My problem is as follows:
I have a data set containing several factor variables, which have the same categories. I need to find the category, which occurs most frequently for each row. In case of ties an arbitrary value can be chosen, although it would be great if I can have more control over it.
My data set contains over a hundred factors. However, the structure is something like that:
df = data.frame(id = 1:3
var1 = c("red","yellow","green")
var2 = c("red","yellow","green")
var3 = c("yellow","orange","green")
var4 = c("orange","green","yellow"))
df
# id var1 var2 var3 var4
# 1 1 red red yellow orange
# 2 2 yellow yellow orange green
# 3 3 green green green yellow
The solution should be a variable within the data frame, for example var5, which contains the most frequent category for each row. It can be a factor or a numeric vector (in case the data need to be converted first to numeric vectors)
In this case, I would like to have this solution:
df$var5
# [1] "red" "yellow" "green"
Any advice will be much appreciated! Thanks in advance!
Something like :
apply(df,1,function(x) names(which.max(table(x))))
[1] "red" "yellow" "green"
In case there is a tie, which.max takes the first max value. From the which.max help page :
Determines the location, i.e., index of the (first) minimum or maximum of a numeric vector.
Ex :
var4 <- c("yellow","green","yellow")
df <- data.frame(cbind(id, var1, var2, var3, var4))
> df
id var1 var2 var3 var4
1 1 red red yellow yellow
2 2 yellow yellow orange green
3 3 green green green yellow
apply(df,1,function(x) names(which.max(table(x))))
[1] "red" "yellow" "green"