splot

features

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

• Density distributions (overlaid on histograms when there is noby variable)
• Scatter plots with prediction lines
• Bar or line graphs with error bars

For each type, multipley variables or data at levels of aby variable are shown in the same plot frame,
and data at levels of one or twobetween 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 fromr-project.org.

Release (version 0.5.4)

install.packages("splot")

Development (version 0.5.5)

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

Then load the package:

library(splot)

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:

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: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")