R Bootcamp Day 2: Exploring the tidyverse
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Goals
By the end of the day, you will:
- Learn how to read in and examining Data
- Begin understanding tidyverse “grammar”
- Begin understanding the grammar of data manipulation,
dplyr - Begin understanding what a pipe is and how to use pipes to chain together
tidyverseverbs
0. Quick Refresher & Warm up
Before we get started with new content, let’s do a quick review of what we covered last time.
Exercise 0.1a
Create a vector, x, and make it the numbers 1 through 20. Change the 3rd and fourth element so that they are 5 times greater.
Last time, we ended by installing two packages, the rio package and the tidyverse package. This time, we’re going to learn how to use them to get our data into R, manipulate it, and visualize it. We’ll load them both now:
library(tidyverse)## -- Attaching packages ------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.1 v purrr 0.3.4
## v tibble 3.0.1 v dplyr 0.8.5
## v tidyr 1.1.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts ---------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(rio)1. Reading data into R
Now that we have the rio package and a basic idea of how packages work, let’s use it to read in some data! If you’re used to a GUI system like excel or SPSS, reading in data in R can be a little bit confusing at first.
Reading in data generally has two slightly challenging aspects for new users:
You need to call a function that works with a particular data format (csv, txt, sav, etc.).
You need to tell R where to look.
We’ll use import() from rio, which does the first part for us. We just call import() and it calls the right read function given the file’s extension (.csv, .txt, .sav, .xlsx, etc.).
1.1 Working Directories & File Paths
On to challeng # 2. When R looks for a file, it has a starting point. This is called the working directory. The working directory that you’re currently in is displayed in the console window and the files tab. You can also get it with the getwd() function:
getwd()## [1] "C:/Users/coryc3133/OneDrive - Umich/Work/Costello-academic-site/content/courses/rbootcamp"For this tutorial, your working directory should be wherever you downloaded the materials and opened the r project. If you opened the .Rmd, you should be in the uoregon_r_bootcamp directory already. You can change your working directory with:
setwd("PATH")It is considered bad practice to use setwd() and it typically will not work in an Rmd; you can see the author’s rationale for that here.
My recommondation is to either:
- Use an R project. This is a relatively foolproof way of doing things.
- Use .Rmds and always open Rstudio from the particular Rmd. This is a little less foolproof.
- Use .Rmds and set the working directory in the console or through R Studio’s GUI. This is the least foolproof.
1 is probably best practice. 2 can save some time but may not be worth it. 3 is too risky for my tastes. If you do need to set the working directory from R Studio’s GUI, do the following:
Session > Set Working Directory > Choose Directory
You can choose the folder you want to work in. The code for setting the working directory will populate in the console. You can then copy/paste this into your code if you’d like.
1.2 Importing & Exporting data with rio
1.2.1 Importing Data
As I mentioned earlier, the import() function from rio really simplifies reading data into R. Let’s see that first hand by reading in the pragmatic_scales_data, a csv file:
library(rio)
ps_data <- import("pragmatic_scales_data.csv")Let’s say that ps_data were a .sav SPSS data file. In rio, this is no problem, it will call the right function to read in .sav files. Let’s give it a try, reading in the pragmatic_scales_data.csv located in the data subdirectory of our current working directory:
ps_data <- import("data/ps_data.sav")Notice that all I had to change was where to look (telling it to go to the data/ subdirectory and the file called ps_data.csv)
You can also import data from a website. For example, this dataset is also hosted on github, so we could download it from there using import() too:
ps_data <- import("https://raw.githubusercontent.com/Coryc3133/uoregon_r_bootcamp/master/pragmatic_scales_data.csv")You can see all of the file formats rio works with by running ?import.
1.2.2 Exporting Data
You can also use rio to export your data, saving it in any of the formats that it works with. This is really simple and works just like import(), but is called export(). For export(), you provide the R dataframe object you want to export, and the path/name for the new file. For example, let’s say I want to export ps_data as an xlsx file and put it into the /data subdirectory. I could do that with export:
export(ps_data,
"data/ps_data.xlsx")Exercise 1.2a
I made a mistake when creating this and left the datasets in the
uoregon_r_bootcampfolder instead of putting them into the/datadirectory. Let’s fix that. We already fixed ps_data, but now I want you to fixanother_data_set.csv. First import the data asanother_df:
Exercise 1.2b
Now I want you to export the data and save it into the
data/directory. Make sure the name of the dataframe isanother_data_set, and make sure you save it out as a csv.
Exercise 1.2c
One of my colleagues insists we send them a .sav file so that they can run the analyses in SPSS. Make another copy of
another_data_setin thedata/subdirectory that is in the .sav format.
Exercise 1.2d
Finally, let’s read one of these datasets to make sure everything worked as expected. Import the .sav version of another_data_set as
another_df.
1.3 Examining Your Data
Now that your data is in R, you may want to take a look at it. There are a few different ways to do that, which each offer different information.
4.3.1 View
One way is to click on the View button in the environment pane. You should see ps_data in the environment pane with a little data icon at the far right. Click on that icon. You’ll notice that this ran View(ps_data) in the console. We can do that with code:
View(ps_data)Note that the V in View() is capital!.
1.3.2 head and tail
You can also see just the first few rows of a dataframe with head():
head(ps_data)## subid item correct age condition
## 1 M22 faces 1 2.00 Label
## 2 M22 houses 1 2.00 Label
## 3 M22 pasta 0 2.00 Label
## 4 M22 beds 0 2.00 Label
## 5 T22 beds 0 2.13 Label
## 6 T22 faces 0 2.13 LabelThis can be useful when you have very large datasets as it is much faster than the View function. head() prints 6 rows by default, but you can increase or decrease that with the n = argument. For example, imagine we want to see the first 20 rows:
head(ps_data, n = 20)## subid item correct age condition
## 1 M22 faces 1 2.00 Label
## 2 M22 houses 1 2.00 Label
## 3 M22 pasta 0 2.00 Label
## 4 M22 beds 0 2.00 Label
## 5 T22 beds 0 2.13 Label
## 6 T22 faces 0 2.13 Label
## 7 T22 houses 1 2.13 Label
## 8 T22 pasta 1 2.13 Label
## 9 T17 pasta 0 2.32 Label
## 10 T17 faces 0 2.32 Label
## 11 T17 houses 0 2.32 Label
## 12 T17 beds 0 2.32 Label
## 13 M3 faces 0 2.38 Label
## 14 M3 houses 1 2.38 Label
## 15 M3 pasta 1 2.38 Label
## 16 M3 beds 1 2.38 Label
## 17 T19 faces 0 2.47 Label
## 18 T19 houses 0 2.47 Label
## 19 T19 pasta 1 2.47 Label
## 20 T19 beds 1 2.47 Labeltail is the complement to head, displaying just the final rows from a dataframe:
tail(ps_data)## subid item correct age condition
## 583 MSCH84 pasta 1 2.83 No Label
## 584 MSCH84 beds 0 2.83 No Label
## 585 MSCH85 faces 0 2.69 No Label
## 586 MSCH85 houses 0 2.69 No Label
## 587 MSCH85 pasta 0 2.69 No Label
## 588 MSCH85 beds 0 2.69 No Label1.3.3 Examine Structure with str
We saw str a little earlier when we first introduced dataframes. It’s worth mentioning it again because it can be so useful when you import data to see how the read function interpreted the variables. Let’s see:
str(ps_data)## 'data.frame': 588 obs. of 5 variables:
## $ subid : chr "M22" "M22" "M22" "M22" ...
## $ item : chr "faces" "houses" "pasta" "beds" ...
## $ correct : int 1 1 0 0 0 0 1 1 0 0 ...
## $ age : num 2 2 2 2 2.13 2.13 2.13 2.13 2.32 2.32 ...
## $ condition: chr "Label" "Label" "Label" "Label" ...1.3.4 Summary
The summary() funciton can be used to get a quick sense of each of the variables in a dataframe. It displays summary information for each variable. It displays different kinds of information depending on the variable’s type.
summary(ps_data)## subid item correct age
## Length:588 Length:588 Min. :0.0000 Min. :2.000
## Class :character Class :character 1st Qu.:0.0000 1st Qu.:2.850
## Mode :character Mode :character Median :0.0000 Median :3.460
## Mean :0.4473 Mean :3.525
## 3rd Qu.:1.0000 3rd Qu.:4.290
## Max. :1.0000 Max. :4.960
## condition
## Length:588
## Class :character
## Mode :character
##
##
## 2. More Advanced Indexing and Modifying a data frame in base R. (optional Content)
2.1 Indexing
Let’s start by reviewing indexing dataframes.
2.1.1 Bracket Indexing with Numerical Indices and Names
Recall that uou can select entries in the data frame just like indexing a matrix, i.e., [row, column]
ps_data[1, 5]## [1] "Label"ps_data[1, "condition"]## [1] "Label"And you can get a whole row or column by leaving the other dimension empty. Let’s get all rows of condition:
ps_data[, "condition"]## [1] "Label" "Label" "Label" "Label" "Label" "Label"
## [7] "Label" "Label" "Label" "Label" "Label" "Label"
## [13] "Label" "Label" "Label" "Label" "Label" "Label"
## [19] "Label" "Label" "Label" "Label" "Label" "Label"
## [25] "Label" "Label" "Label" "Label" "Label" "Label"
## [31] "Label" "Label" "Label" "Label" "Label" "Label"
## [37] "Label" "Label" "Label" "Label" "Label" "Label"
## [43] "Label" "Label" "Label" "Label" "Label" "Label"
## [49] "Label" "Label" "Label" "Label" "Label" "Label"
## [55] "Label" "Label" "Label" "Label" "Label" "Label"
## [61] "Label" "Label" "Label" "Label" "Label" "Label"
## [67] "Label" "Label" "Label" "Label" "Label" "Label"
## [73] "Label" "Label" "Label" "Label" "Label" "Label"
## [79] "Label" "Label" "Label" "Label" "Label" "Label"
## [85] "Label" "Label" "Label" "Label" "Label" "Label"
## [91] "Label" "Label" "Label" "Label" "Label" "Label"
## [97] "Label" "Label" "Label" "Label" "Label" "Label"
## [103] "Label" "Label" "Label" "Label" "Label" "Label"
## [109] "Label" "Label" "Label" "Label" "Label" "Label"
## [115] "Label" "Label" "Label" "Label" "Label" "Label"
## [121] "Label" "Label" "Label" "Label" "Label" "Label"
## [127] "Label" "Label" "Label" "Label" "Label" "Label"
## [133] "Label" "Label" "Label" "Label" "Label" "Label"
## [139] "Label" "Label" "Label" "Label" "Label" "Label"
## [145] "Label" "Label" "Label" "Label" "Label" "Label"
## [151] "Label" "Label" "Label" "Label" "Label" "Label"
## [157] "Label" "Label" "Label" "Label" "Label" "Label"
## [163] "Label" "Label" "Label" "Label" "Label" "Label"
## [169] "Label" "Label" "Label" "Label" "Label" "Label"
## [175] "Label" "Label" "Label" "Label" "Label" "Label"
## [181] "Label" "Label" "Label" "Label" "Label" "Label"
## [187] "Label" "Label" "Label" "Label" "Label" "Label"
## [193] "Label" "Label" "Label" "Label" "Label" "Label"
## [199] "Label" "Label" "Label" "Label" "Label" "Label"
## [205] "Label" "Label" "Label" "Label" "Label" "Label"
## [211] "Label" "Label" "Label" "Label" "Label" "Label"
## [217] "Label" "Label" "Label" "Label" "Label" "Label"
## [223] "Label" "Label" "Label" "Label" "Label" "Label"
## [229] "Label" "Label" "Label" "Label" "Label" "Label"
## [235] "Label" "Label" "Label" "Label" "Label" "Label"
## [241] "Label" "Label" "Label" "Label" "Label" "Label"
## [247] "Label" "Label" "Label" "Label" "Label" "Label"
## [253] "Label" "Label" "Label" "Label" "Label" "Label"
## [259] "Label" "Label" "Label" "Label" "Label" "Label"
## [265] "Label" "Label" "Label" "Label" "Label" "Label"
## [271] "Label" "Label" "Label" "Label" "Label" "Label"
## [277] "Label" "Label" "Label" "Label" "Label" "Label"
## [283] "Label" "Label" "Label" "Label" "Label" "Label"
## [289] "Label" "Label" "Label" "Label" "Label" "Label"
## [295] "Label" "Label" "Label" "Label" "Label" "Label"
## [301] "No Label" "No Label" "No Label" "No Label" "No Label" "No Label"
## [307] "No Label" "No Label" "No Label" "No Label" "No Label" "No Label"
## [313] "No Label" "No Label" "No Label" "No Label" "No Label" "No Label"
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## [553] "No Label" "No Label" "No Label" "No Label" "No Label" "No Label"
## [559] "No Label" "No Label" "No Label" "No Label" "No Label" "No Label"
## [565] "No Label" "No Label" "No Label" "No Label" "No Label" "No Label"
## [571] "No Label" "No Label" "No Label" "No Label" "No Label" "No Label"
## [577] "No Label" "No Label" "No Label" "No Label" "No Label" "No Label"
## [583] "No Label" "No Label" "No Label" "No Label" "No Label" "No Label"2.1.2 Indexing with $
Recall that you can also get a column from a df by using df$column. For example, we could get condition:
ps_data$condition## [1] "Label" "Label" "Label" "Label" "Label" "Label"
## [7] "Label" "Label" "Label" "Label" "Label" "Label"
## [13] "Label" "Label" "Label" "Label" "Label" "Label"
## [19] "Label" "Label" "Label" "Label" "Label" "Label"
## [25] "Label" "Label" "Label" "Label" "Label" "Label"
## [31] "Label" "Label" "Label" "Label" "Label" "Label"
## [37] "Label" "Label" "Label" "Label" "Label" "Label"
## [43] "Label" "Label" "Label" "Label" "Label" "Label"
## [49] "Label" "Label" "Label" "Label" "Label" "Label"
## [55] "Label" "Label" "Label" "Label" "Label" "Label"
## [61] "Label" "Label" "Label" "Label" "Label" "Label"
## [67] "Label" "Label" "Label" "Label" "Label" "Label"
## [73] "Label" "Label" "Label" "Label" "Label" "Label"
## [79] "Label" "Label" "Label" "Label" "Label" "Label"
## [85] "Label" "Label" "Label" "Label" "Label" "Label"
## [91] "Label" "Label" "Label" "Label" "Label" "Label"
## [97] "Label" "Label" "Label" "Label" "Label" "Label"
## [103] "Label" "Label" "Label" "Label" "Label" "Label"
## [109] "Label" "Label" "Label" "Label" "Label" "Label"
## [115] "Label" "Label" "Label" "Label" "Label" "Label"
## [121] "Label" "Label" "Label" "Label" "Label" "Label"
## [127] "Label" "Label" "Label" "Label" "Label" "Label"
## [133] "Label" "Label" "Label" "Label" "Label" "Label"
## [139] "Label" "Label" "Label" "Label" "Label" "Label"
## [145] "Label" "Label" "Label" "Label" "Label" "Label"
## [151] "Label" "Label" "Label" "Label" "Label" "Label"
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## [169] "Label" "Label" "Label" "Label" "Label" "Label"
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## [223] "Label" "Label" "Label" "Label" "Label" "Label"
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## [235] "Label" "Label" "Label" "Label" "Label" "Label"
## [241] "Label" "Label" "Label" "Label" "Label" "Label"
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## [253] "Label" "Label" "Label" "Label" "Label" "Label"
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## [271] "Label" "Label" "Label" "Label" "Label" "Label"
## [277] "Label" "Label" "Label" "Label" "Label" "Label"
## [283] "Label" "Label" "Label" "Label" "Label" "Label"
## [289] "Label" "Label" "Label" "Label" "Label" "Label"
## [295] "Label" "Label" "Label" "Label" "Label" "Label"
## [301] "No Label" "No Label" "No Label" "No Label" "No Label" "No Label"
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## [577] "No Label" "No Label" "No Label" "No Label" "No Label" "No Label"
## [583] "No Label" "No Label" "No Label" "No Label" "No Label" "No Label"2.1.3 Indexing with Logical Tests
Up to this point, we’ve only covered indexing by numerical index or name. But, you can also index via logical tests. To do this in base R, we use the which() function, which returns the indices where a condition is true. We can test if things are equal, not equal, greater, or lesser using the following symbols:
| Test | symbol |
|---|---|
| Equal | == |
| Not equal | != |
| Greater than | > |
| Lesser than | < |
| Greater than or Equal to | >= |
| Lesser than or Equal to | <= |
Let’s put this to use and get all of the indices where condition is equal to label:
which(ps_data$condition == "Label")## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
## [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
## [235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
## [253] 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270
## [271] 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
## [289] 289 290 291 292 293 294 295 296 297 298 299 300We can combine this with [] indexing to do even more powerful subsetting. For example, we can put this which() call within the row position to extract the rows for subjects in the “Label” condition.
ps_data[which(ps_data$condition == "Label"),]## subid item correct age condition
## 1 M22 faces 1 2.00 Label
## 2 M22 houses 1 2.00 Label
## 3 M22 pasta 0 2.00 Label
## 4 M22 beds 0 2.00 Label
## 5 T22 beds 0 2.13 Label
## 6 T22 faces 0 2.13 Label
## 7 T22 houses 1 2.13 Label
## 8 T22 pasta 1 2.13 Label
## 9 T17 pasta 0 2.32 Label
## 10 T17 faces 0 2.32 Label
## 11 T17 houses 0 2.32 Label
## 12 T17 beds 0 2.32 Label
## 13 M3 faces 0 2.38 Label
## 14 M3 houses 1 2.38 Label
## 15 M3 pasta 1 2.38 Label
## 16 M3 beds 1 2.38 Label
## 17 T19 faces 0 2.47 Label
## 18 T19 houses 0 2.47 Label
## 19 T19 pasta 1 2.47 Label
## 20 T19 beds 1 2.47 Label
## 21 T20 faces 1 2.50 Label
## 22 T20 houses 1 2.50 Label
## 23 T20 pasta 0 2.50 Label
## 24 T20 beds 1 2.50 Label
## 25 T21 faces 1 2.58 Label
## 26 T21 houses 1 2.58 Label
## 27 T21 pasta 1 2.58 Label
## 28 T21 beds 0 2.58 Label
## 29 M26 faces 1 2.59 Label
## 30 M26 houses 1 2.59 Label
## 31 M26 pasta 0 2.59 Label
## 32 M26 beds 1 2.59 Label
## 33 T18 faces 1 2.61 Label
## 34 T18 houses 0 2.61 Label
## 35 T18 pasta 1 2.61 Label
## 36 T18 beds 0 2.61 Label
## 37 T12 beds 0 2.72 Label
## 38 T12 faces 0 2.72 Label
## 39 T12 houses 1 2.72 Label
## 40 T12 pasta 0 2.72 Label
## 41 T16 faces 1 2.73 Label
## 42 T16 houses 0 2.73 Label
## 43 T16 pasta 1 2.73 Label
## 44 T16 beds 1 2.73 Label
## 45 T7 faces 1 2.74 Label
## 46 T7 houses 0 2.74 Label
## 47 T7 pasta 0 2.74 Label
## 48 T7 beds 0 2.74 Label
## 49 T9 houses 0 2.79 Label
## 50 T9 faces 1 2.79 Label
## 51 T9 pasta 0 2.79 Label
## 52 T9 beds 1 2.79 Label
## 53 T5 faces 1 2.80 Label
## 54 T5 houses 1 2.80 Label
## 55 T5 pasta 0 2.80 Label
## 56 T5 beds 1 2.80 Label
## 57 T14 faces 1 2.83 Label
## 58 T14 houses 1 2.83 Label
## 59 T14 pasta 0 2.83 Label
## 60 T14 beds 1 2.83 Label
## 61 T2 houses 0 2.83 Label
## 62 T2 faces 0 2.83 Label
## 63 T2 pasta 1 2.83 Label
## 64 T2 beds 1 2.83 Label
## 65 T15 faces 0 2.85 Label
## 66 T15 houses 0 2.85 Label
## 67 T15 pasta 1 2.85 Label
## 68 T15 beds 0 2.85 Label
## 69 M13 houses 0 2.88 Label
## 70 M13 beds 1 2.88 Label
## 71 M13 faces 1 2.88 Label
## 72 M13 pasta 0 2.88 Label
## 73 M12 faces 1 2.88 Label
## 74 M12 houses 0 2.88 Label
## 75 M12 pasta 1 2.88 Label
## 76 M12 beds 0 2.88 Label
## 77 T13 beds 0 2.89 Label
## 78 T13 faces 0 2.89 Label
## 79 T13 houses 1 2.89 Label
## 80 T13 pasta 1 2.89 Label
## 81 T8 faces 1 2.91 Label
## 82 T8 houses 0 2.91 Label
## 83 T8 pasta 1 2.91 Label
## 84 T8 beds 1 2.91 Label
## 85 T1 faces 1 2.95 Label
## 86 T1 houses 0 2.95 Label
## 87 T1 pasta 0 2.95 Label
## 88 T1 beds 1 2.95 Label
## 89 M15 faces 1 2.98 Label
## 90 M15 houses 1 2.98 Label
## 91 M15 pasta 1 2.98 Label
## 92 M15 beds 1 2.98 Label
## 93 T11 faces 1 2.99 Label
## 94 T11 houses 0 2.99 Label
## 95 T11 pasta 1 2.99 Label
## 96 T11 beds 1 2.99 Label
## 97 T10 faces 0 3.00 Label
## 98 T10 houses 1 3.00 Label
## 99 T10 pasta 1 3.00 Label
## 100 T10 beds 1 3.00 Label
## 101 T3 faces 1 3.09 Label
## 102 T3 houses 1 3.09 Label
## 103 T3 pasta 1 3.09 Label
## 104 T3 beds 1 3.09 Label
## 105 T6 faces 1 3.10 Label
## 106 T6 houses 1 3.10 Label
## 107 T6 pasta 1 3.10 Label
## 108 T6 beds 1 3.10 Label
## 109 M32 beds 1 3.19 Label
## 110 M32 faces 1 3.19 Label
## 111 M32 houses 0 3.19 Label
## 112 M32 pasta 1 3.19 Label
## 113 M1 faces 0 3.20 Label
## 114 M1 beds 1 3.20 Label
## 115 M1 pasta 0 3.20 Label
## 116 M1 houses 0 3.20 Label
## 117 C16 faces 0 3.22 Label
## 118 C16 houses 0 3.22 Label
## 119 C16 pasta 1 3.22 Label
## 120 C16 beds 1 3.22 Label
## 121 T4 faces 1 3.24 Label
## 122 T4 houses 0 3.24 Label
## 123 T4 pasta 0 3.24 Label
## 124 T4 beds 1 3.24 Label
## 125 C17 faces 1 3.25 Label
## 126 C17 houses 0 3.25 Label
## 127 C17 pasta 1 3.25 Label
## 128 C17 beds 0 3.25 Label
## 129 C6 faces 0 3.26 Label
## 130 C6 houses 1 3.26 Label
## 131 C6 pasta 1 3.26 Label
## 132 C6 beds 1 3.26 Label
## 133 M10 faces 1 3.28 Label
## 134 M10 houses 1 3.28 Label
## 135 M10 beds 1 3.28 Label
## 136 M10 pasta 1 3.28 Label
## 137 M31 faces 0 3.30 Label
## 138 M31 houses 1 3.30 Label
## 139 M31 pasta 1 3.30 Label
## 140 M31 beds 1 3.30 Label
## 141 C3 houses 0 3.46 Label
## 142 C3 pasta 1 3.46 Label
## 143 C3 beds 1 3.46 Label
## 144 C3 faces 1 3.46 Label
## 145 C10 faces 0 3.46 Label
## 146 C10 houses 0 3.46 Label
## 147 C10 pasta 1 3.46 Label
## 148 C10 beds 1 3.46 Label
## 149 M18 faces 0 3.46 Label
## 150 M18 houses 1 3.46 Label
## 151 M18 pasta 1 3.46 Label
## 152 M18 beds 1 3.46 Label
## 153 M16 faces 0 3.50 Label
## 154 M16 houses 0 3.50 Label
## 155 M16 pasta 0 3.50 Label
## 156 M16 beds 1 3.50 Label
## 157 M23 faces 1 3.52 Label
## 158 M23 houses 0 3.52 Label
## 159 M23 pasta 1 3.52 Label
## 160 M23 beds 1 3.52 Label
## 161 C7 faces 0 3.55 Label
## 162 C7 houses 1 3.55 Label
## 163 C7 pasta 0 3.55 Label
## 164 C7 beds 0 3.55 Label
## 165 C12 faces 1 3.56 Label
## 166 C12 houses 0 3.56 Label
## 167 C12 pasta 1 3.56 Label
## 168 C12 beds 1 3.56 Label
## 169 C15 faces 1 3.59 Label
## 170 C15 houses 1 3.59 Label
## 171 C15 pasta 1 3.59 Label
## 172 C15 beds 1 3.59 Label
## 173 M29 faces 0 3.72 Label
## 174 M29 houses 1 3.72 Label
## 175 M29 pasta 1 3.72 Label
## 176 M29 beds 1 3.72 Label
## 177 C20 faces 1 3.75 Label
## 178 C20 houses 1 3.75 Label
## 179 C20 pasta 1 3.75 Label
## 180 C20 beds 1 3.75 Label
## 181 M11 faces 1 3.82 Label
## 182 M11 houses 0 3.82 Label
## 183 M11 pasta 1 3.82 Label
## 184 M11 beds 1 3.82 Label
## 185 C9 beds 1 3.82 Label
## 186 C9 faces 1 3.82 Label
## 187 C9 houses 1 3.82 Label
## 188 C9 pasta 1 3.82 Label
## 189 C24 faces 1 3.85 Label
## 190 C24 houses 0 3.85 Label
## 191 C24 pasta 0 3.85 Label
## 192 C24 beds 1 3.85 Label
## 193 C22 faces 0 3.92 Label
## 194 C22 houses 0 3.92 Label
## 195 C22 pasta 1 3.92 Label
## 196 C22 beds 1 3.92 Label
## 197 C8 faces 1 3.92 Label
## 198 C8 houses 1 3.92 Label
## 199 C8 pasta 1 3.92 Label
## 200 C8 beds 1 3.92 Label
## 201 M4 faces 1 3.96 Label
## 202 M4 houses 1 3.96 Label
## 203 M4 pasta 1 3.96 Label
## 204 M4 beds 1 3.96 Label
## 205 M6 faces 0 4.50 Label
## 206 M6 houses 1 4.50 Label
## 207 M6 pasta 1 4.50 Label
## 208 M6 beds 0 4.50 Label
## 209 C19 faces 1 4.14 Label
## 210 C19 houses 0 4.14 Label
## 211 C19 pasta 0 4.14 Label
## 212 C19 beds 1 4.14 Label
## 213 C1 faces 1 4.16 Label
## 214 C1 houses 1 4.16 Label
## 215 C1 pasta 1 4.16 Label
## 216 C1 beds 1 4.16 Label
## 217 M19 beds 1 4.16 Label
## 218 M19 faces 0 4.16 Label
## 219 M19 houses 0 4.16 Label
## 220 M19 pasta 1 4.16 Label
## 221 C11 faces 1 4.22 Label
## 222 C11 houses 0 4.22 Label
## 223 C11 pasta 1 4.22 Label
## 224 C11 beds 1 4.22 Label
## 225 M9 faces 1 4.26 Label
## 226 M9 houses 1 4.26 Label
## 227 M9 pasta 1 4.26 Label
## 228 M9 beds 1 4.26 Label
## 229 M2 faces 1 4.28 Label
## 230 M2 houses 0 4.28 Label
## 231 M2 pasta 1 4.28 Label
## 232 M2 beds 1 4.28 Label
## 233 C5 faces 1 4.29 Label
## 234 C5 houses 1 4.29 Label
## 235 C5 pasta 1 4.29 Label
## 236 C5 beds 1 4.29 Label
## 237 M30 beds 1 4.33 Label
## 238 M30 faces 1 4.33 Label
## 239 M30 houses 0 4.33 Label
## 240 M30 pasta 1 4.33 Label
## 241 C13 faces 0 4.38 Label
## 242 C13 houses 1 4.38 Label
## 243 C13 pasta 0 4.38 Label
## 244 C13 beds 1 4.38 Label
## 245 C4 faces 1 4.55 Label
## 246 C4 houses 1 4.55 Label
## 247 C4 pasta 1 4.55 Label
## 248 C4 beds 1 4.55 Label
## 249 C14 faces 1 4.57 Label
## 250 C14 houses 1 4.57 Label
## 251 C14 pasta 0 4.57 Label
## 252 C14 beds 1 4.57 Label
## 253 M17 faces 1 4.58 Label
## 254 M17 houses 1 4.58 Label
## 255 M17 pasta 1 4.58 Label
## 256 M17 beds 1 4.58 Label
## 257 C2 faces 1 4.60 Label
## 258 C2 houses 1 4.60 Label
## 259 C2 pasta 1 4.60 Label
## 260 C2 beds 1 4.60 Label
## 261 C23 faces 0 4.62 Label
## 262 C23 houses 1 4.62 Label
## 263 C23 pasta 1 4.62 Label
## 264 C23 beds 0 4.62 Label
## 265 M20 faces 0 4.64 Label
## 266 M20 houses 0 4.64 Label
## 267 M20 pasta 1 4.64 Label
## 268 M20 beds 1 4.64 Label
## 269 M21 faces 1 4.64 Label
## 270 M21 houses 1 4.64 Label
## 271 M21 pasta 1 4.64 Label
## 272 M21 beds 1 4.64 Label
## 273 C21 faces 1 4.73 Label
## 274 C21 houses 0 4.73 Label
## 275 C21 pasta 1 4.73 Label
## 276 C21 beds 1 4.73 Label
## 277 M24 faces 1 4.82 Label
## 278 M24 houses 1 4.82 Label
## 279 M24 pasta 1 4.82 Label
## 280 M24 beds 1 4.82 Label
## 281 M5 faces 0 4.84 Label
## 282 M5 houses 0 4.84 Label
## 283 M5 pasta 0 4.84 Label
## 284 M5 beds 1 4.84 Label
## 285 M7 faces 1 4.89 Label
## 286 M7 houses 1 4.89 Label
## 287 M7 pasta 1 4.89 Label
## 288 M7 beds 0 4.89 Label
## 289 M8 faces 1 4.89 Label
## 290 M8 houses 1 4.89 Label
## 291 M8 pasta 1 4.89 Label
## 292 M8 beds 1 4.89 Label
## 293 C18 faces 0 4.95 Label
## 294 C18 houses 1 4.95 Label
## 295 C18 pasta 1 4.95 Label
## 296 C18 beds 1 4.95 Label
## 297 M25 faces 1 4.96 Label
## 298 M25 houses 1 4.96 Label
## 299 M25 pasta 1 4.96 Label
## 300 M25 beds 1 4.96 LabelOr, we could get all of the rows where subjects are greater than or equal to 2.5 years old:
ps_data[which(ps_data$age >= 2.5),]## subid item correct age condition
## 21 T20 faces 1 2.50 Label
## 22 T20 houses 1 2.50 Label
## 23 T20 pasta 0 2.50 Label
## 24 T20 beds 1 2.50 Label
## 25 T21 faces 1 2.58 Label
## 26 T21 houses 1 2.58 Label
## 27 T21 pasta 1 2.58 Label
## 28 T21 beds 0 2.58 Label
## 29 M26 faces 1 2.59 Label
## 30 M26 houses 1 2.59 Label
## 31 M26 pasta 0 2.59 Label
## 32 M26 beds 1 2.59 Label
## 33 T18 faces 1 2.61 Label
## 34 T18 houses 0 2.61 Label
## 35 T18 pasta 1 2.61 Label
## 36 T18 beds 0 2.61 Label
## 37 T12 beds 0 2.72 Label
## 38 T12 faces 0 2.72 Label
## 39 T12 houses 1 2.72 Label
## 40 T12 pasta 0 2.72 Label
## 41 T16 faces 1 2.73 Label
## 42 T16 houses 0 2.73 Label
## 43 T16 pasta 1 2.73 Label
## 44 T16 beds 1 2.73 Label
## 45 T7 faces 1 2.74 Label
## 46 T7 houses 0 2.74 Label
## 47 T7 pasta 0 2.74 Label
## 48 T7 beds 0 2.74 Label
## 49 T9 houses 0 2.79 Label
## 50 T9 faces 1 2.79 Label
## 51 T9 pasta 0 2.79 Label
## 52 T9 beds 1 2.79 Label
## 53 T5 faces 1 2.80 Label
## 54 T5 houses 1 2.80 Label
## 55 T5 pasta 0 2.80 Label
## 56 T5 beds 1 2.80 Label
## 57 T14 faces 1 2.83 Label
## 58 T14 houses 1 2.83 Label
## 59 T14 pasta 0 2.83 Label
## 60 T14 beds 1 2.83 Label
## 61 T2 houses 0 2.83 Label
## 62 T2 faces 0 2.83 Label
## 63 T2 pasta 1 2.83 Label
## 64 T2 beds 1 2.83 Label
## 65 T15 faces 0 2.85 Label
## 66 T15 houses 0 2.85 Label
## 67 T15 pasta 1 2.85 Label
## 68 T15 beds 0 2.85 Label
## 69 M13 houses 0 2.88 Label
## 70 M13 beds 1 2.88 Label
## 71 M13 faces 1 2.88 Label
## 72 M13 pasta 0 2.88 Label
## 73 M12 faces 1 2.88 Label
## 74 M12 houses 0 2.88 Label
## 75 M12 pasta 1 2.88 Label
## 76 M12 beds 0 2.88 Label
## 77 T13 beds 0 2.89 Label
## 78 T13 faces 0 2.89 Label
## 79 T13 houses 1 2.89 Label
## 80 T13 pasta 1 2.89 Label
## 81 T8 faces 1 2.91 Label
## 82 T8 houses 0 2.91 Label
## 83 T8 pasta 1 2.91 Label
## 84 T8 beds 1 2.91 Label
## 85 T1 faces 1 2.95 Label
## 86 T1 houses 0 2.95 Label
## 87 T1 pasta 0 2.95 Label
## 88 T1 beds 1 2.95 Label
## 89 M15 faces 1 2.98 Label
## 90 M15 houses 1 2.98 Label
## 91 M15 pasta 1 2.98 Label
## 92 M15 beds 1 2.98 Label
## 93 T11 faces 1 2.99 Label
## 94 T11 houses 0 2.99 Label
## 95 T11 pasta 1 2.99 Label
## 96 T11 beds 1 2.99 Label
## 97 T10 faces 0 3.00 Label
## 98 T10 houses 1 3.00 Label
## 99 T10 pasta 1 3.00 Label
## 100 T10 beds 1 3.00 Label
## 101 T3 faces 1 3.09 Label
## 102 T3 houses 1 3.09 Label
## 103 T3 pasta 1 3.09 Label
## 104 T3 beds 1 3.09 Label
## 105 T6 faces 1 3.10 Label
## 106 T6 houses 1 3.10 Label
## 107 T6 pasta 1 3.10 Label
## 108 T6 beds 1 3.10 Label
## 109 M32 beds 1 3.19 Label
## 110 M32 faces 1 3.19 Label
## 111 M32 houses 0 3.19 Label
## 112 M32 pasta 1 3.19 Label
## 113 M1 faces 0 3.20 Label
## 114 M1 beds 1 3.20 Label
## 115 M1 pasta 0 3.20 Label
## 116 M1 houses 0 3.20 Label
## 117 C16 faces 0 3.22 Label
## 118 C16 houses 0 3.22 Label
## 119 C16 pasta 1 3.22 Label
## 120 C16 beds 1 3.22 Label
## 121 T4 faces 1 3.24 Label
## 122 T4 houses 0 3.24 Label
## 123 T4 pasta 0 3.24 Label
## 124 T4 beds 1 3.24 Label
## 125 C17 faces 1 3.25 Label
## 126 C17 houses 0 3.25 Label
## 127 C17 pasta 1 3.25 Label
## 128 C17 beds 0 3.25 Label
## 129 C6 faces 0 3.26 Label
## 130 C6 houses 1 3.26 Label
## 131 C6 pasta 1 3.26 Label
## 132 C6 beds 1 3.26 Label
## 133 M10 faces 1 3.28 Label
## 134 M10 houses 1 3.28 Label
## 135 M10 beds 1 3.28 Label
## 136 M10 pasta 1 3.28 Label
## 137 M31 faces 0 3.30 Label
## 138 M31 houses 1 3.30 Label
## 139 M31 pasta 1 3.30 Label
## 140 M31 beds 1 3.30 Label
## 141 C3 houses 0 3.46 Label
## 142 C3 pasta 1 3.46 Label
## 143 C3 beds 1 3.46 Label
## 144 C3 faces 1 3.46 Label
## 145 C10 faces 0 3.46 Label
## 146 C10 houses 0 3.46 Label
## 147 C10 pasta 1 3.46 Label
## 148 C10 beds 1 3.46 Label
## 149 M18 faces 0 3.46 Label
## 150 M18 houses 1 3.46 Label
## 151 M18 pasta 1 3.46 Label
## 152 M18 beds 1 3.46 Label
## 153 M16 faces 0 3.50 Label
## 154 M16 houses 0 3.50 Label
## 155 M16 pasta 0 3.50 Label
## 156 M16 beds 1 3.50 Label
## 157 M23 faces 1 3.52 Label
## 158 M23 houses 0 3.52 Label
## 159 M23 pasta 1 3.52 Label
## 160 M23 beds 1 3.52 Label
## 161 C7 faces 0 3.55 Label
## 162 C7 houses 1 3.55 Label
## 163 C7 pasta 0 3.55 Label
## 164 C7 beds 0 3.55 Label
## 165 C12 faces 1 3.56 Label
## 166 C12 houses 0 3.56 Label
## 167 C12 pasta 1 3.56 Label
## 168 C12 beds 1 3.56 Label
## 169 C15 faces 1 3.59 Label
## 170 C15 houses 1 3.59 Label
## 171 C15 pasta 1 3.59 Label
## 172 C15 beds 1 3.59 Label
## 173 M29 faces 0 3.72 Label
## 174 M29 houses 1 3.72 Label
## 175 M29 pasta 1 3.72 Label
## 176 M29 beds 1 3.72 Label
## 177 C20 faces 1 3.75 Label
## 178 C20 houses 1 3.75 Label
## 179 C20 pasta 1 3.75 Label
## 180 C20 beds 1 3.75 Label
## 181 M11 faces 1 3.82 Label
## 182 M11 houses 0 3.82 Label
## 183 M11 pasta 1 3.82 Label
## 184 M11 beds 1 3.82 Label
## 185 C9 beds 1 3.82 Label
## 186 C9 faces 1 3.82 Label
## 187 C9 houses 1 3.82 Label
## 188 C9 pasta 1 3.82 Label
## 189 C24 faces 1 3.85 Label
## 190 C24 houses 0 3.85 Label
## 191 C24 pasta 0 3.85 Label
## 192 C24 beds 1 3.85 Label
## 193 C22 faces 0 3.92 Label
## 194 C22 houses 0 3.92 Label
## 195 C22 pasta 1 3.92 Label
## 196 C22 beds 1 3.92 Label
## 197 C8 faces 1 3.92 Label
## 198 C8 houses 1 3.92 Label
## 199 C8 pasta 1 3.92 Label
## 200 C8 beds 1 3.92 Label
## 201 M4 faces 1 3.96 Label
## 202 M4 houses 1 3.96 Label
## 203 M4 pasta 1 3.96 Label
## 204 M4 beds 1 3.96 Label
## 205 M6 faces 0 4.50 Label
## 206 M6 houses 1 4.50 Label
## 207 M6 pasta 1 4.50 Label
## 208 M6 beds 0 4.50 Label
## 209 C19 faces 1 4.14 Label
## 210 C19 houses 0 4.14 Label
## 211 C19 pasta 0 4.14 Label
## 212 C19 beds 1 4.14 Label
## 213 C1 faces 1 4.16 Label
## 214 C1 houses 1 4.16 Label
## 215 C1 pasta 1 4.16 Label
## 216 C1 beds 1 4.16 Label
## 217 M19 beds 1 4.16 Label
## 218 M19 faces 0 4.16 Label
## 219 M19 houses 0 4.16 Label
## 220 M19 pasta 1 4.16 Label
## 221 C11 faces 1 4.22 Label
## 222 C11 houses 0 4.22 Label
## 223 C11 pasta 1 4.22 Label
## 224 C11 beds 1 4.22 Label
## 225 M9 faces 1 4.26 Label
## 226 M9 houses 1 4.26 Label
## 227 M9 pasta 1 4.26 Label
## 228 M9 beds 1 4.26 Label
## 229 M2 faces 1 4.28 Label
## 230 M2 houses 0 4.28 Label
## 231 M2 pasta 1 4.28 Label
## 232 M2 beds 1 4.28 Label
## 233 C5 faces 1 4.29 Label
## 234 C5 houses 1 4.29 Label
## 235 C5 pasta 1 4.29 Label
## 236 C5 beds 1 4.29 Label
## 237 M30 beds 1 4.33 Label
## 238 M30 faces 1 4.33 Label
## 239 M30 houses 0 4.33 Label
## 240 M30 pasta 1 4.33 Label
## 241 C13 faces 0 4.38 Label
## 242 C13 houses 1 4.38 Label
## 243 C13 pasta 0 4.38 Label
## 244 C13 beds 1 4.38 Label
## 245 C4 faces 1 4.55 Label
## 246 C4 houses 1 4.55 Label
## 247 C4 pasta 1 4.55 Label
## 248 C4 beds 1 4.55 Label
## 249 C14 faces 1 4.57 Label
## 250 C14 houses 1 4.57 Label
## 251 C14 pasta 0 4.57 Label
## 252 C14 beds 1 4.57 Label
## 253 M17 faces 1 4.58 Label
## 254 M17 houses 1 4.58 Label
## 255 M17 pasta 1 4.58 Label
## 256 M17 beds 1 4.58 Label
## 257 C2 faces 1 4.60 Label
## 258 C2 houses 1 4.60 Label
## 259 C2 pasta 1 4.60 Label
## 260 C2 beds 1 4.60 Label
## 261 C23 faces 0 4.62 Label
## 262 C23 houses 1 4.62 Label
## 263 C23 pasta 1 4.62 Label
## 264 C23 beds 0 4.62 Label
## 265 M20 faces 0 4.64 Label
## 266 M20 houses 0 4.64 Label
## 267 M20 pasta 1 4.64 Label
## 268 M20 beds 1 4.64 Label
## 269 M21 faces 1 4.64 Label
## 270 M21 houses 1 4.64 Label
## 271 M21 pasta 1 4.64 Label
## 272 M21 beds 1 4.64 Label
## 273 C21 faces 1 4.73 Label
## 274 C21 houses 0 4.73 Label
## 275 C21 pasta 1 4.73 Label
## 276 C21 beds 1 4.73 Label
## 277 M24 faces 1 4.82 Label
## 278 M24 houses 1 4.82 Label
## 279 M24 pasta 1 4.82 Label
## 280 M24 beds 1 4.82 Label
## 281 M5 faces 0 4.84 Label
## 282 M5 houses 0 4.84 Label
## 283 M5 pasta 0 4.84 Label
## 284 M5 beds 1 4.84 Label
## 285 M7 faces 1 4.89 Label
## 286 M7 houses 1 4.89 Label
## 287 M7 pasta 1 4.89 Label
## 288 M7 beds 0 4.89 Label
## 289 M8 faces 1 4.89 Label
## 290 M8 houses 1 4.89 Label
## 291 M8 pasta 1 4.89 Label
## 292 M8 beds 1 4.89 Label
## 293 C18 faces 0 4.95 Label
## 294 C18 houses 1 4.95 Label
## 295 C18 pasta 1 4.95 Label
## 296 C18 beds 1 4.95 Label
## 297 M25 faces 1 4.96 Label
## 298 M25 houses 1 4.96 Label
## 299 M25 pasta 1 4.96 Label
## 300 M25 beds 1 4.96 Label
## 317 MSCH52 faces 0 2.50 No Label
## 318 MSCH52 houses 1 2.50 No Label
## 319 MSCH52 pasta 0 2.50 No Label
## 320 MSCH52 beds 1 2.50 No Label
## 321 MSCH38 faces 0 2.59 No Label
## 322 MSCH38 houses 0 2.59 No Label
## 323 MSCH38 pasta 1 2.59 No Label
## 324 MSCH38 beds 0 2.59 No Label
## 325 MSCH43 faces 0 2.71 No Label
## 326 MSCH43 houses 0 2.71 No Label
## 327 MSCH43 pasta 0 2.71 No Label
## 328 MSCH43 beds 0 2.71 No Label
## 329 MSCH49 faces 0 2.88 No Label
## 330 MSCH49 houses 0 2.88 No Label
## 331 MSCH49 pasta 0 2.88 No Label
## 332 MSCH49 beds 0 2.88 No Label
## 333 MSCH45 faces 0 2.90 No Label
## 334 MSCH45 houses 0 2.90 No Label
## 335 MSCH45 pasta 0 2.90 No Label
## 336 MSCH45 beds 1 2.90 No Label
## 337 MSCH42 faces 1 2.93 No Label
## 338 MSCH42 houses 0 2.93 No Label
## 339 MSCH42 pasta 0 2.93 No Label
## 340 MSCH42 beds 0 2.93 No Label
## 341 MSCH53 faces 1 2.99 No Label
## 342 MSCH53 houses 1 2.99 No Label
## 343 MSCH53 pasta 0 2.99 No Label
## 344 MSCH53 beds 0 2.99 No Label
## 345 SCH35 faces 0 3.02 No Label
## 346 SCH35 houses 0 3.02 No Label
## 347 SCH35 pasta 0 3.02 No Label
## 348 SCH35 beds 0 3.02 No Label
## 349 MSCH40 faces 0 3.02 No Label
## 350 MSCH40 houses 1 3.02 No Label
## 351 MSCH40 pasta 0 3.02 No Label
## 352 MSCH40 beds 1 3.02 No Label
## 353 SCH34 faces 0 3.06 No Label
## 354 SCH34 houses 0 3.06 No Label
## 355 SCH34 pasta 0 3.06 No Label
## 356 SCH34 beds 0 3.06 No Label
## 357 SCH33 faces 0 3.06 No Label
## 358 SCH33 houses 0 3.06 No Label
## 359 SCH33 pasta 0 3.06 No Label
## 360 SCH33 beds 0 3.06 No Label
## 361 MSCH41 faces 0 3.18 No Label
## 362 MSCH41 houses 0 3.18 No Label
## 363 MSCH41 pasta 0 3.18 No Label
## 364 MSCH41 beds 0 3.18 No Label
## 365 SCH37 beds 0 3.27 No Label
## 366 SCH37 faces 1 3.27 No Label
## 367 SCH37 houses 0 3.27 No Label
## 368 SCH37 pasta 1 3.27 No Label
## 369 SCH32 faces 1 3.27 No Label
## 370 SCH32 houses 0 3.27 No Label
## 371 SCH32 pasta 0 3.27 No Label
## 372 SCH32 beds 0 3.27 No Label
## 373 SCH36 beds 0 3.33 No Label
## 374 SCH36 faces 0 3.33 No Label
## 375 SCH36 houses 1 3.33 No Label
## 376 SCH36 pasta 1 3.33 No Label
## 377 SCH11 beds 0 3.41 No Label
## 378 SCH12 faces 0 3.41 No Label
## 379 SCH12 houses 0 3.41 No Label
## 380 SCH12 pasta 0 3.41 No Label
## 381 SCH12 beds 0 3.41 No Label
## 382 SCH18 faces 0 3.45 No Label
## 383 SCH18 houses 0 3.45 No Label
## 384 SCH18 pasta 0 3.45 No Label
## 385 SCH18 beds 0 3.45 No Label
## 386 MSCH48 faces 0 3.50 No Label
## 387 MSCH48 houses 1 3.50 No Label
## 388 MSCH48 pasta 0 3.50 No Label
## 389 MSCH48 beds 0 3.50 No Label
## 390 SCH25 faces 0 3.54 No Label
## 391 SCH25 houses 1 3.54 No Label
## 392 SCH25 pasta 1 3.54 No Label
## 393 SCH25 beds 0 3.54 No Label
## 394 SCH31 faces 0 3.71 No Label
## 395 SCH31 houses 0 3.71 No Label
## 396 SCH31 pasta 0 3.71 No Label
## 397 SCH31 beds 0 3.71 No Label
## 398 MSCH46 faces 0 3.76 No Label
## 399 MSCH46 houses 0 3.76 No Label
## 400 MSCH46 pasta 1 3.76 No Label
## 401 MSCH46 beds 0 3.76 No Label
## 402 SCH11 faces 1 3.82 No Label
## 403 SCH11 houses 1 3.82 No Label
## 404 SCH11 pasta 1 3.82 No Label
## 405 SCH29 faces 0 3.83 No Label
## 406 SCH29 houses 0 3.83 No Label
## 407 SCH29 pasta 0 3.83 No Label
## 408 SCH29 beds 0 3.83 No Label
## 409 MSCH39 beds 1 3.93 No Label
## 410 MSCH39 pasta 0 3.93 No Label
## 411 MSCH39 houses 0 3.94 No Label
## 412 MSCH39 faces 0 3.94 No Label
## 413 SCH28 faces 0 4.02 No Label
## 414 SCH28 houses 0 4.02 No Label
## 415 SCH28 pasta 0 4.02 No Label
## 416 SCH28 beds 0 4.02 No Label
## 417 SCH22 faces 0 4.02 No Label
## 418 SCH22 houses 0 4.02 No Label
## 419 SCH22 pasta 0 4.02 No Label
## 420 SCH22 beds 1 4.02 No Label
## 421 SCH24 faces 0 4.07 No Label
## 422 SCH24 houses 0 4.07 No Label
## 423 SCH24 pasta 1 4.07 No Label
## 424 SCH24 beds 0 4.07 No Label
## 425 SCH27 faces 0 4.09 No Label
## 426 SCH27 houses 0 4.09 No Label
## 427 SCH27 pasta 1 4.09 No Label
## 428 SCH27 beds 0 4.09 No Label
## 429 SCH17 faces 0 4.25 No Label
## 430 SCH17 houses 0 4.25 No Label
## 431 SCH17 pasta 1 4.25 No Label
## 432 SCH17 beds 0 4.25 No Label
## 433 SCH10 faces 0 4.32 No Label
## 434 SCH10 houses 0 4.32 No Label
## 435 SCH10 pasta 0 4.32 No Label
## 436 SCH10 beds 1 4.32 No Label
## 437 SCH9 faces 0 4.37 No Label
## 438 SCH9 houses 0 4.37 No Label
## 439 SCH9 pasta 0 4.37 No Label
## 440 SCH9 beds 0 4.37 No Label
## 441 SCH20 faces 0 4.39 No Label
## 442 SCH20 houses 0 4.39 No Label
## 443 SCH20 pasta 0 4.39 No Label
## 444 SCH20 beds 0 4.39 No Label
## 445 SCH6 faces 0 4.41 No Label
## 446 SCH6 houses 0 4.41 No Label
## 447 SCH6 pasta 0 4.41 No Label
## 448 SCH6 beds 0 4.41 No Label
## 449 SCH7 faces 1 4.41 No Label
## 450 SCH7 houses 0 4.41 No Label
## 451 SCH7 pasta 0 4.41 No Label
## 452 SCH7 beds 0 4.41 No Label
## 453 SCH15 faces 1 4.42 No Label
## 454 SCH15 houses 0 4.42 No Label
## 455 SCH15 pasta 0 4.42 No Label
## 456 SCH15 beds 0 4.42 No Label
## 457 SCH30 faces 0 4.44 No Label
## 458 SCH30 houses 0 4.44 No Label
## 459 SCH30 pasta 1 4.44 No Label
## 460 SCH30 beds 0 4.44 No Label
## 461 SCH3 faces 0 4.47 No Label
## 462 SCH3 houses 0 4.47 No Label
## 463 SCH3 pasta 0 4.47 No Label
## 464 SCH3 beds 0 4.47 No Label
## 465 SCH26 faces 0 4.47 No Label
## 466 SCH26 houses 0 4.47 No Label
## 467 SCH26 pasta 1 4.47 No Label
## 468 SCH26 beds 0 4.47 No Label
## 469 SCH8 faces 0 4.52 No Label
## 470 SCH8 houses 0 4.52 No Label
## 471 SCH8 pasta 0 4.52 No Label
## 472 SCH8 beds 0 4.52 No Label
## 473 SCH16 faces 0 4.55 No Label
## 474 SCH16 houses 0 4.55 No Label
## 475 SCH16 pasta 0 4.55 No Label
## 476 SCH16 beds 1 4.55 No Label
## 477 SCH14 faces 0 4.58 No Label
## 478 SCH14 houses 0 4.58 No Label
## 479 SCH14 pasta 0 4.58 No Label
## 480 SCH14 beds 1 4.58 No Label
## 481 SCH2 faces 0 4.61 No Label
## 482 SCH2 houses 0 4.61 No Label
## 483 SCH2 pasta 0 4.61 No Label
## 484 SCH2 beds 0 4.61 No Label
## 485 SCH5 faces 0 4.61 No Label
## 486 SCH5 houses 0 4.61 No Label
## 487 SCH5 pasta 0 4.61 No Label
## 488 SCH5 beds 0 4.61 No Label
## 489 SCH13 faces 0 4.75 No Label
## 490 SCH13 houses 0 4.75 No Label
## 491 SCH13 pasta 0 4.75 No Label
## 492 SCH13 beds 0 4.75 No Label
## 493 SCH21 faces 0 4.76 No Label
## 494 SCH21 houses 0 4.76 No Label
## 495 SCH21 pasta 0 4.76 No Label
## 496 SCH21 beds 0 4.76 No Label
## 497 SCH19 faces 0 4.79 No Label
## 498 SCH19 houses 0 4.79 No Label
## 499 SCH19 pasta 0 4.79 No Label
## 500 SCH19 beds 1 4.79 No Label
## 501 SCH23 faces 0 4.82 No Label
## 502 SCH23 houses 0 4.82 No Label
## 503 SCH23 pasta 0 4.82 No Label
## 504 SCH23 beds 0 4.82 No Label
## 505 SCH1 faces 0 4.82 No Label
## 506 SCH1 houses 0 4.82 No Label
## 507 SCH1 pasta 0 4.82 No Label
## 508 SCH1 beds 0 4.82 No Label
## 509 MSCH66 faces 0 3.50 No Label
## 510 MSCH66 houses 0 3.50 No Label
## 511 MSCH66 pasta 1 3.50 No Label
## 512 MSCH66 beds 0 3.50 No Label
## 513 MSCH67 faces 0 3.24 No Label
## 514 MSCH67 houses 1 3.24 No Label
## 515 MSCH67 pasta 0 3.24 No Label
## 516 MSCH67 beds 1 3.24 No Label
## 517 MSCH68 faces 0 3.94 No Label
## 518 MSCH68 houses 0 3.94 No Label
## 519 MSCH68 pasta 0 3.94 No Label
## 520 MSCH68 beds 0 3.94 No Label
## 521 MSCH69 faces 0 2.72 No Label
## 522 MSCH69 houses 1 2.72 No Label
## 523 MSCH69 pasta 1 2.72 No Label
## 524 MSCH69 beds 0 2.72 No Label
## 529 MSCH71 faces 1 3.14 No Label
## 530 MSCH71 houses 1 3.14 No Label
## 531 MSCH71 pasta 1 3.14 No Label
## 532 MSCH71 beds 0 3.14 No Label
## 533 MSCH72 faces 1 3.72 No Label
## 534 MSCH72 houses 1 3.72 No Label
## 535 MSCH72 pasta 0 3.72 No Label
## 536 MSCH72 beds 0 3.72 No Label
## 537 MSCH73 faces 0 3.10 No Label
## 538 MSCH73 houses 0 3.10 No Label
## 539 MSCH73 pasta 0 3.10 No Label
## 540 MSCH73 beds 0 3.10 No Label
## 545 MSCH75 faces 0 3.67 No Label
## 546 MSCH75 houses 0 3.67 No Label
## 547 MSCH75 pasta 0 3.67 No Label
## 548 MSCH75 beds 0 3.66 No Label
## 549 MSCH76 faces 0 2.58 No Label
## 550 MSCH76 houses 0 2.58 No Label
## 551 MSCH76 pasta 0 2.58 No Label
## 552 MSCH76 beds 0 2.58 No Label
## 553 MSCH77 faces 0 2.55 No Label
## 554 MSCH77 houses 0 2.55 No Label
## 555 MSCH77 pasta 0 2.55 No Label
## 556 MSCH77 beds 1 2.55 No Label
## 561 MSCH79 faces 0 2.70 No Label
## 562 MSCH79 houses 1 2.70 No Label
## 563 MSCH79 pasta 0 2.70 No Label
## 564 MSCH79 beds 1 2.70 No Label
## 565 MSCH80 faces 0 2.76 No Label
## 566 MSCH80 houses 0 2.76 No Label
## 567 MSCH80 pasta 0 2.76 No Label
## 568 MSCH80 beds 0 2.76 No Label
## 569 MSCH81 faces 1 2.84 No Label
## 570 MSCH81 houses 0 2.84 No Label
## 571 MSCH81 pasta 0 2.84 No Label
## 572 MSCH81 beds 0 2.84 No Label
## 581 MSCH84 faces 0 2.83 No Label
## 582 MSCH84 houses 0 2.83 No Label
## 583 MSCH84 pasta 1 2.83 No Label
## 584 MSCH84 beds 0 2.83 No Label
## 585 MSCH85 faces 0 2.69 No Label
## 586 MSCH85 houses 0 2.69 No Label
## 587 MSCH85 pasta 0 2.69 No Label
## 588 MSCH85 beds 0 2.69 No LabelYou can also use logical tests for columns, though that is a little trickier. Let’s get all of the columns that start with the letter c. We can look for variables that start with c by using str_detect() on the column names, looking for entries that start with c "^c".
ps_data[,grep("^c", colnames(ps_data))]## correct condition
## 1 1 Label
## 2 1 Label
## 3 0 Label
## 4 0 Label
## 5 0 Label
## 6 0 Label
## 7 1 Label
## 8 1 Label
## 9 0 Label
## 10 0 Label
## 11 0 Label
## 12 0 Label
## 13 0 Label
## 14 1 Label
## 15 1 Label
## 16 1 Label
## 17 0 Label
## 18 0 Label
## 19 1 Label
## 20 1 Label
## 21 1 Label
## 22 1 Label
## 23 0 Label
## 24 1 Label
## 25 1 Label
## 26 1 Label
## 27 1 Label
## 28 0 Label
## 29 1 Label
## 30 1 Label
## 31 0 Label
## 32 1 Label
## 33 1 Label
## 34 0 Label
## 35 1 Label
## 36 0 Label
## 37 0 Label
## 38 0 Label
## 39 1 Label
## 40 0 Label
## 41 1 Label
## 42 0 Label
## 43 1 Label
## 44 1 Label
## 45 1 Label
## 46 0 Label
## 47 0 Label
## 48 0 Label
## 49 0 Label
## 50 1 Label
## 51 0 Label
## 52 1 Label
## 53 1 Label
## 54 1 Label
## 55 0 Label
## 56 1 Label
## 57 1 Label
## 58 1 Label
## 59 0 Label
## 60 1 Label
## 61 0 Label
## 62 0 Label
## 63 1 Label
## 64 1 Label
## 65 0 Label
## 66 0 Label
## 67 1 Label
## 68 0 Label
## 69 0 Label
## 70 1 Label
## 71 1 Label
## 72 0 Label
## 73 1 Label
## 74 0 Label
## 75 1 Label
## 76 0 Label
## 77 0 Label
## 78 0 Label
## 79 1 Label
## 80 1 Label
## 81 1 Label
## 82 0 Label
## 83 1 Label
## 84 1 Label
## 85 1 Label
## 86 0 Label
## 87 0 Label
## 88 1 Label
## 89 1 Label
## 90 1 Label
## 91 1 Label
## 92 1 Label
## 93 1 Label
## 94 0 Label
## 95 1 Label
## 96 1 Label
## 97 0 Label
## 98 1 Label
## 99 1 Label
## 100 1 Label
## 101 1 Label
## 102 1 Label
## 103 1 Label
## 104 1 Label
## 105 1 Label
## 106 1 Label
## 107 1 Label
## 108 1 Label
## 109 1 Label
## 110 1 Label
## 111 0 Label
## 112 1 Label
## 113 0 Label
## 114 1 Label
## 115 0 Label
## 116 0 Label
## 117 0 Label
## 118 0 Label
## 119 1 Label
## 120 1 Label
## 121 1 Label
## 122 0 Label
## 123 0 Label
## 124 1 Label
## 125 1 Label
## 126 0 Label
## 127 1 Label
## 128 0 Label
## 129 0 Label
## 130 1 Label
## 131 1 Label
## 132 1 Label
## 133 1 Label
## 134 1 Label
## 135 1 Label
## 136 1 Label
## 137 0 Label
## 138 1 Label
## 139 1 Label
## 140 1 Label
## 141 0 Label
## 142 1 Label
## 143 1 Label
## 144 1 Label
## 145 0 Label
## 146 0 Label
## 147 1 Label
## 148 1 Label
## 149 0 Label
## 150 1 Label
## 151 1 Label
## 152 1 Label
## 153 0 Label
## 154 0 Label
## 155 0 Label
## 156 1 Label
## 157 1 Label
## 158 0 Label
## 159 1 Label
## 160 1 Label
## 161 0 Label
## 162 1 Label
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## 164 0 Label
## 165 1 Label
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## 167 1 Label
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## 170 1 Label
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## 174 1 Label
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## 179 1 Label
## 180 1 Label
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## 182 0 Label
## 183 1 Label
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## 185 1 Label
## 186 1 Label
## 187 1 Label
## 188 1 Label
## 189 1 Label
## 190 0 Label
## 191 0 Label
## 192 1 Label
## 193 0 Label
## 194 0 Label
## 195 1 Label
## 196 1 Label
## 197 1 Label
## 198 1 Label
## 199 1 Label
## 200 1 Label
## 201 1 Label
## 202 1 Label
## 203 1 Label
## 204 1 Label
## 205 0 Label
## 206 1 Label
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## 208 0 Label
## 209 1 Label
## 210 0 Label
## 211 0 Label
## 212 1 Label
## 213 1 Label
## 214 1 Label
## 215 1 Label
## 216 1 Label
## 217 1 Label
## 218 0 Label
## 219 0 Label
## 220 1 Label
## 221 1 Label
## 222 0 Label
## 223 1 Label
## 224 1 Label
## 225 1 Label
## 226 1 Label
## 227 1 Label
## 228 1 Label
## 229 1 Label
## 230 0 Label
## 231 1 Label
## 232 1 Label
## 233 1 Label
## 234 1 Label
## 235 1 Label
## 236 1 Label
## 237 1 Label
## 238 1 Label
## 239 0 Label
## 240 1 Label
## 241 0 Label
## 242 1 Label
## 243 0 Label
## 244 1 Label
## 245 1 Label
## 246 1 Label
## 247 1 Label
## 248 1 Label
## 249 1 Label
## 250 1 Label
## 251 0 Label
## 252 1 Label
## 253 1 Label
## 254 1 Label
## 255 1 Label
## 256 1 Label
## 257 1 Label
## 258 1 Label
## 259 1 Label
## 260 1 Label
## 261 0 Label
## 262 1 Label
## 263 1 Label
## 264 0 Label
## 265 0 Label
## 266 0 Label
## 267 1 Label
## 268 1 Label
## 269 1 Label
## 270 1 Label
## 271 1 Label
## 272 1 Label
## 273 1 Label
## 274 0 Label
## 275 1 Label
## 276 1 Label
## 277 1 Label
## 278 1 Label
## 279 1 Label
## 280 1 Label
## 281 0 Label
## 282 0 Label
## 283 0 Label
## 284 1 Label
## 285 1 Label
## 286 1 Label
## 287 1 Label
## 288 0 Label
## 289 1 Label
## 290 1 Label
## 291 1 Label
## 292 1 Label
## 293 0 Label
## 294 1 Label
## 295 1 Label
## 296 1 Label
## 297 1 Label
## 298 1 Label
## 299 1 Label
## 300 1 Label
## 301 1 No Label
## 302 0 No Label
## 303 1 No Label
## 304 0 No Label
## 305 0 No Label
## 306 0 No Label
## 307 0 No Label
## 308 0 No Label
## 309 0 No Label
## 310 0 No Label
## 311 0 No Label
## 312 0 No Label
## 313 0 No Label
## 314 0 No Label
## 315 0 No Label
## 316 0 No Label
## 317 0 No Label
## 318 1 No Label
## 319 0 No Label
## 320 1 No Label
## 321 0 No Label
## 322 0 No Label
## 323 1 No Label
## 324 0 No Label
## 325 0 No Label
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## 327 0 No Label
## 328 0 No Label
## 329 0 No Label
## 330 0 No Label
## 331 0 No Label
## 332 0 No Label
## 333 0 No Label
## 334 0 No Label
## 335 0 No Label
## 336 1 No Label
## 337 1 No Label
## 338 0 No Label
## 339 0 No Label
## 340 0 No Label
## 341 1 No Label
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## 343 0 No Label
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## 346 0 No Label
## 347 0 No Label
## 348 0 No Label
## 349 0 No Label
## 350 1 No Label
## 351 0 No Label
## 352 1 No Label
## 353 0 No Label
## 354 0 No Label
## 355 0 No Label
## 356 0 No Label
## 357 0 No Label
## 358 0 No Label
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## 364 0 No Label
## 365 0 No Label
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## 384 0 No Label
## 385 0 No Label
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## 387 1 No Label
## 388 0 No Label
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## 392 1 No Label
## 393 0 No Label
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## 399 0 No Label
## 400 1 No Label
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## 405 0 No Label
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## 407 0 No Label
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## 409 1 No Label
## 410 0 No Label
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## 413 0 No Label
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## 415 0 No Label
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## 418 0 No Label
## 419 0 No Label
## 420 1 No Label
## 421 0 No Label
## 422 0 No Label
## 423 1 No Label
## 424 0 No Label
## 425 0 No Label
## 426 0 No Label
## 427 1 No Label
## 428 0 No Label
## 429 0 No Label
## 430 0 No Label
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## 432 0 No Label
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## 436 1 No Label
## 437 0 No Label
## 438 0 No Label
## 439 0 No Label
## 440 0 No Label
## 441 0 No Label
## 442 0 No Label
## 443 0 No Label
## 444 0 No Label
## 445 0 No Label
## 446 0 No Label
## 447 0 No Label
## 448 0 No Label
## 449 1 No Label
## 450 0 No Label
## 451 0 No Label
## 452 0 No Label
## 453 1 No Label
## 454 0 No Label
## 455 0 No Label
## 456 0 No Label
## 457 0 No Label
## 458 0 No Label
## 459 1 No Label
## 460 0 No Label
## 461 0 No Label
## 462 0 No Label
## 463 0 No Label
## 464 0 No Label
## 465 0 No Label
## 466 0 No Label
## 467 1 No Label
## 468 0 No Label
## 469 0 No Label
## 470 0 No Label
## 471 0 No Label
## 472 0 No Label
## 473 0 No Label
## 474 0 No Label
## 475 0 No Label
## 476 1 No Label
## 477 0 No Label
## 478 0 No Label
## 479 0 No Label
## 480 1 No Label
## 481 0 No Label
## 482 0 No Label
## 483 0 No Label
## 484 0 No Label
## 485 0 No Label
## 486 0 No Label
## 487 0 No Label
## 488 0 No Label
## 489 0 No Label
## 490 0 No Label
## 491 0 No Label
## 492 0 No Label
## 493 0 No Label
## 494 0 No Label
## 495 0 No Label
## 496 0 No Label
## 497 0 No Label
## 498 0 No Label
## 499 0 No Label
## 500 1 No Label
## 501 0 No Label
## 502 0 No Label
## 503 0 No Label
## 504 0 No Label
## 505 0 No Label
## 506 0 No Label
## 507 0 No Label
## 508 0 No Label
## 509 0 No Label
## 510 0 No Label
## 511 1 No Label
## 512 0 No Label
## 513 0 No Label
## 514 1 No Label
## 515 0 No Label
## 516 1 No Label
## 517 0 No Label
## 518 0 No Label
## 519 0 No Label
## 520 0 No Label
## 521 0 No Label
## 522 1 No Label
## 523 1 No Label
## 524 0 No Label
## 525 0 No Label
## 526 0 No Label
## 527 0 No Label
## 528 1 No Label
## 529 1 No Label
## 530 1 No Label
## 531 1 No Label
## 532 0 No Label
## 533 1 No Label
## 534 1 No Label
## 535 0 No Label
## 536 0 No Label
## 537 0 No Label
## 538 0 No Label
## 539 0 No Label
## 540 0 No Label
## 541 1 No Label
## 542 0 No Label
## 543 0 No Label
## 544 1 No Label
## 545 0 No Label
## 546 0 No Label
## 547 0 No Label
## 548 0 No Label
## 549 0 No Label
## 550 0 No Label
## 551 0 No Label
## 552 0 No Label
## 553 0 No Label
## 554 0 No Label
## 555 0 No Label
## 556 1 No Label
## 557 0 No Label
## 558 0 No Label
## 559 0 No Label
## 560 1 No Label
## 561 0 No Label
## 562 1 No Label
## 563 0 No Label
## 564 1 No Label
## 565 0 No Label
## 566 0 No Label
## 567 0 No Label
## 568 0 No Label
## 569 1 No Label
## 570 0 No Label
## 571 0 No Label
## 572 0 No Label
## 573 1 No Label
## 574 0 No Label
## 575 1 No Label
## 576 0 No Label
## 577 0 No Label
## 578 0 No Label
## 579 1 No Label
## 580 0 No Label
## 581 0 No Label
## 582 0 No Label
## 583 1 No Label
## 584 0 No Label
## 585 0 No Label
## 586 0 No Label
## 587 0 No Label
## 588 0 No LabelYou can also do more complicated logical tests by including & for AND and | for OR. For example, let’s get subjects that were in the label condition and less than 3 years old:
ps_data[which(ps_data$condition == "Label" & ps_data$age < 3),]## subid item correct age condition
## 1 M22 faces 1 2.00 Label
## 2 M22 houses 1 2.00 Label
## 3 M22 pasta 0 2.00 Label
## 4 M22 beds 0 2.00 Label
## 5 T22 beds 0 2.13 Label
## 6 T22 faces 0 2.13 Label
## 7 T22 houses 1 2.13 Label
## 8 T22 pasta 1 2.13 Label
## 9 T17 pasta 0 2.32 Label
## 10 T17 faces 0 2.32 Label
## 11 T17 houses 0 2.32 Label
## 12 T17 beds 0 2.32 Label
## 13 M3 faces 0 2.38 Label
## 14 M3 houses 1 2.38 Label
## 15 M3 pasta 1 2.38 Label
## 16 M3 beds 1 2.38 Label
## 17 T19 faces 0 2.47 Label
## 18 T19 houses 0 2.47 Label
## 19 T19 pasta 1 2.47 Label
## 20 T19 beds 1 2.47 Label
## 21 T20 faces 1 2.50 Label
## 22 T20 houses 1 2.50 Label
## 23 T20 pasta 0 2.50 Label
## 24 T20 beds 1 2.50 Label
## 25 T21 faces 1 2.58 Label
## 26 T21 houses 1 2.58 Label
## 27 T21 pasta 1 2.58 Label
## 28 T21 beds 0 2.58 Label
## 29 M26 faces 1 2.59 Label
## 30 M26 houses 1 2.59 Label
## 31 M26 pasta 0 2.59 Label
## 32 M26 beds 1 2.59 Label
## 33 T18 faces 1 2.61 Label
## 34 T18 houses 0 2.61 Label
## 35 T18 pasta 1 2.61 Label
## 36 T18 beds 0 2.61 Label
## 37 T12 beds 0 2.72 Label
## 38 T12 faces 0 2.72 Label
## 39 T12 houses 1 2.72 Label
## 40 T12 pasta 0 2.72 Label
## 41 T16 faces 1 2.73 Label
## 42 T16 houses 0 2.73 Label
## 43 T16 pasta 1 2.73 Label
## 44 T16 beds 1 2.73 Label
## 45 T7 faces 1 2.74 Label
## 46 T7 houses 0 2.74 Label
## 47 T7 pasta 0 2.74 Label
## 48 T7 beds 0 2.74 Label
## 49 T9 houses 0 2.79 Label
## 50 T9 faces 1 2.79 Label
## 51 T9 pasta 0 2.79 Label
## 52 T9 beds 1 2.79 Label
## 53 T5 faces 1 2.80 Label
## 54 T5 houses 1 2.80 Label
## 55 T5 pasta 0 2.80 Label
## 56 T5 beds 1 2.80 Label
## 57 T14 faces 1 2.83 Label
## 58 T14 houses 1 2.83 Label
## 59 T14 pasta 0 2.83 Label
## 60 T14 beds 1 2.83 Label
## 61 T2 houses 0 2.83 Label
## 62 T2 faces 0 2.83 Label
## 63 T2 pasta 1 2.83 Label
## 64 T2 beds 1 2.83 Label
## 65 T15 faces 0 2.85 Label
## 66 T15 houses 0 2.85 Label
## 67 T15 pasta 1 2.85 Label
## 68 T15 beds 0 2.85 Label
## 69 M13 houses 0 2.88 Label
## 70 M13 beds 1 2.88 Label
## 71 M13 faces 1 2.88 Label
## 72 M13 pasta 0 2.88 Label
## 73 M12 faces 1 2.88 Label
## 74 M12 houses 0 2.88 Label
## 75 M12 pasta 1 2.88 Label
## 76 M12 beds 0 2.88 Label
## 77 T13 beds 0 2.89 Label
## 78 T13 faces 0 2.89 Label
## 79 T13 houses 1 2.89 Label
## 80 T13 pasta 1 2.89 Label
## 81 T8 faces 1 2.91 Label
## 82 T8 houses 0 2.91 Label
## 83 T8 pasta 1 2.91 Label
## 84 T8 beds 1 2.91 Label
## 85 T1 faces 1 2.95 Label
## 86 T1 houses 0 2.95 Label
## 87 T1 pasta 0 2.95 Label
## 88 T1 beds 1 2.95 Label
## 89 M15 faces 1 2.98 Label
## 90 M15 houses 1 2.98 Label
## 91 M15 pasta 1 2.98 Label
## 92 M15 beds 1 2.98 Label
## 93 T11 faces 1 2.99 Label
## 94 T11 houses 0 2.99 Label
## 95 T11 pasta 1 2.99 Label
## 96 T11 beds 1 2.99 LabelOr we might want subjects rows where the item is either faces or houses.
ps_data[which(ps_data$item == "faces" | ps_data$item == "houses"),]## subid item correct age condition
## 1 M22 faces 1 2.00 Label
## 2 M22 houses 1 2.00 Label
## 6 T22 faces 0 2.13 Label
## 7 T22 houses 1 2.13 Label
## 10 T17 faces 0 2.32 Label
## 11 T17 houses 0 2.32 Label
## 13 M3 faces 0 2.38 Label
## 14 M3 houses 1 2.38 Label
## 17 T19 faces 0 2.47 Label
## 18 T19 houses 0 2.47 Label
## 21 T20 faces 1 2.50 Label
## 22 T20 houses 1 2.50 Label
## 25 T21 faces 1 2.58 Label
## 26 T21 houses 1 2.58 Label
## 29 M26 faces 1 2.59 Label
## 30 M26 houses 1 2.59 Label
## 33 T18 faces 1 2.61 Label
## 34 T18 houses 0 2.61 Label
## 38 T12 faces 0 2.72 Label
## 39 T12 houses 1 2.72 Label
## 41 T16 faces 1 2.73 Label
## 42 T16 houses 0 2.73 Label
## 45 T7 faces 1 2.74 Label
## 46 T7 houses 0 2.74 Label
## 49 T9 houses 0 2.79 Label
## 50 T9 faces 1 2.79 Label
## 53 T5 faces 1 2.80 Label
## 54 T5 houses 1 2.80 Label
## 57 T14 faces 1 2.83 Label
## 58 T14 houses 1 2.83 Label
## 61 T2 houses 0 2.83 Label
## 62 T2 faces 0 2.83 Label
## 65 T15 faces 0 2.85 Label
## 66 T15 houses 0 2.85 Label
## 69 M13 houses 0 2.88 Label
## 71 M13 faces 1 2.88 Label
## 73 M12 faces 1 2.88 Label
## 74 M12 houses 0 2.88 Label
## 78 T13 faces 0 2.89 Label
## 79 T13 houses 1 2.89 Label
## 81 T8 faces 1 2.91 Label
## 82 T8 houses 0 2.91 Label
## 85 T1 faces 1 2.95 Label
## 86 T1 houses 0 2.95 Label
## 89 M15 faces 1 2.98 Label
## 90 M15 houses 1 2.98 Label
## 93 T11 faces 1 2.99 Label
## 94 T11 houses 0 2.99 Label
## 97 T10 faces 0 3.00 Label
## 98 T10 houses 1 3.00 Label
## 101 T3 faces 1 3.09 Label
## 102 T3 houses 1 3.09 Label
## 105 T6 faces 1 3.10 Label
## 106 T6 houses 1 3.10 Label
## 110 M32 faces 1 3.19 Label
## 111 M32 houses 0 3.19 Label
## 113 M1 faces 0 3.20 Label
## 116 M1 houses 0 3.20 Label
## 117 C16 faces 0 3.22 Label
## 118 C16 houses 0 3.22 Label
## 121 T4 faces 1 3.24 Label
## 122 T4 houses 0 3.24 Label
## 125 C17 faces 1 3.25 Label
## 126 C17 houses 0 3.25 Label
## 129 C6 faces 0 3.26 Label
## 130 C6 houses 1 3.26 Label
## 133 M10 faces 1 3.28 Label
## 134 M10 houses 1 3.28 Label
## 137 M31 faces 0 3.30 Label
## 138 M31 houses 1 3.30 Label
## 141 C3 houses 0 3.46 Label
## 144 C3 faces 1 3.46 Label
## 145 C10 faces 0 3.46 Label
## 146 C10 houses 0 3.46 Label
## 149 M18 faces 0 3.46 Label
## 150 M18 houses 1 3.46 Label
## 153 M16 faces 0 3.50 Label
## 154 M16 houses 0 3.50 Label
## 157 M23 faces 1 3.52 Label
## 158 M23 houses 0 3.52 Label
## 161 C7 faces 0 3.55 Label
## 162 C7 houses 1 3.55 Label
## 165 C12 faces 1 3.56 Label
## 166 C12 houses 0 3.56 Label
## 169 C15 faces 1 3.59 Label
## 170 C15 houses 1 3.59 Label
## 173 M29 faces 0 3.72 Label
## 174 M29 houses 1 3.72 Label
## 177 C20 faces 1 3.75 Label
## 178 C20 houses 1 3.75 Label
## 181 M11 faces 1 3.82 Label
## 182 M11 houses 0 3.82 Label
## 186 C9 faces 1 3.82 Label
## 187 C9 houses 1 3.82 Label
## 189 C24 faces 1 3.85 Label
## 190 C24 houses 0 3.85 Label
## 193 C22 faces 0 3.92 Label
## 194 C22 houses 0 3.92 Label
## 197 C8 faces 1 3.92 Label
## 198 C8 houses 1 3.92 Label
## 201 M4 faces 1 3.96 Label
## 202 M4 houses 1 3.96 Label
## 205 M6 faces 0 4.50 Label
## 206 M6 houses 1 4.50 Label
## 209 C19 faces 1 4.14 Label
## 210 C19 houses 0 4.14 Label
## 213 C1 faces 1 4.16 Label
## 214 C1 houses 1 4.16 Label
## 218 M19 faces 0 4.16 Label
## 219 M19 houses 0 4.16 Label
## 221 C11 faces 1 4.22 Label
## 222 C11 houses 0 4.22 Label
## 225 M9 faces 1 4.26 Label
## 226 M9 houses 1 4.26 Label
## 229 M2 faces 1 4.28 Label
## 230 M2 houses 0 4.28 Label
## 233 C5 faces 1 4.29 Label
## 234 C5 houses 1 4.29 Label
## 238 M30 faces 1 4.33 Label
## 239 M30 houses 0 4.33 Label
## 241 C13 faces 0 4.38 Label
## 242 C13 houses 1 4.38 Label
## 245 C4 faces 1 4.55 Label
## 246 C4 houses 1 4.55 Label
## 249 C14 faces 1 4.57 Label
## 250 C14 houses 1 4.57 Label
## 253 M17 faces 1 4.58 Label
## 254 M17 houses 1 4.58 Label
## 257 C2 faces 1 4.60 Label
## 258 C2 houses 1 4.60 Label
## 261 C23 faces 0 4.62 Label
## 262 C23 houses 1 4.62 Label
## 265 M20 faces 0 4.64 Label
## 266 M20 houses 0 4.64 Label
## 269 M21 faces 1 4.64 Label
## 270 M21 houses 1 4.64 Label
## 273 C21 faces 1 4.73 Label
## 274 C21 houses 0 4.73 Label
## 277 M24 faces 1 4.82 Label
## 278 M24 houses 1 4.82 Label
## 281 M5 faces 0 4.84 Label
## 282 M5 houses 0 4.84 Label
## 285 M7 faces 1 4.89 Label
## 286 M7 houses 1 4.89 Label
## 289 M8 faces 1 4.89 Label
## 290 M8 houses 1 4.89 Label
## 293 C18 faces 0 4.95 Label
## 294 C18 houses 1 4.95 Label
## 297 M25 faces 1 4.96 Label
## 298 M25 houses 1 4.96 Label
## 301 MSCH47 faces 1 2.01 No Label
## 302 MSCH47 houses 0 2.01 No Label
## 305 MSCH50 faces 0 2.03 No Label
## 306 MSCH50 houses 0 2.03 No Label
## 309 MSCH51 faces 0 2.07 No Label
## 310 MSCH51 houses 0 2.07 No Label
## 313 MSCH44 faces 0 2.25 No Label
## 314 MSCH44 houses 0 2.25 No Label
## 317 MSCH52 faces 0 2.50 No Label
## 318 MSCH52 houses 1 2.50 No Label
## 321 MSCH38 faces 0 2.59 No Label
## 322 MSCH38 houses 0 2.59 No Label
## 325 MSCH43 faces 0 2.71 No Label
## 326 MSCH43 houses 0 2.71 No Label
## 329 MSCH49 faces 0 2.88 No Label
## 330 MSCH49 houses 0 2.88 No Label
## 333 MSCH45 faces 0 2.90 No Label
## 334 MSCH45 houses 0 2.90 No Label
## 337 MSCH42 faces 1 2.93 No Label
## 338 MSCH42 houses 0 2.93 No Label
## 341 MSCH53 faces 1 2.99 No Label
## 342 MSCH53 houses 1 2.99 No Label
## 345 SCH35 faces 0 3.02 No Label
## 346 SCH35 houses 0 3.02 No Label
## 349 MSCH40 faces 0 3.02 No Label
## 350 MSCH40 houses 1 3.02 No Label
## 353 SCH34 faces 0 3.06 No Label
## 354 SCH34 houses 0 3.06 No Label
## 357 SCH33 faces 0 3.06 No Label
## 358 SCH33 houses 0 3.06 No Label
## 361 MSCH41 faces 0 3.18 No Label
## 362 MSCH41 houses 0 3.18 No Label
## 366 SCH37 faces 1 3.27 No Label
## 367 SCH37 houses 0 3.27 No Label
## 369 SCH32 faces 1 3.27 No Label
## 370 SCH32 houses 0 3.27 No Label
## 374 SCH36 faces 0 3.33 No Label
## 375 SCH36 houses 1 3.33 No Label
## 378 SCH12 faces 0 3.41 No Label
## 379 SCH12 houses 0 3.41 No Label
## 382 SCH18 faces 0 3.45 No Label
## 383 SCH18 houses 0 3.45 No Label
## 386 MSCH48 faces 0 3.50 No Label
## 387 MSCH48 houses 1 3.50 No Label
## 390 SCH25 faces 0 3.54 No Label
## 391 SCH25 houses 1 3.54 No Label
## 394 SCH31 faces 0 3.71 No Label
## 395 SCH31 houses 0 3.71 No Label
## 398 MSCH46 faces 0 3.76 No Label
## 399 MSCH46 houses 0 3.76 No Label
## 402 SCH11 faces 1 3.82 No Label
## 403 SCH11 houses 1 3.82 No Label
## 405 SCH29 faces 0 3.83 No Label
## 406 SCH29 houses 0 3.83 No Label
## 411 MSCH39 houses 0 3.94 No Label
## 412 MSCH39 faces 0 3.94 No Label
## 413 SCH28 faces 0 4.02 No Label
## 414 SCH28 houses 0 4.02 No Label
## 417 SCH22 faces 0 4.02 No Label
## 418 SCH22 houses 0 4.02 No Label
## 421 SCH24 faces 0 4.07 No Label
## 422 SCH24 houses 0 4.07 No Label
## 425 SCH27 faces 0 4.09 No Label
## 426 SCH27 houses 0 4.09 No Label
## 429 SCH17 faces 0 4.25 No Label
## 430 SCH17 houses 0 4.25 No Label
## 433 SCH10 faces 0 4.32 No Label
## 434 SCH10 houses 0 4.32 No Label
## 437 SCH9 faces 0 4.37 No Label
## 438 SCH9 houses 0 4.37 No Label
## 441 SCH20 faces 0 4.39 No Label
## 442 SCH20 houses 0 4.39 No Label
## 445 SCH6 faces 0 4.41 No Label
## 446 SCH6 houses 0 4.41 No Label
## 449 SCH7 faces 1 4.41 No Label
## 450 SCH7 houses 0 4.41 No Label
## 453 SCH15 faces 1 4.42 No Label
## 454 SCH15 houses 0 4.42 No Label
## 457 SCH30 faces 0 4.44 No Label
## 458 SCH30 houses 0 4.44 No Label
## 461 SCH3 faces 0 4.47 No Label
## 462 SCH3 houses 0 4.47 No Label
## 465 SCH26 faces 0 4.47 No Label
## 466 SCH26 houses 0 4.47 No Label
## 469 SCH8 faces 0 4.52 No Label
## 470 SCH8 houses 0 4.52 No Label
## 473 SCH16 faces 0 4.55 No Label
## 474 SCH16 houses 0 4.55 No Label
## 477 SCH14 faces 0 4.58 No Label
## 478 SCH14 houses 0 4.58 No Label
## 481 SCH2 faces 0 4.61 No Label
## 482 SCH2 houses 0 4.61 No Label
## 485 SCH5 faces 0 4.61 No Label
## 486 SCH5 houses 0 4.61 No Label
## 489 SCH13 faces 0 4.75 No Label
## 490 SCH13 houses 0 4.75 No Label
## 493 SCH21 faces 0 4.76 No Label
## 494 SCH21 houses 0 4.76 No Label
## 497 SCH19 faces 0 4.79 No Label
## 498 SCH19 houses 0 4.79 No Label
## 501 SCH23 faces 0 4.82 No Label
## 502 SCH23 houses 0 4.82 No Label
## 505 SCH1 faces 0 4.82 No Label
## 506 SCH1 houses 0 4.82 No Label
## 509 MSCH66 faces 0 3.50 No Label
## 510 MSCH66 houses 0 3.50 No Label
## 513 MSCH67 faces 0 3.24 No Label
## 514 MSCH67 houses 1 3.24 No Label
## 517 MSCH68 faces 0 3.94 No Label
## 518 MSCH68 houses 0 3.94 No Label
## 521 MSCH69 faces 0 2.72 No Label
## 522 MSCH69 houses 1 2.72 No Label
## 525 MSCH70 faces 0 2.31 No Label
## 526 MSCH70 houses 0 2.31 No Label
## 529 MSCH71 faces 1 3.14 No Label
## 530 MSCH71 houses 1 3.14 No Label
## 533 MSCH72 faces 1 3.72 No Label
## 534 MSCH72 houses 1 3.72 No Label
## 537 MSCH73 faces 0 3.10 No Label
## 538 MSCH73 houses 0 3.10 No Label
## 541 MSCH74 faces 1 2.34 No Label
## 542 MSCH74 houses 0 2.34 No Label
## 545 MSCH75 faces 0 3.67 No Label
## 546 MSCH75 houses 0 3.67 No Label
## 549 MSCH76 faces 0 2.58 No Label
## 550 MSCH76 houses 0 2.58 No Label
## 553 MSCH77 faces 0 2.55 No Label
## 554 MSCH77 houses 0 2.55 No Label
## 557 MSCH78 faces 0 2.43 No Label
## 558 MSCH78 houses 0 2.43 No Label
## 561 MSCH79 faces 0 2.70 No Label
## 562 MSCH79 houses 1 2.70 No Label
## 565 MSCH80 faces 0 2.76 No Label
## 566 MSCH80 houses 0 2.76 No Label
## 569 MSCH81 faces 1 2.84 No Label
## 570 MSCH81 houses 0 2.84 No Label
## 573 MSCH82 faces 1 2.46 No Label
## 574 MSCH82 houses 0 2.46 No Label
## 577 MSCH83 faces 0 2.37 No Label
## 578 MSCH83 houses 0 2.37 No Label
## 581 MSCH84 faces 0 2.83 No Label
## 582 MSCH84 houses 0 2.83 No Label
## 585 MSCH85 faces 0 2.69 No Label
## 586 MSCH85 houses 0 2.69 No LabelExercise 2.1a
Get the first 10 rows of the item column from the
ps_datadf.
Exercise 2.1b
Using logical indexing, get all of the rows where age is greater than or equal to 3.5 and item equals “faces”.
Exercise 2.1c (Bonus)
Using logical indexing, get all of the columns that start with either s or a (Hint: you only need 1 grep call and | can be used within a string).
3. Introduction to the tidyverse
We installed and loaded the tidyverse earlier and now we’ll learn some of the basics. “The tidyverse is an opionated collection of R packages designed for data science”. It’s a suite of packages designed with a consistent philosophy and aesthetic. This is nice because all of the packages are designed to work well together, providing a consistent framework to do many of the most common tasks in R including:
- data cleaning (
tidyr) - data manipulating (
dplyr) - data visualization (
ggplot2) - working with strings (
stringr) - working with factors (
forcats)
Among others. We’ll be using functions from each of these packages today and tomorrow.
Today we’ll just focus on data manipulation with dplyr and data visualization with ggplot2
Three qualities of the tidyverse are worth mentioning at the outset:
packages are designed to be like grammars for their task, so we’ll be using terms like verbs to discuss the tidyverse. The idea is that you can string these grammatical elements together to form more complex statements, just like with language.
The first argument of (basically) every function is data. This is very handy, especially when it comes to piping (discussed below).
Variable names are usually not quoted.
The last thing I want to be sure to mention is that the tidyverse packages all have helpful cheatsheets. I think these are one of the handiest R resources out there, and I look at them regularly.
Without further ado, let’s get started with some basic use of dplyr:
3.1 dplyr
dplyr is a grammar of data manipulation. It is made up of several verbs for common data manipulation tasks.
3.1.1 Selecting Columns
The select() is the first verb we’ll cover and is how we can subset columns. If you’re like me, you’ll soon find it much easier to use than the bracket subsetting we did earlier.
select() is the verb for selecting columns from a dataframe. The first argument is data followed which columns you would like to select.
3.1.1.1 Basics of Select
You can indicate the columns you want to select using unquoted names. For example, let’s select just age from ps_data
select(ps_data, age)## age
## 1 2.00
## 2 2.00
## 3 2.00
## 4 2.00
## 5 2.13
## 6 2.13
## 7 2.13
## 8 2.13
## 9 2.32
## 10 2.32
## 11 2.32
## 12 2.32
## 13 2.38
## 14 2.38
## 15 2.38
## 16 2.38
## 17 2.47
## 18 2.47
## 19 2.47
## 20 2.47
## 21 2.50
## 22 2.50
## 23 2.50
## 24 2.50
## 25 2.58
## 26 2.58
## 27 2.58
## 28 2.58
## 29 2.59
## 30 2.59
## 31 2.59
## 32 2.59
## 33 2.61
## 34 2.61
## 35 2.61
## 36 2.61
## 37 2.72
## 38 2.72
## 39 2.72
## 40 2.72
## 41 2.73
## 42 2.73
## 43 2.73
## 44 2.73
## 45 2.74
## 46 2.74
## 47 2.74
## 48 2.74
## 49 2.79
## 50 2.79
## 51 2.79
## 52 2.79
## 53 2.80
## 54 2.80
## 55 2.80
## 56 2.80
## 57 2.83
## 58 2.83
## 59 2.83
## 60 2.83
## 61 2.83
## 62 2.83
## 63 2.83
## 64 2.83
## 65 2.85
## 66 2.85
## 67 2.85
## 68 2.85
## 69 2.88
## 70 2.88
## 71 2.88
## 72 2.88
## 73 2.88
## 74 2.88
## 75 2.88
## 76 2.88
## 77 2.89
## 78 2.89
## 79 2.89
## 80 2.89
## 81 2.91
## 82 2.91
## 83 2.91
## 84 2.91
## 85 2.95
## 86 2.95
## 87 2.95
## 88 2.95
## 89 2.98
## 90 2.98
## 91 2.98
## 92 2.98
## 93 2.99
## 94 2.99
## 95 2.99
## 96 2.99
## 97 3.00
## 98 3.00
## 99 3.00
## 100 3.00
## 101 3.09
## 102 3.09
## 103 3.09
## 104 3.09
## 105 3.10
## 106 3.10
## 107 3.10
## 108 3.10
## 109 3.19
## 110 3.19
## 111 3.19
## 112 3.19
## 113 3.20
## 114 3.20
## 115 3.20
## 116 3.20
## 117 3.22
## 118 3.22
## 119 3.22
## 120 3.22
## 121 3.24
## 122 3.24
## 123 3.24
## 124 3.24
## 125 3.25
## 126 3.25
## 127 3.25
## 128 3.25
## 129 3.26
## 130 3.26
## 131 3.26
## 132 3.26
## 133 3.28
## 134 3.28
## 135 3.28
## 136 3.28
## 137 3.30
## 138 3.30
## 139 3.30
## 140 3.30
## 141 3.46
## 142 3.46
## 143 3.46
## 144 3.46
## 145 3.46
## 146 3.46
## 147 3.46
## 148 3.46
## 149 3.46
## 150 3.46
## 151 3.46
## 152 3.46
## 153 3.50
## 154 3.50
## 155 3.50
## 156 3.50
## 157 3.52
## 158 3.52
## 159 3.52
## 160 3.52
## 161 3.55
## 162 3.55
## 163 3.55
## 164 3.55
## 165 3.56
## 166 3.56
## 167 3.56
## 168 3.56
## 169 3.59
## 170 3.59
## 171 3.59
## 172 3.59
## 173 3.72
## 174 3.72
## 175 3.72
## 176 3.72
## 177 3.75
## 178 3.75
## 179 3.75
## 180 3.75
## 181 3.82
## 182 3.82
## 183 3.82
## 184 3.82
## 185 3.82
## 186 3.82
## 187 3.82
## 188 3.82
## 189 3.85
## 190 3.85
## 191 3.85
## 192 3.85
## 193 3.92
## 194 3.92
## 195 3.92
## 196 3.92
## 197 3.92
## 198 3.92
## 199 3.92
## 200 3.92
## 201 3.96
## 202 3.96
## 203 3.96
## 204 3.96
## 205 4.50
## 206 4.50
## 207 4.50
## 208 4.50
## 209 4.14
## 210 4.14
## 211 4.14
## 212 4.14
## 213 4.16
## 214 4.16
## 215 4.16
## 216 4.16
## 217 4.16
## 218 4.16
## 219 4.16
## 220 4.16
## 221 4.22
## 222 4.22
## 223 4.22
## 224 4.22
## 225 4.26
## 226 4.26
## 227 4.26
## 228 4.26
## 229 4.28
## 230 4.28
## 231 4.28
## 232 4.28
## 233 4.29
## 234 4.29
## 235 4.29
## 236 4.29
## 237 4.33
## 238 4.33
## 239 4.33
## 240 4.33
## 241 4.38
## 242 4.38
## 243 4.38
## 244 4.38
## 245 4.55
## 246 4.55
## 247 4.55
## 248 4.55
## 249 4.57
## 250 4.57
## 251 4.57
## 252 4.57
## 253 4.58
## 254 4.58
## 255 4.58
## 256 4.58
## 257 4.60
## 258 4.60
## 259 4.60
## 260 4.60
## 261 4.62
## 262 4.62
## 263 4.62
## 264 4.62
## 265 4.64
## 266 4.64
## 267 4.64
## 268 4.64
## 269 4.64
## 270 4.64
## 271 4.64
## 272 4.64
## 273 4.73
## 274 4.73
## 275 4.73
## 276 4.73
## 277 4.82
## 278 4.82
## 279 4.82
## 280 4.82
## 281 4.84
## 282 4.84
## 283 4.84
## 284 4.84
## 285 4.89
## 286 4.89
## 287 4.89
## 288 4.89
## 289 4.89
## 290 4.89
## 291 4.89
## 292 4.89
## 293 4.95
## 294 4.95
## 295 4.95
## 296 4.95
## 297 4.96
## 298 4.96
## 299 4.96
## 300 4.96
## 301 2.01
## 302 2.01
## 303 2.01
## 304 2.01
## 305 2.03
## 306 2.03
## 307 2.03
## 308 2.03
## 309 2.07
## 310 2.07
## 311 2.07
## 312 2.07
## 313 2.25
## 314 2.25
## 315 2.25
## 316 2.25
## 317 2.50
## 318 2.50
## 319 2.50
## 320 2.50
## 321 2.59
## 322 2.59
## 323 2.59
## 324 2.59
## 325 2.71
## 326 2.71
## 327 2.71
## 328 2.71
## 329 2.88
## 330 2.88
## 331 2.88
## 332 2.88
## 333 2.90
## 334 2.90
## 335 2.90
## 336 2.90
## 337 2.93
## 338 2.93
## 339 2.93
## 340 2.93
## 341 2.99
## 342 2.99
## 343 2.99
## 344 2.99
## 345 3.02
## 346 3.02
## 347 3.02
## 348 3.02
## 349 3.02
## 350 3.02
## 351 3.02
## 352 3.02
## 353 3.06
## 354 3.06
## 355 3.06
## 356 3.06
## 357 3.06
## 358 3.06
## 359 3.06
## 360 3.06
## 361 3.18
## 362 3.18
## 363 3.18
## 364 3.18
## 365 3.27
## 366 3.27
## 367 3.27
## 368 3.27
## 369 3.27
## 370 3.27
## 371 3.27
## 372 3.27
## 373 3.33
## 374 3.33
## 375 3.33
## 376 3.33
## 377 3.41
## 378 3.41
## 379 3.41
## 380 3.41
## 381 3.41
## 382 3.45
## 383 3.45
## 384 3.45
## 385 3.45
## 386 3.50
## 387 3.50
## 388 3.50
## 389 3.50
## 390 3.54
## 391 3.54
## 392 3.54
## 393 3.54
## 394 3.71
## 395 3.71
## 396 3.71
## 397 3.71
## 398 3.76
## 399 3.76
## 400 3.76
## 401 3.76
## 402 3.82
## 403 3.82
## 404 3.82
## 405 3.83
## 406 3.83
## 407 3.83
## 408 3.83
## 409 3.93
## 410 3.93
## 411 3.94
## 412 3.94
## 413 4.02
## 414 4.02
## 415 4.02
## 416 4.02
## 417 4.02
## 418 4.02
## 419 4.02
## 420 4.02
## 421 4.07
## 422 4.07
## 423 4.07
## 424 4.07
## 425 4.09
## 426 4.09
## 427 4.09
## 428 4.09
## 429 4.25
## 430 4.25
## 431 4.25
## 432 4.25
## 433 4.32
## 434 4.32
## 435 4.32
## 436 4.32
## 437 4.37
## 438 4.37
## 439 4.37
## 440 4.37
## 441 4.39
## 442 4.39
## 443 4.39
## 444 4.39
## 445 4.41
## 446 4.41
## 447 4.41
## 448 4.41
## 449 4.41
## 450 4.41
## 451 4.41
## 452 4.41
## 453 4.42
## 454 4.42
## 455 4.42
## 456 4.42
## 457 4.44
## 458 4.44
## 459 4.44
## 460 4.44
## 461 4.47
## 462 4.47
## 463 4.47
## 464 4.47
## 465 4.47
## 466 4.47
## 467 4.47
## 468 4.47
## 469 4.52
## 470 4.52
## 471 4.52
## 472 4.52
## 473 4.55
## 474 4.55
## 475 4.55
## 476 4.55
## 477 4.58
## 478 4.58
## 479 4.58
## 480 4.58
## 481 4.61
## 482 4.61
## 483 4.61
## 484 4.61
## 485 4.61
## 486 4.61
## 487 4.61
## 488 4.61
## 489 4.75
## 490 4.75
## 491 4.75
## 492 4.75
## 493 4.76
## 494 4.76
## 495 4.76
## 496 4.76
## 497 4.79
## 498 4.79
## 499 4.79
## 500 4.79
## 501 4.82
## 502 4.82
## 503 4.82
## 504 4.82
## 505 4.82
## 506 4.82
## 507 4.82
## 508 4.82
## 509 3.50
## 510 3.50
## 511 3.50
## 512 3.50
## 513 3.24
## 514 3.24
## 515 3.24
## 516 3.24
## 517 3.94
## 518 3.94
## 519 3.94
## 520 3.94
## 521 2.72
## 522 2.72
## 523 2.72
## 524 2.72
## 525 2.31
## 526 2.31
## 527 2.31
## 528 2.31
## 529 3.14
## 530 3.14
## 531 3.14
## 532 3.14
## 533 3.72
## 534 3.72
## 535 3.72
## 536 3.72
## 537 3.10
## 538 3.10
## 539 3.10
## 540 3.10
## 541 2.34
## 542 2.34
## 543 2.34
## 544 2.34
## 545 3.67
## 546 3.67
## 547 3.67
## 548 3.66
## 549 2.58
## 550 2.58
## 551 2.58
## 552 2.58
## 553 2.55
## 554 2.55
## 555 2.55
## 556 2.55
## 557 2.43
## 558 2.43
## 559 2.43
## 560 2.43
## 561 2.70
## 562 2.70
## 563 2.70
## 564 2.70
## 565 2.76
## 566 2.76
## 567 2.76
## 568 2.76
## 569 2.84
## 570 2.84
## 571 2.84
## 572 2.84
## 573 2.46
## 574 2.46
## 575 2.46
## 576 2.46
## 577 2.37
## 578 2.37
## 579 2.37
## 580 2.37
## 581 2.83
## 582 2.83
## 583 2.83
## 584 2.83
## 585 2.69
## 586 2.69
## 587 2.69
## 588 2.69You can select more columns by enetring them, separated by a comma. Let’s get age and condition:
select(ps_data, age, condition)## age condition
## 1 2.00 Label
## 2 2.00 Label
## 3 2.00 Label
## 4 2.00 Label
## 5 2.13 Label
## 6 2.13 Label
## 7 2.13 Label
## 8 2.13 Label
## 9 2.32 Label
## 10 2.32 Label
## 11 2.32 Label
## 12 2.32 Label
## 13 2.38 Label
## 14 2.38 Label
## 15 2.38 Label
## 16 2.38 Label
## 17 2.47 Label
## 18 2.47 Label
## 19 2.47 Label
## 20 2.47 Label
## 21 2.50 Label
## 22 2.50 Label
## 23 2.50 Label
## 24 2.50 Label
## 25 2.58 Label
## 26 2.58 Label
## 27 2.58 Label
## 28 2.58 Label
## 29 2.59 Label
## 30 2.59 Label
## 31 2.59 Label
## 32 2.59 Label
## 33 2.61 Label
## 34 2.61 Label
## 35 2.61 Label
## 36 2.61 Label
## 37 2.72 Label
## 38 2.72 Label
## 39 2.72 Label
## 40 2.72 Label
## 41 2.73 Label
## 42 2.73 Label
## 43 2.73 Label
## 44 2.73 Label
## 45 2.74 Label
## 46 2.74 Label
## 47 2.74 Label
## 48 2.74 Label
## 49 2.79 Label
## 50 2.79 Label
## 51 2.79 Label
## 52 2.79 Label
## 53 2.80 Label
## 54 2.80 Label
## 55 2.80 Label
## 56 2.80 Label
## 57 2.83 Label
## 58 2.83 Label
## 59 2.83 Label
## 60 2.83 Label
## 61 2.83 Label
## 62 2.83 Label
## 63 2.83 Label
## 64 2.83 Label
## 65 2.85 Label
## 66 2.85 Label
## 67 2.85 Label
## 68 2.85 Label
## 69 2.88 Label
## 70 2.88 Label
## 71 2.88 Label
## 72 2.88 Label
## 73 2.88 Label
## 74 2.88 Label
## 75 2.88 Label
## 76 2.88 Label
## 77 2.89 Label
## 78 2.89 Label
## 79 2.89 Label
## 80 2.89 Label
## 81 2.91 Label
## 82 2.91 Label
## 83 2.91 Label
## 84 2.91 Label
## 85 2.95 Label
## 86 2.95 Label
## 87 2.95 Label
## 88 2.95 Label
## 89 2.98 Label
## 90 2.98 Label
## 91 2.98 Label
## 92 2.98 Label
## 93 2.99 Label
## 94 2.99 Label
## 95 2.99 Label
## 96 2.99 Label
## 97 3.00 Label
## 98 3.00 Label
## 99 3.00 Label
## 100 3.00 Label
## 101 3.09 Label
## 102 3.09 Label
## 103 3.09 Label
## 104 3.09 Label
## 105 3.10 Label
## 106 3.10 Label
## 107 3.10 Label
## 108 3.10 Label
## 109 3.19 Label
## 110 3.19 Label
## 111 3.19 Label
## 112 3.19 Label
## 113 3.20 Label
## 114 3.20 Label
## 115 3.20 Label
## 116 3.20 Label
## 117 3.22 Label
## 118 3.22 Label
## 119 3.22 Label
## 120 3.22 Label
## 121 3.24 Label
## 122 3.24 Label
## 123 3.24 Label
## 124 3.24 Label
## 125 3.25 Label
## 126 3.25 Label
## 127 3.25 Label
## 128 3.25 Label
## 129 3.26 Label
## 130 3.26 Label
## 131 3.26 Label
## 132 3.26 Label
## 133 3.28 Label
## 134 3.28 Label
## 135 3.28 Label
## 136 3.28 Label
## 137 3.30 Label
## 138 3.30 Label
## 139 3.30 Label
## 140 3.30 Label
## 141 3.46 Label
## 142 3.46 Label
## 143 3.46 Label
## 144 3.46 Label
## 145 3.46 Label
## 146 3.46 Label
## 147 3.46 Label
## 148 3.46 Label
## 149 3.46 Label
## 150 3.46 Label
## 151 3.46 Label
## 152 3.46 Label
## 153 3.50 Label
## 154 3.50 Label
## 155 3.50 Label
## 156 3.50 Label
## 157 3.52 Label
## 158 3.52 Label
## 159 3.52 Label
## 160 3.52 Label
## 161 3.55 Label
## 162 3.55 Label
## 163 3.55 Label
## 164 3.55 Label
## 165 3.56 Label
## 166 3.56 Label
## 167 3.56 Label
## 168 3.56 Label
## 169 3.59 Label
## 170 3.59 Label
## 171 3.59 Label
## 172 3.59 Label
## 173 3.72 Label
## 174 3.72 Label
## 175 3.72 Label
## 176 3.72 Label
## 177 3.75 Label
## 178 3.75 Label
## 179 3.75 Label
## 180 3.75 Label
## 181 3.82 Label
## 182 3.82 Label
## 183 3.82 Label
## 184 3.82 Label
## 185 3.82 Label
## 186 3.82 Label
## 187 3.82 Label
## 188 3.82 Label
## 189 3.85 Label
## 190 3.85 Label
## 191 3.85 Label
## 192 3.85 Label
## 193 3.92 Label
## 194 3.92 Label
## 195 3.92 Label
## 196 3.92 Label
## 197 3.92 Label
## 198 3.92 Label
## 199 3.92 Label
## 200 3.92 Label
## 201 3.96 Label
## 202 3.96 Label
## 203 3.96 Label
## 204 3.96 Label
## 205 4.50 Label
## 206 4.50 Label
## 207 4.50 Label
## 208 4.50 Label
## 209 4.14 Label
## 210 4.14 Label
## 211 4.14 Label
## 212 4.14 Label
## 213 4.16 Label
## 214 4.16 Label
## 215 4.16 Label
## 216 4.16 Label
## 217 4.16 Label
## 218 4.16 Label
## 219 4.16 Label
## 220 4.16 Label
## 221 4.22 Label
## 222 4.22 Label
## 223 4.22 Label
## 224 4.22 Label
## 225 4.26 Label
## 226 4.26 Label
## 227 4.26 Label
## 228 4.26 Label
## 229 4.28 Label
## 230 4.28 Label
## 231 4.28 Label
## 232 4.28 Label
## 233 4.29 Label
## 234 4.29 Label
## 235 4.29 Label
## 236 4.29 Label
## 237 4.33 Label
## 238 4.33 Label
## 239 4.33 Label
## 240 4.33 Label
## 241 4.38 Label
## 242 4.38 Label
## 243 4.38 Label
## 244 4.38 Label
## 245 4.55 Label
## 246 4.55 Label
## 247 4.55 Label
## 248 4.55 Label
## 249 4.57 Label
## 250 4.57 Label
## 251 4.57 Label
## 252 4.57 Label
## 253 4.58 Label
## 254 4.58 Label
## 255 4.58 Label
## 256 4.58 Label
## 257 4.60 Label
## 258 4.60 Label
## 259 4.60 Label
## 260 4.60 Label
## 261 4.62 Label
## 262 4.62 Label
## 263 4.62 Label
## 264 4.62 Label
## 265 4.64 Label
## 266 4.64 Label
## 267 4.64 Label
## 268 4.64 Label
## 269 4.64 Label
## 270 4.64 Label
## 271 4.64 Label
## 272 4.64 Label
## 273 4.73 Label
## 274 4.73 Label
## 275 4.73 Label
## 276 4.73 Label
## 277 4.82 Label
## 278 4.82 Label
## 279 4.82 Label
## 280 4.82 Label
## 281 4.84 Label
## 282 4.84 Label
## 283 4.84 Label
## 284 4.84 Label
## 285 4.89 Label
## 286 4.89 Label
## 287 4.89 Label
## 288 4.89 Label
## 289 4.89 Label
## 290 4.89 Label
## 291 4.89 Label
## 292 4.89 Label
## 293 4.95 Label
## 294 4.95 Label
## 295 4.95 Label
## 296 4.95 Label
## 297 4.96 Label
## 298 4.96 Label
## 299 4.96 Label
## 300 4.96 Label
## 301 2.01 No Label
## 302 2.01 No Label
## 303 2.01 No Label
## 304 2.01 No Label
## 305 2.03 No Label
## 306 2.03 No Label
## 307 2.03 No Label
## 308 2.03 No Label
## 309 2.07 No Label
## 310 2.07 No Label
## 311 2.07 No Label
## 312 2.07 No Label
## 313 2.25 No Label
## 314 2.25 No Label
## 315 2.25 No Label
## 316 2.25 No Label
## 317 2.50 No Label
## 318 2.50 No Label
## 319 2.50 No Label
## 320 2.50 No Label
## 321 2.59 No Label
## 322 2.59 No Label
## 323 2.59 No Label
## 324 2.59 No Label
## 325 2.71 No Label
## 326 2.71 No Label
## 327 2.71 No Label
## 328 2.71 No Label
## 329 2.88 No Label
## 330 2.88 No Label
## 331 2.88 No Label
## 332 2.88 No Label
## 333 2.90 No Label
## 334 2.90 No Label
## 335 2.90 No Label
## 336 2.90 No Label
## 337 2.93 No Label
## 338 2.93 No Label
## 339 2.93 No Label
## 340 2.93 No Label
## 341 2.99 No Label
## 342 2.99 No Label
## 343 2.99 No Label
## 344 2.99 No Label
## 345 3.02 No Label
## 346 3.02 No Label
## 347 3.02 No Label
## 348 3.02 No Label
## 349 3.02 No Label
## 350 3.02 No Label
## 351 3.02 No Label
## 352 3.02 No Label
## 353 3.06 No Label
## 354 3.06 No Label
## 355 3.06 No Label
## 356 3.06 No Label
## 357 3.06 No Label
## 358 3.06 No Label
## 359 3.06 No Label
## 360 3.06 No Label
## 361 3.18 No Label
## 362 3.18 No Label
## 363 3.18 No Label
## 364 3.18 No Label
## 365 3.27 No Label
## 366 3.27 No Label
## 367 3.27 No Label
## 368 3.27 No Label
## 369 3.27 No Label
## 370 3.27 No Label
## 371 3.27 No Label
## 372 3.27 No Label
## 373 3.33 No Label
## 374 3.33 No Label
## 375 3.33 No Label
## 376 3.33 No Label
## 377 3.41 No Label
## 378 3.41 No Label
## 379 3.41 No Label
## 380 3.41 No Label
## 381 3.41 No Label
## 382 3.45 No Label
## 383 3.45 No Label
## 384 3.45 No Label
## 385 3.45 No Label
## 386 3.50 No Label
## 387 3.50 No Label
## 388 3.50 No Label
## 389 3.50 No Label
## 390 3.54 No Label
## 391 3.54 No Label
## 392 3.54 No Label
## 393 3.54 No Label
## 394 3.71 No Label
## 395 3.71 No Label
## 396 3.71 No Label
## 397 3.71 No Label
## 398 3.76 No Label
## 399 3.76 No Label
## 400 3.76 No Label
## 401 3.76 No Label
## 402 3.82 No Label
## 403 3.82 No Label
## 404 3.82 No Label
## 405 3.83 No Label
## 406 3.83 No Label
## 407 3.83 No Label
## 408 3.83 No Label
## 409 3.93 No Label
## 410 3.93 No Label
## 411 3.94 No Label
## 412 3.94 No Label
## 413 4.02 No Label
## 414 4.02 No Label
## 415 4.02 No Label
## 416 4.02 No Label
## 417 4.02 No Label
## 418 4.02 No Label
## 419 4.02 No Label
## 420 4.02 No Label
## 421 4.07 No Label
## 422 4.07 No Label
## 423 4.07 No Label
## 424 4.07 No Label
## 425 4.09 No Label
## 426 4.09 No Label
## 427 4.09 No Label
## 428 4.09 No Label
## 429 4.25 No Label
## 430 4.25 No Label
## 431 4.25 No Label
## 432 4.25 No Label
## 433 4.32 No Label
## 434 4.32 No Label
## 435 4.32 No Label
## 436 4.32 No Label
## 437 4.37 No Label
## 438 4.37 No Label
## 439 4.37 No Label
## 440 4.37 No Label
## 441 4.39 No Label
## 442 4.39 No Label
## 443 4.39 No Label
## 444 4.39 No Label
## 445 4.41 No Label
## 446 4.41 No Label
## 447 4.41 No Label
## 448 4.41 No Label
## 449 4.41 No Label
## 450 4.41 No Label
## 451 4.41 No Label
## 452 4.41 No Label
## 453 4.42 No Label
## 454 4.42 No Label
## 455 4.42 No Label
## 456 4.42 No Label
## 457 4.44 No Label
## 458 4.44 No Label
## 459 4.44 No Label
## 460 4.44 No Label
## 461 4.47 No Label
## 462 4.47 No Label
## 463 4.47 No Label
## 464 4.47 No Label
## 465 4.47 No Label
## 466 4.47 No Label
## 467 4.47 No Label
## 468 4.47 No Label
## 469 4.52 No Label
## 470 4.52 No Label
## 471 4.52 No Label
## 472 4.52 No Label
## 473 4.55 No Label
## 474 4.55 No Label
## 475 4.55 No Label
## 476 4.55 No Label
## 477 4.58 No Label
## 478 4.58 No Label
## 479 4.58 No Label
## 480 4.58 No Label
## 481 4.61 No Label
## 482 4.61 No Label
## 483 4.61 No Label
## 484 4.61 No Label
## 485 4.61 No Label
## 486 4.61 No Label
## 487 4.61 No Label
## 488 4.61 No Label
## 489 4.75 No Label
## 490 4.75 No Label
## 491 4.75 No Label
## 492 4.75 No Label
## 493 4.76 No Label
## 494 4.76 No Label
## 495 4.76 No Label
## 496 4.76 No Label
## 497 4.79 No Label
## 498 4.79 No Label
## 499 4.79 No Label
## 500 4.79 No Label
## 501 4.82 No Label
## 502 4.82 No Label
## 503 4.82 No Label
## 504 4.82 No Label
## 505 4.82 No Label
## 506 4.82 No Label
## 507 4.82 No Label
## 508 4.82 No Label
## 509 3.50 No Label
## 510 3.50 No Label
## 511 3.50 No Label
## 512 3.50 No Label
## 513 3.24 No Label
## 514 3.24 No Label
## 515 3.24 No Label
## 516 3.24 No Label
## 517 3.94 No Label
## 518 3.94 No Label
## 519 3.94 No Label
## 520 3.94 No Label
## 521 2.72 No Label
## 522 2.72 No Label
## 523 2.72 No Label
## 524 2.72 No Label
## 525 2.31 No Label
## 526 2.31 No Label
## 527 2.31 No Label
## 528 2.31 No Label
## 529 3.14 No Label
## 530 3.14 No Label
## 531 3.14 No Label
## 532 3.14 No Label
## 533 3.72 No Label
## 534 3.72 No Label
## 535 3.72 No Label
## 536 3.72 No Label
## 537 3.10 No Label
## 538 3.10 No Label
## 539 3.10 No Label
## 540 3.10 No Label
## 541 2.34 No Label
## 542 2.34 No Label
## 543 2.34 No Label
## 544 2.34 No Label
## 545 3.67 No Label
## 546 3.67 No Label
## 547 3.67 No Label
## 548 3.66 No Label
## 549 2.58 No Label
## 550 2.58 No Label
## 551 2.58 No Label
## 552 2.58 No Label
## 553 2.55 No Label
## 554 2.55 No Label
## 555 2.55 No Label
## 556 2.55 No Label
## 557 2.43 No Label
## 558 2.43 No Label
## 559 2.43 No Label
## 560 2.43 No Label
## 561 2.70 No Label
## 562 2.70 No Label
## 563 2.70 No Label
## 564 2.70 No Label
## 565 2.76 No Label
## 566 2.76 No Label
## 567 2.76 No Label
## 568 2.76 No Label
## 569 2.84 No Label
## 570 2.84 No Label
## 571 2.84 No Label
## 572 2.84 No Label
## 573 2.46 No Label
## 574 2.46 No Label
## 575 2.46 No Label
## 576 2.46 No Label
## 577 2.37 No Label
## 578 2.37 No Label
## 579 2.37 No Label
## 580 2.37 No Label
## 581 2.83 No Label
## 582 2.83 No Label
## 583 2.83 No Label
## 584 2.83 No Label
## 585 2.69 No Label
## 586 2.69 No Label
## 587 2.69 No Label
## 588 2.69 No LabelYou can also use columns’ positions. We could get subid, the first column, by supplying a 1:
select(ps_data, 1)## subid
## 1 M22
## 2 M22
## 3 M22
## 4 M22
## 5 T22
## 6 T22
## 7 T22
## 8 T22
## 9 T17
## 10 T17
## 11 T17
## 12 T17
## 13 M3
## 14 M3
## 15 M3
## 16 M3
## 17 T19
## 18 T19
## 19 T19
## 20 T19
## 21 T20
## 22 T20
## 23 T20
## 24 T20
## 25 T21
## 26 T21
## 27 T21
## 28 T21
## 29 M26
## 30 M26
## 31 M26
## 32 M26
## 33 T18
## 34 T18
## 35 T18
## 36 T18
## 37 T12
## 38 T12
## 39 T12
## 40 T12
## 41 T16
## 42 T16
## 43 T16
## 44 T16
## 45 T7
## 46 T7
## 47 T7
## 48 T7
## 49 T9
## 50 T9
## 51 T9
## 52 T9
## 53 T5
## 54 T5
## 55 T5
## 56 T5
## 57 T14
## 58 T14
## 59 T14
## 60 T14
## 61 T2
## 62 T2
## 63 T2
## 64 T2
## 65 T15
## 66 T15
## 67 T15
## 68 T15
## 69 M13
## 70 M13
## 71 M13
## 72 M13
## 73 M12
## 74 M12
## 75 M12
## 76 M12
## 77 T13
## 78 T13
## 79 T13
## 80 T13
## 81 T8
## 82 T8
## 83 T8
## 84 T8
## 85 T1
## 86 T1
## 87 T1
## 88 T1
## 89 M15
## 90 M15
## 91 M15
## 92 M15
## 93 T11
## 94 T11
## 95 T11
## 96 T11
## 97 T10
## 98 T10
## 99 T10
## 100 T10
## 101 T3
## 102 T3
## 103 T3
## 104 T3
## 105 T6
## 106 T6
## 107 T6
## 108 T6
## 109 M32
## 110 M32
## 111 M32
## 112 M32
## 113 M1
## 114 M1
## 115 M1
## 116 M1
## 117 C16
## 118 C16
## 119 C16
## 120 C16
## 121 T4
## 122 T4
## 123 T4
## 124 T4
## 125 C17
## 126 C17
## 127 C17
## 128 C17
## 129 C6
## 130 C6
## 131 C6
## 132 C6
## 133 M10
## 134 M10
## 135 M10
## 136 M10
## 137 M31
## 138 M31
## 139 M31
## 140 M31
## 141 C3
## 142 C3
## 143 C3
## 144 C3
## 145 C10
## 146 C10
## 147 C10
## 148 C10
## 149 M18
## 150 M18
## 151 M18
## 152 M18
## 153 M16
## 154 M16
## 155 M16
## 156 M16
## 157 M23
## 158 M23
## 159 M23
## 160 M23
## 161 C7
## 162 C7
## 163 C7
## 164 C7
## 165 C12
## 166 C12
## 167 C12
## 168 C12
## 169 C15
## 170 C15
## 171 C15
## 172 C15
## 173 M29
## 174 M29
## 175 M29
## 176 M29
## 177 C20
## 178 C20
## 179 C20
## 180 C20
## 181 M11
## 182 M11
## 183 M11
## 184 M11
## 185 C9
## 186 C9
## 187 C9
## 188 C9
## 189 C24
## 190 C24
## 191 C24
## 192 C24
## 193 C22
## 194 C22
## 195 C22
## 196 C22
## 197 C8
## 198 C8
## 199 C8
## 200 C8
## 201 M4
## 202 M4
## 203 M4
## 204 M4
## 205 M6
## 206 M6
## 207 M6
## 208 M6
## 209 C19
## 210 C19
## 211 C19
## 212 C19
## 213 C1
## 214 C1
## 215 C1
## 216 C1
## 217 M19
## 218 M19
## 219 M19
## 220 M19
## 221 C11
## 222 C11
## 223 C11
## 224 C11
## 225 M9
## 226 M9
## 227 M9
## 228 M9
## 229 M2
## 230 M2
## 231 M2
## 232 M2
## 233 C5
## 234 C5
## 235 C5
## 236 C5
## 237 M30
## 238 M30
## 239 M30
## 240 M30
## 241 C13
## 242 C13
## 243 C13
## 244 C13
## 245 C4
## 246 C4
## 247 C4
## 248 C4
## 249 C14
## 250 C14
## 251 C14
## 252 C14
## 253 M17
## 254 M17
## 255 M17
## 256 M17
## 257 C2
## 258 C2
## 259 C2
## 260 C2
## 261 C23
## 262 C23
## 263 C23
## 264 C23
## 265 M20
## 266 M20
## 267 M20
## 268 M20
## 269 M21
## 270 M21
## 271 M21
## 272 M21
## 273 C21
## 274 C21
## 275 C21
## 276 C21
## 277 M24
## 278 M24
## 279 M24
## 280 M24
## 281 M5
## 282 M5
## 283 M5
## 284 M5
## 285 M7
## 286 M7
## 287 M7
## 288 M7
## 289 M8
## 290 M8
## 291 M8
## 292 M8
## 293 C18
## 294 C18
## 295 C18
## 296 C18
## 297 M25
## 298 M25
## 299 M25
## 300 M25
## 301 MSCH47
## 302 MSCH47
## 303 MSCH47
## 304 MSCH47
## 305 MSCH50
## 306 MSCH50
## 307 MSCH50
## 308 MSCH50
## 309 MSCH51
## 310 MSCH51
## 311 MSCH51
## 312 MSCH51
## 313 MSCH44
## 314 MSCH44
## 315 MSCH44
## 316 MSCH44
## 317 MSCH52
## 318 MSCH52
## 319 MSCH52
## 320 MSCH52
## 321 MSCH38
## 322 MSCH38
## 323 MSCH38
## 324 MSCH38
## 325 MSCH43
## 326 MSCH43
## 327 MSCH43
## 328 MSCH43
## 329 MSCH49
## 330 MSCH49
## 331 MSCH49
## 332 MSCH49
## 333 MSCH45
## 334 MSCH45
## 335 MSCH45
## 336 MSCH45
## 337 MSCH42
## 338 MSCH42
## 339 MSCH42
## 340 MSCH42
## 341 MSCH53
## 342 MSCH53
## 343 MSCH53
## 344 MSCH53
## 345 SCH35
## 346 SCH35
## 347 SCH35
## 348 SCH35
## 349 MSCH40
## 350 MSCH40
## 351 MSCH40
## 352 MSCH40
## 353 SCH34
## 354 SCH34
## 355 SCH34
## 356 SCH34
## 357 SCH33
## 358 SCH33
## 359 SCH33
## 360 SCH33
## 361 MSCH41
## 362 MSCH41
## 363 MSCH41
## 364 MSCH41
## 365 SCH37
## 366 SCH37
## 367 SCH37
## 368 SCH37
## 369 SCH32
## 370 SCH32
## 371 SCH32
## 372 SCH32
## 373 SCH36
## 374 SCH36
## 375 SCH36
## 376 SCH36
## 377 SCH11
## 378 SCH12
## 379 SCH12
## 380 SCH12
## 381 SCH12
## 382 SCH18
## 383 SCH18
## 384 SCH18
## 385 SCH18
## 386 MSCH48
## 387 MSCH48
## 388 MSCH48
## 389 MSCH48
## 390 SCH25
## 391 SCH25
## 392 SCH25
## 393 SCH25
## 394 SCH31
## 395 SCH31
## 396 SCH31
## 397 SCH31
## 398 MSCH46
## 399 MSCH46
## 400 MSCH46
## 401 MSCH46
## 402 SCH11
## 403 SCH11
## 404 SCH11
## 405 SCH29
## 406 SCH29
## 407 SCH29
## 408 SCH29
## 409 MSCH39
## 410 MSCH39
## 411 MSCH39
## 412 MSCH39
## 413 SCH28
## 414 SCH28
## 415 SCH28
## 416 SCH28
## 417 SCH22
## 418 SCH22
## 419 SCH22
## 420 SCH22
## 421 SCH24
## 422 SCH24
## 423 SCH24
## 424 SCH24
## 425 SCH27
## 426 SCH27
## 427 SCH27
## 428 SCH27
## 429 SCH17
## 430 SCH17
## 431 SCH17
## 432 SCH17
## 433 SCH10
## 434 SCH10
## 435 SCH10
## 436 SCH10
## 437 SCH9
## 438 SCH9
## 439 SCH9
## 440 SCH9
## 441 SCH20
## 442 SCH20
## 443 SCH20
## 444 SCH20
## 445 SCH6
## 446 SCH6
## 447 SCH6
## 448 SCH6
## 449 SCH7
## 450 SCH7
## 451 SCH7
## 452 SCH7
## 453 SCH15
## 454 SCH15
## 455 SCH15
## 456 SCH15
## 457 SCH30
## 458 SCH30
## 459 SCH30
## 460 SCH30
## 461 SCH3
## 462 SCH3
## 463 SCH3
## 464 SCH3
## 465 SCH26
## 466 SCH26
## 467 SCH26
## 468 SCH26
## 469 SCH8
## 470 SCH8
## 471 SCH8
## 472 SCH8
## 473 SCH16
## 474 SCH16
## 475 SCH16
## 476 SCH16
## 477 SCH14
## 478 SCH14
## 479 SCH14
## 480 SCH14
## 481 SCH2
## 482 SCH2
## 483 SCH2
## 484 SCH2
## 485 SCH5
## 486 SCH5
## 487 SCH5
## 488 SCH5
## 489 SCH13
## 490 SCH13
## 491 SCH13
## 492 SCH13
## 493 SCH21
## 494 SCH21
## 495 SCH21
## 496 SCH21
## 497 SCH19
## 498 SCH19
## 499 SCH19
## 500 SCH19
## 501 SCH23
## 502 SCH23
## 503 SCH23
## 504 SCH23
## 505 SCH1
## 506 SCH1
## 507 SCH1
## 508 SCH1
## 509 MSCH66
## 510 MSCH66
## 511 MSCH66
## 512 MSCH66
## 513 MSCH67
## 514 MSCH67
## 515 MSCH67
## 516 MSCH67
## 517 MSCH68
## 518 MSCH68
## 519 MSCH68
## 520 MSCH68
## 521 MSCH69
## 522 MSCH69
## 523 MSCH69
## 524 MSCH69
## 525 MSCH70
## 526 MSCH70
## 527 MSCH70
## 528 MSCH70
## 529 MSCH71
## 530 MSCH71
## 531 MSCH71
## 532 MSCH71
## 533 MSCH72
## 534 MSCH72
## 535 MSCH72
## 536 MSCH72
## 537 MSCH73
## 538 MSCH73
## 539 MSCH73
## 540 MSCH73
## 541 MSCH74
## 542 MSCH74
## 543 MSCH74
## 544 MSCH74
## 545 MSCH75
## 546 MSCH75
## 547 MSCH75
## 548 MSCH75
## 549 MSCH76
## 550 MSCH76
## 551 MSCH76
## 552 MSCH76
## 553 MSCH77
## 554 MSCH77
## 555 MSCH77
## 556 MSCH77
## 557 MSCH78
## 558 MSCH78
## 559 MSCH78
## 560 MSCH78
## 561 MSCH79
## 562 MSCH79
## 563 MSCH79
## 564 MSCH79
## 565 MSCH80
## 566 MSCH80
## 567 MSCH80
## 568 MSCH80
## 569 MSCH81
## 570 MSCH81
## 571 MSCH81
## 572 MSCH81
## 573 MSCH82
## 574 MSCH82
## 575 MSCH82
## 576 MSCH82
## 577 MSCH83
## 578 MSCH83
## 579 MSCH83
## 580 MSCH83
## 581 MSCH84
## 582 MSCH84
## 583 MSCH84
## 584 MSCH84
## 585 MSCH85
## 586 MSCH85
## 587 MSCH85
## 588 MSCH85Or, you can say which variable you don’t want by prefacing its name or index with a -. For example, let’s get rid of age.
select(ps_data, -age)## subid item correct condition
## 1 M22 faces 1 Label
## 2 M22 houses 1 Label
## 3 M22 pasta 0 Label
## 4 M22 beds 0 Label
## 5 T22 beds 0 Label
## 6 T22 faces 0 Label
## 7 T22 houses 1 Label
## 8 T22 pasta 1 Label
## 9 T17 pasta 0 Label
## 10 T17 faces 0 Label
## 11 T17 houses 0 Label
## 12 T17 beds 0 Label
## 13 M3 faces 0 Label
## 14 M3 houses 1 Label
## 15 M3 pasta 1 Label
## 16 M3 beds 1 Label
## 17 T19 faces 0 Label
## 18 T19 houses 0 Label
## 19 T19 pasta 1 Label
## 20 T19 beds 1 Label
## 21 T20 faces 1 Label
## 22 T20 houses 1 Label
## 23 T20 pasta 0 Label
## 24 T20 beds 1 Label
## 25 T21 faces 1 Label
## 26 T21 houses 1 Label
## 27 T21 pasta 1 Label
## 28 T21 beds 0 Label
## 29 M26 faces 1 Label
## 30 M26 houses 1 Label
## 31 M26 pasta 0 Label
## 32 M26 beds 1 Label
## 33 T18 faces 1 Label
## 34 T18 houses 0 Label
## 35 T18 pasta 1 Label
## 36 T18 beds 0 Label
## 37 T12 beds 0 Label
## 38 T12 faces 0 Label
## 39 T12 houses 1 Label
## 40 T12 pasta 0 Label
## 41 T16 faces 1 Label
## 42 T16 houses 0 Label
## 43 T16 pasta 1 Label
## 44 T16 beds 1 Label
## 45 T7 faces 1 Label
## 46 T7 houses 0 Label
## 47 T7 pasta 0 Label
## 48 T7 beds 0 Label
## 49 T9 houses 0 Label
## 50 T9 faces 1 Label
## 51 T9 pasta 0 Label
## 52 T9 beds 1 Label
## 53 T5 faces 1 Label
## 54 T5 houses 1 Label
## 55 T5 pasta 0 Label
## 56 T5 beds 1 Label
## 57 T14 faces 1 Label
## 58 T14 houses 1 Label
## 59 T14 pasta 0 Label
## 60 T14 beds 1 Label
## 61 T2 houses 0 Label
## 62 T2 faces 0 Label
## 63 T2 pasta 1 Label
## 64 T2 beds 1 Label
## 65 T15 faces 0 Label
## 66 T15 houses 0 Label
## 67 T15 pasta 1 Label
## 68 T15 beds 0 Label
## 69 M13 houses 0 Label
## 70 M13 beds 1 Label
## 71 M13 faces 1 Label
## 72 M13 pasta 0 Label
## 73 M12 faces 1 Label
## 74 M12 houses 0 Label
## 75 M12 pasta 1 Label
## 76 M12 beds 0 Label
## 77 T13 beds 0 Label
## 78 T13 faces 0 Label
## 79 T13 houses 1 Label
## 80 T13 pasta 1 Label
## 81 T8 faces 1 Label
## 82 T8 houses 0 Label
## 83 T8 pasta 1 Label
## 84 T8 beds 1 Label
## 85 T1 faces 1 Label
## 86 T1 houses 0 Label
## 87 T1 pasta 0 Label
## 88 T1 beds 1 Label
## 89 M15 faces 1 Label
## 90 M15 houses 1 Label
## 91 M15 pasta 1 Label
## 92 M15 beds 1 Label
## 93 T11 faces 1 Label
## 94 T11 houses 0 Label
## 95 T11 pasta 1 Label
## 96 T11 beds 1 Label
## 97 T10 faces 0 Label
## 98 T10 houses 1 Label
## 99 T10 pasta 1 Label
## 100 T10 beds 1 Label
## 101 T3 faces 1 Label
## 102 T3 houses 1 Label
## 103 T3 pasta 1 Label
## 104 T3 beds 1 Label
## 105 T6 faces 1 Label
## 106 T6 houses 1 Label
## 107 T6 pasta 1 Label
## 108 T6 beds 1 Label
## 109 M32 beds 1 Label
## 110 M32 faces 1 Label
## 111 M32 houses 0 Label
## 112 M32 pasta 1 Label
## 113 M1 faces 0 Label
## 114 M1 beds 1 Label
## 115 M1 pasta 0 Label
## 116 M1 houses 0 Label
## 117 C16 faces 0 Label
## 118 C16 houses 0 Label
## 119 C16 pasta 1 Label
## 120 C16 beds 1 Label
## 121 T4 faces 1 Label
## 122 T4 houses 0 Label
## 123 T4 pasta 0 Label
## 124 T4 beds 1 Label
## 125 C17 faces 1 Label
## 126 C17 houses 0 Label
## 127 C17 pasta 1 Label
## 128 C17 beds 0 Label
## 129 C6 faces 0 Label
## 130 C6 houses 1 Label
## 131 C6 pasta 1 Label
## 132 C6 beds 1 Label
## 133 M10 faces 1 Label
## 134 M10 houses 1 Label
## 135 M10 beds 1 Label
## 136 M10 pasta 1 Label
## 137 M31 faces 0 Label
## 138 M31 houses 1 Label
## 139 M31 pasta 1 Label
## 140 M31 beds 1 Label
## 141 C3 houses 0 Label
## 142 C3 pasta 1 Label
## 143 C3 beds 1 Label
## 144 C3 faces 1 Label
## 145 C10 faces 0 Label
## 146 C10 houses 0 Label
## 147 C10 pasta 1 Label
## 148 C10 beds 1 Label
## 149 M18 faces 0 Label
## 150 M18 houses 1 Label
## 151 M18 pasta 1 Label
## 152 M18 beds 1 Label
## 153 M16 faces 0 Label
## 154 M16 houses 0 Label
## 155 M16 pasta 0 Label
## 156 M16 beds 1 Label
## 157 M23 faces 1 Label
## 158 M23 houses 0 Label
## 159 M23 pasta 1 Label
## 160 M23 beds 1 Label
## 161 C7 faces 0 Label
## 162 C7 houses 1 Label
## 163 C7 pasta 0 Label
## 164 C7 beds 0 Label
## 165 C12 faces 1 Label
## 166 C12 houses 0 Label
## 167 C12 pasta 1 Label
## 168 C12 beds 1 Label
## 169 C15 faces 1 Label
## 170 C15 houses 1 Label
## 171 C15 pasta 1 Label
## 172 C15 beds 1 Label
## 173 M29 faces 0 Label
## 174 M29 houses 1 Label
## 175 M29 pasta 1 Label
## 176 M29 beds 1 Label
## 177 C20 faces 1 Label
## 178 C20 houses 1 Label
## 179 C20 pasta 1 Label
## 180 C20 beds 1 Label
## 181 M11 faces 1 Label
## 182 M11 houses 0 Label
## 183 M11 pasta 1 Label
## 184 M11 beds 1 Label
## 185 C9 beds 1 Label
## 186 C9 faces 1 Label
## 187 C9 houses 1 Label
## 188 C9 pasta 1 Label
## 189 C24 faces 1 Label
## 190 C24 houses 0 Label
## 191 C24 pasta 0 Label
## 192 C24 beds 1 Label
## 193 C22 faces 0 Label
## 194 C22 houses 0 Label
## 195 C22 pasta 1 Label
## 196 C22 beds 1 Label
## 197 C8 faces 1 Label
## 198 C8 houses 1 Label
## 199 C8 pasta 1 Label
## 200 C8 beds 1 Label
## 201 M4 faces 1 Label
## 202 M4 houses 1 Label
## 203 M4 pasta 1 Label
## 204 M4 beds 1 Label
## 205 M6 faces 0 Label
## 206 M6 houses 1 Label
## 207 M6 pasta 1 Label
## 208 M6 beds 0 Label
## 209 C19 faces 1 Label
## 210 C19 houses 0 Label
## 211 C19 pasta 0 Label
## 212 C19 beds 1 Label
## 213 C1 faces 1 Label
## 214 C1 houses 1 Label
## 215 C1 pasta 1 Label
## 216 C1 beds 1 Label
## 217 M19 beds 1 Label
## 218 M19 faces 0 Label
## 219 M19 houses 0 Label
## 220 M19 pasta 1 Label
## 221 C11 faces 1 Label
## 222 C11 houses 0 Label
## 223 C11 pasta 1 Label
## 224 C11 beds 1 Label
## 225 M9 faces 1 Label
## 226 M9 houses 1 Label
## 227 M9 pasta 1 Label
## 228 M9 beds 1 Label
## 229 M2 faces 1 Label
## 230 M2 houses 0 Label
## 231 M2 pasta 1 Label
## 232 M2 beds 1 Label
## 233 C5 faces 1 Label
## 234 C5 houses 1 Label
## 235 C5 pasta 1 Label
## 236 C5 beds 1 Label
## 237 M30 beds 1 Label
## 238 M30 faces 1 Label
## 239 M30 houses 0 Label
## 240 M30 pasta 1 Label
## 241 C13 faces 0 Label
## 242 C13 houses 1 Label
## 243 C13 pasta 0 Label
## 244 C13 beds 1 Label
## 245 C4 faces 1 Label
## 246 C4 houses 1 Label
## 247 C4 pasta 1 Label
## 248 C4 beds 1 Label
## 249 C14 faces 1 Label
## 250 C14 houses 1 Label
## 251 C14 pasta 0 Label
## 252 C14 beds 1 Label
## 253 M17 faces 1 Label
## 254 M17 houses 1 Label
## 255 M17 pasta 1 Label
## 256 M17 beds 1 Label
## 257 C2 faces 1 Label
## 258 C2 houses 1 Label
## 259 C2 pasta 1 Label
## 260 C2 beds 1 Label
## 261 C23 faces 0 Label
## 262 C23 houses 1 Label
## 263 C23 pasta 1 Label
## 264 C23 beds 0 Label
## 265 M20 faces 0 Label
## 266 M20 houses 0 Label
## 267 M20 pasta 1 Label
## 268 M20 beds 1 Label
## 269 M21 faces 1 Label
## 270 M21 houses 1 Label
## 271 M21 pasta 1 Label
## 272 M21 beds 1 Label
## 273 C21 faces 1 Label
## 274 C21 houses 0 Label
## 275 C21 pasta 1 Label
## 276 C21 beds 1 Label
## 277 M24 faces 1 Label
## 278 M24 houses 1 Label
## 279 M24 pasta 1 Label
## 280 M24 beds 1 Label
## 281 M5 faces 0 Label
## 282 M5 houses 0 Label
## 283 M5 pasta 0 Label
## 284 M5 beds 1 Label
## 285 M7 faces 1 Label
## 286 M7 houses 1 Label
## 287 M7 pasta 1 Label
## 288 M7 beds 0 Label
## 289 M8 faces 1 Label
## 290 M8 houses 1 Label
## 291 M8 pasta 1 Label
## 292 M8 beds 1 Label
## 293 C18 faces 0 Label
## 294 C18 houses 1 Label
## 295 C18 pasta 1 Label
## 296 C18 beds 1 Label
## 297 M25 faces 1 Label
## 298 M25 houses 1 Label
## 299 M25 pasta 1 Label
## 300 M25 beds 1 Label
## 301 MSCH47 faces 1 No Label
## 302 MSCH47 houses 0 No Label
## 303 MSCH47 pasta 1 No Label
## 304 MSCH47 beds 0 No Label
## 305 MSCH50 faces 0 No Label
## 306 MSCH50 houses 0 No Label
## 307 MSCH50 pasta 0 No Label
## 308 MSCH50 beds 0 No Label
## 309 MSCH51 faces 0 No Label
## 310 MSCH51 houses 0 No Label
## 311 MSCH51 pasta 0 No Label
## 312 MSCH51 beds 0 No Label
## 313 MSCH44 faces 0 No Label
## 314 MSCH44 houses 0 No Label
## 315 MSCH44 pasta 0 No Label
## 316 MSCH44 beds 0 No Label
## 317 MSCH52 faces 0 No Label
## 318 MSCH52 houses 1 No Label
## 319 MSCH52 pasta 0 No Label
## 320 MSCH52 beds 1 No Label
## 321 MSCH38 faces 0 No Label
## 322 MSCH38 houses 0 No Label
## 323 MSCH38 pasta 1 No Label
## 324 MSCH38 beds 0 No Label
## 325 MSCH43 faces 0 No Label
## 326 MSCH43 houses 0 No Label
## 327 MSCH43 pasta 0 No Label
## 328 MSCH43 beds 0 No Label
## 329 MSCH49 faces 0 No Label
## 330 MSCH49 houses 0 No Label
## 331 MSCH49 pasta 0 No Label
## 332 MSCH49 beds 0 No Label
## 333 MSCH45 faces 0 No Label
## 334 MSCH45 houses 0 No Label
## 335 MSCH45 pasta 0 No Label
## 336 MSCH45 beds 1 No Label
## 337 MSCH42 faces 1 No Label
## 338 MSCH42 houses 0 No Label
## 339 MSCH42 pasta 0 No Label
## 340 MSCH42 beds 0 No Label
## 341 MSCH53 faces 1 No Label
## 342 MSCH53 houses 1 No Label
## 343 MSCH53 pasta 0 No Label
## 344 MSCH53 beds 0 No Label
## 345 SCH35 faces 0 No Label
## 346 SCH35 houses 0 No Label
## 347 SCH35 pasta 0 No Label
## 348 SCH35 beds 0 No Label
## 349 MSCH40 faces 0 No Label
## 350 MSCH40 houses 1 No Label
## 351 MSCH40 pasta 0 No Label
## 352 MSCH40 beds 1 No Label
## 353 SCH34 faces 0 No Label
## 354 SCH34 houses 0 No Label
## 355 SCH34 pasta 0 No Label
## 356 SCH34 beds 0 No Label
## 357 SCH33 faces 0 No Label
## 358 SCH33 houses 0 No Label
## 359 SCH33 pasta 0 No Label
## 360 SCH33 beds 0 No Label
## 361 MSCH41 faces 0 No Label
## 362 MSCH41 houses 0 No Label
## 363 MSCH41 pasta 0 No Label
## 364 MSCH41 beds 0 No Label
## 365 SCH37 beds 0 No Label
## 366 SCH37 faces 1 No Label
## 367 SCH37 houses 0 No Label
## 368 SCH37 pasta 1 No Label
## 369 SCH32 faces 1 No Label
## 370 SCH32 houses 0 No Label
## 371 SCH32 pasta 0 No Label
## 372 SCH32 beds 0 No Label
## 373 SCH36 beds 0 No Label
## 374 SCH36 faces 0 No Label
## 375 SCH36 houses 1 No Label
## 376 SCH36 pasta 1 No Label
## 377 SCH11 beds 0 No Label
## 378 SCH12 faces 0 No Label
## 379 SCH12 houses 0 No Label
## 380 SCH12 pasta 0 No Label
## 381 SCH12 beds 0 No Label
## 382 SCH18 faces 0 No Label
## 383 SCH18 houses 0 No Label
## 384 SCH18 pasta 0 No Label
## 385 SCH18 beds 0 No Label
## 386 MSCH48 faces 0 No Label
## 387 MSCH48 houses 1 No Label
## 388 MSCH48 pasta 0 No Label
## 389 MSCH48 beds 0 No Label
## 390 SCH25 faces 0 No Label
## 391 SCH25 houses 1 No Label
## 392 SCH25 pasta 1 No Label
## 393 SCH25 beds 0 No Label
## 394 SCH31 faces 0 No Label
## 395 SCH31 houses 0 No Label
## 396 SCH31 pasta 0 No Label
## 397 SCH31 beds 0 No Label
## 398 MSCH46 faces 0 No Label
## 399 MSCH46 houses 0 No Label
## 400 MSCH46 pasta 1 No Label
## 401 MSCH46 beds 0 No Label
## 402 SCH11 faces 1 No Label
## 403 SCH11 houses 1 No Label
## 404 SCH11 pasta 1 No Label
## 405 SCH29 faces 0 No Label
## 406 SCH29 houses 0 No Label
## 407 SCH29 pasta 0 No Label
## 408 SCH29 beds 0 No Label
## 409 MSCH39 beds 1 No Label
## 410 MSCH39 pasta 0 No Label
## 411 MSCH39 houses 0 No Label
## 412 MSCH39 faces 0 No Label
## 413 SCH28 faces 0 No Label
## 414 SCH28 houses 0 No Label
## 415 SCH28 pasta 0 No Label
## 416 SCH28 beds 0 No Label
## 417 SCH22 faces 0 No Label
## 418 SCH22 houses 0 No Label
## 419 SCH22 pasta 0 No Label
## 420 SCH22 beds 1 No Label
## 421 SCH24 faces 0 No Label
## 422 SCH24 houses 0 No Label
## 423 SCH24 pasta 1 No Label
## 424 SCH24 beds 0 No Label
## 425 SCH27 faces 0 No Label
## 426 SCH27 houses 0 No Label
## 427 SCH27 pasta 1 No Label
## 428 SCH27 beds 0 No Label
## 429 SCH17 faces 0 No Label
## 430 SCH17 houses 0 No Label
## 431 SCH17 pasta 1 No Label
## 432 SCH17 beds 0 No Label
## 433 SCH10 faces 0 No Label
## 434 SCH10 houses 0 No Label
## 435 SCH10 pasta 0 No Label
## 436 SCH10 beds 1 No Label
## 437 SCH9 faces 0 No Label
## 438 SCH9 houses 0 No Label
## 439 SCH9 pasta 0 No Label
## 440 SCH9 beds 0 No Label
## 441 SCH20 faces 0 No Label
## 442 SCH20 houses 0 No Label
## 443 SCH20 pasta 0 No Label
## 444 SCH20 beds 0 No Label
## 445 SCH6 faces 0 No Label
## 446 SCH6 houses 0 No Label
## 447 SCH6 pasta 0 No Label
## 448 SCH6 beds 0 No Label
## 449 SCH7 faces 1 No Label
## 450 SCH7 houses 0 No Label
## 451 SCH7 pasta 0 No Label
## 452 SCH7 beds 0 No Label
## 453 SCH15 faces 1 No Label
## 454 SCH15 houses 0 No Label
## 455 SCH15 pasta 0 No Label
## 456 SCH15 beds 0 No Label
## 457 SCH30 faces 0 No Label
## 458 SCH30 houses 0 No Label
## 459 SCH30 pasta 1 No Label
## 460 SCH30 beds 0 No Label
## 461 SCH3 faces 0 No Label
## 462 SCH3 houses 0 No Label
## 463 SCH3 pasta 0 No Label
## 464 SCH3 beds 0 No Label
## 465 SCH26 faces 0 No Label
## 466 SCH26 houses 0 No Label
## 467 SCH26 pasta 1 No Label
## 468 SCH26 beds 0 No Label
## 469 SCH8 faces 0 No Label
## 470 SCH8 houses 0 No Label
## 471 SCH8 pasta 0 No Label
## 472 SCH8 beds 0 No Label
## 473 SCH16 faces 0 No Label
## 474 SCH16 houses 0 No Label
## 475 SCH16 pasta 0 No Label
## 476 SCH16 beds 1 No Label
## 477 SCH14 faces 0 No Label
## 478 SCH14 houses 0 No Label
## 479 SCH14 pasta 0 No Label
## 480 SCH14 beds 1 No Label
## 481 SCH2 faces 0 No Label
## 482 SCH2 houses 0 No Label
## 483 SCH2 pasta 0 No Label
## 484 SCH2 beds 0 No Label
## 485 SCH5 faces 0 No Label
## 486 SCH5 houses 0 No Label
## 487 SCH5 pasta 0 No Label
## 488 SCH5 beds 0 No Label
## 489 SCH13 faces 0 No Label
## 490 SCH13 houses 0 No Label
## 491 SCH13 pasta 0 No Label
## 492 SCH13 beds 0 No Label
## 493 SCH21 faces 0 No Label
## 494 SCH21 houses 0 No Label
## 495 SCH21 pasta 0 No Label
## 496 SCH21 beds 0 No Label
## 497 SCH19 faces 0 No Label
## 498 SCH19 houses 0 No Label
## 499 SCH19 pasta 0 No Label
## 500 SCH19 beds 1 No Label
## 501 SCH23 faces 0 No Label
## 502 SCH23 houses 0 No Label
## 503 SCH23 pasta 0 No Label
## 504 SCH23 beds 0 No Label
## 505 SCH1 faces 0 No Label
## 506 SCH1 houses 0 No Label
## 507 SCH1 pasta 0 No Label
## 508 SCH1 beds 0 No Label
## 509 MSCH66 faces 0 No Label
## 510 MSCH66 houses 0 No Label
## 511 MSCH66 pasta 1 No Label
## 512 MSCH66 beds 0 No Label
## 513 MSCH67 faces 0 No Label
## 514 MSCH67 houses 1 No Label
## 515 MSCH67 pasta 0 No Label
## 516 MSCH67 beds 1 No Label
## 517 MSCH68 faces 0 No Label
## 518 MSCH68 houses 0 No Label
## 519 MSCH68 pasta 0 No Label
## 520 MSCH68 beds 0 No Label
## 521 MSCH69 faces 0 No Label
## 522 MSCH69 houses 1 No Label
## 523 MSCH69 pasta 1 No Label
## 524 MSCH69 beds 0 No Label
## 525 MSCH70 faces 0 No Label
## 526 MSCH70 houses 0 No Label
## 527 MSCH70 pasta 0 No Label
## 528 MSCH70 beds 1 No Label
## 529 MSCH71 faces 1 No Label
## 530 MSCH71 houses 1 No Label
## 531 MSCH71 pasta 1 No Label
## 532 MSCH71 beds 0 No Label
## 533 MSCH72 faces 1 No Label
## 534 MSCH72 houses 1 No Label
## 535 MSCH72 pasta 0 No Label
## 536 MSCH72 beds 0 No Label
## 537 MSCH73 faces 0 No Label
## 538 MSCH73 houses 0 No Label
## 539 MSCH73 pasta 0 No Label
## 540 MSCH73 beds 0 No Label
## 541 MSCH74 faces 1 No Label
## 542 MSCH74 houses 0 No Label
## 543 MSCH74 pasta 0 No Label
## 544 MSCH74 beds 1 No Label
## 545 MSCH75 faces 0 No Label
## 546 MSCH75 houses 0 No Label
## 547 MSCH75 pasta 0 No Label
## 548 MSCH75 beds 0 No Label
## 549 MSCH76 faces 0 No Label
## 550 MSCH76 houses 0 No Label
## 551 MSCH76 pasta 0 No Label
## 552 MSCH76 beds 0 No Label
## 553 MSCH77 faces 0 No Label
## 554 MSCH77 houses 0 No Label
## 555 MSCH77 pasta 0 No Label
## 556 MSCH77 beds 1 No Label
## 557 MSCH78 faces 0 No Label
## 558 MSCH78 houses 0 No Label
## 559 MSCH78 pasta 0 No Label
## 560 MSCH78 beds 1 No Label
## 561 MSCH79 faces 0 No Label
## 562 MSCH79 houses 1 No Label
## 563 MSCH79 pasta 0 No Label
## 564 MSCH79 beds 1 No Label
## 565 MSCH80 faces 0 No Label
## 566 MSCH80 houses 0 No Label
## 567 MSCH80 pasta 0 No Label
## 568 MSCH80 beds 0 No Label
## 569 MSCH81 faces 1 No Label
## 570 MSCH81 houses 0 No Label
## 571 MSCH81 pasta 0 No Label
## 572 MSCH81 beds 0 No Label
## 573 MSCH82 faces 1 No Label
## 574 MSCH82 houses 0 No Label
## 575 MSCH82 pasta 1 No Label
## 576 MSCH82 beds 0 No Label
## 577 MSCH83 faces 0 No Label
## 578 MSCH83 houses 0 No Label
## 579 MSCH83 pasta 1 No Label
## 580 MSCH83 beds 0 No Label
## 581 MSCH84 faces 0 No Label
## 582 MSCH84 houses 0 No Label
## 583 MSCH84 pasta 1 No Label
## 584 MSCH84 beds 0 No Label
## 585 MSCH85 faces 0 No Label
## 586 MSCH85 houses 0 No Label
## 587 MSCH85 pasta 0 No Label
## 588 MSCH85 beds 0 No LabelYou could also get rid of by referencing its index:
select(ps_data, -5)## subid item correct age
## 1 M22 faces 1 2.00
## 2 M22 houses 1 2.00
## 3 M22 pasta 0 2.00
## 4 M22 beds 0 2.00
## 5 T22 beds 0 2.13
## 6 T22 faces 0 2.13
## 7 T22 houses 1 2.13
## 8 T22 pasta 1 2.13
## 9 T17 pasta 0 2.32
## 10 T17 faces 0 2.32
## 11 T17 houses 0 2.32
## 12 T17 beds 0 2.32
## 13 M3 faces 0 2.38
## 14 M3 houses 1 2.38
## 15 M3 pasta 1 2.38
## 16 M3 beds 1 2.38
## 17 T19 faces 0 2.47
## 18 T19 houses 0 2.47
## 19 T19 pasta 1 2.47
## 20 T19 beds 1 2.47
## 21 T20 faces 1 2.50
## 22 T20 houses 1 2.50
## 23 T20 pasta 0 2.50
## 24 T20 beds 1 2.50
## 25 T21 faces 1 2.58
## 26 T21 houses 1 2.58
## 27 T21 pasta 1 2.58
## 28 T21 beds 0 2.58
## 29 M26 faces 1 2.59
## 30 M26 houses 1 2.59
## 31 M26 pasta 0 2.59
## 32 M26 beds 1 2.59
## 33 T18 faces 1 2.61
## 34 T18 houses 0 2.61
## 35 T18 pasta 1 2.61
## 36 T18 beds 0 2.61
## 37 T12 beds 0 2.72
## 38 T12 faces 0 2.72
## 39 T12 houses 1 2.72
## 40 T12 pasta 0 2.72
## 41 T16 faces 1 2.73
## 42 T16 houses 0 2.73
## 43 T16 pasta 1 2.73
## 44 T16 beds 1 2.73
## 45 T7 faces 1 2.74
## 46 T7 houses 0 2.74
## 47 T7 pasta 0 2.74
## 48 T7 beds 0 2.74
## 49 T9 houses 0 2.79
## 50 T9 faces 1 2.79
## 51 T9 pasta 0 2.79
## 52 T9 beds 1 2.79
## 53 T5 faces 1 2.80
## 54 T5 houses 1 2.80
## 55 T5 pasta 0 2.80
## 56 T5 beds 1 2.80
## 57 T14 faces 1 2.83
## 58 T14 houses 1 2.83
## 59 T14 pasta 0 2.83
## 60 T14 beds 1 2.83
## 61 T2 houses 0 2.83
## 62 T2 faces 0 2.83
## 63 T2 pasta 1 2.83
## 64 T2 beds 1 2.83
## 65 T15 faces 0 2.85
## 66 T15 houses 0 2.85
## 67 T15 pasta 1 2.85
## 68 T15 beds 0 2.85
## 69 M13 houses 0 2.88
## 70 M13 beds 1 2.88
## 71 M13 faces 1 2.88
## 72 M13 pasta 0 2.88
## 73 M12 faces 1 2.88
## 74 M12 houses 0 2.88
## 75 M12 pasta 1 2.88
## 76 M12 beds 0 2.88
## 77 T13 beds 0 2.89
## 78 T13 faces 0 2.89
## 79 T13 houses 1 2.89
## 80 T13 pasta 1 2.89
## 81 T8 faces 1 2.91
## 82 T8 houses 0 2.91
## 83 T8 pasta 1 2.91
## 84 T8 beds 1 2.91
## 85 T1 faces 1 2.95
## 86 T1 houses 0 2.95
## 87 T1 pasta 0 2.95
## 88 T1 beds 1 2.95
## 89 M15 faces 1 2.98
## 90 M15 houses 1 2.98
## 91 M15 pasta 1 2.98
## 92 M15 beds 1 2.98
## 93 T11 faces 1 2.99
## 94 T11 houses 0 2.99
## 95 T11 pasta 1 2.99
## 96 T11 beds 1 2.99
## 97 T10 faces 0 3.00
## 98 T10 houses 1 3.00
## 99 T10 pasta 1 3.00
## 100 T10 beds 1 3.00
## 101 T3 faces 1 3.09
## 102 T3 houses 1 3.09
## 103 T3 pasta 1 3.09
## 104 T3 beds 1 3.09
## 105 T6 faces 1 3.10
## 106 T6 houses 1 3.10
## 107 T6 pasta 1 3.10
## 108 T6 beds 1 3.10
## 109 M32 beds 1 3.19
## 110 M32 faces 1 3.19
## 111 M32 houses 0 3.19
## 112 M32 pasta 1 3.19
## 113 M1 faces 0 3.20
## 114 M1 beds 1 3.20
## 115 M1 pasta 0 3.20
## 116 M1 houses 0 3.20
## 117 C16 faces 0 3.22
## 118 C16 houses 0 3.22
## 119 C16 pasta 1 3.22
## 120 C16 beds 1 3.22
## 121 T4 faces 1 3.24
## 122 T4 houses 0 3.24
## 123 T4 pasta 0 3.24
## 124 T4 beds 1 3.24
## 125 C17 faces 1 3.25
## 126 C17 houses 0 3.25
## 127 C17 pasta 1 3.25
## 128 C17 beds 0 3.25
## 129 C6 faces 0 3.26
## 130 C6 houses 1 3.26
## 131 C6 pasta 1 3.26
## 132 C6 beds 1 3.26
## 133 M10 faces 1 3.28
## 134 M10 houses 1 3.28
## 135 M10 beds 1 3.28
## 136 M10 pasta 1 3.28
## 137 M31 faces 0 3.30
## 138 M31 houses 1 3.30
## 139 M31 pasta 1 3.30
## 140 M31 beds 1 3.30
## 141 C3 houses 0 3.46
## 142 C3 pasta 1 3.46
## 143 C3 beds 1 3.46
## 144 C3 faces 1 3.46
## 145 C10 faces 0 3.46
## 146 C10 houses 0 3.46
## 147 C10 pasta 1 3.46
## 148 C10 beds 1 3.46
## 149 M18 faces 0 3.46
## 150 M18 houses 1 3.46
## 151 M18 pasta 1 3.46
## 152 M18 beds 1 3.46
## 153 M16 faces 0 3.50
## 154 M16 houses 0 3.50
## 155 M16 pasta 0 3.50
## 156 M16 beds 1 3.50
## 157 M23 faces 1 3.52
## 158 M23 houses 0 3.52
## 159 M23 pasta 1 3.52
## 160 M23 beds 1 3.52
## 161 C7 faces 0 3.55
## 162 C7 houses 1 3.55
## 163 C7 pasta 0 3.55
## 164 C7 beds 0 3.55
## 165 C12 faces 1 3.56
## 166 C12 houses 0 3.56
## 167 C12 pasta 1 3.56
## 168 C12 beds 1 3.56
## 169 C15 faces 1 3.59
## 170 C15 houses 1 3.59
## 171 C15 pasta 1 3.59
## 172 C15 beds 1 3.59
## 173 M29 faces 0 3.72
## 174 M29 houses 1 3.72
## 175 M29 pasta 1 3.72
## 176 M29 beds 1 3.72
## 177 C20 faces 1 3.75
## 178 C20 houses 1 3.75
## 179 C20 pasta 1 3.75
## 180 C20 beds 1 3.75
## 181 M11 faces 1 3.82
## 182 M11 houses 0 3.82
## 183 M11 pasta 1 3.82
## 184 M11 beds 1 3.82
## 185 C9 beds 1 3.82
## 186 C9 faces 1 3.82
## 187 C9 houses 1 3.82
## 188 C9 pasta 1 3.82
## 189 C24 faces 1 3.85
## 190 C24 houses 0 3.85
## 191 C24 pasta 0 3.85
## 192 C24 beds 1 3.85
## 193 C22 faces 0 3.92
## 194 C22 houses 0 3.92
## 195 C22 pasta 1 3.92
## 196 C22 beds 1 3.92
## 197 C8 faces 1 3.92
## 198 C8 houses 1 3.92
## 199 C8 pasta 1 3.92
## 200 C8 beds 1 3.92
## 201 M4 faces 1 3.96
## 202 M4 houses 1 3.96
## 203 M4 pasta 1 3.96
## 204 M4 beds 1 3.96
## 205 M6 faces 0 4.50
## 206 M6 houses 1 4.50
## 207 M6 pasta 1 4.50
## 208 M6 beds 0 4.50
## 209 C19 faces 1 4.14
## 210 C19 houses 0 4.14
## 211 C19 pasta 0 4.14
## 212 C19 beds 1 4.14
## 213 C1 faces 1 4.16
## 214 C1 houses 1 4.16
## 215 C1 pasta 1 4.16
## 216 C1 beds 1 4.16
## 217 M19 beds 1 4.16
## 218 M19 faces 0 4.16
## 219 M19 houses 0 4.16
## 220 M19 pasta 1 4.16
## 221 C11 faces 1 4.22
## 222 C11 houses 0 4.22
## 223 C11 pasta 1 4.22
## 224 C11 beds 1 4.22
## 225 M9 faces 1 4.26
## 226 M9 houses 1 4.26
## 227 M9 pasta 1 4.26
## 228 M9 beds 1 4.26
## 229 M2 faces 1 4.28
## 230 M2 houses 0 4.28
## 231 M2 pasta 1 4.28
## 232 M2 beds 1 4.28
## 233 C5 faces 1 4.29
## 234 C5 houses 1 4.29
## 235 C5 pasta 1 4.29
## 236 C5 beds 1 4.29
## 237 M30 beds 1 4.33
## 238 M30 faces 1 4.33
## 239 M30 houses 0 4.33
## 240 M30 pasta 1 4.33
## 241 C13 faces 0 4.38
## 242 C13 houses 1 4.38
## 243 C13 pasta 0 4.38
## 244 C13 beds 1 4.38
## 245 C4 faces 1 4.55
## 246 C4 houses 1 4.55
## 247 C4 pasta 1 4.55
## 248 C4 beds 1 4.55
## 249 C14 faces 1 4.57
## 250 C14 houses 1 4.57
## 251 C14 pasta 0 4.57
## 252 C14 beds 1 4.57
## 253 M17 faces 1 4.58
## 254 M17 houses 1 4.58
## 255 M17 pasta 1 4.58
## 256 M17 beds 1 4.58
## 257 C2 faces 1 4.60
## 258 C2 houses 1 4.60
## 259 C2 pasta 1 4.60
## 260 C2 beds 1 4.60
## 261 C23 faces 0 4.62
## 262 C23 houses 1 4.62
## 263 C23 pasta 1 4.62
## 264 C23 beds 0 4.62
## 265 M20 faces 0 4.64
## 266 M20 houses 0 4.64
## 267 M20 pasta 1 4.64
## 268 M20 beds 1 4.64
## 269 M21 faces 1 4.64
## 270 M21 houses 1 4.64
## 271 M21 pasta 1 4.64
## 272 M21 beds 1 4.64
## 273 C21 faces 1 4.73
## 274 C21 houses 0 4.73
## 275 C21 pasta 1 4.73
## 276 C21 beds 1 4.73
## 277 M24 faces 1 4.82
## 278 M24 houses 1 4.82
## 279 M24 pasta 1 4.82
## 280 M24 beds 1 4.82
## 281 M5 faces 0 4.84
## 282 M5 houses 0 4.84
## 283 M5 pasta 0 4.84
## 284 M5 beds 1 4.84
## 285 M7 faces 1 4.89
## 286 M7 houses 1 4.89
## 287 M7 pasta 1 4.89
## 288 M7 beds 0 4.89
## 289 M8 faces 1 4.89
## 290 M8 houses 1 4.89
## 291 M8 pasta 1 4.89
## 292 M8 beds 1 4.89
## 293 C18 faces 0 4.95
## 294 C18 houses 1 4.95
## 295 C18 pasta 1 4.95
## 296 C18 beds 1 4.95
## 297 M25 faces 1 4.96
## 298 M25 houses 1 4.96
## 299 M25 pasta 1 4.96
## 300 M25 beds 1 4.96
## 301 MSCH47 faces 1 2.01
## 302 MSCH47 houses 0 2.01
## 303 MSCH47 pasta 1 2.01
## 304 MSCH47 beds 0 2.01
## 305 MSCH50 faces 0 2.03
## 306 MSCH50 houses 0 2.03
## 307 MSCH50 pasta 0 2.03
## 308 MSCH50 beds 0 2.03
## 309 MSCH51 faces 0 2.07
## 310 MSCH51 houses 0 2.07
## 311 MSCH51 pasta 0 2.07
## 312 MSCH51 beds 0 2.07
## 313 MSCH44 faces 0 2.25
## 314 MSCH44 houses 0 2.25
## 315 MSCH44 pasta 0 2.25
## 316 MSCH44 beds 0 2.25
## 317 MSCH52 faces 0 2.50
## 318 MSCH52 houses 1 2.50
## 319 MSCH52 pasta 0 2.50
## 320 MSCH52 beds 1 2.50
## 321 MSCH38 faces 0 2.59
## 322 MSCH38 houses 0 2.59
## 323 MSCH38 pasta 1 2.59
## 324 MSCH38 beds 0 2.59
## 325 MSCH43 faces 0 2.71
## 326 MSCH43 houses 0 2.71
## 327 MSCH43 pasta 0 2.71
## 328 MSCH43 beds 0 2.71
## 329 MSCH49 faces 0 2.88
## 330 MSCH49 houses 0 2.88
## 331 MSCH49 pasta 0 2.88
## 332 MSCH49 beds 0 2.88
## 333 MSCH45 faces 0 2.90
## 334 MSCH45 houses 0 2.90
## 335 MSCH45 pasta 0 2.90
## 336 MSCH45 beds 1 2.90
## 337 MSCH42 faces 1 2.93
## 338 MSCH42 houses 0 2.93
## 339 MSCH42 pasta 0 2.93
## 340 MSCH42 beds 0 2.93
## 341 MSCH53 faces 1 2.99
## 342 MSCH53 houses 1 2.99
## 343 MSCH53 pasta 0 2.99
## 344 MSCH53 beds 0 2.99
## 345 SCH35 faces 0 3.02
## 346 SCH35 houses 0 3.02
## 347 SCH35 pasta 0 3.02
## 348 SCH35 beds 0 3.02
## 349 MSCH40 faces 0 3.02
## 350 MSCH40 houses 1 3.02
## 351 MSCH40 pasta 0 3.02
## 352 MSCH40 beds 1 3.02
## 353 SCH34 faces 0 3.06
## 354 SCH34 houses 0 3.06
## 355 SCH34 pasta 0 3.06
## 356 SCH34 beds 0 3.06
## 357 SCH33 faces 0 3.06
## 358 SCH33 houses 0 3.06
## 359 SCH33 pasta 0 3.06
## 360 SCH33 beds 0 3.06
## 361 MSCH41 faces 0 3.18
## 362 MSCH41 houses 0 3.18
## 363 MSCH41 pasta 0 3.18
## 364 MSCH41 beds 0 3.18
## 365 SCH37 beds 0 3.27
## 366 SCH37 faces 1 3.27
## 367 SCH37 houses 0 3.27
## 368 SCH37 pasta 1 3.27
## 369 SCH32 faces 1 3.27
## 370 SCH32 houses 0 3.27
## 371 SCH32 pasta 0 3.27
## 372 SCH32 beds 0 3.27
## 373 SCH36 beds 0 3.33
## 374 SCH36 faces 0 3.33
## 375 SCH36 houses 1 3.33
## 376 SCH36 pasta 1 3.33
## 377 SCH11 beds 0 3.41
## 378 SCH12 faces 0 3.41
## 379 SCH12 houses 0 3.41
## 380 SCH12 pasta 0 3.41
## 381 SCH12 beds 0 3.41
## 382 SCH18 faces 0 3.45
## 383 SCH18 houses 0 3.45
## 384 SCH18 pasta 0 3.45
## 385 SCH18 beds 0 3.45
## 386 MSCH48 faces 0 3.50
## 387 MSCH48 houses 1 3.50
## 388 MSCH48 pasta 0 3.50
## 389 MSCH48 beds 0 3.50
## 390 SCH25 faces 0 3.54
## 391 SCH25 houses 1 3.54
## 392 SCH25 pasta 1 3.54
## 393 SCH25 beds 0 3.54
## 394 SCH31 faces 0 3.71
## 395 SCH31 houses 0 3.71
## 396 SCH31 pasta 0 3.71
## 397 SCH31 beds 0 3.71
## 398 MSCH46 faces 0 3.76
## 399 MSCH46 houses 0 3.76
## 400 MSCH46 pasta 1 3.76
## 401 MSCH46 beds 0 3.76
## 402 SCH11 faces 1 3.82
## 403 SCH11 houses 1 3.82
## 404 SCH11 pasta 1 3.82
## 405 SCH29 faces 0 3.83
## 406 SCH29 houses 0 3.83
## 407 SCH29 pasta 0 3.83
## 408 SCH29 beds 0 3.83
## 409 MSCH39 beds 1 3.93
## 410 MSCH39 pasta 0 3.93
## 411 MSCH39 houses 0 3.94
## 412 MSCH39 faces 0 3.94
## 413 SCH28 faces 0 4.02
## 414 SCH28 houses 0 4.02
## 415 SCH28 pasta 0 4.02
## 416 SCH28 beds 0 4.02
## 417 SCH22 faces 0 4.02
## 418 SCH22 houses 0 4.02
## 419 SCH22 pasta 0 4.02
## 420 SCH22 beds 1 4.02
## 421 SCH24 faces 0 4.07
## 422 SCH24 houses 0 4.07
## 423 SCH24 pasta 1 4.07
## 424 SCH24 beds 0 4.07
## 425 SCH27 faces 0 4.09
## 426 SCH27 houses 0 4.09
## 427 SCH27 pasta 1 4.09
## 428 SCH27 beds 0 4.09
## 429 SCH17 faces 0 4.25
## 430 SCH17 houses 0 4.25
## 431 SCH17 pasta 1 4.25
## 432 SCH17 beds 0 4.25
## 433 SCH10 faces 0 4.32
## 434 SCH10 houses 0 4.32
## 435 SCH10 pasta 0 4.32
## 436 SCH10 beds 1 4.32
## 437 SCH9 faces 0 4.37
## 438 SCH9 houses 0 4.37
## 439 SCH9 pasta 0 4.37
## 440 SCH9 beds 0 4.37
## 441 SCH20 faces 0 4.39
## 442 SCH20 houses 0 4.39
## 443 SCH20 pasta 0 4.39
## 444 SCH20 beds 0 4.39
## 445 SCH6 faces 0 4.41
## 446 SCH6 houses 0 4.41
## 447 SCH6 pasta 0 4.41
## 448 SCH6 beds 0 4.41
## 449 SCH7 faces 1 4.41
## 450 SCH7 houses 0 4.41
## 451 SCH7 pasta 0 4.41
## 452 SCH7 beds 0 4.41
## 453 SCH15 faces 1 4.42
## 454 SCH15 houses 0 4.42
## 455 SCH15 pasta 0 4.42
## 456 SCH15 beds 0 4.42
## 457 SCH30 faces 0 4.44
## 458 SCH30 houses 0 4.44
## 459 SCH30 pasta 1 4.44
## 460 SCH30 beds 0 4.44
## 461 SCH3 faces 0 4.47
## 462 SCH3 houses 0 4.47
## 463 SCH3 pasta 0 4.47
## 464 SCH3 beds 0 4.47
## 465 SCH26 faces 0 4.47
## 466 SCH26 houses 0 4.47
## 467 SCH26 pasta 1 4.47
## 468 SCH26 beds 0 4.47
## 469 SCH8 faces 0 4.52
## 470 SCH8 houses 0 4.52
## 471 SCH8 pasta 0 4.52
## 472 SCH8 beds 0 4.52
## 473 SCH16 faces 0 4.55
## 474 SCH16 houses 0 4.55
## 475 SCH16 pasta 0 4.55
## 476 SCH16 beds 1 4.55
## 477 SCH14 faces 0 4.58
## 478 SCH14 houses 0 4.58
## 479 SCH14 pasta 0 4.58
## 480 SCH14 beds 1 4.58
## 481 SCH2 faces 0 4.61
## 482 SCH2 houses 0 4.61
## 483 SCH2 pasta 0 4.61
## 484 SCH2 beds 0 4.61
## 485 SCH5 faces 0 4.61
## 486 SCH5 houses 0 4.61
## 487 SCH5 pasta 0 4.61
## 488 SCH5 beds 0 4.61
## 489 SCH13 faces 0 4.75
## 490 SCH13 houses 0 4.75
## 491 SCH13 pasta 0 4.75
## 492 SCH13 beds 0 4.75
## 493 SCH21 faces 0 4.76
## 494 SCH21 houses 0 4.76
## 495 SCH21 pasta 0 4.76
## 496 SCH21 beds 0 4.76
## 497 SCH19 faces 0 4.79
## 498 SCH19 houses 0 4.79
## 499 SCH19 pasta 0 4.79
## 500 SCH19 beds 1 4.79
## 501 SCH23 faces 0 4.82
## 502 SCH23 houses 0 4.82
## 503 SCH23 pasta 0 4.82
## 504 SCH23 beds 0 4.82
## 505 SCH1 faces 0 4.82
## 506 SCH1 houses 0 4.82
## 507 SCH1 pasta 0 4.82
## 508 SCH1 beds 0 4.82
## 509 MSCH66 faces 0 3.50
## 510 MSCH66 houses 0 3.50
## 511 MSCH66 pasta 1 3.50
## 512 MSCH66 beds 0 3.50
## 513 MSCH67 faces 0 3.24
## 514 MSCH67 houses 1 3.24
## 515 MSCH67 pasta 0 3.24
## 516 MSCH67 beds 1 3.24
## 517 MSCH68 faces 0 3.94
## 518 MSCH68 houses 0 3.94
## 519 MSCH68 pasta 0 3.94
## 520 MSCH68 beds 0 3.94
## 521 MSCH69 faces 0 2.72
## 522 MSCH69 houses 1 2.72
## 523 MSCH69 pasta 1 2.72
## 524 MSCH69 beds 0 2.72
## 525 MSCH70 faces 0 2.31
## 526 MSCH70 houses 0 2.31
## 527 MSCH70 pasta 0 2.31
## 528 MSCH70 beds 1 2.31
## 529 MSCH71 faces 1 3.14
## 530 MSCH71 houses 1 3.14
## 531 MSCH71 pasta 1 3.14
## 532 MSCH71 beds 0 3.14
## 533 MSCH72 faces 1 3.72
## 534 MSCH72 houses 1 3.72
## 535 MSCH72 pasta 0 3.72
## 536 MSCH72 beds 0 3.72
## 537 MSCH73 faces 0 3.10
## 538 MSCH73 houses 0 3.10
## 539 MSCH73 pasta 0 3.10
## 540 MSCH73 beds 0 3.10
## 541 MSCH74 faces 1 2.34
## 542 MSCH74 houses 0 2.34
## 543 MSCH74 pasta 0 2.34
## 544 MSCH74 beds 1 2.34
## 545 MSCH75 faces 0 3.67
## 546 MSCH75 houses 0 3.67
## 547 MSCH75 pasta 0 3.67
## 548 MSCH75 beds 0 3.66
## 549 MSCH76 faces 0 2.58
## 550 MSCH76 houses 0 2.58
## 551 MSCH76 pasta 0 2.58
## 552 MSCH76 beds 0 2.58
## 553 MSCH77 faces 0 2.55
## 554 MSCH77 houses 0 2.55
## 555 MSCH77 pasta 0 2.55
## 556 MSCH77 beds 1 2.55
## 557 MSCH78 faces 0 2.43
## 558 MSCH78 houses 0 2.43
## 559 MSCH78 pasta 0 2.43
## 560 MSCH78 beds 1 2.43
## 561 MSCH79 faces 0 2.70
## 562 MSCH79 houses 1 2.70
## 563 MSCH79 pasta 0 2.70
## 564 MSCH79 beds 1 2.70
## 565 MSCH80 faces 0 2.76
## 566 MSCH80 houses 0 2.76
## 567 MSCH80 pasta 0 2.76
## 568 MSCH80 beds 0 2.76
## 569 MSCH81 faces 1 2.84
## 570 MSCH81 houses 0 2.84
## 571 MSCH81 pasta 0 2.84
## 572 MSCH81 beds 0 2.84
## 573 MSCH82 faces 1 2.46
## 574 MSCH82 houses 0 2.46
## 575 MSCH82 pasta 1 2.46
## 576 MSCH82 beds 0 2.46
## 577 MSCH83 faces 0 2.37
## 578 MSCH83 houses 0 2.37
## 579 MSCH83 pasta 1 2.37
## 580 MSCH83 beds 0 2.37
## 581 MSCH84 faces 0 2.83
## 582 MSCH84 houses 0 2.83
## 583 MSCH84 pasta 1 2.83
## 584 MSCH84 beds 0 2.83
## 585 MSCH85 faces 0 2.69
## 586 MSCH85 houses 0 2.69
## 587 MSCH85 pasta 0 2.69
## 588 MSCH85 beds 0 2.69You can also use : to select or de-select a range of variables. This can be done with reference to their numerical index:
# select first three:
select(ps_data, 1:3)## subid item correct
## 1 M22 faces 1
## 2 M22 houses 1
## 3 M22 pasta 0
## 4 M22 beds 0
## 5 T22 beds 0
## 6 T22 faces 0
## 7 T22 houses 1
## 8 T22 pasta 1
## 9 T17 pasta 0
## 10 T17 faces 0
## 11 T17 houses 0
## 12 T17 beds 0
## 13 M3 faces 0
## 14 M3 houses 1
## 15 M3 pasta 1
## 16 M3 beds 1
## 17 T19 faces 0
## 18 T19 houses 0
## 19 T19 pasta 1
## 20 T19 beds 1
## 21 T20 faces 1
## 22 T20 houses 1
## 23 T20 pasta 0
## 24 T20 beds 1
## 25 T21 faces 1
## 26 T21 houses 1
## 27 T21 pasta 1
## 28 T21 beds 0
## 29 M26 faces 1
## 30 M26 houses 1
## 31 M26 pasta 0
## 32 M26 beds 1
## 33 T18 faces 1
## 34 T18 houses 0
## 35 T18 pasta 1
## 36 T18 beds 0
## 37 T12 beds 0
## 38 T12 faces 0
## 39 T12 houses 1
## 40 T12 pasta 0
## 41 T16 faces 1
## 42 T16 houses 0
## 43 T16 pasta 1
## 44 T16 beds 1
## 45 T7 faces 1
## 46 T7 houses 0
## 47 T7 pasta 0
## 48 T7 beds 0
## 49 T9 houses 0
## 50 T9 faces 1
## 51 T9 pasta 0
## 52 T9 beds 1
## 53 T5 faces 1
## 54 T5 houses 1
## 55 T5 pasta 0
## 56 T5 beds 1
## 57 T14 faces 1
## 58 T14 houses 1
## 59 T14 pasta 0
## 60 T14 beds 1
## 61 T2 houses 0
## 62 T2 faces 0
## 63 T2 pasta 1
## 64 T2 beds 1
## 65 T15 faces 0
## 66 T15 houses 0
## 67 T15 pasta 1
## 68 T15 beds 0
## 69 M13 houses 0
## 70 M13 beds 1
## 71 M13 faces 1
## 72 M13 pasta 0
## 73 M12 faces 1
## 74 M12 houses 0
## 75 M12 pasta 1
## 76 M12 beds 0
## 77 T13 beds 0
## 78 T13 faces 0
## 79 T13 houses 1
## 80 T13 pasta 1
## 81 T8 faces 1
## 82 T8 houses 0
## 83 T8 pasta 1
## 84 T8 beds 1
## 85 T1 faces 1
## 86 T1 houses 0
## 87 T1 pasta 0
## 88 T1 beds 1
## 89 M15 faces 1
## 90 M15 houses 1
## 91 M15 pasta 1
## 92 M15 beds 1
## 93 T11 faces 1
## 94 T11 houses 0
## 95 T11 pasta 1
## 96 T11 beds 1
## 97 T10 faces 0
## 98 T10 houses 1
## 99 T10 pasta 1
## 100 T10 beds 1
## 101 T3 faces 1
## 102 T3 houses 1
## 103 T3 pasta 1
## 104 T3 beds 1
## 105 T6 faces 1
## 106 T6 houses 1
## 107 T6 pasta 1
## 108 T6 beds 1
## 109 M32 beds 1
## 110 M32 faces 1
## 111 M32 houses 0
## 112 M32 pasta 1
## 113 M1 faces 0
## 114 M1 beds 1
## 115 M1 pasta 0
## 116 M1 houses 0
## 117 C16 faces 0
## 118 C16 houses 0
## 119 C16 pasta 1
## 120 C16 beds 1
## 121 T4 faces 1
## 122 T4 houses 0
## 123 T4 pasta 0
## 124 T4 beds 1
## 125 C17 faces 1
## 126 C17 houses 0
## 127 C17 pasta 1
## 128 C17 beds 0
## 129 C6 faces 0
## 130 C6 houses 1
## 131 C6 pasta 1
## 132 C6 beds 1
## 133 M10 faces 1
## 134 M10 houses 1
## 135 M10 beds 1
## 136 M10 pasta 1
## 137 M31 faces 0
## 138 M31 houses 1
## 139 M31 pasta 1
## 140 M31 beds 1
## 141 C3 houses 0
## 142 C3 pasta 1
## 143 C3 beds 1
## 144 C3 faces 1
## 145 C10 faces 0
## 146 C10 houses 0
## 147 C10 pasta 1
## 148 C10 beds 1
## 149 M18 faces 0
## 150 M18 houses 1
## 151 M18 pasta 1
## 152 M18 beds 1
## 153 M16 faces 0
## 154 M16 houses 0
## 155 M16 pasta 0
## 156 M16 beds 1
## 157 M23 faces 1
## 158 M23 houses 0
## 159 M23 pasta 1
## 160 M23 beds 1
## 161 C7 faces 0
## 162 C7 houses 1
## 163 C7 pasta 0
## 164 C7 beds 0
## 165 C12 faces 1
## 166 C12 houses 0
## 167 C12 pasta 1
## 168 C12 beds 1
## 169 C15 faces 1
## 170 C15 houses 1
## 171 C15 pasta 1
## 172 C15 beds 1
## 173 M29 faces 0
## 174 M29 houses 1
## 175 M29 pasta 1
## 176 M29 beds 1
## 177 C20 faces 1
## 178 C20 houses 1
## 179 C20 pasta 1
## 180 C20 beds 1
## 181 M11 faces 1
## 182 M11 houses 0
## 183 M11 pasta 1
## 184 M11 beds 1
## 185 C9 beds 1
## 186 C9 faces 1
## 187 C9 houses 1
## 188 C9 pasta 1
## 189 C24 faces 1
## 190 C24 houses 0
## 191 C24 pasta 0
## 192 C24 beds 1
## 193 C22 faces 0
## 194 C22 houses 0
## 195 C22 pasta 1
## 196 C22 beds 1
## 197 C8 faces 1
## 198 C8 houses 1
## 199 C8 pasta 1
## 200 C8 beds 1
## 201 M4 faces 1
## 202 M4 houses 1
## 203 M4 pasta 1
## 204 M4 beds 1
## 205 M6 faces 0
## 206 M6 houses 1
## 207 M6 pasta 1
## 208 M6 beds 0
## 209 C19 faces 1
## 210 C19 houses 0
## 211 C19 pasta 0
## 212 C19 beds 1
## 213 C1 faces 1
## 214 C1 houses 1
## 215 C1 pasta 1
## 216 C1 beds 1
## 217 M19 beds 1
## 218 M19 faces 0
## 219 M19 houses 0
## 220 M19 pasta 1
## 221 C11 faces 1
## 222 C11 houses 0
## 223 C11 pasta 1
## 224 C11 beds 1
## 225 M9 faces 1
## 226 M9 houses 1
## 227 M9 pasta 1
## 228 M9 beds 1
## 229 M2 faces 1
## 230 M2 houses 0
## 231 M2 pasta 1
## 232 M2 beds 1
## 233 C5 faces 1
## 234 C5 houses 1
## 235 C5 pasta 1
## 236 C5 beds 1
## 237 M30 beds 1
## 238 M30 faces 1
## 239 M30 houses 0
## 240 M30 pasta 1
## 241 C13 faces 0
## 242 C13 houses 1
## 243 C13 pasta 0
## 244 C13 beds 1
## 245 C4 faces 1
## 246 C4 houses 1
## 247 C4 pasta 1
## 248 C4 beds 1
## 249 C14 faces 1
## 250 C14 houses 1
## 251 C14 pasta 0
## 252 C14 beds 1
## 253 M17 faces 1
## 254 M17 houses 1
## 255 M17 pasta 1
## 256 M17 beds 1
## 257 C2 faces 1
## 258 C2 houses 1
## 259 C2 pasta 1
## 260 C2 beds 1
## 261 C23 faces 0
## 262 C23 houses 1
## 263 C23 pasta 1
## 264 C23 beds 0
## 265 M20 faces 0
## 266 M20 houses 0
## 267 M20 pasta 1
## 268 M20 beds 1
## 269 M21 faces 1
## 270 M21 houses 1
## 271 M21 pasta 1
## 272 M21 beds 1
## 273 C21 faces 1
## 274 C21 houses 0
## 275 C21 pasta 1
## 276 C21 beds 1
## 277 M24 faces 1
## 278 M24 houses 1
## 279 M24 pasta 1
## 280 M24 beds 1
## 281 M5 faces 0
## 282 M5 houses 0
## 283 M5 pasta 0
## 284 M5 beds 1
## 285 M7 faces 1
## 286 M7 houses 1
## 287 M7 pasta 1
## 288 M7 beds 0
## 289 M8 faces 1
## 290 M8 houses 1
## 291 M8 pasta 1
## 292 M8 beds 1
## 293 C18 faces 0
## 294 C18 houses 1
## 295 C18 pasta 1
## 296 C18 beds 1
## 297 M25 faces 1
## 298 M25 houses 1
## 299 M25 pasta 1
## 300 M25 beds 1
## 301 MSCH47 faces 1
## 302 MSCH47 houses 0
## 303 MSCH47 pasta 1
## 304 MSCH47 beds 0
## 305 MSCH50 faces 0
## 306 MSCH50 houses 0
## 307 MSCH50 pasta 0
## 308 MSCH50 beds 0
## 309 MSCH51 faces 0
## 310 MSCH51 houses 0
## 311 MSCH51 pasta 0
## 312 MSCH51 beds 0
## 313 MSCH44 faces 0
## 314 MSCH44 houses 0
## 315 MSCH44 pasta 0
## 316 MSCH44 beds 0
## 317 MSCH52 faces 0
## 318 MSCH52 houses 1
## 319 MSCH52 pasta 0
## 320 MSCH52 beds 1
## 321 MSCH38 faces 0
## 322 MSCH38 houses 0
## 323 MSCH38 pasta 1
## 324 MSCH38 beds 0
## 325 MSCH43 faces 0
## 326 MSCH43 houses 0
## 327 MSCH43 pasta 0
## 328 MSCH43 beds 0
## 329 MSCH49 faces 0
## 330 MSCH49 houses 0
## 331 MSCH49 pasta 0
## 332 MSCH49 beds 0
## 333 MSCH45 faces 0
## 334 MSCH45 houses 0
## 335 MSCH45 pasta 0
## 336 MSCH45 beds 1
## 337 MSCH42 faces 1
## 338 MSCH42 houses 0
## 339 MSCH42 pasta 0
## 340 MSCH42 beds 0
## 341 MSCH53 faces 1
## 342 MSCH53 houses 1
## 343 MSCH53 pasta 0
## 344 MSCH53 beds 0
## 345 SCH35 faces 0
## 346 SCH35 houses 0
## 347 SCH35 pasta 0
## 348 SCH35 beds 0
## 349 MSCH40 faces 0
## 350 MSCH40 houses 1
## 351 MSCH40 pasta 0
## 352 MSCH40 beds 1
## 353 SCH34 faces 0
## 354 SCH34 houses 0
## 355 SCH34 pasta 0
## 356 SCH34 beds 0
## 357 SCH33 faces 0
## 358 SCH33 houses 0
## 359 SCH33 pasta 0
## 360 SCH33 beds 0
## 361 MSCH41 faces 0
## 362 MSCH41 houses 0
## 363 MSCH41 pasta 0
## 364 MSCH41 beds 0
## 365 SCH37 beds 0
## 366 SCH37 faces 1
## 367 SCH37 houses 0
## 368 SCH37 pasta 1
## 369 SCH32 faces 1
## 370 SCH32 houses 0
## 371 SCH32 pasta 0
## 372 SCH32 beds 0
## 373 SCH36 beds 0
## 374 SCH36 faces 0
## 375 SCH36 houses 1
## 376 SCH36 pasta 1
## 377 SCH11 beds 0
## 378 SCH12 faces 0
## 379 SCH12 houses 0
## 380 SCH12 pasta 0
## 381 SCH12 beds 0
## 382 SCH18 faces 0
## 383 SCH18 houses 0
## 384 SCH18 pasta 0
## 385 SCH18 beds 0
## 386 MSCH48 faces 0
## 387 MSCH48 houses 1
## 388 MSCH48 pasta 0
## 389 MSCH48 beds 0
## 390 SCH25 faces 0
## 391 SCH25 houses 1
## 392 SCH25 pasta 1
## 393 SCH25 beds 0
## 394 SCH31 faces 0
## 395 SCH31 houses 0
## 396 SCH31 pasta 0
## 397 SCH31 beds 0
## 398 MSCH46 faces 0
## 399 MSCH46 houses 0
## 400 MSCH46 pasta 1
## 401 MSCH46 beds 0
## 402 SCH11 faces 1
## 403 SCH11 houses 1
## 404 SCH11 pasta 1
## 405 SCH29 faces 0
## 406 SCH29 houses 0
## 407 SCH29 pasta 0
## 408 SCH29 beds 0
## 409 MSCH39 beds 1
## 410 MSCH39 pasta 0
## 411 MSCH39 houses 0
## 412 MSCH39 faces 0
## 413 SCH28 faces 0
## 414 SCH28 houses 0
## 415 SCH28 pasta 0
## 416 SCH28 beds 0
## 417 SCH22 faces 0
## 418 SCH22 houses 0
## 419 SCH22 pasta 0
## 420 SCH22 beds 1
## 421 SCH24 faces 0
## 422 SCH24 houses 0
## 423 SCH24 pasta 1
## 424 SCH24 beds 0
## 425 SCH27 faces 0
## 426 SCH27 houses 0
## 427 SCH27 pasta 1
## 428 SCH27 beds 0
## 429 SCH17 faces 0
## 430 SCH17 houses 0
## 431 SCH17 pasta 1
## 432 SCH17 beds 0
## 433 SCH10 faces 0
## 434 SCH10 houses 0
## 435 SCH10 pasta 0
## 436 SCH10 beds 1
## 437 SCH9 faces 0
## 438 SCH9 houses 0
## 439 SCH9 pasta 0
## 440 SCH9 beds 0
## 441 SCH20 faces 0
## 442 SCH20 houses 0
## 443 SCH20 pasta 0
## 444 SCH20 beds 0
## 445 SCH6 faces 0
## 446 SCH6 houses 0
## 447 SCH6 pasta 0
## 448 SCH6 beds 0
## 449 SCH7 faces 1
## 450 SCH7 houses 0
## 451 SCH7 pasta 0
## 452 SCH7 beds 0
## 453 SCH15 faces 1
## 454 SCH15 houses 0
## 455 SCH15 pasta 0
## 456 SCH15 beds 0
## 457 SCH30 faces 0
## 458 SCH30 houses 0
## 459 SCH30 pasta 1
## 460 SCH30 beds 0
## 461 SCH3 faces 0
## 462 SCH3 houses 0
## 463 SCH3 pasta 0
## 464 SCH3 beds 0
## 465 SCH26 faces 0
## 466 SCH26 houses 0
## 467 SCH26 pasta 1
## 468 SCH26 beds 0
## 469 SCH8 faces 0
## 470 SCH8 houses 0
## 471 SCH8 pasta 0
## 472 SCH8 beds 0
## 473 SCH16 faces 0
## 474 SCH16 houses 0
## 475 SCH16 pasta 0
## 476 SCH16 beds 1
## 477 SCH14 faces 0
## 478 SCH14 houses 0
## 479 SCH14 pasta 0
## 480 SCH14 beds 1
## 481 SCH2 faces 0
## 482 SCH2 houses 0
## 483 SCH2 pasta 0
## 484 SCH2 beds 0
## 485 SCH5 faces 0
## 486 SCH5 houses 0
## 487 SCH5 pasta 0
## 488 SCH5 beds 0
## 489 SCH13 faces 0
## 490 SCH13 houses 0
## 491 SCH13 pasta 0
## 492 SCH13 beds 0
## 493 SCH21 faces 0
## 494 SCH21 houses 0
## 495 SCH21 pasta 0
## 496 SCH21 beds 0
## 497 SCH19 faces 0
## 498 SCH19 houses 0
## 499 SCH19 pasta 0
## 500 SCH19 beds 1
## 501 SCH23 faces 0
## 502 SCH23 houses 0
## 503 SCH23 pasta 0
## 504 SCH23 beds 0
## 505 SCH1 faces 0
## 506 SCH1 houses 0
## 507 SCH1 pasta 0
## 508 SCH1 beds 0
## 509 MSCH66 faces 0
## 510 MSCH66 houses 0
## 511 MSCH66 pasta 1
## 512 MSCH66 beds 0
## 513 MSCH67 faces 0
## 514 MSCH67 houses 1
## 515 MSCH67 pasta 0
## 516 MSCH67 beds 1
## 517 MSCH68 faces 0
## 518 MSCH68 houses 0
## 519 MSCH68 pasta 0
## 520 MSCH68 beds 0
## 521 MSCH69 faces 0
## 522 MSCH69 houses 1
## 523 MSCH69 pasta 1
## 524 MSCH69 beds 0
## 525 MSCH70 faces 0
## 526 MSCH70 houses 0
## 527 MSCH70 pasta 0
## 528 MSCH70 beds 1
## 529 MSCH71 faces 1
## 530 MSCH71 houses 1
## 531 MSCH71 pasta 1
## 532 MSCH71 beds 0
## 533 MSCH72 faces 1
## 534 MSCH72 houses 1
## 535 MSCH72 pasta 0
## 536 MSCH72 beds 0
## 537 MSCH73 faces 0
## 538 MSCH73 houses 0
## 539 MSCH73 pasta 0
## 540 MSCH73 beds 0
## 541 MSCH74 faces 1
## 542 MSCH74 houses 0
## 543 MSCH74 pasta 0
## 544 MSCH74 beds 1
## 545 MSCH75 faces 0
## 546 MSCH75 houses 0
## 547 MSCH75 pasta 0
## 548 MSCH75 beds 0
## 549 MSCH76 faces 0
## 550 MSCH76 houses 0
## 551 MSCH76 pasta 0
## 552 MSCH76 beds 0
## 553 MSCH77 faces 0
## 554 MSCH77 houses 0
## 555 MSCH77 pasta 0
## 556 MSCH77 beds 1
## 557 MSCH78 faces 0
## 558 MSCH78 houses 0
## 559 MSCH78 pasta 0
## 560 MSCH78 beds 1
## 561 MSCH79 faces 0
## 562 MSCH79 houses 1
## 563 MSCH79 pasta 0
## 564 MSCH79 beds 1
## 565 MSCH80 faces 0
## 566 MSCH80 houses 0
## 567 MSCH80 pasta 0
## 568 MSCH80 beds 0
## 569 MSCH81 faces 1
## 570 MSCH81 houses 0
## 571 MSCH81 pasta 0
## 572 MSCH81 beds 0
## 573 MSCH82 faces 1
## 574 MSCH82 houses 0
## 575 MSCH82 pasta 1
## 576 MSCH82 beds 0
## 577 MSCH83 faces 0
## 578 MSCH83 houses 0
## 579 MSCH83 pasta 1
## 580 MSCH83 beds 0
## 581 MSCH84 faces 0
## 582 MSCH84 houses 0
## 583 MSCH84 pasta 1
## 584 MSCH84 beds 0
## 585 MSCH85 faces 0
## 586 MSCH85 houses 0
## 587 MSCH85 pasta 0
## 588 MSCH85 beds 0# de-select last three:
select(ps_data, -(1:3)) # - requires parenthetical sequence## age condition
## 1 2.00 Label
## 2 2.00 Label
## 3 2.00 Label
## 4 2.00 Label
## 5 2.13 Label
## 6 2.13 Label
## 7 2.13 Label
## 8 2.13 Label
## 9 2.32 Label
## 10 2.32 Label
## 11 2.32 Label
## 12 2.32 Label
## 13 2.38 Label
## 14 2.38 Label
## 15 2.38 Label
## 16 2.38 Label
## 17 2.47 Label
## 18 2.47 Label
## 19 2.47 Label
## 20 2.47 Label
## 21 2.50 Label
## 22 2.50 Label
## 23 2.50 Label
## 24 2.50 Label
## 25 2.58 Label
## 26 2.58 Label
## 27 2.58 Label
## 28 2.58 Label
## 29 2.59 Label
## 30 2.59 Label
## 31 2.59 Label
## 32 2.59 Label
## 33 2.61 Label
## 34 2.61 Label
## 35 2.61 Label
## 36 2.61 Label
## 37 2.72 Label
## 38 2.72 Label
## 39 2.72 Label
## 40 2.72 Label
## 41 2.73 Label
## 42 2.73 Label
## 43 2.73 Label
## 44 2.73 Label
## 45 2.74 Label
## 46 2.74 Label
## 47 2.74 Label
## 48 2.74 Label
## 49 2.79 Label
## 50 2.79 Label
## 51 2.79 Label
## 52 2.79 Label
## 53 2.80 Label
## 54 2.80 Label
## 55 2.80 Label
## 56 2.80 Label
## 57 2.83 Label
## 58 2.83 Label
## 59 2.83 Label
## 60 2.83 Label
## 61 2.83 Label
## 62 2.83 Label
## 63 2.83 Label
## 64 2.83 Label
## 65 2.85 Label
## 66 2.85 Label
## 67 2.85 Label
## 68 2.85 Label
## 69 2.88 Label
## 70 2.88 Label
## 71 2.88 Label
## 72 2.88 Label
## 73 2.88 Label
## 74 2.88 Label
## 75 2.88 Label
## 76 2.88 Label
## 77 2.89 Label
## 78 2.89 Label
## 79 2.89 Label
## 80 2.89 Label
## 81 2.91 Label
## 82 2.91 Label
## 83 2.91 Label
## 84 2.91 Label
## 85 2.95 Label
## 86 2.95 Label
## 87 2.95 Label
## 88 2.95 Label
## 89 2.98 Label
## 90 2.98 Label
## 91 2.98 Label
## 92 2.98 Label
## 93 2.99 Label
## 94 2.99 Label
## 95 2.99 Label
## 96 2.99 Label
## 97 3.00 Label
## 98 3.00 Label
## 99 3.00 Label
## 100 3.00 Label
## 101 3.09 Label
## 102 3.09 Label
## 103 3.09 Label
## 104 3.09 Label
## 105 3.10 Label
## 106 3.10 Label
## 107 3.10 Label
## 108 3.10 Label
## 109 3.19 Label
## 110 3.19 Label
## 111 3.19 Label
## 112 3.19 Label
## 113 3.20 Label
## 114 3.20 Label
## 115 3.20 Label
## 116 3.20 Label
## 117 3.22 Label
## 118 3.22 Label
## 119 3.22 Label
## 120 3.22 Label
## 121 3.24 Label
## 122 3.24 Label
## 123 3.24 Label
## 124 3.24 Label
## 125 3.25 Label
## 126 3.25 Label
## 127 3.25 Label
## 128 3.25 Label
## 129 3.26 Label
## 130 3.26 Label
## 131 3.26 Label
## 132 3.26 Label
## 133 3.28 Label
## 134 3.28 Label
## 135 3.28 Label
## 136 3.28 Label
## 137 3.30 Label
## 138 3.30 Label
## 139 3.30 Label
## 140 3.30 Label
## 141 3.46 Label
## 142 3.46 Label
## 143 3.46 Label
## 144 3.46 Label
## 145 3.46 Label
## 146 3.46 Label
## 147 3.46 Label
## 148 3.46 Label
## 149 3.46 Label
## 150 3.46 Label
## 151 3.46 Label
## 152 3.46 Label
## 153 3.50 Label
## 154 3.50 Label
## 155 3.50 Label
## 156 3.50 Label
## 157 3.52 Label
## 158 3.52 Label
## 159 3.52 Label
## 160 3.52 Label
## 161 3.55 Label
## 162 3.55 Label
## 163 3.55 Label
## 164 3.55 Label
## 165 3.56 Label
## 166 3.56 Label
## 167 3.56 Label
## 168 3.56 Label
## 169 3.59 Label
## 170 3.59 Label
## 171 3.59 Label
## 172 3.59 Label
## 173 3.72 Label
## 174 3.72 Label
## 175 3.72 Label
## 176 3.72 Label
## 177 3.75 Label
## 178 3.75 Label
## 179 3.75 Label
## 180 3.75 Label
## 181 3.82 Label
## 182 3.82 Label
## 183 3.82 Label
## 184 3.82 Label
## 185 3.82 Label
## 186 3.82 Label
## 187 3.82 Label
## 188 3.82 Label
## 189 3.85 Label
## 190 3.85 Label
## 191 3.85 Label
## 192 3.85 Label
## 193 3.92 Label
## 194 3.92 Label
## 195 3.92 Label
## 196 3.92 Label
## 197 3.92 Label
## 198 3.92 Label
## 199 3.92 Label
## 200 3.92 Label
## 201 3.96 Label
## 202 3.96 Label
## 203 3.96 Label
## 204 3.96 Label
## 205 4.50 Label
## 206 4.50 Label
## 207 4.50 Label
## 208 4.50 Label
## 209 4.14 Label
## 210 4.14 Label
## 211 4.14 Label
## 212 4.14 Label
## 213 4.16 Label
## 214 4.16 Label
## 215 4.16 Label
## 216 4.16 Label
## 217 4.16 Label
## 218 4.16 Label
## 219 4.16 Label
## 220 4.16 Label
## 221 4.22 Label
## 222 4.22 Label
## 223 4.22 Label
## 224 4.22 Label
## 225 4.26 Label
## 226 4.26 Label
## 227 4.26 Label
## 228 4.26 Label
## 229 4.28 Label
## 230 4.28 Label
## 231 4.28 Label
## 232 4.28 Label
## 233 4.29 Label
## 234 4.29 Label
## 235 4.29 Label
## 236 4.29 Label
## 237 4.33 Label
## 238 4.33 Label
## 239 4.33 Label
## 240 4.33 Label
## 241 4.38 Label
## 242 4.38 Label
## 243 4.38 Label
## 244 4.38 Label
## 245 4.55 Label
## 246 4.55 Label
## 247 4.55 Label
## 248 4.55 Label
## 249 4.57 Label
## 250 4.57 Label
## 251 4.57 Label
## 252 4.57 Label
## 253 4.58 Label
## 254 4.58 Label
## 255 4.58 Label
## 256 4.58 Label
## 257 4.60 Label
## 258 4.60 Label
## 259 4.60 Label
## 260 4.60 Label
## 261 4.62 Label
## 262 4.62 Label
## 263 4.62 Label
## 264 4.62 Label
## 265 4.64 Label
## 266 4.64 Label
## 267 4.64 Label
## 268 4.64 Label
## 269 4.64 Label
## 270 4.64 Label
## 271 4.64 Label
## 272 4.64 Label
## 273 4.73 Label
## 274 4.73 Label
## 275 4.73 Label
## 276 4.73 Label
## 277 4.82 Label
## 278 4.82 Label
## 279 4.82 Label
## 280 4.82 Label
## 281 4.84 Label
## 282 4.84 Label
## 283 4.84 Label
## 284 4.84 Label
## 285 4.89 Label
## 286 4.89 Label
## 287 4.89 Label
## 288 4.89 Label
## 289 4.89 Label
## 290 4.89 Label
## 291 4.89 Label
## 292 4.89 Label
## 293 4.95 Label
## 294 4.95 Label
## 295 4.95 Label
## 296 4.95 Label
## 297 4.96 Label
## 298 4.96 Label
## 299 4.96 Label
## 300 4.96 Label
## 301 2.01 No Label
## 302 2.01 No Label
## 303 2.01 No Label
## 304 2.01 No Label
## 305 2.03 No Label
## 306 2.03 No Label
## 307 2.03 No Label
## 308 2.03 No Label
## 309 2.07 No Label
## 310 2.07 No Label
## 311 2.07 No Label
## 312 2.07 No Label
## 313 2.25 No Label
## 314 2.25 No Label
## 315 2.25 No Label
## 316 2.25 No Label
## 317 2.50 No Label
## 318 2.50 No Label
## 319 2.50 No Label
## 320 2.50 No Label
## 321 2.59 No Label
## 322 2.59 No Label
## 323 2.59 No Label
## 324 2.59 No Label
## 325 2.71 No Label
## 326 2.71 No Label
## 327 2.71 No Label
## 328 2.71 No Label
## 329 2.88 No Label
## 330 2.88 No Label
## 331 2.88 No Label
## 332 2.88 No Label
## 333 2.90 No Label
## 334 2.90 No Label
## 335 2.90 No Label
## 336 2.90 No Label
## 337 2.93 No Label
## 338 2.93 No Label
## 339 2.93 No Label
## 340 2.93 No Label
## 341 2.99 No Label
## 342 2.99 No Label
## 343 2.99 No Label
## 344 2.99 No Label
## 345 3.02 No Label
## 346 3.02 No Label
## 347 3.02 No Label
## 348 3.02 No Label
## 349 3.02 No Label
## 350 3.02 No Label
## 351 3.02 No Label
## 352 3.02 No Label
## 353 3.06 No Label
## 354 3.06 No Label
## 355 3.06 No Label
## 356 3.06 No Label
## 357 3.06 No Label
## 358 3.06 No Label
## 359 3.06 No Label
## 360 3.06 No Label
## 361 3.18 No Label
## 362 3.18 No Label
## 363 3.18 No Label
## 364 3.18 No Label
## 365 3.27 No Label
## 366 3.27 No Label
## 367 3.27 No Label
## 368 3.27 No Label
## 369 3.27 No Label
## 370 3.27 No Label
## 371 3.27 No Label
## 372 3.27 No Label
## 373 3.33 No Label
## 374 3.33 No Label
## 375 3.33 No Label
## 376 3.33 No Label
## 377 3.41 No Label
## 378 3.41 No Label
## 379 3.41 No Label
## 380 3.41 No Label
## 381 3.41 No Label
## 382 3.45 No Label
## 383 3.45 No Label
## 384 3.45 No Label
## 385 3.45 No Label
## 386 3.50 No Label
## 387 3.50 No Label
## 388 3.50 No Label
## 389 3.50 No Label
## 390 3.54 No Label
## 391 3.54 No Label
## 392 3.54 No Label
## 393 3.54 No Label
## 394 3.71 No Label
## 395 3.71 No Label
## 396 3.71 No Label
## 397 3.71 No Label
## 398 3.76 No Label
## 399 3.76 No Label
## 400 3.76 No Label
## 401 3.76 No Label
## 402 3.82 No Label
## 403 3.82 No Label
## 404 3.82 No Label
## 405 3.83 No Label
## 406 3.83 No Label
## 407 3.83 No Label
## 408 3.83 No Label
## 409 3.93 No Label
## 410 3.93 No Label
## 411 3.94 No Label
## 412 3.94 No Label
## 413 4.02 No Label
## 414 4.02 No Label
## 415 4.02 No Label
## 416 4.02 No Label
## 417 4.02 No Label
## 418 4.02 No Label
## 419 4.02 No Label
## 420 4.02 No Label
## 421 4.07 No Label
## 422 4.07 No Label
## 423 4.07 No Label
## 424 4.07 No Label
## 425 4.09 No Label
## 426 4.09 No Label
## 427 4.09 No Label
## 428 4.09 No Label
## 429 4.25 No Label
## 430 4.25 No Label
## 431 4.25 No Label
## 432 4.25 No Label
## 433 4.32 No Label
## 434 4.32 No Label
## 435 4.32 No Label
## 436 4.32 No Label
## 437 4.37 No Label
## 438 4.37 No Label
## 439 4.37 No Label
## 440 4.37 No Label
## 441 4.39 No Label
## 442 4.39 No Label
## 443 4.39 No Label
## 444 4.39 No Label
## 445 4.41 No Label
## 446 4.41 No Label
## 447 4.41 No Label
## 448 4.41 No Label
## 449 4.41 No Label
## 450 4.41 No Label
## 451 4.41 No Label
## 452 4.41 No Label
## 453 4.42 No Label
## 454 4.42 No Label
## 455 4.42 No Label
## 456 4.42 No Label
## 457 4.44 No Label
## 458 4.44 No Label
## 459 4.44 No Label
## 460 4.44 No Label
## 461 4.47 No Label
## 462 4.47 No Label
## 463 4.47 No Label
## 464 4.47 No Label
## 465 4.47 No Label
## 466 4.47 No Label
## 467 4.47 No Label
## 468 4.47 No Label
## 469 4.52 No Label
## 470 4.52 No Label
## 471 4.52 No Label
## 472 4.52 No Label
## 473 4.55 No Label
## 474 4.55 No Label
## 475 4.55 No Label
## 476 4.55 No Label
## 477 4.58 No Label
## 478 4.58 No Label
## 479 4.58 No Label
## 480 4.58 No Label
## 481 4.61 No Label
## 482 4.61 No Label
## 483 4.61 No Label
## 484 4.61 No Label
## 485 4.61 No Label
## 486 4.61 No Label
## 487 4.61 No Label
## 488 4.61 No Label
## 489 4.75 No Label
## 490 4.75 No Label
## 491 4.75 No Label
## 492 4.75 No Label
## 493 4.76 No Label
## 494 4.76 No Label
## 495 4.76 No Label
## 496 4.76 No Label
## 497 4.79 No Label
## 498 4.79 No Label
## 499 4.79 No Label
## 500 4.79 No Label
## 501 4.82 No Label
## 502 4.82 No Label
## 503 4.82 No Label
## 504 4.82 No Label
## 505 4.82 No Label
## 506 4.82 No Label
## 507 4.82 No Label
## 508 4.82 No Label
## 509 3.50 No Label
## 510 3.50 No Label
## 511 3.50 No Label
## 512 3.50 No Label
## 513 3.24 No Label
## 514 3.24 No Label
## 515 3.24 No Label
## 516 3.24 No Label
## 517 3.94 No Label
## 518 3.94 No Label
## 519 3.94 No Label
## 520 3.94 No Label
## 521 2.72 No Label
## 522 2.72 No Label
## 523 2.72 No Label
## 524 2.72 No Label
## 525 2.31 No Label
## 526 2.31 No Label
## 527 2.31 No Label
## 528 2.31 No Label
## 529 3.14 No Label
## 530 3.14 No Label
## 531 3.14 No Label
## 532 3.14 No Label
## 533 3.72 No Label
## 534 3.72 No Label
## 535 3.72 No Label
## 536 3.72 No Label
## 537 3.10 No Label
## 538 3.10 No Label
## 539 3.10 No Label
## 540 3.10 No Label
## 541 2.34 No Label
## 542 2.34 No Label
## 543 2.34 No Label
## 544 2.34 No Label
## 545 3.67 No Label
## 546 3.67 No Label
## 547 3.67 No Label
## 548 3.66 No Label
## 549 2.58 No Label
## 550 2.58 No Label
## 551 2.58 No Label
## 552 2.58 No Label
## 553 2.55 No Label
## 554 2.55 No Label
## 555 2.55 No Label
## 556 2.55 No Label
## 557 2.43 No Label
## 558 2.43 No Label
## 559 2.43 No Label
## 560 2.43 No Label
## 561 2.70 No Label
## 562 2.70 No Label
## 563 2.70 No Label
## 564 2.70 No Label
## 565 2.76 No Label
## 566 2.76 No Label
## 567 2.76 No Label
## 568 2.76 No Label
## 569 2.84 No Label
## 570 2.84 No Label
## 571 2.84 No Label
## 572 2.84 No Label
## 573 2.46 No Label
## 574 2.46 No Label
## 575 2.46 No Label
## 576 2.46 No Label
## 577 2.37 No Label
## 578 2.37 No Label
## 579 2.37 No Label
## 580 2.37 No Label
## 581 2.83 No Label
## 582 2.83 No Label
## 583 2.83 No Label
## 584 2.83 No Label
## 585 2.69 No Label
## 586 2.69 No Label
## 587 2.69 No Label
## 588 2.69 No LabelAnd you can even use ranges of variable names.
# select first three
select(ps_data, subid:correct)## subid item correct
## 1 M22 faces 1
## 2 M22 houses 1
## 3 M22 pasta 0
## 4 M22 beds 0
## 5 T22 beds 0
## 6 T22 faces 0
## 7 T22 houses 1
## 8 T22 pasta 1
## 9 T17 pasta 0
## 10 T17 faces 0
## 11 T17 houses 0
## 12 T17 beds 0
## 13 M3 faces 0
## 14 M3 houses 1
## 15 M3 pasta 1
## 16 M3 beds 1
## 17 T19 faces 0
## 18 T19 houses 0
## 19 T19 pasta 1
## 20 T19 beds 1
## 21 T20 faces 1
## 22 T20 houses 1
## 23 T20 pasta 0
## 24 T20 beds 1
## 25 T21 faces 1
## 26 T21 houses 1
## 27 T21 pasta 1
## 28 T21 beds 0
## 29 M26 faces 1
## 30 M26 houses 1
## 31 M26 pasta 0
## 32 M26 beds 1
## 33 T18 faces 1
## 34 T18 houses 0
## 35 T18 pasta 1
## 36 T18 beds 0
## 37 T12 beds 0
## 38 T12 faces 0
## 39 T12 houses 1
## 40 T12 pasta 0
## 41 T16 faces 1
## 42 T16 houses 0
## 43 T16 pasta 1
## 44 T16 beds 1
## 45 T7 faces 1
## 46 T7 houses 0
## 47 T7 pasta 0
## 48 T7 beds 0
## 49 T9 houses 0
## 50 T9 faces 1
## 51 T9 pasta 0
## 52 T9 beds 1
## 53 T5 faces 1
## 54 T5 houses 1
## 55 T5 pasta 0
## 56 T5 beds 1
## 57 T14 faces 1
## 58 T14 houses 1
## 59 T14 pasta 0
## 60 T14 beds 1
## 61 T2 houses 0
## 62 T2 faces 0
## 63 T2 pasta 1
## 64 T2 beds 1
## 65 T15 faces 0
## 66 T15 houses 0
## 67 T15 pasta 1
## 68 T15 beds 0
## 69 M13 houses 0
## 70 M13 beds 1
## 71 M13 faces 1
## 72 M13 pasta 0
## 73 M12 faces 1
## 74 M12 houses 0
## 75 M12 pasta 1
## 76 M12 beds 0
## 77 T13 beds 0
## 78 T13 faces 0
## 79 T13 houses 1
## 80 T13 pasta 1
## 81 T8 faces 1
## 82 T8 houses 0
## 83 T8 pasta 1
## 84 T8 beds 1
## 85 T1 faces 1
## 86 T1 houses 0
## 87 T1 pasta 0
## 88 T1 beds 1
## 89 M15 faces 1
## 90 M15 houses 1
## 91 M15 pasta 1
## 92 M15 beds 1
## 93 T11 faces 1
## 94 T11 houses 0
## 95 T11 pasta 1
## 96 T11 beds 1
## 97 T10 faces 0
## 98 T10 houses 1
## 99 T10 pasta 1
## 100 T10 beds 1
## 101 T3 faces 1
## 102 T3 houses 1
## 103 T3 pasta 1
## 104 T3 beds 1
## 105 T6 faces 1
## 106 T6 houses 1
## 107 T6 pasta 1
## 108 T6 beds 1
## 109 M32 beds 1
## 110 M32 faces 1
## 111 M32 houses 0
## 112 M32 pasta 1
## 113 M1 faces 0
## 114 M1 beds 1
## 115 M1 pasta 0
## 116 M1 houses 0
## 117 C16 faces 0
## 118 C16 houses 0
## 119 C16 pasta 1
## 120 C16 beds 1
## 121 T4 faces 1
## 122 T4 houses 0
## 123 T4 pasta 0
## 124 T4 beds 1
## 125 C17 faces 1
## 126 C17 houses 0
## 127 C17 pasta 1
## 128 C17 beds 0
## 129 C6 faces 0
## 130 C6 houses 1
## 131 C6 pasta 1
## 132 C6 beds 1
## 133 M10 faces 1
## 134 M10 houses 1
## 135 M10 beds 1
## 136 M10 pasta 1
## 137 M31 faces 0
## 138 M31 houses 1
## 139 M31 pasta 1
## 140 M31 beds 1
## 141 C3 houses 0
## 142 C3 pasta 1
## 143 C3 beds 1
## 144 C3 faces 1
## 145 C10 faces 0
## 146 C10 houses 0
## 147 C10 pasta 1
## 148 C10 beds 1
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## 150 M18 houses 1
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## 154 M16 houses 0
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## 160 M23 beds 1
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## 162 C7 houses 1
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## 165 C12 faces 1
## 166 C12 houses 0
## 167 C12 pasta 1
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## 182 M11 houses 0
## 183 M11 pasta 1
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## 226 M9 houses 1
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## 242 C13 houses 1
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## 248 C4 beds 1
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## 250 C14 houses 1
## 251 C14 pasta 0
## 252 C14 beds 1
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## 254 M17 houses 1
## 255 M17 pasta 1
## 256 M17 beds 1
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## 258 C2 houses 1
## 259 C2 pasta 1
## 260 C2 beds 1
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## 262 C23 houses 1
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## 274 C21 houses 0
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## 286 M7 houses 1
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## 300 M25 beds 1
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## 350 MSCH40 houses 1
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## 354 SCH34 houses 0
## 355 SCH34 pasta 0
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## 360 SCH33 beds 0
## 361 MSCH41 faces 0
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## 363 MSCH41 pasta 0
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## 366 SCH37 faces 1
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## 368 SCH37 pasta 1
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## 374 SCH36 faces 0
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## 378 SCH12 faces 0
## 379 SCH12 houses 0
## 380 SCH12 pasta 0
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## 383 SCH18 houses 0
## 384 SCH18 pasta 0
## 385 SCH18 beds 0
## 386 MSCH48 faces 0
## 387 MSCH48 houses 1
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## 389 MSCH48 beds 0
## 390 SCH25 faces 0
## 391 SCH25 houses 1
## 392 SCH25 pasta 1
## 393 SCH25 beds 0
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## 395 SCH31 houses 0
## 396 SCH31 pasta 0
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## 399 MSCH46 houses 0
## 400 MSCH46 pasta 1
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## 402 SCH11 faces 1
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## 406 SCH29 houses 0
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## 410 MSCH39 pasta 0
## 411 MSCH39 houses 0
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## 414 SCH28 houses 0
## 415 SCH28 pasta 0
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## 418 SCH22 houses 0
## 419 SCH22 pasta 0
## 420 SCH22 beds 1
## 421 SCH24 faces 0
## 422 SCH24 houses 0
## 423 SCH24 pasta 1
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## 426 SCH27 houses 0
## 427 SCH27 pasta 1
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## 429 SCH17 faces 0
## 430 SCH17 houses 0
## 431 SCH17 pasta 1
## 432 SCH17 beds 0
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## 434 SCH10 houses 0
## 435 SCH10 pasta 0
## 436 SCH10 beds 1
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## 438 SCH9 houses 0
## 439 SCH9 pasta 0
## 440 SCH9 beds 0
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## 442 SCH20 houses 0
## 443 SCH20 pasta 0
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## 446 SCH6 houses 0
## 447 SCH6 pasta 0
## 448 SCH6 beds 0
## 449 SCH7 faces 1
## 450 SCH7 houses 0
## 451 SCH7 pasta 0
## 452 SCH7 beds 0
## 453 SCH15 faces 1
## 454 SCH15 houses 0
## 455 SCH15 pasta 0
## 456 SCH15 beds 0
## 457 SCH30 faces 0
## 458 SCH30 houses 0
## 459 SCH30 pasta 1
## 460 SCH30 beds 0
## 461 SCH3 faces 0
## 462 SCH3 houses 0
## 463 SCH3 pasta 0
## 464 SCH3 beds 0
## 465 SCH26 faces 0
## 466 SCH26 houses 0
## 467 SCH26 pasta 1
## 468 SCH26 beds 0
## 469 SCH8 faces 0
## 470 SCH8 houses 0
## 471 SCH8 pasta 0
## 472 SCH8 beds 0
## 473 SCH16 faces 0
## 474 SCH16 houses 0
## 475 SCH16 pasta 0
## 476 SCH16 beds 1
## 477 SCH14 faces 0
## 478 SCH14 houses 0
## 479 SCH14 pasta 0
## 480 SCH14 beds 1
## 481 SCH2 faces 0
## 482 SCH2 houses 0
## 483 SCH2 pasta 0
## 484 SCH2 beds 0
## 485 SCH5 faces 0
## 486 SCH5 houses 0
## 487 SCH5 pasta 0
## 488 SCH5 beds 0
## 489 SCH13 faces 0
## 490 SCH13 houses 0
## 491 SCH13 pasta 0
## 492 SCH13 beds 0
## 493 SCH21 faces 0
## 494 SCH21 houses 0
## 495 SCH21 pasta 0
## 496 SCH21 beds 0
## 497 SCH19 faces 0
## 498 SCH19 houses 0
## 499 SCH19 pasta 0
## 500 SCH19 beds 1
## 501 SCH23 faces 0
## 502 SCH23 houses 0
## 503 SCH23 pasta 0
## 504 SCH23 beds 0
## 505 SCH1 faces 0
## 506 SCH1 houses 0
## 507 SCH1 pasta 0
## 508 SCH1 beds 0
## 509 MSCH66 faces 0
## 510 MSCH66 houses 0
## 511 MSCH66 pasta 1
## 512 MSCH66 beds 0
## 513 MSCH67 faces 0
## 514 MSCH67 houses 1
## 515 MSCH67 pasta 0
## 516 MSCH67 beds 1
## 517 MSCH68 faces 0
## 518 MSCH68 houses 0
## 519 MSCH68 pasta 0
## 520 MSCH68 beds 0
## 521 MSCH69 faces 0
## 522 MSCH69 houses 1
## 523 MSCH69 pasta 1
## 524 MSCH69 beds 0
## 525 MSCH70 faces 0
## 526 MSCH70 houses 0
## 527 MSCH70 pasta 0
## 528 MSCH70 beds 1
## 529 MSCH71 faces 1
## 530 MSCH71 houses 1
## 531 MSCH71 pasta 1
## 532 MSCH71 beds 0
## 533 MSCH72 faces 1
## 534 MSCH72 houses 1
## 535 MSCH72 pasta 0
## 536 MSCH72 beds 0
## 537 MSCH73 faces 0
## 538 MSCH73 houses 0
## 539 MSCH73 pasta 0
## 540 MSCH73 beds 0
## 541 MSCH74 faces 1
## 542 MSCH74 houses 0
## 543 MSCH74 pasta 0
## 544 MSCH74 beds 1
## 545 MSCH75 faces 0
## 546 MSCH75 houses 0
## 547 MSCH75 pasta 0
## 548 MSCH75 beds 0
## 549 MSCH76 faces 0
## 550 MSCH76 houses 0
## 551 MSCH76 pasta 0
## 552 MSCH76 beds 0
## 553 MSCH77 faces 0
## 554 MSCH77 houses 0
## 555 MSCH77 pasta 0
## 556 MSCH77 beds 1
## 557 MSCH78 faces 0
## 558 MSCH78 houses 0
## 559 MSCH78 pasta 0
## 560 MSCH78 beds 1
## 561 MSCH79 faces 0
## 562 MSCH79 houses 1
## 563 MSCH79 pasta 0
## 564 MSCH79 beds 1
## 565 MSCH80 faces 0
## 566 MSCH80 houses 0
## 567 MSCH80 pasta 0
## 568 MSCH80 beds 0
## 569 MSCH81 faces 1
## 570 MSCH81 houses 0
## 571 MSCH81 pasta 0
## 572 MSCH81 beds 0
## 573 MSCH82 faces 1
## 574 MSCH82 houses 0
## 575 MSCH82 pasta 1
## 576 MSCH82 beds 0
## 577 MSCH83 faces 0
## 578 MSCH83 houses 0
## 579 MSCH83 pasta 1
## 580 MSCH83 beds 0
## 581 MSCH84 faces 0
## 582 MSCH84 houses 0
## 583 MSCH84 pasta 1
## 584 MSCH84 beds 0
## 585 MSCH85 faces 0
## 586 MSCH85 houses 0
## 587 MSCH85 pasta 0
## 588 MSCH85 beds 0# deselect first three
select(ps_data, -(subid:correct))## age condition
## 1 2.00 Label
## 2 2.00 Label
## 3 2.00 Label
## 4 2.00 Label
## 5 2.13 Label
## 6 2.13 Label
## 7 2.13 Label
## 8 2.13 Label
## 9 2.32 Label
## 10 2.32 Label
## 11 2.32 Label
## 12 2.32 Label
## 13 2.38 Label
## 14 2.38 Label
## 15 2.38 Label
## 16 2.38 Label
## 17 2.47 Label
## 18 2.47 Label
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## 35 2.61 Label
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## 37 2.72 Label
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## 47 2.74 Label
## 48 2.74 Label
## 49 2.79 Label
## 50 2.79 Label
## 51 2.79 Label
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## 221 4.22 Label
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## 373 3.33 No Label
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## 402 3.82 No Label
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## 431 4.25 No Label
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## 449 4.41 No Label
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## 491 4.75 No Label
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## 501 4.82 No Label
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## 506 4.82 No Label
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## 509 3.50 No Label
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## 519 3.94 No Label
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## 521 2.72 No Label
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## 523 2.72 No Label
## 524 2.72 No Label
## 525 2.31 No Label
## 526 2.31 No Label
## 527 2.31 No Label
## 528 2.31 No Label
## 529 3.14 No Label
## 530 3.14 No Label
## 531 3.14 No Label
## 532 3.14 No Label
## 533 3.72 No Label
## 534 3.72 No Label
## 535 3.72 No Label
## 536 3.72 No Label
## 537 3.10 No Label
## 538 3.10 No Label
## 539 3.10 No Label
## 540 3.10 No Label
## 541 2.34 No Label
## 542 2.34 No Label
## 543 2.34 No Label
## 544 2.34 No Label
## 545 3.67 No Label
## 546 3.67 No Label
## 547 3.67 No Label
## 548 3.66 No Label
## 549 2.58 No Label
## 550 2.58 No Label
## 551 2.58 No Label
## 552 2.58 No Label
## 553 2.55 No Label
## 554 2.55 No Label
## 555 2.55 No Label
## 556 2.55 No Label
## 557 2.43 No Label
## 558 2.43 No Label
## 559 2.43 No Label
## 560 2.43 No Label
## 561 2.70 No Label
## 562 2.70 No Label
## 563 2.70 No Label
## 564 2.70 No Label
## 565 2.76 No Label
## 566 2.76 No Label
## 567 2.76 No Label
## 568 2.76 No Label
## 569 2.84 No Label
## 570 2.84 No Label
## 571 2.84 No Label
## 572 2.84 No Label
## 573 2.46 No Label
## 574 2.46 No Label
## 575 2.46 No Label
## 576 2.46 No Label
## 577 2.37 No Label
## 578 2.37 No Label
## 579 2.37 No Label
## 580 2.37 No Label
## 581 2.83 No Label
## 582 2.83 No Label
## 583 2.83 No Label
## 584 2.83 No Label
## 585 2.69 No Label
## 586 2.69 No Label
## 587 2.69 No Label
## 588 2.69 No Label3.1.1.2 Helper functions
The best part of select is that it has special helper function to perform common kinds of selection tasks.
starts_with
For example, let’s say we want all the variables that start with ‘c’. We can use the starts_with() helper function:
select(ps_data, starts_with("c"))## correct condition
## 1 1 Label
## 2 1 Label
## 3 0 Label
## 4 0 Label
## 5 0 Label
## 6 0 Label
## 7 1 Label
## 8 1 Label
## 9 0 Label
## 10 0 Label
## 11 0 Label
## 12 0 Label
## 13 0 Label
## 14 1 Label
## 15 1 Label
## 16 1 Label
## 17 0 Label
## 18 0 Label
## 19 1 Label
## 20 1 Label
## 21 1 Label
## 22 1 Label
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## 24 1 Label
## 25 1 Label
## 26 1 Label
## 27 1 Label
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## 29 1 Label
## 30 1 Label
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## 50 1 Label
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## 52 1 Label
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## 54 1 Label
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## 63 1 Label
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## 70 1 Label
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## 100 1 Label
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## 106 1 Label
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## 109 1 Label
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## 119 1 Label
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## 130 1 Label
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## 147 1 Label
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## 149 0 Label
## 150 1 Label
## 151 1 Label
## 152 1 Label
## 153 0 Label
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## 155 0 Label
## 156 1 Label
## 157 1 Label
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## 159 1 Label
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## 215 1 Label
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## 225 1 Label
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## 228 1 Label
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## 232 1 Label
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## 234 1 Label
## 235 1 Label
## 236 1 Label
## 237 1 Label
## 238 1 Label
## 239 0 Label
## 240 1 Label
## 241 0 Label
## 242 1 Label
## 243 0 Label
## 244 1 Label
## 245 1 Label
## 246 1 Label
## 247 1 Label
## 248 1 Label
## 249 1 Label
## 250 1 Label
## 251 0 Label
## 252 1 Label
## 253 1 Label
## 254 1 Label
## 255 1 Label
## 256 1 Label
## 257 1 Label
## 258 1 Label
## 259 1 Label
## 260 1 Label
## 261 0 Label
## 262 1 Label
## 263 1 Label
## 264 0 Label
## 265 0 Label
## 266 0 Label
## 267 1 Label
## 268 1 Label
## 269 1 Label
## 270 1 Label
## 271 1 Label
## 272 1 Label
## 273 1 Label
## 274 0 Label
## 275 1 Label
## 276 1 Label
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## 278 1 Label
## 279 1 Label
## 280 1 Label
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## 282 0 Label
## 283 0 Label
## 284 1 Label
## 285 1 Label
## 286 1 Label
## 287 1 Label
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## 289 1 Label
## 290 1 Label
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## 292 1 Label
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## 294 1 Label
## 295 1 Label
## 296 1 Label
## 297 1 Label
## 298 1 Label
## 299 1 Label
## 300 1 Label
## 301 1 No Label
## 302 0 No Label
## 303 1 No Label
## 304 0 No Label
## 305 0 No Label
## 306 0 No Label
## 307 0 No Label
## 308 0 No Label
## 309 0 No Label
## 310 0 No Label
## 311 0 No Label
## 312 0 No Label
## 313 0 No Label
## 314 0 No Label
## 315 0 No Label
## 316 0 No Label
## 317 0 No Label
## 318 1 No Label
## 319 0 No Label
## 320 1 No Label
## 321 0 No Label
## 322 0 No Label
## 323 1 No Label
## 324 0 No Label
## 325 0 No Label
## 326 0 No Label
## 327 0 No Label
## 328 0 No Label
## 329 0 No Label
## 330 0 No Label
## 331 0 No Label
## 332 0 No Label
## 333 0 No Label
## 334 0 No Label
## 335 0 No Label
## 336 1 No Label
## 337 1 No Label
## 338 0 No Label
## 339 0 No Label
## 340 0 No Label
## 341 1 No Label
## 342 1 No Label
## 343 0 No Label
## 344 0 No Label
## 345 0 No Label
## 346 0 No Label
## 347 0 No Label
## 348 0 No Label
## 349 0 No Label
## 350 1 No Label
## 351 0 No Label
## 352 1 No Label
## 353 0 No Label
## 354 0 No Label
## 355 0 No Label
## 356 0 No Label
## 357 0 No Label
## 358 0 No Label
## 359 0 No Label
## 360 0 No Label
## 361 0 No Label
## 362 0 No Label
## 363 0 No Label
## 364 0 No Label
## 365 0 No Label
## 366 1 No Label
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## 368 1 No Label
## 369 1 No Label
## 370 0 No Label
## 371 0 No Label
## 372 0 No Label
## 373 0 No Label
## 374 0 No Label
## 375 1 No Label
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## 379 0 No Label
## 380 0 No Label
## 381 0 No Label
## 382 0 No Label
## 383 0 No Label
## 384 0 No Label
## 385 0 No Label
## 386 0 No Label
## 387 1 No Label
## 388 0 No Label
## 389 0 No Label
## 390 0 No Label
## 391 1 No Label
## 392 1 No Label
## 393 0 No Label
## 394 0 No Label
## 395 0 No Label
## 396 0 No Label
## 397 0 No Label
## 398 0 No Label
## 399 0 No Label
## 400 1 No Label
## 401 0 No Label
## 402 1 No Label
## 403 1 No Label
## 404 1 No Label
## 405 0 No Label
## 406 0 No Label
## 407 0 No Label
## 408 0 No Label
## 409 1 No Label
## 410 0 No Label
## 411 0 No Label
## 412 0 No Label
## 413 0 No Label
## 414 0 No Label
## 415 0 No Label
## 416 0 No Label
## 417 0 No Label
## 418 0 No Label
## 419 0 No Label
## 420 1 No Label
## 421 0 No Label
## 422 0 No Label
## 423 1 No Label
## 424 0 No Label
## 425 0 No Label
## 426 0 No Label
## 427 1 No Label
## 428 0 No Label
## 429 0 No Label
## 430 0 No Label
## 431 1 No Label
## 432 0 No Label
## 433 0 No Label
## 434 0 No Label
## 435 0 No Label
## 436 1 No Label
## 437 0 No Label
## 438 0 No Label
## 439 0 No Label
## 440 0 No Label
## 441 0 No Label
## 442 0 No Label
## 443 0 No Label
## 444 0 No Label
## 445 0 No Label
## 446 0 No Label
## 447 0 No Label
## 448 0 No Label
## 449 1 No Label
## 450 0 No Label
## 451 0 No Label
## 452 0 No Label
## 453 1 No Label
## 454 0 No Label
## 455 0 No Label
## 456 0 No Label
## 457 0 No Label
## 458 0 No Label
## 459 1 No Label
## 460 0 No Label
## 461 0 No Label
## 462 0 No Label
## 463 0 No Label
## 464 0 No Label
## 465 0 No Label
## 466 0 No Label
## 467 1 No Label
## 468 0 No Label
## 469 0 No Label
## 470 0 No Label
## 471 0 No Label
## 472 0 No Label
## 473 0 No Label
## 474 0 No Label
## 475 0 No Label
## 476 1 No Label
## 477 0 No Label
## 478 0 No Label
## 479 0 No Label
## 480 1 No Label
## 481 0 No Label
## 482 0 No Label
## 483 0 No Label
## 484 0 No Label
## 485 0 No Label
## 486 0 No Label
## 487 0 No Label
## 488 0 No Label
## 489 0 No Label
## 490 0 No Label
## 491 0 No Label
## 492 0 No Label
## 493 0 No Label
## 494 0 No Label
## 495 0 No Label
## 496 0 No Label
## 497 0 No Label
## 498 0 No Label
## 499 0 No Label
## 500 1 No Label
## 501 0 No Label
## 502 0 No Label
## 503 0 No Label
## 504 0 No Label
## 505 0 No Label
## 506 0 No Label
## 507 0 No Label
## 508 0 No Label
## 509 0 No Label
## 510 0 No Label
## 511 1 No Label
## 512 0 No Label
## 513 0 No Label
## 514 1 No Label
## 515 0 No Label
## 516 1 No Label
## 517 0 No Label
## 518 0 No Label
## 519 0 No Label
## 520 0 No Label
## 521 0 No Label
## 522 1 No Label
## 523 1 No Label
## 524 0 No Label
## 525 0 No Label
## 526 0 No Label
## 527 0 No Label
## 528 1 No Label
## 529 1 No Label
## 530 1 No Label
## 531 1 No Label
## 532 0 No Label
## 533 1 No Label
## 534 1 No Label
## 535 0 No Label
## 536 0 No Label
## 537 0 No Label
## 538 0 No Label
## 539 0 No Label
## 540 0 No Label
## 541 1 No Label
## 542 0 No Label
## 543 0 No Label
## 544 1 No Label
## 545 0 No Label
## 546 0 No Label
## 547 0 No Label
## 548 0 No Label
## 549 0 No Label
## 550 0 No Label
## 551 0 No Label
## 552 0 No Label
## 553 0 No Label
## 554 0 No Label
## 555 0 No Label
## 556 1 No Label
## 557 0 No Label
## 558 0 No Label
## 559 0 No Label
## 560 1 No Label
## 561 0 No Label
## 562 1 No Label
## 563 0 No Label
## 564 1 No Label
## 565 0 No Label
## 566 0 No Label
## 567 0 No Label
## 568 0 No Label
## 569 1 No Label
## 570 0 No Label
## 571 0 No Label
## 572 0 No Label
## 573 1 No Label
## 574 0 No Label
## 575 1 No Label
## 576 0 No Label
## 577 0 No Label
## 578 0 No Label
## 579 1 No Label
## 580 0 No Label
## 581 0 No Label
## 582 0 No Label
## 583 1 No Label
## 584 0 No Label
## 585 0 No Label
## 586 0 No Label
## 587 0 No Label
## 588 0 No LabelThat is way simpler than the base R solution we discussed above, which was
ps_data[, grep("^c", colnames(ps_data))]## correct condition
## 1 1 Label
## 2 1 Label
## 3 0 Label
## 4 0 Label
## 5 0 Label
## 6 0 Label
## 7 1 Label
## 8 1 Label
## 9 0 Label
## 10 0 Label
## 11 0 Label
## 12 0 Label
## 13 0 Label
## 14 1 Label
## 15 1 Label
## 16 1 Label
## 17 0 Label
## 18 0 Label
## 19 1 Label
## 20 1 Label
## 21 1 Label
## 22 1 Label
## 23 0 Label
## 24 1 Label
## 25 1 Label
## 26 1 Label
## 27 1 Label
## 28 0 Label
## 29 1 Label
## 30 1 Label
## 31 0 Label
## 32 1 Label
## 33 1 Label
## 34 0 Label
## 35 1 Label
## 36 0 Label
## 37 0 Label
## 38 0 Label
## 39 1 Label
## 40 0 Label
## 41 1 Label
## 42 0 Label
## 43 1 Label
## 44 1 Label
## 45 1 Label
## 46 0 Label
## 47 0 Label
## 48 0 Label
## 49 0 Label
## 50 1 Label
## 51 0 Label
## 52 1 Label
## 53 1 Label
## 54 1 Label
## 55 0 Label
## 56 1 Label
## 57 1 Label
## 58 1 Label
## 59 0 Label
## 60 1 Label
## 61 0 Label
## 62 0 Label
## 63 1 Label
## 64 1 Label
## 65 0 Label
## 66 0 Label
## 67 1 Label
## 68 0 Label
## 69 0 Label
## 70 1 Label
## 71 1 Label
## 72 0 Label
## 73 1 Label
## 74 0 Label
## 75 1 Label
## 76 0 Label
## 77 0 Label
## 78 0 Label
## 79 1 Label
## 80 1 Label
## 81 1 Label
## 82 0 Label
## 83 1 Label
## 84 1 Label
## 85 1 Label
## 86 0 Label
## 87 0 Label
## 88 1 Label
## 89 1 Label
## 90 1 Label
## 91 1 Label
## 92 1 Label
## 93 1 Label
## 94 0 Label
## 95 1 Label
## 96 1 Label
## 97 0 Label
## 98 1 Label
## 99 1 Label
## 100 1 Label
## 101 1 Label
## 102 1 Label
## 103 1 Label
## 104 1 Label
## 105 1 Label
## 106 1 Label
## 107 1 Label
## 108 1 Label
## 109 1 Label
## 110 1 Label
## 111 0 Label
## 112 1 Label
## 113 0 Label
## 114 1 Label
## 115 0 Label
## 116 0 Label
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## 119 1 Label
## 120 1 Label
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## 130 1 Label
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## 140 1 Label
## 141 0 Label
## 142 1 Label
## 143 1 Label
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## 145 0 Label
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## 147 1 Label
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## 150 1 Label
## 151 1 Label
## 152 1 Label
## 153 0 Label
## 154 0 Label
## 155 0 Label
## 156 1 Label
## 157 1 Label
## 158 0 Label
## 159 1 Label
## 160 1 Label
## 161 0 Label
## 162 1 Label
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## 165 1 Label
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## 167 1 Label
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## 169 1 Label
## 170 1 Label
## 171 1 Label
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## 175 1 Label
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## 178 1 Label
## 179 1 Label
## 180 1 Label
## 181 1 Label
## 182 0 Label
## 183 1 Label
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## 185 1 Label
## 186 1 Label
## 187 1 Label
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## 190 0 Label
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## 195 1 Label
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## 200 1 Label
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## 203 1 Label
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## 205 0 Label
## 206 1 Label
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## 209 1 Label
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## 212 1 Label
## 213 1 Label
## 214 1 Label
## 215 1 Label
## 216 1 Label
## 217 1 Label
## 218 0 Label
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## 220 1 Label
## 221 1 Label
## 222 0 Label
## 223 1 Label
## 224 1 Label
## 225 1 Label
## 226 1 Label
## 227 1 Label
## 228 1 Label
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## 230 0 Label
## 231 1 Label
## 232 1 Label
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## 234 1 Label
## 235 1 Label
## 236 1 Label
## 237 1 Label
## 238 1 Label
## 239 0 Label
## 240 1 Label
## 241 0 Label
## 242 1 Label
## 243 0 Label
## 244 1 Label
## 245 1 Label
## 246 1 Label
## 247 1 Label
## 248 1 Label
## 249 1 Label
## 250 1 Label
## 251 0 Label
## 252 1 Label
## 253 1 Label
## 254 1 Label
## 255 1 Label
## 256 1 Label
## 257 1 Label
## 258 1 Label
## 259 1 Label
## 260 1 Label
## 261 0 Label
## 262 1 Label
## 263 1 Label
## 264 0 Label
## 265 0 Label
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## 267 1 Label
## 268 1 Label
## 269 1 Label
## 270 1 Label
## 271 1 Label
## 272 1 Label
## 273 1 Label
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## 275 1 Label
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## 284 1 Label
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## 300 1 Label
## 301 1 No Label
## 302 0 No Label
## 303 1 No Label
## 304 0 No Label
## 305 0 No Label
## 306 0 No Label
## 307 0 No Label
## 308 0 No Label
## 309 0 No Label
## 310 0 No Label
## 311 0 No Label
## 312 0 No Label
## 313 0 No Label
## 314 0 No Label
## 315 0 No Label
## 316 0 No Label
## 317 0 No Label
## 318 1 No Label
## 319 0 No Label
## 320 1 No Label
## 321 0 No Label
## 322 0 No Label
## 323 1 No Label
## 324 0 No Label
## 325 0 No Label
## 326 0 No Label
## 327 0 No Label
## 328 0 No Label
## 329 0 No Label
## 330 0 No Label
## 331 0 No Label
## 332 0 No Label
## 333 0 No Label
## 334 0 No Label
## 335 0 No Label
## 336 1 No Label
## 337 1 No Label
## 338 0 No Label
## 339 0 No Label
## 340 0 No Label
## 341 1 No Label
## 342 1 No Label
## 343 0 No Label
## 344 0 No Label
## 345 0 No Label
## 346 0 No Label
## 347 0 No Label
## 348 0 No Label
## 349 0 No Label
## 350 1 No Label
## 351 0 No Label
## 352 1 No Label
## 353 0 No Label
## 354 0 No Label
## 355 0 No Label
## 356 0 No Label
## 357 0 No Label
## 358 0 No Label
## 359 0 No Label
## 360 0 No Label
## 361 0 No Label
## 362 0 No Label
## 363 0 No Label
## 364 0 No Label
## 365 0 No Label
## 366 1 No Label
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## 368 1 No Label
## 369 1 No Label
## 370 0 No Label
## 371 0 No Label
## 372 0 No Label
## 373 0 No Label
## 374 0 No Label
## 375 1 No Label
## 376 1 No Label
## 377 0 No Label
## 378 0 No Label
## 379 0 No Label
## 380 0 No Label
## 381 0 No Label
## 382 0 No Label
## 383 0 No Label
## 384 0 No Label
## 385 0 No Label
## 386 0 No Label
## 387 1 No Label
## 388 0 No Label
## 389 0 No Label
## 390 0 No Label
## 391 1 No Label
## 392 1 No Label
## 393 0 No Label
## 394 0 No Label
## 395 0 No Label
## 396 0 No Label
## 397 0 No Label
## 398 0 No Label
## 399 0 No Label
## 400 1 No Label
## 401 0 No Label
## 402 1 No Label
## 403 1 No Label
## 404 1 No Label
## 405 0 No Label
## 406 0 No Label
## 407 0 No Label
## 408 0 No Label
## 409 1 No Label
## 410 0 No Label
## 411 0 No Label
## 412 0 No Label
## 413 0 No Label
## 414 0 No Label
## 415 0 No Label
## 416 0 No Label
## 417 0 No Label
## 418 0 No Label
## 419 0 No Label
## 420 1 No Label
## 421 0 No Label
## 422 0 No Label
## 423 1 No Label
## 424 0 No Label
## 425 0 No Label
## 426 0 No Label
## 427 1 No Label
## 428 0 No Label
## 429 0 No Label
## 430 0 No Label
## 431 1 No Label
## 432 0 No Label
## 433 0 No Label
## 434 0 No Label
## 435 0 No Label
## 436 1 No Label
## 437 0 No Label
## 438 0 No Label
## 439 0 No Label
## 440 0 No Label
## 441 0 No Label
## 442 0 No Label
## 443 0 No Label
## 444 0 No Label
## 445 0 No Label
## 446 0 No Label
## 447 0 No Label
## 448 0 No Label
## 449 1 No Label
## 450 0 No Label
## 451 0 No Label
## 452 0 No Label
## 453 1 No Label
## 454 0 No Label
## 455 0 No Label
## 456 0 No Label
## 457 0 No Label
## 458 0 No Label
## 459 1 No Label
## 460 0 No Label
## 461 0 No Label
## 462 0 No Label
## 463 0 No Label
## 464 0 No Label
## 465 0 No Label
## 466 0 No Label
## 467 1 No Label
## 468 0 No Label
## 469 0 No Label
## 470 0 No Label
## 471 0 No Label
## 472 0 No Label
## 473 0 No Label
## 474 0 No Label
## 475 0 No Label
## 476 1 No Label
## 477 0 No Label
## 478 0 No Label
## 479 0 No Label
## 480 1 No Label
## 481 0 No Label
## 482 0 No Label
## 483 0 No Label
## 484 0 No Label
## 485 0 No Label
## 486 0 No Label
## 487 0 No Label
## 488 0 No Label
## 489 0 No Label
## 490 0 No Label
## 491 0 No Label
## 492 0 No Label
## 493 0 No Label
## 494 0 No Label
## 495 0 No Label
## 496 0 No Label
## 497 0 No Label
## 498 0 No Label
## 499 0 No Label
## 500 1 No Label
## 501 0 No Label
## 502 0 No Label
## 503 0 No Label
## 504 0 No Label
## 505 0 No Label
## 506 0 No Label
## 507 0 No Label
## 508 0 No Label
## 509 0 No Label
## 510 0 No Label
## 511 1 No Label
## 512 0 No Label
## 513 0 No Label
## 514 1 No Label
## 515 0 No Label
## 516 1 No Label
## 517 0 No Label
## 518 0 No Label
## 519 0 No Label
## 520 0 No Label
## 521 0 No Label
## 522 1 No Label
## 523 1 No Label
## 524 0 No Label
## 525 0 No Label
## 526 0 No Label
## 527 0 No Label
## 528 1 No Label
## 529 1 No Label
## 530 1 No Label
## 531 1 No Label
## 532 0 No Label
## 533 1 No Label
## 534 1 No Label
## 535 0 No Label
## 536 0 No Label
## 537 0 No Label
## 538 0 No Label
## 539 0 No Label
## 540 0 No Label
## 541 1 No Label
## 542 0 No Label
## 543 0 No Label
## 544 1 No Label
## 545 0 No Label
## 546 0 No Label
## 547 0 No Label
## 548 0 No Label
## 549 0 No Label
## 550 0 No Label
## 551 0 No Label
## 552 0 No Label
## 553 0 No Label
## 554 0 No Label
## 555 0 No Label
## 556 1 No Label
## 557 0 No Label
## 558 0 No Label
## 559 0 No Label
## 560 1 No Label
## 561 0 No Label
## 562 1 No Label
## 563 0 No Label
## 564 1 No Label
## 565 0 No Label
## 566 0 No Label
## 567 0 No Label
## 568 0 No Label
## 569 1 No Label
## 570 0 No Label
## 571 0 No Label
## 572 0 No Label
## 573 1 No Label
## 574 0 No Label
## 575 1 No Label
## 576 0 No Label
## 577 0 No Label
## 578 0 No Label
## 579 1 No Label
## 580 0 No Label
## 581 0 No Label
## 582 0 No Label
## 583 1 No Label
## 584 0 No Label
## 585 0 No Label
## 586 0 No Label
## 587 0 No Label
## 588 0 No Labelends_with
Select columns that end with some character:
select(ps_data, ends_with("e"))## age
## 1 2.00
## 2 2.00
## 3 2.00
## 4 2.00
## 5 2.13
## 6 2.13
## 7 2.13
## 8 2.13
## 9 2.32
## 10 2.32
## 11 2.32
## 12 2.32
## 13 2.38
## 14 2.38
## 15 2.38
## 16 2.38
## 17 2.47
## 18 2.47
## 19 2.47
## 20 2.47
## 21 2.50
## 22 2.50
## 23 2.50
## 24 2.50
## 25 2.58
## 26 2.58
## 27 2.58
## 28 2.58
## 29 2.59
## 30 2.59
## 31 2.59
## 32 2.59
## 33 2.61
## 34 2.61
## 35 2.61
## 36 2.61
## 37 2.72
## 38 2.72
## 39 2.72
## 40 2.72
## 41 2.73
## 42 2.73
## 43 2.73
## 44 2.73
## 45 2.74
## 46 2.74
## 47 2.74
## 48 2.74
## 49 2.79
## 50 2.79
## 51 2.79
## 52 2.79
## 53 2.80
## 54 2.80
## 55 2.80
## 56 2.80
## 57 2.83
## 58 2.83
## 59 2.83
## 60 2.83
## 61 2.83
## 62 2.83
## 63 2.83
## 64 2.83
## 65 2.85
## 66 2.85
## 67 2.85
## 68 2.85
## 69 2.88
## 70 2.88
## 71 2.88
## 72 2.88
## 73 2.88
## 74 2.88
## 75 2.88
## 76 2.88
## 77 2.89
## 78 2.89
## 79 2.89
## 80 2.89
## 81 2.91
## 82 2.91
## 83 2.91
## 84 2.91
## 85 2.95
## 86 2.95
## 87 2.95
## 88 2.95
## 89 2.98
## 90 2.98
## 91 2.98
## 92 2.98
## 93 2.99
## 94 2.99
## 95 2.99
## 96 2.99
## 97 3.00
## 98 3.00
## 99 3.00
## 100 3.00
## 101 3.09
## 102 3.09
## 103 3.09
## 104 3.09
## 105 3.10
## 106 3.10
## 107 3.10
## 108 3.10
## 109 3.19
## 110 3.19
## 111 3.19
## 112 3.19
## 113 3.20
## 114 3.20
## 115 3.20
## 116 3.20
## 117 3.22
## 118 3.22
## 119 3.22
## 120 3.22
## 121 3.24
## 122 3.24
## 123 3.24
## 124 3.24
## 125 3.25
## 126 3.25
## 127 3.25
## 128 3.25
## 129 3.26
## 130 3.26
## 131 3.26
## 132 3.26
## 133 3.28
## 134 3.28
## 135 3.28
## 136 3.28
## 137 3.30
## 138 3.30
## 139 3.30
## 140 3.30
## 141 3.46
## 142 3.46
## 143 3.46
## 144 3.46
## 145 3.46
## 146 3.46
## 147 3.46
## 148 3.46
## 149 3.46
## 150 3.46
## 151 3.46
## 152 3.46
## 153 3.50
## 154 3.50
## 155 3.50
## 156 3.50
## 157 3.52
## 158 3.52
## 159 3.52
## 160 3.52
## 161 3.55
## 162 3.55
## 163 3.55
## 164 3.55
## 165 3.56
## 166 3.56
## 167 3.56
## 168 3.56
## 169 3.59
## 170 3.59
## 171 3.59
## 172 3.59
## 173 3.72
## 174 3.72
## 175 3.72
## 176 3.72
## 177 3.75
## 178 3.75
## 179 3.75
## 180 3.75
## 181 3.82
## 182 3.82
## 183 3.82
## 184 3.82
## 185 3.82
## 186 3.82
## 187 3.82
## 188 3.82
## 189 3.85
## 190 3.85
## 191 3.85
## 192 3.85
## 193 3.92
## 194 3.92
## 195 3.92
## 196 3.92
## 197 3.92
## 198 3.92
## 199 3.92
## 200 3.92
## 201 3.96
## 202 3.96
## 203 3.96
## 204 3.96
## 205 4.50
## 206 4.50
## 207 4.50
## 208 4.50
## 209 4.14
## 210 4.14
## 211 4.14
## 212 4.14
## 213 4.16
## 214 4.16
## 215 4.16
## 216 4.16
## 217 4.16
## 218 4.16
## 219 4.16
## 220 4.16
## 221 4.22
## 222 4.22
## 223 4.22
## 224 4.22
## 225 4.26
## 226 4.26
## 227 4.26
## 228 4.26
## 229 4.28
## 230 4.28
## 231 4.28
## 232 4.28
## 233 4.29
## 234 4.29
## 235 4.29
## 236 4.29
## 237 4.33
## 238 4.33
## 239 4.33
## 240 4.33
## 241 4.38
## 242 4.38
## 243 4.38
## 244 4.38
## 245 4.55
## 246 4.55
## 247 4.55
## 248 4.55
## 249 4.57
## 250 4.57
## 251 4.57
## 252 4.57
## 253 4.58
## 254 4.58
## 255 4.58
## 256 4.58
## 257 4.60
## 258 4.60
## 259 4.60
## 260 4.60
## 261 4.62
## 262 4.62
## 263 4.62
## 264 4.62
## 265 4.64
## 266 4.64
## 267 4.64
## 268 4.64
## 269 4.64
## 270 4.64
## 271 4.64
## 272 4.64
## 273 4.73
## 274 4.73
## 275 4.73
## 276 4.73
## 277 4.82
## 278 4.82
## 279 4.82
## 280 4.82
## 281 4.84
## 282 4.84
## 283 4.84
## 284 4.84
## 285 4.89
## 286 4.89
## 287 4.89
## 288 4.89
## 289 4.89
## 290 4.89
## 291 4.89
## 292 4.89
## 293 4.95
## 294 4.95
## 295 4.95
## 296 4.95
## 297 4.96
## 298 4.96
## 299 4.96
## 300 4.96
## 301 2.01
## 302 2.01
## 303 2.01
## 304 2.01
## 305 2.03
## 306 2.03
## 307 2.03
## 308 2.03
## 309 2.07
## 310 2.07
## 311 2.07
## 312 2.07
## 313 2.25
## 314 2.25
## 315 2.25
## 316 2.25
## 317 2.50
## 318 2.50
## 319 2.50
## 320 2.50
## 321 2.59
## 322 2.59
## 323 2.59
## 324 2.59
## 325 2.71
## 326 2.71
## 327 2.71
## 328 2.71
## 329 2.88
## 330 2.88
## 331 2.88
## 332 2.88
## 333 2.90
## 334 2.90
## 335 2.90
## 336 2.90
## 337 2.93
## 338 2.93
## 339 2.93
## 340 2.93
## 341 2.99
## 342 2.99
## 343 2.99
## 344 2.99
## 345 3.02
## 346 3.02
## 347 3.02
## 348 3.02
## 349 3.02
## 350 3.02
## 351 3.02
## 352 3.02
## 353 3.06
## 354 3.06
## 355 3.06
## 356 3.06
## 357 3.06
## 358 3.06
## 359 3.06
## 360 3.06
## 361 3.18
## 362 3.18
## 363 3.18
## 364 3.18
## 365 3.27
## 366 3.27
## 367 3.27
## 368 3.27
## 369 3.27
## 370 3.27
## 371 3.27
## 372 3.27
## 373 3.33
## 374 3.33
## 375 3.33
## 376 3.33
## 377 3.41
## 378 3.41
## 379 3.41
## 380 3.41
## 381 3.41
## 382 3.45
## 383 3.45
## 384 3.45
## 385 3.45
## 386 3.50
## 387 3.50
## 388 3.50
## 389 3.50
## 390 3.54
## 391 3.54
## 392 3.54
## 393 3.54
## 394 3.71
## 395 3.71
## 396 3.71
## 397 3.71
## 398 3.76
## 399 3.76
## 400 3.76
## 401 3.76
## 402 3.82
## 403 3.82
## 404 3.82
## 405 3.83
## 406 3.83
## 407 3.83
## 408 3.83
## 409 3.93
## 410 3.93
## 411 3.94
## 412 3.94
## 413 4.02
## 414 4.02
## 415 4.02
## 416 4.02
## 417 4.02
## 418 4.02
## 419 4.02
## 420 4.02
## 421 4.07
## 422 4.07
## 423 4.07
## 424 4.07
## 425 4.09
## 426 4.09
## 427 4.09
## 428 4.09
## 429 4.25
## 430 4.25
## 431 4.25
## 432 4.25
## 433 4.32
## 434 4.32
## 435 4.32
## 436 4.32
## 437 4.37
## 438 4.37
## 439 4.37
## 440 4.37
## 441 4.39
## 442 4.39
## 443 4.39
## 444 4.39
## 445 4.41
## 446 4.41
## 447 4.41
## 448 4.41
## 449 4.41
## 450 4.41
## 451 4.41
## 452 4.41
## 453 4.42
## 454 4.42
## 455 4.42
## 456 4.42
## 457 4.44
## 458 4.44
## 459 4.44
## 460 4.44
## 461 4.47
## 462 4.47
## 463 4.47
## 464 4.47
## 465 4.47
## 466 4.47
## 467 4.47
## 468 4.47
## 469 4.52
## 470 4.52
## 471 4.52
## 472 4.52
## 473 4.55
## 474 4.55
## 475 4.55
## 476 4.55
## 477 4.58
## 478 4.58
## 479 4.58
## 480 4.58
## 481 4.61
## 482 4.61
## 483 4.61
## 484 4.61
## 485 4.61
## 486 4.61
## 487 4.61
## 488 4.61
## 489 4.75
## 490 4.75
## 491 4.75
## 492 4.75
## 493 4.76
## 494 4.76
## 495 4.76
## 496 4.76
## 497 4.79
## 498 4.79
## 499 4.79
## 500 4.79
## 501 4.82
## 502 4.82
## 503 4.82
## 504 4.82
## 505 4.82
## 506 4.82
## 507 4.82
## 508 4.82
## 509 3.50
## 510 3.50
## 511 3.50
## 512 3.50
## 513 3.24
## 514 3.24
## 515 3.24
## 516 3.24
## 517 3.94
## 518 3.94
## 519 3.94
## 520 3.94
## 521 2.72
## 522 2.72
## 523 2.72
## 524 2.72
## 525 2.31
## 526 2.31
## 527 2.31
## 528 2.31
## 529 3.14
## 530 3.14
## 531 3.14
## 532 3.14
## 533 3.72
## 534 3.72
## 535 3.72
## 536 3.72
## 537 3.10
## 538 3.10
## 539 3.10
## 540 3.10
## 541 2.34
## 542 2.34
## 543 2.34
## 544 2.34
## 545 3.67
## 546 3.67
## 547 3.67
## 548 3.66
## 549 2.58
## 550 2.58
## 551 2.58
## 552 2.58
## 553 2.55
## 554 2.55
## 555 2.55
## 556 2.55
## 557 2.43
## 558 2.43
## 559 2.43
## 560 2.43
## 561 2.70
## 562 2.70
## 563 2.70
## 564 2.70
## 565 2.76
## 566 2.76
## 567 2.76
## 568 2.76
## 569 2.84
## 570 2.84
## 571 2.84
## 572 2.84
## 573 2.46
## 574 2.46
## 575 2.46
## 576 2.46
## 577 2.37
## 578 2.37
## 579 2.37
## 580 2.37
## 581 2.83
## 582 2.83
## 583 2.83
## 584 2.83
## 585 2.69
## 586 2.69
## 587 2.69
## 588 2.69contains
Select columns that contain a character.
select(ps_data, contains("i"))## subid item condition
## 1 M22 faces Label
## 2 M22 houses Label
## 3 M22 pasta Label
## 4 M22 beds Label
## 5 T22 beds Label
## 6 T22 faces Label
## 7 T22 houses Label
## 8 T22 pasta Label
## 9 T17 pasta Label
## 10 T17 faces Label
## 11 T17 houses Label
## 12 T17 beds Label
## 13 M3 faces Label
## 14 M3 houses Label
## 15 M3 pasta Label
## 16 M3 beds Label
## 17 T19 faces Label
## 18 T19 houses Label
## 19 T19 pasta Label
## 20 T19 beds Label
## 21 T20 faces Label
## 22 T20 houses Label
## 23 T20 pasta Label
## 24 T20 beds Label
## 25 T21 faces Label
## 26 T21 houses Label
## 27 T21 pasta Label
## 28 T21 beds Label
## 29 M26 faces Label
## 30 M26 houses Label
## 31 M26 pasta Label
## 32 M26 beds Label
## 33 T18 faces Label
## 34 T18 houses Label
## 35 T18 pasta Label
## 36 T18 beds Label
## 37 T12 beds Label
## 38 T12 faces Label
## 39 T12 houses Label
## 40 T12 pasta Label
## 41 T16 faces Label
## 42 T16 houses Label
## 43 T16 pasta Label
## 44 T16 beds Label
## 45 T7 faces Label
## 46 T7 houses Label
## 47 T7 pasta Label
## 48 T7 beds Label
## 49 T9 houses Label
## 50 T9 faces Label
## 51 T9 pasta Label
## 52 T9 beds Label
## 53 T5 faces Label
## 54 T5 houses Label
## 55 T5 pasta Label
## 56 T5 beds Label
## 57 T14 faces Label
## 58 T14 houses Label
## 59 T14 pasta Label
## 60 T14 beds Label
## 61 T2 houses Label
## 62 T2 faces Label
## 63 T2 pasta Label
## 64 T2 beds Label
## 65 T15 faces Label
## 66 T15 houses Label
## 67 T15 pasta Label
## 68 T15 beds Label
## 69 M13 houses Label
## 70 M13 beds Label
## 71 M13 faces Label
## 72 M13 pasta Label
## 73 M12 faces Label
## 74 M12 houses Label
## 75 M12 pasta Label
## 76 M12 beds Label
## 77 T13 beds Label
## 78 T13 faces Label
## 79 T13 houses Label
## 80 T13 pasta Label
## 81 T8 faces Label
## 82 T8 houses Label
## 83 T8 pasta Label
## 84 T8 beds Label
## 85 T1 faces Label
## 86 T1 houses Label
## 87 T1 pasta Label
## 88 T1 beds Label
## 89 M15 faces Label
## 90 M15 houses Label
## 91 M15 pasta Label
## 92 M15 beds Label
## 93 T11 faces Label
## 94 T11 houses Label
## 95 T11 pasta Label
## 96 T11 beds Label
## 97 T10 faces Label
## 98 T10 houses Label
## 99 T10 pasta Label
## 100 T10 beds Label
## 101 T3 faces Label
## 102 T3 houses Label
## 103 T3 pasta Label
## 104 T3 beds Label
## 105 T6 faces Label
## 106 T6 houses Label
## 107 T6 pasta Label
## 108 T6 beds Label
## 109 M32 beds Label
## 110 M32 faces Label
## 111 M32 houses Label
## 112 M32 pasta Label
## 113 M1 faces Label
## 114 M1 beds Label
## 115 M1 pasta Label
## 116 M1 houses Label
## 117 C16 faces Label
## 118 C16 houses Label
## 119 C16 pasta Label
## 120 C16 beds Label
## 121 T4 faces Label
## 122 T4 houses Label
## 123 T4 pasta Label
## 124 T4 beds Label
## 125 C17 faces Label
## 126 C17 houses Label
## 127 C17 pasta Label
## 128 C17 beds Label
## 129 C6 faces Label
## 130 C6 houses Label
## 131 C6 pasta Label
## 132 C6 beds Label
## 133 M10 faces Label
## 134 M10 houses Label
## 135 M10 beds Label
## 136 M10 pasta Label
## 137 M31 faces Label
## 138 M31 houses Label
## 139 M31 pasta Label
## 140 M31 beds Label
## 141 C3 houses Label
## 142 C3 pasta Label
## 143 C3 beds Label
## 144 C3 faces Label
## 145 C10 faces Label
## 146 C10 houses Label
## 147 C10 pasta Label
## 148 C10 beds Label
## 149 M18 faces Label
## 150 M18 houses Label
## 151 M18 pasta Label
## 152 M18 beds Label
## 153 M16 faces Label
## 154 M16 houses Label
## 155 M16 pasta Label
## 156 M16 beds Label
## 157 M23 faces Label
## 158 M23 houses Label
## 159 M23 pasta Label
## 160 M23 beds Label
## 161 C7 faces Label
## 162 C7 houses Label
## 163 C7 pasta Label
## 164 C7 beds Label
## 165 C12 faces Label
## 166 C12 houses Label
## 167 C12 pasta Label
## 168 C12 beds Label
## 169 C15 faces Label
## 170 C15 houses Label
## 171 C15 pasta Label
## 172 C15 beds Label
## 173 M29 faces Label
## 174 M29 houses Label
## 175 M29 pasta Label
## 176 M29 beds Label
## 177 C20 faces Label
## 178 C20 houses Label
## 179 C20 pasta Label
## 180 C20 beds Label
## 181 M11 faces Label
## 182 M11 houses Label
## 183 M11 pasta Label
## 184 M11 beds Label
## 185 C9 beds Label
## 186 C9 faces Label
## 187 C9 houses Label
## 188 C9 pasta Label
## 189 C24 faces Label
## 190 C24 houses Label
## 191 C24 pasta Label
## 192 C24 beds Label
## 193 C22 faces Label
## 194 C22 houses Label
## 195 C22 pasta Label
## 196 C22 beds Label
## 197 C8 faces Label
## 198 C8 houses Label
## 199 C8 pasta Label
## 200 C8 beds Label
## 201 M4 faces Label
## 202 M4 houses Label
## 203 M4 pasta Label
## 204 M4 beds Label
## 205 M6 faces Label
## 206 M6 houses Label
## 207 M6 pasta Label
## 208 M6 beds Label
## 209 C19 faces Label
## 210 C19 houses Label
## 211 C19 pasta Label
## 212 C19 beds Label
## 213 C1 faces Label
## 214 C1 houses Label
## 215 C1 pasta Label
## 216 C1 beds Label
## 217 M19 beds Label
## 218 M19 faces Label
## 219 M19 houses Label
## 220 M19 pasta Label
## 221 C11 faces Label
## 222 C11 houses Label
## 223 C11 pasta Label
## 224 C11 beds Label
## 225 M9 faces Label
## 226 M9 houses Label
## 227 M9 pasta Label
## 228 M9 beds Label
## 229 M2 faces Label
## 230 M2 houses Label
## 231 M2 pasta Label
## 232 M2 beds Label
## 233 C5 faces Label
## 234 C5 houses Label
## 235 C5 pasta Label
## 236 C5 beds Label
## 237 M30 beds Label
## 238 M30 faces Label
## 239 M30 houses Label
## 240 M30 pasta Label
## 241 C13 faces Label
## 242 C13 houses Label
## 243 C13 pasta Label
## 244 C13 beds Label
## 245 C4 faces Label
## 246 C4 houses Label
## 247 C4 pasta Label
## 248 C4 beds Label
## 249 C14 faces Label
## 250 C14 houses Label
## 251 C14 pasta Label
## 252 C14 beds Label
## 253 M17 faces Label
## 254 M17 houses Label
## 255 M17 pasta Label
## 256 M17 beds Label
## 257 C2 faces Label
## 258 C2 houses Label
## 259 C2 pasta Label
## 260 C2 beds Label
## 261 C23 faces Label
## 262 C23 houses Label
## 263 C23 pasta Label
## 264 C23 beds Label
## 265 M20 faces Label
## 266 M20 houses Label
## 267 M20 pasta Label
## 268 M20 beds Label
## 269 M21 faces Label
## 270 M21 houses Label
## 271 M21 pasta Label
## 272 M21 beds Label
## 273 C21 faces Label
## 274 C21 houses Label
## 275 C21 pasta Label
## 276 C21 beds Label
## 277 M24 faces Label
## 278 M24 houses Label
## 279 M24 pasta Label
## 280 M24 beds Label
## 281 M5 faces Label
## 282 M5 houses Label
## 283 M5 pasta Label
## 284 M5 beds Label
## 285 M7 faces Label
## 286 M7 houses Label
## 287 M7 pasta Label
## 288 M7 beds Label
## 289 M8 faces Label
## 290 M8 houses Label
## 291 M8 pasta Label
## 292 M8 beds Label
## 293 C18 faces Label
## 294 C18 houses Label
## 295 C18 pasta Label
## 296 C18 beds Label
## 297 M25 faces Label
## 298 M25 houses Label
## 299 M25 pasta Label
## 300 M25 beds Label
## 301 MSCH47 faces No Label
## 302 MSCH47 houses No Label
## 303 MSCH47 pasta No Label
## 304 MSCH47 beds No Label
## 305 MSCH50 faces No Label
## 306 MSCH50 houses No Label
## 307 MSCH50 pasta No Label
## 308 MSCH50 beds No Label
## 309 MSCH51 faces No Label
## 310 MSCH51 houses No Label
## 311 MSCH51 pasta No Label
## 312 MSCH51 beds No Label
## 313 MSCH44 faces No Label
## 314 MSCH44 houses No Label
## 315 MSCH44 pasta No Label
## 316 MSCH44 beds No Label
## 317 MSCH52 faces No Label
## 318 MSCH52 houses No Label
## 319 MSCH52 pasta No Label
## 320 MSCH52 beds No Label
## 321 MSCH38 faces No Label
## 322 MSCH38 houses No Label
## 323 MSCH38 pasta No Label
## 324 MSCH38 beds No Label
## 325 MSCH43 faces No Label
## 326 MSCH43 houses No Label
## 327 MSCH43 pasta No Label
## 328 MSCH43 beds No Label
## 329 MSCH49 faces No Label
## 330 MSCH49 houses No Label
## 331 MSCH49 pasta No Label
## 332 MSCH49 beds No Label
## 333 MSCH45 faces No Label
## 334 MSCH45 houses No Label
## 335 MSCH45 pasta No Label
## 336 MSCH45 beds No Label
## 337 MSCH42 faces No Label
## 338 MSCH42 houses No Label
## 339 MSCH42 pasta No Label
## 340 MSCH42 beds No Label
## 341 MSCH53 faces No Label
## 342 MSCH53 houses No Label
## 343 MSCH53 pasta No Label
## 344 MSCH53 beds No Label
## 345 SCH35 faces No Label
## 346 SCH35 houses No Label
## 347 SCH35 pasta No Label
## 348 SCH35 beds No Label
## 349 MSCH40 faces No Label
## 350 MSCH40 houses No Label
## 351 MSCH40 pasta No Label
## 352 MSCH40 beds No Label
## 353 SCH34 faces No Label
## 354 SCH34 houses No Label
## 355 SCH34 pasta No Label
## 356 SCH34 beds No Label
## 357 SCH33 faces No Label
## 358 SCH33 houses No Label
## 359 SCH33 pasta No Label
## 360 SCH33 beds No Label
## 361 MSCH41 faces No Label
## 362 MSCH41 houses No Label
## 363 MSCH41 pasta No Label
## 364 MSCH41 beds No Label
## 365 SCH37 beds No Label
## 366 SCH37 faces No Label
## 367 SCH37 houses No Label
## 368 SCH37 pasta No Label
## 369 SCH32 faces No Label
## 370 SCH32 houses No Label
## 371 SCH32 pasta No Label
## 372 SCH32 beds No Label
## 373 SCH36 beds No Label
## 374 SCH36 faces No Label
## 375 SCH36 houses No Label
## 376 SCH36 pasta No Label
## 377 SCH11 beds No Label
## 378 SCH12 faces No Label
## 379 SCH12 houses No Label
## 380 SCH12 pasta No Label
## 381 SCH12 beds No Label
## 382 SCH18 faces No Label
## 383 SCH18 houses No Label
## 384 SCH18 pasta No Label
## 385 SCH18 beds No Label
## 386 MSCH48 faces No Label
## 387 MSCH48 houses No Label
## 388 MSCH48 pasta No Label
## 389 MSCH48 beds No Label
## 390 SCH25 faces No Label
## 391 SCH25 houses No Label
## 392 SCH25 pasta No Label
## 393 SCH25 beds No Label
## 394 SCH31 faces No Label
## 395 SCH31 houses No Label
## 396 SCH31 pasta No Label
## 397 SCH31 beds No Label
## 398 MSCH46 faces No Label
## 399 MSCH46 houses No Label
## 400 MSCH46 pasta No Label
## 401 MSCH46 beds No Label
## 402 SCH11 faces No Label
## 403 SCH11 houses No Label
## 404 SCH11 pasta No Label
## 405 SCH29 faces No Label
## 406 SCH29 houses No Label
## 407 SCH29 pasta No Label
## 408 SCH29 beds No Label
## 409 MSCH39 beds No Label
## 410 MSCH39 pasta No Label
## 411 MSCH39 houses No Label
## 412 MSCH39 faces No Label
## 413 SCH28 faces No Label
## 414 SCH28 houses No Label
## 415 SCH28 pasta No Label
## 416 SCH28 beds No Label
## 417 SCH22 faces No Label
## 418 SCH22 houses No Label
## 419 SCH22 pasta No Label
## 420 SCH22 beds No Label
## 421 SCH24 faces No Label
## 422 SCH24 houses No Label
## 423 SCH24 pasta No Label
## 424 SCH24 beds No Label
## 425 SCH27 faces No Label
## 426 SCH27 houses No Label
## 427 SCH27 pasta No Label
## 428 SCH27 beds No Label
## 429 SCH17 faces No Label
## 430 SCH17 houses No Label
## 431 SCH17 pasta No Label
## 432 SCH17 beds No Label
## 433 SCH10 faces No Label
## 434 SCH10 houses No Label
## 435 SCH10 pasta No Label
## 436 SCH10 beds No Label
## 437 SCH9 faces No Label
## 438 SCH9 houses No Label
## 439 SCH9 pasta No Label
## 440 SCH9 beds No Label
## 441 SCH20 faces No Label
## 442 SCH20 houses No Label
## 443 SCH20 pasta No Label
## 444 SCH20 beds No Label
## 445 SCH6 faces No Label
## 446 SCH6 houses No Label
## 447 SCH6 pasta No Label
## 448 SCH6 beds No Label
## 449 SCH7 faces No Label
## 450 SCH7 houses No Label
## 451 SCH7 pasta No Label
## 452 SCH7 beds No Label
## 453 SCH15 faces No Label
## 454 SCH15 houses No Label
## 455 SCH15 pasta No Label
## 456 SCH15 beds No Label
## 457 SCH30 faces No Label
## 458 SCH30 houses No Label
## 459 SCH30 pasta No Label
## 460 SCH30 beds No Label
## 461 SCH3 faces No Label
## 462 SCH3 houses No Label
## 463 SCH3 pasta No Label
## 464 SCH3 beds No Label
## 465 SCH26 faces No Label
## 466 SCH26 houses No Label
## 467 SCH26 pasta No Label
## 468 SCH26 beds No Label
## 469 SCH8 faces No Label
## 470 SCH8 houses No Label
## 471 SCH8 pasta No Label
## 472 SCH8 beds No Label
## 473 SCH16 faces No Label
## 474 SCH16 houses No Label
## 475 SCH16 pasta No Label
## 476 SCH16 beds No Label
## 477 SCH14 faces No Label
## 478 SCH14 houses No Label
## 479 SCH14 pasta No Label
## 480 SCH14 beds No Label
## 481 SCH2 faces No Label
## 482 SCH2 houses No Label
## 483 SCH2 pasta No Label
## 484 SCH2 beds No Label
## 485 SCH5 faces No Label
## 486 SCH5 houses No Label
## 487 SCH5 pasta No Label
## 488 SCH5 beds No Label
## 489 SCH13 faces No Label
## 490 SCH13 houses No Label
## 491 SCH13 pasta No Label
## 492 SCH13 beds No Label
## 493 SCH21 faces No Label
## 494 SCH21 houses No Label
## 495 SCH21 pasta No Label
## 496 SCH21 beds No Label
## 497 SCH19 faces No Label
## 498 SCH19 houses No Label
## 499 SCH19 pasta No Label
## 500 SCH19 beds No Label
## 501 SCH23 faces No Label
## 502 SCH23 houses No Label
## 503 SCH23 pasta No Label
## 504 SCH23 beds No Label
## 505 SCH1 faces No Label
## 506 SCH1 houses No Label
## 507 SCH1 pasta No Label
## 508 SCH1 beds No Label
## 509 MSCH66 faces No Label
## 510 MSCH66 houses No Label
## 511 MSCH66 pasta No Label
## 512 MSCH66 beds No Label
## 513 MSCH67 faces No Label
## 514 MSCH67 houses No Label
## 515 MSCH67 pasta No Label
## 516 MSCH67 beds No Label
## 517 MSCH68 faces No Label
## 518 MSCH68 houses No Label
## 519 MSCH68 pasta No Label
## 520 MSCH68 beds No Label
## 521 MSCH69 faces No Label
## 522 MSCH69 houses No Label
## 523 MSCH69 pasta No Label
## 524 MSCH69 beds No Label
## 525 MSCH70 faces No Label
## 526 MSCH70 houses No Label
## 527 MSCH70 pasta No Label
## 528 MSCH70 beds No Label
## 529 MSCH71 faces No Label
## 530 MSCH71 houses No Label
## 531 MSCH71 pasta No Label
## 532 MSCH71 beds No Label
## 533 MSCH72 faces No Label
## 534 MSCH72 houses No Label
## 535 MSCH72 pasta No Label
## 536 MSCH72 beds No Label
## 537 MSCH73 faces No Label
## 538 MSCH73 houses No Label
## 539 MSCH73 pasta No Label
## 540 MSCH73 beds No Label
## 541 MSCH74 faces No Label
## 542 MSCH74 houses No Label
## 543 MSCH74 pasta No Label
## 544 MSCH74 beds No Label
## 545 MSCH75 faces No Label
## 546 MSCH75 houses No Label
## 547 MSCH75 pasta No Label
## 548 MSCH75 beds No Label
## 549 MSCH76 faces No Label
## 550 MSCH76 houses No Label
## 551 MSCH76 pasta No Label
## 552 MSCH76 beds No Label
## 553 MSCH77 faces No Label
## 554 MSCH77 houses No Label
## 555 MSCH77 pasta No Label
## 556 MSCH77 beds No Label
## 557 MSCH78 faces No Label
## 558 MSCH78 houses No Label
## 559 MSCH78 pasta No Label
## 560 MSCH78 beds No Label
## 561 MSCH79 faces No Label
## 562 MSCH79 houses No Label
## 563 MSCH79 pasta No Label
## 564 MSCH79 beds No Label
## 565 MSCH80 faces No Label
## 566 MSCH80 houses No Label
## 567 MSCH80 pasta No Label
## 568 MSCH80 beds No Label
## 569 MSCH81 faces No Label
## 570 MSCH81 houses No Label
## 571 MSCH81 pasta No Label
## 572 MSCH81 beds No Label
## 573 MSCH82 faces No Label
## 574 MSCH82 houses No Label
## 575 MSCH82 pasta No Label
## 576 MSCH82 beds No Label
## 577 MSCH83 faces No Label
## 578 MSCH83 houses No Label
## 579 MSCH83 pasta No Label
## 580 MSCH83 beds No Label
## 581 MSCH84 faces No Label
## 582 MSCH84 houses No Label
## 583 MSCH84 pasta No Label
## 584 MSCH84 beds No Label
## 585 MSCH85 faces No Label
## 586 MSCH85 houses No Label
## 587 MSCH85 pasta No Label
## 588 MSCH85 beds No LabelThere are others too, but these are the most common. Here is a table of all of them.
| function | what it does |
|---|---|
starts_with() |
selects columns starting with a string |
ends_with() |
selects columns that end with a string |
contains() |
selects columns that contain a string |
matches() |
selects columns that match a regular expression |
num_ranges() |
selects columns that match a numerical range |
one_of() |
selects columns whose names match entries in a character vector |
everything() |
selects all columns |
last_col() |
selects last column; can include an offset. |
Each of these can be very useful in a given scenario.
Exercise 3.1.1a
Select all of the variables that contain the letter e in their name.
3.1.2 Filtering rows
filter() is the next verb we’ll cover today, and is used to extract rows based on logical tests.
Like select(), its first argument is the data, followed by conditions for filtering data. For example, let’s say we want to filter rows for cases in the “No Label” condition.
filter(ps_data, condition == "No Label")## subid item correct age condition
## 1 MSCH47 faces 1 2.01 No Label
## 2 MSCH47 houses 0 2.01 No Label
## 3 MSCH47 pasta 1 2.01 No Label
## 4 MSCH47 beds 0 2.01 No Label
## 5 MSCH50 faces 0 2.03 No Label
## 6 MSCH50 houses 0 2.03 No Label
## 7 MSCH50 pasta 0 2.03 No Label
## 8 MSCH50 beds 0 2.03 No Label
## 9 MSCH51 faces 0 2.07 No Label
## 10 MSCH51 houses 0 2.07 No Label
## 11 MSCH51 pasta 0 2.07 No Label
## 12 MSCH51 beds 0 2.07 No Label
## 13 MSCH44 faces 0 2.25 No Label
## 14 MSCH44 houses 0 2.25 No Label
## 15 MSCH44 pasta 0 2.25 No Label
## 16 MSCH44 beds 0 2.25 No Label
## 17 MSCH52 faces 0 2.50 No Label
## 18 MSCH52 houses 1 2.50 No Label
## 19 MSCH52 pasta 0 2.50 No Label
## 20 MSCH52 beds 1 2.50 No Label
## 21 MSCH38 faces 0 2.59 No Label
## 22 MSCH38 houses 0 2.59 No Label
## 23 MSCH38 pasta 1 2.59 No Label
## 24 MSCH38 beds 0 2.59 No Label
## 25 MSCH43 faces 0 2.71 No Label
## 26 MSCH43 houses 0 2.71 No Label
## 27 MSCH43 pasta 0 2.71 No Label
## 28 MSCH43 beds 0 2.71 No Label
## 29 MSCH49 faces 0 2.88 No Label
## 30 MSCH49 houses 0 2.88 No Label
## 31 MSCH49 pasta 0 2.88 No Label
## 32 MSCH49 beds 0 2.88 No Label
## 33 MSCH45 faces 0 2.90 No Label
## 34 MSCH45 houses 0 2.90 No Label
## 35 MSCH45 pasta 0 2.90 No Label
## 36 MSCH45 beds 1 2.90 No Label
## 37 MSCH42 faces 1 2.93 No Label
## 38 MSCH42 houses 0 2.93 No Label
## 39 MSCH42 pasta 0 2.93 No Label
## 40 MSCH42 beds 0 2.93 No Label
## 41 MSCH53 faces 1 2.99 No Label
## 42 MSCH53 houses 1 2.99 No Label
## 43 MSCH53 pasta 0 2.99 No Label
## 44 MSCH53 beds 0 2.99 No Label
## 45 SCH35 faces 0 3.02 No Label
## 46 SCH35 houses 0 3.02 No Label
## 47 SCH35 pasta 0 3.02 No Label
## 48 SCH35 beds 0 3.02 No Label
## 49 MSCH40 faces 0 3.02 No Label
## 50 MSCH40 houses 1 3.02 No Label
## 51 MSCH40 pasta 0 3.02 No Label
## 52 MSCH40 beds 1 3.02 No Label
## 53 SCH34 faces 0 3.06 No Label
## 54 SCH34 houses 0 3.06 No Label
## 55 SCH34 pasta 0 3.06 No Label
## 56 SCH34 beds 0 3.06 No Label
## 57 SCH33 faces 0 3.06 No Label
## 58 SCH33 houses 0 3.06 No Label
## 59 SCH33 pasta 0 3.06 No Label
## 60 SCH33 beds 0 3.06 No Label
## 61 MSCH41 faces 0 3.18 No Label
## 62 MSCH41 houses 0 3.18 No Label
## 63 MSCH41 pasta 0 3.18 No Label
## 64 MSCH41 beds 0 3.18 No Label
## 65 SCH37 beds 0 3.27 No Label
## 66 SCH37 faces 1 3.27 No Label
## 67 SCH37 houses 0 3.27 No Label
## 68 SCH37 pasta 1 3.27 No Label
## 69 SCH32 faces 1 3.27 No Label
## 70 SCH32 houses 0 3.27 No Label
## 71 SCH32 pasta 0 3.27 No Label
## 72 SCH32 beds 0 3.27 No Label
## 73 SCH36 beds 0 3.33 No Label
## 74 SCH36 faces 0 3.33 No Label
## 75 SCH36 houses 1 3.33 No Label
## 76 SCH36 pasta 1 3.33 No Label
## 77 SCH11 beds 0 3.41 No Label
## 78 SCH12 faces 0 3.41 No Label
## 79 SCH12 houses 0 3.41 No Label
## 80 SCH12 pasta 0 3.41 No Label
## 81 SCH12 beds 0 3.41 No Label
## 82 SCH18 faces 0 3.45 No Label
## 83 SCH18 houses 0 3.45 No Label
## 84 SCH18 pasta 0 3.45 No Label
## 85 SCH18 beds 0 3.45 No Label
## 86 MSCH48 faces 0 3.50 No Label
## 87 MSCH48 houses 1 3.50 No Label
## 88 MSCH48 pasta 0 3.50 No Label
## 89 MSCH48 beds 0 3.50 No Label
## 90 SCH25 faces 0 3.54 No Label
## 91 SCH25 houses 1 3.54 No Label
## 92 SCH25 pasta 1 3.54 No Label
## 93 SCH25 beds 0 3.54 No Label
## 94 SCH31 faces 0 3.71 No Label
## 95 SCH31 houses 0 3.71 No Label
## 96 SCH31 pasta 0 3.71 No Label
## 97 SCH31 beds 0 3.71 No Label
## 98 MSCH46 faces 0 3.76 No Label
## 99 MSCH46 houses 0 3.76 No Label
## 100 MSCH46 pasta 1 3.76 No Label
## 101 MSCH46 beds 0 3.76 No Label
## 102 SCH11 faces 1 3.82 No Label
## 103 SCH11 houses 1 3.82 No Label
## 104 SCH11 pasta 1 3.82 No Label
## 105 SCH29 faces 0 3.83 No Label
## 106 SCH29 houses 0 3.83 No Label
## 107 SCH29 pasta 0 3.83 No Label
## 108 SCH29 beds 0 3.83 No Label
## 109 MSCH39 beds 1 3.93 No Label
## 110 MSCH39 pasta 0 3.93 No Label
## 111 MSCH39 houses 0 3.94 No Label
## 112 MSCH39 faces 0 3.94 No Label
## 113 SCH28 faces 0 4.02 No Label
## 114 SCH28 houses 0 4.02 No Label
## 115 SCH28 pasta 0 4.02 No Label
## 116 SCH28 beds 0 4.02 No Label
## 117 SCH22 faces 0 4.02 No Label
## 118 SCH22 houses 0 4.02 No Label
## 119 SCH22 pasta 0 4.02 No Label
## 120 SCH22 beds 1 4.02 No Label
## 121 SCH24 faces 0 4.07 No Label
## 122 SCH24 houses 0 4.07 No Label
## 123 SCH24 pasta 1 4.07 No Label
## 124 SCH24 beds 0 4.07 No Label
## 125 SCH27 faces 0 4.09 No Label
## 126 SCH27 houses 0 4.09 No Label
## 127 SCH27 pasta 1 4.09 No Label
## 128 SCH27 beds 0 4.09 No Label
## 129 SCH17 faces 0 4.25 No Label
## 130 SCH17 houses 0 4.25 No Label
## 131 SCH17 pasta 1 4.25 No Label
## 132 SCH17 beds 0 4.25 No Label
## 133 SCH10 faces 0 4.32 No Label
## 134 SCH10 houses 0 4.32 No Label
## 135 SCH10 pasta 0 4.32 No Label
## 136 SCH10 beds 1 4.32 No Label
## 137 SCH9 faces 0 4.37 No Label
## 138 SCH9 houses 0 4.37 No Label
## 139 SCH9 pasta 0 4.37 No Label
## 140 SCH9 beds 0 4.37 No Label
## 141 SCH20 faces 0 4.39 No Label
## 142 SCH20 houses 0 4.39 No Label
## 143 SCH20 pasta 0 4.39 No Label
## 144 SCH20 beds 0 4.39 No Label
## 145 SCH6 faces 0 4.41 No Label
## 146 SCH6 houses 0 4.41 No Label
## 147 SCH6 pasta 0 4.41 No Label
## 148 SCH6 beds 0 4.41 No Label
## 149 SCH7 faces 1 4.41 No Label
## 150 SCH7 houses 0 4.41 No Label
## 151 SCH7 pasta 0 4.41 No Label
## 152 SCH7 beds 0 4.41 No Label
## 153 SCH15 faces 1 4.42 No Label
## 154 SCH15 houses 0 4.42 No Label
## 155 SCH15 pasta 0 4.42 No Label
## 156 SCH15 beds 0 4.42 No Label
## 157 SCH30 faces 0 4.44 No Label
## 158 SCH30 houses 0 4.44 No Label
## 159 SCH30 pasta 1 4.44 No Label
## 160 SCH30 beds 0 4.44 No Label
## 161 SCH3 faces 0 4.47 No Label
## 162 SCH3 houses 0 4.47 No Label
## 163 SCH3 pasta 0 4.47 No Label
## 164 SCH3 beds 0 4.47 No Label
## 165 SCH26 faces 0 4.47 No Label
## 166 SCH26 houses 0 4.47 No Label
## 167 SCH26 pasta 1 4.47 No Label
## 168 SCH26 beds 0 4.47 No Label
## 169 SCH8 faces 0 4.52 No Label
## 170 SCH8 houses 0 4.52 No Label
## 171 SCH8 pasta 0 4.52 No Label
## 172 SCH8 beds 0 4.52 No Label
## 173 SCH16 faces 0 4.55 No Label
## 174 SCH16 houses 0 4.55 No Label
## 175 SCH16 pasta 0 4.55 No Label
## 176 SCH16 beds 1 4.55 No Label
## 177 SCH14 faces 0 4.58 No Label
## 178 SCH14 houses 0 4.58 No Label
## 179 SCH14 pasta 0 4.58 No Label
## 180 SCH14 beds 1 4.58 No Label
## 181 SCH2 faces 0 4.61 No Label
## 182 SCH2 houses 0 4.61 No Label
## 183 SCH2 pasta 0 4.61 No Label
## 184 SCH2 beds 0 4.61 No Label
## 185 SCH5 faces 0 4.61 No Label
## 186 SCH5 houses 0 4.61 No Label
## 187 SCH5 pasta 0 4.61 No Label
## 188 SCH5 beds 0 4.61 No Label
## 189 SCH13 faces 0 4.75 No Label
## 190 SCH13 houses 0 4.75 No Label
## 191 SCH13 pasta 0 4.75 No Label
## 192 SCH13 beds 0 4.75 No Label
## 193 SCH21 faces 0 4.76 No Label
## 194 SCH21 houses 0 4.76 No Label
## 195 SCH21 pasta 0 4.76 No Label
## 196 SCH21 beds 0 4.76 No Label
## 197 SCH19 faces 0 4.79 No Label
## 198 SCH19 houses 0 4.79 No Label
## 199 SCH19 pasta 0 4.79 No Label
## 200 SCH19 beds 1 4.79 No Label
## 201 SCH23 faces 0 4.82 No Label
## 202 SCH23 houses 0 4.82 No Label
## 203 SCH23 pasta 0 4.82 No Label
## 204 SCH23 beds 0 4.82 No Label
## 205 SCH1 faces 0 4.82 No Label
## 206 SCH1 houses 0 4.82 No Label
## 207 SCH1 pasta 0 4.82 No Label
## 208 SCH1 beds 0 4.82 No Label
## 209 MSCH66 faces 0 3.50 No Label
## 210 MSCH66 houses 0 3.50 No Label
## 211 MSCH66 pasta 1 3.50 No Label
## 212 MSCH66 beds 0 3.50 No Label
## 213 MSCH67 faces 0 3.24 No Label
## 214 MSCH67 houses 1 3.24 No Label
## 215 MSCH67 pasta 0 3.24 No Label
## 216 MSCH67 beds 1 3.24 No Label
## 217 MSCH68 faces 0 3.94 No Label
## 218 MSCH68 houses 0 3.94 No Label
## 219 MSCH68 pasta 0 3.94 No Label
## 220 MSCH68 beds 0 3.94 No Label
## 221 MSCH69 faces 0 2.72 No Label
## 222 MSCH69 houses 1 2.72 No Label
## 223 MSCH69 pasta 1 2.72 No Label
## 224 MSCH69 beds 0 2.72 No Label
## 225 MSCH70 faces 0 2.31 No Label
## 226 MSCH70 houses 0 2.31 No Label
## 227 MSCH70 pasta 0 2.31 No Label
## 228 MSCH70 beds 1 2.31 No Label
## 229 MSCH71 faces 1 3.14 No Label
## 230 MSCH71 houses 1 3.14 No Label
## 231 MSCH71 pasta 1 3.14 No Label
## 232 MSCH71 beds 0 3.14 No Label
## 233 MSCH72 faces 1 3.72 No Label
## 234 MSCH72 houses 1 3.72 No Label
## 235 MSCH72 pasta 0 3.72 No Label
## 236 MSCH72 beds 0 3.72 No Label
## 237 MSCH73 faces 0 3.10 No Label
## 238 MSCH73 houses 0 3.10 No Label
## 239 MSCH73 pasta 0 3.10 No Label
## 240 MSCH73 beds 0 3.10 No Label
## 241 MSCH74 faces 1 2.34 No Label
## 242 MSCH74 houses 0 2.34 No Label
## 243 MSCH74 pasta 0 2.34 No Label
## 244 MSCH74 beds 1 2.34 No Label
## 245 MSCH75 faces 0 3.67 No Label
## 246 MSCH75 houses 0 3.67 No Label
## 247 MSCH75 pasta 0 3.67 No Label
## 248 MSCH75 beds 0 3.66 No Label
## 249 MSCH76 faces 0 2.58 No Label
## 250 MSCH76 houses 0 2.58 No Label
## 251 MSCH76 pasta 0 2.58 No Label
## 252 MSCH76 beds 0 2.58 No Label
## 253 MSCH77 faces 0 2.55 No Label
## 254 MSCH77 houses 0 2.55 No Label
## 255 MSCH77 pasta 0 2.55 No Label
## 256 MSCH77 beds 1 2.55 No Label
## 257 MSCH78 faces 0 2.43 No Label
## 258 MSCH78 houses 0 2.43 No Label
## 259 MSCH78 pasta 0 2.43 No Label
## 260 MSCH78 beds 1 2.43 No Label
## 261 MSCH79 faces 0 2.70 No Label
## 262 MSCH79 houses 1 2.70 No Label
## 263 MSCH79 pasta 0 2.70 No Label
## 264 MSCH79 beds 1 2.70 No Label
## 265 MSCH80 faces 0 2.76 No Label
## 266 MSCH80 houses 0 2.76 No Label
## 267 MSCH80 pasta 0 2.76 No Label
## 268 MSCH80 beds 0 2.76 No Label
## 269 MSCH81 faces 1 2.84 No Label
## 270 MSCH81 houses 0 2.84 No Label
## 271 MSCH81 pasta 0 2.84 No Label
## 272 MSCH81 beds 0 2.84 No Label
## 273 MSCH82 faces 1 2.46 No Label
## 274 MSCH82 houses 0 2.46 No Label
## 275 MSCH82 pasta 1 2.46 No Label
## 276 MSCH82 beds 0 2.46 No Label
## 277 MSCH83 faces 0 2.37 No Label
## 278 MSCH83 houses 0 2.37 No Label
## 279 MSCH83 pasta 1 2.37 No Label
## 280 MSCH83 beds 0 2.37 No Label
## 281 MSCH84 faces 0 2.83 No Label
## 282 MSCH84 houses 0 2.83 No Label
## 283 MSCH84 pasta 1 2.83 No Label
## 284 MSCH84 beds 0 2.83 No Label
## 285 MSCH85 faces 0 2.69 No Label
## 286 MSCH85 houses 0 2.69 No Label
## 287 MSCH85 pasta 0 2.69 No Label
## 288 MSCH85 beds 0 2.69 No LabelOr we could select observations from the “No Label” condition for kids 3 years old or younger:
filter(ps_data, condition == "No Label" & age <= 3)## subid item correct age condition
## 1 MSCH47 faces 1 2.01 No Label
## 2 MSCH47 houses 0 2.01 No Label
## 3 MSCH47 pasta 1 2.01 No Label
## 4 MSCH47 beds 0 2.01 No Label
## 5 MSCH50 faces 0 2.03 No Label
## 6 MSCH50 houses 0 2.03 No Label
## 7 MSCH50 pasta 0 2.03 No Label
## 8 MSCH50 beds 0 2.03 No Label
## 9 MSCH51 faces 0 2.07 No Label
## 10 MSCH51 houses 0 2.07 No Label
## 11 MSCH51 pasta 0 2.07 No Label
## 12 MSCH51 beds 0 2.07 No Label
## 13 MSCH44 faces 0 2.25 No Label
## 14 MSCH44 houses 0 2.25 No Label
## 15 MSCH44 pasta 0 2.25 No Label
## 16 MSCH44 beds 0 2.25 No Label
## 17 MSCH52 faces 0 2.50 No Label
## 18 MSCH52 houses 1 2.50 No Label
## 19 MSCH52 pasta 0 2.50 No Label
## 20 MSCH52 beds 1 2.50 No Label
## 21 MSCH38 faces 0 2.59 No Label
## 22 MSCH38 houses 0 2.59 No Label
## 23 MSCH38 pasta 1 2.59 No Label
## 24 MSCH38 beds 0 2.59 No Label
## 25 MSCH43 faces 0 2.71 No Label
## 26 MSCH43 houses 0 2.71 No Label
## 27 MSCH43 pasta 0 2.71 No Label
## 28 MSCH43 beds 0 2.71 No Label
## 29 MSCH49 faces 0 2.88 No Label
## 30 MSCH49 houses 0 2.88 No Label
## 31 MSCH49 pasta 0 2.88 No Label
## 32 MSCH49 beds 0 2.88 No Label
## 33 MSCH45 faces 0 2.90 No Label
## 34 MSCH45 houses 0 2.90 No Label
## 35 MSCH45 pasta 0 2.90 No Label
## 36 MSCH45 beds 1 2.90 No Label
## 37 MSCH42 faces 1 2.93 No Label
## 38 MSCH42 houses 0 2.93 No Label
## 39 MSCH42 pasta 0 2.93 No Label
## 40 MSCH42 beds 0 2.93 No Label
## 41 MSCH53 faces 1 2.99 No Label
## 42 MSCH53 houses 1 2.99 No Label
## 43 MSCH53 pasta 0 2.99 No Label
## 44 MSCH53 beds 0 2.99 No Label
## 45 MSCH69 faces 0 2.72 No Label
## 46 MSCH69 houses 1 2.72 No Label
## 47 MSCH69 pasta 1 2.72 No Label
## 48 MSCH69 beds 0 2.72 No Label
## 49 MSCH70 faces 0 2.31 No Label
## 50 MSCH70 houses 0 2.31 No Label
## 51 MSCH70 pasta 0 2.31 No Label
## 52 MSCH70 beds 1 2.31 No Label
## 53 MSCH74 faces 1 2.34 No Label
## 54 MSCH74 houses 0 2.34 No Label
## 55 MSCH74 pasta 0 2.34 No Label
## 56 MSCH74 beds 1 2.34 No Label
## 57 MSCH76 faces 0 2.58 No Label
## 58 MSCH76 houses 0 2.58 No Label
## 59 MSCH76 pasta 0 2.58 No Label
## 60 MSCH76 beds 0 2.58 No Label
## 61 MSCH77 faces 0 2.55 No Label
## 62 MSCH77 houses 0 2.55 No Label
## 63 MSCH77 pasta 0 2.55 No Label
## 64 MSCH77 beds 1 2.55 No Label
## 65 MSCH78 faces 0 2.43 No Label
## 66 MSCH78 houses 0 2.43 No Label
## 67 MSCH78 pasta 0 2.43 No Label
## 68 MSCH78 beds 1 2.43 No Label
## 69 MSCH79 faces 0 2.70 No Label
## 70 MSCH79 houses 1 2.70 No Label
## 71 MSCH79 pasta 0 2.70 No Label
## 72 MSCH79 beds 1 2.70 No Label
## 73 MSCH80 faces 0 2.76 No Label
## 74 MSCH80 houses 0 2.76 No Label
## 75 MSCH80 pasta 0 2.76 No Label
## 76 MSCH80 beds 0 2.76 No Label
## 77 MSCH81 faces 1 2.84 No Label
## 78 MSCH81 houses 0 2.84 No Label
## 79 MSCH81 pasta 0 2.84 No Label
## 80 MSCH81 beds 0 2.84 No Label
## 81 MSCH82 faces 1 2.46 No Label
## 82 MSCH82 houses 0 2.46 No Label
## 83 MSCH82 pasta 1 2.46 No Label
## 84 MSCH82 beds 0 2.46 No Label
## 85 MSCH83 faces 0 2.37 No Label
## 86 MSCH83 houses 0 2.37 No Label
## 87 MSCH83 pasta 1 2.37 No Label
## 88 MSCH83 beds 0 2.37 No Label
## 89 MSCH84 faces 0 2.83 No Label
## 90 MSCH84 houses 0 2.83 No Label
## 91 MSCH84 pasta 1 2.83 No Label
## 92 MSCH84 beds 0 2.83 No Label
## 93 MSCH85 faces 0 2.69 No Label
## 94 MSCH85 houses 0 2.69 No Label
## 95 MSCH85 pasta 0 2.69 No Label
## 96 MSCH85 beds 0 2.69 No LabelWe can also filter for observations that meet one condition or another, using | for OR. Let’s get observations for kids younger than 3 or in the no label condition
filter(ps_data, condition == "Label" | age <= 3)## subid item correct age condition
## 1 M22 faces 1 2.00 Label
## 2 M22 houses 1 2.00 Label
## 3 M22 pasta 0 2.00 Label
## 4 M22 beds 0 2.00 Label
## 5 T22 beds 0 2.13 Label
## 6 T22 faces 0 2.13 Label
## 7 T22 houses 1 2.13 Label
## 8 T22 pasta 1 2.13 Label
## 9 T17 pasta 0 2.32 Label
## 10 T17 faces 0 2.32 Label
## 11 T17 houses 0 2.32 Label
## 12 T17 beds 0 2.32 Label
## 13 M3 faces 0 2.38 Label
## 14 M3 houses 1 2.38 Label
## 15 M3 pasta 1 2.38 Label
## 16 M3 beds 1 2.38 Label
## 17 T19 faces 0 2.47 Label
## 18 T19 houses 0 2.47 Label
## 19 T19 pasta 1 2.47 Label
## 20 T19 beds 1 2.47 Label
## 21 T20 faces 1 2.50 Label
## 22 T20 houses 1 2.50 Label
## 23 T20 pasta 0 2.50 Label
## 24 T20 beds 1 2.50 Label
## 25 T21 faces 1 2.58 Label
## 26 T21 houses 1 2.58 Label
## 27 T21 pasta 1 2.58 Label
## 28 T21 beds 0 2.58 Label
## 29 M26 faces 1 2.59 Label
## 30 M26 houses 1 2.59 Label
## 31 M26 pasta 0 2.59 Label
## 32 M26 beds 1 2.59 Label
## 33 T18 faces 1 2.61 Label
## 34 T18 houses 0 2.61 Label
## 35 T18 pasta 1 2.61 Label
## 36 T18 beds 0 2.61 Label
## 37 T12 beds 0 2.72 Label
## 38 T12 faces 0 2.72 Label
## 39 T12 houses 1 2.72 Label
## 40 T12 pasta 0 2.72 Label
## 41 T16 faces 1 2.73 Label
## 42 T16 houses 0 2.73 Label
## 43 T16 pasta 1 2.73 Label
## 44 T16 beds 1 2.73 Label
## 45 T7 faces 1 2.74 Label
## 46 T7 houses 0 2.74 Label
## 47 T7 pasta 0 2.74 Label
## 48 T7 beds 0 2.74 Label
## 49 T9 houses 0 2.79 Label
## 50 T9 faces 1 2.79 Label
## 51 T9 pasta 0 2.79 Label
## 52 T9 beds 1 2.79 Label
## 53 T5 faces 1 2.80 Label
## 54 T5 houses 1 2.80 Label
## 55 T5 pasta 0 2.80 Label
## 56 T5 beds 1 2.80 Label
## 57 T14 faces 1 2.83 Label
## 58 T14 houses 1 2.83 Label
## 59 T14 pasta 0 2.83 Label
## 60 T14 beds 1 2.83 Label
## 61 T2 houses 0 2.83 Label
## 62 T2 faces 0 2.83 Label
## 63 T2 pasta 1 2.83 Label
## 64 T2 beds 1 2.83 Label
## 65 T15 faces 0 2.85 Label
## 66 T15 houses 0 2.85 Label
## 67 T15 pasta 1 2.85 Label
## 68 T15 beds 0 2.85 Label
## 69 M13 houses 0 2.88 Label
## 70 M13 beds 1 2.88 Label
## 71 M13 faces 1 2.88 Label
## 72 M13 pasta 0 2.88 Label
## 73 M12 faces 1 2.88 Label
## 74 M12 houses 0 2.88 Label
## 75 M12 pasta 1 2.88 Label
## 76 M12 beds 0 2.88 Label
## 77 T13 beds 0 2.89 Label
## 78 T13 faces 0 2.89 Label
## 79 T13 houses 1 2.89 Label
## 80 T13 pasta 1 2.89 Label
## 81 T8 faces 1 2.91 Label
## 82 T8 houses 0 2.91 Label
## 83 T8 pasta 1 2.91 Label
## 84 T8 beds 1 2.91 Label
## 85 T1 faces 1 2.95 Label
## 86 T1 houses 0 2.95 Label
## 87 T1 pasta 0 2.95 Label
## 88 T1 beds 1 2.95 Label
## 89 M15 faces 1 2.98 Label
## 90 M15 houses 1 2.98 Label
## 91 M15 pasta 1 2.98 Label
## 92 M15 beds 1 2.98 Label
## 93 T11 faces 1 2.99 Label
## 94 T11 houses 0 2.99 Label
## 95 T11 pasta 1 2.99 Label
## 96 T11 beds 1 2.99 Label
## 97 T10 faces 0 3.00 Label
## 98 T10 houses 1 3.00 Label
## 99 T10 pasta 1 3.00 Label
## 100 T10 beds 1 3.00 Label
## 101 T3 faces 1 3.09 Label
## 102 T3 houses 1 3.09 Label
## 103 T3 pasta 1 3.09 Label
## 104 T3 beds 1 3.09 Label
## 105 T6 faces 1 3.10 Label
## 106 T6 houses 1 3.10 Label
## 107 T6 pasta 1 3.10 Label
## 108 T6 beds 1 3.10 Label
## 109 M32 beds 1 3.19 Label
## 110 M32 faces 1 3.19 Label
## 111 M32 houses 0 3.19 Label
## 112 M32 pasta 1 3.19 Label
## 113 M1 faces 0 3.20 Label
## 114 M1 beds 1 3.20 Label
## 115 M1 pasta 0 3.20 Label
## 116 M1 houses 0 3.20 Label
## 117 C16 faces 0 3.22 Label
## 118 C16 houses 0 3.22 Label
## 119 C16 pasta 1 3.22 Label
## 120 C16 beds 1 3.22 Label
## 121 T4 faces 1 3.24 Label
## 122 T4 houses 0 3.24 Label
## 123 T4 pasta 0 3.24 Label
## 124 T4 beds 1 3.24 Label
## 125 C17 faces 1 3.25 Label
## 126 C17 houses 0 3.25 Label
## 127 C17 pasta 1 3.25 Label
## 128 C17 beds 0 3.25 Label
## 129 C6 faces 0 3.26 Label
## 130 C6 houses 1 3.26 Label
## 131 C6 pasta 1 3.26 Label
## 132 C6 beds 1 3.26 Label
## 133 M10 faces 1 3.28 Label
## 134 M10 houses 1 3.28 Label
## 135 M10 beds 1 3.28 Label
## 136 M10 pasta 1 3.28 Label
## 137 M31 faces 0 3.30 Label
## 138 M31 houses 1 3.30 Label
## 139 M31 pasta 1 3.30 Label
## 140 M31 beds 1 3.30 Label
## 141 C3 houses 0 3.46 Label
## 142 C3 pasta 1 3.46 Label
## 143 C3 beds 1 3.46 Label
## 144 C3 faces 1 3.46 Label
## 145 C10 faces 0 3.46 Label
## 146 C10 houses 0 3.46 Label
## 147 C10 pasta 1 3.46 Label
## 148 C10 beds 1 3.46 Label
## 149 M18 faces 0 3.46 Label
## 150 M18 houses 1 3.46 Label
## 151 M18 pasta 1 3.46 Label
## 152 M18 beds 1 3.46 Label
## 153 M16 faces 0 3.50 Label
## 154 M16 houses 0 3.50 Label
## 155 M16 pasta 0 3.50 Label
## 156 M16 beds 1 3.50 Label
## 157 M23 faces 1 3.52 Label
## 158 M23 houses 0 3.52 Label
## 159 M23 pasta 1 3.52 Label
## 160 M23 beds 1 3.52 Label
## 161 C7 faces 0 3.55 Label
## 162 C7 houses 1 3.55 Label
## 163 C7 pasta 0 3.55 Label
## 164 C7 beds 0 3.55 Label
## 165 C12 faces 1 3.56 Label
## 166 C12 houses 0 3.56 Label
## 167 C12 pasta 1 3.56 Label
## 168 C12 beds 1 3.56 Label
## 169 C15 faces 1 3.59 Label
## 170 C15 houses 1 3.59 Label
## 171 C15 pasta 1 3.59 Label
## 172 C15 beds 1 3.59 Label
## 173 M29 faces 0 3.72 Label
## 174 M29 houses 1 3.72 Label
## 175 M29 pasta 1 3.72 Label
## 176 M29 beds 1 3.72 Label
## 177 C20 faces 1 3.75 Label
## 178 C20 houses 1 3.75 Label
## 179 C20 pasta 1 3.75 Label
## 180 C20 beds 1 3.75 Label
## 181 M11 faces 1 3.82 Label
## 182 M11 houses 0 3.82 Label
## 183 M11 pasta 1 3.82 Label
## 184 M11 beds 1 3.82 Label
## 185 C9 beds 1 3.82 Label
## 186 C9 faces 1 3.82 Label
## 187 C9 houses 1 3.82 Label
## 188 C9 pasta 1 3.82 Label
## 189 C24 faces 1 3.85 Label
## 190 C24 houses 0 3.85 Label
## 191 C24 pasta 0 3.85 Label
## 192 C24 beds 1 3.85 Label
## 193 C22 faces 0 3.92 Label
## 194 C22 houses 0 3.92 Label
## 195 C22 pasta 1 3.92 Label
## 196 C22 beds 1 3.92 Label
## 197 C8 faces 1 3.92 Label
## 198 C8 houses 1 3.92 Label
## 199 C8 pasta 1 3.92 Label
## 200 C8 beds 1 3.92 Label
## 201 M4 faces 1 3.96 Label
## 202 M4 houses 1 3.96 Label
## 203 M4 pasta 1 3.96 Label
## 204 M4 beds 1 3.96 Label
## 205 M6 faces 0 4.50 Label
## 206 M6 houses 1 4.50 Label
## 207 M6 pasta 1 4.50 Label
## 208 M6 beds 0 4.50 Label
## 209 C19 faces 1 4.14 Label
## 210 C19 houses 0 4.14 Label
## 211 C19 pasta 0 4.14 Label
## 212 C19 beds 1 4.14 Label
## 213 C1 faces 1 4.16 Label
## 214 C1 houses 1 4.16 Label
## 215 C1 pasta 1 4.16 Label
## 216 C1 beds 1 4.16 Label
## 217 M19 beds 1 4.16 Label
## 218 M19 faces 0 4.16 Label
## 219 M19 houses 0 4.16 Label
## 220 M19 pasta 1 4.16 Label
## 221 C11 faces 1 4.22 Label
## 222 C11 houses 0 4.22 Label
## 223 C11 pasta 1 4.22 Label
## 224 C11 beds 1 4.22 Label
## 225 M9 faces 1 4.26 Label
## 226 M9 houses 1 4.26 Label
## 227 M9 pasta 1 4.26 Label
## 228 M9 beds 1 4.26 Label
## 229 M2 faces 1 4.28 Label
## 230 M2 houses 0 4.28 Label
## 231 M2 pasta 1 4.28 Label
## 232 M2 beds 1 4.28 Label
## 233 C5 faces 1 4.29 Label
## 234 C5 houses 1 4.29 Label
## 235 C5 pasta 1 4.29 Label
## 236 C5 beds 1 4.29 Label
## 237 M30 beds 1 4.33 Label
## 238 M30 faces 1 4.33 Label
## 239 M30 houses 0 4.33 Label
## 240 M30 pasta 1 4.33 Label
## 241 C13 faces 0 4.38 Label
## 242 C13 houses 1 4.38 Label
## 243 C13 pasta 0 4.38 Label
## 244 C13 beds 1 4.38 Label
## 245 C4 faces 1 4.55 Label
## 246 C4 houses 1 4.55 Label
## 247 C4 pasta 1 4.55 Label
## 248 C4 beds 1 4.55 Label
## 249 C14 faces 1 4.57 Label
## 250 C14 houses 1 4.57 Label
## 251 C14 pasta 0 4.57 Label
## 252 C14 beds 1 4.57 Label
## 253 M17 faces 1 4.58 Label
## 254 M17 houses 1 4.58 Label
## 255 M17 pasta 1 4.58 Label
## 256 M17 beds 1 4.58 Label
## 257 C2 faces 1 4.60 Label
## 258 C2 houses 1 4.60 Label
## 259 C2 pasta 1 4.60 Label
## 260 C2 beds 1 4.60 Label
## 261 C23 faces 0 4.62 Label
## 262 C23 houses 1 4.62 Label
## 263 C23 pasta 1 4.62 Label
## 264 C23 beds 0 4.62 Label
## 265 M20 faces 0 4.64 Label
## 266 M20 houses 0 4.64 Label
## 267 M20 pasta 1 4.64 Label
## 268 M20 beds 1 4.64 Label
## 269 M21 faces 1 4.64 Label
## 270 M21 houses 1 4.64 Label
## 271 M21 pasta 1 4.64 Label
## 272 M21 beds 1 4.64 Label
## 273 C21 faces 1 4.73 Label
## 274 C21 houses 0 4.73 Label
## 275 C21 pasta 1 4.73 Label
## 276 C21 beds 1 4.73 Label
## 277 M24 faces 1 4.82 Label
## 278 M24 houses 1 4.82 Label
## 279 M24 pasta 1 4.82 Label
## 280 M24 beds 1 4.82 Label
## 281 M5 faces 0 4.84 Label
## 282 M5 houses 0 4.84 Label
## 283 M5 pasta 0 4.84 Label
## 284 M5 beds 1 4.84 Label
## 285 M7 faces 1 4.89 Label
## 286 M7 houses 1 4.89 Label
## 287 M7 pasta 1 4.89 Label
## 288 M7 beds 0 4.89 Label
## 289 M8 faces 1 4.89 Label
## 290 M8 houses 1 4.89 Label
## 291 M8 pasta 1 4.89 Label
## 292 M8 beds 1 4.89 Label
## 293 C18 faces 0 4.95 Label
## 294 C18 houses 1 4.95 Label
## 295 C18 pasta 1 4.95 Label
## 296 C18 beds 1 4.95 Label
## 297 M25 faces 1 4.96 Label
## 298 M25 houses 1 4.96 Label
## 299 M25 pasta 1 4.96 Label
## 300 M25 beds 1 4.96 Label
## 301 MSCH47 faces 1 2.01 No Label
## 302 MSCH47 houses 0 2.01 No Label
## 303 MSCH47 pasta 1 2.01 No Label
## 304 MSCH47 beds 0 2.01 No Label
## 305 MSCH50 faces 0 2.03 No Label
## 306 MSCH50 houses 0 2.03 No Label
## 307 MSCH50 pasta 0 2.03 No Label
## 308 MSCH50 beds 0 2.03 No Label
## 309 MSCH51 faces 0 2.07 No Label
## 310 MSCH51 houses 0 2.07 No Label
## 311 MSCH51 pasta 0 2.07 No Label
## 312 MSCH51 beds 0 2.07 No Label
## 313 MSCH44 faces 0 2.25 No Label
## 314 MSCH44 houses 0 2.25 No Label
## 315 MSCH44 pasta 0 2.25 No Label
## 316 MSCH44 beds 0 2.25 No Label
## 317 MSCH52 faces 0 2.50 No Label
## 318 MSCH52 houses 1 2.50 No Label
## 319 MSCH52 pasta 0 2.50 No Label
## 320 MSCH52 beds 1 2.50 No Label
## 321 MSCH38 faces 0 2.59 No Label
## 322 MSCH38 houses 0 2.59 No Label
## 323 MSCH38 pasta 1 2.59 No Label
## 324 MSCH38 beds 0 2.59 No Label
## 325 MSCH43 faces 0 2.71 No Label
## 326 MSCH43 houses 0 2.71 No Label
## 327 MSCH43 pasta 0 2.71 No Label
## 328 MSCH43 beds 0 2.71 No Label
## 329 MSCH49 faces 0 2.88 No Label
## 330 MSCH49 houses 0 2.88 No Label
## 331 MSCH49 pasta 0 2.88 No Label
## 332 MSCH49 beds 0 2.88 No Label
## 333 MSCH45 faces 0 2.90 No Label
## 334 MSCH45 houses 0 2.90 No Label
## 335 MSCH45 pasta 0 2.90 No Label
## 336 MSCH45 beds 1 2.90 No Label
## 337 MSCH42 faces 1 2.93 No Label
## 338 MSCH42 houses 0 2.93 No Label
## 339 MSCH42 pasta 0 2.93 No Label
## 340 MSCH42 beds 0 2.93 No Label
## 341 MSCH53 faces 1 2.99 No Label
## 342 MSCH53 houses 1 2.99 No Label
## 343 MSCH53 pasta 0 2.99 No Label
## 344 MSCH53 beds 0 2.99 No Label
## 345 MSCH69 faces 0 2.72 No Label
## 346 MSCH69 houses 1 2.72 No Label
## 347 MSCH69 pasta 1 2.72 No Label
## 348 MSCH69 beds 0 2.72 No Label
## 349 MSCH70 faces 0 2.31 No Label
## 350 MSCH70 houses 0 2.31 No Label
## 351 MSCH70 pasta 0 2.31 No Label
## 352 MSCH70 beds 1 2.31 No Label
## 353 MSCH74 faces 1 2.34 No Label
## 354 MSCH74 houses 0 2.34 No Label
## 355 MSCH74 pasta 0 2.34 No Label
## 356 MSCH74 beds 1 2.34 No Label
## 357 MSCH76 faces 0 2.58 No Label
## 358 MSCH76 houses 0 2.58 No Label
## 359 MSCH76 pasta 0 2.58 No Label
## 360 MSCH76 beds 0 2.58 No Label
## 361 MSCH77 faces 0 2.55 No Label
## 362 MSCH77 houses 0 2.55 No Label
## 363 MSCH77 pasta 0 2.55 No Label
## 364 MSCH77 beds 1 2.55 No Label
## 365 MSCH78 faces 0 2.43 No Label
## 366 MSCH78 houses 0 2.43 No Label
## 367 MSCH78 pasta 0 2.43 No Label
## 368 MSCH78 beds 1 2.43 No Label
## 369 MSCH79 faces 0 2.70 No Label
## 370 MSCH79 houses 1 2.70 No Label
## 371 MSCH79 pasta 0 2.70 No Label
## 372 MSCH79 beds 1 2.70 No Label
## 373 MSCH80 faces 0 2.76 No Label
## 374 MSCH80 houses 0 2.76 No Label
## 375 MSCH80 pasta 0 2.76 No Label
## 376 MSCH80 beds 0 2.76 No Label
## 377 MSCH81 faces 1 2.84 No Label
## 378 MSCH81 houses 0 2.84 No Label
## 379 MSCH81 pasta 0 2.84 No Label
## 380 MSCH81 beds 0 2.84 No Label
## 381 MSCH82 faces 1 2.46 No Label
## 382 MSCH82 houses 0 2.46 No Label
## 383 MSCH82 pasta 1 2.46 No Label
## 384 MSCH82 beds 0 2.46 No Label
## 385 MSCH83 faces 0 2.37 No Label
## 386 MSCH83 houses 0 2.37 No Label
## 387 MSCH83 pasta 1 2.37 No Label
## 388 MSCH83 beds 0 2.37 No Label
## 389 MSCH84 faces 0 2.83 No Label
## 390 MSCH84 houses 0 2.83 No Label
## 391 MSCH84 pasta 1 2.83 No Label
## 392 MSCH84 beds 0 2.83 No Label
## 393 MSCH85 faces 0 2.69 No Label
## 394 MSCH85 houses 0 2.69 No Label
## 395 MSCH85 pasta 0 2.69 No Label
## 396 MSCH85 beds 0 2.69 No Labeldplyr also has a few helper functions for more advanced filtering. One that is pretty useful is between(). Let’s use it to get kids between ages 2.1 and 2.5:
filter(ps_data, between(age, 2.1, 2.5))## subid item correct age condition
## 1 T22 beds 0 2.13 Label
## 2 T22 faces 0 2.13 Label
## 3 T22 houses 1 2.13 Label
## 4 T22 pasta 1 2.13 Label
## 5 T17 pasta 0 2.32 Label
## 6 T17 faces 0 2.32 Label
## 7 T17 houses 0 2.32 Label
## 8 T17 beds 0 2.32 Label
## 9 M3 faces 0 2.38 Label
## 10 M3 houses 1 2.38 Label
## 11 M3 pasta 1 2.38 Label
## 12 M3 beds 1 2.38 Label
## 13 T19 faces 0 2.47 Label
## 14 T19 houses 0 2.47 Label
## 15 T19 pasta 1 2.47 Label
## 16 T19 beds 1 2.47 Label
## 17 T20 faces 1 2.50 Label
## 18 T20 houses 1 2.50 Label
## 19 T20 pasta 0 2.50 Label
## 20 T20 beds 1 2.50 Label
## 21 MSCH44 faces 0 2.25 No Label
## 22 MSCH44 houses 0 2.25 No Label
## 23 MSCH44 pasta 0 2.25 No Label
## 24 MSCH44 beds 0 2.25 No Label
## 25 MSCH52 faces 0 2.50 No Label
## 26 MSCH52 houses 1 2.50 No Label
## 27 MSCH52 pasta 0 2.50 No Label
## 28 MSCH52 beds 1 2.50 No Label
## 29 MSCH70 faces 0 2.31 No Label
## 30 MSCH70 houses 0 2.31 No Label
## 31 MSCH70 pasta 0 2.31 No Label
## 32 MSCH70 beds 1 2.31 No Label
## 33 MSCH74 faces 1 2.34 No Label
## 34 MSCH74 houses 0 2.34 No Label
## 35 MSCH74 pasta 0 2.34 No Label
## 36 MSCH74 beds 1 2.34 No Label
## 37 MSCH78 faces 0 2.43 No Label
## 38 MSCH78 houses 0 2.43 No Label
## 39 MSCH78 pasta 0 2.43 No Label
## 40 MSCH78 beds 1 2.43 No Label
## 41 MSCH82 faces 1 2.46 No Label
## 42 MSCH82 houses 0 2.46 No Label
## 43 MSCH82 pasta 1 2.46 No Label
## 44 MSCH82 beds 0 2.46 No Label
## 45 MSCH83 faces 0 2.37 No Label
## 46 MSCH83 houses 0 2.37 No Label
## 47 MSCH83 pasta 1 2.37 No Label
## 48 MSCH83 beds 0 2.37 No LabelExercise 3.2a
Get Kids between the ages of 3 and 4 using
filter()and thebetween()helper function.
Exercise 3.2b
Get Kids between ages of 3 and 4 using
filter()without using thebetween()function.
3.3. Pipes
Pipes come from the magrittr library are available when you load the tidyverse (probably unnecessary sidenote: they’re technically imported with dplyr when you call library(tidyverse)). Pipes are a way to write strings of functions more easily, creating pipelines. They are extremely powerful and useful. A pipe looks like this:
%>%
You can enter a pipe with the shortcut CTRL+Shift+M for PC; CMD+Shift+M for Mac.
3.3.1 A quick side note about the term pipe
As mentioned above, a pipe in piping syntax is symbolized by %>%. However, another character is sometimes called a pipe, which is the vertical bar |, which we saw with logical tests above (| means or in logical statements).
3.3.2 The logic of piping syntax
The general idea of piping syntax, is that we have some function on the left hand and right hand side of the pipe. The function on the left side is evaluated, and then the output of that function is passed to the function on the right hand side of the pipe as the first argument of that (RHS) function. Let’s start with a simple example. We’ll get the sum of the age variable from the ps_data.
You can think of pipes as standing in for then.
ps_data$age %>% # LHS is age vector from ps_data
sum() # pass that to the sum function## [1] 2072.44As you can see, on the left hand side of the pipe %>%, we have the age vector from ps_data. On the right hand side, we have the function sum(), so the piped syntax is basically saying Take age from PS_data then get the sum.
We can make this look even a little cleaner by using the select() function:
ps_data %>% # take the data, then...
select(age) %>% # select age, then...
sum() # take the sum## [1] 2072.44Notice that we entered age as an argument in select and it looks like the first argument. Looks can be deceiving; the first argument is actually .data = ps_data, but that is hidden from view when piping.
Style Tip: It’s typically considered good practice to not have more than one pipe per line.
Bad:
ps_data %>% select(age) %>% sum() ## [1] 2072.44Good:
ps_data %>%
select(age) %>%
sum() ## [1] 2072.443.3.3 Why use pipes?
The most important and most often mentioned reasons to use pipes are cleanliness (which I hear is next to godliness) and efficiency:
- Cleaner code
- This is nice, because it helps make your code more readable by other humans (including your future self).
Piped:
ps_data %>% # take the data, then...
select(age) %>% # select age, then...
sum()## [1] 2072.44VS Nested:
sum(select(ps_data, age), na.rm = TRUE)## [1] 2072.44- Cleaner environment
- When you use pipes, you have basically no reason to save objects from intermediary steps in your data wrangling / analysis workflow, because you can just pass output from function to function without saving it.
- Finding object you’re looking for is easier.
- Auto complete (with tab) a little more efficient.
- Efficiency
- This is efficiency for you, the person doing the coding (not more efficient computing).
- Naming objects is hard; piping means coming up with fewer names.
- More error-proof
- Because naming is hard, you might accidentally re-use a name and make an error.
3.3.4 A note about Scaling
The gains in cleanliness and efficiency scale with the complexity of what you’re doing.
Let’s say, we wanted to take our PS_data, filter for observations from kids between 2.5 and 3.2, and then select just the subject id and age variables, and then get unique kids (using the unique() function on the subject id).
Without pipes, you’ll either end up with some difficult to read code:
unique(select(filter(ps_data, age > 2.5 | age < 3.2), age, subid))or some throwaway objects:
data_subset_age <- filter(ps_data, age > 2.5 | age > 3.2)
data_subset_age_ids <- select(data_subset_age, subid, age)
unique(data_subset_age_ids)With pipes, we can avoid these issues:
ps_data %>% # take the data, then...
filter(age > 2.5 | age > 3.2) %>% # filter for kids between 2.5 and 3.2, then...
select(subid, age) %>% # select subject id and centered age, then...
unique() # get unique rows## subid age
## 1 T21 2.58
## 5 M26 2.59
## 9 T18 2.61
## 13 T12 2.72
## 17 T16 2.73
## 21 T7 2.74
## 25 T9 2.79
## 29 T5 2.80
## 33 T14 2.83
## 37 T2 2.83
## 41 T15 2.85
## 45 M13 2.88
## 49 M12 2.88
## 53 T13 2.89
## 57 T8 2.91
## 61 T1 2.95
## 65 M15 2.98
## 69 T11 2.99
## 73 T10 3.00
## 77 T3 3.09
## 81 T6 3.10
## 85 M32 3.19
## 89 M1 3.20
## 93 C16 3.22
## 97 T4 3.24
## 101 C17 3.25
## 105 C6 3.26
## 109 M10 3.28
## 113 M31 3.30
## 117 C3 3.46
## 121 C10 3.46
## 125 M18 3.46
## 129 M16 3.50
## 133 M23 3.52
## 137 C7 3.55
## 141 C12 3.56
## 145 C15 3.59
## 149 M29 3.72
## 153 C20 3.75
## 157 M11 3.82
## 161 C9 3.82
## 165 C24 3.85
## 169 C22 3.92
## 173 C8 3.92
## 177 M4 3.96
## 181 M6 4.50
## 185 C19 4.14
## 189 C1 4.16
## 193 M19 4.16
## 197 C11 4.22
## 201 M9 4.26
## 205 M2 4.28
## 209 C5 4.29
## 213 M30 4.33
## 217 C13 4.38
## 221 C4 4.55
## 225 C14 4.57
## 229 M17 4.58
## 233 C2 4.60
## 237 C23 4.62
## 241 M20 4.64
## 245 M21 4.64
## 249 C21 4.73
## 253 M24 4.82
## 257 M5 4.84
## 261 M7 4.89
## 265 M8 4.89
## 269 C18 4.95
## 273 M25 4.96
## 277 MSCH38 2.59
## 281 MSCH43 2.71
## 285 MSCH49 2.88
## 289 MSCH45 2.90
## 293 MSCH42 2.93
## 297 MSCH53 2.99
## 301 SCH35 3.02
## 305 MSCH40 3.02
## 309 SCH34 3.06
## 313 SCH33 3.06
## 317 MSCH41 3.18
## 321 SCH37 3.27
## 325 SCH32 3.27
## 329 SCH36 3.33
## 333 SCH11 3.41
## 334 SCH12 3.41
## 338 SCH18 3.45
## 342 MSCH48 3.50
## 346 SCH25 3.54
## 350 SCH31 3.71
## 354 MSCH46 3.76
## 358 SCH11 3.82
## 361 SCH29 3.83
## 365 MSCH39 3.93
## 367 MSCH39 3.94
## 369 SCH28 4.02
## 373 SCH22 4.02
## 377 SCH24 4.07
## 381 SCH27 4.09
## 385 SCH17 4.25
## 389 SCH10 4.32
## 393 SCH9 4.37
## 397 SCH20 4.39
## 401 SCH6 4.41
## 405 SCH7 4.41
## 409 SCH15 4.42
## 413 SCH30 4.44
## 417 SCH3 4.47
## 421 SCH26 4.47
## 425 SCH8 4.52
## 429 SCH16 4.55
## 433 SCH14 4.58
## 437 SCH2 4.61
## 441 SCH5 4.61
## 445 SCH13 4.75
## 449 SCH21 4.76
## 453 SCH19 4.79
## 457 SCH23 4.82
## 461 SCH1 4.82
## 465 MSCH66 3.50
## 469 MSCH67 3.24
## 473 MSCH68 3.94
## 477 MSCH69 2.72
## 481 MSCH71 3.14
## 485 MSCH72 3.72
## 489 MSCH73 3.10
## 493 MSCH75 3.67
## 496 MSCH75 3.66
## 497 MSCH76 2.58
## 501 MSCH77 2.55
## 505 MSCH79 2.70
## 509 MSCH80 2.76
## 513 MSCH81 2.84
## 517 MSCH84 2.83
## 521 MSCH85 2.69See, so much easier to read, and not flooding our environment with clutter and not taxing our already taxed minds with having to come up with a bunch of names. And keep in mind this is just chaining a few of commands together; it really pays off as you do more and more complicated things.
3.3.5 Saving the output of your pipe
Keep in mind that, like everything in R, you have to tell R to save the output of your pipe using the <-.
unique_filtered_data <- ps_data %>% # take the data, then...
filter(age > 2.5 | age > 3.2) %>% # filter for kids between 2.5 and 3.2, then...
select(subid, age) %>% # select subject id and centered age, then...
unique() # get unique rowsExercise 3.3a
Take the
ps_datadata set. Using select and filter, get the number correct for kids at least 4 years old (note: there are several ways to do this, but the sum() function may be helpful). The output of your pipe should be a single number.
4. Diving deeper into dplyr
We gained a little bit of familiarity with dplyr above as an intro to the tidyverse. Now, we’ll dive a little deeper and explore some of the other ways we can transform data using the grammar of data manipulation.
4.1 More on select()
Let’s review what we learned using about selecting columns. We’ll use the starwars dataset, which comes with dplyr.
4.1.1 basic selects
Recall that one easy way to select columns is to use select(), providing the data as the first argument (w/ or w/o pipes) and desired unquoted column names separated by a comma:
starwars %>%
select(name, homeworld)## # A tibble: 87 x 2
## name homeworld
## <chr> <chr>
## 1 Luke Skywalker Tatooine
## 2 C-3PO Tatooine
## 3 R2-D2 Naboo
## 4 Darth Vader Tatooine
## 5 Leia Organa Alderaan
## 6 Owen Lars Tatooine
## 7 Beru Whitesun lars Tatooine
## 8 R5-D4 Tatooine
## 9 Biggs Darklighter Tatooine
## 10 Obi-Wan Kenobi Stewjon
## # ... with 77 more rows4.1.2 selecting a range
Recall that we can also select a range of variables, by name. Let’s get everything from name to homeworld:
starwars %>%
select(name:homeworld)## # A tibble: 87 x 9
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke~ 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl~ red 33 <NA>
## 4 Dart~ 202 136 none white yellow 41.9 male
## 5 Leia~ 150 49 brown light brown 19 female
## 6 Owen~ 178 120 brown, gr~ light blue 52 male
## 7 Beru~ 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Bigg~ 183 84 black light brown 24 male
## 10 Obi-~ 182 77 auburn, w~ fair blue-gray 57 male
## # ... with 77 more rows, and 1 more variable: homeworld <chr>4.1.3 selecting with helper functions
The helper fucntions allow us to do even more advanced column selection. For example, we can get all of the columns that contain the string "color":
starwars %>%
select(contains("color"))## # A tibble: 87 x 3
## hair_color skin_color eye_color
## <chr> <chr> <chr>
## 1 blond fair blue
## 2 <NA> gold yellow
## 3 <NA> white, blue red
## 4 none white yellow
## 5 brown light brown
## 6 brown, grey light blue
## 7 brown light blue
## 8 <NA> white, red red
## 9 black light brown
## 10 auburn, white fair blue-gray
## # ... with 77 more rows4.1.4 Re-arranging columns with select
I didn’t mention this before, but we can also use select to re-arrange our columns. This is one of the main uses for the everything() helper function. Let’s re-arrange things so that homeworld comes first, followed by name, then by the remainin columns
starwars %>%
select(homeworld, name, everything())## # A tibble: 87 x 13
## homeworld name height mass hair_color skin_color eye_color birth_year
## <chr> <chr> <int> <dbl> <chr> <chr> <chr> <dbl>
## 1 Tatooine Luke~ 172 77 blond fair blue 19
## 2 Tatooine C-3PO 167 75 <NA> gold yellow 112
## 3 Naboo R2-D2 96 32 <NA> white, bl~ red 33
## 4 Tatooine Dart~ 202 136 none white yellow 41.9
## 5 Alderaan Leia~ 150 49 brown light brown 19
## 6 Tatooine Owen~ 178 120 brown, gr~ light blue 52
## 7 Tatooine Beru~ 165 75 brown light blue 47
## 8 Tatooine R5-D4 97 32 <NA> white, red red NA
## 9 Tatooine Bigg~ 183 84 black light brown 24
## 10 Stewjon Obi-~ 182 77 auburn, w~ fair blue-gray 57
## # ... with 77 more rows, and 5 more variables: gender <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>Exercise 4.1a
Re-arrange the
starwarscolumns such that all of the columns that start with h are at the beginning, followed by the remaining columns.
Exercise 4.2a
Select name, species, hair_color, skin_color, and eye_color from the starwars data. Use at least one helper function.
4.2 More on filter()
Recall that filter() is sort of like the complement to select, and is for filtering the data for certain observations (rows).
4.2.1 Filters with one condition
Filters can be relatively simple. For example, we could filter for starwars characters that are less than 100cm in height:
starwars %>%
filter(height < 100)## # A tibble: 7 x 13
## name height mass hair_color skin_color eye_color birth_year gender homeworld
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 R2-D2 96 32 <NA> white, bl~ red 33 <NA> Naboo
## 2 R5-D4 97 32 <NA> white, red red NA <NA> Tatooine
## 3 Yoda 66 17 white green brown 896 male <NA>
## 4 Wick~ 88 20 brown brown brown 8 male Endor
## 5 Dud ~ 94 45 none blue, grey yellow NA male Vulpter
## 6 Ratt~ 79 15 none grey, blue unknown NA male Aleen Mi~
## 7 R4-P~ 96 NA none silver, r~ red, blue NA female <NA>
## # ... with 4 more variables: species <chr>, films <list>, vehicles <list>,
## # starships <list>Or we could do even more advanced filtering, such as by filter for observations 1 SD above the average height:
starwars %>%
filter(height > mean(height, na.rm = TRUE) + sd(height, na.rm = TRUE)) # don't forget na.rm!## # A tibble: 7 x 13
## name height mass hair_color skin_color eye_color birth_year gender homeworld
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Chew~ 228 112 brown unknown blue 200 male Kashyyyk
## 2 Roos~ 224 82 none grey orange NA male Naboo
## 3 Yara~ 264 NA none white yellow NA male Quermia
## 4 Lama~ 229 88 none grey black NA male Kamino
## 5 Taun~ 213 NA none grey black NA female Kamino
## 6 Grie~ 216 159 none brown, wh~ green, y~ NA male Kalee
## 7 Tarf~ 234 136 brown brown blue NA male Kashyyyk
## # ... with 4 more variables: species <chr>, films <list>, vehicles <list>,
## # starships <list>Speaking of NAs, let’s see which characters have missing mass by filtering for rows where mass is NA.
starwars %>%
filter(mass == NA)It give us an empty df. What happened? NA is a special case when it comes to logical filtering. A value can’t equal NA, it is NA. This is a subtle distinction. I try to remember it by reminding myself that an unknown could be equal to anything, but it definitely is an unknown.
So, if we want to filter for NAs, we have to use is.na(), which returns TRUE if an entry is NA. Let’s try to get characters with missing mass again:
starwars %>%
filter(is.na(mass)) # note you wrap the variable in is.na()## # A tibble: 28 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Wilh~ 180 NA auburn, g~ fair blue 64 male
## 2 Mon ~ 150 NA auburn fair blue 48 female
## 3 Arve~ NA NA brown fair brown NA male
## 4 Fini~ 170 NA blond fair blue 91 male
## 5 Rugo~ 206 NA none green orange NA male
## 6 Ric ~ 183 NA brown fair blue NA male
## 7 Watto 137 NA black blue, grey yellow NA male
## 8 Quar~ 183 NA black dark brown 62 male
## 9 Shmi~ 163 NA black fair brown 72 female
## 10 Bib ~ 180 NA none pale pink NA male
## # ... with 18 more rows, and 5 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>To get the values that aren’t missing, you have to put a ! before is.na(), so it looks like !is.na(variable). Let’s get every observation that isn’t mising on mass:
starwars %>%
filter(!is.na(mass))## # A tibble: 59 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke~ 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl~ red 33 <NA>
## 4 Dart~ 202 136 none white yellow 41.9 male
## 5 Leia~ 150 49 brown light brown 19 female
## 6 Owen~ 178 120 brown, gr~ light blue 52 male
## 7 Beru~ 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Bigg~ 183 84 black light brown 24 male
## 10 Obi-~ 182 77 auburn, w~ fair blue-gray 57 male
## # ... with 49 more rows, and 5 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>This works because ! converts a logical value to its opposite:
!TRUE## [1] FALSE!FALSE## [1] TRUE! 5 == 5## [1] FALSE! 5 == 4## [1] TRUE4.2.2 Filter with multiple conditions
Recall that we can combine conditions using & or |. For example, we could get observations 1 SD above the mean of height and below the mean of mass
starwars %>%
filter(height > mean(height, na.rm = TRUE) + sd(height, na.rm = TRUE) &
mass < mean(mass, na.rm = TRUE))## # A tibble: 2 x 13
## name height mass hair_color skin_color eye_color birth_year gender homeworld
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Roos~ 224 82 none grey orange NA male Naboo
## 2 Lama~ 229 88 none grey black NA male Kamino
## # ... with 4 more variables: species <chr>, films <list>, vehicles <list>,
## # starships <list>We could get observations either 1 SD above or below the mean of height too:
starwars %>%
filter(height > mean(height, na.rm = TRUE) + sd(height, na.rm = TRUE) |
height < mean(height, na.rm = TRUE) + sd(height, na.rm = TRUE))## # A tibble: 81 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke~ 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl~ red 33 <NA>
## 4 Dart~ 202 136 none white yellow 41.9 male
## 5 Leia~ 150 49 brown light brown 19 female
## 6 Owen~ 178 120 brown, gr~ light blue 52 male
## 7 Beru~ 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Bigg~ 183 84 black light brown 24 male
## 10 Obi-~ 182 77 auburn, w~ fair blue-gray 57 male
## # ... with 71 more rows, and 5 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>You can also filter based on character variables. For example, we could get ever character grey hair:
starwars %>%
filter(hair_color == "grey")## # A tibble: 1 x 13
## name height mass hair_color skin_color eye_color birth_year gender homeworld
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Palp~ 170 75 grey pale yellow 82 male Naboo
## # ... with 4 more variables: species <chr>, films <list>, vehicles <list>,
## # starships <list>Or characters that have grey or brown hair:
starwars %>%
filter(hair_color == "grey" |
hair_color == "brown")## # A tibble: 19 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Leia~ 150 49 brown light brown 19 female
## 2 Beru~ 165 75 brown light blue 47 female
## 3 Chew~ 228 112 brown unknown blue 200 male
## 4 Han ~ 180 80 brown fair brown 29 male
## 5 Wedg~ 170 77 brown fair hazel 21 male
## 6 Jek ~ 180 110 brown fair blue NA male
## 7 Palp~ 170 75 grey pale yellow 82 male
## 8 Arve~ NA NA brown fair brown NA male
## 9 Wick~ 88 20 brown brown brown 8 male
## 10 Qui-~ 193 89 brown fair blue 92 male
## 11 Ric ~ 183 NA brown fair blue NA male
## 12 Cordé 157 NA brown light brown NA female
## 13 Clie~ 183 NA brown fair blue 82 male
## 14 Dormé 165 NA brown light brown NA female
## 15 Tarf~ 234 136 brown brown blue NA male
## 16 Raym~ 188 79 brown light brown NA male
## 17 Rey NA NA brown light hazel NA female
## 18 Poe ~ NA NA brown light brown NA male
## 19 Padm~ 165 45 brown light brown 46 female
## # ... with 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>Or characters that don’t have brown hair:
starwars %>%
filter(hair_color != "brown")## # A tibble: 64 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke~ 172 77 blond fair blue 19 male
## 2 Dart~ 202 136 none white yellow 41.9 male
## 3 Owen~ 178 120 brown, gr~ light blue 52 male
## 4 Bigg~ 183 84 black light brown 24 male
## 5 Obi-~ 182 77 auburn, w~ fair blue-gray 57 male
## 6 Anak~ 188 84 blond fair blue 41.9 male
## 7 Wilh~ 180 NA auburn, g~ fair blue 64 male
## 8 Yoda 66 17 white green brown 896 male
## 9 Palp~ 170 75 grey pale yellow 82 male
## 10 Boba~ 183 78.2 black fair brown 31.5 male
## # ... with 54 more rows, and 5 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>You may be wandering if there is some way to extract those observations that have “grey” listed as one of several colors.
It’s a little tricky, but it can be done by using the stringr function str_detect(). Time permitting, we’ll cover stringr in a bit more detail tomorrow, but str_detect() can be used to look for a string in an object; it returns a logical (TRUE or FALSE) based on whether or not the string is found in that object. It is a lot like grep() which we saw earlier, but has the string/data as its first argument (a la tidyverse convention). Let’s use it in combination with filter() to get these salt & pepper starwars characters:
starwars %>%
filter(str_detect(hair_color, "grey")) # first argument is string var,## # A tibble: 3 x 13
## name height mass hair_color skin_color eye_color birth_year gender homeworld
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Owen~ 178 120 brown, gr~ light blue 52 male Tatooine
## 2 Wilh~ 180 NA auburn, g~ fair blue 64 male Eriadu
## 3 Palp~ 170 75 grey pale yellow 82 male Naboo
## # ... with 4 more variables: species <chr>, films <list>, vehicles <list>,
## # starships <list> # followed by pattern it looks forSince str_detect() returns a logical, we can negate it with ! to get the opposite (observations that don’t contain the string "grey" in hair_color):
starwars %>%
filter(!str_detect(hair_color, "grey"))## # A tibble: 79 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke~ 172 77 blond fair blue 19 male
## 2 Dart~ 202 136 none white yellow 41.9 male
## 3 Leia~ 150 49 brown light brown 19 female
## 4 Beru~ 165 75 brown light blue 47 female
## 5 Bigg~ 183 84 black light brown 24 male
## 6 Obi-~ 182 77 auburn, w~ fair blue-gray 57 male
## 7 Anak~ 188 84 blond fair blue 41.9 male
## 8 Chew~ 228 112 brown unknown blue 200 male
## 9 Han ~ 180 80 brown fair brown 29 male
## 10 Wedg~ 170 77 brown fair hazel 21 male
## # ... with 69 more rows, and 5 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>Exercise 4.2a
Starting with the starwars data frame, filter for characters thats birth year is at least 200. Select just their name, species, and birth_year.
Exercise 4.2b
Starting with the starwars data frame, filter for characters that don’t have blue in their eye color (hint: it could have more than one color) and print their name, eye_color, and species.
Exercise 4.2c
Starting with the starwars data frame, filter for droids from Tatooine or humans from Naboo. Select just name, homeworld, species, hair, eye, and skin color and make sure the columns in that order.
4.3 Transforming data with mutate() and transmute()
The next verbs from dplyr we’ll discuss are mutate() and transmute(). mutate() is for adding columns to a dataframe. transmute() is for replacing columns in a dataframe. Let’s start with mutate()
4.3.1 mutate()
Recall from last time that we can add columns to a dataframe in base R using data$new_col <-. For example, we could add height in meters to our starwars data:
starwars$height_in_m = starwars$height/100
starwars ## # A tibble: 87 x 14
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke~ 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl~ red 33 <NA>
## 4 Dart~ 202 136 none white yellow 41.9 male
## 5 Leia~ 150 49 brown light brown 19 female
## 6 Owen~ 178 120 brown, gr~ light blue 52 male
## 7 Beru~ 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Bigg~ 183 84 black light brown 24 male
## 10 Obi-~ 182 77 auburn, w~ fair blue-gray 57 male
## # ... with 77 more rows, and 6 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>, height_in_m <dbl># cleaning it back up
starwars <- starwars %>%
select(-height_in_m)In the tidyverse, we use mutate() instead. mutate() requires data as its first argument, followed by a set of expressions defining new columns, separated by a comma. For example, we could calculate height in meters and millimeters:
starwars <- starwars %>%
mutate(height_in_mm = height*10, # height in millimeters
height_in_m = height/100) # height in metersmutate() performs each calculation in order, so you can use variables created earlier within the same mutate call in later operations. For example, let’s z score height and mass (using the base R function scale()) and then average them together using the base R function rowMeans():
starwars %>%
mutate(height_z = scale(height),
mass_z = scale(mass),
height_mass_z = rowMeans(data.frame(height_z, mass_z), na.rm = TRUE))## # A tibble: 87 x 18
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke~ 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl~ red 33 <NA>
## 4 Dart~ 202 136 none white yellow 41.9 male
## 5 Leia~ 150 49 brown light brown 19 female
## 6 Owen~ 178 120 brown, gr~ light blue 52 male
## 7 Beru~ 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Bigg~ 183 84 black light brown 24 male
## 10 Obi-~ 182 77 auburn, w~ fair blue-gray 57 male
## # ... with 77 more rows, and 10 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>, height_in_mm <dbl>,
## # height_in_m <dbl>, height_z[,1] <dbl>, mass_z[,1] <dbl>,
## # height_mass_z <dbl>As you can see, getting row means (i.e., mean for each row across a set of columns) can be a little clunky in mutate(). The rowwise() function is designed to make this easier. To use it, we call it before a mutate() call. Let’s try it
starwars %>%
rowwise() %>%
mutate(height_z = scale(height),
mass_z = scale(mass),
height_mass_z = mean(c(height_z, mass_z), na.rm = TRUE))## Source: local data frame [87 x 18]
## Groups: <by row>
##
## # A tibble: 87 x 18
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke~ 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl~ red 33 <NA>
## 4 Dart~ 202 136 none white yellow 41.9 male
## 5 Leia~ 150 49 brown light brown 19 female
## 6 Owen~ 178 120 brown, gr~ light blue 52 male
## 7 Beru~ 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Bigg~ 183 84 black light brown 24 male
## 10 Obi-~ 182 77 auburn, w~ fair blue-gray 57 male
## # ... with 77 more rows, and 10 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>, height_in_mm <dbl>,
## # height_in_m <dbl>, height_z <dbl>, mass_z <dbl>, height_mass_z <dbl>Oops, as you can see we got NaN, because it’s trying to get z scores for each row, which are not defined. We would need to do two mutate calls and use the rowwise() function between them:
starwars %>%
mutate(height_z = scale(height),
mass_z = scale(mass)) %>%
rowwise() %>%
mutate(height_mass_z = mean(c(height_z, mass_z), na.rm = TRUE))## Source: local data frame [87 x 18]
## Groups: <by row>
##
## # A tibble: 87 x 18
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke~ 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl~ red 33 <NA>
## 4 Dart~ 202 136 none white yellow 41.9 male
## 5 Leia~ 150 49 brown light brown 19 female
## 6 Owen~ 178 120 brown, gr~ light blue 52 male
## 7 Beru~ 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Bigg~ 183 84 black light brown 24 male
## 10 Obi-~ 182 77 auburn, w~ fair blue-gray 57 male
## # ... with 77 more rows, and 10 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>, height_in_mm <dbl>,
## # height_in_m <dbl>, height_z[,1] <dbl>, mass_z[,1] <dbl>,
## # height_mass_z <dbl>You can overwrite columns with mutate(). For example, gender is currently a character, but we could turn it using mutate to overwrite it:
starwars %>%
mutate(gender = factor(gender))## # A tibble: 87 x 15
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <fct>
## 1 Luke~ 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl~ red 33 <NA>
## 4 Dart~ 202 136 none white yellow 41.9 male
## 5 Leia~ 150 49 brown light brown 19 female
## 6 Owen~ 178 120 brown, gr~ light blue 52 male
## 7 Beru~ 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Bigg~ 183 84 black light brown 24 male
## 10 Obi-~ 182 77 auburn, w~ fair blue-gray 57 male
## # ... with 77 more rows, and 7 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>, height_in_mm <dbl>,
## # height_in_m <dbl>4.3.2 transmute()
transmute() is similar to mutate, but instead of returning the original data frame with the new (additional) columns, it returns just the new columns. For example, we could use transmute if we just wanted a dataframe of z scored height and mass:
starwars %>%
transmute(height_z = scale(height),
mass_z = scale(mass))## # A tibble: 87 x 2
## height_z[,1] mass_z[,1]
## <dbl> <dbl>
## 1 -0.0678 -0.120
## 2 -0.212 -0.132
## 3 -2.25 -0.385
## 4 0.795 0.228
## 5 -0.701 -0.285
## 6 0.105 0.134
## 7 -0.269 -0.132
## 8 -2.22 -0.385
## 9 0.249 -0.0786
## 10 0.220 -0.120
## # ... with 77 more rowsNote that this basically equivalent to doing a mutate() followed by a select() for the newly mutated columns.
Exercise 4.3a
Create a new variable called bmi that is each character’s bmi. bmi = kg / m^2. Mass is already in kg units, but height is currently in cm units, so height will need to be converted (1m = 100cm).
Exercise 4.3b
This time, do the same thing, but use transmute so that the resulting dataframe has just name, height in meters, mass, and bmi (hint: you can always mutate or transmute a variable to be equal to itself)
4.4 Grouping Data with group_by()
group_by() is another dplyr function that creates groups based on one or more variables in the data. This affects all kinds of things that you then do with the data, such as mutating. It is pretty simple to use. It requires data as its first argument, and the you name the variables (unquoted) to group by separated by a column.
For example, we could group our starwars data by characters’ homeworld:
starwars %>%
group_by(homeworld)## # A tibble: 87 x 15
## # Groups: homeworld [49]
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke~ 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl~ red 33 <NA>
## 4 Dart~ 202 136 none white yellow 41.9 male
## 5 Leia~ 150 49 brown light brown 19 female
## 6 Owen~ 178 120 brown, gr~ light blue 52 male
## 7 Beru~ 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Bigg~ 183 84 black light brown 24 male
## 10 Obi-~ 182 77 auburn, w~ fair blue-gray 57 male
## # ... with 77 more rows, and 7 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>, height_in_mm <dbl>,
## # height_in_m <dbl>Okay, by the looks of it, nothing happened. But, if we paste it into the console, you can see that it did change something. It adds some meta-data saying that homeworld is a grouping factor, but that is it.
But, it has powerful effects on how other functions interact with our grouped dataset. Let’s see what happens when we take the mean mass with and without grouping:
starwars %>%
mutate(mean_mass = mean(mass, na.rm = TRUE)) %>%
select(homeworld, mean_mass, everything()) # re-arrange for easy viewing## # A tibble: 87 x 16
## homeworld mean_mass name height mass hair_color skin_color eye_color
## <chr> <dbl> <chr> <int> <dbl> <chr> <chr> <chr>
## 1 Tatooine 97.3 Luke~ 172 77 blond fair blue
## 2 Tatooine 97.3 C-3PO 167 75 <NA> gold yellow
## 3 Naboo 97.3 R2-D2 96 32 <NA> white, bl~ red
## 4 Tatooine 97.3 Dart~ 202 136 none white yellow
## 5 Alderaan 97.3 Leia~ 150 49 brown light brown
## 6 Tatooine 97.3 Owen~ 178 120 brown, gr~ light blue
## 7 Tatooine 97.3 Beru~ 165 75 brown light blue
## 8 Tatooine 97.3 R5-D4 97 32 <NA> white, red red
## 9 Tatooine 97.3 Bigg~ 183 84 black light brown
## 10 Stewjon 97.3 Obi-~ 182 77 auburn, w~ fair blue-gray
## # ... with 77 more rows, and 8 more variables: birth_year <dbl>, gender <chr>,
## # species <chr>, films <list>, vehicles <list>, starships <list>,
## # height_in_mm <dbl>, height_in_m <dbl>Now, let’s see what happens if we group_by() first:
starwars %>%
group_by(homeworld) %>%
mutate(mean_mass = mean(mass, na.rm = TRUE)) %>%
select(homeworld, mean_mass, everything())## # A tibble: 87 x 16
## # Groups: homeworld [49]
## homeworld mean_mass name height mass hair_color skin_color eye_color
## <chr> <dbl> <chr> <int> <dbl> <chr> <chr> <chr>
## 1 Tatooine 85.4 Luke~ 172 77 blond fair blue
## 2 Tatooine 85.4 C-3PO 167 75 <NA> gold yellow
## 3 Naboo 64.2 R2-D2 96 32 <NA> white, bl~ red
## 4 Tatooine 85.4 Dart~ 202 136 none white yellow
## 5 Alderaan 64 Leia~ 150 49 brown light brown
## 6 Tatooine 85.4 Owen~ 178 120 brown, gr~ light blue
## 7 Tatooine 85.4 Beru~ 165 75 brown light blue
## 8 Tatooine 85.4 R5-D4 97 32 <NA> white, red red
## 9 Tatooine 85.4 Bigg~ 183 84 black light brown
## 10 Stewjon 77 Obi-~ 182 77 auburn, w~ fair blue-gray
## # ... with 77 more rows, and 8 more variables: birth_year <dbl>, gender <chr>,
## # species <chr>, films <list>, vehicles <list>, starships <list>,
## # height_in_mm <dbl>, height_in_m <dbl>You can also group by multiple factors, which you can just add as comma-separated unquoted variable names. For example, let’s group by homeworld and species and get the average mass:
starwars %>%
group_by(homeworld, species) %>%
mutate(mean_mass = mean(mass, na.rm = TRUE)) %>%
select(homeworld, mean_mass, everything())## # A tibble: 87 x 16
## # Groups: homeworld, species [58]
## homeworld mean_mass name height mass hair_color skin_color eye_color
## <chr> <dbl> <chr> <int> <dbl> <chr> <chr> <chr>
## 1 Tatooine 96 Luke~ 172 77 blond fair blue
## 2 Tatooine 53.5 C-3PO 167 75 <NA> gold yellow
## 3 Naboo 32 R2-D2 96 32 <NA> white, bl~ red
## 4 Tatooine 96 Dart~ 202 136 none white yellow
## 5 Alderaan 64 Leia~ 150 49 brown light brown
## 6 Tatooine 96 Owen~ 178 120 brown, gr~ light blue
## 7 Tatooine 96 Beru~ 165 75 brown light blue
## 8 Tatooine 53.5 R5-D4 97 32 <NA> white, red red
## 9 Tatooine 96 Bigg~ 183 84 black light brown
## 10 Stewjon 77 Obi-~ 182 77 auburn, w~ fair blue-gray
## # ... with 77 more rows, and 8 more variables: birth_year <dbl>, gender <chr>,
## # species <chr>, films <list>, vehicles <list>, starships <list>,
## # height_in_mm <dbl>, height_in_m <dbl>Exercise 4.4a
Create two new variables called
species_mean_massthat is equal to the average mass per species, andspecies_centered_massthat is equal to each character’s mass minus their species’ mean (i.e., center mass within species). Select name, species, and all of the mass variables.
4.5 Summarizing data with summarize()
The next dplyr verb we’ll cover is summarize(), which is used to summarize across rows of a dataset. It, like all tidyverse functions, requires data as its first argument, and then you enter your summary formulas separated by commas; it looks pretty similar to mutate() and transmute(). The outcoming dataset will have just the variables you summarized and lose everything else.
Just like in mutate(), each expression is new_var_name = EXPRESSION.
Let’s use summarize on the starwars dataset to get the mean mass across all observations:
starwars %>%
summarize(mean_mass = mean(mass, na.rm = TRUE))## # A tibble: 1 x 1
## mean_mass
## <dbl>
## 1 97.3We can also add some more summary statistis. For example, let’s get the sd and n as well:
starwars %>%
summarize(mean_mass = mean(mass, na.rm = TRUE),
sd_mass = sd(mass, na.rm = TRUE),
n = n())## # A tibble: 1 x 3
## mean_mass sd_mass n
## <dbl> <dbl> <int>
## 1 97.3 169. 874.5.1 Combining group_by() and summarize()
group_by() and summarize() can be combined to get group-level statistics. This is a great way to make tables of descriptive stats in R or to create aggregated datasets for some purposes.
To use these together, you just run group_by() followed by summarize() in a pipeline. Let’s take a look at the mass statistics per species:
starwars %>%
group_by(species) %>% # just add the group_by() call
summarize(mean_mass = mean(mass, na.rm = TRUE),
sd_mass = sd(mass, na.rm = TRUE),
n = n())## # A tibble: 38 x 4
## species mean_mass sd_mass n
## <chr> <dbl> <dbl> <int>
## 1 Aleena 15 NA 1
## 2 Besalisk 102 NA 1
## 3 Cerean 82 NA 1
## 4 Chagrian NaN NA 1
## 5 Clawdite 55 NA 1
## 6 Droid 69.8 51.0 5
## 7 Dug 40 NA 1
## 8 Ewok 20 NA 1
## 9 Geonosian 80 NA 1
## 10 Gungan 74 11.3 3
## # ... with 28 more rowsLet’s clean that up a bit by filtering out species with just 1 observation:
starwars %>%
group_by(species) %>% # just add the group_by() call
summarize(mean_mass = mean(mass, na.rm = TRUE),
sd_mass = sd(mass, na.rm = TRUE),
n = n()) %>%
filter(n > 1)## # A tibble: 9 x 4
## species mean_mass sd_mass n
## <chr> <dbl> <dbl> <int>
## 1 Droid 69.8 51.0 5
## 2 Gungan 74 11.3 3
## 3 Human 82.8 19.4 35
## 4 Kaminoan 88 NA 2
## 5 Mirialan 53.1 4.38 2
## 6 Twi'lek 55 NA 2
## 7 Wookiee 124 17.0 2
## 8 Zabrak 80 NA 2
## 9 <NA> 48 NA 5Of course you can use multiple groups in group_by() to get crosstabs:
starwars %>%
group_by(species, gender) %>% # just change the group_by() call
summarize(mean_mass = mean(mass, na.rm = TRUE),
sd_mass = sd(mass, na.rm = TRUE),
n = n()) %>%
filter(n > 1)## # A tibble: 10 x 5
## # Groups: species [7]
## species gender mean_mass sd_mass n
## <chr> <chr> <dbl> <dbl> <int>
## 1 Droid none 140 NA 2
## 2 Droid <NA> 46.3 24.8 3
## 3 Gungan male 74 11.3 3
## 4 Human female 56.3 16.3 9
## 5 Human male 87.0 16.5 26
## 6 Mirialan female 53.1 4.38 2
## 7 Wookiee male 124 17.0 2
## 8 Zabrak male 80 NA 2
## 9 <NA> female 48 NA 3
## 10 <NA> male NaN NaN 2Exercise 4.5a
Returning to the
ps_data, get the total number correct (call itnum_correct), the total number of item/condition combinations (call itnum_trials), and the proportion correct (call itprop_correct) for each condition and item using group_by and summarize.
4.6 Brief Overview of Other dpyr verbs
I wanted to tell you quickly about some other useful dplyr verbs we don’t have time to go into depth with, but that are useful to know about.
4.6.1 arrange()
Arrange can be used to re-arrange the rows of a dataset based on some variable(s) values. It requires the data, then each variable you want to arrange the data by separated by a comma.
For example, we can arrange the data by mass:
starwars %>%
arrange(mass)## # A tibble: 87 x 15
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Ratt~ 79 15 none grey, blue unknown NA male
## 2 Yoda 66 17 white green brown 896 male
## 3 Wick~ 88 20 brown brown brown 8 male
## 4 R2-D2 96 32 <NA> white, bl~ red 33 <NA>
## 5 R5-D4 97 32 <NA> white, red red NA <NA>
## 6 Sebu~ 112 40 none grey, red orange NA male
## 7 Dud ~ 94 45 none blue, grey yellow NA male
## 8 Padm~ 165 45 brown light brown 46 female
## 9 Wat ~ 193 48 none green, gr~ unknown NA male
## 10 Sly ~ 178 48 none pale white NA female
## # ... with 77 more rows, and 7 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>, height_in_mm <dbl>,
## # height_in_m <dbl>You can make it descending order by wrapping the variable in desc():
starwars %>%
arrange(desc(mass))## # A tibble: 87 x 15
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Jabb~ 175 1358 <NA> green-tan~ orange 600 herma~
## 2 Grie~ 216 159 none brown, wh~ green, y~ NA male
## 3 IG-88 200 140 none metal red 15 none
## 4 Dart~ 202 136 none white yellow 41.9 male
## 5 Tarf~ 234 136 brown brown blue NA male
## 6 Owen~ 178 120 brown, gr~ light blue 52 male
## 7 Bossk 190 113 none green red 53 male
## 8 Chew~ 228 112 brown unknown blue 200 male
## 9 Jek ~ 180 110 brown fair blue NA male
## 10 Dext~ 198 102 none brown yellow NA male
## # ... with 77 more rows, and 7 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>, height_in_mm <dbl>,
## # height_in_m <dbl>And, you can provide multiple variables to sort on, and it will do each in order:
starwars %>%
arrange(height, mass)## # A tibble: 87 x 15
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Yoda 66 17 white green brown 896 male
## 2 Ratt~ 79 15 none grey, blue unknown NA male
## 3 Wick~ 88 20 brown brown brown 8 male
## 4 Dud ~ 94 45 none blue, grey yellow NA male
## 5 R2-D2 96 32 <NA> white, bl~ red 33 <NA>
## 6 R4-P~ 96 NA none silver, r~ red, blue NA female
## 7 R5-D4 97 32 <NA> white, red red NA <NA>
## 8 Sebu~ 112 40 none grey, red orange NA male
## 9 Gasg~ 122 NA none white, bl~ black NA male
## 10 Watto 137 NA black blue, grey yellow NA male
## # ... with 77 more rows, and 7 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>, height_in_mm <dbl>,
## # height_in_m <dbl>If you tell it to arrange by a character variable, it will order them alphabetically:
starwars %>%
arrange(name)## # A tibble: 87 x 15
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Ackb~ 180 83 none brown mot~ orange 41 male
## 2 Adi ~ 184 50 none dark blue NA female
## 3 Anak~ 188 84 blond fair blue 41.9 male
## 4 Arve~ NA NA brown fair brown NA male
## 5 Ayla~ 178 55 none blue hazel 48 female
## 6 Bail~ 191 NA black tan brown 67 male
## 7 Barr~ 166 50 black yellow blue 40 female
## 8 BB8 NA NA none none black NA none
## 9 Ben ~ 163 65 none grey, gre~ orange NA male
## 10 Beru~ 165 75 brown light blue 47 female
## # ... with 77 more rows, and 7 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>, height_in_mm <dbl>,
## # height_in_m <dbl>4.6.2 rename()
rename() can be used to rename variables. It requires data as its first argument, then the rename expressions in the form new_name = old_name. For example, we could rename name from the starwars data:
starwars %>%
rename(char_name = name)## # A tibble: 87 x 15
## char_name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke Sky~ 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl~ red 33 <NA>
## 4 Darth Va~ 202 136 none white yellow 41.9 male
## 5 Leia Org~ 150 49 brown light brown 19 female
## 6 Owen Lars 178 120 brown, gr~ light blue 52 male
## 7 Beru Whi~ 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Biggs Da~ 183 84 black light brown 24 male
## 10 Obi-Wan ~ 182 77 auburn, w~ fair blue-gray 57 male
## # ... with 77 more rows, and 7 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>, height_in_mm <dbl>,
## # height_in_m <dbl>And, you can provide multiple renames:
starwars %>%
rename(char_name = name,
height_cm = height,
mass_kg = mass)## # A tibble: 87 x 15
## char_name height_cm mass_kg hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke Sky~ 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl~ red 33 <NA>
## 4 Darth Va~ 202 136 none white yellow 41.9 male
## 5 Leia Org~ 150 49 brown light brown 19 female
## 6 Owen Lars 178 120 brown, gr~ light blue 52 male
## 7 Beru Whi~ 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Biggs Da~ 183 84 black light brown 24 male
## 10 Obi-Wan ~ 182 77 auburn, w~ fair blue-gray 57 male
## # ... with 77 more rows, and 7 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>, height_in_mm <dbl>,
## # height_in_m <dbl>4.6.3 *_join()
dplyr has some useful functions for joining datasets together based on a unique key (or set of key variables) to match on. For example, if we had a dataset called lightsabers that has various starwars characters’ lightsaber colors:
lightsabers <- data.frame(name = c("Luke Skywalker",
"Darth Vader",
"Obi-Wan Kenobi",
"Dooku"),
lightsaber_color = c("green",
"red",
"blue",
"red"))We could join this to our starwars data using one of the join functions, such as left_join(), which keeps everythin in the left hand dataset (the first datatset in the arguments) and only records that match in the right hand dataset:
left_join(starwars,
lightsabers)## Joining, by = "name"## # A tibble: 87 x 16
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke~ 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl~ red 33 <NA>
## 4 Dart~ 202 136 none white yellow 41.9 male
## 5 Leia~ 150 49 brown light brown 19 female
## 6 Owen~ 178 120 brown, gr~ light blue 52 male
## 7 Beru~ 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Bigg~ 183 84 black light brown 24 male
## 10 Obi-~ 182 77 auburn, w~ fair blue-gray 57 male
## # ... with 77 more rows, and 8 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>, height_in_mm <dbl>,
## # height_in_m <dbl>, lightsaber_color <chr>And let’s see what happens if we reverse the order:
left_join(lightsabers,
starwars)## Joining, by = "name"## name lightsaber_color height mass hair_color skin_color
## 1 Luke Skywalker green 172 77 blond fair
## 2 Darth Vader red 202 136 none white
## 3 Obi-Wan Kenobi blue 182 77 auburn, white fair
## 4 Dooku red 193 80 white fair
## eye_color birth_year gender homeworld species
## 1 blue 19.0 male Tatooine Human
## 2 yellow 41.9 male Tatooine Human
## 3 blue-gray 57.0 male Stewjon Human
## 4 brown 102.0 male Serenno Human
## films
## 1 Revenge of the Sith, Return of the Jedi, The Empire Strikes Back, A New Hope, The Force Awakens
## 2 Revenge of the Sith, Return of the Jedi, The Empire Strikes Back, A New Hope
## 3 Attack of the Clones, The Phantom Menace, Revenge of the Sith, Return of the Jedi, The Empire Strikes Back, A New Hope
## 4 Attack of the Clones, Revenge of the Sith
## vehicles
## 1 Snowspeeder, Imperial Speeder Bike
## 2
## 3 Tribubble bongo
## 4 Flitknot speeder
## starships
## 1 X-wing, Imperial shuttle
## 2 TIE Advanced x1
## 3 Jedi starfighter, Trade Federation cruiser, Naboo star skiff, Jedi Interceptor, Belbullab-22 starfighter
## 4
## height_in_mm height_in_m
## 1 1720 1.72
## 2 2020 2.02
## 3 1820 1.82
## 4 1930 1.93There are also more complicated joins that you can learn more about in Ch. 13 of R for Data Science
4.6.4 distinct()
distinct() can be used to get distinct entries within a dataset. It requires data as its first argument, and then variables you want to be distinct. Let’s start with just one variable, distinct homeworlds in starwars:
starwars %>%
distinct(homeworld)## # A tibble: 49 x 1
## homeworld
## <chr>
## 1 Tatooine
## 2 Naboo
## 3 Alderaan
## 4 Stewjon
## 5 Eriadu
## 6 Kashyyyk
## 7 Corellia
## 8 Rodia
## 9 Nal Hutta
## 10 Bestine IV
## # ... with 39 more rowsYou can also get distinct combination by supplying more than 1 variable:
starwars %>%
distinct(homeworld, species)## # A tibble: 58 x 2
## homeworld species
## <chr> <chr>
## 1 Tatooine Human
## 2 Tatooine Droid
## 3 Naboo Droid
## 4 Alderaan Human
## 5 Stewjon Human
## 6 Eriadu Human
## 7 Kashyyyk Wookiee
## 8 Corellia Human
## 9 Rodia Rodian
## 10 Nal Hutta Hutt
## # ... with 48 more rowsNote that by default it give us just the variables included in the distinct() call. We can change this by changing the .keep_all argument to TRUE:
starwars %>%
distinct(homeworld, species, .keep_all = TRUE)## # A tibble: 58 x 15
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke~ 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl~ red 33 <NA>
## 4 Leia~ 150 49 brown light brown 19 female
## 5 Obi-~ 182 77 auburn, w~ fair blue-gray 57 male
## 6 Wilh~ 180 NA auburn, g~ fair blue 64 male
## 7 Chew~ 228 112 brown unknown blue 200 male
## 8 Han ~ 180 80 brown fair brown 29 male
## 9 Gree~ 173 74 <NA> green black 44 male
## 10 Jabb~ 175 1358 <NA> green-tan~ orange 600 herma~
## # ... with 48 more rows, and 7 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>, height_in_mm <dbl>,
## # height_in_m <dbl>4.6.5 count()
count() is an easy way to get the count of a variable in a dataset. It’s basically a shortcut of group_by() and summarize(n = n()). It requires data as its first argument, then the variables you want counts of. For example, let’s get counts of species in starwars:
starwars %>%
count(species)## # A tibble: 38 x 2
## species n
## <chr> <int>
## 1 Aleena 1
## 2 Besalisk 1
## 3 Cerean 1
## 4 Chagrian 1
## 5 Clawdite 1
## 6 Droid 5
## 7 Dug 1
## 8 Ewok 1
## 9 Geonosian 1
## 10 Gungan 3
## # ... with 28 more rowsYou can also get counts of combinations of variables by adding more variables, separated by a comma:
starwars %>%
count(species, homeworld) %>%
arrange(desc(n))## # A tibble: 58 x 3
## species homeworld n
## <chr> <chr> <int>
## 1 Human Tatooine 8
## 2 Human Naboo 5
## 3 Human <NA> 5
## 4 Gungan Naboo 3
## 5 Human Alderaan 3
## 6 Droid Tatooine 2
## 7 Droid <NA> 2
## 8 Human Corellia 2
## 9 Human Coruscant 2
## 10 Kaminoan Kamino 2
## # ... with 48 more rowsExercise 4.6a
Take the
ps_data, make sure that it has only distinct kids (based on subid), but keep all of the variables. Put the dataset in order from most frequent to least frequent age.