Explain how you would make this plot interactive using the packages we have discussed in this unit.

Note: A description of the dataset used to create this visualisation is given in Section C.

Write one paragraph of interpretation explaining the key insights that can be drawn from the data shown.

Explain data density and data-ink-ratio in the context of graphical design principles.

Explain why the following plot is bad and suggest at least three ways to improve it.

Now your manager wants users to see the exact revenue value for each slice in the room type pie chart. Explain how you this can be achieved in two different ways in Power BI and state when to use which one.

Your manager changed her mind suddenly and wanted the first pie chart to show daily booking revenue instead of total revenue. Explain why this is not effective. Suggest an alternative visualisation that can be used and justify your choice.

In Power BI, applying a slicer to a report automatically filters all visuals on the page.

The following two commands will always produce the same set of rows in the final result

salary %>% arrange(desc(age)) %>% filter(age > 30)
salary %>% filter(age > 30) %>% arrange(desc(age))

Note: Assume age is a column in the data frame salary.

In R, the expression mean(c(TRUE, FALSE, TRUE)) will return an error because logical values cannot be averaged.