Today, we’re going to cover another variable class in R: factors. We’ll review how to create and modify factors.
A (very) short glossary of terms
factor - A type of variable in R that takes on a limited number of different values, i.e., a categorical variable.
level - All possible ‘categories’ of a factor.
Set-up
For the past few weeks, we’ve been exploring the world of tidyverse. We’ll continue to use tidyverse, and specifically, some functions from forcats, which is newly built into tidyverse.
library(tidyverse)
We’ll use a subset of the General Social Survey because it has a number of categorical variables. It can be loaded from forcats.
# Load the data
forcats::gss_cat
Looking at the data + a quick review of ggplot & piping
# Take a first look
gss_cat
For a quick review, take a look at past office hours sessions on ggplot and piping.
How many observations are in the dataset?
How many variables are in the dataset?
How many of them are factor variables?
What are the levels of the factor variables?
Like many things in R, there are multiple ways to take a look at the levels of a factor. Here are a few:
levels(gss_cat$race) # Access the levels directly
## [1] "Other" "Black" "White" "Not applicable"
gss_cat %>% count(race) # Count observations in each level
## # A tibble: 3 x 2
## race n
## <fct> <int>
## 1 Other 1959
## 2 Black 3129
## 3 White 16395
Note that each of these commands give different output – count gives you the number of observations in each category, but doesn’t include levels with zero counts in its table output.
Let’s take a look at some visualizations.
ggplot(gss_cat, aes(race)) +
geom_bar()
Note that ggplot drops levels that don’t have any values. ggplot has an option to show all levels:
## [1] Dec Apr <NA> Mar
## Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Changing factor order
You can take a look at all of the functions in the forcats package here.
When defining levels, their order matters. Taking a look at month_levels that we assigned above, we will see that the order in which we assigned the levels is retained.
It can be useful to change the order of factor levels for visualizations.
We’ll go over summarise and group_by in detail next week, but let’s use them here to quickly construct something we can use for our visualization example.
There’s not much of a pattern here that we can see. Let’s reorder the factor levels according to tvhours using fct_reorder from the forcats package.
relig_summary %>% # Call the dataset that you want to use
# Redefine the levels of the `relig` factor variable
mutate(relig = fct_reorder(relig, tvhours)) %>%
ggplot(aes(tvhours, relig)) +
geom_point()
We can see from this example that fct_reorder reorders the order of factors according to another variable.
Think before reordering – some categorical variables already have a principaled order. In the following example, it wouldn’t make sense to reorder income levels by increasing age.
It could also make sense to move the “Not applicable” to a spot that makes more sense. For this, we’ll use fct_relevel. Instead of designating a second variable to define the order, fct_relevel takes any levels that you want to move to the front of the list.
## # A tibble: 8 x 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
## 7 Democrat, weak 3690
## 8 Democrat, strong 3490
Using case_when() to create new levels conditionally
case_when() is an alternative to ifelse(). It can be useful when you want to create new variables that relies on existing variables (i.e., with mutate).
Let’s recode the age variable using case_when().
gss_cat %>%
mutate(
age_cat = case_when(
age < 40 ~ "youngest",
age >= 40 | age <= 50 ~ "middle",
age > 50 ~ "oldest"
)
)
## # A tibble: 21,483 x 10
## year marital age race rincome partyid relig denom tvhours age_cat
## <int> <fct> <int> <fct> <fct> <fct> <fct> <fct> <int> <chr>
## 1 2000 Never m… 26 White $8000 t… Ind,ne… Prot… Sout… 12 younge…
## 2 2000 Divorced 48 White $8000 t… Not st… Prot… Bapt… NA middle
## 3 2000 Widowed 67 White Not app… Indepe… Prot… No d… 2 middle
## 4 2000 Never m… 39 White Not app… Ind,ne… Orth… Not … 4 younge…
## 5 2000 Divorced 25 White Not app… Not st… None Not … 1 younge…
## 6 2000 Married 25 White $20000 … Strong… Prot… Sout… NA younge…
## 7 2000 Never m… 36 White $25000 … Not st… Chri… Not … 3 younge…
## 8 2000 Divorced 44 White $7000 t… Ind,ne… Prot… Luth… NA middle
## 9 2000 Married 44 White $25000 … Not st… Prot… Other 0 middle
## 10 2000 Married 47 White $25000 … Strong… Prot… Sout… 3 middle
## # ... with 21,473 more rows
The TRUE here captures everything that hasn’t met one of the earlier criteria. Here’s another example:
A helpful function to find which variables are NA – and keep them that way – is is.na(). So if you know the variable you’re working with has missing values, the first line of your case_when() statement might be: is.na ~ NA_character_. One tricky thing with case_when() occurs when you have NA values. It turns out that there’s actually a different version of NA that you have to use with case_when() whenever you want to create an NA. In the above example, if we wanted to code all the “other” observations as missing, we would have to give them the value NA_character_ because we’re assigning the other values to be character labels. (We can later turn the whole variable into a factor variable.) If instead they were numbers, we could use NA_real_.
