Visualisation Tools!

In this series, I attempted the same visualisation task in three different tools: PowerBI, Tableau, and R, to compare the methods and approach for visualising the same dataset and producing the same/similar results.

• PowerBI: Using Power BI to explore the NSW Penalty Notices data set
• Tableau: First Time Tableau User!
• R language: Using R markdown and ggplots for data visualisation

In all three examples, I started with data preparation tasks, then getting some initial insights through summarisation to explore min, max and other summary stats such as the median. Then I used stack bar charts to compare values in different categories. I explored different ways to control the style and formatting of such plots.

Reusability in PowerBI and Tableau is done through embedding, copying and pasting visualisation elements. In R, one can create functions to encapsulate the code for plots.

We use visualisations to communicate insights, in this series, applying a forecasting model is one way to show predicted increase of fines revenue overtime. Both PowerBI and Tableau have built-in features to produce time-series forecasts, in R the user must first fit a forecasting model, use it to produce a forecast data set then visualise the results.

Both PowerBI and Tableau provide online services to publish the developed reports and dashboards for sharing with others. Rmarkdown can be compiled into a document and shared with others offline, as a PDF or Word document, or online through services such as RPubs.

All of the above and more are explored in more detail in the three blog posts.

In summary, I’d pick PowerBI over any other tool to produce data narratives for communicating insights with impact, Tableau is an equally capable tool. So between these two tools, it often comes down to personal preference and availability of the tool within your organisation. Both tools are suitable self-serve BI tools for business users and data scientists.

For complex analysis of data, exploratory data analysis, predictive and forecast modeling, I would pick R language with ggplot, or python with libraries such as matplotlib and seaborn (which I did not cover in this series) as the tools of choice.

My personal preference remains PowerBI though! what’s yours?