---
title: "Spatial data example"
output: 
  rmarkdown::html_vignette:
    check_title: FALSE
vignette: >
  %\VignetteIndexEntry{Spatial data example}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{css, echo = F}
body {
  text-align: justify;
}

p {
  font-weight: 'normal';
  font-size: '16px
}
```

```{r, eval = TRUE, echo = F}
execute <- T
if(!all(c('maps', 'mapproj') %in% installed.packages())){
  execute <- F
  print("This vignetted requires the `maps` and `mapproj` packages to be installed for execution.")
}
```

<p>In this example we illustrate the capabilities of the `PieGlyph` package to overlay pie-chart glyphs on a map to illustrate spatial patterns in attributes.</p>

<p>We show an example where the proportion of votes received by the Republic and Democratic parties in each US state is show for a hypothetical election.</p>

<p>These plots can be created easily with existing techniques too. However, due to the pie-charts being tied with the plot dimensions it isn't possible to visualise the map in different geographical projections or change the aspect ratio of the plot without converting the pie-charts into ellipses. `PieGlyph` offers a solution to this problem.</p>

### Load Packages
```{r setup, warning=F, message=F, eval = execute}
library(PieGlyph)
library(ggplot2)
library(dplyr)
```

### Create data

##### Load Map data
<p>Load the geographical information including the latitude and longitude for the states in USA.</p>

```{r create-boundaries, eval = execute}
states_boundaries <- map_data("state")
```
<p>The dataset contains 15537 rows describing the geographical boundaries each state (except Alaska and Hawaii) in USA. The `long`, `lat` and `region` are the columns of interest to us. `long` and `lat` describe the longitude and latitudes respectively of the boundaries of the states, while `region` contains the names of each state.</p>

```{r boundaries, eval = execute}
head(states_boundaries)
```

<p>Create fake elections results each state in `states_boundaries` data</p>

```{r create-votes, eval = execute}
set.seed(123)

# Get names of state names from map data
votes_data <- data.frame('State' = tolower(state.name))

# Simulate percentage of votes received in each state by the Democratic, Republic and other parties
votes_data <- votes_data %>% 
                mutate('Democratic' = round(runif(50, 1, 100)),
                       'Republic' = round(runif(50, 1, (100 - Democratic))),
                       'Other' = 100 - Democratic - Republic)

# Add the latitude and longitude of the geographical centers of the states to place the pies 
votes_data <- votes_data %>% 
                mutate('pie_lat' = state.center$y,
                       'pie_long' = state.center$x)

# Filter out any states that weren't present in the map_data
votes_data <- votes_data %>% filter(State %in% unique(states_boundaries$region))
```

<p>The dataset contains 48 rows describing the percentage of votes different parties got in the respective state. `State` describes the state name, `Democaratic`, `Republic` and `Other` describe the percent of votes the parties got in the state. `pie_lat` and `pie_long` describe the geographical centre of each state (this is where the pie will be placed on the plot). </p>  

```{r votes_data, eval = execute}
head(votes_data)
```

### Create plot
##### Create map

```{r map, warning = F, fig.align='center', fig.width=7, eval = execute, fig.alt="Map of US"}
map <- ggplot(states_boundaries, aes(x = long, y = lat)) +
        # Add states and their borders
        geom_polygon(aes(group = group),
                     fill = 'darkseagreen', colour = 'black')+
        # Axis titles
        labs(x = 'Longitude', y ='Latitude')+
        # Blue background for the sea behind
        theme(panel.background = element_rect(fill = 'lightsteelblue2'))+
        # Coordinate system for maps
        coord_map()
map
```

##### Add pie charts showing proportion of votes for different in each states

```{r glyph-merc, warning = F, fig.align='center', fig.width=7, warning = F, eval = execute, fig.alt="US map with pie-glyphs overlaid"}
plot <- map + 
  # Add pie-charts for each state
  geom_pie_glyph(aes(y = pie_lat, x = pie_long),
                 data = votes_data, colour = 'black',
                 slices = c('Democratic','Republic','Other'))+
  # Colours of the pie sectors
  scale_fill_manual(values = c('#047db7','#c52d25', 'grey'), name = 'Party')+
  # Place legend on top of the plot
  theme(legend.position = 'top')
plot
```

<p>As the pie-charts are created independent of the axes and plot dimensions in `PieGlyph`, they are unaffected by any change in the map projection</p>
```{r glyph-albers, warning = F, fig.align='center', fig.width=7, warning = F, message = F, eval = execute, fig.alt="US map with pie-glyphs overlaid and albers projection"}
plot +
  # Different map projection
  coord_map('albers', lat0 = 45.5, lat1 = 29.5)
```

```{r glyph-gnomonic, warning = F, fig.align='center', fig.width=7, warning = F, message = F, eval = execute, fig.alt="US map with pie-glyphs overlaid with gnomonic projection"}
plot +
  # Different map projection
  coord_map('gnomonic')
```


.
