30  Home Exercises

Take the movie dialogue datasets from last week and do the following:

  1. Using the metadata dataset, chart the average gross per year using an appropriate geom.

    1. Using scales (i.e. don’t filter the dataset first), limit the chart x axis to the years 1990 - 2000

    2. Set the axis labels on this chart to display every two years (1990, 1992, etc.)

    3. Set the color of the geom of the chart to something other than the default.

  2. Create a bar graph of the gross takings for each movie in the Star Wars franchise.

    1. Reorder the chart going from the highest to the lowest takings

    2. Flip the chart so that the movie names are on the vertical axis.

    3. (optional to practice previous week again) Give the bars a black outline, set a custom fill colour, and decrease the transparency slightly.

  3. Import the Nobel Prize winners ‘Laureates’ dataset.

    1. Create a bar chart charting the number of winners from each continent per year.

    2. Change the palette for the fill of the bars to ‘Dark2’.

    3. Set the major breaks on the y axis to every 20 and the minor breaks to every 5.

  4. Import the Titanic dataset and do the following:

    1. calculate the average fare paid for each embarking location, and draw it as a bar chart.

    2. Change the default scale to a gradient starting with the colour aquamarine and ending with the colour deepskyblue.

    3. Reverse the legend bar and set the height to 10 cm.

  5. (slightly more challenging). Last week we looked at gender in the IMDB dataset. Can you create a chart which investigates which genres became more or less equal over time? Limit your investigations to a sensible number of the top genres to help with visualisation, or group the genres together into 6 or 7 categories. Once you’re done, change 5 scale elements.

Tip

The genres in the IMDB genre dataset list up to three genres for each movie, separated by a comma. This isn’t very helpful for data analysis or visualisation - let’s assume it would be better to treat each movie as having separate observations for each genre listed. One way to do this is by using some new functions: separate() and pivot_longer(). You may also need to clean the results slightly using str_trim().