Handling missing conversion factors

In GDPuc: Easily Convert GDP Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

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 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
)
x$result

x$cfs

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 = "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

x$cfs

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

x$cfs

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

x$cfs

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

x$cfs

# 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

x$cfs

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 = "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 = 1,
  return_cfs = TRUE
)
x$result

x$cfs

# Why is the deflator above not 1? That is because for VEN, 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.

GDPuc documentation built on Sept. 11, 2024, 6:17 p.m.