Great painters can teach us a lot about how to compose a good palet for our plot. That is why we look here at a way to algorithmically derive a colour palette from any jpeg image. Application of derived palettes on ggplot is also showed.
We are ready for the third R-Lab, the monthly appointment where we co-work together on a real data science problem using R. This time the R-Lab is promoted by nothing but the Assessorato alla Partecipazione, Cittadinanza Attiva e Open Data of the Comune di Milano! We will access their municipality budget data, and use one day of joint work for designing and realizing an R Shiny app that allows citizens to visualize and explore the city revenues and spending info.
In R-Lab#2 we realized a Shinyapp that shows the earthquakes evolution over the Bove-Vettore fault. Here report, code, slides and app!
R has become an essential tool in oceanography and marine ecology. For instance, R is specifically used to read, process and represent in situ oceanographic data and to manage satellite data in order to produce high temporal and spatial resolution maps useful to synoptically explore and monitoring vast areas of the world oceans. In this post we briefly describe a practical use of R in conjunction with satellite data to identify marine bioregions of the Labrador Sea with different patters in the phytoplankton seasonal cycle.
Detect sentinel values, recode factor variables, replace missing values: a tutorial on various steps in data preparation using R.
Have you ever tried to set multiple legends for the same aesthetics of a ggplot graph? Here you will discover how to do it
In the R environment, different packages to draw maps are available. I lost the count by now; surely, sp and ggmap deserve consideration. Despite the great availability of R functions dedicated to this topic, in the past, when I needed to draw a very basic map of Italy with regions marked with different colours (namely a choropleth map), I had a bit of difficulties.
Some time ago, I was contacted from guys at Packt Publishing. Their just published the Building Interactive Graphs with ggplot2 and Shiny online course and they ask me my (humble) opinion.
In Part 1 of this series we moved the first steps into building our Sales Dashboard in R. In this Part 2 we explore additional ways to display sales related data.
If you haven't read Part 1, it is highly recommended that you do so first because we will build on what was covered there.