rwc.Rmd

---
title: "Rugby World Cup"
output: html_notebook
---

# Analysing data on England players at the Rugby World Cup

```{r}
#Use this line if you don't have rio installed
# install.packages("rio")
#Import data
rwcdata <- rio::import("England Rugby.xlsx", sheet = 2)
#Import specific data for chart
regionsandpositions <- rio::import("rugby-world-cup.csv")
colnames(regionsandpositions) <- c("Region","Position","Players","regtotal")
```

## The stacked bar chart

We've adapted some code from [the BBC R Cookbook](https://bbc.github.io/rcookbook/#make_a_stacked_bar_chart).

First we install the packages needed:

```{r}
install.packages('devtools')
devtools::install_github('bbc/bbplot')
```


```{r}
#This line of code installs the pacman page if you do not have it installed - if you do, it simply loads the package
if(!require(pacman))install.packages("pacman")

pacman::p_load('dplyr', 'tidyr', 'gapminder',
               'ggplot2',  'ggalt',
               'forcats', 'R.utils', 'png', 
               'grid', 'ggpubr', 'scales',
               'bbplot')
```

This section of code we don't use because it generates the data in the right format - we've already done that.

```{r}
'
#prepare data
stacked_df <- gapminder %>% 
  filter(year == 2007) %>%
  mutate(lifeExpGrouped = cut(lifeExp, 
                    breaks = c(0, 50, 65, 80, 90),
                    labels = c("Under 50", "50-65", "65-80", "80+"))) %>%
  group_by(continent, lifeExpGrouped) %>%
  summarise(continentPop = sum(as.numeric(pop)))

#set order of stacks by changing factor levels
stacked_df$lifeExpGrouped = factor(stacked_df$lifeExpGrouped, levels = rev(levels(stacked_df$lifeExpGrouped)))
'
```

Here we generate the stacked bar chart

```{r}
#create plot - specify data frame and x and y axes
stackedbarforrob <- ggplot(data = regionsandpositions, 
            aes(x = reorder(Region,regtotal), y = Players))+
  #Specify fill colour is based on position
  geom_col(aes(fill = Position), width = 0.7) +
  #Add the BBC styling
  bbc_style() +
  #Add colour scheme
  scale_fill_viridis_d(direction = -1) +
  #Add a black line at the bottom of the bars
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  #Add the legend (subtitle can be filled too if you want)
  labs(title = "Where England's rugby players come from",
       subtitle = "") +
  #Position the legend
  theme(legend.position = "top", 
        legend.justification = "left") +
  guides(fill = guide_legend(reverse = TRUE)) 
#Flip from vertical to horizontal bars
stackedbarforrob <- stackedbarforrob + coord_flip()
#Show the results
stackedbarforrob
#Save because we can
ggsave("stackedbarforrob.png", width = 10, height = 7, units = "in")
```

## A sankey diagram

```{r}
#Code adapted from https://www.r-graph-gallery.com/321-introduction-to-interactive-sankey-diagram-2.html
# Library
if(!require(networkD3)){
  install.packages("networkD3")
}
library(networkD3)
library(dplyr)
 
# A connection data frame is a list of flows with intensity for each flow
links <- regionsandpositions
colnames(links) <- c("source","target","value")

# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodes <- data.frame(
  name=c(as.character(links$source), 
  as.character(links$target)) %>% unique()
)
 
# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
links$IDsource <- match(links$source, nodes$name)-1 
links$IDtarget <- match(links$target, nodes$name)-1
 
# Make the Network
p <- sankeyNetwork(Links = links, Nodes = nodes,
              Source = "IDsource", Target = "IDtarget",
              Value = "value", NodeID = "name", 
              sinksRight=FALSE)
p


```