How to change color of data points on a boxplot based on a factored variable using ggplot R

Question

I am trying to make a series of graphs that are based on a binomial variable. I want to add data points to the graph based on a different factored variable with 3 levels. I have been trying to use geom_jitter which worked to put the points on the box plot but I havent been able to change the colors to represent the different levels of the factored variable.

Here is the code I have been using

longg <- ggplot(long, aes(x = mbbase, y= beta)) + 
  geom_boxplot() + facet_wrap(~test) + 
  ylab("Beta") + 
  theme_cleveland() +
  scale_fill_viridis(discrete = TRUE, alpha=0.09) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + 
  theme(axis.title.x = element_blank()) + 
  geom_jitter( size=0.7, alpha=1, width = 0.05)

Here is an example of the graph I want with the mtcars data but instead of a numeric variable as the color id like a factored variable with 3 levels but I only want the color of the data points to change without adding a new box plot for each level of the factored variable

It would be easier to help if you create a small reproducible example along with expected output. Read about how to give a reproducible example. — Ronak Shah
– Ronak Shah, Commented Mar 27, 2021 at 2:59
@RonakShah I updated the question with an example hopefully that helps! — Julia L
– Julia L, Commented Mar 27, 2021 at 18:11

s__ · Accepted Answer · 2021-03-27 21:48:41Z

1

With mtcars, you can try this:

library(ggplot2)
library(dplyr)
library(viridis)

mtcars %>%
# optional: divide the column to color in three. There are more elegant ways
#to do it, but in this way probably it's easier to use it in your data
mutate(new_carb = as.factor(ifelse(carb %in% c(1,2),1,
                                 ifelse(carb %in% c(3,4),2,3)))) %>%
ggplot( aes(x = as.factor(am), y= mpg)) + 
  geom_boxplot() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
        axis.title.x = element_blank()) +
  geom_boxplot(outlier.shape=NA) + 
  # add the color here
  geom_jitter(aes(color = new_carb),
                  size=0.7, alpha=1, width = 0.05) +
  scale_color_viridis(discrete = TRUE, alpha=0.09)

answered Mar 27, 2021 at 21:48

s__

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