/covid-gtrends

Replication Package for: The Evolution of the COVID-19 Pandemic Through the Lens of Google Seaches

MIT LicenseMIT

Replication package for: The Evolution of the COVID-19 Pandemic Through the Lens of Google Searches

To replicate analysis

  1. Clone this repository
  2. In _main.R, change github_file_path to point to the github repo.
  3. Run _main.R; this runs all scripts needed to replicate the analysis, including data cleaning and generating all tables and figures. Tables and figures are exported to: Paper Figures and Tables/

Code

Main Script

  • _main.R: Main script that runs all code, including data cleaning and analysis.

Organization

Code is organized into two main folders.

  • DataWork includes all code to replicate analysis of the paper.

  • Dashboard contains code to develop the dashboard associated with this project.

DataWork

The DataWork folder is organized into the below folders. The number indicates code that must be run before others. For example, code in 01_ should be run before 02_; however, folders with the same number can be run in any order.

  • 01_process_ancillary_data: Cleans individual datasets used throughout the analysis, including downloading data from specific sources (for example, downloading and cleaning data from the World Development Indicators).
  • 02_translate_search_terms: Translates each search term into different languages
  • 03_determine_most_common_language: For each country, determines the most common language used to make Google searches.
  • 04_scrape_gtrends_data: Scrapes Google search data across countries and keywords, relying on the gtrendsR package
  • 05_clean_gtrends_data: Cleans Google search data into analysis-ready datasets, including merging in data from other sources.
  • 06_analysis: All code for analysis, including generating figures and tables.

Dashboard

To prepare data for the dashboard, the /Dashboard/_dash_main.R should be run.

Data

Data can be found in the /Data folder here. Within /Data, there is a folder for each dataset. Each dataset folder generally contains a /RawData and /FinalData folder, where /RawData contains data downloaded from its source and FinalData contains data processed from code. Each folder within /Data includes a readme that documents the source of the data.