For personal reasons I am trying to track the number of NCOV-19 confirmed cases in Italy as well as the number of deaths (since I live in Italy, it is not difficult to guess the personal reason…). I am thus regularly monitoring news from the italian official sources like “Regione Lombardia” and “Protezione Civile”.
PaletteR, the package that allows you to create an optimized palette from an image, has been staying around for nearly two years, and #rstats users have made a lot of great stuff with it. I had therefore took the time to collect what I have found around the web, just to celebrate this beauty. You can find them below in a slideshow.
One of the most frustrating issues I face in my professional life is the plentitude of ineffective reports generated within my company. Wherever I look around me is plenty of junk charts, like barplot showing useless 3D effects or ambiguous and crowded pie charts. I do understand the root causes of this desperate state of the art: people have always less time to dedicate to reports crafting, and even less to dedicate to their plot. In the crazy and speedy-going working life, my colleagues have no time and for learning data visualization principles. Even so, this remains quite a big problem since a lot of time and money-wasting consequences come from poorly crafted reports and plots:
I live in Italy, and more precisely in Milan, a city known for fashion and design events. During a lunch break I was visiting the Pinacoteca di Brera, a 200 centuries old museum. This museum is full of incredible paintings from the Renaissance period. During my visit I was particularly impressed from one of them: “La Vergine con il Bambino, angeli e Santi”, by Piero della Francesca.
I have recently completed a great reading: Edward Tufte’s The visual display of quantitative information. In the dataviz realm, this is some kind of fundamental book. This book was some kind of structural break in the history of data visualization. In ’70s and ’80s graphics were considered a way to entertain less educated readers. Their ability to make available new insights and communicate them effectively was underestimated.
this short post is exactly what it seems: a showcase of all ggplot2 themes available within the ggplot2 package. I was doing such a list for myself ( you know that feeling …“how would it look like with this theme? let’s try this one…”) and at the end I thought it could have be useful for my readers. At least this post will save you the time of trying all differents themes just to have a sense of how they look like.