ball

TABLE:

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Field Goal Stats by Position
Position	Count	Minimum	Average	Maximum
Forward	4	50.9	54.25	59.1
Guard	4	46.6	52.90	58.0
Center	4	40.8	50.85	59.9

CODE:

# Installing and loading required packages

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("gtExtras"))
  install.packages("gtExtras")

library(tidyverse)
library(gtExtras)

# Installing and loading required packages

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("gtExtras"))
  install.packages("gtExtras")

library(tidyverse)
library(gtExtras)

# Specify made-up data for 12 players and turn it into a data frame
# Note: I used ChatGPT to help generate this data

Player <- c(
  "Player 1",
  "Player 2",
  "Player 3",
  "Player 4",
  "Player 5",
  "Player 6",
  "Player 7",
  "Player 8",
  "Player 9",
  "Player 10",
  "Player 11",
  "Player 12"
)

Position <- c(
  "Guard",
  "Center",
  "Center",
  "Guard",
  "Forward",
  "Center",
  "Forward",
  "Guard",
  "Center",
  "Forward",
  "Guard",
  "Forward"
)

FG_Percentage <- c(58.0,
                   44.9,
                   40.8,
                   46.6,
                   59.1,
                   57.8,
                   53.9,
                   52.8,
                   59.9,
                   53.1,
                   54.2,
                   50.9)

basketball_players <- data.frame(Player, Position, FG_Percentage)

FG_Average <- mean(FG_Percentage)

basketball_players <- basketball_players %>%
  mutate(
    FG_Category = case_when(
      FG_Percentage < FG_Average ~ "Below average",
      FG_Percentage == FG_Average ~ "Average",
      FG_Percentage > FG_Average ~ "Above average",
      .default = "Error"
    )
  )
# Making the table

Player_table <- gt(basketball_players) %>%
  tab_header("Player positions and FG stats") %>%
  cols_align(align = "left") %>%
  gt_theme_538

# Showing the table

Player_table

# Load required libraries
library(tidyverse)
library(gt)

# Create sample data
Player <- c("Player 1", "Player 2", "Player 3", "Player 4", "Player 5", 
            "Player 6", "Player 7", "Player 8", "Player 9", "Player 10", 
            "Player 11", "Player 12")

Position <- c("Guard", "Center", "Center", "Guard", "Forward", 
              "Center", "Forward", "Guard", "Center", "Forward", 
              "Guard", "Forward")

FG_Percentage <- c(58.0, 44.9, 40.8, 46.6, 59.1, 57.8, 
                   53.9, 52.8, 59.9, 53.1, 54.2, 50.9)

basketball_players <- data.frame(Player, Position, FG_Percentage)

# Compute summary statistics
summary_stats <- basketball_players %>%
  group_by(Position) %>%
  summarise(
    Count = n(),
    Minimum = min(FG_Percentage),
    Average = mean(FG_Percentage),
    Maximum = max(FG_Percentage)
  ) %>%
  arrange(desc(Average))  # Sort by Average in descending order

# Create table using gt
Player_table <- gt(summary_stats) %>%
  tab_header(title = "Field Goal Stats by Position") %>%
  cols_align(align = "left")

# Display the table
Player_table