Question

This is something which data analysts do all the time (especially when working with survey data which features missing responses.) It’s common to first multiply impute a set of compete data matrices, fit models to each of these matrices, and then combine the results. At the moment I’m doing things by hand and looking for a more elegant solution.

Imagine there’s 5 *.csv files in the working directory, named dat1.csv, dat2.csv, … dat5.csv. I want to estimate the same linear model using each data set.

Given this answer, a first step is to gather a list of the files, which I do with the following

csvdat <- list.files(pattern="dat.*csv")

Now I want to do something like

for(x in csvdat) {
    lm.which(csvdat == "x") <- lm(y ~ x1 + x2, data = x)
}

The “which” statement is my silly way of trying to number each model in turn, using the location in the csvdat list the loop is currently up to. that is, I’d like this loop to return a set of 5 lm objects with the names lm.1, lm.2, etc

Is there some simple way to create these objects, and name them so that I can easily indicate which data set they correspond to?

Thanks for your help!

Answer 1

Another approach is to use the plyr package to do the looping. Using the example constructed by @chl, here is how you would do it

require(plyr)

# read csv files into list of data frames
data_frames = llply(csvdat, read.csv)

# run regression models on each data frame
regressions = llply(data_frames, lm, formula = y ~ .)
names(regressions) = csvdat