Data Prescription

.title[
# Data Prescription
]
.subtitle[
## Introducing R for Nursing Analytics
]
.author[
### Dr. Bret Staudt Willet
]
.date[
### December 18, 2024
]

---

# <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M160 32c-35.3 0-64 28.7-64 64V320c0 35.3 28.7 64 64 64H512c35.3 0 64-28.7 64-64V96c0-35.3-28.7-64-64-64H160zM396 138.7l96 144c4.9 7.4 5.4 16.8 1.2 24.6S480.9 320 472 320H328 280 200c-9.2 0-17.6-5.3-21.6-13.6s-2.9-18.2 2.9-25.4l64-80c4.6-5.7 11.4-9 18.7-9s14.2 3.3 18.7 9l17.3 21.6 56-84C360.5 132 368 128 376 128s15.5 4 20 10.7zM192 128a32 32 0 1 1 64 0 32 32 0 1 1 -64 0zM48 120c0-13.3-10.7-24-24-24S0 106.7 0 120V344c0 75.1 60.9 136 136 136H456c13.3 0 24-10.7 24-24s-10.7-24-24-24H136c-48.6 0-88-39.4-88-88V120z"/></svg>

**View the slides:**

[bretsw.com/nursing24-workshop](https://bretsw.com/nursing24-workshop)

---

# <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> <br><br> **Part 1:** <br> Introduction to R

---

# <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 128a16 16 0 1 1 0 32 16 16 0 1 1 0-32zm112 16a16 16 0 1 1 32 0 16 16 0 1 1 -32 0z"/></svg> Our Constructivist Approach

1. I'll introduce some concepts

1. You'll try some code

1. We'll all discuss together

<div class="caption">
<p><a href="https://xkcd.com/627/" target="_blank">'Tech Support Cheat Sheet' from XKCD</a></p>
</div>

---

---

- It is increasingly used in academia

- It is cross-platform, open-source, and freely-available

- It is a programming language and quite flexible

- It is capable of carrying out basic and complex statistical analyses

- It is able to work with data small (*n* = 10) and large (*n* = 1,000,000+) efficiently

- There is a great, inclusive community of users and developers

---

### To download R

- Visit [**cran.r-project.org**](https://cran.r-project.org/) to download R
- Find your operating system (Mac, Windows, or Linux)
- Download the 'latest release' on the page for your operating system and download and install the application

### To download RStudio Desktop

- Visit [**posit.co**](https://posit.co/download/rstudio-desktop/) to download RStudio Desktop from the parent company, Posit
- Find your operating system (Mac, Windows, or Linux)
- Download the 'latest release' on the page for your operating system and download and install the application

### RMarkdown

- RMarkdown is a data analysis "notebook" that combines text with code and output
- It is a great file type to use when beginning to use R and to create reproducible analyses
- It is fun to use because you can generate different types of output (Word, PDF, and even web-based)

---

---

What do you think this code will do?

``` r
starwars
```

---

Let's see!

``` r
starwars
```

```
## # A tibble: 87 × 14
##    name     height  mass hair_color skin_color eye_color birth_year sex   gender
##    <chr>     <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
##  1 Luke Sk…    172    77 blond      fair       blue            19   male  mascu…
##  2 C-3PO       167    75 <NA>       gold       yellow         112   none  mascu…
##  3 R2-D2        96    32 <NA>       white, bl… red             33   none  mascu…
##  4 Darth V…    202   136 none       white      yellow          41.9 male  mascu…
##  5 Leia Or…    150    49 brown      light      brown           19   fema… femin…
##  6 Owen La…    178   120 brown, gr… light      blue            52   male  mascu…
##  7 Beru Wh…    165    75 brown      light      blue            47   fema… femin…
##  8 R5-D4        97    32 <NA>       white, red red             NA   none  mascu…
##  9 Biggs D…    183    84 black      light      brown           24   male  mascu…
## 10 Obi-Wan…    182    77 auburn, w… fair       blue-gray       57   male  mascu…
## # ℹ 77 more rows
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## #   vehicles <list>, starships <list>
```

---

What do you think this code will do?

``` r
glimpse(starwars)
```

---

Let's see!

``` r
glimpse(starwars)
```

```
## Rows: 87
## Columns: 14
## $ name       <chr> "Luke Skywalker", "C-3PO", "R2-D2", "Darth Vader", "Leia Or…
## $ height     <int> 172, 167, 96, 202, 150, 178, 165, 97, 183, 182, 188, 180, 2…
## $ mass       <dbl> 77.0, 75.0, 32.0, 136.0, 49.0, 120.0, 75.0, 32.0, 84.0, 77.…
## $ hair_color <chr> "blond", NA, NA, "none", "brown", "brown, grey", "brown", N…
## $ skin_color <chr> "fair", "gold", "white, blue", "white", "light", "light", "…
## $ eye_color  <chr> "blue", "yellow", "red", "yellow", "brown", "blue", "blue",…
## $ birth_year <dbl> 19.0, 112.0, 33.0, 41.9, 19.0, 52.0, 47.0, NA, 24.0, 57.0, …
## $ sex        <chr> "male", "none", "none", "male", "female", "male", "female",…
## $ gender     <chr> "masculine", "masculine", "masculine", "masculine", "femini…
## $ homeworld  <chr> "Tatooine", "Tatooine", "Naboo", "Tatooine", "Alderaan", "T…
## $ species    <chr> "Human", "Droid", "Droid", "Human", "Human", "Human", "Huma…
## $ films      <list> <"A New Hope", "The Empire Strikes Back", "Return of the J…
## $ vehicles   <list> <"Snowspeeder", "Imperial Speeder Bike">, <>, <>, <>, "Imp…
## $ starships  <list> <"X-wing", "Imperial shuttle">, <>, <>, "TIE Advanced x1",…
```

---

What do you think this code will do?

``` r
starwars %>% 
  filter(height <= 150) %>% 
  arrange(height) %>%
  select(name, sex, height, mass, homeworld)
```

---

Let's see!

``` r
starwars %>% 
  filter(height <= 120) %>% 
  arrange(height) %>%
  select(name, sex, height, mass, homeworld)
```

```
## # A tibble: 8 × 5
##   name                  sex   height  mass homeworld  
##   <chr>                 <chr>  <int> <dbl> <chr>      
## 1 Yoda                  male      66    17 <NA>       
## 2 Ratts Tyerel          male      79    15 Aleen Minor
## 3 Wicket Systri Warrick male      88    20 Endor      
## 4 Dud Bolt              male      94    45 Vulpter    
## 5 R2-D2                 none      96    32 Naboo      
## 6 R4-P17                none      96    NA <NA>       
## 7 R5-D4                 none      97    32 Tatooine   
## 8 Sebulba               male     112    40 Malastare
```

---

What do you think this code will do?

``` r
starwars %>% 
  unnest(starships) %>% 
  select(name, gender, starships) %>%
  head(15)
```

---

Let's see!

``` r
starwars %>% 
  unnest(starships) %>% 
  select(starships, name, gender) %>%
  head(15)
```

```
## # A tibble: 15 × 3
##    starships                name              gender   
##    <chr>                    <chr>             <chr>    
##  1 X-wing                   Luke Skywalker    masculine
##  2 Imperial shuttle         Luke Skywalker    masculine
##  3 TIE Advanced x1          Darth Vader       masculine
##  4 X-wing                   Biggs Darklighter masculine
##  5 Jedi starfighter         Obi-Wan Kenobi    masculine
##  6 Trade Federation cruiser Obi-Wan Kenobi    masculine
##  7 Naboo star skiff         Obi-Wan Kenobi    masculine
##  8 Jedi Interceptor         Obi-Wan Kenobi    masculine
##  9 Belbullab-22 starfighter Obi-Wan Kenobi    masculine
## 10 Naboo fighter            Anakin Skywalker  masculine
## 11 Trade Federation cruiser Anakin Skywalker  masculine
## 12 Jedi Interceptor         Anakin Skywalker  masculine
## 13 Millennium Falcon        Chewbacca         masculine
## 14 Imperial shuttle         Chewbacca         masculine
## 15 Millennium Falcon        Han Solo          masculine
```

---

What do you think this code will do?

``` r
starwars %>% 
  unnest(starships) %>% 
  mutate(vehicles = strsplit(starships, ",")) %>%
  unnest(starships) %>% 
  select(name, gender, starships) %>% 
  group_by(gender) %>% 
  count()
```

---

Let's see!

``` r
starwars %>% 
  unnest(starships) %>% 
  mutate(vehicles = strsplit(starships, ",")) %>%
  unnest(starships) %>% 
  select(name, gender, starships) %>% 
  group_by(gender) %>% 
  count() %>%
  arrange(desc(n))
```

```
## # A tibble: 3 × 2
## # Groups:   gender [3]
##   gender        n
##   <chr>     <int>
## 1 masculine    26
## 2 feminine      3
## 3 <NA>          2
```

---

``` r
starwars %>% 
  ggplot() +
  geom_point(aes(x = mass, y = height, color = gender))
```

---

# <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 Data

---

### Step 1: Register a Fitbit developer app

This example of health analytics utilizes the {fitbitr} R package ([Kaye, 2021](https://matthewrkaye.com/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
fitbitr::generate_fitbitr_token(
  app_name = Sys.getenv('FITBIT_APP_NAME'),
  client_id = Sys.getenv('FITBIT_CLIENT_ID'),
  client_secret = Sys.getenv('FITBIT_CLIENT_SECRET'),
  callback = Sys.getenv('FITBIT_REDIRECT_URL'),
  cache = FALSE
)
```

---

---

What do you think this code will do?

``` r
start_date <- 
  lubridate::mdy("12/01/2024") - lubridate::days(1094) 
end_date <- 
  lubridate::mdy("12/01/2024")

steps_df <- 
  get_steps(start_date, end_date)
```

---

### Look at your daily steps

``` r
tail(steps_df, 15)
```

```
## # A tibble: 15 × 2
##    date       steps
##    <date>     <dbl>
##  1 2024-11-17 12815
##  2 2024-11-18  4123
##  3 2024-11-19  8120
##  4 2024-11-20  7895
##  5 2024-11-21  9340
##  6 2024-11-22  5913
##  7 2024-11-23  3793
##  8 2024-11-24  5958
##  9 2024-11-25  8504
## 10 2024-11-26  5596
## 11 2024-11-27  5505
## 12 2024-11-28  4927
## 13 2024-11-29 10361
## 14 2024-11-30  6436
## 15 2024-12-01  8648
```

---

``` r
steps_df %>%
  ggplot(aes(x = date, y = steps)) +
  geom_point()
```

---

``` r
steps_df %>%
  ggplot(aes(x = date, y = steps)) +
  geom_point() +
  geom_smooth()
```

---

``` r
steps_df %>%
  ggplot(aes(x = date, y = steps)) +
  geom_point() +
  geom_smooth() +
  xlab(NULL) +
  ylab("Steps per Day")
```

---

``` r
steps_df %>%
  ggplot(aes(x = date, y = steps)) +
  geom_point() +
  geom_smooth() +
  xlab(NULL) +
  ylab("Steps per Day") +
  ggtitle("Number of Daily Steps")
```

---

``` r
steps_df %>%
  ggplot(aes(x = date, y = steps)) +
  geom_point() +
  geom_smooth() +
  xlab(NULL) +
  ylab("Steps per Day") +
  ggtitle("Number of Daily Steps") +
  theme_bw()
```

---

``` r
steps_df %>%
  ggplot(aes(x = date, y = steps)) +
  geom_point(color = "blue", alpha = 0.5) +
  geom_smooth(color = "green") +
  xlab(NULL) +
  ylab("Steps per Day") +
  ggtitle("Number of Daily Steps") +
  theme_bw()
```

---

``` r
steps_df %>%
  ggplot(aes(x = date, y = steps)) +
  geom_point(color = "blue", alpha = 0.5) +
  geom_smooth(color = "green") +
  xlab(NULL) +
  scale_y_continuous(labels = scales::comma) +
  ylab("Steps per Day") +
  ggtitle("Number of Daily Steps") +
  theme_bw()
```

---

What do you think this code will do?

``` r
hr <- get_heart_rate_intraday(lubridate::mdy("11/28/2024"))
```

---

Let's see!

``` r
head(hr, 15)
```

```
## # A tibble: 15 × 2
##    time                heart_rate
##    <dttm>                   <dbl>
##  1 2024-11-28 08:59:56         70
##  2 2024-11-28 09:00:06         72
##  3 2024-11-28 09:00:11         82
##  4 2024-11-28 09:00:16         78
##  5 2024-11-28 09:00:26         79
##  6 2024-11-28 09:00:36         82
##  7 2024-11-28 09:00:41         83
##  8 2024-11-28 09:00:46         83
##  9 2024-11-28 09:01:01         89
## 10 2024-11-28 09:01:06         91
## 11 2024-11-28 09:01:11         88
## 12 2024-11-28 09:01:16         84
## 13 2024-11-28 09:01:21         81
## 14 2024-11-28 09:01:26         78
## 15 2024-11-28 09:01:31         74
```

---

``` r
ggplot(hr, aes(x = time, y = heart_rate)) +
  geom_point(alpha = 0.5) +
  geom_smooth() +
  xlab(NULL) +
  ylab("Beats per Minute") +
  ggtitle("Thanksgiving Heart Rate") +
  theme_bw()
```

---

What do you think this code will do?

``` r
steps_intraday <- get_steps_intraday(lubridate::mdy("11/28/2024"))
```

---

Let's see!

``` r
head(steps_intraday, 15)
```

```
## # A tibble: 15 × 2
##    time                steps
##    <dttm>              <dbl>
##  1 2024-11-28 00:00:00     0
##  2 2024-11-28 00:01:00     0
##  3 2024-11-28 00:02:00     0
##  4 2024-11-28 00:03:00     0
##  5 2024-11-28 00:04:00     0
##  6 2024-11-28 00:05:00     0
##  7 2024-11-28 00:06:00     0
##  8 2024-11-28 00:07:00     0
##  9 2024-11-28 00:08:00     0
## 10 2024-11-28 00:09:00     0
## 11 2024-11-28 00:10:00     0
## 12 2024-11-28 00:11:00     0
## 13 2024-11-28 00:12:00     0
## 14 2024-11-28 00:13:00     0
## 15 2024-11-28 00:14:00     0
```
---

``` r
ggplot(steps_intraday, aes(x = time, y = steps)) +
  geom_point(alpha = 0.5) +
  geom_smooth() +
  xlab(NULL) +
  ylab("Steps by Minute") +
  ggtitle("Thanksgiving Steps") +
  theme_bw()
```

---

``` r
ggplot(comparison_day, aes(x = steps, y = heart_rate)) +
  geom_point() +
  xlab("Steps per Minute") +
  ylab("Beats per Minute") +
  ggtitle("Heart Rate vs. Steps") +
  theme_bw()
```

---

What do you think this code will do?

``` r
activity_df <- get_activity_summary(lubridate::mdy("11/28/2024"))
```

---

Let's see!

``` r
glimpse(activity_df)
```

```
## Rows: 1
## Columns: 12
## $ date                   <date> 2024-11-28
## $ calories_out           <dbl> 2450
## $ activity_calories      <dbl> 623
## $ calories_bmr           <dbl> 1613
## $ active_score           <dbl> -1
## $ steps                  <dbl> 4927
## $ sedentary_minutes      <dbl> 1310
## $ lightly_active_minutes <dbl> 87
## $ fairly_active_minutes  <dbl> 25
## $ very_active_minutes    <dbl> 18
## $ marginal_calories      <dbl> 573
## $ resting_heart_rate     <dbl> 59
```

---

What do you think this code will do?

``` r
health_df = NULL

for (i in 0:60) {
  new_row =
    tibble(date = lubridate::mdy("12/01/2024") - i,
           resting_hr = get_activity_summary(date)$resting_heart_rate,
           steps = get_activity_summary(date)$steps
           )
  health_df <-
    health_df %>%
    bind_rows(new_row)
}
```

---

Let's see!

``` r
head(health_df, 15)
```

```
## # A tibble: 15 × 3
##    date       resting_hr steps
##    <date>          <dbl> <dbl>
##  1 2024-12-01         61  8648
##  2 2024-11-30         NA  6436
##  3 2024-11-29         NA 10361
##  4 2024-11-28         59  4927
##  5 2024-11-27         NA  5505
##  6 2024-11-26         NA  5596
##  7 2024-11-25         NA  8504
##  8 2024-11-24         58  5958
##  9 2024-11-23         58  3793
## 10 2024-11-22         NA  5913
## 11 2024-11-21         59  9340
## 12 2024-11-20         59  7895
## 13 2024-11-19         NA  8120
## 14 2024-11-18         59  4123
## 15 2024-11-17         NA 12815
```

---

``` 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()
```

---

``` r
ggplot(health_df, aes(x = steps, y = resting_hr)) +
geom_point() +
geom_smooth() +
xlab("Daily Steps") +
scale_x_continuous(labels = scales::comma) +
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_point() +
geom_smooth() +
xlim(0, 15000) +
scale_x_continuous(label = scales::comma) +
xlab("Daily Steps") +
ylim(0, 70) +
ylab("Beats per Minute while Resting") +
ggtitle("Resting Heart Rate vs. Daily Steps") +
theme_bw()
```

---

---

- Download a copy of this repository: https://github.com/bretsw/nursing24-workshop

- 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.

<hr>

### Reflect:

- What other comparisons might you make?

- How else might you analyze these Fitbit data?

- What data sources might you explore in your professional context?

---

# <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

---

# <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> Resources

**Beginners:**

- [RStudio Beginners' Guide](https://education.rstudio.com/learn/beginner/)
- Book: [*Data Science in Education Using R*](https://datascienceineducation.com)
  - [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)

<hr>

**Intermediates:**

- [RStudio Intermediates' Guide](https://education.rstudio.com/learn/intermediate/)
- Book: [*R for Data Science*](https://r4ds.had.co.nz/)

<hr>

**Experts:**

- [RStudio Experts' Guide](https://education.rstudio.com/learn/expert/)
- Book: [*Data Analysis in Medicine and Health using R*](https://bookdown.org/drki_musa/dataanalysis/)
- Book: [*Learning Statistics with R*](https://learningstatisticswithr.com/)

---

# <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> 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
  - [Posit Community forum](https://forum.posit.co/) (highly recommended!)
  - [Contact Bret!](https://bretsw.com)
- Describe your woes to a GenAI chatbot (e.g., ChatGPT)
- General strategies on learning more: [Chapter 17 of *Data Science in Education Using R*](https://datascienceineducation.com/c17.html)

---

# <svg aria-hidden="true" role="img" viewBox="0 0 320 512" style="height:1em;width:0.62em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M80 160c0-35.3 28.7-64 64-64h32c35.3 0 64 28.7 64 64v3.6c0 21.8-11.1 42.1-29.4 53.8l-42.2 27.1c-25.2 16.2-40.4 44.1-40.4 74V320c0 17.7 14.3 32 32 32s32-14.3 32-32v-1.4c0-8.2 4.2-15.8 11-20.2l42.2-27.1c36.6-23.6 58.8-64.1 58.8-107.7V160c0-70.7-57.3-128-128-128H144C73.3 32 16 89.3 16 160c0 17.7 14.3 32 32 32s32-14.3 32-32zm80 320a40 40 0 1 0 0-80 40 40 0 1 0 0 80z"/></svg> <br><br> Questions

<hr>

<hr>