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Categorical variables, including nominal and ordinal variables in R programming language are called factor variables. For example, gender(male/female) is nominal, and survey results (excellent, good, normal, bad) have ordinal values. Categorical variables are useful because many data analysis operations are related to values in different categories, such as contingency tables between two categorical variables for independence analysis, hypothesis testing of homogeneity of variances, just name a few. So usually, the values of those categories must have to be set as factor type in R before the analysis can go further.
In R language, function factor() is used to set or create variable/vector as factor type. The basic form of the function is
factor(var, order)
Where var is a vector, and order is set to TRUE if the values in the vector are ordered.
The following example shows creation of a data frame, in which variable ‘blood’ is an unordered factor variable, and variable ‘status’ is an ordered variable.
#create variable vectors
ID <- c(3, 4, 5, 6)
age <- c(15, 14, 18, 12)
blood <- c("Type5", "Type6", "Type5", "Type6")
status <- c("Poor", "Improved", "Excellent", "Poor")
# to set vector ‘blood’ as an factor without order
blood <- factor(blood)
# to set vector ‘status’ as ordered factor
status <- factor(status, order = TRUE)
# to create a data frame
pdata <- data.frame(ID, age, blood, status)
#to show structure of data frame
str(pdata)
'data.frame': 4 obs. of 4 variables:
$ ID : num 3 4 5 6
$ age : num 15 14 18 12
$ blood : Factor w/ 2 levels "Type5","Type6": 1 2 1 2
$ status: Ord.factor w/ 3 levels "Excellent"<"Improved"<..: 3 2 1 3
#get summary information of data frame
summary(pdata)
> summary(pdata)
ID age blood status
Min. :3.00 Min. :12.00 Type5:2 Excellent:1
1st Qu.:3.75 1st Qu.:13.50 Type6:2 Improved :1
Median :4.50 Median :14.50 Poor :2
Mean :4.50 Mean :14.75
3rd Qu.:5.25 3rd Qu.:15.75
Max. :6.00 Max. :18.00
>
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