Artwork by @allison_horst
Tip: If on a uni computer sign into OneDrive
Before you do this challenge make sure you have decided where you will save your R project. If you are on a university computer saving it in the cloud on your uni onedrive will mean you can access it later. Check you are signed into your uni one drive: open the file explorer > C drive > Users > find your username > OneDrive - University of Leeds and sign in.
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”.
- Browse to choose where you want to save it.
- Click the “Create Project” button
For future computer labs, if you want to open this same RStudio
project now it has been created, 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.
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.
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.
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.
Remember that the .csv data file is now saved 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$species
penguins$bill_length_mmPassing the penguins data frame through the structure
function str will show you the type of data for each
variable.
str(penguins)‘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$bill_length_mm, na.rm = TRUE)But we can’t use the mean function on a factor.
mean(penguins$species, na.rm = TRUE)Tip: 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.
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, ]
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
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## 20 Adelie
## 21 Adelie
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## 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
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## 63 Adelie
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## 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
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## 160 Gentoo
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## 162 Gentoo
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## 277 Chinstrap
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## 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" "Adelie"
## [8] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [15] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [22] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [29] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [36] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [43] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [50] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [57] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [64] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [71] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [78] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [85] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [92] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [99] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [106] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [113] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [120] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [127] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [134] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [141] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
## [148] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie" "Gentoo" "Gentoo"
## [155] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [162] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [169] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [176] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [183] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [190] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [197] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [204] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [211] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [218] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [225] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [232] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [239] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [246] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [253] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [260] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [267] "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo" "Gentoo"
## [274] "Gentoo" "Gentoo" "Gentoo" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [281] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [288] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [295] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [302] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [309] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [316] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [323] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [330] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [337] "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap" "Chinstrap"
## [344] "Chinstrap"Also, calls the first column.
penguins[1, ]## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
## 1 Adelie Torgersen 39.1 18.7 181 3750 male 2007Tip: 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, there are many sources of help!
Gen AI can give you the specific help you need when your code won’t work or when you are trying to do something new in R. It is recommended that you use copilot which the university provides for all staff and students. The data you enter into this version of copilot is not shared and therefore more secure.
If you want to use GenAI go to https://uoldigital.net/copilot and use your university username and password to sign in.
Don’t rely solely on AI
You have to know the basics of R so that you can understand the code AI suggests. This means you can identify when GenAI hasn’t quite understood your prompt and why the code it is giving you isn’t working.
Try to avoid using code written by Gen AI and then getting into a spiral by repeatedly asking it to debug that code.
GenAI is only as good as the prompt you provide. Here’s some tips
The university has a guide for students on the use of Gen AI in assessments. Your assessment must include a section similar to the below.
Acknowledgement
I acknowledge the use of Copilot (https://m365.cloud.microsoft/chat) to debug R code when cleaning and analysing my data.
Every package author writes help files for their functions. Each help page is broken down into sections (Description, Usage, Arguments etc). The last section Examples is very useful.
Either of the commands below will bring up the help for the function
log.
?log
help(log)When your code doesn’t work: seek help from the R community. 9 times out of 10, the answers you are seeking have already been answered on Stack Overflow.
Other internet resources to look out for while googling are:
Adapted from R for Reproducible Scientific Analysis licensed CC_BY 4.0 by The Carpentries