Programmatic access

You can access refbank data in R through the refbank package or directly using the Redivis API for Python.

The codebook contains descriptions of all tables and what each column means.

R

1. Install the refbankr R package:

remotes::install_github("refbank/refbankr")

2. Generate and set an API token.

3. Access the data:

library(refbankr)

# Load table as tidyverse tibble
datasets <- get_datasets()

# experiment-level information about the conditions
conditions <- get_conditions()

# trial-level information about who was in the trial, what the stimuli were, and when in a game it occurred
trials <- get_trials()

# information about participant choices on each trial
choices <- get_choices()

# language data for each trial
messages <- get_messages()

# meta-data about stimuli
images <- get_images()

# meta-data about participants
participants <- get_players()

By default, functions return all datasets in the current version of the data, but you can specify a different version, or a specific set of datasets.

# learn what the current version number is
get_current_version()

# specify a specific version
# or specific datasets
trials <- get_trials(version="7.3", datasets=c("hawkins2023_frompartners", "hawkins2021_respect"))

For testing, you can limit the number of results retrieved.

trials <- get_trials(max_results=100)
# this is non-deterministic in which items are returned

For convenience, you can get some tables with other tables joined in already.

messages <- get_messages(include_trial_data=T,
                         include_player_data=T,
                         include_image_data=T,
                         include_condition_data=T)
                         
choices <- get_choices(include_trial_data=T,
                       include_player_data=T,
                       include_image_data=T,
                       include_condition_data=T)


trials <- get_trials(include_image_data=T,
                    include_condition_data=T)
                         

You can also download the image files where available.

download_image_files(destination="images/")

In addition to the primary data tables, there are also derived tables of processed data, such as vector embeddings of the describer language on each trial, and cosine similarities between these embeddings.

embeddings <- get_sbert_embeddings()

similarities <- get_cosine_similarities(sim_type=c("to_last"))

See refbankr github repository for more information.

Python

1. Install the redivis-python client library:

pip install --upgrade redivis

2. Generate and set an API token.

3. Access the data:

import redivis

user = redivis.user("mcfrank")
dataset = user.dataset("refbank")
table = dataset.table("summary")

# Load table as a dataframe
df = table.to_pandas_dataframe()

View documentation