See the warning in ?subset.
Passing the column name of lvar is not the same as passing the 'contextual
column' (as I coin it in these circumstances).
You can solve it by indeed using [] instead.
For my own comfort, here is the relevant line from your original function:
Data.tmp <- subset(Fulldf, lvar==subgroup, select=c("xvar","yvar"))
Which should become something like (untested but should be close):
Data.tmp <- Fulldf[Fulldf[,"lvar"]==subgroup, c("xvar","yvar")]
This should be a lot easier to translate based on column names, as the
column names are now used as such.
HTH,
Nick Sabbe
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-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of E Hofstadler
Sent: vrijdag 1 april 2011 13:09
To: r-help@r-project.org
Subject: [R] programming: telling a function where to look for the entered
variables
Hi there,
Could someone help me with the following programming problem..?
I have written a function that works for my intended purpose, but it
is quite closely tied to a particular dataframe and the names of the
variables in this dataframe. However, I'd like to use the same
function for different dataframes and variables. My problem is that
I'm not quite sure how to tell my function in which dataframe the
entered variables are located.
Here's some reproducible data and the function:
# create reproducible data
set.seed(124)
xvar <- sample(0:3, 1000, replace = T)
yvar <- sample(0:1, 1000, replace=T)
zvar <- rnorm(100)
lvar <- sample(0:1, 1000, replace=T)
Fulldf <- as.data.frame(cbind(xvar,yvar,zvar,lvar))
Fulldf$xvar <- factor(xvar, labels=c("blue","green","red","yellow"))
Fulldf$yvar <- factor(yvar, labels=c("area1","area2"))
Fulldf$lvar <- factor(lvar, labels=c("yes","no"))
and here's the function in the form that it currently works: from a
subset of the dataframe Fulldf, a contingency table is created (in my
actual data, several other operations are then performed on that
contingency table, but these are not relevant for the problem in
question, therefore I've deleted it) .
# function as it currently works: tailored to a particular dataframe
(Fulldf)
myfunct <- function(subgroup){ # enter a particular subgroup for which
the contingency table should be calculated (i.e. a particular value of
the factor lvar)
Data.tmp <- subset(Fulldf, lvar==subgroup, select=c("xvar","yvar"))
#restrict dataframe to given subgroup and two columns of the original
dataframe
Data.tmp <- na.omit(Data.tmp) # exclude missing values
indextable <- table(Data.tmp$xvar, Data.tmp$yvar) # make contingency table
return(indextable)
}
#Since I need to use the function with different dataframes and
variable names, I'd like to be able to tell my function the name of
the dataframe and variables it should use for calculating the index.
This is how I tried to modify the first part of the #function, but it
didn't work:
# function as I would like it to work: independent of any particular
dataframe or variable names (doesn't work)
myfunct.better <- function(subgroup, lvarname, yvarname, dataframe){
#enter the subgroup, the variable names to be used and the dataframe
in which they are found
Data.tmp <- subset(dataframe, lvarname==subgroup, select=c("xvar",
deparse(substitute(yvarname)))) # trying to subset the given dataframe
for the given subgroup of the given variable. The variable "xvar"
happens to have the same name in all dataframes) but the variable
yvarname has different names in the different dataframes
Data.tmp <- na.omit(Data.tmp)
indextable <- table(Data.tmp$xvar, Data.tmp$yvarname) # create the
contingency table on the basis of the entered variables
return(indextable)
}
calling
myfunct.better("yes", lvarname=lvar, yvarname=yvar, dataframe=Fulldf)
results in the following error:
Error in `[.data.frame`(x, r, vars, drop = drop) :
undefined columns selected
My feeling is that R doesn't know where to look for the entered
variables (lvar, yvar), but I'm not sure how to solve this problem. I
tried using with() and even attach() within the function, but that
didn't work.
Any help is greatly appreciated.
Best,
Esther
P.S.:
Are there books that elaborate programming in R for beginners -- and I
mean things like how to best use vectorization instead of loops and
general "best practice" tips for programming. Most of the books I've
been looking at focus on applying R for particular statistical
analyses, and only comparably briefly deal with more general
programming aspects. I was wondering if there's any books or tutorials
out there that cover the latter aspects in a more elaborate and
systematic way...?
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______________________________________________
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.
