library(tidyverse)Introduction to the tidyverse
R
tidyverse
R-SIG
R-SIG 26.02.24
1 Introduction
Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table. (Wickham, 2014).
The tidyverse is composed of multiple packages, all following a common philosophy, and facilitating many aspects of coding in R, for example data wrangling and plotting. It is not really necessary to learn the tidyverse syntax in order to be proficient in R. However, I find it easier to understand and write Code in, at least in most cases. In the end, it is a question of preference what you want to learn and use. Most code will probably be composed from base R functions and tidyverse functions.
You can find an overview of the included packages at the offical tidyverse documentation.
A more thorough introduction into the tidyverse can be found here.
2 Some tidyverse features
2.1 Tibbles
A special type of data frame are the so called tibbles. Tibbles are a modern version of data frames and the standard data frame type of the tidyverse, as they have some advantageous characteristics (e.g., note the more informative printing of the data frame). So don’t be confused if you run into them, in general they behave like data frames. Take a look at the Exercises, or at a more thorough Example if you want to learn more.
2.2 The Pipe Operator
tidyverse code is often written using the pipe operator %>% (read as ‘then do’), which makes it easy to connect multiple function calls.
Some notes on the pipe syntax, also see Exercises:
- If we don’t have any additional arguments we want to put into the function, we can just write the function name without any brackets.
- The pipe operator will give the result of the last function as input into the next function.
- If we want to clearly state which of the function arguments should receive the input, we can write a
., which can be read as output of the previous function call.
3 Workstation organization
3.1 RStudio Projects
Over time, it will become increasingly hard to organize all your files, working directories and workspaces in a sensible manner. A reasonable big project will consist of multiple script files, data, output and plots. To keep everything toghether, RStudio Projects can be used (highly recommended). Therefore, when starting a new project in R, the first thing you should do is to create a RStudio project.
You can create a new RStudio project by clicking on File - New Project in the RStudio window. You can either create a totally new directory, or choose an already existing folder for the project.
3.2 Code organization
Within your project folder, I would suggest that you create subfolders to save your Scripts, data, outputs … in. For example, you could create a folder named R, where all your R Scripts will go. You can do the same for data, plots etc. This will help you to structure your working directory and make it easier to find specific files.
3.3 Absolute paths vs. relative paths
I can head to a specific file by using the full path (absolute path): "C:/Users/hafiznij/Documents/GitHub/IQB-Methods/posts/r_sig/24_01_26_tidyverse_intro/raw_data/winners.rda". This approach has some disadvantages: it will only work on my notebook. If I want to continue my project on another device, I will have to change the path. The same goes for other people who want to work with my project. So, to keep these paths more reproducable, we should always use relative paths: "./raw_data/winners.rda". This will always work independently of the device I am working on, as long as I am in the correct working directory.
Network drives
One exception might be paths to files on the IQB network drives, like T: … Because these are always the same for every one, absolute paths will work just fine for everything lying on here.
The working directory is the path R is currently working in. I can obtain it by typing:
getwd()[1] "/home/runner/work/IQB-Methods/IQB-Methods/docs/r_sig/24_01_26_tidyverse_intro"Luckily, RStudio projects set the working directory automatically, so we don’t really have to deal with that.
Now take a look at the working directory and the relative path I used for loading the winners.rda. Notice something? Correct, both paths combined equal the absolute path to the file. So by splitting it up, we obtain a more reproducible path, that works independently of where the current working directory is.
The
here package
Another great way to deal with the path confusion is to use the here package. It can build the paths relative to the directory where your R Studio project is saved in. For example, "./raw_data/winners.rda" becomes here::here("raw_data", "winners.rda"). This is not incredibly important right now, especially if you have all your files in the same folder. But it can become very valuable with increasing project complexity and file structure, so look into it if you want to get a head start! I also I have to use it sometimes during the tutorial because of the way I have organized my project, so don’t be confused! It is just another way to build file paths. Look here (:D) if you want to learn more about the package.
4 Exercise
Create a new RStudio project. Create the folders
R,dataandplots. Create a new R-Script which lies in yourRfolder.Write the following code using the pipe-operator from the
tidyverse:
sum(seq(from = 1, to = mean(c(45:100), na.rm = TRUE), by = 0.1))
Solution
library(tidyverse)
c(45:100) %>%
mean(na.rm = TRUE) %>%
seq(from = 1, to = ., by = 0.1) %>%
sum[1] 26313Much nicer to read, right?
- If we don’t have any additional arguments we want to put into the function, we can just write the function name without any brackets, like we do at the end with
sum. - The pipe operator will give the result of the last function as input into the next function. That’s why we don’t have to specify the vector within the
mean()function. - If we want to clearly state which of the function arguments should receive the input, we can write a
., which can be read as output of the previous function call. That’s what we do in theseq()function. It calculates a sequence from1to the mean ofc(45:100).
- Install and load the
palmerpenguinspackage.
Solution
# install.packages("palmerpenguins")
library(palmerpenguins)- Transform the
penguins-tibble (available after loading the package) into adata.frame.
Solution
penguins_frame <- as.data.frame(penguins)- Compare how both objects (tibble and data.frame) are printed into the console. Which differences can you see?
Solution
penguins# A tibble: 344 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Adelie Torgersen 39.1 18.7 181 3750
2 Adelie Torgersen 39.5 17.4 186 3800
3 Adelie Torgersen 40.3 18 195 3250
4 Adelie Torgersen NA NA NA NA
5 Adelie Torgersen 36.7 19.3 193 3450
6 Adelie Torgersen 39.3 20.6 190 3650
7 Adelie Torgersen 38.9 17.8 181 3625
8 Adelie Torgersen 39.2 19.6 195 4675
9 Adelie Torgersen 34.1 18.1 193 3475
10 Adelie Torgersen 42 20.2 190 4250
# ℹ 334 more rows
# ℹ 2 more variables: sex <fct>, year <int>penguins_frame species island bill_length_mm bill_depth_mm flipper_length_mm
1 Adelie Torgersen 39.1 18.7 181
2 Adelie Torgersen 39.5 17.4 186
3 Adelie Torgersen 40.3 18.0 195
4 Adelie Torgersen NA NA NA
5 Adelie Torgersen 36.7 19.3 193
6 Adelie Torgersen 39.3 20.6 190
7 Adelie Torgersen 38.9 17.8 181
8 Adelie Torgersen 39.2 19.6 195
9 Adelie Torgersen 34.1 18.1 193
10 Adelie Torgersen 42.0 20.2 190
11 Adelie Torgersen 37.8 17.1 186
12 Adelie Torgersen 37.8 17.3 180
13 Adelie Torgersen 41.1 17.6 182
14 Adelie Torgersen 38.6 21.2 191
15 Adelie Torgersen 34.6 21.1 198
16 Adelie Torgersen 36.6 17.8 185
17 Adelie Torgersen 38.7 19.0 195
18 Adelie Torgersen 42.5 20.7 197
19 Adelie Torgersen 34.4 18.4 184
20 Adelie Torgersen 46.0 21.5 194
21 Adelie Biscoe 37.8 18.3 174
22 Adelie Biscoe 37.7 18.7 180
23 Adelie Biscoe 35.9 19.2 189
24 Adelie Biscoe 38.2 18.1 185
25 Adelie Biscoe 38.8 17.2 180
26 Adelie Biscoe 35.3 18.9 187
27 Adelie Biscoe 40.6 18.6 183
28 Adelie Biscoe 40.5 17.9 187
29 Adelie Biscoe 37.9 18.6 172
30 Adelie Biscoe 40.5 18.9 180
31 Adelie Dream 39.5 16.7 178
32 Adelie Dream 37.2 18.1 178
33 Adelie Dream 39.5 17.8 188
34 Adelie Dream 40.9 18.9 184
35 Adelie Dream 36.4 17.0 195
36 Adelie Dream 39.2 21.1 196
37 Adelie Dream 38.8 20.0 190
38 Adelie Dream 42.2 18.5 180
39 Adelie Dream 37.6 19.3 181
40 Adelie Dream 39.8 19.1 184
41 Adelie Dream 36.5 18.0 182
42 Adelie Dream 40.8 18.4 195
43 Adelie Dream 36.0 18.5 186
44 Adelie Dream 44.1 19.7 196
45 Adelie Dream 37.0 16.9 185
46 Adelie Dream 39.6 18.8 190
47 Adelie Dream 41.1 19.0 182
48 Adelie Dream 37.5 18.9 179
49 Adelie Dream 36.0 17.9 190
50 Adelie Dream 42.3 21.2 191
51 Adelie Biscoe 39.6 17.7 186
52 Adelie Biscoe 40.1 18.9 188
53 Adelie Biscoe 35.0 17.9 190
54 Adelie Biscoe 42.0 19.5 200
55 Adelie Biscoe 34.5 18.1 187
56 Adelie Biscoe 41.4 18.6 191
57 Adelie Biscoe 39.0 17.5 186
58 Adelie Biscoe 40.6 18.8 193
59 Adelie Biscoe 36.5 16.6 181
60 Adelie Biscoe 37.6 19.1 194
61 Adelie Biscoe 35.7 16.9 185
62 Adelie Biscoe 41.3 21.1 195
63 Adelie Biscoe 37.6 17.0 185
64 Adelie Biscoe 41.1 18.2 192
65 Adelie Biscoe 36.4 17.1 184
66 Adelie Biscoe 41.6 18.0 192
67 Adelie Biscoe 35.5 16.2 195
68 Adelie Biscoe 41.1 19.1 188
69 Adelie Torgersen 35.9 16.6 190
70 Adelie Torgersen 41.8 19.4 198
71 Adelie Torgersen 33.5 19.0 190
72 Adelie Torgersen 39.7 18.4 190
73 Adelie Torgersen 39.6 17.2 196
74 Adelie Torgersen 45.8 18.9 197
75 Adelie Torgersen 35.5 17.5 190
76 Adelie Torgersen 42.8 18.5 195
77 Adelie Torgersen 40.9 16.8 191
78 Adelie Torgersen 37.2 19.4 184
79 Adelie Torgersen 36.2 16.1 187
80 Adelie Torgersen 42.1 19.1 195
81 Adelie Torgersen 34.6 17.2 189
82 Adelie Torgersen 42.9 17.6 196
83 Adelie Torgersen 36.7 18.8 187
84 Adelie Torgersen 35.1 19.4 193
85 Adelie Dream 37.3 17.8 191
86 Adelie Dream 41.3 20.3 194
87 Adelie Dream 36.3 19.5 190
88 Adelie Dream 36.9 18.6 189
89 Adelie Dream 38.3 19.2 189
90 Adelie Dream 38.9 18.8 190
91 Adelie Dream 35.7 18.0 202
92 Adelie Dream 41.1 18.1 205
93 Adelie Dream 34.0 17.1 185
94 Adelie Dream 39.6 18.1 186
95 Adelie Dream 36.2 17.3 187
96 Adelie Dream 40.8 18.9 208
97 Adelie Dream 38.1 18.6 190
98 Adelie Dream 40.3 18.5 196
99 Adelie Dream 33.1 16.1 178
100 Adelie Dream 43.2 18.5 192
101 Adelie Biscoe 35.0 17.9 192
102 Adelie Biscoe 41.0 20.0 203
103 Adelie Biscoe 37.7 16.0 183
104 Adelie Biscoe 37.8 20.0 190
105 Adelie Biscoe 37.9 18.6 193
106 Adelie Biscoe 39.7 18.9 184
107 Adelie Biscoe 38.6 17.2 199
108 Adelie Biscoe 38.2 20.0 190
109 Adelie Biscoe 38.1 17.0 181
110 Adelie Biscoe 43.2 19.0 197
111 Adelie Biscoe 38.1 16.5 198
112 Adelie Biscoe 45.6 20.3 191
113 Adelie Biscoe 39.7 17.7 193
114 Adelie Biscoe 42.2 19.5 197
115 Adelie Biscoe 39.6 20.7 191
116 Adelie Biscoe 42.7 18.3 196
117 Adelie Torgersen 38.6 17.0 188
118 Adelie Torgersen 37.3 20.5 199
119 Adelie Torgersen 35.7 17.0 189
120 Adelie Torgersen 41.1 18.6 189
121 Adelie Torgersen 36.2 17.2 187
122 Adelie Torgersen 37.7 19.8 198
123 Adelie Torgersen 40.2 17.0 176
124 Adelie Torgersen 41.4 18.5 202
125 Adelie Torgersen 35.2 15.9 186
126 Adelie Torgersen 40.6 19.0 199
127 Adelie Torgersen 38.8 17.6 191
128 Adelie Torgersen 41.5 18.3 195
129 Adelie Torgersen 39.0 17.1 191
130 Adelie Torgersen 44.1 18.0 210
131 Adelie Torgersen 38.5 17.9 190
132 Adelie Torgersen 43.1 19.2 197
133 Adelie Dream 36.8 18.5 193
134 Adelie Dream 37.5 18.5 199
135 Adelie Dream 38.1 17.6 187
136 Adelie Dream 41.1 17.5 190
137 Adelie Dream 35.6 17.5 191
138 Adelie Dream 40.2 20.1 200
139 Adelie Dream 37.0 16.5 185
140 Adelie Dream 39.7 17.9 193
141 Adelie Dream 40.2 17.1 193
142 Adelie Dream 40.6 17.2 187
143 Adelie Dream 32.1 15.5 188
144 Adelie Dream 40.7 17.0 190
145 Adelie Dream 37.3 16.8 192
146 Adelie Dream 39.0 18.7 185
147 Adelie Dream 39.2 18.6 190
148 Adelie Dream 36.6 18.4 184
149 Adelie Dream 36.0 17.8 195
150 Adelie Dream 37.8 18.1 193
151 Adelie Dream 36.0 17.1 187
152 Adelie Dream 41.5 18.5 201
153 Gentoo Biscoe 46.1 13.2 211
154 Gentoo Biscoe 50.0 16.3 230
155 Gentoo Biscoe 48.7 14.1 210
156 Gentoo Biscoe 50.0 15.2 218
157 Gentoo Biscoe 47.6 14.5 215
158 Gentoo Biscoe 46.5 13.5 210
159 Gentoo Biscoe 45.4 14.6 211
160 Gentoo Biscoe 46.7 15.3 219
161 Gentoo Biscoe 43.3 13.4 209
162 Gentoo Biscoe 46.8 15.4 215
163 Gentoo Biscoe 40.9 13.7 214
164 Gentoo Biscoe 49.0 16.1 216
165 Gentoo Biscoe 45.5 13.7 214
166 Gentoo Biscoe 48.4 14.6 213
167 Gentoo Biscoe 45.8 14.6 210
168 Gentoo Biscoe 49.3 15.7 217
169 Gentoo Biscoe 42.0 13.5 210
170 Gentoo Biscoe 49.2 15.2 221
171 Gentoo Biscoe 46.2 14.5 209
172 Gentoo Biscoe 48.7 15.1 222
173 Gentoo Biscoe 50.2 14.3 218
174 Gentoo Biscoe 45.1 14.5 215
175 Gentoo Biscoe 46.5 14.5 213
176 Gentoo Biscoe 46.3 15.8 215
177 Gentoo Biscoe 42.9 13.1 215
178 Gentoo Biscoe 46.1 15.1 215
179 Gentoo Biscoe 44.5 14.3 216
180 Gentoo Biscoe 47.8 15.0 215
181 Gentoo Biscoe 48.2 14.3 210
182 Gentoo Biscoe 50.0 15.3 220
183 Gentoo Biscoe 47.3 15.3 222
184 Gentoo Biscoe 42.8 14.2 209
185 Gentoo Biscoe 45.1 14.5 207
186 Gentoo Biscoe 59.6 17.0 230
187 Gentoo Biscoe 49.1 14.8 220
188 Gentoo Biscoe 48.4 16.3 220
189 Gentoo Biscoe 42.6 13.7 213
190 Gentoo Biscoe 44.4 17.3 219
191 Gentoo Biscoe 44.0 13.6 208
192 Gentoo Biscoe 48.7 15.7 208
193 Gentoo Biscoe 42.7 13.7 208
194 Gentoo Biscoe 49.6 16.0 225
195 Gentoo Biscoe 45.3 13.7 210
196 Gentoo Biscoe 49.6 15.0 216
197 Gentoo Biscoe 50.5 15.9 222
198 Gentoo Biscoe 43.6 13.9 217
199 Gentoo Biscoe 45.5 13.9 210
200 Gentoo Biscoe 50.5 15.9 225
201 Gentoo Biscoe 44.9 13.3 213
202 Gentoo Biscoe 45.2 15.8 215
203 Gentoo Biscoe 46.6 14.2 210
204 Gentoo Biscoe 48.5 14.1 220
205 Gentoo Biscoe 45.1 14.4 210
206 Gentoo Biscoe 50.1 15.0 225
207 Gentoo Biscoe 46.5 14.4 217
208 Gentoo Biscoe 45.0 15.4 220
209 Gentoo Biscoe 43.8 13.9 208
210 Gentoo Biscoe 45.5 15.0 220
211 Gentoo Biscoe 43.2 14.5 208
212 Gentoo Biscoe 50.4 15.3 224
213 Gentoo Biscoe 45.3 13.8 208
214 Gentoo Biscoe 46.2 14.9 221
215 Gentoo Biscoe 45.7 13.9 214
216 Gentoo Biscoe 54.3 15.7 231
217 Gentoo Biscoe 45.8 14.2 219
218 Gentoo Biscoe 49.8 16.8 230
219 Gentoo Biscoe 46.2 14.4 214
220 Gentoo Biscoe 49.5 16.2 229
221 Gentoo Biscoe 43.5 14.2 220
222 Gentoo Biscoe 50.7 15.0 223
223 Gentoo Biscoe 47.7 15.0 216
224 Gentoo Biscoe 46.4 15.6 221
225 Gentoo Biscoe 48.2 15.6 221
226 Gentoo Biscoe 46.5 14.8 217
227 Gentoo Biscoe 46.4 15.0 216
228 Gentoo Biscoe 48.6 16.0 230
229 Gentoo Biscoe 47.5 14.2 209
230 Gentoo Biscoe 51.1 16.3 220
231 Gentoo Biscoe 45.2 13.8 215
232 Gentoo Biscoe 45.2 16.4 223
233 Gentoo Biscoe 49.1 14.5 212
234 Gentoo Biscoe 52.5 15.6 221
235 Gentoo Biscoe 47.4 14.6 212
236 Gentoo Biscoe 50.0 15.9 224
237 Gentoo Biscoe 44.9 13.8 212
238 Gentoo Biscoe 50.8 17.3 228
239 Gentoo Biscoe 43.4 14.4 218
240 Gentoo Biscoe 51.3 14.2 218
241 Gentoo Biscoe 47.5 14.0 212
242 Gentoo Biscoe 52.1 17.0 230
243 Gentoo Biscoe 47.5 15.0 218
244 Gentoo Biscoe 52.2 17.1 228
245 Gentoo Biscoe 45.5 14.5 212
246 Gentoo Biscoe 49.5 16.1 224
247 Gentoo Biscoe 44.5 14.7 214
248 Gentoo Biscoe 50.8 15.7 226
249 Gentoo Biscoe 49.4 15.8 216
250 Gentoo Biscoe 46.9 14.6 222
251 Gentoo Biscoe 48.4 14.4 203
252 Gentoo Biscoe 51.1 16.5 225
253 Gentoo Biscoe 48.5 15.0 219
254 Gentoo Biscoe 55.9 17.0 228
255 Gentoo Biscoe 47.2 15.5 215
256 Gentoo Biscoe 49.1 15.0 228
257 Gentoo Biscoe 47.3 13.8 216
258 Gentoo Biscoe 46.8 16.1 215
259 Gentoo Biscoe 41.7 14.7 210
260 Gentoo Biscoe 53.4 15.8 219
261 Gentoo Biscoe 43.3 14.0 208
262 Gentoo Biscoe 48.1 15.1 209
263 Gentoo Biscoe 50.5 15.2 216
264 Gentoo Biscoe 49.8 15.9 229
265 Gentoo Biscoe 43.5 15.2 213
266 Gentoo Biscoe 51.5 16.3 230
267 Gentoo Biscoe 46.2 14.1 217
268 Gentoo Biscoe 55.1 16.0 230
269 Gentoo Biscoe 44.5 15.7 217
270 Gentoo Biscoe 48.8 16.2 222
271 Gentoo Biscoe 47.2 13.7 214
272 Gentoo Biscoe NA NA NA
273 Gentoo Biscoe 46.8 14.3 215
274 Gentoo Biscoe 50.4 15.7 222
275 Gentoo Biscoe 45.2 14.8 212
276 Gentoo Biscoe 49.9 16.1 213
277 Chinstrap Dream 46.5 17.9 192
278 Chinstrap Dream 50.0 19.5 196
279 Chinstrap Dream 51.3 19.2 193
280 Chinstrap Dream 45.4 18.7 188
281 Chinstrap Dream 52.7 19.8 197
282 Chinstrap Dream 45.2 17.8 198
283 Chinstrap Dream 46.1 18.2 178
284 Chinstrap Dream 51.3 18.2 197
285 Chinstrap Dream 46.0 18.9 195
286 Chinstrap Dream 51.3 19.9 198
287 Chinstrap Dream 46.6 17.8 193
288 Chinstrap Dream 51.7 20.3 194
289 Chinstrap Dream 47.0 17.3 185
290 Chinstrap Dream 52.0 18.1 201
291 Chinstrap Dream 45.9 17.1 190
292 Chinstrap Dream 50.5 19.6 201
293 Chinstrap Dream 50.3 20.0 197
294 Chinstrap Dream 58.0 17.8 181
295 Chinstrap Dream 46.4 18.6 190
296 Chinstrap Dream 49.2 18.2 195
297 Chinstrap Dream 42.4 17.3 181
298 Chinstrap Dream 48.5 17.5 191
299 Chinstrap Dream 43.2 16.6 187
300 Chinstrap Dream 50.6 19.4 193
301 Chinstrap Dream 46.7 17.9 195
302 Chinstrap Dream 52.0 19.0 197
303 Chinstrap Dream 50.5 18.4 200
304 Chinstrap Dream 49.5 19.0 200
305 Chinstrap Dream 46.4 17.8 191
306 Chinstrap Dream 52.8 20.0 205
307 Chinstrap Dream 40.9 16.6 187
308 Chinstrap Dream 54.2 20.8 201
309 Chinstrap Dream 42.5 16.7 187
310 Chinstrap Dream 51.0 18.8 203
311 Chinstrap Dream 49.7 18.6 195
312 Chinstrap Dream 47.5 16.8 199
313 Chinstrap Dream 47.6 18.3 195
314 Chinstrap Dream 52.0 20.7 210
315 Chinstrap Dream 46.9 16.6 192
316 Chinstrap Dream 53.5 19.9 205
317 Chinstrap Dream 49.0 19.5 210
318 Chinstrap Dream 46.2 17.5 187
319 Chinstrap Dream 50.9 19.1 196
320 Chinstrap Dream 45.5 17.0 196
321 Chinstrap Dream 50.9 17.9 196
322 Chinstrap Dream 50.8 18.5 201
323 Chinstrap Dream 50.1 17.9 190
324 Chinstrap Dream 49.0 19.6 212
325 Chinstrap Dream 51.5 18.7 187
326 Chinstrap Dream 49.8 17.3 198
327 Chinstrap Dream 48.1 16.4 199
328 Chinstrap Dream 51.4 19.0 201
329 Chinstrap Dream 45.7 17.3 193
330 Chinstrap Dream 50.7 19.7 203
331 Chinstrap Dream 42.5 17.3 187
332 Chinstrap Dream 52.2 18.8 197
333 Chinstrap Dream 45.2 16.6 191
334 Chinstrap Dream 49.3 19.9 203
335 Chinstrap Dream 50.2 18.8 202
336 Chinstrap Dream 45.6 19.4 194
337 Chinstrap Dream 51.9 19.5 206
338 Chinstrap Dream 46.8 16.5 189
339 Chinstrap Dream 45.7 17.0 195
340 Chinstrap Dream 55.8 19.8 207
341 Chinstrap Dream 43.5 18.1 202
342 Chinstrap Dream 49.6 18.2 193
343 Chinstrap Dream 50.8 19.0 210
344 Chinstrap Dream 50.2 18.7 198
body_mass_g sex year
1 3750 male 2007
2 3800 female 2007
3 3250 female 2007
4 NA <NA> 2007
5 3450 female 2007
6 3650 male 2007
7 3625 female 2007
8 4675 male 2007
9 3475 <NA> 2007
10 4250 <NA> 2007
11 3300 <NA> 2007
12 3700 <NA> 2007
13 3200 female 2007
14 3800 male 2007
15 4400 male 2007
16 3700 female 2007
17 3450 female 2007
18 4500 male 2007
19 3325 female 2007
20 4200 male 2007
21 3400 female 2007
22 3600 male 2007
23 3800 female 2007
24 3950 male 2007
25 3800 male 2007
26 3800 female 2007
27 3550 male 2007
28 3200 female 2007
29 3150 female 2007
30 3950 male 2007
31 3250 female 2007
32 3900 male 2007
33 3300 female 2007
34 3900 male 2007
35 3325 female 2007
36 4150 male 2007
37 3950 male 2007
38 3550 female 2007
39 3300 female 2007
40 4650 male 2007
41 3150 female 2007
42 3900 male 2007
43 3100 female 2007
44 4400 male 2007
45 3000 female 2007
46 4600 male 2007
47 3425 male 2007
48 2975 <NA> 2007
49 3450 female 2007
50 4150 male 2007
51 3500 female 2008
52 4300 male 2008
53 3450 female 2008
54 4050 male 2008
55 2900 female 2008
56 3700 male 2008
57 3550 female 2008
58 3800 male 2008
59 2850 female 2008
60 3750 male 2008
61 3150 female 2008
62 4400 male 2008
63 3600 female 2008
64 4050 male 2008
65 2850 female 2008
66 3950 male 2008
67 3350 female 2008
68 4100 male 2008
69 3050 female 2008
70 4450 male 2008
71 3600 female 2008
72 3900 male 2008
73 3550 female 2008
74 4150 male 2008
75 3700 female 2008
76 4250 male 2008
77 3700 female 2008
78 3900 male 2008
79 3550 female 2008
80 4000 male 2008
81 3200 female 2008
82 4700 male 2008
83 3800 female 2008
84 4200 male 2008
85 3350 female 2008
86 3550 male 2008
87 3800 male 2008
88 3500 female 2008
89 3950 male 2008
90 3600 female 2008
91 3550 female 2008
92 4300 male 2008
93 3400 female 2008
94 4450 male 2008
95 3300 female 2008
96 4300 male 2008
97 3700 female 2008
98 4350 male 2008
99 2900 female 2008
100 4100 male 2008
101 3725 female 2009
102 4725 male 2009
103 3075 female 2009
104 4250 male 2009
105 2925 female 2009
106 3550 male 2009
107 3750 female 2009
108 3900 male 2009
109 3175 female 2009
110 4775 male 2009
111 3825 female 2009
112 4600 male 2009
113 3200 female 2009
114 4275 male 2009
115 3900 female 2009
116 4075 male 2009
117 2900 female 2009
118 3775 male 2009
119 3350 female 2009
120 3325 male 2009
121 3150 female 2009
122 3500 male 2009
123 3450 female 2009
124 3875 male 2009
125 3050 female 2009
126 4000 male 2009
127 3275 female 2009
128 4300 male 2009
129 3050 female 2009
130 4000 male 2009
131 3325 female 2009
132 3500 male 2009
133 3500 female 2009
134 4475 male 2009
135 3425 female 2009
136 3900 male 2009
137 3175 female 2009
138 3975 male 2009
139 3400 female 2009
140 4250 male 2009
141 3400 female 2009
142 3475 male 2009
143 3050 female 2009
144 3725 male 2009
145 3000 female 2009
146 3650 male 2009
147 4250 male 2009
148 3475 female 2009
149 3450 female 2009
150 3750 male 2009
151 3700 female 2009
152 4000 male 2009
153 4500 female 2007
154 5700 male 2007
155 4450 female 2007
156 5700 male 2007
157 5400 male 2007
158 4550 female 2007
159 4800 female 2007
160 5200 male 2007
161 4400 female 2007
162 5150 male 2007
163 4650 female 2007
164 5550 male 2007
165 4650 female 2007
166 5850 male 2007
167 4200 female 2007
168 5850 male 2007
169 4150 female 2007
170 6300 male 2007
171 4800 female 2007
172 5350 male 2007
173 5700 male 2007
174 5000 female 2007
175 4400 female 2007
176 5050 male 2007
177 5000 female 2007
178 5100 male 2007
179 4100 <NA> 2007
180 5650 male 2007
181 4600 female 2007
182 5550 male 2007
183 5250 male 2007
184 4700 female 2007
185 5050 female 2007
186 6050 male 2007
187 5150 female 2008
188 5400 male 2008
189 4950 female 2008
190 5250 male 2008
191 4350 female 2008
192 5350 male 2008
193 3950 female 2008
194 5700 male 2008
195 4300 female 2008
196 4750 male 2008
197 5550 male 2008
198 4900 female 2008
199 4200 female 2008
200 5400 male 2008
201 5100 female 2008
202 5300 male 2008
203 4850 female 2008
204 5300 male 2008
205 4400 female 2008
206 5000 male 2008
207 4900 female 2008
208 5050 male 2008
209 4300 female 2008
210 5000 male 2008
211 4450 female 2008
212 5550 male 2008
213 4200 female 2008
214 5300 male 2008
215 4400 female 2008
216 5650 male 2008
217 4700 female 2008
218 5700 male 2008
219 4650 <NA> 2008
220 5800 male 2008
221 4700 female 2008
222 5550 male 2008
223 4750 female 2008
224 5000 male 2008
225 5100 male 2008
226 5200 female 2008
227 4700 female 2008
228 5800 male 2008
229 4600 female 2008
230 6000 male 2008
231 4750 female 2008
232 5950 male 2008
233 4625 female 2009
234 5450 male 2009
235 4725 female 2009
236 5350 male 2009
237 4750 female 2009
238 5600 male 2009
239 4600 female 2009
240 5300 male 2009
241 4875 female 2009
242 5550 male 2009
243 4950 female 2009
244 5400 male 2009
245 4750 female 2009
246 5650 male 2009
247 4850 female 2009
248 5200 male 2009
249 4925 male 2009
250 4875 female 2009
251 4625 female 2009
252 5250 male 2009
253 4850 female 2009
254 5600 male 2009
255 4975 female 2009
256 5500 male 2009
257 4725 <NA> 2009
258 5500 male 2009
259 4700 female 2009
260 5500 male 2009
261 4575 female 2009
262 5500 male 2009
263 5000 female 2009
264 5950 male 2009
265 4650 female 2009
266 5500 male 2009
267 4375 female 2009
268 5850 male 2009
269 4875 <NA> 2009
270 6000 male 2009
271 4925 female 2009
272 NA <NA> 2009
273 4850 female 2009
274 5750 male 2009
275 5200 female 2009
276 5400 male 2009
277 3500 female 2007
278 3900 male 2007
279 3650 male 2007
280 3525 female 2007
281 3725 male 2007
282 3950 female 2007
283 3250 female 2007
284 3750 male 2007
285 4150 female 2007
286 3700 male 2007
287 3800 female 2007
288 3775 male 2007
289 3700 female 2007
290 4050 male 2007
291 3575 female 2007
292 4050 male 2007
293 3300 male 2007
294 3700 female 2007
295 3450 female 2007
296 4400 male 2007
297 3600 female 2007
298 3400 male 2007
299 2900 female 2007
300 3800 male 2007
301 3300 female 2007
302 4150 male 2007
303 3400 female 2008
304 3800 male 2008
305 3700 female 2008
306 4550 male 2008
307 3200 female 2008
308 4300 male 2008
309 3350 female 2008
310 4100 male 2008
311 3600 male 2008
312 3900 female 2008
313 3850 female 2008
314 4800 male 2008
315 2700 female 2008
316 4500 male 2008
317 3950 male 2008
318 3650 female 2008
319 3550 male 2008
320 3500 female 2008
321 3675 female 2009
322 4450 male 2009
323 3400 female 2009
324 4300 male 2009
325 3250 male 2009
326 3675 female 2009
327 3325 female 2009
328 3950 male 2009
329 3600 female 2009
330 4050 male 2009
331 3350 female 2009
332 3450 male 2009
333 3250 female 2009
334 4050 male 2009
335 3800 male 2009
336 3525 female 2009
337 3950 male 2009
338 3650 female 2009
339 3650 female 2009
340 4000 male 2009
341 3400 female 2009
342 3775 male 2009
343 4100 male 2009
344 3775 female 2009tibblesonly print 10 rows by default, data.frames a lot more.tibblesonly print as many columns as possible in one row, which looks a lot cleaner.- On top, the
tibbleshows us how many rows and columns there are in our data. NAsare printed in red intibbles(not in this output, but try it yourself).- The data-type of each column is printed on top of the column in
tibbles.
- Save your penguins
data.frameand your penguinstibbleas.RDSfiles in a dedicateddatafolder in your R-project. Use relative paths!
Solution
saveRDS(penguins, file = "./data/penguins.RDS")
saveRDS(penguins_frame, file = "./data/penguins_frame.RDS")- Load your penguins
data.frameand your penguinstibbleinto R. Use theherepackage.
Solution
library(here)
readRDS(here::here("data", "penguins.RDS"))
readRDS(here::here("data", "penguins_frame.RDS"))Footnotes
Image by Barn Images on Unsplash.↩︎