---
title: "Premier League 2018-2019"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    social: [ "twitter", "facebook", "menu"]
    source_code: embed
    theme: readable
---

```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(tidyverse)
library(DT)
library(rpivotTable)
library(ggplot2)
library(ggthemes)
library(dplyr)
library(openintro)
library(highcharter)
library(scales)
library(janitor)
```


```{r}
epl <- read_csv("C:\\Users\\ald04\\Desktop\\RR\\Datascience\\Football\\epl_1819.csv",n_max = 20)
epl_transfer <- read_csv("C:\\Users\\ald04\\Desktop\\RR\\Datascience\\Football\\transfer.csv",n_max = 20)
# Cleaning ----------------------------------------------------------------
epl_transfer$end_2016 <- as.numeric(gsub(",","",epl_transfer$end_2016))
epl_transfer$end_2015 <- as.numeric(gsub(",","",epl_transfer$end_2015))
epl_transfer$end_2014 <- as.numeric(gsub(",","",epl_transfer$end_2014))
epl_transfer$end_2012 <- as.numeric(gsub(",","",epl_transfer$end_2012))
epl_transfer$end_2011 <- as.numeric(gsub(",","",epl_transfer$end_2011))
# Formatting --------------------------------------------------------------
epl_mod <- gather(epl_transfer,"period","amount",2:11)
Liv<- epl %>% 
  filter(Team == "Liverpool")
epl <- clean_names(epl)

```

```{r}
mycolors <- c("blue", "#FFC125", "darkgreen", "darkorange")
```

Home Page
=====================================

Row
-------------------------------------

### Premier League  2018 2019

```{r}
valueBox(paste("Season Review"),
        color = "#fbf2e9")
```


### **League Champions**

```{r}
valueBox(paste("Manchester City"),
         icon = "fa-crown",
         color = "Light Gray")

```


### **Goals Scored**

```{r}
valueBox(sum(epl$attack_scored),
         icon = "fa-bullseye",
         color = "Light Gray")
```

### **Transfer Fees**

```{r}
transfer_value <-epl_mod %>% 
  filter(period == "end_2019") %>% 
  tally(abs(round(amount))/1000)
valueBox(paste(round(transfer_value,2),"Billion"),
          color = "Light Gray",
          icon = 'fa-coins')
```

### **Highest Transfers Fees (Chelsea)**

```{r}

HighSpend <- epl_mod %>% 
  filter(period == "end_2019" & team == "Chelsea") %>% 
  select(amount) %>% 
  mutate(amount = amount * -1)

valueBox(paste(HighSpend$amount,"Million"),
         icon = 'fa-piggy-bank',
          color = "Light Gray")
```


Row
-------------------------------

###  **Total TV Revenue  Payout**

```{r}
valueBox(paste(round(sum(epl$finance_tv_revenue)/1000000000,2), "Billion"),
        color = "Light Gray",
         icon = "fa-tv")

```

### **Golden Boot Winners**

```{r}
valueBox(paste("M. Salah,  
               S. Mane, 
               P. Aubameyang"),
         color = "Light Gray",
         icon = "fa-medal")
```

### **FPA Player of the Season**

```{r}
valueBox(paste("Virgil Van Dyke"),
         color = "Light Gray",
         icon = "fa-trophy")
```

Row
------------------------------------
### **Top Scoring Teams**

```{r}
epl %>% 
  group_by(team) %>% 
  tally(attack_scored) %>% 
  top_n(5) %>%
  arrange(n) %>% 
  ggplot(aes(reorder(team,n),n))+
  geom_col(fill = "#FAAB18", show.legend = FALSE) +
  geom_text(aes(label = n), col = "white", size = 10,nudge_y = -6) +
  coord_flip() +
  theme_fivethirtyeight (base_size = 15) +
  labs(title ="Highest Scoring Teams",
       x = "",
       y = "")
```

### **Sky Sports & BT Sports Payout**

```{r}
epl %>% 
  group_by(team) %>% 
  tally(finance_tv_revenue) %>% 
  top_n(5) %>%
  arrange(n) %>% 
  ggplot(aes(reorder(team,n),n))+
  geom_col(fill = "#FAAB18", show.legend = FALSE) +
  #geom_text(aes(label = round(n/1000000,1)), col = "black", size = 10) +
  coord_flip() +
  theme_fivethirtyeight (base_size = 15) +
  labs(title ="Top TV Revenue Earners(£)",
       x = "",
       y = "")+
  scale_y_continuous(labels = comma)
```

Net Spend
========================================

### Net Spend

```{r}
 NetSpendBar<- epl_mod %>%  
  ggplot(aes(period,amount,fill = amount))+
  geom_col()+
scale_x_discrete(expand = c(0,0),labels = c("2010","2011","2012","2013","2014",
                                            "2015","2016","2017","2018","2019"))+
scale_fill_continuous(high = "green",low = "red")+
  labs(title = "Club Transfer Spend 2010 - 2019",
       x = "End of Season",
       y = "Amount in Millions (£)") +
  geom_hline(yintercept=0, size=1, col = "grey")+
  coord_flip()+
   theme(legend.position="none",
   panel.background = element_rect(fill = '#4682B4'),
   plot.background=element_rect(fill="#4682B4"),    ##4B6A94
   panel.grid.minor = element_blank(),
   #panel.grid.minor = element_line(colour = "white"),
   panel.grid.major = element_line(colour = "white"),
   panel.grid.major.y = element_blank(),
   #panel.grid.major.x = element_blank(),
   #panel.grid.minor = element_blank(),
   plot.title=element_text(size=15, 
   face="bold",colour = "white",hjust=0.5,lineheight=1.2),  # title
  plot.subtitle=element_text(size=13, colour = "black",face="bold"),  # subtitle
  plot.caption=element_text(size=13,colour = "white"),  # caption
  axis.title.x=element_text(size=13,colour = "white", face = "bold",),  # X axis title
  axis.title.y=element_text(size=13,colour = "white", face = "bold"),  # Y axis title
  axis.text.x=element_text(size=10,colour = "white", face = "bold"),  # X axis text
  axis.text.y=element_text(size=10,colour="white",hjust=1, face="bold"),
  axis.line.x = element_line(colour = "#FFFFFF", size=1, lineend = "butt"),
  axis.line.y = element_line(colour = "#FFFFFF", size=1),
  axis.ticks.x = element_line(colour = "#FFFFFF"),
  axis.ticks.y = element_line(colour = "#FFFFFF"))+
  facet_wrap(.~team)
NetSpendBar
```

Data Table
========================================

```{r}
datatable(epl,
          caption = "Premier League 2018 2019",
          rownames = T,
          filter = "top",
          options = list(pageLength = 25))
```