About Data Visualizations: Line Graphs

Categories: Quantitative, Visuals

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Collecting, analyzing, and reporting with data can be daunting. The person that SAGE Publishing — the parent of MethodSpace — turns to when it has questions is Diana Aleman – editor extraordinaire for SAGE Stats and U.S. Political Stats. And now she is bringing her trials, tribulations, and expertise with data to you in this blog, Tips with Diana. Stay tuned for Diana’s experiences, tips, and tricks with finding, analyzing and visualizing data. View Diana’s blog in its native habitat HERE.

What is a line graph?

From news articles to math class, line graphs show up everywhere, so it’s important to understand them.

A line graph is a data visualization type used to track how values change over time. They are particularly useful for tracking trends, which help us more quickly and easily determine when data changed and consider what outside factor could have contributed to that change. For instance, in the personal bankruptcy chart below we see a noticeable drop and rise in the average personal bankruptcy rate between 2005 and 2010 – right around the onset of the Great Recession. Coincidence? I think not.

The above shows a line graph tracking the median average rate of personal bankruptcy in the United States.

Line graphs are used by people who work with data regularly like business owners, budgeters, and statisticians; however, the everyday person is equally likely to have created a line graph at some point in his or her life. They are extremely versatile, which is what makes them so popular and therefore important to learn how to create.

Tips on creating a line graph

Excel is where you are most likely to end up creating a line graph from scratch and thanks to the chart feature, this work is easier than ever. Generally, however, you should follow the guidelines below!

  1. Be sure to have data for your X axis (horizontal) and your Y axis (vertical). For line graphs, the X axis is usually a time variable like years. The Y axis typically measures the dependent variable, i.e. the data that you want to track.
  2. Add labels and scales for each axis.
  3. Next, plot your data points.
  4. Finally, title your chart. Your title should be succinct and convey the key takeaway of the graph.

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