When comparing 2 sets of data, one question we always ask is,
- How is first set of numbers different from second set?
A classic example of this is, lets say you are comparing productivity figures of your company with industry averages. Merely seeing both your series as lines (or columns etc.) is not going to tell you the full story. But if we can shade our productivity line in red or green when it is under or above industry average… now that would be awesome! Something like below:
The above chart tells us where we are lagging and where we are good. It will let us ask poking questions about the gap and find answers (may be removing coffee machine from 2nd floor last May was a bad idea!)
So how do we create such a chart?
PS: This chart and article is inspired from a question asked by arobbins & excellent solution provided by Hui here.
Creating a shaded line chart in Excel – step by step tutorial
1. Place your data in Excel
Lay out your data like this.
2. Add 3 extra columns – min, lower, upper
If you look at the chart closely, you will realize it is a collection of 4 sets of data. See this illustration to understand.
Write formulas to load values in to min, lower (green) & upper (red) series.
- Min is minimum of productivity and ind. average
- Lower (green) is difference between productivity and ind. average (or NA() if negative)
- Upper (red) is difference between ind. average and productivity (or NA() if negative)
3. Create a stacked area chart from this data
Select all the 4 series (productivity, min, lower & upper) and create a stacked area chart.
This is how it looks.
4. Format the productivity series as line
Right click on productivity series and using “Change series chart type” option, change it to line chart.
5. Make the min series transparent
Select min series and fill it with “No color”
6. Format lower & upper in green & red colors respectively
And you are done!
Optional: adjust series formatting, add grid lines etc.
As a bonus, you can add vertical grid lines (so that we can understand the red green changes easily) and format the horizontal axis. You can also move around the legend and remove the words “min” from it.
This will make the chart look really awesome.
Is this the only way to compare productivity with industry averages?
Although our shaded line chart is an excellent way to visualize differences between 2 series of data, I kept thinking if there are other ways to compare this.
After a bit of doodling & drawing inspiration from various charts I have seen earlier, here are 4 more options we can consider.
Option 1 – Productivity vs. variance wrt Ind. average
This chart shows the variance (industry average-productity) at bottom so that we can easily look at overall trend & understand how we fared with respect to industry.
To create this chart, you just have to calculate the variance in a separate column and create a column & line chart combination (column for variance & line for productivity). Once such a chart is ready, go to fill options for the column chart and check invert colors if negative option and set up green & red colors!
Option 2 – Productivity vs. better or worse indicators
This chart just shows whether productivity surpassed industry average or not in a boolean state (green for yes, red for no)
This chart is a combination of line & column chart with same principle as above (invert if negative option).
Option 2 (made using Excel 2010 Sparklines)
You can create this chart very easily with Excel 2010 sparklines. Line chart for productivity and win-loss chart for better or worse indicators.
Option 3 – Collapsed Productivity vs. variance wrt Ind. average
Since the color is already telling us whether variance is negative or positive, we can collapse both to same side of axis (thus saving some space & reducing redundant information).
To create this chart, we need two series of data – positive variance & negative variance as 2 sets of areas on the chart.
Option 4 – Collapsed Productivity vs. better or worse indicators
Well, this is same as option 2 but collapsed.
Download Example workbook
Click here to download the Excel workbook containing all these examples. You can also see detailed steps for making the shaded line chart in it.
How do you compare one series with another?
I must confess that I never made shaded line chart until today. For smaller data sets (<15 items), I usually compare by making column charts or thermo-meter charts. These are easy to make and easy to understand. For larger data sets, I try to make dynamic charts so that I can choose which series to include in comparison or make indexed charts.
Now that I learned how to set up shaded line charts, I will try them in my upcoming projects & consulting assignments to see how they fare.
What about you? Which types of charts do you use to compare one series with another? Please share your techniques & implementations using comments. I would love to learn more from you.
Compare often? Check out these charts
If you compare apples to apples (or to an occasional bushel of oranges) for living, then check out these charting tutorials & techniques.
WARNING: After learning these techniques, Suddenly you will become incomparably awesome in your office.
12 Responses to “29 Excel Formula Tips for all Occasions [and proof that PHD readers truly rock]”
Some great contributions here.
Gotta love the Friday 13th formula 😀
Great tips from you all! Thanks a lot for sharing! bsamson, particularly you helped me on a terribly annoying task. 🙂
(BTW, Chandoo, it's not exactly "Find if a range is normally distributed" what my suggestion does. It checks if two proportions are statistically different. I probably gave you a bad explanation on twitter, but it'd be probably better if you fix it here... 🙂 )
Great compilation Chandoo
For the "Clean your text before you lookup"
=VLOOKUP(CLEAN(TRIM(E20)),F5:G18,2,0)
I would like to share a method to convert a number-stored-as-text before you lookup:
=VLOOKUP(E20+0,F5:G18,2,0)
@Peder, yeah, I loved that formula
@Aires: Sorry, I misunderstood your formula. Corrected the heading now.
@John.. that is a cool tip.
Hey Chandoo,
That p-value formula is really great for a statistics person like me.
What a p-value essentially is, is the probability that the results obtained from a statistical test aren't valid. So for example, if my p value is .05, there's a 5% probability that my results are wrong.
You can play with this if you install the Data Analysis Toolpak (which will perform some statistical tests for you AND provide the P Value.)
Let's say for example I've got two weeks of data (separated into columns) with the number of hours worked per day. I want to find out if the total number of hours I worked in week two were really all the different than week one.
Week1 Week2
10 11
12 9
9 10
7 8
5 8
Go to Data > Data Analysis > T-Test Assuming Unequal Variances > OK
In the Variable 1 Box, select the range of data for week 1.
In the Variable 2 Box, select the range of data for week 2.
Check "Labels"
In the Alpha box, select a value (in percentage terms) for how tolerant you are of error.
.05 is the general standard; that is to say I am willing to accept a 95% level of confidence that my result is accuarate.
Select a range output.
Excel calculates a number of results: Average (mean) for each week's data, etc.
You'll notice however that there are two P Values; one-tail and two-tail. (one tail tests are for > or .05), the number of hours I worked in week two is statistically equivalent to the number of hours I worked in week one.
So here’s a way you might want to use this. You put up a new entry on your blog. You think it’s the best entry ever! So you pull your webstats for this week and compare it to last week. You gather data for each week on the length of time a visitor spends on your website. The question you’re trying to prove statistically is whether there’s an average increase in the amount of time spent on your website this week as compared to last week (as a result of your fancy new blog post). You can run the same statistical test I illustrated above to find out. Incidentally, it matters very little to the stat test whether the quantity of visitors differs or not.
Anyhow, the Data Analysis toolpack doesn't perform a lot of stat tests that folks like me would like to have access to. In those cases I have to either use different software, or write some very complicated mathematical formulas. Having this p-value formula makes my life a LOT easier!
Thanks!
Eric~
Fantastic stuf..One line explanation is cool.
Thanks to all the contributors
OS
Take FirstName, MI, LastName in access (you can fix it to work in excel) capitalize first letter of each and lowercase the rest and add ". " if MI exists then same for last name:
Full Name: Format(Left([FirstName],1),">") & Format(Right([FirstName]),Len([FirstName])-1),"") & ". ","") & Format(Left([LastName],1),">") & Format(Right([LastName],Len([LastName])-1),"<")
I teach excel, access, etc etc for a living and i have my access students build this formula one step at a time from the inside out to show how formulas can be made even if it looks complicated. Yes I know I could just do IsNull([MI]) and reverse the order in the Iif() function but the point here is to nest as many functions as possible one by one (also I illustrate how it will fail without the Not() as it is)
Extract the month from a date
The easiest formula for this is =MONTH(a1)
It will return a 1 for January, 2 for February etc.
if in a column we write the value of total person for eg. 10 if we spent 1.33 paise each person then how we get total amount in next column and the result will in round form plzzzzz solve my problem sir................... thank u
@Anjali
If the value 10 is in B2 and 1.33 paise is in C2 the formula in D2 could be =B2*C2
If the values are a column of values you can copy the formula down by copy/paste or drag the small black handle at the bottom right corner of cell D2
kindly share with me new forumulas.
How to convert a figure like 870.70 into 870 but 871.70 into 880 using excel formula ? Please help.