splot

An R package to ease data visualization

https://github.com/miserman/splot

Science Score: 23.0%

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    1 of 2 committers (50.0%) from academic institutions
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    Low similarity (11.9%) to scientific vocabulary

Keywords

data-visualization r
Last synced: 9 months ago · JSON representation

Repository

An R package to ease data visualization

Basic Info
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  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 3
Topics
data-visualization r
Created over 9 years ago · Last pushed over 2 years ago
Metadata Files
Readme Changelog

README.md

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) R install.packages("splot") Development (version 0.5.5) ```R

install.packages("remotes")

remotes::install_github("miserman/splot") Then load the package: R library(splot) ```

examples

Make some data: random group and x variables, and a y variable related to x: R group = rep(c("group 1", "group 2"), 50) x = rnorm(100) y = x * .5 + rnorm(100) The distribution of y: R splot(y) A scatter plot between y and x: R splot(y ~ x) Same data with a quadratic model: R splot(y ~ x + x^2 + x^3) Same data separated by group: R splot(y ~ x * group) Could also separate by median or standard deviations of x: R splot(y ~ x * x) splot(y ~ x * x, split = "sd") Summarize with a bar plot: R splot(y ~ x * group, type = "bar") Two-level y variable with a probability prediction line: ```R

make some new data for this example:

a discrete y variable and related x variable:

ybin = rep(c(1, 5), 50) xcon = y_bin * .4 + rnorm(100)

lines = "prob" for a prediction line from a logistic model:

splot(ybin ~ xcon, lines = "prob") ```

Owner

  • Name: Micah Iserman
  • Login: miserman
  • Kind: user

GitHub Events

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Last synced: over 1 year ago

All Time
  • Total Commits: 220
  • Total Committers: 2
  • Avg Commits per committer: 110.0
  • Development Distribution Score (DDS): 0.077
Past Year
  • Commits: 7
  • Committers: 1
  • Avg Commits per committer: 7.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
miserman m****n@t****u 203
Micah m****n@g****m 17
Committer Domains (Top 20 + Academic)
ttu.edu: 1

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Last synced: 10 months ago

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  • Average comments per issue: 0
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Past Year
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 340 last-month
  • Total dependent packages: 1
  • Total dependent repositories: 2
  • Total versions: 11
  • Total maintainers: 1
cran.r-project.org: splot

Simplified Plotting for Data Exploration

  • Versions: 11
  • Dependent Packages: 1
  • Dependent Repositories: 2
  • Downloads: 340 Last month
Rankings
Dependent packages count: 18.3%
Dependent repos count: 19.4%
Forks count: 28.0%
Average: 29.4%
Stargazers count: 34.7%
Downloads: 46.8%
Maintainers (1)
Last synced: 10 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.1 depends
  • grDevices * depends
  • graphics * depends
  • stats * depends
  • knitr * suggests
  • rmarkdown * suggests