Can one use 'subset' on data from .csv files of known structure but varying details?

I'm trying to work on data from .csv files of known general format but varying group and measure names. I can get a data.frame using:

mydata=read.csv(file.choose(),header=T)    
mydata

    GroupNames  Measure1    Measure2    Measure3  etc
1   group1      value1      value1
2   group1      value2      value2
3   gro开发者_开发问答up2      value3      value3
4   group2      value4      value4
5   group2      value5      value5
6   group3      value6      value6
7   group3      value7      value7

etc

Is there a way to subset the data and do the required tests if I don't know the numbers of groups or Measures (or their names) ahead of time?

I can get the first row using:

names(mydata)
[1] "GroupNames" "Measure1" "Measure2" "Measure3" 

I can get the groups using:

Groups<-levels(factor(mydata[[1]]))
Groups
[1] "group1" "group2"  "group3"

I can create a subset using:

g1<-subset(mydata, GroupNames %in% Groups[1])
g1
    GroupNames  Measure1    Measure2    Measure3  etc
1   group1      value1      value1
2   group1      value2      value2

but how do I automatically put "GroupNames" in the above subset command without knowing it ahead of time? Current experiments using:

Titles<-names(mydata)

then

g1<-subset(mydata, Titles[1] %in% Groups[1])

fail, and return:

[1] GroupNames Measure1 Measure2 Measure3     
<0 rows> (or 0-length row.names)

Sorry, but I am a beginner...

---

You're not far off, actually. You just need to pass an object instead of a character string when using subset. It should work if you skip the Titles business and just do this:

g1<-subset(mydata, mydata[[1]] %in% Groups[1])