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
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.
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