CShapesMaps.Rmd

---
title: "Building historical maps with CShapes"
author: "Bernhard Bieri"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Building historical maps with CShapes}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.align = "center"
)
pkgs <- c("WDI", "ggplot2", "dplyr", "countrycode", "manystates")
lapply(pkgs, library, character.only = TRUE)
```

Providing visualizations about of the structure of the international
governance system at certain times is desirable, but not always straightforward.
While not aiming at being a comprehensive guide to the
[different ways of creating maps in R](https://geocompr.robinlovelace.net/),
this articles introduces the process of building maps in R with data
from [`{cshapes}` package](https://cran.r-project.org/web/packages/cshapes/index.html)
and GDP data from the World Bank.

In the following example, we'll see how to build a heatmap showing GDP per
capita in 1980.

## Step 1: Import the data from `{cshapes}` and the World Bank

First, we extract data from the `{WDI}` package of the
[NY.GDP.PCAP.KD](https://data.worldbank.org/indicator/NY.GDP.PCAP.KD)
indicator of the World Bank which yields information
about the GDP per capita of countries in 2010 $.
We then clean this data and then convert the ISO country code to
the COW number we have in the `{cshapes}` dataset
with the `{countrycode}` package.

```{r data, echo=TRUE, warning=FALSE, message=FALSE}
# Extract GDP from World Bank for the period between 1980 and 1990
library(WDI)
library(countrycode)
library(dplyr)

GDP <- WDI::WDI(indicator = 'NY.GDP.PCAP.KD', start = 1980, end = 1980)
GDP <- dplyr::slice(GDP, 50:266)
GDP <- dplyr::as_tibble(GDP) %>%
  dplyr::mutate(cowID = as.character(countrycode::countrycode(GDP$iso2c,
                                                       origin = "iso2c",
                                                       destination = "cowc",
                                                       warn = FALSE)))
```

Second, we use the `{cshapes}` package by importing a world map
in a way that is consistent with the many packages universe with the
`import_cshapes()` function.

```{r cshapes, echo=TRUE, warning=FALSE, message=FALSE}
# Import map data with import_cshapes()
mapdata <- manystates::import_cshapes(date = "1980-01-01")
```

Finally, we perform a `left_join()` between both dataframes to combine mapping
data (especially the `geometry` variable which contains the shapes of countries)
and data about GDP in 1980.
Since this is a historical map, it takes the information about states and
distances according what they were in 1980 (e.g. West and East Germany or USSR).

```{r join, echo=TRUE, warning=FALSE, message=FALSE}
# Left join on cowNr
data <- dplyr::left_join(mapdata, GDP, by = "cowID")
```

## Step 2: Create a heatmap

To plot the data, we leverage `{ggplot}` and its `geom_sf()`
layer that takes a vector of polygons as an input
(in this case the `geometry` variable of the combined
dataframe) to create a map.
We also use a neat theme from the `{hrbrthemes}`
package and tweak it a little for a prettier output.

```{r plot, echo=TRUE, warning=FALSE, message=FALSE}
library(ggplot2)
library(hrbrthemes) # for a pretty map

ggplot2::ggplot(data = data$geometry) + # Main plot options
  geom_sf(mapping = aes(fill = data$NY.GDP.PCAP.KD), lwd = 0.1) + # Map options
  scale_fill_viridis_c(option = "plasma", na.value = "grey80") + # Color options
  labs(title = "GDP per capita in 1980",
       subtitle = "Some fact about the GDP in 1980.",
       caption = "Source: World Bank (retrieved 15/07/2021)",
       fill = "GDP per capita") + # Define labels
  hrbrthemes::theme_ipsum() + # Set a default theme
  theme(panel.background = element_rect(fill = "light blue"), # Make oceans blue
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_blank()) # Remove grids
```