---
title: "Handling missing conversion factors"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Handling missing conversion factors}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

```{r, include = FALSE}
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.


```{r}
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$cfs
```
To eliminate the warning:
```{r}
x <- convertGDP(
  gdp = my_gdp, 
  unit_in = "constant 2005 Int$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"](https://pik-piam.github.io/GDPuc/articles/warn.html)).

### `replace_NAs` = 0  

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

```{r}
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.

```{r}
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.

```{r}
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!**

```{r}
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.

```{r}
# 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
```

### `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. 

```{r}
# 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

x$cfs
```