# 5 Data Visualization 5.5 Line chart

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Line charts, especially useful in the fields of statistics and science, are more popular than all other graphs combined because their visual characteristics reveal data trends clearly and these charts are easy to create.

A line chart is a visual comparison of how two variables—shown on the x- and y-axes—are related or vary with each other. It shows related information by drawing a continuous line between all the points on a grid.

Line charts compare two variables: one is plotted along the x-axis (horizontal) and the other along the y-axis (vertical). The y-axis in a line chart usually indicates quantity (e.g. dollars, litres) or percentage, while the horizontal x-axis often measures units of time. As a result, the line chart is often viewed as a time series graph. For example, if you wanted to graph the height of a baseball pitch over time, you could measure the time variable along the x-axis, and the height along the y-axis. Although they do not present specific data as well as tables do, line charts are able to show relationships more clearly than tables do. Line charts can also depict multiple series and hence are usually the best candidate for time series data and frequency distribution.

Vertical bar charts and line charts share a similar purpose. The vertical bar chart, however, reveals a change in magnitude, whereas the line chart is used to show a change in direction.

In summary, line charts:

• show specific values of data well,
• reveal trends and relationships between data,
• compare trends in different groups.

Graphs can give a distorted image of the data. If scales on the axes of a line graph force data to appear a certain way, then a graph can even reveal a trend that is entirely different from the one intended. This happens when the intervals between adjacent points along the axis may be dissimilar, or when the same data charted in two graphs using different scales appear different.

Example 1 – Plotting a trend over time

Chart 5.5.1 shows one obvious trend, the fluctuation in the labour force from January to July. The number of students at Andrew’s high school who are members of the labour force is scaled using intervals on the y-axis, while the time variable is plotted on the x-axis.

The number of students participating in the labour force was 252 in January, 252 in February, 255 in March, 256 in April, 282 in May, 290 in June and 319 in July. When examined further, the line chart indicates that the labour force participation of these students was at a plateau for the first four months (January to April), and for the next three months (May to July) the number increased steadily.

Data table for Chart 5.5.1 ﻿
Data table for Chart 5.5.1
Table summary
This table displays the results of Data table for Chart 5.5.1. The information is grouped by Month (appearing as row headers), Number of students (appearing as column headers).
Month Number of students
January 250
February 250
March 255
April 260
May 280
June 290
July 315
Example 2 – Comparing two related variables

Chart 5.5.2 is a single line chart comparing two items. In this example, time is not a factor. The chart compares the average number of dollars donated by the age of the donors. According to the trend in the chart, the older the donor, the more money he or she donates. The 17-year-old donors donate, on average, \$84. For the 19-year-olds, the average donation increased by \$26 to make the average donation of that age group \$110.

Data table for Chart 5.5.2 ﻿
Data table for Chart 5.5.2
Table summary
This table displays the results of Data table for Chart 5.5.2. The information is grouped by Age (appearing as row headers), Average donation (\$) (appearing as column headers).
Age Average donation (\$)
15 36
16 52
17 83
18 100
19 110
Example 3 – Using correct scale

When drawing an axis, it is important that you use the correct scale. Otherwise, the line’s shape can give readers the wrong impression about the data. Compare Chart 5.5.3 with Chart 5.5.4:

Data table for Chart 5.5.3 ﻿
Data table for Chart 5.5.3
Table summary
This table displays the results of Data table for Chart 5.5.3. The information is grouped by Month (appearing as row headers), Number of crime offenders (appearing as column headers).
Month Number of crime offenders
January 418
February 399
March 391
April 372
May 372

Data table for Chart 5.5.4 ﻿
Data table for Chart 5.5.4
Table summary
This table displays the results of Data table for Chart 5.5.4. The information is grouped by Month (appearing as row headers), Number of crime offenders (appearing as column headers).
Month Number of crime offenders
January 418
February 399
March 391
April 372
May 372

Using a scale of 350 to 430 (Chart 5.5.3) focuses on a small range of values. It does not accurately depict the trend in guilty crime offenders between January and May since it exaggerates that trend. However, choosing a scale of 0 to 450 (Chart 5.5.4) better displays how small the decline in the number of guilty crime offenders really was.

Both charts can be useful depending on the context. The important thing to remember is that you should pay attention to the scale that is being used when interpreting a graph.

Example 4 – Multiple line graphs

A multiple line chart can effectively compare similar items over the same period of time, as you can see in Chart 5.5.5 which compares the use of cell phones by gender.

Data table for Chart 5.5.5 ﻿
Data table for Chart 5.5.5
Table summary
This table displays the results of Data table for Chart 5.5.5. The information is grouped by Year (appearing as row headers), Number of men (thousands), Number of women (thousands) and Total number (thousands) (appearing as column headers).
Year Number of men (thousands) Number of women (thousands) Total number (thousands)
2012 150.0 147.5 297.5
2013 165.0 157.5 322.5
2014 177.5 160.0 337.5
2015 155.0 177.5 332.5
2016 162.5 182.5 345.0
2017 175.0 180.0 355.0
2018 195.0 187.5 382.5

Chart 5.5.5 is an example of a good chart. The message is clearly stated in the title, and each of the line graphs is properly labelled. It is easy to see from this chart that the total cell phone use has been rising steadily since 2012, except for a one-year period (2015) where the numbers drop slightly. The pattern of use for women and men seems to be quite similar with very small discrepancies between them.

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