We Helped With This R Studio Programming Homework: Have A Similar One?

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############## Homework # 4 ##############
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# Directions:
# In Assignment 4, you will transform a sample of 
# 2500 trump tweets for analysis, using data with over 
# 30,000 tweets. You will use the preprocessCorpus function
# to simplify the transformation and then demonstrate and
# understanding of the transformation from a corpus to 
# a DTM or TDM.

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######### Preliminary Code #########
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## Get/Set Your Working Directory
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## Load Packages (libraries)
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######### Solutions #########
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# 1. First, import the data from the .csv file (trump_tweets.csv)
#    as a dataframe names trumpt. Identify the ID column and column with text
#    that you would like to analyze and rename them or create them as necessary
#    to create the Corpus from a DataframeSource. 
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# ANSWER #

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# 2. Run a command using the set.seed function and your birthday,
#    as in HW #3. Using Hw #3 as a guide, use the sample()
#    function to create a sample of 2500 tweets without replacement.
#    Name your subset of tweets "trump_sub". Then, remove the 
#    original dataframe.
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# ANSWER #

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# 3. Create the Corpus object named trumpcorp. Then, use the preprocessCorpus() 
#    function, which you loaded into your workspace, to cleanse the corpus.
#    Use lemmatization, keep hashtags, remove SMART stopwords 
#    and do not preserve intraword punctuation.
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# ANSWER #

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# 4. Create a Document Term Matrix, named trumpDTM, with default settings.
#    View the high-level DTM information. How many Terms are in your
#    DTM?
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# ANSWER #

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# 5. Now, apply a a maximum term length of 15 and a minimum term length of 4 to the DTM. 
#    How does this impact the number of terms? How many terms are there in the DTM with 
#    min and max term lengths? How does this impact the size of your DTM?
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# ANSWER #

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# 6. Use the appropriate functions to determine 
#    which terms appear more than 100 times in your TDM? 
#    and 250 times in your TDM? What do you notice about
#    these terms? Use the appropriate function to find terms associated
#    with terms appearing 250 times. What does 
#    Donald Trump talk about most on Twitter based on your sample? 
#    In your opinion, are these the most important terms?
#    Why or why not? Explain.
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# ANSWER #

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# 7. Next, consider the sparsity of the DTM. Based on the
#    sparsity, Based on this, should you remove Sparse 
#    Terms? Explain. If you remove terms with .995 
#    sparsity, how many terms remain? If you remove terms
#    with .99 sparsity, how many remain? Which would you
#    use and why? Explain.
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# ANSWER #
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# 8. tfidf weighting is probably the most popular weighting used in Text Mining. 
#    Apply tfidf weighting to your matrix (which removes sparse terms). Do not use normalization. 
#    View the 50 highest weighted terms.  In your opinion, do these seem
#    to be the most important terms in your sample of trump tweets?
#    why or why not?
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# ANSWER #
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