Hi,
On Aug 9, 2009, at 11:29 AM, Frank Schäffer wrote:
Hi,
I' ve read in several files with measurements into R data frames(works
flawlessly). Each dataframe is named by the location of measurement
and
contains hundreds of rows and about 50 columns like this
dataframe1.
date measurment_1 .... mesurement_n
1
2
3
..
..
..
n
Just as an aside, it's somehow considered more R-idiomatic to store
all of these tables in a list (of tables) and access them as
mydata[[1]], mydata[[2]], ..., mydata[[n]]. Assuming the datafiles are
'filename.1.txt', 'filename.2.txt', etc. You might do this like so:
mydata <- lapply(paste('filename', 1:n, 'txt', sep='.'), read.table,
header=TRUE, sep=...)
To test that all colnames are the same, you could do something like.
names1 <- colnames(mydata[[1]])
all(sapply(2:n, function(dat) length(intersect(names1,
colnames(mydata[[n]]))) == length(names1)))
For further processing I need to check whether or not ncol and
colnames are
the same for all dataframes.
Also I need to add a new column to each dataframe with contain the
name of the
dataframe, so that this column can be treated as factor in later
processing
(after merging some seleted dataframes to one)
I tried out
for (i in 1:length(ls()){
print(ncol(ls()[i])
}
but this does not work because r returns a "character" for i and
therefore
"NULL" as result.
Reading the output of ls() into a list also does not work.
How can I accomplish this task??
If you still want to do it this way, see: ?get
for example:
for (varName in paste('dataframe', 1:n, sep='')) {
cat(colnames(get(varName)))
}
HTH,
-steve
--
Steve Lianoglou
Graduate Student: Computational Systems Biology
| Memorial Sloan-Kettering Cancer Center
| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact
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