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I'm new to loops and I have a problem with calling variable from i'th data frame.

I'm able to call each data frame correctly, but when I should call a specified variable inside each data frame problems come:

Example:

for (i in 1:15) {
    assign(
      paste("model", i, sep = ""), 
    (lm(response ~ variable, data = eval(parse(text = paste("data", i, sep = "")))))
    )
    plot(data[i]$response, predict.lm(eval(parse(text = paste("model", i, sep = ""))))) #plot obs vs preds
}

Here I'm doing a simple one variable linear model 15 times, which works just fine. Problems come when I try to plot the results. How should I call data[i] response?

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  • 4
    Why are you using assign and eval? You can use a list to store all your datasets. Commented Jul 28, 2013 at 11:10
  • 1
    If you are a beginner and you find yourself using assign, eval or parse, there is an extremely high probability that there is a much better way to do this in R. As @VincentZoonekynd pointed out, use a list. Commented Jul 28, 2013 at 12:31
  • Do you have any links to this kind of looping structures? I've tried to find some, but I'm not sure if they are what I'm looking for. Each dataset (n=15) has 68 variables and 284 obs. I'd also like to change the variable I call. Any help is appreciated. Commented Jul 28, 2013 at 14:41
  • You should use lapply for the loop. Commented Jul 28, 2013 at 14:46
  • data[i] is not the same as an object named "data<i>" where the <i> is meant to be a digit or sequence of digits, Commented Jul 28, 2013 at 20:08

2 Answers 2

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Let's say there are multiple dataframes with names: data1 ...data15 and that there are no other data-objects that begin with the letters: d,a,t,a. Lets also assume that in each of those dataframes are columns named 'response' and 'variable'. The this would gather the dataframes into a list and draw separate plots for the linear regression lines.

dlist <- lapply ( ls(patt='^data'), get)
lapply(dlist, function(df) 
                 plot(NA, xlim=range(df$variable), ylim=range(df$response)
                 abline( coef( lm(response ~ variable, data=df) ) )
         )

If you wanted to name the dataframes in that list, you could use your paste code to supply names:

names(dlist) <- paste("data", i, sep = "")

There are many other assignments you could make in the context of this loop, but you would need to describe the desired results better than with failed efforts.

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Here's modified code that should work. It does one variable lm-model and calculates correlation of predicted and observed values and stores it into an empty matrix. It also plots these values.
Thanks Thomas for help. 

par(mfrow=c(4,5))
results.matrix <- matrix(NA, nrow = 20, ncol = 2)
colnames(results.matrix) <- c("Subset","Correlation")

for (i in 1:length(datalist)) {
    model <- lm(response ~ variable, data = datalist[[i]])
    pred <- predict.lm(model)
    cor <- (cor.test(pred, datalist[[i]]$response))
    plot(pred, datalist[[i]]$response, xlab="pred", ylab="obs")
    results.matrix[i, 1] <- i
    results.matrix[i, 2] <- cor$estimate
}
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4 Comments

for (i in datalist) should be for (i in 1:length(datalist)).
model <- lm(response ~ variable, data = i) should be model <- lm(response ~ variable, data = datalist[[i]]).
cor <- (cor.test(pred, i$response) should be cor <- (cor.test(pred, datalist[[i]]$response).
You should edit your answer to include those changes so that it's a working solution.

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