frequency table and group by multiple variables in r

Question

Folks, I need an elegant way of creating frequency count and group by multiple variables. Output should be a dataframe. I know the answer lies somewhere in using dplyr and data.table which I am still learning. I tried this link but I want to do this using dplyr and data.table.

Here is the sample data from the same link -

ID <- seq(1:177)
Age <- sample(c("0-15", "16-29", "30-44", "45-64", "65+"), 177, replace = TRUE)
Sex <- sample(c("Male", "Female"), 177, replace = TRUE)
Country <- sample(c("England", "Wales", "Scotland", "N. Ireland"), 177, replace = TRUE)
Health <- sample(c("Poor", "Average", "Good"), 177, replace = TRUE)
Survey <- data.frame(Age, Sex, Country, Health)

Here is the output I am looking for. Thanks and appreciate your help!

Answer 1

We can use dcast from data.table

library(data.table)
dcast(setDT(Survey), Age + Sex ~Health, value.var = "Country",
                   length)[, Total := Average + Good + Poor][]

---

If we don't want to type the column names, use Reduce with +

dcast(setDT(Survey), Age + Sex ~Health, value.var = "Country",
                length)[, Total := Reduce(`+`, .SD), .SDcols = Average:Poor][]

Answer 2

Here is a method using data.table and tidyr but not dcast . First, you count observations with .N in j by the variables of interest

Survey[, .N, by=.(Age, Sex, Health)]

returning:

 Age   Sex     Health   N
 30-44 Female  Average  10
 65+   Female  Poor     9
 0-15  Male    Average  3
 16-29 Male    Average  6
 30-44 Male    Good     6
 45-64 Female  Average  8

Then, use spread from tidyr to turn your column of choice into a set of new columns (one for each unique value) populated by N

spread(Survey[, .N, by=.(Age, Sex, Health)], Health, N)