README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# hexmap

<!-- badges: start -->
[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
<!-- badges: end -->

The hexmap R package aims to automate the conversion of geospatial polygons into hexagonal grids. This conversion allows users to better visualise statistical values associated with each geospatial region. 

## Installation

You can install the development version of hexmap like so:

``` r
install.packages("remotes")
remotes::install_github("numbats/hexmap")
```

## Method 

1. Make a cartogram so that each region has an approximately equal area (optional)
2. Create a hexagonal grid based on this cartogram or other geospatial object
3. Assign the region to the "closest" hexagon

## Example: Australian federal election 2022


```{r, message = FALSE}
library(hexmap)
library(sf)
library(cartogram)
library(ggplot2)
library(dplyr)
set.seed(1)

winner_map <- function(data) {
  party_colors <- c("ALP" = "red3", "LNP" = "blue3", "GRN" = "seagreen", "Other" = "yellow2")
  ggplot(data) + 
    geom_sf(aes(fill = winner)) +
    theme_void() + 
    scale_fill_manual(values = party_colors) 
}

state_map <- function(data) {
  ggplot(data) + 
    geom_sf(aes(fill = state)) +
    theme_void() 
}
```

The data `aec2022` in hexmap is an `sf` object with a simplified Australian federal electoral boundary and the corresponding party that won the 2022 Australian federal election for that electoral division.

### Choropleth map

```{r plot-choropleth, message = FALSE}
winner_map(aec2022)
```

### Cartogram

```{r plot-cartogram, message = FALSE, warning = FALSE, cache = TRUE}
aec2022carto <- aec2022 %>% 
  st_transform(crs = "+init=epsg:3112") %>% 
  mutate(weight = 1) %>% 
  cartogram_cont("weight", itermax = 50)

winner_map(aec2022carto)
state_map(aec2022carto)
```

### `geogrid` hexagonal map

```{r plot-geogrid, warning = FALSE, cache = TRUE}
hex <- geogrid::calculate_grid(aec2022carto, grid_type = "hexagonal")
res <- geogrid::assign_polygons(aec2022carto, hex)

state_map(res)
```



## Related 

- [`geogrid`](https://github.com/jbaileyh/geogrid) package