Calculating Median and Mode of numerical variables for different subgroups in R

Question

I have customer call data and I want to get the median and mode for the call success rate for different subgroups.

My variables are: Customer ID, Employment Status (Retired, Employed, Unemployed), count of calls, count of successful calls, call success rate (successful calls/number of calls)

So far I worked in Excel to get the average call reachability. Mode and median are not easy to calculate with Excel, because it can only do it for up to 255 recods I believe. I know that the summary command in R provides you with summary statistics for each variable but what if you want to get the median and mode of each subgroup. Meaning: Median and Mode of call succcess rate for Retired, Employed and Unemployed customers separately? Why I want to calculate the Median and Mode, although I have the average already? Because both are more resistant to outliers. But I am open for discussion if you think it is not necessary :)

Answer 1

assuming your data is stored in an object named df you can do:

tapply(df$S_Calls, df$Emp_Stat, median)

As for the mode, oddly enough R does not have a built in function for that. You could define one yourself using:

mode_stat <- function(x) {
  ux <- na.omit(unique(x))
  ux[which.max(tabulate(match(x, ux)))]
}

and then do in a similar fashion:

tapply(df$S_Calls, df$Emp_Stat, mode_stat)

Answer 2

Since you mentioned that you have the data in excel, first import it in R:

df <- readxl::excel_sheets(path = "file.xlsx")

then calculate median per Emp_Stat:

sapply(unique(df$Emp_Stat), function(x){
    sapply(df[df$Emp_Stat == x, ], median)
})

and for mode:

# for every Emp_Stat
sapply(unique(df$Emp_Stat), function(x1){
    # for every column
    sapply(df[df$Emp_Stat == x1, ], function(x2){
        which.max(table(x2))
    })
})

Ps. What I mentioned and the answer Iyar Lin provided are essentially the same, just using different functions.