aah perfect, exactly what i was looking for!

Thank you

On Fri, Jun 7, 2013 at 3:24 PM, arun kirshna [via R]
<ml-node+s789695n4668923h47@n4.nabble.com> wrote:
> HI,
> Try:
> ?split()
>
> source("http://www.openintro.org/stat/data/cdc.R")
>  str(cdc)
> #'data.frame':    20000 obs. of  9 variables:
> # $ genhlth : Factor w/ 5 levels "excellent","very good",..: 3 3 3 3 2 2 2 2
> 3 3 ...
> # $ exerany : num  0 0 1 1 0 1 1 0 0 1 ...
> # $ hlthplan: num  1 1 1 1 1 1 1 1 1 1 ...
> # $ smoke100: num  0 1 1 0 0 0 0 0 1 0 ...
> # $ height  : num  70 64 60 66 61 64 71 67 65 70 ...
> # $ weight  : int  175 125 105 132 150 114 194 170 150 180 ...
> # $ wtdesire: int  175 115 105 124 130 114 185 160 130 170 ...
> # $ age     : int  77 33 49 42 55 55 31 45 27 44 ...
> # $ gender  : Factor w/ 2 levels "m","f": 1 2 2 2 2 2 1 1 2 1 ...
> cdc$genhlth<- as.character(cdc$genhlth)
> cdclst1<- split(cdc,cdc$genhlth)
> lapply(cdclst1,head,2)
> #$excellent
> #     genhlth exerany hlthplan smoke100 height weight wtdesire age gender
> #11 excellent       1        1        1     69    186      175  46      m
> #13 excellent       1        0        1     66    185      220  21      m
> #
> #$fair
> #   genhlth exerany hlthplan smoke100 height weight wtdesire age gender
> #12    fair       1        1        1     69    168      148  62      m
> #15    fair       1        0        0     69    170      170  23      m
> #
> #$good
> #  genhlth exerany hlthplan smoke100 height weight wtdesire age gender
> #1    good       0        1        0     70    175      175  77      m
> #2    good       0        1        1     64    125      115  33      f
> #
> #$poor
> #   genhlth exerany hlthplan smoke100 height weight wtdesire age gender
> #53    poor       1        1        1     62    140      130  64      f
> #79    poor       1        1        0     63    142      120  52      f
>
> #$`very good`
> #    genhlth exerany hlthplan smoke100 height weight wtdesire age gender
> #5 very good       0        1        0     61    150      130  55      f
> #6 very good       1        1        0     64    114      114  55      f
>
>
> sapply(cdclst1,nrow)
> #excellent      fair      good      poor very good
> #     4657      2019      5675       677      6972
>
> cdcGood<-cdclst1[["good"]]
>   str(cdcGood)
> #'data.frame':    5675 obs. of  9 variables:
> # $ genhlth : chr  "good" "good" "good" "good" ...
> # $ exerany : num  0 0 1 1 0 1 1 0 1 1 ...
> # $ hlthplan: num  1 1 1 1 1 1 1 0 1 1 ...
> # $ smoke100: num  0 1 1 0 1 0 1 1 1 1 ...
> # $ height  : num  70 64 60 66 65 70 73 67 75 65 ...
> # $ weight  : int  175 125 105 132 150 180 185 156 200 160 ...
> # $ wtdesire: int  175 115 105 124 130 170 175 150 190 140 ...
> # $ age     : int  77 33 49 42 27 44 79 47 43 54 ...
> # $ gender  : Factor w/ 2 levels "m","f": 1 2 2 2 2 1 1 1 1 2 ...
>
>
> A.K.
>
>
>>Hi I am trying to figure out how to subset a bunch of data. As an example I
>> am using the cdc data from openintro.org.
>>
>>In the first column with the name "genhlth" there are various
> options that the persons could respond. For exmaple "good" "very good"
> and "poor". Now >what i would like to do is to seperate the data so that
> everyone who answered good are stored in one variable and everyone who
> answered poor are in >another variable.
>>
>>Now I know i could just do subset(cdc, cdc$genhlth == "poor") to
>  get the poor, but would really like for a code that would seperate data
>  into each >group, regardless of what the text or the number of groups
> are.
>>
>>Can anyone give me a hint?
>
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