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("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")

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.

`sjp.kfold_cv()`

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

- Argument
`scatter.plot`

was renamed to`show.scatter`

. - Argument
`varl.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)`

).