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econid

Overview

The econid R package is a foundational building block of the econdataverse family of packages aimed at helping economists and financial professionals work with sovereign-level economic data. The package is aimed at domain experts in economics and finance who need to analyze and join data across multiple sources, but who aren’t necessarily R programming experts.

Motivation

Economic and financial datasets present unique challenges when working with country-level data:

  1. Mixed Entity Types

Datasets often combine different types of entities in the same “country” column:

  • Countries and sovereign states
  • Territories and administrative regions (e.g., Puerto Rico, Hong Kong)
  • Geographic or economic aggregates (e.g., “Sub-Saharan Africa”, “Low Income Countries”)
  • International institutions (e.g., “World Bank”, “IMF”)
  1. Inconsistent Naming

The same entity might appear in various formats:

  • Different codes (ISO-2, ISO-3, numeric codes)
  • Various name formats (e.g., “United States”, “US”, “U.S.A.”)
  • Historical names or non-English variants
  1. Complex Analysis Needs

Researchers often need to:

  • Compare individual countries with regional aggregates (e.g., Nigeria vs. Sub-Saharan Africa)
  • Join data across datasets with different naming conventions
  • Handle ambiguous cases (e.g., “Congo” could refer to multiple countries)
  • Work with specialized entities not in standard ISO lists

econid addresses these challenges through:

  • Robust name standardization with clear entity type identification
  • Flexible customization options for special cases
  • Warning systems for missing or ambiguous matches
  • Tools for fuzzy searching, filtering, and joining across datasets

Design Philosophy

The design philosophy of the package follows tidyverse design principles and the tidy tools manifesto. We strive to practice human-centered design, with clear documentation and examples and graceful handling of edge cases. We invite you to submit suggestions for improvements and extensions on the package’s Github Issues page.

We have designed the package to handle only the most common entities financial and economic professionals might encounter in a dataset (249 in total), not to handle every edge case. However, the package allows users to extend the standardization list with custom entities to flexibly accommodate any unconventional use case.

Installation

To install the package from CRAN, you can use the install.packages() function:

To install a development version from GitHub, you can use the remotes package:

remotes::install_github("Teal-Insights/r-econid")

Then, load the package in your R session or Quarto or RMarkdown notebook:

Usage

Below is a high-level overview of how econid works in practice, followed by a more detailed description of the main function and its parameters. The examples and tests illustrate typical usage patterns.

Use these patterns to explore the package and integrate it into your data cleaning workflows. For finer-grained operations (e.g., fuzzy filter and search), keep an eye on the package for future enhancements.

Package Summary

  1. Input validation
    The package checks if your input dataset and specified columns exist. It also ensures you only request valid output columns (e.g., "entity_name", "entity_id", "entity_type", "iso2c", and "iso3c"). Any invalid columns raise an error.

  2. Name and code matching
    The function standardize_entity() looks in your dataset for names (and optionally codes) that might match an entity. It:

    • Converts the names to UTF-8 for consistent processing.
    • Calls internal functions to try matching each entry via case-insensitive regex patterns.
    • If multiple columns are provided, it attempts to match on each in sequence, prioritizing matches from earlier columns.
    • If multiple matches exist for a single row, a warning is raised (unless suppressed).

  3. Merging standardized columns
    Once the function finds a match, it returns a new or augmented data frame with standardized columns (e.g., "entity_id", "entity_name", "entity_type", etc.). You control exactly which standardized columns appear via the output_cols argument.

  4. Handling missing and custom cases

    • Custom entities can be added using add_entity_pattern() before standardization
    • When an entity cannot be matched, it shows NA in the standardized columns.
    • You can specify how to fill missing values using the fill_mapping parameter.
    • You can optionally specify a default entity type for unmatched entries (default_entity_type).
    • Warnings are issued for ambiguous matches if warn_ambiguous is TRUE.

Workflow

Workflow

standardize_entity() Function 

# Basic example
df <- data.frame(
  entity = c("United States", "China", "NotACountry"),
  code = c("USA", "CHN", "ZZZ"),
  obs_value = c(1, 2, 3)
)

# Using with dplyr pipeline
library(dplyr)

df |>
  standardize_entity(entity, code) |>
  filter(!is.na(entity_id)) |>
  mutate(entity_category = case_when(
    entity_type == "economy" ~ "Country",
    TRUE ~ "Other"
  )) |>
  select(entity_name, entity_category, obs_value)
##     entity_name entity_category obs_value
## 1 United States         Country         1
## 2         China         Country         2

You can also use the function directly without a pipeline:

standardize_entity(
  data = df,
  entity, code,
  output_cols = c("entity_id", "entity_name", "entity_type"),
  fill_mapping = c(entity_name = "entity"),
  default_entity_type = NA_character_,
  warn_ambiguous = TRUE
)
##   entity_id   entity_name entity_type        entity code obs_value
## 1       USA United States     economy United States  USA         1
## 2       CHN         China     economy         China  CHN         2
## 3      <NA>   NotACountry        <NA>   NotACountry  ZZZ         3

Parameters

  • data
    A data frame (or tibble) containing the entities to be standardized.

  • …
    Columns containing entity names and/or IDs. These can be specified using unquoted column names (e.g., entity_name) or quoted column names (e.g., "entity_name"). Must specify at least one column. If multiple columns are specified, the function tries each in sequence, prioritizing matches from earlier columns.

  • output_cols (optional)
    A character vector of columns to include in the final output. Valid options:

    • "entity_id"
    • "entity_name"
    • "entity_type"
    • "iso3c"
    • "iso2c"

    Defaults to c("entity_id", "entity_name", "entity_type").

  • prefix (optional)
    A character string to prefix the output column names. Useful when standardizing multiple entities in the same dataset (e.g., “country”, “counterpart”).

  • fill_mapping (optional)
    A named character vector specifying how to fill missing values when no entity match is found. Names should be output column names (without prefix), and values should be input column names (from ...).

  • default_entity_type (optional)
    A character scalar ("economy", "organization", "aggregate", or "other") to assign as the entity type where no match is found. This value only applies if "entity_type" is requested in output_cols. The four valid values were selected to cover the most common economic use cases:

    • "economy": A legal or quasi-legal jurisdiction such as a country or autonomous region (e.g., “United States”, “Democratic Autonomous Administration of North and East Syria”)
    • "organization": An institution or organization such as a bank or international agency (e.g., “World Bank”, “IMF”)
    • "aggregate": A geographic or economic aggregate such as a region or development group (e.g., “Sub-Saharan Africa”, “Low Income Countries”)
    • "other": Anything that doesn’t fit into the other categories (e.g., “Elon Musk”, “The Moon”)

  • warn_ambiguous (optional)
    A logical indicating whether to warn if a single row in data can match more than one entity. Defaults to TRUE.

  • overwrite (optional)
    A logical indicating whether to overwrite existing entity columns. Defaults to TRUE.

  • warn_overwrite (optional)
    A logical indicating whether to warn when overwriting existing entity columns. Defaults to TRUE.

  • .before (optional)
    Column name or position to insert the standardized columns before. If NULL (default), columns are inserted at the beginning of the dataframe. Can be a character vector specifying the column name or a numeric value specifying the column index.

Returns

A data frame (or tibble) the same size as data, augmented with the requested standardized columns.

Working with Multiple Entities

The standardize_entity() function can be used to standardize multiple entities in the same dataset by using the prefix parameter:

df <- data.frame(
  country_name = c("United States", "France"),
  counterpart_name = c("China", "Germany")
)

df |>
  standardize_entity(country_name) |>
  standardize_entity(counterpart_name, prefix = "counterpart")
##   counterpart_entity_id counterpart_entity_name counterpart_entity_type
## 1                   CHN                   China                 economy
## 2                   DEU                 Germany                 economy
##   entity_id   entity_name entity_type  country_name counterpart_name
## 1       USA United States     economy United States            China
## 2       FRA        France     economy        France          Germany

add_entity_pattern() Function

The add_entity_pattern() function allows you to add custom entity patterns to the package. This is useful if you need to standardize entities that are not in the default list.

add_entity_pattern(
  "BJ-CITY",
  "Beijing City",
  entity_type = "economy",
  aliases = c("Beijing Municipality")
)

df_custom <- data.frame(entity = c("United States", "Beijing Municipality"))
result_custom <- standardize_entity(df_custom, entity)
print(result_custom)
##   entity_id   entity_name entity_type               entity
## 1       USA United States     economy        United States
## 2   BJ-CITY  Beijing City     economy Beijing Municipality

reset_custom_entity_patterns() Function

The reset_custom_entity_patterns() function allows you to clear all custom entity patterns that have been added during the current R session. This is useful when you want to start fresh with only the default entity patterns.

Contributing

We welcome your feedback and contributions! Please submit suggestions for improvements and extensions on the package’s Github Issues page.