(2) Fill the R codes in this markdown file and answer related questions. (3) After you finish, convert the markdown file into a html version and submit both the markdown and html files to dpbi.rutgers@gmail.com. (4) Your grade will be based on the correctness of your answers and the neatness of your markdown and html files. This dataset contains the sales transactions of an international shoe store chain. Your task is to analyze this dataset. Question 1: Import this dataset into R and run str(). How many transaction records and variables we have? (10pts). Answer: Question 2: Count (1) the number of stores in each country; (2) the number of SKUs and (3) which years we have sales records (Hint: R-function unique()) (10pts). Answer: Question 3: Sort total store sales in dollar values from high to low. What are the top and bottom 5 stores? (Hint: dplyr library – arrange(), group_by(), summarize() and %˃%) (10pts) Answer: Questin 4: Continue on Question 3 and generate a pareto chart (use pareto.chart() in library qcc and rename the ylabel and title appropriately). If we need to close stores that count for less than one percentage of the total chain store sales, which stores shall we close? (10pts) Answer: Question 5: Rank the number of sales of each SKU from high to low. What are the top and bottom 3 SKUs? (Hint: dplyr library – arrange(), group_by(), summarize(), n() and %˃%) (10pts) Answer: Question 6: Continue on Question 5 and generate two bar charts of the sales (use geom_bar) for the top and bottom 20 SKUs separately. Measure the percentage of sales difference between the top and bottom 20 SKUs. (10pts) Answer: Question 7: Generate the four time series of the total monthly dollar sales of each country and plot them in one chart (use geom_line()). Which country has the largest monthly sales? and which country has the largest monthly sales per store? (10pts) Answer: Question 8: Count the total number of discount vs no-discount sales within each month. Generate a bar chart of discount/no-discount sales (use geom_bar(position ＝ ���fill���)) for each month. Are no-discount sales higher or lower than discount sales? (10pts) Answer: Question 9: Tabulate the number of sales and average sales price of each SKU in the two groups of Gender (male vs.��female). Generate a dot plot of price vs sales (mark male/female with different colors and add separate linear regression lines). What���s the relationship between sales and price? (10pts) Answer: Question 10: How much male shoe sales are higher than female shoe sales if the price effect is ignorable? Calculate your estimate by a statistical method (e.g., regression). (10pts) Answer:
