Category Archives: Charts

Eat, Sleep, Report, Repeat!

Repeaters are a visualisation feature introduced in v9.1.  They are effective when you want to repeat a formatted section of a report by one variable.  They can save so much time as you don’t have to go through the tedious, error-prone task of recreating the same section many times by copying and pasting manually.  Imagine the time you’d save setting the design up just once and have the repetition handled by XLCubed!

Here’s an example of a repeater:

Our repeater is based on an XLCubed grid, filtered by Geography, with a panel consisting of formulae with XLCubed In-Cell charts and PictureLinks.  Additionally, each panel is conditionally formatted, with the colouring based on ratio of 3-month and 12-month average sales.

The great thing about XLCubed Repeaters is that you can include any XLCubed or Excel content in them!

Let’s see how easy it is to design our repeater – click Visualise > Repeater on the XLCubed ribbon and in the Designer window define the hierarchy to be repeated by dragging it to the Repeater Setup area.  Similarly, you can also set up any additional filters.

Click OK and then set the content of the repeater by setting up the three key ranges:

  • Repeat range – The entire area required to generate the report for one repetition. It needs to include all the source data and formulae used in the area to be displayed – the area within the blue border below
  • Render range – the display area which will be repeated and shown in the final report – the area within red border
  • Input range – the location where the member parameter will be inserted by the repeater, the cell with a green border. This will be used as the selection criteria for XLCubed Grids or formulae within the ‘Repeat Range’.

You can move/resize the repeater, set borders and margins from the Appearance tab.

You can easily include more items in your repeater by moving them in the Render range.

Remember, you can use any Excel content – in the example below we have a standard Excel chart that we want to also include at the top of our repeater:

Drag the chart into the top of your Render Range (resize as necessary) and your chart now appears like this:

It’s as simple as that!  Set up your repeater once and no more worrying that you’ve missed vital bits of information!

For more details see the Repeaters page on our Wiki:  https://help.xlcubed.com/Repeaters

…and a YouTube video which runs through this example:

https://www.youtube.com/watch?v=d3hYF8vkz-M

For all you football fans out there, our blog on the Premier League transfer window also has an example highlighting the use of repeaters:  https://blog.xlcubed.com/2017/09/charting-the-premier-league-transfer-window/

As always we would love to hear your feedback!

Charting the Premier League Transfer Window

This summer English Premier league clubs spent more than ever before on player transfers, a staggering £1.47bn in total. Some spent a lot more than others, and while PSG are making the Financial Fair Play headlines globally, the EPL clubs as a group spent more than any other league.

There are lots of ways to analyse spending, and rather than write a detailed analysis or opinion piece (as I’d doubtless end up being biased), I’ve taken the opportunity to simply present the transfer activity in a few different visualisations and readers can draw their own conclusions.

The first is a card based approach, with one ‘card’ per club. For each club we can see

  • Overall transfer activity (total revenue + total income),
  • Net transfer activity (spending – revenue)
  • A customised bullet graph showing transfer Spending : Revenue
  • Bars by player, showing incoming players (spending) in red and player sales (i.e. revenue) in blue

We’ve had some internal debate, but as the focus is on spending, actual spending is shown as positive numbers (in red) and revenue is shown as negative (blue).

I initially built the view for one club, as below, and then used a new ‘Repeater’ feature being introduced to XLCubed later this year which was a big time saver.

The repeater allowed me simply to replicate that view for the other clubs as below rather than build it 20 times. More to come on that in the next few months.

Click for a larger view. (yes, it’s a chart of two halves…)

The clubs are ordered from top left to bottom right by overall Activity. Using that approach Manchester City are top as they not only bought heavily, but also had significant sales, as did Chelsea. Perhaps surprisingly Everton are third, both due to higher spending than normal and also the sale of Romelu Lukaku for an eye-watering £76m.

Note that while this view is in many ways a Small Multiple approach, the spending axes do not have a shared scale as that makes the charts difficult to read for clubs with a smaller spend.

If we had ranked by Net spend, Manchester United would actually be top as while City and Chelsea both spent more on players, United had very little sales to offset spending.

A few other points of interest are that both the North London clubs, Arsenal and Tottenham actually had a net income over this transfer window.

The club view below is ranked by net spend, and gives an easy comparison by club.

TreeMaps can also be interesting in this context. I’ve used them here to take a look at spending by club by position, and also by age band to provide a viewpoints on where clubs have been focusing on the pitch and whether on the short or long-term.

Taking playing position first, it varies significantly across clubs. Of the 3 largest spenders Manchester City have focused most heavily on defence, Chelsea on midfield and Manchester United on Forwards (albeit on 1 expensive forward).

Club Spending by Position

Looking at age band of the players purchased, as would probably be expected the 22-25 age band is the biggest spending category for most clubs. The players are established, but their expected peak years are still to come, and their market value will likely remain high if they were sold in a few years. All Liverpool’s purchases were in this age band.

Club Spending by Age band

Clubs looking for an instant fix may also invest in slightly older players already at their peak, and the 26-29 band has the second highest level of spend.

 

The transfer window could be charted endlessly, but in the end only time will tell if the clubs have spent wisely. Although wisely is a relative term in this context of course.

 

 

User control of dashboard layouts

Dashboard sheets were introduced with V9, primarily as a way to deliver mobile-friendly reporting with a responsive UI to auto-fit any screen size. Specific Targets which define the layout can be defined to optimise the layout for different devices and are automatically applied depending on the device type.

Another use-case which is less obvious but can also be very useful is to allow users to choose between a number of predefined layouts.

For example, on a specific report there may be just 3 or 4 slicers which are typically used, but occasionally users may need access to a much larger list of slicers to filter by. It would be a shame to clutter the report for everyone permanently with all the slicers as it makes the selection process less intuitive, and probably forces us to use only combo boxes to save space. Ideally we’d like users to be able to switch from a ‘Quick Slicer’ view to an ‘All slicer’ view.

Another example would be where users want to include additional dashboard items, or remove items to get a larger view of a data table.

These scenarios and others can be handled by giving users control over which Dashboard Target is active via a slicer.

In the example shown below the button slicer allows switching between a ‘Quick slicer’ view with the 3 primary slicers shown as list boxes, an ‘All Slicer’ view with all 9 slicers available as combo boxes, and a ‘Table View’ which maximises the space for the data table and removes the charts.


So how do I…?

Firstly, you’ll need to define the various Targets which you want the user to choose between (see here for the details).

Next you need to add a slicer allowing the user to select between Target layouts. This slicer will be based on an Excel range, and will output its selection to another cell which you specify. It’s easiest if the input range for the slicer exactly matches your Dashboard Target Names (otherwise you can use vlookups to cross-match). Of course you’ll need to enable that slicer on each of the targets to allow the users to switch views.

Finally, we can use the XL3SetProperty() formula to set the active Target for the dashboard, based on the output of the slicer we just set up. The syntax is:

XL3SetProperty(Object Type, Object Name, Property to set, value to set the property to)

The screenshot above shows the slicer and formula setup – hope it proves useful for some of you!

How to gauge data through charts – Creating Gauge Charts

A common question that comes up in support for XL Cubed is how to add charts that look like a dial, or a gauge. Something like the below:

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These are actually very easy to make and publish to the web, plus they have the further bonus of adding something different to make your reports look more professional.

Once you have your data ready, add a new doughnut chart and configure it to show the information you want it to.

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This will give you a simple doughnut chart.

 

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Next up, pick the cell that contains the information you want to show in the middle of the doughnut chart and reference it in another cell. For example, in the below example we have the two numbers that make up our doughnut chart in cells B3 and B4. Cell E3 contains the information we want to show in the middle of the doughnut chart.

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As you can see, the formatting is different in E3 to the other cells. This is because we have formatted the cell to show the data how we want it to appear in the chart.

Once we are at this stage, it is just a case of transferring the number to the middle of the doughnut chart. You can do this by selecting the formatted cell, in our case E3, copying it and then paste special as a ‘Linked Picture’ anywhere in the worksheet (we will move it into the chart in the next step).

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The ‘Linked Picture’ appears as a cell but it actually acts like a picture so, lastly, move the picture into the middle of the doughnut chart so it looks how you want it, then, right click on the new picture and select ‘Send to Back’

 

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As the cell is a ‘Linked Picture’ Any changes you make to the cell you copied, formatting or data, will update the image.

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Your Gauge Chart is complete! These charts also look good when published to the web.

 

Report Flexibility, with Control

Sometimes we want to let report users modify the structure of a report but to govern exactly what they can and can’t do. While Grids can be restricted at a granular level to enable and disable functionality, that approach still requires some degree of product knowledge by the user.

XLCubed provides the XL3SetProperty() formula, which enables manipulation of many of the core objects such as Grids, Slicers and Small Multiples. It means report users can have simple slicer selections to change the structure of a report, what’s being displayed in a chart, or to vary the chart type. It gives flexibility within the report, but requires no product knowledge from the end user which can be crucial when delivering web reports on a widespread basis.

One common example of usage is where the hierarchy to be viewed in a grid needs to change based on the measure a user selects (depending on the structure of the cube some measures may not be applicable for all hierarchies). Typically that would need to be handled in two Grids, but we can use XL3SetProperty to bring this together, and also to give user choice on the associated Small Multiple Chart view.

The final published report is shown below:

 

S1

 

If the user selects an “Internet” measure, we show Customer Geography on rows, whereas a “Reseller” measure should show Reseller Type on rows. The same logic applies to the Small Multiple chart. In the screenshot below, the user has selected Reseller Gross Profit as the measure, and ‘Stacked Column’ as the chart type. You can see that the hierarchy on rows has been switched, as has the split within the individual charts, allowing the user to easily vary their view of the data with simple button selectors.

 

S2

 

This is implemented through the following key points:

  • A lookup table in Excel to determine what hierarchy is applicable for each measure
  • An Excel list showing the available chart types – this is used in the Chart Type slicer
    • The chart slicer outputs its selection into cell $AG$10
  • The measure slicer is linked directly to the grid and the small multiple, but also outputs its selection to an Excel cell ($A$B4)
  • A vlookup determines which hierarchy to use based on the selected measure
  • Three XL3SetProperty() formulae now control what is displayed based on user selections:
    • $AB$7 – sets the grid rows
    • $AB$8 – sets the small multiple columns
    • $AB$7 – sets the chart type

 

Formulae

 

The approach gives a deep level of access to the key XLCubed reporting objects, and enables controlled flexibility within web and mobile-delivered reports. No programming is needed, just a mid-level understanding of Excel itself, and XLCubed.

This is just one example of what the approach can achieve – it’s really limited only by imagination. See XL3SetProperty() for more detail, or contact us if you’d like the example workbook.

Bump Charts in XLCubed

So today’s blog is about adding Bump Charts in Excel using v8 XLCubed.

Initially a Bump Chart looks the same as a line chart – the difference is they plot the rank position rather than the actual value.

Let’s imagine that I sell a product in a marketplace with 10 other competitors. I may like to see how the rank position of my product and the competition changes over time to check if I’m gaining or losing market position. It’s a common scenario in pharma, where we have a good customer base.

You will usually want dates on the category axis so the trends are shown across time. The series then holds the items to be compared, in this case the products.

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Our example has been set up with Measures on Headers, Product Categories on Series and Date Calendar on Categories.  For more information on using Small Multiples in XLCubed please visit Small Multiple Charts.

The currently selected measure is Reseller Order Quantities (selected though the Measures slicer)

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for the eleven months prior to April 2008 (selected through the Date slicer)

 

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for a subset of products.

Looking at the bump chart you can see that I’ve selected Road Bikes and Mountain Bikes for easy comparison.  You can quickly see that the rank position for Road Bikes dropped quite dramatically from May 2007, picked up again in September before dropping again in November and rising in December through to February 2008.  The change for Mountain Bikes, on the other hand, was less dramatic, rising and falling slightly, steadying in February 2008 before dropping again the following month.

To create a bump chart just select Line – Bump as the Chart Type on your Small Multiple chart. The neat part is that all the rankings are worked out for you behind the scenes, without the need for lots of complex Excel gymnastics trying to work through the full result set month by month.

Excel heat maps made easy!

With the recent release of version 8 we’re going to blog about a number of the new features, starting with how to create a heat map in Excel.

Here’s a fairly large table showing sales for thirty six products across twenty six US states:

 v8B1

There’s a lot of data here but it’s not giving us any helpful information as the table is too large to see any pattern or comparison.

A heat map could be a useful way to give a quick visual picture of the spread of the sales volume. Let’s add a simple heat map, new in version 8 of XLCubed.

Select the data area in the table, and then from the XLCubed ribbon select the InCell-Chart group, and heat map:

v8B2

 

As we have already selected the data area to be charted this prompt is already showing the correct cell locations.

Choose the formula destination (where the formula controlling the chart will be located), and the Chart destination (where the top left cell in the chart area will be located).

We can now define the look of the heat map in the Chart Format dialog:

v8B3

 

 

We have set the low and high colours to define a blue colour gradient.

Outlying values could potentially skew the chart so you have the option to exclude these by setting minimum and maximum values.  Select the icon to use, squares in our case, and the number of steps or bands to split the range of values into.

We have pre-arranged the Excel cell sizes to be squares, and this is the resulting heat map:

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You can now quickly assimilate the spread of values in a glance, and note the higher sales volumes in Maine, Michigan and Missouri for Road, Touring and Mountain Bikes.

To alter the formatting of the chart simply double click on any one of the squares in the heat map, or on the chart formula to bring up the formatting dialog.

If you are not already a user of XLCubed you can get started with an evaluation of XLCubed by going to our registration page.

Excel BI myths debunked – #6: No report sharing & distribution

Here we continue our theme on the myths which get propagated about Excel based BI. The next argument is that Excel BI cannot handle widespread report sharing and distribution. Base case we actually agree with this one, and that’s why we invested in developing XLCubed Web Edition specifically to address it.

Understandably, sharing an Excel workbook around hundreds or thousands of users is not something which many companies will consider. A web based distribution approach is much lighter and easier to manage. The drawback is that most web based report design environments lack the flexibility and latent user skill base of Excel. XLCubed provides a simple way to push data-connected reports developed in Excel to a portal based environment, where report consumers don’t require any software installed locally, other than a browser. The reports can also be accessed interactively through our native mobile apps for Apple, Android and Windows phone 8.

XLCubed Web is self-sufficient and does not require SharePoint. For customers with SharePoint and keen to retain it as a centralised environment – no problem, XLCubed Web can integrate so tightly within SharePoint the end users won’t even know it’s there.

Excel based users can become web and mobile report designers in minutes. XLCubed uses Excel as a key part of the BI solution rather than as the entire BI solution, and it’s that which allows us to address the sharing problem, along with the other myths we have identified in this blog series.

 from any version of Excel:

ipxl

…to web…

ipwb

…to mobile.

ipip

Some Excel BI myths debunked #2: Inflexible Charting

#2: Inflexible Charting

Continuing our discussion of common criticisms of Excel focused BI, let’s take a look at charting.

“Excel charts are static, inflexible and you need to start from scratch if you want to change them.“

The flipside is that everyone knows how to use them, and in reality many charts in business reporting are in effect static – the numbers being charted change, but the chart layout and number of elements being charted stays the same.

Of course there are cases when charts can vary considerably with the data, or perhaps you would like to be able to drill into more detail on the chart, or to quickly display multiple charts split by one variable. Excel charting can’t handle those scenarios, but XLCubed caters for it through Small Multiples.  The example below depicts river water quality in different regions of England. It could be built in native Excel, but would be a painful and time consuming process. With XLCubed it’s a drag and drop process in our small multiple designer.

waterqualitysmalt

If the number of regions being reported changes, the number of charts being plotted will automatically stay in sync, and there is a direct data connection rather than having to maintain Excel ranges etc.

Sometimes with charting small is beautiful. Perhaps we just want the key numbers with a Sparkline alongside, or a bullet graph or bar chart to display actual to target. Native Excel 2010 and 2013 can handle the Sparkline, but not the ability to then drill the report and have the Sparklines extend, and there is also the issue of needing to bring the data itself into Excel before charting it.

XLCubed Grids can contain dynamic in-cell charts which build the charts as part of the query, and as such are drillable and remove the need to maintain a data range in Excel as shown below.

DrillIncell

So XLCubed brings the type of dynamic charting being described to Excel, and provides a simple web and mobile deployment option.

 

 

Bandlines in XLCubed

In early January this year Stephen Few introduced the concept of Bandlines. He identified a useful extension to Sparklines, making use of shaded or coloured horizontal bands to provide more information on the context of the trend line itself. See Stephen’s article on Bandlines and the thinking behind them for a detail description.

The Sparklines are ideal for showing individual trends in a small amount of screen real estate, and we use them extensively in dashboards, typically in a ‘visual table’. By definition Sparklines are small, and to make the trend easily readable, they are typically scaled individually so that each Sparkline uses the whole vertical axis. This means they do not give any impression of the scale of the numbers involved across different rows. It’s possible to use a common scale, and while sometimes that works more often it means many of the rows with smaller values are excessively flattened.

Bandlines address this by introducing horizontal shaded areas depicting the lower, middle and upper quartiles and the median represented by a line. The user can determine the context of the bands. The two most common examples would be plotting recent trend in the context of a longer period, or plotting individual rows in the context of the overall set of data being displayed.

We think Bandlines add real value, so hats off once again to Stephen, and we’re pleased to say that Bandlines are now available in the current version of XLCubed (see here for more detail).

The screenshots below show two examples, displayed in two colour schemes.

 

Bandlines3

The charts depict historic margin by store. The ‘Banding across all stores’ charts show the 30-day trend for the individual store, set in the quartile context of the data for all 11 stores in the table. We can see that for the Gilroy store in row 1, while the margin has varied, it remained in the top quartile when set against all stores for almost the whole period.

The ‘Banding by store, 90 days’ charts show the individual 30 day trend, set in the context of the previous 90 days for the individual store. This helps provide much more historical context, but the line itself still focuses on the more recent trend. Stockton is probably most noteworty here as across the 30 day period it has dropped from the top quartile into the 1st quartile across the whole 90 day period.

We’d love to hear your thoughts (and also which colour scheme works best!), we will also be adding Sparkstrips in the near future so watch this space.