Aggregate multiple rows of the same data.frame in R based on common values in given columns

Question

I have a data.frame that looks like this:

# set example data
df <- read.table(textConnection("item\tsize\tweight\tvalue
A\t2\t3\t4
A\t2\t3\t6
B\t1\t2\t3
C\t3\t2\t1
B\t1\t2\t4
B\t1\t2\t2"), header = TRUE)

# print example data
df

  item size weight value
1    A    2      3     4
2    A    2      3     6
3    B    1      2     3
4    C    3      2     1
5    B    1      2     4
6    B    1      2     2

As you can see the size and weight columns do not add any complexity since they are the same for each item. However, there can be multiple values for the same item.

I want to collapse the data.frame to have one row per item using the mean value:

  item size weight value
1    A    2      3     5
3    B    1      2     3
4    C    3      2     1

I guess I have to use the aggregate function but I could not figure out how exactly I can get the above result.

Answer 1

Nowadays, this is what I would do:

library(dplyr)

df %>%
  group_by(item, size, weight) %>%
  summarize(value = mean(value)) %>%
  ungroup

This yields the following result:

# A tibble: 3 x 4
   item  size weight value
  <chr> <int>  <int> <dbl>
1     A     2      3     5
2     B     1      2     3
3     C     3      2     1

I will leave the accepted answer as such as I specifically asked for aggregate, but I find the dplyr solution the most readable.

Answer 2

Here is the solution using the ddply from plyr package:

library(plyr)
ddply(df,.(item),colwise(mean))
  item size weight value
1    A    2      3     5
2    B    1      2     3
3    C    3      2     1

Answer 3

aggregate(value ~ item + size + weight, FUN = mean, data=df)

  item size weight value
1    B    1      2     3
2    C    3      2     1
3    A    2      3     5

Answer 4

df$value <- ave(df$value,df$item,FUN=mean)
df[!duplicated(df$item),]

  item size weight value
1    A    2      3     5
3    B    1      2     3
4    C    3      2     1