Our Constructivist Approach

1. We'll introduce some concepts
2. You'll try some code
3. We'll all discuss together

Why Learn R?

• It is increasingly used in education

• 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

• Visit rstudio.com to download RStudio
• 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

Getting Started with RStudio

Activities:

1. Running a single RMarkdown chunk
2. Running another RMarkdown chunk
3. Rendering an RMarkdown document to a PDF
4. Creating your first visualization

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 %>% 
  filter(sex == "female") %>% 
  select(name, hair_color, skin_color, homeworld)

Try it Out!

Let's see!

starwars %>% 
  filter(sex == "female") %>% 
  select(name, hair_color, skin_color, homeworld)

## # A tibble: 16 × 4
##    name               hair_color skin_color          homeworld
##    <chr>              <chr>      <chr>               <chr>    
##  1 Leia Organa        brown      light               Alderaan 
##  2 Beru Whitesun lars brown      light               Tatooine 
##  3 Mon Mothma         auburn     fair                Chandrila
##  4 Shmi Skywalker     black      fair                Tatooine 
##  5 Ayla Secura        none       blue                Ryloth   
##  6 Adi Gallia         none       dark                Coruscant
##  7 Cordé              brown      light               Naboo    
##  8 Luminara Unduli    black      yellow              Mirial   
##  9 Barriss Offee      black      yellow              Mirial   
## 10 Dormé              brown      light               Naboo    
## 11 Zam Wesell         blonde     fair, green, yellow Zolan    
## 12 Taun We            none       grey                Kamino   
## 13 Jocasta Nu         white      fair                Coruscant
## 14 Shaak Ti           none       red, blue, white    Shili    
## 15 Rey                brown      light               <NA>     
## 16 Padmé Amidala      brown      light               Naboo

Try it Out!

What do you think this code will do?

starwars %>% 
  filter(sex %in% c("male", "none"),
         height <= 150) %>% 
  select(name, sex, height, mass, homeworld) %>%
  arrange(desc(height))

Try it Out!

Let's see!

starwars %>% 
  filter(sex %in% c("male", "none"),
         height <= 150) %>% 
  arrange(height) %>%
  select(name, sex, height, mass, homeworld)

## # A tibble: 10 × 5
##    name                  sex   height  mass homeworld  
##    <chr>                 <chr>  <int> <dbl> <chr>      
##  1 Yoda                  male      66    17 <NA>       
##  2 Ratts Tyerell         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  
##  9 Gasgano               male     122    NA Troiken    
## 10 Watto                 male     137    NA Toydaria

Try it Out!

starwars %>% 
  unnest(starships) %>% 
  select(name, gender, starships) %>%
  head(10)

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

Try it Out!

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

## # A tibble: 3 × 2
## # Groups:   gender [3]
##   gender        n
##   <chr>     <int>
## 1 feminine      3
## 2 masculine    27
## 3 <NA>          1

Exploring further

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> <"The Empire Strikes Back", "Revenge of the Sith", "Return…
## $ vehicles   <list> <"Snowspeeder", "Imperial Speeder Bike">, <>, <>, <>, "Imp…
## $ starships  <list> <"X-wing", "Imperial shuttle">, <>, <>, "TIE Advanced x1",…

Visualizing data

starwars %>% 
  ggplot() +
  geom_point(aes(x = mass, y = height, color = gender),
             alpha = 0.5
             ) +
  theme_bw()

Resources

Beginners:

• RStudio Beginners' Guide
• Book: Data Science in Education Using R
  • See Chapter 12 - Walkthrough 6: Exploring Relationships Using Social Network Analysis With Social Media Data
  • Physical copy of DSIEUR
  • Even more resources from DSIEUR

Intermediates:

• RStudio Intermediates' Guide
• {tidytags} package notes
• Book: R for Data Science

Experts:

• RStudio Experts' Guide
• Book: Learning Statistics with R
• Data Science in Education Using R
  • See Chapter 20.3 Appendix C - Social Network Influence and Selection Models
• SNA resources: Dr. Ken Frank's website

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 (highly recommended!)
  • Twitter hashtag: #RStats
  • Contact Bret!
• General strategies on learning more: Chapter 17 of Data Science in Education Using R