The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Usage

library(idmc)

The simple use for the idmc package is to retrieve the data from the API directly into R.

df <- idmc_get_data()
df
#> # A tibble: 51,408 × 37
#>        id country     iso3  latitude longitude centroid  role  displacement_type
#>     <int> <chr>       <chr>    <dbl>     <dbl> <chr>     <chr> <chr>            
#>  1 224900 Portugal    PRT       39.9     -8.65 [39.9112… Reco… Disaster         
#>  2 224767 India       IND       20.4     82.2  [20.4311… Reco… Conflict         
#>  3 224915 Spain       ESP       36.7     -6.08 [36.6898… Reco… Disaster         
#>  4 224622 Mexico      MEX       17.4    -92.5  [17.3704… Reco… Conflict         
#>  5 224624 Mexico      MEX       16.8    -92.5  [16.8476… Reco… Conflict         
#>  6 224623 Mexico      MEX       16.7    -91.9  [16.6675… Reco… Conflict         
#>  7 225567 Cameroon    CMR       10.7     13.8  [10.6662… Reco… Conflict         
#>  8 224647 New Zealand NZL      -37.7    176.   [-37.700… Reco… Disaster         
#>  9 225566 Cameroon    CMR       12.7     14.4  [12.7429… Reco… Conflict         
#> 10 224437 Mexico      MEX       17.6    -93.0  [17.6197… Reco… Disaster         
#> # ℹ 51,398 more rows
#> # ℹ 29 more variables: qualifier <chr>, figure <int>, displacement_date <date>,
#> #   displacement_start_date <date>, displacement_end_date <date>, year <int>,
#> #   event_id <int>, event_name <chr>, event_codes <chr>,
#> #   event_code_types <chr>, event_start_date <date>, event_end_date <date>,
#> #   category <chr>, subcategory <chr>, type <chr>, subtype <chr>,
#> #   standard_popup_text <chr>, event_url <chr>, event_info <chr>, …

This data frame, with variables described in the API documentation, includes 1 row per event. We can normalize this to daily displacement, assuming uniform distribution of displacement between start and end date, for all countries and type of displacement. idmc_transform_daily().

idmc_transform_daily(df)
#> # A tibble: 922,896 × 5
#>    iso3  country    displacement_type date       displacement_daily
#>    <chr> <chr>      <chr>             <date>                  <dbl>
#>  1 AB9   Abyei Area Conflict          2018-01-01                  0
#>  2 AB9   Abyei Area Conflict          2018-01-02                  0
#>  3 AB9   Abyei Area Conflict          2018-01-03                  0
#>  4 AB9   Abyei Area Conflict          2018-01-04                  0
#>  5 AB9   Abyei Area Conflict          2018-01-05                  0
#>  6 AB9   Abyei Area Conflict          2018-01-06                  0
#>  7 AB9   Abyei Area Conflict          2018-01-07                  0
#>  8 AB9   Abyei Area Conflict          2018-01-08                  0
#>  9 AB9   Abyei Area Conflict          2018-01-09                  0
#> 10 AB9   Abyei Area Conflict          2018-01-10                  0
#> # ℹ 922,886 more rows

While there are a few other parameters you can play around with in these functions, this is the primary purpose of this simple package.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.