Comparison charts are invaluable tools in Excel, widely used across business, education, and research to visually represent data. These charts not only simplify complex information but also highlight key trends and comparisons. A comparison chart in Excel is a visual representation that allows users to compare different items or datasets. These charts are crucial when you need to show differences or similarities between values, track changes over time, or illustrate part-to-whole relationships.
In this article, we’ll compare a company’s sales, expenses, and overall profits by year. Here is some sample data:
Types of Comparison Charts in Excel
There are many types of charts you can use in Excel to compare data. Here are a few examples of common charts you might use when comparing data, and how they look:
Bar Chart: Creating a bar chart in Excel starts with selecting your data and choosing the ‘Bar Chart’ option from the ‘Insert’ tab. Bar charts are particularly useful for comparing individual items or categories. To enhance readability, consider adjusting the bar colors and adding data labels. In the bar chart below, you can easily compare sales versus expenses versus profits, and also compare those values by year.
Column Chart: Similar to bar charts but oriented vertically, column charts are ideal for showing changes over time. After selecting your data, choose ‘Column Chart’ from the ‘Insert’ tab. Play with colors and axes to make your chart stand out. Whether you prefer to go with a column chart or a bar chart may simply come down to your preference.
Line Chart: Line charts are perfect for tracking trends over periods. Select your data, click ‘Insert’, and then ‘Line Chart’. Customize your line chart by changing line styles and adding markers for key data points. Line charts may be more useful when there are fluctuations that you want to plot. Here is the chart based on the current sample data:
Here’s a look at the chart when there are greater fluctuations in the data:
Pie Chart: For part-to-whole comparisons, pie charts are your go-to option. After selecting the data, find ‘Pie Chart’ under the ‘Insert’ tab. Enhance your pie chart by experimenting with different slice colors and adding a legend for clarity. This is ideal when you want to compare individual parts of a greater total. Suppose you wanted to analyze what made up the company’s sales. This is where a pie chart might be most appropriate:
Excel has many more charts available for you to use, but these are good starting options when doing analysis. After you’ve selected the right chart, there are further enhancements you can focus on.
Tips for creating effective comparison charts
Here are some tips and things you can focus on to make your charts even better:
Simplify and Focus: Avoid cluttering your chart with too much information. Focus on the key data points you want to compare. This can sometimes mean creating multiple charts instead of trying to fit everything into one.
Use Appropriate Scale and Axes: Ensure that your axes are scaled properly to accurately reflect the differences in data. Misleading scales can lead to incorrect interpretations.
Color and Design: Use color effectively to differentiate data sets and draw attention to key points. However, be mindful of color blindness and avoid using colors that might be hard to distinguish.
Clear Labels and Legends: Use labels and legends that clearly describe what each part of your chart represents. Avoid jargon or abbreviations that might not be understood by all viewers.
Consistent Formatting: Keep formatting like font size, color schemes, and line styles consistent across all charts, especially when they will be viewed together.
Data Integrity: Ensure your data is accurate and up to date. Misleading or incorrect data can harm credibility.
Accessibility: Make your charts accessible to everyone, including those with visual impairments. This can involve using larger text, high-contrast colors, and providing alternative text descriptions where necessary.
Checklist for creating comparison charts
[ ] Chart Type Selection: Choose the most appropriate chart type for your data.
[ ] Data Accuracy: Verify the data for accuracy and relevance.
[ ] Simplification: Remove unnecessary data or split into multiple charts if needed.
[ ] Scaling and Axes: Check that axes are scaled properly to accurately represent the data.
[ ] Color Usage: Use distinct colors to differentiate data sets; consider color blindness.
[ ] Labels and Legends: Ensure all parts of the chart are clearly labeled.
[ ] Consistent Formatting: Maintain consistent formatting across all elements.
[ ] Review for Clarity: Check if the chart conveys the intended message clearly.
[ ] Accessibility Compliance: Ensure the chart is accessible to all audiences.
[ ] Feedback: If possible, get feedback from others to see if the chart is easily understandable.
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A good way to gauge the strength of the U.S. economy and how well it is returning to normal level is by looking at Las Vegas’ visitor data. The Las Vegas Convention and Visitors Authority (LVCVA) has plenty of important metrics that it tracks on its website. From the number of visitors to the city to occupancy levels to daily room rates, and other key performance indicators (KPIs). Using data from that website, which you can find here, I’ll guide you through the step-by-stop process as to how you can build a dashboard to track some of those key metrics.
Step 1. Preparing and consolidating the data
One of the most important parts of data analysis is to clean up your data from the beginning. By doing this, you’ll avoid headaches later on. It’ll also make it easier for you to do analysis in the first place. To get a proper glimpse of how Las Vegas is doing now, it’ll be useful to track multiple years. On the LVCVA website, you can download data for multiple years. For this example, I’m going to download data from 2019 through to 2023 YTD.
This is what one of the files looks like:
As of writing this article, data for 2023 is available up until the end of July. Since the data is organized in the same format on all of the files I’m downloading, I can just copy and paste one year after another. The key is for the rows to line up.
But I still need to clean up this data. One problem is that there are gaps between the months. Once I’ve loaded all the years together, I’ll remove those blank columns. The easiest way to do this is the highlight the top row. Then, press F5 and select Special. There will be an option to select Blanks:
Then, all those gaps are selected:
If your right-click on any one of them, select Delete, choose Entire Column, and press OK. Now those columns are gone:
There’s still one problem here. The way the data is structured right now isn’t useful when creating pivot tables. And if you’re creating a dashboard, you’ll want to be able to create pivot tables easily. Doing so can make it easy to create reports on the fly and easy to make changes. It’s easier to have dates going vertically than horizontally to scroll through data. So what I will do is use the TRANSPOSE function to flip it. All that’s necessary here is to use the function and select your entire data set. Then, voila:
Before I make any further changes, I want to convert this into values. Since I used the TRANSPOSE function, it’s sitting as an array. To change this, I’ll select the entire data set, press CTRL+C, and then press CTRL+SHIFT+V to paste as values. If you don’t have that functionality on your version of Microsoft Excel, right-click and select Paste Special and click on Values.
I will also add a few more columns to make the analysis easier. I’ll create a column for the month and year. This will involve using the MONTH and YEAR functions. The only argument that is needed is the original date, which in the screenshot above, appears under ‘Tourism Indicators.’
And since I want to compare 2023 to 2019, I’ll add a column for ‘Current Period’ and ‘Comparable Period.’ The point of this is to make sure that I can filter the current YTD values against the same values from 2019. Since I have data up until July, any comparisons should also run up until July 2019. For the Current Period, I’m using the MAXIFS function to grab the maximum value for the Month field for the current year (I can use the TODAY function to make it dynamically pull in the current year). Then, for the Comparable Period column, I can compare the Month field to see if it is less than or equal to the Current Period. If it is, then I’ll set the value as a “Y” to indicate it falls in the comparable period or “N” if it doesn’t. This way, if I come across month 8 and my current period only goes up to 7, it will mark that as an “N” which will allow me to easily filter out those results.
Lastly, I will convert all this data into a table. The purpose of this is so that I can easily reference the different fields later on, without having to remember column letters. To convert this into a table, select Insert and click on Table. Then, on the Table Design tab, you can name the table something that’s easy to remember. In my example, I’ll refer to it as tblConsolidated.
Step 2: Identifying the KPIs to track
Before rushing out to create the pivot tables, it’s important to know what you want to track. You don’t want to create a pivot table and track everything possible, otherwise it won’t be a useful summary, which is what a good dashboard should aim to do. That’s why you should devote some time to identify what some of your KPIs should be.
There are a lot of metrics on here and these are the ones that I am going to use, which will help gauge how active and busy Las Vegas is:
Visitor volume. Obviously the number of people visiting the city is a great indication of how many people there are.
Occupancy levels. If hotels are booked up, that’s another positive sign that the city is busy.
RevPAR. This takes the room revenue divided by the number of available rooms. It shows how well a hotel is filling up its rooms at a given rate.
Average Daily Rate. This is partly reflected in RevPAR but it can be a useful indicator as people are more familiar with room prices than they are with RevPAR, especially those who visit Las Vegas often.
En/Deplaned Passengers. This is a helpful metric to know how much out-of-town traffic there is coming to the city.
Average Daily Auto Traffic. With this metric, readers can see how busy the roads are.
Gaming Revenue (Las Vegas Strip). This is another important KPI because it tracks how much people are spending at casinos.
Step 3: Creating the pivot tables
Now it’s on to creating a pivot table for each KPI you want to track. To make this process easier, just create a pivot table one time, and then copy it for as many charts that you want to create. This way, you don’t have to go back and select Insert->Pivot Table over and over again. Just make sure to leave enough room so that they don’t overlap, otherwise you’ll encounter errors.
It’s also a good idea to label your pivot tables by going into the PivotTable Analyze tab. For a pivot table to track visitor volume, you might want to call that ptVisitorvolume, for example. This will be helpful later on if you want to change charts and aren’t sure what PivotTable1 relates to. You’ll also likely want to change the default formatting for a pivot table:
To change the format, don’t just highlight the cells and make the changes, otherwise they’ll revert back once you update the data. Instead, right-click on one of the values and select Value Field Settings. Then, select Number Format and apply the formatting you want to apply to that field.
What I also like to do is put all the pivot tables on a separate tab to keep them organized, while all the charts will go on a main tab dedicated for the dashboard.
Step 4: Creating the charts
When creating your charts, one thing to consider is how you want the data to be visualized. You can do this as part of the stage to identify KPIs. For visitor volume, for example, I’ll use a line chart since I want to see the month-over-month progression. This will also make it easy to compare against multiple years.
Since these are charts created from pivot tables, they are pivot charts, and they come with drop-down options:
They aren’t terribly appealing and to get rid of them, click on the chart, select the PivotChart Analyze tab and unselect the option for Field Buttons:
One thing that can help with creating charts is by using Excel’s existing Chart Styles, which are on the Design tab (which is visible if the chart is selected):
This can be an easy way to customize your charts without having to do so manually.
You may also want to adjust how the data is displayed. Visitor volume, for example, may make sense to leave as the default, which is a summation. But when looking at ADR or RevPAR, you wouldn’t want to sum those values up. Instead, you may want to calculate the average instead. To do that, right-click on one of the fields and select Summarize Values By and select Average
Now, you’ll see an average based on period, which makes more sense than summing up prices.
At this point, it comes down to your personal preferences as to how you want to design the charts, and it would be far too deep to try and get into all those options here. However, I’d suggest mixing up a bit of bar and column charts and also changing up the colors so your dashboard doesn’t look like the same item over and over.
Some additional things you may want to consider are:
Adding data labels. And if you do use them, consider not using axis labels;
Using legends where and when make sense to do so;
Adding background images to your charts to have a different look and feel to them;
Having descriptive titles to help summarize what the chart is displaying;
Not plotting too much on on chart. You may want to consider plotting years instead of months;
Not using a border color so that your charts blend in with the background.
Here are a couple of charts I created with images in the background to make it clear what they are showing:
Step 5: Adding key numbers at the top for further emphasis
Charts are good, but what can also be useful is to put key numbers right at the top so that readers don’t have to spend much time looking for the most important metrics. For example, using formulas, you can pull in the total number of visitors for the period, the occupancy rate, the ADR, RevPAR, and other items, based on the latest information.
While these can be good to include in charts, by making them big and allowing them to stand out as soon as you open up the dashboard, it can help drive the point home even further.
In the example above, I have a list of the current metrics along with the growth rate or comparable percentage from 2019, to help show how the metric is doing compared to that year. You could also add conditional formatting to this to highlight where there is an improvement and where things may have worsened.
Step 6: Finishing touches
Once you’ve got your charts and metrics all on there, the last piece of the puzzle is to add a title as well as any icons or images that may be relevant to help give some added pop to your dashboard. If you go to the Insert tab, you can use that to pull in pictures from the internet. Excel also has built-in icons and stock images that you can use, just by doing a search:
This can be an easy way to help your dashboard stand out even further. Here’s a snapshot of the dashboard I created:
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If you’ve got a big, complex spreadsheet with lots of formulas, it can be slow to run. In those situations, turning off calculations can be a life saver. But the downside of doing so, is that you might forget that those calculations haven’t been updated. Relying on stale values can be risky and lead to poor decision making and analysis.
Thankfully, there’s a new feature in Excel that now helps you find and identify those values easily.
Finding stale values
For this example, I’m going to use a simple table. It shows product IDs, prices, quantities, and total sales.
The only calculation that happens here is in the total sales column, where price is multiplied by quantity. If the calculations are on, changing either the price or quantity fields will change the value in the total sales field automatically. But if I turn on Manual Calculations, then the calculation won’t happen until I either set the calculations to Automatic, or to manually force calculations (e.g. by pressing F9).
To turn off calculations in Excel, go to the Formulas tab and select Calculations Options, where you’ll see the following options:
The one danger is that if you set your calculations to Manual, it will change the setting for all the workbooks you currently have open. This change isn’t just set to one sheet or workbook.
In the above screenshot, the calculations are set to Manual. And if you’ve updated to the latest version of Excel, you’ll see the option at the bottom: Format Stale Values. If you check this off, you will now see different formatting for calculations that Excel hasn’t updated.
After checking that off and making changes to some of the quantities in my table, some of the values in the total sales column haven’t updated. And it’s easy to see which ones those are:
There are now strikethroughs showing for the values which aren’t updated. This tells you that those values are no longer accurate. As you can see from the value of $172.50 where the corresponding quantity is 50 and the price is $5.75, the total sales based on that calculation should be $287.50. Without applying the formatting for stale values, it would be difficult to notice that the value of $172.50 is incorrect.
Once the values are recalculated, either by manually triggering them (F9) or by changing them back to automatic, then the strikethrough goes away. And that’s because the value has also been updated:
If you never turn your calculations off and set them to manual, you’ll never need to use this feature of stale formatting. But if you do occasionally turn off calculations, then it can be valuable to you as it can help you avoid errors and making incorrect decisions based on outdated information.
If you don’t see this option available yet then it may not be available on your version of Excel. You need Microsoft/Office 365 and for the latest beta updates to be installed. Eventually, however, it will be rolled out to all 365 users.. But if you want new features as soon as they are available, be sure to sign up for the (free) Office Insiders Program to ensure that you get them earlier than the general rollout.
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In this post, I’m going to show you how to group dates in a pivot table by month. By doing this, you can do analysis by month rather than individual day. And that will also make it easier to plot the data on a chart.
For this example, I’m going to use TSA passenger volumes as my data set and analyze them by month and year. Here is the data I’m going to use, which runs up until Aug. 6, 2023:
If I load this into a pivot table, my fields are as follows:
I have the date field which shows the current year’s dates. And there is also a field for each year, which contains the passenger volumes. If I put the Date in the Rows section of the pivot table and then years into the values section, then my pivot table looks like this:
There are a few things that I need to fix for this analysis to work:
I need to change each year field so that it is taking an average instead of summing the values. If I leave it as is, summing the values may not be helpful as the months are not going to be identical eah year. Taking an average will help smooth the data.
The formatting should be changed so that the values are separated by commas. This will make it easier to visually see the data. The numbers are too big and can be difficult to interpret in their current format.
The Row labels are broken down by year. But I already have the year values going across. This is not necessary and I need to have only the month values.
Here’s how to address these items.
To change the year field so that it takes an average, right-click on the field and select the option to summarize as an average:
Repeat this for each field, so that everything says average. To fix the number formatting, right-click on each field and select Value Field Settings:
Change the formatting to Number and check off the option for the 1000 separator. Repeat these steps for the other fields as well.
Next, for the date grouping, right-click on any of the date values and select the Group button:
At the following dialog box, uncheck years and quarters and just leave Months:
After making all those changes, my pivot table now looks like this:
It’s now easier to compare the different months and years. And it’s also easier to put it on a chart. If I insert a line chart, it’s easy to spot the trends by a monthly and yearly basis:
This is a PivotChart, as it evident from the grey drop-down options. If you prefer to get rid of the filters, go to the PivotChart Analyze tab and uncheck the Field Buttons option. Now you’ll have a cleaner chart layout. In the below example, I have also moved the legend to the bottom:
As you can see, by grouping your pivot table dates by month, it becomes easier to analyze data. And by not doing a daily analysis, it’s possible to look at the data from a year-to-date view to compare the monthly averages. This way, you are able to still see the story behind the data without having a crowded chart.
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The TREND function in Excel is a powerful tool that allows users to perform linear regression analysis and make predictions based on existing data. This function is particularly valuable for professionals dealing with data analysis, financial modeling, and forecasting. In this article, I will go over how the function works, and provide step-by-step instructions on how to utilize it in your Excel worksheets.
Using the TREND function
To use the TREND function, follow the steps below:
1. Organize the data
Before you can use the function, you need to have your data organized so that it includes at least two columns. One needs to be for the independent variables, or the x-values, and another one for the dependent variables, or y-values. It is necessary for the data to be aligned correctly so that the information correctly relates to one another (i.e. you don’t want the wrong values lined up next to one another).
Below is sample data for a company which sells seasonal products. In warmer weather, revenue rises while in cooler temperatures, sales are lower.
2. Calculate the Trend Line
With the data populated, you can now enter it into the TREND function in Excel. This involves specifying the following arguments:
In the above example, the known_y’s are the sales, the known_x’s are the average monthly temperatures. If I don’t fill in any new_x’s or specify the constant, the function will still try and plot out the rest of the values:
The problem in this scenario is that it doesn’t take into account the temperature; it simply assumes a similar trend as before. The function is much more useful if I have forecasted monthly temperatures. That way, the trend calculation will take that into account. Suppose I fill in the data, telling Excel that I expect the temperatures to be much warmer over the next 12 months:
With the previous forecast off to the right, you can see that the TREND function has adjusted to reflect the newer information. Thus, the more data you plug into the function, the more reliable the forecast will be. Otherwise, it will simply assume the same patterns will repeat from before, which may not necessarily be the case.
There is an additional argument in the function that you can also adjust, and that is the constant. If you set it to false it will be 0. If set to true, then the formula will calculate it. This is the b variable which is part of the y=mx+b equation. If you expect there to always be a minimum, a constant amount, then you may want this to be calculated. If, however, the data can fluctuate wildly, then you may want to set it to true so that there is no intercept. Here’s a comparison with the above data both when there is a constant and when there isn’t:
The forecast in green is where the argument is set to false (constant is set to zero) and blue is where it is true and a constant is calculated. From the chart below, you can see that there isn’t a big difference but the highs are higher and the lows are lower when there is a constant. This may, however, not always be the case as it will depend on your individual data set.
Create a chart to differentiate between actuals and forecast
One thing you may find helpful to do when creating a forecast is to put those amounts on a different column:
By doing this, you leave yourself space to add actuals later on and to compare them against your forecast. You can also create a chart with the forecast being a different series. In the below chart, I have used a dotted line to show the forecast while the actuals remain solid. For the first forecast amount, I set it to the same as the actual. This way, when I create the chart below, there are no gaps and it is merely a continuation of the line.
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Creating a dashboard can be an effective and efficient way to pool in many data points. In this post, I’ll show you how to create a dashboard that factors in several economic indicators, including inflation, interest rates, housing starts, GDP, unemployment, and the performance of the stock market. It will utilize power query and allow you to easily refresh the data.
Creating and collecting the data points
To make the data that I’m dynamic, I will also use a variable for the current date, so that the data will automatically update. In this example, it will be called todaysdate which is equal to the following formula:
Below are the sources for the data that I will use in creating this dashboard along with the Power Query links I will use (along with the variable for the date). I’ll also set up the Power Query links as named ranges in the Excel spreadsheet, making it easy to reference them within the queries.
The name is case-sensitive so if you use a named range that is all in lowercase as I have done, then those references also need to be in lowercase in Power Query. However, for the purposes of this example, you don’t need to use named ranges and it is an optional step.
Creating the Power Query connections
To create a Power Query connection, I’m going to start by going into the Data tab and selecting From Web under the Get & Transform Data section. For the unemployment rate data, I’ll use the link for that:
After click on OK, I’ll select the table that I want to use, which is the first one on the list:
I’ll click on the Transform Data button before loading it. What I will do is split the Month column so that I have both a Month and Year field. To do this, I’ll select the column, right-click and select the option to Split by Delimiter and use a space. I’ll also use this opportunity to put in my named range for the data link. In the Power Query window, under the Home tab, there’s an option to click on the Advanced Editor. Here, I’ll enter my NamedRange variable and use that when referencing the Source:
When you’re running a query for the first time, you may see a warning asking you about Privacy Levels. Set these to Public and select Save.
Now it’s time to repeat the steps for the other data sources.
Transforming the data in Power Query
There will be some adjustments that need to be made along the way when loading the data. For example, for the data that comes from the FRED website, there are some rows at the top that need to be removed:
In this case, I’ll need to click the Remove Rows button at the top, and specify that I want to Remove Top Rows and enter a value of 11, to remove the first 11.
For the housing and inflation data, I need to make additional adjustments since the data is raw and doesn’t show the percent change, which is what I want. Here are the steps I’m going to take for those queries:
Unpivoting the data. This is important for the sake of making sure that months are not going across and are instead going vertically. Refer to this post on how to flip and unpivot data in Power Query.
Generating previous and current period data. I’ll create a calculated column to calculate the current period and the previous period. After the current period column is created (by simply joining the month and year together), I’ll duplicate the query so that there is an additional table for the inflation data. As for the previous period, this involves subtracting 1 from the year to get the previous year’s values. Then, the year and month are concatenated:
Doing a lookup of the prior-year period. I’ll now merge the query with the one I copied earlier (the other inflation period). This involves doing a lookup of the previous period on the other table’s current period. The goal here is to get the prior-year period’s value. Here’s an overview of how to merge queries in Power Query.
Calculating the percent change. Once the prior-year period’s value is loaded and on the same row, I can create a custom column to calculate the year-over-year change, which is just the new value / old value -1.
Removing unneeded values. The final steps involve removing any blank values from the inflation rate and removing and periods that contain the word “HALF” indicating half-year values. Lastly, I’ll split the columns back out so I again have the year and month broken out, this time, along with the inflation rate %:
These steps will be similar for the housing data, except I won’t need to unpivot the data since it isn’t broken out by month and year.
Creating the pivot tables and linking to the data
Now that the data is loaded, the next step is to link to it or create pivot tables, to populate the dashboard. For the unemployment data, I will summarize the average by year:
For the GDP tab, I’ll pull in just the four most recent quarters. To do this, I can use the INDEX function and the COUNTA function to grab the furthest values. For the most recent period, I can use the following formula:
For more recent periods, I’ll deduct 1, 2, and 3 from the COUNTA value:
The interest rates I will leave as is as that data can chart smoothly given that there normally aren’t many interest rate changes.
For the inflation rate, I will again take the average annual rate using a pivot table but only looking at data since 2010:
On the housing tab, I will break out the average housing starts by quarter, again using a pivot table:
Creating the dashboard
Now that the pivot tables are set up, I can start putting together the dashboard.
For starters, I’m going to go for a clear, dark background, setting it to black. I’m going to create headers for each of the different categories: Unemployment, GDP, Interest, Inflation, Housing Starts, and Stock Market. I’ll link to the key data, referencing the key metric that I want from each tab. Each header will take up three columns, with a space between each one:
What I will also do is create some conditional formatting rules for these values so that they can appear green or red based on their values. Refer to this post for an in-depth overview on conditional formatting. Below the values, I will also extract the date of the most recent data and put it within a formula, to show when the data was last updated:
Next, I’ll create the charts for the different pivot tables. This is really down to preference and style, but I’ll use a combination of bar, column, and line charts to display the data. Here’s how the dashboard looks after adding a title:
And with the data all coming from the web and utilizing Power Query, you can simply just refresh the data to pull the latest numbers, making your dashboard dynamic and easily updateable.
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Did you know that you can group numbers in Excel using tags? By just listing all the categories an item should belong to, you can make it easier to group them. In this post, I’ll show you how you can use tags in Excel to efficiently summarize different categories.
Suppose you wanted to list all the possible streaming services you might subscribe to. You might have a list that looks something like this:
This is fine if you want to compare them or even tally them all up. But what if you wanted to look at different scenarios, such as what if you select some of these services, but not all of them? This is where tags can be really helpful. Let’s say I want to create the following categories:
Each category will have a different mix of services. Here’s how I can use tags to make that happen. I’ll create another column next to the price where I specify all the categories a service will fall under:
In the above example, Netflix is included in every package but HBO Max is only included in Tier 3. Next, what I’m going to do is create columns for each one of these tags, such as follows:
Without using tags, you might be tempted to put a checkmark to determine which service belongs in which category. But that’s not necessary here. Instead, I’m going to use a function to determine whether to pull in the price or not.
Using a formula to determine if a tag is found
The key to making this work is the SEARCH function. This will look within the tag values to see if there is a match. If there is, then the price will be populated within the corresponding category. To check if the ‘basic’ keyword is found within the tags related to Netflix (assume this is cell C2), this is how that formula would look:
This will return a value of 1, indicating that the term is found at the very start of the string. If you use the function to look for the word ‘kids’ then it would return a value of 8 as that comes after ‘basic in my example.’ Of key importance here is that there is a number. If there isn’t a number and instead there is an error, that means that the tag wasn’t found. I will adjust the formula as follows to check if there is a number:
This will return a value of either TRUE or FALSE. But the formula needs to go further than just identifying if the tag was found. It needs to pull in the corresponding value. To do this, I’ll need an IF statement to extract the value from column B:
By freezing the formulas and copying this across the other categories, this formula will now allow me to pull in the amounts correctly based on the tags:
But let’s say you don’t even want to do this, you just want to quickly group the totals without these extra columns. You can also do that with the help of tags.
Summarizing the totals by category
You don’t need to create a column for each group if you don’t want to. You summarize the total in just an array formula. Simply use the formula referenced earlier and include it within a SUM function, while referencing the entire range:
This is the same logic as before, except this time the values will be totaled together. On older versions of Excel, you may need to use CTRL+SHIFT+ENTER after entering this formula for it to correctly compute as an array. But if you’re using a newer version, you don’t need to. If you copy the formula to the other categories, you’ll be able to sum the values by without the need for additional columns:
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In this post, I’m going to show you how you can easily calculate variances in Excel. I will also go over how to group variances and how using pivot tables, charts, and conditional formatting can help save you time in reviewing them.
For this example, I’m going to use data from the S&P 500 as stock prices frequently fluctuate. To start, I’m going to download the data from the past year. I’m going to remove everything except the closing values just to keep this example simple:
Calculating the variances
The calculate the variance in these data points, what I need to do is to take the current closing price, and subtract the previous day’s closing price from it. That will tell me how much of a move there was that day. On June 7, for instance, the S&P 500 fell from 4,229.89 on June 4 (the previous trading day) to 4,226.52. If I minus the current day’s close from the previous, I get a value of -3.37.
But we can dig a lot deeper than just looking at the difference in price. Let’s also create a field to indicate whether these variances are positive or negative. To do that, I’ll create another column called ‘Direction.’ For this calculation, I will take a look at the value in column C (where my variance is) and create a simple IF formula:
Here’s what my sheet looks like now:
Although you can determine whether it is positive or negative from the variance field, by creating another column you can quickly filter if you want to look at all the negative or positive values. Another column I’ll insert here is for the percentage change.
To do this, what I will do is take the variance amount and divide it by the previous day’s closing price. This will tell me how much the price has moved as a percentage of what its value was the day before — which is much more useful than just looking at the raw value. After inserting the column, I have the total variance, variance %, and which direction it went in:
I changed the variance % field to show percentages and I added a few decimal places since the percentages are fairly small. To add decimal places, go to the Numbers group on the Home tab and click the following button on the left:
The one on the left will add decimal places while the one on the right will remove them.
However, what if you don’t care about positives or negatives and are just interested in the absolute value of the changes? I’ll cover that next.
Calculating changes in absolute value
With absolute value, you remove the positive or negative indicator. And to calculate a variance this way, you just need to add a formula to the calculation in the variance field. Rather than this:
You would enter this:
Now, my variances update and I no longer have a use for the Direction field since all the values will be positive:
Alternatively, you could also just create another column specifically for the change in absolute value.
Now that the variances have been created, what you may want to do next is to group them.
Why would you want to group variances? The big advantage in doing so is they can make it easier to analyze a large data set by showing you where the bulk of the variances are.
Rather than creating a bunch of IF statements, what I’ll do is create a table to show where the variances belong:
I’ve created a named range called VarianceTable for this. And now, all I need to is use a VLOOKUP formula to find which category a variance belongs in. Since I’m not using an exact match, I will set the last argument in the function to ‘TRUE’ :
Now I have a category field instead of the Direction:
But this doesn’t tell me a whole lot. I could filter by the category. However, a better approach is to create a quick pivot table that shows me a summary of where the values fall:
And from that, I can quickly display these variances on a chart:
Another way you can help identify extreme values in variances is by using conditional formatting. To apply conditional formatting, select either the variance column or the variance % column and under the Conditional Formatting button on the Home tab, you can select either Data Bars or Color Scales. I prefer using Data Bars since there are fewer colors:
Then, my variances are easier to visualize and to see where the highs and lows are:
When you are analyzing variances, using conditional formatting, pivot tables, and charts can help you summarize your findings.
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Speeding saves time but it also can put yourself and other people in harm’s way. But how about if you only speed 5 miles over the speed limit? What about 10 miles? Below, I’ll do a sensitivity analysis in Excel to calculate just how much time you are saving by speeding over various time intervals.
Setting up the file and creating the formulas
I’m going to create a base value of 50 mph to serve as my default speed. I will also create a variable for the interval to determine the different rates I want to move by for my sensitivity analysis. Here is what it looks like thus far:
The formulas for the actual speed are off to the right. I am just taking the previous speed (or in the case of the first value, the default speed) and incrementing it by the interval. Doing it this way will make it easy if I want to adjust the interval or base speed variables without having to manually update the other values.
Next, I need to set up my calculation to determine the time that is saved. At 55 mph, over the course of an hour, I will have traveled 5 miles more than if I was traveling 50 mph (let’s assume this is the legal, posted rate). And since 5 miles is 10% of the 50mph I would be going on an hourly basis, that equates to 6 minutes (10% of 60 minutes) of additional driving that I would do at the posted rate. That is the time saved by speeding at a rate of 55 mph. To put this into a calculation, I first need to take the difference in speed:
C6 is the first value in the speed column and C2 is the default speed. I also need to divide this by the default speed to get the % of an hour this would represent:
The next step is to multiple all of this by 60 (number of minutes in an hour) to convert this into minutes:
Now, I can copy this formula down and now I have the time savings per hour by the different speeds:
Next, what I will do is add different time intervals. I don’t want to strictly look at just a single hour. It will be helpful to set up various different periods. To do this, I’ll create a header for the number of minutes and adjust my formula so that it references the header rather than just multiples by 60:
Now, what I will do is set up more periods and copy the formulas across. Here is what the time savings look like across 15, 30, 45, 60, and 120 minute periods:
To better visualize this, I will create a line chart that shows this data:
The important takeaway from all of this is that for short trips of 30 minutes or less, you aren’t saving even 10 minutes worth of time unless you are speeding excessively (70 mph vs 50 mph), which is not just dangerous but can run you the risk of getting a ticket. But over a few hours of driving, even a 5 mph bump up in speed can save you 12 minutes. It is a safer and more sustainable option to go slower and gradually accumulate time savings.
If you want a quick way to do these calculations without using a spreadsheet, simply calculate how much faster you are going than the speed limit and convert that into a percentage. Then, multiply that by the number of minutes that you are driving for. In the example of going 70 in a 50 zone, you would be 40% over the speed limit. Multiply that by 15 minutes of driving time, and the time saved would be 6 minutes. The formula looks as follows:
I’m not advocating for driving fast and the purpose of this was simply to calculate the theoretical time savings in Excel. If you have other suggestions for problems to solve in Excel, please contact me with your ideas.
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You can use Excel to create models, templates, and also to do analysis. This will be the first in a new series of posts to do with Excel-related analysis and how to set up a question and answer it accurately. In this analysis, I’m going to look at how much money you can be losing by letting it sit idle. Specifically, I’ll analyze an investment that pays you a dividend every quarter and look at two scenarios — one where you reinvest dividends back into the stock and one where you don’t. How much of a difference that can make over a 30-year period could surprise you.
At the bottom of the page, I’ll leave the file available for download if you want to take a look at my work and follow along and to see just how much of a difference there is when you don’t reinvest dividends.
Scenario 1: collecting the dividend payments and not reinvesting them
The assumptions and fields
In the simplest scenario, let’s set it up that you don’t reinvest dividends back into the company. To create a template for this in Excel, we’ll need to know the price of the stock, how much you’re investing, and how much the company pays in dividends (which is usually on a quarterly basis), and the growth rate. To keep it simple, we’ll assume the dividend payments will never change and so the amount that you receive in dividends will remain constant.
This is a useful assumption to make when making this type of comparison so that you can isolate one variable, which in this case is whether you reinvest the dividend payments or not. It’s safe to assume if there is a benefit of reinvesting dividend payments, it’ll be even greater if the payouts increase over time, so it’s unnecessary to incorporate dividend growth into the model in order to do this analysis.
I’ll also set the price of the stock at $100, the quarterly dividend at $1.25, and the amount to invest at $10,000. There will be a calculated field to determine the number of shares, which will take the amount invested and divide that by the price of the stock. With a $10,000 investment, you would be able to own 100 shares of a stock that’s priced at $100. I’ll also assume that the stock will rise by 5% every year. These are what my inputs and calculations look like so far:
Setting up the headers
Next, we’ll need to set up the headers for the actual model where the results will be populated. The fields I’ll include are the year, the starting portfolio value, the dividend amount, the cumulative dividend, the ending portfolio value, and the portfolio + dividend.
In the year field, I’m just going to increment the numbers 1 to 30 to show the portfolio’s progression over 30 years. You can do this a few different ways. Besides manually entering the numbers 1 to 30 in, you can enter the number 1 in first and then create a formula that just adds one to the number above, and then copy it down. Another option is to enter the values 1 and 2 in the first two rows, select those two cells, and then copy that down. Since you are selecting multiple items, Excel will know the pattern and that you want to increment by 1 each time. Otherwise, just trying to copy 1 down will give you a series of 1’s. For some examples of how this works, check out this post on how to autofill data in Excel.
For the starting portfolio value, I will just link to the initial amount invested and in subsequent periods this will be equal to the previous year’s ending portfolio value.
To calculate the dividend amount, all I need to do here is enter the number of shares, multiply them by the quarterly dividend, and then multiply that by 4, since the payments are quarterly. My formula looks as follows in the first cell:
$C$7 is the number of shares and $C$3 is the quarterly dividend. Since I’m not reinvesting any dividends, my amount invested will remain the same and that also means that I won’t collect more dividends (since I’m assuming the dividend rate will remain unchanged). This means that every year, I’m expecting to collect $500 in dividend income as I’m taking 100 shares, multiplying them by $1.25 and then by 4 payments.
The cumulative dividend field is an easy calculation as it’s just adding the total of all the dividend payments. You can calculate the cumulative value by using the SUM formula, freezing the first cell, but not the last one. In cell D12, my formula looks as follows:
My dividend payments are in column C. While the first cell is frozen, the second one is not and the calculation will expand as I copy this formula down.
The ending portfolio value is calculated by taking the starting portfolio value and multiplying it by the growth factor — which in this case is 5%.
The last formula is the portfolio + dividend calculation. This will tell me what the total value of my investment is after factoring the growth in share price as well as all the dividend income I’ve collected over the years. This is a simple calculation of just adding the ending portfolio value (in column E) with the cumulative dividend in column D).
With all of my formulas copied down, this is what my values look like over the 30-year period:
The dividend payments total $15,000 after 30 years and the portfolio will rise to a value of $43,219.42 by the end of the period. Combined, the value of this investment is $58,219.42 when adding the dividend income on top of all the growth the stock is expected to achieve over the years.
Now, let’s switch over to the other scenario, where you reinvest dividends to buy more shares of the company.
Scenario 2: reinvesting the dividend income
This scenario will be more complicated because now the number of shares owned will change every year if you were to reinvest the full amount of dividends you earn.
I’ll need to make some changes to the structure of the template. First, I’ll want to track the number of shares that are owned over the years as that will determine how much dividends will be collected. I’ll also need to calculate the expected stock price to determine how many additional shares I can buy with the dividend income. And I also won’t need the cumulative dividend since the payments will be reinvested back into the stock.
The stock price field will rise by 5% each year and its formula will be simple as it will just rise by the growth rate. As for the number of shares, that will start with the initial purchase of 100 shares and then in future periods it will take the dividend amount and divide it by the stock price to determine the number of additional shares that can be purchased. The dividend calculation will then take the number of shares, multiply it by the quarterly dividend and then again by 4 quarters
With those changes, here’s what the model looks like if the dividend income is reinvested:
At $92,169.05, you’re making $33,949.62 more by reinvesting the dividend back into the stock. This, of course, assumes that the stock will continue to grow at a rate of 5% and that you’ll do nothing with that dividend income but let it sit in the first scenario. But the point is still the same: the cost of letting money sit idle can be significant. In the second scenario, your portfolio will be worth 58% more than it would be in the first scenario.
Now, if you were to invest the dividend income from the first scenario into other investments, then the difference would likely be smaller. However, for the purpose of this analysis, it’s clear that there’s a big advantage of reinvesting dividend income. One variable that wasn’t considered in this analysis is the discount that companies sometimes offer investors when reinvesting dividend income, which could result in more shares and greater returns over the long term under the second scenario. But again, for the sake of simplicity, that was left out but it’s an example of another reason why reinvesting dividend income can be very beneficial.
Proving out the variances
The last part of this analysis involves proving these differences out, comparing when you reinvest dividends versus when you don’t. This is an important part in order to show where the variances came from and to illustrate that the calculations are correct.
Two key areas that contribute to the differences between these two models are the loss of dividend income by not holding more shares and also the loss of portfolio value by not benefiting from the full incremental growth each year.
To do this, let’s create another table that summarizes the variances. The first field here will be the portfolio change, which will just look at the difference in portfolio values between the two models in each year.
Next, the loss of growth column will calculate how much growth is lost by not reinvesting the dividend income. This is calculated by taking the difference in starting values and multiplying that by the growth factor of 5%. Since the dividend income isn’t reinvested, the starting portfolio value will be lower in the first scenario, which means the amount of growth earned will be less than under the second scenario.
The loss of dividend income is the next source of variation because with fewer shares in the first scenario, that will mean less dividend income. To calculate this variance, we’ll need to take the difference in the number of shares and multiply that by the quarterly dividend and by 4, for the number of payments during the year.
Lastly, there is a field for the cumulative loss, which is important as it’s a running total of the losses from dividend income and growth. This should match up the total portfolio change field, and I’ve added a check column to calculate the difference and ensure everything nets out to zero.
Here’s what the variance table looks like:
As you can see, the bulk of the losses originate from the loss of growth as the impact of compounding can significantly affect your overall returns over the long term when you don’t reinvest dividends.
To see this file in more detail, you can download it from here.
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