How can I get column data to be added based on a group designation using R?

The data set that I'm working with is similar to the one below (although the example is of a much smaller scale, the data I'm working with is 10's of thousands of rows) and I haven't been able to figure out how to get R to add up column data based on the group number. Essentially I want to be able to get the number of green(s), blue(s), and red(s) added up for all of group 81 and 66 separately and then be able to use that informatio开发者_如何学运维n to calculate percentages.

txt <- "Group Green Blue Red Total
81 15 10 21 46
81 10 10 10 30
81 4 8 0 12
81 42 2 2 46
66 11 9 1 21
66 5 14 5 24 
66 7 5 2 14
66 1 16 3 20
66 22 4 2 28"

dat <- read.table(textConnection(txt), sep = " ", header = TRUE)

I've spent a good deal of time trying to figure out how to use some of the functions on my own hoping I would stumble across a proper way to do it, but since I'm such a new basic user I feel like I have hit a wall that I cannot progress past without help.

One way is via aggregate. Assuming your data is in an object x:

aggregate(. ~ Group, data=x, FUN=sum)
#   Group Green Blue Red Total
# 1    66    46   48  13   107
# 2    81    71   30  33   134

Both of the answers above are perfect examples of how to address this type of problem. Two other options exist within reshape and plyr

library(reshape)
cast(melt(dat, "Group"), Group ~ ..., sum)

library(plyr)
ddply(dat, "Group", function(x) colSums(x[, -1]))

I would suggest that @Joshua's answer is neater, but two functions you should learn are apply and tapply. If a is your data set, then:

## apply calculates the sum of each row
> total = apply(a[,2:4], 1, sum)
## tapply calculates the sum based on each group
> tapply(total, a$Group, sum)
 66  81 
107 134