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:


By entering a formula as the first argument in the splot function (e.g., splot(y ~ x)), you can make

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 0.5.2)


Development (version 0.5.3)


Then load the package:



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')