a sliceable Gantt chart that automatically updates and populates itself when you add more rows to your dataset (i.e., no tedious manual updates).

How to Make Sliceable Gantt Charts in Excel

You can watch the high-level tutorial here:

What’s Inside

• 0:00 Intro
• 1:08 The end product: Sliceable in Excel. or printed/PDFd
• 1:52 Gantt chart options in Excel: 1) Stacked bar chart or 2) Inside cells, like this
• 3:50 Dataset
• 5:51 Pivot table
• 6:29 Slicer
• 6:40 List of projects and their amounts
• 8:30 Helper cells to the left and above
• 9:55 AND formula to fill in the body of the table
• 11:18 Conditional formatting
• 12:50 Theme Colors
• 13:40 Your Homework List
• 14:17 Want more details? Watch the 2.5-hr livestream
• 14:37 Download this Gantt chart

Download the Excel File

It’s here.

Related Resources

• The full livestream where I created this from scratch
• Helper cells

However, the spacecraft never finished its mission. In fact, upon reaching Mars the next year, the $125 million spacecraft promptly crash landed into Mars, disintegrating in the atmosphere.

What could have caused such a crash landing?

Was it a freak meteor strike?

Faulty equipment?

ALIENS, perhaps?!?

The answer, surprisingly, is that the crash was caused by a classic case of BAD DATA.

That’s right–this spacecraft, this wonder of science, was rendered useless by bad data being entered into its flawless system. The Mars Climate Orbiter was designed to work on metric units, but unfortunately commands for the spacecraft were being sent from Earth in English units.

The result was a $125 million conversion error.

Collecting Survey Data at HOPE International

So what exactly does this have to do with spreadsheets? I’m glad you asked. I work with the nonprofit HOPE International as a Listening, Monitoring, and Evaluation Analyst. The mission of HOPE is to invest in the dreams of families in the world’s underserved communities as we proclaim and live the Gospel.

My team contributes to that by facilitating listening to those we serve, primarily through administering surveys and analyzing the data. Our surveys focus on many things–impact, experience, satisfaction, etc.–but regardless of the focus area, I always can’t WAIT to dive into the results.

When you spend so long crafting a questionnaire, translating it just right, and training enumerators to administer the survey, it’s nearly impossible to resist jumping into analysis once the results are in.

However, I’ve found that this is precisely what I must do–resist the urge to jump straight into analysis.

This is because, just as in the case of the Mars Climate Orbiter, a perfectly designed analysis system with flawless pivot tables will amount to nothing (or worse, a $125 million error) without proper data flowing into the system.

That’s right–I’m talking about DATA CLEANING.

Why Data Cleaning?

Data cleaning is an essential part of our survey process.

There have been many real-world situations where the results would have been biased or even completely incorrect had we not first taken the time to clean the data.

Here are a few situations we’ve encountered in the past:

1. Duplicate survey responses caused by system error, or a respondent accidentally taking a survey twice.
2. Pretest/training responses being included with the actual data from survey administration.
3. Surveys being completed in an extremely short amount of time, where most if not all of the answer choices were blank.
4. Data entry errors, such as accidentally copying a response in Excel across multiple rows and erasing original responses.

As you can see, the issues above would cause drastic differences if not corrected through a data cleaning process.

As tempting as it is to jump straight into crafting pivot tables and analyzing the results of the survey, engaging in a thorough cleaning and recoding of the data is vital to ensuring accurate results.

6 Data Cleaning Steps to Save You Millions

I’d like to show you what we do for our data cleaning process, and how Simple Spreadsheets helped to make this process even stronger.

In this article, you’ll learn:

1. How to check for duplicates (for example, if someone accidentally took the same survey twice);
2. How to check the survey for changes (for example, if translation typos were found after going live);
3. How to check for outliers in survey duration (how long it takes someone to complete a survey);
4. How to Use COUNTA and COUNTBLANK;
5. How to Recode Variables with IF Statements; and
6. How to Combine Datasets Together with VLOOKUP.

Yes, all of these data cleaning steps can be completed in Microsoft Excel.

(1) How to Look for Duplicates

One of the most important steps in our data cleaning process is to look for “duplicates.”

Duplicates are two (or more) entries that are either exactly the same, or match on a critical piece of information (like ID number or name).

It’s crucial that we identify these duplicates and resolve them before starting analysis. Otherwise, our results will not be accurate, and will instead overrepresent the duplicated entries.

Which Variable(s) Should Be Unique?

To check for duplicates, first identify the key variables in your data set that should be unique for each respondent.

For instance, our clients have an identification number which is unique to them. This field should not be duplicated in a data set.

Highlight the Duplicates in a Different Color

Once you determine your key variables, there is a simple Excel process that you can follow in order to identify and sort through your duplicates:

• Step 1- Highlight the column of interest.
• Step 2- In the Excel ribbon, select “Home” > ”Conditional Formatting” > ”Highlight Cells Rules” >”Duplicate Values.”
• Step 3- In the pop-up window, choose a highlight color of your choice and press “OK.” This will highlight all of the cells in the selected column that contain duplicate values.

Once these steps have been followed, any duplicates for the criteria you selected will be highlighted.

Manually Examine Each of the Duplicate Entries

I like to then filter the column where it only contains the duplicate values, sort in ascending order, and then manually go down the list to analyze each duplicate pair (or trio, etc).

Doing this manually really helps you to get a feel for the data, and understand whether the duplicates are truly duplicates, or whether there is some other systematic issue at play.

If the duplicates match exactly in all fields in the survey, then they are “true duplicates.” We usually keep the response that was entered first and remove the other response.

If they don’t match exactly in all of the fields, then we connect with our team that administered the survey and try to determine together how to handle the entries, whether removing them entirely, keeping some, or keeping all.

(2) How to Check the Survey for Version Changes

Another important step in the process is to check survey versions for any notable changes.

When we are administering a survey, we do everything we can to test the survey beforehand, in order to not make any changes during the administration.

However, unforeseen changes to translation, wording, or even whole questions sometimes need to be made during the administration process, and it’s important to check if any of these changes could impact how data is interpreted.

For instance, if the first 10 respondents to a survey saw this question:

“How satisfied are you with the training curriculum?”

• Very satisfied
• Satisfied
• Neither satisfied nor unsatisfied
• Very unsatisfied
• Very unsatisfied

And the rest of the respondents saw this question:

“How satisfied are you with the training curriculum?”

• Very satisfied
• Satisfied
• Neither satisfied nor unsatisfied
• Unsatisfied
• Very unsatisfied

Then the fourth answer would mean two different things, depending on when the survey was taken.

In a large survey that is being translated into multiple languages, it is quite possible that small details like this go unnoticed, even through quality checks and testing.

Compare Spreadsheets with the “Compare Files” Add-In for Excel

In order to avoid having to meticulously analyze each version of the survey row by row in Excel, we utilize the “Compare Files” function.

This is located in the “Inquire” tab as an add-in for Excel, but I highly recommend you download it.

It saves a considerable amount of time comparing two spreadsheets.

To use this function:

• Simply open the spreadsheets you want to compare at the same time.
• Click “Compare Files.”
• Choose the files you would like to compare.
• Press the “Compare” button.

Excel will then open a third document which lists all the differences (and their categories).

Our team then goes through this document to see if any critical changes were made to the survey during administration, and we account for these changes accordingly in the analysis.

(3) How to Check for Outliers in Survey Duration

Lastly, a simple but important step in our data cleaning process is to check the duration of a survey.

Usually, we determine the average time it took to complete the survey, and then manually investigate any responses that were much faster or much slower than that average length.

These could just be outliers, or they could be surveys that weren’t finished, system errors, data entry errors, etc.

We also look for “straightlining,” which is when a respondent answers the same response to each question (usually in order to just get the survey over with faster).

Removing any responses that are errors and accounting for straightlining is an important factor in our analysis.

(4) How to Use COUNTA and COUNTBLANK in Excel

The Simple Spreadsheets course both affirmed the current steps in our data cleaning process (particularly in the area of handling duplicates), and added new tools into our toolbox!

One simple tool that I’ve found helpful is the COUNTA and COUNTBLANK functions.

These functions are two sides of the same coin.

• COUNTA returns the number of cells that are not empty in a specified range.
• COUNTBLANK returns the number of cells that are blank in a specified range.

We’ve used these two functions to quickly assess whether our data passes the “sniff test.”

For instance, if there is a question that we designed as mandatory for everyone in the survey but only half of the cells are populated, there is something wrong with our dataset and we need to investigate further.

Some of the possible causes could be that the question was not marked as mandatory in the survey software, the data was entered incorrectly, there was an error in translation, etc.

Basically, by using these two functions for each column in our dataset, we can get a bird’s-eye-view of the pattern of responses to each question in the survey.

(5) How to Recode Variables with IF Statements in Excel

Recoding was a game-changer for me in the data cleaning process.

Before taking Simple Spreadsheets, I didn’t know how to make the data do what we needed it to do for our analyses.

For instance, maybe the geographical information in our database was captured in cities, but I needed to organize it into regions for our stratified random sample.

Or, maybe the data contained registration dates for clients, but I needed to organize them into different categories of tenure.

I didn’t know any method to do this besides manually going through the data and recategorizing by hand.

Needless to say–WOW did Simple Spreadsheets save me time!

The IF function allowed me to recategorize data by using a simple formula.

For a practical example, I had a list of bank branches that I needed to group together into different regions. Instead of doing this manually, I was able to use the IF formula to create different groupings for the regions all at once.

(6) How to Combine Datasets Together with VLOOKUP in Excel

VLOOKUP was also an extremely helpful formula for me to get the data sets to do what we needed them to do.

Often we will have multiple datasets that we need to merge together, because we have different sources of information.

Because most of our clients have Client ID numbers, I was able to use these numbers as the common source of information in the VLOOKUP function, thus merging together datasets in minutes with confidence.

Save Yourself $125 million

I honestly can’t count the amount of times that the data cleaning process has brought us helpful insights that both ensure we have accurate results, and helped us to improve our processes in the future so that we avoid/account for any potential errors.

Simple Spreadsheets was a great help in affirming and bolstering our data cleaning process, and I hope that this article gives you a jump start into creating a similar process that suits your needs.

It’s not always the most fun process (although I’ve grown to really love it and have earned the title of “Detective” on my team 😊), but it is CRUCIAL to ensuring a good result.

Just ask NASA…a million dollar data cleaning system would still have saved them $124 million in the long run.

—

When COVID-19 pushed many events online, I decided to host a virtual Christmas trivia event for my family.

Participants answered questions over four different rounds in teams of three to six people. The rounds each had five questions and all questions were Christmas- or New Year-themed.

Before: Compiling the Data in a Spreadsheet

To support scorekeeping, teams had an individual score sheet where they wrote and marked their own answers.

I had a master score sheet that would automatically pull their scores together so all teams scores were combined into one page.

I’d then show this master score sheet via screen share at half-time and at the end of the event.

The master score sheet looked like this:

While it brought all the scores into one place, it wasn’t very easy for my participants to quickly pull out the key information (i.e., how well their team was doing).

I decided to apply some of the lessons I learned in the Dashboard Design course to make the sheet more accessible.

After: My Trivia Night Dashboard

First, I had to decide what type of dashboard I wanted to make.

In the course, Ann provided a handy Dashboard Cheat Sheet that helped me see different options.

I decided because I had one time point and wanted to compare categories (i.e., teams) that bars would likely be best.

I also decided to convert the numerical scores into percentages because not all rounds had the same number of possible points. Percentages would be a more consistent indicator.

Now it was time to make the dashboard.

It was easy to follow along step by step with Ann’s stacked bar dashboard video tutorial.

I made the following dashboard using the Data Bars feature in Microsoft Excel:

What I Learned about Dashboards and Excel

Not only were the steps easy to follow, but I also learned about better dashboard and Excel practices.

These tips help make your life easier and your dashboards more editable and readable.

Some of my lessons learned include:

• Always put a title, subtitle, and date on the dashboards.
• If your text is in a colour, make it bold so it is easier to read.
• Add a white border around cells to add white space.
• Use cell styles and Theme Colours to make formatting more consistent and easier to edit (I somehow did not know about this in Excel even though I use it regularly in Word).
• Give yourself a bit of time to do the final editing to make it sure fits on a page

With this dashboard, I found it much easier to see:

• How well teams did in each round (e.g., team 6 struggled with Round 4, but excelled in Round 3).
• How teams compared to each other.
• How hard each round was (e.g., Round 2 was on average harder than Round 3).

Designing a Second Dashboard

Encouraged by my dashboard attempts, I decided to try one more dashboard.

I wanted to know within each round, which questions did teams get right and wrong.

This would help me identify which questions were too easy and which were too hard. It’s a fine balance to get when hosting trivia!

I thought about including it in the same dashboard above, but I then I watched one of Ann’s videos about the four types of dashboards.

This reminded me that it’s okay (and even better) to make different dashboards for different audiences.

I had to do a bit of data cleaning first. I ended up with a table that showed for each question in each round the percent of teams that got that question fully correct:

While it had the information that I needed, I found it hard to identify any patterns or the take home message.

I remembered that in the Dashboard Design course Ann had a video on how to compare categories using heatmaps. (Here’s a blog post tutorial you can read.)

I used the steps to create this:

What I Learned from My Second Dashboard

As with the first dashboard, there were some great tricks.

Essentially, if you’re doing something manually (like changing the text colour to white on the darker cells or individually colouring cells) there is almost always a better way! You can use Excel’s conditional formatting to automatically color-code background fills and/or font colors.

I found it much easier to identify patterns both within round and across rounds.

For example, teams generally had a harder time with questions in Round 2 than they did with Round 3 (there are more lighter cells).

Using this dashboard, I could easily pick out questions which were too hard and too easy.

Questions That Were Too Hard

Round 1 – Question 4: What is the name of this dish and where is it eaten on January 6? (Answer: Rosca de Reyes; Mexico)

Round 2 – Question 2: What is the highest grossing Christmas movie (according to Wikipedia)?

Options: a. The Grinch b. Krampus c. The Polar Express d. Elf

(Answer: The Grinch)

Questions That Were Too Easy

Round 4 – Question 2: What fruit is a traditional stocking stuffer?

(Answer: Citrus fruit like an orange, mandarin, clementine)

Round 3 – Question 4: What performance is this song played in? (Bonus: Who is the composer?)

(Answer: The Nutcracker; Tchaikovsky)

(Sound clip source: Dance of the Sugar Plum Fairy Kevin MacLeod (incompetech.com)

Licensed under Creative Commons: By Attribution 3.0 License, via Wikimedia Commons)

I really enjoyed how approachable and practical the videos in this course were.

I can’t wait to continue to apply the tips and techniques I learned both at work and for fun!

Maybe at next year’s trivia I’ll have to test some of the dashboard designs for comparing change over time.

Connect with Emily

LinkedIn: https://www.linkedin.com/in/emilysross/

Ready to escape the bar chart? I talked about Designs to Start Using Instead at the DataScienceGO conference in April 2021.

Watch the Conference Session

Choose Your Own Adventure

This was a Choose Your Own Adventure presentation, where I gave the conference attendees a chance to vote for the chart families they wanted to learn about.

These non-linear presentations aren’t for everyone. You need to be extremely comfortable with the topic area and with presenting. You can learn more about Choose Your Own Adventure presentations here.

Exploratory Data Visualization

First, we talked about my three favorite techniques for exploratory data visualization:

1. Spark lines
2. Data Bars
3. Heat Tables

Spark Lines

Want to add miniature trend lines to your spreadsheet? Here’s how:

• Highlight the top row of your dataset (the numbers that you want to visualize).
• Go to the Insert tab.
• Click on the Sparklines button.
• Follow the instructions: Choose where you want the sparklines to be placed. I usually position them off to the right side of my dataset.

Data Bars

Want to explore your dataset with miniature horizontal bars? Here’s how:

• Highlight the data you want to visualize.
• Stay on the Home tab.
• Click on the Conditional Formatting button.
• Choose a solid-filled Data Bar.

Heat Tables

We can also explore our dataset with instant color-coding. Here’s how to add a heat map or heat table to your spreadsheet:

• Highlight the data you want to visualize.
• Stay on the Home tab.
• Click on the Conditional Formatting button.
• Choose a Color Scale.

PLEASE avoid the inaccessible options—anything with red and green. Most of the Conditional Formatting options are absolute garbage, to be honest. Here’s a blog post that lists which Conditional Formatting options to avoid altogether—and what to use instead.

Want more info? In the video, you’ll see me open Excel and provide how-to tutorials.

Maps

Next, we discussed a few options for maps.

Choropleth Maps

You’ve seen this one: The regular ol’ color-coded map, or choropleth. Big numbers are dark. These maps are familiar and intuitive.

But, there’s a problem with regular maps: The large places can dwarf the small places. No matter how dark we color-in tiny Delaware, for example, the larger places like Texas and Alaska will always steal the show.

Cartographers have a name for this misleading issue with regular maps. It’s called The Alaska Effect.

Tile Grid Maps

Don’t worry, we’re not doomed by The Alaska Effect! There are a couple alternatives worth mentioning.

Square tile grid maps can help us overcome The Alaska Effect. Every location is the same shape and size, so now our audience only has to look at color. In other words, since Delaware and Texas are the same shape and size, we’re free to focus entirely on color.

BUT.

There’s a learning curve with tile grid maps. They’re almost too novel. Sometimes we spend more time focusing on why our home state isn’t in the right spot than on actually finding patterns in the data.

Tile grid maps have become more and more common over the years. In the video, I show you some real-life examples from the Urban Institute, the Washington Post, Child Trends, CNN, and National Geographic.

Hex Maps

Rather than using squares…. What if we try hexagons?

With six edges, hex maps give us more flexibility in arranging the shapes. That way, the maps can look closer to real-life maps.

In the video, I discuss some additional advantage of hex maps:

• Hex maps combat the Alaska Effect.
• Hex maps include more of the correct neighboring states compared to square maps.
• Hex maps include the correct southern tips.
• Hex maps include more notches for the Great Lakes.
• Hex maps visualize the correct four corners of the U.S.

And of course, hex maps aren’t just for the United States. You can create maps for zip codes, Census tracts, states, provinces, countries, etc. In the video, I show you a waffle map of African countries.

I can’t tell you how many times I’ve heard this from workshop participants.

Bill Gates, are you reading this??

Microsoft Excel is lonnnnng overdue for some updates.

Don’t get me wrong—it’s still my favorite program. With 750 million users worldwide, I won’t be switching to anything else. It’s used by every business professional I know for at least part of their workflow.

Earlier this week, I was invited to guest lecture at Baruch College. Thanks to Professor Mahmoud Kamal Ahmadi for inviting me!

I’m normally very zen about data visualization. I expected to bring that peace of mind to Professor Ahmadi’s students.

Here’s my calm before the storm selfie:

“Sure, some of Excel’s default formatting is hard to decipher. And isn’t accessible for people with disabilities,” I’ve said a million times. “But with some behind-the-scenes editing, we can still make powerful visualizations inside Excel.”

I’m getting tired of making excuses for Microsoft.

Shouldn’t they know better by now??

I started to teach the Baruch College students about exploratory data visualization with conditional formatting. I couldn’t help but rant about the bad formatting as I went. It was 8pm at night. My filter had disappeared; I couldn’t help it. Sorry not sorry, Microsoft.

Wait, What’s Conditional Formatting??

Conditional Formatting is a fancy way of saying “if-then visuals.”

If the number is above 50, then fill the cell with red.

If the number is below average, make the font bold.

On and on.

Conditional Formatting is Ann K. Emery’s favorite button in Excel (along with the pivot tables button). It’s hiding in plain sight on our Home tab.

How to Use Conditional Formatting in Microsoft Excel

Conditional Formatting lets us create near-instant visuals.

These visuals are helpful for both exploratory and explanatory purposes. Exploratory data visualization is for us, the spreadsheet users and graph-makers. These near-instant visuals help us uncover patterns. Explanatory data visualization is typically for others, like our supervisor, Board of Directors, or other stakeholder groups. These near-instant visuals can be shared with others inside of dashboards, scorecards, and one-pagers to explain key findings to our audiences.

Here’s how to use Conditional Formatting in Microsoft Excel:

1. Highlight or select some of the values in your spreadsheet. You can use Conditional Formatting on numbers, percentages, currency, and even words.
2. Go to the Home tab.
3. Click on the Conditional Formatting button.
4. Choose one of the options, like Highlight Cells Rules, Top/Bottom Rules, or Data Bars.
5. Enjoy your near-instant visual!
6. Edit edit edit. With the cells still selected, go back to the Conditional Formatting button. On the very bottom of the list, you’ll see an option for Manage Rules. This is a fancy way of saying edit. You can adjust most aspects of your visual: the colors, the cutoff values, more.

Conditional Formatting in Microsoft Excel that Should Be Retired ASAP

Conditional Formatting is mostly excellent.

I love the speed. I love the instant understanding I get by seeing my numbers come to life.

But I hate hate hate the ableism.

Some of the Conditional Formatting options are terrible for people with color vision deficiencies.

Others are terrible for grayscale printing. (When was the last time you got excited about paying for colored ink cartridges?)

Others are just terribly busy-looking and could easily be improved with Graphic Design 101 skills.

Here are 24 of Excel’s conditional formatting techniques that should be retired ASAP.

These visuals are:

• too time-consuming to read,
• not accessible, or
• look like they’re from 1995.

3 Arrows (Colored)

Nice try, but so busy. I’ll show you something better in a moment.

3 Triangles

A bit easier to skim, but we can do better.

4 Arrows (Colored)

Why do the yellow arrows point in two different directions?

3 Arrows (Gray)

4 Arrows (Gray)

5 Arrows (Gray)

There’s nothing useful here. Don’t make me write three different sentences about why these three options are worthless.

3 Traffic Lights (Unrimmed)

I’m about to become really unpopular with 99% of people who make business dashboards, but….

We should really stop using traffic light color-coding altogether.

Green-red color combos aren’t accessible for people with color vision deficiencies (more on this later).

Sure, red-green combos are intuitive. For those of us who can see them. But they’re useless for everyone else.

3 Traffic Lights (Rimmed)

Let’s make the colors even smaller and harder to see.

Red to Black

These tones are confusing to me. Don’t we associate both red and black as “bad” colors? Why is red = high percentages? And black = low percentages? Sure, Excel lets us flip-flop these colors, but the question remains—aren’t red and black both “bad?”

4 Traffic Lights

Now, Excel is saying that black is even worse than red?? These inconsistencies kill me. And since when do traffic lights have 4 different colors? I’ve never seen a black lightbulb in a traffic light. So more for intuitive traffic light coding.

3 Signs

I want to love these. Although the 3 Signs design would technically pass 508 compliance accessibility guidelines (because our viewers aren’t relying on color alone—they can also see the different shapes) it’s still so busy.

There’s also the issue of combining both categorical coding (a diamond vs. triangle vs. circle) with diverging coding (red is worst, yellow is medium, green is best). Forgive the jargon, but as a research methods geek, this bothers me.

3 Symbols (Circled)

These tiny symbols would be impossible to skim at a glance in a tiny spreadsheet.

3 Flags

GAH. Probably the hardest to read from this bunch.

3 Symbols (Uncircled)

Maybe the easiest to skim from this bunch? But still a bit busy.

3 Stars

This design gets creativity points.

5 Quarters

These aren’t so bad to skim right now—because I’ve already organized the spreadsheet from lowest to highest. Imagine a mismatched list (e.g., 20%, then 80%, then 10%, then 50%….). It would get messy.

5 Boxes

There could be more contrast between the gray and blue, i.e., it would be easier to read if the gray was a bit lighter, or the blue a bit darker.

4 Ratings

I actually love bar and column charts for at-a-glance findings.

But, these would be easier to read if they were horizontal bar charts, not vertical column charts.

I’ll show you an example with horizontal bars in a moment.

5 Ratings

Same shortcomings here.

Data Bars (Gradient)

Speaking of bar and column charts… Do you see how much easier it is to compare bars than columns?

BUT, not these gradient bars. We need to retire these. The most important part of the bar chart is the right-most endpoint. So why does Microsoft fade these out to lighter colors… therefore making the most important thing harder to see???

Data Bars (Solid)

Winner winner chicken dinner! More of these, please.

Green-Yellow Red Color Scales

Yes, I know what you’re thinking. This stoplight coding is intuitive.

But only for those of us who can see red and green as distinct colors. For people with color vision deficiencies, this color scheme is worthless.

Let’s retire this ableist color scale from Excel.

Green-White-Red Color Scales

Not accessible. Ableist.

Blue-White-Red Color Scales

Better than the red-green color coding, since at least it’s legible for people with color vision deficiencies.

But, this wouldn’t print well in grayscale. More on this in a moment.

Green-Yellow Color Scale

Not horrible… but not as clear as it could be.

Green-White Color Scale

Winner winner chicken dinner! More of these, please.

Look at the green-yellow and green-white options next to each other.

Do you see how the green-white color scale is easier to read? The white is, well, whiter than the yellow. Therefore, there’s even more contrast when compared to the green.

This scale is colorblind-friendly and grayscale-friendly.

Red-White Color Scale

Winner winner chicken dinner! More of these, please.

This is the opposite tone of the green-white color scale.

In other words, use this color scale to emphasize that low = bad.

How about Grayscale Printing?

We should always assume that someone will print our visuals. That printing may happen in grayscale, not full color, to save money.

I did a quick grayscale test on these color scales to show you what they’d look like.

Do you see how the first three are worthless? The fourth one, green-yellow, is okay. The last two are the easiest to read.

(In Dataviz Jargon: Transform that diverging scale into a sequential scale. It’s harder to notice differences between two different hues, like red and blue, than to notice differences between gradations, like light green vs. dark green. And it’s impossible to read diverging scales in grayscale.)

How about Color Vision Deficiencies?

I also did a color-blindness check.

First, I uploaded a screenshot to the Color Vision Deficiency Simulator website.

Next, I investigated what the color scales would look like for someone with protanopia. Eek.

The first four are worthless.

Green-yellow is okay.

Green-white and red-white are best.

Well-Formatted Conditional Formatting in Microsoft Excel Worth Keeping

Keeping score? Here are the conditional formatting visuals we can keep using:

• Green-White Color Scale
• Red-White Color Scale
• Data Bars

I’ll add another keeper to the list: Squares and circles made with the Webdings symbol font.

For example, we can use Webdings g’s and the rept() function to create an icon array, as shown above.

Or, we can use Webdings g’s and c’s to create a series of filled-unfilled squares.

Or, we can use Webdings n’s to create a series of light-dark circles.

It’s faster to skim a list of filled-unfilled squares, or light-dark circles, than to skim the stars, flags, or mini column charts shown earlier.

How to Add Conditional Formatting to Your Microsoft Excel Spreadsheet

Want to create conditional formatting to explore initial patterns in your spreadsheet? Here are links to detailed tutorials:

• How to Create Data Bars in Microsoft Excel
• How to Make a Heat Table in Microsoft Excel
• More examples of easy-to-skim symbols and icons made with symbol fonts (Webdings and Wingdings)
• Webdings example: Re-envisioning a University’s Monthly Report: Two Reports with Two Different Purposes      
• Webdings example: Visualizing Your Annual Survey Results: Four Makeovers That Didn’t Work, and the Fifth That Did

Your Turn

What are some additional features of Excel that should be retired? Or added?

And why rely on Excel’s default settings when you can easily format your spreadsheet to fit your exact preferences?

There are numerous formatting adjustments you can make to any of the numbers in your Excel file. You can automatically add commas to large numbers, you can automatically round numbers up or down, and you can even automatically fill in cells with the color of your choice.

Adding Commas to Large Numbers

Does your spreadsheet have a bunch of huuuuuge numbers, like 99535767? Sometimes it’s easier to “see” these numbers when they’ve got commas, like 99,535,767 instead of 99535767. Rather than painstakingly adding those commas by hand, use Excel’s built-in Comma Style button.

Rounding Numbers Up or Down

Want to round your numbers up or down? You’ve got two options.

First, you could use the Increase Decimal or Decrease Decimal buttons. The decimal places are still there, inside your original cells, but now they’re just hidden out of view. Try clicking on one of the cells and you’ll see all those millions of decimal places hanging out in the Formula Bar.

Your second option is the =round() function. This function has two pieces. First, tell Excel which cell contains the number that needs some rounding (e.g., A1). Second, tell Excel how many decimal places you want (e.g., 2). Now, when you use the Increase Decimal or Decrease Decimal buttons, you’ll notice that the number has been truncated. Try clicking on one of the cells and only the first two decimal places will show up in your Formula Bar.

Color-Coding Numbers Greater Than a Certain Value

It’s easy to color-code your numbers by hand if you’re only dealing with 5, 10, or 20 numbers. But it’s even easier to automatically color-code your numbers with Excel’s Conditional Formatting feature.

Highlight or select the range of information that you want to color-code. On the Home tab, click on the Conditional Formatting icon. You’ll notice a couple drop-downs within drop-downs that contain dozens of color-coding features for you to explore.

Sometimes I want to see how many numbers fell above a certain value. For example, I might want to see how many people were above age 35. Highlight or select the age values, click on the Conditional Formatting icon, select Highlight Cells Rules, and then select Greater Than.

As shown in the pop-up window, you’ll get to customize your cut-off value (I changed my cut-off from 47 to 35 in this example). You’ll also get to customize your colors. For instance, you might want everyone older than 35 to show up as red, yellow, or green.

Color-Coding Numbers that Fall Between Two Values

Excel’s Conditional Formatting icon has endless possibilities. In this example, I color-code everyone between the age of 25 and 45.

On your Home tab, simply click Conditional Formatting, Highlight Cells Rules, and Between. The subsequent pop-up window gives you customization options that can be tweaked to fit your specifications.

Color-Coding the Largest 10 Numbers

Or, maybe you’re interested in seeing the top 10 highest ages from your list.
Go to Conditional Formatting, Top/Bottom Rules, and Top 10 Items. You might want those highest items to appear green, red, or yellow. Or customize the colors further by selecting Custom Format from the pop-up menu.

Color-Coding the Top 10% of Numbers

Interested in making the top percentage of items stand out?

Go to Conditional Formatting, Top/Bottom Rules, and Top 10% Items. In the pop-up window, select the exact percentage you’re interested in–10%, 25%, or 50%, etc.–and then select the colors of your choosing.

Checking for Duplicate Numbers

Whenever I’m dealing with peoples’ names or ID numbers, I like to make sure each person is only listed once on my spreadsheet. Removing duplicate entries early on in the process ensures that my numbers will be accurate later on.

It would take a lot of time – and mental energy! – to scan a long list for double-entries. No matter how hard I was paying attention, I would probably miss one or two. So, let’s let Excel do the hard work for us.

Excel’s Conditional Formatting will change the color of duplicates to make them stand out. For example, you can add a red or yellow fill to those cells. Highlight your list of numbers and go to Conditional Formatting on the Home tab. Select the first option from the list, Highlight Cells Rules. Then, select Duplicate Values. You’ll get a new pop-up window that gives you plenty of color options. In my example, I selected a light green fill with dark green text.

My dataset contained one set of duplicates—person 116 was accidentally listed twice. Once I spotted the error, I deleted the double-entry by highlighting one of those double rows, right-clicking, and selecting Delete.

Undoing the Instant Color-Coding

Want to remove that instant color-coding and get your spreadsheet back to its original state? Conditional Formatting is easy to undo.

Go to Conditional Formatting –> Clear Rules. You can remove your conditional formatting from just a section of cells that you’ve already highlighted (Clear Rules from Selected Cells) or from everywhere within your sheet at once (Clear Rules from Entire Sheet).

Want to learn more spreadsheet strategies? I partnered with Udemy to share more than 50 of my favorite time-saving tips. Read my full guidebook at https://www.udemy.com/tutorials/learn-excel/.