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datacommons

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Access the Google Data Commons API V2. Data Commons provides programmatic access to statistical and demographic data from dozens of sources organized in a knowledge graph.

Installation

You can install datacommons from CRAN via:

install.packages("datacommons")

You can install the development version of datacommons from GitHub with:

# install.packages("pak")
pak::pak("tidy-intelligence/r-datacommons")

Usage

Load the package:

library(datacommons)

Get a free API key for Data Commons here. Set the Data Commons API key as the DATACOMMONS_API_KEY environment variable using the helper function and restart your R session to load the key:

dc_set_api_key("YOUR_API_KEY")

If you want to use a custom Data Commons instance, then you can also set the DATACOMMONS_BASE_URL environment varibale on the project or global level:

dc_set_base_url("YOUR_BASE_URL")

Get a data frame with US population data from World Development Indicators:

country_level <- dc_get_observations(
  date = "all",
  variable_dcids = "Count_Person",
  entity_dcids = "country/USA",
  return_type = "data.frame",
  filter_facet_id = 3981252704
)
head(country_level, 5)
#>   entity_dcid              entity_name variable_dcid    variable_name date
#> 1 country/USA United States of America  Count_Person Total population 1960
#> 2 country/USA United States of America  Count_Person Total population 1961
#> 3 country/USA United States of America  Count_Person Total population 1962
#> 4 country/USA United States of America  Count_Person Total population 1963
#> 5 country/USA United States of America  Count_Person Total population 1964
#>       value   facet_id                 facet_name
#> 1 180671000 3981252704 WorldDevelopmentIndicators
#> 2 183691000 3981252704 WorldDevelopmentIndicators
#> 3 186538000 3981252704 WorldDevelopmentIndicators
#> 4 189242000 3981252704 WorldDevelopmentIndicators
#> 5 191889000 3981252704 WorldDevelopmentIndicators

If you want to get different population numbers from the US Census on the state level:

state_level <- dc_get_observations(
  variable_dcids = "Count_Person",
  date = 2021,
  parent_entity = "country/USA",
  entity_type = "State",
  return_type = "data.frame",
  filter_facet_id = 2176550201
)
head(state_level, 5)
#>   entity_dcid entity_name variable_dcid    variable_name date    value
#> 1    geoId/01     Alabama  Count_Person Total population 2021  5039877
#> 2    geoId/02      Alaska  Count_Person Total population 2021   732673
#> 3    geoId/04     Arizona  Count_Person Total population 2021  7276316
#> 4    geoId/05    Arkansas  Count_Person Total population 2021  3025891
#> 5    geoId/06  California  Count_Person Total population 2021 39237836
#>     facet_id                    facet_name
#> 1 2176550201 USCensusPEP_Annual_Population
#> 2 2176550201 USCensusPEP_Annual_Population
#> 3 2176550201 USCensusPEP_Annual_Population
#> 4 2176550201 USCensusPEP_Annual_Population
#> 5 2176550201 USCensusPEP_Annual_Population

Contributing

Contributions to oecdoda are welcome! If you’d like to contribute, please follow these steps:

  1. Create an issue: Before making changes, create an issue describing the bug or feature you’re addressing.
  2. Fork the repository: After receiving supportive feedback from the package authors, fork the repository to your GitHub account.
  3. Create a branch: Create a branch for your changes with a descriptive name.
  4. Make your changes: Implement your bug fix or feature.
  5. Test your changes: Run tests to ensure your changes don’t break existing functionality.
  6. Submit a pull request: Push your changes to your fork and submit a pull request to the main repository.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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