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Finish up with factors

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Fixes #728
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Hadley Wickham
2022-05-04 08:41:40 -05:00
parent 0a4a5c3d55
commit 79a761664a
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64
factors.Rmd
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@@ -1,24 +1,29 @@
# Factors # Factors
```{r, results = "asis", echo = FALSE}
status("complete")
```
## Introduction ## Introduction
Factors are used for categorical variables, variables that have a fixed and known set of possible values. Factors are used for categorical variables, variables that have a fixed and known set of possible values.
They are also useful when you want to display character vectors in a non-alphabetical order. They are also useful when you want to display character vectors in a non-alphabetical order.
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/). 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/).
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. 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.
An early version of the paper helped motivate and scope the forcats package; thanks Amelia & Nick! An early version of the paper helped motivate and scope the forcats package; thanks Amelia & Nick!
### Prerequisites ### Prerequisites
To work with factors, we'll use the **forcats** package, which is part of the core tidyverse. Base R some basic tools for creating and manipulating factors.
We'll supplement these with the **forcats** package, which is part of the core tidyverse.
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. 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.
```{r setup, message = FALSE} ```{r setup, message = FALSE}
library(tidyverse) library(tidyverse)
``` ```
## Creating factors ## Factor basics
Imagine that you have a variable that records month: Imagine that you have a variable that records month:
@@ -58,7 +63,7 @@ y1
sort(y1) sort(y1)
``` ```
And any values not in the set will be silently converted to NA: And any values not in the level will be silently converted to NA:
```{r} ```{r}
y2 <- factor(x2, levels = month_levels) y2 <- factor(x2, levels = month_levels)
@@ -107,7 +112,7 @@ gss_cat
(Remember, since this dataset is provided by a package, you can get more information about the variables with `?gss_cat`.) (Remember, since this dataset is provided by a package, you can get more information about the variables with `?gss_cat`.)
When factors are stored in a tibble, you can't see their levels so easily. When factors are stored in a tibble, you can't see their levels so easily.
One way to see them is with `count()`: One way to view them is with `count()`:
```{r} ```{r}
gss_cat |> gss_cat |>
@@ -125,26 +130,6 @@ ggplot(gss_cat, aes(race)) +
geom_bar() geom_bar()
``` ```
By default, ggplot2 will drop levels that don't have any values.
You can force them to display with:
```{r}
#> fig.alt: >
#> The same bar chart as the last plot, but now with an missing bar on
#> the far right with label "Not applicable".
ggplot(gss_cat, aes(race)) +
geom_bar() +
scale_x_discrete(drop = FALSE)
```
These levels represent valid values that simply did not occur in this dataset.
In dplyr::count() set the `.drop` option to `FALSE`, to show these.
```{r}
gss_cat |>
count(race, .drop = FALSE)
```
When working with factors, the two most common operations are changing the order of the levels, and changing the values of the levels. When working with factors, the two most common operations are changing the order of the levels, and changing the values of the levels.
Those operations are described in the sections below. Those operations are described in the sections below.
@@ -183,7 +168,7 @@ ggplot(relig_summary, aes(tvhours, relig)) +
geom_point() geom_point()
``` ```
It is difficult to interpret this plot because there's no overall pattern. It is hard to read this plot because there's no overall pattern.
We can improve it by reordering the levels of `relig` using `fct_reorder()`. We can improve it by reordering the levels of `relig` using `fct_reorder()`.
`fct_reorder()` takes three arguments: `fct_reorder()` takes three arguments:
@@ -207,7 +192,9 @@ For example, you could rewrite the plot above as:
```{r, eval = FALSE} ```{r, eval = FALSE}
relig_summary |> relig_summary |>
mutate(relig = fct_reorder(relig, tvhours)) |> mutate(
relig = fct_reorder(relig, tvhours)
) |>
ggplot(aes(tvhours, relig)) + ggplot(aes(tvhours, relig)) +
geom_point() geom_point()
``` ```
@@ -253,8 +240,8 @@ ggplot(rincome_summary, aes(age, fct_relevel(rincome, "Not applicable"))) +
Why do you think the average age for "Not applicable" is so high? Why do you think the average age for "Not applicable" is so high?
Another type of reordering is useful when you are coloring the lines on a plot. Another type of reordering is useful when you are coloring the lines on a plot.
`fct_reorder2()` reorders the factor by the `y` values associated with the largest `x` values. `fct_reorder2(f, x, y)` reorders the factor `f` by the `y` values associated with the largest `x` values.
This makes the plot easier to read because the line colurs line up with the legend. 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.
```{r, fig.align = "default", out.width = "50%", fig.width = 4} ```{r, fig.align = "default", out.width = "50%", fig.width = 4}
#| fig.alt: #| fig.alt:
@@ -288,7 +275,7 @@ ggplot(by_age, aes(age, prop, colour = fct_reorder2(marital, age, prop))) +
``` ```
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. 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.
You may want to combine with `fct_rev()`. Combine it with `fct_rev()` if you want the largest values on the right, not the left.
```{r} ```{r}
#| fig.alt: > #| fig.alt: >
@@ -324,6 +311,7 @@ gss_cat |> count(partyid)
The levels are terse and inconsistent. The levels are terse and inconsistent.
Let's tweak them to be longer and use a parallel construction. Let's tweak them to be longer and use a parallel construction.
Like most rename and recoding functions in the tidyverse, the new values go on the left and the old values go on the right:
```{r} ```{r}
gss_cat |> gss_cat |>
@@ -340,7 +328,7 @@ gss_cat |>
count(partyid) count(partyid)
``` ```
`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. `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.
To combine groups, you can assign multiple old levels to the same new level: To combine groups, you can assign multiple old levels to the same new level:
@@ -362,7 +350,7 @@ gss_cat |>
count(partyid) count(partyid)
``` ```
You must use this technique with care: if you group together categories that are truly different you will end up with misleading results. Use this technique with care: if you group together categories that are truly different you will end up with misleading results.
If you want to collapse a lot of levels, `fct_collapse()` is a useful variant of `fct_recode()`. If you want to collapse a lot of levels, `fct_collapse()` is a useful variant of `fct_recode()`.
For each new variable, you can provide a vector of old levels: For each new variable, you can provide a vector of old levels:
@@ -371,16 +359,16 @@ For each new variable, you can provide a vector of old levels:
gss_cat |> gss_cat |>
mutate( mutate(
partyid = fct_collapse(partyid, partyid = fct_collapse(partyid,
other = c("No answer", "Don't know", "Other party"), "other" = c("No answer", "Don't know", "Other party"),
rep = c("Strong republican", "Not str republican"), "rep" = c("Strong republican", "Not str republican"),
ind = c("Ind,near rep", "Independent", "Ind,near dem"), "ind" = c("Ind,near rep", "Independent", "Ind,near dem"),
dem = c("Not str democrat", "Strong democrat") "dem" = c("Not str democrat", "Strong democrat")
) )
) |> ) |>
count(partyid) count(partyid)
``` ```
Sometimes you just want to lump together all the small groups to make a plot or table simpler. Sometimes you just want to lump together the small groups to make a plot or table simpler.
That's the job of the `fct_lump_*()` family of functions. That's the job of the `fct_lump_*()` family of functions.
`fct_lump_lowfreq()` is a simple starting point that progressively lumps the smallest groups categories into "Other", always keeping "Other" as the smallest category. `fct_lump_lowfreq()` is a simple starting point that progressively lumps the smallest groups categories into "Other", always keeping "Other" as the smallest category.
@@ -400,6 +388,8 @@ gss_cat |>
print(n = Inf) print(n = Inf)
``` ```
Read the documentation to learn about `fct_lump_min()` and `fct_lump_prop()` which are useful in other cases.
### Exercises ### Exercises
1. How have the proportions of people identifying as Democrat, Republican, and Independent changed over time? 1. How have the proportions of people identifying as Democrat, Republican, and Independent changed over time?

13
missing-values.Rmd
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@@ -40,6 +40,7 @@ There are two missing values in this dataset:
One way to think about the difference is with this Zen-like koan: One way to think about the difference is with this Zen-like koan:
> An explicit missing value is the presence of an absence.\ > An explicit missing value is the presence of an absence.\
>
> An implicit missing value is the absence of a presence. > An implicit missing value is the absence of a presence.
### Pivoting ### Pivoting
@@ -239,6 +240,18 @@ health |>
Main con of this approach is that you need to carefully specify the `fill` argument so that Main con of this approach is that you need to carefully specify the `fill` argument so that
By default, ggplot2 will drop levels that don't have any values.
You can force them to display with by using `drop = FALSE` on the discrete axis:
```{r}
#| fig.alt: >
#| The same bar chart as the last plot, but now with an missing bar on
#| the far right with label "Not applicable".
ggplot(gss_cat, aes(race)) +
geom_bar() +
scale_x_discrete(drop = FALSE)
```
## NaN ## NaN
Special not a number. Special not a number.