Nothing
RStudio and code
In r4ds.tutorials: Tutorials for "R for Data Science"
library(learnr)
library(tutorial.helpers)
library(tidyverse)
library(knitr)
knitr::opts_chunk$set(echo = FALSE)
knitr::opts_chunk$set(out.width = '90%')
options(tutorial.exercise.timelimit = 60,
tutorial.storage = "local")
Introduction
This tutorial covers Chapter 2 Workflow: basics, Chapter 4 Workflow: code style, Chapter 6 Workflow: scripts and projects, and some material from Chapter 28 Quarto from R for Data Science (2e) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund. You will learn how to use R scripts and how to create RStudio projects.
Checking your setup
Until now, you have been typing your code into the neat boxes in our tutorials. What if you want to start working on your own?
This tutorial assumes that you have read and completed all the steps in the "Getting Started wth Tutorials" tutorial from the tutorial.helpers package. We will ask you to copy/paste the command/response so often, we will be shortening this instruction to CP/CR.
Exercise 1
You should have installed the tidyverse package, among others. If you haven't yet, run install.package('tidyverse') in the Console.
Load the tidyverse package into your R Console using the library() function.
Run search() in the Console to see the libraries that you've currently loaded. CP/CR. That is, copy and paste that command and the result into the space below.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
There should be strings such as "package:tidyverse" in the output. These are the packages that are currently loaded in your workspace.
Exercise 2
In the Console, run:
rstudioapi::readRStudioPreference(
name = "load_workspace",
default = "Code failed."
)
CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
The result should be FALSE. If the result was not FALSE, you should run this command in the Console:
tutorial.helpers::set_rstudio_settings()
This function ensures that your settings for RStudio are sensible.
Exercise 3
Look at the top left of your Console pane. Here you should see the version of R you are running. It should look something like the image below.
include_graphics("images/r_version.png")
Type R.version$version.string into the Console and hit Enter. CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
This should be the same as the version you saw at the top left of the Console pane. Make sure it is the most recently released version of R.
Exercise 4
Shortly, we will create a new RStudio project for you to work in. Before we do this, it might be useful to understand where you are right now.
In the R Console, run getwd(). CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
This function returns the "path" to your current R working directory. A path is simply the information which allows you to reach a location on your computer. In this case, if you wanted to find the folder from which R is running, you would follow the path returned by getwd().
More info on paths can be found here.
Exercise 5
Whenever you start a new RStudio project, you will want to store it in a location which you can easily find. Right now, you are likely in an arbitrary location on your computer. It would be much more useful to have a location dedicated to your RStudio projects.
From the Console, run dir.exists("~/Desktop/"). CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
If this returns TRUE, then you have a "typical" computer set up, with a Desktop in your home directory. (The tilde symbol, ~, refers to a user's home directory.) We recommend that you store your RStudio projects there. If the return value is FALSE, you will need to create a location by hand. However, more experienced computer users should feel free to ignore this advice and set up their project directories as they see fit, at their own risk.
Exercise 6
Let's make a folder, and call it projects, and store it on our desktop. From the Console, run dir.create("~/Desktop/projects/"). CP/CR. (This command will only work if dir.exists("~/Desktop/") returned TRUE.)
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
If that command works, you are all set. (If you get a message about the directory already exists, then you are also all set.) If not, you will need to create a projects directory by hand. Helpful resources can be found here for Windows, and here for macOS.
NOTE: Do not store this folder inside of a cloud storage directory such as OneDrive. This can cause heaps of unforeseen problems for you in the future.
The path to your R projects should not include any folders with spaces (or other weird characters) in their names. For example, this is a reasonable location for R projects if you are using a Mac:
/Users/dkane/Desktop/projects/
The next example is bad. Do not use spaces or weird characters!
/Users/dkane/Desktop/my projects/
R Projects
Good data scientists keep their work organized. You should place your work in different RStudio projects, which are simply folders (that is, directories) on your computer with some helper files. Each separate RStudio project is a folder within your main folder for your R work, i.e., /Users/dkane/Desktop/projects/. We use the terms "RStudio project" and "R project" interchangeably.
Exercise 1
Read all of the instructions before starting this exercise.
You will be forced to close the tutorial during project creation. You'll be able to re-open the tutorial and get back to where you left off once the project is created.
The first step is to click File -> New Project from the main menu:
include_graphics("images/new_project.png")
Select New Directory:
include_graphics("images/new_project_2.png")
Select New Project:
include_graphics("images/new_project_3.png")
Yes, it is confusing that this is the second time you have selected New Project! But that's only due to the fact that RStudio offers many options for projects.
Use analysis-1 for the "Directory name". In the "Create project as subdirectory of" box, use your new projects folder.
include_graphics("images/new_project_4.png")
Ensure that all the check boxes are unchecked. Click "Create Project."
Now that you have read all the directions, we can go through the steps. But note that, when you first click File -> New Project, you will get the "Terminate Jobs" warning. (Recall the discussion from the "Getting Started with Tutorials" tutorial in the tutorial.helpers package.)
And that is OK! Go ahead and click "Terminate Jobs" and follow the above steps. You will need to restart the tutorial. Your R session will be located in this new project. For the rest of this tutorial, we will be working in analysis-1.
Once you restart the tutorial, run getwd() in the Console. CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
This path should now be different than our previous path, similar to the path below. Creating/opening an R project moves the R session to that directory.
/Users/dkane/Desktop/projects/analysis-1/
If your path does not include your projects folder, you probably did not fill in the "Create project as subdirectory of:" box correctly. It should be the path to your projects folder, including projects itself.
Exercise 2
Run list.files(). CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
list.files() returns files contained in your current R project. This should return only the file analysis-1.Rproj.
Keeping all the files associated with a given project --- input data, R scripts, analytic results, and figures --- together in one directory is such a wise and common practice that RStudio has built-in support for this via projects.
Everything you need is in one place and cleanly separated from all the other projects that you are working on.
Exercise 3
In the Console, run:
tutorial.helpers::show_file("analysis-1.Rproj")
CP/CR.
show_file() is a function which we have written which makes it easy for you to demonstrate, and for us to check, that you have done what we wanted you to do.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
This should return lines containing some basic settings for your R project. For example, RestoreWorkspace is set to Default. Each R project has its own Rproj file in which its settings are recorded.
Indeed, the definition of an R project --- from the point of view of RStudio --- is a directory with an associated Rproj file. ("Folder" and "directory" are used interchangeably.)
Script 1
So far, we have only worked in the R Console, but it's quite difficult to type more than a few lines of code into the Console at once. One solution is to use R scripts, files which contain a permanent copy of our code.
Exercise 1
Create an R script by clicking the File -> New File drop down menu at the top left of your RStudio Session, and then selecting R Script.
An untitled, empty file should pop up above the R Console in the Source pane. By opening a script, you have caused the Source pane to open, the fourth common pane within RStudio. (The three other panes are Console, Environment and Output. Within each pane, there are usually multiple tabs.)
You can also create a new R script using the keyboard shortcut Cmd/Ctrl + Shift + N.
Type 5 * 5 into your R script file. Save the file. Name it script-1.R. (Note that R will provide the .R suffix, so you only need to type script-1.)
In the Console, run:
tutorial.helpers::show_file("script-1.R")
CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
You might have gotten this warning:
Warning message:
In readLines("script-1.R") : incomplete final line found on 'script-1.R'
This means that your file need a blank line at the end. As a rule of thumb, it is always a good idea to have the last line of any text file be blank.
R scripts are permanent copies of your code. You can save them and also work with them interactively.
Exercise 2
Note the "Source" button, which is in the top right corner of the Source pane. If you hover your cursor over it, you will see "Source the contents of the active document." Press the Source button. (We will sometimes shorten the instruction "press the source button" to just "source" the script.)
Note what happens in the Console. CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 2)
You answer should look like this:
> source("~/Desktop/projects/analysis-1/script-1.R", echo=TRUE)
> 5 * 5
[1] 25
>
Whenever you run an R script file by hitting the "Source" button, Rstudio will send all the code in the file to the R Console. Because echo=TRUE, the Console prints out both the code itself (that is the "echo") and the result of executing that code.
Exercise 3
There are often "shortcut key" combinations which perform the same task as clicking a button. Placing your cursor inside the R script window and using the keyboard shortcut (Cmd/Ctrl) + Shift + (Return/Enter) is the same as hitting the "Source" button.
Note that Return is the name of a Mac key and Enter the name of the corresponding Windows key, just like Cmd ("Command") is a Mac key and Ctrl ("Control") is the equivalent Windows key. In other words, on the Mac, we hit Cmd + Shift + Return while on Windows it is Ctrl + Shift + Enter. However, typing Return/Enter all the time is annoying, so, going forward, we will just use Enter. Mac users are expected to remember that this means the Return key.
Try it now. CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 2)
Using shortcut keys is quicker and more professional than clicking buttons.
Exercise 4
In your R script, type 6 + 3 in the line after 5 * 5. Save the file with Cmd/Ctrl + S. (Note that the S in that shortcut key just refers to the "S" key on your keyboard. You don't need to capitalize it.)
"Source" this file. CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 2)
The answer is what you might expect:
> source("~/Desktop/projects/analysis-1/script-1.R", echo=TRUE)
> 5 * 5
[1] 25
> 6 + 3
[1] 9
>
Each line is echoed. Each line is executed. Finally, the results of each line, if there are any, are printed.
The output you see is the same as what would happen if you copied each line to the Console and hit Enter after each one.
Exercise 5
Instead of sourcing the entire file, we can just execute (or "run") a single line. In script-1.R, place your cursor on the same line as 6 + 3 and click the "Run" button to the left of the "Source" button. CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 2)
You should get:
> 6 + 3
[1] 9
>
Instead of both lines in the script executing, only the second line does. And the process by which this happens has nothing to do with the entire script. Run is not the same thing as Source. With Run, you are just copy/pasting one line from the script to the Console at a time. With Source, you're copy/pasting all lines from the script, but executing them one at a time.
Exercise 6
The shortcut key for running a single line of code is Cmd/Ctrl + Enter. In your R script, place your cursor on the same line as 5 * 5 and hit Cmd/Ctrl + Enter. CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 2)
Only the first line of the script is executed. Cmd/Ctrl + Enter is probably the single most commonly used shortcut key because it is very handy to execute your code line-by-line.
Exercise 7
Go back to the first line in your R script. Change 5 * 5 to x <- 5 * 5, thereby creating an object named x with a value of 25. Save the file. Click the "Source" button. CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
Your answer should look like:
> source("~/Desktop/projects/analysis-1/script-1.R", echo=TRUE)
> x <- 5 * 5
> 6 + 3
[1] 9
>
Note how the x <- 5 * 5 is executed (and echoed) but nothing is printed. The assignment operator (<-) does not generate a printed result.
Exercise 8
In the Console tab, run ls(). CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 2)
ls() returns a list of objects present in your environment. These are the same objects displayed in your Environment tab in the Environment pane in the upper right window in RStudio. You should see that it returned x, since you defined x using your R script.
Script 2
Let's create this plot:
hist_p <- ggplot(data = diamonds,
mapping = aes(x = carat)) +
geom_histogram(bins = 100,
color = "white") +
scale_y_continuous(labels = scales::comma_format()) +
labs(title = "Histogram of Carat (Weight) among 50,000 Diamonds",
subtitle = "Carats just at or above meaningful numbers are very common",
x = "Carat",
y = "Number",
caption = "diamonds data set from ggplot2 package")
hist_p
Exercise 1
Click File -> New File -> R Script. At the top of the Source pane, the file should be called Untitled1 (or something similar).
Click the "Save current document" icon -- the disk image to the left of the "Source on Save" box and below the script title. Or use the shortcut Cmd/Ctrl + S to save the script. Let's call this file script-2. The .R suffix is added automatically.
In the Console, run list.files(). CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 2)
Save your scripts (with informative names) in the project, edit them, run them line-by-line or in their entirety. Restart R frequently to make sure you’ve captured everything in your scripts.
Scripts and projects give you a solid workflow that will serve you well in the future. Always remember to create one RStudio project for each data analysis project. That project may contain multiple scripts files, along with other material.
Exercise 2
Restart your R session using Session -> Restart R. This will cause R to bring up the "Terminate Jobs" box. Go ahead and terminate the tutorial. Then, restart the tutorial once R restarts.
From the Console, run list.files(). CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
All the files we had from before --- analysis-1.Rproj, script-1.R, and script-2.R --- still exist. Our work, if we have saved it, is preserved even when R (or RStudio) restarts.
Exercise 3
From the Console, run ls(). CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
x is gone! The environment --- the place in the computer where R creates and uses objects --- is cleaned, by default, each time R restarts. This is a good thing! For our work to be "reproducible," we want to be able to start from nothing except our code.
Exercise 4
At the top of script-2.R, type
library(tidyverse)
You will see, in the top bar of the Source pane, that script-2.R, the name of the file, is now a different color, and has an asterisk. This indicates that there are unsaved changes in the file.
Run the entire file with Ctrl + Shift + Enter. Note how much material --- the "Attaching core tidyverse packages" and so on --- is produced because, by default, we are sourcing the file with echo=TRUE.
Run search() in the Console. CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 2)
This function returns a list of loaded packages. This should include the string package:tidyverse.
Exercise 5
Skip a line and add the following comment to the file:
# This is an example of a code comment within an R script.
Save the file.
In the Console, run:
tutorial.helpers::show_file("script-2.R")`
CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 2)
Did you remember to have a blank line at the end of the file? If not, you got a warning. Always have a blank line at the end of any text file.
Comments begin with a hash (also known as a "pound sign"): #. R will ignore everything on the line to the right of the hash when trying to run the code.
Comments are an extremely useful tool when writing code. If they're placed well, it becomes much easier to debug (find and fix mistakes) if something goes wrong.
There are a few "guidelines" to writing comments, for example:
- Comments should be used sparingly.
- Comments are unnecessary and in fact distracting if they state the obvious.
- Comments that contradict the code are worse than no comments. Always make a priority of keeping the comments up-to-date when the code changes!
Typically, when reading code, you should be able to answer 3 questions fairly easily, for every line:
- What is this code doing,
- How is it doing it, and
- Why is it being done.
The what and how can be deduced from the code itself. The why, though, is a bit trickier, and it is highly recommended that you use comments to make it easier to tell why something is being done.
# This is an example of a poor comment:
...
x <- x + 1 # Increment x
...
# This is an example of a good comment:
...
x <- x + 1 # Account for borders
...
Exercise 6
Change the code comment in script-2.R from "This is an example of a code comment within an R script." to a one sentence description of one of your recent meals. Save the file.
In the Console, run:
tutorial.helpers::show_file("script-2.R")
CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 2)
Get in the habit of adding comments.
Figuring out why something was done is much more difficult than understanding how it was done. For example, geom_smooth() has an argument called span, which controls the smoothness of the curve, with larger values yielding a smoother curve. Suppose you decide to change the value of span from its default of 0.75 to 0.9: it’s easy for a future reader to understand what is happening, but unless you note your thinking in a comment, no one will understand why you changed the default.
Exercise 7
Skip a line after your comment. Call ggplot(), setting data to diamonds and mapping x to carat within aes().
Run the entire file again. Note the sloppiness of the word "run" in this instruction. It is not always clear what "run" means. For example, it does not mean to click the "Run" button! In general, "run" means to "execute" all the code in the file, most commonly by either using Ctrl + Shift + Enter or by pressing the "Source" button.
This should generate a blank plot in the Plots tab in the Output pane at the bottom right of your RStudio session.
In the Console, run:
tutorial.helpers::show_file("script-2.R")
CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 2)
ggplot() will generate a blank plot, at least until a geom layer is added.
Exercise 8
Add a layer with geom_histogram(). Change the border color in our graph by setting color to "white" within geom_histogram, and change the number of columns in our plot by setting the bins argument to 100. Remember that, to connect the ggplot() call to geom_histogram(), we need a +.
Run the file. Again, to "run" a script generally means to "execute" it, meaning sending it to the R process. We have learned two ways to (easily) execute an entire file: pressing the "Source" button or hitting the associated shortcut key: Ctrl + Shift + Enter.
This should generate bars on your plot. Did you remember to place a + after the ggplot() call and before the geom_histogram() layer?
In the Console, run:
tutorial.helpers::show_file("script-2.R")
CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 2)
The color argument used here modifies the border color of our columns. To change the fill color, use the fill argument.
Exercise 9
It would be nice if the numbers on the Y-axis were formatted better, i.e., with commas. The easiest way to do that is to add this line to the script.
scale_y_continuous(labels = scales::comma_format())
Do so. Run the file. Note that you don't need to save the file first. If you execute a file with either "Source" or Ctrl + Shift + Enter, RStudio will save the file automatically.
Don't forget that you will need a + after the call to geom_histogram().
In the Console, run:
tutorial.helpers::show_file("script-2.R")
CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
We use scale_y_continuous() (and scale_x_continuous()) to modify the labels and the breaks in numeric axes. The scales package provides a variety of useful formatting tools, including comma_format(), dollar_format() and others.
Exercise 10
Now let's make our graph look a little nicer by adding a labs() layer with an appropriate title, subtitle, and axis labels.
Reminder, this is what our graph should look like
hist_p
Run your R script file to see your completed plot. Again, note the language here! Above, we made a distinction between the "Source" and "Run" buttons above --- and that distinction is real. However, in casual language, we use the word "run" to generically mean "Tell the computer to execute the commands in this file." We don't necessarily care how that is achieved.
In the Console, run:
tutorial.helpers::show_file("script-2.R")
CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 8)
Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread. Even as a very new programmer, it’s a good idea to work on your code style. Using a consistent style makes it easier for others (including future-you!) to read your work and is particularly important if you need to get help from someone else.
Note the nice formatting. After the ggplot() call, the next two commands --- geom_histogram() and labs() --- are both indented the same amount. Similarly, arguments to a given function --- like bins and color --- are lined up with each other.
Exercise 11
Now we have the code which creates a plot in script-2.R. How do we use this plot for other things? Right now we have to run script-2.R every time we want to see the plot, which is a little inconvenient.
We can use the ggsave() function. Type ?ggsave in the R Console. This will open the help page for ggsave(). Copy and paste the Description from the help page into the space below.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 5)
ggsave() is used to save plots as individual files, separate from the code which created the plot. In this case we will be using the "png" format.
Exercise 12
We want to save our entire graph to an object, which can be done by setting our plot equal to a variable. Call this variable hist_p.
Now using the ggsave() function we can create a png of our plot from our code. In ggsave(), set plot to hist_p, file to "diamonds.png".
Your entire file should look like:
library(tidyverse)
# Had an OK filet mignon at Cheesecake Factory.
hist_p <- ggplot(data = diamonds,
mapping = aes(x = carat)) +
geom_histogram(bins = 100,
color = "white") +
scale_y_continuous(labels = scales::comma_format()) +
labs(title = "Histogram of Carat (Weight) among 50,000 Diamonds",
subtitle = "Carats just at or above meaningful numbers are very common",
x = "Carat",
y = "Count",
caption = "diamonds data set from ggplot2 package")
ggsave(plot = hist_p, file = "diamonds.png")
Run your code.
In the Console, run list.files(). CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 2)
You should see the diamonds.png file which contains your plot. Look in the bottom right corner of your workspace (i.e., in the Output pane) in the Files tab. Click on the diamonds.png file to view your plot.
If the title text flows out of the image, you might want to modify the scale argument in ggsave() to find a better image size. scale is set to 1 by default.
Script 3
Let's make another plot, this time in a script inside of a different RStudio project. We'll be making this plot:
car_plot <- ggplot(mpg, aes(displ, hwy, colour = class)) +
geom_point()
car_plot
Exercise 1
Type getwd() into the Console. CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 5)
Your answer should look like this:
"/Users/dkane/Desktop/projects/project-1"
Somewhere in the path should be project-1, the name of the RStudio project you're in right now.
Exercise 2
Type list.files() into the Console. CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
This command shows the files in the directory.
Note that "project" has multiple meanings. First, "project" is a generic term for an RStudio project, generally with a sensible name. Second, project-1, note the use of typewriter font, is the name of a specific RStudio project. Third, project-1 --- the same string in the same font --- can refer to the directory in which the RStudio project is located.
Exercise 3
Do not start this exercise until you have read all the instructions. This exercise will force you to close the tutorial, so you won't be able to access the instructions while completing the exercise. Once you're done with all the steps in this exercise, re-open the tutorial to continue.
We want to make the plot in a script that's inside of a different RStudio project. To do that, we need to actually create the new project! The way to do that is quite simple, as RStudio has a handy menu for managing projects:
include_graphics("images/rs-proj-menu-locator.png")
To make a new project, press the "New Project..." button. You'll be asked to close the tutorial here. Do so, and you'll soon see the New Project Wizard.
include_graphics('images/new_proj_wizard.png')
We want to make a project in a new directory, so select that option.
Now, you'll be asked for a project type. Keep it simple and select "New Project". Name it project-2 and create it as a subdirectory of (folder inside of) your projects folder. Make sure all the checkboxes are unchecked, and hit "Create Project"!
include_graphics('images/new_proj_type.png')
Don't forget to open up the tutorial from the menu in the top right again once you're done.
Exercise 4
Type getwd() into the Console. CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 5)
Your answer should look like this:
"/Users/dkane/Desktop/projects/project-2"
Somewhere in the path should be project-2, the name of the RStudio project you just made and are now in.
Exercise 5
Now, we have to create the script where we'll be creating our plot.
In the menu bar on the top of the window, press File -> New File -> R Script. Type 5 * 5 and a newline into the file and hit Ctrl/Cmd + S to save it. Save it as script-3.r (the .r is automatically supplied by the dialog box when you save, so you only need to type script-3).
In the console, run list.files(). CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
You should see script-3.r somewhere in the output.
Exercise 6
Go back to editing script-3.r. Get rid of everything that's already there. Copy-paste the below code into the script, and save with Ctrl/Cmd + S.
car_plot <- ggplot(mpg, aes(displ, hwy, colour = class)) +
geom_point()
car_plot
Don't forget to have an empty line at the end of the file!
Once you're done, source the script with the "Source" button in the top right.
Back in the Console, run tutorial.helpers::show_file('script-3.r'). CP/CR.
question_text(NULL,
answer(NULL, correct = TRUE),
allow_retry = TRUE,
try_again_button = "Edit Answer",
incorrect = NULL,
rows = 3)
The output should have the same chunk of code as the one you were given to copy-paste above.
Summary
This tutorial covered Chapter 2 Workflow: basics, Chapter 4 Workflow: code style, Chapter 6 Workflow: scripts and projects, and some material in Chapter 28 Quarto from R for Data Science (2e) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund. You learned how to use R scripts and how to create RStudio projects.
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library(learnr) library(tutorial.helpers) library(tidyverse) library(knitr) knitr::opts_chunk$set(echo = FALSE) knitr::opts_chunk$set(out.width = '90%') options(tutorial.exercise.timelimit = 60, tutorial.storage = "local")
Introduction
This tutorial covers Chapter 2 Workflow: basics, Chapter 4 Workflow: code style, Chapter 6 Workflow: scripts and projects, and some material from Chapter 28 Quarto from R for Data Science (2e) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund. You will learn how to use R scripts and how to create RStudio projects.
Checking your setup
Until now, you have been typing your code into the neat boxes in our tutorials. What if you want to start working on your own?
This tutorial assumes that you have read and completed all the steps in the "Getting Started wth Tutorials" tutorial from the tutorial.helpers package. We will ask you to copy/paste the command/response so often, we will be shortening this instruction to CP/CR.
Exercise 1
You should have installed the tidyverse package, among others. If you haven't yet, run install.package('tidyverse') in the Console.
Load the tidyverse package into your R Console using the library() function.
Run search() in the Console to see the libraries that you've currently loaded. CP/CR. That is, copy and paste that command and the result into the space below.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
There should be strings such as "package:tidyverse" in the output. These are the packages that are currently loaded in your workspace.
Exercise 2
In the Console, run:
rstudioapi::readRStudioPreference( name = "load_workspace", default = "Code failed." )
CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
The result should be FALSE. If the result was not FALSE, you should run this command in the Console:
tutorial.helpers::set_rstudio_settings()
This function ensures that your settings for RStudio are sensible.
Exercise 3
Look at the top left of your Console pane. Here you should see the version of R you are running. It should look something like the image below.
include_graphics("images/r_version.png")
Type R.version$version.string into the Console and hit Enter. CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
This should be the same as the version you saw at the top left of the Console pane. Make sure it is the most recently released version of R.
Exercise 4
Shortly, we will create a new RStudio project for you to work in. Before we do this, it might be useful to understand where you are right now.
In the R Console, run getwd(). CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
This function returns the "path" to your current R working directory. A path is simply the information which allows you to reach a location on your computer. In this case, if you wanted to find the folder from which R is running, you would follow the path returned by getwd().
More info on paths can be found here.
Exercise 5
Whenever you start a new RStudio project, you will want to store it in a location which you can easily find. Right now, you are likely in an arbitrary location on your computer. It would be much more useful to have a location dedicated to your RStudio projects.
From the Console, run dir.exists("~/Desktop/"). CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
If this returns TRUE, then you have a "typical" computer set up, with a Desktop in your home directory. (The tilde symbol, ~, refers to a user's home directory.) We recommend that you store your RStudio projects there. If the return value is FALSE, you will need to create a location by hand. However, more experienced computer users should feel free to ignore this advice and set up their project directories as they see fit, at their own risk.
Exercise 6
Let's make a folder, and call it projects, and store it on our desktop. From the Console, run dir.create("~/Desktop/projects/"). CP/CR. (This command will only work if dir.exists("~/Desktop/") returned TRUE.)
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
If that command works, you are all set. (If you get a message about the directory already exists, then you are also all set.) If not, you will need to create a projects directory by hand. Helpful resources can be found here for Windows, and here for macOS.
NOTE: Do not store this folder inside of a cloud storage directory such as OneDrive. This can cause heaps of unforeseen problems for you in the future.
The path to your R projects should not include any folders with spaces (or other weird characters) in their names. For example, this is a reasonable location for R projects if you are using a Mac:
/Users/dkane/Desktop/projects/
The next example is bad. Do not use spaces or weird characters!
/Users/dkane/Desktop/my projects/
R Projects
Good data scientists keep their work organized. You should place your work in different RStudio projects, which are simply folders (that is, directories) on your computer with some helper files. Each separate RStudio project is a folder within your main folder for your R work, i.e., /Users/dkane/Desktop/projects/. We use the terms "RStudio project" and "R project" interchangeably.
Exercise 1
Read all of the instructions before starting this exercise.
You will be forced to close the tutorial during project creation. You'll be able to re-open the tutorial and get back to where you left off once the project is created.
The first step is to click File -> New Project from the main menu:
include_graphics("images/new_project.png")
Select New Directory:
include_graphics("images/new_project_2.png")
Select New Project:
include_graphics("images/new_project_3.png")
Yes, it is confusing that this is the second time you have selected New Project! But that's only due to the fact that RStudio offers many options for projects.
Use analysis-1 for the "Directory name". In the "Create project as subdirectory of" box, use your new projects folder.
include_graphics("images/new_project_4.png")
Ensure that all the check boxes are unchecked. Click "Create Project."
Now that you have read all the directions, we can go through the steps. But note that, when you first click File -> New Project, you will get the "Terminate Jobs" warning. (Recall the discussion from the "Getting Started with Tutorials" tutorial in the tutorial.helpers package.)
And that is OK! Go ahead and click "Terminate Jobs" and follow the above steps. You will need to restart the tutorial. Your R session will be located in this new project. For the rest of this tutorial, we will be working in analysis-1.
Once you restart the tutorial, run getwd() in the Console. CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
This path should now be different than our previous path, similar to the path below. Creating/opening an R project moves the R session to that directory.
/Users/dkane/Desktop/projects/analysis-1/
If your path does not include your projects folder, you probably did not fill in the "Create project as subdirectory of:" box correctly. It should be the path to your projects folder, including projects itself.
Exercise 2
Run list.files(). CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
list.files() returns files contained in your current R project. This should return only the file analysis-1.Rproj.
Keeping all the files associated with a given project --- input data, R scripts, analytic results, and figures --- together in one directory is such a wise and common practice that RStudio has built-in support for this via projects.
Everything you need is in one place and cleanly separated from all the other projects that you are working on.
Exercise 3
In the Console, run:
tutorial.helpers::show_file("analysis-1.Rproj")
CP/CR.
show_file() is a function which we have written which makes it easy for you to demonstrate, and for us to check, that you have done what we wanted you to do.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
This should return lines containing some basic settings for your R project. For example, RestoreWorkspace is set to Default. Each R project has its own Rproj file in which its settings are recorded.
Indeed, the definition of an R project --- from the point of view of RStudio --- is a directory with an associated Rproj file. ("Folder" and "directory" are used interchangeably.)
Script 1
So far, we have only worked in the R Console, but it's quite difficult to type more than a few lines of code into the Console at once. One solution is to use R scripts, files which contain a permanent copy of our code.
Exercise 1
Create an R script by clicking the File -> New File drop down menu at the top left of your RStudio Session, and then selecting R Script.
An untitled, empty file should pop up above the R Console in the Source pane. By opening a script, you have caused the Source pane to open, the fourth common pane within RStudio. (The three other panes are Console, Environment and Output. Within each pane, there are usually multiple tabs.)
You can also create a new R script using the keyboard shortcut Cmd/Ctrl + Shift + N.
Type 5 * 5 into your R script file. Save the file. Name it script-1.R. (Note that R will provide the .R suffix, so you only need to type script-1.)
In the Console, run:
tutorial.helpers::show_file("script-1.R")
CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
You might have gotten this warning:
Warning message:
In readLines("script-1.R") : incomplete final line found on 'script-1.R'
This means that your file need a blank line at the end. As a rule of thumb, it is always a good idea to have the last line of any text file be blank.
R scripts are permanent copies of your code. You can save them and also work with them interactively.
Exercise 2
Note the "Source" button, which is in the top right corner of the Source pane. If you hover your cursor over it, you will see "Source the contents of the active document." Press the Source button. (We will sometimes shorten the instruction "press the source button" to just "source" the script.)
Note what happens in the Console. CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 2)
You answer should look like this:
> source("~/Desktop/projects/analysis-1/script-1.R", echo=TRUE)
> 5 * 5
[1] 25
>
Whenever you run an R script file by hitting the "Source" button, Rstudio will send all the code in the file to the R Console. Because echo=TRUE, the Console prints out both the code itself (that is the "echo") and the result of executing that code.
Exercise 3
There are often "shortcut key" combinations which perform the same task as clicking a button. Placing your cursor inside the R script window and using the keyboard shortcut (Cmd/Ctrl) + Shift + (Return/Enter) is the same as hitting the "Source" button.
Note that Return is the name of a Mac key and Enter the name of the corresponding Windows key, just like Cmd ("Command") is a Mac key and Ctrl ("Control") is the equivalent Windows key. In other words, on the Mac, we hit Cmd + Shift + Return while on Windows it is Ctrl + Shift + Enter. However, typing Return/Enter all the time is annoying, so, going forward, we will just use Enter. Mac users are expected to remember that this means the Return key.
Try it now. CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 2)
Using shortcut keys is quicker and more professional than clicking buttons.
Exercise 4
In your R script, type 6 + 3 in the line after 5 * 5. Save the file with Cmd/Ctrl + S. (Note that the S in that shortcut key just refers to the "S" key on your keyboard. You don't need to capitalize it.)
"Source" this file. CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 2)
The answer is what you might expect:
> source("~/Desktop/projects/analysis-1/script-1.R", echo=TRUE)
> 5 * 5
[1] 25
> 6 + 3
[1] 9
>
Each line is echoed. Each line is executed. Finally, the results of each line, if there are any, are printed.
The output you see is the same as what would happen if you copied each line to the Console and hit Enter after each one.
Exercise 5
Instead of sourcing the entire file, we can just execute (or "run") a single line. In script-1.R, place your cursor on the same line as 6 + 3 and click the "Run" button to the left of the "Source" button. CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 2)
You should get:
> 6 + 3 [1] 9 >
Instead of both lines in the script executing, only the second line does. And the process by which this happens has nothing to do with the entire script. Run is not the same thing as Source. With Run, you are just copy/pasting one line from the script to the Console at a time. With Source, you're copy/pasting all lines from the script, but executing them one at a time.
Exercise 6
The shortcut key for running a single line of code is Cmd/Ctrl + Enter. In your R script, place your cursor on the same line as 5 * 5 and hit Cmd/Ctrl + Enter. CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 2)
Only the first line of the script is executed. Cmd/Ctrl + Enter is probably the single most commonly used shortcut key because it is very handy to execute your code line-by-line.
Exercise 7
Go back to the first line in your R script. Change 5 * 5 to x <- 5 * 5, thereby creating an object named x with a value of 25. Save the file. Click the "Source" button. CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
Your answer should look like:
> source("~/Desktop/projects/analysis-1/script-1.R", echo=TRUE) > x <- 5 * 5 > 6 + 3 [1] 9 >
Note how the x <- 5 * 5 is executed (and echoed) but nothing is printed. The assignment operator (<-) does not generate a printed result.
Exercise 8
In the Console tab, run ls(). CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 2)
ls() returns a list of objects present in your environment. These are the same objects displayed in your Environment tab in the Environment pane in the upper right window in RStudio. You should see that it returned x, since you defined x using your R script.
Script 2
Let's create this plot:
hist_p <- ggplot(data = diamonds, mapping = aes(x = carat)) + geom_histogram(bins = 100, color = "white") + scale_y_continuous(labels = scales::comma_format()) + labs(title = "Histogram of Carat (Weight) among 50,000 Diamonds", subtitle = "Carats just at or above meaningful numbers are very common", x = "Carat", y = "Number", caption = "diamonds data set from ggplot2 package") hist_p
Exercise 1
Click File -> New File -> R Script. At the top of the Source pane, the file should be called Untitled1 (or something similar).
Click the "Save current document" icon -- the disk image to the left of the "Source on Save" box and below the script title. Or use the shortcut Cmd/Ctrl + S to save the script. Let's call this file script-2. The .R suffix is added automatically.
In the Console, run list.files(). CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 2)
Save your scripts (with informative names) in the project, edit them, run them line-by-line or in their entirety. Restart R frequently to make sure you’ve captured everything in your scripts.
Scripts and projects give you a solid workflow that will serve you well in the future. Always remember to create one RStudio project for each data analysis project. That project may contain multiple scripts files, along with other material.
Exercise 2
Restart your R session using Session -> Restart R. This will cause R to bring up the "Terminate Jobs" box. Go ahead and terminate the tutorial. Then, restart the tutorial once R restarts.
From the Console, run list.files(). CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
All the files we had from before --- analysis-1.Rproj, script-1.R, and script-2.R --- still exist. Our work, if we have saved it, is preserved even when R (or RStudio) restarts.
Exercise 3
From the Console, run ls(). CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
x is gone! The environment --- the place in the computer where R creates and uses objects --- is cleaned, by default, each time R restarts. This is a good thing! For our work to be "reproducible," we want to be able to start from nothing except our code.
Exercise 4
At the top of script-2.R, type
library(tidyverse)
You will see, in the top bar of the Source pane, that script-2.R, the name of the file, is now a different color, and has an asterisk. This indicates that there are unsaved changes in the file.
Run the entire file with Ctrl + Shift + Enter. Note how much material --- the "Attaching core tidyverse packages" and so on --- is produced because, by default, we are sourcing the file with echo=TRUE.
Run search() in the Console. CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 2)
This function returns a list of loaded packages. This should include the string package:tidyverse.
Exercise 5
Skip a line and add the following comment to the file:
# This is an example of a code comment within an R script.
Save the file.
In the Console, run:
tutorial.helpers::show_file("script-2.R")`
CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 2)
Did you remember to have a blank line at the end of the file? If not, you got a warning. Always have a blank line at the end of any text file.
Comments begin with a hash (also known as a "pound sign"): #. R will ignore everything on the line to the right of the hash when trying to run the code.
Comments are an extremely useful tool when writing code. If they're placed well, it becomes much easier to debug (find and fix mistakes) if something goes wrong.
There are a few "guidelines" to writing comments, for example:
- Comments should be used sparingly.
- Comments are unnecessary and in fact distracting if they state the obvious.
- Comments that contradict the code are worse than no comments. Always make a priority of keeping the comments up-to-date when the code changes!
Typically, when reading code, you should be able to answer 3 questions fairly easily, for every line:
- What is this code doing,
- How is it doing it, and
- Why is it being done.
The what and how can be deduced from the code itself. The why, though, is a bit trickier, and it is highly recommended that you use comments to make it easier to tell why something is being done.
# This is an example of a poor comment: ... x <- x + 1 # Increment x ... # This is an example of a good comment: ... x <- x + 1 # Account for borders ...
Exercise 6
Change the code comment in script-2.R from "This is an example of a code comment within an R script." to a one sentence description of one of your recent meals. Save the file.
In the Console, run:
tutorial.helpers::show_file("script-2.R")
CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 2)
Get in the habit of adding comments.
Figuring out why something was done is much more difficult than understanding how it was done. For example, geom_smooth() has an argument called span, which controls the smoothness of the curve, with larger values yielding a smoother curve. Suppose you decide to change the value of span from its default of 0.75 to 0.9: it’s easy for a future reader to understand what is happening, but unless you note your thinking in a comment, no one will understand why you changed the default.
Exercise 7
Skip a line after your comment. Call ggplot(), setting data to diamonds and mapping x to carat within aes().
Run the entire file again. Note the sloppiness of the word "run" in this instruction. It is not always clear what "run" means. For example, it does not mean to click the "Run" button! In general, "run" means to "execute" all the code in the file, most commonly by either using Ctrl + Shift + Enter or by pressing the "Source" button.
This should generate a blank plot in the Plots tab in the Output pane at the bottom right of your RStudio session.
In the Console, run:
tutorial.helpers::show_file("script-2.R")
CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 2)
ggplot() will generate a blank plot, at least until a geom layer is added.
Exercise 8
Add a layer with geom_histogram(). Change the border color in our graph by setting color to "white" within geom_histogram, and change the number of columns in our plot by setting the bins argument to 100. Remember that, to connect the ggplot() call to geom_histogram(), we need a +.
Run the file. Again, to "run" a script generally means to "execute" it, meaning sending it to the R process. We have learned two ways to (easily) execute an entire file: pressing the "Source" button or hitting the associated shortcut key: Ctrl + Shift + Enter.
This should generate bars on your plot. Did you remember to place a + after the ggplot() call and before the geom_histogram() layer?
In the Console, run:
tutorial.helpers::show_file("script-2.R")
CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 2)
The color argument used here modifies the border color of our columns. To change the fill color, use the fill argument.
Exercise 9
It would be nice if the numbers on the Y-axis were formatted better, i.e., with commas. The easiest way to do that is to add this line to the script.
scale_y_continuous(labels = scales::comma_format())
Do so. Run the file. Note that you don't need to save the file first. If you execute a file with either "Source" or Ctrl + Shift + Enter, RStudio will save the file automatically.
Don't forget that you will need a + after the call to geom_histogram().
In the Console, run:
tutorial.helpers::show_file("script-2.R")
CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
We use scale_y_continuous() (and scale_x_continuous()) to modify the labels and the breaks in numeric axes. The scales package provides a variety of useful formatting tools, including comma_format(), dollar_format() and others.
Exercise 10
Now let's make our graph look a little nicer by adding a labs() layer with an appropriate title, subtitle, and axis labels.
Reminder, this is what our graph should look like
hist_p
Run your R script file to see your completed plot. Again, note the language here! Above, we made a distinction between the "Source" and "Run" buttons above --- and that distinction is real. However, in casual language, we use the word "run" to generically mean "Tell the computer to execute the commands in this file." We don't necessarily care how that is achieved.
In the Console, run:
tutorial.helpers::show_file("script-2.R")
CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 8)
Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread. Even as a very new programmer, it’s a good idea to work on your code style. Using a consistent style makes it easier for others (including future-you!) to read your work and is particularly important if you need to get help from someone else.
Note the nice formatting. After the ggplot() call, the next two commands --- geom_histogram() and labs() --- are both indented the same amount. Similarly, arguments to a given function --- like bins and color --- are lined up with each other.
Exercise 11
Now we have the code which creates a plot in script-2.R. How do we use this plot for other things? Right now we have to run script-2.R every time we want to see the plot, which is a little inconvenient.
We can use the ggsave() function. Type ?ggsave in the R Console. This will open the help page for ggsave(). Copy and paste the Description from the help page into the space below.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 5)
ggsave() is used to save plots as individual files, separate from the code which created the plot. In this case we will be using the "png" format.
Exercise 12
We want to save our entire graph to an object, which can be done by setting our plot equal to a variable. Call this variable hist_p.
Now using the ggsave() function we can create a png of our plot from our code. In ggsave(), set plot to hist_p, file to "diamonds.png".
Your entire file should look like:
library(tidyverse) # Had an OK filet mignon at Cheesecake Factory. hist_p <- ggplot(data = diamonds, mapping = aes(x = carat)) + geom_histogram(bins = 100, color = "white") + scale_y_continuous(labels = scales::comma_format()) + labs(title = "Histogram of Carat (Weight) among 50,000 Diamonds", subtitle = "Carats just at or above meaningful numbers are very common", x = "Carat", y = "Count", caption = "diamonds data set from ggplot2 package") ggsave(plot = hist_p, file = "diamonds.png")
Run your code.
In the Console, run list.files(). CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 2)
You should see the diamonds.png file which contains your plot. Look in the bottom right corner of your workspace (i.e., in the Output pane) in the Files tab. Click on the diamonds.png file to view your plot.
If the title text flows out of the image, you might want to modify the scale argument in ggsave() to find a better image size. scale is set to 1 by default.
Script 3
Let's make another plot, this time in a script inside of a different RStudio project. We'll be making this plot:
car_plot <- ggplot(mpg, aes(displ, hwy, colour = class)) + geom_point() car_plot
Exercise 1
Type getwd() into the Console. CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 5)
Your answer should look like this:
"/Users/dkane/Desktop/projects/project-1"
Somewhere in the path should be project-1, the name of the RStudio project you're in right now.
Exercise 2
Type list.files() into the Console. CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
This command shows the files in the directory.
Note that "project" has multiple meanings. First, "project" is a generic term for an RStudio project, generally with a sensible name. Second, project-1, note the use of typewriter font, is the name of a specific RStudio project. Third, project-1 --- the same string in the same font --- can refer to the directory in which the RStudio project is located.
Exercise 3
Do not start this exercise until you have read all the instructions. This exercise will force you to close the tutorial, so you won't be able to access the instructions while completing the exercise. Once you're done with all the steps in this exercise, re-open the tutorial to continue.
We want to make the plot in a script that's inside of a different RStudio project. To do that, we need to actually create the new project! The way to do that is quite simple, as RStudio has a handy menu for managing projects:
include_graphics("images/rs-proj-menu-locator.png")
To make a new project, press the "New Project..." button. You'll be asked to close the tutorial here. Do so, and you'll soon see the New Project Wizard.
include_graphics('images/new_proj_wizard.png')
We want to make a project in a new directory, so select that option.
Now, you'll be asked for a project type. Keep it simple and select "New Project". Name it project-2 and create it as a subdirectory of (folder inside of) your projects folder. Make sure all the checkboxes are unchecked, and hit "Create Project"!
include_graphics('images/new_proj_type.png')
Don't forget to open up the tutorial from the menu in the top right again once you're done.
Exercise 4
Type getwd() into the Console. CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 5)
Your answer should look like this:
"/Users/dkane/Desktop/projects/project-2"
Somewhere in the path should be project-2, the name of the RStudio project you just made and are now in.
Exercise 5
Now, we have to create the script where we'll be creating our plot.
In the menu bar on the top of the window, press File -> New File -> R Script. Type 5 * 5 and a newline into the file and hit Ctrl/Cmd + S to save it. Save it as script-3.r (the .r is automatically supplied by the dialog box when you save, so you only need to type script-3).
In the console, run list.files(). CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
You should see script-3.r somewhere in the output.
Exercise 6
Go back to editing script-3.r. Get rid of everything that's already there. Copy-paste the below code into the script, and save with Ctrl/Cmd + S.
car_plot <- ggplot(mpg, aes(displ, hwy, colour = class)) + geom_point() car_plot
Don't forget to have an empty line at the end of the file!
Once you're done, source the script with the "Source" button in the top right.
Back in the Console, run tutorial.helpers::show_file('script-3.r'). CP/CR.
question_text(NULL, answer(NULL, correct = TRUE), allow_retry = TRUE, try_again_button = "Edit Answer", incorrect = NULL, rows = 3)
The output should have the same chunk of code as the one you were given to copy-paste above.
Summary
This tutorial covered Chapter 2 Workflow: basics, Chapter 4 Workflow: code style, Chapter 6 Workflow: scripts and projects, and some material in Chapter 28 Quarto from R for Data Science (2e) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund. You learned how to use R scripts and how to create RStudio projects.
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