Data discovery: seasonal speed
The Endeavour 2024-01-23
[This article was first published on R – What You're Doing Is Rather Desperate, 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.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Just writing this one quickly as it’s been hanging around my browser tabs for weeks…
I wrote Taking steps (in XML) almost 7 years ago and once in a while, I still grab Apple Health data from my phone and play around with it in R for a few minutes. Sometimes, curve fitting to a cloud of points generates a surprise.
library(tidyverse)library(xml2)theme_set(theme_bw())health_data <- read_xml("~/Documents/apple_health_export/export.xml")ws <- xml_find_all(health_data, ".//Record[@type='HKQuantityTypeIdentifierWalkingSpeed']") %>% map(xml_attrs) %>% map_df(as.list)ws %>% mutate(Date = ymd_hms(creationDate), value = as.numeric(value)) %>% ggplot(aes(Date, value)) + geom_point(size = 1, alpha = 0.2, color = "grey70", fill = "grey70") + geom_smooth() + labs(y = "Walking speed (km/h)", title = "Walking speed data", subtitle = "Apple Health 2020 - 2023")Result:
Huh. Looks seasonal. Looks faster in the (southern) winter. Has that been reported before? Sure has.
It didn’t impress everyone but I thought it was interesting.
To leave a comment for the author, please follow the link and comment on their blog: R – What You're Doing Is Rather Desperate.
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.