---
title: "ICC T20 World Cup 2022 Team of the Tournament"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    theme:
      version: 4
      bg: "#FDF7F7"
      fg: "#101010" 
      primary: "#320073"
      navbar-bg: "#ff00a5"
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(dplyr)
library(tidyverse)
library(htmltools)
library(kableExtra)
library(knitr)
library(scatterplot3d)
```

Team Summary
==============================

## ICC T20 World Cup 2022 Team of the Tournament

```{r}
#getwd()
# Read player data
player_data <- read.csv("t20_csv_files/final_player_data.csv")
playing11 <- read.csv("t20_csv_files/playing11.csv")

# Function to generate HTML code for displaying images
image_html <- function(url) {
  img_tag <- sprintf('<img src="%s" style="width:100px;height:100px;">', url)
  htmltools::HTML(img_tag)
}

# Create a new column with image HTML code
playing11$image <- lapply(playing11$image, image_html)

playing11 <- playing11[,c("image", "name", "role", "team", "description")]

# Display table with images
table_with_images <- knitr::kable(playing11, "html", escape = FALSE) %>%
  kableExtra::kable_styling(full_width = FALSE)

# Create HTML widget to render the table
table_widget <- htmltools::HTML(table_with_images)

# Output the table widget
table_widget
```

Selection Criteria:

Openers (2): 

* Batting average > 30

* Batting strike rate > 140

* Innings batted > 3

* Boundary % > 50

* Batting position < 3
 

Anchors (3):

* Batting average > 40

* Batting strike rate > 125

* Innings Batted > 3

* Average balls faced > 20

* Batting position > 2


All rounders (2):

* Batting average > 15

* Batting strike rate > 140

* Innings batted > 2

* Batting position > 4

* Innings bowled > 2

* Economy < 7

* Bowling strike rate < 20


Fast bowlers (3)*:

* Innings bowled > 4

* Economy < 7

* Bowling strike rate < 16

* Bowling style = "fast"

* Bowling average < 20

* Dot ball % > 40


Legspinner (1):

 * Innings bowled > 4

 * Economy < 7

 * Bowling strike rate < 16

 * Bowling style = "legbreak"

 * Bowling average < 20

  
Note: Sam Curran was initially picked as a fast bowler, but since he is an all-rounder, he is classified as an all-rounder in the super team.

Openers {data-orientation=columns}
==============================

Column
---------------------------------

### List of potential openers (scroll)

```{r}
openers <- player_data %>% 
  filter(battingAvg > 30, battingSR > 140, innsBatted > 3, boundaryPer > 50, battingPos < 3) %>% 
  arrange(desc(battingAvg)) %>% 
  select(image, name, team, totalRuns, battingAvg, battingSR, innsBatted, boundaryPer, battingPos, avgBallsFaced)

# Create a new column with image HTML code
openers$image <- lapply(openers$image, image_html)
table_with_images <- knitr::kable(openers, "html", escape = FALSE) %>%
  kableExtra::kable_styling(full_width = FALSE) %>% 
  kableExtra::row_spec(1:2, background = "lightgreen")
table_widget <- htmltools::HTML(table_with_images)
table_widget
```

Column
---------------------------------

### 3d scatterplot comparing batting average, batting strike rate, and boundary percentage
```{r}

s3d <- scatterplot3d(openers$battingAvg, openers$battingSR, openers$boundaryPer,
                 color="blue", pch=19,        # filled blue circles
                 main="Batting Avg vs Batting SR vs Boundary %",
                 xlab = "Batting Average", ylab = "Batting Strike Rate", zlab = "Boundary Percentage")
    s3d.coords <- s3d$xyz.convert(openers$battingAvg, openers$battingSR, openers$boundaryPer) # convert 3D coords to 2D projection
    text(s3d.coords$x, s3d.coords$y,             # x and y coordinates
         labels=openers$name,               # text to plot
         cex=1, pos=4)  
```

### Selection Criteria:

Sorted by batting average

* Batting average > 30

* Batting strike rate > 140

* Innings batted > 3

* Boundary % > 50

* Batting position < 3

Anchors{data-orientation=columns}
==============================

Column
---------------------------------

### List of potential anchors (scroll)

```{r}
anchors <- player_data %>% 
  filter(battingAvg > 40, battingSR > 125, innsBatted > 3, avgBallsFaced > 20, battingPos > 2) %>% 
  arrange(desc(battingAvg)) %>% 
  select(image, name, team, totalRuns, battingAvg, battingSR, innsBatted, boundaryPer, battingPos, avgBallsFaced)
# Create a new column with image HTML code
anchors$image <- lapply(anchors$image, image_html)
table_with_images <- knitr::kable(anchors, "html", escape = FALSE) %>%
  kableExtra::kable_styling(full_width = FALSE) %>% 
  kableExtra::row_spec(1:3, background = "lightgreen")
table_widget <- htmltools::HTML(table_with_images)
table_widget
```

Column
---------------------------------

### 3d scatterplot comparing batting average, batting strike rate, and boundary percentage
```{r}

s3d <- scatterplot3d(anchors$battingAvg, anchors$battingSR, anchors$boundaryPer,
                 color="blue", pch=19,        # filled blue circles
                 main="Batting Avg vs Batting SR vs Boundary %",
                 xlab = "Batting Average", ylab = "Batting Strike Rate", zlab = "Boundary Percentage")
    s3d.coords <- s3d$xyz.convert(anchors$battingAvg, anchors$battingSR, anchors$boundaryPer) # convert 3D coords to 2D projection
    text(s3d.coords$x, s3d.coords$y,             # x and y coordinates
         labels=anchors$name,               # text to plot
         cex=1, pos=4)  
```

### Selection Criteria:

Sorted by batting average

* Batting average > 40

* Batting strike rate > 125

* Innings Batted > 3

* Average balls faced > 20

* Batting position > 2

All Rounders {data-orientation=columns}
==============================

Column
---------------------------------

### List of potential all rounders (scroll)
```{r}
all_rounders <- player_data %>% 
  filter(battingAvg > 15, battingSR > 140, innsBatted > 2, battingPos > 4, economy < 7, bowlingSR < 20) %>% 
  arrange(desc(battingSR)) %>% 
  select(image, name, team, totalRuns, battingAvg, battingSR, innsBatted, boundaryPer, battingPos, bowlingStyle, totalWickets, economy, bowlingAvg, bowlingSR, innsBowled)
# Create a new column with image HTML code
all_rounders$image <- lapply(all_rounders$image, image_html)
table_with_images <- knitr::kable(all_rounders, "html", escape = FALSE) %>%
  kableExtra::kable_styling(full_width = FALSE) %>% 
  kableExtra::row_spec(1:2, background = "lightgreen")
table_widget <- htmltools::HTML(table_with_images)
table_widget
```

Column
---------------------------------

### 3d scatterplot comparing batting average, batting strike rate, and boundary percentage (open image in a new tab for a better view) 
```{r}

s3d <- scatterplot3d(all_rounders$battingAvg, all_rounders$battingSR, all_rounders$boundaryPer,
                 color="blue", pch=19,        # filled blue circles
                 main="Batting Avg vs Batting SR vs Boundary %",
                 xlab = "Batting Average", ylab = "Batting Strike Rate", zlab = "Boundary Percentage")
    s3d.coords <- s3d$xyz.convert(all_rounders$battingAvg, all_rounders$battingSR, all_rounders$boundaryPer) # convert 3D coords to 2D projection
    text(s3d.coords$x, s3d.coords$y,             # x and y coordinates
         labels=all_rounders$name,               # text to plot
         cex=1, pos=4)  
```

### 3d scatterplot comparing economy, bowling average, and bowling strike rate (open image in a new tab for a better view)
```{r}
options(repr.plot.width = 8, repr.plot.height = 6)
s3d <- scatterplot3d(all_rounders$economy, all_rounders$bowlingAvg, all_rounders$bowlingSR,
                 color="blue", pch=19,        # filled blue circles
                 main="Economy vs Bowling Average vs Bowling SR",
                 xlab = "Economy", ylab = "Bowling Average", zlab = "Bowling Strike Rate")
    s3d.coords <- s3d$xyz.convert(all_rounders$economy, all_rounders$bowlingAvg, all_rounders$bowlingSR) # convert 3D coords to 2D projection
    text(s3d.coords$x, s3d.coords$y,             # x and y coordinates
         labels=all_rounders$name,               # text to plot
         cex=1, pos=4)  
```

### Selection Criteria:

Sorted by batting strike rate

* Batting average > 15

* Batting strike rate > 140

* Innings batted > 2

* Batting position > 4

* Innings bowled > 2

* Economy < 7

* Bowling strike rate < 20

Fast Bowlers {data-orientation=columns}
==============================

Column
---------------------------------
### List of potential fast bowlers
```{r}
fast_bowlers <- player_data %>% 
  filter(innsBowled > 4, economy < 7, bowlingSR < 16, grepl("fast", bowlingStyle, fixed = T), bowlingAvg < 20, dotBallPer > 40) %>% 
  arrange(bowlingAvg) %>% 
  select(image, name, team, totalWickets, economy, bowlingAvg, bowlingSR, innsBowled, runsConceded, ballsBowled, dotBallPer)
# Create a new column with image HTML code
fast_bowlers$image <- lapply(fast_bowlers$image, image_html)
table_with_images <- knitr::kable(fast_bowlers, "html", escape = FALSE) %>%
  kableExtra::kable_styling(full_width = FALSE) %>% 
  kableExtra::row_spec(1:3, background = "lightgreen")
table_widget <- htmltools::HTML(table_with_images)
table_widget
```

Column
---------------------------------

### 3d scatterplot comparing economy, bowling average, and bowling strike rate
```{r}
options(repr.plot.width = 8, repr.plot.height = 6)
s3d <- scatterplot3d(fast_bowlers$economy, fast_bowlers$bowlingAvg, fast_bowlers$bowlingSR,
                 color="blue", pch=19,        # filled blue circles
                 main="Economy vs Bowling Average vs Bowling SR",
                 xlab = "Economy", ylab = "Bowling Average", zlab = "Bowling Strike Rate")
    s3d.coords <- s3d$xyz.convert(fast_bowlers$economy, fast_bowlers$bowlingAvg, fast_bowlers$bowlingSR) # convert 3D coords to 2D projection
    text(s3d.coords$x, s3d.coords$y,             # x and y coordinates
         labels=fast_bowlers$name,               # text to plot
         cex=1, pos=4)  
```

### Selection Criteria:

Sorted by bowling average

* Innings bowled > 4

* Economy < 7

* Bowling strike rate < 16

* Bowling style = "fast"

* Bowling average < 20

* Dot ball % > 40

Legspinners {data-orientation=columns}
==============================

Column
---------------------------------
### List of potential legspinners
```{r}
mystery_spinner <- player_data %>% 
  filter(innsBowled > 4, economy < 7, bowlingSR < 16, grepl("legbreak", bowlingStyle, fixed = T), bowlingAvg < 20) %>% 
  arrange(bowlingAvg) %>% 
  select(image, name, team, totalWickets, economy, bowlingAvg, bowlingSR, innsBowled, runsConceded, ballsBowled, dotBallPer)
# Create a new column with image HTML code
mystery_spinner$image <- lapply(mystery_spinner$image, image_html)
table_with_images <- knitr::kable(mystery_spinner, "html", escape = FALSE) %>%
  kableExtra::kable_styling(full_width = FALSE) %>% 
  kableExtra::row_spec(1:1, background = "lightgreen")
table_widget <- htmltools::HTML(table_with_images)
table_widget
```

Column
---------------------------------

```{r}
options(repr.plot.width = 8, repr.plot.height = 6)
s3d <- scatterplot3d(mystery_spinner$economy, mystery_spinner$bowlingAvg, mystery_spinner$bowlingSR,
                 color="blue", pch=19,        # filled blue circles
                 main="Economy vs Bowling Average vs Bowling SR",
                 xlab = "Economy", ylab = "Bowling Average", zlab = "Bowling Strike Rate")
    s3d.coords <- s3d$xyz.convert(mystery_spinner$economy, mystery_spinner$bowlingAvg, mystery_spinner$bowlingSR) # convert 3D coords to 2D projection
    text(s3d.coords$x, s3d.coords$y,             # x and y coordinates
         labels=mystery_spinner$name,               # text to plot
         cex=1, pos=4)  
```


### Selection Criteria:

Sorted by bowling average

 * Innings bowled > 4

 * Economy < 7

 * Bowling strike rate < 16

 * Bowling style = "legbreak"

 * Bowling average < 20