records-batting.Rmd

---
title: "Batting Records"
description: |
  A collection of various records throughout our history! 
author:
  - name: James Day
date: "`r Sys.Date()`"
output: distill::distill_article
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
	echo = FALSE,
	message = FALSE,
	warning = FALSE
)
library(dplyr)
library(readr)
library(purrr)
library(stringr)
library(here)
library(reactable)
library(htmltools)
library(knitr)
library(rmarkdown)
```

```{r load data, include=FALSE, echo = FALSE}
batting <- read_csv(here::here("data", "batting_combined.csv"))
```
```{r}
df <- batting %>%
  filter(team == "The Nutty Cuckoo Super Kings") %>%
  mutate(not_out = Dismissal == "Not Out",
         Score = ifelse(not_out, paste0(Runs, "*"), Runs),
         Opposition = ifelse(home_team == "The Nutty Cuckoo Super Kings", away_team, home_team),
         Fifty = Runs >= 50,
         `50s` = ifelse(Runs >= 50, 1, 0),
         `30s` = ifelse(Runs >= 30 & Runs < 50, 1, 0),
         `0s` = ifelse(Runs == 0 & Dismissal != "Not Out", 1, 0),
         runs_made = Runs,
         date = format(date, "%d/%m/%Y"))

```
# Best Batting

```{r}
df %>%
  arrange(desc(Runs + (not_out/2))) %>%
  filter(row_number(desc(Runs)) < 6) %>%
  select(date, season, Batsmen, Score, Balls, SR, Opposition)  %>%
  kable(digits = 1)
  

```
# Runs
### Most Runs (all time)

```{r}
df %>%
  group_by(Batsmen) %>%
  summarise(Runs = sum(runs_made, na.rm = TRUE),
            not_outs = sum(Dismissal == "Not Out", na.rm = TRUE),
            Innings = n(),
            Avg = Runs/(Innings-not_outs)) %>%
  arrange(desc(Runs), Innings) %>%
  select(Batsmen, Innings, Runs, Avg) %>%
  filter(row_number(desc(Runs)) < 6) %>%
  kable(digits = 1)



```

### Most Runs (season)
```{r}
df %>%
  group_by(Batsmen, season) %>%
  summarise(Runs = sum(runs_made, na.rm = TRUE),
            not_outs = sum(Dismissal == "Not Out", na.rm = TRUE),
            Innings = n(),
            Avg = Runs/(Innings-not_outs)) %>%
  arrange(desc(Runs), Innings) %>%
  select(season, Batsmen, Innings, Runs, Avg) %>%
  ungroup() %>%
  filter(row_number(desc(Runs)) < 6) %>%
  kable(digits = 1)

```


### Highest Average (all time, min 5 innings)

```{r}
df %>%
  group_by(Batsmen) %>%
  summarise(Runs = sum(runs_made, na.rm = TRUE),
            not_outs = sum(Dismissal == "Not Out", na.rm = TRUE),
            Innings = n(),
            Avg = Runs/(Innings-not_outs)) %>%
  arrange(desc(Avg), Innings) %>%
  select(Batsmen, Innings, Runs, Avg) %>%
  ungroup() %>%
  filter(Innings > 5) %>%
  filter(row_number(desc(Avg)) < 6) %>%
  kable(digits = 1)

```


### Highest Average (season, min 3 innings)
```{r}
df %>%
  group_by(Batsmen, season) %>%
  summarise(Runs = sum(runs_made, na.rm = TRUE),
            not_outs = sum(Dismissal == "Not Out", na.rm = TRUE),
            Innings = n(),
            Avg = Runs/(Innings-not_outs)) %>%
  arrange(desc(Avg), Innings) %>%
  select(season, Batsmen, Innings, Runs, Avg) %>%
  ungroup() %>%
  filter(Innings >= 3) %>%
  filter(Avg != "Inf") %>%
  filter(row_number(desc(Avg)) < 6) %>%
  kable(digits = 1)

```


# Strike Rate
### Highest SR (all time, min 10 innings)

```{r}
df %>%
  group_by(Batsmen) %>%
  summarise(SR = sum(Runs, na.rm = TRUE)/sum(Balls, na.rm = TRUE) * 100,
            Runs = sum(Runs, na.rm = TRUE),
            Innings = n()) %>%
  filter(Innings > 9) %>%
  arrange(desc(SR), desc(Runs)) %>%
  select(Batsmen, Innings, Runs, SR) %>%
  filter(row_number(desc(SR)) < 6) %>%
  kable(digits = 1)

```

### Highest SR (season, min 3 innings)
```{r}
df %>%
  group_by(season, Batsmen) %>%
  summarise(SR = sum(Runs, na.rm = TRUE)/sum(Balls, na.rm = TRUE) * 100,
            Runs = sum(Runs, na.rm = TRUE),
            Innings = n()) %>%
  filter(Innings > 2) %>%
  arrange(desc(SR), desc(Runs)) %>%
  select(season, Batsmen, Innings, Runs, SR) %>%
  ungroup() %>%
  filter(row_number(desc(SR)) < 6) %>%
  kable(digits = 1)

```


### Highest SR (innings, min 10 balls)
```{r}
df %>%
  filter(Balls > 9) %>%
  arrange(desc(SR), desc(Runs)) %>%
  select(date, season, Batsmen, Runs, Balls, SR, Opposition) %>%
  filter(row_number(desc(SR)) < 6) %>%
  kable(digits = 1)

```


### Fastest Fifty

```{r}
df %>%
  filter(Runs > 49) %>%
  arrange(Balls, desc(SR)) %>%
  select(date, season, Batsmen, Runs, Balls, SR, Opposition) %>%
  filter(row_number(desc(SR)) < 6) %>%
  kable(digits = 1)

```

# Boundaries
### Most Sixes (career)
```{r}
df %>%
  group_by(Batsmen) %>%
  summarise(Sixes = sum(`6s`, na.rm = TRUE),
            Innings = n()) %>%
  arrange(desc(Sixes), Innings) %>%
  select(Batsmen, Innings, Sixes) %>%
  filter(row_number(desc(Sixes)) < 6) %>%
  kable(digits = 1)

```



### Most Sixes (season)

```{r}
df %>%
  group_by(season, Batsmen) %>%
  summarise(Sixes = sum(`6s`, na.rm = TRUE),
            Innings = n()) %>%
  arrange(desc(Sixes), Innings) %>%
  select(season, Batsmen, Innings, Sixes) %>%
  ungroup() %>%
  filter(row_number(desc(Sixes)) < 6) %>%
  kable(digits = 1)

```

### Most Sixes (innings)
```{r}
df %>%
  mutate(Sixes = `6s`) %>%
  arrange(desc(Sixes), Balls) %>%
  select(date, season, Batsmen, Runs, Balls, Sixes, Opposition) %>%
  filter(row_number(desc(Sixes)) < 6) %>%
  kable(digits = 1)

```

### Most Fours (career)
```{r}
df %>%
  group_by(Batsmen) %>%
  summarise(Fours = sum(`4s`, na.rm = TRUE),
            Innings = n()) %>%
  arrange(desc(Fours), Innings) %>%
  select(Batsmen, Innings, Fours) %>%
  filter(row_number(desc(Fours)) < 6) %>%
  kable(digits = 1)

```



### Most Fours (season)

```{r}
df %>%
  group_by(season, Batsmen) %>%
  summarise(Fours = sum(`4s`, na.rm = TRUE),
            Innings = n()) %>%
  arrange(desc(Fours), Innings) %>%
  select(season, Batsmen, Innings, Fours) %>%
  ungroup() %>%
  filter(row_number(desc(Fours)) < 6) %>%
  kable(digits = 1)

```


### Most Fours (innings)
```{r}
df %>%
  mutate(Fours = `4s`) %>%
  arrange(desc(Fours), Balls) %>%
  select(date, season, Batsmen, Runs, Balls, Fours, Opposition) %>%
  filter(row_number(desc(Fours)) < 6) %>%
  kable(digits = 1)

```

# Milestones
### Most Fifties (career)
```{r}
df %>%
  group_by(Batsmen) %>%
  summarise(Fifties = sum(Fifty, na.rm = TRUE),
            Innings = n()) %>%
  arrange(desc(Fifties), Innings) %>%
  select(Batsmen, Innings, Fifties) %>%
  filter(row_number(desc(Fifties)) < 6) %>%
  kable(digits = 1)

```


### Most Fifties (season)
```{r}
df %>%
  group_by(season, Batsmen) %>%
  summarise(Fifties = sum(Fifty, na.rm = TRUE),
            Innings = n()) %>%
  arrange(desc(Fifties), Innings) %>%
  select(season, Batsmen, Innings, Fifties) %>%
  ungroup %>%
  filter(row_number(desc(Fifties)) < 6) %>%
  kable(digits = 1)
```
### Most Not Outs (career)
```{r}
df %>%
  group_by(Batsmen) %>%
  summarise(NO = sum(not_out, na.rm = TRUE),
            Innings = n()) %>%
  arrange(desc(NO), Innings) %>%
  select(Batsmen, Innings, NO) %>%
  filter(row_number(desc(NO)) < 6) %>%
  kable(digits = 1)

```


### Most Not Outs (season)
```{r}
df %>%
  group_by(season, Batsmen) %>%
  summarise(NO = sum(not_out, na.rm = TRUE),
            Innings = n()) %>%
  arrange(desc(NO), Innings) %>%
  select(season, Batsmen, Innings, NO) %>%
  ungroup %>%
  filter(row_number(desc(NO)) < 6) %>%
  kable(digits = 1)
```