How to get a frequency table of all columns of complete data frame in R?

Question

I want to create a frequency table from a data frame and save it in excel. Using table() function i can only create frequency of a particular column. But I want to create frequency table for all the columns altogether, and for each column the levels or type of variables may differ too. Like kind of summary of a data frame but there will not be mean or other measures, only frequencies. I was trying something like this

 for(i in 1:230){
 rm(tb)
 tb<-data.frame(table(mydata[i]))
 tb2<-cbind(tb2,tb)
 }

But it's showing the following Error

Error in data.frame(..., check.names = FALSE) : arguments imply differing number of rows: 15, 12

In place of cbind() I also used data.frame() but the Error didn't changed.

Answer 1

Maybe an rbind solution is better as it allows you to handle variables with different levels:

dt = data.frame(x = c("A","A","B","C"),
                y = c(1,1,2,1))

dt

#   x y
# 1 A 1
# 2 A 1
# 3 B 2
# 4 C 1

dt_res = data.frame()

for (i in 1:ncol(dt)){

dt_temp = data.frame(t(table(dt[,i])))
dt_temp$Var1 = names(dt)[i]

dt_res = rbind(dt_res, dt_temp)

}

names(dt_res) = c("Variable","Levels","Freq")

dt_res

#   Variable Levels Freq
# 1        x      A    2
# 2        x      B    1
# 3        x      C    1
# 4        y      1    3
# 5        y      2    1

And an alternative (probably faster) process using apply:

dt = data.frame(x = c("A","A","B","C"),
                y = c(1,1,2,1))

dt

ff = function(x){

  y = data.frame(t(table(x)))
  y$Var1 = NULL
  names(y) = c("Levels","Freq")
  return(y)
}

dd = do.call(rbind, apply(dt, 2, ff)) 

dd

#     Levels Freq
# x.1      A    2
# x.2      B    1
# x.3      C    1
# y.1      1    3
# y.2      2    1


# extract variable names from row names
dd$Variable = sapply(row.names(dd), function(x) unlist(strsplit(x,"[.]"))[1])

dd

#     Levels Freq Variable
# x.1      A    2        x
# x.2      B    1        x
# x.3      C    1        x
# y.1      1    3        y
# y.2      2    1        y