readme_raw.Rmd

---
title: "The Game of Thrones Colour Palettes"
author:
  - "Alejandro Jiménez Rico"
date: "`r Sys.Date()`"
output:
  md_document:
    variant: markdown_github
    toc: true
    toc_depth: 1
---


This package provides a round of palettes inspired by the *Game of Thrones* TV show.

At its first version, it simply contains the palettes of some of the houses of Westeros. They have been chosen manually, taking into account its consistency with all the existing branding of the franchise, but its suitability for data visualisation. 

> Information visualization is just a language with everything to be discovered. 

The colour palette should be beautiful, useful for plotting data and should relate to desired style; in this case, should relate to the _Song of Ice and Fire_ world. Some of the colours might change in future versions, in order to find this balance between suitability for plotting and relatability to the images from the universe.

<center><img src="https://raw.githubusercontent.com/aljrico/aljrico.github.io/master/_posts/images/show_scales2-1.png" ></center>




Installation
-------------

Just copy and execute this bunch of code and you'll have the last version of the package installed:


```{r, eval = FALSE}
devtools::install_github("aljrico/gameofthrones")
```

And you can now use it:


```{r}
library(gameofthrones)
```

Usage
-----

The default colour scale of the package is the one of the house *Targaryen*. If you prefer to choose another one, you'll need to specify which house you want the palette from.

Let's say that you want a palette made from the house **Targaryen**.


```{r}
pal <- got(250, option = "Targaryen")
image(volcano, col = pal)
```

<center><img src="https://raw.githubusercontent.com/aljrico/aljrico.github.io/master/_posts/images/unnamed-chunk-3-1.png" ></center>

Or that you want burn something down using *wildfire*.

```{r}
pal <- got(250, option = "Wildfire")
image(volcano, col = pal)
```


### ggplot2

Of course, this package has specific functions to behave seamlessly with the best data visiualisation library available. 
The package contains colour scale functions for **ggplot2** plots: `scale_color_got()` and `scale_fill_got()`.


Here is a made up example using the colours from the house of **Martell**,


```{r}
library(ggplot2)
ggplot(data.frame(x = rnorm(1e4), y = rnorm(1e4)), aes(x = x, y = y)) +
  geom_hex() + 
	coord_fixed() +
  scale_fill_got(option = "Martell") + 
	theme_bw()
```

and **Baratheon**


```{r}
ggplot(data.frame(x = rnorm(1e4), y = rnorm(1e4)), aes(x = x, y = y)) +
  geom_hex() + 
	coord_fixed() +
  scale_fill_got(option = "Baratheon") + 
	theme_bw()
```

You can also use it to create this cloropeths of the U.S Unemployment:

```{r}
unemp <- read.csv("http://datasets.flowingdata.com/unemployment09.csv",
                  header = FALSE, stringsAsFactors = FALSE)
names(unemp) <- c("id", "state_fips", "county_fips", "name", "year",
                  "?", "?", "?", "rate")
unemp$county <- tolower(gsub(" County, [A-Z]{2}", "", unemp$name))
unemp$county <- gsub("^(.*) parish, ..$","\\1", unemp$county)
unemp$state <- gsub("^.*([A-Z]{2}).*$", "\\1", unemp$name)
county_df <- map_data("county", projection = "albers", parameters = c(39, 45))
names(county_df) <- c("long", "lat", "group", "order", "state_name", "county")
county_df$state <- state.abb[match(county_df$state_name, tolower(state.name))]
county_df$state_name <- NULL
state_df <- map_data("state", projection = "albers", parameters = c(39, 45))
choropleth <- merge(county_df, unemp, by = c("state", "county"))
choropleth <- choropleth[order(choropleth$order), ]
ggplot(choropleth, aes(long, lat, group = group)) +
  geom_polygon(aes(fill = rate), colour = alpha("white", 1 / 2), size = 0.2) +
  geom_polygon(data = state_df, colour = "white", fill = NA) +
  coord_fixed() +
  theme_minimal() +
  ggtitle("US unemployment rate by county") +
	labs(subtitle = "House Tully palette") +
  theme(axis.line = element_blank(), axis.text = element_blank(),
        axis.ticks = element_blank(), axis.title = element_blank()) +
  scale_fill_got(option = "Tully")
```



But what if you want discrete scales? These functions also can be used for discrete scales with the argument `discrete = TRUE`. This argument, when TRUE, sets a finite number of sufficiently spaced colours within the selected palette to plot your data.


```{r}
library(gridExtra)

gg1 <- ggplot(diamonds, aes(factor(color), fill=factor(cut))) +  
	geom_bar(colour = "black") +
  scale_fill_got(discrete = TRUE, option = "Margaery") +
	ggtitle("Lady Margaery") +
	theme_minimal()

gg2 <- ggplot(diamonds, aes(factor(color), fill=factor(cut))) +  
	geom_bar(colour = "black") +
  scale_fill_got(discrete = TRUE, option = "Daenerys", direction = - 1) +
	ggtitle("Daenerys Stormborn") +
	theme_minimal()

grid.arrange(gg1,gg2)
```

Note that you can also play with the direction of the colour map. Either the default `direction = 1` or the opposite `direction = -1`.

```{r}
library(geojsonio)
library(tidyverse)
spdf <- geojson_read("https://raw.githubusercontent.com/gregoiredavid/france-geojson/master/communes.geojson",  what = "sp")

# Since it is a bit to much data, I select only a subset of it:
spdf = spdf[ substr(spdf@data$code,1,2)  %in% c("06", "83", "13", "30", "34", "11", "66") , ]

# I need to fortify the data AND keep trace of the commune code! (Takes 2 minutes)
library(broom)
spdf_fortified <- tidy(spdf, region = "code")



# read data
data=read.table("https://www.r-graph-gallery.com/wp-content/uploads/2017/12/data_on_french_states.csv", header=T, sep=";")
head(data)

# Make the merge
spdf_fortified = spdf_fortified %>%
	left_join(. , data, by=c("id"="depcom"))

# Note that if the number of restaurant is NA, it is in fact 0
spdf_fortified$nb_equip[ is.na(spdf_fortified$nb_equip)] = 0.001

p1 <- ggplot() +
	geom_polygon(data = spdf_fortified, aes(fill = nb_equip, x = long, y = lat, group = group) , size=0, alpha=0.9) +
	theme_void() +
	scale_fill_got(option = "Wildfire", trans = "log", breaks=c(1,5,10,20,50,100), name="Number of restaurant", guide = guide_legend( keyheight = unit(3, units = "mm"), keywidth=unit(12, units = "mm"), label.position = "bottom", title.position = 'top', nrow=1) ) +
	theme(legend.position = "none") + 
	coord_map()

p2 <- ggplot() +
	geom_polygon(data = spdf_fortified, aes(fill = nb_equip, x = long, y = lat, group = group) , size=0, alpha=0.9) +
	theme_void() +
	scale_fill_got(option = "Wildfire", direction = -1, trans = "log", breaks=c(1,5,10,20,50,100), name="Number of restaurant", guide = guide_legend( keyheight = unit(3, units = "mm"), keywidth=unit(12, units = "mm"), label.position = "bottom", title.position = 'top', nrow=1) ) +
	theme(legend.position = "none") +
	coord_map()

grid.arrange(p1,p2, ncol = 2)
```


```{r}
gg1 <- ggplot(diamonds, aes(carat, fill = cut)) +
  geom_density(position = "stack") +
	scale_fill_got_d(option = "Stark2")

gg2 <- ggplot(mpg, aes(class)) +
	geom_bar(aes(fill = drv), position = position_stack(reverse = TRUE), colour = "black") +
 coord_flip() +
	scale_fill_got_d(option = "Daenerys") +
 theme(legend.position = "top") +
	ylab("") +
	xlab("Class")

grid.arrange(gg1,gg2)
```


```{r}

gg <- ggplot(diamonds, aes(carat, stat(count), fill = cut)) +
  geom_density(position = "fill") +
	xlab("") + 
	ylab("")
gg1 <- gg +
	scale_fill_got(discrete = TRUE, option = "white_walkers", name = "") +
	ggtitle("White Walkers Palette")

gg2 <- gg +
	scale_fill_got(discrete = TRUE, option = "Lannister", name = "") +
	ggtitle("House Lannister Palette")

gg3 <-gg +
	scale_fill_got(discrete = TRUE, option = "Tyrell", name = "") +
	ggtitle("House Tyrell Palette")

gg4 <- gg +
	scale_fill_got(discrete = TRUE, option = "Stark", name = "") +
	ggtitle("House Stark Palette")

gg5 <- gg +
	scale_fill_got(discrete = TRUE, option = "Tully", name = "") +
	ggtitle("House Tully Palette")

gg6 <- gg +
	scale_fill_got(discrete = TRUE, option = "Martell", name = "") +
	ggtitle("House Martell Palette")

grid.arrange(gg1,gg2,gg3,gg4,gg5,gg6)
```