README.md

jspsychread

The goal of jspsychread is to transform the data collected in jsPsych and stored in JSON format into R. The data are then available as tibbles (one trial per line) and you can filter them etc.

Installation

The package is in active development. You can install the current version from GitHub with following code:

# Install devtools package if necessary
if(!"devtools" %in% rownames(installed.packages())) install.packages("devtools")

# Install the stable verion from GitHub
devtools::install_github("visionlabels/jspsychread")

How to start

Check the example below which covers the main parts of the workflow. More detailed walk-through is included in the package vignette (see vignette("jspsychread")).

Example

This is a basic example which shows you how to solve a common problem:

library(tidyverse)
library(jspsychread)

## basic example code

fn <- demo_file("jspsych-video-button-response.json")
d  <- read_jspsych(fn)

# parse results of a specific trial types
d %>%
  filter(trial_type == trial_types$preload) %>%
  select(record, trial_index, raw) %>%
  process_records(.using = parse_preload) %>%
  unnest(processed)

d %>%
  filter(trial_type == trial_types$html_button_response) %>%
  select(record, trial_index, raw) %>%
  process_records(.using = parse_html_button_response) %>%
  unnest(processed)

d %>%
  filter(trial_type == trial_types$video_button_response) %>%
  select(record, trial_index, raw) %>%
  process_records(.using = parse_video_button_response) %>%
  unnest(processed)

In the examples above, you explicitly defined what function to use to process the trial data. Since version 0.3.0, process_records can auto-detect the parser based on the trial_type column.

It is still required that all trials in the tibble are of the same type and you should use filter or group_by to separate different trial types. It is an intentional decision. With diverse trial types in the same tibble, it would not be possible to unnest it.

You can use simpler call:

d %>%
  filter(trial_type == trial_types$html_button_response) %>%
  process_records(.using = parse_html_button_response) %>%
  unnest(processed)

Or even parse everything into a list of tibbles.

dl <-
  d %>% 
  group_split(trial_type) %>% 
  map(~ process_records(.x))
dl
dl %>% map(~ .x %>% unnest(processed))