foe_2013

Lenguaje R en Linux

Incorporación de R en el mundo empresarial

El análisis de los grandes datos en forma de datos de medios sociales es un ejemplo

Si bien otro software de análisis puede realizar funciones similares, R le brinda un medio para explorar los datos de formas diferentes, rápidamente, en un entorno, luego compartirlo con otros de forma gráfic

used to set ad prices, find new drugs more quickly or fine-tune financial models

R is also the name of a popular programming language used by a growing number of data analysts inside corporations and academia.

Data sets that were previously unmanageable are now opening up worlds of possibility due to flexible and powerful platforms to manage the scale and complexity associated with crunching massive data

can deliver 10-100x performance improvements at a fraction of the cost compared to traditional analytics vendors.

Advanced R computations

R You Ready for R?

R helps people deal with large volumes of data in a wide variety of industries

the software is open source, meaning people can pick it up for free and make their own changes to the code

What makes R particularly powerful is that statisticians and statistically minded people around the world have contributed packages to the R Group and maintain a very active news group offering suggestions and help. The growing collection of packages and the ease with which they interact with each other and the core R is perhaps the greatest advantage of R

Using R in 12 simple steps for personality research (These steps are not meant to limit what can be done with R, but merely to describe how to do the analysis for the most basic of research projects and to give a first experience with R). Install R on your computer or go to a machine that has it. Download the psych package as well as other recommended packages from CRAN using the install.packages function, or using the package installer in the GUI. To get packages recommended for a particular research field, use the ctv package to install a particular task view. Note, these first two steps need to be done only once! Activate the psych package or other desired packages using e.g., library(psych). This needs to be done every time you start R. Or, it is possible to modify the startup parameters for R so that certain libraries are loaded automatically. Enter your data using a text editor and save as a text file (perhaps comma delimited if using a spreadsheet program such as Excel or OpenOffice) Read the data file or copy and paste from the clipboard (using, e.g., read.clipboard). Find basic descriptive statistics (e.g., means, standard deviations, minimum and maxima) using describe. Prepare a simple descriptive graph (e.g, a box plot) of your variables. Find the correlation matrix to give an overview of relationships (if the number is not too great, a scatter plot matrix or SPLOM plot is very useful, this can be done with pairs.panels. If you have an experimental variable, do the appropriate multiple regression using stan- dardized or at least zero centered scores. If you want to do a factor analysis or principal components analysis, use the factanal or fa and principal function To score items and create a scale and find various reliability estimates, use score.items and perhaps omega. Graph the results.

R-based application to predict pipeline cracking for an international oil and gas company R-based application to optimize product recipe for a multinational food and beverage conglomerate Implementing a hedge fund's backtesting system in R  

R-statistics blog

around 245 active R bloggers 

We both had an interest in statistical computing and saw a common need for a better software environment in our Macintosh teaching laboratory. We saw no suitable commercial environment and we began to experiment to see what might be involved in developing one ourselves.

Publicado el 10/10/2012 Introduction to R Programming is an open enrollment, live, interactive online course offered by the non-profit Georgia R School (http://georgia-r-school.org). All of the videos associated with this

Thanks for theþff resource!

Create beautiful and unique data visualizations Representing complex data with charts and graphs is an essential part of the data analysis process, and R goes far beyond the traditional bar chart and line plot. Heavily influenced by thought leaders in data visualization like Bill Cleveland and Edward Tufte, R makes it easy to draw meaning from multidimensional data with multi-panel charts, 3-D surfaces and more. The custom charting capabilities of R are featured in many of the stunning infographics seen in the New York Times, The Economist, and the FlowingData blog.