Here we are at the last post in Blogging A to Z! Today, I want to talk about adding additional axes to your ggplot, using the options for fill or color. While these aren’t true z-axes in the geometric sense, I think of them as a third, z, axis.

Some of you may be surprised to learn that fill and color are different, and that you could use one or both in a given plot.

Color refers to the outline of the object (bar, piechart wedge, etc.), while fill refers to the inside of the object. For scatterplots, the default shape doesn’t have a fill, so you’d just use color to change the appearance of those points.

Let’s recreate the pages read over 2019 chart, but this time, I’ll just use fiction books and separate them as either fantasy or other fiction; this divides that dataset pretty evenly in half. Here’s how I’d generate the pages read over time separately by those two genre categories.

library(tidyverse)

## -- Attaching packages ------------------------------------------- tidyverse 1.3.0 --

## <U+2713> ggplot2 3.2.1     <U+2713> purrr   0.3.3
## <U+2713> tibble  2.1.3     <U+2713> dplyr   0.8.3
## <U+2713> tidyr   1.0.0     <U+2713> stringr 1.4.0
## <U+2713> readr   1.3.1     <U+2713> forcats 0.4.0

## -- Conflicts ---------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

reads2019 <- read_csv("~/Downloads/Blogging A to Z/SaraReads2019_allchanges.csv",
                      col_names = TRUE)

## Parsed with column specification:
## cols(
##   Title = col_character(),
##   Pages = col_double(),
##   date_started = col_character(),
##   date_read = col_character(),
##   Book.ID = col_double(),
##   Author = col_character(),
##   AdditionalAuthors = col_character(),
##   AverageRating = col_double(),
##   OriginalPublicationYear = col_double(),
##   read_time = col_double(),
##   MyRating = col_double(),
##   Gender = col_double(),
##   Fiction = col_double(),
##   Childrens = col_double(),
##   Fantasy = col_double(),
##   SciFi = col_double(),
##   Mystery = col_double(),
##   SelfHelp = col_double()
## )

fantasy <- reads2019 %>%
  filter(Fiction == 1) %>%
  mutate(date_read = as.Date(date_read, format = '%m/%d/%Y'),
         Fantasy = factor(Fantasy, levels = c(0,1),
                          labels = c("Other Fiction",
                                     "Fantasy"))) %>%
  group_by(Fantasy) %>%
  mutate(GenreRead = order_by(date_read, cumsum(Pages))) %>%
  ungroup()

Now I’d just plug that information into my ggplot code, but add a third variable in the aesthetics (aes) for ggplot – color = Fantasy.

library(scales)

## 
## Attaching package: 'scales'

## The following object is masked from 'package:purrr':
## 
##     discard

## The following object is masked from 'package:readr':
## 
##     col_factor

myplot <- fantasy %>%
  ggplot(aes(date_read, GenreRead, color = Fantasy)) +
  geom_point() +
  xlab("Date") +
  ylab("Pages") +
  scale_x_date(date_labels = "%b",
               date_breaks = "1 month") +
  scale_y_continuous(labels = comma, breaks = seq(0,30000,5000)) +
  labs(color = "Genre of Fiction")

This plot uses the default R colorscheme. I could change those colors, using an existing colorscheme, or define my own. Let’s make a fivethirtyeight style figure, using their theme for the overall plot, and their color scheme for the genre variable.

library(ggthemes)

## Warning: package 'ggthemes' was built under R version 3.6.3

myplot +
  scale_color_fivethirtyeight() +
  theme_fivethirtyeight()

myplot +
  scale_color_manual(values = c("#4b0082","#ffd700")) +
  theme_minimal()