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## ---- echo = FALSE, message = FALSE-------------------------------------------
library("rprime")
library("knitr")
opts_chunk$set(
comment = "#>",
error = FALSE,
tidy = FALSE,
collapse = TRUE)
## -----------------------------------------------------------------------------
library("plyr")
reduce_sails <- function(sails_path) {
sails_lines <- read_eprime(sails_path)
sails_frames <- FrameList(sails_lines)
# Trials occur at level 3
sails_frames <- keep_levels(sails_frames, 3)
sails <- to_data_frame(sails_frames)
# Tidy up
to_pick <- c("Eprime.Basename", "Running", "Module", "Sound",
"Sample", "Correct", "Response")
sails <- sails[to_pick]
running_map <- c(TrialLists = "Trial", PracticeBlock = "Practice")
sails$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
}
## -----------------------------------------------------------------------------
head(reduce_sails("data/SAILS/SAILS_001X00XS1.txt"))
## -----------------------------------------------------------------------------
sails_paths <- list.files("data/SAILS/", pattern = ".txt", full.names = TRUE)
sails_paths
ensemble <- ldply(sails_paths, reduce_sails)
## -----------------------------------------------------------------------------
# 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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