Skip to contents

splot

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

installation

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:

examples

Make some data: random group and x variables, and a y variable related to x:

group = rep(c("group 1", "group 2"), 50)
x = rnorm(100)
y = x * .5 + rnorm(100)

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:

splot(y ~ x * x)
splot(y ~ x * x, split = "sd")

Summarize with a bar plot:

splot(y ~ x * group, type = "bar")

Two-level y variable with a probability prediction line:

# make some new data for this example:
# a discrete y variable and related x variable:
y_bin = rep(c(1, 5), 50)
x_con = y_bin * .4 + rnorm(100)

# lines = "prob" for a prediction line from a logistic model:
splot(y_bin ~ x_con, lines = "prob")