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Category: Visualization

Using Slicers In Power BI To Filter Chart Categories

Published 2018-12-11 by Kevin Feasel

Prathy Kamasani shows how you can use slicers in Power BI to filter out specific categories in a line chart:

The logic is to create a table with the DAX function UNION. Each Table expression in UNION function represents a value of slicer. Apart from that slicer related value, all the rest of the values are blanks.  It is key to have them as blanks than zero’s, we don’t see any data.

In other words, pivoting the table to turn one measure with several different category values into one measure per category.  If you know the number of categories (4 in this case), this solution can work well for you.

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Visualizing Traditional Japanese Color Palettes

Published 2018-11-30 by Kevin Feasel

Chisato den Engelsen looks at 465 traditional color palettes used in Japan:

Since each of colours had name, I also was curious if there are some characters that are used more often than other. Colour name was written in two ways in this website. One in Kanji and other in Hiragana.

I love wordcloud2 to visualize the wordcloud, so I can see which characters appears more often the others.

It’s an interesting exercise and all of the R code is included.  Be sure to check out the list of colors with a character representing “rat” or “mouse” in the name.  H/T R-Bloggers

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Creating Custom Power BI Themes

Published 2018-11-28 by Kevin Feasel

Cathrine Wilhelmsen shows us how to create a custom theme for Power BI dashboards:

Power BI comes with several built-in themes and a whole gallery full of custom themes available for download. But what if you still can’t find the perfect look for your reports? No problem! Just create your own custom Power BI themes 🙂

…sounds simple enough, right? It only takes a few minutes to create a custom Power BI theme with a color palette of your choice. Whoosh – instant custom branding!

But if you are like me, simple color changes might not be enough. Maybe you want finer control of borders, fonts, labels, or other visual elements. Or maybe you just don’t want to keep changing the same settings over and over and over again in multiple visualizations and reports. (Please don’t do that.)

You can control all of these things in custom Power BI themes. It is, however, not quite as simple as creating a color palette… yet. (You never know when the Power BI product team will blow your mind with a new update!) But for now, we need to define custom themes in JSON files.

Click through to learn how to do some of these changes through the power of editing JSON files.

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Building A Gantt Chart With Plotly

Published 2018-11-27 by Kevin Feasel

Ellen Talbot shows us how to embrace our inner micromanagers:

Something a little different today for a quick chat about my latest project and why I’m finding the plotly package so helpful!

Are you like me and physically can’t function unless you’ve got a to do list in front of you? Well even if you’re not, imagine my pain while I’m wearing my non – Locke Data hat and trying to plan out the final year of my PhD thesis!

I needed something that updated easily, something visual and something to keep my supervisors in the know. I’ve previously made gantt charts using LaTeX but found it ridiculously clunky to get working and decided there had to be a better way. And if I could include interactivity then all the better, which is how I discovered plotly.

Admittedly, I like gantt charts more than almost any developer I’ve ever met.  They always look so pretty and are wonderful depictions of a world which will never be.

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Quick Geospatial Data Plots In R And Python

Published 2018-11-26 by Kevin Feasel

Harry McLellan shows us how we can use R and Python to generate quick-and-dirty plots of geospatial data:

Now R has some useful packages like ggmap, mapdata and ggplot2 which allow you to source you map satellite images directly from google maps, but this does require a free google API key to source from the cloud. These packages can also plot the map around the data as I am currently trimming the map to fit the data. But for a fair test I also used a simplistic pre-built map in R. This was from the package rworldmap, which allows plotting at a country level with defined borders. Axes can be scaled to act like a zoom function but without a higher resolutions map or raster satellite image map it is pointless to go past a country level.

There’s a lot more you can do with both languages, but when you just want a plot in a few lines of code, both are up to the task.

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Working With Temporal Line Charts In Power BI

Published 2018-11-20 by Kevin Feasel

Marco Russo shows off a few things you can do with Power BI to make displaying temporal data in line charts better:

The first line is related to the week ending on February 2nd, so Sales Amount includes only 2 days (February 1st and 2nd) excluding the amount of other 5 days in the same week (January 27th to 31st). The same happens in the last week, which includes June 29th and 30th but does not include sales for the remaining 5 days in the same week (July 1st to 5th). This also explains why the report includes a week ending in July 2008 even though the Month slicer only includes dates up to June 2018.

We can create a measure that removes incomplete weeks from the calculation, as shown in the following code. A similar technique could be used for incomplete months and quarters.

There are some interesting techniques that Marco shows off, including hiding incomplete weeks.

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Editing ArcGIS Maps In Power BI

Published 2018-11-01 by Kevin Feasel

Jason Bonello shows us the types of changes we can make to ArcGIS maps in Power BI:

Map themes – This allows a change in the style for the map and once can choose from location only, heatmaps or clustering (the last two are only available for point layers, that is when you select Points in the Location Type). Through the clustering option, one could group individual location points into larger circular clusters that fall within a cluster radius – giving a high level view and then the ability to drill down into each region. If heatmaps are chosen any values in the Size or Color will be ignored and the tooltips will not be available.

Read the whole thing.

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Visualizing A Correlation Matrix With corrplot

Published 2018-10-24 by Kevin Feasel

Kristian Larsen demonstrates the corrplot package in R:

First we need to read the packages into the R library. For descriptive statistics of the dataset we use the skimr package and for visualization of correlation matrix we use the corrplot package. We will work with windspeed dataset from the bReeze package:

# Read packages into R library
library(bReeze)
library(corrplot)
library(skimr)

Click through for the demo.

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Using cdata To Created Faceted Plots

Published 2018-10-22 by Kevin Feasel

Nina Zumel shows how to use the cdata package to create faceted ggplot2 plots:

First, load the packages and data:

library("ggplot2")
library("cdata")

iris <- data.frame(iris)

Now define the data-shaping transform, or control table. The control table is basically a picture that sketches out the final data shape that I want. I want to specify the x and y columns of the plot (call these the value columns of the data frame) and the column that I am faceting by (call this the key column of the data frame). And I also need to specify how the key and value columns relate to the existing columns of the original data frame.

Read on to see how you can use cdata to tie together different faceted plots.

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