Joyce Robbins 2024-08-27
This package will eventually contain functions, data, and templates to accompany data visualization courses.
If you encounter problems or have questions, please open an issue or start/contribute to a discussion.
As of now, it contains two functions: draw_biplot() and
plot_missing().
There are other options for drawing biplots in the ggplot2
framework; ggbiplot() in the ordr
package is an excellent choice.
The main contributions of draw_biplot() are ease of use and option to
calibrate only one of the axes. Calibration calculations are performed
by calibrate() in the calibrate
package.
Currently, draw_biplot() takes a data frame, performs principal
components analysis (PCA) on the numeric columns using prcomp() and
draws a biplot using the first non-numeric column as labels for the
principal component scores (points). Additional options besides PCA may
be added in the future.
plot_missing() was designed as a replacement for extracat::visna()
which is no longer available on CRAN. It has improved labeling and the
option to label axes as percents or numbers.
library(redav)
swiss$country <- rownames(swiss)
draw_biplot(swiss)
draw_biplot(attitude)
draw_biplot(attitude, key_axis = "raises") +
labs(title = "The Chatterjee-Price Attitude Data",
subtitle = "package: datasets (base R)")
s77 <- as.data.frame(state.x77)
s77$state_name <- rownames(s77)
draw_biplot(s77)
draw_biplot(s77, key_axis = "Murder", ticklab = 0:16, project = FALSE,
point_color="deepskyblue3") + theme_classic()
draw_biplot(s77, mult = 1)
draw_biplot(s77, points = FALSE)
library(redav)
data(CHAIN, package = "mi")
plot_missing(CHAIN)
plot_missing(CHAIN, percent = FALSE)
plot_missing(CHAIN, max_rows = 4)
plot_missing(CHAIN, max_cols = 3)
plot_missing(CHAIN, num_char = 5)
plot_missing(CHAIN, max_rows = 4, max_cols = 3, num_char = 5, percent = FALSE)
plot_missing(mtcars)
Rendered from Readme.Rmd.