Working with multiple files"

library("rprime")
library("knitr")
opts_chunk$set(
  comment = "#>",
  error = FALSE,
  tidy = FALSE,
  collapse = TRUE)

Suppose we want to load all the Eprime files in a directory and combine the results in dataframe.

My strategy in this scenario is to figure out what I need to do for a single file and then wrap those steps in a function that takes a filepath to a txt file and returns a dataframe. After some exploration and interactive programming, I come up with the following function.

library("plyr")
reduce_sails <- function(sails_path) {
  sails_lines <- read_eprime(sails_path)
  sails_frames <- FrameList(sails_lines)

  # Trials occur at level 3
  sails_frames <- keep_levels(sails_frames, 3)
  sails <- to_data_frame(sails_frames)

  # Tidy up
  to_pick <- c("Eprime.Basename", "Running", "Module", "Sound", 
               "Sample", "Correct", "Response")
  sails <- sails[to_pick]
  running_map <- c(TrialLists = "Trial", PracticeBlock = "Practice")
  sails$Running <- running_map[sails$Running]

  # Renumber trials in the practice and experimental blocks separately.
  # Numerically code correct response.
  sails <- ddply(sails, .(Running), mutate, 
                 TrialNumber = seq(from = 1, to = length(Running)),
                 CorrectResponse = ifelse(Correct == Response, 1, 0))
  sails$Sample <- NULL

  # Optionally, one might save the processed file via: 
  # csv <- paste0(file_path_sans_ext(sails_path), ".csv")
  # write.csv(sails, csv, row.names = FALSE)
  sails
}

Here's a preview of what the function returns when given a filepath.

head(reduce_sails("data/SAILS/SAILS_001X00XS1.txt"))

Now that the function works on one file, I can use ldply to apply the function to several files, returning results in a single dataframe. (For dplyr, I would lapply the function to each path to get a list of dataframes, then use bind_rows to combine into a single dataframe.)

sails_paths <- list.files("data/SAILS/", pattern = ".txt", full.names = TRUE)
sails_paths
ensemble <- ldply(sails_paths, reduce_sails)

Finally, with all of the subjects' data contained in a single dataframe, I can use ddply plus summarise and compute summary scores at different levels of aggregation within each subject.

# Score trials within subjects
overall <- ddply(ensemble, .(Eprime.Basename, Running), summarise, 
                 Score = sum(CorrectResponse),
                 PropCorrect = Score / length(CorrectResponse))
overall

# Score modules within subjects
modules <- ddply(ensemble, .(Eprime.Basename, Running, Module), summarise, 
                 Score = sum(CorrectResponse),
                 PropCorrect = mean(CorrectResponse))
modules


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rprime documentation built on May 1, 2019, 7:56 p.m.