Practical session

Overview

Teaching: 10 min
Exercises: 120 min

Questions

• Can you apply the theory to some practical problems?

Objectives

• Able to inspect data

• Able to create a function

• Able to subset data

• Able to manipulate data

• Able to manipulate flow control

• Able to plot simple plots

Set up a new project.

Create a new project and new script. Add a data folder to the project and place the gapminder_data.csv in the data folder. Load the gapminder_data.csv into Spyder. Load pandas and matplotlib into your script.

Inspect the data.

It is important to get an understanding of what is contained in a dataset before you start working with it.

Find:

1. The number of records in the data set.
2. The number of attributes for a record.
3. The column names.
4. The data type of each attribute
5. What are the values for the first 10 records.
6. What are the values for the last record in the dataset.

Subsetting tasks.

1. Aim: Extract observations collected for the year 1957

2. Aim: Extract all columns except 1 through to 4

3. Aim: Extract the rows where the life expectancy is longer the 80 years

4. Aim: Extract the first row, and the fourth and fifth columns (continent and lifeExp).

5. Aim: Extract rows that contain information for the years 2002 and 2007 (advanced)

More subsetting

1. Create a new data.frame called gapminder_small that only contains rows 1 through 9 and 19 through 23.

2. Suppose we want to pull out information from southeast Asia. How would we subset data from just those countries? For this question we would consider Myanmar, Thailand, Cambodia, Vietnam, and Laos as SE Asia. (advanced)

Include flow control

1. Use an if() statement to print a suitable message reporting whether there are any records from 2002 in the gapminder dataset.

2. Modify your code to remove hardcoded variables so that you can provide any year to be checked.

3. Now modify your code so you can check two years at the same time.

Create a function

Define a function that calculates and returns the Gross Domestic Product of a nation (GDP * POP) from the data available in the dataset.

Now modify your code so that the returned GDP is added as a new column on the data set.

Create a function so that if given a country or a year it returns just the data for this selection (e.g., You could specify ‘Australia’ and get back all years for Australia, you could specify ‘2007’ and get all countries in 2007, or you could specify ‘Australia’ in ‘2007’ and just receive the data for Australia in 2007).

Add defensive programming

What happens if we pass unexpected values into your function? (Try putting numeric values in as a country or logical value in as a year)

Add some additional checks into your function to make sure that the correct arguments are being passed in. Hint: are we entering a valid country or year?.

Manipulating data and simple plots

1. Make a new column in the gapminder data frame that contains population in units of millions of people.

2. On a single graph, plot population, in millions, against year, for all countries. Do not worry about identifying which country is which.

3. Repeat the exercise, graphing only for China, India, and Indonesia. Again, do not worry about which is which.

More plotting

1. Plot a scatter plot of GDP against life expectancy.

2. Now modify the plot to show how life expectancy has changed over time.

3. Now colour code to the points to show continent.

4. Scatter plot might not be the best option for showing change over time. Change it to a line plot.

Recap & reflect

Consider the material we have covered today:

• Are all concepts clear?
• Are you confident you could apply them to your data?
• Are there any other tools you feel you need?
• What section did you find the most difficult?
• What content may you need more support with?

Key Points

• Solving problems with R and RStudio will improve your skills and confidence