Vyas_Project3_Report

Project 3 - Exploring Visualizations ALY6000 71053 Introduction to Analytics SEC 27 Module 3 Prepared by: Anvita Vyas (NUID:002962386) For: Prof.Herath Gedara, Chinthaka Pathum Dinesh Submission Date: 10 October 2023

Introduction The third project of the course presents an opportunity to visualize data through R programming. The project entails working on the books data. The project involved data cleaning and data analysis through visualization. It culminates in a comprehensive analysis of the dataset, providing us the chance to analyze the data visually and understand different relations between the variables. Overview In this project, we will analyze the dataset about books, which was collected from Goodreads. The dataset includes details such as book titles, authors, ratings, pages, and more. The objective of this assignment is to give a chance to explore functions related to data cleaning, exploratory data analysis, and to create compelling visualizations. Moreover, we would also explore functions that help with basic statistics by computing population and sample statistics. The aim of the assignment is to draw insights from the data by visualization and understanding statistics. Key Findings 1. Data processing a. This process involves data loading and cleaning. The first step is loading the packages. For this assignment, we have used tidyverse by p_load(tidyverse) . Loading that package helped with reading the CSV by read.csv(). To work with date and time we have loaded library(lubridate) . To clean the data we had to download the dplyr library by library(dplyr) and load janitor by p_load(janitor) . To help with data visualization we loaded library(ggplot2), library(plotly), library(ggQC), library(ggthemes). 2. Data Cleaning a. This assignment had a huge focus on data cleaning so that the data was easy to work with. We first worked on cleaning the names by clean_names(). To deal with date and cleaning the date we used mdy() and year() functions. 3. Data Manipulation a. For data manipulation, we used functions like the vector function c() to select the data we require. To manipulate the data we also used select() , filter() , arrange() , group_by() , and mutate() functions. The data manipulation helped with analyzing the data more clearly as well as keeping the data more focused on what is needed. 4. Statistical Analysis 1

The key findings demonstrate how various operations and functions in R programming can help with data analysis to extract meaningful information from datasets. Key Visualizations Question 3 (Data Analysis) 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 is “Histogram of Book Ratings.” – The graph is filled with the color “red.” – Set a binwidth of .25. – Use theme_bw(). Fig 3. Input to create a histogram Fig 4. Output, the histogram 3

Question 4 (Data Analysis) Create a boxplot of the number pages per book in the dataset with the following requirements. – The boxplot is horizontal. – The x-axis is labeled “Pages.” – The title is “Box Plot of Page Counts.” – Fill the boxplot with the color magenta. – Use the theme theme_economist from the ggthemes package. Fig 5. Input to create a box plot 4

Fig 8. Output - the Pareto chart Question 7 (Data Analysis) 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.” 6

– The graph is titled “Scatter Plot of Pages vs. Rating.” – Use the theme theme_tufte(). Fig 9. Input to create a scatter plot Fig 10. Output - the scatter plot Question 9 (Data Analysis) Create a line plot with from this data frame with points representing the counts per year from 1990 - 2020. Color the points for each year with the average rating. Format with the following specifications: – The graph is titled “Total Number of Books Rated Per Year.” 7

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. Fig 13. Input Fig 14. Output Question 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. Fig 15. Input 9

Fig 16. Output Comparison of Sample Stat with Population Stats Population Stat Sample Stat Comparison Average 3.98 Sample 1: 3.99 Sample 2: 3.97 Sample 3: 3.97 We can notice the average across all 3 sample stats and the population stats are very similar and around the same range. Variance 0.0963 Sample 1: 0.103 Sample 2: 0.0955 Sample 3: 0.0728 Here Samples 1 & 2 are much closer to the population variance whereas Sample 3 is skewed and is further from the variance, giving us a thought to think about. Standard Deviation 0.31 Sample 1: 0.321 Sample 2: 0.309 Sample 3: 0.270 Similar to the variance even here Samples 1 & 2 have a similar sd as the population however Sample 3 sd has a little difference from the population variance. Showing the data in sample 3 has some skewed data points. 10

Fig 19. Output - Visualization 2 12

Executive Summary The Analysis of Top Authors Book Count and Average Rating Introduction This analysis focuses on gaining insights in regard to the contribution of authors and the quality of their work. To conduct this analysis we created two visualizations focusing on metrics like book counts for authors and their corresponding average ratings . Findings The first graph, "Top Authors with Most Books," shows the top authors based on the number of books they have published. Karen Kingsbury emerges at the top with the most books published. Nora Roberts and Mercedes Lackey follow suit. The second graph, "Top Authors Average Rating," focuses on the perceived quality of the authors' work, as reflected by the average rating of their books. Karen Kingsbury emerges on the top again with the highest rating. However, CLAMP William Flanagan, although producing fewer works, has a high average rating as well as Terry Pratchett indicating a strong reader response and consistent quality in their writing. Conclusion In conclusion, when comparing both visualizations, it is evident that Karen Kingsbury is an author who has not only consistently delivered books but has maintained great quality and received high ratings. We can also infer that having published more books does not guarantee high ratings. Although some authors successfully achieve both high volume and quality, we do have some that still get high ratings even though the volume of books published is lower. 13

References Bluman, A. G. (2018). Elementary statistics: A step by step approach . McGraw-Hill Education. Cardillo, C. (2020, April 12). Data Manipulation with dplyr . RPubs. https://rpubs.com/odenipinedo/data-manipulation-with-dplyr Kabacoff, R. (2022). R in action: Data analysis and graphics with R and Tidyverse . Manning Publications. Madhugiri, D. (2023, July 26). A comprehensive guide on GGPLOT2 in R. Analytics Vidhya. https://www.analyticsvidhya.com/blog/2022/03/a-comprehensive-guide-on-ggplot2-in-r/# :~:text=ggplot%20is%20a%20popular%20data,structured%20way%20to%20create%20v isualizations R Tutorial . R tutorial. (n.d.). https://www.w3schools.com/r/default.asp 15