Pacers New Players

Loading relevant libraries

library(rvest)
library(ggplot2)
library(tidyr)
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

Fetching player stats from Basketball Reference

url <- 'https://www.basketball-reference.com/leagues/NBA_2025_per_game.html'
webpage <- read_html(url)
tables <- html_table(webpage, fill = TRUE)
player_stats <- tables[[1]]

Cleaning the data

player_stats <- player_stats %>%
  filter(!is.na(Player)) %>%
  mutate_at(vars(STL, BLK, `DRB`), as.numeric)

Filtering relevant stats for Pacers and the selected player (Cam Johnson example)

pacers_stats <- player_stats %>% filter(Team == 'IND')
cam_johnson_stats <- player_stats %>% filter(Player == 'Cameron Johnson')

Combining Pacers stats with the selected player.

combined_stats <- bind_rows(pacers_stats, cam_johnson_stats)

Selecting relevant columns for visualization

selected_stats <- combined_stats %>% select(Player, STL, BLK, `DRB`)

Melting the data for ggplot using tidyr

melted_stats <- selected_stats %>%
  pivot_longer(cols = -Player, names_to = "Statistic", values_to = "Value")

Plotting the graph

ggplot(melted_stats, aes(x = Player, y = Value, fill = Player)) +
  geom_bar(stat = 'identity', position = 'dodge') +
  facet_wrap(~Statistic, scales = 'free') +
  theme_minimal() +
  labs(title = 'Cam Johnson vs. Indiana Pacers: Defensive Statistics Comparison',
       x = 'Player',
       y = 'Value',
        fill = 'Player')