Every cyclist has a particular important climb. It might not be a big deal to anyone else, but any climb can be important!
My favorite local climb goes by the name of ‘Lochen’. It’s located outside of my local hometown Balingen in the southwest of Germany. It’s about 4.4 kilometers long with an average gradient of 6.9%.
This doesn’t sound like a hard climb. It might not even register as a regular big climb for most cyclists. But for me it’s one of the most iconic climbs.
In the following post, I will let different versions of me race against each other on my favorite local climb!
In order to reproduce the analysis, perform the following steps:
- Clone the repository
- Install the packages listed in the
libraries.Rfile - Run the target pipeline by executing
targets::tar_make()command
The data originates from my personal Strava account. If you have a Strava account and want to query your data like I do here, you can have a look at one of my previous posts.
The data are a bunch of arrow files, that you can query via dpylr syntax thanks to the DuckDB package.
Deselect heartrate measurements and restrict the spatial data to a
bounding box. Add information about the type and the start date of each
activity.
poi <- function(df_act, paths_meas, target_file, act_type,
lng_min, lng_max, lat_min, lat_max) {
act_col_types <- schema(
moving = boolean(), velocity_smooth = double(),
grade_smooth = double(), distance = double(),
altitude = double(), heartrate = int32(), time = int32(),
lat = double(), lng = double(), cadence = int32(),
watts = int32(), id = string())
strava_db <- open_dataset(
paths_meas, format = "arrow", schema = act_col_types) |>
to_duckdb()
df_strava_poi <- strava_db |>
filter(
lng >= lng_min, lng <= lng_max, lat >= lat_min, lat <= lat_max) |>
select(-heartrate) |>
collect() |>
left_join(select(df_act, id, type, start_date), by = "id")
}
## # A tibble: 40,439 × 13
## moving velocity_smooth grade_smooth distance altitude time lat lng
## <lgl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
## 1 TRUE 3 3.4 22882. 637. 3925 48.2 8.85
## 2 TRUE 3 1.7 22885. 637. 3926 48.2 8.85
## 3 TRUE 2.9 3.4 22887. 637 3927 48.2 8.85
## 4 TRUE 3 3.4 22890. 637 3928 48.2 8.85
## 5 TRUE 2.9 3.3 22893. 637. 3929 48.2 8.85
## 6 TRUE 3 3.3 22896. 637. 3930 48.2 8.85
## 7 TRUE 2.9 3.3 22899. 637. 3931 48.2 8.85
## 8 TRUE 3 5 22902. 637. 3932 48.2 8.85
## 9 TRUE 3 4.9 22905. 638. 3933 48.2 8.85
## 10 TRUE 3 6.7 22908. 638. 3934 48.2 8.85
## # … with 40,429 more rows, and 5 more variables: cadence <int>, watts <int>,
## # id <chr>, type <chr>, start_date <dttm>
Further preprocess the raw data. Keep only rows, where I was moving and
turn the start date from datetime to date. Adjust the time column so
that every activity starts at time 0.
lochen <- function(df_lochen_raw) {
df_lochen_raw |>
group_by(id) |>
mutate(
time_delta = time - lag(time),
start_date = as_date(start_date)) |>
replace_na(list(time_delta = 0)) |>
filter(moving) |>
mutate(time = cumsum(time_delta)) |>
select(-time_delta)
}
## # A tibble: 40,365 × 13
## # Groups: id [36]
## moving velocity_smooth grade_smooth distance altitude time lat lng
## <lgl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
## 1 TRUE 3 3.4 22882. 637. 0 48.2 8.85
## 2 TRUE 3 1.7 22885. 637. 1 48.2 8.85
## 3 TRUE 2.9 3.4 22887. 637 2 48.2 8.85
## 4 TRUE 3 3.4 22890. 637 3 48.2 8.85
## 5 TRUE 2.9 3.3 22893. 637. 4 48.2 8.85
## 6 TRUE 3 3.3 22896. 637. 5 48.2 8.85
## 7 TRUE 2.9 3.3 22899. 637. 6 48.2 8.85
## 8 TRUE 3 5 22902. 637. 7 48.2 8.85
## 9 TRUE 3 4.9 22905. 638. 8 48.2 8.85
## 10 TRUE 3 6.7 22908. 638. 9 48.2 8.85
## # … with 40,355 more rows, and 5 more variables: cadence <int>, watts <int>,
## # id <chr>, type <chr>, start_date <date>
Make a first static ggplot visualisation. Keep the plot rather minimal.
Use ggplot2::theme_void as a general theme:
vis_lochen <- function(df_lochen) {
df_lochen |>
ggplot(
aes(x = lng, y = lat, group = id)) +
geom_path(alpha = 0.2) +
theme(
axis.ticks.x = element_blank(), legend.position = "bottom") +
labs(x = element_blank(), y = element_blank(), color = "Activity Year")
}
As you can see there are lot of paths on one road. These are my bike rides on the ‘Lochen’ pass.
Some paths don’t seem to match. These are activities of another type in the same region as my bike rides. These activities don’t use the main road and stand out in the plot.
To further explore the data, make a first animated visualisation with
the gganimate package:
vis_anim_lochen <- function(gg_lochen) {
gg_lochen +
transition_reveal(along = time)
}
In this animated version of the plot, you can see that there are further problems in the data. Not all bike rides start at the bottom of the climb. You can guess which activities start at the top of the climb, by looking at the general speed of the animation. Determine these activities:
wrong_direction <- function(df_lochen) {
df_lat_lng_start <- df_lochen |>
group_by(id) |>
summarise(
start_lat = lat[time == min(time)], start_lng = lng[time == min(time)])
midway <- min(df_lochen$lat) + (max(df_lochen$lat) - min(df_lochen$lat)) / 2
df_lat_lng_start |>
filter(start_lat < midway)
}
Filter the activities for bike rides. Exclude activities that start at the top of the climb. Repeat the above animated plot:
lochen_ride <- function(df_lochen, df_wrong_direction) {
df_lochen |>
filter(type == "Ride") |>
anti_join(df_wrong_direction, by = "id")
}
Now it looks much cleaner and the rides are more comparable to one another.
For the final version of the animation, add small points to point out my
position at the time. Color these positions by the year of the
activity Reduce the speed of the animation a little bit, to display
smaller differences in the rides.
vis_anim_lochen_final <- function(gg_lochen_ride) {
gg_endpoints <- gg_lochen_ride +
geom_point(shape = 4, aes(color = as_factor(year(start_date))))
gg_anim_endpoints <- vis_anim_lochen(gg_endpoints)
animate(gg_anim_endpoints, fps = 7)
}
I very much like how the plot turned out. I hope I can add many more layers to this animation in the future!