function in aggregate applied to specific columns only

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, na.rm=TRUE)

This doesn't work, one cannot take the mean of a factor (gender).   
Is there any way of specifying which columns to use for the mean?  I  
want to aggregate by student, obtaining mean scores, and assume any  
other factors are unchanging in a specific student, ie. gender.

Thanks
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aggregate(basicSub["score"], basicSub["student"], mean)

with(basicSub, tapply(score, student, mean))

library(doBy)
summaryBy(. ~ student, basicSub)

library(sqldf)
sqldf("select student, avg(score) from basicSub group by student")

summary(score ~ student, basicSub)

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, na.rm=TRUE)

This doesn't work, one cannot take the mean of a factor (gender). ?Is there any way of specifying which columns to use for the mean? ?I want to aggregate by student, obtaining mean scores, and assume any other factors are unchanging in a specific student, ie. gender.

Thanks
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That makes eight solutions. Any others?  :)

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 print method;  to return a vector, do instead

as.vector(with(basicSub, by(score, student, mean)))

[1] 55.0 60.0 67.5
You can cbind the unique student IDs to get a matrix result.

ave() is used to map the average (or comparable summary) to each
observation.
By itself, it returns a vector of the same length as the number of
observations:

with(basicSub, ave(score, student))

[1] 55.0 60.0 67.5 67.5 55.0

It's more useful if you want to add the means to the data frame:

transform(basicSub, avg = ave(score, student))

 student gender score  avg
1       1      m    50 55.0
2       2      m    60 60.0
3       3      f    70 67.5
4       3      f    65 67.5
5       1      m    60 55.0

That makes eight solutions. Any others?  :)

Dennis


On Sun, Jan 3, 2010 at 8:14 PM, Gabor Grothendieck
<ggrothendieck at gmail.com>wrote:

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))

  1    2    3
55.0 60.0 67.5

3. summaryBy in doBy package

library(doBy)
summaryBy(. ~ student, basicSub)

 student score.mean
1       1       55.0
2       2       60.0
3       3       67.5

4. sqldf in sqldf package.  Uses SQL:

library(sqldf)
sqldf("select student, avg(score) from basicSub group by student")

 student avg(score)
1       1       55.0
2       2       60.0
3       3       67.5

5. summary.formula in Hmisc

summary(score ~ student, basicSub)

score    N=5

+-------+-+-+-----+
|       | |N|score|
+-------+-+-+-----+
|student|1|2|55.0 |
|       |2|1|60.0 |
|       |3|2|67.5 |
+-------+-+-+-----+
|Overall| |5|61.0 |
+-------+-+-+-----+

6. plyr (see Dennis Murphy's solution in this thread)


On Sun, Jan 3, 2010 at 10:46 PM, david hilton shanabrook
<dhshanab at acad.umass.edu> 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 <- aggregate(basicSub, by=list(basicSub$student), 
FUN=mean,

na.rm=TRUE)

This doesn't work, one cannot take the mean of a factor (gender).  Is

there any way of specifying which columns to use for the mean?  I want to
aggregate by student, obtaining mean scores, and assume any other factors
are unchanging in a specific student, ie. gender.

Thanks
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