Sport data from Runalyze

R-bloggers 2025-04-18

[This article was first published on r.iresmi.net, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Runners – CC-BY by ~jar{}

I already explored how to get your activities from Strava. Maybe you use Runalyze instead? In that case you can do some web scraping to export your data.

library(httr)library(rvest)library(glue)library(readr)library(dplyr)library(stringr)library(tidyr)library(lubridate)library(hms)library(janitor)library(ggplot2)library(forcats)library(tibble)library(purrr)

Runalyze allows you to download your tabular data as a CSV (so no tracks). The only complicated part is the login: you have to get a token.

token <- GET("https://runalyze.com/login") |>   content() |>  html_elements("input[name=_csrf_token]") |>   html_attr("value")POST("https://runalyze.com/login",     body = list(       "_username" = Sys.getenv("RUNALYZE_U"),       "_password" = Sys.getenv("RUNALYZE_P"),       "_remember_me" = "on",       "_csrf_token" = token))GET("https://runalyze.com/_internal/data/activities/all",    write_disk(glue("{tempdir()}/runalyze.csv"), overwrite = TRUE))

Fairly easy. Then, you get all your activities.

There is also an API for other uses, but I didn’t try it. I didn’t try to get the GPS data either (seems less straightforward).

# to build from : distinct(activites, sportid)sports <- tribble(  ~sportid, ~sport,               ~colour,  400452,   "running",            "yellow",  400454,   "cycling",            "orange",  422335,   "nordic skiing",      "lightblue",  422336,   "mountain skiing",    "blue",  453960,   "alpinism",           "darkgreen",  400453,   "swimming",           "deepskyblue",  400455,   "stretching",         "pink",  1304290,  "walking and others", "grey",    # hiking  400456,   "walking and others", "grey",    # crossfit  400457,   "walking and others", "grey") |> # hiking  mutate(sport = fct_rev(as_factor(sport)))activities <- read_csv(glue("{tempdir()}/runalyze.csv"),                       guess_max = 1e4) |>   clean_names() |>   mutate(across(c(time, created, edited), as_datetime),         across(c(s, elapsed_time), hms),         vdate = ymd(paste("2024", month(time), day(time), sep = "-"))) |>   left_join(sports, join_by(sportid))

activities |>   group_by(ym = format(time, "%Y-%m"), sport) |>   summarise(time_s = sum(s, na.rm = TRUE),            distance = sum(distance, na.rm = TRUE),            .groups = "drop") |>   mutate(hours = as.numeric(time_s, "hours")) |>   ggplot(aes(ym, hours, fill = sport)) +  geom_col(just = 0) +  scale_x_discrete(    breaks = \(x) keep(x, substr(x, 6, 7) == "01"),    labels = \(x) ifelse(substr(x, 6, 7) == "01", substr(x, 1, 4), "")) +  scale_fill_manual(values = sports |>                       select(sport, colour) |>                       deframe()) +  labs(title = "Activities",       subtitle = "Monthly time",       x = "month",       y = "h",       fill = "activities") +  theme(axis.title.y = element_text(angle = 0, vjust = 0.5))

Figure 1: My data from Runalyze

To leave a comment for the author, please follow the link and comment on their blog: r.iresmi.net.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Continue reading: Sport data from Runalyze