What is data analysis using spreadsheet?

There are a variety of methods that may be utilized to analyze data. Many statistical packages are available, including Microsoft Excel, which is free and can often be used for simple, efficient analysis.

Using the table below as an example, several methods of data analysis in Excel will be examined, including the sort function and the Pivot Table. The sort function is best used for relatively small databases, while the Pivot Table is helpful for analyzing larger datasets and quickly grouping items. Utilizing the sort function on the data set below, it is possible to count the number of people with allergies and determine how many of them are male or female. The data is sorted first by diagnosis and then by gender.

Data Analysis Example

Hints for Analyzing Data

Before using the sort function or Pivot Tables, the data must be “cleaned.” This means that the first step in data analysis is to go through the data and ensure that the style of data entry is consistent within columns. In this case, for diagnoses, it is important to make sure that only one word, phrase, or abbreviation is used to describe each diagnosis. If multiple words are used to describe the same thing— for example, if “allergy” is written in the database as “allerg,” “allergy,” and “allergies” — the analysis will be more difficult, so it is best to choose one term and use it consistently. It may be necessary to change the terminology used in the dataset in order to be consistent throughout; such changes ought to be made at this preliminary stage. If there are multiple diagnoses for a single subject, it is important to list the diagnoses separately. It may be necessary to create additional columns labeled “Diagnosis 2” and “Diagnosis 3,” listing one diagnosis in each column. (If this is the case, it is possible to create multiple Pivot Tables and manually add the results together.)

Using the Sort Function in Excel

Using Excel 2016 for Windows, first select the data (“Control-A” selects all). On the top of the Excel tool bar, choose the “Data” tab. Then, click the sort function (circled below in blue). In the window that pops up, click “Sort by ‘Diagnosis.’” To sort again by gender, click the button in the upper-left corner of the window that says “Add Level.” Then, click “Gender” and the “OK” button. (See picture below.)

Sorting is a great tool to identify trends and to analyze small amounts of data. In the example above, once the data are sorted by diagnosis and then by gender, simply count the number of people with each diagnosis and record the gender breakdown, either manually or using the Excel “COUNTIFS” formula. To use the formula to count the number of females with an allergy diagnosis, select an empty cell and type “=COUNTIFS” followed by the range and criteria. For this example, the first range is D2:D23 and the first criteria is “Allergy.” The second range is C2:C23 and the second criteria is “F.” See the picture below for the proper formulaic notation.

Press “Enter,” and the number of individuals who have an allergy diagnosis and are female is revealed in the cell.

This formula is an excellent way to count specific data if it is too time consuming to count manually. Simply alter the range and criteria in the formula to examine different subgroups.

Using Pivot Tables in Excel

With large data sets, manually counting or using a formula to count can be tedious and create opportunities for error. Pivot Tables will automatically sort data and list values, producing efficient and accurate information. To create a Pivot Table, select the data, click on the “Insert” tab, and then select “Pivot Table.” (For Macs, click on the “Data” tab, followed by “Pivot Table.”)

The Pivot Table will open in a new sheet of the Excel file. The next step is to add values. On the right side, there is a box that says “Choose fields to add to report.” To first sort by diagnosis, drag the “Diagnosis” label (the one with the checkbox next to it) into the “Rows” box. It will look like this:

To next determine how many people had each diagnosis, drag “Diagnosis” (the one with the checkbox next to it) to the box with the heading “Values.” It should look like this:

To sort by gender, drag “Gender” to the box with the heading “Rows,” and Excel provides an automatic breakdown, which can be used to calculate percentages and to create graphs.

Alternatively, to sort first by gender, and then by diagnosis, switch the order of “Gender” and “Diagnosis” in the “Rows” box.

This is just one example of how Pivot Tables can be used. Fields can be added or removed as necessary. It may be helpful to practice dragging different fields to different categories in order to develop an understanding of how Pivot Tables work.

Creating a Data Display

Once the data are analyzed, it is often useful to create a display so that others can quickly and easily understand the results. One way to do this is to create a chart using Excel. First, create another table to more easily show the breakdown of number of males and females with a certain diagnosis. Do this by dragging the “Gender” field from the “Rows” category to “Columns.”

Next, click “PivotChart” under the “Analyze” tab, and select the option “Stacked Column.” This shows the number of males and females with each diagnosis, stacked on top of each other.

After the chart has been created, click on the green plus sign in the upper right corner of the chart to add chart and axis titles, add data labels, format the color scheme, and hide the field settings.

Once the chart is customized, it clearly displays important trends in the data. For example, from this chart, one can quickly see that no females were diagnosed with conjunctivitis or presbyopia.

The Importance of Reporting All Results

When analyzing data, it is critical to report all results, even if they seem insignificant. It is also essential to not lump data analyses together and make generalizations. For example, a researcher conducting a study on the effectiveness of a visual aid to increase knowledge of cataracts administers a 10-question survey to patients before and after showing them the visual aid. The researcher finds that the visual aid increases the overall number of questions answered correctly. This is a good start, but it is not enough. It is critical that the researcher analyze the results of each individual question. Just knowing that the intervention increases overall knowledge provides little information about the strengths and weaknesses of the intervention. Perhaps the intervention caused a significant increase in the number of people understanding what a cataract is, but not the number of people understanding proper post-operative procedures. This is important to know because the intervention can then be modified to better convey the necessary information.

Do you want quick insights into your data but don’t know where to start? 

Spreadsheet software like Excel or Google Sheets are widely used tools for analyzing data. Google Sheets has some built-in quick analysis features that can help you get an overview of your data and some useful insights.

While you can still analyze your data with Google Analytics, the user interface isn’t great for exploring data. On the other hand, Google Sheets provides ease of transforming and analyzing data with its visualization tools and built-in functionalities.

Google Sheets can also be useful to archive data long-term and collaborate with other tools such as Google Data Studio.

In this guide on data analysis with Google Sheets, we’re going to take a look at some of these built-in functionalities.

So let’s dive in.

An overview of what we’ll cover in this guide:

Exporting Data from Google Analytics to Google Sheets

Alright, before we start in Google Sheets, we actually need to have some data available. So I’ve logged into the Google Analytics demo account. Note that this is demonstrated using Universal Analytics, but you can also pull other data from a Google Analytics 4 property.

Here, we can go to Conversions → E-commerce → Product Performance report and see that there is some useful data here that we can analyze.

Now, for the next step you should copy this data into Google Sheets.

We could do this manually, but that will be quite cumbersome. However, Google has an export function that we can use. 

Click on Export → Google Sheets. 

And then Import the data.

Now if you would like to automate the whole process, my top recommendation is a tool called Supermetrics. You can install it as an add-on to your account and then pull the data directly from the API. This tool is then accessible under Add-ons in Google Sheets.

You can also export data from Google Analytics to Google Sheets using the Google Analytics add-on.

Now that we have this sheet available with all our data, let’s look at how we can analyze this data.

Preparation: Cleaning and Formatting Data

First up, let’s clean this data. So, we will delete any cells that don’t belong to our data set. 

Also, we will get rid of any empty rows on the top of the sheet so that we have our first row as a header row. For this, select the empty rows on the top, right-click and select Delete rows 1 – 6. 

It’s also important to make this data visually appealing so that we can know at a quick glance what we’re looking at. 

Let’s make our header row bold. For this, select the header row and click on the Bold option.

We could also change the background color, for example to blue, or change the font color, and so on.

There are also other options to make the data visually appealing and more accessible when you look at it at a quick glance. 

Now, if you scroll down to the end of the sheet, you can see some additional data that is not required for now. So you can get rid of this. 

Select the data and then press the delete button.

Also, the last row in the table adds up all the data above. We will do this summation ourselves in the sheet. So, we’ll get rid of this for now. 

Here as well, you can select the data and then press the delete button.

You should always make sure that your data is in the correct format and is convenient to read in order to go over to the analysis step.

Now, we are left with the raw data that we want to analyze. Thus, this is our first step in data analysis and now we are ready to analyze a clean data set.

Quick Way to do Data Analysis with Google Sheets

Next, let’s do a quick analysis in Google Sheets with the built-in tools that we have. We can utilize them right away when we spot something in the data that we want to analyze.

Sorting

Firstly, how can you sort this table? Currently, it’s already sorted by the Product Revenue, but we can sort by any other column as well. All we would need to do is hover over the column that we want to sort it by, click on the drop-down, and select Sort sheet A → Z. 

Once we sort this, we get the lowest quantities on the top. However, we have also sorted our first header row here, which has mixed our labels up with our data. Let’s undo this by pressing Ctrl+Z. 

Now in order to prevent this, we need to freeze the header row. You can do this by simply dragging down the top-left cell of your spreadsheet. So now the first row is frozen. And when we scroll down, it actually stays in place. 

Now, hover over column E and click on the drop-down menu. From this, select Sort sheet Z → A. This will show us our highest quantities first. And we can see which product has been sold most often in terms of the quantity that was put into the basket.

Thus, you can get quick insights by sorting these columns or by finding particular outliers.

So if you go back and Sort sheet A → Z, you can see the products that were never sold. But some of these products have been put into the basket quite often. This can be very helpful data that should be looked into.

Quick Sum Functions

If we find anything that strikes our analytic fancy, we can take a quick look with the Quick Sum functions.

We can do this by simply marking the cells that we want to analyze. So for example, let’s go with the first 10 products. We will mark the Quantity column.  And then, in the right bottom corner, we can see a function that has quickly summed up all the values that we had in our range of the cells.

So our top 10 products that were sold, make up about 9000 units. Let’s now mark the whole quantity column just by clicking on column E. We can see that 17,600 products were sold overall.

Thus, we get a quick ratio of how much the best 10 products or the most sold products are in comparison to the overall pie. In this case, it would be over half of the product.

So again, this is quick analysis just through the built-in features of Google Sheets. 

Once we click on the box in the bottom-right corner, we can actually see other data such as the Avg, Min, Max, Count and Count Numbers, which are actually the numbers minus the header row. 

Thus, without doing any kind of fancy functional analysis, we have a quick overview of our data.

Filtering

Now, you might also want to filter out data. For example, there are a lot of values here that didn’t sell anything. And we want to see the products that actually sold something. 

How would we achieve that? Well, there is a built-in button, which is the Filter button. We can just click on it with our header row in focus. 

This will summarize our data in the table and we get these quick filter buttons in the header row. We can click on these quick filter buttons. It will show all the values that are in that particular column. You can search for a specific range or value by clicking on Filter by values…

You can also use the Filter by condition… function. For example, if we want to view all only our sales greater than zero, we can filter out all the products that didn’t sell at all. Here, you can add a condition greater than 0 for column E i.e. Quantity.

Thus, this is a quick way to filter our data. We also have some other mechanisms. 

Let us take another example. For this, first we get rid of the current filter. Select the condition as None under Filter by condition…

Now, let’s go to the Product column i.e. Column A and say, we only want to have products from the brand Nest.

Firstly, let’s clear all the filters from the column. For this, click on the filter button beside the column and click on Clear.

Now, type Nest in the search box and click on Select all → OK.

Thus, we only have Nest products in the table and we can compare them easily. 

This was a quick way to use the built-in functionality of filter functions within Google Sheets. Now, click on Select all so that we have all the values again in Column A.

Artificial Intelligence

Moving forward, the last built-in feature that I want to show you is something newer. Google has been introducing artificial intelligence to just about all of its products. You can access the artificial intelligence functions in Google Sheets by clicking on the Explore button in the bottom right corner. 

When you click on Explore, Google will give you some suggestions of questions guide our data analysis. For example, we might want to ask for the average of order quantity. So, let’s click on Average of Avg. QTY.

The value of the average order quantity would be 1.068. Thus, we’ve analyzed column E.

Here, we don’t even have to fill in any kind of formula. Google Sheets does it automatically. 

We can see the formula by clicking on See formula. And that is the formula that we would put into the formula bar. 

This gives us a quick analysis on the fly.

If you’re interested in some of these questions you can scroll down.  

We have some graphs that were analyzed, like the Basket-to-Detail Rate.

Also, we have the graph for Unique purchases to know how it is distributed. 

We could even ask a question under Ask a question about your data.

For example, we can ask how many units were sold of a particular product. The count here for this Maze Pen is 4. We can also see the formula by clicking on See formula.

But, this is not quite correct. If you look at the formula, you can see that Google Sheets is counting how many times the name of the product “Maze Pen” appeared in column A. Ideally, the AI engine should be able to pick up that we wanted the quantity of this product. 

Let’s rephrase our question. And then we can get the correct answer back.

This is a bit counterintuitive, but this is a machine learning algorithm that will hopefully understand all our questions in the near future. The more we use it, the better it will get.

This is again a quick way to do analysis in Google Sheets.

Alright, so there you have it. This is how you can use the built-in functionalities of Google Sheets such as sorting, filtering and artificial intelligence for quick and easy data analysis.

But, the functionalities of Google Sheets doesn’t end here. You can use it to create pivot tables or build entire Google Analytics Dashboards.

Do you use Google Sheets to analyze your marketing data? Or do you prefer doing it on Google Analytics itself? Do you know any other quick analysis functionalities of Google Sheets? Feel free to comment below!