bless your chart - 010: Generational rebounder

Load data and manipulate

Code

rbs <- read_csv("unc_boards.csv") %>% 
       filter(Rebounds > 600) %>% 
       mutate(Type = if_else(RPG > 9.8, "Over 10.0 avg", "Under 10 avg"),
              Label = if_else(RPG > 9.8 | Rebounds > 1000, Player, ""))

Set the themes for the plot

Code

# theme
theme_me <- function () {
  theme_minimal(base_size = 10, base_family = "RobotoCondensed-Regular") %+replace%
    theme (
      plot.title = element_text(
        hjust = 0.5,
        size = 24,
        face = "bold"
      ),
      plot.subtitle = element_text(
        hjust = 0.5,
        size = 10,
        lineheight = 0.25,
        vjust = -0.5
      ),
      plot.caption = element_text(
        hjust = 1,
        size = 6,
        lineheight = 0.35,
        margin = margin(t = 20)
      ),
      panel.grid.minor = element_blank(),
      plot.background = element_rect(fill = "floral white", color = "floral white")
    )
}

colors = c("#2b8cbe", "#045a8d")

Make the scatterplot with the encircle

Code

bacot <- rbs %>%
    mutate(Label = if_else(Player == "Armando Bacot", "", Label)) %>% 
    ggplot(aes(x=Rebounds, y = Games, color=Type)) +
    geom_point() +
    geom_label_repel(aes(label = Label),
                     box.padding   = 0.35, 
                     point.padding = 0.5,
                     segment.color = 'grey50') +
    geom_vline(xintercept = 1000,
             linetype = 'dashed',
             color = "#dddddd") +
      geom_hline(yintercept = 100,
             linetype = 'dashed',
             color = "#dddddd") +
  scale_color_manual(values = colors) +
  scale_x_continuous(breaks = seq(600, 1250, 50), limits = c(600, 1250)) +
  scale_y_continuous(breaks = seq(50, 160, 10), limits = c(50, 160)) +
  geom_encircle(data = filter(rbs, 
                              Player == "Armando Bacot"), aes(fill = "lightpink"),
                s_shape=0.2, expand=0.01, alpha=0.1) +
  theme_me() +
  theme(legend.position = "none", plot.title = element_markdown()) +
  labs(
    y = "Career Games",
    x = "Career Rebounds",
    title = "<span style='color:#56a0d3'>Armando Bacot</span>  \nGenerational rebounder ",
    caption = "@dadgumboxscores | January 8, 2023 | data via goheels.com"
  ) +
  annotate(
    geom = 'label',
    y = 120,
    x = 1165,
    label = "Armando Bacot \n1170 rebounds \n115 career games",
    fontface = 'bold',
    colour = "#56a0d3",
    size = 4,
    fill = "lightpink",
    alpha = 0.1
  ) 

# save the chart
ggsave(
  "bacot.png",
  bacot,
  w = 9.5,
  h = 9.5,
  dpi = 300,
)

bacot

Make the table

Code

table <- rbs %>%
  filter(Rebounds > 1000 | RPG > 9.8) %>% 
  mutate(Years = if_else(Player == "Armando Bacot", "2019-present", Years)) %>% 
  select(Player, Years, Rebounds, Games, RPG) %>%
  arrange(-Rebounds) %>% 
  gt() %>%
  gt_merge_stack(col1 = Player, col2 = Years) %>%
  gt_highlight_rows(
    rows = c(2),
    fill = "#d0e4f3",
    bold_target_only = TRUE,
    target_col = c(RPG),
  ) %>%
  gt_theme_nytimes() %>%
  # set title + caption + subtitle
  tab_header(title = "North Carolina Career Rebound Leaders") %>%
  tab_source_note(source_note = "@dadgumboxscores | January 8, 2023 | data via goheels.com")  %>%
  # fix font of source note
  tab_options (source_notes.font.size = px(10),
               table.font.size = px(12))
  
gtsave(table, 'rpg_table.png', vwidth = 600, vheight = 600, expand = 10)

table

North Carolina Career Rebound Leaders
Player	Rebounds	Games	RPG
Tyler Hansbrough 2005-09	1219	142	8.6
Armando Bacot 2019-present	1170	115	10.2
Sam Perkins 1980-84	1167	135	8.6
George Lynch 1989-93	1097	140	7.8
Billy Cunningham 1962-65	1062	69	15.4
Kennedy Meeks 2013-17	1052	144	7.3
Brice Johnson 2012-16	1035	148	7.0
Antawn Jamison 1995-98	1027	104	9.9
Mitch Kupchak 1972-76	1006	119	8.5
Brad Daugherty 1982-86	1003	135	7.4
Rusty Clark 1966-69	929	91	10.2
Pete Brennan 1955-58	854	81	10.5
Lennie Rosenbluth 1954-57	790	76	10.4
Sean May 2002-05	771	77	10.0
Doug Moe 1958-61	635	60	10.6
@dadgumboxscores \| January 8, 2023 \| data via goheels.com