```
library(forcats)
<- factor(c("G234", "G452", "G136"))
x <- fct_recode(x, Physics = "G234", Math = "G452", Chemistry = "G136")
y y
```

```
[1] Physics Math Chemistry
Levels: Chemistry Physics Math
```

As what we have mentioned in the previous chapter, R sorts levels of factors in alphabetical order by default. In this chapter we will talk about working with factors using **forcats** package, which can be helpful when you managing categorical variables.

**Don’t** directly assign levels with `levels()<-`

. Instead, using `fct_recode()`

.

```
library(forcats)
<- factor(c("G234", "G452", "G136"))
x <- fct_recode(x, Physics = "G234", Math = "G452", Chemistry = "G136")
y y
```

```
[1] Physics Math Chemistry
Levels: Chemistry Physics Math
```

For the binned, ordinal data with levels out of order, `fct_relevel()`

can be used to set a correct order.

```
library(tibble)
library(ggplot2)
<- tibble(MotherAge = c("15-19 years", "20-24 years", "25-29 years", "30-34 years", "35-39 years", "40-44 years", "45-49 years", "50 years and over", "Under 15 years"), Num = c(229.715, 850.509, 1152.311, 1094.693, 527.996, 111.848, 8.171, .754, 2.5))
Births2015
ggplot(Births2015, aes(fct_relevel(MotherAge, "Under 15 years"), Num)) +
geom_col() +
coord_flip() +
scale_y_continuous(breaks = seq(0, 1250, 250)) +
ggtitle("United States Births, 2015", subtitle = "in thousands") +
theme_grey(16) +
labs(y = "mother age", x = "count")
```

The following examples give three circumstances when using `fct_relevel()`

.

- Using
`fct_relevel()`

to move levels to the beginning:

```
<- c("A", "B", "C", "move1", "D", "E", "move2", "F")
x fct_relevel(x, "move1", "move2")
```

```
[1] A B C move1 D E move2 F
Levels: move1 move2 A B C D E F
```

- Using
`fct_relevel()`

to move levels after an item (by position):

```
<- c("A", "B", "C", "move1", "D", "E", "move2", "F")
x fct_relevel(x, "move1", "move2", after = 4) # move after the fourth item
```

```
[1] A B C move1 D E move2 F
Levels: A B C D move1 move2 E F
```

- Using
`fct_relevel()`

to move levels to the end

```
<- c("A", "B", "C", "move1", "D", "E", "move2", "F")
x fct_relevel(x, "move1", "move2", after = Inf)
```

```
[1] A B C move1 D E move2 F
Levels: A B C D E F move1 move2
```

If the row order is correct, use `fct_inorder()`

:

```
<- data.frame(temperature = factor(c("cold", "warm", "hot")),
df count = c(15, 5, 22))
# row order is correct (think: factor in ROW order)
ggplot(df, aes(x = fct_inorder(temperature), y = count)) +
geom_col() +
theme_grey(16) +
labs( x = "temperature")
```

Usually, unbinned, nominal data should be sorted by frequency order, which can be achieved using `fct_infreq()`

(default is decreasing order of frequency)

```
<- data.frame(
df color = c("orange","blue", "red","brown","yellow", "green", "orange", "red", "yellow","blue","blue","red","orange","blue","red","orange","orange")
)
ggplot(df, aes(fct_infreq(color))) +
geom_bar() +
theme_grey(16)
```

For binned, nominal data which should be sorted by frequency order, use `fct_reorder()`

. In the following example **count** is used, generally you can also apply **mean**,**median**, etc. to `.fun`

inside `fct_reorder()``.

```
<- data.frame(
pack1 color = c("blue", "brown", "green", "orange", "red", "yellow"),
count = c(13, 7, 12, 9, 7, 8)
)
ggplot(pack1, aes(fct_reorder(color, count, .desc = TRUE), count)) +
geom_col() +
theme_grey(16) +
labs(x = "color")
```

For prominent NA bars which should not be eliminated, use `fct_explicit_na(x)`

. And using `fct_rev(x)`

to reverse the factor level **doesn’t** help.

```
library(dplyr)
<- data.frame(temperature = factor(c("cold", "warm", "hot", NA)), count = c(15, 5, 22, 12))
df
%>%
df mutate(temperature = fct_explicit_na(temperature, "NA") %>%
fct_relevel("NA", "hot", "warm", "cold")) %>%
ggplot(aes(x = temperature, y = count)) +
geom_col() +
coord_flip() +
theme_grey(16) +
labs(x = "temperature")
```

For analyzing categorical variables, the first step is always to decide whether the class is ordinal or nominal.

fct_recode(x, …) – change names of levels

fct_inorder(x) – set level order of x to row order

fct_relevel(x, …) – manually set the order of levels of x

fct_reorder(x, y) – reorder x by y

fct_infreq(x) – order the levels of x by decreasing frequency

fct_rev(x) – reverse the order of factor levels of x

fct_explicit_na(x) – turn NAs into a real factor level

Sometimes you want to transfer a continuous variable to a categorical variable. For example, you might want assign grades to final scores of a course. In the following example, we generated a data set of test scores randomly and we assign grades based on some thresholds. We then apply function `cut`

. (You can similarly use `case_when`

)

```
set.seed(2022)
<- round(runif(100, min = 70, max = 100))
testscore
<- data.frame(testscore) |>
df mutate(grade = cut(testscore, breaks = seq(70, 100, 10),
labels = c("C", "B", "A"), right = FALSE,
include.lowest = TRUE))
head(df)
```

```
testscore grade
1 94 A
2 89 B
3 74 C
4 86 B
5 76 C
6 89 B
```