You can use one of the following methods to calculate the standard deviation by group in R:

Method 1: Use base R

aggregate(df$col_to_aggregate, list(df$col_to_group_by), FUN=sd) 

Method 2: Use dplyr

library(dplyr)

df %>%
  group_by(col_to_group_by) %>%
  summarise_at(vars(col_to_aggregate), list(name=sd))

Method 3: Use data.table

library(data.table)

setDT(df)

dt[ ,list(sd=sd(col_to_aggregate)), by=col_to_group_by]

The following examples show how to use each of these methods in practice with the following data frame in R:

#create data frame
df <- data.frame(team=rep(c('A', 'B', 'C'), each=6),
                 points=c(8, 10, 12, 12, 14, 15, 10, 11, 12,
                          18, 22, 24, 3, 5, 5, 6, 7, 9))

#view data frame
df

   team points
1     A      8
2     A     10
3     A     12
4     A     12
5     A     14
6     A     15
7     B     10
8     B     11
9     B     12
10    B     18
11    B     22
12    B     24
13    C      3
14    C      5
15    C      5
16    C      6
17    C      7
18    C      9

Method 1: Calculate Standard Deviation by Group Using Base R

The following code shows how to use the aggregate() function from base R to calculate the standard deviation of points scored by team:

#calculate standard deviation of points by team
aggregate(df$points, list(df$team), FUN=sd)

  Group.1        x
1       A 2.562551
2       B 6.013873
3       C 2.041241

Method 2: Calculate Standard Deviation by Group Using dplyr

The following code shows how to use the group_by() and summarise_at() functions from the dplyr package to calculate the standard deviation of points scored by team:

library(dplyr) #calculate standard deviation of points scored by team df %>%
  group_by(team) %>%
  summarise_at(vars(points), list(name=sd))

# A tibble: 3 x 2
  team   name
   
1 A      2.56
2 B      6.01
3 C      2.04

Method 3: Calculate Standard Deviation by Group Using data.table

The following code shows how to calculate the standard deviation of points scored by team using functions from the data.table package:

library(data.table) #convert data frame to data table 
setDT(df)

#calculate standard deviation of points scored by team df[ ,list(sd=sd(points)), by=team]

   team       sd
1:    A 2.562551
2:    B 6.013873
3:    C 2.041241

Notice that all three methods return the same results.

Note: If you’re working with an extremely large data frame, it’s recommended to use the dplyr or data.table approach since these packages perform much faster than base R.