README.md

In kemacdonald/iChartAnalyzeR: Analyze PEEK Data iChart Style

iChartAnalyzeR

iChartAnalyzeR is a R-package that contains utility functions for analyzing eyetracking studies in the style of the Language Learning Lab at Stanford University.

⚠️ Warning: This package is early work in development and subject to potentially code-breaking changes.

Setup

Install iChartAnalyzeR

Install iChartAnalyzeR from this GitHub repository:

install.packages('devtools')
devtools::install_github("kemacdonald/iChartAnalyzeR")

Usage

The following code snippet shows how to use the basic functionality of iChartAnalyzeR. Note that for any of these functions to work, the data have to be in iChart format with the same column naming conventions.

library(iChartAnalyzeR)

## Set the working directory to wherever the iChart is stored on your local machine or server
setwd("~/Desktop/tobii_to_ichart_test/test_Rscripts/")

## Read and Preprocess iChart
d <- readiChart(iChartFile = "Habla2_25_iChart_wide.txt", sampling_rate = 17)
d <- computeStatistics(d, cleanWindowStart = 0, cleanWindowEnd = 2300,
                       accuracyWindowStart = 300, accuracyWindowEnd = 1800,
                       save_results = TRUE)
d <- filteriChart(d, minRT = 300, maxRT = 1800, maxfirstgap = 30, maxlonggap = 30, save_results = TRUE)
d <- defineOnset(d, critonset = 0, includeAways = FALSE)

## Describe the iChart
describeiChart(d)

## Filter out Prescreened Trials
d_analysis <- filterPrescreened(d, save_results = TRUE)
describeiChart(d_analysis)

## Rename condition
d_analysis <- renameCondition(d_analysis, oldCondition = "Experimental", newCondition = "Vanilla")
describeiChart(d_analysis)

## Filter out unknown words
d_analysis <- removeUnknownWords(d_analysis, knownWords_file = "knows.txt", knows_threshold = 3)
describeiChart(d_analysis)

## Compute aggregate accuracy and RT
acc <- poolData(d_analysis,
               dependent="Accuracy",
               include_T_initial = TRUE,
               RejectFirstGap=TRUE,
               RejectLongestGap=TRUE,
               RejectRT=FALSE,
               save_results = TRUE)

rt <- poolData(d_analysis,
              dependent = "RT",
              include_T_initial = TRUE,
              RejectFirstGap = TRUE,
              RejectLongestGap = TRUE,
              RejectRT = TRUE,
              save_results = TRUE)

## Generate and save graph values for Profile Plot
pp_gvs <- generatePPgraphValues(d_analysis,
                                filter_criteria = list('GoodFirstGap', 'GoodLongestGap'),
                                group_cols = list("Condition"),
                                gvStartWindow = 0, gvEndWindow = 3000,
                                save_results = F)

## Make a Profile Plot from the graph values
makeProfilePlot(pp_gvs, plotStartWindow = 0, plotEndWindow = 2300, smoothing_factor = 5,
                save_results = TRUE)

## Generate and save graph values for Onset Contingency Plot
oc_gvs <- generateOCgraphValues(d_analysis,
                                filter_criteria = list('GoodFirstGap', 'GoodLongestGap'),
                                group_cols = list("Condition"),
                                gvStartWindow = 0, gvEndWindow = 3000,
                                save_results = TRUE)

## Make Onset Contingency Plot
makeOCplot(oc_gvs, plotStartWindow = 0, plotEndWindow = 1800, smoothing_factor = 5,
           save_results = TRUE)

kemacdonald/iChartAnalyzeR documentation built on May 7, 2019, 8:44 a.m.