/
readspan.R
438 lines (409 loc) · 23 KB
/
readspan.R
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#' Raw Tidy Data for Reading Span
#'
#' Converts the messy e-prime data file into a tidy raw data file that is
#' easy to work with.
#'
#' @param x dataframe
#' @param include_col c(): list of additional columns to include
#' @param taskVersion old or new version. Required for different types of
#' data files from older or newer versions of the complex-span tasks.
#' First leave out the argument, if it does not work
#' then try taskVersion = "old".
#' @export
#'
raw_readspan <- function(x, include_col = c(), taskVersion = "new") {
exit_task_error <- FALSE
if (stringr::str_detect(x$ExperimentName[1], "RspanShort")) {
taskVersion <- "oswald"
}
if (taskVersion == "new") {
x <- dplyr::mutate(x,
`Procedure[Block]` =
ifelse(is.na(`Procedure[Block]`),
"TaskProc", `Procedure[Block]`))
x <- dplyr::filter(x, `Procedure[Block]` == "TaskProc")
if (!("AvgReadTime" %in% colnames(x))) {
x <- dplyr::mutate(x, AvgReadTime = NA)
}
}
if (taskVersion == "old" | taskVersion == "oswald") {
x <- dplyr::filter(x, `Procedure[Block]` == "SessionProc")
x <- dplyr::mutate(x, ReadACC = NA, AvgReadTime = NA)
}
if (!("AvgReadTime" %in% colnames(x))) {
x <- dplyr::mutate(x, AvgReadTime = NA)
}
if (NA %in% unique(x$`Running[Trial]`)) {
x <- tidyr::fill(x, `Running[Trial]`, .direction = "down")
x <- dplyr::mutate(x, `Procedure[SubTrial]` =
ifelse(is.na(`Procedure[SubTrial]`),
"recall", `Procedure[SubTrial]`))
exit_task_error <- TRUE
}
x <- dplyr::rename(x, SetSize = setsz)
x <-
dplyr::mutate(x,
SubTrialProc =
dplyr::case_when(`Procedure[SubTrial]` == "TrialProc" |
`Procedure[SubTrial]` == "TrialProc1" |
`Procedure[SubTrial]` == "TrialProc2" ~
"ProcessingTask",
`Procedure[SubTrial]` == "recall" |
`Procedure[SubTrial]` == "recall1" |
`Procedure[SubTrial]` == "recall2" ~
"Recall",
TRUE ~ as.character(NA)),
Block = dplyr::case_when(`Running[Trial]` == "BlockList1" ~ 1,
`Running[Trial]` == "BlockList2" ~ 2,
`Running[Trial]` == "BlockList3" ~ 3,
TRUE ~ as.double(NA)))
if (taskVersion == "oswald") {
x <- dplyr::mutate(x,
Block = dplyr::case_when(BlockList.Sample <= 3 ~ 1,
BlockList.Sample > 3 ~ 2,
TRUE ~ as.double(NA)))
}
blocks <- length(unique(x$Block))
if (taskVersion != "oswald") {
if (blocks == 1) {
x <- dplyr::mutate(x, Block = 1)
} else if (blocks == 2) {
x <-
dplyr::mutate(x,
SENSEBOTH.RT =
dplyr::case_when(SubTrialProc == "ProcessingTask" &
Block == 1 ~ as.double(SENSEBOTH.RT),
SubTrialProc == "ProcessingTask" &
Block == 2 ~ as.double(SENSEBOTH1.RT),
TRUE ~ as.double(NA)),
showSentence.RT =
dplyr::case_when(SubTrialProc == "ProcessingTask" &
Block == 1 ~ as.double(showSentence.RT),
SubTrialProc == "ProcessingTask" &
Block == 2 ~ as.double(showSentence1.RT),
TRUE ~ as.double(NA)),
CollectClick.RT =
dplyr::case_when(SubTrialProc == "Recall" &
Block == 1 ~
as.double(CollectClick.RT),
SubTrialProc == "Recall" &
Block == 2 ~
as.double(CollectClick2.RT),
TRUE ~ as.double(NA)),
SENSEBOTH.ACC =
dplyr::case_when(SubTrialProc == "ProcessingTask" &
Block == 1 ~ as.double(SENSEBOTH.ACC),
SubTrialProc == "ProcessingTask" &
Block == 2 ~ as.double(SENSEBOTH1.ACC),
TRUE ~ as.double((NA))))
} else if (blocks == 3) {
x <-
dplyr::mutate(x,
SENSEBOTH.RT =
dplyr::case_when(SubTrialProc == "ProcessingTask" &
Block == 1 ~ as.double(SENSEBOTH.RT),
SubTrialProc == "ProcessingTask" &
Block == 2 ~ as.double(SENSEBOTH1.RT),
SubTrialProc == "ProcessingTask" &
Block == 3 ~ as.double(SENSEBOTH2.RT),
TRUE ~ as.double(NA)),
showSentence.RT =
dplyr::case_when(SubTrialProc == "ProcessingTask" &
Block == 1 ~ as.double(showSentence.RT),
SubTrialProc == "ProcessingTask" &
Block == 2 ~ as.double(showSentence1.RT),
SubTrialProc == "ProcessingTask" &
Block == 3 ~ as.double(showSentence2.RT),
TRUE ~ as.double(NA)),
CollectClick.RT =
dplyr::case_when(SubTrialProc == "Recall" &
Block == 1 ~
as.double(CollectClick.RT),
SubTrialProc == "Recall" &
Block == 2 ~
as.double(CollectClick2.RT),
SubTrialProc == "Recall" &
Block == 3 ~
as.double(CollectClick3.RT),
TRUE ~ as.double(NA)),
SENSEBOTH.ACC =
dplyr::case_when(SubTrialProc == "ProcessingTask" &
Block == 1 ~ as.double(SENSEBOTH.ACC),
SubTrialProc == "ProcessingTask" &
Block == 2 ~ as.double(SENSEBOTH1.ACC),
SubTrialProc == "ProcessingTask" &
Block == 3 ~ as.double(SENSEBOTH2.ACC),
TRUE ~ as.double((NA))))
}
}
x <- dplyr::group_by(x, Subject, Block, Trial)
x <- dplyr::mutate(x,
RT =
dplyr::case_when(SubTrialProc == "ProcessingTask" ~
as.double(SENSEBOTH.RT) +
as.double(showSentence.RT),
SubTrialProc == "Recall" ~
as.double(CollectClick.RT),
TRUE ~ as.double(NA)),
clear_items =
dplyr::case_when(SubTrialProc == "Recall" &
WordSelection == "Clear" ~ 1,
SubTrialProc == "Recall" &
WordSelection == "clear" ~ 1,
TRUE ~ as.numeric(NA)),
clear_items =
zoo::na.locf(clear_items, fromLast = TRUE, na.rm = FALSE),
clear_items =
ifelse(SubTrialProc == "ProcessingTask",
as.numeric(NA), clear_items),
button_click =
dplyr::case_when(SubTrialProc == "Recall" &
WordSelection == "Enter" ~ 1,
SubTrialProc == "Recall" &
WordSelection == "Exit" ~ 1,
SubTrialProc == "Recall" &
WordSelection == "Clear" ~ 1,
SubTrialProc == "Recall" &
WordSelection == "clear" ~ 1,
SubTrialProc == "Recall" &
WordSelection == "InvalidResponse" ~
1,
TRUE ~ 0),
recall_response_type =
dplyr::case_when(SubTrialProc == "Recall" &
button_click == 0 &
clear_items == 1 ~ "clear",
SubTrialProc == "Recall" &
button_click == 1 ~ "button",
SubTrialProc == "Recall" &
button_click == 0 ~ "recall",
TRUE ~ as.character(NA)),
recall_response =
dplyr::case_when(SubTrialProc == "Recall" ~
as.character(WordSelection),
TRUE ~ as.character(NA)),
AvgReadTime =
ifelse(!is.na(AvgReadTime) &
AvgReadTime == "?",
as.numeric(NA), AvgReadTime),
remove =
dplyr::case_when(SubTrialProc == "Recall" &
is.na(WordSelection) ~ 1,
TRUE ~ 0))
x <- dplyr::filter(x, remove == 0)
if (exit_task_error == TRUE) {
x <- dplyr::group_by(x, Subject, Block, Trial, SubTrialProc)
x <- dplyr::mutate(x,
SubTrial = dplyr::row_number(),
SetSize =
dplyr::case_when(is.na(SetSize) &
SubTrialProc == "ProcessingTask" ~
as.double(max(SubTrial)),
TRUE ~ as.double(SetSize)))
x <- dplyr::ungroup(x)
x <- tidyr::fill(x, SetSize, .direction = "down")
}
x <- dplyr::group_by(x, Subject, Block, Trial, recall_response_type)
x <- dplyr::mutate(x, recall_position = dplyr::row_number())
x <- dplyr::group_by(x, Subject, Block, Trial)
suppressWarnings({
x <- dplyr::mutate(x,
SubTrial = dplyr::row_number(),
position_1 =
ifelse(SubTrial == 1, letterstimuli, as.character(NA)),
position_1 = zoo::na.locf(position_1, na.rm = FALSE),
position_2 =
ifelse(SubTrial == 2, letterstimuli, as.character(NA)),
position_2 = zoo::na.locf(position_2, na.rm = FALSE),
position_3 =
ifelse(SubTrial == 3, letterstimuli, as.character(NA)),
position_3 = zoo::na.locf(position_3, na.rm = FALSE),
position_4 =
ifelse(SubTrial == 4, letterstimuli, as.character(NA)),
position_4 = zoo::na.locf(position_4, na.rm = FALSE),
position_5 =
ifelse(SubTrial == 5, letterstimuli, as.character(NA)),
position_5 = zoo::na.locf(position_5, na.rm = FALSE),
position_6 =
ifelse(SubTrial == 6, letterstimuli, as.character(NA)),
position_6 = zoo::na.locf(position_6, na.rm = FALSE),
position_7 =
ifelse(SubTrial == 7, letterstimuli, as.character(NA)),
position_7 = zoo::na.locf(position_7, na.rm = FALSE),
position_8 =
ifelse(SubTrial == 8, letterstimuli, as.character(NA)),
position_8 = zoo::na.locf(position_8, na.rm = FALSE),
position_9 =
ifelse(SubTrial == 9, letterstimuli, as.character(NA)),
position_9 = zoo::na.locf(position_9, na.rm = FALSE),
memory_item =
dplyr::case_when(recall_response_type == "recall" &
recall_position == 1 ~
as.character(position_1),
recall_response_type == "recall" &
recall_position == 2 ~
as.character(position_2),
recall_response_type == "recall" &
recall_position == 3 ~
as.character(position_3),
recall_response_type == "recall" &
recall_position == 4 ~
as.character(position_4),
recall_response_type == "recall" &
recall_position == 5 ~
as.character(position_5),
recall_response_type == "recall" &
recall_position == 6 ~
as.character(position_6),
recall_response_type == "recall" &
recall_position == 7 ~
as.character(position_7),
recall_response_type == "recall" &
recall_position == 8 ~
as.character(position_8),
recall_response_type == "recall" &
recall_position == 9 ~
as.character(position_9),
TRUE ~ as.character(NA)),
CorrectResponse =
ifelse(SubTrialProc == "ProcessingTask",
as.character(`CorrectAnswer[SubTrial]`),
ifelse(SubTrialProc == "Recall",
as.character(memory_item),
as.character(NA))),
Accuracy =
dplyr::case_when(SubTrialProc == "ProcessingTask" ~
as.double(SENSEBOTH.ACC),
SubTrialProc == "Recall" &
CorrectResponse == WordSelection ~
as.double(1),
SubTrialProc == "Recall" &
CorrectResponse != WordSelection ~
as.double(0),
TRUE ~ as.double((NA))),
Response =
dplyr::case_when(SubTrialProc == "ProcessingTask" &
Accuracy == 1 ~ CorrectResponse,
SubTrialProc == "ProcessingTask" &
Accuracy == 0 &
CorrectResponse == "TRUE" ~ "FALSE",
SubTrialProc == "ProcessingTask" &
Accuracy == 0 &
CorrectResponse == "FALSE" ~ "TRUE",
SubTrialProc == "Recall" ~
as.character(WordSelection),
TRUE ~ as.character(NA)),
Response = dplyr::case_when(Response == "Blank" ~ "-",
Response == "blank" ~ "-",
TRUE ~ Response),
MemoryItem = letterstimuli,
Processing.correct =
ifelse(SubTrialProc == "ProcessingTask",
Accuracy, as.numeric(NA)),
Processing.correct =
stats::ave(Processing.correct,
FUN = function(x) sum(x, na.rm = TRUE)),
Recall.correct =
ifelse(SubTrialProc == "Recall",
Accuracy, as.numeric(NA)),
Recall.correct =
stats::ave(Recall.correct,
FUN = function(x) sum(x, na.rm = TRUE)),
Partial.unit = Recall.correct / SetSize,
Absolute.unit = ifelse(Recall.correct == SetSize, 1, 0),
Partial.load = Recall.correct,
Absolute.load =
ifelse(Recall.correct == SetSize, Recall.correct, 0))
})
x <- dplyr::ungroup(x)
x <- dplyr::select(x, Subject, Block, Trial, SetSize, Processing.correct,
Recall.correct, Partial.unit, Partial.load,
Absolute.unit, Absolute.load, SubTrial, SubTrialProc,
RT, Accuracy, Response, CorrectResponse, MemoryItem,
include_col, SessionDate, SessionTime, recall_response_type)
x <- dplyr::distinct(x)
# add columns with sequence of target memory and recalled items
x_tr <- dplyr::mutate(x,
Response =
dplyr::case_when(is.na(Response) ~ as.character(NA),
Response == "TRUE" ~ "TRUE",
Response == "FALSE" ~ "FALSE",
Response == "-" ~ "-",
TRUE ~ Response))
x_tr <- dplyr::filter(x_tr, recall_response_type == "recall" |
SubTrialProc == "ProcessingTask")
x_tr <- dplyr::group_by(x_tr, Subject, Block, Trial, SubTrialProc)
x_tr <- dplyr::mutate(x_tr, SubTrial = dplyr::row_number())
x_tr <- dplyr::ungroup(x_tr)
x_tr <- tidyr::pivot_wider(x_tr,
id_cols = c(Subject, Block, Trial),
names_from = c(SubTrialProc, SubTrial),
values_from = c(MemoryItem, Response))
x_tr <- tidyr::unite(x_tr, "MemoryTargets",
dplyr::contains("MemoryItem_ProcessingTask"),
sep = "", na.rm = TRUE)
x_tr <- tidyr::unite(x_tr, "Recalled",
dplyr::contains("Response_Recall"),
sep = "", na.rm = TRUE)
x_tr <- dplyr::select(x_tr, Subject, Block, Trial, MemoryTargets, Recalled)
x <- merge(x, x_tr, by = c("Subject", "Block", "Trial"))
x <- dplyr::relocate(x, MemoryTargets, Recalled, .after = SetSize)
# add columns for edit distance load and unit scores
x <- englelab::edit_distance(x)
x <- dplyr::relocate(x, EditDistance.unit, EditDistance.load,
.after = Processing.correct)
# remove Recall.correct. It is redundant with Partial.load
x <- dplyr::select(x, -Recall.correct, -recall_response_type)
return(x)
}
#' Calculate Reading Span Scores
#'
#' Calculate various span scores from the output of `raw_readspan()`
#' @param x dataframe
#' @export
#'
score_readspan <- function(x) {
message("If you use the Edit Distance scores, then cite: ",
"Gonthier, C. (2022). An easy way to improve scoring of memory span tasks ",
"The edit distance, beyond “correct recall in the correct serial position.” ",
"Behavior Research Methods, 16. https://doi.org/10.3758/s13428-022-01908-2")
if ("Running[Trial]" %in% colnames(x)) {
x <- englelab::raw_readspan(x)
}
x_recall <- dplyr::distinct(x, Subject, Block, Trial, SetSize,
EditDistance.unit, EditDistance.load,
Partial.unit, Partial.load,
Absolute.unit, Absolute.load)
x_recall <- dplyr::summarise(x_recall,
ReadSpan.EditDistanceScore = sum(EditDistance.load),
ReadSpan.EditDistanceUnit = mean(EditDistance.unit),
ReadSpan.EditDistanceLoad =
sum(EditDistance.load) / sum(SetSize),
ReadSpan.PartialScore = sum(Partial.load),
ReadSpan.PartialUnit = mean(Partial.unit),
ReadSpan.PartialLoad =
sum(Partial.load) / sum(SetSize),
ReadSpan.AbsoluteScore = sum(Absolute.load),
ReadSpan.AbsoluteUnit = mean(Absolute.unit),
ReadSpan.AbsoluteLoad =
sum(Absolute.load) / sum(SetSize),
ReadSpan.Trials = dplyr::n(),
ReadSpan.MemoryItems = sum(SetSize))
x_processing <- dplyr::filter(x, SubTrialProc == "ProcessingTask")
x_processing <- dplyr::summarise(x_processing,
Read.ACC = mean(Accuracy, na.rm = TRUE),
Read.RT_mean = mean(RT, na.rm = TRUE),
Read.RT_sd = sd(RT, na.rm = TRUE),
SessionDate = dplyr::first(SessionDate),
SessionTime = dplyr::first(SessionTime))
x <- tryCatch(dplyr::full_join(x_recall, x_processing),
error = function(c){
if (!FALSE) {dplyr::bind_cols(x_recall, x_processing)}
else {dplyr::full_join(x_recall, x_processing)}
})
x <- dplyr::relocate(x,
ReadSpan.Trials, ReadSpan.MemoryItems,
SessionDate, SessionTime,
.after = dplyr::last_col())
return(x)
}