My sjPlot-package was just updated on CRAN with some – as I think – useful new features.

First, I have added some vignettes to the package (based on the existing online-documentation) that cover some core features and principles of the sjPlot-package, so you have direct access to these manuals within R. The vignettes are also online on CRAN.

A second feature is the better support for black & white figures. There are two ways to create plots in black and white or greyscale. For bar plots, geom.colors = "gs" creates a plot using a greyscale (based on scales::grey_pal()).

library(sjPlot)
library(sjmisc)
library(ggplot2)
theme_set(theme_bw())
data(efc)
sjp.grpfrq(efc$e42dep, efc$c172code, geom.colors = "gs")

Similar to barplots, lineplots can be plotted in greyscale as well (with geom.colors = "gs"). However, in most cases lines colored in greyscale are difficult to distinguish. In this case, certain plot types in sjPlot support black & white figures with different linetypes.

Following plot-types allow black & white figures:

• sjp.grpfrq(type = „line“)
• sjp.int()
• sjp.lm(type = „pred“)
• sjp.glm(type = „pred“)
• sjp.lmer(type = „pred“)
• sjp.glmer(type = „pred“)

Use geom.colors = "bw" to create a b/w-plot.

# create binrary response
y <- ifelse(efc$neg_c_7 < median(na.omit(efc$neg_c_7)), 0, 1)
# create data frame for fitting model
df <- data.frame(
  y = to_factor(y),
  sex = to_factor(efc$c161sex),
  dep = to_factor(efc$e42dep),
  barthel = efc$barthtot,
  education = to_factor(efc$c172code)
)
# set variable label for response
set_label(df$y) <- "High Negative Impact"
# fit model
fit <- glm(y ~., data = df, family = binomial(link = "logit"))
# print predicted propbabilities
sjp.glm(fit, type = "pred", vars = c("barthel", "sex","dep"), geom.colors = "bw")

Different linetypes do not apply to other linetyped plots (like sjp.lm(type = "eff") or sjp.lm(type = "slope")), because these usually only plot a single line – so there’s no need for different linetypes, you can just set geom.colors = "black" (or geom.colors = "bw").

With this new feature, it’s pretty easy to create plots for (print) journals that require b/w or greyscaled figures.