An R package to ease data visualization.
The aim of this package is to make visualization an early part of the data analysis process by automating a few common plotting tasks.
In terms of design, it has three general principles:
 Flexibility: splot prefers to try to make a reasonable plot from any input, rather than erroring out.
 Minimal specification: You should be able to make most plots with just a formula input, and the same formula should generally be compatible with multiple plot types, regardless of variable types.
 Tweakability: Though splot is focused on quick, automated plotting, you should be able to adjust any aspect of the display with additional arguments, if you find a plot you want to display elsewhere.
features
By entering a formula as the first argument in the splot
function (e.g., splot(y ~ x)
), you can make
 Density distributions (overlaid on histograms when there is no
by
variable)  Scatter plots with prediction lines
 Bar or line graphs with error bars
For each type, multiple y
variables or data at levels of a by
variable are shown in the same plot frame,
and data at levels of one or two between
variables are shown in separate plot frames, organized in a grid.
resources
 Introduction

Documentation

Style Guide
 Gallery
 Applied examples: Exploring Data  Refining a Result
installation
Download R from rproject.org.
Release (version 0.5.3)
install.packages('splot')
Development (version 0.5.4)
# install.packages('remotes')
remotes::install_github('miserman/splot')
Then load the package:
examples
Make some data: random group and x variables, and a y variable related to x:
The distribution of y:
splot(y)
A scatter plot between y and x:
splot(y ~ x)
Same data with a quadratic model:
splot(y ~ x + x^2 + x^3)
Same data separated by group:
splot(y ~ x * group)
Could also separate by median or standard deviations of x:
Summarize with a bar plot:
splot(y ~ x * group, type='bar')
Twolevel y variable with a probability prediction line: