R Bootcamp (in-class tutorial)

Based on https://datacarpentry.org/r-socialsci/ (chapters 1-4)

• In-class part: Introduction to R and starting with data and data wrangling

This part features selected issues.

• After class: please complete chapters 1-4 at your own pace.

• What is R? What is RStudio?
• Why learn R?
  • no point and clicking (good!)
  • great for reproducibility
  • interdisciplinary & extensible
  • supports all data formats
  • can produce high-quality graphics
  • has a large community
  • it's free, open-source and cross-platform!
  • works even in the cloud!
• But… can be geeky + many ways to do the “same thing” (-> confusing!), e.g., data.table vs. dplyr

• Under the File menu, click on New project, choose New directory, then New project
• Enter a name for this new folder (or “directory”) and choose a convenient location for it. This will be your working directory for the rest of the day (e.g., ~/data-carpentry)
• Click on Create project
• Create a new file where we will type our scripts. Go to File > New File > R script. Click the save icon on your toolbar and save your script as “script.R”.

• four panes: source code, console, environment/history, files
• setting and checking working directories (setwd() and getwd())
• relative directories vs. absolute directories, use of here()

• Directory structure (for tutorial; different structures used later)

dir.create("data") # continue w/ other directories

• Can you come up with other ways to create a folder/directory structure?

• Running code (ctrl + R, buttons, running code top-down)
• Installing additional packages (package tab or install.packages())
  
  
• DO: Let's all verify package tidyverse is installed!

• as a calculator
• variable assignment
• calculating with variables
• = vs. ==
• functions
  • using functions (e.g., round())
  • getting help on functions
  • what functions exist? –> cheat sheet for base R
• vectors and data types (c(1,2,3), c("Berlin", "Barcelona"), NA)
• subsetting vectors (explicitly, conditionally with logical operators)

1. Assign the following values to the variable rooms: 1, 2, 1, 8
2. Use the function max() to calculate the maximum number of rooms
3. Modify rooms by adding NA to it: 1, 2, 1, 8, NA (why do we need NAs?)
4. Recalculate the maximum & check the help (?max, followed by enter) to see how to tackle the problem!
5. Write a bit of code to return the third value of rooms
6. Write a bit of code to return the number of rooms larger or equal to 2. Tip: length() & conditional subsetting required!

• individual values vs. “holding data” (rows, colums)
• difference between Excel & R? Each column holds the same data type!
• R tutorial proceeds w/ non-marketing data; here, we use some music streaming data instead
  
  

• Download the data to your computer

download.file("https://raw.githubusercontent.com/hannesdatta/course-dprep/master/content/docs/modules/week4/regional-global-daily-latest.csv", "streams.csv")

• Load the data into R using the tidyverse read_csv() function

library(tidyverse)
streams <- read_csv('streams.csv', skip=1)

• Then, take a look at the data by typing in the console…

streams # full preview
head(streams) # limited preview
view(streams) # point-and click view
nrow(streams) # number of rows ("size")
summary(streams) # show basic summary statistics

Tips:

• Beware of “looking” at large datasets! nrow(streams) is your friend!
• You may have to experiment with options in read_csv, e.g., separation characters.

• $column_name shows data, e.g., streams$Artist
• make use of tabs for auto-completion!
• converting character variables to factor variables (and back); as.factor()
• self-study preview: using dates!

select(): subsetting/selecting columns

streams %>% select(`Track Name`)

# A tibble: 200 × 1
   `Track Name`                     
   <chr>                            
 1 STAY (with Justin Bieber)        
 2 INDUSTRY BABY (feat. Jack Harlow)
 3 THATS WHAT I WANT                
 4 Heat Waves                       
 5 Woman                            
 6 Bad Habits                       
 7 Pepas                            
 8 MONTERO (Call Me By Your Name)   
 9 Shivers                          
10 Beggin'                          
# ℹ 190 more rows

filter(): subset rows on conditions

streams %>% filter(`Track Name` == "Pepas")

# A tibble: 1 × 5
  Position `Track Name` Artist  Streams URL                                     
     <dbl> <chr>        <chr>     <dbl> <chr>                                   
1        7 Pepas        Farruko 4048596 https://open.spotify.com/track/5fwSHlTE…

mutate(): create new columns by using information from other columns

streams <- streams %>% mutate(weekly_streams = `Streams` * 7)
streams %>% select(`Track Name`, `Streams`, weekly_streams)

# A tibble: 200 × 3
   `Track Name`                      Streams weekly_streams
   <chr>                               <dbl>          <dbl>
 1 STAY (with Justin Bieber)         8469665       59287655
 2 INDUSTRY BABY (feat. Jack Harlow) 7189005       50323035
 3 THATS WHAT I WANT                 4554200       31879400
 4 Heat Waves                        4476838       31337866
 5 Woman                             4201276       29408932
 6 Bad Habits                        4158040       29106280
 7 Pepas                             4048596       28340172
 8 MONTERO (Call Me By Your Name)    3762718       26339026
 9 Shivers                           3742188       26195316
10 Beggin'                           3646313       25524191
# ℹ 190 more rows

group_by() and summarize(): create summary statistics on grouped data

streams %>% group_by(Artist) %>% summarize(total_streams = sum(Streams))

# A tibble: 121 × 2
   Artist        total_streams
   <chr>                 <dbl>
 1 $uicideboy$          862781
 2 24kGoldn            1293398
 3 A-Wall               817164
 4 ATB                  757095
 5 Ariana Grande       2539856
 6 Aventura            2149469
 7 Avicii              1610117
 8 BTS                 4739470
 9 Baby Keem           1558924
10 Bad Bunny           3567213
# ℹ 111 more rows

arrange(): sort results; use desc() to show in descending order

streams %>% group_by(Artist) %>% summarize(total_streams = sum(Streams)) %>% arrange(desc(total_streams))

# A tibble: 121 × 2
   Artist         total_streams
   <chr>                  <dbl>
 1 Lil Nas X           24334525
 2 Doja Cat            14534676
 3 Drake               13979533
 4 Olivia Rodrigo      13589501
 5 Ed Sheeran           9810986
 6 The Kid LAROI        9353107
 7 The Weeknd           6671164
 8 Billie Eilish        6634519
 9 J Balvin             5970510
10 Måneskin             5484032
# ℹ 111 more rows

• count(): count discrete values

streams %>% count(Streams>1E6)

# A tibble: 2 × 2
  `Streams > 1e+06`     n
  <lgl>             <int>
1 FALSE                98
2 TRUE                102

• select(): subset columns
• filter(): subset rows on conditions
• mutate(): create new columns by using information from other columns
• group_by() and summarize(): create summary statistics on grouped data
• arrange(): sort results
• count(): count discrete values

1. Show only the columns Artist and Track Name from streams. Tip: select()
2. Show all observations by Lil Nas X. Tip: filter()
3. Sum up all streams by artist: Tip: group_by() and summarize()
4. Sort the results from (3) in descending order

Tip: also check out the dplyr cheatsheet

• We use RStudio for prototyping code
• For “production”, we usually run our code via the terminal/command line
• First, we have to store our code as a .R file
• Then, we can call it via the terminal
  • R --vanilla < filename.R (gives output)
  • Rscript filename.R (gives only output if made explicit)

Let's try it out!

• Complete this tutorial at Data Carpentry
• Form and register teams! (remember: mix skills!)
• Review course material/course website