Visual Review: Area Chart

I spend a lot of time using line and column charts, but in trying to overlay different types of information for different time periods, I have discovered that the Area Chart is really effective and underutilized (at least by me).

Today I'm using it to overlay two lockdown time periods and visualize the total number of COVID 19 cases in each period. 

View the live Power BI Report

Area Chart

Scores

Data Clarity: 8/10

Versatility: 8/10

Fun Factor: 7/10

PBI Certified: Yes (Built-in default visual)

Description

The Basic Area Chart is one of the original built-in visuals. It is useful for showing trends over time. Specifically it highlights the volume trending over time, because it shades the area under the line. If you remember back to high school calculus that area under the line/curve is the volume. Area charts do a great job of focusing the eye on the full volume, making it easier to see the big picture and compare values. I've found it's a good compromise to using cumulative values - just the simple act of shading the area under the line causes the human eye to include all the historical values with the current value and aggregate them. 

As you hover over the area chart, you also get a line showing where you are, which again makes it easy to compare the two regions to left and right of your cursor and see the difference in VOLUME for the two areas. 

How to

Fields

The Area Chart accepts data into the following field categories: 

• Axis
• Legend
• Values
• Secondary values
• Small multiples
• Tooltips

Axis

This should ideally be a date, time, duration or continuous (usually time based) field. In my example I have used 'Days in Lockdown' as my continuous time measure.

Legend

This can be used for categorizing your data. Either use Legend OR put multiple fields in the Values category. If you have something in legend, it will determine the different colors and lines (we call these 'series' in the data viz world). In my example I have opted to use multiple values, so must leave legend empty.

Values

Almost every Power BI visual has a values pane - put your explicit measures here. If your 'Legend' category is empty, you have the option to put multiple values in the Values category. In my example I have two measures in here to show the Alert Level in Auckland on any given day:

• Alert Level AKL March 2020 
• Alert Level AKL August 2021

Secondary values

This enables you to visualize data with different scales on the same chart. I have used it to overlay the Alert Levels behind the COVID Cases Reported. The highest alert level is 4, while the highest number of NZ COVID Cases Reported on a single day is over 80. If these were both plotted on the same scale we wouldn't be able to see the change in alert levels very easily. In my example, I have put the COVID Cases Reported in the secondary values:

• Lockdown March 2020 Cases
• Lockdown August 2021 Cases

Small multiples

This lets you create multiple charts without the need to copy paste. It ensures the scale and axes are consistent across all the visuals, ensuring that your report reader can safely compare and pick up on patterns across the visualizations. It's a relatively new feature that has been added to all the core Power BI visuals and I have blogged before about Small Multiples. 

Tooltips

Tooltips are also available on most Power BI visuals - I will be doing a separate blog on Tooltips where I compare the built in tooltips to Report Page Tooltips.

Formatting Options

Once you've got the data into the visual, it's time to format it nicely. Select the visual, then click on the format paint roller to view the formatting options. 

General

For most visuals this has the responsiveness, size, position, layer order and alt text. The Area Chart General formatting (and line chart too) have an extra option in here for High Density Sampling. 

> High Density Sampling

High Density Sampling is a feature that ensures Power BI visuals capture all the outliers in your data and provide a true representative sample even when working with many data points. By default this is set to 'On' and you should leave it that way.

In the visual below, you can see that with high density sampling turned on, there's a break in the pink negative areas. With high density sampling turned off, that break is missed because the visual is only plotting regular samples at specific time periods, and misses the maximum value in that range. The high density sampling algorithm ensures that the min and max values are always plotted, and therefore we see the more accurate representation of the data which indicates a break in the negative pink area:

Legend

The 'Legend' section will only appear under formatting options if you have either:

• A field in the 'Legend' category, OR
• Multiple fields in the 'Values' category, OR
• Multiple fields in the 'Secondary values' category, OR
• A field or fields in both the 'Values' and 'Secondary values' categories

I particularly like to play around with the 'Style' section of the Legend. Depending on what options you select further down (in Shapes section) you may want to change the default Style.

X axis

By default, this will be set to 'Continuous' which will ensure you can see the entire time range on one screen, but may also result in some sampling, so make sure you've selected the right 'High Density Sampling' option from the General section when Continuous is set to on. 

This dataset has been curated so I decided how many days I wanted to visualize, therefore the 'End' of x axis set to auto will plot all 100 days I created in my GENERATESERIES column for the x axis. You may wish to use the Conditional formatting option here to find the last point in time with data and ensure the x axis stops there. Wonderful power and flexibility! You can even apply conditional formatting to the axis labels (for example on a categorical axis you could change the color for weekends vs weekdays - how cool is that?)!

Y axis

The Y axis settings are mostly a mirror of the X axis. If you're using multiple fields in the 'Values' you may want to turn on and change the Title of the Y axis so that people know which numbers relate to which areas in the chart.

Secondary Y Axis

These settings only appear if you're using the 'Secondary Values' and are the same as the Y axis settings.

Zoom sliders

This is a relatively new feature that I'll leave for another post on another day, but basically it lets the user change the axes start and end values dynamically to focus on what they want to see.

Data colors

Unfortunately, there is no conditional formatting for data colors in the Area Chart, which is one of the things keeping it from getting a higher score in versatility. It's possible to set the data colors specific to each category or series if you're using multiple values or legend.

Data labels

Data labels can be customized by series - just scroll to the bottom and turn customize series to 'on' (will only work if Legend is a formatting option). 

An important thing to note - the data labels don't enable you to show the series label for multiple values fields, but you can change the color so that it's easier to tell which numbers relate to which series. Which of the above images do you prefer? I don't like using color as the only distinguishing feature, so this is another reason this visual doesn't get higher scores for data clarity.

Shapes 

This might be where the fun factor comes in, but it's also a really useful way to differentiate between your series. 

I'm using the area chart to compare two distinct time periods - and to forecast based on previous values. Therefore I want the shapes for the previous 'forecast' to be dashed, making the actual current values stand out more. You can also adjust the stroke width and turn on markers to distinguish data points from the line connection. If you have stepped data (such as my Alert Levels) that you don't want a diagonal line for, turn the stepped layout to 'On'.

Plot area

This just contains the standard plot area format options - adjust the transparency or add a background image. 

Note: this is different to the Background image and transparency - plot area does not include the legend and title of the visual.

Title

Includes all the beautiful title formatting options, including conditional formatting and word wrap if you have long titles or tiny screens.

Background

This is where you set the transparency and image for the entire visual - useful if you have something behind this visual that you want to hide or show (which sometimes happens when I'm creating mobile only visuals since Power BI has no option to hide a visual in desktop but show in mobile). 

Otherwise I leave these as defaults.

Lock aspect

Turn this on if you want the visual to behave like images in Word or PowerPoint. 

When Lock aspect is set to Off (the default), you can grab the corner of your visual and drag it anywhere - changing the aspect ration of the visual as you do. 

When Lock aspect is set to On, you can grab the corner of your visual and it will only let you drag it along the diagonal of the existing visual - thereby maintaining the current aspect ratio. 

If you resize using the side, top, bottom or General section of the visual Lock aspect has no impact. 

Border

If you turn this on, you can add rounded corners by adjusting the 'radius'.

Shadow

Make sure you leave space between your visuals if using the Shadow effect. 

Tooltip

This is how you can change what the additional information that shows up on hover looks like - I'll detail these settings in a separate post because they are very cool and deserve their own post.

Visual header

A while back the visual header was updated to a new look and feel, and now you can control which features show up at the top of the visual when users hover or select the visual. 

Note: This only impacts the header behavior in PowerBI.com so you need to publish the report to see the impact of the visual header changes.

Custom Visual Review: Sankey

Auckland: Living City

Today's post is inspired the the Auckland Museum Living City exhibit that projects larger than life data visualizations onto the floors and walls of Auckland Museum. Check it out if you're in New Zealand, or sneak a peak at the Auckland Museum Living City Instagram post for my overseas readers. 

Sankey Custom Visual

Scores

Data Clarity: 6/10

Versatility: 3/10

Fun Factor: 6/10

PBI Certified: Yes

Description

The Sankey Chart is a specialized type of visual designed to display the flow of data from source to destination. For that reason, it can be difficult to find the right data to use with this visual or you may need to perform many transformations on your data model to get it into a format ready to use with a Sankey visual. 

I have used it to help display the breakdown of complex hierarchies - turning it into a flowchart of the parent - child relationships in the dataset. It's commonly used for the energy industry, but as Auckland Museum have shown it works well with anything that moves in which you want to show the relationships from start to finish. 

How To

Format the visual

The Sankey visual enables the end user to manipulate the data by dragging and dropping the nodes to move the links around and change the overall flow of the visual. 

The Sankey visual has a few basic formatting options: 

Data labels: This will turn on the labels for the nodes. Of course you have the typical font size, color, etc. There is also an option here to turn on Force display, which will ensure that all nodes are labeled without the user needing to hover over the visual. This is great for small datasets, but can make larger datasets look very cluttered. Unfortunately, there is no option to specify which nodes to force display for, it's either: default, all or nothing.

Data link labels: This will turn on the labels for ALL the data links. You'll see in the video below that this can be time consuming and clutter!

Links: This simply lets you choose a color for all the links. You must individually set the color for each unique link combination.

Nodes: This lets you choose a color for each of the nodes, again this must be done individually for each node. 

Scale settings: This is a useful option that lets you change the scale. The default is set to 'Provide min optimal height of node' which can make it difficult to see which node has a larger population/weight. Turning this off can make some nodes too small to see, but also displays the nodes accurately to scale. There is also an option for logarithmic scales here.

Cycles displaying: Not applicable for this dataset, but useful for displaying the flow of items that can return back to their source. 

All the other formatting options are as standard visuals, formatting for title, size, background etc. 

This is definitely a time consuming visual to set up, but the rewards can be great if you put in the effort and have the right data to display. 

Model the Data

The most challenging part of using the Sankey visual might just be getting your data formatted properly. If you're wanting to use the Sankey visual for a dataset that isn't curated for Source and Destination (such as showing multi-level hierarchical parent child relationships), you may need to transform the data to make it all visible in the Sankey. 

In the video below I show one method for turning a flat tabular table into a Source > Destination table for Sankey visualization. Here you'll see how to create a series of queries that reference and group your raw data, and then finally append all those queries into one long table for the Sankey visual.

Custom Visual Review: Happy Father's Day!

In honor of Father's Day, I thought I'd tell a story of the history of the holiday and explore the Timeline Storyteller Custom Visual for Power BI.

Custom Visual Review: Timeline Storyteller

Scores

Data Clarity: 8/10

Versatility: 8/10

Fun Factor: 8/10

PBI Certified: No

Description

The Timeline Storyteller works on data where date dimension is key to the story. As long as you have a date to work with, the storyteller is quite versatile in how it allows you to display and manipulate your data. 

How To

The Timeline Storyteller doesn't have many options in the Format menu - everything is built into the visual itself. This allows it to be versatile, but also means the report user has the ability to edit the story and configuration of the visual. 

This makes it a bit confusing for someone new to the visual. In the report above - how do you interact with the visual? I have built a story in Playback Mode that matches the article I used as data source, however the buttons to toggle through the scenes are at the bottom of the visual. You may decide to explore and play with the data on your own before you get to my story! That's part of what makes it unique and versatile, but I'd like to see some development in this area of end user experience.

View Published Story

To view recorded scenes, click the arrow keys in the bottom left of the visual. When you get to the end of the story, Timeline Storyteller will automatically start the story from the beginning.

Select an item in the Legend to filter by that Category. You can also collapse the Legend if it is in your way.

Hover over the squares to see the Category description for that item.

You cannot record new scenes in view mode. Click the square icon to move into edit mode.

Edit Story

To create a story that others can flick through, you need to be in edit mode of the visual. Go ahead - you can test this out in the Power BI file above. Simply click the square icon in the Playback menu.

This will bring up three panes: 

• Timeline representation | Scale | Layout: Top of visual
• Data | Annotate | Filter: Left of visual
• Record Scenes for Playback Mode: Bottom of visual

From a report developer perspective, it's very much like Power Point in that you can drag and drop the callouts to wherever you want. Unfortunately, it doesn't allow you to change the order of callouts - so you need to add them in the order you want. 

Timeline representation | Scale | Layout

The example above uses data that spans decades, so I was limited in some of the display options I could choose, but this is most definitely a fun way to display timelines. If you aren't concerned with keeping the data chronological, but just need it to be in sequence, you have many options for how it's displayed, including a 'spiral' design where you can draw any shape you want!

Data | Annotate | Filter

You can use this pane to add notes and images to your story. Warning: If you click the 'Reset' icon it will clear all scenes (refresh your browser to get them back to what the report designer created).

Try adding an annotation and an image.

Record Scenes for Playback Mode

Remember to click 'Record' icon whenever you have a scene you like. It's always possible to delete a scene, but much harder to recreate it. It also wasn't possible to reorder scenes, so I had to be careful when developing my Playback Mode scenes to do them in the right order.

Custom Visual Review: Charticulator

This is not your ordinary custom visual - this is EVERY custom visual. Charticulator puts the power to design and develop custom visuals to meet your precise requirements in your hands - with no coding required!

Custom Visual: Charticulator

Scores

Data Clarity: 10/10

Versatility: 10/10

Fun Factor: 10/10

PBI Certified: It's developed by Microsoft (but it's in preview!)

Description

You read that right - 10/10 in all categories. The Charticulator Custom Visual is a Microsoft developed preview visual that enables you to create your own Power BI visualizations using simple images, your own data, and a bit of patience. Since this visual gives you the power to develop your own custom visualization, the way we use this visual is a bit different to most other custom visuals. 

• It will take you a bit more time, especially if you've never used Charticulator before.
• It will give you a lot more flexibility, especially if you're constantly searching for formatting options that don't exist in the built in visual library.
• It will make you feel like a superstar for having built your very own visual.

Let's start by seeing how to use Charticulator. I'm going to expand slightly on the Microsoft Charticulator Blog.

How to

Step 0: Get Charticulator

First, we need to get the Charticulator custom visual. You can do this the same way you get any custom visual, just download it from the marketplace. 

The Charticulator visual will appear on the bottom row of the Visualizations pane. Let's put this in a blank new page in our report. Select the Charticulator visual icon to add it to the report.

Now we're ready to follow the steps in the Charticulator visual itself.

Step 1: Start with adding and assigning your data to visual and create a charticulator visual.

For this example, we're going to use a nice simple dataset - the freediving data from a previous blog post. It has three columns: Depth, Pressure and Volume.

Our goal is to plot the percent change in Volume of our lungs as Depth changes when freediving, so we'll add Depth and Volume to the Data box in the Visualizations pane.

Step 2: Import existing template or start creating from blank visual

Since this is our first time using Charticulator, we don't have any of our own templates (though there are plenty of Charticulator Templates available for download).

In order to develop our own visualization, we must hover over the Charticulator visual header and click 'Edit'.

This will open the Charticulator visual in focus mode and give you a screen that looks a bit like this: 

Click 'Create chart' and the Charticulator design canvas will open up.

Now we're ready to start creating. Check out the Charticulator Tutorials for more info on Glyphs, Layers, Attributes and how to use the Charticulator designer, or follow along with the Freediving example below. You can download all the necessary Charticulator Freedive Sample Data to follow along.

Glyph

We'll start with our glyph - glyphs can be shapes, images or lines.

Since we're trying to visualize the change in lung volume, we'll use an image of human lungs. Transparent files are best if you have them, for this example I've used .png file.

To add an image, click the Icon button in the ribbon at the top. 

I want the lungs to be the center of the chart, so I will select the center of the Glyph graph at top right to put the Icon placeholder in the middle. 

Layers

You'll notice that you now see Icon1 under Glyph in the 'Layers' section of the designer. Select Icon1 here to edit its Attributes.

Attributes

Now we'll give it a name - Lungs, and browse on our computer to find the image we want to use for this icon. 

Finally, select the desired alignment for this Glyph relative to the position we chose earlier. I want them both centred.

Bind Data

Note that in the .gif above, I typed a static size for my Glyph, mainly so we could see it for alignment purposes. We want the size of the lungs to change based on the Volume selected in the data. Click the Link next to the 'Size' and choose the 'Volume' data field. Now set the Domain and Range start and end sizes until you get the size you want for your icon. I set Domain Start to 0 and Domain End to 1, Range Start to 0 and Range End to 10,000. Hit tab after entering each value to save, then click away from the pop up to return to the Designer main screen.

Save

This is a simple visual, but it will work. Click Save at the top left of the Charticulator designer. There are many ways to save your Charticulator visual. If you want to use it again you can click the Export arrow to export it as a template. This action is still in preview from Power BI Desktop, so you'll have to copy and save as a .tmplt file. 

Test

We're now ready to test our visual. One of the BEST FEATURES about the Charticulator Custom Visual being integrated into Power BI Desktop is that we can save, test, and rework our custom visual without needing to export and import the custom visual each time. 

Click 'Back to Report' to return to the Power BI report. You'll now see a beautiful set of lungs on the page. 

Add a slicer for Depth and only allow single selections. BONUS: Create a parameter to make this slicer a slider as in the sample file. See the Parameter How To blog if you're not familiar with DAX parameters.

It works, but could use a bit of refining. Let's go back into Edit mode and see what else we can do.

PlotSegment

So far, we've just managed to resize our glyph based on the data, which is handy, but what if we want its position to change relative to the data selected? To make that happen, we need to return to the Layers area and select the PlotSegment1 layer. 

Now in the Attributes section, we'll rename this to PlotSegmentDepth.

Click the link next to Y Axis and choose 'Depth' data field. 

This will put avg(Depth) in the Data field. Our depth ranges from 0 to 65, but since we're diving down, we want this to be negative, so put a - symbol in front of the avg(Depth) so that it reads -avg(Depth). Now set the range to  -65 to 0. Be sure to untick the Auto range min value and Auto range max value boxes in order to keep the Y-axis consistent for any depth selected!

Go ahead, save and test this to see how it looks.

Add Meaning by expanding your Glyph

We're making good progress, but this visual still doesn't tell the story I want it to - it's too difficult to see the change in size of lung volume when we can only see one depth at a time. We want to compare to the surface volume, so I'm going to add a semi-transparent lung at full surface volume. 

Return to Edit mode and start at the beginning. Note we're still working with our original Glyph. Recall that a Glyph can be a shape, icon, lines, drawings. Well, it can also be a combination of all of the above. Let's add another Icon to our Glyph. Click the Icon button in the ribbon, select the new icon from the Layers area and format it in the Attributes area: give it the same size as you specified in your Range End, the same alignment as the original LungsVolumeIcon and don't bind it to any data. 

Save and test the visual. 

Wow!

That's pretty cool. So many possibilities with this Charticulator Custom Visual. We haven't even started to look at text labels, color, and stacking Glyphs, but I hope I've inspired you to go and have a bit of a play with it yourself. Let me know what you create.

Eleventh Day of Christmas: Enlighten Data Story

   🎵 On the eleventh day of Christmas, my true love gave to me... 

Data storytelling, 

Data driven emotions,

Meaningful Switches, 

Syncing Slicers Slicing,

Conditional Drill-through Button,

Verified User Selections, 

Dynamic titles,

Custom theme colors,

Appended queries,

Table expanded columns

and

a PDF in Power BI. 🎵

Custom Visual Review: Enlighten Data Story

Scores

Data Clarity: 7/10

Versatility: 8/10

Fun Factor: 3/10

PBI Certified: No

Description

The Enlighten Data Story custom visual allows you to add key facts and figures directly to a text narrative. It's so simple that it is truly effective and provides endless opportunities for data storytelling. 

While you could achieve a similar effect with a custom DAX measure concatenation and a card visual, the Enlighten Data Story highlights the data figures to help them stand out from the rest of the narrative. 

Formatting Options

The Enlighten Data Story has the typical formatting options for General, Title, Bacground, Lock aspect ration, Border, Shadow and Visual header. There is nothing extra or different in these categories. 

The two additional formatting categories that make the Englighten Data Story stand out are the 'Story' and 'Data' categories. 

Story

This allows you to format the static story element of your narrative. You can set the Font colour (yes it's a Kiwi-made visual so colour is spelled with a 'u' in it) and Font size. 

In the Text section, type the narrative you wish to display. A # acts as a placeholder (though this can be changed in the Data formatting category) and will return the value of the data fields you have added to the Data values section of the Fields for this visual. For example, type: 

Total sales are # for the period #.

Then add [Total Sales] measure and [Current Period] measure to the Data values of the visual. You must add them in the order you want them to appear in your narrative. If you have three fields but only two #, only the first two fields will display in your data story. If you have more # than fields, the extra # will show up in your data story. 

Align allows you to left, center or right align the entire narrative. Scrolling enables is 'On' by default, in case your narrative gets too long to fit in the space you have allocated.

Pro tip: Try adding <br> to your story to add a line break (this one may be easy to copy and paste in as it tries to reformat the HTML code).

Total sales are <br> # <br> for the # period.

Data

This allows you to customize the Data values that you add to the narrative. The first thing you can do is edit the placeholder, handy if you need to display # as part of your data story. In this case, simply pick a symbol that you know you won't need in your data story. 

You can set the Font colour and Font size for the data values, as well as set Bold to 'On' or toggle it off. This enables you to make your data figures stand out from the narrative in your data story, as they can take on a different size and colour to the rest of your text. 

You can also change the native formatting of the fields. For example you may prefer to write 1,300,000 as 1.3M instead. 

The 'Blank label' box is my favorite feature. We're still waiting for this feature in native Power BI visuals, but Enlighten have given us the option to replace a Blank() result with any value we desire in the Englighten Data Story. You might put 'NA' or 'Unknown' or 'Not available' or in some cases even '0' in this box to make your data story easier to read. You can see an example of this in the Enlighten Data Story Formatting image above.

'Show tooltips' is set to 'On' by default, but if you are using mostly measures, you may prefer to turn this off. The Enlighten Data Story doesn't have a separate 'tooltip' field area, so there's not much added information provided in a tooltip for a measure that you can't already see in the data story.

Report Example

We used the Enlighten Data Story visual to help compose Santa's letters letting each person know if they're on the naughty or nice list. Recall we created a measure using SWITCH to help display what Santa would say: 

Letter =
SWITCH (
    SELECTEDVALUE ( 'Santa List'[Naughty or Nice?] ),
    "Nice", "Looks like you have been nice in 2020. Keep up the good work!",
    "Naughty",
        "Looks like you have been naughty in 2020. Best change your ways before Christmas.",
    "Pick a name."
)

Now we can use that in our Enlighten Data Story. 

How to:

1. Open the SantaList.pbix file we've been working with. 
2. Select the 'Check the List' page - it should have the Comicgen visual there and not much else.
3. Click the three dots in the Visualizations pane > Get more visuals and then search for and add the Enlighten Data Story. 
4. Add the Enlighten Data Story visual to an empty space on your report page. 
5. Add the SantaList[Name] column and [Letter] measure to the Data values for the visual.
6. Click the paint roller to format the visual.
7. Expand the Story section and type: Dear #, <br> # <br> Yours truly, <br> Santa
8. Test your buttons, slicers and report functionality. It should be pretty functional by now. 
9. Add a background photo or splash of color to your pages if desired.
10. Save the file.

Tune in again tomorrow for the final gift in the 12 Days of Christmas series where we'll refine the user experience of returning to the home page and look at how to use bookmarks to reset the report filters so that you don't have to clear the name from the last search before selecting a new name. 

Tenth Day of Christmas: Comicgen Custom Visual

  🎵 On the tenth day of Christmas, my true love gave to me... 

Data driven emotions,

Meaningful Switches, 

Syncing Slicers Slicing,

Conditional Drill-through Button,

Verified User Selections, 

Dynamic titles,

Custom theme colors,

Appended queries,

Table expanded columns

and

a PDF in Power BI. 🎵

Custom Visual Review: Comicgen 

Scores

Data Clarity: 4/10

Versatility: 7/10

Fun Factor: 10/10

PBI Certified: No

Description

The Comicgen custom visual is a great way to bring emotion to your data. It uses cartoon drawings to display different emotions and poses based on data. 

I must admit, I would never have sought out this visual in a business scenario, but maybe it has a place. Humans are more likely to remember emotive material than neutral material (Hamann, 2001). Emotive material is something we can connect with, usually through remembered experiences. Words such as laughter, joyful and success are more memorable than the neutral words of month, sales, and invest. So, if you want people to connect with your data and remember your report, add more emotion to it. 

Sure, this leaves more open to interpretation, so you definitely want to back up this visual with some hard facts and figures, but maybe there's a reason emojis are so popular and perhaps there is a place for them in business reporting. I'd love to hear your thoughts. 

Overall, it's a fun visual and emotions are applicable to almost any dataset so it is fairly versatile (that is if we can learn to accept comics in our business reports). 

Formatting Options

Like most Power BI visuals, Comicgen has the following formatting options: 

• General
• Title
• Background
• Lock Aspect Ratio
• Border
• Shadow
• Visual Header

There is nothing special in any of these options, just the typical settings that all visuals have. However, Comicgen has one additional formatting option called 'Comic Settings' that makes the visual work.

Comic Settings

This is where all the magic happens. Comic settings lets you adjust the character, pose and emotion of your visual. It's possible to pick a static pose and emotion, or tell the visual to update based on your data. 

Comic

At the time of writing this post, the Comicgen visual has 7 comic options available: Ava, Bean, Evan, Dee, Dey, Priya and Ringo. These must be preselected, and cannot update with a change in your data. 

Pose

The Comicgen visual has many poses to choose from: Ponting Right (pointingright), Ponting Up (pointingup), Yuhoo (yuhoo), Super Perfect (superperfect), Holding Laptop (holdinglaptop), Angry Frustrated (angryfrustrated), Hands Folded (handsfolded), Hands on Hip (handsonhip), Holding Book (holdingbook), Reading Paper (readingpaper), Thumbs Up (thumbsup), Thinking hmm (thinkinghmm). 

In addition to all those options above, there is an option for 'Data Driven' emotion. In order to use the 'Data Driven' option, you must have either a measure or column which returns exactly the value in (brackets) in the list above. Basically, you have to spell the poses exactly as they are written in the visual, but without the spaces. For some reason, they fixed the spelling mistake on 'pointing' for the data driven, but not in the format pane when you select it manually. ;-)

Emotion

The Comicgen visual currently has seven emotions: Normal, Laugh, Sad, Angry, Worried, Surprised, Wink. 

Emotions can also be 'Data Driven'. So far, all emotions are single words so you don't need to worry about removing spaces. Make sure you write the data exactly as in the list, and your visual will work.

Mirror

This is such a simple option, but so important. You may have been distraught to discover that Dey can only point right and not left, well don't worry, just change the Mirror setting from 'Regular' to 'Mirror' and he'll point left. 

Only Face

This is another handy option, and I used it to place my comic inside the donut hole of my donut chart. The default is 'False', which means you can see the body and poses of your comic, but you do have the option to set this to 'True'. Unfortunately, there's a lot of white empty space when this option is set to 'True', so the responsive sizing of this visual could use some improvement, but with some clever layering it can work. 

Report Example

Alright, so how do we put this Comicgen visual to use in our SantaList.pbix file? Most datasets will not contain perfect pose and emotion words created for this visual, so we'll use the DAX SWITCH function that we looked at yesterday to convert our data into something this visual can understand. Recall we wrote a measure for pose and a measure for emotion: 

Pose =
SWITCH (
    SELECTEDVALUE ( 'Santa List'[Naughty or Nice?] ),
    "Nice", "Yuhoo",
    "Naughty", "AngryFrustrated",
    "HandsFolded"
)

Emotion =
SWITCH (
    SELECTEDVALUE ( 'Santa List'[Naughty or Nice?] ),
    "Nice", "Laugh",
    "Naughty", "worried",
    "normal"
)

Today, we will use those two measures in our Comicgen visual. 

How To: 

1. Open the SantaList.pbix file.
2. In the Visualizations pane, click the three dots, then select Get more visuals. 
3. Search for 'Comic' and add the Comicgen visual to your report.
4. Select the 'Check the List' page of your report. It's still looking pretty empty.
5. Add the Comicgen visual to this page.
6. Put the [Emotion] measure in the Emotion Data box.
7. Put the [Pose] measure in the Pose Data box. 
8. Click the paint roller and expand the Comic Settings section. 
9. Select any Comic you like.
10. Select 'Data Driven' for Pose.
11. Select 'Data Driven' for Emotion.
12. Save and test your file. It should show a different pose if you select a name on the naughty list. 

It's starting to come together! Tune in again tomorrow for the next gift in the 12 Days of Christmas series where we'll look at another custom visual, the Enlighten Data Story, and see how this can help us compose Santa's letter. 

New Zealand Region Map for Synoptic Panel Power BI Custom Visual

If you've attended one of my Power BI training sessions, you may have heard me recommend the Synoptic Panel Custom Visual for Power BI developed by OKViz. This visual is highly customisable and interactive without requiring any custom coding. It enables you to turn any image into a data chart, with the ability to customise colours based on data values from your live data model. I have used this visual for reporting health & safety incidents by location and body part, stock levels by factory region, and customer information by region.

I have had a few questions lately about how to easily map NZ data by region, so thought I would share my own NZ regional map that I have created. It is a regional map for New Zealand District Health Board regions to go alongside the NZ 2018 Census data that I often use for NZ reporting and statistics. When used in conjunction with the Synoptic Panel custom visual, you can create regional shaded maps for New Zealand based on your data to look something like this:

To use this visual, simply download the Synoptic Panel from the AppSource/Marketplace, and download my .svg file. Your data must have a column containing NZ region names matching the DHB spelling. Place this field in the categories field of the visual. Place whatever value you want to display (population, revenue, number of customers or sales or incidents) in the measure field. The image above was created using explicit states to set 5 distinct colours, but you can also turn on saturation to make the transition between colours more gradual based on the actual value of the measure. 

As always, post any questions or comments below and let me know how you get on!

Download the .svg map file and sample data from my sample data site.