### Install cricketdata from GitHub

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

Installs the development version of the cricketdata package directly from GitHub. Requires the devtools package to be installed first.

```r
install.packages("devtools")
devtools::install_github("robjhyndman/cricketdata")
```

--------------------------------

### Example: Download Meta Data for Multiple Players

Source: https://pkg.robjhyndman.com/cricketdata/reference/fetch_player_meta.html

This example demonstrates how to download meta data for multiple players by passing a vector of their Cricinfo player IDs to the `fetch_player_meta` function. The results are stored in the `aus_women` tibble.

```R
if (FALSE) { # \dontrun{
# Download meta data on Meg Lanning and Ellyse Perry
aus_women <- fetch_player_meta(c(329336, 275487))
} # }
```

--------------------------------

### Install cricketdata from CRAN

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

Installs the stable version of the cricketdata package from CRAN. Ensure dependencies are installed for full functionality.

```r
install.packages("cricketdata", dependencies = TRUE)
```

--------------------------------

### Install Development Version of cricketdata

Source: https://pkg.robjhyndman.com/cricketdata/index.html

Install the development version of the cricketdata package from Github using the pak package manager.

```r
pak::pak("robjhyndman/cricketdata")
```

--------------------------------

### Example: Fetching WBBL Data

Source: https://pkg.robjhyndman.com/cricketdata/reference/fetch_cricsheet.html

Demonstrates how to fetch ball-by-ball, match, and player data for the WBBL competition. This code is for demonstration and will not run unless the condition is met.

```R
if (FALSE) { # \dontrun{
wbbl_bbb <- fetch_cricsheet(competition = "wbbl", type = "bbb")
wbbl_match <- fetch_cricsheet(competition = "wbbl", type = "match")
wbbl_player <- fetch_cricsheet(competition = "wbbl", type = "player")
} # }
```

--------------------------------

### Load cricketdata, dplyr, and ggplot2 Libraries

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricinfo.html

Loads the necessary R libraries for data manipulation and visualization. Ensure these packages are installed before running.

```r
library(cricketdata)
library(dplyr)
library(ggplot2)
```

--------------------------------

### Examples of Fetching Cricinfo Data

Source: https://pkg.robjhyndman.com/cricketdata/reference/fetch_cricinfo.html

Demonstrates how to use the `fetch_cricinfo` function to retrieve specific datasets, such as women's T20 data for Australia and men's ODI bowling data for India.

```R
if (FALSE) { # \dontrun{
auswt20 <- fetch_cricinfo("T20", "Women", country = "Aust")
IndiaODIBowling <- fetch_cricinfo("ODI", "men", "bowling", country = "india")
} # }
```

--------------------------------

### Install Stable Version of cricketdata

Source: https://pkg.robjhyndman.com/cricketdata/index.html

Install the stable version of the cricketdata package from CRAN using the pak package manager.

```r
# install.packages("pak")
pak::pak("cricketdata")
```

--------------------------------

### Load cricketdata and Libraries

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricsheet.html

Loads necessary R libraries for data manipulation, visualization, and fetching Cricsheet data. Ensure these packages are installed before running.

```r
library(cricketdata)
library(readr)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(stringr)
library(showtext)
#> Loading required package: sysfonts
#> Loading required package: showtextdb
library(ggplot2)
library(gghighlight)
library(ggtext)
library(patchwork)
```

--------------------------------

### Fetch Cricsheet Data

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricsheet.html

Fetches data from Cricsheet by specifying the type (ball-by-ball, match, player), gender, and competition. Example shown for WBBL female data.

```r
# Fetch ball-by-ball data
wbbl_bbb <- fetch_cricsheet(competition = "wbbl", gender = "female")

# Fetch match metadata
wbbl_match_info <- fetch_cricsheet(competition = "wbbl", type = "match", gender = "female")
```

--------------------------------

### Find Player ID using Name

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

Use `find_player_id` to get a player's Cricinfo ID from their name. This ID is essential for fetching individual player data.

```R
# Fetching a player, Meg Lanning's, ID
meg_lanning_id <- find_player_id("Meg Lanning")$ID
```

```R
meg_lanning_id
#> [1] 329336
```

--------------------------------

### fetch_player_meta

Source: https://pkg.robjhyndman.com/cricketdata/reference/index.html

Fetches player meta data.

```APIDOC
## fetch_player_meta()

### Description
Fetch Player Meta Data.

### Usage
fetch_player_meta()

### Details
This function retrieves metadata associated with players.
```

--------------------------------

### fetch_player_data

Source: https://pkg.robjhyndman.com/cricketdata/reference/index.html

Fetches player data.

```APIDOC
## fetch_player_data()

### Description
Fetch Player Data.

### Usage
fetch_player_data()

### Details
This function is used to retrieve player-specific data.
```

--------------------------------

### Fetch Player Metadata by ID

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

Fetches meta data for specified player IDs using the fetch_player_meta() function. This is useful for obtaining detailed information on individual players for advanced modeling.

```r
# Download meta data on Meg Lanning and Ellyse Perry
aus_women <- fetch_player_meta(c(329336, 275487))
```

```r
aus_women |>
  select(-name) |>
  knitr::kable(
    digits = 1, align = "c", format = "pipe",
    col.names = c(
      "ID", "FullName", "Country", "DOB", "BattingStyle", "BowlingStyle"
    )
  )
```

--------------------------------

### Download and Plot Player Data

Source: https://pkg.robjhyndman.com/cricketdata/reference/fetch_player_data.html

Demonstrates downloading player data for different match types and activities, and then plotting T20 batting scores using dplyr and ggplot2.

```R
if (FALSE) { # \dontrun{
# Download data on some players
EllysePerry <- fetch_player_data(275487, "T20", "batting")
RahulDravid <- fetch_player_data(28114, "ODI", "fielding")
LasithMalinga <- fetch_player_data(49758, "Test", "bowling")

# Create a plot for Ellyse Perry's T20 scores
library(dplyr)
library(ggplot2)
EllysePerry |>
  filter(!is.na(Runs)) |>
  ggplot(aes(x = Start_Date, y = Runs, col = Dismissal, na.rm = TRUE)) +
  geom_point() +
  ggtitle("Ellyse Perry's T20 Scores")
} # } 
```

--------------------------------

### update_player_meta

Source: https://pkg.robjhyndman.com/cricketdata/reference/index.html

Updates player_meta.

```APIDOC
## update_player_meta()

### Description
Update player_meta.

### Usage
update_player_meta()

### Details
This function is used to update the player_meta data.
```

--------------------------------

### Display Player Metadata

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

Filters and selects specific columns from the player_meta dataset, then formats the output for display using knitr::kable. Use this to view a subset of player information.

```r
player_meta |>
  filter(!is.na(playing_role)) |>
  select(-cricinfo_id, -unique_name, -name) |>
  head() |>
  knitr::kable(
    digits = 1, align = "c", format = "pipe",
    col.names = c(
      "ID", "FullName", "Country", "DOB", "BirthPlace",
      "BattingStyle", "BowlingStyle", "PlayingRole"
    )
  )
```

--------------------------------

### fetch_player_meta()

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

Fetches player meta data such as full name, country of representation, date of birth, bowling and batting hand, bowling style, and playing role. This data is useful for advanced modeling.

```APIDOC
## fetch_player_meta()

### Description
Fetch the player’s meta data such as full name, country of representation, data of birth, bowling and batting hand, bowling style, and playing role. This meta data is useful for advance modeling, e.g, age curves, batter profile against bowling types etc.

### Arguments
* `playerid`: A vector of player IDs as given in Cricinfo profiles. Integer or character.

### Example
```R
# Download meta data on Meg Lanning and Ellyse Perry
aus_women <- fetch_player_meta(c(329336, 275487))
aus_women |>
  select(-name) |>
  knitr::kable(
    digits = 1, align = "c", format = "pipe",
    col.names = c(
      "ID", "FullName", "Country", "DOB", "BattingStyle", "BowlingStyle"
    )
  )
```
```

--------------------------------

### Fetch Player Data Function Signature

Source: https://pkg.robjhyndman.com/cricketdata/reference/fetch_player_data.html

Defines the parameters for fetching player data, including player ID, match type, and activity.

```R
fetch_player_data(
  playerid,
  matchtype = c("test", "odi", "t20"),
  activity = c("batting", "bowling", "fielding")
)
```

--------------------------------

### Update Player Meta Data

Source: https://pkg.robjhyndman.com/cricketdata/reference/update_player_meta.html

Fetches the latest player meta data. Optionally, it can re-download all data from ESPNCricinfo if `start_again` is set to TRUE.

```APIDOC
## update_player_meta

### Description
Updates the player_meta data set with current information available online.

### Usage
```R
update_player_meta(start_again = FALSE)
```

### Arguments
* `start_again` (logical) - If TRUE, downloads all data from ESPNCricinfo without using player_meta as a starting point. This can take a long time.

### Value
A tibble containing meta data on cricket players.

### Examples
```R
# Update data to current
new_player_meta <- update_player_meta()
```
```

--------------------------------

### Fetch Indian Test Fielding Data

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricinfo.html

Retrieves fielding statistics for Indian Men in Test matches. This data can be used to analyze player performance in the field.

```r
Indfielding <- fetch_cricinfo("Test", "Men", "Fielding", country = "India")
```

--------------------------------

### Load Google Fonts for Plotting

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricsheet.html

Imports 'Roboto Condensed' and 'Staatliches' fonts from Google Fonts for use in plots. Ensures automatic display of loaded fonts.

```r
# Import fonts from Google Fonts
font_add_google("Roboto Condensed", "roboto_con")
font_add_google("Staatliches", "staat")
showtext_auto()
```

--------------------------------

### Fetch IPL Ball-by-Ball Data

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

Fetches all ball-by-ball data for the Indian Premier League (IPL). The fetched data can be inspected using glimpse().

```R
ipl_bbb <- fetch_cricsheet("bbb", "male", "ipl")

ipl_bbb |>
  glimpse()
```

--------------------------------

### Fetch Meg Lanning's ODI Batting Data

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricinfo.html

Fetches ODI batting data for a specific player, Meg Lanning, by first finding her player ID. It also adds a 'NotOut' column.

```r
meg_lanning_id <- find_player_id("Meg Lanning")$ID
MegLanning <- fetch_player_data(meg_lanning_id, "ODI") |>
  mutate(NotOut = (Dismissal == "not out")) |>
  mutate(NotOut = tidyr::replace_na(NotOut, FALSE))
```

--------------------------------

### Display and Summarize Women's T20 Bowling Data

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

Displays a glimpse of the fetched T20 bowling data for women and then creates a formatted table of key career statistics. This is useful for a quick overview of player performance.

```R
# Looking at data
wt20 |>
  glimpse()
#> Rows: 2,753
#> Columns: 16
#> $ Player             <chr> "M Schutt", "Nida Dar", "DB Sharma", "S Ecclestone"…
#> $ Country            <chr> "Australia", "Pakistan", "India", "England", "THA",…
#> $ Start              <int> 2013, 2010, 2016, 2016, 2018, 2008, 2008, 2007, 200…
#> $ End                <int> 2025, 2024, 2024, 2025, 2025, 2025, 2021, 2023, 202…
#> $ Matches            <int> 122, 160, 124, 96, 99, 167, 117, 113, 145, 98, 112,…
#> $ Innings            <int> 121, 152, 121, 95, 98, 137, 113, 112, 122, 93, 111,…
#> $ Overs              <dbl> 413.1, 510.2, 436.5, 355.2, 331.4, 407.5, 395.3, 39…
#> $ Maidens            <int> 10, 13, 12, 10, 23, 8, 6, 21, 6, 21, 17, 30, 4, 13,…
#> $ Runs               <int> 2632, 2910, 2642, 2089, 1363, 2385, 2206, 2291, 227…
#> $ Wickets            <int> 149, 144, 138, 137, 126, 126, 125, 123, 118, 116, 1…
#> $ Average            <dbl> 17.664430, 20.208333, 19.144928, 15.248175, 10.8174…
#> $ Economy            <dbl> 6.370311, 5.702155, 6.048073, 5.878987, 4.109548, 5…
#> $ StrikeRate         <dbl> 16.63758, 21.26389, 18.99275, 15.56204, 15.79365, 1…
#> $ BestBowlingInnings <chr> "5/15", "5/21", "4/10", "4/18", "5/5", "4/12", "5/1…
#> $ FourWickets        <int> 4, 1, 1, 2, 5, 4, 4, 0, 1, 6, 1, 2, 4, 1, 3, 2, 3, …
#> $ FiveWickets        <int> 1, 1, 0, 0, 1, 0, 3, 2, 0, 2, 0, 0, 0, 2, 3, 1, 0, …

# Table showing certain features of the data
wt20 |>
  select(Player, Country, Matches, Runs, Wickets, Economy, StrikeRate) |>
  head() |>
  knitr::kable(
    digits = 2, align = "c",
    caption = "Women Player career profile for international T20"
  )
```

--------------------------------

### fetch_cricsheet

Source: https://pkg.robjhyndman.com/cricketdata/reference/index.html

Fetches ball-by-ball, match, and player data from Cricsheet.

```APIDOC
## fetch_cricsheet()

### Description
Fetch ball-by-ball, match and player data from Cricsheet and return a tibble.

### Usage
fetch_cricsheet()

### Details
This function retrieves various types of data from Cricsheet, including ball-by-ball, match, and player information, and returns it as a tibble.
```

--------------------------------

### Plotting Batting Average per Season with gghighlight

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricsheet.html

Generates a line plot showing average runs per innings for each season, highlighting specific players. Requires ggplot2 and gghighlight libraries. Customizes theme and text elements for presentation.

```r
batting_per_season |>
  ggplot(aes(
    x = season, y = runs_per_innings_avg,
    group = striker, colour = is_healy
  )) +
  geom_line(linewidth = 2, colour = "#F80F61FF") +
  gghighlight(is_healy,
    label_key = striker,
    label_params = aes(
      size = 6, force_pull = 0.1, nudge_y = 10, label.size = 1,
      family = "roboto_con", label.padding = 0.5,
      fill = "#19232FFF",
      colour = "#F80F61FF"
    ),
    unhighlighted_params = list(size = 1, color = "#187999FF")
  ) +
  labs(
    title = "WBBL: Average runs scored per innings (3+ innings)",
    x = NULL, y = NULL,
    caption = "**Source:** Cricsheet.org // **Plot:** @jacquietran"
  ) +
  theme_minimal() +
  theme(
    text = element_text(size = 18, family = "roboto_con", colour = "#FFFFFF"),
    plot.title = element_text(family = "staat", margin = margin(0, 0, 15, 0)),
    plot.caption = element_markdown(size = NULL, margin = margin(15, 0, 0, 0)),
    axis.text = element_text(colour = "#FFFFFF"),
    legend.position = "none",
    panel.grid.major = element_line(linetype = "dashed"),
    panel.grid.minor = element_blank(),
    plot.background = element_rect(
      fill = "#171F2AFF", colour = NA
    ),
    panel.spacing = unit(2, "lines"),
    plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), "cm")
  )
```

--------------------------------

### Access Player Meta Data

Source: https://pkg.robjhyndman.com/cricketdata/reference/player_meta.html

Load and view the player_meta dataset. This dataset contains player names and attributes from ESPNCricinfo.

```r
player_meta
```

--------------------------------

### Subset WBBL07 Match Metadata in R

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricsheet.html

Filters and selects specific columns from the `wbbl_match_info` dataset to create a tidy subset for WBBL07 games played up to a certain date. Ensures `match_id` is a factor for consistent analysis.

```r
# Subset match metadata for WBB07 games
wbbl07_match_info_tidy <- wbbl_match_info |>
  filter(season == "2021/22", date <= "2021/11/07") |>
  select(
    match_id, winner, winner_runs, winner_wickets, method, outcome,
    eliminator
  ) |>
  mutate(match_id = factor(match_id))
```

--------------------------------

### fetch_cricinfo

Source: https://pkg.robjhyndman.com/cricketdata/reference/index.html

Fetches data from Cricinfo.

```APIDOC
## fetch_cricinfo()

### Description
Fetch Data from Cricinfo.

### Usage
fetch_cricinfo()

### Details
This function is used to retrieve data from the Cricinfo website.
```

--------------------------------

### Filter Dismissals by Striker (R)

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricsheet.html

Filters the dismissals_by_ball_number_summary dataset to include only dismissals for a specific striker. Useful for isolating player-specific statistics.

```r
dismissals_by_ball_number_summary_devine <- dismissals_by_ball_number_summary |>
  filter(striker == "SFM Devine")
```

```r
dismissals_by_ball_number_summary_knight <- dismissals_by_ball_number_summary |>
  filter(striker == "HC Knight")
```

--------------------------------

### Fetch Player Meta Data

Source: https://pkg.robjhyndman.com/cricketdata/reference/fetch_player_meta.html

Fetches meta data for specified players using their Cricinfo player IDs.

```APIDOC
## Fetch Player Meta Data

### Description
Fetches player meta data from ESPNCricinfo and returns a tibble with one line per player. To identify the players, use their Cricinfo player IDs. The simplest way to find this is to look up their Cricinfo Profile page. The number at the end of the URL is the ID. For example, Meg Lanning's profile page is https://www.espncricinfo.com/cricketers/meg-lanning-329336, so her ID is 329336.

### Usage
```R
fetch_player_meta(playerid)
```

### Arguments

*   **playerid** (Integer or character vector) - A vector of player IDs as given in Cricinfo profiles.

### Value
A tibble containing meta data on the selected players, with one row for each player.

### Examples

```R
# Download meta data on Meg Lanning and Ellyse Perry
aus_women <- fetch_player_meta(c(329336, 275487))
```
```

--------------------------------

### Generate Table of Top IPL 2022 Batters

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

Generates a formatted table summarizing the top 10 batters in the IPL 2022 season, including their total runs, balls faced, strike rate, dot percentage, and boundary percentage. This provides a concise overview of high-performing players.

```R
ipl_bbb |>
  filter(season == "2022") |>
  group_by(striker) |>
  summarize(
    Runs = sum(runs_off_bat), BallsFaced = n() - sum(!is.na(wides)),
    StrikeRate = Runs / BallsFaced,
    DotPercent = sum(runs_off_bat == 0) * 100 / BallsFaced,
    BoundaryPercent = sum(runs_off_bat %in% c(4, 6)) * 100 / BallsFaced
  ) |>
  arrange(desc(Runs)) |
  rename(Batter = striker) |
  slice(1:10) |>
  knitr::kable(digits = 1, align = "c")
```

--------------------------------

### Fetch Cricsheet Ball-by-Ball Data

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

Download ball-by-ball data from Cricsheet. Specify the data type, gender, and competition. The raw T20 data is further processed to add analytical features.

```R
# Indian Premier League (IPL) Ball-by-Ball Data
# fetch_cricsheet(type = "bbb", gender = "male", competition = "ipl")
```

--------------------------------

### update_player_meta()

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

This function consults the directory of all players available on the cricsheet website and includes the meta data of new players into the `player_meta` data frame. Data for new players is scraped from ESPNCricinfo.

```APIDOC
## update_player_meta()

### Description
This function is supposed to consult the directory of all players available on cricsheet website and include the meta data of new players into the `player_meta` data frame. The data for new players will be scraped from the ESPNCricinfo.
```

--------------------------------

### Fetch Australian Men's ODI Data by Innings

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricinfo.html

Fetches Australian Men's ODI batting data, with each row representing an innings. Specify format and country.

```r
# Fetch all Australian Men's ODI data by innings
menODI <- fetch_cricinfo("ODI", "Men", "Batting", type = "innings", country = "Australia")
```

--------------------------------

### Fetch Player Meta Data

Source: https://pkg.robjhyndman.com/cricketdata/reference/fetch_player_meta.html

Use this function to download meta data for specific players from ESPNCricinfo. Requires player IDs, which can be found in the URL of their Cricinfo profile pages. The function returns a tibble with one row per player.

```R
fetch_player_meta(playerid)
```

--------------------------------

### Load cricketdata and dplyr Libraries

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

Loads the necessary cricketdata and dplyr libraries for data manipulation and analysis. dplyr is commonly used for data wrangling tasks.

```r
library(cricketdata)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(ggplot2)
```

--------------------------------

### Update Player Meta Data

Source: https://pkg.robjhyndman.com/cricketdata/reference/update_player_meta.html

Call this function to update the player meta data to the most current version available online. The function returns a tibble with player meta information.

```R
new_player_meta <- update_player_meta()
```

--------------------------------

### Fetch USA Men's ODI Batting Data by Innings

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

Fetches innings-by-innings batting data for male players representing the USA in One Day Internationals. This is useful for detailed analysis of individual performances in specific matches.

```R
# Fetch all USA Men's ODI data by innings
menODI <- fetch_cricinfo("ODI", "Men", "Batting",
  type = "innings",
  country = "United States of America"
)
```

--------------------------------

### Build Cricket Dismissals Plot (R)

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricsheet.html

Generates a ggplot2 boxplot showing the percentage of dismissals by ball number for a specific player. Uses geom_boxplot for distribution and geom_point for individual player data.

```r
showtext_auto()

# Healy
p1 <- dismissals_by_ball_number_summary |>
  ggplot(aes(x = ball_num_in_over, y = dismissals_pct)) +
  geom_boxplot(
    fill = "#FFFFFF", colour = "#FFFFFF", size = .5,
    alpha = 0.25, notch = TRUE, outlier.shape = NA, coef = 0
  ) +
  geom_point(
    data = dismissals_by_ball_number_summary_healy,
    colour = "#F80F61FF", size = 3
  ) +
  labs(
    title = "WBBL: % dismissals by ball number",
    subtitle = "AJ Healy"
  ) +
  plot_features
```

```r
# Mooney
p2 <- dismissals_by_ball_number_summary |>
  ggplot(aes(x = ball_num_in_over, y = dismissals_pct)) +
  geom_boxplot(
    fill = "#FFFFFF", colour = "#FFFFFF", size = .5,
    alpha = 0.25, notch = TRUE, outlier.shape = NA, coef = 0
  ) +
  geom_point(
    data = dismissals_by_ball_number_summary_mooney,
    colour = "#FA6900FF", size = 3
  ) +
  labs(subtitle = "BL Mooney") +
  plot_features
```

```r
# Lanning
p3 <- dismissals_by_ball_number_summary |>
  ggplot(aes(x = ball_num_in_over, y = dismissals_pct)) +
  geom_boxplot(
    fill = "#FFFFFF", colour = "#FFFFFF", size = .5,
    alpha = 0.25, notch = TRUE, outlier.shape = NA, coef = 0
  ) +
  geom_point(
    data = dismissals_by_ball_number_summary_lanning,
    colour = "#018821FF", size = 3
  ) +
  labs(subtitle = "MM Lanning") +
  plot_features
```

```r
# Perry
p4 <- dismissals_by_ball_number_summary |>
  ggplot(aes(x = ball_num_in_over, y = dismissals_pct)) +
  geom_boxplot(
    fill = "#FFFFFF", colour = "#FFFFFF", size = .5,
    alpha = 0.25, notch = TRUE, outlier.shape = NA, coef = 0
  ) +
  geom_point(
    data = dismissals_by_ball_number_summary_perry,
    colour = "#F80F61FF", size = 3
  ) +
  labs(subtitle = "EA Perry") +
  plot_features
```

```r
# Devine
p5 <- dismissals_by_ball_number_summary |>
  ggplot(aes(x = ball_num_in_over, y = dismissals_pct)) +
  geom_boxplot(
    fill = "#FFFFFF", colour = "#FFFFFF", size = .5,
    alpha = 0.25, notch = TRUE, outlier.shape = NA, coef = 0
  ) +
  geom_point(
    data = dismissals_by_ball_number_summary_devine,
    colour = "#FA6900FF", size = 3
  ) +
  labs(subtitle = "SFM Devine") +
  plot_features
```

```r
# Knight
p6 <- dismissals_by_ball_number_summary |>
  ggplot(aes(x = ball_num_in_over, y = dismissals_pct)) +
  geom_boxplot(
    fill = "#FFFFFF", colour = "#FFFFFF", size = .5,
    alpha = 0.25, notch = TRUE, outlier.shape = NA, coef = 0
  ) +
  geom_point(
    data = dismissals_by_ball_number_summary_knight,
    colour = "#95C65CFF", size = 3
  ) +
  labs(
    subtitle = "HC Knight",
    caption = "**Source:** Cricsheet.org // **Plot:** @jacquietran"
  ) +
  plot_features
```

--------------------------------

### Calculating League-Wide Dismissal Rate by Ball Number

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricsheet.html

Merges ball-facing and dismissal data to calculate the league-wide dismissal rate per ball number. Filters for batters facing over 200 balls and excludes balls beyond the 6th. Also extracts data for specific players.

```r
# Merge data and summarise to league-wide dismissals rate by ball number
dismissals_by_ball_number_summary <- left_join(
  ball_number_faced_summary, dismissals_by_ball_number,
  by = c("ball_num_in_over", "striker")
) |>
  tidyr::replace_na(list(dismissals_n = 0)) |>
  group_by(striker) |
  mutate(total_balls_faced = sum(balls_faced)) |
  ungroup() |
  mutate(dismissals_pct = round(dismissals_n / balls_faced * 100, 2)) |
  # Include those who have faced more than 200 balls total
  filter(total_balls_faced >= 200) |
  # Exclude balls beyond 6 - infrequent occurrences
  filter(ball_num_in_over < 7)

# Extract data for specific players
# Healy
dismissals_by_ball_number_summary_healy <- dismissals_by_ball_number_summary |
  filter(striker == "AJ Healy")
# Mooney
dismissals_by_ball_number_summary_mooney <- dismissals_by_ball_number_summary |
  filter(striker == "BL Mooney")
# Lanning
dismissals_by_ball_number_summary_lanning <- dismissals_by_ball_number_summary |
  filter(striker == "MM Lanning")
# Perry
dismissals_by_ball_number_summary_perry <- dismissals_by_ball_number_summary |
  filter(striker == "EA Perry")
```

--------------------------------

### player_meta

Source: https://pkg.robjhyndman.com/cricketdata/reference/index.html

Provides meta data on players listed at ESPNCricinfo.

```APIDOC
## player_meta

### Description
Meta data on players listed at ESPNCricinfo.

### Details
This object contains metadata for players found on ESPNCricinfo.
```

--------------------------------

### Visualize Indian Men Test Fielding Performance

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricinfo.html

Creates a scatter plot comparing the number of matches played against dismissals for Indian Men in Test cricket, differentiating wicketkeepers. Requires dplyr and ggplot2.

```r
Indfielding |>
  mutate(wktkeeper = (CaughtBehind > 0) | (Stumped > 0)) |>
  ggplot(aes(x = Matches, y = Dismissals, col = wktkeeper)) +
  geom_point() +
  ggtitle("Indian Men Test Fielding")
```

--------------------------------

### fetch_cricinfo Function

Source: https://pkg.robjhyndman.com/cricketdata/reference/fetch_cricinfo.html

Fetches data from ESPNCricinfo and returns it as a tibble. All arguments are case-insensitive and partially matched.

```APIDOC
## fetch_cricinfo

### Description
Fetches data from ESPNCricinfo and return a tibble. All arguments are case-insensitive and partially matched.

### Usage
```R
fetch_cricinfo(
  matchtype = c("test", "odi", "t20"),
  sex = c("men", "women"),
  activity = c("batting", "bowling", "fielding"),
  type = c("career", "innings"),
  country = NULL
)
```

### Arguments

*   **matchtype** (Character) - Indicates test (default), odi, or t20.
*   **sex** (Character) - Indicates men (default) or women.
*   **activity** (Character) - Indicates batting (default), bowling or fielding.
*   **type** (Character) - Indicates innings-by-innings or career (default) data.
*   **country** (Character, Optional) - Indicates country. The default is to fetch data for all countries.

### Value
A `tibble` object, similar to a `data.frame`.

### Examples
```R
# Fetch T20 data for women in Australia
auswt20 <- fetch_cricinfo("T20", "Women", country = "Aust")

# Fetch ODI bowling data for men in India
IndiaODIBowling <- fetch_cricinfo("ODI", "men", "bowling", country = "india")
```
```

--------------------------------

### Fetch Cricsheet Data Function Signature

Source: https://pkg.robjhyndman.com/cricketdata/reference/fetch_cricsheet.html

Defines the arguments for fetching data: type (bbb, match, player), gender (female, male), and competition code.

```R
fetch_cricsheet(
  type = c("bbb", "match", "player"),
  gender = c("female", "male"),
  competition = "tests"
)
```

--------------------------------

### Fetch Women's T20 Bowling Data

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricinfo.html

Fetches all available Women's T20 bowling data from ESPNCricinfo. Respect ESPNCricinfo's terms of use.

```r
# Fetch all Women's T20 data
wt20 <- fetch_cricinfo("T20", "Women", "Bowling")
```

--------------------------------

### player_meta Dataset

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

A dataset containing player’s and cricket officials meta data such as full name, country of representation, date of birth, bowling and batting hand, bowling style, and playing role. Over 11,000 player’s and officials data is available, scraped from the ESPNCricinfo website.

```APIDOC
## player_meta

### Description
It is a data set containing player’s and cricket officials meta data such as full name, country of representation, data of birth, bowling and batting hand, bowling style, and playing role. More than 11,000 player’s and officials data is available. This data was scraped from ESPNCricinfo website.

### Example
```R
player_meta |>
  filter(!is.na(playing_role)) |>
  select(-cricinfo_id, -unique_name, -name) |>
  head() |>
  knitr::kable(
    digits = 1, align = "c", format = "pipe",
    col.names = c(
      "ID", "FullName", "Country", "DOB", "BirthPlace",
      "BattingStyle", "BowlingStyle", "PlayingRole"
    )
  )
```
```

--------------------------------

### Analyze IPL 2022 Batters Boundary and Dot Percentage

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

Calculates and visualizes the boundary and dot ball percentages for top batters in the IPL 2022 season. This plot helps identify players with high boundary-hitting ability and low dot ball frequency.

```R
ipl_bbb |>
  filter(season == "2022") |>
  group_by(striker) |>
  summarize(
    Runs = sum(runs_off_bat), BallsFaced = n() - sum(!is.na(wides)),
    StrikeRate = Runs / BallsFaced, DotPercent = sum(runs_off_bat == 0) * 100 / BallsFaced,
    BoundaryPercent = sum(runs_off_bat %in% c(4, 6)) * 100 / BallsFaced
  ) |>
  arrange(desc(Runs)) |> 
  rename(Batter = striker) |>
  slice(1:20) |>
  ggplot(aes(y = BoundaryPercent, x = DotPercent, size = BallsFaced)) +
  geom_point(color = "red", alpha = 0.3) +
  geom_text(aes(label = Batter),
    vjust = -0.5, hjust = 0.5, color = "#013369",
    position = position_dodge(0.9), size = 3
  ) +
  ylab("Boundary Percent") +
  xlab("Dot Percent") +
  ggtitle("IPL 2022: Top 20 Batters")
```

--------------------------------

### Compute Meg Lanning's Batting Average

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricinfo.html

Calculates Meg Lanning's batting average from her ODI data, excluding innings where she was not out. Requires dplyr.

```r
# Compute batting average
MLave <- MegLanning |>
  summarise(
    Innings = sum(!is.na(Runs)),
    Average = sum(Runs, na.rm = TRUE) / (Innings - sum(NotOut, na.rm=TRUE))
  ) |>
  pull(Average)
names(MLave) <- paste("Average =", round(MLave, 2))
```

--------------------------------

### Tidying Ball-by-Ball Cricket Data

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricsheet.html

Prepares ball-by-ball cricket data to calculate the number of balls faced and dismissals for each ball number within an over. Filters out specific seasons and ball numbers, and applies a minimum balls faced threshold.

```r
# Create new variable for ball number in each over
ball_number_faced <- wbbl_bbb_tidy |
  mutate(ball_num_in_over = sub(".*\\.", "", ball))

# Summarise number of balls faced of each ball number, per batter
ball_number_faced_summary <- ball_number_faced |
  group_by(ball_num_in_over, striker) |
  summarise(balls_faced = n(), .groups = "drop")

# Dismissals by ball number
dismissals_by_ball_number <- ball_number_faced |
  select(ball_num_in_over, striker, wicket_type) |
  filter(wicket_type != "") |
  group_by(ball_num_in_over, striker) |
  summarise(dismissals_n = n(), .groups = "drop")
```

--------------------------------

### Access Cricsheet Codes Data Frame

Source: https://pkg.robjhyndman.com/cricketdata/reference/cricsheet_codes.html

This snippet shows how to access the `cricsheet_codes` data frame, which contains competition names and codes used by Cricsheet. No specific imports are required as it's a built-in dataset.

```R
cricsheet_codes
```

--------------------------------

### Find Player ID

Source: https://pkg.robjhyndman.com/cricketdata/reference/find_player_id.html

Searches for a player by name on cricinfo.com and returns a table of matching players, their IDs, and the teams they played for.

```APIDOC
## find_player_id

### Description
Find a player id from cricinfo.com.

### Usage
```R
find_player_id(searchstring)
```

### Arguments
* `searchstring` (character vector) - Part of a player name(s) to search for.

### Value
A table of matching players, their ids, and teams they played for.

### See Also
`fetch_player_data()`, `fetch_player_meta()`

### Examples
```R
if (FALSE) {
  # Find player ID for 'Perry'
  (perry <- find_player_id("Perry"))
  
  # Fetch player data using the found ID
  EllysePerry <- fetch_player_data(perry[2, "ID"], "test")
}
```
```

--------------------------------

### Plot ODI Scores with ggplot2

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricinfo.html

Generates a scatter plot of runs scored against the date for ODI matches. Includes a horizontal line at the average score and uses color to indicate if the player was not out. Requires the ggplot2 library and a data frame named MegLanning with Date, Runs, NotOut, and MLave columns.

```r
ggplot(MegLanning) +
  geom_hline(aes(yintercept = MLave), col = "gray") +
  geom_point(aes(x = Date, y = Runs, col = NotOut)) +
  ggtitle("Meg Lanning ODI Scores") +
  scale_y_continuous(sec.axis = sec_axis(~., breaks = MLave))
```

--------------------------------

### fetch_player_data

Source: https://pkg.robjhyndman.com/cricketdata/reference/fetch_player_data.html

Fetches individual player data from all matches played. The function will scrape the data from ESPNCricinfo and return a tibble with one line per innings for all games a player has played. To identify a player, use their Cricinfo player ID.

```APIDOC
## fetch_player_data

### Description
Fetches individual player data from all matches played. The function will scrape the data from ESPNCricinfo and return a tibble with one line per innings for all games a player has played. To identify a player, use their Cricinfo player ID. The simplest way to find this is to look up their Cricinfo Profile page. The number at the end of the URL is the ID. For example, Meg Lanning's profile page is http://www.espncricinfo.com/australia/content/player/329336.html, so her ID is 329336.

### Usage
```R
fetch_player_data(
  playerid,
  matchtype = c("test", "odi", "t20"),
  activity = c("batting", "bowling", "fielding")
)
```

### Arguments 

*   **playerid** (Integer or character) - The player ID as given in the Cricinfo profile.
*   **matchtype** (character vector) - Which type of cricket matches do you want? Tests, ODIs or T20s? Not case-sensitive. Defaults to c("test", "odi", "t20").
*   **activity** (character vector) - Which type of activities do you want? Batting, Bowling or Fielding? Not case-sensitive. Defaults to c("batting", "bowling", "fielding").

### Value
A tibble containing data on the selected player, with one row for every innings of every match in which they have played.

### Examples

```R
# Download data on some players
EllysePerry <- fetch_player_data(275487, "T20", "batting")
RahulDravid <- fetch_player_data(28114, "ODI", "fielding")
LasithMalinga <- fetch_player_data(49758, "Test", "bowling")

# Create a plot for Ellyse Perry's T20 scores
library(dplyr)
library(ggplot2)
EllysePerry |> 
  filter(!is.na(Runs)) |> 
  ggplot(aes(x = Start_Date, y = Runs, col = Dismissal, na.rm = TRUE)) + 
  geom_point() + 
  ggtitle("Ellyse Perry's T20 Scores")
```
```

--------------------------------

### Calculate Team Strike Rates per Innings

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricsheet.html

Calculates rolling strike rates, identifies wicket balls, and creates descriptive labels for each innings. Excludes matches with 'tie' or 'no result' outcomes.

```r
team_strike_rate <- wbbl07_bbb_tidy |>
  # Exclude matches that ended with a Super Over ("tie")
  # and matches that were called off ("no result")
  filter(!outcome_batting_team %in% c("tie", "no result")) |>
  group_by(match_id, innings) |> 
  mutate(
    rolling_strike_rate = round(
      runs_cumulative / balls_cumulative * 100, 1
    ),
    wicket_ball_num = case_when(
      !is.na(wicket_type) ~ balls_cumulative,
      TRUE ~ NA_real_
    ),
    wicket_strike_rate = case_when(
      !is.na(wicket_type) ~ rolling_strike_rate,
      TRUE ~ NA_real_
    ),
    innings_description = case_when(
      innings == 1 ~ "Batting 1st",
      innings == 2 ~ "Batting 2nd"
    ),
    bowling_team_short = word(bowling_team, -1),
    start_date_day = lubridate::day(start_date),
    start_date_month = lubridate::month(start_date),
    match_details = glue::glue(
      "{innings_description} vs. {bowling_team_short} ({start_date_day}/{start_date_month})"
    )
  ) |> 
  arrange(match_id, innings, balls_cumulative) |> 
  mutate(
    match_details = factor(
      match_details,
      levels = unique(match_details)
    ),
    outcome_batting_team = factor(
      outcome_batting_team,
      levels = c("won", "lost")
    )
  )
```

--------------------------------

### Fetch Cricinfo Data Function Signature

Source: https://pkg.robjhyndman.com/cricketdata/reference/fetch_cricinfo.html

Defines the signature for the `fetch_cricinfo` function, specifying available arguments for match type, sex, activity, data type, and country.

```R
fetch_cricinfo(
  matchtype = c("test", "odi", "t20"),
  sex = c("men", "women"),
  activity = c("batting", "bowling", "fielding"),
  type = c("career", "innings"),
  country = NULL
)
```

--------------------------------

### Fetch Player Data by ID

Source: https://pkg.robjhyndman.com/cricketdata/articles/cricketdata_R_pkg.html

Retrieve all match data for a player using their Cricinfo ID. This function scrapes data from ESPNCricinfo and returns a tibble. You can specify match type and activity (batting/bowling/fielding).

```R
# Fetching the player Meg Lanning's playing data
MegLanning <- fetch_player_data(meg_lanning_id, "ODI") |>
  mutate(NotOut = (Dismissal == "not out"))
```

```R
dim(MegLanning)
#> [1] 103  14
names(MegLanning)
#>  [1] "Date"       "Innings"    "Opposition" "Ground"     "Runs"      
#>  [6] "Mins"       "BF"         "X4s"        "X6s"        "SR"        
#> [11] "Pos"        "Dismissal"  "Inns"       "NotOut"
```

```R
# Compute batting average
MLave <- MegLanning |>
  filter(!is.na(Runs)) |>
  summarise(Average = sum(Runs) / (n() - sum(NotOut))) |>
  pull(Average)
names(MLave) <- paste("Average =", round(MLave, 2))

# Plot ODI scores
ggplot(MegLanning) + 
  geom_hline(aes(yintercept = MLave), col = "gray") + 
  geom_point(aes(x = Date, y = Runs, col = NotOut)) + 
  ggtitle("Meg Lanning ODI Scores") + 
  scale_y_continuous(sec.axis = sec_axis(~., breaks = MLave))
```