How to create bivariate plots to compare groups (in R, using lattice and gplots)

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Task

Suppose we have a dataset with different treatment conditions and an outcome variable, and we want to perform exploratory data analysis. How would we visually compare the treatment conditions with regards to the outcome variable?

Related tasks:

• How to create basic plots
• How to add details to a plot
• How to create a histogram
• How to create a box (and whisker) plot
• How to change axes, ticks, and scale in a plot
• How to plot interaction effects of treatments

Solution

We use a built-in dataset called ToothGrowth that discusses the length of the teeth (len) in each of 10 guinea pigs at three Vitamin C dosage levels ($0.5$, $1$, and $2$ mg) with two delivery methods - orange juice or ascorbic acid (supp).

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# You can replace this example data frame with your own data
df <- ToothGrowth

If you wish to understand the distribution of the length of the tooth based on the delivery methods, you can construct a bivariate histogram plot.

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# install.packages( "lattice" ) # if you have not already done this
library(lattice)
histogram( ~ len | supp, data = df)

To visualize the summary statistics of the length of the tooth based on the delivery methods, you can construct a bivariate box plot.

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bwplot(df$len ~ df$supp)
# Or the following code produces a similar figure, using the mosaic package:
# boxplot(len ~ supp, data = df)

To plot the means for both treatment levels of supp for the len column, we load the gplots package and use the plotmeans function.

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# install.packages( "gplots" ) # if you have not already done this
library(gplots)
plotmeans(df$len ~ df$supp)

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Attaching package: ‘gplots’


The following object is masked from ‘package:stats’:

    lowess

Content last modified on 24 July 2023.

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Contributed by Krtin Juneja (KJUNEJA@falcon.bentley.edu)