Learning Goal: I’m working on a r multi-part question and need support to help m

Learning Goal: I’m working on a r multi-part question and need support to help me learn.
here is the notes https://online.stat.psu.edu/stat510/node/674 (Note: This is Lesson 13 in the Online Notes)
In order to receive credit for homework, all responses must include how an answer is obtained, not just the numeric solution. Please write professionally and identify or highlight relevant output. If you weave in answers to the homework assignment, please bold or highlight your response to help your answer stand out from the rest of the document.

Part 1 Create a chart showing the words with the greatest contribution to posit

Part 1 Create a chart showing the words with the greatest contribution to positive or negative sentiment in the AP articles. Show all the code from the necessary packages untll you can produce the chart. Comment your code line by line.
Create charts showing the terms with the highest tf-idf from each of four selected inaugural addresses. Eliminate the ? term. Show all the code from the necessary packages untll you can produce the chart. Comment your code line by line.
Create charts showing over time how the frequency of the terms god, america, foreign, immigrant, union, constitution, and freedom have changed over time. Each term should be in it’s own chart. Include a smoothed regression line with confidence interval. Label your axes. Label the chart with the word in question. Comment your code line by line. (Note the lower case initial letters of proper nouns. Does this make a difference in your analysis?)
Part 2 Show stacked bar charts of the most common terms within each of 2 topics from the Associated Press articles in the topicmodels package. Color the charts by topic. Comment your code line by line.
Show a stacked bar chart showing the words that have a Beta greater than 1/1000 in at least one topic with the greatest difference in Beta between topic 1 and topic 2. comment each line of your code
Here is link to Text https://bookdown.org/Maxine/tidy-text-mining/use your words so that there is no plagiarism. I need the rmd file so that I can execute the code on my windows and word doc of the output you executed. Always repeat the question you are answering. Thank you!
Requirements: as needed to understand code/ graphs

PART 1 Using the attached files of around 3200 tweets per person, show a histog

PART 1 Using the attached files of around 3200 tweets per person, show a histogram (frequency distribution) of the tweets of both Dave and Julia. Use `UTC` to create the time stamp. Remember that the case of column headers matters.
Make a dataframe of word frequency for each of Dave and Julia. Plot the frequencies against each other. Include a dividing line in red showing words nearby that are similar in frequency and words more distant which are shared less frequently.
Create a stacked chart comparing the odds ratios of the top 15 words used by each tweeter. Remove twitter handles from the list of words. Calculate the word usage ratios (usage v. total) and display it on a log scale. Do you notice any interesting differences? Does anything stand out as a difference?
PART 2 Using the tweet files: Create time series charts for each tweeter showing how word usage has changed over time. Show for three words. You may have to manipulate a parameter to show Comment your code, line by line.
Show a graph for each tweeter revealing the ten words with the highest number of retweets. Comment your code, line by line.
Here is link to Text https://bookdown.org/Maxine/tidy-text-mining/ use your words so that there is no plagiarism. I need the rmd file so that I can execute the code on my windows and word doc of the output you executed. Always repeat the question you are answering. Thank you!
Requirements: as needed for the comments and graphs

PART 1 Using the attached files of around 3200 tweets per person, show a histog

PART 1 Using the attached files of around 3200 tweets per person, show a histogram (frequency distribution) of the tweets of both Dave and Julia. Use `UTC` to create the time stamp. Remember that the case of column headers matters.
Make a dataframe of word frequency for each of Dave and Julia. Plot the frequencies against each other. Include a dividing line in red showing words nearby that are similar in frequency and words more distant which are shared less frequently.
Create a stacked chart comparing the odds ratios of the top 15 words used by each tweeter. Remove twitter handles from the list of words. Calculate the word usage ratios (usage v. total) and display it on a log scale. Do you notice any interesting differences? Does anything stand out as a difference?
PART 2 Using the tweet files: Create time series charts for each tweeter showing how word usage has changed over time. Show for three words. You may have to manipulate a parameter to show Comment your code, line by line.
Show a graph for each tweeter revealing the ten words with the highest number of retweets. Comment your code, line by line.
Here is link to Text https://bookdown.org/Maxine/tidy-text-mining/ use your words so that there is no plagiarism. I need the rmd file so that I can execute the code on my windows and word doc of the output you executed. Always repeat the question you are answering. Thank you!
Requirements: as needed for code/ graphs   |   .doc file

Part 1 Create a chart showing the words with the greatest contribution to posit

Part 1 Create a chart showing the words with the greatest contribution to positive or negative sentiment in the AP articles. Show all the code from the necessary packages untll you can produce the chart. Comment your code line by line.
Create charts showing the terms with the highest tf-idf from each of four selected inaugural addresses. Eliminate the ? term. Show all the code from the necessary packages untll you can produce the chart. Comment your code line by line.
Create charts showing over time how the frequency of the terms god, america, foreign, immigrant, union, constitution, and freedom have changed over time. Each term should be in it’s own chart. Include a smoothed regression line with confidence interval. Label your axes. Label the chart with the word in question. Comment your code line by line. (Note the lower case initial letters of proper nouns. Does this make a difference in your analysis?)
Part 2 Show stacked bar charts of the most common terms within each of 2 topics from the Associated Press articles in the topicmodels package. Color the charts by topic. Comment your code line by line.
Show a stacked bar chart showing the words that have a Beta greater than 1/1000 in at least one topic with the greatest difference in Beta between topic 1 and topic 2. comment each line of your code
Here is link to Text https://bookdown.org/Maxine/tidy-text-mining/ use your words so that there is no plagiarism. I need the rmd file so that I can execute the code on my windows and word doc of the output you executed. Always repeat the question you are answering. Thank you!
Requirements: as needed for the code/ graphs   |   .doc file

PART 1 Using the attached files of around 3200 tweets per person, show a histog

PART 1 Using the attached files of around 3200 tweets per person, show a histogram (frequency distribution) of the tweets of both Dave and Julia. Use `UTC` to create the time stamp. Remember that the case of column headers matters.
Make a dataframe of word frequency for each of Dave and Julia. Plot the frequencies against each other. Include a dividing line in red showing words nearby that are similar in frequency and words more distant which are shared less frequently.
Create a stacked chart comparing the odds ratios of the top 15 words used by each tweeter. Remove twitter handles from the list of words. Calculate the word usage ratios (usage v. total) and display it on a log scale. Do you notice any interesting differences? Does anything stand out as a difference?
PART 2 Using the tweet files: Create time series charts for each tweeter showing how word usage has changed over time. Show for three words. You may have to manipulate a parameter to show Comment your code, line by line.
Show a graph for each tweeter revealing the ten words with the highest number of retweets. Comment your code, line by line.
Here is link to Text https://bookdown.org/Maxine/tidy-text-mining/ use your words so that there is no plagiarism. I need the rmd file so that I can execute the code on my windows and word doc of the output you executed. Always repeat the question you are answering. Thank you!
Requirements: as needed for code/ graphs

Create a chart showing the words with the greatest contribution to positive or n

Create a chart showing the words with the greatest contribution to positive or negative sentiment in the AP articles. Show all the code from the necessary packages untll you can produce the chart. Comment your code line by line.
Create charts showing the terms with the highest tf-idf from each of four selected inaugural addresses. Eliminate the ? term. Show all the code from the necessary packages untll you can produce the chart. Comment your code line by line.
Create charts showing over time how the frequency of the terms god, america, foreign, immigrant, union, constitution, and freedom have changed over time. Each term should be in it’s own chart. Include a smoothed regression line with confidence interval. Label your axes. Label the chart with the word in question. Comment your code line by line. (Note the lower case initial letters of proper nouns. Does this make a difference in your analysis?)
use your words so that there is no plagiarism. I need the rmd file so that I can execute the code on my windows and word doc of the output you executed. Always repeat the question you are answering. Thank you!TEXT BOOK https://bookdown.org/Maxine/tidy-text-mining/
Requirements: as needed for the code/ graphs

Learning Goal: I’m working on a r report and need an explanation and answer to h

Learning Goal: I’m working on a r report and need an explanation and answer to help me learn.
(R Language) Determining poisonous mushrooms in data with decision tree models
You will document in a report the results of each step of the mining process, analyze and interpret the results. Suggest the characteristics to use when determining if a mushroom is safe to eat. Make recommendations for additional analysis and variables to examine to build other classifications such as use of the mushrooms that are not poisonous.
The report should include the following:
Code walk through: in this section provide a step by step explanation of how the code is interacting with and/or transforming the data. Provide examples from the output to support your explanations.
Analysis: Based on the output, analyze the data and the relationships revealed about the variables of interest. Explains the insights provided by the output. Use visualizations to support your analysis.
Interpretation and Recommendations: Interpret the results of your analysis and explain what the results mean for the data owner. Provide recommendations for actions to be taken based on your interpretation. Support those with the data. Explain why and what explicit variables you suggest incorporating. For example, median income by city and state from the census.gov website might be useful for examining home ownership.
Both the r-code file and the word file are required.No of pages 7

Learning Goal: I’m working on a r report and need an explanation and answer to h

Learning Goal: I’m working on a r report and need an explanation and answer to help me learn.
(R Language) Determining poisonous mushrooms in data with decision tree models
Details
You will document in a report the results of each step of the mining process, analyze and interpret the results. Suggest the characteristics to use when determining if a mushroom is safe to eat. Make recommendations for additional analysis and variables to examine to build other classifications such as use of the mushrooms that are not poisonous.
The report should include the following:
Code walk through: in this section provide a step by step explanation of how the code is interacting with and/or transforming the data. Provide examples from the output to support your explanations.
Analysis: Based on the output, analyze the data and the relationships revealed about the variables of interest. Explains the insights provided by the output. Use visualizations to support your analysis.
Interpretation and Recommendations: Interpret the results of your analysis and explain what the results mean for the data owner. Provide recommendations for actions to be taken based on your interpretation. Support those with the data. Explain why and what explicit variables you suggest incorporating. For example, median income by city and state from the census.gov website might be useful for examining home ownership.
Both the r-code file and the word file are required.
Apa style formatting.
No of pages 8 (1800 words)
References page required and intext citations are must