Hadley Wickham
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factors.Rmd
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factors.Rmd
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# Factors
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```{r, results = "asis", echo = FALSE}
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status("complete")
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```
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## Introduction
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Factors are used for categorical variables, variables that have a fixed and known set of possible values.
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They are also useful when you want to display character vectors in a non-alphabetical order.
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If, after reading this chapter, you want to learn more about factors, I recommend reading Amelia McNamara and Nicholas Horton's paper, [*Wrangling categorical data in R*](https://peerj.com/preprints/3163/).
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If you want to learn more about factors after reading this chapter, I recommend reading Amelia McNamara and Nicholas Horton's paper, [*Wrangling categorical data in R*](https://peerj.com/preprints/3163/).
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This paper lays out some of the history discussed in [*stringsAsFactors: An unauthorized biography*](https://simplystatistics.org/posts/2015-07-24-stringsasfactors-an-unauthorized-biography/) and [*stringsAsFactors = \<sigh\>*](http://notstatschat.tumblr.com/post/124987394001/stringsasfactors-sigh), and compares the tidy approaches to categorical data outlined in this book with base R methods.
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An early version of the paper helped motivate and scope the forcats package; thanks Amelia & Nick!
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### Prerequisites
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To work with factors, we'll use the **forcats** package, which is part of the core tidyverse.
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Base R some basic tools for creating and manipulating factors.
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We'll supplement these with the **forcats** package, which is part of the core tidyverse.
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It provides tools for dealing with **cat**egorical variables (and it's an anagram of factors!) using a wide range of helpers for working with factors.
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```{r setup, message = FALSE}
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library(tidyverse)
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```
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## Creating factors
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## Factor basics
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Imagine that you have a variable that records month:
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@ -58,7 +63,7 @@ y1
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sort(y1)
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```
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And any values not in the set will be silently converted to NA:
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And any values not in the level will be silently converted to NA:
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```{r}
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y2 <- factor(x2, levels = month_levels)
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@ -107,7 +112,7 @@ gss_cat
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(Remember, since this dataset is provided by a package, you can get more information about the variables with `?gss_cat`.)
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When factors are stored in a tibble, you can't see their levels so easily.
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One way to see them is with `count()`:
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One way to view them is with `count()`:
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```{r}
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gss_cat |>
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@ -125,26 +130,6 @@ ggplot(gss_cat, aes(race)) +
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geom_bar()
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```
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By default, ggplot2 will drop levels that don't have any values.
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You can force them to display with:
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```{r}
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#> fig.alt: >
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#> The same bar chart as the last plot, but now with an missing bar on
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#> the far right with label "Not applicable".
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ggplot(gss_cat, aes(race)) +
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geom_bar() +
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scale_x_discrete(drop = FALSE)
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```
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These levels represent valid values that simply did not occur in this dataset.
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In dplyr::count() set the `.drop` option to `FALSE`, to show these.
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```{r}
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gss_cat |>
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count(race, .drop = FALSE)
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```
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When working with factors, the two most common operations are changing the order of the levels, and changing the values of the levels.
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Those operations are described in the sections below.
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@ -183,7 +168,7 @@ ggplot(relig_summary, aes(tvhours, relig)) +
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geom_point()
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```
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It is difficult to interpret this plot because there's no overall pattern.
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It is hard to read this plot because there's no overall pattern.
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We can improve it by reordering the levels of `relig` using `fct_reorder()`.
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`fct_reorder()` takes three arguments:
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@ -207,7 +192,9 @@ For example, you could rewrite the plot above as:
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```{r, eval = FALSE}
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relig_summary |>
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mutate(relig = fct_reorder(relig, tvhours)) |>
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mutate(
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relig = fct_reorder(relig, tvhours)
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) |>
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ggplot(aes(tvhours, relig)) +
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geom_point()
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```
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@ -253,8 +240,8 @@ ggplot(rincome_summary, aes(age, fct_relevel(rincome, "Not applicable"))) +
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Why do you think the average age for "Not applicable" is so high?
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Another type of reordering is useful when you are coloring the lines on a plot.
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`fct_reorder2()` reorders the factor by the `y` values associated with the largest `x` values.
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This makes the plot easier to read because the line colurs line up with the legend.
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`fct_reorder2(f, x, y)` reorders the factor `f` by the `y` values associated with the largest `x` values.
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This makes the plot easier to read because the colors of the line at the far right of the plot will line up with the legend.
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```{r, fig.align = "default", out.width = "50%", fig.width = 4}
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#| fig.alt:
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@ -288,7 +275,7 @@ ggplot(by_age, aes(age, prop, colour = fct_reorder2(marital, age, prop))) +
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```
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Finally, for bar plots, you can use `fct_infreq()` to order levels in increasing frequency: this is the simplest type of reordering because it doesn't need any extra variables.
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You may want to combine with `fct_rev()`.
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Combine it with `fct_rev()` if you want the largest values on the right, not the left.
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```{r}
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#| fig.alt: >
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@ -324,6 +311,7 @@ gss_cat |> count(partyid)
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The levels are terse and inconsistent.
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Let's tweak them to be longer and use a parallel construction.
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Like most rename and recoding functions in the tidyverse, the new values go on the left and the old values go on the right:
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```{r}
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gss_cat |>
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count(partyid)
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```
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`fct_recode()` will leave levels that aren't explicitly mentioned as is, and will warn you if you accidentally refer to a level that doesn't exist.
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`fct_recode()` will the leave levels that aren't explicitly mentioned as is, and will warn you if you accidentally refer to a level that doesn't exist.
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To combine groups, you can assign multiple old levels to the same new level:
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count(partyid)
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```
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You must use this technique with care: if you group together categories that are truly different you will end up with misleading results.
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Use this technique with care: if you group together categories that are truly different you will end up with misleading results.
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If you want to collapse a lot of levels, `fct_collapse()` is a useful variant of `fct_recode()`.
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For each new variable, you can provide a vector of old levels:
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gss_cat |>
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mutate(
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partyid = fct_collapse(partyid,
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other = c("No answer", "Don't know", "Other party"),
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rep = c("Strong republican", "Not str republican"),
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ind = c("Ind,near rep", "Independent", "Ind,near dem"),
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dem = c("Not str democrat", "Strong democrat")
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"other" = c("No answer", "Don't know", "Other party"),
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"rep" = c("Strong republican", "Not str republican"),
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"ind" = c("Ind,near rep", "Independent", "Ind,near dem"),
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"dem" = c("Not str democrat", "Strong democrat")
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)
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) |>
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count(partyid)
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```
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Sometimes you just want to lump together all the small groups to make a plot or table simpler.
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Sometimes you just want to lump together the small groups to make a plot or table simpler.
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That's the job of the `fct_lump_*()` family of functions.
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`fct_lump_lowfreq()` is a simple starting point that progressively lumps the smallest groups categories into "Other", always keeping "Other" as the smallest category.
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print(n = Inf)
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```
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Read the documentation to learn about `fct_lump_min()` and `fct_lump_prop()` which are useful in other cases.
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### Exercises
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1. How have the proportions of people identifying as Democrat, Republican, and Independent changed over time?
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@ -40,6 +40,7 @@ There are two missing values in this dataset:
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One way to think about the difference is with this Zen-like koan:
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> An explicit missing value is the presence of an absence.\
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>
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> An implicit missing value is the absence of a presence.
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### Pivoting
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@ -239,6 +240,18 @@ health |>
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Main con of this approach is that you need to carefully specify the `fill` argument so that
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By default, ggplot2 will drop levels that don't have any values.
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You can force them to display with by using `drop = FALSE` on the discrete axis:
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```{r}
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#| fig.alt: >
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#| The same bar chart as the last plot, but now with an missing bar on
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#| the far right with label "Not applicable".
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ggplot(gss_cat, aes(race)) +
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geom_bar() +
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scale_x_discrete(drop = FALSE)
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```
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## NaN
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Special not a number.
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