# Dealing with Factors

Factors are used to represent categorical data and can be unordered or ordered. One can think of a factor as an integer vector where each integer has a label. In fact, factors are built on top of integer vectors using two attributes: the class(), “factor”, which makes them behave differently from regular integer vectors, and the levels(), which defines the set of allowed values. Factors are important in statistical modeling and are treated specially by modelling functions like lm() and glm(). This section will provide you the basics of managing categorical data as factors.

## Creating, Converting & Inspecting Factors

Factor objects can be created with the factor() function:

# create a factor string
gender <- factor(c("male", "female", "female", "male", "female"))
gender
## [1] male   female female male   female
## Levels: female male

# inspect to see if it is a factor class
class(gender)
## [1] "factor"

# show that factors are just built on top of integers
typeof(gender)
## [1] "integer"

# See the underlying representation of factor
unclass(gender)
## [1] 2 1 1 2 1
## attr(,"levels")
## [1] "female" "male"

# what are the factor levels?
levels(gender)
## [1] "female" "male"

# show summary of counts
summary(gender)
## female   male
##      3      2


If we have a vector of character strings or integers we can easily convert to factors:

group <- c("Group1", "Group2", "Group2", "Group1", "Group1")
str(group)
##  chr [1:5] "Group1" "Group2" "Group2" "Group1" "Group1"

# convert from characters to factors
as.factor(group)
## [1] Group1 Group2 Group2 Group1 Group1
## Levels: Group1 Group2


## Ordering, Revaluing, & Dropping Factor Levels

We can easily order, revalue, and drop factor levels as the following illustrates.

### Ordering Levels

When creating a factor we can control the ordering of the levels by using the levels argument:

# when not specified the default puts order as alphabetical
gender <- factor(c("male", "female", "female", "male", "female"))
gender
## [1] male   female female male   female
## Levels: female male

# specifying order
gender <- factor(c("male", "female", "female", "male", "female"),
levels = c("male", "female"))
gender
## [1] male   female female male   female
## Levels: male female


We can also create ordinal factors in which a specific order is desired by using the ordered = TRUE argument. This will be reflected in the output of the levels as shown below in which low < middle < high:

ses <- c("low", "middle", "low", "low", "low", "low", "middle", "low", "middle",
"middle", "middle", "middle", "middle", "high", "high", "low", "middle",
"middle", "low", "high")

# create ordinal levels
ses <- factor(ses, levels = c("low", "middle", "high"), ordered = TRUE)
ses
##  [1] low    middle low    low    low    low    middle low    middle middle
## [11] middle middle middle high   high   low    middle middle low    high
## Levels: low < middle < high

# you can also reverse the order of levels if desired
factor(ses, levels=rev(levels(ses)))
##  [1] low    middle low    low    low    low    middle low    middle middle
## [11] middle middle middle high   high   low    middle middle low    high
## Levels: high < middle < low


### Revalue Levels

To recode factor levels I usually use the revalue() function from the plyr package.

plyr::revalue(ses, c("low" = "small", "middle" = "medium", "high" = "large"))
##  [1] small  medium small  small  small  small  medium small  medium medium
## [11] medium medium medium large  large  small  medium medium small  large
## Levels: small < medium < large


Using the :: notation allows you to access the revalue() function without having to fully load the plyr package.

### Dropping Levels

When you want to drop unused factor levels, use droplevels():

ses2 <- ses[ses != "middle"]

# lets say you have no observations in one level
summary(ses2)
##    low middle   high
##      8      0      3

# you can drop that level if desired
droplevels(ses2)
##  [1] low  low  low  low  low  low  high high low  low  high
## Levels: low < high