For loop to import .csv files containing text (tweets)

Question

I have a folder with 60-some .csv files containing "scraped" tweets. Each file has 6 columns: one is simply the "position" in which they got downloaded (might as well get rid of it), then I have one for the date, one for the time and then the 3 important ones: ticker (e.g. AAPL, MSFT...), body of the tweet a lastly a label available only for a small part of them. What I need to do is to run a for loop to be able to apply svm and NaiveBayes to every file so to predict the label for non-labeled tweets. I've already run both algorithms on the training set, the output from this loop would be the test set. Thank You in advance.

When asking for help, you should include a simple [reproducible example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) with sample input and desired output that can be used to test and verify possible solutions. Ask a specific question that can be unambiguously answered. — MrFlick, Feb 26 '18 at 21:48
I wasnt able to find the question you all linked, but I seem to have found an answer there, thank you all for the answers — TheClutch01, Feb 27 '18 at 14:13

score 2 · Answer 1 · edited Feb 26 '18 at 22:11

We can get a character vector of all relevant files with list.files

fn <- list.files(pattern = ".csv$")

Then use a member of the *apply family to read in the data and apply a function; for example, something like this:

lst <- lapply(fn, function(x) {
    df <-read.csv(x);
    ## Example: Fit a linear model and return lm object
    return(lm(y ~ x, data = df));
})

lst then contains a list of lm objects. If you replace lm with svm, the return objects will be a list of results from a support vector machine classification.

For loop to import .csv files containing text (tweets)

1 Answers1