README.Rmd

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# planetaryhdi

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[![License: CC BY
4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14355748.svg)](https://zenodo.org/doi/10.5281/zenodo.14355748)
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The goal of planetaryhdi is to make data provide a cleaned datasets of planetary pressures-adjusted HDI Index for all countries.

## Installation

You can install the development version of planetaryhdi from
[GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("openwashdata/planetaryhdi")
```

```{r}
## Run the following code in console if you don't have the packages
## install.packages(c("dplyr", "knitr", "readr", "stringr", "gt", "kableExtra"))
library(dplyr)
library(knitr)
library(readr)
library(stringr)
library(gt)
library(kableExtra)
```

Alternatively, you can download the individual datasets as a CSV or XLSX
file from the table below.

1.  Click Download CSV. A window opens that displays the CSV in
    your browser.
2.  Right-click anywhere inside the window and select "Save Page As...".
3.  Save the file in a folder of your choice.

```{r, echo=FALSE, message=FALSE, warning=FALSE}

extdata_path <- "https://github.com/openwashdata/planetaryhdi/raw/main/inst/extdata/"

read_csv("data-raw/dictionary.csv") |> 
  distinct(file_name) |> 
  dplyr::mutate(file_name = str_remove(file_name, ".rda")) |> 
  dplyr::rename(dataset = file_name) |> 
  mutate(
    CSV = paste0("[Download CSV](", extdata_path, dataset, ".csv)"),
    XLSX = paste0("[Download XLSX](", extdata_path, dataset, ".xlsx)")
  ) |> 
  knitr::kable()

```

## Data

The package provides access to Planetary pressures-adjusted HDI Index for all countries and UNDP defined sub-region in one dataset.

```{r}
library(planetaryhdi)
```

### planetaryhdi

The dataset has
`r nrow(planetaryhdi)` observations and `r ncol(planetaryhdi)`
variables

```{r}
planetaryhdi |> 
  head(3) |> 
  gt::gt() |>
  gt::as_raw_html()
```

For an overview of the variable names, see the following table.

```{r echo=FALSE, message=FALSE, warning=FALSE}
readr::read_csv("data-raw/dictionary.csv", locale = locale(encoding = "UTF-8")) |>
  mutate(across(everything(), ~ iconv(., from = "UTF-8", to = "UTF-8", sub = ""))) |> # Replace invalid UTF-8 characters with ""
  dplyr::filter(file_name == "planetaryhdi.rda") |>
  dplyr::select(variable_name:description) |>
  knitr::kable() |>
  kableExtra::kable_styling("striped") |>
  kableExtra::scroll_box(height = "200px")
```

## Example

### Planetary Pressures Adjusted HDI Index for all countries

```{r}
library(planetaryhdi)
library(ggplot2)
library(rnaturalearthdata)
library(rnaturalearth)
library(dplyr)

# 2022 HDI worldwide 
world <- ne_countries(scale = "medium", returnclass = "sf")

# Check that planetaryhdi has the necessary columns
if(!all(c("iso3c", "phdi") %in% colnames(planetaryhdi))) {
  stop("planetaryhdi must have columns 'iso3c' and 'phdi'.")
}

# Join the world data with the planetaryhdi data
world_map_data <- world |> 
  left_join(planetaryhdi, by = c("iso_a3" = "iso3c"))

# Handle any NA values that might have been introduced by the join
world_map_data$phdi[is.na(world_map_data$phdi)] <- NA

# Define HDI color palette
hdi_colors <- c("#d73027", "#fc8d59", "#fee08b", "#fdae61", "#fdd49e", "#feedde", 
                "#d9ef8b", "#a6d96a", "#66bd63", "#1a9850", "#00441b", "#003300", "#001a00", 
                "#e0e0e0") 

# Plot the map
ggplot(data = world_map_data) +
  geom_sf(aes(fill = cut(phdi, 
                         breaks = c(-Inf, 0.399, 0.449, 0.499, 0.549, 0.599, 0.649, 0.699, 
                                    0.749, 0.799, 0.849, 0.899, 0.950, Inf), 
                         labels = c("≤ 0.399", "0.400–0.449", "0.450–0.499", "0.500–0.549", 
                                    "0.550–0.599", "0.600–0.649", "0.650–0.699", 
                                    "0.700–0.749", "0.750–0.799", "0.800–0.849", 
                                    "0.850–0.899", "0.900–0.950", "≥ 0.950")))) +
  scale_fill_manual(values = hdi_colors, na.value = "gray90", name = "HDI 2022 Brackets") +
  theme_minimal() +
  labs(title = "World Planetary Pressures Adjusted HDI (2022)") +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.grid = element_blank())

```


### Effects of Adjusting HDI with planetary pressures

```{r fig.width=8, fig.height=6, message=FALSE, warning=FALSE}
library(planetaryhdi)
library(ggplot2)

# Ensure the dataset has the necessary columns (hdi and phdi)
if(!all(c("iso3c", "hdi", "phdi") %in% colnames(planetaryhdi))) {
  stop("planetaryhdi must have columns 'hdi' and 'phdi'.")
}

# Create the scatter plot
ggplot(planetaryhdi, aes(x = hdi, y = phdi, label = iso3c)) +
  geom_point(aes(color = phdi), size = 1) +  # Points colored by phdi values
  geom_text(aes(label = iso3c), hjust = 1.5, vjust = 1.5, size = 1.5) +  # Labels for countries
  theme_minimal() +
  labs(title = "HDI vs Planetary Pressures Adjusted HDI (2022)",
       x = "Human Development Index (HDI)",
       y = "Planetary Pressures Adjusted HDI (phdi)") +
  theme(axis.text = element_text(size = 10),  # Adjust axis text size
        axis.title = element_text(size = 12),  # Adjust axis title size
        legend.position = "none")  # Remove the legend

```

## License

Data are available as
[CC-BY](https://github.com/openwashdata/%7B%7B%7Bpackagename%7D%7D%7D/blob/main/LICENSE.md).

## Citation

Please cite this package using:

```{r}
citation("planetaryhdi")
```