Our Constructivist Approach

1. I'll introduce some concepts

2. You'll try some code

3. We'll all discuss together

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

Setting up R

To download R

• Visit 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 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)

Try it Out!

What do you think this code will do?

starwars

Try it Out!

Let's see!

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>

Try it Out!

What do you think this code will do?

glimpse(starwars)

Try it Out!

Let's see!

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",…

Try it Out!

What do you think this code will do?

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

Try it Out!

Let's see!

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

Try it Out!

What do you think this code will do?

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

Try it Out!

Let's see!

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

Try it Out!

What do you think this code will do?

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

Try it Out!

Let's see!

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

Visualizing Data

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

Set up the Fitbit API

Step 1: Register a Fitbit developer app

Here's how I completed the form:

• 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

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.

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!

Set up the Fitbit API

Step 3: Retrieve your Fitbit data

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
)

Try it Out!

What do you think this code will do?

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)

Try it Out!

Look at your daily steps

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

Picture it!

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

Picture it!

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

Picture it!

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

Picture it!

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

Picture it!

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()

Picture it!

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()

Picture it!

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()

Try it Out!

What do you think this code will do?

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

Try it Out!

Let's see!

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

Picture it!

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()

Try it Out!

What do you think this code will do?

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

Try it Out!

Let's see!

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

Picture it!

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()

Picture it!

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()

Try it Out!

What do you think this code will do?

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

Try it Out!

Let's see!

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

Try it Out!

What do you think this code will do?

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)
}

Try it Out!

Let's see!

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

Picture it!

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()

Picture it!

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()

Picture it!

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()

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.

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?

Resources

Beginners:

• RStudio Beginners' Guide
• Book: Data Science in Education Using R
  • Physical copy of DSIEUR
  • Even more resources from DSIEUR

Intermediates:

• RStudio Intermediates' Guide
• Book: R for Data Science

Experts:

• RStudio Experts' Guide
• Book: Data Analysis in Medicine and Health using R
• Book: Learning Statistics with R

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 (highly recommended!)
  • Contact Bret!
• Describe your woes to a GenAI chatbot (e.g., ChatGPT)
• General strategies on learning more: Chapter 17 of Data Science in Education Using R