NeuroIS-package: NeuroIS: The R-Package for Neuro Information Systems Research...

Description Details Author(s) Examples

Description

NeuroIS has become an established method in IS research for investigating user behavior, perception, and even neurophysiology. This same named package NeuroIS is a freely-available open source R-package for analyzing NeuroIS data, enabling scholars to investigate the bio-physiological fundations of human-computer interaction in individual as well as in group settings.

Details

[1] http://im.iism.kit.edu/1093_1100.php

Package: Brownie
Type: Package
Version: 0.0.0.9000
Date: 2017-31-01
License: GPL-3
Depends: R (>= 3.0)

Author(s)

Dominik Jung d.jung@kit.edu

Sven Michalczyk sven.michalczyk@icloud.com

Examples

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### 0. Preparations & Import
library(NeuroIS)

import(path = "data/jump_and_rest", prefix = "jump_and_rest", 
       create_physio_object = T, physio_mv_dir = T)

### 1. Do central pages exist in terms of duration or click frequency?
summary(data = jump_and_rest_web, objectives = F)

### 2. At which pages did the proband jump?
marker <- marker(data = jump_and_rest_web, subject = 1, path_marker = "data/jump_and_rest/physio" )

start_time <- min(jump_and_rest_web[jump_and_rest_web$SUBJECT_ID_SUBJECT == 1]$Time, na.rm = T)

summary(jump_and_rest_physio, type = "eda", subject = 1, marker = marker, start_time = start_time) 

plot(jump_and_rest_physio, type = "eda", subject = 1, marker = marker, start_time)

plot(jump_and_rest_physio, type = "ecg", subject = 1, marker = "data/jump_and_rest/physio/marker1.csv",
     start_time, physio_unisens_dir = "data/jump_and_rest/physio/")

### 3. Which elements on a central page did the mouse last on?
web_summary <- summary(data = jump_and_rest_web, objectives = F)
urls <- web_summary$URL
take_screenshot(urls = urls)
plot(jump_and_rest_web, url = urls[4], type = "motion", subject = 1,
     alpha = 0.1, size = 3, color = "purple")

### 4. What<e2><80><99>s the probability to go from one page to another, based on the subject?
library(clickstream)

cls <- as_clickstream(data = jump_and_rest_web, objectives = F, attribution ="last")
summary(cls)
mc <- fitMarkovChain(clickstreamList = cls, order = 1, control = list(optimizer = "quadratic"))
summary(mc)
plot(mc, order = 1)

Fiddleman/BrownieR documentation built on May 6, 2019, 4:36 p.m.