What is the number of booking requests for each type of hotel in every country in the dataset? Display the largest

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Tasks:

Data Wrangling and visualization (60 Marks)

For all tables below, you need to use the RMarkdown functionality to present tables. If you just present the results without formatting them as a table, you will not be able to get the full mark for the question.

1. Display the first 10 rows of the dataset using RMarkdown (1 marks)

2. Generate the general statistics of the dataset using summary() function (2 mark).

3. What is the number of booking requests for each type of hotel in every country in the dataset? Display the largest 10 (5 marks)

4. Create a bar chart that shows the number booking requests per each type of hotel, not country-specific (i.e. just City and Resort). The chart should have a custom title and custom labelled axes. Include a legend. (10 marks)

5. Create a bar chart that shows the number of booking requests per each type of hotel in top 10 countries (by number of booking). Use faceting to show charts for each country next to each other in one graph). The chart should have a custom title and custom labelled axes. Include a legend. (10 marks)

6. What is the number of cancellations per year for Resort Hotel? Present as a table (5 marks)

7. What is the average lead time for each type of hotel for every year? (5 marks)

8. Present the above information as a horizontal bar chart with each type of hotel next to each other. The chart should have a custom title and custom labelled axes. Include a legend. (5 marks)

9 Add a column to the dataset with the average daily rate for each type of hotel in each country for each market segment. Display the top 15 most expensive as a table. (10 marks)

10. Create a line chart with the 20 most expensive locations for City Hotel. The chart should have a custom title and custom labelled axes. Include a legend. (5 marks)

11. Based on the updated dataset create a new dataset that includes only hotel type, country, newly calculated daily rate. Save the modified dataset (in step 9) as well as the new dataset a file called modifieddata.csv and newdata.csv (3 marks).

Attachments:

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