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
`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.

- Introduction
- Documentation

- Style
Guide

- Gallery
- Applied examples: Exploring Data | Refining a Result

Download R from r-project.org.

Release (version 0.5.3)

`install.packages('splot')`

Development (version 0.5.4)

```
# install.packages('remotes')
::install_github('miserman/splot') remotes
```

Then load the package:

`library(splot)`

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

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

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:

```
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:
= rep(c(1, 5), 50)
y_bin = y_bin * .4 + rnorm(100)
x_con
# lines = 'prob' for a prediction line from a logistic model:
splot(y_bin ~ x_con, lines = 'prob')
```