2. Fitbit

.title[
# 2. Fitbit
]
.subtitle[
## Analytics Sandbox
]
.author[
### K. Bret Staudt Willet | Florida State University
]
.date[
### January 18, 2023
]

---

# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M228.3 469.1L47.6 300.4c-4.2-3.9-8.2-8.1-11.9-12.4h87c22.6 0 43-13.6 51.7-34.5l10.5-25.2 49.3 109.5c3.8 8.5 12.1 14 21.4 14.1s17.8-5 22-13.3L320 253.7l1.7 3.4c9.5 19 28.9 31 50.1 31H476.3c-3.7 4.3-7.7 8.5-11.9 12.4L283.7 469.1c-7.5 7-17.4 10.9-27.7 10.9s-20.2-3.9-27.7-10.9zM503.7 240h-132c-3 0-5.8-1.7-7.2-4.4l-23.2-46.3c-4.1-8.1-12.4-13.3-21.5-13.3s-17.4 5.1-21.5 13.3l-41.4 82.8L205.9 158.2c-3.9-8.7-12.7-14.3-22.2-14.1s-18.1 5.9-21.8 14.8l-31.8 76.3c-1.2 3-4.2 4.9-7.4 4.9H16c-2.6 0-5 .4-7.3 1.1C3 225.2 0 208.2 0 190.9v-5.8c0-69.9 50.5-129.5 119.4-141C165 36.5 211.4 51.4 244 84l12 12 12-12c32.6-32.6 79-47.5 124.6-39.9C461.5 55.6 512 115.2 512 185.1v5.8c0 16.9-2.8 33.5-8.3 49.1z"/></svg> <br><br> **Part 2:** <br> Fitbit

---

### Step 1: Register a Fitbit developer app

This health assessment utilizes the {fitbitr} R package ([Kaye, 2021](https://mrkaye97.github.io/fitbitr/)). The functions in the package are relatively straightforward, but getting access to the [Fitbit API](https://dev.fitbit.com/build/reference/web-api/) set up is a bit tricky.

---

### Step 1: Register a Fitbit developer app

The first thing to do is register a new Fitbit developer app at [dev.fitbit.com](https://dev.fitbit.com/apps/new).

---

### Step 1: Register a Fitbit developer app

Here's how I completed the form:

<hr>

- **Application Name:** `r-health-tracker`
- **Description:** `fitbitr script`
- **Application Website URL:** `https://github.com/bretsw/fitbit` (replace with your GitHub repo)
- **Organization:** `bretsw` (replace with your GitHub user name)
- **Organization Website URL:** `https://github.com/bretsw/fitbit` (replace with your GitHub repo)
- **Terms of Service URL:** `https://github.com/bretsw/fitbit` (replace with your GitHub repo)
- **Privacy Policy URL:** `https://github.com/bretsw/fitbit` (replace with your GitHub repo)
- **OAuth 2.0 Application Type:** `Personal`
- **Redirct URL:** `http://localhost:1410/`
- **Default Access Type:** `Read Only`

---

### Step 1: Register a Fitbit developer app

I got a warning from the dev website that the redirect URL needs to be an `https`, but it needs to be in the exact format I have above, because this is how R is set up to take you back to your R console. If you have to, complete the initial form with `https://localhost:1410/` initially, then go back and edit it before you run your R script.

Once you submit your Fitbit dev app, you will be taken to a webpage that lists your **OAuth 2.0 Client ID** and **Client Secret**.

---

### Step 2: Record your dev app credentials

---

### Step 2: Record your dev app credentials

Be sure to make a copy of your Fitbit dev app **OAuth 2.0 Client ID** and **Client Secret**. I find it easiest to securely store these in my local R user environment. You can open this file with the command `usethis::edit_r_environ()` and then retrieve stored variables with `Sys.getenv()`.

In my R script below, note that you should save your credentials in the form `FITBIT_CLIENT_ID = XXXXXX` and `FITBIT_CLIENT_SECRET = XXXXXX`.

Once you have your credentials stored, close the .Renviron file and quit out of R entirely. When you restart R, your credentials are ready to go. You can check if things are stored correctly by running: `Sys.getenv('FITBIT_CLIENT_ID')` and `Sys.getenv('FITBIT_CLIENT_SECRET')`.

Just be sure that you don't share these credentials with anyone!

---

### Step 3: Retrieve your Fitbit data

---

### Step 3: Retrieve your Fitbit data

```r
generate_token(client_id = Sys.getenv('FITBIT_CLIENT_ID'), 
               client_secret = Sys.getenv('FITBIT_CLIENT_SECRET')
)
```

---

# <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M181.5 197.1l12.9 6.4c5.9 3 12.4 4.5 19.1 4.5c23.5 0 42.6-19.1 42.6-42.6V144c0-35.3-28.7-64-64-64H128c-35.3 0-64 28.7-64 64v21.4c0 23.5 19.1 42.6 42.6 42.6c6.6 0 13.1-1.5 19.1-4.5l12.9-6.4 8.4-4.2L135.1 185c-4.5-3-7.1-8-7.1-13.3V168c0-13.3 10.7-24 24-24h16c13.3 0 24 10.7 24 24v3.7c0 5.3-2.7 10.3-7.1 13.3l-11.8 7.9 8.4 4.2zm-8.6 49.4L160 240l-12.9 6.4c-12.6 6.3-26.5 9.6-40.5 9.6c-3.6 0-7.1-.2-10.6-.6v.6c0 35.3 28.7 64 64 64h64c17.7 0 32 14.3 32 32s-14.3 32-32 32H384V336 320c0-23.7 12.9-44.4 32-55.4c9.4-5.4 20.3-8.6 32-8.6V240c0-26.5 21.5-48 48-48c8.8 0 16 7.2 16 16v32 16 48c0 8.8 7.2 16 16 16s16-7.2 16-16V204.3c0-48.2-30.8-91-76.6-106.3l-8.5-2.8c-8-2.7-12.6-11.1-10.4-19.3s10.3-13.2 18.6-11.6l19.9 4C576 86.1 640 164.2 640 254.9l0 1.1h0c0 123.7-100.3 224-224 224h-1.1H256h-.6C132 480 32 380 32 256.6V256 216.8c-10.1-14.6-16-32.3-16-51.4V144l0-1.4C6.7 139.3 0 130.5 0 120c0-13.3 10.7-24 24-24h2.8C44.8 58.2 83.3 32 128 32h64c44.7 0 83.2 26.2 101.2 64H296c13.3 0 24 10.7 24 24c0 10.5-6.7 19.3-16 22.6l0 1.4v21.4c0 1.4 0 2.8-.1 4.3c12-6.2 25.7-9.6 40.1-9.6h8c17.7 0 32 14.3 32 32s-14.3 32-32 32h-8c-13.3 0-24 10.7-24 24v8h56.4c-15.2 17-24.4 39.4-24.4 64H320c-42.3 0-78.2-27.4-91-65.3c-5.1 .9-10.3 1.3-15.6 1.3c-14.1 0-27.9-3.3-40.5-9.6zM96 160c-8.8 0-16-7.2-16-16s7.2-16 16-16s16 7.2 16 16s-7.2 16-16 16zm144-16c0 8.8-7.2 16-16 16s-16-7.2-16-16s7.2-16 16-16s16 7.2 16 16z"/></svg> <br><br> Try it Out!

---

What do you think this code will do?

```r
start_date <- lubridate::today() - lubridate::years(2)
end_date <- lubridate::today()

steps_df <- steps(start_date, end_date)

write_csv(steps_df, "data/fitbit-steps.csv")
```

---

### Look at your daily steps

```r
glimpse(steps_df)
```

```
## Rows: 731
## Columns: 2
## $ date  <date> 2021-01-18, 2021-01-19, 2021-01-20, 2021-01-21, 2021-01-22, 202…
## $ steps <dbl> 12838, 10826, 11052, 12304, 9067, 5008, 5936, 8994, 9242, 11000,…
```

---

```r
ggplot(steps_df, aes(x=date, y=steps)) +
  geom_point() +
  geom_smooth() +
  geom_vline(aes(xintercept = as.Date("2020-03-11")), color = 'green') + 
  xlab(NULL) +
  ylab("Steps per Day") +
  ggtitle("Number of Daily Steps") +
  theme_bw()
```

---

---

What do you think this code will do?

```r
hr <- heart_rate_intraday(lubridate::today() - 1)

write_csv(hr, "data/fitbit-hr.csv")
```

---

### Look at yesterday's heart rate

```r
glimpse(hr)
```

```
## Rows: 1,045
## Columns: 2
## $ time       <dttm> 2023-01-17 00:00:00, 2023-01-17 00:01:00, 2023-01-17 00:02…
## $ heart_rate <dbl> 76, 68, 73, 80, 74, 79, 72, 87, 81, 69, 75, 84, 63, 64, 66,…
```
---

```r
ggplot(hr, aes(x=time, y=heart_rate)) +
  geom_point() +
  geom_smooth() +
  xlab(NULL) +
  ylab("Beats per Minute") +
  ggtitle("Yesterday's Heart Rate") +
  theme_bw()
```

---

---

What do you think this code will do?

```r
yesterday_df <- activity_summary(lubridate::today() - 1)

write_csv(yesterday_df, "data/fitbit-yesterday-df.csv")
```

---

```r
glimpse(yesterday_df)
```

```
## Rows: 1
## Columns: 14
## $ date                   <date> 2023-01-17
## $ active_score           <dbl> -1
## $ activity_calories      <dbl> 853
## $ calories_bmr           <dbl> 1852
## $ calories_out           <dbl> 2625
## $ elevation              <dbl> 0
## $ fairly_active_minutes  <dbl> 0
## $ floors                 <dbl> 0
## $ lightly_active_minutes <dbl> 188
## $ marginal_calories      <dbl> 443
## $ resting_heart_rate     <dbl> 58
## $ sedentary_minutes      <dbl> 1252
## $ steps                  <dbl> 5099
## $ very_active_minutes    <dbl> 0
```

---

What do you think this code will do?

```r
health_df = NULL

for (i in 0:60) {
  new_row =
    tibble(date = lubridate::today() - i,
           resting_hr = activity_summary(date)$resting_heart_rate,
           steps = activity_summary(date)$steps
           )
  health_df <-
    health_df %>%
    bind_rows(new_row)
}

write_csv(health_df, "data/fitbit-health-df.csv")
```

---

### Look at yesterday's heart rate

```r
glimpse(health_df)
```

```
## Rows: 61
## Columns: 3
## $ date       <date> 2023-01-18, 2023-01-17, 2023-01-16, 2023-01-15, 2023-01-14…
## $ steps      <dbl> 105, 5099, 7161, 5281, 7645, 5875, 6366, 6336, 6099, 8664, …
## $ resting_hr <dbl> NA, 58, 57, 57, 57, 58, 58, 58, NA, 57, 57, 58, 58, 58, 60,…
```

---

This block of code looks at my resting heart rate across the past 2 months (60 days). Note that a Fitbit dev app's rate limit is 150 API requests per hour for each user who has consented to share their data; and it resets at the top of each hour. This means that you are limited to retrieving the `activity_summary()` data for 150 days at a time. You could store this data, wait an hour, and the retrieve the next 150 days if you wanted to.

---

```r
ggplot(health_df, aes(x=date, y=resting_hr)) +
  geom_point() +
  geom_smooth() +
  xlab(NULL) +
  ylab("Beats per Minute while Resting") +
  ggtitle("Resting Heart Rate") +
  theme_bw()
```

---

---

What do you think this code will do?

```r
ggplot(health_df, aes(x=steps, y=resting_hr)) +
  geom_point() +
  geom_smooth() +
  xlab("Daily Steps") +
  ylab("Beats per Minute while Resting") +
  ggtitle("Resting Heart Rate vs. Daily Steps") +
  theme_bw()
```

---

---

```r
ggplot(health_df, aes(x=steps, y=resting_hr)) +
  geom_jitter(alpha=0.6) +
  geom_smooth(method='lm') +
  xlab("Daily Steps") +
  ylab("Beats per Minute while Resting") +
  ggtitle("Resting Heart Rate vs. Daily Steps") +
  theme_bw()
```

---

---

---

- Download a copy of this repository.
- If you have a Fitbit, see if you can retrieve and analyze your own data.
- Regardless, use the saved data in the "data" folder to play around a bit more, changing different parameters.
- Reflect: 
  - What other comparisons might you make?
  - How else might you analyze these Fitbit data?

---

# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M40 48C26.7 48 16 58.7 16 72v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V72c0-13.3-10.7-24-24-24H40zM192 64c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zm0 160c-17.7 0-32 14.3-32 32s14.3 32 32 32H480c17.7 0 32-14.3 32-32s-14.3-32-32-32H192zM16 232v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V232c0-13.3-10.7-24-24-24H40c-13.3 0-24 10.7-24 24zM40 368c-13.3 0-24 10.7-24 24v48c0 13.3 10.7 24 24 24H88c13.3 0 24-10.7 24-24V392c0-13.3-10.7-24-24-24H40z"/></svg> <br><br> Appendix: <br> Helpful Resources <br> and Troubleshooting

---

# Resources

**Beginners:**
- [RStudio Beginners' Guide](https://education.rstudio.com/learn/beginner/)
- Book: [*Data Science in Education Using R*](https://datascienceineducation.com)
  - See [Chapter 12](https://datascienceineducation.com/c12.html) - Walkthrough 6: Exploring Relationships Using Social Network Analysis With Social Media Data
  - [Physical copy of DSIEUR](https://www.routledge.com/Data-Science-in-Education-Using-R/Estrellado-Freer-Mostipak-Rosenberg-Velasquez/p/book/9780367422257)
  - [Even more resources from DSIEUR](https://datascienceineducation.com/c18.html)

**Intermediates:**
- [RStudio Intermediates' Guide](https://education.rstudio.com/learn/intermediate/)
- [{tidytags} package notes](https://docs.ropensci.org/tidytags/index.html)
- Book: [*R for Data Science*](http://r4ds.had.co.nz/)

**Experts:**
- [RStudio Experts' Guide](https://education.rstudio.com/learn/expert/)
- Book: [*Learning Statistics with R*](https://learningstatisticswithr.com/)
- [*Data Science in Education Using R*](https://datascienceineducation.com)
  - See [Chapter 20.3 Appendix C](https://datascienceineducation.com/c20.html#c20c) - Social Network Influence and Selection Models
- SNA resources: [Dr. Ken Frank's website](https://sites.google.com/msu.edu/kenfrank/social-network-resources)

---

# Troubleshooting

- Try to find out what the specific problem is
  -  Identify what is *not* causing the problem
- "Unplug and plug it back in" - restart R; close and reopen R
- Seek out workshops and other learning opportunities
- Reach out to others! Sharing what is causing an issue can often help to clarify the problem
  - [RStudio Community forum](https://community.rstudio.com/) (highly recommended!)
  - Twitter hashtag: [#RStats](https://twitter.com/search?q=%23RStats&src=typeahead_click&f=live)
  - [Contact Bret!](https://bretsw.com)
- General strategies on learning more: [Chapter 17 of *Data Science in Education Using R*](https://datascienceineducation.com/c17.html)