# How to group by multiple columns in dataframe using R and do aggregate function

I have a dataframe with columns as defined below. I have provided one set of example, similar to this I have many countries with loan amount and gender variables

      country          loan_amount          gender
1      Austia              175                F
2      Austia              100                F
3      Austia              825                M
4      Austia              175                F
5      Austia             1025                M
6      Austia              225                F


Here I need to group by countries and then for each country, I need to calculate loan percentage by gender in new columns, so that new columns will have male percentage of total loan amount for that country and female percentage of total loan amount for that country. I need to do two group_by function, first to group all countries together and after that group genders to calculate loan percent.

    Total loan amount = 2525
female_prcent = 175+100+175+225/2525 = 26.73
male_percent = 825+1025/2525 = 73.26


The output should be as below:

      country            female_percent   male_percent
1      Austia              26.73            73.26


I am trying to do this in R. I tried the below function, but my R session is not producing any result and it is terminating.

  df %>%
group_by(country, gender) %>%
summarise_each(funs(sum))


Could someone help me in achieving this output? I think this can be achieved using dplyr function, but I am struck inbetween.

– Drj
Jun 20, 2016 at 11:33

library(dplyr)
library(tidyr)
df %>% group_by(country, gender) %>%
summarise(total_loan_amount =sum(loan_amount)) %>%
ungroup() %>%
transmute(country = country,  female_percent = F / (F+M), male_percent = M /(F+M))


results in

 Source: local data frame [1 x 3]

country female_percent male_percent
(fctr)          (dbl)        (dbl)
1  Austia      0.2673267    0.7326733


I am sure there are better ways of doing it. Below is my simplistic take.

library(dplyr); library(reshape2)
Summary <- df %>%
group_by(country, gender) %>%
summarise(Net = sum(loan_amount))

final <- recast(Summary, country~gender, id.var = c("country", "gender"))
final <- mutate(final, F_percent = final$F/(final$F+final$M), M_percent = final$M/(final$F+final$M) )


Naming the columns with better names and retaining or dropping certain columns should now be easy.

I know this is an old post, but just thought I'd share my solution, which I think is a bit cleaner.

library(tidyverse)

df  <- data.frame(stringsAsFactors=FALSE,
country = c("Austia", "Austia", "Austia", "Austia", "Austia", "Austia"),
loan_amount = c(175, 100, 825, 175, 1025, 225),
gender = c("F", "F", "M", "F", "M", "F")
)

df %>%
group_by(country)  %>%
summarize(female_percent = sum(loan_amount[gender=="F"]) / sum(loan_amount),
male_percent = 1-female_percent)