I would like to get multiple frequency tables within a single table.
Here is my data:
df<-read.table(text=" group score night ticket book gender course
A Y 1 0 0 Male M
A Y 1 0 0 Female N
A N 1 1 1 Female N
A Y 2 1 1 Female M
A Y 2 1 1 Male N
A Y 2 0 0 Female N
A N 3 1 0 Male N
B N 3 1 1 Female N
B N 1 0 1 Female M
B Y 1 0 1 Female M
",header=TRUE)
and the output would be :
Frequency Percent
Group
A 7 70
B 3 30
score
Y 4 40
N 6 60
night
1 5 50
2 3 30
3 2 20
book
0 4 40
1 6 60
gender
Female 7 70
Male 3 30
course
M 4 40
N 6 60
I have used the following codes:
df%>%
group_by( group, score, night, ticket, book, gender, course) %>%
summarise(n = n()) %>%
mutate(freq = n / sum(n)
But it did not work for.
A general solution would be to apply the table function on each column of your frame. Typically table returns a named-vector, but you want a more frame-like presentation, so we'll augment that with as.data.frame.table.
lst2 <- lapply(df, function(x) {
out <- as.data.frame.table(table(x))
out$Pct <- 100*out$Freq/sum(out$Freq)
out
})
# or code-golf:
# lapply(df, function(x) transform(as.data.frame.table(table(x)), Pct = 100*Freq/sum(Freq)))
lst2
# $group
# x Freq Pct
# 1 A 7 70
# 2 B 3 30
# $score
# x Freq Pct
# 1 N 4 40
# 2 Y 6 60
# $night
# x Freq Pct
# 1 1 5 50
# 2 2 3 30
# 3 3 2 20
# $ticket
# x Freq Pct
# 1 0 5 50
# 2 1 5 50
# $book
# x Freq Pct
# 1 0 4 40
# 2 1 6 60
# $gender
# x Freq Pct
# 1 Female 7 70
# 2 Male 3 30
# $course
# x Freq Pct
# 1 M 4 40
# 2 N 6 60
You can combine all of these elements with something like:
do.call(rbind, c(Map(cbind, nm=names(lst2), lst2), list(make.row.names = FALSE)))
# nm x Freq Pct
# 1 group A 7 70
# 2 group B 3 30
# 3 score N 4 40
# 4 score Y 6 60
# 5 night 1 5 50
# 6 night 2 3 30
# 7 night 3 2 20
# 8 ticket 0 5 50
# 9 ticket 1 5 50
# 10 book 0 4 40
# 11 book 1 6 60
# 12 gender Female 7 70
# 13 gender Male 3 30
# 14 course M 4 40
# 15 course N 6 60
Edited to remove the row names by default.