Statistics with R Warning Here are the notes I took while discovering and using the statistical environment R. However, I do not claim any competence in the domains I tackle: I hope you will find those notes useful, but keep you eyes open -- errors and bad advice are still lurking in those pages... Should you want it, I have prepared a quick-and-dirty PDF version of this document. The old, French version is still available, in HTML or as a single file. You may also want all the code in this document. 1. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Summer 2010 — R: ggplot2 Intro Contents Intro When it comes to producing graphics in R, there are basically three options for your average user. base graphics I've written up a pretty comprehensive description for use of base graphics here, and don't intend to extend beyond that. Both and make creating plots of multivariate data easier. The website for ggplot2 is here: Basics is meant to be an implementation of the Grammar of Graphics, hence gg-plot. Plots convey information through various aspects of their aesthetics. x position y position size of elements shape of elements color of elements The elements in a plot are geometric shapes, like points lines line segments bars text Some of these geometries have their own particular aesthetics. points point shape point size lines line type line weight bars y minimum y maximum fill color outline color text label value The values represented in the plot are the product of various statistics. Layer by Layer Displaying Statistics
Visualizing Tables with plot.table Home > R > Visualizing Tables with plot.table plot.table function in the Systematic Investor Toolbox is a flexible table drawing routine. plot.table has a simple interface and takes following parameters: plot.matrix – matrix with data you want to plotsmain – text to draw in (top, left) cell; default value is blank stringhighlight – Either TRUE/FALSE to indicate if you want to color each cell based on its numeric value Or a matrix with colors for each cellcolorbar – TRUE/FALSE flag to indicate if you want to draw colorbar Here is a few examples how you can use plot.table function to create summary reports. First, let’s load Systematic Investor Toolbox: To create basic plot.table: To create plot.table with colorbar: Next, I want to show a more practical example of plot.table function. I will show more examples of plot.table in the future posts. To view the complete source code for this example, please have a look at the plot.table.test() function in plot.table.r at github. Like this:
Gapminder: Unveiling the beauty of statistics for a fact based world view. Learning R R library(stringr) [1] "1 Introduction" [3] "Climate projections of the Intergovernmental Panel on Climate Change (IPCC) forecast a general increase of seasonal temperatures in the present century across the temperate zone, aggravated by decreasing amounts of summer rainfall in certain regions at lower latitudes (Christensen et al. 2007). [5] "In this study, we aim to (1) identify the limiting macroclimatic factors and to (2) predict the future boundaries of beech (Fagus sylvatica L.) and sessile oak (Quercus petraea (Mattuschka) Liebl.) forests in a region highly vulnerable to climatic extremes. [7] "Beech and sessile oak forests of Hungary are to a large extent “trailing edge” populations (Hampe and Petit 2005), which should be preferably modelled using specific modelling strategies (Thuiller et al. 2008). extr1 <- unlist(str_extract_all(txt, pattern = "\\(.*? extr2 <- extr1[grep("[0-9]{4}", extr1)] (str_extract(extr2, "[A-Z].*[0-9]")) [1] "Christensen et al. 2007" [2] "Fischlin et al. 2007"
Curriculum Vitae (mis à jour le: 25/01/2011) Pierre Lafaye de Micheaux Né le 27 mars 1973 à Paris. Marié avec deux enfants. Nationalités : canadienne, française, suisse. Séjours (courts) dans d’autres laboratoires de recherche universitaire Conférences invitées1 Sydney, Australie (2014). Mini-cours et tutoriels Invitation de chercheurs Bourses et subventions Distinction académique Fonctions électives Responsabilités administratives Université de Montréal, Département de Mathématiques et de Statistique Université Pierre Mendès France, Département STID de l’IUT2 Arbitrage d’articles de revues Bernoulli, Canadian Journal of Statistics, Cognitive Computation, Computational Statistics, Computational Statistics and Data Analysis, Frontiers Neuroscience, Journal of Multivariate Analysis, Journal of Statistical Planning and Inference, Journal of Statistical Software, Mathematical Reviews, Medical Image Computing and Computer Assisted Intervention (MICCAI) Proceedings, Statistical Methodology. Comités éditoriaux Thèmes de recherche privilégiés 2014?
R Reference Card Polygon Overlay Analysis Download data and R Code for this example Project Requirement: Polygon Overlay operations determine the spatial coincidence (if any) of two polygon data layers, or between polygon and point layer, usually creating a new data layer in the process. Three useful (and widely used) polygon overlay operations are: Intersection (logical AND): The common or shared area between two overlapping polygons. Union (logical OR): The combined areas of two possibly overlapping polygons. Point-in-Polygon (logical AND): Between a point and polygon layer, the subset of points located within the polygon boundary. Here, we demonstrate overlay operations using a collection of point and polygon species range data sets collected in South America, and methods from the PBSmapping package. 1) What is the area of each Species Range? Input Data / Format: Point File: Mammalian Species Sightings (ESRI Point Shape File) from NatureServe data set. Base Map: DIVA-GIS Global Administrative Boundaries. Workflow: Discussion:
RStudio Server Amazon Machine Image (AMI) - Louis Aslett Current AMI Quick Reference (27nd Jun 2015)Amazon instance type reference Click to launch through AWS web interface: What’s new recently? Easy Dropbox setup to make syncing files on/off server easy, including selective folder sync. Preinstalled RStudioAMI R package for server control. HVM AMIs for full current generation instance support. < Back to homepage Amazon’s EC2 platform provides a convenient environment for rapidly procuring computational resources in the cloud. To get started with the Amazon cloud, you must first signup for an AWS account if you don’t already have one. Click here for a simple video guide to using the AMIs listed here, or for more detailed information read on. What is this? If you want to run a server in the Amazon cloud, you have to select what system you are going to bootup. In particular, many common tools and dependencies are built-in. Why an RStudio AMI? AMI Release History * N/A since these data centres were not yet open when the images were built. Usage
developers:projects:gsoc2012:ropensci Summary: Dynamic access and visualization of scientific data repositories Description: rOpenSci is a collaborative effort to develop R-based tools for facilitating Open Science. Projects in rOpenSci fall into two categories: those for working with the scientific literature, and those for working directly with the databases. Visit the active development hub of each project on github, where you can see and download source-code, see updates, and follow or join the developer discussions of issues. Most of the packages work through an API provided by the resource (database, paper archive) to access data and bring it within reach of R’s powerful manipulation. See a complete list of our R packages currently in development. The student could choose to work on a package for a particular data repository of interest, or develop tools for visualization and exploration that could function across the existing packages.
Interactive graphics for data analysis: principles and examples - Martin Theus, Simon Urbanek