---
title: "Customize your maps"
output: rmarkdown::html_vignette
author: "Richard Aubrey White, Chi Zhang"
date: "2021-01-15"
vignette: >
  %\VignetteIndexEntry{Customize your maps}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---


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

```{r setup}
library(csmaps)
library(ggplot2)
library(data.table)
library(magrittr)
```

This vignette covers colouring regions (either automatically by `location_code`
or with your own categories), adding labels, and enriching a map with outside
data such as population figures from `csdata`.

## Colored maps

### Automatic coloring by `location_code`

```{r fig.height=6, fig.width=6}
pd <- copy(csmaps::nor_county_map_b2024_split_dt)

q <- ggplot()
q <- q + csmaps::annotate_oslo_nor_map_bxxxx_split_dt()
q <- q + geom_polygon(
  data = pd, 
  mapping = aes(
    x = long, 
    y = lat,
    group = group,
    fill = location_code
  ),  
  color="black",
  linewidth = 0.4
)
q <- q + theme_void()
q <- q + coord_quickmap()
q <- q + labs(title = "")
q
```

### Customized coloring with external information

It is also possible to specify the color by user-defined groups. Here we show an example of assigning different (pseudo) risk level to each county. 

```{r fig.height=6, fig.width=6, warning=FALSE}
pd <- copy(csmaps::nor_county_map_b2024_split_dt)

# assign each location a random category for different colors
location_info <- unique(pd[,c("location_code")])
location_info[,category:=rep(
  c("Good","Normal","Neutral","Bad","Very Bad"),
  each=3)[1:.N]
]
location_info[,category:=factor(
  category,
  levels=c("Good","Normal","Neutral","Bad","Very Bad")
  )
]
print(location_info)

# join the map data.table
pd[
  location_info,
  on="location_code",
  category:=category
]

q <- ggplot()
q <- q + csmaps::annotate_oslo_nor_map_bxxxx_split_dt()
q <- q + geom_polygon(
  data = pd, 
  mapping = aes(
    x = long, 
    y = lat,
    group = group,
    fill = category
  ),  
  color="black",
  linewidth = 0.4
)
q <- q + coord_quickmap()
q <- q + labs(title="")
q <- q + theme_void()
q
```

## Labeled maps

We can add labels of county index onto the maps. There are several options for adding texts on a graph in `ggplot2`. We recommend `geom_label()` to add the labels if no label overlap occurs, otherwise we recommend using `ggrepel::geom_label_repel()`. 

```{r fig.height=6, fig.width=6}
q <- ggplot()
q <- q + csmaps::annotate_oslo_nor_map_bxxxx_split_dt()
q <- q + geom_polygon(
  data = pd, 
  mapping = aes(
    x = long, 
    y = lat,
    group = group,
    fill = category
  ),  
  color="black",
  linewidth = 0.4
)
q <- q + geom_label(
  data = csmaps::nor_county_position_geolabels_b2024_split_dt,
  mapping = aes(
    x = long, 
    y = lat,
    label = location_code
  )
)
# ggrepel::geom_label_repel() for avoiding overlap
q <- q + theme_void()
q <- q + coord_quickmap()
q <- q + labs(title = "")
q

```

Labels can be easily added to other layouts, such as Oslo wards.

```{r fig.height=6, fig.width=6}
q <- ggplot(mapping = aes(x = long, y = lat))
q <- q + geom_polygon(
  data = csmaps::oslo_ward_map_b2024_default_dt,
  mapping = aes(group = group),
  color = "black",
  fill = "white",
  linewidth = 0.4
)
q <- q + geom_label(
  data = csmaps::oslo_ward_position_geolabels_b2024_default_dt,
  mapping = aes(label = location_code),
  color = "red",
  size = 3,
  label.size = 0.1,
  label.r = grid::unit(0, "lines")
)
q <- q + theme_void()
q <- q + coord_quickmap()
q

```

## Enrich plot with additional data 

It is convenient to use `csdata` package to enrich Norway and Oslo maps with external information, such as location name and population.

### Add county name and population to Norway map

```{r fig.height=6, fig.width=6}
# enrich with population and location name
dpop_2024 <- csdata::nor_population_by_age_cats()[
  calyear==2024 &
  granularity_geo %in% "county"
]

# join, create label
labels <- copy(csmaps::nor_county_position_geolabels_b2024_split_dt)
labels[
  dpop_2024, 
  on = "location_code",
  pop_total := pop_jan1_n
]
labels[
  csdata::nor_locations_names(), 
  on = "location_code",
  location_name_short := location_name_short
]
labels[, label := paste0(location_name_short, '\n', pop_total)]
print(head(labels))

# plot
pd <- copy(csmaps::nor_county_map_b2024_split_dt)

q <- ggplot()
q <- q + csmaps::annotate_oslo_nor_map_bxxxx_split_dt()
q <- q + geom_polygon(
  data = pd, 
  mapping = aes(
    x = long,
    y = lat,
    group = group,
    fill = location_code
  ),
  color="black",
  linewidth = 0.4
)
q <- q + ggrepel::geom_label_repel(
  data = labels[repel==TRUE],
  mapping = aes(x = long, y = lat, label = label),
  size = 3,
  label.size = 0.1,
  label.r = grid::unit(0, "lines"),
  min.segment.length = 0
)
q <- q + geom_label(
  data = labels[repel==FALSE],
  mapping = aes(x = long, y = lat, label = label),
  size = 3,
  label.size = 0.1,
  label.r = grid::unit(0, "lines")
)
q <- q + theme_void()
q <- q + coord_quickmap()
q <- q + labs(title = "")
q

```

### Add location name for ward and population for Oslo map

```{r fig.height=6, fig.width=6}
# enrich with population and location name
dpop_2024 <- csdata::nor_population_by_age_cats()[calyear==2024]

# join, create label
labels <- copy(csmaps::oslo_ward_position_geolabels_b2024_default_dt)
labels[
  dpop_2024, 
  on = "location_code",
  pop_total := pop_jan1_n
]
labels[
  csdata::nor_locations_names(), 
  on = "location_code",
  location_name_short := location_name_short
]
labels[, label := paste0(location_name_short, '\n', pop_total)]
print(head(labels))

q <- ggplot(mapping = aes(x = long, y = lat))
q <- q + geom_polygon(
  data = csmaps::oslo_ward_map_b2024_default_dt,
  mapping = aes(group = group),
  color = "black",
  fill = "white",
  linewidth = 0.4
)
q <- q + geom_label(
  data = labels,
  mapping = aes(label = label),
  color = "red",
  size = 3,
  label.size = 0.1,
  label.r = grid::unit(0, "lines")
)
q <- q + theme_void()
q <- q + coord_quickmap()
q

```

