An Updated corrgram

In earlier posts, I have been looking at different ways to get an idea of what scales and demographic variables in my data are related to other scales and demographic variables in my data.

In a post from a while back, I looked at corrgrams (Wright, 2021). I think that there are many ways to explore data visually, but have come to like the simplicity of both the code for, and the visual presentation of, corrgrams for many purposes.

Here I use the data from a post on making a network graph.

load("../network-graph/mydata.RData")

library(corrgram) # corrgrams

corrgram(mydata, 
         order=TRUE, 
         lower.panel = panel.pie, # lower panel is pie charts
         upper.panel = panel.cor, # upper panel is correlations
         text.panel = panel.txt, # text panel is variable
         diag.panel=panel.minmax, # minimum and maximum
         main = "Correlation of Scales in Our Data") 

References

Wright, K. (2021). Corrgram: Plot a correlogram. https://doi.org/10.32614/CRAN.package.corrgram