Data discovery: seasonal speed
This article is originally published at https://nsaunders.wordpress.com
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.
Thanks for visiting r-craft.org
This article is originally published at https://nsaunders.wordpress.com
Please visit source website for post related comments.
