openwashdata Data Package Hackathon

Mian Zhong

Global Health Engineering, ETH Zurich

June 21, 2024

Welcome!

Data Package - Raw Data

Conceptually

• Raw Dataset(s)

  ID	Date	Country
  1	21.06.2024	U.S.A.
  2	June 22 2024	United States
  …	…	…

Technically

• Spreadsheets, PDF reports, Hand-written documents

Data Package - Tidy Data

Conceptually

• Tidy/Processed Dataset(s)

  id	date	country
  1	2024-06-21	United States
  2	2024-06-22	United States
  …	…	…

Technically

• .rda Data Objects

Data Package - Dictionary

Conceptually

• Dictionary

variable	description
id	The unique ID
date	Date in the format YYYY-MM-DD
country	Full name of the country

Technically

• Dictionary csvfile
• R Roxygen documentation

Data Package - Summary

Conceptually

• Raw Dataset(s)
• Tidy/Processed Dataset(s)
• Dictionary

Technically

• Spreadsheets, PDF reports, Hand-written documents
• .rda Data Objects
• Dictionary csvfile
• R Roxygen documentation
• R package metadata
  • DESCRIPTION
  • CITATION

openwashdata Workflow

• Initialize package repository

• Create dataset

• Document dataset

• Communicate dataset

openwashdata Workflow

• Initialize package repository

  • create GitHub repo
  • create local repo
  • sync local with GitHub

• Create dataset(s)

  • Import raw data
  • Clean data to tidy version
  • Export tidy data
  • Write data dictionary

• Document dataset(s)

  • write package metadata (e.g. authors)
  • document dataset in R

• Communicate Dataset(s)

  • README

  • Website

  • Tutorial/Example

washr Overview

A set of automation tools to set up an openwashdata data package in a consistent structure. It helps to reduce manual entries on tidy data exporting, README writing, pkgdown configuration and content, and etc.

Create Dataset

1. Initialize raw data directory

setup_rawdata()

2. Import raw data into data-raw

3. Go to data-raw/data-processing.R and happy data cleaning

Document Dataset

1. Update Package Metadata

update_description()
# add author information(All authors)
usethis::use_author(given = "Mian", family = "Zhong",  
                    role = c("aut", "cre"), email = "mzhong@ethz.ch", 
                    comment = c(ORCID = "0009-0009-4546-7214"))
usethis::use_author(given = "Lars", family = "Schöbitz",  role = "aut", 
                    email = "lschoebitz@ethz.ch", 
                    comment = c(ORCID = "0000-0003-2196-5015"))

2. Create a dictionary for the dataset

setup_dictionary()

Document Dataset

3. Go to data-raw/dictionary.csv, and fill in the descriptions of the variables
4. Convert the dictionary csvfile to R documentation

setup_roxygen()

Go to R/ and fill in the title and description for the dataset.

Communicate Dataset

1. Create README and a stand-alone webpage

setup_readme()
# Go to README.Rmd and complete it
build_readme()
setup_website(has_example = TRUE)