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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("strengejacke/sjPlot")

Please note the package dependencies when installing from GitHub. The GitHub version of this package may depend on latest GitHub versions of my other packages, so you may need to install those first, if you encounter any problems. Here's the order for installing packages from GitHub:

sjlabelledsjmiscsjstatsggeffectssjPlot

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

35,447

Version

2.4.0

License

GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Last Published

October 19th, 2017

Functions in sjPlot (2.4.0)

plot_model

Plot regression models
plot_models

Forest plot of multiple regression models
dist_norm

Plot normal distributions
dist_t

Plot t-distributions
dist_chisq

Plot chi-squared distributions
dist_f

Plot F distributions
efc

Sample dataset from the EUROFAMCARE project
plot_grid

Arrange list of plots as grid
reexports

Objects exported from other packages
set_theme

Set global theme options for sjp-functions
sjc.cluster

Compute hierarchical or kmeans cluster analysis
sjc.dend

Compute hierarchical cluster analysis and visualize group classification
sjc.kgap

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

Compute quick cluster analysis
sjp.aov1

Plot One-Way-Anova tables
sjp.chi2

Plot Pearson's Chi2-Test of multiple contingency tables
sjc.elbow

Compute elbow values of a k-means cluster analysis
sjc.grpdisc

Compute a linear discriminant analysis on classified cluster groups
sjp.corr

Plot correlation matrix
sjp.fa

Plot FA results
sjp.kfold_cv

Plot model fit from k-fold cross-validation
sjp.likert

Plot likert scales as centered stacked bars
sjp.glmer

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

Plot grouped proportional tables
sjPlot-package

Data Visualization for Statistics in Social Science
sjPlot-themes

Modify plot appearance
sjp.frq

Plot frequencies of variables
sjp.glm

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

Plot stacked proportional bars
sjp.grpfrq

Plot grouped or stacked frequencies
sjp.int

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

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

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

Plot PCA results
sjp.poly

Plot polynomials for (generalized) linear regression
sjp.xtab

Plot contingency tables
sjt.df

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

Summary of factor analysis as HTML table
sjt.frq

Summary of frequencies as HTML table
sjt.glm

Summary of generalized linear models as HTML table
sjp.resid

Plot predicted values and their residuals
sjp.scatter

Plot (grouped) scatter plots
sjt.lmer

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

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

Summary of principal component analysis as HTML table
sjplot

Wrapper to create plots and tables within a pipe-workflow
sjt.itemanalysis

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

Summary of linear regression as HTML table
sjt.glmer

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

Summary of grouped means as HTML table
sjt.xtab

Summary of contingency tables as HTML table
view_df

View structure of labelled data frames
sjt.corr

Summary of correlations as HTML table
sjt.stackfrq

Summary of stacked frequencies as HTML table