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?
- 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
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 (
1 2 # 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.
1 2 3 # 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.
1 2 3 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
we load the
gplots package and use the
1 2 3 # install.packages( "gplots" ) # if you have not already done this library(gplots) plotmeans(df$len ~ df$supp)
1 2 3 4 5 6 Attaching package: ‘gplots’ The following object is masked from ‘package:stats’: lowess
Content last modified on 24 July 2023.
Contributed by Krtin Juneja (KJUNEJA@falcon.bentley.edu)