332-hexbin-chloropleth-cartogram.Rmd

---
title: "Basic Cartogram with R"
descriptionMeta: "This post describes how to apply the cartogram method to a hexbin map. Each region is represented as a hexagon which size is distorted according to a numeric variable. Reproducible R code provided."
descriptionTop: "This post explains how to use the `[cartogram](cartogram.html)` method on a `[hexbin map](hexbin-map.html)`. Each region is depicted as a hexagon, with its **size altered based on a numeric variable**, utilizing the `cartogram` package."
sectionText: "Cartogram section"
sectionLink: "cartogram.html"
DataToVizText: "Data to Viz"
DataToVizLink: "data-to-viz.com/graph/cartogram.html"
url: "332-hexbin-chloropleth-cartogram"
output:
  html_document:
      self_contained: false    
      mathjax: default
      lib_dir: libs
      template: template_rgg.html
      css: style.css
      toc: TRUE
      toc_float: TRUE
      toc_depth: 2
      df_print: "paged"
---


```{r global options, include = FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE, dev = "ragg_png", dpi = 300)
```

<div class="container">




# Basic hexbin map
***

<div class = "row">

<div class = "col-md-6  col-sm-12 align-self-center">

The first step is to build a basic [hexbin map](hexbin-map.html) of the US. Note that the gallery dedicates a [whole section](hexbin-map.html) to this kind of map.

Hexagons boundaries are provided [here](https://team.carto.com/u/andrew/tables/andrew.us_states_hexgrid/public/map). You have to download it at the `geojson` format and load it in R thanks to the `st_read() / read_sf()` functions.

Then you get a geospatial object that you can plot using the `plot()` function. This is widely explained in the [background map](map.html) section of the gallery.

</div>


<div class = "col-md-6  col-sm-12">

```{r thecode, echo=FALSE, out.width = "100%", fig.height=7}
# library
library(tidyverse)
library(sf)
library(RColorBrewer)

# Hexagons boundaries at geojson format were found here, and stored on my github https://team.carto.com/u/andrew/tables/andrew.us_states_hexgrid/public/map.

# Load this file. (Note: I stored in a folder called DATA)
my_sf <- read_sf("DATA/us_states_hexgrid.geojson.json")

# Bit of reformatting
my_sf <- my_sf %>%
  mutate(google_name = gsub(" \\(United States\\)", "", google_name))

# Show it
plot(st_geometry(my_sf))
```
</div>
</div>



```{r thecode, eval=FALSE}
```














# Distort hexagone size with `cartogram` {#carto}
***

<div class = "row">

<div class = "col-md-6  col-sm-12 align-self-center">

The geospatial object has an attached data frame that provides several information for each region.

We need to add a new column to this data frame. This column will provide the **population per state**, available at `.csv` format [here](https://raw.githubusercontent.com/holtzy/R-graph-gallery/master/DATA/pop_US.csv). 

We can thus use the `cartogram` library to **distort the size of each state** (=hexagon), proportionally to this column. The new geospatial object we get can be drawn with the same `plot` function.

</div>


<div class = "col-md-6  col-sm-12">

```{r thecode2, echo=FALSE, out.width = "100%", fig.height=7}
# Library
library(cartogram)

# Load the population per states (source: https://www.census.gov/data/tables/2017/demo/popest/nation-total.html)
pop <- read.table("https://raw.githubusercontent.com/holtzy/R-graph-gallery/master/DATA/pop_US.csv", sep = ",", header = T)
pop$pop <- pop$pop / 1000000

# merge both
my_sf <- my_sf %>% left_join(., pop, by = c("google_name" = "state"))

# Compute the cartogram, using this population information
# First we need to change the projection, we use Mercator (AKA Google Maps, EPSG 3857)
my_sf_merc <- st_transform(my_sf, 3857)
cartogram <- cartogram_cont(my_sf_merc, "pop")

# Back to original projection
cartogram <- st_transform(cartogram, st_crs(my_sf))

# First look!
plot(st_geometry(cartogram))
```
</div>
</div>



```{r thecode2, eval=FALSE}
```










# Cartogram and choropleth {#choro}
***

To get a satisfying result, let's:

- **color hexagons** according to their population
- add **legend**
- add **background** color
- add **title**
- add state name. Label position is computed thanks to the `gCentroid()` function. The centroid can be understand as the **center of a polygon**, and by so a good **proxy to place the label**.


```{r thecode3, echo=FALSE, fig.align="center", out.width = "80%", fig.height=5}
# Library
# plot
ggplot(cartogram) +
  geom_sf(aes(fill = pop), linewidth = 0.05, alpha = 0.9, color = "black") +
  scale_fill_gradientn(
    colours = brewer.pal(7, "BuPu"), name = "population (in M)",
    labels = scales::label_comma(),
    guide = guide_legend(
      keyheight = unit(3, units = "mm"),
      keywidth = unit(12, units = "mm"),
      title.position = "top",
      label.position = "bottom"
    )
  ) +
  geom_sf_text(aes(label = iso3166_2), color = "white", size = 3, alpha = 0.6) +
  theme_void() +
  ggtitle("Another look on the US population") +
  theme(
    legend.position = c(0.5, 0.9),
    legend.direction = "horizontal",
    text = element_text(color = "#22211d"),
    plot.background = element_rect(fill = "#f5f5f9", color = NA),
    panel.background = element_rect(fill = "#f5f5f9", color = NA),
    legend.background = element_rect(fill = "#f5f5f9", color = NA),
    plot.title = element_text(size = 22, hjust = 0.5, color = "#4e4d47", margin = margin(b = -0.1, t = 0.4, l = 2, unit = "cm")),
  )
```






```{r thecode3, eval=FALSE}
```



# Going further

This post explains how to use the `cartogram` method on a hexbin map.

You might be interested in interested in [2d density hexbin map](329-hexbin-map-for-distribution.html), and more generally in the [hexbin map section](hexbin-map.html) of the gallery.



<!-- Close container -->
</div>




```{r, echo=FALSE}
htmltools::includeHTML("htmlChunkRelatedMap.html")
```