Barplots, boxplots or normality plots are displayed according to the nature of described variable. These plots are useful to explore visually whether a continuous variable follows a normal distribution or to identify possible outliers or rare categories, etc.

Step 1. Install the package

Install compareGroups package from CRAN and then load it by typing:

install.packages("compareGroups")
library(compareGroups)

Step 2. Load data

Load the PREDIMED example data available in compareGroups package:

data(predimed)

Setp 3. Computations

First use compareGroups function to store all values used to perform plots afterwards.

res <- compareGroups(group ~ . - toevent, data = predimed)


You can use varinfo function to recover the original name of variables (not labels which are displayed in the results).

varinfo(res)

--- Analyzed variable names ----

   Orig varname Shown varname                  
1  group        Intervention group             
2  sex          Sex                            
3  age          Age                            
4  smoke        Smoking                        
5  bmi          Body mass index                
6  waist        Waist circumference            
7  wth          Waist-to-height ratio          
8  htn          Hypertension                   
9  diab         Type-2 diabetes                
10 hyperchol    Dyslipidemia                   
11 famhist      Family history of premature CHD
12 hormo        Hormone-replacement therapy    
13 p14          MeDiet Adherence score         
14 event        AMI, stroke, or CV Death

Step 4. Perform plots

by using the plot method which takes the results created by compareGroups function. Inside “[” brackets you can select which variable to plot. And, indicating bivar=TRUE a bivariate plot is performed, i.e. stratifying by groups.

  1. For categorical variables a barplot is performed, stratifying by groups (right plot) or not (left plot):
plot(res['diab'])
plot(res['diab'],bivar=TRUE)

  1. For continuous variables boxplots or normality plots are performed depending whether groups are considered or not, respectively.
plot(res['bmi'])
plot(res['bmi'],bivar=TRUE)