Starting out with esquisse

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.

Using ggplot to explore #AFLW 2019 performances

I have been looking at the #AFLW 2019 data. I took the opportunity to include some CRAN packages I have not used before.

The data (two csv files) and my code are in a GitHub repository (link). My code is very basic and reflects my own thinking out loud as a I learn more about R.

In the past, I have tended to bookmark R suggestions and yet never manage to return to them as the list gets longer. My new practice is to create an R file to explore packages or code that strike me as interesting.

I used patchwork (link) and ggforce (link) in addition to ggrepel (link) to look at the data in the context of ggplot2 (link).

I was particularly interested in how patchwork helped me combine a range of images.

These work really well as PDF A4 pages and I thought they would be helpful summaries to stimulate conversation.

The three plot example above is: