You’re reading the first edition of R4DS; for the latest on this topic see the Factors chapter in the second edition.
In R, factors are used to work with 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.
Historically, factors were much easier to work with than characters. As a result, many of the functions in base R automatically convert characters to factors. This means that factors often crop up in places where they’re not actually helpful. Fortunately, you don’t need to worry about that in the tidyverse, and can focus on situations where factors are genuinely useful.
To work with factors, we’ll use the forcats package, which is part of the core tidyverse. It provides tools for dealing with categorical variables (and it’s an anagram of factors!) using a wide range of helpers for working with factors.
If you want to learn more about factors, I recommend reading Amelia McNamara and Nicholas Horton’s paper, Wrangling categorical data in R. This paper lays out some of the history discussed in stringsAsFactors: An unauthorized biography and stringsAsFactors = <sigh>, and compares the tidy approaches to categorical data outlined in this book with base R methods. An early version of the paper help motivate and scope the forcats package; thanks Amelia & Nick!
Imagine that you have a variable that records month:
x1 <- c("Dec", "Apr", "Jan", "Mar")
Using a string to record this variable has two problems:
There are only twelve possible months, and there’s nothing saving you from typos:
x2 <- c("Dec", "Apr", "Jam", "Mar")
It doesn’t sort in a useful way:
sort(x1) #>  "Apr" "Dec" "Jan" "Mar"
You can fix both of these problems with a factor. To create a factor you must start by creating a list of the valid levels:
month_levels <- c( "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec" )
Now you can create a factor:
And any values not in the set will be silently converted to NA:
y2 <- factor(x2, levels = month_levels) y2 #>  Dec Apr <NA> Mar #> Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
If you want a warning, you can use
y2 <- parse_factor(x2, levels = month_levels) #> Warning: 1 parsing failure. #> row col expected actual #> 3 -- value in level set Jam
If you omit the levels, they’ll be taken from the data in alphabetical order:
factor(x1) #>  Dec Apr Jan Mar #> Levels: Apr Dec Jan Mar
Sometimes you’d prefer that the order of the levels match the order of the first appearance in the data. You can do that when creating the factor by setting levels to
unique(x), or after the fact, with
If you ever need to access the set of valid levels directly, you can do so with
levels(f2) #>  "Dec" "Apr" "Jan" "Mar"
It’s often useful to change the order of the factor levels in a visualisation. For example, imagine you want to explore the average number of hours spent watching TV per day across religions:
f, the factor whose levels you want to modify.
x, a numeric vector that you want to use to reorder the levels.
fun, a function that’s used if there are multiple values of
xfor each value of
f. The default value is
Reordering religion makes it much easier to see that people in the “Don’t know” category watch much more TV, and Hinduism & Other Eastern religions watch much less.
relig_summary %>% mutate(relig = fct_reorder(relig, tvhours)) %>% ggplot(aes(tvhours, relig)) + geom_point()
What if we create a similar plot looking at how average age varies across reported income level?
Here, arbitrarily reordering the levels isn’t a good idea! That’s because
rincome already has a principled order that we shouldn’t mess with. Reserve
fct_reorder() for factors whose levels are arbitrarily ordered.
However, it does make sense to pull “Not applicable” to the front with the other special levels. You can use
fct_relevel(). It takes a factor,
f, and then any number of levels that you want to move to the front of the line.
Why do you think the average age for “Not applicable” is so high?
Another type of reordering is useful when you are colouring the lines on a plot.
fct_reorder2() reorders the factor by the
y values associated with the largest
x values. This makes the plot easier to read because the line colours line up with the legend.
by_age <- gss_cat %>% filter(!is.na(age)) %>% count(age, marital) %>% group_by(age) %>% mutate(prop = n / sum(n)) ggplot(by_age, aes(age, prop, colour = marital)) + geom_line(na.rm = TRUE) ggplot(by_age, aes(age, prop, colour = fct_reorder2(marital, age, prop))) + geom_line() + labs(colour = "marital")
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
More powerful than changing the orders of the levels is changing their values. This allows you to clarify labels for publication, and collapse levels for high-level displays. The most general and powerful tool is
fct_recode(). It allows you to recode, or change, the value of each level. For example, take the
The levels are terse and inconsistent. Let’s tweak them to be longer and use a parallel construction.
gss_cat %>% mutate(partyid = fct_recode(partyid, "Republican, strong" = "Strong republican", "Republican, weak" = "Not str republican", "Independent, near rep" = "Ind,near rep", "Independent, near dem" = "Ind,near dem", "Democrat, weak" = "Not str democrat", "Democrat, strong" = "Strong democrat" )) %>% count(partyid) #> # A tibble: 10 × 2 #> partyid n #> <fct> <int> #> 1 No answer 154 #> 2 Don't know 1 #> 3 Other party 393 #> 4 Republican, strong 2314 #> 5 Republican, weak 3032 #> 6 Independent, near rep 1791 #> # ℹ 4 more rows
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.
To combine groups, you can assign multiple old levels to the same new level:
gss_cat %>% mutate(partyid = fct_recode(partyid, "Republican, strong" = "Strong republican", "Republican, weak" = "Not str republican", "Independent, near rep" = "Ind,near rep", "Independent, near dem" = "Ind,near dem", "Democrat, weak" = "Not str democrat", "Democrat, strong" = "Strong democrat", "Other" = "No answer", "Other" = "Don't know", "Other" = "Other party" )) %>% count(partyid) #> # A tibble: 8 × 2 #> partyid n #> <fct> <int> #> 1 Other 548 #> 2 Republican, strong 2314 #> 3 Republican, weak 3032 #> 4 Independent, near rep 1791 #> 5 Independent 4119 #> 6 Independent, near dem 2499 #> # ℹ 2 more rows
You must use this technique with care: if you group together categories that are truly different you will end up with misleading results.
gss_cat %>% mutate(partyid = fct_collapse(partyid, other = c("No answer", "Don't know", "Other party"), rep = c("Strong republican", "Not str republican"), ind = c("Ind,near rep", "Independent", "Ind,near dem"), dem = c("Not str democrat", "Strong democrat") )) %>% count(partyid) #> # A tibble: 4 × 2 #> partyid n #> <fct> <int> #> 1 other 548 #> 2 rep 5346 #> 3 ind 8409 #> 4 dem 7180
Sometimes you just want to lump together all the small groups to make a plot or table simpler. That’s the job of
The default behaviour is to progressively lump together the smallest groups, ensuring that the aggregate is still the smallest group. In this case it’s not very helpful: it is true that the majority of Americans in this survey are Protestant, but we’ve probably over collapsed.
Instead, we can use the
n parameter to specify how many groups (excluding other) we want to keep:
gss_cat %>% mutate(relig = fct_lump(relig, n = 10)) %>% count(relig, sort = TRUE) %>% print(n = Inf) #> # A tibble: 10 × 2 #> relig n #> <fct> <int> #> 1 Protestant 10846 #> 2 Catholic 5124 #> 3 None 3523 #> 4 Christian 689 #> 5 Other 458 #> 6 Jewish 388 #> 7 Buddhism 147 #> 8 Inter-nondenominational 109 #> 9 Moslem/islam 104 #> 10 Orthodox-christian 95