That makes eight solutions. Any others? :)
A ninth was detailed in two other threads last month. The first link
compares to ave().
http://tolstoy.newcastle.edu.au/R/e8/help/09/12/9014.html
http://tolstoy.newcastle.edu.au/R/e8/help/09/12/8830.html
Dennis Murphy djmu...@gmail.com wrote in
I want to use aggregate with the mean function on specific columns
gender - factor(c(m, m, f, f, m))
student - c(0001, 0002, 0003, 0003, 0001)
score - c(50, 60, 70, 65, 60)
basicSub - data.frame(student, gender, score)
basicSubMean - aggregate(basicSub, by=list(basicSub$student), FUN=mean,
On Jan 3, 2010, at 10:46 PM, david hilton shanabrook wrote:
I want to use aggregate with the mean function on specific columns
gender - factor(c(m, m, f, f, m))
student - c(0001, 0002, 0003, 0003, 0001)
score - c(50, 60, 70, 65, 60)
basicSub - data.frame(student, gender, score)
basicSubMean -
Hi:
Perhaps the plyr package would be useful. It contains functions colwise(),
numcolwise() and
catcolwise() that will perform the same operation on the stated type of
object. In this case,
numcolwise() is appropriate:
str(basicSub)
'data.frame': 5 obs. of 3 variables:
$ student: num 1 2 3
You want this?
basicSubMean - aggregate(basicSub[c(score)], by=list(basicSub$student),
FUN=mean, na.rm=TRUE)
basicSubMean
Group.1 score
1 1 55.0
2 2 60.0
3 3 67.5
bests
milton
On Sun, Jan 3, 2010 at 10:46 PM, david hilton shanabrook
dhsha...@acad.umass.edu wrote:
I
Here are 6 ways:
1. aggregate
aggregate(basicSub[score], basicSub[student], mean)
student score
1 1 55.0
2 2 60.0
3 3 67.5
2. tapply
with(basicSub, tapply(score, student, mean))
123
55.0 60.0 67.5
3. summaryBy in doBy package
library(doBy)
summaryBy(. ~
Just for the fun of it, here are two more: by and ave.
with(basicSub, by(score, student, mean))
student: 1
[1] 55
student: 2
[1] 60
student: 3
[1] 67.5
Not my favorite
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