### Matrix Span Processing Script ### ### Author: James Stone ### ### Version: 1.0 ### ### For use with the matrix span task available at http://www.cognitivetools.uk/ ### #required library library(stringr) # Set the working directory of R to the folder that hosts the output files from the matrix span task # ### function that takes the data frame from an output file and returns that persons summary info ### matrix_span_processing <- function(data.frame) { num.trials <- nrow(data.frame) fta.score <- 0 for (i in 1:num.trials) { if (data.frame$trial.matrix_span.result[i] == "success") { fta.score <- fta.score + data.frame$trial.matrix_span.load[i] } } #highest load with a fully correct response if (length(data.frame$trial.matrix_span.load[data.frame$trial.matrix_span.result == "success"]) == 0) { #no successes max.span <- NA } else { max.span <- max(data.frame$trial.matrix_span.load[data.frame$trial.matrix_span.result == "success"]) } #span two trials correct if (nrow(subset(data.frame, trial.matrix_span.load == 2)) == 0) { #then there were no span size 2 trials, therefore span.2.corr <- NA } else { span.2.corr <- sum(data.frame$trial.matrix_span.points[data.frame$trial.matrix_span.load == 2]) } #span three trials correct if (nrow(subset(data.frame, trial.matrix_span.load == 3)) == 0) { #then there were no span size 3 trials, therefore span.3.corr <- NA } else { span.3.corr <- sum(data.frame$trial.matrix_span.points[data.frame$trial.matrix_span.load == 3]) } #span four trials correct if (nrow(subset(data.frame, trial.matrix_span.load == 4)) == 0) { #then there were no span size 4 trials, therefore span.4.corr <- NA } else { span.4.corr <- sum(data.frame$trial.matrix_span.points[data.frame$trial.matrix_span.load == 4]) } #span five trials correct if (nrow(subset(data.frame, trial.matrix_span.load == 5)) == 0) { #then there were no span size 5 trials, therefore span.5.corr <- NA } else { span.5.corr <- sum(data.frame$trial.matrix_span.points[data.frame$trial.matrix_span.load == 5]) } #span six trials correct if (nrow(subset(data.frame, trial.matrix_span.load == 6)) == 0) { #then there were no span size 6 trials, therefore span.6.corr <- NA } else { span.6.corr <- sum(data.frame$trial.matrix_span.points[data.frame$trial.matrix_span.load == 6]) } #span seven trials correct if (nrow(subset(data.frame, trial.matrix_span.load == 7)) == 0) { #then there were no span size 7 trials, therefore span.7.corr <- NA } else { span.7.corr <- sum(data.frame$trial.matrix_span.points[data.frame$trial.matrix_span.load == 7]) } #span eight trials correct if (nrow(subset(data.frame, trial.matrix_span.load == 8)) == 0) { #then there were no span size 8 trials, therefore span.8.corr <- NA } else { span.8.corr <- sum(data.frame$trial.matrix_span.points[data.frame$trial.matrix_span.load == 8]) } #span nine trials correct if (nrow(subset(data.frame, trial.matrix_span.load == 9)) == 0) { #then there were no span size 9 trials, therefore span.9.corr <- NA } else { span.9.corr <- sum(data.frame$trial.matrix_span.points[data.frame$trial.matrix_span.load == 9]) } ###finally, to get prop score, and number individual successes need to break down the recall ###segment to a per element basis, not the per trial basis we get as output. #create a new data frame where every row is one response within a trial #number of rows will be the sum of the loads num.elements <- sum(data.frame$trial.matrix_span.load) ID <- numeric(num.elements) trial.no <- numeric(num.elements) load <- numeric(num.elements) question <- numeric(num.elements) response <- numeric(num.elements) success <- numeric(num.elements) counter <- 1 for (i in 1:nrow(data.frame)) { num_elements <- data.frame$trial.matrix_span.load[i] tmp_question <- as.character(data.frame$trial.matrix_span.question[i]) tmp_response <- as.character(data.frame$trial.matrix_span.response[i]) q <- str_split_fixed(tmp_question,", ", num_elements) r <- str_split_fixed(tmp_response,", ", num_elements) if (nchar(q[1]) == 2) { q[1] <- substr(q[1],2,2) } else if (nchar(q[1]) == 3) { q[1] <- substr(q[1],2,3) } if (nchar(r[1]) == 2) { r[1] <- substr(r[1],2,2) } else if (nchar(r[1]) == 3) { r[1] <- substr(r[1],2,3) } if (nchar(q[num_elements]) == 2) { q[num_elements] <- substr(q[num_elements],1,1) } else if (nchar(q[num_elements]) == 3) { q[num_elements] <- substr(q[num_elements],1,2) } else if (nchar(q[num_elements]) == 4) { q[num_elements] <- substr(q[num_elements],1,3) } if (nchar(r[num_elements]) == 2) { r[num_elements] <- substr(r[num_elements],1,1) } else if (nchar(r[num_elements]) == 3) { r[num_elements] <- substr(r[num_elements],1,2) } else if (nchar(r[num_elements]) == 4) { r[num_elements] <- substr(r[num_elements],1,3) } q.vec <- as.numeric(q) r.vec <- as.numeric(r) for (j in 1:data.frame$trial.matrix_span.load[i]) { ID[counter] <- data.frame$user.yearOfBirth[1] trial.no[counter] <- data.frame$trial.matrix_span.trialNo[i] load[counter] <- data.frame$trial.matrix_span.load[i] question[counter] <- q.vec[j] response[counter] <- r.vec[j] success[counter] <- q.vec[j] == r.vec[j] counter <- counter + 1 } } data.frame.indi <- as.data.frame(cbind(ID,trial.no,load,question,response,success)) number.successes <- sum(data.frame.indi$success) props <- numeric(num.trials) for (i in 1:num.trials) { this.trial.load <- nrow(subset(data.frame.indi, trial.no == i)) this.trial.successes <- sum(data.frame.indi$success[data.frame.indi$trial.no == i]) props[i] <- this.trial.successes / this.trial.load } prop.score <- mean(props) p.summary <- c(fta.score, prop.score, number.successes, max.span, span.2.corr, span.3.corr, span.4.corr, span.5.corr, span.6.corr, span.7.corr, span.8.corr, span.9.corr) return(p.summary) } ##now can use that function on all our data files for matrix span #put all matrix span files into one folder and set that folder as working directory compile_matrix_span_data <- function(filestring) { raw.data <- read.csv(filestring) ps <- unique(raw.data$session.subject.subject.code) matrix.span.data <- matrix(nrow=length(ps), ncol=12) for (i in 1:length(ps)) { #subset data frame with first unique subj code sub.raw.data <- subset(raw.data, session.subject.subject.code == ps[i]) tmp.matrix.summary <- matrix_span_processing(sub.raw.data) matrix.span.data[i, ] <- tmp.matrix.summary } matrix.span.data <- as.data.frame(matrix.span.data) matrix.span.data$user <- ps matrix.names <- c("fta.score", "prop.score", "number.successes", "max.span", "span.2.corr", "span.3.corr", "span.4.corr", "span.5.corr", "span.6.corr", "span.7.corr", "span.8.corr", "span.9.corr", "pcode") names(matrix.span.data) <- matrix.names return(matrix.span.data) } matrix.span.data <- compile_matrix_span_data("datafile.csv") rm(compile_matrix_span_data, matrix_span_processing)