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sjPlot - Data Visualization for Statistics in Social Science

Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, PCA and correlation matrices, cluster analyses, scatter plots, Likert scales, effects plots of interaction terms in regression models, constructing index or score variables and much more.

Installation

Latest development build

To install the latest development snapshot (see latest changes below), type following commands into the R console:

library(devtools)
devtools::install_github("sjPlot/devel")

Please note that the latest development snapshot most likely depends on the latest build of the sjmisc-package, so you probably want to install it as well:

devtools::install_github("sjPlot/sjmisc")

Officiale, stable release

     

To install the latest stable release from CRAN, type following command into the R console:

install.packages("sjPlot")

Documentation and examples

Citation

In case you want / have to cite my package, please use citation('sjPlot') for citation information. Since core functionality of package depends on the ggplot-package, consider citing this package as well.

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Version

Install

install.packages('sjPlot')

Monthly Downloads

20,068

Version

2.1.0

License

GPL-3

Issues

Pull Requests

Stars

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Maintainer

Last Published

September 27th, 2016

Functions in sjPlot (2.1.0)

sjc.cluster

Compute hierarchical or kmeans cluster analysis
save_plot

Save ggplot-figure for print publication
dist_t

Plot t-distributions
adjust_plot_range

Adjust y range of ggplot-objects
set_theme

Set default theme for sjp-functions
dist_f

Plot F distributions
dist_chisq

Plot chi-squared distributions
dist_norm

Plot normal distributions
sjc.dend

Compute hierarchical cluster analysis and visualize group classification
plot_grid

Arrange list of plots as grid
sjp.corr

Plot correlation matrix
sjc.qclus

Compute quick cluster analysis
sjp.chi2

Plot Pearson's Chi2-Test of multiple contingency tables
sjp.glmer

Plot estimates, predictions or effects of generalized linear mixed effects models
sjc.grpdisc

Compute a linear discriminant analysis on classified cluster groups
sjc.kgap

Compute gap statistics for k-means-cluster
sjc.elbow

Compute elbow values of a k-means cluster analysis
sjp.aov1

Plot One-Way-Anova tables
sjp.frq

Plot frequencies of variables
sjp.glm

Plot estimates, predictions or effects of generalized linear models
sjp.grpfrq

Plot grouped or stacked frequencies
sjp.pca

Plot PCA results
sjp.likert

Plot likert scales as centered stacked bars
sjp.poly

Plot polynomials for (generalized) linear regression
sjp.lmm

Plot estimates of multiple fitted lm(er)'s
sjp.lmer

Plot estimates, predictions or effects of linear mixed effects models
sjp.lm

Plot estimates, predictions or effects of linear models
sjp.int

Plot interaction effects of (generalized) linear (mixed) models
sjp.glmm

Plot estimates of multiple fitted glm(er)'s
sjp.gpt

Plot grouped proportional tables
sjp.resid

Plot predicted values and their residuals
sjt.corr

Summary of correlations as HTML table
sjp.xtab

Plot contingency tables
sjp.scatter

Plot (grouped) scatter plots
sjt.df

Show (description of) data frame as HTML table
sjt.frq

Summary of frequencies as HTML table
sjt.glm

Summary of generalized linear models as HTML table
sjp.setTheme

Set global theme options for sjp-functions
sjp.stackfrq

Plot stacked proportional bars
sjPlot-package

Data Visualization for Statistics in Social Science
sjt.pca

Summary of principal component analysis as HTML table
sjt.lm

Summary of linear regression as HTML table
sjt.grpmean

Summary of grouped means as HTML table
sjt.lmer

Summary of linear mixed effects models as HTML table
sjt.xtab

Summary of contingency tables as HTML table
sjt.mwu

Summary of Mann-Whitney-Test as HTML table
sjt.glmer

Summary of generalized linear mixed models as HTML table
sjt.itemanalysis

Summary of item analysis of an item scale as HTML table
sjt.stackfrq

Summary of stacked frequencies as HTML table
view_df

View structure of labelled data frames