library(tidyverse)
library(FITfileR)
library(dplyr)
library(patchwork)
library(leaflet)
library(modelr)
training_raw <- read_csv("activities/activities.csv") %>%
janitor::clean_names()
training_summary =
training_raw %>%
filter(activity_type == "Run") %>%
select(activity_id, activity_date, activity_name, activity_description, elapsed_time_6, distance_7, max_heart_rate_8, relative_effort_9, max_speed, average_speed, elevation_gain, elevation_loss, max_grade, average_grade, max_cadence, average_cadence, average_heart_rate, calories, weather_temperature, dewpoint, humidity, wind_speed) %>%
filter(activity_id >= 6910869137) %>%
separate(activity_date, c("month_date", "year", "time"), sep = ", ") %>%
mutate(
date = str_c(month_date, year, sep = " "),
date = as.Date(date, format = "%b%d%Y")
) %>%
select(-month_date, -year) %>%
mutate(
elapsed_time_min = elapsed_time_6 / 60,
distance_km = distance_7,
max_heart_rate = max_heart_rate_8,
relative_effort = relative_effort_9
) %>%
select(-elapsed_time_6, -distance_7, -max_heart_rate_8, -relative_effort_9)
activity_summaries =
training_summary %>%
select(activity_id, date, time, activity_name, activity_description)
tidy_training =
training_summary %>%
select(-activity_name, -activity_description) %>%
select(activity_id, date, time, distance_km, elapsed_time_min, max_speed, average_speed, max_heart_rate, average_heart_rate, relative_effort, everything()) %>%
filter(date != "2022-03-31",
date != "2022-04-03")In order to display Emma’s progress throughout the NYC Marathon an
interactive map was created. Utilizing data from the
marathon_day data set, cleaning began with converting the
distance measurement from kilometers to miles, in order to best display
the race. From this, each observation was grouped by mileage (1-26),
with a Start (Mile 0) and Finish (Mile 26.2) observation being added as
well. Variables position_longitude and
position_latitude were recoded to longitude
and latitude within the data set. A new data set
marathon_speed was generated in order to group all
observations based on mile as well as convert speed from its initial
unit of meters per second to miles per hour. The data was then
aggregated in order to create averages for each of Emma’s mile
statistics such as speed, heart rate, cadence, as well as altitude.
Leaflet was then used to display all mile markers along the 26.2-mile
route, in addition to a pop-up that includes Emma’s summary statistics
for each running stint of the marathon.
marathon_day <- read_csv("activities/marathon_day.csv")
marathon_day =
marathon_day %>%
mutate(
distance_m = distance*0.000621371
) %>%
mutate(group = case_when(
between(distance_m, 0,0.9999) ~ "Start",
between(distance_m, 1, 1.9999) ~ "1",
between(distance_m, 2, 2.9999) ~ "2",
between(distance_m, 3, 3.9999) ~ "3",
between(distance_m, 4, 4.9999) ~ "4",
between(distance_m, 5, 5.9999) ~ "5",
between(distance_m, 6, 6.9999) ~ "6",
between(distance_m, 7, 7.9999) ~ "7",
between(distance_m, 8, 8.9999) ~ "8",
between(distance_m, 9, 9.9999) ~ "9",
between(distance_m, 10, 10.9999) ~ "10",
between(distance_m, 11, 11.9999) ~ "11",
between(distance_m, 12, 12.9999) ~ "12",
between(distance_m, 13, 13.9999) ~ "13",
between(distance_m, 14, 14.9999) ~ "14",
between(distance_m, 15, 15.9999) ~ "15",
between(distance_m, 16, 2.9999) ~ "16",
between(distance_m, 17, 17.9999) ~ "17",
between(distance_m, 18, 18.9999) ~ "18",
between(distance_m, 19, 19.9999) ~ "19",
between(distance_m, 20, 20.9999) ~ "20",
between(distance_m, 21, 21.9999) ~ "21",
between(distance_m, 22, 22.9999) ~ "22",
between(distance_m, 23, 23.9999) ~ "23",
between(distance_m, 24, 24.9999) ~ "24",
between(distance_m, 25, 25.9999) ~ "25",
between(distance_m, 26, 26.9999) ~ "26-Finish"))
#Recode
marathon_geo =
marathon_day %>%
mutate(
latitude = position_lat,
longitude = position_long) %>%
select(-position_lat, -position_long)
marathon_speed =
marathon_geo %>%
group_by(group) %>%
mutate(
speed_mile =
enhanced_speed*2.237)
#Aggregate Data
agg = aggregate(marathon_speed,
by = list(marathon_geo$group),
FUN = mean)
leaflet() %>%
addTiles() %>%
addCircleMarkers(data = agg,
lng = ~longitude,
lat = ~latitude,
label = ~Group.1,
radius = 7,
color = "orange",
stroke = TRUE, fillOpacity = 0.75,
popup = ~paste("Mile:", Group.1,
"<br>Speed:", speed_mile,
"<br>HR:", heart_rate,
"<br>Cadence:", cadence,
"<br>Altitude:", enhanced_altitude)) %>%
addProviderTiles(providers$CartoDB.Positron)The leaflet plot above demonstrates Emma’s NYCM racing path. Each marker indicates the mile number and average speed (miles/hour), heart rate, cadence, and altitude for that mile. As a note, Strava failed to record information for Mile 3, and is therefore not included in the leaflet above.