Calculate the mean by group

user_1517860 0
Viewed 194766

I have a large data frame that looks similar to this:

df <- data.frame(dive = factor(sample(c("dive1","dive2"), 10, replace=TRUE)),
                 speed = runif(10)
                 )
> df
    dive      speed
1  dive1 0.80668490
2  dive1 0.53349584
3  dive2 0.07571784
4  dive2 0.39518628
5  dive1 0.84557955
6  dive1 0.69121443
7  dive1 0.38124950
8  dive2 0.22536126
9  dive1 0.04704750
10 dive2 0.93561651

My goal is to obtain the average of values in one column when another column is equal to a certain value and repeat this for all values. i.e. in the example above I would like to return an average for the column speed for every unique value of the column dive. So when dive==dive1, the average for speed is this and so on for each value of dive.

r-faq dataframe r
8 Answers

Adding alternative base R approach, which remains fast under various cases.

rowsummean <- function(df) {
  rowsum(df$speed, df$dive) / tabulate(df$dive)
}

Borrowing the benchmarks from @Ari:

10 rows, 2 groups

res1

10 million rows, 10 groups

res2

10 million rows, 1000 groups

res3

user_19537180

With the new funtion across:

df %>% 
  group_by(dive) %>% 
  summarise(across(speed, mean, na.rm = TRUE))
user_129904330

We already have tons of options to get mean by group, adding one more from mosaic package. 

mosaic::mean(speed~dive, data = df)
#dive1 dive2 
#0.579 0.440

This returns a named numeric vector, if needed a dataframe we can wrap it in stack

stack(mosaic::mean(speed~dive, data = df))

#  values   ind
#1  0.579 dive1
#2  0.440 dive2

data

set.seed(123)
df <- data.frame(dive=factor(sample(c("dive1","dive2"),10,replace=TRUE)),
                 speed=runif(10))
user_39629140

Using collapse

library(collapse)
library(magrittr)
df %>% 
   fgroup_by(dive) %>%
   fsummarise(speed = fmean(speed))
#   dive     speed
#1 dive1 0.5788479
#2 dive2 0.4401514

data

set.seed(123)
df <- data.frame(dive=factor(sample(c("dive1","dive2"),10,replace=TRUE)),
             speed=runif(10))
user_37322710

Extending answer provided by RCchelsie - If anyone wants to get mean calculate by group for all columns in the dataframe:

df %>% 
  group_by(dive) %>% 
  summarise(across(.cols=everything(), mean, na.rm=TRUE))
user_62540410
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