So, what makes a good chart or visualization? The answer depends on a few factors, most of which boils down to the dataset you are working with. Think of a dataset as a table of rows and columns that has consistency for each column or field available. For example, Year should have values of 2013, 2014, and 2015 all in the same consistent format. If your dataset has inconsistent formats or a mix of values, then cleansing your dataset before creating the chart is recommended. Data cleansing, or scrubbing, is the process of fixing or removing inaccurate records from your dataset. Charts need uniformity for reasons such as sorting the year values in ascending order to present a trend accurately.
Let's go with a simple example, as shown in the following diagram. Here, on the left-hand side of this dataset, we have a uniform table of data with four rows, two columns, and a header row. To ensure that you understand this concept...
