variable scope & resolution in R function - r

Question

I want to loop through the vars in a dataframe, calling lm() on each one, and so I wrote this:
findvars <- function(x = samsungData, dv = 'activity', id = 'subject') {
# Loops through the possible predictor vars, does an lm() predicting the dv
# from each, and returns a data.frame of coefficients, one row per IV.
r <- data.frame()
# All varnames apart from the dependent var, and the case identifier
ivs <- setdiff(names(x), c(dv, id))
for (iv in ivs) {
print(paste("trying", iv))
m <- lm(dv ~ iv, data = x, na.rm = TRUE)
# Take the absolute value of the coefficient, then transpose.
c <- t(as.data.frame(sapply(m$coefficients, abs)))
c$iv <- iv # which IV produced this row?
r <- c(r, c)
}
return(r)
}
This doesn't work, I believe b/c the formula in the lm() call consists of function-local variables that hold strings naming vars in the passed-in dataframe (e.g., "my_dependant_var" and "this_iv") as opposed to pointers to the actual variable objects.
I tried wrapping that formula in eval(parse(text = )), but could not get that to work.
If I'm right about the problem, can someone explain to me how to get R to resolve the contents of those vars iv & dv into the pointers I need? Or if I'm wrong, can someone explain what else is going on?
Many thanks!
Here is some repro code:
library(datasets)
data(USJudgeRatings)
findvars(x = USJudgeRatings, dv = 'CONT', id = 'DILG')

Answer 1

So there's enough bad stuff happening in your function besides your trouble with the formula, that I think someone should walk you through it all. Here are some annotations, followed by a better version:
#For small examples, "growing" objects isn't a huge deal,
# but you will regret it very, very quickly. It's a bad
# habit. Learn to ditch it now. So don't inititalize
# empty lists and data frames.
r <- data.frame()
ivs <- setdiff(names(x), c(dv, id))
for (iv in ivs) {
print(paste("trying", iv))
#There is no na.rm argument to lm, only na.action
m <- lm(dv ~ iv, data = x, na.rm = TRUE)
#Best not to name variables c, its a common function, see two lines from now!
# Also, use the coef() extractor functions, not $. That way, if/when
# authors change the object structure your code won't break.
#Finally, abs is vectorized, no need for sapply
c <- t(as.data.frame(sapply(m$coefficients, abs)))
#This is probably best stored in the name
c$iv <- iv # which IV produced this row?
#Growing objects == bad! Also, are you sure you know what happens when
# you concatenate two data frames?
r <- c(r, c)
}
return(r)
}
Try something like this instead:
findvars <- function(x,dv,id){
ivs <- setdiff(names(x),c(dv,id))
#initialize result list of the appropriate length
result <- setNames(vector("list",length(ivs)),ivs)
for (i in seq_along(ivs)){
result[[i]] <- abs(coef(lm(paste(dv,ivs[i],sep = "~"),data = x,na.action = na.omit)))
}
result
}