Lab 6:

Preparation

• Read/ annotate: Recipe #6. You can refer back to this document to help you at any point during this lab activity.
• Note: do your best to employ what you've learned and use other existing resources (R documentation, web searches, etc.).

Objectives

• Gain experience working with coding strategies such as control statements, custom functions, and iteration.
• Practice working with direct downloads and API interfaces to acquire data.
• Implement organizational strategies for organizing data in reproducible fashion.

Instructions

Setup

1. Create a new R Markdown document. Title it "Lab #6" and provide add your name as the author.
2. Edit the front matter to have rendered R Markdown documents print pretty tabular datasets.
3. Delete all the material below the front matter.
4. Add a code chunk directly below the header named 'setup' and add the code to load the following packages and any others you end up using in this lab report. Add message=FALSE to this code chunk to suppress messages.

• tidyverse
• tadr
• rtweet

Tasks

1. Create two level-1 header sections named: "Direct downloads" and "API interfaces".
2. Follow the instructions that follow adding the relevant prose description and code chunks to the corresponding sections.

• Make sure to provide descriptions of your steps between code chunks and code comments within the code chunks!

Direct downloads

The goal in this section will to be to download and decompress a corpus file and save the contents to disk.

• Source the functions/functions.R file to load the get_compressed_data() function into the current R session.
  • Note that the function appears in the 'Environment' pane in RStudio.
• In the R Console, check the arguments that the function requires. Use args() with the function name as the only argument (do not quote the function name).
  • Describe the arguments of the function and predict what they will do.
• Navigate to the ACTIVE-ES Corpus v.02. This is part of the ACTIV-ES Corpus a comparable Spanish corpus comprised of film dialogue from Argentine, Mexican and Spanish productions.
  • Click on the 'tagged.zip' file. Right-click on the 'Download' button and copy the link address to the .zip file.
• Create and name a code chunk that will download and decompress the .zip file into the data/original/actives/ directory.
  • Confirm that the .zip file was downloaded into the correct directory.
• Create and name a code chunk to report the directory structure. Include the following code in this code chunk:

fs::dir_tree(recurse = 2)

API interfaces

The goal in this section will be to interface the Twitter API with the rtweet package. I will use the stream_tweets() function to stream live tweets from Twitter for 5 minutes (300 seconds).

• Read the pre-established Twitter authentication token (student_token.rds) and assign it to an object named student_token. Use the read_rds() function.
• In the R Console, run the following code to test whether the Twitter API is up and working.

stream_usa <- 
  stream_tweets(lookup_coords("usa"), timeout = 10, token = student_token) # NOT RUN (test in R Console)

• Create and name a code chunk. Add the following function to this code chunk. Review what it does and add code comments describing each line.

stream_usa <- function(file, timeout = 10, token = student_token, force = FALSE) {
  # Function:
  # Stream tweets from the US and save results to a csv file
  
  if(!file.exists(file) | force == TRUE) {
    message("Getting ready to stream.")
    if(!dir.exists(dirname(file))) {
      dir.create(path = dirname(file), showWarnings = FALSE, recursive = TRUE)
      message("Directory created.")
    }
    
    stream <- 
      rtweet::stream_tweets(lookup_coords("usa"), 
                            timeout = timeout, 
                            token = token) %>% 
      lat_lng()
    
    rtweet::save_as_csv(x = stream, file_name = file)
    message("Stream file saved!")
    
  } else {
    message("Stream file already exists. Set 'force = TRUE' to overwrite existing data.")
  }
}

• Create and name a code chunk. Run the stream_usa() function setting the file argument to the path to the file that you want to create and the timeout argument to 300.
• Create and name a code chunk to report the directory structure, as you did in the previous section.
• Create and name a code chunk to read in the .csv file you created. Use the read_csv() function and assign the results to stream_twitter_usa.
• Create and name a code chunk to source the functions/functions.R file for the plot_tweet_langs() function (if you have not already). Run the plot_tweet_langs() function with our tweets as the only argument.

Assessment

Add a level-1 section which describes your learning in this lab.

Some questions to consider:

• What did you learn?
• What was most/ least challenging?
• What resources did you consult?
• What more would you like to know about?

Submission

1. To prepare your lab report for submission on Canvas you will need to Knit your R Markdown document to PDF or Word.
2. Download this file to your computer.
3. Go to the Canvas submission page for Lab #6 and submit your PDF/Word document as a 'File Upload'. Add any comments you would like to pass on to me about the lab in the 'Comments...' box in Canvas.