Have a look at Figure 2. It shows the grades for English and Mathematics along the different terms displayed on a table. Now look at Figure 3 that shows the grades for English and Mathematics along the different terms displayed on a column chart.
In each figure do you find easier to explain what happened throughout the year? How long does it take to identify the higher grades for both English and Mathematics and in which term they were attained? Is it quicker than on Figure 2 or 3? Now imagine you need to understand what happened in terms of grades for hundreds of students, do you think that just looking at the data on a table format would be enough?
Very often we need to create visuals to help us to better understand the data. This process of turning data into pictures or graphs that are easy to understand is known as data visualisation.
The use of data visualisation is very important because it simplifies complex information, highlights trends (for example we can if our grades are improving with time), and it is also more attractive, interactive, and colourful visuals are more attractive than plain numbers and text.
Let’s now take a look into how we can create data visualisations for numerical data. As we discussed before, numerical data represents the count or measure of something using numbers. Numerical data can be broken down into two additional categories of data: discrete and continuous.
Discrete data values
Discrete values are when the numerical data can only be in whole numbers, as there is only a certain number of values. Think about your school, a discrete data value would be the number of students in your class. You can count 20, 21,22 students but you will never have 20.5 students.
A good way to represent discrete data is through the use of bar charts or pie charts. Imagine you are analysing your classmates’ preferences for different school subjects (for example English, Mathematics, Sciences and PE). You could use a pie chart to show the proportion of each preference (Figure 4 -A), or bar chart to show the number of students who prefer each type (Figure 4 -B).
Continuous data values
Continuous numerical data values will fall anywhere within a range of measurements. With a vast number of options, continuous data values can be slightly different to each other. For example, continuous data could be something like the weather, it can be 23°, 23.5° or 23.57° and so on. In addition, continuous data may change over time, while the weather was 23° today, it may be 27.85° tomorrow.
A good way to represent continuous data that change over time is through the use of line graphs. For example, you could use a line graph to show how your height has increased every year since you were born. The x-axis (horizontal line) might show the years, and the y-axis (vertical line) would show your height in centimetres. As you grow, you draw a dot for each year and connect the dots to make a line (Figure 5 – A). This same data can also be visualised through a column chart, where the x-axis (horizontal line) could also show the height of each bar would be the measurement of your height (Figure 5 – B).
On the next chapter is Creating a column chart, where you will be able to create your own data visual!
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[…] The next chapter is Data Visualisation. […]