# Deep Dive: Understanding 27 Different PowerPoint Charts

## About this lesson

Improve your skills by understanding 27 different PowerPoint (and Excel) chart types in under 25 minutes.

Charts and graphs are powerful tools for explaining and persuading, but there are so many choices. Let us walk through all the choices and what each one means.

These charts are the same in PowerPoint and Excel and provide an excellent introduction to the variety of charts and why each one has a unique place in your toolbox.

## Topics

01:24 The Pie Chart

02:51 The Bar of Pie Chart

03:54 The Bar Chart

04:45 The Clustered Bar Chart

05:18 The Column Chart

05:38 The 3D Column Chart

06:13 The Clustered Column Chart

07:28 The Stacked Column Chart

08:07 The Line Chart

09:32 The Area Chart

10:10 The 3-D Area Chart

11:06 The Stacked Area Chart

11:36 The 100% Area Chart

12:24 Analyzing Data with Charts

12:56 The Two Axe Line Chart

14:14 The Combo Chart

14:57 The “Added Total Line

15:39 The Running Average Trendline Chart

16:44 Trend Forecasting Chart

17:15 The XY Scatter Plot Chart

17:48 The PowerPoint Specialty Charts

18:11 The Histogram Chart

18:49 The Stock Chart

19:16 The Treemap Chart

19:41 The Radar Chart

20:20 The Sunburst Chart

20:57 The Funnel Chart

21:31 The Surface Chart

## Details

**Subject** Microsoft PowerPoint

**Software Compatibility** All versions for both Mac and Windows

**Level**

**Course Completed**

**PDF Files**
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## Transcript

0:04

Charts are superb method for telling a story, explaining situations or analyzing data, and PowerPoint has you covered with charts.Maybe too many charts for us to comprehend. The opening menu shows you 16 different types of charts. But guess what? There’s even more. Hi, I’m Les from Power Up Training. And I’m going to show you in the next 30 minutes 27 different charts to help you understand how best to utilize your tools. While we have more detailed tutorials on PowerPoint chart creation process here at Power Up Training, it does no good if you don’t pick the right graph to visualize your message. This tutorials going to show you all the various charts used by PowerPoint, and even Microsoft Excel as they’re the same. And I promise not to use any of the examples that you find in other tutorials showing the company sales numbers. Instead, my charts will reveal realistic examples to answer different types of real world questions. And if you wish, you can use them to do sales numbers too. We have 27 different charts to cover with Chapter time codes listed below. So let’s get started. With a pie chart, the pie chart is excellent for understanding the relationship between the data points. For our example, we’re looking at a fictional package delivery company and the number of vehicles owned. The key is that we’re looking at the proportional size of each type of vehicle as compared to each other, not the total number, but the relationship to the whole. Instantly, we see that they own way more bikes than any other category. While a table of numbers can tell the whole story, the pie chart instantly drives home the point that the bikes are the biggest category, not by just a little bit, but by all the other ones combined. So while the pie chart can only accommodate one series of data. And this examples it is the number of vehicles per category, it would not accommodate any other data, such as a second series of the total dollar value of our fleet. Multiple data series won’t work in a pie chart. In a few moments, we’ll see alternative charts that can handle that beforehand. Let’s look at a different kind of pie chart that can solve a common problem when there’s a subset of data that is too small to visualize, because the categories get lost in the small “numberness.” And they’re squeezed together to where they’re hard hard to understand hat chart is the Bar of Pie. Here, we show just three main categories on the big main pie. And then we break out the other grouping slices to expand the microscopic view for vans and SUVs and skateboards in its own bar. Well, this is visually clear, it can be a bit misleading. As you distract your viewer from the bigger picture of the largest slice which are visually distorted by the real size of the smaller other groupings. It’s really those other items that are minor compared to the bigger groupings. Here’s a warning. If you have too many items in your pie chart, they can get lost. nine slices in this pie chart are too many unless our point is that Seattle and Portland are the largest locations for delivery vehicles. But even then, the way this chart is laid out, it makes it a bit more work for your viewers to figure that out. Although if I were to add in some labels, it might help. Better to use a bar chart. Here, the data runs horizontal from left to right. And the key factors compared to the pie chart are that the actual data numbers are used here, not the percentages to the whole. So we see that Seattle has the biggest fleet with 225 instantly understood both the relationship and the actual number count. An extra bonus is that the full city name is displayed so you can use longer labels in the bar charts. To improve this chart, I probably go back and sort the city’s fleet by size. When we’re displaying a simple set of data, the number of vehicles the bar chart can accommodate multiple data series, which a pie chart can’t do.

4:45

This is a variation of the bar chart called a clustered bar chart. The same concept, but we’ve now included vehicle count by city with both the ones powered by gas such as trucks and vans and those by footpath For our bicycle and skateboard delivery vehicles, you can add more than two data series. But for a PowerPoint presentation, I warn you not to include too many data series, as a message may get lost when this slide is shown, unless you plan to explain the details, while presenting an alternative slide is the column chart, it just looks like a bar chart has been turned 90 degrees. The way we’re displaying it here is really just the same as our bar chart, but just a different look. In fact, let’s get me out of the way so that we can see more of the charts on our screen. This is another column chart, in fact, the exact same column chart and the same data. But instead, we’ve selected a different presentation style, of three dimensional it gives the visual depth and design look. However, I need to caution you to be careful. In an attempt to look pretty, we could lose some clarity. This simple one data series chart is easy to visualize the various fleet sizes by city. If you had a data values that were similar, or too many cities, it could get lost when turning into 3d. clustered column chart is another variation, or more precisely, the same column chart. But now with additional data series added up to now, we’ve only seen charts with a single set of data. But just adding a second column, we now have columns that are grouped side by side. This chart is joined are two different classification of transport vehicles, the first set of foot power delivery bikes and skateboards, and the second series of guests trucks and vans. So for Portland, we see 80 foot power delivery vehicles, and 87 gas fuel vans. Specifically, we can visually compare by city, the ratio between fuel types and a rough clue of the total combined size. The instant takeaway would be how San Francisco has way more gas powered vehicles to combat the city’s Hills compared to foot power, and also how Eugene is our smallest fleet relative to the other cities here are showing only two datasets. But we can add more. Once again, too much data can get lost in a column chart. The stacked column chart is another variation is for multiple series of data, and it puts them together on top of each other. Unlike the cluster chart from before where you can compare, the size of each series side by side. The stacked column chart is emphasizing the total of the series added together here is instantly recognizable, that Seattle has the biggest fleet by city, and Eugene has the smallest. And as a minor bonus, you get to view how the individual components add up to the total. It is secondary, however, to the total the line chart. Up to this point, we’ve been using almost the same datasets to focus on different aspects of the relationship between cities or vehicle types. And while we could use a line chart with that same data, it would be technically not correct. The line chart shows the fleet size totals by city, but the connector lines are indicating a flow of time or a sequencing of events. So this is the wrong data for line chart. So instead for our line chart, we will illustrate the trend of vehicle purchases by year showing the ups and downs of our purchases through time. The line chart naturally draws our eyes through the years the see the rise and fall over time. And line charts can accommodate more than just one data series. In this chart, we’re examining year by year purchases of vehicles by type. This is great for spotting trends, such as the move from buying mostly foot powered bikes and skateboards in the early years to more and more gas driven vans and trucks in the latter years, and line charts can handle more than two data series. But as always, be careful not to overfill your chart with too many series. The Area Chart. The area chart is really just a line chart with a color fill below each line.

9:41

They can look visually striking with the bolder look. But they can have issues depending on the numbers in your data series. This is the same chart as our previous line chart. But because the foot vehicles are in front, it hides completely this smaller numbers for the gas vehicles until 2016. So while it may look sharp, is not always the right choice based off your data. But don’t give up on the area chart quite yet. If we instead use the three dimensional area chart, we now have a different viewing perspective. And we can see to some degree behind the peaks of the data in the front row. This chart variation almost works. But now we can’t see the foot powered data series starting in 2019. However, there might be a fixed depending on your numeric data. Here, I just moved the gas Data Set to the back. And now we can see all the data points in our 3d area chart. For big picture stories, 3d area charts can be a great addition. But they’re a lousy choice if your numeric numbers are close in value, as it’s hard to distinguish their actual height in size in this looking down perspective, but there are some area charts that are both visually striking, and also precise and communicating numeric values. The stacked area chart conveys both the continuum of time but also the total value and the relative size of each data series. It shows the total and the components in that great two purpose chart. As we can see this smaller and smaller percentage of foot vehicle purchases over time. If we really want to drive home the point of the changing ratios than the 100% area chart is excellent. Here, PowerPoint takes our data points and converts them into ratios, or percentages of the total. For example, in the year 2013 foot vehicles were almost 100% of our purchases. And by 2020, it was almost 100% for gas vehicles. While there’s no indication the total purchase counts it does show the relationship. And the cool thing is you just put in the data series numbers, and PowerPoint does all the math calculations for you. You can consider this a pie chart that travels over time. PowerPoint charts are not just for illustration, but can also be used for analysis. Here is a line chart, we’ve added a third data series of packages delivered as compared to our vehicle purchase type. It is obvious on this slide that our package delivery counts have grown greatly over time. But the issue is that the scale of the packages is in hundreds of 1000s easily dwarfing our small vehicle count numbers. To solve this, we introduce the two axis line chart, basically a chart that has the left vertical axis showing the numeric count for the vehicles purchased with the right vertical axis, the numeric scale for the packages delivered. Note that the axis labels are used to clarify, which is tied to which now that the data is magnified in the line chart. The analysis part is easy. We can determine that as soon as we increase the number of gas vehicle purchases are packaged delivery counts followed, all in one picture. Let’s look at a different data set. This time, we’re charting customer interactions by 1) phone calls 2) webpage forms and 3) our newly introduced iPhone app. And of course, since we’re looking at monthly numbers, the line chart is the best choice.

13:56

If we change the iPhone app line color to red, and make it bold, you can see that this method of interactions with customers quickly grew to the most popular technique. And once I explained that to you, it becomes obvious though we can do better with PowerPoint charts. Here is a new chart type the combo chart. With the combo chart, each data series can be a different chart type overlaid on top of each other. Here, I’ve turned phone and web data series into columns with a lighter color shading and then left the iPhone order app as the line in red and bold. Now, it is much more obvious that the iPhone app became the most popular method without my having to point it out. Depending on the situation. You can also make the secondary y axis also known as a vertical axis. Use a different set of scale numbers for comparisons. Let’s go a bit deeper to show why different Hard choices can alter the understanding. Here’s a simple line chart, where I’ve added a fourth series, which is the grand total month by month, I already pointed out that the iPhone app became our dominant tool for our customers. Now, look at this alternative chart, the stacked line chart, which we saw earlier, the top line represents the total, see how the lines match, but it’s made up of the three datasets combined and stacked on top. And suddenly, the growth of the iPhone app is not as apparent. So choosing the right chart to highlight your message is important. And I have another reason to make the total line separate and distinct. As we’re be able to do some fancy statistical analysis on that specific line. The first statistical analysis is to smooth out the variability of our monthly numbers by adding in a trailing four month average, because of the red dotted line, and you’re able to get rid of some of that variability noise. The cool thing is that we can use PowerPoint to do all the math to create this analytical chart. Here is a quick side trip of how to perform this analysis in PowerPoint, see how we have four data series. Now I’m going to right click on the total line and choose Add Trendline where I’m presented with dozens of choices, such as the moving averages, where I can now select the trailing number of data points to perform the math on. Also note that there are other more sophisticated statistical tools that we’ll see in the next graph, including the ability to do future projections.

16:44

And here is that same data, but without the moving average. Instead, we use the trend line technique to add an exponential statistical analysis plus a future projection of total usage. While simple to do, I must warn you that without some background statistical understanding, you can get yourself into some big trouble. For those that know the R squared value for this line is point six, six, which is not a very high correlation. One more common analysis chart is the XY Plot chart, which operates on just two and only two datasets. That concept is to plot the Intersect values on the vertical and horizontal axe. Here, we see a soft correlation that packages under 10 pounds, typically need less than 1.5 miles to travel for delivery. The understanding of this type of chart goes beyond this tutorial. That brings us up to nine specialty charts. Some of these will not be found on older versions of PowerPoint, starting to drop off prior to office 2013. And for the Mac users with a recent version of PowerPoint, you do need to dig deeper into the menus under such categories as hierarchy, or statistical or waterfall to find them, but they are there. Let’s get going with histogram. The histogram chart will take a single set of data and grouped them together showing the total instances for each grouping. Here, we’re looking at a large number of packages and their weight. PowerPoint has done the math for us and created three pound groupings. So we had 13 packages that were between zero and three pounds, and then 24 packages between three and six pounds. For this dataset, we can see that most of our packages are under 12 pounds. Once again, PowerPoint did all the heavy math walrus. The stock chart is a specific chart for a specific situation during a specific trading period, typically done in days, but it could be weeks or months. There are three datasets. The highest price reached during that time period, and the lowest and then the ending price at the end of the period. It works for stock pricing cryptocurrencies or other financial instruments, but it must use these three data series. The treemap chart is sort of like a pie chart that’s in a rectangular shape. This chart answers the question of how much percentage space does each vehicle take up in our parking garage. The treemap shows percentage usage, not the total numbers. So of the 100% of usage, trucks and vans take up most of the floor space. While the treemap was working with a single data series, the radar chart works with two or more datasets. Here we have a mapping of the number of deliveries versus the staff size by city and it jumps out the Sacramento does many more deliveries Even with a small staff size. Typically the radar chart is hard for novices to understand, which is why I don’t use it in the presentation. But for a technical audience, it might work. Note that you can also handle more than two data series. For charts like that makes my head spin. This Starburst chart lets you break a typical pie chart into subgroups. Once again, just one data set of total staff by department. But we’ve also grouped the departments by location where PowerPoint has totaled them up for us automatically. Here we see that the Portland PDX staffing is the largest of the collection. And when we look at the outer ring, we see that PDX has a larger Driver Set of employees plus an HR and exec department that does not exist in the other cities.

20:57

The funnel chart may look just like a bar chart that’s coming from the center, left and right. But in reality, the funnel chart is specialized in that it typically shows the activities funneling down. Here, we start off with a number of visitors to our website. And of those 2500 visits. Only 925 visitors go to the web form application. And some of those webform applications are abandoned, resulting in only 750 orders. And after cancellation, we’re typically only do 690 deliveries. The surface chart will visualize three datasets over the x axis, the Y axis and the Z axis. This is a real three dimensional chart, not just one this done for graphical enhancements. Here we’re mapping the number of repairs by city and by vehicle type. In our example, it becomes obvious that San Francisco with all of us Hills causes our vehicles to have higher repair needs. This specific dataset works as is obvious, but I find people have difficulty seeing 3d spatial data representation on a two dimensional Slide screen. And if we’re talking hard to understand at first glance, then we must discuss box and whisker. For a room of scientists. This may look obvious, but for general business audience, we must dig deep into statistics. A single element in the box and whisker shows the statistical analysis with an indicator of our outliers at the upper and lower extremes, and then the range of the top quartile and the bottom quartile. And then in the middle, we’ll see a line indicator for the median inside the two middle quartiles. If this is your expertise, you’ll appreciate that PowerPoint will do all the math for you. And our last PowerPoint chart is the waterfall. This is another complex at first chart illustrates how charts can hide facts or surface hidden trends. If we asked our head of HR, how stable our workforce has been, this simple bar chart showing a small decrease of five people and then return to 100. By the third quarter makes us look very stable. But look at this specialized waterfall chart. It shows the changes between the major milestones. For example, in the first quarter, we lost 25 People who quit, and another 10 who were fired, which is over a third of our staff. And then we hired back 30 new people before the end of the quarter. So the start and ending points looks stable. But behind the curtains, we see a lot of staffing churn. So while a waterfall chart is a bit more confusing at first glance, it can bring amazing insight for your PowerPoint audience to understand the real story that would get lost in a traditional chart all on a single slide, at this point, you now understand the immense wealth of the tool that PowerPoint gives to you to analyze and present your data with over 25 different charts to subscribe and explore our expanding wealth PowerPoint Tutorials, including more chart creation training, or go to our website to see all of our free training tutorials, better organized than the YouTube playlists, including our anatomy of a line chart. Until next time, Go Power Up!