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Tools to Analyze Economic Data

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tidy-intelligence/r-econtools

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Provides a consistent set of functions for enriching and analyzingsovereign-level economic data. Economists, data scientists, andfinancial professionals can use the package to add standardizedidentifiers, demographic and macroeconomic indicators, and derivedmetrics such as gross domestic product per capita or governmentexpenditure shares.

The package is part of theEconDataverse family of packages aimedat helping economists and financial professionals work withsovereign-level economic data.

Installation

You can installecontools fromCRAN via:

install.packages("econtools")

You can install the development version ofecontools fromGitHub with:

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

Usage

This is a basic example which shows you how to solve a common problem:

library(econtools)
df<-data.frame(id= rep("USA",5),name= c("United States","United.states","US","USA","United States"),year=2019:2023,gross_domestic_product= c(2.15e13,2.14e13,2.37e13,2.60e13,2.77e13))add_population_column(df,id_column="name",id_type="regex")#>    id          name year gross_domestic_product entity_id population#> 1 USA United States 2019               2.15e+13       USA  340110988#> 2 USA United.states 2020               2.14e+13       USA  340110988#> 3 USA            US 2021               2.37e+13       USA  340110988#> 4 USA           USA 2022               2.60e+13       USA  340110988#> 5 USA United States 2023               2.77e+13       USA  340110988

The simplest way to add additional information is using an ISO 3166-1alpha-3 code.

Add most recent population number:

add_population_column(df,id_column="id")#>    id          name year gross_domestic_product population#> 1 USA United States 2019               2.15e+13  340110988#> 2 USA United.states 2020               2.14e+13  340110988#> 3 USA            US 2021               2.37e+13  340110988#> 4 USA           USA 2022               2.60e+13  340110988#> 5 USA United States 2023               2.77e+13  340110988

Add population by year:

add_population_column(df,id_column="id",date_column="year")#>    id          name year gross_domestic_product population#> 1 USA United States 2019               2.15e+13  330226227#> 2 USA United.states 2020               2.14e+13  331577720#> 3 USA            US 2021               2.37e+13  332099760#> 4 USA           USA 2022               2.60e+13  334017321#> 5 USA United States 2023               2.77e+13  336806231

Similarly, for poverty ratio:

add_poverty_ratio_column(df,id_column="id",date_column="year")#>    id          name year gross_domestic_product poverty_ratio#> 1 USA United States 2019               2.15e+13           1.0#> 2 USA United.states 2020               2.14e+13           0.5#> 3 USA            US 2021               2.37e+13           0.5#> 4 USA           USA 2022               2.60e+13           1.2#> 5 USA United States 2023               2.77e+13           1.2

Create a new column that calculates a value relative to the population,for instance GDP per capita:

add_population_share_column(df,id_column="id",date_column="year",value_column="gross_domestic_product")#>    id          name year gross_domestic_product population population_share#> 1 USA United States 2019               2.15e+13  330226227         65106.88#> 2 USA United.states 2020               2.14e+13  331577720         64539.92#> 3 USA            US 2021               2.37e+13  332099760         71364.10#> 4 USA           USA 2022               2.60e+13  334017321         77840.27#> 5 USA United States 2023               2.77e+13  336806231         82243.13

Add income levels via:

add_income_level_column(df,id_column="id")#>    id          name year gross_domestic_product income_level_id#> 1 USA United States 2019               2.15e+13             HIC#> 2 USA United.states 2020               2.14e+13             HIC#> 3 USA            US 2021               2.37e+13             HIC#> 4 USA           USA 2022               2.60e+13             HIC#> 5 USA United States 2023               2.77e+13             HIC#>   income_level_name#> 1       High income#> 2       High income#> 3       High income#> 4       High income#> 5       High income

If you want to use another column and automatically map identifiers to anewentity_id column using theeconid package:

add_population_column(df,id_column="name",id_type="regex")#>    id          name year gross_domestic_product entity_id population#> 1 USA United States 2019               2.15e+13       USA  340110988#> 2 USA United.states 2020               2.14e+13       USA  340110988#> 3 USA            US 2021               2.37e+13       USA  340110988#> 4 USA           USA 2022               2.60e+13       USA  340110988#> 5 USA United States 2023               2.77e+13       USA  340110988

If you only want to add ISO-3 codes:

add_iso3_codes_column(df,"name")#>    id          name year gross_domestic_product iso3_code#> 1 USA United States 2019               2.15e+13       USA#> 2 USA United.states 2020               2.14e+13       USA#> 3 USA            US 2021               2.37e+13       USA#> 4 USA           USA 2022               2.60e+13       USA#> 5 USA United States 2023               2.77e+13       USA

You can also add a column with standardized names:

add_short_names_column(df,"name")#>    id          name year gross_domestic_product    short_name#> 1 USA United States 2019               2.15e+13 United States#> 2 USA United.states 2020               2.14e+13 United States#> 3 USA            US 2021               2.37e+13 United States#> 4 USA           USA 2022               2.60e+13 United States#> 5 USA United States 2023               2.77e+13 United States

Finally, you can add columns from IMF World Economic Outlook (WEO) datasuch as the GDP in national currency

add_gdp_column(df,id_column="id",date_column="year",usd=FALSE)#>    id          name year gross_domestic_product          gdp#> 1 USA United States 2019               2.15e+13 2.153998e+13#> 2 USA United.states 2020               2.14e+13 2.135412e+13#> 3 USA            US 2021               2.37e+13 2.368118e+13#> 4 USA           USA 2022               2.60e+13 2.600690e+13#> 5 USA United States 2023               2.77e+13 2.772072e+13

Or the government expenditure (only available in national currency):

add_gov_exp_column(df,id_column="id",date_column="year")#>    id          name year gross_domestic_product      gov_exp#> 1 USA United States 2019               2.15e+13 7.715392e+12#> 2 USA United.states 2020               2.14e+13 9.562061e+12#> 3 USA            US 2021               2.37e+13 1.023444e+13#> 4 USA           USA 2022               2.60e+13 9.578022e+12#> 5 USA United States 2023               2.77e+13 1.028794e+13

And share of government expenditure to GDP:

add_gov_exp_share_column(df,id_column="id",date_column="year")#>    id          name year gross_domestic_product gov_exp_share#> 1 USA United States 2019               2.15e+13       0.35819#> 2 USA United.states 2020               2.14e+13       0.44779#> 3 USA            US 2021               2.37e+13       0.43218#> 4 USA           USA 2022               2.60e+13       0.36829#> 5 USA United States 2023               2.77e+13       0.37113

Contributing

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

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

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