(Well, the entire lookup family—vlookup, hlookup, index-match, and xlookup.)

In this blog post, you’ll learn:

• What vlookup is used for;
• Why vlookup can be tricky; and
• How to fill in the four pieces of the formula.

What Vlookup Is Used for in Excel

Vlookup helps us merge data from various tables, sheets, and files into a single table that we can use for our analyses.

Why Vlookup is Tricky for Novices

Sometimes Excel novices are hesitant to try vlookup because it requires that you fill in four different pieces of information.

Learning the Excel lingo here is truly like learning a new language. Stick with it and keep practicing, and you’ll be a fluent vlookup user in no time!

Here’s the information that we’ll need to complete: =vlookup(lookup_value,table_array,col_index_num,[range_lookup])

Let’s walk through each of the four segments of the vlookup function.

Our Fictional Scenario

I’ve got five people: Ann, Isaac, Tony, Keely, and Dan. I’ve also got two different tables of data: Favorite Color and Favorite Food.

Let’s pretend I want to create a single dataset that contains both the colors and the foods together.

In a perfect world, I’d be able to copy and paste the colors and foods together.

But in the real world, we’ve typically got different numbers of people in each of the original tables. For example, we’ve got information about Ann, Isaac, Tony, and Dan in our Favorite Color table, but we’ve only got information about Ann, Keely, and Isaac in our Favorite Food table, so a simple copy and paste isn’t possible.

Sure, with just five people, we could fill in this information by hand. But what if our dataset contains information about 50 people? Or 50,000 people? Copying and pasting could take all day, and we’d probably make a million mistakes along the way. Vlookup to the rescue!

How to Use =Vlookup() in Microsoft Excel

Here’s how to fill in each of the four pieces of the vlookup formula.

Step 1: Fill in the lookup_value

First, let’s fill in the lookup_value, which is the first piece of the vlookup function.

The lookup_value is the cell that contains the person’s name or ID number that we’re interested in. These names or ID numbers are the links that connect all the tables together.

The names or ID numbers must be located in the first column of each table–in the first column of your new combined dataset and in the first column of every single table from which you’re pulling data.

In this example, watch as I type =vlookup( into cell B8. Next, click on the cell that contains the name or ID number that you want to look up in one of your other tables. Then, insert a comma, which moves us on to the second section of the function.

So far, my function reads: =vlookup(A8,

Step 2: Fill in the table_array

Second, we have to indicate the table_array.

The table array is the table or dataset from which we’re pulling data.

In this example, we want to get information from the Favorite Color table into our master table down below. The table_array for the Favorite Color table is A1:B5. In other words, that table begins in cell A1 and ends in cell B5.

My function reads: =vlookup(A8,A1:B5,

Step 3: Fill in the col_index_num

Third, we have to indicate the col_index_num.

This column index number is the number of the column we care about. Just type in the number of the column you’re interested in.

For example, we want to know favorite colors, which are located in the second column of our Favorite Color table, so we type a 2 into the vlookup function. As usual, conclude with a comma to move on to the fourth and final segment of our function.

My function reads: =vlookup(A8,A1:B5,2,

Step 4: Fill in the range_lookup

Fourth, we need to indicate the range_lookup..

We have to type the word true or false into the fourth and final section of our vlookup function.

A true will give us an approximate match and a false will give us the exact information we’re looking for. We obviously want precise information, so type false into the function and end with a closing parenthesis.

My completed function reads: =vlookup(A8,A1:B5,2,false)

We can see that Ann’s favorite color is blue.

A Second Vlookup Example

Let’s go through a second vlookup example to make sure the four pieces of the function make sense.

We’ll continue creating a master table that combines content from both the Favorite Color and Favorite Food tables into a single table.

First, in cell C8, type =vlookup(A8, to set the lookup_value as Ann.

Second, indicate the boundaries of the Favorite Food table that we want to pay attention to. My function now reads =vlookup(A8,D1:E4

Third, tell Excel which column of the Favorite Food table to focus on. The foods are listed in the second column of that mini-table, so enter a 2 into the vlookup function. My function says =vlookup(A8,D1:E4,2

Finally, type false into the function and close your parentheses. The completed function says =vlookup(A8,D1:E4,2,false) and tells us that Ann’s favorite food is pizza. 

Vlookup takes time to sink in, so go easy on yourself if you don’t “get it” right away. I promise that the time-savings from vlookup are worth the learning curve.

Smart, hardworking person #1: I love the idea of using data to drive decisions, but spreadsheets can be such a drag. It takes forever to finish all the monthly reports that my organization is required to submit.

Person #2: Agreed. Spreadsheets totally suck. Just figuring out the ‘average’ scores takes too long.

#1: I have to type all the numbers into my calculator to add them up…

#2: … and then count how many people are included! Right! And divide by that number!  And then I have to do those same tallies again the next month.

Oh noooooooooo!

A tiny piece of my soul crumbles every time I hear these comments.

You were put on this planet to do more than manual data analysis.

There’s a better way.

I love Excel, but that doesn’t mean I want to spend more time than necessary using it. Instead, I use these time-saving strategies to save my limited mental energy for tasks that are more important.

Here are four statistical formulas that every spreadsheet user should know.

(There are many, many, many additional formulas that you show know. But this is a good place to get started.)

Statistical Formula #1: Calculating the Average or Mean

Ten years ago, I was a consultant to Federal and state education agencies, so my brain is still packed full of education-related datasets. Humor me and pretend like you work on similar datasets for a minute. (The formula is the same regardless of your content area.)

Let’s pretend you want to calculate the average (a.k.a. mean) score for students.

Think back to high school math class: To calculate the average, you add up all the scores and then divide that sum by 5 because there are 5 students in the group. These are the steps that the poor conference-goers were doing by hand with their calculator.

Let Excel’s =average() function handle the calculations for you.

Type =average( and then click on the range of scores (so B2 through B6 for the reading scores). Add a ) to the end of the function and press Enter on the keyboard.

Your function will say =average(B2:B6) and the result is an average score of 49.2.

Once you calculate the average reading score in Column B, you can also drag that function over to the right to find the average math score, as I demonstrate below.

This is a simple dataset with just 5 scores, but the formula works the same with huge datasets of thousands of entries, too. Sometimes people worry that data analysis gets more complicated with larger datasets. Not necessarily. Formulas just become more critical on larger datasets. It’s time-consuming to do manual calculations on small datasets, and it’s impossible to do manual calculations on large datasets.

Statistical Formula #2: Median

You remember the median from high school math class, too, right?

Think of a median as a middle value. You sort the dataset from the smallest number to the largest number. The median is the number in the middle of that list.

Medians aren’t just nice-to-have calculations—they’re critical for reporting on skewed distributions. But that’s a post for another day… unless you want to comment on this blog post and practice giving your best explain-it-to-your-kindergartener explanation of a normal vs. skewed distribution, and a mean vs. median? Go for it, brave friends.

To calculate the median reading score, type =median(B2:B6) into cell B8 and press Enter.

You’ll get a result of 41.

Over the years, I’ve heard a few of my workshop participants wonder if the median still works even if your spreadsheet isn’t sorted from smallest to largest first. Yep, it still works! Excel does the behind-the-scenes sorting for you. That’s built into the formula. Your computer scans that section of numbers… sorts it… and finds the middle value. Your numbers can be in any order whatsoever.

Statistical Formula #3: Mode

The mode is the most-occurring number in a set of numbers.

To find the mode for this sample dataset, you would type =mode(B2:B6).

The most common reading score is 41 and the most common math score is 68.

Statistical Formula #4: Standard Deviation

Remember the term standard deviation from your high school math class?

The standard deviation basically measures how stretched out your set of numbers is.

Is there a bunch of reallllllly small numbers along with a bunch of realllllly big numbers? Or are the numbers all fairly similar across the board?

A higher standard deviation means the numbers really vary, while a small standard deviation means the scores are pretty similar to one another.

To calculate the standard deviation for our sample numbers, you would type =stdev(B2:B6) and get a result of 20.8.

Fellow data friends, I challenge you to comment on this blog post with your best explain-it-to-your-kindergartener definition. Phew! This is a hard term to describe!

Feeling Good??

I try to vary my blog content between beginner and advanced topics. This post is purposefully towards the beginner end of the spectrum.

Some of you work in spreadsheets all day every day like me, and you can do these formulas (and a million more) in your sleep. Other people are only in spreadsheets once in while because they’re busy leading organizations, writing grants, and managing teams of staff.

Occasional spreadsheet users, this post is for you! I hope you got a confidence boost and realized that you’re already on the right track with your current spreadsheet knowledge.

Your Turn

There are so many useful spreadsheet formulas. Comment below with your favorite formula, especially the formulas you think the occasional spreadsheet users would get the most benefit from.

Last time, we made a streamlined version of the basic bar chart by adjusting Excel’s default settings. Basic bar charts are great when you’re just looking at simple patterns, like one series of data at a time.

However, sometimes we need to compare several series of data at one time. For example, the Innovation Network team and I recently compared the capacity of 12 different coalitions using our coalition assessment tool. We calculated coalition capacity in 7 different areas, such as Basic Functioning and Structure and the Ability to Cultivate and Develop Champions. To learn more about the tool, check out some materials from a recent presentation here.

The “before” chart: Here’s a clustered (aka cluttered) bar chart based on fictional data from the coalition assessment tool. The default Excel chart to compare Coalition A and Coalition B might look something like this:

A default clustered bar chart in Excel. Bleh.

Or, you could use the switch row/column feature to make a default chart like this:

Another default clustered bar chart in Excel.

These default clustered bar charts work… kind of. Well… not really. Actually, no, they really don’t work at all! My brain needs a couple minutes to read the default charts and really think about comparisons.

The “after” chart: Luckily, with a little Excel elbow grease, we can make the patterns pop out even faster. Here’s a side by side bar chart:

Now, my brain can see the patterns almost immediately: Both coalitions scored highest on Basic Functioning and Structure. Both coalitions scored lowest on Sustainability, with Reputation and Visibility scoring pretty low as well. Coalition A scored higher than Coalition B on every section of the assessment. The overall scores were 80% for Coalition A and 50% for Coalition B. Phew! This is much easier for my brain.

The dataviz challenge: Re-create the “after” version of the side by side bar chart in Excel, R, or some other free software program. When you’re finished, email me or tweet a screenshot to @annkemery.

Tips for beginner Excel users: First, learn how to make a basic bar chart. Then, check out this powerpoint for tips on making a side by side bar chart.

Bonus for advanced Excel users: Instead of copying this dataset exactly, think about how you might use a side by side bar chart in your own work. Can you re-create this chart using your own data or your own color scheme?

The prize for playing: Beer or coffee, my treat, the next time you’re in DC; a professional development opportunity; and bragging rights.

I’ll post the how-to guide in two weeks, on June 10, 2013. Happy charting!

First, let’s take a closer look at default Excel bar charts. The “before” bar chart is shown below. In my example, we’re examining how a nonprofit has grown over the past six years. This is a simple metric so we don’t need anything more complicated than a basic bar chart.

You’ve probably seen these exact charts before. There’s nothing technically wrong with this default bar chart. It doesn’t distort the data. It doesn’t mislead the readers. Default charts are fine for internal use like informal staff meetings. However, a few tweaks can really improve the formatting.

The “after” chart is shown below. Now, my brain sees the pattern immediately. There are fewer distractions — no border, no tick marks, no grid lines. In other words, this bar chart passes the Squint Test — when you squint your eyes, and everything gets a little blurry, you should still be able to detect the overall shape of the data. There shouldn’t be extra ink like borders, tick marks, or grid lines getting in the way.

I also infused a few of my personal preferences into the chart’s formatting. My eyes do better with the larger font. Being a data nerd, I prefer having data labels on my bars so I can read the exact values (rather than having to estimate values in the default chart). The white space and custom color palette make the chart look like something a graphic designer might create. I purposefully focused attention on the 2012 bar by using a darker color.

Like I mentioned, these last few adjustments aren’t required. Passing the Squint Test is more important than satisfying everyone’s personal preferences. There are multiple correct ways to improve chart formatting.

The dataviz challenge: Re-create the “after” version of the basic bar chart in Excel or R. No expensive software allowed. You can re-create this exact bar chart, or you can tweak the color scheme and numbers to fit an example from your own work. The goal is to practice manipulating the settings in Excel and to create a basic bar chart that’s easy to read and understand. When you’re finished, email me or tweet a screenshot to @annkemery.

Bonus: Beginners can probably make this bar chart in 30 minutes or less (for example, during your lunch break). You’ll get faster with practice. I bet that advanced Excel users can enter the data, insert the chart, and adjust the chart in 3 minutes or less. Time yourself. Advanced users, how fast are you?

The prize for playing: Beer or coffee, my treat, the next time you’re in DC; a professional development opportunity; and bragging rights.

I’ll post the how-to guide in two weeks, on April 24, 2013. Happy charting!