aat-analysis 0.0.7

This project analyzes mobile AAT data.

AAT Analysis

This package helps with analyzing mobile AAT data.

Install

pip install aat_analysis

How to use

#%run utils.ipynb # Some utility functions
#%run make_condition_templates.ipynb # Defines expected data based on resources
#%run json_to_df.ipynb # Turns raw json data into dataframes and calculates responses, rts, and force

from aat_analysis.make_condition_templates import make_condition_templates
from aat_analysis.json_to_df import json_to_df
from aat_analysis.utils import merge_data

#from aat_analysis.

Define folder paths

• raw should include the raw data from your experiment
• external should include the contents of the Resources folder of your experiment app
• interim and processed can be empty

external_folder = "../data/external/"
interim_folder = "../data/interim/"
raw_data_folder = "../data/raw/"
processed_data_file = "../data/processed/data.csv"

Preprocess data

# Creates empty dataframes to define expected data for each condition
templates = make_condition_templates(external_folder)
# Preprocesses data for each participant and moves it to interim
json_to_df(raw_data_folder, external_folder, interim_folder, templates)
# Merges interim data and stores it for further analysis
data = merge_data(interim_folder, drop=['interpolated','interpolated_gyro'])
data.to_csv(processed_data_file)

100%|█████████████████████████████████████████████| 3/3 [00:27<00:00,  9.24s/it]

AAT data

The selected columns below contain all data needed to calculate approach tendencies for each session, participant, and stimulus type. The additional data in the dataframe (not shown) are answers to other questions and some additional AAT variables.

data[['participant','condition','session','trial','is_practice','stimulus_set','stimulus','correct_response','response','accuracy','rt','force']]

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	participant	condition	session	trial	is_practice	stimulus_set	stimulus	correct_response	response	accuracy	rt	force
0	kmahu0zq	condition_2	final_session	1	False	None	None	None	NA	False	NaN	NaN
1	kmahu0zq	condition_2	introduction_session_2	1	True	practice_food	stim_0154	push	ND	False	NaN	8.124186
2	kmahu0zq	condition_2	introduction_session_2	2	True	practice_objects	stim_1276	pull	pull	True	1206.0	12.130466
3	kmahu0zq	condition_2	introduction_session_2	3	True	practice_objects	stim_1264	pull	ND	False	NaN	1.651279
4	kmahu0zq	condition_2	introduction_session_2	4	True	practice_objects	stim_1277	pull	pull	True	629.0	18.342323
...	...	...	...	...	...	...	...	...	...	...	...	...
6166	kmah8va6	condition_2	push_food_before_lunch_d5	132	False	unhealthy_tempting	stim_0025	pull	pull	True	346.0	9.024626
6167	kmah8va6	condition_2	push_food_before_lunch_d5	133	False	unhealthy_non_tempting	stim_0125	pull	pull	True	363.0	5.820239
6168	kmah8va6	condition_2	push_food_before_lunch_d5	134	False	healthy_non_tempting	stim_0226	pull	pull	True	492.0	8.345508
6169	kmah8va6	condition_2	push_food_before_lunch_d5	135	False	healthy_tempting	stim_0201	pull	pull	True	450.0	5.539470
6170	kmah8va6	condition_2	push_food_before_lunch_d5	136	False	objects	stim_1035	push	pull	False	308.0	6.589124

6171 rows × 12 columns