In jtr13/redav: R for EDAV

# DO NOT EDIT Readme.md, EDIT Readme.Rmd instead
knitr::opts_chunk$set(echo = TRUE, message=FALSE, warning=FALSE)

redav

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.

## Installation
# This package is not on CRAN. Install with:
remotes::install_github("jtr13/redav")

Examples

`draw_biplot()`

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)

`plot_missing()`

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)