Almost every year I participate in the Coffeeneuring Challenge. You can find out more what coffeeneuring is and check out my past adventures over on my bike blog. But basically you have to bike to seven different coffee locations over the course of about six weeks. Now that the 2022 season is over, it seemed like a good opportunity to use my coffeeneuring data for the #30DayMapChallenge.
First we read in the spreadsheet where I kept track of my rides and destinations.
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| name | date | category | strava | geometry |
|---|---|---|---|---|
| Crema Café | 2022-10-15 | coffee shop | https://www.strava.com/activities/7966997401 | POINT (-89.32413 43.07862) |
| Paradiddle’s Café | 2022-10-09 | coffee shop | https://www.strava.com/activities/7939047633 | POINT (-88.99078 43.19471) |
| Alice Good | 2022-10-21 | coffee shop | https://www.strava.com/activities/7998212282 | POINT (-89.53358 42.98808) |
| Hodge Podge | 2022-11-06 | coffee shop | https://www.strava.com/activities/8079719303 | POINT (-89.51824 42.99536) |
| Grace | 2022-11-20 | coffee shop | https://www.strava.com/activities/8146150714 | POINT (-89.51179 43.09536) |
| Rock River, Byron | 2022-10-23 | coffee outside | https://www.strava.com/activities/8009683012 | POINT (-89.25426 42.12329) |
| Leopold Pump Track | 2022-10-30 | coffee outside | https://www.strava.com/activities/8044807447 | POINT (-89.41957 43.03036) |
| Firefly | 2022-10-16 | coffee shop | https://www.strava.com/activities/7973704511 | POINT (-89.38395 42.92652) |
We can map the destinations:
You may wonder about the one dot very far south. This was on a very long two-day ride to Illinois I did in October!
Next we add lines for the actual rides to these destinations. I don’t have access to the Strava API, and so I manually downloaded the gpx files and saved them in a folder. We use purrr to read them in and simplify the tracks with st_simplify to reduce the size.
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process_gpx <- function(x){
df <- read_GPX(x)
df[[1]] |> #read_GPX outputs list
st_simplify(dTolerance = 100) #distance is in meters
}
tracks <- list.files("data/", full.names = T) |>
map_dfr(process_gpx)Let’s add the tracks to the above map:
We have all the data elements together now. Let’s create a nice static map. For that we need a base map. The Stamen Watercolor is always a nice option. Note the +c(...) after the bounding box command: This enlarges the bounding box in each direction so that our destinations aren’t at the very edge of the map and to make the map a little more square.
Rather than using color to distinguish between the category of destination, we can use custom icons with the tmap_icons() function. I created two icons in Inkscape, based on Fontawesome icons.
Now it’s just a matter of putting all the layers together and making adjustments:
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my_icons <- tmap_icons(c("img/coffee-inside.png", "img/coffee-outside.png"))
tmap_mode("plot")
tm_shape(basemap) +
tm_rgb() +
tm_shape(tracks) +
tm_lines(lwd = 1.5,
col = "darkgreen") +
tm_shape(destinations) +
tm_symbols(
shape = "category",
shapes = my_icons,
size = .1,
title.shape = "Type of destination",
border.col = NULL,
alpha = .8
) +
tm_text(
"name",
size = .5,
remove.overlap = T,
col = "white",
just = "left",
bg.color = "darkgrey",
bg.alpha = .7
) +
tm_compass() +
tm_layout(
legend.outside = T,
main.title = "Harald’s 2022 Coffeeneuring Season",
fontfamily = "Roboto Condensed"
)