Load the AidData Global Chinese Development Finance 2.0 Dataset

get_gcdf2_dataset() makes it easy to load AidData's Global Chinese Development Finance Dataset, Version 2.0 (GCDF 2.0). By default, the function adds standardized country names that make it easier to merge it with other datasets. The standardized country names are described in the documentation for gcdf2_standardized_countrynames. Setting standardized_countrynames = FALSE is equivalent to simply calling the gcdf2_dataset data object. The data definitions are available in the gcdf2_data_dictionary.

Usage

get_gcdf2_dataset(standardized_countrynames = TRUE)

Arguments

standardized_countrynames

set to TRUE by default, this argument attaches standardized country names to make it easier to join the dataset with other datasets.

Value

get_gcdf2_dataset() returns a tibble with 13,427 observations.

Examples

# returns the GCDF 2.0 dataset, with standardized country names
get_gcdf2_dataset()
#> # A tibble: 13,427 × 73
#>    country_name iso3c country_or_regional aid_data_tuff_project_id
#>    <chr>        <chr> <chr>                                  <dbl>
#>  1 Afghanistan  AFG   country                                53631
#>  2 Afghanistan  AFG   country                                53632
#>  3 Afghanistan  AFG   country                                53633
#>  4 Afghanistan  AFG   country                                53634
#>  5 Afghanistan  AFG   country                                53636
#>  6 Afghanistan  AFG   country                                53637
#>  7 Afghanistan  AFG   country                                53644
#>  8 Afghanistan  AFG   country                                53999
#>  9 Afghanistan  AFG   country                                54396
#> 10 Afghanistan  AFG   country                                56587
#> # ℹ 13,417 more rows
#> # ℹ 69 more variables: recommended_for_aggregates <chr>, umbrella <chr>,
#> #   financier_country <chr>, recipient <chr>, recipient_region <chr>,
#> #   commitment_year <dbl>, commitment_year_estimated <chr>,
#> #   implementation_start_year <dbl>, completion_year <dbl>, title <chr>,
#> #   description <chr>, staff_comments <chr>, status <chr>, intent <chr>,
#> #   flow_type <chr>, concessional <chr>, flow_class <chr>, sector_code <dbl>, …

# returns the GCDF 2.0 dataset as-is.
get_gcdf2_dataset(standardized_countrynames = FALSE)
#> # A tibble: 13,427 × 70
#>    aid_data_tuff_project_id recommended_for_aggrega…¹ umbrella financier_country
#>                       <dbl> <chr>                     <chr>    <chr>            
#>  1                    53631 Yes                       No       China (People's …
#>  2                    53632 Yes                       No       China (People's …
#>  3                    53633 Yes                       No       China (People's …
#>  4                    53634 Yes                       No       China (People's …
#>  5                    53636 Yes                       No       China (People's …
#>  6                    53637 Yes                       No       China (People's …
#>  7                    53644 Yes                       No       China (People's …
#>  8                    53999 Yes                       No       China (People's …
#>  9                    54396 No                        No       China (People's …
#> 10                    56587 Yes                       No       China (People's …
#> # ℹ 13,417 more rows
#> # ℹ abbreviated name: ¹​recommended_for_aggregates
#> # ℹ 66 more variables: recipient <chr>, recipient_region <chr>,
#> #   commitment_year <dbl>, commitment_year_estimated <chr>,
#> #   implementation_start_year <dbl>, completion_year <dbl>, title <chr>,
#> #   description <chr>, staff_comments <chr>, status <chr>, intent <chr>,
#> #   flow_type <chr>, concessional <chr>, flow_class <chr>, sector_code <dbl>, …

# the latter is equivalent to simply calling the [gcdf2_dataset] data object
gcdf2_dataset
#> # A tibble: 13,427 × 70
#>    aid_data_tuff_project_id recommended_for_aggrega…¹ umbrella financier_country
#>                       <dbl> <chr>                     <chr>    <chr>            
#>  1                    53631 Yes                       No       China (People's …
#>  2                    53632 Yes                       No       China (People's …
#>  3                    53633 Yes                       No       China (People's …
#>  4                    53634 Yes                       No       China (People's …
#>  5                    53636 Yes                       No       China (People's …
#>  6                    53637 Yes                       No       China (People's …
#>  7                    53644 Yes                       No       China (People's …
#>  8                    53999 Yes                       No       China (People's …
#>  9                    54396 No                        No       China (People's …
#> 10                    56587 Yes                       No       China (People's …
#> # ℹ 13,417 more rows
#> # ℹ abbreviated name: ¹​recommended_for_aggregates
#> # ℹ 66 more variables: recipient <chr>, recipient_region <chr>,
#> #   commitment_year <dbl>, commitment_year_estimated <chr>,
#> #   implementation_start_year <dbl>, completion_year <dbl>, title <chr>,
#> #   description <chr>, staff_comments <chr>, status <chr>, intent <chr>,
#> #   flow_type <chr>, concessional <chr>, flow_class <chr>, sector_code <dbl>, …