Press "Enter" to skip to content

Category: Visualization

Plotly And Power BI

Published 2017-07-10 by Kevin Feasel

Leila Etaati shows how to use Plotly to generate interactive R charts in Power BI:

In the last two posts (Part 1 and 2), I have explained the main process of creating the R custom Visual Packages in Power BI. there are some parts that still need improvement which I will do in next posts. In this post, I am going to show different R charts that can be used in power BI and when we should used them for what type of data, these are Facet jitter chart, Pie chart, Polar Scatter Chart, Multiple Box Plot, and Column Width Chart. I follow the same process I did in Post 1 and Post 2. I just change the R scripts  and will explain how to use these graphs

Leila includes several examples of chart types and shows that it’s pretty easy to get this working.

Comments closed

R’s iGraph + SQL Server Graphs

Published 2017-07-07 by Kevin Feasel

Dennes Torres has a post which shows how to use R’s iGraph library to visualize graphs created in SQL Server 2017:

The possibility to use both technologies together is very interesting. Using graph objects we can store relationships between elements, for example, relationships between forum members. Using R scripts we can build a cluster graph from the stored graph information, illustrating the relationships in the graph.

The script below creates a database for our example with a subset of the objects used in my article and a few more relationship records between the forum members.

Click through for the script.

Comments closed

Bar Plot Alternatives

Published 2017-06-29 by Kevin Feasel

Alboukadel Kassambara shows off a couple alternatives to bar charts:

Cleveland’s dot plot

Color y text by groups. Use y.text.col = TRUE.

ggdotchart(dfm, x = "name", y = "mpg",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "descending",                       # Sort value in descending order
           rotate = TRUE,                                # Rotate vertically
           dot.size = 2,                                 # Large dot size
           y.text.col = TRUE,                            # Color y text by groups
           ggtheme = theme_pubr()                        # ggplot2 theme
           )+
  theme_cleveland()                                      # Add dashed grids

I like the lollipop chart example.

Comments closed

Rotating Tiles Custom Visual

Published 2017-06-21 by Kevin Feasel

Devin Knight continues his Power BI custom visuals series:

In this module you will learn how to use the Rotating Tile Custom Visual.  The Rotating Tile gives you the ability to display multiple metrics on a single visual that rotates through each value you wish to display.  This allows you to save valuable space on your reports!

This feels like the type of thing that works on a dashboard but would get frustrating if you used it for time-sensitive data or data which required thoughtful analysis.

Comments closed

Great Circles In R

Published 2017-06-16 by Kevin Feasel

Yan Holtz shows how to draw great circles using an R package called geosphere:

This post explains how to draw connection lines between several localizations on a map, using R. The method proposed here relies on the use of the gcIntermediate function from the geosphere package. Instead of making straight lines, it offers to draw the shortest routes, using great circles. A special care is given for situations where cities are very far from each other and where the shortest connection thus passes behind the map.

Now we know how to make pretty-looking global route charts.

Comments closed

Data Visualization Basics

Published 2017-06-13 by Kevin Feasel

Kameerath Kareem describes a few basic visualizations and explains when you might use them:

Cumulative distribution graph is a commonly used chart type to express the performance metrics in percentile; it plots the percent of users who had performance metric greater or lesser than the threshold for the website.

The graph below shows the CDF graph for web page response time

From the CDF graph above, we see that at the 90th percentile, the web page response time of a website is 10.3 seconds. This means that 10% of the users in the time frame that the data was collected in had an overall web page load time of more than 10.3 seconds.

These are metrics as they relate to systems operations, but the general rules apply elsewhere as well.  Also, 10.3 seconds to load a webpage seems…slow.

Comments closed

Dot-Density Maps In R

Published 2017-06-08 by Kevin Feasel

Paul Campbell shows how to build a dot density map in R:

To get me started I invested in the expert guidance of data-visualiser-extraordinaire Nathan Yau, aka Flowing Data. Nathan has a whole host of tutorials on how to make really great visualisations in R (including a brand new course focused on mapping) and thankfully one of them deals with how to plot dot density using base R.

Now with a better understanding of the task at hand, I needed to find the required ethnicity data and shapefiles. I recently saw a video of Amelia McNamara’s great talk at the OpenVis Conference titled ‘How spatial polygons shape our world’. The .shp file really is a glorious thing and it seems that the spatial polygon makers are the unsung heros of the datavis world, so a big round of applause for all those guys is in order.

Anyway, I digress. Luckily for me, the good folks over at the London DataStore have a vast array of Shapefiles that go from Borough level all the way down to Super Output Area level. I’m going to use the Output Areas as the boundaries for the dots and the much broader Borough boundaries for ploting area labels and borders.

The ethnic group data itself was sourced from the Nomis website which has a handy 2011 Census table finder tool where you can easily download an Ethnic Group csv file for London output areas. Vamonos.

I’m going to give this a second reading; it’s a great example of how to go from functional to beautiful.  H/T David Smith

Comments closed

Creating Nicer Reports

Published 2017-06-08 by Kevin Feasel

Reid Havens has a few tips for making Power BI reports look nicer:

This is less of a single applied step as it is multiple formatting practices applied throughout the report. I’ve already hit on this subject a little bit in the two previous Power BI visual design practices in regards to using complimentary colors. The two key takeaways in this section are object formatting and color coordination.

Of all my best practices I’m showcasing here I’d say this one is the most subjective. However I think that maintaining complimentary colors goes a long ways to creating a professional looking report. I also have a strong dislike for the default title design for visualizations in Power BI. By default it is left aligned and a grey color (AGAIN…hard to read!). I center that sucker and color the background. An added benefit to coloring the title background is it actually forces me to make sure my objects are aligned, otherwise it is VERY noticeable now if they aren’t.

Definitely read the comments on this one, as some of these tips are subjective.

Comments closed