Handling missing conversion factors

The replace_NAs argument

Use the replace_NAs argument in convertGDP to handle missing conversion factors.

replace_NAs = NULL or NA

By default, replace_NAs is NULL, and NAs are returned along with a warning. Set replace_NAs = NA to explicitly return NAs without the warning.

Below, the return_cfs argument is set to TRUE to inspect the conversion factors, along side the result.

library(GDPuc)

# Test with Aruba -> iso3c = ABW
my_gdp <- tibble::tibble(
  iso3c = c("ABW"),
  year = 2010:2014,
  value = 100:104
)

x <- convertGDP(
  gdp = my_gdp,
  unit_in = "constant 2005 Int$PPP",
  unit_out = "constant 2019 Int$PPP",
  return_cfs = TRUE
)
#> Warning: NAs have been generated for countries lacking conversion factors!
x$result
#> # A tibble: 5 × 3
#>   iso3c  year value
#>   <chr> <int> <dbl>
#> 1 ABW    2010    NA
#> 2 ABW    2011    NA
#> 3 ABW    2012    NA
#> 4 ABW    2013    NA
#> 5 ABW    2014    NA

x$cfs
#> # A tibble: 1 × 4
#>   iso3c 2005 PPP conversion factor in (LCU per international $…¹ 2019 …² 2019 …³
#>   <chr>                                                    <dbl>   <dbl>   <dbl>
#> 1 ABW                                                       1.22      NA      NA
#> # … with abbreviated variable names
#> #   ¹​`2005 PPP conversion factor in (LCU per international $)`,
#> #   ²​`2019 value of base 2005 GDP deflator in (constant 2019 LCU per constant 2005 LCU)`,
#> #   ³​`2019 PPP conversion factor in (LCU per international $)`

To eliminate the warning:

x <- convertGDP(
  gdp = my_gdp, 
  unit_in = "constant 2005 Int$PPP", 
  unit_out = "constant 2019 Int$PPP",
  replace_NAs = NA
)

You can also use the GDPuc.warn option to suppress warnings from convertGDP in general (see “Silence warnings”).

replace_NAs = 0

If set to 0, resulting NAs are set to 0.

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

x <- convertGDP(
  gdp = my_gdp,
  unit_in = "constant 2005 Int$PPP",
  unit_out = "constant 2019 Int$PPP",
  replace_NAs = 0,
  return_cfs = TRUE
)
x$result
#> # A tibble: 5 × 3
#>   iso3c  year value
#>   <chr> <int> <dbl>
#> 1 ABW    2010     0
#> 2 ABW    2011     0
#> 3 ABW    2012     0
#> 4 ABW    2013     0
#> 5 ABW    2014     0

x$cfs
#> # A tibble: 1 × 4
#>   iso3c 2005 PPP conversion factor in (LCU per international $…¹ 2019 …² 2019 …³
#>   <chr>                                                    <dbl>   <dbl>   <dbl>
#> 1 ABW                                                       1.22      NA      NA
#> # … with abbreviated variable names
#> #   ¹​`2005 PPP conversion factor in (LCU per international $)`,
#> #   ²​`2019 value of base 2005 GDP deflator in (constant 2019 LCU per constant 2005 LCU)`,
#> #   ³​`2019 PPP conversion factor in (LCU per international $)`

replace_NAs = “no_conversion”

If set to “no_conversion”, NAs are replaced with the values in the gdp argument.

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

x <- convertGDP(
  gdp = my_gdp,
  unit_in = "constant 2005 Int$PPP",
  unit_out = "constant 2019 Int$PPP",
  replace_NAs = "no_conversion",
  return_cfs = TRUE
)
x$result
#> # A tibble: 5 × 3
#>   iso3c  year value
#>   <chr> <int> <dbl>
#> 1 ABW    2010   100
#> 2 ABW    2011   101
#> 3 ABW    2012   102
#> 4 ABW    2013   103
#> 5 ABW    2014   104

x$cfs
#> # A tibble: 1 × 4
#>   iso3c 2005 PPP conversion factor in (LCU per international $…¹ 2019 …² 2019 …³
#>   <chr>                                                    <dbl>   <dbl>   <dbl>
#> 1 ABW                                                       1.22      NA      NA
#> # … with abbreviated variable names
#> #   ¹​`2005 PPP conversion factor in (LCU per international $)`,
#> #   ²​`2019 value of base 2005 GDP deflator in (constant 2019 LCU per constant 2005 LCU)`,
#> #   ³​`2019 PPP conversion factor in (LCU per international $)`

replace_NAs = “linear”

If set to “linear”, missing conversion factors are inter- and extrapolated linearly. For the extrapolation, the closest 5 data points are used.

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

x <- convertGDP(
  gdp = my_gdp,
  unit_in = "constant 2005 Int$PPP",
  unit_out = "constant 2019 Int$PPP",
  replace_NAs = "linear",
  return_cfs = TRUE
)
x$result
#> # A tibble: 5 × 3
#>   iso3c  year value
#>   <chr> <int> <dbl>
#> 1 ABW    2010  116.
#> 2 ABW    2011  117.
#> 3 ABW    2012  118.
#> 4 ABW    2013  120.
#> 5 ABW    2014  121.

x$cfs
#> # A tibble: 1 × 4
#>   iso3c 2005 PPP conversion factor in (LCU per international $…¹ 2019 …² 2019 …³
#>   <chr>                                                    <dbl>   <dbl>   <dbl>
#> 1 ABW                                                       1.22    1.30    1.37
#> # … with abbreviated variable names
#> #   ¹​`2005 PPP conversion factor in (LCU per international $)`,
#> #   ²​`2019 value of base 2005 GDP deflator in (constant 2019 LCU per constant 2005 LCU)`,
#> #   ³​`2019 PPP conversion factor in (LCU per international $)`

replace_NAs = “regional_average”

If set to “regional_average”, the regional GDP-weighted averages will be used. Requires a region-mapping, and a column in the source object with GDP data at PPP, to be used as weight. May lead to misleading results, use with care!

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

my_mapping_data_frame <- tibble::tibble(
  iso3c = c("ABW", "BRA", "ARG", "COL"),
  region = "LAM"
)

x <- convertGDP(
  gdp = my_gdp,
  unit_in = "constant 2005 Int$PPP",
  unit_out = "constant 2019 Int$PPP",
  replace_NAs = "regional_average",
  with_regions = my_mapping_data_frame,
  return_cfs = TRUE
)
x$result
#> # A tibble: 5 × 3
#>   iso3c  year value
#>   <chr> <int> <dbl>
#> 1 ABW    2010  3.75
#> 2 ABW    2011  3.79
#> 3 ABW    2012  3.83
#> 4 ABW    2013  3.86
#> 5 ABW    2014  3.90

x$cfs
#> # A tibble: 1 × 4
#>   iso3c 2005 PPP conversion factor in (LCU per international $…¹ 2019 …² 2019 …³
#>   <chr>                                                    <dbl>   <dbl>   <dbl>
#> 1 ABW                                                       1.22    6.44    210.
#> # … with abbreviated variable names
#> #   ¹​`2005 PPP conversion factor in (LCU per international $)`,
#> #   ²​`2019 value of base 2005 GDP deflator in (constant 2019 LCU per constant 2005 LCU)`,
#> #   ³​`2019 PPP conversion factor in (LCU per international $)`

# Compare the 2019 PPP with the 2005 PPP. They are not in the same order of magnitude. 
# Obviously, being a part of the same region, does not mean the currencies are of the same strength.

replace_NAs = c(“linear”, “…”)

If a vector is passed, with “linear” as first element, then the operations are done in sequence. For example for c(“linear”, 0), missing conversion factors are first inter- and extrapolated linearly but if any missing conversion factors still lead to NAs, these are replaced with 0.

# Create an imaginary country XXX, and add it to the Latin America region
my_gdp <- tibble::tibble(
  iso3c = c("ABW", "XXX"),
  year = 2010,
  value = 100
)

my_mapping_data_frame <- tibble::tibble(
  iso3c = c("ABW", "BRA", "ARG", "COL", "XXX"),
  region = "LAM"
)

x <- convertGDP(
  gdp = my_gdp,
  unit_in = "constant 2005 Int$PPP",
  unit_out = "constant 2019 Int$PPP",
  replace_NAs = c("linear", 0),
  with_regions = my_mapping_data_frame,
  return_cfs = TRUE
)
x$result
#> # A tibble: 2 × 3
#>   iso3c  year value
#>   <chr> <dbl> <dbl>
#> 1 ABW    2010  116.
#> 2 XXX    2010    0

x$cfs
#> # A tibble: 2 × 4
#>   iso3c 2005 PPP conversion factors in (LCU per international …¹ 2019 …² 2019 …³
#>   <chr>                                                    <dbl>   <dbl>   <dbl>
#> 1 ABW                                                       1.22    1.37      NA
#> 2 XXX                                                       1.30   NA         NA
#> # … with abbreviated variable names
#> #   ¹​`2005 PPP conversion factors in (LCU per international $)`,
#> #   ²​`2019 value of base 2005 GDP deflators in (constant 2019 LCU per constant 2005 LCU)`,
#> #   ³​`2019 PPP conversion factors in (LCU per international $)`

Deprecated: replace_NAs = 1

If set to 1, missing conversion factors are set to 1. To be deprecated, use with care!

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

x <- convertGDP(
  gdp = my_gdp,
  unit_in = "constant 2005 Int$PPP",
  unit_out = "constant 2019 Int$PPP",
  replace_NAs = 1,
  return_cfs = TRUE
)
#> Warning: The `replace_NAs` argument of `convertGDP()` should not be 1 as of GDPuc 0.7.0.
#> ℹ The deprecated feature was likely used in the GDPuc package.
#>   Please report the issue at <]8;;https://github.com/pik-piam/GDPuc/issueshttps://github.com/pik-piam/GDPuc/issues]8;;>.
x$result
#> # A tibble: 5 × 3
#>   iso3c  year value
#>   <chr> <int> <dbl>
#> 1 ABW    2010  103.
#> 2 ABW    2011  104.
#> 3 ABW    2012  105.
#> 4 ABW    2013  106.
#> 5 ABW    2014  107.

x$cfs
#> # A tibble: 1 × 4
#>   iso3c 2005 PPP conversion factor in (LCU per international $…¹ 2019 …² 2019 …³
#>   <chr>                                                    <dbl>   <dbl>   <dbl>
#> 1 ABW                                                       1.22   0.840       1
#> # … with abbreviated variable names
#> #   ¹​`2005 PPP conversion factor in (LCU per international $)`,
#> #   ²​`2019 value of base 2005 GDP deflator in (constant 2019 LCU per constant 2005 LCU)`,
#> #   ³​`2019 PPP conversion factor in (LCU per international $)`

# Why is the deflator above not 1? That is because for ABW, only the deflator value in 2019 was set to 1. 
# In 2005 the deflator was in the order of magnitude of 100. Obviously setting the deflator to 1 in 2019 is 
# completely misleading.