Getting Started with Analysis and Charts

Key Terms:

• Google Sheets Explore: An automated feature that allows the user to view various insights on a selected range of data without knowledge of function notation.

• Charts: Built-in visualizations of a range of data

  • Headers: y-axis type variables

  • Labels: x-axis type variables

  • Series: Variables to plot on the y-axis

Google Sheets Explore

As the name suggests, this feature is unique to Google Sheets and can provide a variety of interesting information without having to learn complicated formulas to calculate specific quantities from a set of data. This can be accessed in the bottom right corner of the interface, with a green icon and the word “Explore”. Depending on the specific data that is provided, various insights will be displayed, such as suggested visualizations, correlation between variables, and suggestions for visual formatting.

The most interesting part of Google Sheets Explore is the Answers functionality, which allows users to input questions about the data in common language and receive answers. For example, in a table of daily sales data for various products, we might want to find the top sellers on each day to know which products to promote at our store on each day. We can simply ask, in (fairly) common language: “Top 10 Product by Tuesday” to see the top 10 products on Tuesday, where our column title for our products is “Product” and our column title for Tuesday sales is “Tuesday”. Obviously, there are other ways to achieve this; we could have used a filter to be able to sort the sheet by any day, and get more than 10 results, but this would require knowledge of how to apply filters correctly. We might not even know that we wanted this information until we viewed various ways to analyze the given dataset. Google Sheets Explore can provide good hints as to what types of analysis we might want to perform ourselves, and then quickly tell us the results to see if this analysis is worth continuing before we spend time developing a formula or other method to find the information ourselves.

The best way to use this feature is to “explore”; it won’t be so useful if we want to permanently see a specific calculation, but it can guide us to finding out what kind of calculations we want to perform in the first place. It could be fun to just look at all the possibilities without writing and figuring out how to perform each one!

Charts

Like Google Sheets Explore, Google will suggest various charts that it thinks would fit your specific dataset; the quality of these recommendations wildly varies, so it is best to have some idea of what type of chart would be most appropriate for your purposes.

The terminology to decide what should exactly be plotted are “headers” for y-axis type variables (information that applies mostly to the y-axis) and “labels” for x-axis type variables (like what each bar represents on a bar chart). In general, a good policy is to experiment with the 3 checkboxes at the bottom of the Setup tab: switch rows/columns, use row x as headers, and use column y as labels. There are situations where these buttons might not do anything, so there is no hard-and-fast rule for how one should approach creating a chart. The general idea is that the automated chart will usually come somewhat close to the intended result, and any major functional adjustments will come from Setup, while cosmetic changes will come from Customize. Although Customize mostly deals with cosmetic changes, one feature of note comes from the Series tab, which allows you to implement error bars and trendlines of various types on a graph. Once again, every visualization’s purpose is different, so it is best to go with a general strategy of major adjustments in the Setup tab, and cosmetic changes in the Customize tab rather than memorizing a specific sequence of actions to create a single visualization.