Exercise 06

Making figures with ggplot2

Published

April 23, 2025

Modified

April 1, 2025

Dates

We’ll work on this exercise in-class on March 27, 2025.

The write-up is due on Thursday, April 3, 2025.

Goals

Create some simple figures in R using the ggplot2 package.
Gain an appreciation of the costs and benefits of scripting the generation of figures.

Assignment

Background

You may complete this assignment in two ways:

Use RStudio on your personal computer or a Penn State lab computer to write your R code and generate your figures. Then create a document with your code and generated figures.
Use Quarto on Posit.cloud.

Resources

You may find some of the explanation and code contained in this tutorial helpful.

The tutorial on making and visualizing data may also be useful.

We’ll be analyzing the survey data one of your classmates collected.

For students using Posit.cloud

Log in to Posit.cloud.
Open the ex06 assignment.
Open the exercise-06.qmd Quarto document.

Read and follow the directions in the document. You may edit the document and save your changes. Press the ‘render’ button (magenta highlighting) to convert the Quarto source document into an MS Word output document. Make sure to change Dr. Gilmore’s name to yours (cyan highlighting).

Figure 1: Screenshot from exercise-06.qmd file in the ex06 Posit.cloud assignment.

For students not using Posit.cloud

Download the data.

Visit the cleaned CSV file on GitHub:

https://github.com/psu-psychology/psych-490-data-viz-2025-spring/blob/main/src/exercises/csv/ex06-data.csv

Figure 2: Screenshot of GitHub data file.

Press on the download button to download the data to your computer.

Move the downloaded file.

You’ll want to copy and paste or move the file to the working directory where RStudio loads. To find this on your computer, open RStudio and run getwd() in the console. Here is what the results of that look like on Gilmore’s computer:

Figure 3: Results of running `getwd()` in the RStudio console.

Yours will differ from this. Copy your file to this directory/folder.

Load the data into a data frame.

Run the following code in your console to load the CSV file as a data frame.

survey <- read.csv("ex06-data.csv")

Confirm that the file loaded by running the following:

str(survey)

You should see something like this:

Figure 4: Results of running the `str()` command on the `survey` data frame.

If you get stuck

If you are struggling at this step, then it is usually because your CSV file is not in a location where RStudio can find it.

It will be much easier for us to help you if we can see you in-person (in class or in a separate meeting) or via Zoom with screen-sharing.

By all means, try to fix the problem yourself, but don’t spend hours doing so.

If you successfully downloaded the CSV and loaded it as a data frame, you’re nearly ready for the fun part(s).

Load R packages needed to make your code simpler.

Before we load the packages, we need to install them: Run the following code:

install.packages("tidyverse")

Then, to load the packages into your computer’s active memory, run the following code:

1library(dplyr)
2library(ggplot2)
3library(forcats)

1: Loads the dplyr library.
2: Loads the ggplot2 library.
3: Loads the forcats library.

survey |>
  count(fav_flavor) |>
  ggplot() +
  aes(x = fav_flavor, y = n) +
  geom_col()

Add colors to the bars

To do this, you’ll need to add a fill aesthetic to the aes() command. You can have ggplot assign colors automatically based on the values in some variable like fav_flavor by adding fill = fav_flavor to the list of aesthetics in the previous code. The code below is almost correct. Fix it and make a more colorful plot.

survey |>
  count(fav_flavor) |>
  ggplot() +
1  aes(x = fav_flavor, y = n, fill = ) +
  geom_col()

1: This line is incomplete. Fix it and make the plot.

If you want all the bars to be the same color, add a fill aesthetic to geom_col().

survey |>
  count(fav_flavor) |>
  ggplot() +
  aes(x = fav_flavor, y = n) +
1  geom_col(fill = "lightblue")

1: Makes all of the bars a light blue. Try a different color like “orange”, or “violet”.

Try making a colorful plot of another variable like best_pet or n_concerts.

Add order to a plot.

As a final exercise, try to make a plot that shows the bars in order based on the number of responses. Here’s a section of code that does this for the fav_flavor data.

survey |>
  count(fav_flavor) |>
1  mutate(fav_flavor_sorted = fct_reorder(fav_flavor, n)) |>
  ggplot() +
2  aes(x = fav_flavor_sorted, y = n, fill = fav_flavor_sorted) +
  geom_col()

1: Uses the fct_reorder() function from the forcats package to create an ordered factor based on fav_flavor and the count stored in n.
2: Use the new sorted variable as the aesthetics in our plot.

Note

Here we use the mutate() function to create a new variable in our data frame.

Submit

The code you wrote in following the steps above.
The results of running your code.
Comments about what you observed.

--- title: "Exercise 06" subtitle: "Making figures with ggplot2" --- ## Dates {-} We'll work on this exercise in-class on March 27, 2025. The write-up is [due]{.orange_due} on Thursday, April 3, 2025. ## Goals {-} 1. Create some simple figures in R using the `ggplot2` package. 2. Gain an appreciation of the costs and benefits of scripting the generation of figures. ## Assignment {-} ### Background You may complete this assignment in two ways: 1. Use RStudio on your personal computer or a Penn State lab computer to write your R code and generate your figures. Then create a document with your code and generated figures. 2. Use Quarto on Posit.cloud. ::: {.callout-note} ## Resources You may find some of the explanation and code contained in [this tutorial](../tutorials/tutorial-r-ggplot.qmd) helpful. The [tutorial](../tutorials/tutorial-making-data.qmd) on making and visualizing data may also be useful. ::: We'll be analyzing the survey data one of your classmates collected. ### For students using Posit.cloud 1. Log in to Posit.cloud. 2. Open the `ex06` assignment. 3. Open the `exercise-06.qmd` Quarto document. Read and follow the directions in the document. You may edit the document and save your changes. Press the 'render' button (magenta highlighting) to convert the Quarto source document into an MS Word output document. Make sure to change Dr. Gilmore's name to yours (cyan highlighting). ![Screenshot from exercise-06.qmd file in the ex06 Posit.cloud assignment.](../include/img/posit-cloud-ex-06.png){#fig-posit-cloud-ex-06} #### For students *not* using Posit.cloud 1. Download the data. Visit the cleaned CSV file on GitHub: <https://github.com/psu-psychology/psych-490-data-viz-2025-spring/blob/main/src/exercises/csv/ex06-data.csv> ![Screenshot of GitHub data file.](../include/img/github-ex06-data.png){#fig-github-ex06-data} Press on the download button to download the data to your computer. 2. Move the downloaded file. You'll want to copy and paste or move the file to the working directory where RStudio loads. To find this on your computer, open RStudio and run `getwd()` in the console. Here is what the results of that look like on Gilmore's computer: ![Results of running `getwd()` in the RStudio console.](../include/img/getwd.png){#fig-getwd} Yours will differ from this. Copy your file to this directory/folder. 3. Load the data into a data frame. Run the following code in your console to load the CSV file as a data frame. ```r survey <- read.csv("ex06-data.csv") ``` 4. Confirm that the file loaded by running the following: ```r str(survey) ``` You should see something like this: ![Results of running the `str()` command on the `survey` data frame.](../include/img/str-survey.png){#fig-str-survey} ::: {.callout-warning} ## If you get stuck If you are struggling at this step, then it is usually because your CSV file is not in a location where RStudio can find it. It will be *much* easier for us to help you if we can see you in-person (in class or in a separate meeting) or via Zoom with screen-sharing. By all means, try to fix the problem yourself, but don't spend hours doing so. ::: If you successfully downloaded the CSV and loaded it as a data frame, you're nearly ready for the fun part(s). 5. Load R packages needed to make your code simpler. Before we load the packages, we need to install them: Run the following code: ```r install.packages("tidyverse") ``` Then, to load the packages into your computer's active memory, run the following code: ```r library(dplyr) # <1> library(ggplot2) # <2> library(forcats) # <3> ``` 1. Loads the `dplyr` library. 2. Loads the `ggplot2` library. 3. Loads the `forcats` library. 5. Make a plot of the favorite flavor (`fav_flavor`) data. ```r survey |> count(fav_flavor) |> ggplot() + aes(x = fav_flavor, y = n) + geom_col() ``` 6. Add colors to the bars To do this, you'll need to add a `fill` aesthetic to the `aes()` command. You can have `ggplot` assign colors automatically based on the values in some variable like `fav_flavor` by adding `fill = fav_flavor` to the list of aesthetics in the previous code. The code below is *almost* correct. Fix it and make a more colorful plot. ```r survey |> count(fav_flavor) |> ggplot() + aes(x = fav_flavor, y = n, fill = ) + # <1> geom_col() ``` 1. This line is incomplete. Fix it and make the plot. If you want *all* the bars to be the same color, add a `fill` aesthetic to `geom_col()`. ```r survey |> count(fav_flavor) |> ggplot() + aes(x = fav_flavor, y = n) + geom_col(fill = "lightblue") # <1> ``` 1. Makes all of the bars a light blue. Try a different color like "orange", or "violet". 7. Go further. Try making a colorful plot of another variable like `best_pet` or `n_concerts`. 8. Add order to a plot. As a final exercise, try to make a plot that shows the bars in order based on the number of responses. Here's a section of code that does this for the `fav_flavor` data. ```r survey |> count(fav_flavor) |> mutate(fav_flavor_sorted = fct_reorder(fav_flavor, n)) |> # <1> ggplot() + aes(x = fav_flavor_sorted, y = n, fill = fav_flavor_sorted) + # <2> geom_col() ``` 1. Uses the `fct_reorder()` function from the `forcats` package to create an ordered factor based on `fav_flavor` and the count stored in `n`. 2. Use the new sorted variable as the aesthetics in our plot. ::: {.callout-note} Here we use the `mutate()` function to create a new variable in our data frame. ::: ### Submit {-} 1. The code you wrote in following the steps above. 2. The results of running your code. 3. Comments about what you observed.