Often you may want to calculate the sum by group in R. There are three methods you can use to do so:
Method 1: Use base R.
aggregate(df$col_to_aggregate, list(df$col_to_group_by), FUN=sum)
Method 2: Use the dplyr() package.
library(dplyr)
df %>%
group_by(col_to_group_by) %>%
summarise(Freq = sum(col_to_aggregate))
Method 3: Use the data.table package.
library(data.table)
dt[ ,list(sum=sum(col_to_aggregate)), by=col_to_group_by]
The following examples show how to use each of these methods in practice.
Method 1: Calculate Sum by Group Using Base R
The following code shows how to use the aggregate() function from base R to calculate the sum of the points scored by team in the following data frame:
#create data frame df #view data frame df team pts rebs 1 a 5 8 2 a 8 8 3 b 14 9 4 b 18 3 5 b 5 8 6 c 7 7 7 c 7 4 #find sum of points scored by team aggregate(df$pts, list(df$team), FUN=sum) Group.1 x 1 a 13 2 b 37 3 c 14
Method 2: Calculate Sum by Group Using dplyr
The following code shows how to use the group_by() and summarise() functions from the dplyr package to calculate the sum of points scored by team in the following data frame:
library(dplyr) #create data frame df #find sum of points scored by team df %>% group_by(team) %>% summarise(Freq = sum(pts)) # A tibble: 3 x 2 team Freq1 a 13 2 b 37 3 c 14
Method 3: Calculate Sum by Group Using data.table
The following code shows how to use the data.table package to calculate the sum of points scored by team in the following data frame:
library(data.table) #create data frame df #convert data frame to data table setDT(df) #find sum of points scored by team df[ ,list(sum=sum(pts)), by=team] team sum 1: a 13 2: b 37 3: c 14
Notice that all three methods return identical results.
Note: If you have an extremely large dataset, the data.table method will work the fastest among the three methods listed here.
Additional Resources
How to Calculate the Mean by Group in R
How to Calculate Quantiles by Group in R