Access and Analyze Official Development Assistance (ODA) using the OECD API. ODA data includes sovereign-level aid data such as key aggregates (DAC1), geographical distributions (DAC2A), project-level data (CRS), and multilateral contributions (Multisystem).
The package is part of the EconDataverse family of packages aimed at helping economists and financial professionals work with sovereign-level economic data. For a Python implementation with a similar interface, see oda-reader.
This package is a product of Christoph Scheuch and not sponsored by or affiliated with the OECD in any way, except for the use of the OECD API.
Installation
You can install oecdoda from CRAN via:
install.packages("oecdoda")You can install the development version of oecdoda from GitHub with:
# install.packages("pak")
pak::pak("tidy-intelligence/r-oecdoda")Usage
Load the package:
oecdoda simplifies access to multiple datasets from the OECD API. Each dataset provides specific filters, which can be listed using:
oda_list_filters()
#> $`DSD_DAC1@DF_DAC1`
#> [1] "donor" "measure" "untied" "flow_type" "unit_measure"
#> [6] "price_base" "period"
#>
#> $`DSD_DAC2@DF_DAC2A`
#> [1] "donor" "recipient" "measure" "unit_measure" "price_base"
#>
#> $`DSD_CRS@DF_CRS`
#> [1] "donor" "recipient" "sector" "measure" "channel"
#> [6] "modality" "flow_type" "price_base" "md_dim" "md_id"
#> [11] "unit_measure"
#>
#> $`DSD_GREQ@DF_CRS_GREQ`
#> [1] "donor" "recipient" "sector" "measure" "channel"
#> [6] "modality" "flow_type" "price_base" "md_dim" "md_id"
#> [11] "unit_measure"
#>
#> $`DSD_MULTI@DF_MULTI`
#> [1] "donor" "recipient" "sector" "measure" "channel"
#> [6] "flow_type" "price_base" "md_dim" "md_id" "unit_measure"Downloading DAC1 Data
The DAC1 dataset includes key aggregates of ODA flows and grant equivalents from DAC members and other providers, as well as other resource flows to developing countries.
oda_get_dac1(
start_year = 2018,
end_year = 2022,
filters = list(
donor = c("FRA", "USA"),
measure = 11017,
flow_type = 1160,
unit_measure = "XDC",
price_base = "V"
)
)
#> # A tibble: 5 × 14
#> entity_id entity_name series_id series_name flow_type_id flow_type_name year
#> <chr> <chr> <int> <chr> <int> <chr> <int>
#> 1 FRA France 11017 Bilateral l… 1160 Grant equival… 2018
#> 2 FRA France 11017 Bilateral l… 1160 Grant equival… 2020
#> 3 FRA France 11017 Bilateral l… 1160 Grant equival… 2019
#> 4 FRA France 11017 Bilateral l… 1160 Grant equival… 2022
#> 5 FRA France 11017 Bilateral l… 1160 Grant equival… 2021
#> # ℹ 7 more variables: value <dbl>, unit_measure_id <chr>,
#> # unit_measure_name <chr>, price_base_id <chr>, price_base_name <chr>,
#> # unit_multiplier_id <int>, unit_multiplier_name <chr>Downloading DAC2A Data
The DAC2A dataset provides data on the geographical distribution of bilateral and multilateral ODA disbursements to developing countries and territories on the DAC List of ODA recipients. Data is available by donor, recipient, and aid type (e.g., grants, loans, technical cooperation, or philanthropic flows).
oda_get_dac2a(
start_year = 2018,
end_year = 2022,
filters = list(
donor = "GBR",
recipient = c("GTM", "CHN"),
measure = 106,
price_base = "Q"
)
)
#> # A tibble: 10 × 16
#> entity_id entity_name counterpart_id counterpart_name series_id series_name
#> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 GBR United Kingd… CHN China (People’s… 106 Imputed mu…
#> 2 GBR United Kingd… GTM Guatemala 106 Imputed mu…
#> 3 GBR United Kingd… CHN China (People’s… 106 Imputed mu…
#> 4 GBR United Kingd… GTM Guatemala 106 Imputed mu…
#> 5 GBR United Kingd… CHN China (People’s… 106 Imputed mu…
#> 6 GBR United Kingd… GTM Guatemala 106 Imputed mu…
#> 7 GBR United Kingd… CHN China (People’s… 106 Imputed mu…
#> 8 GBR United Kingd… GTM Guatemala 106 Imputed mu…
#> 9 GBR United Kingd… CHN China (People’s… 106 Imputed mu…
#> 10 GBR United Kingd… GTM Guatemala 106 Imputed mu…
#> # ℹ 10 more variables: flow_type_id <chr>, flow_type_name <chr>, year <int>,
#> # value <dbl>, unit_measure_id <chr>, unit_measure_name <chr>,
#> # price_base_id <chr>, price_base_name <chr>, unit_multiplier_id <int>,
#> # unit_multiplier_name <chr>Downloading CRS Data
The CRS dataset provides granular, activity-level statistics on who provides what aid, to where, and for what purpose, on a flow basis or a grant-equivalent basis.
oda_get_crs(
start_year = 2018,
end_year = 2022,
filters = list(
donor = c("AUT", "FRA", "USA"),
recipient = "BIH",
measure = 100,
channel = 60000,
price_base = "Q"
)
)
#> # A tibble: 1,257 × 20
#> entity_id entity_name counterpart_id counterpart_name series_id series_name
#> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 USA United States BIH Bosnia and Herz… 100 Official D…
#> 2 USA United States BIH Bosnia and Herz… 100 Official D…
#> 3 USA United States BIH Bosnia and Herz… 100 Official D…
#> 4 USA United States BIH Bosnia and Herz… 100 Official D…
#> 5 USA United States BIH Bosnia and Herz… 100 Official D…
#> 6 USA United States BIH Bosnia and Herz… 100 Official D…
#> 7 USA United States BIH Bosnia and Herz… 100 Official D…
#> 8 USA United States BIH Bosnia and Herz… 100 Official D…
#> 9 USA United States BIH Bosnia and Herz… 100 Official D…
#> 10 USA United States BIH Bosnia and Herz… 100 Official D…
#> # ℹ 1,247 more rows
#> # ℹ 14 more variables: flow_type_id <chr>, flow_type_name <chr>,
#> # channel_id <int>, channelt_name <chr>, modality_id <chr>,
#> # modality_name <chr>, year <int>, value <dbl>, unit_measure_id <chr>,
#> # unit_measure_name <chr>, price_base_id <chr>, price_base_name <chr>,
#> # unit_multiplier_id <int>, unit_multiplier_name <chr>Downloading Multisystem Data
The Multisystem dataset presents providers’ total use of the multilateral system, including both core contributions to multilateral organizations and bilateral aid channeled through these organizations.
oda_get_multisystem(
start_year = 2018,
end_year = 2022,
filters = list(
donor = "DAC",
recipient = "DPGC",
sector = 1000,
measure = 10
)
)
#> # A tibble: 3,152 × 18
#> entity_id entity_name counterpart_id counterpart_name series_id series_name
#> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 DAC DAC countries DPGC Developing coun… 10 Core contr…
#> 2 DAC DAC countries DPGC Developing coun… 10 Core contr…
#> 3 DAC DAC countries DPGC Developing coun… 10 Core contr…
#> 4 DAC DAC countries DPGC Developing coun… 10 Core contr…
#> 5 DAC DAC countries DPGC Developing coun… 10 Core contr…
#> 6 DAC DAC countries DPGC Developing coun… 10 Core contr…
#> 7 DAC DAC countries DPGC Developing coun… 10 Core contr…
#> 8 DAC DAC countries DPGC Developing coun… 10 Core contr…
#> 9 DAC DAC countries DPGC Developing coun… 10 Core contr…
#> 10 DAC DAC countries DPGC Developing coun… 10 Core contr…
#> # ℹ 3,142 more rows
#> # ℹ 12 more variables: flow_type_id <chr>, flow_type_name <chr>,
#> # channel_id <int>, channel_name <chr>, year <int>, value <dbl>,
#> # unit_measure_id <chr>, unit_measure_name <chr>, price_base_id <chr>,
#> # price_base_name <chr>, unit_multiplier_id <int>, unit_multiplier_name <chr>Rate Limiting
oecdoda automatically handles the limits of the OECD API of 20 calls per hour (as of July 2025). According to the OECD Service Team, this limit will be relaxed eventually. If you want to manually change the limits for your session, use the following options:
options(
oecdoda.rate_capacity = 10,
oecdoda.rate_fill_time = 60
)Contributing
Contributions to oecdoda are welcome! If you’d like to contribute, please follow these steps:
- Create an issue: Before making changes, create an issue describing the bug or feature you’re addressing.
- Fork the repository: After receiving supportive feedback from the package authors, fork the repository to your GitHub account.
- Create a branch: Create a branch for your changes with a descriptive name.
- Make your changes: Implement your bug fix or feature.
- Test your changes: Run tests to ensure your changes don’t break existing functionality.
- Submit a pull request: Push your changes to your fork and submit a pull request to the main repository.