Share • LinkedIn • Facebook • Twitter 38 Dashboards have become somewhat fashionable in the reporting and analytics world, especially beyond the realms of Microsoft Excel. Having worked as an analytics professional in and for a few companies now, I have crossed paths with two very different tools that whip out analytics dashboards that are pleasing to the eyes, viz. - Tableau and R shiny. Tableau was founded in 2003 in Mountain View, California and offers five key products from its suite of data visualisation software. I was introduced to Tableau as a dashboard tool, however I find its core strength to be exploratory data analysis. With its ability to quickly drag-and-drop variables from a loaded database it is an absolute gun when it comes to viewing trends, discovering patterns and identifying outliers. Princess mononoke symphonic suite rar file.

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R is a free open-source programming language that first appeared in 1993 and has gained recent popularity by analytics professionals and data scientists alike due to the development of a wide array of packages being developed by programmers for data wrangling, visualisation, modelling techniques, etc. R Studio is the GUI (Graphical User Interface) of choice for many, and shiny is a popular dashboard package created by, who along with, Chief Scientist at R Studio, have been key contributors to the popularity of R. With that bit of background in mind, I'm going to dive right into a feature comparison between the two across ten factors. Please note that the comparison is purely from the perspective of an analyst who is meant to conduct a data analysis and create a cyclical dashboard to report findings. I am also only comparing Tableau to the free version of R shiny - the paid R Shiny server subscription does have features that overcome some of the drawbacks of the free version. (1) Speed - with respect to dashboard creation While Tableau works great with extracts of data, I've personally found it to be a bit laggy with a live connection. Often, changing the variables of a chart on your dashboard becomes a long affair, particularly if there are calculations involved. Moreover creating individual graphs, duplicating and changing variables, et al is tedious with your mouse hard at work - a coder's nightmare! R shiny is code-based so creating the actual dashboard does not impact speed. Replicating graphs and code is a cinch and a seasoned R dashboard creator can be much more productive in R shiny. Winner: R shiny (2) House-keeping - with respect to regular maintenance Being open-source and constantly evolving, R programs often require regular up-keep, especially when packages dependent on your analysis are updated or rendered outdated with an R update.

Winner: Tableau (3) Malleability - ability to change the dashboard This is quite important in my list as stakeholders often ask for modifications and updates to their dashboards. The ability to change and move around your graphs, figures and numbers is one functionality where Tableau underwhelms. For starters, resizing the canvas to fit more charts stretches all floating objects on your dashboard which then requires a lot of manual work to individually modify the dimensions of everything on the screen. Shiny, on the other hand, has a neat fluid page feature where you can split your dashboard into rows and columns within more rows and columns. There's nothing quite like Ctrl+C and Ctrl+V. Winner: R shiny (4) Permissions and access Access control is readily available in Tableau, with the ability to provide different kinds of access and permissions to subscribers. This facility is not available in R shiny without subscribing to its paid service. Winner: Tableau (5) Automation Because R was traditionally built as a statistical analysis and data wrangling tool, automation of data manipulation steps in your analysis is easy and quite akin to SAS. The big advantage for R is that even if you choose to decommission any dashboard, your data steps can be reused later. With Tableau, anything you do with calculated fields and parameters is strictly governed and will remain within the confines of the Tableau ecosystem. Often I find myself on Google looking at complicated walk-throughs to accomplish simple feats such as displaying a calculated field (e.g.