This document shows examples how to create b/w figures, e.g. if you don’t want colored figures for print-journals.
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
library(sjPlot) library(sjmisc) library(sjlabelled) library(ggplot2) theme_set(theme_bw()) data(efc) plot_grpfrq(efc$e42dep, efc$c172code, geom.colors = "gs")
Similar to barplots, lineplots - mostly from
plot_model() - can be plotted in greyscale as well (with
colors = "gs"). However, in most cases lines colored in
greyscale are difficult to distinguish. In this case,
plot_model() supports black & white figures with
different linetypes. Use
colors = "bw" to create a
# create binrary response <- ifelse(efc$neg_c_7 < median(na.omit(efc$neg_c_7)), 0, 1) y # create data frame for fitting model <- data.frame( df 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 <- glm(y ~., data = df, family = binomial(link = "logit")) fit # plot marginal effects plot_model( fit, type = "pred", terms = c("barthel", "sex","dep"), colors = "bw", ci.lvl = NA )
Different linetypes do not apply to all linetyped plots, if these
usually only plot a single line - so there’s no need for different
linetypes, and you can just set
colors = "black" (or
colors = "bw").
# plot coefficients plot_model(fit, colors = "black")