ALY 6000_Project_3 v0.1(2)

Project 3 ALY 6000 Project Instructions In this project, you will examine data collected from the website www.goodreads.com , archived on www.kaggle.com , and modified for this project. Specifically, you will create different forms of informative and compelling visualizations in R. You will also draw conclusions from the data and report on them in written form, exploring the statistical ideas of samples and populations and the measures of dispersion and central tendency. When completed you will submit your work as Lastname_Project3.R and Lastname_Project3_Report.pdf. For example, if your name is Sally Smith, then your files would be titled Smith_Project3 Project Setup By now, you should be familiar with basic project setup. 1. Create a new project called "Lastname-Project3". 2. Create an R script file within your project called "Lastname-Project3.R". 3. Include your name, the date, and the class in a comment as the first line of the script. 4. Include the environment reset code at the start of your script. 5. Load any standard packages you might need. Project 3 Instructions 1. Download the file books.csv from Canvas and read the dataset into R. Call this data set books. Rows: 52448 Columns: 23 ── Column specification ──────────────────────────────────────────────────────── Delimiter: "," chr (17): title, series, author, description, language, isbn, genres, charac... dbl (6): rating, pages, numRatings, likedPercent, bbeScore, bbeVotes ℹ Use `spec()` to retrieve the full column specification for this data. ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Cleaning the data set 1. The janitor package contains helpful functions that perform basic maintenance of your data frame. Use the clean_name function to standardize the names in your data frame. 2. The lubridate package contains helpful functions to convert dates represented as strings to dates represented as dates. Convert the first_publish_date column to a type date using the mdy function. 3. Using the year function in lubridate, extract the year from the first_publish_date column place it in a new column named year. 4. Reduce your dataset to only include books published between 1990 and 2020 (inclusive). 5. Remove the following columns from the data set: publish_date, edition, characters, price, genres, setting, and isbn. Call this data set books_publishers 6. Keep only books that are fewer than 1200 pages. Data Analysis 1. Use the glimpse function to produce a long view of the dataset. 2. Use the summary function to produce a breakdown of the statistics of the dataset. 3. Create a rating histogram with the following criteria. – The y-axis is labeled “Number of Books.” – The x-axis is labeled “Rating.” – The title of the graph “Histogram of Book Ratings.” – The graph is filled with the color “red.” – Set a binwidth of .25. – Use theme_bw(). Warning: There was 1 warning in `mutate()`. ℹ In argument: `first_publish_date = mdy(first_publish_date)`. Caused by warning: ! 1186 failed to parse.

5. Group the data by publisher and produce a summary data frame, book_summary, containing each publisher and their associated number of books in the dataset. With that data frame, make the following refinements: – Remove any rows that contain NAs. – Remove any publishers with fewer than 250 books. – Order the data frame by the total number of books in descending order. – Make the publisher into a factor with the levels defined by the current ordering of the publisher. – Add a column, cum_count, to the data frame with cumulative count of books. – Add a column, rel_freq, to the data frame with the relative frequency of books. – Add a column, cum_freq, to the data frame with the cumulative relative frequency of books. publisher total_book s cum_coun t rel_fre q cum_fre q <fct> <int> <int> <dbl> <dbl> 1 Random House 2607 2607 0.428 0.428 2 Harper Collins 1512 4119 0.248 0.676 3 Simon and Schuster 663 4782 0.109 0.785 4 MacMillan 541 5323 0.0888 0.874 5 Hatchette 493 5816 0.0809 0.955 6 Scholastic Books 277 6093 0.0455 1 6. Using the data frame constructed in the prior problem, create a Pareto Chart with an ogive of cumulative counts formatted with the following additional criteria: # A tibble: 6 × 5

– The bars are filled with the color cyan. – The x-axis label is “Publisher.” – The y-axis label is “Number of Books.” – The title is “Pareto and Ogive of Publisher Book Counts (1990 - 2020).” – Use the theme theme_clean(). – Rotate the x-axis labels by 45 degrees (consider the ggeasy package). 7. Create a scatter plot of pages vs. rating for the books data frame with the following requirements: – Color the points based on the year of publication. – The x-axis is labeled “Pages.” – The y-axis is labeled “Rating.” – The graph is titled “Scatter Plot of Pages vs. Rating.” – Use the theme theme_tufte().

# A tibble: 1 × 3 avg_rating variance s d <dbl><dbl> <dbl> 13.980.0963 0.310 10. R has built-in functions to computer the sample mean ( mean) , sample variance ( var), and sample standard deviation ( sd). Create your own functions to compute the average, to compute the population variance ( pop_var) and the population standard deviation ( sd_var ). You may not use the three built-in functions listed above, but may use other built-in functions. All three functions should accept a single vector of values and return the corresponding computed result. 11. Consider the complete dataset of books to be the population you are analyzing. Compute population stats for the average, variance, and standard deviation of the book rating. 12. Create three samples of size 100 from the books data frame. Compute sample statistics for mean, variance and standard deviation of the book rating. Compare these results with the population stats in your report. 13. Create one or more additional visualizations based on the existing data or additional analysis that you perform. 14. Write an executive summary report that contains an overview of your analysis, the visualizations you created with textual descriptions of key takeaways, and any key statistics that were computed in your analysis.

Submitting to Canvas When you are satisfied with your solution. 1. Remove any lines in your code that have “install.packages.” 2. Remove any lines in your code that use the view function. 3. Submit 2 files under the assignment in Canvas. 1. Your R script named Lastname_Project3.R file. 2. A pdf of your report titled Lastname_Project3_Report.pdf . Your report should contain the following information formatted as specified: 1. Title Page Include your name, assignment title, and submission date Introduction and Key Findings Include an overview of the assignment and any findings Conclusion/Recommendations Include evidence-based recommendations and visualizations or direct presentation of tabular data Works Cited Include all sources, including YouTube videos, instruction materials, Google search results, and texts that informed your study of statistics and R Your report should be as concise as possible while maintaining fluency. Your key findings will be strongest if supported by visualizations or direct presentation of tabular data. Your summary must adhere to APA guidelines, including page numbers on each page (including the title page) in the upper right corner. See the following examples for title pages , citations , and general APA formatting . Congratulations on completing Project 3!