T D James tdjames1

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

New R package to fetch climate data from FetchClimate webservice

Francisco Rodriguez-Sanchez
22 April 2016

Trying new RFc package to get climate data into R from FetchClimate service.

See all the available climate variables here.

	#' When plotting multiple data series that share a common x axis but different y axes,
	#' we can just plot each graph separately. This suffers from the drawback that the shared axis will typically
	#' not align across graphs due to different plot margins.
	#' One easy solution is to reshape2::melt() the data and use ggplot2's facet_grid() mapping. However, there is
	#' no way to label individual y axes.
	#' facet_grid() and facet_wrap() were designed to plot small multiples, where both x- and y-axis ranges are
	#' shared acros all plots in the facetting. While the facet_ calls allow us to use different scales with
	#' the \code{scales = "free"} argument, they should not be used this way.
	#' A more robust approach is to the grid package grid.draw(), rbind() and ggplotGrob() to create a grid of
	#' individual plots where the plot axes are properly aligned within the grid.

	#Load library mass and set seed
	library(MASS)
	set.seed(100)

	# We are going to use 3 random variables
	m <- 3

	# Number of samples to be drawn
	n <- 2000