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Data visualization helps deliver clear messages to your audience. Data loses value without proper context and visualization. Charts break down complex data and assist with effective communication of results.
Interpreting a bar chart is easier than scrolling through a spreadsheet. At the onset of your project, you must visualize data at your disposal. Doing so helps you interpret the results and places you in a good position to interpret and present the same.
Research extensively and find the best chart to show trends over time. Before you prepare a visual diagram, assess why you need it in the first place. Establish whether there was another way of presenting the data.
What message would you like to deliver to your audience? Decide on the variables, data points, and scale. After that, find a chart that would best go with the data. What are the different variables you would like to utilize?
With dozens of chart types available, choosing the most suitable can be a tough task. Read on, learn more about trends, and discover the best chart.
In this blog you will learn:
Google Sheets and Microsoft Excel offer you numerous charts but if you want to opt for some third-party library CharExpo is a gem.
Considering that you already have the data, the dilemma is in determining the best graph type. The solution lies in understanding your readers, the people you want to share the information in your possession with, i.e., the audience. Are they an experienced lot? What is their familiarity with data analysis?
Here are simple guidelines to help pick the best chart to show trends over time:
For data presentation to bear fruit, you must identify your readers. Are they skilled executive marketers? If yes, then you have an easy task since they are already familiar with graphs, charts, and other ways of data presentation.
However, a general audience with little to no knowledge of data analysis requires something more straightforward. Here are the suggested charts for less experienced audiences:
The above are simple data presentation charts. However, these may not show information in depth. The following are what we recommend for readers that are experts in the field:
More than anyone else, marketers perfectly know how important it is to use color in brand identity. People relate and recognize brands by their colors. What many may not realize is that data is also related to your brand.
Therefore, while presenting data in a visual format, you must retain your brand’s color schemes. Pie charts and bar graphs present you with opportunities to play around with color. Think of yourself as a designer who must win their audience by including plenty of colors.
By retaining your brand colors, readers recognize your identity and retain the information presented.
Line charts are the best visual presentation for emphasizing change over time. Consider two variables, one on the vertical axis and the second on the horizontal axis. For a better understanding, the variable on the vertical axis will remain constant while the one on the horizontal axis is continuous.
Line charts can demonstrate the change by depicting change through line segments that move from left to right. As the movement takes place, observe the slope which will move up or down.
The horizontal axis requires a variable whose values change progressively at regular intervals to facilitate measurement. The variable must be such that it allows you to make hourly, daily, weekly, or monthly observations as warranted by the situation. As the analyst, you decide on the interval size depending on the type of data under observation.
Turning our focus to the vertical axis, you will capture a numeric variable for each interval on the horizontal axis. In most cases, what appears on this axis is a statistical summary such as an average value or total across events depicted on the horizontal axis. You can also plot multiple lines for trend comparison to show data changes over a specified time frame.
As a starting point, ensure that you plot your bars against a baseline that begins from zero. That way, your readers can see how bar lengths vary. Besides, someone can compare different bar lengths since they all have a common baseline. Also, your data comes off as genuine.
On the contrary, charts with a scale gap could result in a comparison misrepresentation. Why is that so? Bar length and actual values ratio will not match. As a result, someone reading the chart will arrive at incorrect conclusions.
Change over time is progressive, and this is something you must show in your charts. Therefore, as you consider plotting data, decide on the order your chart bars will follow. Usually, data analysts prefer to have the longest bar at the beginning with the shortest one at the end.
Nonetheless, a reader can still compare bar lengths regardless of the order. However, such a comparison takes a bit of their time as they must move their eyes back and forth several times. Sorting the bars in a sequence cuts a reader the slack.
There are exceptions where your categories follow an inherent order and changing them would distort your presentation. In such a situation, you let inherent ordering stay.
Color is an excellent way of depicting change over time. Google Sheets and Microsoft Excel include tools that apply different colors to each bar by default. While default coloring is fine, you might end up distracting the user. Someone could interpret the colors differently, yet that is not what you intended.
To remove ambiguity, adopt meaningful use of color. For instance, you can highlight some columns where you want the reader to focus on a specific data aspect. Think of it as telling a story but with an emphasis on some characters or events.
Data presentation must be simple. Otherwise, readers will not understand your message. They will see it as just another statistical image. The order in which you arrange bar charts is critical. Except for natural order, e.g., time and age, have your bars appearing in ascending or descending order of data values. Avoid alphabetical and other arbitrary setups.
Having so many labels on a chart makes it look cluttered and untidy. As a rule of thumb, get rid of elements that have no relevance to the message you wish to communicate. Label each bar with what it represents. Also, remove the horizontal and vertical axes plus grid lines.
You will realize that your bar chart looks appealing and does not have visual clutter. More so, the data you present becomes noticeable.
Decide on a reasonable size for your chart, not too big or small. Also, maintain a standard width for each bar while also keeping a consistent space between them.
Data visualization possibilities are endless, with many chart options. We have identified a line chart as the best chart to show trends over time from our discussion. Nonetheless, you must narrow down on a specific one since there are numerous options at your disposal.
Likewise, dozens of tools exist to assist with deriving line graphs from data sets. Presenting change over time in a visual format can, at times, be a challenging task. Rather than seeking different connectors to pull data sets, you can rely on one add-on.
ChartExpo is a Google Sheets and Excel add-on that lightens your data visualization task. It comes with in-built charts enabling you to create a line chart with only a few clicks.
As mentioned earlier, line charts are best suited for showing trends since changes against time take a linear approach. Population growth, demand forecasts, and units sold are all examples of quantitative data.
These occurrences are best visualized through line charts. We can demonstrate how to use ChartExpo in Google Sheets by using a data set example like the one below:
| Year | TV | Mobiles | Sound System |
| 2015 | 700 | 1500 | 1000 |
| 2016 | 600 | 1400 | 900 |
| 2017 | 700 | 2000 | 1300 |
| 2018 | 1200 | 1800 | 800 |
| 2019 | 980 | 1900 | 1100 |
| 2020 | 700 | 2500 | 1200 |
To begin, open your ChartExpo add-on. If you have not installed it yet, you can directly install ChartExpo add-on for Google Sheets
It will load and appear on your screen’s right-hand corner. Select the Multi Series Line Chart from the list of General Analysis Charts as shown in the image below:
Next, choose your dimensions and metrics and after that, click the “Create Chart” button.
Here is what your chart will look like on your computer screen:
If you go back to the original data set sale data of TV, Mobile and Sound System are all you can see. At the onset, you could see that some years had higher amounts than others. Visualizing the trend was difficult, almost impossible.
However, you can spot trends in the three very easily, you can see that TV sales were good till 2018 but after that, it started a decline.
Other useful pointers for creating an effective line chart are having a zero baseline, maintaining a sequential category, using color sparingly, removing clutter, and labeling your diagram.
The answer is now clear, line charts. Most data analysts prefer using a line chart as compared to other types. If you want to plot changes and trends over time, a line chart is your best option. Line charts compare data, reveal differences across categories, show trends while also revealing highs and lows.
Numerous visualization methods exist, and all show changes over time, also referred to as trends. Examples of these methods are as follows:
While these have numerous similarities, the difference is in how you connect data ranges to create lines.
Line graphs assist a data analyst in showing trends which could be a rise or drop, increase, or decrease. With these charts, you can compare facts by displaying quantities to arrive at a comparison. What is more, a reader can quickly establish relationships between the visually presented categories.
Line chart scores highly here since you can use it to map continuous data. Examples of instances where you use line graphs include identifying traffic spikes, mapping an increase or decrease in sales for a specific year, weather reports, and so on.
Raw data is impossible to interpret since it has no structure. By tabulating it in a spreadsheet, you can at least begin to realize some order. Nonetheless, data tabulations do not necessarily reveal trends over time. A perfect example is when you have tons of data in numerous spreadsheets.
Line graphs display change over time through data points spread across two axes. Straight lines connect these data points. When structuring a line graph, the horizontal axis (x-axis) is where you plot your independent data. Dependent data goes to the vertical (y-axis).
A line chart is, therefore, the best chart to show trends over time. It shows trends and data variables clearly. Besides, a line graph assists readers with making predictions for the future. However, for a data set comparison being useful, you must use the same scale on both axes.
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