R Script (Graduate Level)

Can you help me understand this R question?

Provide an “R” script that includes code and explanatory #comments for the following steps:

Load the full 2018-2020 workspace.

1) Choose a set of key words or phrases that are useful for your team project and use GloVe word embeddings to find additional synonyms within the corpus. List any new words as a #comment

2) Generate a frequency table showing the appearances per document of your key words/phrases using the dfm_select or dfm_lookup function.

3) Use the kwic function to extract a text window around one of your key words or phrases and combine the pre- and post- windows.

4) Choose one of the following and analyze the text windows: readability, lexical diversity, or one of the sentiment analysis approaches.

5) Write a few sentences at the end about what this analysis shows you (include this as # comments at the end of your R script).

6) # Please use the stringr and regex syntax to view all instances of “wage,” # “wages,” “Wage,” and “Wages” in the second document. # Which member of Congress utters the word “wage” (or a variant thereof) in this document?

7) # Extract all instances of “wage” (and its variants) that occur within 50 # characters of the word “living” in the first 50 documents. Save these matches # to an object named “living_wage”

# How many matches did you find?

# Which of the first 10 documents has the highest number of matches?

# What were the phrases captured by the regex?

# Now run the code again, expanding the window to 100, 200, and 500 characters. # Does the regex find any additional phrases? If so, what are they?

8) # Use the kwic command to extract a 100-token window around the regex you wrote # for Problem 7. Save this kwic object as “lw_window” and convert it into a data

# frame named “df_lw_window”.

# What are the dimensions of this data frame?

9) As you may have noticed, many words are split in half by a hyphenation followed

# by at least one space (“- “). This is a function of how the PDF documents were

# originally formatted and the difficulty of converting these documents to plain

# text. # Write a regex to replace all occurrences of this break in the first 10 documents

# in the cr_txt object and create a new data frame named “cr_txt_cleaned.”

# Then check the text to make sure that you have performed this replacement

# properly.

10) We will be leveraging the Congressional Record’s relatively uniform

# structure to split the text at the beginning of a speaker’s statement. These

# transitions (1) start with a new line, (2) include the word “Mr.” or “Ms.”, and

# list the name of the speaker in ALL CAPS.

# Write a regex to match and extract all instances of a new speaker in the first

# 10 documents. Save these matches in an object named “speakers” and convert # this list object into a data frame.

11) Segment the text by speaker

12) Merge in covariates of interest (State, Minimum Wage, Union)

13) Troubleshoot failed merges by harmonizing member names;

14) Choose at least one text comparison method and generate a comparison between subsets of your text, (example speeches by Republicans versus by Democrats and Independents)

15) Write a few sentences at the end about what this comparison shows you (include this as # comments at the end of your R script).

Requirements: R Data Script – 15 Questions

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