data-raw/README.md

In qdercon/pstpipeline: Probabilistic Selection Task (PST) pipeline: parsing, analysis, simulation, and plotting

Using the raw data outside of pstpipeline

The task data is stored here as an R data object (all_res_split.RDS). The raw experiment outputs for each partcipant are too large to store on GitHub; this data is outputted from the import_multiple parsing function. The other two .csv files are from an earlier study by Gillan et al. (2016), the use of which is explained further in this notebook.

The data object is structured as a nested list. As analyses were done separately in distancing participants, it is first separated by group (non-distanced and distanced). Within each group, there are then four data frames: ppt_info (demographic and exclusion info); training (training phase data); test (test phase data); and gillan_questions (psychiatric questionnaire question answers).

Note that with the exception of questionnaire questions, the data includes all 995 participants, so you may want to filter out excluded participants. The ppt_info data frame includes a column called exclusion which is TRUE for excluded participants and FALSE for non-excluded participants (see the paper for more information on exclusion criteria).

If you use the data for any published work, please consider citing the paper:

Dercon†, Q., Mehrhof†, S. Z., Sandhu, T. R., Hitchcock, C., Lawson, R. P., Pizzagalli, D. A., Dalgleish, T., & Nord, C. L. (2023). A core component of psychological therapy causes adaptive changes in computational learning mechanisms. Psychological Medicine, 1–11. https://doi.org/10.1017/S0033291723001587

In R

The .RDS file can be easily loaded into R using the following code:

all_res_split <- readRDS("data-raw/all_res_split.RDS")

Then, the various data frames can be accessed using $ or [[ ]] syntax. For example, to access the training data frame for the non-distanced group, you would call:

training_data <- all_res_split$non_distanced$training

If required, an easy way to remove the distancing group nesting would be as follows (the pipe requires R >= 4.1.0):

all_res <- purrr::transpose(all_res_split) |> purrr::map(dplyr::bind_rows)

In Python

It is probably easiest to first import the data into R as detailed above and then save the data you require as .csv files. However, should you wish to do this in Python, you can use the rpy2 package to convert the RDS into pandas data frames. This can be run in Google Colab to avoid local installs.

import pandas as pd
import rpy2.robjects as ro
import urllib

from rpy2.robjects import pandas2ri
from rpy2.robjects.conversion import localconverter

## download data from GitHub
url = "https://github.com/qdercon/pstpipeline/blob/main/data-raw/all_res_split.RDS?raw=true"
urllib.request.urlretrieve(url, "all_res_split.RDS")

## load RDS file
readRDS = ro.r['readRDS']
r_df = readRDS('all_res_split.RDS')

## convert to rpy2 ListVector that pandas can read
with localconverter(ro.default_converter + pandas2ri.converter):
  non_distanced = ro.conversion.rpy2py(r_df[0])
  distanced = ro.conversion.rpy2py(r_df[1])

## convert training data to pandas data frames
with localconverter(ro.default_converter + pandas2ri.converter):
  non_dis_train = ro.conversion.rpy2py(non_distanced.rx('training')[0])
  dis_train = ro.conversion.rpy2py(distanced.rx('training')[0])

non_dis_train.head()

qdercon/pstpipeline documentation built on June 1, 2025, 1:11 p.m.