In the code fragment, I used 'by' to actually compute the min value (part of
the statement with the eval) - and I agree that an apply would work there
wonderfully.
However, my hope was to use an apply for the subsetting of the data.frame's
columns, so that I could then use an apply to compute the min across each row
of the subsets.
Something that would give me the results of the following, but programmatically:
apply(the.data[,1], 1, min) #min of the first column
apply(the.data[,2:3], 1, min) #min of the next 2 columns
apply(the.data[,4:6], 1, min) #min of the next 3 columns
apply(the.data[,7:10], 1, min) #min of the next 4 columns
...
apply(the.data[,46:55], 1, min)#min of the next 10 columns
Like, can I make a vector of levels with 'rep(1:10,1:10)', and then apply the
function across all columns in each level? And then how could I cbind them
together?
Thanks for any help,
Bill
On Jun 8, 2010, at 5:08 PM, Jannis wrote:
> you should have found a solution for that in the help page of apply.
>
> just run
>
> min.values = apply(the.data,1,min)
>
> the '1' marks the direction (e.g. whether apply is applied to rows or
> columns), it could be a 2 as well. Check that yourself in the apply
> documentation.
>
> Then run rbind(the.data,min.values) (could be cbind as well, I am not sure
> again ;-) ) and you get what you want.
>
> 09wkj schrieb:
>> I am mainly a Java/C++ programmer, so my mind is used to iterating over data
>> with for loops. After a long break, I am trying to get back into the "R
>> mindset", but I could not find a solution in the documentation for the
>> applys, aggregate, or by.
>>
>> I have a data.frame where each row is an entry with 10 groups of
>> measurements. The first measurement spans 1 column, the second spans 2
>> columns, third 3, and so on (55 total columns). What I want to do is add to
>> my data.frame 10 new columns containing the minimum value of each
>> measurement.
>>
>> dim(the.data)
>> [1] 1679 55
>>
>>
>>> colnames(the.data)
>>>
>> [1] "k.1.1" "k.2.1" "k.2.2" "k.3.1" "k.3.2" "k.3.3" "k.4.1"
>> [8] "k.4.2" "k.4.3" "k.4.4" "k.5.1" "k.5.2" "k.5.3" "k.5.4"
>> [15] "k.5.5" "k.6.1" "k.6.2" "k.6.3" "k.6.4" "k.6.5" "k.6.6"
>> [22] "k.7.1" "k.7.2" "k.7.3" "k.7.4" "k.7.5" "k.7.6" "k.7.7"
>> [29] "k.8.1" "k.8.2" "k.8.3" "k.8.4" "k.8.5" "k.8.6" "k.8.7"
>> [36] "k.8.8" "k.9.1" "k.9.2" "k.9.3" "k.9.4" "k.9.5" "k.9.6"
>> [43] "k.9.7" "k.9.8" "k.9.9" "k.10.1" "k.10.2" "k.10.3" "k.10.4"
>> [50] "k.10.5" "k.10.6" "k.10.7" "k.10.8" "k.10.9" "k.10.10"
>>
>> I want to add to the.data new columns: min.k.1, min.k.2, ..., min.k.10
>>
>> This is the section of code I would like to improve, hopefully getting rid
>> of the eval and the for loop:
>>
>> for(k in 1:10){
>> s <- subset(the.data, select=paste("k", k, 1:k, sep="."))
>> eval(parse(text = paste("the.data$min.k.", k, "<-as.vector(by(s,
>> 1:nrow(s), min))", sep="")))
>> }
>>
>> Thanks for any help,
>> Bill
>> ______________________________________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
>
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