Introduction

The OECD (Organisation for Economic Co-operation and Development) provides extensive databases for economic, social, and environmental indicators. Accessing these programmatically through R is efficient and reproducible. In this article, we explore two popular R packages for accessing OECD data—oecd and rsdmx—and discuss critical updates to the OECD Developer API that have impacted package functionality.

We also provide practical examples, emphasize the importance of applying filters during data retrieval, and guide users on how to work with the latest tools to ensure seamless data access.

Why Programmatic Access Matters

Accessing data programmatically offers several benefits:

1. Customization: Tailor requests to retrieve only the data you need (e.g., specific countries, indicators, and years).

2. Efficiency: Save time and bandwidth by filtering data before download.

3. Reproducibility: Ensure that analyses can be easily updated or shared.

4. Automation: Streamline workflows by automating data extraction.

OECD Data Explorer: Exploring and Accessing Data

The OECD provides programmatic access to OECD data for OECD countries and selected non-member economies through a RESTful application programming interface (API) based on the SDMX standard. The APIs allow developers to easily query the OECD data in several ways to create innovative software applications which use dynamically updated OECD data.

The OECD Data Explorer is an interactive web-based platform that allows users to explore, visualize, and download data from the OECD databases. It is particularly useful for users who want to manually browse through datasets before deciding on specific data points for analysis. Here, we provide an overview of the OECD Data Explorer, including how to navigate the platform, customize filters, and access API links for programmatic use.

The OECD Data Explorer is available at: https://data-explorer.oecd.org/

When you visit the site, you are greeted with a clean interface for navigating through datasets. The platform organizes data into themes such as;

• Economy

• Education

• Environment

• Health

• Innovation and Technology

• Employment

Each theme contains various datasets that can be explored interactively.

The OECD Package: Accessing OECD Data in R

The oecd package is an R package designed to provide a convenient interface for accessing data from the OECD Developer API. It allows users to:

• Explore available datasets in the OECD databases.

• Retrieve filtered data programmatically for specific countries, indicators, and time periods.

• Work with data in a reproducible way directly within R.

However, the version of the OECD package available on CRAN is currently outdated due to recent changes in the OECD API (2024). These changes have impacted the functionality of some key features in the CRAN release. You can find more information about changes in the OECD API from https://www.oecd.org/en/data/insights/data-explainers/2024/09/OECD-DE-FAQ.html.

To overcome these limitations, it is recommended to use the updated version of the OECDpackage available on GitHub, which is fully compatible with the latest OECD API.

For installation and usage details, refer to the updated package repository:
https://github.com/expersso/OECD

Installing the Updated oecd Package:

# Install devtools if not already installed
install.packages("devtools")

# Install the updated oecd package from GitHub
devtools::install_github("expersso/OECD")

The updated version of the OECDpackage simplifies interaction with the OECD API, focusing on just two core functions: get_data_structure() and get_dataset(). Here’s a brief overview of their functionality and arguments:

library(OECD)
dataset_unemprate <- "OECD.SDD.TPS,DSD_LFS@DF_IALFS_UNE_M,1.0"
data_str <- get_data_structure(dataset_unemprate)
str(data_str, max.level = 1)

List of 15
 $ VAR_DESC               :'data.frame':    17 obs. of  2 variables:
 $ CL_ACTIVITY_ISIC4      :'data.frame':    958 obs. of  2 variables:
 $ CL_ADJUSTMENT          :'data.frame':    17 obs. of  2 variables:
 $ CL_AGE                 :'data.frame':    308 obs. of  2 variables:
 $ CL_AREA                :'data.frame':    469 obs. of  2 variables:
 $ CL_SECTOR              :'data.frame':    216 obs. of  2 variables:
 $ CL_SEX                 :'data.frame':    7 obs. of  2 variables:
 $ CL_TRANSFORMATION      :'data.frame':    59 obs. of  2 variables:
 $ CL_UNIT_MEASURE        :'data.frame':    670 obs. of  2 variables:
 $ CL_WORKER_STATUS_ICSE93:'data.frame':    13 obs. of  2 variables:
 $ CL_MEASURE_LFS_TPS     :'data.frame':    30 obs. of  2 variables:
 $ CL_DECIMALS            :'data.frame':    16 obs. of  2 variables:
 $ CL_FREQ                :'data.frame':    34 obs. of  2 variables:
 $ CL_OBS_STATUS          :'data.frame':    20 obs. of  4 variables:
 $ CL_UNIT_MULT           :'data.frame':    31 obs. of  4 variables:

get_dataset(
  dataset,
  filter = NULL,
  start_time = NULL,
  end_time = NULL,
  last_n_observations = NULL,
  ...
)

data_filters_unemprate <- "BEL+AUS+AUT+CAN+DNK+FRA+DEU+GRC+HUN+IRL+ITA+JPN+NLD+NZL+NOR+PRT+SVN+ESP+SWE+CHE+USA+GBR+TUR..PT_LF_SUB._Z.Y._T.Y_GE15..M"

df <- get_dataset(dataset = dataset_unemprate,
                  filter = data_filters_unemprate,
                  start_time = 2014)

head(df)

  ACTIVITY ADJUSTMENT    AGE DECIMALS FREQ  MEASURE OBS_STATUS ObsValue
1       _Z          Y Y_GE15        1    M UNE_LF_M          A      3.6
2       _Z          Y Y_GE15        1    M UNE_LF_M          A      3.7
3       _Z          Y Y_GE15        1    M UNE_LF_M          A      5.5
4       _Z          Y Y_GE15        1    M UNE_LF_M          A      5.6
5       _Z          Y Y_GE15        1    M UNE_LF_M          A      5.7
6       _Z          Y Y_GE15        1    M UNE_LF_M          A      4.3
  REF_AREA SEX TIME_PERIOD TRANSFORMATION UNIT_MEASURE UNIT_MULT
1      USA  _T     2019-06             _Z    PT_LF_SUB         0
2      USA  _T     2019-07             _Z    PT_LF_SUB         0
3      BEL  _T     2024-06             _Z    PT_LF_SUB         0
4      BEL  _T     2024-07             _Z    PT_LF_SUB         0
5      BEL  _T     2024-08             _Z    PT_LF_SUB         0
6      DEU  _T     2015-08             _Z    PT_LF_SUB         0

Using the rsdmx Package

The rsdmx package allows interaction with the OECD Developer API through SDMX format. It is particularly useful if you prefer working directly with API URLs.

install.packages("rsdmx")

# Load the rsdmx package
library(rsdmx)

Warning: package 'rsdmx' was built under R version 4.3.3

# Define the API URL for unemployment rates
oecd_url <- "https://sdmx.oecd.org/public/rest/data/OECD.SDD.TPS,DSD_LFS@DF_IALFS_UNE_M,1.0/BEL+AUS+AUT+CAN+DNK+FRA+DEU+GRC+HUN+IRL+ITA+JPN+NLD+NZL+NOR+PRT+SVN+ESP+SWE+CHE+USA+GBR+TUR..PT_LF_SUB._Z.Y._T.Y_GE15..M?startPeriod=2023-11&dimensionAtObservation=AllDimensions"

# Step 1: Fetch the data
unemployment_data <- readSDMX(oecd_url)

# Step 2: Convert to a data frame
unemployment_df <- as.data.frame(unemployment_data)

# View the data
head(unemployment_df)

  TIME_PERIOD REF_AREA  MEASURE UNIT_MEASURE TRANSFORMATION ADJUSTMENT SEX
1     2024-03      ITA UNE_LF_M    PT_LF_SUB             _Z          Y  _T
2     2024-04      ITA UNE_LF_M    PT_LF_SUB             _Z          Y  _T
3     2024-08      BEL UNE_LF_M    PT_LF_SUB             _Z          Y  _T
4     2024-09      BEL UNE_LF_M    PT_LF_SUB             _Z          Y  _T
5     2024-10      BEL UNE_LF_M    PT_LF_SUB             _Z          Y  _T
6     2023-12      SVN UNE_LF_M    PT_LF_SUB             _Z          Y  _T
     AGE ACTIVITY FREQ obsValue UNIT_MULT DECIMALS OBS_STATUS
1 Y_GE15       _Z    M      6.9         0        1          A
2 Y_GE15       _Z    M      6.7         0        1          A
3 Y_GE15       _Z    M      5.7         0        1          A
4 Y_GE15       _Z    M      5.8         0        1          A
5 Y_GE15       _Z    M      5.8         0        1          A
6 Y_GE15       _Z    M      3.4         0        1          A

Conclusion

Both oecd and rsdmx allow you to specify filters directly in your API request, which is critical for:

1. Time Efficiency: Smaller, focused datasets download faster.

2. Storage Optimization: Filtering minimizes the size of the retrieved dataset.

3. Simpler Analysis: Pre-filtered data reduces the need for extensive preprocessing.

When working with OECD databases in R, the updated version of the oecd package (available on GitHub) is a reliable choice, provided you install it from its GitHub repository. If you prefer working directly with API URLs, the rsdmx package is another strong option.

Regardless of the package, applying filters in your data requests is essential to ensure efficiency and reproducibility. By integrating these tools into your workflow, you can access OECD data programmatically and focus on the analysis itself.

References

1. OECD Data Explorer

2. OECD Data via API

3. Updated oecd Package on GitHub

4. rsdmx Package Documentation

5. OECD Data API documentation

6. Upgrading your queries from the legacy OECD.Stat APIs to the new OECD Data API