readme.md

The Global Human Day

The data this week comes from the The Human Chronome Project an initiative based at McGill University in Montreal, from their paper The global human day in PNAS and the associated dataset on Zenodo.

The daily activities of ≈8 billion people occupy exactly 24 h per day, placing a strict physical limit on what changes can be achieved in the world. These activities form the basis of human behavior, and because of the global integration of societies and economies, many of these activities interact across national borders. This project estimates how all humans spend their time using a generalized, physical outcome–based categorization that facilitates the integration of data from hundreds of diverse datasets.

See their supplementary materials for details about their methods and additional visualizations.

The Zenodo dataset includes the input data and scripts used to create the datasets used in the paper. The datasets are from the outputData file "all_countries.csv", "global_human_day.csv", "global_economic_activity.csv" and inputData "country_regions.csv". The outputData files are aggregated output data from data collected, created from the scripts in the 'scripts' directory.

h/t Data is Plural 2023-07-13 newsletter for the dataset.

The Data

# Option 1: tidytuesdayR package 
## install.packages("tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2023-09-12')
## OR
tuesdata <- tidytuesdayR::tt_load(2023, week = 37)

all_countries <- tuesdata$all_countries
country_regions <- tuesdata$country_regions
global_human_day <- tuesdata$global_human_day
global_economic_activity <- tuesdata$global_economic_activity

# Option 2: Read directly from GitHub

all_countries <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-09-12/all_countries.csv')
country_regions <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-09-12/country_regions.csv')
global_human_day <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-09-12/global_human_day.csv')
global_economic_activity <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-09-12/global_economic_activity.csv')

How to Participate

• Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our suggestion is to use the data provided to practice your data tidying and plotting techniques, and to consider for yourself what nuances might underlie these relationships.
• Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
• Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.

Data Dictionary

all_countries.csv

variable	class	description
Category	character	M24 categories
Subcategory	character	M24 subcategories
country_iso3	character	Country code in iso3
region_code	character	Region code
population	double	Population
hoursPerDayCombined	double	Hours per day combined for the country
uncertaintyCombined	double	Uncertainty combined. Uncertainty is in units variance.

country_regions.csv

variable	class	description
region_code	character	Region code
region_name	character	Region name
country_name	character	Country name
M49_code	double	M49 code
country_iso2	character	Country code in iso2
country_iso3	character	Country code in iso3
alt_country_name	character	Alternative country name
alt_country_name1	character	Alternative country name 1
alt_country_name2	character	Alternative country name 2
alt_country_name3	character	Alternative country name 3
alt_country_name4	character	Alternative country name 4
alt_country_name5	character	Alternative country name 5
alt_country_name6	character	Alternative country name 6
other_code1	character	Other country code 1
other_code2	character	Other country code 2

global_human_day.csv

variable	class	description
Subcategory	character	M24 subcategory
hoursPerDay	double	Hours per day for all countries
uncertainty	double	Uncertainty in units variance.

global_economic_activity.csv

variable	class	description
Subcategory	character	M24 subcategory
hoursPerDay	double	Hours per day for all countries.
uncertainty	double	Uncertainty in units variance.

Cleaning Script

library(tidyverse)

# Read in the data file all_countries.csv from https://zenodo.org/record/8040631

all_countries <- read_csv("all_countries.csv")

# Change variable name to be consistent between files

colnames(all_countries)[3] <- "country_iso3"

# Remove columns on data status

all_countries = subset(all_countries, select = -c(dataStatus,dataStatusEconomic))

# write out data
readr::write_csv(
  all_countries, "all_countries.csv")