PASS Summit is an annual pilgrimage to Mecca. A journey that we all make in the Autumn to dip our bodies in the ever-loving glow of SQL Server.  It is a chance to spend a week with thousands of like-minded people all with which love data. From beginners to experts, we come from all corners of the world to descend upon Seattle and mingle with Microsoft all in the name of learning and networking in the world of data.  Ahhhhh, I am excited once again writing these words and thinking about the things I will do the first week of November in the Pacific Northwest.  My bags are packed and I am ready to go but I am getting a little ahead of myself as the trip is not for another six weeks.

Will you be there?  Are you on the fence?  Do you need a gentle push?  Feel free to ask me anything here or on twitter.  Let me also remind you that next week the price will be going up so maybe you need to act today!  Register Now!  Come by and say hi and introduce yourself as I would love to meet you in Seattle.

When Power BI first debuted, one of the features that it lacked was the ability to change a color scheme on a wholesale basis. At that point (and still today) you can change colors for individual data points and that can be tedious with a lot of data points and/or trying to match up corporate color schemes or with existing corporate SSRS reporting standards.  With the advent of Themes in Power BI, you can change a color theme for an entire report easily and quickly and copy that into your next report.

There is one important caveat if you have existing reports: If you have modified all of the colors in a report, implementing a Theme will not change the existing color scheme in a report as you’ve customized those colors already and the theme will not change that. This could be considered a feature or a bug depending upon your perspective. Microsoft considers that a feature. Plan accordingly.

The color themes are simple JSON files that look like the following (this one we created at the following site https://powerbi.tips/tools/advanced-color-theme-generator/):

{
	"name": "MyTheme",
	"dataColors": [
		"#121a56",
		"#2337c7",
		"#23abc7",
		"#23c77b",
		"#23c730",
		"#c423c7",
		"#c73223"
	],
	"background": "#c7c423",
	"foreground": "#b223c7",
	"tableAccent": "#0b090c"
}

In order to change themes, we select the Switch Theme button on the main ribbon, as shown in Figure 1.  Power BI gives us a few provided options.  We can also download themes from the Microsoft Power BI community.

Figure 1 – Themes

Here we have returned to our Hank Aaron data and reports from earlier in the series.  The colors in Figure 2 are before we apply any theme.  We could use the JSON file that I created above to get a new color theme.

Figure 2 – Before Theme

After choosing Import Theme from the Switch Theme ribbon, we chose our manually created JSON file to produce the following hideous report shown in Figure 3.  Notice that some colors such as in the pie chart never changed. We covered that earlier, they will not change as they’ve been customized.  We probably should have created a corporate color scheme or gone with one of the provided themes. as this looks horrible.  Have fun with your color themes, but not this much fun.  Stay tuned!

Figure 3 – After Theme

Today we will be discussing Drillthrough. Drillthrough allows you to drill through to more details. For example, you have a chart that summarizes Sales Amounts by Years.  When you see this visualization, you realize that it would be useful to be able to drill through the chart and see all of the data that is summarized for the selected year.  Let’s try this using our AdventureWorksDW2014 data-driven Power BI report from the last few posts.  If you remember we had a Stacked Column Chart displaying Sales Amount by the Order Date hierarchy.

Let’s create a second report page and name it Order Details.  On this page, add Order Date (as a date and not a hierarchy) and Sales Amount from FactInternetSales and FirstName and LastName from DimCustomer. This will give us some basic order details as shown in Figure 1.

Figure 1 – Order Details

On the Order Details page, drag the Year value from Order Date in FactInternetSales to the Drillthrough filter area as shown in Figure 2. This will be our link to the main page to allow us to drillthrough based on the year of the order date. So when we are viewing Sales by Year on our main chart, we can choose to drillthrough and see all of the individual order that made up that year.

Figure 2 – Drillthrough Filter

Keep in mind that you can hide these drillthrough detail report pages and allow them only to be seen on the drill through action.  Because of this feature, Power BI places an arrow in the upper left hand corner to allow you to return from whence you came.  You can edit the appearance of the arrow and when the end-user presses the arrow they are returned to the original report.  Personally, I chose to make the arrow a little larger and change the color to red to make it more obvious as shown in Figure 3.  We should also at this point name the report page tab at the bottom of the report so that when we try to drill through it will be obvious as to what page we want to drillthrough to as shown in Figure 4.  Here we see the page name of Order Details to the right of the drillthrough menu. If there were multiple pages we would have to choose a particular page here.

Figure 3 – Detail with Return Arrow

So now that we’ve gotten a little ahead of ourselves, let me explain how we got to that menu.  In our Stacked Column Chart, we are at the Sales Amount by Year hierarchy level of our visual, thus the OrderDate year is in scope for our data. This is important because we chose Year as our drillthrough filter.  If we right-click on one of the years inside the chart, we can drill through as shown in Figure 4. If you are not hovering over a particular year, then the drillthrough option will not be available in the context menu.  It is also important to note that if you drill down or move to the next level and do not have year in scope, then the drillthrough feature to the year filter on Order Details will not be available.  Similarly, you could create additional report pages to provide different details to those levels as well. Stay tuned for our next adventure with Power BI!!!

Figure 4 – Let’s Drillthrough Now!

Now that we’ve learned how to drill down in a matrix table, let’s see how to drill down in a visualization.  Highlight the matrix that we used in days twenty and twenty-one and select the Stacked Column Chart in the visualization menu. This will convert our matrix table to the stacked column chart using the same selected fields OrderDate and SalesAmount as shown in Figure 1.

Figure 1 – Stack Column Chart

This is the same data now represented as a chart.  If we right click on one of the stacks, as shown in Figure 2, we see a Drill Down option. Notice that there is also a Show Next Level and Expand to Next Level options.

Figure 2 – Drill Down Options

• If we choose Drill Down it takes us to Sales Amount by Year and Quarter and in this case it is 2013.
• If we choose Show Next Level, it takes us to Sales Amount by Quarter. This is an aggregation for all years by quarter. There is a difference here between seeing a drill down into 2013 data and looking at the next level of the hierarchy.
• If we choose Expand to Next Level, as shown in Figure 3, we see a break-down by year and quarter for Sales Amount.

Figure 3 – Aggregates by Year and Quarter

These are subtle differences but very important differences.  We can continue drilling up or down to see the various levels for this chart in much the same way that we did for the matrix table. Note that the up and down arrows are available above the visualization here as well. Stay tuned for the drillthrough in our next installment.

To recap quickly, we were using the AdventureWorksDW2014 data warehouse and we added  SalesAmount and OrderDate from the FactInternetSales table.  We want to insert a matrix table onto the report as those work with the drill down feature whereas the regular table visualization does not.

Figure 1 – Initial Sales Amounts

In Figure 2, we can see drill down icons for the Matrix visualization. The down arrow on the right side of the toolbar enables the drill down capabilities for the visualization.  The three arrow icons in the left hand corner have the following functionality:

Figure 2 – Drill Down Icons

• The Up Arrow drills up a level from the current level of the hierarchy.
• The Double Arrow drills down to the next level of the hierarchy (as seen in Figure 3).

Figure 3 – Move Down One Level

• The Single to Double Arrow expands down all to the next level of the hierarchy (as shown in Figure 4).

Figure 4 – All Down One Level

With this feature we can roll up and down allowing Power BI to do the aggregations along the way.  In Figure 5, we can see the bottom level showing sales aggregates for year, quarter, month, and day. At this point you can visualize the roll up for the totals.  We have now learned to use the drill down feature of Power BI. Congratulations!

Figure 5 – All The Way Down

Today we are going to demonstrate the drill down functionality in Power BI.  One of the key requirements for the drill down feature is the need for a hierarchy to allow us to drill up and/or down. The previous Hank Aaron data did not lend itself well to hierarchies.  One of the most common automatic hierarchies is the date hierarchy. When we have a date field in our data, Power BI will automatically generate a date hierarchy which consists of year, month, day, and quarter. That gives us four levels to drill up or down with.

Because it is common in business intelligence reporting to generate reports from the data warehouse, it seems only logical that we are going to use the AdventureWorksDW2014 version of sample data to demonstrate this functionality. The free sample data warehouse database can be download from Microsoft here.

For demonstration purposes, connect to the AdventureWorksDW2014 database and load the fact and dimension tables as shown in Figure 1 below.

Figure 1 – Load OrderDate Hierarchy and SalesAmount

Notice how OrderDate is split out into Year, Quarter, Month, and Day fields. This is a hierarchy. If we do not want to have one of these levels in our hierarchy we can remove it here by selecting the X on the right side of the field on the left side of Figure 1.  For example, maybe we do not care to get aggregate rollups for quarters, therefore we can remove the Quarter field from this hierarchy here. Thus our drill down will only consist of year, month, and day aggregations.

At the same time, I should mention that if we chose not use the drill down functionality on this particular field hierarchy and we just want to view the date as a date, we can switch back to a regular date field as shown in Figure 2.

Stay tuned to the next installment to see the drill down in action!

Figure 2- Selecting a Regular Date Instead

For the next few blog posts we are going to discuss taking Power BI to another level. We are going to discuss drill down (or up) and drillthrough.

Let’s explain the terminology first!

With drill down, we can go from a high level on a report like Year to Date Sales to the lowest detail that we wish to show.  We can decide how many levels of drilling.  So let’s say our first level is corporate-wide so we might see one row for 2017 that has sales data totals for the entire corporation. If we drill down to the next level, we can see that inside the corporation we now have totals by global regions. If we drill down a little further we might see totals by country and then totals by smaller regions and so on.  Drilling up is simply going back up that chain.  This adds an entirely new dimension to our reports that eliminates the need for multiple static reports.

Drillthrough, on the other hand, provides the ability to drill through to another page or report for detail and thus filter by the field that you selected when you chose drillthrough.  For example, when we have our drill down and we get to the country detail on our report mentioned above, we could drillthrough to a second report that shows sales details and it would then filter by that country.

If this sounds a little confusing, do not fret…we will go through it in the next couple of posts.  Stay Tuned!

Let’s look at visuals today.  The hardest part to designing a Power BI report is deciding which visuals to use that will best convey the message of your report or dashboard. I could go into a detailed analysis of each of the default visuals, but a few days ago I came upon the following visual reference from SQLBI.com (which is an excellent and highly recommended Power BI reference site) and thought I would pass it along instead of reinventing the wheel:

The page where the image was found (http://www.sqlbi.com/ref/power-bi-visuals-reference/) has a download option for the PDF for your daily use.  Enjoy!

It is just what you think it would be: question and answer, only this time your Power BI dashboard end-user asks the question of your data and the software returns an answer!!!! This sounds like some straight-up Skynet Terminators taking over the world science fiction, right? Well, of course, we are data people so now WE HAVE THE ULTIMATE COSMIC POWER!!! OK, maybe not that cool, but still!

Let’s dive in and take a look!  In Figure 1, we see our original dashboard and if you look right above the pinned insight “G and PA” you can see the “Ask a question about your data” field area where we can begin typing. Once you click into this field it will show you a different screen without the pinned visualizations and will show you some sample columns to select or you can begin typing your question.

Figure 1 – Dashboard

In essence, Q&A provides answers in the form of charts and graphs to natural language questions in real-time type-ahead fashion. Watch the progression in Figures 2 through 5 as we type ‘hr by year’ as our question. Also, notice that typing HR first gives us a 755 HR total in the chart responding to our request.  Remember that our dataset is called Standard Batting so you can see that Figure 2 shows us the HR column of that dataset as a selection. This is useful if the column was found in multiple locations.

Figure 2 – Typing HR for Home Runs

Figure 3 – Continuing to Type for Home Runs

Figure 4 – Power BI Guessing the Column is Year

Figure 5 – Now We Have an Answer to Our Question

We’ve now successfully utilized the Q&A feature of PowerBI.com.  If you did not see that feature, make sure that you have it turned on as shown in Figure 6 below. Stay tuned for the next day in this series!

Figure 6 – Turn on Q&A in the Dashboard Settings

Now that we’ve uploaded our report to PowerBI.com, we can take advantage of a neat little feature called Quick Insights. From the Reports screen, select the light bulb icon and generate the insights as shown in Figure 1.

Figure 1 – Quick Insights

This generates some helpful and interesting insights from our dataset. However, some of them might be useless because we know our data and might lead to a “buyer beware” situation.  In addition, some of them are obvious to baseball fans, such as Figure 2, but with your data maybe you want a correlation pointed out in the data.  We generally know that the more games you play, the more plate appearances you should have. But now we have data to back up the correlation!

Figure 2 – Correlation between Plate Appearances and Games

There were some interesting insights provided such as Figure 3. The tool found a correlation between walks (BB) and age for a certain subset of data: when Hank Aaron played for the Milwaukee Braves. Is that because he became a better, more patient hitter?

Figure 3 – Correlation Between Age and Walks (BB)

In Figure 4, we have an additional correlation, between Strike Outs and Walks (BB) for the Milwaukee Braves. Fascinating!

Figure 4 – Correlation Between Walks and Strike Outs

Keep in mind that there were many insights generated for our data set. Many of them were not useful in this example such as the number of times Hank was caught stealing by the position he played. Because we know the data, there really is not a useful need for this relationship. But we get to decide which insights are useful and save those to our dashboard by clicking on the push pin icon in the upper corner. This will prompt us to select or create a new dashboard, as seen in Figure 5. We will explore dashboards further in day seventeen. Stay tuned!

Figure 5 – Pin the Insight to a Dashboard

The first step to doing this is creating an account on PowerBI.com. Don’t worry, there is a free account which lets you publish up to 1GB of data, share dashboards, and  have access to cloud sources.  If you need more than that, pricing information can be found here.

On the top ribbon, select the Publish icon. You will then be prompted for your PowerBI.com account and password as shown in Figure 1.

Figure 1 – Enter Account Information

At this point, the report will be published to the Power BI service as shown in Figure 2 and 3. That was easy, right?

Figure 2 – Publishing to the Power BI service

Our report is now in the cloud! Are you excited yet? I am! Stay tuned to see where we go next!

Figure 3 – Our Report in the Cloud

Being a consultant for Innovative Architects, this makes my job much harder because I may tell a client one thing in March and then that is incorrect in April. I am perfectly OK with that because: NEW FEATURES!!!!

So if you are following this blog in real-time, you are going to want to update your Power BI as well to the March 2017 version to prevent any incompatibilities with my version.  Look at the features and improvements in Figure 1.

Figure 1 – New Features

Check out the last item on the list…split column by delimiter.   Does that sound familiar?  It should as we did that manually using calculated columns in Day Ten. This is the perfect example of what I referred to earlier at IA. I may have told a client that you could not easily split a column by delimiter. Now you can. Isn’t that fabulous?  Thanks Microsoft!  Below is the video from them on the new features.

Stay tuned to see where we go next!

While we were away, I tidied up the slicer that we created yesterday with a nice title, a border, I changed the fonts, etc. In other words, I tried to make it match the format of our page as shown in Figure 1. We also selected the Select All Option in the slicer so that we could return to the original functionality of the visuals before the slicer was added. It feels like the same visual theme in our slicer.

Figure 1 – Tidy the Slicer Up

Now we want to add another slicer.  Select the home run (HR) field from the Field pane on the right hand of the screen. Select the Slicer from the Visualization pane to turn the new bar graph into a slicer. Select Dropdown and configure it to match the first slicer as mentioned above and shown in Figure 2. Select the ellipses (…) option above the selected slicer object and select Z->A Sort by HR to sort the home runs from most to least in our drop down. Notice that the two slicers are related and limit your options compared to the choices you make in the other slicer. This also filters all of the visuals on your page. This is cool, powerful, and sometimes terribly awkward.

Figure 2 – Home Run Slicer

Now let’s go for broke and add a third slicer. Repeat the above steps using the team (Tm) field only this time let’s leave it as a list of check boxes since Hank only played for three teams. As you can see in Figure 3, we’ve selected the Milwaukee Braves (MLN) and the Milwaukee Brewers (MIL). This filters our other two slicers to rows of data that only matches those two teams. We could at this point slice these down further and this isn’t really the best example for when you would actually need three filters in a visual but I hope it helps you to get a sense that they are all connected and all control the visuals on the page. In other words, all of them are connected and feed off each other. It is unlimited cosmic power, sort of. Use it wisely. Stay tuned for our next adventure!

Figure 3 – A Third Filter

Figure 1 – Slicer

Welcome back to day twelve of our series on Success in Power BI! Here is the link to refresh your memory on yesterday’s work on our Hank Aaron project. We now have two visuals on our page and today we are going to add a slicer. Some of my favorite meals came from slicers…wait that doesn’t seem right. Well in that case just ignore Figure 1.

Just like delicious cold cuts, the Power BI data slicer is used to take a large amount of data and make it smaller. In this case we use slicers to filter data, in other words, to get to the heart of the data. Make sense? It is a visual filter made available on our page to filter through the data in real time. Exciting! Let’s pick up where we left off….remember when I said we needed some extra space at the bottom of the page? Good, you’re now ready to begin if your page looks like Figure 2.

Figure 2 – Let’s Slice This Data Like Roast Beef

Figure 3 – Adding the Age Field

1. Click somewhere in the white space at the bottom of the page to ensure that neither of our visuals are selected and then select the Age field in the Fields pane on the right hand side. This will default in a nice little clustered column chart (Figure 3) where it adds the ages from each of our records to give us a grand total of 713. This data is useless as this is not a field to sum up. But we need it here, for now.
2. With that visual still selected let’s choose the Slicer in the Visualization pane as shown in Figure 4 using the blue arrow. Our clustered column chart has now changed to a list of check boxes representing each age of data in the data set.
3. Check some boxes and watch how the TWO visuals change. We are seeing his totals move depending upon the age. This. Is. Awesome!
4. Hold the Ctrl button and select multiple ages and watch the visuals change as shown in Figure 5. Notice the changes in the line and clustered column chart. You can actually see that some ages are missing by the gaps. Even the trend lines move. We must be careful with this to prevent a misleading representation.

Figure 4 – Slicing Time!

Figure 5 – Slice and Diced

Figure 6 – Drop Down Slicer

That list of check boxes is really unnecessarily expensive in page real estate terms. We can change that into a drop down to prevent losing our valuable visual space. If you hover over it on the right hand corner you will see the word List with a down arrow. Select that and choose Dropdown. Our list is now much smaller as shown in Figure 7. What will we think of tomorrow? Stay Tuned!

Figure 7 – Less Page Real Estate Slicer

In order to do that, we need to add another column as these statistics track hits (H), doubles (2B), triples (3B), and home runs (HR) but they do not track singles (1B). I’ve often wondered why when you are looking at a player statistics, the single was relegated as pedestrian to not be depicted in its own column. However, we are going to give it its proper due.

1. Go to the data pane on the left hand ribbon.
2. Right click somewhere in the data and select Add Column.  I wanted to point out that you can skip step one by right clicking on the Fields pane on the right hand side and simply choose Add Column there as well.
3. Replace ‘Column =’ with the following (as shown in Figure 1): 1B = ‘Standard Batting'[H] – ‘Standard Batting'[2B] – ‘Standard Batting'[3B] – ‘Standard Batting'[HR]

Figure 1 – Singles Column

Now we are prepared to create a pie chart, but first we must make some room and size down our existing visualization on the page leaving some white space. Click on the white space so that our existing visual is not selected so when we click on the Pie Chart visualization it adds a new visual and does not change our existing line and clustered column chart to a pie chart. Click on the Pie Chart in the Visualization pane as shown in figure 2 with the red arrow.

Figure 2 – Add a Pie Chart

If you notice under the Visualization pane, we have a blank canvas with no fields dragged into fill the pie chart.  Select the singles (1B), doubles (2B), triples (3B), and home runs in the Fields pane on the right hand side. This will insert them as values and populate our pie chart as shown in Figure 2. Note that I’ve already made it a little prettier with a change in colors and title. I’ve also added hits (H) to the Tooltips section so that when you hover over home runs for example you can also see how many total hits for example. The hover automagically adds a percentage so it is nice to see what the percentage was based upon.

Figure 3 – Pie Chart Configured

Notice the white space I left at the bottom, that is for tomorrow’s post. Stay tuned!

We are going to switch gears today and manipulate some of our data set for future lessons. We have the Awards field currently which has a few different awards delimited by comma. We want to separate out this data into separate columns to report on.

1. Go back to the data view (on the left hand ribbon middle icon).
2. Right click anywhere inside the grid and choose New Column (as shown in Figure 1).
3. Type in the following (replacing Column = as shown in Figure 2):  AllStar = IF((FIND(“AS”,’Standard Batting'[Awards],1,0)) > 0,”Yes”,”No”)
4. Repeat steps two and three: GoldenGlove = IF(FIND(“GG”,’Standard Batting'[Awards],1,0) > 0,”Yes”,”No”)

Figure 1 – Adding a New Column

This language is called DAX or Data Analysis Expressions and it is a library of functions and operators that are used to build expressions and formulas for Analysis Services (SSAS), Power BI Desktop, and Power Pivot for Excel.  Here is an MSDN reference for DAX. And here is a quick reference that I refer to in order to know which function will fulfill my needs.

To summarize what we did above in the steps: we added two new columns called AllStar and GoldenGlove. We looked at the Awards column for AS or GG and used some DAX functionality to return a Yes or a No into these separate columns. Now these awards have more meaning to use other than the long string of awards for that year. Stay tuned to see how we use them!

Figure 2 – Using DAX for a New Column

Yesterday we made an assertion about Hank Aaron’s on-base percentage (OBP) remaining fairly flat over his career that could have been caused by more walks as a result of him being a home run hitter. This is a common strategy in baseball where the pitcher will walk a home run hitter to prevent a home run when there are runners on base. We should test that theory using our data!  Change your visual to the Line and clustered column chart in the Visualizations pane. Your visual should appear like Figure 1.

Figure 1 – Clustered Column Chart with Trends

Now remove the batting average (BA) field from the Column values to avoid confusion as we want to see if walks (BB) correlate with on-base percentages (OB) and home runs (HR) for Hank Aaron. First off, let’s change the title to something a little more reflective of our comparison. Then, add the BB field to the Line values as depicted by the black arrow in Figure 2. Also, notice that a trend line was added for these in a dashed version of the color used to depict the walk line.

Figure 2 – Adding the Walk Line

Now we will add the home run (HR) field to the Line values area as shown with the red arrow in Figure 3. It is interesting to note that according to the trend line, Aaron’s walks (BB) increased as he got older yet the home runs (HR) decreased, as we would have expected with a declining physical stature. It also doesn’t appear that there is a correlation to walks (BB) versus home runs (HR), at least not a strong one.

Figure 3 – Adding Home Runs

Lucky for us that in 1955 Major League Baseball began tracking intentional walks (IBB) as those were more related to a home run hitter’s prowess. Regular walks could be from poor pitching and were not necessarily intended to pitch around a good hitter. The distinction is important to show correlation in this theory. Also, keep in mind 1955 was Aaron’s second year so we will not see data for his rookie year.

However, there still does not seem to be a correlation as some years like at age 32 he had 44 home runs and only 15 intentional walks and his on-base percentage was lower than most other years despite being a strong home run performance year.  Likewise two years later his home run total dropped to 29 but his intentional walks spiked to 23, the highest in his career. The trend for intentional walks does follow the home run trend downward.

Does this data give us an accurate analysis? If we were presenting this to the board with a couple of visualizations that we’ve created, would they make an informed decision? Would you? Stay tuned!

Figure 4 – Replacing Walks with Intentional Walks

Figure 1 – Reset the Area Chart

In the Analytics pane, add a Trend Line as shown in Figure 2. Notice that it inserts a dashed black line, but even more interesting is that it defaults a setting called Combine Series to On. In essence, we are seeing a trend line for both the on-base percentage and the batting average.  It is as we would expect with a player aging, his performance trends downward. Change the default for Combine Series to Off and let’s see how it changes our story.

Figure 2 – Combined Trend Line

Now we have TWO trend lines as shown in Figure 3, one for OBP and one for BA. In addition, they are defaulted to the colors of the original lines but these are dashed for delineation: Automagically!!! Now when we compare those two sets of trend lines (Figures 2 and 3) you should see a different slope. The on-base percentage was more flat and consistent while the batting average dropped more rapidly than the combined line. That is fascinating and it shows that even as he aged he was still consistently able to get on base. Being a home run hitter, some of that was attributed to more walks as pitchers would rather give up an intentional walk than a home run especially with runners on base.  Let’s look at that in a future day.  Changing this setting altered our story and gave us more insight than a combined trend line. It may not always.

Figure 3 – Separate Trend Lines

What else can we do as far as analytics are concerned? Well…we can also add Average Lines, Median Lines, and Percentile Lines. Give them all a try to see what the differences are. But, a click or two and you have added instant analytics in your visuals. Pretty nifty, huh? Stay tuned for more Power BI!

What more can we do with our visualization? What if we do not like the Line Chart visual? If you select the Area Chart visual you can quickly change your visual to the graph shown in Figure 1.

Figure 1 – Easily Switched to an Area Chart

Try some other visuals.  Better yet, try them all. Personally, I do not think any of the other visuals work with this set of selected values. For example, in Figure 2 we see a Clustered Column Chart which to me isn’t as easy to see the natural trend line but it works. I think the original Area Chart works best in this scenario. Let’s switch back to that visual moving forward.

Figure 2 – Clustered Column Chart

Speaking of trend lines, let’s add some simple analytics such as a Min and Max line. We can easily add a Min line for the BA and the OBP. Select the third icon in the pane below the Visualizations called Analytics as shown in Figure 3 with the red arrow. Select Min Line and add two Min Lines and configure them as shown with the blue arrows. We need the colors and alignment to show in our area chart. For batting average, I used a bright yellow line and for on-base percentage a red. I then used a black font and moved it to the right and under the line. If the colors are hard to see you could switch back to a regular line chart to prevent the color clash.

Figure 3 – Adding Min Lines

Now we can do the same to add in some Max Lines using straight lines with the same respective color for either OBP or BA as showing in Figure 4.  I changed these to be solid lines to avoid confusion. The visual is sort of busy but it works, right? Maybe the color choices are not perfect and those can always be tweaked. But we’ve added some base analytics to our visual. We are rock stars, right? Stay tuned for more.

Figure 4 – Adding Max Lines