Boxplots in Jamovi

Cheatsheet

Published

July 27, 2024

This work was developed using resources that are available under a Creative Commons Attribution 4.0 International License, made available on the SOLES Open Educational Resources repository by the School of Life and Environmental Sciences, The University of Sydney.

1 About

The boxplot is a visual representation of a dataset’s distribution, showing the median, quartiles, and outliers. It is useful for comparing distributions between groups and identifying outliers within a single group.

  • You have Jamovi installed ideally 2.5.7.0 or later.
  • You can follow instructions to select, click and drag elements in Jamovi.

The data should be in a long format (also known as tidy data), where each row is an observation and each column is a variable (Figure 1). If your data is not already structured this way, reshape it manually in a spreadsheet program or in R using the pivot_longer() function from the tidyr package.

Sex BW
F 2.15
M 2.55
F 2.95
F 2.70
M 2.20
F 1.85
M 2.55
M 2.60

 

F M
2.15 2.55
2.95 2.20
2.70 2.55
1.85 2.60
Figure 1: Data should be in long format (left) where each row is an observation and each column is a variable. This is the preferred format for most statistical software. Wide format (right) is also common, but may require additional steps to analyse or visualise in some instances.

2 Data

For this cheatsheet we will use part of the possums dataset used in BIOL2022 labs.

3 Import data

  1. Click on the Menu icon:
  2. Select Open > Browse, and navigate to the downloaded file.
  3. Click Open to load the data.

4 Plot

  1. Click on the Analyses tab.
  2. Select Exploration > Descriptives.
  3. Add Sex to the “Split by” box.
  4. Add BW to the “Variables” box.
  5. In the “Plots” tab, select Boxplot.
Figure 2: How to import data and create a boxplot in Jamovi. Click on the image to expand it.

5 Export

To export the plot, right click on the plot, select Image > Export… > Browse and rename the file before clicking on the Save button.

Figure 3: A popup window should appear when you right click on a plot, where you can export the image. Click on the image to expand it.