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.
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
- 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.
Download R from r-project.org.
Release (version 0.5.4)
Development (version 0.5.5)
# install.packages("remotes") remotes::install_github("miserman/splot")
Then load the package:
Make some data: random group and x variables, and a y variable related to x:
The distribution of 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")
Two-level y variable with a probability prediction line: