# P is for percent

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We’ve used ggplots throughout this blog series, but today, I want to introduce another package that helps you customize scales on your ggplots – the scales package. I use this package most frequently to format scales as percent. There aren’t a lot of good ways to use percents with my dataset, but one example would be to calculate the percentage each book contributes to the total pages I read in 2019.

library(tidyverse) ## -- Attaching packages ------------------------------------------- tidyverse 1.3.0 -- ##ggplot2 3.2.1purrr 0.3.3 ##tibble 2.1.3dplyr 0.8.3 ##tidyr 1.0.0stringr 1.4.0 ##readr 1.3.1forcats 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_allrated.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() ## ) reads2019 <- reads2019 %>% mutate(perpage = Pages/sum(Pages))

The new variable, perpage, is a proportion. But if I display those data with a figure, I want them to be percentages instead. Here’s how to do that. (If you don’t already have the scales package, add install.packages(“scales”) at the beginning of this code.)

library(scales) ## ## Attaching package: 'scales' ## The following object is masked from 'package:purrr': ## ## discard ## The following object is masked from 'package:readr': ## ## col_factor reads2019 %>% ggplot(aes(perpage)) + geom_histogram() + scale_x_continuous(labels = percent, breaks = seq(0,.05,.005)) + xlab("Percentage of Total Pages Read") + ylab("Books") ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

You need to make sure you load the scales package before you add the labels = percent attribute, or you’ll get an error message. Alternatively, you can tell R to use the scales package just for this attribute by adding scales:: before percent. This trick becomes useful when you have lots of packages loaded that use the same function names, because R will use the most recently loaded package for that function, and mask it from any other packages.

This post also seems like a great opportunity to hop on my statistical highhorse and talk about the difference between a histogram and a bar chart. Why is this important? With everything going on in the world – pandemics, political elections, etc. – I’ve seen lots of comments on others’ intelligence, many of which show a misunderstanding of the most well-known histogram: the standard normal curve. You see, raw data, even from a huge number of people and *even* on a standardized test, like a cognitive ability (aka: IQ) test, is **never** as clean or pretty as it appears in a histogram.

Histograms use a process called “binning”, where ranges of scores are combined to form one of the bars. The bins can be made bigger (including a larger range of scores) or smaller, and smaller bins will start showing the jagged nature of most data, even so-called normally distributed data.

As one example, let’s show what my percent figure would look like as a bar chart instead of a histogram (like the one above).

reads2019 %>% ggplot(aes(perpage)) + geom_bar() + scale_x_continuous(labels = percent, breaks = seq(0,.05,.005)) + xlab("Percentage of Total Pages Read") + ylab("Books")