I need a graph in Power BI that is a line graph but has more than 2 y-axes.
Each line has to have its own y-axis (own scale and color). The lines are from categories (like an ID) that need to be selectable with a slicer.
I have tried many custom graphs, but none seem to match my requirements. Plotly has functionality to build such graphs, but it is not supported in the python visual and it does not work in the R visual. I tried the plotly.js visual with the https://github.com/plotly/react-chart-editor editor. I could build the right graph but for each "trace" I had to manually filter out the ID's of the separate lines, instead of doing it with a slicer.
Quick attempt with plotly.js in Power BI looks like this:
I'd rather avoid writing my own custom visual, but any advice or tips are welcome.
Related
I'm trying to plotting multiple lines in one single graph. I've combined two of them using dual axis, but I don't know how should I add another.
Thank you in advance for any help.
Since you have mentioned that you want to show multiple measures in one viz it means that one axis is sufficient for all these measures. Therefore, Instead of creating dual axis chart, do it like this-
Step-1 Build line chart with one measure.
Step-2 Drop another measure on the axis directly instead of rows/column shelf where you'll see a double bar icon like the screenshot.
step-3 Drop additional mesaures either in similar ways or onto measure values pane created automatically. See a demonstration screenshot below on sample superstore
I know it is possible to create such double pie charts in excel like this:
http://chandoo.org/wp/2009/12/02/group-small-slices-in-pie-charts/
but can SPSS or R do this also?
In relation to R:
The answer to the title question is "yes" ... see ?pie
As for the second question, the one in the body - it would be possible but would involve some coding. You'd have to draw two pie charts side by side (which could be managed with two calls to pie) and use segments or arrows (and text if necessary) to do the additional components of the plot.
Here's a rough example:
That required the fig argument of par to get them side-by-side.
(That example required a little fiddling to get right, but it would be possible to write a function to automate the details.)
The main issue I can see would be 'why on earth would you do it?' -- pie charts are a poor way of conveying information of this form. There are alternatives that result in much better ability to distinguish values, and less bias (such as what you get when comparing nearly horizontal vs nearly vertical slices).
I know it is possible to create such double pie charts in excel like this:
http://chandoo.org/wp/2009/12/02/group-small-slices-in-pie-charts/
but can SPSS or R do this also?
In relation to R:
The answer to the title question is "yes" ... see ?pie
As for the second question, the one in the body - it would be possible but would involve some coding. You'd have to draw two pie charts side by side (which could be managed with two calls to pie) and use segments or arrows (and text if necessary) to do the additional components of the plot.
Here's a rough example:
That required the fig argument of par to get them side-by-side.
(That example required a little fiddling to get right, but it would be possible to write a function to automate the details.)
The main issue I can see would be 'why on earth would you do it?' -- pie charts are a poor way of conveying information of this form. There are alternatives that result in much better ability to distinguish values, and less bias (such as what you get when comparing nearly horizontal vs nearly vertical slices).
I'm getting familiar with Graphviz and wonder if it's doable to generate a diagram/graph like the one below (not sure what you call it). If not, does anyone know what's a good open source framework that does it? (pref, C++, Java or Python).
According to Many Eyes, this is a bubble chart. They say:
It is especially useful for data sets with dozens to hundreds of values, or with values that differ by several orders of magnitude.
...
To see the exact value of a circle on the chart, move your mouse over it. If you are charting more than one dimension, use the menu to choose which dimension to show. If your data set has multiple numeric columns, you can choose which column to base the circle sizes on by using the menu at the bottom of the chart.
Thus, any presentation with a lot of bubbles in it (especially with many small bubbles) would have to be dynamic to respond to the mouse.
My usual practice with bubble charts is to show three or four variables (x, y and another variable through the size of the bubble, and perhaps another variable with the color or shading of the bubble). With animation, you can show development over time too - see GapMinder. FlowingData provides a good example with a tutorial on how to make static bubble charts in R.
In the example shown in the question, though, the bubbles appear to be located somewhat to have similar companies close together. Even then, the exact design criteria are unclear to me. For example, I'd have expected Volkswagen to be closer to General Motors than Pfizer is (if some measure of company similarity is used to place the bubbles), but that isn't so in this diagram.
You could use Graphviz to produce a static version of a bubble chart, but there would be quite a lot of work involved to do so. You would have to preprocess the data to calculate a similarity matrix, obtain edge weights from that matrix, assign colours and sizes to each bubble and then have the preprocessing script write the Graphviz file with all edges hidden and run the Graphviz file through neato to draw it.
I'm trying to use canvas to build a line graph between 2 date points. I need the date measurements along the bottom of the graph to change based on the start and end date data, which can vary widely between what information is grabbed by the application. Are there any plugins that do this, specifically/dynamically?
There is a huge abundance of chart and graph plotting libraries that can do this, and gRaphaël seems to do what you ask for quite well.