Adding a column of means by group to original data

Question

This question already has an answer here:

• Calculate group mean (or other summary stats) and assign to original data 4 answers

I want to add a column of means based on factor column in R data.frame. Like this:

df1 <- data.frame(X = rep(x = LETTERS[1:2], each = 3), Y = 1:6)
df2 <- aggregate(data = df1, Y ~ X, FUN = mean)
df3 <- merge(x = df1, y = df2, by = "X", suffixes = c(".Old",".New"))
df3
#   X Y.Old Y.New
# 1 A     1     2
# 2 A     2     2
# 3 A     3     2
# 4 B     4     5
# 5 B     5     5
# 6 B     6     5

To accomplish this problem I've to create two unnecessary data.frames. I'd like to know a way to append a column of means by factor column into my original data.frame without creating any extra data.frames. Thanks for your time and help.

Answer 1

ddply and transform to the rescue (although I'm sure you'll get at least 4 different ways to do this):

library(plyr)
ddply(df1,.(X),transform,Y.New = mean(Y))
  X Y Y.New
1 A 1     2
2 A 2     2
3 A 3     2
4 B 4     5
5 B 5     5
6 B 6     5

Answer 2

This is what the ave function is for.

df1$Y.New <- ave(df1$Y, df1$X)

Answer 3

Joran answered beautifully, This is not an answer to your question but an extension of the conversation. If you're looking for table of means for two categorical variable's relationship to a dependent here's the Hadley function for that:

cast(CO2, Type ~ Treatment, value="uptake", fun.aggregate=mean, margins=TRUE)

Here's a head view of CO2 data, and a look at the means table:

> head(CO2)
  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4
3   Qn1 Quebec nonchilled  250   34.8
4   Qn1 Quebec nonchilled  350   37.2
5   Qn1 Quebec nonchilled  500   35.3
6   Qn1 Quebec nonchilled  675   39.2

> library(reshape)

> cast(CO2, Type ~ Treatment, mean, margins=TRUE)  
         Type nonchilled  chilled    (all)
1      Quebec   35.33333 31.75238 33.54286
2 Mississippi   25.95238 15.81429 20.88333
3       (all)   30.64286 23.78333 27.21310

Answer 4

Two alternative ways of doing this:

1. with the dplyr package:

library(dplyr)
df1 <- df1 %>% 
  group_by(X) %>% 
  mutate(Y.new = mean(Y))

2. with the data.table package:

library(data.table)
setDT(df1)[, Y.new := mean(Y), by = X]

both give the following result:

> df1
   X Y Y.new
1: A 1     2
2: A 2     2
3: A 3     2
4: B 4     5
5: B 5     5
6: B 6     5