How to get summary statistics by group

问题:

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?

回答1:

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.

回答2:

dplyr package could be nice alternative to this problem:

library(\'dplyr\')
df %>% group_by(group) %>% summarize(mean=mean(dt), sum=sum(dt))

回答3:

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)

回答4:

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) 

回答5:

take a look at the plyr package. Specifically, ddply

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

回答6:

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

回答7:

I just found a wonderful R package tables. You can tabulate data by as many categories as you desire and calculate multiple statistics for multiple variables - it truly is amazing!

But wait, there\'s more! The package has functions to generate LaTeX code for your tables for easy import to your documents.

回答8:

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 

回答9:

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.