Visualizations types examples: Ribbon Chart, Waterfall, Scatter Chart, Bubble Chart, Dot Plot Chart

1. 11/26/2020 Ribbon Chart
1/1
Sales Volume by Year and Region Name
Year
1995 2000 2005 2010 2015
33K
31K
61K
24K
61K
42K
41K
49K
25K
43K
26K
52K
30K
40K
48K
61K
38K
26K
17K
14K
30K
10K
27K
21K
19K
11K12K
23K
20K
24K
19K
29K
26K
14K 11K
19K
15K 14K
28K
12K
27K
19K
24K
21K 13K18K
25K
13K
27K
27K
15K
19K
16K
17K
13K 11K
25K
10K
25K
16K21K
11K11K
18K17K 21K 24K 24K
12K 10K 13K 16K
30K
27K
51K
22K
51K
37K
38K
24K
42K
39K
50K
24K
49K
43K
27K
35K
35K
24K
30K
26K
55K
21K
35K
44K
52K
42K
37K
52K
37K
42K
52K
22K
28K
26K
34K
47K
Region Name Greater Manchester Merseyside South Yorkshire Tyne and Wear West Midlands West Yorkshire
Ribbon Chart
You can create ribbon charts to visualize data, and quickly discover which data category has the highest rank (largest value). Ribbon charts are effective at showing rank change, with the highest range (value) always displayed on top for each time period.
We can see that we've got West Yorkshire starting off third and then it comes up to second. Then goes down to third again.
Similarly we can see very clearly that South Yorkshire tarts at fifth and then becomes fourth in 2001, before slipping back down.
Ribbon Chart allows us to see the volume with the total. So it's equivalent to the stacked, but it doesn't display them in a fixed order like the stacked charts do.
Instead, it puts at the top the one which is the biggest and at the bottom the one which is the smallest.
Format + Ribbons section or Format + Data Labels
So Ribbons are useful when you want to know the ranking of individual categories, together with their size.
So it serves a different purpose to the line charts and the stacked area charts, but it does make for an interesting topic of conversation.

2. 11/26/2020 Waterfall Chart - Cumulative
1/1
SalesVolume by Year
0.0M
0.5M
1.0M
1.5M
2.0M
2.5M
3.0M
3.5M
Date Year
SalesVolume
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Total
Increase Decrease Total
Waterfall Chart: Cumulative
WATERFALL CHARTS:
- show a running total as Power BI adds and subtracts values,
- useful for understanding how an initial value is affected by a series of positive and negative changes,
- are quite useful when you're want to have an allocated cumulative count or if you want to drill down into the significant figures.
For instance, you might be calculating a company's profits and seeing what the most profitable items were and which made the most loss. So you can be auditing the major changes.
Example: How many homes did we sell by the end of 2004?
Using a Waterfall Chart, each year's sales volumes gets added to the previous year. As we can see, 243,065 is the Sales Volume in 2004.

3. 11/26/2020 Waterfall Chart - Breakdown
1/1
SalesVolume by Year and RegionName
0K
50K
100K
150K
200K
Year
SalesVolume
1995
Other
WestMidlands
GreaterManches…
1996
Other
GreaterManches…
WestYorkshire
1997
Merseyside
Other
GreaterManches…
1998
Other
GreaterManches…
WestYorkshire
1999
Other
SouthYorkshire
GreaterManches…
2000
Other
WestYorkshire
GreaterManches…
2001
Other
GreaterManches…
WestYorkshire
2002
Merseyside
Other
WestMidlands
2003
Other
Merseyside
SouthYorkshire
2004
WestYorkshire
GreaterManches…
Other
2005
Other
GreaterManches…
WestYorkshire
2006
Other
WestMidlands
WestYorkshire
2007
WestYorkshire
GreaterManches…
Other
2008
WestMidlands
GreaterManches…
Other
2009
WestMidlands
Other
Increase Decrease Total Other
Waterfall Chart: Breakdown
If we add Region Name into the Breakdown, each of the Regions gets added into all of the years. We can see that when we get to 2004 and 2005, we have got some negative sales volume compared to the previous year. And then 2009
drops off completely.
Format + Sentiment colors:
Increase - Green: means that these are your big advances.
Decrease - Red: means these are your biggest decliners.
To see the most 2 significant (the biggest bottom and the biggest top): Format + Breakdown + 2.
Note: it is showing 3 breakdowns, as the others are going to be wrapped up into Other. Also, as soon as I put in a breakdown, it is not longer cumulative (the end figure is the totality for that year).
For example, we can see that in 2003, Merseyside has got up 2000 units and West Midlands has gone down by 2000 units.

4. 11/26/2020 Line Chart
1/1
Line Chart: Average House Price change
So we can see that we start off at around 4% (1997) go all the way up to +28% (2004), and then all the way down to -9.6 (2009).
Note: Change the aggregation for 12m%change
12m% Change by Year
-10
-5
0
5
10
15
20
25
30
Year
12m%Change
2000 2005 2010 2015
0.0
5.4
28.6
-9.6
3.8
3.0
-3.3
4.7
4.2
3.8
9.6
7.0
0.6
7.3
26.1
8.2
-3.0
-0.6

5. 11/26/2020 Waterfall 2
1/1
Waterfall Chart: Average House Price change
So we can see that:
- between 2001 and 2002: we have the biggest raisers in South Yorkshire and Tyne and Wear
- between 2004 and 2005: we have got huge declines in Tyne and Wear and Merseyside.
So it just allows a different view of your data and see how it's been going over time.
12m% Change by Year and RegionName
-10
0
10
20
30
40
50
60
Year
12m%Change
1996
WestMidlands
GreaterManchester
Other
1997
Other
SouthYorkshire
WestYorkshire
1998
Merseyside
SouthYorkshire
Other
1999
WestMidlands
GreaterManchester
Other
2000
Merseyside
TyneandWear
Other
2001
SouthYorkshire
TyneandWear
Other
2002
SouthYorkshire
TyneandWear
Other
2003
Merseyside
Other
WestMidlands
2004
Other
TyneandWear
Merseyside
2005
Other
WestYorkshire
GreaterManchester
2006
Other
SouthYorkshire
Merseyside
2007
Other
WestYorkshire
GreaterManchester
2008
Other
SouthYorkshire
GreaterManchester
2009
WestMidlands
WestYorkshire
Other
2010
Other
WestYorkshire
WestMidlands
Increase Decrease Total Other

6. 11/26/2020 Scatter Chart
1/1
Scatter Chart: Comparing two different values
How does the Sales volume vary according to Price Inflation?
- If houses are rising quickly, do we have more Sales Volume? So people are trying to buy the houses before the prices get too high.
- If it's going down, do we have a reduction in the Sales Volume?
- Are people frightened and not want to buy?
So let's compare SUM Sales Volume and AVG House Price Inflation, using a Scatter chart.
Note:
- add Date into Details, so we don't have it overall.
- drag Region Name to Legend, so we have each Region in a different colour. So we can now see the difference in sales volume for the various Regions.
Sales Volume and Sum of 12m% Change by Year and RegionName
-200
-100
0
100
200
300
400
500
Sales Volume
Sumof12m%Change
10K 20K 30K 40K 50K 60K
RegionName Greater Manchester Merseyside South Yorkshire Tyne and Wear West Midlands West Yorkshire

7. 11/26/2020 Scatter Chart2
1/1
Scatter Chart: Showing Dates at once
So the question is: So, do we want all of the dates to be shown at once, or do we want it to be shown more as a presentation?
What if I want to concentrate on one year at a time?
- Drag Year down from Details to Play Axis. Clicking on the Play Button, we can see the dots for each year and how they change over time.
- Add Sales Volume to Size: the bigger the Sales Volume, the bigger the Size.
Clicking on the Play Button, we can see that the further right we go, the more the circles get bigger. But as soon as we get to the left-hand side, they start reducing in size.
Sales Volume, Sum of 12m% Change and Sales Volume by RegionName and Year
-200
-100
0
100
200
300
400
500
Sales Volume
Sumof12m%Change
10K 20K 30K 40K 50K 60K
2016
RegionName
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Greater Manchester Merseyside South Yorkshire Tyne and Wear West Midlands West Yorkshire

8. 11/26/2020 Bubble Chart
1/1
Bubble Chart: Comparing three different values
So the idea about this is generally to have three independent variables, numeric. So let's have a different, a third value, for the size.
For example, let's add Average Price in the Size. Note: Change the aggregation to Average.
So now, we can see that the cumulative effect of all the house price inflation. But when we get around to 2007, this is when the house prices were at their maximum, and then they slightly declined.
However, they were still fairly big even though there is some negative inflation, and even though sales values are down.
We still have much lower house prices in 1996 compared to the peak of the negative house price inflation around 2009.
SalesVolume, Sum of 12m% change and Average Price by RegionName and Year
-200
-100
0
100
200
300
400
500
SalesVolume
Sumof12m%change
10K 20K 30K 40K 50K 60K
2016
RegionName
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Greater Manchester Merseyside South Yorkshire Tyne and Wear West Midlands West Yorkshire

9. 11/26/2020 Dot Plot Chart
1/1
Dot Plot Chart: Adding a categorical field into X-Axis
To create a dot plot chart, replace the numerical X-Axis field with a categorical field (es.Area Code) and remove Date from Play Axis
Sum of 12m% change and Average Price by RegionName and AreaCode
1,520
1,540
1,560
1,580
1,600
1,620
1,640
1,660
1,680
AreaCode
Sumof12m%change
E11000001 E11000002 E11000003 E11000007 E11000005 E11000006
RegionName Greater Manchester Merseyside South Yorkshire Tyne and Wear West Midlands West Yorkshire

10. 11/26/2020 Format - Scatter Chart
1/1
Scatter Chart: Format
- Change the shape for Mangester: Format + Shape + Customise Series ON
- Add Category labels: Format + Category labels ON
- Add Color Borders: Format + Color Borders ON
Note: It is not possible at the moment to change the speed of Play button.
Sales Volume, Sum of 12m% Change and Sales Volume by RegionName and Year
-200
-100
0
100
200
300
400
500
Sales Volume
Sumof12m%Change
10K 20K 30K 40K 50K 60K
2016
Greater Manchester
West Midlands
West YorkshireMerseyside
South Yorkshire
Tyne and Wear
RegionName
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Greater Manchester Merseyside South Yorkshire Tyne and Wear West Midlands West Yorkshire