sjPlot2.8.17 package

Data Visualization for Statistics in Social Science

dist_chisq

Plot chi-squared distributions

dist_f

Plot F distributions

dist_norm

Plot normal distributions

dist_t

Plot t-distributions

plot_frq

Plot frequencies of variables

plot_gpt

Plot grouped proportional tables

plot_grid

Arrange list of plots as grid

plot_grpfrq

Plot grouped or stacked frequencies

plot_kfold_cv

Plot model fit from k-fold cross-validation

plot_likert

Plot likert scales as centered stacked bars

plot_model

Plot regression models

plot_models

Forest plot of multiple regression models

plot_residuals

Plot predicted values and their residuals

plot_scatter

Plot (grouped) scatter plots

plot_stackfrq

Plot stacked proportional bars

plot_xtab

Plot contingency tables

save_plot

Save ggplot-figure for print publication

set_theme

Set global theme options for sjp-functions

sjp.aov1

Plot One-Way-Anova tables

sjp.chi2

Plot Pearson's Chi2-Test of multiple contingency tables

sjp.corr

Plot correlation matrix

sjp.poly

Plot polynomials for (generalized) linear regression

sjPlot-package

Data Visualization for Statistics in Social Science

sjPlot-themes

Modify plot appearance

sjplot

Wrapper to create plots and tables within a pipe-workflow

tab_corr

Summary of correlations as HTML table

tab_df

Print data frames as HTML table.

tab_fa

Summary of factor analysis as HTML table

tab_itemscale

Summary of item analysis of an item scale as HTML table

tab_model

Print regression models as HTML table

tab_pca

Summary of principal component analysis as HTML table

tab_stackfrq

Summary of stacked frequencies as HTML table

tab_xtab

Summary of contingency tables as HTML table

view_df

View structure of labelled data frames

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, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data.

  • Maintainer: Daniel Lüdecke
  • License: GPL-3
  • Last published: 2024-11-29