All functions

dist_chisq()

Plot chi-squared distributions

dist_f()

Plot F distributions

dist_norm()

Plot normal distributions

dist_t()

Plot t-distributions

efc

Sample dataset from the EUROFAMCARE project

plot_gpt()

Plot grouped proportional tables

plot_grid()

Arrange list of plots as grid

plot_likert()

Plot likert scales as centered stacked bars

plot_model() get_model_data()

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

save_plot()

Save ggplot-figure for print publication

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.elbow()

Compute elbow values of a k-means cluster analysis

sjc.grpdisc()

Compute a linear discriminant analysis on classified cluster groups

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

sjp.corr()

Plot correlation matrix

sjp.fa()

Plot FA results

sjp.frq()

Plot frequencies of variables

sjp.grpfrq()

Plot grouped or stacked frequencies

sjp.kfold_cv()

Plot model fit from k-fold cross-validation

sjp.pca()

Plot PCA results

sjp.poly()

Plot polynomials for (generalized) linear regression

sjp.stackfrq()

Plot stacked proportional bars

sjp.xtab()

Plot contingency tables

sjPlot-package

Data Visualization for Statistics in Social Science

theme_sjplot() theme_sjplot2() theme_blank() theme_538() font_size() label_angle() legend_style() scale_color_sjplot() scale_fill_sjplot() sjplot_pal() show_sjplot_pals() css_theme()

Modify plot appearance

sjplot() sjtab()

Wrapper to create plots and tables within a pipe-workflow

sjt.corr()

Summary of correlations as HTML table

sjt.fa()

Summary of factor analysis as HTML table

sjt.glm()

Summary of generalized linear models 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.lm()

Summary of linear regression as HTML table

sjt.lmer()

Summary of linear mixed effects models as HTML table

sjt.pca()

Summary of principal component analysis as HTML table

sjt.stackfrq()

Summary of stacked frequencies as HTML table

sjt.xtab()

Summary of contingency tables as HTML table

tab_df() tab_dfs()

Print data frames as HTML table.

tab_model()

Print regression models as HTML table

view_df()

View structure of labelled data frames