CODEWITHSUNDEEP
rsummarycategorical-data
Anita
Anita

Reputation: 789

R: making a different between categorical and numeric predictors

I've got the following code:

isNoun <- as.factor(isNoun)
isVerb <- as.factor(isVerb)
labels <- as.factor(labels)

alles <- matrix(c(isNoun, isVerb, length,labels), nrow=388,ncol=4)
alles_df <- as.data.frame(alles)
summary(alles_df)
> summary(alles_df)
       V1               V2                V3               V4        
 Min.   :0.0000   Min.   :0.00000   Min.   : 3.000   Min.   :0.0000  
 1st Qu.:1.0000   1st Qu.:0.00000   1st Qu.: 5.000   1st Qu.:0.0000  
 Median :1.0000   Median :0.00000   Median : 6.500   Median :0.0000  
 Mean   :0.9098   Mean   :0.01546   Mean   : 7.193   Mean   :0.2938  
 3rd Qu.:1.0000   3rd Qu.:0.00000   3rd Qu.: 9.000   3rd Qu.:1.0000  
 Max.   :1.0000   Max.   :1.00000   Max.   :18.000   Max.   :1.0000

where isVerb, isNoun and labels are codes as 0 (= no) and 1 (=yes). Now I would like to treat these three variables as categorical variables, so I would like to get a frequency table of isNoun, isVerb and labels, instead of a mean. I've tried this with the function as.factor(isNoun), but it didn't work.

Upvotes: 0

Views: 304

Answers (1)

Juli&#225;n Urbano
Juli&#225;n Urbano

Reputation: 8488

The problem is that you first create a matrix, whose elements must be of the same type, and then you convert to data frame. You have to create the data frame from the beginning:

alles <- data.frame(isNoun = as.factor(isNoun),
                    isVerb = as.factor(isVerb),
                    length,
                    labels = as.factor(labels))

Upvotes: 2

Related Questions