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.

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.

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:

sjlabelled → sjmisc → sjstats → ggeffects → sjPlot

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

`install.packages("sjPlot")`

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.

- Remove unnecessary imports.
- Add back
`save_plot()`

function. - Revised and updated package vignettes.

`tab_df()`

and`tab_dfs()`

as (more generic) replacement for`sjt.df()`

.`tab_df()`

prints the content of a data frame as HTML table, while`tab_dfs()`

prints a list of data frames into a HTML table. The HTML table is then displayed in the Viewer-pane of RStudio or in a web browser.

`sjt.grpmean()`

is now deprecated. Please use`sjstats::grpmean()`

with argument`out = "viewer"`

instead.`sjt.mwu()`

is now deprecated. Please use`sjstats::mwu()`

with argument`out = "viewer"`

instead.`sjt.df()`

is now deprecated. Please use`sjmisc::descr()`

with argument`out = "viewer"`

or`tab_df()`

instead.

`plot_model()`

now also supports`clm`

-models from package*ordinal*,`polr`

-models from package*MASS*,`multinom`

-models from package*nnet*and`Zelig-relogit`

-models from package*Zelig*.`plot_model()`

gets a`show.legend`

-argument to show or hide the legend for marginal effects plots.`plot_model()`

gets a`se`

-argument to plot (robust) standard errors instead of confidence intervals for coefficient-plots.- Diagnostic plots in
`plot_model()`

(`type = "diag"`

) now also plot diagnostics of random effects from (generalized) linear mixed models. - The
`...`

-argument of`plot_model()`

now also accepts the arguments`sep_in`

and`sep_out`

, which are passed down to`snakecase::to_any_case()`

for case conversion of term labels (axis labels). - The
`title`

-argument in`plot_model()`

now also works for plotting random effects (`type = "re"`

). `sjt.itemanalysis()`

no longer returns a list of score items, but only a data frame of scores.`sjp.grpfrq()`

gets a`show.ci`

-argument to add notches to boxplots.

`view_df()`

did not work with double values (with decimal points) when`show.values = TRUE`

.`view_df()`

caused an error when a variable has completely missing values.`plot_models()`

did not properly remove intercepts from output for survey models, when`show.intercept = FALSE`

.`plot_models()`

did not automatically transform axis for all applicable model types.`get_model_data()`

did not work for marginal effects plots.- Fixed a bug that was introduced during the past update in
`sjt.grpmean()`

, resulting in multiple table outputs and a wrong overall p-value in the summary line. - Model weights are now correctly taken into account for marginal effect plots in
`plot_model()`

. `sjp.likert()`

did not show correct order for factors with character levels, when a neutral category was specified and was not the last factor level.- Fixed issue when plotting random effects (
`type = "re"`

) for specific`brms`

-models.

- The old
`set_theme()`

was removed. Instead, there are some new predifined themes available (see`?"sjPlot-themes"`

). The former`sjp.setThemes()`

was renamed to`set_theme()`

instead.

`plot_model()`

as replacement for`sjp.lm()`

,`sjp.glm()`

,`sjp.lmer()`

,`sjp.glmer()`

and`sjp.int()`

(which will become deprecated in the future, and will later be removed).`get_model_data()`

to get the data from plot-objects created with`plot_model()`

.`font_size()`

,`label_angle()`

and`legend_style()`

as convenient ways to tweak common ggplot-theme-elements.

`view_df()`

now better handles string variables and gets a`show.string.values`

-argument to omit the output of values from string variables.`view_df()`

gets a`max.len`

-argument to truncate output for variables with many values.`view_df()`

displays more information on non-labelled, numeric variables.`sjp.pca()`

and`sjt.pca()`

now give more informative error messages when just one component is extracted.

- Fixed bug with
`rm.terms`

-argument in`plot_models()`

. - In rare situations,
`view_df()`

did not work for string variables with missing values. - Fixed issue with wrong footnote text when saving the output of
`sjt.pca()`

as file. `sjp.xtab()`

did not work when`show.n`

and`show.prc`

were set to`FALSE`

, but`show.values`

was`TRUE`

.

- Fixed issue with latest tidyr-update on CRAN.
- HTML-tables (
`sjt.*`

-functions) displayed in the viewer pane now automatically add a CSS-style for white page background. This fixes an RStudio issue on OS X, where the new look'n'feel used dark backgrounds in the viewer pane, making output hardly readable.

- Revising package code from scratch. Part of the old functions will be replaced by new ones, making the code base easier to maintain and reduce redundant functions by merging them together. In the course of the next updates, functions will first become deprecated and later defunct.
- Changed imports to avoid using deprecated functions.
- Use more informative warning- and error-messages for certain functions.

`plot_models()`

as replacement for`sjp.lmm()`

and`sjp.glmm()`

(which are now deprecated).`sjp.fa()`

and`sjt.fa()`

to plot or print as table the results of factor analyses.

- The mean and standard deviation in the summary of
`sjt.frq()`

were not correctly computed, when`auto.group`

-argument was specified. This bug was introduced in the last update and is not fixed again. - Values of character vectors were not always correctly sorted in
`view_df()`

(if`show.frq = TRUE`

).

- All
`sjt`

-functions can now be directly integrated into knitr-code-chunks, because sjPlot exports a knitr-print-method (see`vignette("sjtbasic", "sjPlot")`

). `sjtab()`

now also works within knitr-documents (see`vignette("sjtbasic", "sjPlot")`

).- Updated Namespace for functions that moved from package
**sjstats**to**sjmisc**.

- Changed defaults for
`save_plot()`

. `save_plot()`

now also supports*svg*-format.- For effect-plots (
`type = "eff"`

), the`axis.title`

-argument can now be used to change the title of y-axes. - For
`sjp.lm()`

,`sjp.glm()`

,`sjp.lmer()`

and`sjp.glmer()`

, if color palette has more values than needed, it is silently shortend to the required length. - When plotting mixed models, argument
`geom.colors`

now also applies to plot-type`type = "ri.slope"`

. - Default correlation-method for
`sjt.corr()`

and`sjp.corr()`

is now`pearson`

. - Argument
`emph.p`

for printing tables of regression models now defaults to`FALSE`

.

- Fixed bug in
`sjt.frq()`

for variables with many missing values and labelled values that did not occur on that variable. - Argument
`value.labels`

had no effect for`sjt.frq()`

. - Automatic label detection in
`sjt.grpmean()`

sometimes not worked for factors without variable labels. `sjp.glm()`

used*Odds Ratios*as default title for y-axis when plotting marginal effects. Fixed, now y-axis is correctly labelled.`sjt.glm()`

used "Odds Ratios" as default column heading for the estimates, even for poisson or other models. Now the string for column headers is selected based on the first model input of the function.- Solved issue with warning in prediction-plots (
`type = "pred"`

) for categorical variables on the x-axis.

- Vignettes were added to this package.

- You can use
`geom.colors = "bw"`

for linetype-plots, to create black & white figures that use different linetypes instead of different colors. `sjp.kfold_cv()`

now also supports poisson and negative binomial regression models.`sjp.pca()`

and`sjt.pca()`

get a`rotation`

-argument, to use either varimax- or oblimin-transformation of factor loadings.- Argument
`show.value`

now also applies to bar plots in`sjp.pca()`

. `sjt.glm()`

, for generalized linar (mixed) models, now shows adjusted standard errors, using the Taylor series-based delta method.- More precise rounding of percentage values in
`sjt.xtab()`

,`sjp.xtab()`

and`sjp.grpfrq()`

. - Cramer's V in
`sjt.xtab()`

is now dentoted as*V*. `sjt.xtab()`

gets a`...`

-argument, to pass down further arguments to the test statistics functions`chisq.test()`

and`fisher.test()`

.`sjt.xtab()`

gets a`statistics`

-argument, to select one of different measures of associations for the table summary.

- Plotting or table output of regression models did not work with null-models (i.e. with intercept only).

`sjp.lm()`

for`type = "ma"`

now uses subtitles in multi-line plot-titles.

- Residuals in
`sjp.kfold_cv()`

had wrong leading sign (i.e. positive residuals were negative and vice versa).

`sjp.kfold_cv()`

to plot model fit from k-fold cross-validation.

- Argument
`scatter.plot`

was renamed to`show.scatter`

. - Argument
`var.labels`

in`sjt.frq()`

was renamed to`title`

. `sjplot()`

and`sjtab()`

also accept grouped data frames, to create plots or tables for all subgroups.- For
`sjp.glm()`

and`sjp.glmer()`

,`type = "pred"`

,`type = "slope"`

,`type = "pred.fe"`

and`type = "fe.slope"`

can now also plot data points when`show.scatter = TRUE`

. Use`point.alpha`

to adjust alpha-level of data points. - For
`sjp.lm()`

,`sjp.lmer()`

,`sjp.glm()`

and`sjp.glmer()`

,`type = "pred"`

and`type = "pred.fe"`

now plot error bars for`show.ci = TRUE`

and a discrete variable on the x-axis. - For
`sjp.glm()`

and`sjp.glmer()`

,`type = "pred"`

and`type = "pred.fe"`

now accept three variables for the`vars`

-argument, to facet grouped predictions by a third variable. - For
`sjp.lm()`

,`sjp.lmer()`

,`sjp.glm()`

and`sjp.glmer()`

, the`...`

-ellipses argument now is also passed down to all errorbars- and smooth-geoms in prediction- and effect-plots, so you can now use the`width`

-argument to show the small stripes at the lower/upper end of the error bars, the`alpha`

-argument to define alpha-level or the`level`

-argument to define the level of confidence bands. `sjp.lm()`

,`sjp.lmer()`

,`sjp.glm()`

and`sjp.glmer()`

get a`point.color`

-argument, do define color of point-geoms when`show.scatter = TRUE`

. If not defined, point-geoms will have same group-color as lines.- Effect-plots (
`type = "eff"`

) now plot data points for discrete variables on the x-axis. `sjt.lm()`

and`sjt.glm()`

get a`robust`

-argument to compute robust standard errors and confidence intervals.`sjp.resid()`

now also returns a plot with the residual pattern,`$pattern`

.- Plot and axis titles from effect-plots can now be changed with
`title`

or`axis.title`

argument. Use a character vector of length > 1 to define (axis) titles for each plot or facet; use`""`

to remove the titles. - Pick better defaults for
`geom.size`

-argument for histogram and density plots in`sjp.frq()`

. - Improved automatic label detection for regression models for plot or table output.

- Restored correct order of categories in
`sjp.xtab()`

and`sjp.grpfrq()`

for stacked bars (`position_stack()`

reversed order since last ggplot2-update), so labels are now correclty positioned again. - Restored correct order of categories in
`sjp.likert()`

, so groups are now in correct order again. - Fixed bug in
`sjt.grpmean()`

for variables with unused value labels (values that were labelled, but did not appear on the vector). - Fixed wrong documentation for
`show.summary`

-argument in`sjt.xtab()`

. `sjt.frq()`

and`sjp.frq()`

showed messed up labels when a labelled vector had both`NA`

values*and*`NaN`

or infinite values.`sjtab()`

did not create tables for`fun = "xtab"`

with additional arguments.

- Effect-plots from
`sjp.int()`

,`sjp.glm()`

and`sjp.glmer()`

now support the`transformation`

-argument from the**effects**-package. For example, when calling`sjp.glm(fit, type = "eff", transformation = NULL)`

, predictions are on their original scale (y-scale) and the title for the y-scale is changed accordingly.

- Restored order of categories in
`sjp.stackfrq()`

, which were reversed by the last ggplot2-update, where`position_stack()`

now sort the stacking order to match grouping order.

- Fixed bug in
`sjplot()`

that caused figures not being plotted in certain situations. - Fixed bug in
`sjp.lmm()`

, which caused an error for plotting multiple mixed models when Intercept was hidden. - Fixed bug in
`sjp.lmm()`

, which caused an error for plotting multiple mixed models when`type = "std"`

or`type = "std2"`

.

- Some fixes needed to be compatible with the latest ggplot2-update.

`sjplot`

, a pipe-friendly wrapper for some of this package's plotting-functions.`sjtab`

, a pipe-friendly wrapper for some of this package's table-functions.

`sjp.resid`

, an experimental function to plot and analyze residuals from linear models.`plot_grid`

to plot a list of ggplot-objects as arranged grid in a single plot.`set_theme`

to use a preset of default themes for plots from the sjp-functions.

- For
`sjp.glmer`

and`sjp.lmer`

, argument`show.ci`

now also applies for plotting random effects (`type = "re"`

, the default), so confidence intervals may not be calculated. This may be useful in some cases where computation of standard errors for random effects caused an error. - Effect plots (
`type = "eff"`

) for`sjp.lm`

,`sjp.glm`

,`sjp.lmer`

and`sjp.glmer`

should now better handle categorical variables and their labels, including using error bars insted of regions for confidence intervals. `table(*, exclude = NULL)`

was changed to`table(*, useNA = "always")`

, because of planned changes in upcoming R version 3.4.`get_option("p_zero")`

was removed, and`sjt.lm`

,`sjt.glm`

,`sjt.lmer`

and`sjt.glmer`

get a`p.zero`

argument.`sjp.setTheme`

no longer sets default theme presets for plots; use`set_theme`

instead.

- A bug introduced in update 2.0.2 caused an error in
`sjp.lm`

for`type = "std"`

. - Effect plots (
`type = "eff"`

) for`sjp.lm`

,`sjp.glm`

,`sjp.lmer`

and`sjp.glmer`

did not plot all predictors, when predictor name was not exactly specified in formula, but transformed inside formula (e.g.`log(pred + 1)`

).