Setting up a project
Artwork by @allison_horst
1 Creating a new Project in R
Challenge 1
- Click the “File” menu button, then “New Project”.
- Click “New Directory”.
- Click “New Project”.
- Type in the name of your project, e.g. “Learning_R”.
- Click the “Create Project” button
The simplest way to open an RStudio project once it has been created
is to click through your file system to get to the directory where it
was saved and double click on the .Rproj file. This will
open RStudio and start your R session in the same directory as the
.Rproj file. Any data, plots or scripts can be saved and
retrieved from this directory.
RStudio projects have the added benefit of allowing you to open multiple projects at the same time each open to its own project directory. This allows you to keep multiple projects open without them interfering with each other.
1.1 Best practices for project organization
In the bottom right pane in RStudio, click the “Files” tab. To
organise your project, create folders called data,
scripts, results, docs by
clicking the folder icon with the green plus sign.
1.1.1 Use a script
The most effective way to work with R is to write the code you want to run in an .R script. You then run the selected lines (by either using a keyboard shortcut or clicking the “Run” button) in the R console.
Open a script (click the white square with the cross in green below
File at the top left). Save the script in your R project folder (click
“File”, then “Save as” and browse to the script folder in
your project). You could call it Lesson 1.
1.1.2 Save the data in the data folder
Artwork by @allison_horst
Challenge 2 View the palmer penguins data here.
- From the window showing the data, save the data as a file (CTRL + S or right mouse click > “Save as”)
- Make sure it’s saved under the name
penguins.csv- Save the file in the
datafolder within your project.We will load and inspect these data shortly.
1.2 Reading help files
Every package author writes help files for their functions. Either of these will load up a help page in RStudio:
?function_name
help(function_name)Each help page is broken down into sections (Description, Usage, Arguments etc). The last section Examples is very useful.
The command below will bring up the help for the function
log.
?logTip: Help files
One of the most daunting aspects of R is the large number of functions available. It would be prohibitive, if not impossible to remember the correct usage for every function you use. Luckily, the help files mean you don’t have to!
1.2.1 When your code doesn’t work: seek help from the R community
If you’re having trouble using a function, 9 times out of 10, the answers you are seeking have already been answered on Stack Overflow.
1.3 Reading in data
We have already saved a .csv data file in our data folder (do the same when you work with your own datasets).
We can load this into R via the following:
penguins <- read.csv(file = "data/penguins.csv")Here we used the function read.csv but there are lots of
different functions for reading in different types of files. For example
read_excel in the readxl package.
Tip: Problems reading in data
If your spreadsheet won’t read in check:
1. You are using the correct function for the type of file, for example
read.csv for csv files
2. You have included the suffix after the name of the file
3. The name of the file is spelled correctly
4. The file exists in the folder you are directing R to. For example, in
our case we would click on the folder named data under the files tab at
the bottom right of RStudio to check.
5. Look at an example on the internet to ensure you have included all
the necessary arguments. read.csv only needs
file = but other functions may need other
arguments.
Once data is in R, you can view it by clicking on it’s name under the Environment window.
In R, datasets are called data frames (df) or sometimes tibbles.
We can begin exploring our data frame right away, pulling out columns
by specifying them using the $ operator:
penguins$species## [1] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [7] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [13] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [19] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [25] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [31] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [37] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [43] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [49] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [55] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [61] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [67] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [73] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [79] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [85] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [91] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [97] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [103] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [109] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [115] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [121] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [127] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [133] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [139] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [145] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [151] "Adelie" "Adelie" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [157] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [163] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [169] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [175] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [181] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [187] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [193] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [199] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [205] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [211] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [217] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [223] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [229] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [235] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [241] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [247] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [253] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [259] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [265] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [271] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [277] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [283] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [289] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [295] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [301] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [307] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [313] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [319] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [325] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [331] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [337] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [343] "Chinstrap" "Chinstrap"penguins$bill_length_mm## [1] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42.0 37.8 37.8 41.1 38.6 34.6
## [16] 36.6 38.7 42.5 34.4 46.0 37.8 37.7 35.9 38.2 38.8 35.3 40.6 40.5 37.9 40.5
## [31] 39.5 37.2 39.5 40.9 36.4 39.2 38.8 42.2 37.6 39.8 36.5 40.8 36.0 44.1 37.0
## [46] 39.6 41.1 37.5 36.0 42.3 39.6 40.1 35.0 42.0 34.5 41.4 39.0 40.6 36.5 37.6
## [61] 35.7 41.3 37.6 41.1 36.4 41.6 35.5 41.1 35.9 41.8 33.5 39.7 39.6 45.8 35.5
## [76] 42.8 40.9 37.2 36.2 42.1 34.6 42.9 36.7 35.1 37.3 41.3 36.3 36.9 38.3 38.9
## [91] 35.7 41.1 34.0 39.6 36.2 40.8 38.1 40.3 33.1 43.2 35.0 41.0 37.7 37.8 37.9
## [106] 39.7 38.6 38.2 38.1 43.2 38.1 45.6 39.7 42.2 39.6 42.7 38.6 37.3 35.7 41.1
## [121] 36.2 37.7 40.2 41.4 35.2 40.6 38.8 41.5 39.0 44.1 38.5 43.1 36.8 37.5 38.1
## [136] 41.1 35.6 40.2 37.0 39.7 40.2 40.6 32.1 40.7 37.3 39.0 39.2 36.6 36.0 37.8
## [151] 36.0 41.5 46.1 50.0 48.7 50.0 47.6 46.5 45.4 46.7 43.3 46.8 40.9 49.0 45.5
## [166] 48.4 45.8 49.3 42.0 49.2 46.2 48.7 50.2 45.1 46.5 46.3 42.9 46.1 44.5 47.8
## [181] 48.2 50.0 47.3 42.8 45.1 59.6 49.1 48.4 42.6 44.4 44.0 48.7 42.7 49.6 45.3
## [196] 49.6 50.5 43.6 45.5 50.5 44.9 45.2 46.6 48.5 45.1 50.1 46.5 45.0 43.8 45.5
## [211] 43.2 50.4 45.3 46.2 45.7 54.3 45.8 49.8 46.2 49.5 43.5 50.7 47.7 46.4 48.2
## [226] 46.5 46.4 48.6 47.5 51.1 45.2 45.2 49.1 52.5 47.4 50.0 44.9 50.8 43.4 51.3
## [241] 47.5 52.1 47.5 52.2 45.5 49.5 44.5 50.8 49.4 46.9 48.4 51.1 48.5 55.9 47.2
## [256] 49.1 47.3 46.8 41.7 53.4 43.3 48.1 50.5 49.8 43.5 51.5 46.2 55.1 44.5 48.8
## [271] 47.2 NA 46.8 50.4 45.2 49.9 46.5 50.0 51.3 45.4 52.7 45.2 46.1 51.3 46.0
## [286] 51.3 46.6 51.7 47.0 52.0 45.9 50.5 50.3 58.0 46.4 49.2 42.4 48.5 43.2 50.6
## [301] 46.7 52.0 50.5 49.5 46.4 52.8 40.9 54.2 42.5 51.0 49.7 47.5 47.6 52.0 46.9
## [316] 53.5 49.0 46.2 50.9 45.5 50.9 50.8 50.1 49.0 51.5 49.8 48.1 51.4 45.7 50.7
## [331] 42.5 52.2 45.2 49.3 50.2 45.6 51.9 46.8 45.7 55.8 43.5 49.6 50.8 50.2Passing the penguins data frame through the structure
function str will show you the type of data for each
variable.
str(penguins)## 'data.frame': 344 obs. of 8 variables:
## $ species : chr "Adelie" "Adelie" "Adelie" "Adelie" ...
## $ island : chr "Torgersen" "Torgersen" "Torgersen" "Torgersen" ...
## $ bill_length_mm : num 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
## $ bill_depth_mm : num 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
## $ flipper_length_mm: int 181 186 195 NA 193 190 181 195 193 190 ...
## $ body_mass_g : int 3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ...
## $ sex : chr "male" "female" "female" NA ...
## $ year : int 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...‘str’ shows us that species is chr which is short for
character. This means R will treat species as a factor (in other words,
discrete data). Bill_length_mm is num which stands for
numeric. int means integer. Both num and
int variables are treated as continuous (scale) data by
R.
We can calculate the mean of bill length.
mean(penguins$bill_length_mm, na.rm = TRUE)## [1] 43.92193But we can’t use the mean function on a factor.
mean(penguins$species, na.rm = TRUE)## Warning in mean.default(penguins$species, na.rm = TRUE): argument is not numeric
## or logical: returning NA## [1] NATip: When functions won’t work
Sometimes R errors are caused by R treating a variable as a factor
when you know it’s a number. Checking what R is “thinking” with
str can help.
1.4 Indexing (square brackets)
The $ operator will specify a variable in a data frame.
You can also use indexing.
Challenge 3
There are several subtly different ways to call variables, observations and elements from data.frames using indexing:
Try out these examples and after a # in your script describe what data is returned by each one.
penguins[1]penguins[1, 1]penguins[, 1]penguins[1, ]
Solution to Challenge 3
penguins[1]## species
## 1 Adelie
## 2 Adelie
## 3 Adelie
## 4 Adelie
## 5 Adelie
## 6 Adelie
## 7 Adelie
## 8 Adelie
## 9 Adelie
## 10 Adelie
## 11 Adelie
## 12 Adelie
## 13 Adelie
## 14 Adelie
## 15 Adelie
## 16 Adelie
## 17 Adelie
## 18 Adelie
## 19 Adelie
## 20 Adelie
## 21 Adelie
## 22 Adelie
## 23 Adelie
## 24 Adelie
## 25 Adelie
## 26 Adelie
## 27 Adelie
## 28 Adelie
## 29 Adelie
## 30 Adelie
## 31 Adelie
## 32 Adelie
## 33 Adelie
## 34 Adelie
## 35 Adelie
## 36 Adelie
## 37 Adelie
## 38 Adelie
## 39 Adelie
## 40 Adelie
## 41 Adelie
## 42 Adelie
## 43 Adelie
## 44 Adelie
## 45 Adelie
## 46 Adelie
## 47 Adelie
## 48 Adelie
## 49 Adelie
## 50 Adelie
## 51 Adelie
## 52 Adelie
## 53 Adelie
## 54 Adelie
## 55 Adelie
## 56 Adelie
## 57 Adelie
## 58 Adelie
## 59 Adelie
## 60 Adelie
## 61 Adelie
## 62 Adelie
## 63 Adelie
## 64 Adelie
## 65 Adelie
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## 69 Adelie
## 70 Adelie
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## 72 Adelie
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## 75 Adelie
## 76 Adelie
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## 78 Adelie
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## 80 Adelie
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## 82 Adelie
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## 84 Adelie
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## 88 Adelie
## 89 Adelie
## 90 Adelie
## 91 Adelie
## 92 Adelie
## 93 Adelie
## 94 Adelie
## 95 Adelie
## 96 Adelie
## 97 Adelie
## 98 Adelie
## 99 Adelie
## 100 Adelie
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## 127 Adelie
## 128 Adelie
## 129 Adelie
## 130 Adelie
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## 133 Adelie
## 134 Adelie
## 135 Adelie
## 136 Adelie
## 137 Adelie
## 138 Adelie
## 139 Adelie
## 140 Adelie
## 141 Adelie
## 142 Adelie
## 143 Adelie
## 144 Adelie
## 145 Adelie
## 146 Adelie
## 147 Adelie
## 148 Adelie
## 149 Adelie
## 150 Adelie
## 151 Adelie
## 152 Adelie
## 153 Gentoo
## 154 Gentoo
## 155 Gentoo
## 156 Gentoo
## 157 Gentoo
## 158 Gentoo
## 159 Gentoo
## 160 Gentoo
## 161 Gentoo
## 162 Gentoo
## 163 Gentoo
## 164 Gentoo
## 165 Gentoo
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## 235 Gentoo
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## 237 Gentoo
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## 239 Gentoo
## 240 Gentoo
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## 245 Gentoo
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## 270 Gentoo
## 271 Gentoo
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## 274 Gentoo
## 275 Gentoo
## 276 Gentoo
## 277 Chinstrap
## 278 Chinstrap
## 279 Chinstrap
## 280 Chinstrap
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## 337 Chinstrap
## 338 Chinstrap
## 339 Chinstrap
## 340 Chinstrap
## 341 Chinstrap
## 342 Chinstrap
## 343 Chinstrap
## 344 ChinstrapCalls the data in column 1.
penguins[1, 1]## [1] "Adelie"Calls the information that is in the first row, first column.
penguins[, 1]## [1] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [7] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [13] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [19] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [25] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [31] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [37] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [43] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [49] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [55] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [61] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [67] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [73] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [79] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [85] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [91] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [97] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [103] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [109] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [115] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [121] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [127] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [133] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [139] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [145] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [151] "Adelie" "Adelie" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [157] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [163] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [169] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [175] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [181] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [187] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [193] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [199] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [205] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [211] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [217] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [223] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [229] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [235] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [241] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [247] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [253] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [259] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [265] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [271] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [277] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [283] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [289] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [295] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [301] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [307] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [313] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [319] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [325] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [331] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [337] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [343] "Chinstrap" "Chinstrap"Also, calls the first column.
penguins[1, ]## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## 1 Adelie Torgersen 39.1 18.7 181 3750
## sex year
## 1 male 2007Adapted from R for Reproducible Scientific Analysis licensed CC_BY 4.0 by The Carpentries