Each week an O’Reilly newsletter arrives in my email inbox. I am not sure when I signed up but I am delighted I did.
This week the newsletter brought an article by Avinash Kaushik titled Responses to Negative Data (link). In it, Avinash discusses the reception of negative news and four data leadership archetypes:
I found the Curious leadership description particularly interesting. Avinash suggests that Curious Ones have two critical attributes: they demonstrate open mindedness in the face of negative data; and they look forward.
I am particularly intrigued by the feedforward aspect of curiosity in changing times. Avinash contextualised this in his opening remark: “A decade ago, data people delivered a lot less bad news because so little could be measured with any degree of confidence”.
His next sentence encouraged me to think in pedagogical and practice terms how we might support those who are learning to analyse data carefully and thoughtfully: “In 2019, we can measure the crap out of so much. Even with the limitations of tools, government regulations, and the astonishing fragmentation of everything (attention, devices, consumption sources, identities and more)”.
I am starting to imagine all sort of learning scenarios where the ‘leader’ can receive news and respond stereotypically … and the conversations we might have to share news effectively.
Each day, Mara Averick (@dataandme) (link) shares some excellent R advice and links on Twitter. For a while, I bookmarked all her suggestions but there were so many of them that I did not manage to return to them. Even allocating them to bookmark folders did not improve my follow up rate.
For the past month or so, I have been creating R scripts in RStudio each day to try out the coding of some of her suggestions. This was the case today with her link to Joachim Gassen’s ExPanDaR 0.4.0 package (link).
I have a GitHub repository for this exploration to share my csv files and code (link). Like most of my efforts it is just a start … and an attempt to share sport examples.
However, I am really interested in the package’s potential for me to have a first look at data, and if appropriate to work through it with coaches to develop their data dashboard … if they think it can be of help to them.
I used the ExPanDaR’s functions to create: a descriptive table (of all variables); a scatter plot; a quantile_trend_graph (distributions of one variable over time); and a list of the 5 most extreme observations in the data frame. I particularly liked the Shiny opportunities I had to plot variables. I am still trying to work out the tooltip functionality for my descriptive table.
My visualisation examples are:
I am looking forward to exploring these functions and other visualisation functions available in ExPanDaR.
I have discovered the esquisse package in R (link). It is described as “a ‘shiny’ gadget to create ‘ggplot2’ charts interactively with drag-and-drop to map your variables. You can quickly visualize your data accordingly to their type, export to ‘PNG’ or ‘PowerPoint’, and retrieve the code to reproduce the chart”.
Information about the package, authors and maintainers can be found on CRAN (link).
I have compiled a brief GitHub repository to share some resources for this introduction. I include the data.frame I used (link).
My first attempt to use esquisse functionality:
I found this package one of the most intuitive CRAN packages I have used. I do have some experience with ggplot2 and understand that I will need to return to it to provide further details. The Shiny format of esquisse really appeals to me. I appreciated the ease of drag and drop that enabled me to modify my visualisations without the need to code.
It will become my first look tool for data visualisation.