# Trying to get frequnecy counts and percent by group of each column in data frame in R

Solution for Trying to get frequnecy counts and percent by group of each column in data frame in R
is Given Below:

I have data that look like this:

``````   pat# sex race    group   bmi
1   F   Black   1   4
2   M   Asian   2   8
3   M   Asian   3   19
4   M   Asian   1   35
5   F   Black   2   12
6   F   Black   3   33
7   M   White   1   2
8   F   Black   2   35
9   M   Asian   3   6
10  F   Black   1   13
11  F   Black   2   18
12  F   Asian   3   1
13  M   White   1   36
14  F   Asian   2   25
15  M   White   3   6
16  M   White   1   20
17  F   Black   2   3
18  M   Asian   3   23
19  F   Black   1   26
20  F   Asian   2   13
21  M   White   3   21
22  M   White   1   16
23  F   Black   2   29
24  F   Black   3   19
25  M   Asian   1   17
26  M   Asian   2   22
27  F   Black   3   26
``````

I would like to get the frequency of each variable and percent by group of each variable, like this:

``````        n           1   2   3
sex M   frequency   %   %   %
F   frequency   %   %   %
``````

next variable:

``````                n          1    2   3
race    White   frequency   %   %   %
Asian   frequency   %   %   %
Black   frequency   %   %   %
``````

There are a lot of variables so I would rather not list each one. I’ve tried to use R’s vector feature (`df[2:30]`) using `xtabs()` and `dplyr` package but am not getting it to work. Which package or function doesn’t matter but would like to make it flexible enough for future data that uses different column names and have different dimensions. Any advice is greatly appreciated!!

One way is to use `janitor` package, but it will also change the totals into percents:

``````library(janitor)

df %>%
tabyl(sex, group) %>%

sex      1      2      3   Total
F 21.43% 50.00% 28.57% 100.00%
M 46.15% 15.38% 38.46% 100.00%

#But we could also choose counts
df %>%
tabyl(sex, group) %>%

sex 1 2 3 Total
F 3 7 4    14
M 6 2 5    13
``````

If you need frequencies, you could do:

``````lapply(df[2:3], table, df\$group)
\$race

1 2 3
Asian 2 4 4
Black 3 5 3
White 4 0 2

\$group

1 2 3
1 9 0 0
2 0 9 0
3 0 0 9
``````

If you need percentages, you will have to define what percentage you need, ie rowwise, columnwise, total etc

If you need rowwise:

``````lapply(df[2:3], function(x)prop.table(table(x, df\$group),1)*100)
\$sex

x          1        2        3
F 21.42857 50.00000 28.57143
M 46.15385 15.38462 38.46154

\$race

x              1        2        3
Asian 20.00000 40.00000 40.00000
Black 27.27273 45.45455 27.27273
White 66.66667  0.00000 33.33333
``````

I was able to do this using the `table()` function and `tigerstats` package. The main problem I was having was R will treat a SAS dataset differently than a CSV dataset. Night and day!

A `tidyverse` way would be –

``````library(tidyverse)

df %>%
count(sex, group) %>%
group_by(sex) %>%
mutate(n = prop.table(n) * 100) %>%
pivot_wider(names_from = group, values_from = n, values_fill = 0)

#  sex     `1`   `2`   `3`
#  <chr> <dbl> <dbl> <dbl>
#1 F      21.4  50    28.6
#2 M      46.2  15.4  38.5
``````

If you want to do this for multiple variables you can use `map`

``````cols <- c('sex', 'race')

map(cols, ~df %>%
count(.data[[.x]], group) %>%
group_by(.data[[.x]]) %>%
mutate(n = prop.table(n) * 100) %>%
pivot_wider(names_from = group, values_from = n, values_fill = 0) %>%
ungroup)

#[[1]]
# A tibble: 2 x 4
#  sex     `1`   `2`   `3`
#  <chr> <dbl> <dbl> <dbl>
#1 F      21.4  50    28.6
#2 M      46.2  15.4  38.5

#[[2]]
# A tibble: 3 x 4
#  race    `1`   `2`   `3`
#  <chr> <dbl> <dbl> <dbl>
#1 Asian  20    40    40
#2 Black  27.3  45.5  27.3
#3 White  66.7   0    33.3
``````