background preloader

R and Data Mining

R and Data Mining

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.

MSISS ST4003 : Data Mining - Louis Aslett MSISS ST4003 : Data Mining 2010-11 < Back to homepage 2009-2010 ST4003 Data Mining lab material This is the labs page for the fourth year undergraduate course in data mining for MSISS and mathematics students, lectured by Dr Myra O'Reagan. Useful Links Introduction to R R reference card RSeek, Google powered search engine of R resources Labs Lab 1 - Examining Data Lab 2 - A Basic Tree Classifier Lab 3 - More Trees Lab 4 - More Programming Concepts and Model Evaluation Lab 5 - Introduction to Neural Networks Lab 6 - Random Forests Lab 7 - Introduction to Support Vector Machines Data Sets Telecom Customer Churn Data (small version) Titanic Survivor Data Cheese Taste Data ESL SVM simulated data

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. 5 of the Best Free and Open Source Data Mining Software The process of extracting patterns from data is called data mining. It is recognized as an essential tool by modern business since it is able to convert data into business intelligence thus giving an informational edge. At present, it is widely used in profiling practices, like surveillance, marketing, scientific discovery, and fraud detection. There are four kinds of tasks that are normally involve in Data mining: * Classification - the task of generalizing familiar structure to employ to new data* Clustering - the task of finding groups and structures in the data that are in some way or another the same, without using noted structures in the data.* Association rule learning - Looks for relationships between variables.* Regression - Aims to find a function that models the data with the slightest error. For those of you who are looking for some data mining tools, here are five of the best open-source data mining software that you could get for free: Orange RapidMiner Weka JHepWork

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?

Apache Mahout: Scalable machine learning and data mining 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

Interactive graphics for data analysis: principles and examples - Martin Theus, Simon Urbanek Visualizing Data: Challenges to Presentation of Quality Graphics—and Solutions Naomi Robbins, a consultant and seminar leader who specializes in the graphical display of data, offers training on the effective presentation of data. She also reviews documents and presentations for clients. She is the author of Creating More Effective Graphs. Three common challenges statisticians and others face when preparing data for presentation include poor options and defaults in many software packages used for creating graphs, managers and colleagues who are socialized to expect figures that attract attention, and poor instructions from conference organizers. Poor Options and Defaults in Many Software Packages Many software programs for drawing charts and graphs offer defaults and options that are full of fancy embellishments that detract from the clear and accurate communication of data. Figure 1 shows the results of an Internet/mail survey of ASA members with six to 15 years of membership. There are numerous other problems with this figure. Summary