---
title: "Black & White Figures for Print Journals"
author: "Daniel Lüdecke"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Black & White Figures for Print Journals}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
```{r echo = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", dev = "png", fig.width = 7, fig.height = 5, message = FALSE, warning = FALSE)
```

This document shows examples how to create b/w figures, e.g. if you don't want colored figures for print-journals.

## Barplots in grey-scaled colors

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()`).

```{r}
library(sjPlot)
library(sjmisc)
library(sjlabelled)
library(ggplot2)
theme_set(theme_bw())
data(efc)
plot_grpfrq(efc$e42dep, efc$c172code, geom.colors = "gs")
```

## Lineplots in b/w with different linetypes

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 b/w-plot.

```{r}
# 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"))

# 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"`).

```{r}
# plot coefficients
plot_model(fit, colors = "black")
```
