Excel: If you do research, you probably will also use Excel at a certain point. For most researchers, that is the first software to organize or analyze any data. It is an essential tool and frequently used, therefor easy to share your results with others with it. I want to suggest this YouTube channel if you want to get more familiar with the features of MS Excel (or most MS programs):
After analyzing your data, to publish your results without using large tables, or really long descriptions, the best to choose an appropriate chart. In this section, I want to give you some examples and general guidelines to correct frequent mistakes in graphs and charts. When you create a chart, always remember it’s purpose:
The goal of a chart is to provide a visual representation of data, making it easier and faster to interpret.
If you need more time to read and understand the chart, it fails the purpose, even if it looks fancy. The focus should be on the data, not the style!
3D effects do not add any value and can even distort results. They can give false impressions of data proportions; they should be avoided in all cases.
Axis Starting Point: Starting the vertical axis above zero in column charts can distort the true differences between data points. This misrepresentation is often exploited in marketing to exaggerate differences, but you should avoid it as a researcher.
Titles: Chart titles are often wasted by stating the obvious. Use the title space to communicate the key message of the chart.
Sorting: Sorting data can make your charts easier to interpret by reducing the effort required to rank data. Try to spare the viewer time to understand the information in your data!
Default chart, default order, distorted axis:
Better choice to share the same data:
Axis Labels: Axis labels should be easy to read and fit horizontally within the chart. If the labels don't fit, consider using a different chart type. E.g. from vertical to horizontal bar chart, to avoid the viewer tilting their head while reading.
Wrong Chart Choice: Stacked charts can make comparing different data series difficult. If the goal is to compare different series, consider using a line chart.
Labels: Excessive labeling can clutter the chart and distract from the key data points. Only label key data points to maintain clarity.
Legends: Legends can slow down data interpretation, as the reader has to constantly refer back and forth. It's more effective to directly label the data if the chart isn't updated frequently. Also, consider if you are publishing in a journal grayscale, your colored chart might be misinterpreted. For this case, use hatching, and test it before submitting.