#packages
library(tidyr)
library(dplyr)
library(ggplot2)
#data
fide_ratings_august <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-23/fide_ratings_august.csv')
fide_ratings_september <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-23/fide_ratings_september.csv')#data cleaning and preparation
chess <- fide_ratings_september |>
filter(!is.na(bday), !is.na(rating)) |>
mutate(age = as.numeric(2026 - bday)) |>
group_by(age) |>
summarise(
avg_rating = mean(rating, na.rm = TRUE),
n = n(),
.groups = "drop"
) |>
filter(n >= 100) |> #filter so it isn't noisy
arrange(age)#Research question: How does age affect rating? For this data analysis, we took 200 thousand chess players from the FIDE tournament, and determined at what age rating tends to peak among these players.
#Final visualization
ggplot(chess, aes(x = age, y = avg_rating)) +
geom_point() +
labs(x = "Age", y = "Average Rating",
title = "Average Chess Rating by Age",
subtitle = "Chess rating tends to peak at age 40, then decline",
caption = "Source: https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-09-23/readme.md \nAll NA values dropped") +
scale_x_continuous(breaks = seq(0, 100, by = 10)) +
theme_minimal() +
geom_smooth() +
theme(
plot.title = element_text(face = "bold", size = 16),
plot.subtitle = element_text(size = 10),
axis.title = element_text(face = "bold", size = 11),
plot.caption = element_text(color = "gray40")
) +
geom_vline(
xintercept = 40,
color = "red",
linewidth = 0.6,
linetype = "dashed"
) +
annotate(
"text",
x = 40,
y = max(chess$avg_rating) + 25,
label = "Rating peak here",
color = "red",
hjust = -0.1,
vjust = 1
)