replace_NAs
argumentUse the replace_NAs
argument in convertGDP
to handle missing conversion factors.
replace_NAs
= NULL or NABy 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 Venezuela -> iso3c = VEN
my_gdp <- tibble::tibble(
iso3c = c("VEN"),
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$result
#> # A tibble: 5 × 3
#> iso3c year value
#> <chr> <int> <dbl>
#> 1 VEN 2010 NA
#> 2 VEN 2011 NA
#> 3 VEN 2012 NA
#> 4 VEN 2013 NA
#> 5 VEN 2014 NA
x$cfs
#> # A tibble: 1 × 4
#> iso3c 2005 PPP conversion fact…¹ 2019 value of base 2…² 2019 PPP conversion …³
#> <chr> <dbl> <dbl> <dbl>
#> 1 VEN 0.842 NA NA
#> # ℹ abbreviated 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$PPP",
replace_NAs = NA
)
You can also use the GDPuc.warn
option to suppress
warnings from convertGDP
in general (see “Silence
warnings”).
replace_NAs
= 0If set to 0, resulting NAs are set to 0.
my_gdp <- tibble::tibble(
iso3c = "VEN",
year = 2010:2014,
value = 100:104
)
x <- convertGDP(
gdp = my_gdp,
unit_in = "constant 2005 Int$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 VEN 2010 0
#> 2 VEN 2011 0
#> 3 VEN 2012 0
#> 4 VEN 2013 0
#> 5 VEN 2014 0
x$cfs
#> # A tibble: 1 × 4
#> iso3c 2005 PPP conversion fact…¹ 2019 value of base 2…² 2019 PPP conversion …³
#> <chr> <dbl> <dbl> <dbl>
#> 1 VEN 0.842 NA NA
#> # ℹ abbreviated 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 $)`
replace_NAs
= “no_conversion”If set to “no_conversion”, NAs are replaced with the values in the gdp argument.
my_gdp <- tibble::tibble(
iso3c = "VEN",
year = 2010:2014,
value = 100:104
)
x <- convertGDP(
gdp = my_gdp,
unit_in = "constant 2005 Int$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 VEN 2010 100
#> 2 VEN 2011 101
#> 3 VEN 2012 102
#> 4 VEN 2013 103
#> 5 VEN 2014 104
x$cfs
#> # A tibble: 1 × 4
#> iso3c 2005 PPP conversion fact…¹ 2019 value of base 2…² 2019 PPP conversion …³
#> <chr> <dbl> <dbl> <dbl>
#> 1 VEN 0.842 NA NA
#> # ℹ abbreviated 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 $)`
replace_NAs
= “linear”If set to “linear”, missing conversion factors are inter- and extrapolated linearly. For the extrapolation, the closest 5 data points are used.
my_gdp <- tibble::tibble(
iso3c = "VEN",
year = 2010:2014,
value = 100:104
)
x <- convertGDP(
gdp = my_gdp,
unit_in = "constant 2005 Int$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 VEN 2010 203.
#> 2 VEN 2011 205.
#> 3 VEN 2012 208.
#> 4 VEN 2013 210.
#> 5 VEN 2014 212.
x$cfs
#> # A tibble: 1 × 4
#> iso3c 2005 PPP conversion fact…¹ 2019 value of base 2…² 2019 PPP conversion …³
#> <chr> <dbl> <dbl> <dbl>
#> 1 VEN 0.842 14.4 5.97
#> # ℹ abbreviated 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 $)`
replace_NAs
= “regional_average”If set to “regional_average”, the regional GDP-weighted averages will be used. Requires a region-mapping, and a column in the source object with GDP data at PPP, to be used as weight. May lead to misleading results, use with care!
my_gdp <- tibble::tibble(
iso3c = "VEN",
year = 2010:2014,
value = 100:104
)
my_mapping_data_frame <- tibble::tibble(
iso3c = c("VEN", "BRA", "ARG", "COL"),
region = "LAM"
)
x <- convertGDP(
gdp = my_gdp,
unit_in = "constant 2005 Int$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 VEN 2010 0.485
#> 2 VEN 2011 0.489
#> 3 VEN 2012 0.494
#> 4 VEN 2013 0.499
#> 5 VEN 2014 0.504
x$cfs
#> # A tibble: 1 × 3
#> iso3c 2019 value of base 2005 GDP deflator in (consta…¹ 2019 PPP conversion …²
#> <chr> <dbl> <dbl>
#> 1 VEN 1.18 205.
#> # ℹ abbreviated names:
#> # ¹`2019 value of base 2005 GDP deflator in (constant 2019 LCU per constant 2005 LCU)`,
#> # ²`2019 PPP conversion factor in (LCU per international $)`
# Compare the 2019 PPP with the 2005 PPP. They are not in the same order of magnitude.
# Obviously, being a part of the same region, does not mean the currencies are of the same strength.
replace_NAs
= c(“linear”, “…”)If a vector is passed, with “linear” as first element, then the operations are done in sequence. For example for c(“linear”, 0), missing conversion factors are first inter- and extrapolated linearly but if any missing conversion factors still lead to NAs, these are replaced with 0.
# Create an imaginary country XXX, and add it to the Latin America region
my_gdp <- tibble::tibble(
iso3c = c("VEN", "XXX"),
year = 2010,
value = 100
)
my_mapping_data_frame <- tibble::tibble(
iso3c = c("VEN", "BRA", "ARG", "COL", "XXX"),
region = "LAM"
)
x <- convertGDP(
gdp = my_gdp,
unit_in = "constant 2005 Int$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 VEN 2010 203.
#> 2 XXX 2010 0
x$cfs
#> # A tibble: 2 × 3
#> iso3c 2019 value of base 2005 GDP deflators in (const…¹ 2019 PPP conversion …²
#> <chr> <dbl> <dbl>
#> 1 VEN 14.4 5.97
#> 2 XXX NA NA
#> # ℹ abbreviated names:
#> # ¹`2019 value of base 2005 GDP deflators in (constant 2019 LCU per constant 2005 LCU)`,
#> # ²`2019 PPP conversion factors in (LCU per international $)`
replace_NAs
= “with_USA”If set to “with_USA”, missing conversion factors are extended using the growth rates of the USA. If that is not possible (for instance if there is no data for any years at all) the data for these countries is converted using the conversion factors of the USA.
# Venezuela is only missing conversion factors in 2019, AIA has no conversion factors at all.
my_gdp <- tibble::tibble(
iso3c = c("VEN", "AIA", "USA"),
value = 100
)
x <- convertGDP(
gdp = my_gdp,
unit_in = "constant 2005 Int$PPP",
unit_out = "constant 2019 Int$PPP",
replace_NAs = "with_USA",
return_cfs = TRUE
)
x$result
#> # A tibble: 3 × 2
#> iso3c value
#> <chr> <dbl>
#> 1 VEN 264.
#> 2 AIA 128.
#> 3 USA 128.
x$cfs
#> # A tibble: 3 × 4
#> iso3c 2005 PPP conversion fact…¹ 2019 value of base 2…² 2019 PPP conversion …³
#> <chr> <dbl> <dbl> <dbl>
#> 1 USA 1 1.28 1
#> 2 VEN 0.842 8.40 2.68
#> 3 AIA 1 1.28 1
#> # ℹ abbreviated 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 $)`