R help - Apr 2005 - basic question

I know this question is very simple, but I am not figure it out


 

I have the data frame:


 

 


 

test<- data.frame(year=c(2000,2000,2001,2001),x=c(54,41,90,15),
y=c(29,2,92,22), z=c(26,68,46,51))


 

test


 

  year    x   y   z


 

1 2000 54 29 26


 

2 2000 41  2  68


 

3 2001 90 92 46


 

4 2001 15 22 51


 

 


 

I want to sum the vectors x, y and z within each year (2000 and 2001) to obtain
this:


 

 


 

year       x   y    z


 

1 2000  95  31 94


 

2 2001 105 114 97


 

 


 

I tried tapply but did not work (or probably I do it wrong)


 

 


 

Any suggestions?  


 

 


 

silva
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On Thu, 2005-04-21 at 16:31 +0100, jose silva wrote:
> I know this question is very simple, but I am not figure it out
> I have the data frame:
> test<- data.frame(year=c(2000,2000,2001,2001),x=c(54,41,90,15),
y=c(29,2,92,22), z=c(26,68,46,51))
> test
>  year x y z
> 1 2000 54 29 26
> 2 2000 41 2 68
> 3 2001 90 92 46
> 4 2001 15 22 51
> I want to sum the vectors x, y and z within each year (2000 and 2001) to
obtain this:
> 
> year x y z
> 1 2000 95 31 94
> 2 2001 105 114 97
> I tried tapply but did not work (or probably I do it wrong)
> 
> Any suggestions? 

tapply() is typically used against a single vector, subsetting by one or
more factors.

In this case, since you want to get the colSums for more than one column
in the data frame, there are a few options:

1. Use by():
> by(test[, -1], test$year, colSums)
test$year: 2000
 x  y  z
95 31 94
------------------------------------------------------
test$year: 2001
  x   y   z
105 114  97



2. Use aggregate():
> aggregate(test[, -1], list(Year = test$year), sum)
  Year   x   y  z
1 2000  95  31 94
2 2001 105 114 97




3. Use split() and then lapply():
> test.s <- split(test, test$year)
> test.s
$"2000"
  year  x  y  z
1 2000 54 29 26
2 2000 41  2 68

$"2001"
  year  x  y  z
3 2001 90 92 46
4 2001 15 22 51
> lapply(test.s, function(x) colSums(x[, -1]))
$"2000"
 x  y  z
95 31 94

$"2001"
  x   y   z
105 114  97


Which you choose may depend upon how you need the output structured for
subsequent use.

See ?by, ?aggregate, ?lapply and ?split for more information.

HTH,

Marc Schwartz

sapply(split(test, test$year), function(x) list(x.s = sum(x$x), y.s = 
sum(x$y), z.s = sum(x$z)))


or for one variable only

aggregate(test$x, list(id = test$year), sum)

cheers
christoph

jose silva wrote:
> I know this question is very simple, but I am not figure it out
> 
> 
>  
> 
> I have the data frame:
> 
> 
>  
> 
>  
> 
> 
>  
> 
> test<- data.frame(year=c(2000,2000,2001,2001),x=c(54,41,90,15),
y=c(29,2,92,22), z=c(26,68,46,51))
> 
> 
>  
> 
> test
> 
> 
>  
> 
>   year    x   y   z
> 
> 
>  
> 
> 1 2000 54 29 26
> 
> 
>  
> 
> 2 2000 41  2  68
> 
> 
>  
> 
> 3 2001 90 92 46
> 
> 
>  
> 
> 4 2001 15 22 51
> 
> 
>  
> 
>  
> 
> 
>  
> 
> I want to sum the vectors x, y and z within each year (2000 and 2001) to
obtain this:
> 
> 
>  
> 
>  
> 
> 
>  
> 
> year       x   y    z
> 
> 
>  
> 
> 1 2000  95  31 94
> 
> 
>  
> 
> 2 2001 105 114 97
> 
> 
>  
> 
>  
> 
> 
>  
> 
> I tried tapply but did not work (or probably I do it wrong)
> 
> 
>  
> 
>  
> 
> 
>  
> 
> Any suggestions?  
> 
> 
>  
> 
>  
> 
> 
>  
> 
> silva
> 	[[alternative HTML version deleted]]
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html
> 
>

Hi all --

     I'm running a Factor Analysis on my dataset, and I've located the 
"factanal()" and "princomp()" methods.  I don't want to
do a PCA, so it
looks like I should use factanal(), but factanal() requires specifying 
the number of factors you expect from the analysis.
    Are there any packages out there explicitly for Exploratory Factor 
Analysis that do not require specifying the number of expected factors?

-- Chris

hi all,

In the library ‘ade4’, there are two eigenanalysis which enable the ordination
of the categorical variables.

1- Multiple Correspondence Analysis (MCA, Tenenhaus & Young 1985) performs
the
multiple correspondence analysis of a factor table (see the
function ‘dudi.acm’). this function is equivalent to functions mca of the 
library(MASS)

2- the “mixed factorial analysis” (Hill & Smith 1976) enables the ordination
of tables mixing quantitative variables and factors (functions ‘dudi.mix’
or ‘dudi.hillsmith’).


I hope this helps,

P.BADY




At 15:47 21/04/2005 -0500, Chris Bergstresser wrote:
>Hi all --
>
>     I'm running a Factor Analysis on my dataset, and I've located
the
> "factanal()" and "princomp()" methods.  I don't
want to do a PCA, so it
> looks like I should use factanal(), but factanal() requires specifying 
> the number of factors you expect from the analysis.
>    Are there any packages out there explicitly for Exploratory Factor 
> Analysis that do not require specifying the number of expected factors?
>
>-- Chris
>
>______________________________________________
>R-help@stat.math.ethz.ch mailing list
>https://stat.ethz.ch/mailman/listinfo/r-help
>PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html
Pierre BADY             <°)))))><
Université Claude Bernard Lyon 1
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