Z is for Additional Axes
This article is originally published at http://www.deeplytrivial.com/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()
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")
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()
library(ggpubr)
## Warning: package 'ggpubr' was built under R version 3.6.3
## Loading required package: magrittr
##
## Attaching package: 'magrittr'
## The following object is masked from 'package:purrr':
##
## set_names
## The following object is masked from 'package:tidyr':
##
## extract
ggpubr::show_point_shapes()
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
reads2019 %>%
mutate(Gender = factor(Gender, levels = c(0,1),
labels = c("Male",
"Female")),
Fiction = factor(Fiction, levels = c(0,1),
labels = c("Non-Fiction",
"Fiction"),
ordered = TRUE)) %>%
group_by(Gender, Fiction) %>%
summarise(Books = n()) %>%
ggplot(aes(Fiction, Books, fill = reorder(Gender, desc(Gender)))) +
geom_col() +
scale_fill_economist() +
xlab("Genre") +
labs(fill = "Author Gender")
reads2019 %>%
mutate(Gender = factor(Gender, levels = c(0,1),
labels = c("Male",
"Female")),
Fiction = factor(Fiction, levels = c(0,1),
labels = c("Non-Fiction",
"Fiction"),
ordered = TRUE)) %>%
group_by(Gender, Fiction) %>%
summarise(Books = n()) %>%
ggplot(aes(Fiction, Books, fill = reorder(Gender, desc(Gender)))) +
geom_col(position = "dodge") +
scale_fill_economist() +
xlab("Genre") +
labs(fill = "Author Gender")
genres <- reads2019 %>%
group_by(Fiction, Childrens, Fantasy, SciFi, Mystery) %>%
summarise(Books = n())
genres <- genres %>%
bind_cols(Genre = c("Non-Fiction",
"General Fiction",
"Mystery",
"Science Fiction",
"Fantasy",
"Fantasy Sci-Fi",
"Children's Fiction",
"Children's Fantasy"))
genres %>%
filter(Genre != "Non-Fiction") %>%
ggplot(aes(reorder(Genre, -Books), Books, fill = Genre)) +
geom_col() +
xlab("Genre") +
scale_x_discrete(labels=function(x){sub("\\s", "\n", x)}) +
scale_fill_economist() +
theme(legend.position = "none")
I hope you enjoyed this series! There's so much more you can do with tidyverse than what I covered this month. Hopefully this has given you enough to get started and sparked your interest to learn more. Once again, I highly recommend checking out R for Data Science.
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This article is originally published at http://www.deeplytrivial.com/
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