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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"))
#> Eprime.Basename Running Module Sound Correct Response TrialNumber
#> 1 SAILS_001X00XS1 Practice LAKE LAKE1.WAV Word Word 1
#> 2 SAILS_001X00XS1 Practice LAKE MAKE.WAV NotWord NotWord 2
#> 3 SAILS_001X00XS1 Practice LAKE LAKE1.WAV Word Word 3
#> 4 SAILS_001X00XS1 Practice LAKE LAKE1.WAV Word Word 4
#> 5 SAILS_001X00XS1 Practice LAKE MAKE.WAV NotWord NotWord 5
#> 6 SAILS_001X00XS1 Practice LAKE LAKE1.WAV Word Word 6
#> CorrectResponse
#> 1 1
#> 2 1
#> 3 1
#> 4 1
#> 5 1
#> 6 1Now 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
#> [1] "data/SAILS/SAILS_001X00XS1.txt" "data/SAILS/SAILS_002X00XS1.txt"
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
#> Eprime.Basename Running Score PropCorrect
#> 1 SAILS_001X00XS1 Practice 10 1.0000000
#> 2 SAILS_001X00XS1 Trial 61 0.8714286
#> 3 SAILS_002X00XS1 Practice 9 0.9000000
#> 4 SAILS_002X00XS1 Trial 57 0.8142857
# Score modules within subjects
modules <- ddply(ensemble, .(Eprime.Basename, Running, Module), summarise,
Score = sum(CorrectResponse),
PropCorrect = mean(CorrectResponse))
modules
#> Eprime.Basename Running Module Score PropCorrect
#> 1 SAILS_001X00XS1 Practice LAKE 10 1.0000000
#> 2 SAILS_001X00XS1 Trial CAT 9 0.9000000
#> 3 SAILS_001X00XS1 Trial LAKE 10 1.0000000
#> 4 SAILS_001X00XS1 Trial RAT 16 0.8000000
#> 5 SAILS_001X00XS1 Trial SUE 26 0.8666667
#> 6 SAILS_002X00XS1 Practice LAKE 9 0.9000000
#> 7 SAILS_002X00XS1 Trial CAT 8 0.8000000
#> 8 SAILS_002X00XS1 Trial LAKE 10 1.0000000
#> 9 SAILS_002X00XS1 Trial RAT 11 0.5500000
#> 10 SAILS_002X00XS1 Trial SUE 28 0.9333333These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.