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copenhagenR-April2024-slides.qmd
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copenhagenR-April2024-slides.qmd
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---
# title: "My Year of Riding Danishly"
# author: "Gregers Kjerulf Dubrow"
format:
revealjs:
theme: [default, custom.scss]
center-title-slide: false
# title-slide-attributes:
# data-background-image: "bike_dragor.jpeg"
# data-background-size: contain
slide-number: c/t
code-link: true
code-overflow: wrap
code-block-height: 1500px
code-line-numbers: false
height: 1080
width: 1920
editor: source
---
##
````{=html}
<!---makes code chunk font larger
```{css echo=FALSE}
.big-code{
font-size: 125%
}
```
--->
````
<center>
<h1>My Year of Riding Danishly</h1>
</center>
<br> <br> <br> <br> <br>
<h2>Gregers Kjerulf Dubrow</h2>
<br>
<h3>CopenhagenR</h3>
<br>
<h3>23 April, 2024</h3>
::: {.absolute bottom="50" right="-400"}
{width="75%" fig-alt="red univega road bike overlooking oresund at dragor"}
:::
##
<center>
<h1>My Year of Riding Danishly</h1>
</center>
::: columns
::: {.column .fragment width="50%"}
<br> <!---<div class=big-code> --->
```{r}
#| eval: false
#| echo: true
library(gregbio)
my_life <- blev_født(danmark) %>%
```
<!---</div> --->
:::
:::
::: {.fragment .absolute top="185" right="400"}
{width="4in" fig-alt="6 month old baby at the steering wheel of a car"}
:::
##
<center>
<h1>My Year of Riding Danishly</h1>
</center>
::: columns
::: {.column width="55%"}
<br>
```{r}
#| eval: false
#| echo: true
library(gregbio)
my_life <- blev_født(danmark) %>%
voksede_op(USA,
state = “Pennsylvania”,
city = “Philadelphia) %>%
undergrad_degree(film_major) %>%
PhD(education_policy) %>%
```
:::
:::
::: {.absolute top="185" right="400"}
{width="4in" fig-alt="6 month old baby at the steering wheel of a car"}
:::
::: {.absolute top="185" right="70"}
{width="3.5in" fig-alt="philadelphia pa city hall"}
:::
##
<center>
<h1>My Year of Riding Danishly</h1>
</center>
::: columns
::: {.column width="55%"}
<br>
```{r}
#| eval: false
#| echo: true
library(gregbio)
my_life <- blev_født(danmark) %>%
voksede_op(USA,
state = “Pennsylvania”,
city = “Philadelphia) %>%
undergrad_degree(film_major) %>%
PhD(education_policy) %>%
career = case_when(
job = faculty ~ FIU, (set_location as Miami, FL)
```
:::
:::
::: {.absolute top="185" right="400"}
{width="4in" fig-alt="6 month old baby at the steering wheel of a car"}
:::
::: {.absolute top="185" right="70"}
{width="3.5in" fig-alt="philadelphia pa city hall"}
:::
::: {.absolute top="450" right="435"}
{width="3.5in" fig-alt="miami beach lifeguard station"}
:::
##
<center>
<h1>My Year of Riding Danishly</h1>
</center>
::: columns
::: {.column width="55%"}
<br>
```{r}
#| eval: false
#| echo: true
library(gregbio)
my_life <- blev_født(danmark) %>%
voksede_op(USA,
state = “Pennsylvania”,
city = “Philadelphia) %>%
undergrad_degree(film_major) %>%
PhD(education_policy) %>%
career = case_when(
job = faculty ~ FIU, (set_location as Miami, FL),
job = data_analyst ~ UC Berkeley & SFSU,
(set_location as SF Bay Area)
```
:::
:::
::: {.absolute top="185" right="400"}
{width="4in" fig-alt="6 month old baby at the steering wheel of a car"}
:::
::: {.absolute top="185" right="70"}
{width="3.5in" fig-alt="philadelphia pa city hall"}
:::
::: {.absolute top="450" right="435"}
{width="3.5in" fig-alt="miami beach lifeguard station"}
:::
::: {.absolute top="450" right="70"}
{width="3.5in" fig-alt="overhead shot of sproul hall uc berkeley"}
:::
##
<center>
<h1>My Year of Riding Danishly</h1>
</center>
::: columns
::: {.column width="55%"}
<br>
```{r}
#| eval: false
#| echo: true
library(gregbio)
my_life <- blev_født(danmark) %>%
voksede_op(USA,
state = “Pennsylvania”,
city = “Philadelphia) %>%
undergrad_degree(film_major) %>%
PhD(education_policy) %>%
career = case_when(
job = faculty ~ FIU, (set_location as Miami, FL),
job = data_analyst ~ UC Berkeley & SFSU,
(set_location as SF Bay Area),
job = career_swerve1 ~ freelance_ESL_teacher,
(set_location as Lyon, FR)
```
:::
:::
::: {.absolute top="185" right="400"}
{width="4in" fig-alt="6 month old baby at the steering wheel of a car"}
:::
::: {.absolute top="185" right="70"}
{width="3.5in" fig-alt="philadelphia pa city hall"}
:::
::: {.absolute top="450" right="435"}
{width="3.5in" fig-alt="miami beach lifeguard station"}
:::
::: {.absolute top="450" right="70"}
{width="3.5in" fig-alt="overhead shot of sproul hall uc berkeley"}
:::
::: {.absolute top="710" right="435"}
{width="3.5in" fig-alt="lyon france"}
:::
##
<center>
<h1>My Year of Riding Danishly</h1>
</center>
::: columns
::: {.column width="55%"}
<br>
```{r}
#| eval: false
#| echo: true
library(gregbio)
my_life <- blev_født(danmark) %>%
voksede_op(USA,
state = “Pennsylvania”,
city = “Philadelphia) %>%
undergrad_degree(film_major) %>%
PhD(education_policy) %>%
career = case_when(
job = faculty ~ FIU, (set_location as Miami, FL),
job = data_analyst ~ UC Berkeley & SFSU,
(set_location as SF Bay Area),
job = career_swerve1 ~ freelance_ESL_teacher,
(set_location as Lyon, FR)
job = career_swerve2 ~
study_abroad_student_services,
(set_location as København, DK)))
```
:::
:::
::: {.absolute top="185" right="400"}
{width="4in" fig-alt="6 month old baby at the steering wheel of a car"}
:::
::: {.absolute top="185" right="70"}
{width="3.5in" fig-alt="philadelphia pa city hall"}
:::
::: {.absolute top="450" right="435"}
{width="3.5in" fig-alt="miami beach lifeguard station"}
:::
::: {.absolute top="450" right="70"}
{width="3.5in" fig-alt="overhead shot of sproul hall uc berkeley"}
:::
::: {.absolute top="710" right="435"}
{width="3.5in" fig-alt="lyon france"}
:::
::: {.absolute top="710" right="70"}
{width="3.5in" fig-alt="front door to ciee copenhagen study center"}
:::
##
<center>
<h1>My Year of Riding Danishly</h1>
</center>
<center>https://www.gregdubrow.io/posts/my-year-of-riding-danishly/</center>
{fig-align="center" height="10in"}
## The plan...
- [Pull the Data](#getdata)
- Via bulk download, the Strava API, and `rStrava` package.
- [EDA with DataExplorer](#eda1)
- Show and run code for exploratory analysis (EDA) using the `DataExplorer` package.
- [EDA with Automated Scatterplots](#eda2)
- Show and run code for EDA using [Cedric Scherer's tutorial on automating plots](https://www.cedricscherer.com/2023/07/05/efficiency-and-consistency-automate-subset-graphics-with-ggplot2-and-purrr/).
- [Tables with `gt`](#tables)
- Show and run code for the tables, including how to align `gt` tables next to each other.
- [Create Charts to Describe My Ride Data](#plots)
- Show and run the `ggplot` code to make some pretty charts.
- [Regression Models](#models)
- Run a few regression models to explain ride outcomes.
## What is Strava?
{fig-align="left" height="2in"}
. . .
<h2>
- App & data platform to track physical exercise - mostly cycling, running, and walking/hiking.
</h2>
. . .
<h2>
- Uses Global Positioning System (GPS) to plot routes.
</h2>
. . .
<h2>
- Social media features, including following other users, adding photos to activity posts, giving kudos to other users on their activities...
</h2>
. . .
<h2>
- Name derived from Swedish word for "strive" - `sträva`
</h2>
## Pulling the data - Bulk download
- Bulk download includes folder with all activities in GPX (GPS Exchange Format) files to build your own maps.
::: {.absolute top="190" left="10"}
{width="5.3in" fig-alt="strava download folder"}
:::
::: {.absolute top="190" left="600"}
{width="3in" fig-alt="strava download folder activities"}
:::
::: {.absolute top="190" left="950"}
{fig-alt="strava download folder activities"}
:::
## Pulling the data - API
- Sign up in your account, project can be anything you want.
::: {.fragment .absolute top="190" left="10"}
{width="8in" fig-alt="strava download folder"}
:::
::: {.fragment .absolute top="190" left="800"}
{width="8in" fig-alt="strava download folder activities"}
:::
## Pulling the data - API
{fig-alt="strava api developer page" fig-align="center" height="11in"}
## Pulling the data - `rStrava` package
<center>https://github.com/fawda123/rStrava</center>
{fig-align="center" height="11in"}
## Let's load the CSV data
```{r}
#| eval: false
#| echo: true
library(tidyverse) # to do tidyverse things
library(tidylog) # to get a log of what's happening to the data
library(janitor) # tools for data cleaning
strava_activities <- readr::read_csv("data/activities.csv") %>%
clean_names() %>%
as_tibble() %>%
rename(elapsed_time = elapsed_time_6, distance = distance_7, max_heart_rate = max_heart_rate_8,
relative_effort = relative_effort_9, commute = commute_10, elapsed_time2 = elapsed_time_16,
distance2 = distance_18, relative_effort2 = relative_effort_38, commute2 = commute_51)
```
::: {.fragment}
{width="11in"}
:::
::: {.fragment}
:::
## Too much date & time wrangling
```{r}
#| eval: false
#| echo: true
mutate(activity_date = str_replace(activity_date, "Jan ", "January "))
separate('activity_date', paste("date", 1:3, sep="_"), sep=",", extra="drop")
mutate(activity_md = str_trim(date_1))
separate('activity_md', paste("activity_md", 1:2, sep="_"), sep=" ", extra="drop")
mutate(activity_mdy = paste0(date_1, ",", date_2))
mutate(activity_ymd = lubridate::mdy(activity_mdy))
mutate(activity_tz = case_when(activity_ymd >= "2022-06-28" ~ "Europe/Copenhagen",
TRUE ~ "US/Pacific"))
```
<br>
::: {.fragment}
<h3>Ok, time to try the API...or wait...is there a package?</h3>
:::
::: {.fragment}
{height=4.75in}
:::
## Using the `rStrava` package - Authorization
In a separate file called stoken.r, I create the access token to call in the script that pulls the data.
```{r}
#| eval: false
#| echo: true
library(rStrava)
app_name <- 'Year of Riding Danishly' # chosen by user
app_client_id <- '---' # an integer, assigned by Strava
app_secret <- '---' # an alphanumeric secret, assigned by Strava
# create the authentication token - cache = TRUE saves it in the working directory
stoken <- httr::config(token = strava_oauth(
app_name, app_client_id, app_secret,
app_scope="activity:read_all", cache = TRUE))
```
stoken object is a list:
::: {.fragment}
{height=7in}
:::
## Using the `rStrava` package - Get the data
```{r}
#| eval: false
#| echo: true
## call the OAuth access token
stoken <- httr::config(token = readRDS('.httr-oauth')[[1]])
## invoke stoken to get data
myact <- get_activity_list(stoken)
```
Returns a list of activities requested
::: {.fragment .absolute top="300" left="10"}
{height=6in}
:::
::: {.fragment .absolute top="300" left="720"}
{height=9.5in}
:::
## Using the `rStrava` package - Get the data
```{r}
#| eval: false
#| echo: true
act_data <- compile_activities(myact) %>%
as_tibble() %>%
glimpse()
```
::: {.fragment}
{height=10in}
:::
## Clean & prep the data
```{r}
#| eval: false
#| echo: true
act_data <- compile_activities(myact) %>%
as_tibble() %>%
mutate(gear_name = case_when(gear_id == "b6298198" ~ "Univega",
gear_id == "b11963967" ~ "Commute bike", TRUE ~ "Not a bike ride")) %>%
mutate(activity_date = lubridate::as_datetime(start_date_local)) %>%
mutate(activity_date_p = as.Date(start_date_local)) %>%
mutate(activity_year = lubridate::year(start_date_local),
activity_month = lubridate::month(start_date_local),
activity_month_t = lubridate::month(start_date_local, label = TRUE, abbr = FALSE),
activity_day = lubridate::day(start_date_local),
activity_md = paste0(activity_month_t, " ", activity_day),
activity_wday = wday(activity_date_p, label = TRUE, abbr = FALSE),
activity_hour = lubridate::hour(activity_date),
activity_min = lubridate::minute(activity_date),
activity_hmt = paste0(activity_hour, ":", activity_min),
activity_hm = hm(activity_hmt),
moving_time_hms = hms::hms(moving_time),
elapsed_time_hms = hms::hms(elapsed_time)) %>%
mutate(location_country = case_when(
timezone == "(GMT+01:00) Europe/Copenhagen" ~ "Denmark",
timezone == "(GMT+01:00) Europe/Paris" ~ "France", TRUE ~ "United States")) %>%
## random edits
mutate(commute = ifelse((activity_year == 2023 & activity_md == "June 14" & name == "Morning commute"),
TRUE, commute))
## merge with variables from CSV but not in API
act_data_csv_ext <- act_data_csv %>%
select(activity_id, calories, average_grade, max_grade, average_elapsed_speed, elevation_loss)
strava_activities_final <- act_data %>%
merge(act_data_csv_ext) %>%
select(activity_id:max_speed, average_elapsed_speed, elevation_gain, elevation_loss, elevation_high, elevation_low,
average_grade, max_grade, location_country:lng_end, average_watts, calories, kilojoules)
```
## Let's do some EDA
<!---load data quietly--->
```{r dataload, message=FALSE, ECHO = FALSE, include = FALSE}
#| message: false
#| echo: false
#| include: false
#| warning: false
#| error: false
library(tidyverse) # to do tidyverse things
library(tidylog) # to get a log of what's happening to the data
library(janitor) # tools for data cleaning
library(modelsummary) # regressions
library(ggtext) # ggplot text helpers
## quietly loads RDS already created
strava_data <- readRDS("~/Data/r/year of riding danishly/data/strava_activities_final.rds")
sumtable <- readRDS("~/Data/r/year of riding danishly/data/sumtable.rds")
rides_mth_type <- readRDS("~/Data/r/year of riding danishly/data/rides_mth_type.rds")
activty_ampm <- readRDS("~/Data/r/year of riding danishly/data/activty_ampm.rds")
strava_models <- strava_data %>%
filter(activity_year == 2023)
```
```{r eda2l}
#| eval: false
#| echo: true
library(DataExplorer) # for EDA
plot_intro(strava_data)
plot_missing(strava_data)
```
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{height=8in}
:::
## Quick aside...some terms that need defining
<br>
Some are obvious (distance in km), these maybe less so...
. . .
- Elapsed time: When I hit "start" on the app to when I hit "stop".
. . .
- Moving time: App's caclulation of how much time was spent in motion.
. . .
- Grade: Rise (elevation) divided by run (distance), x 100
. . .
- Watts: A measurement of power. Strava incorporates rider weight, bike weight, bike type, gradients and speed.
. . .
- Calories: Total energy expended in the time it took to do the workout.
. . .
- Kilojoules: Energy burned by the workout. Formula for kilojoules is (watts x seconds) x 1000.
## Let's do some EDA - Correlations!
```{r edacorrr}
#| eval: false
#| echo: true
strava_data %>%
select(distance_km, elapsed_time, moving_time, max_speed, average_speed, elevation_gain, elevation_loss, elevation_low,
elevation_high, average_grade, max_grade, average_watts, calories, kilojoules) %>%
filter(!is.na(average_watts)) %>%
filter(!is.na(calories)) %>%
plot_correlation(maxcat = 5L, type = "continuous", geom_text_args = list("size" = 4))
```
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:::
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So what do we see here?
- Most of the relationships are positive, some with expectedly near 1:1 relationships, such as distance (in km) and total time for the ride.
- Average speed is positively correlated with distance but the relationship is only at 0.14, the weakest of all positive associations with distance. Average speed correlations are low...near 0, for total elevation gain and negative the higher the average grade of the ride.
- Averge watts, or weighted power output for the ride, has mostly negative correlations. Longer rides in time or distance meant a lower average power per ride segment.
We'll keep these correlations in mind when looking at the scatterplots and then later considering the regression results.
:::
## More EDA - Scatterplots!
- `DataExplorer` has functionality for scatterplots, but each call only allows for one comparison y-axis variable & not much customization to the output, like a smoothing line.
```{r}
#| eval: false
#| echo: true
plot_scatterplot(strava_data_filter, by = "distance_km", nrow = 6L)
```
{height=9in}
## More EDA - Better scatterplots!
- https://www.cedricscherer.com/2023/07/05/efficiency-and-consistency-automate-subset-graphics-with-ggplot2-and-purrr/
```{r}
#| eval: false
#| echo: true
plot_scatter_lm <- function(data, var1, var2, pointsize = 2, transparency = .5, color = "") {
## check if inputs are valid
if (!exists(substitute(data))) stop("data needs to be a data frame.")
if (!is.data.frame(data)) stop("data needs to be a data frame.")
if (!is.numeric(pull(data[var1]))) stop("Column var1 needs to be of type numeric, passed as string.")
if (!is.numeric(pull(data[var2]))) stop("Column var2 needs to be of type numeric, passed as string.")
if (!is.numeric(pointsize)) stop("pointsize needs to be of type numeric.")
if (!is.numeric(transparency)) stop("transparency needs to be of type numeric.")
if (color != "") { if (!color %in% names(data)) stop("Column color needs to be a column of data, passed as string.") }
g <-
ggplot(data, aes(x = !!sym(var1), y = !!sym(var2))) +
geom_point(aes(color = !!sym(color)), size = pointsize, alpha = transparency) +
geom_smooth(aes(color = !!sym(color), color = after_scale(prismatic::clr_darken(color, .3))),
method = "lm", se = FALSE) +
theme_minimal() +
theme(panel.grid.minor = element_blank(), legend.position = "top")
if (color != "") {
if (is.numeric(pull(data[color]))) {
g <- g + scale_color_viridis_c(direction = -1, end = .85) +
guides(color = guide_colorbar(
barwidth = unit(12, "lines"), barheight = unit(.6, "lines"), title.position = "top"))
} else {g <- g + scale_color_brewer(palette = "Set2")}
}
return(g)
}
```
## More EDA - Better scatterplots!
```{r}
#| eval: false
#| echo: true
## 1st plot call - distance as y axis
patchwork::wrap_plots(
map2(c("elapsed_time", "moving_time", "average_speed","average_watts", "calories", "kilojoules"),
c("distance_km", "distance_km", "distance_km", "distance_km", "distance_km", "distance_km"),
~plot_scatter_lm(data = strava_activities_rides, var1 = .x, var2 = .y, pointsize = 3.5) +
theme(plot.margin = margin(rep(15, 4)))))
```
::: {.fragment}
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:::
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- Confirms what we saw in the correlation heatmap & displays ride distributions.
- Positive and almost 1:1 relationships between distance and both time measures, elapsed and moving.
- Negative association with watts that we saw in the correlations. Making a note to take a closer look at how much an effect watts has later on in the regression section.
- Note the outlier ride of 60km and an elapsed time of more than 15,000 seconds...more about that one later.
:::
## A few more scatterplots
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- Average speed decreases as ride time goes up (top left plot)...makes sense.
- Expended more energy (calories & kilojoules) as ride time increased (top left).
- Speed & watts had strong relationship (top right), as we saw in correlation heatmap.
- Surprised energy output has weak association with average speed; perhaps here in flat Denmark there’s only so much energy burn I can hit.
- Strong relationship between kilojoules & elevation.
:::
## Tables with `gt`
```{r}
#| fig.width: 8.0
#| fig.height: 6.0
#| fig-dpi: 300
#| warning: false
#| message: false
#| error: false
#| echo: false
library(gt)
sumtable %>%
select(rides, km_total, elev_total, time_total1, time_total2, cal_total, kiloj_total) %>%
gt() %>%
fmt_number(columns = c(km_total, elev_total, cal_total, kiloj_total), decimals = 0) %>%
cols_label(rides = "Total Rides", km_total = "Total Kilometers",
elev_total = md("Total Elevation *(meters)*"),
time_total1 = md("Total Time *(hours/min/sec)*"),
time_total2 = md("Total Time *(days/hours/min/sec)*"),
cal_total = "Total Calories", kiloj_total = "Total Kilojoules") %>%
cols_align(align = "center", columns = everything()) %>%
tab_style(style = cell_fill(color = "lightgrey"),
locations = cells_body(rows = seq(1, 1, 1))) %>%
tab_style(
style = cell_text(align = "center"),
locations = cells_column_labels(
columns = c(rides, km_total, elev_total, time_total1, time_total2, cal_total, kiloj_total))) %>%
tab_header(title = md("My Year of Riding Danishly<br>*Ride Totals*")) %>%
gtsave("images/gtsummary1.png")
```
```{r}
#| eval: false
#| echo: true
library(gt)
sumtable %>%
select(rides, km_total, elev_total, time_total1, time_total2, cal_total, kiloj_total) %>%
gt() %>%
fmt_number(columns = c(km_total, elev_total, cal_total, kiloj_total), decimals = 0) %>%
cols_label(rides = "Total Rides", km_total = "Total Kilometers",
elev_total = md("Total Elevation *(meters)*"),
time_total1 = md("Total Time *(hours/min/sec)*"),
time_total2 = md("Total Time *(days/hours/min/sec)*"),
cal_total = "Total Calories", kiloj_total = "Total Kilojoules") %>%
cols_align(align = "center", columns = everything()) %>%
tab_style(style = cell_fill(color = "grey"), locations = cells_body(rows = seq(1, 1, 1))) %>%
tab_style( style = cell_text(align = "center"), locations = cells_column_labels(
columns = c(rides, km_total, elev_total, time_total1, time_total2, cal_total, kiloj_total))) %>%
tab_header(title = md("My Year of Riding Danishly<br>*Ride Totals*"))
```
::: {.fragment}
:::
::: {.fragment}
- For the year, more than 440 rides covering 2,500 kilometers.
- I spent the equivalent of more than 5 days on the bike, and burned 60,000+ units of energy. Which means on average, every day I did 1.2 rides,and went about 7 km, a few km more than the average Copenhagener. (*It occurs to me know that I didn't make a count for how many days of the year I rode...an edit to come perhaps...*)
:::
## Tables with `gt`
```{r}
#| fig.width: 8.0
#| fig.height: 6.0
#| fig-dpi: 300
#| warning: false
#| message: false
#| error: false
#| echo: false
sumtable %>%
select(km_avg, km_med, km_min, km_max) %>%
gt() %>%
cols_label(km_avg = "Average", km_med = "Median",
km_min = "Shortest", km_max = "Longest") %>%
cols_align(align = "center", columns = everything()) %>%
tab_style(style = cell_fill(color = "grey"), locations = cells_body(rows = seq(1, 1, 1))) %>%
tab_header(title = md("*Ride Statistics - Distance (in km)*")) %>%
gtsave("images/gtsummary2.png")
sumtable %>%
select(time_avg, time_med, time_min, time_max) %>%
gt() %>%
cols_label(time_avg = "Average", time_med = "Median",
time_min = "Shortest", time_max = "Longest") %>%
cols_align(align = "center", columns = everything()) %>%
tab_style(style = cell_fill(color = "grey"), locations = cells_body(rows = seq(1, 1, 1))) %>%
tab_header(title = md("*Ride Statistics - Time*")) %>%
gtsave("images/gtsummary3.png")
sumtable %>%
select(elev_avg, elev_med, elev_min, elev_max) %>%
gt() %>%
cols_label(elev_avg = "Average", elev_med = "Median",
elev_min = "Lowest", elev_max = "Highest") %>%
cols_align(align = "center", columns = everything()) %>%
tab_style(style = cell_fill(color = "grey"), locations = cells_body(rows = seq(1, 1, 1))) %>%
tab_header(title = md("*Ride Statistics - Elevation (meters)*")) %>%
gtsave("images/gtsummary4.png")
sumtable %>%
select(cal_avg, cal_min, cal_max, kiloj_avg, kiloj_min, kiloj_max) %>%
gt() %>%
cols_label(cal_avg = "Average", cal_min = "Least", cal_max = "Most",
kiloj_avg = "Average", kiloj_min = "Least", kiloj_max = "Most") %>%
cols_align(align = "center", columns = everything()) %>%
tab_spanner(label = "Calories Burned", columns = c(cal_avg, cal_min, cal_max)) %>%
tab_spanner(label = "Kilojoules Burned", columns = c(kiloj_avg, kiloj_min, kiloj_max)) %>%
tab_style(style = cell_fill(color = "grey"), locations = cells_body(rows = seq(1, 1, 1))) %>%
tab_header(title = md("*Ride Statistics - Energy*")) %>%
gtsave("images/gtsummary5.png")
```
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- The rides spanned 1 km to 60 km, with the average & median ride around 4-5km, which makes sense given that my work commute was a bit over 4km and rides to Danish class were ~4km or ~6km depending on location.
<br>
<br>
- The elevation stats are what you’d expect for Denmark, and the average ride burned 140-150 units of energy.
:::
## Let's make some charts with `ggplot2`
```{r}
#| eval: false
#| echo: true
library(patchwork)
ridesplot1 <- rides_mth_type %>%
ggplot(aes(activity_month_abbv, rides_by_month)) +
geom_col(fill = "#C8102E") +
geom_text(aes(label= rides_by_month), color = "white", size = 5, vjust = 1.5) +
labs(x = "", y = "", title = "Spring & Summer Weather = More Rides",
subtitle = glue::glue("*Average Rides / Month = {round(mean(rides_mth_type$rides_by_month, 3))}*")) +
theme_minimal() +
theme(panel.grid = element_blank(), plot.title = element_text(hjust = 0.5),
plot.subtitle = element_markdown(hjust = 0.5),
axis.text.y = element_blank())
ridesplot2 <- rides_mth_type %>%
group_by(ride_type) %>%
mutate(rides_by_type = sum(ride_type_n)) %>%
ungroup() %>%
distinct(rides_by_type, .keep_all = TRUE) %>%
mutate(ride_type_pct = rides_by_type / sum(rides_by_type)) %>%
{. ->> tmp} %>%
ggplot(aes(ride_type, ride_type_pct)) +
geom_col(fill = "#C8102E") +
scale_x_discrete(labels = paste0(tmp$ride_type, "<br>Total Rides = ", tmp$rides_by_type, "")) +
geom_text(data = subset(tmp, ride_type != "Workout"),
aes(label= scales::percent(round(ride_type_pct, 2))), color = "white", size = 5, vjust = 1.5) +
geom_text(data = subset(tmp, ride_type == "Workout"),
aes(label= scales::percent(round(ride_type_pct, 2))), color = "#C8102E", size = 5, vjust = -.5) +
labs(x = "", y = "", title = "Lots of Riding to Work or Danish Class") +
theme_minimal() +
theme(panel.grid = element_blank(), plot.title = element_text(hjust = 0.5),
axis.text.y = element_blank(), axis.text.x = element_markdown())
rm(tmp)
ridesplot1 + ridesplot2
```
## Let's make some charts with `ggplot2`
## Let's make some charts with `ggplot2`
```{r}
#| eval: false
#| echo: true
rides_mth_type %>%
ggplot(aes(activity_month_t, ride_type_pct, fill = ride_type)) +
geom_bar(stat = "identity") +
geom_text(data = subset(rides_mth_type, ride_type != "Workout"),
aes(label = scales::percent(round(ride_type_pct, 2))),
position = position_stack(vjust = 0.5), color= "white", vjust = 1, size = 5) +
labs(x = "", y = "", title = "Most Rides Each Month Were Commutes to/from Work or Danish Class") +
scale_fill_manual(values = c("#0072B2", "#E69F00", "#CC79A7"), labels = c("Commute/<br>Studieskolen", "Other", "Workout")) +
theme_minimal()+
theme(legend.position = "bottom", legend.spacing.x = unit(0, 'cm'),
legend.text = element_markdown(),
legend.key.width = unit(1.5, 'cm'), legend.title = element_blank(),
axis.text.y = element_blank(), plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
guides(fill = guide_legend(label.position = "bottom"))
```