This small package provides functionality to access and manage the application programming interface (API) of the Armed Conflict Location & Event Data Project (ACLED), while requiring a minimal number of dependencies. The function acled.api() makes it easy to retrieve a user-defined sample (or all of the available data) of ACLED, enabling a seamless integration of regular data updates into the research work flow.
When using this package, you acknowledge that you have read ACLED’s terms and conditions of use, and that you agree with their attribution requirements.
You can install the latest release version of acled.api from CRAN with:
You can install the development version from GitLab with:
Using acled.api is straight forward. To download data on, for example, all ACLED conflict events in Europe and Central America that happened between June 2019 and July 2020, you can supply:
library(acled.api) # loads the package
#>
#> Before using this package to download data, you require an ACLED access key.
#> You can request your key by registering with ACLED on https://developer.acleddata.com/.
#> The package may be cited as:
#> Dworschak, Christoph. 2020. "Acled.api: Automated Retrieval of ACLED Conflict
#> Event Data." R package. CRAN version 1.1.8.
#> For the development version of this package, visit <https://gitlab.com/chris-dworschak/acled.api/>
my.data.frame <- acled.api( # stores an ACLED sample in object my.data.frame
email.address = Sys.getenv("ACLED_EMAIL_ADDRESS"),
access.key = Sys.getenv("ACLED_ACCESS_KEY"),
region = c("South Asia", "Central America"),
start.date = "2019-09-01",
end.date = "2020-01-31")
#> Your ACLED data request was successful.
#> Events were retrieved for the period starting 2019-09-01 until 2020-01-31.
my.data.frame[1:5,] # returns the first three observations of the ACLED sample
#> event_id_cnty region country year event_date
#> 1 HND1343 Central America Honduras 2020 2020-01-31
#> 2 GTM2921 Central America Guatemala 2020 2020-01-31
#> 3 IND70618 South Asia India 2020 2020-01-31
#> 4 IND70620 South Asia India 2020 2020-01-31
#> 5 IND70609 South Asia India 2020 2020-01-31
#> source admin1 admin2
#> 1 Proceso Digital Cortes La Lima
#> 2 Dialogos - Observatorio sobre la Violencia Huehuetenango La Libertad
#> 3 Asian News International Bihar Patna
#> 4 Hindustan Times Maharashtra Pune
#> 5 Hindustan Times Bihar Kaimur
#> admin3 location latitude longitude event_type
#> 1 La Lima La Lima 15.4333 -87.9167 Violence against civilians
#> 2 Camoja Grande 15.5823 -91.9006 Violence against civilians
#> 3 Sampatchak Patna 25.5941 85.1356 Protests
#> 4 Pune City Pune 18.5195 73.8553 Protests
#> 5 Bhabua Bhabua 25.0404 83.6074 Battles
#> sub_event_type interaction fatalities tags timestamp
#> 1 Attack 37 1 1618511533
#> 2 Attack 37 1 1618511538
#> 3 Peaceful protest 60 0 crowd size=no report 1649276878
#> 4 Peaceful protest 60 0 crowd size=no report 1649276878
#> 5 Armed clash 44 0 1649276878After the release of versions 1 through 8, ACLED changed their update system to allow for real-time amendments and post-release corrections, thereby forgoing traditional data versioning. This change requires researchers to take additional steps in order to ensure the replicability of their results when using ACLED data. Some tasks, like real-time forecasting models used by practitioners, may not require replicability of intermediate results. However, most research-related tasks assume the possibility of replication at a later stage. This is especially the case for results that are intended for publication, or for an ongoing data project where constant changes to the underlying sample are not desirable.
To this end, downloaded data intended for replicable use must be permanently stored by the analyst. Data downloaded through acled.api() are only stored temporarily in the working space, and may be lost after closing R. Therefore, if replicability is important to the analyst’s task, a call through acled.api() should occur only once at the beginning of the data project, immediately followed by, e.g., saveRDS(downloaded.data, file = "my_acled_data.rds"). This locally stored data file can then be used again at a later point by calling readRDS(file = "my_acled_data.rds"), and ensures that the analysis sample stays constant over time.
ACLED provides a time stamp for each individual observation (column timestamp), enabling researchers to do “micro versioning” of data points if necessary, and to verify congruence across samples. Starting from version 1.0.9, the function acled.api() includes the timestamp variable in its default API call. More recently, ACLED also introduced a discussion of data versioning in its API Guide.