Melbourne Marathon 2025 Finishing Times by age group[OC]

made in RStudio by scraping the multisport Australia website. My favourite part of this visualisation is overlaying two separate geom_points to create a glowing look with a bright dot surrounded by a faint little halo. 🙂

library(rvest)
library(tidyverse)

marathonresults <- vector(mode = “list”, length = length(242))

for (i in 1:242){
marathonresults[[i]] <- paste0(“https://www.multisportaustralia.com.au/races/melbourne-marathon-2025/events/1?page=”, i)
}

marathonresults <- unlist(marathonresults)

marathonresults2 <- vector(mode = “list”, length = 242)

for (i in seq_along(marathonresults)){
marathonresults2[[i]] <- read_html(marathonresults[[i]]) %>% html_table()
}

stragglers<- read_html(“https://www.multisportaustralia.com.au/races/melbourne-marathon-2025/events/1?page=243″) %>% html_table()

stragglecrew<- stragglers[[1]] %>% select(-8) %>%
filter(Pos!=”DNS”) %>% select(time = `Net Time`, age = `Category (Pos)`)

mara3<- marathonresults2 %>% bind_rows() %>% select(time = `Net Time`, age = `Category (Pos)`) %>%
rbind(stragglecrew)

That is a really nice graph! You can see the decline by age and speed groups pretty vell, which is a totally fun extra layer of information.