# Getting information on the conversion process

## The `verbose` argument

Set the `verbose` argument in `convertGDP` to `TRUE` to print out the underlying conversion steps and factors.

    library(GDPuc)

    my_gdp <- tibble::tibble(
      iso3c = "USA",
      year = 2010:2014,
      value = 100:104
    )

    convertGDP(
      gdp = my_gdp,
      unit_in = "constant 2005 LCU",
      unit_out = "constant 2017 Int$PPP",
      verbose = TRUE
    )
    #> ℹ Converting GDP with conversion factors from wb_wdi:
    #> constant 2005 LCU → constant 2017 LCU
    #> 2017 value of base 2005 GDP deflator in (constant 2017 LCU per constant 2005
    #> LCU) used:
    #> USA: 1.23136
    #> constant 2017 LCU → constant 2017 Int$PPP
    #> 2017 PPP conversion factor in (LCU per international $) used:
    #> USA: 1
    #> # A tibble: 5 × 3
    #>   iso3c  year value
    #>   <chr> <int> <dbl>
    #> 1 USA    2010  123.
    #> 2 USA    2011  124.
    #> 3 USA    2012  126.
    #> 4 USA    2013  127.
    #> 5 USA    2014  128.

The verbosity can also be controlled via the option `GDPuc.verbose`.

    options(GDPuc.verbose = TRUE)

    convertGDP(
      gdp = my_gdp,
      unit_in = "constant 2005 LCU",
      unit_out = "constant 2017 Int$PPP"
    )
    #> ℹ Converting GDP with conversion factors from wb_wdi:
    #> constant 2005 LCU → constant 2017 LCU
    #> 2017 value of base 2005 GDP deflator in (constant 2017 LCU per constant 2005
    #> LCU) used:
    #> USA: 1.23136
    #> constant 2017 LCU → constant 2017 Int$PPP
    #> 2017 PPP conversion factor in (LCU per international $) used:
    #> USA: 1
    #> # A tibble: 5 × 3
    #>   iso3c  year value
    #>   <chr> <int> <dbl>
    #> 1 USA    2010  123.
    #> 2 USA    2011  124.
    #> 3 USA    2012  126.
    #> 4 USA    2013  127.
    #> 5 USA    2014  128.

    options(GDPuc.verbose = FALSE)

## The `return_cfs` argument

Set the `return_cfs` argument in `convertGDP` to `TRUE` to return a list of length 2, with the result and a the conversion factors used.

    convertGDP(
      gdp = my_gdp,
      unit_in = "constant 2005 LCU",
      unit_out = "constant 2017 Int$PPP",
      return_cfs = TRUE
    )
    #> $result
    #> # A tibble: 5 × 3
    #>   iso3c  year value
    #>   <chr> <int> <dbl>
    #> 1 USA    2010  123.
    #> 2 USA    2011  124.
    #> 3 USA    2012  126.
    #> 4 USA    2013  127.
    #> 5 USA    2014  128.
    #>
    #> $cfs
    #> # A tibble: 1 × 3
    #>   iso3c 2017 value of base 2005 GDP deflator in (consta…¹ 2017 PPP conversion …²
    #>   <chr>                                             <dbl>                  <dbl>
    #> 1 USA                                                1.23                      1
    #> # ℹ abbreviated names:
    #> #   ¹​`2017 value of base 2005 GDP deflator in (constant 2017 LCU per constant 2005 LCU)`,
    #> #   ²​`2017 PPP conversion factor in (LCU per international $)`

## Conversion method

This package makes us of country-specific GDP deflators, Market Exchange Rates (MER), and Purchasing Power Parity (PPP) conversion factors to convert GDP values. Setting the `verbose` argument to `TRUE` should make the conversion process transparent and allow you to analyze the individual steps taken. All conversion functions were successfully tested on the World Bank’s World Development Indicator (WDI) data: given any 2 WDI GDP series, `convertGDP` will reliably convert the one to the other.

That being said, converting GDP series can be complex and the use of this package should not absolve one of thinking carefully on what conversion is being done and how. When using the provided `wd_wdi` source, this specifically concerns conversions using PPPs and MERs together.
That is because the PPP conversion factors provided by the World Bank are based off of linked GDP deflators, while the MERs are not. That means, that converting the World Bank’s current international dollar PPP series into LCU, will result in the “GDP: linked series (current LCU)”, while converting the current US dollar MER series into LCU, will result in the “GDP (current LCU)” series. Therefore, when linked and non-lined GDP deflators differ (which they do more often for developing countries, and in general the further into the past one looks), converting from current IntPPP to current USMER will not result in the exact same series as given in the WDI data.