[r] How to get summary statistics by group

I'm trying to get multiple summary statistics in R/S-PLUS grouped by categorical column in one shot. I found couple of functions, but all of them do one statistic per call, like `aggregate().

data <- c(62, 60, 63, 59, 63, 67, 71, 64, 65, 66, 68, 66, 
          71, 67, 68, 68, 56, 62, 60, 61, 63, 64, 63, 59)
grp <- factor(rep(LETTERS[1:4], c(4,6,6,8)))
df <- data.frame(group=grp, dt=data)
mg <- aggregate(df$dt, by=df$group, FUN=mean)    
mg <- aggregate(df$dt, by=df$group, FUN=sum)    

What I'm looking for is to get multiple statistics for the same group like mean, min, max, std, ...etc in one call, is that doable?

The psych package has a great option for grouped summary stats:

library(psych)
    
describeBy(dt, group="grp")

produces lots of useful stats including mean, median, range, sd, se.

While some of the other approaches work, this is pretty close to what you were doing and only uses base r. If you know the aggregate command this may be more intuitive.

with( df , aggregate( dt , by=list(group) , FUN=summary)  )

dplyr package could be nice alternative to this problem:

library(dplyr)

df %>% 
  group_by(group) %>% 
  summarize(mean = mean(dt),
            sum = sum(dt))

To get 1st quadrant and 3rd quadrant

df %>% 
  group_by(group) %>% 
  summarize(q1 = quantile(dt, 0.25),
            q3 = quantile(dt, 0.75))

after 5 long years I'm sure not much attention is going to be received for this answer, But still to make all options complete, here is the one with data.table

library(data.table)
setDT(df)[ , list(mean_gr = mean(dt), sum_gr = sum(dt)) , by = .(group)]
#   group mean_gr sum_gr
#1:     A      61    244
#2:     B      66    396
#3:     C      68    408
#4:     D      61    488 

Besides describeBy, the doBy package is an another option. It provides much of the functionality of SAS PROC SUMMARY. Details: http://www.statmethods.net/stats/descriptives.html

Using Hadley Wickham's purrr package this is quite simple. Use split to split the passed data_frame into groups, then use map to apply the summary function to each group.

library(purrr)

df %>% split(.$group) %>% map(summary)

take a look at the plyr package. Specifically, ddply

ddply(df, .(group), summarise, mean=mean(dt), sum=sum(dt))

There's many different ways to go about this, but I'm partial to describeBy in the psych package:

describeBy(df$dt, df$group, mat = TRUE) 

I'll put in my two cents for tapply().

tapply(df$dt, df$group, summary)

You could write a custom function with the specific statistics you want to replace summary.

First, it depends on your version of R. If you've passed 2.11, you can use aggreggate with multiple results functions(summary, by instance, or your own function). If not, you can use the answer made by Justin.