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how to calculate the mean of a group in a table

5 messages · tornanddesperate, Pete Brecknock, David Winsemius +1 more

#
Hi its me again

I don't mean to get on your nerves, but the use of R proofs to be a bit more
complicated than envisaged.

I would like to calculate the mean of a group of values, here
"wage_accepted". The group is determined by the stage and period, so in the
end there should be a column with the values of the wages in period 1
stage1, period 1 stage 2, period 2 stage1... Unfortunately, I haven't much
of a clue on how to program this. Could you tell me how the function should
roughly look like ? if-else, loops included or not ? in the end ?

   treatment session period stage wage_accepted type
1          1       1      1     1            25  low
2          1       1      1     1            19  low
3          1       1      1     1            15  low
4          1       1      1     2            32 high
5          1       1      1     2            13  low
6          1       1      1     2            14  low
7          1       1      2     1            17  low
8          1       1      2     1            12  low

I am also very grateful if someone could point out a good internet tutorial
resource for R functions such as if-else and loops and how to use them in
conjunction with tables. It should contain simple examples if possible. 

As ever indepted to you,

Matthias



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#
tornanddesperate wrote:
A couple of possible approaches ....

# read in your data
lines <-"treatment session period stage wage_accepted type
1       1      1     1            25  low
1       1      1     1            19  low
1       1      1     1            15  low
1       1      1     2            32 high
1       1      1     2            13  low
1       1      1     2            14  low
1       1      2     1            17  low
1       1      2     1            12  low"

d <- read.table(textConnection(lines), header = TRUE)
closeAllConnections()

# 1. using the aggregate function
aggregate(d$wage_accepted, list(period=d$period, stage=d$stage), FUN=mean)

# 2. using the by function
by(d$wage_accepted,list(d$period,d$stage),FUN=mean)

As for tutorial resources, I would recommend visiting CRAN at
http://cran.r-project.org/ ... follow the "Manuals" link on the left hand
side of the page. 

HTH

Pete 
 

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#
On May 7, 2011, at 2:24 PM, tornanddesperate wrote:

            
I see Pete Brecknock has given you two workable answers with `by` and  
`aggregate`. According to the tapply help page, those functions are  
both based on tapply, and so it should be no surprise that tapply is  
also workable:

  tapply(d$wage_accepted, list(period=d$period, stage=d$stage), mean)
       stage
period        1        2
      1 19.66667 19.66667
      2 14.50000       NA

But results are returned as a table, which I find more usable since I  
can then use the inheritance of tables from matrix properties and  
divide tapply-summary calculations by the results of table() done with  
the same marginal factors. On the other hand aggregate can deliver  
multiple column results where tapply can only deliver the results from  
one column. Their argument requirements are somewhat different, eg.  
tapply and by do not need the name of the FUN argument but aggregate  
does, and I keep needing to refresh my memory on those.
#
I see you already have three solutions but, just for the heck of it, here's another. I am trying to get familiar with the reshape2 package and your question was a good exercise for me. 


With your data set named xx:

library(reshape2)

yy <- melt(xx, id=c("period", "treatment", "session",
     "type","stage"), measured=c("wage_accepted"))

dcast(yy, period + stage ~ variable, mean)

As for your request for tutorials for loops and if-else materials, I cannot make any better suggestions than you already have. However, in R, if you are thinking of using these it usually means you are not thinking properly in R.  There are "usually" faster and better was of achieving the desired results in R. Sometimes a loop or if-else is appropriate but usually in many fewer instances than in most other languages.

By the way type is a reserved(? name in R. Not a problem here but it's safer not to use reserved words in R.
--- On Sat, 5/7/11, tornanddesperate <MatthiasNeff at gmx.ch> wrote:

            
2 days later
#
Just wanted to thank everybody here for their great help. I tried the
"tapply" and it worked brilliantly. By changing its parameters I can easily
compute the values for the other treatments as well. Without help, I
probably would just have despaired.

Have a great time

mat

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