5-diet_logging

5 - diet logging

Diet logging using a smartphone app involves collecting data on food and drink consumption through a mobile application. These data include information such as types of food, serving sizes, nutritional information and the times of consumption. The data is used to track dietary habits and can be used in scientific research to gain insights into the dietary habits of a population and to correlate to other temporal measurements and events.

The information is stored in 2 parquet file: diet_logging.parquet, raw_diet_logging.parquet which contain the processed and raw diet logging data respectively.

from pheno_utils import PhenoLoader

dl = PhenoLoader('diet_logging', age_sex_dataset=None)
dl

Warning: index is not unique for diet_logging.parquet
Warning: index is not unique for raw_diet_logging.parquet

DataLoader for diet_logging with
15 fields
2 tables: ['diet_logging', 'raw_diet_logging']

dl.dict

	field_string	description_string	parent_dataframe	relative_location	value_type	units	item_type	array	cohorts	data_type	debut	pandas_dtype	sampling_rate
tabular_field_name
collection_timestamp	Collection timestamp	Collection timestamp	NaN	diet_logging.parquet	Time	Time	Data	Single	10K	Time Series	2019-01-29	datetime64[ns, Asia/Jerusalem]	NaN
collection_date	Date	Datetime column relecting the time food item w...	NaN	diet_logging.parquet	Time	Time	Data	Single	10K	Time Series	2019-09-01	datetime64[ns]	NaN
food_id	Food ID	IDs in the diet logging app representing speci...	NaN	diet_logging.parquet	Categorical (single)	None	Data	Single	10K	Time Series	2019-09-01	integer	NaN
logging_day	Logging day per participant	Integer indicating which day of logging period	NaN	diet_logging.parquet	Integer	None	Data	Single	10K	Time Series	2019-09-01	float	NaN
weight	Weight	Weight of food item logged	NaN	diet_logging.parquet	Continuous	g	Data	Single	10K	Time Series	2019-09-01	float	NaN
short_food_name	Short food name	Classifcation of food item logged into a short...	NaN	diet_logging.parquet	Categorical (single)	None	Data	Single	10K	Time Series	2019-09-01	object	NaN
food_category	Food category	Classifcation of food item logged into a food ...	NaN	diet_logging.parquet	Categorical (single)	None	Data	Single	10K	Time Series	2019-09-01	object	NaN
product_name	Product name	Product name of food logged	NaN	diet_logging.parquet	Categorical (single)	None	Data	Single	10K	Time Series	2019-09-01	object	NaN
calories	Calories	Calories of food item logged	NaN	diet_logging.parquet	Continuous	kcal	Data	Single	10K	Time Series	2019-09-01	float	NaN
carbohydrate_g	Carbohydrate intake per food logged	Carbohydrate intake per food logged	NaN	diet_logging.parquet	Continuous	g	Data	Single	10K	Time Series	2019-09-01	float	NaN
llipid_g	Fat intake per food logged	Fat intake per food logged	NaN	diet_logging.parquet	Continuous	g	Data	Single	10K	Time Series	2019-09-01	float	NaN
protein_g	Protein intake per food logged	Protein intake per food logged	NaN	diet_logging.parquet	Continuous	g	Data	Single	10K	Time Series	2019-09-01	float	NaN
sodium_mg	Sodium intake per food logged	Sodium intake per food logged	NaN	diet_logging.parquet	Continuous	mg	Data	Single	10K	Time Series	2019-09-01	float	NaN
alcohol_g	Alcohol intake per food logged	Alcohol intake per food logged	NaN	diet_logging.parquet	Continuous	g	Data	Single	10K	Time Series	2019-09-01	float	NaN
dietary_fiber_g	Dietary fiber intake per food logged	Dietary fiber intake per food logged	NaN	diet_logging.parquet	Continuous	g	Data	Single	10K	Time Series	2019-09-01	float	NaN
local_timestamp	Local timestamp	Local timestamp of food logging	NaN	diet_logging.parquet	Time	Time	Data	Single	10K	Time Series	2019-09-01	datetime64[ns]	NaN
eaten_in_restaurant	Eaten at restaurant indication	Indication if food was eatn at home or at a re...	NaN	diet_logging.parquet	Boolean	None	Data	Single	10K	Time Series	2019-09-01	bool	NaN
total_logging_days	Total number of days logged	Total number of days diet was logged per resea...	NaN	raw_diet_logging.parquet	Integer	None	Data	Single	10K	Time Series	2019-09-01	integer	NaN