Pivot Tables

 

Pivot Tables: Use Cases & Why They’re So Helpful (Chapter 10)

Course: Ads, Fads, & Consumer Culture | Week 10 Topic

What We Learned This Week

This week, we learned about pivot tables and why they are such a useful tool for working with large datasets. In Chapter 10 of Effective Data Visualization by Stephanie Evergreen, the focus is on making data easier to understand by organizing it in ways that reduce confusion and highlight patterns. Pivot tables help with that because they can quickly summarize data without changing the original spreadsheet.

Definition: A pivot table lets you “rotate” and reorganize data so you can see totals, comparisons, and trends fast.

Why Pivot Tables Are Helpful

• They summarize huge datasets instantly (sum, count, average, etc.).
• They help you spot patterns you might miss in raw rows and columns.
• They reduce manual math and limit errors from hand-calculations.
• They are flexible—you can drag fields around and explore different questions quickly.
• They support clearer visuals because they organize the data you’ll chart later.

Real-World Use Cases (Examples to Share)

1) Retail & Sales (Products, Regions, Seasons)

A common use case is analyzing sales performance. For example, a business could use a pivot table to compare sales totals by product category (rows) across years or months (columns), and then filter by region. This makes it easy to answer questions like:

• Which product category sells the most overall?
• Which region brings in the most revenue?
• Do sales spike during holidays or certain months?

This is helpful because companies can make decisions based on evidence—like what to restock, what to advertise more, or what to discontinue.

2) Social Media & Marketing Performance

Pivot tables are also useful for marketing analytics. A creator or brand can summarize metrics like likes, comments, shares, and click-through rates by platform (TikTok, Instagram, etc.) and by content type (video, carousel, story). This helps answer questions like:

• Which platform performs best for engagement?
• What type of post gets the most clicks?
• Did engagement increase after a campaign started?

This is helpful because it turns random numbers into clear comparisons, which supports better strategy (posting schedule, content choices, targeting).

3) Education & Academic Data

Schools can use pivot tables to analyze grades, attendance, or enrollment. For example, a pivot table could show average test scores by class section, or attendance totals by month. This makes it easier to identify patterns like:

• Which topics students struggle with the most?
• Do attendance drops happen around certain weeks?
• Are certain sections performing differently?

This is helpful because it supports decisions that improve learning, like adding review sessions or adjusting pacing.

4) Personal Tracking (Budgeting, Habits, Time)

Pivot tables work for personal life too. Someone could track spending and use a pivot table to group purchases by category (food, gas, rent) and by month. This helps you see:

• Where your money goes most each month
• How spending changes over time
• What categories you could cut back on

This is helpful because it turns daily messy data into a clean summary you can actually use.

Connection Back to Chapter 10

Chapter 10 emphasizes making information easier to understand by organizing and presenting it clearly. Pivot tables support that idea because they reduce cognitive overload: instead of staring at hundreds of rows, you can see a structured summary that highlights comparisons and trends. That makes pivot tables an important tool not only for analysis, but also for preparing data for charts and visuals that communicate clearly.

Conclusion

Overall, pivot tables are helpful because they let you explore data quickly, answer questions efficiently, and turn raw spreadsheets into useful insights. Whether you’re analyzing sales, marketing results, academic data, or personal budgeting, pivot tables make large datasets feel manageable and meaningful.

 

Making and Evaluating a Pie Chart

This blog entry records my experience in making and analyzing the effectiveness of a pie chart. The objective of this assignment was to select a dataset that is suited for a pie chart and then produce a pie chart and see how effective the choice made turns out to be.

Choosing a Strong Use Case

In my pie chart, I decided to use a data set that breaks down a sum into a small group of easily identifiable categories. In my pie chart, I have opted to show breakdowns of how people normally spend their time, which I have categorized into five groups: sleeping, working or attending school, leisure activities, house chores, and everything else.

This data is very suitable for making a pie chart since the data is the composition of a whole, with the total adding up to 100%. The data is also very manageable, with just five categories, making the graph very readable. The data has large enough differences for comparison.

In Effective Data Visualization, author Stephanie Evergreen puts forth that a pie chart is most effective when done with a small number of categories and attempting to determine the relationship of each segment to a complete unit. The current data meets this qualification, so a pie chart is appropriate here.

Source used to support pie chart use:

https://www.data-to-viz.com/graph/pie.html

Creating the Pie Chart

The graph I made for my assignment is a pie chart. I made the graph using Excel. I made sure that the graph is very easy to understand. I labeled each part of the graph both with its category and its percent. That way, the graph does not require interpretation.

I kept using only basic colors and did not incorporate any 3D effects that may lead to distorted views of pizza slice sizes. Additionally, I added a title and a source of information. With that, the overall design is basic, which allows the overall messages of the image to be obtained at a glance.

Pie Chart Image:

pie chart

Evaluating the Design Choice

Although the pie chart is good for indicating proportions, I wondered if perhaps another graph would be better at indicating the data with more accuracy. A bar graph would help allow comparisons between precise values. This would be particularly useful with values that are similar in amount.

However, since the objective was to highlight how each category contributes to the whole day, the use of a pie chart was appropriate. Another way to present the data would be by using a bar chart or a “donut” chart, but neither would add much value to the data. Through this, one was able to learn that the use of a pie chart is appropriate when one wishes to make general observations rather than taking exact readings.

Design Tips and Best Practices

While researching effective pie chart design, I learned several best practices:

• Limit the number of slices to no more than five
• Avoid 3-D effects that distort area perception
• Label slices directly when possible

Use simple, high-contrast colors. These guidelines helped me create a cleaner and more readable chart. Additionally, they reinforced the notion that I should use pie charts rarely and with great intention.

Final Reflection

This assignment has made me realize the conditions under which a pie chart would be used. I have been able to produce a graph that effectively conveys its message by comparing the different options that were considered.

In summary, this exercise has demonstrated that, if used appropriately, the use of pie charts can be effective, but it is a technique that requires careful data selection and design.

Benchmark Comparisons in Data Visualization

Category: Benchmark

Benchmarks serve a critical role in the visualization of data because, through them, the viewer can assess the data based on a target or standard. Rather than displaying a set of figures, data visualization through benchmarks helps the audience answer the question of whether the data meets the expected standards. This article will examine a data visualization posted by the New York Times containing a benchmark. 

Selected Visualization

The visualization that I selected is from The New York Times, and it compares inflation rates in the U.S. to a long-term target inflation rate of 2% set by the Federal Reserve. This graph is a line graph that shows inflation over time, including a line that represents the inflation targeting set by the Federal Reserve.

Link to original visualization:

https://www.nytimes.com/interactive/2023/06/13/business/economy/inflation-fed-interest-rates.html

How the Benchmark Is Displayed

In the chart, the benchmark is illustrated using a horizontal dotted line positioned at the level of 2% inflation. This dotted line stays steady while the inflation data wobbles around it. The benchmark is also marked on the chart, thereby eliminating any confusion that it might be an economic indicator. It is the Fed target.

The use of a lighter color in the benchmark line compared to that of the bold line indicates inflation, making it easier to concentrate attention on the data, though the benchmark line still serves as a reference.

Effectiveness of the Benchmark

The reason this is effective as a benchmark is that it shows whether inflation is meeting, exceeding, or falling short of what the Fed wants immediately. Without this information, changes to inflation levels could be observed, but they wouldn’t be put into context.

In this case, the flows are shown over the whole chart, meaning that viewers are able to make informed comparisons of inflation at any point in time. Even those with little knowledge of economics would be able to grasp the meaning behind the graphic.

Reflection

On the whole, this type of visualization uses the benchmark in a clear and effective way. It is easier to understand with the dashed line, labeling, and the difference in colors between the information and the benchmark.

In this example, a simple benchmarking exercise is used to make a chart more meaningful, as an assembly of numbers, rather than just a simple series of numbers on a chart.

Pivot Tables Armani Johns

      When I first learned pivot tables, I didn’t realize how much the layout of the data affects whether Excel can even work with it. These images show exactly why structure matters.

    The “incorrect” example looks like a normal report, but pivot tables can’t use data that’s spread across wide rows with months as separate columns. There’s no single column that represents “Month” so Excel has nothing to group or summarize. It also hides the real structure of the data, making analysis way harder than it needs to be.

    The “correct” version fixes that by putting each characteristic, Company, Region, Month, Product, and Sales into its own column. Each row becomes one clean data record. This format lets pivot tables slice, filter, group, and summarize the data instantly. It basically turns messy reports into something Excel can actually think with.

    This image shows the difference between a normal report layout and a layout that actually works for pivot tables. On the left, the data is spread across multiple sections. Months are across the top, products are listed down the side, and values are buried inside a matrix. Again, Excel can’t analyze this structure because it doesn’t know which column represents which variable. So on the right side this issue is fixed that by breaking every characteristic into its own separate column. Each row becomes one clean “record” that describes a single data point. This tidy, column-based format is exactly what pivot tables need in order to sort, group, filter, and summarize information correctly.

Pivot tables are helpful because once your data is structured like this, you can answer questions in seconds, like total sales per region, best-selling products, or monthly trends without doing any deep analysis manually. Good structure gives you easy insights!

Pivot Tables - Angelys Valdez

Why Do We Need Pivot Tables?

Raw data can be intimidating especially when there is a lot of rows and columns of numbers, dates, and names. That is where Pivot Tables come in to help us out! Chapter 10 introduces Pivot Tables and its importance. Pivot Tables are a dynamic tool on Microsoft Excel that helps to quickly summarize large sets of data. You can organize information into categories, calculate averages, compare values, and rearrange data view. It is a way to "pivot" your perspective on data to rotate fields around until it is the way you want. Most people do not need more data, but they need more clarity. Once you understand how to use them, you will never look at data the same way again. 

Figure 8.1

Why Pivot Tables Matter

They save us a lot of time. Instead of writing out formulas or manually sorting through data, pivot tables can do that for you. With a few clicks, you can calculate goals, averages, counts, percentages, sums, and more. Pivot Tables also reveal any trends in the data you may have missed. You may not see which person made the highest total revenue over a certain period of time compared to another person who that same amount in their first year. This also shows which categories if any need improvement.

Row Labels	Average of Score	How Many Tests Taken In This Subj.
English	84.83333333	6
Alex	78	1
Ashley	96	1
Bella	85	1
Chris	74	1
Dana	93	1
Evan	83	1
Math	85.5	6
Alex	85	1
Ashley	87	1
Bella	92	1
Chris	78	1
Dana	95	1
Evan	76	1
Science	88.5	6
Alex	88	1
Ashley	93	1
Bella	88	1
Chris	81	1
Dana	89	1
Evan	92	1
Grand Total	86.27777778	18

Figure 8.2

This is a pivot table I created to represent student test scores. The categories used were subjects and grade levels which included Math, Science, and English in grades 9-11. The pivot table calculated the total average in that class for each subject and overall. 

Pivot Tables

 

                                                                            Pivot Tables

As a junior in college studying financing, I have come across a lot of pivot tables especially during accounting. Excel is a major source that is used when creating tables to task on profits and revenues. Above I selected an example of one may look like each quarter, one thing about a business's there's four quarters in a year and there's a lot of data that is inputted to showcase the margins of that quarter. 

Accounting can be hard to grasp at first but is something that has to be handled carefully because it all has to make sense and as in company profits is something to see rise positively.  

Pivot Tables - Davinia

 Pivot Tables - Davinia Brito 

A pivot table is a table typically created in Excel, used to quickly summarize and organize large datasets. It rearranges or pivots information by putting them into categories, rows, columns, values, and filters. Once you do this, you are able to calculate totals and averages without having to alter the original dataset. They are commonly used in charts in tables in order to spot trends, group information, and create clean reports.  

Above is an example of a set dataset along with its pivot table. In the example provided, the pivot table takes the individual sales records and groups them by Department, calculating their total sales for each group. This is helpful because instead of manually adding sales for Clothing, Electronics, and Home Goods, the pivot table automatically collects these values and shows that Electronics generated the highest sales, followed by Clothing and Home Goods. This makes it easier and helpful in order to rearrange throughout fields without changing the original set.