index.Rmd

---
title: "Data Prescription"
subtitle: "Introducing R for Nursing Analytics"
author: "Dr. Bret Staudt Willet"
date: "December 18, 2024"
output:
  xaringan::moon_reader:
    lib_dir: libs
    seal: true
    css: [default, 'custom.css', 'custom-fonts.css']
    nature:
      titleSlideClass: [left, bottom]
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
---

```{r setup, include=FALSE}
#usethis::use_git_ignore(c("*.csv", "*.rds"))
options(htmltools.dir.version = FALSE)

library(knitr)
library(xaringan)
library(fontawesome)
library(tidyverse)
library(fitbitr)
```

class: inverse, center, middle

# `r fa("fas fa-images", fill = "#fff")`

**View the slides:** 

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

---







class: inverse, center, middle

# `r fa("fab fa-r-project", fill = "#fff")` <br><br> **Part 1:** <br> Introduction to R

---

# `r fa("fas fa-otter", fill = "#fff")` Our Constructivist Approach

--

1. I'll introduce some concepts

--

1. You'll try some code

--

1. We'll all discuss together

--

```{r, out.width="320px", echo=FALSE, fig.align="center"}
include_graphics("img/tech_support_cheat_sheet.png")
```

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

---

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# `r fa("fab fa-r-project", fill = "#fff")` <br><br> Background <br> on R and RStudio

---

# `r fa("fab fa-r-project", fill = "#fff")` Why Learn R?

--

- 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

---

# `r fa("fab fa-r-project", fill = "#fff")` Setting up R

### 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)






---

class: inverse, center, middle

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

What do you think this code will do?

```{r, eval=FALSE}
starwars
```

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

Let's see!

```{r, eval=TRUE}
starwars
```

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

What do you think this code will do?

```{r, eval=FALSE}
glimpse(starwars)
```

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

Let's see!

```{r, eval=TRUE}
glimpse(starwars)
```

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

What do you think this code will do?

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

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

Let's see!

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

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

What do you think this code will do?

```{r, eval=FALSE}
starwars %>% 
  unnest(starships) %>% 
  select(name, gender, starships) %>%
  head(15)
```

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

Let's see!

```{r, eval=TRUE}
starwars %>% 
  unnest(starships) %>% 
  select(starships, name, gender) %>%
  head(15)
```

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

What do you think this code will do?

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

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

Let's see!

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

---

# `r fa("fas fa-otter", fill = "#fff")` Visualizing Data

```{r, warning=FALSE, eval=TRUE, fig.width=8, fig.height=4.5, dpi=200, fig.align="center"}
starwars %>% 
  ggplot() +
  geom_point(aes(x = mass, y = height, color = gender))
```













---

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# `r fa("fas fa-heart-pulse", fill = "#fff")` <br><br> **Part 2:** <br> Fitbit Data

---

class: inverse, center, middle

# `r fa("fas fa-heart-pulse", fill = "#fff")` <br><br> Set up the <br> Fitbit API

### 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. 

---

class: inverse, center, middle

# `r fa("fas fa-heart-pulse", fill = "#fff")` <br><br> Set up the <br> Fitbit API

### 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).

---

# `r fa("fas fa-heart-pulse", fill = "#fff")` Set up the Fitbit API

### 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`

---

# `r fa("fas fa-heart-pulse", fill = "#fff")` Set up the Fitbit API

### 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**. 

---

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# `r fa("fas fa-heart-pulse", fill = "#fff")` <br><br> Set up the <br> Fitbit API

### Step 2: Record your dev app credentials

---

# `r fa("fas fa-heart-pulse", fill = "#fff")` Set up the Fitbit API

### 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!

---

class: inverse, center, middle

# `r fa("fas fa-heart-pulse", fill = "#fff")` <br><br> Set up the <br> Fitbit API

### Step 3: Retrieve your Fitbit data

---

# `r fa("fas fa-heart-pulse", fill = "#fff")` Set up the Fitbit API

### Step 3: Retrieve your Fitbit data

```{r, message=FALSE, eval=FALSE}
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
)
```









---

class: inverse, center, middle

# `r fa("fas fa-otter", fill = "#fff")` <br><br> Try it Out! 

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

What do you think this code will do?

```{r, eval=FALSE}
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)
```

```{r, include=FALSE, eval=FALSE}
write_csv(steps_df, "data/steps2024.csv")
```

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

### Look at your daily steps

```{r, include=FALSE, message=FALSE}
steps_df <- read_csv("data/steps2024.csv")
```

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

---

# `r fa("fas fa-chart-line", fill = "#fff")` Picture it!

```{r, message=FALSE, warning=FALSE, fig.width=8, fig.height=4.5, dpi=200, fig.align="center", out.width="75%"}
steps_df %>%
  ggplot(aes(x = date, y = steps)) +
  geom_point()
```

---

# `r fa("fas fa-chart-line", fill = "#fff")` Picture it!

```{r, message=FALSE, warning=FALSE, fig.width=8, fig.height=4.5, dpi=200, fig.align="center", out.width="75%"}
steps_df %>%
  ggplot(aes(x = date, y = steps)) +
  geom_point() +
  geom_smooth()
```

---

# `r fa("fas fa-chart-line", fill = "#fff")` Picture it!

```{r, message=FALSE, warning=FALSE, fig.width=8, fig.height=4.5, dpi=200, fig.align="center", out.width="75%"}
steps_df %>%
  ggplot(aes(x = date, y = steps)) +
  geom_point() +
  geom_smooth() +
  xlab(NULL) +
  ylab("Steps per Day")
```

---

# `r fa("fas fa-chart-line", fill = "#fff")` Picture it!

```{r, message=FALSE, warning=FALSE, fig.width=8, fig.height=4.5, dpi=200, fig.align="center", out.width="75%"}
steps_df %>%
  ggplot(aes(x = date, y = steps)) +
  geom_point() +
  geom_smooth() +
  xlab(NULL) +
  ylab("Steps per Day") +
  ggtitle("Number of Daily Steps")
```

---

# `r fa("fas fa-chart-line", fill = "#fff")` Picture it!

```{r, message=FALSE, warning=FALSE, fig.width=8, fig.height=4.5, dpi=200, fig.align="center", out.width="75%"}
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 fa("fas fa-chart-line", fill = "#fff")` Picture it!

```{r, message=FALSE, warning=FALSE, fig.width=8, fig.height=4.5, dpi=200, fig.align="center", out.width="75%"}
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 fa("fas fa-chart-line", fill = "#fff")` Picture it!

```{r, message=FALSE, warning=FALSE, fig.width=8, fig.height=4.5, dpi=200, fig.align="center", out.width="75%"}
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()
```

```{r, include=FALSE, eval=FALSE}
ggsave(file="img/daily-steps2024.png", width=8, height=4.5)
```






---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

What do you think this code will do?

```{r, eval=FALSE}
hr <- get_heart_rate_intraday(lubridate::mdy("11/28/2024"))
```

```{r, include=FALSE, eval=FALSE}
write_csv(hr, "data/hr-11-28-2024.csv")
```

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

Let's see!

```{r, include=FALSE, message=FALSE}
hr <- read_csv("data/hr-11-28-2024.csv")
```

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

---

# `r fa("fas fa-chart-line", fill = "#fff")` Picture it!

```{r, message=FALSE, warning=FALSE, fig.width=8, fig.height=4.5, dpi=200, fig.align="center", out.width="75%"}
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()
```

```{r, include=FALSE, eval=FALSE}
ggsave(file="img/heart-rate-thanksgiving2024.png", width=8, height=4.5)
```





---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

What do you think this code will do?

```{r, eval=FALSE}
steps_intraday <- get_steps_intraday(lubridate::mdy("11/28/2024"))
```

```{r, include=FALSE, eval=FALSE}
write_csv(steps_intraday, "data/intraday-steps-11-28-2024.csv")
```

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

Let's see!

```{r, include=FALSE, message=FALSE}
steps_intraday <- read_csv("data/intraday-steps-11-28-2024.csv")
```

```{r}
head(steps_intraday, 15)
```
---

# `r fa("fas fa-chart-line", fill = "#fff")` Picture it!

```{r, message=FALSE, warning=FALSE, fig.width=8, fig.height=4.5, dpi=200, fig.align="center", out.width="75%"}
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, include=FALSE, eval=FALSE}
ggsave(file="img/intraday-steps11-28-2024.png", width=8, height=4.5)
```




---

# `r fa("fas fa-chart-line", fill = "#fff")` Picture it!

```{r, include=FALSE, message=FALSE}
comparison_day <- 
  hr %>%
  full_join(steps_intraday, by = "time")
```

```{r, message=FALSE, warning=FALSE, fig.width=8, fig.height=4.5, dpi=200, fig.align="center", out.width="75%"}
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()
```






---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

What do you think this code will do?

```{r, eval=FALSE}
activity_df <- get_activity_summary(lubridate::mdy("11/28/2024"))
```

```{r, include=FALSE, eval=FALSE}
write_csv(activity_df, "data/activity-11-28-2024.csv")
```

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

Let's see!

```{r, include=FALSE, message=FALSE}
activity_df <- read_csv("data/activity-11-28-2024.csv")
```

```{r}
glimpse(activity_df)
```




---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

What do you think this code will do?

```{r, eval=FALSE}
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)
}
```

```{r, include=FALSE, eval=FALSE}
write_csv(health_df, "data/activity-trend-2024.csv")
```

---

# `r fa("fas fa-otter", fill = "#fff")` Try it Out!

Let's see!

```{r, include=FALSE, message=FALSE}
health_df <- 
  read_csv("data/activity-trend-2024.csv")
```

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

---

# `r fa("fas fa-chart-line", fill = "#fff")` Picture it!

```{r, message=FALSE, warning=FALSE, fig.width=8, fig.height=4.5, dpi=200, fig.align="center", out.width="75%"}
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, include=FALSE, eval=FALSE}
ggsave(file="img/resting-heart-rate-over-time-2024.png", width=8, height=4.5)
```

---

# `r fa("fas fa-chart-line", fill = "#fff")` Picture it!

```{r, message=FALSE, warning=FALSE, fig.width=8, fig.height=4.5, dpi=200, fig.align="center", out.width="75%"}
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, include=FALSE, eval=FALSE}
ggsave(file="img/resting-heart-rate-vs-daily-steps-2024.png", width=8, height=4.5)
```

---

# `r fa("fas fa-chart-line", fill = "#fff")` Picture it!

```{r, message=FALSE, warning=FALSE, fig.width=8, fig.height=4.5, dpi=200, fig.align="center", out.width="75%"}
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()
```

```{r, include=FALSE, eval=FALSE}
ggsave(file="img/resting-heart-rate-vs-daily-steps-2024-zoomed.png", width=8, height=4.5)
```






---

class: inverse, center, middle

# `r fa("fas fa-code", fill = "#fff")` <br><br> Next Steps

---

# `r fa("fas fa-code", fill = "#fff")` Next Steps

--

- 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?






---

class: inverse, center, middle

# `r fa("fas fa-list", fill = "#fff")` <br><br> Appendix: <br> Helpful Resources <br> and Troubleshooting

---

# `r fa("fas fa-list", fill = "#fff")` 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/)



---

# `r fa("fas fa-list", fill = "#fff")` 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)



---

class: inverse, center, middle

# `r fa("fas fa-question", fill = "#fff")` <br><br> Questions

<hr>

`r fa("fas fa-envelope", fill = "#fff")` [bret.staudtwillet@fsu.edu](mailto:bret.staudtwillet@fsu.edu) | `r fa("fas fa-globe", fill = "#fff")` [bretsw.com](https://bretsw.com) | `r fa("fab fa-github", fill = "#fff")` [GitHub](https://github.com/bretsw/)

<hr>

`r fa("fas fa-images", fill = "#fff")` Slides: [bretsw.com/nursing24-workshop](https://bretsw.com/nursing24-workshop)

`r fa("fas fa-code", fill = "#fff")` Source Materials: [github.com/bretsw/nursing24-workshop](https://github.com/bretsw/nursing24-workshop)

`r fa("fas fa-book-open", fill = "#fff")` Workbook: [github.com/bretsw/nursing24-workshop/blob/main/index.Rmd](https://github.com/bretsw/nursing24-workshop/blob/main/workbook.Rmd)