Lenguaje R en Linux
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juandavidvelasquez.2011.pdf (objeto application/pdf) - 0 views
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Lenguaje R en Linux | - 0 views
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El lenguaje R es un sistema para análisis estadístico y gráficos creado por Ross Ihaka y Robert Gentleman
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pero, en Debian, no existen esos archivos. Sin embargo, lanzando synaptic, lo ubiqué con r-cran (por lo del proyecto) e instalando uno de sus paquetes específicos (en este caso fue r-cran-tseries), descargó también el r-base-core como “dependencia”; además de otros paquetes adicionales.
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Para copiar los valores de una variable se usa la función c(). Entonces, para x: x <- c(1,2,3) [Enter] Para y: y <- c(5,7,9) [Enter] y el resultado se visualiza con: lm(y ~ x) [Enter]
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Si se instala el paquete r-base-html se podrá acceder a una documentación más extensa con sólo invocar help.start().
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los usuarios de Windows también pueden acceder al lenguaje R y el comportamiento del interprete, según leí en la bibliografía al respecto, se realiza de una manera similar al de los sistemas tipo UNIX.
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'R' language bringing statistical analytics to the masses (Q&A) | Software, Interrupted... - 0 views
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Predictive analytics has a proven track record in the business intelligence field. Wal-Mart, for one, has used predictive analytics to better understand the habits and tendencies of their customers. Predictive analytics was a driving force in influencing their competitive pricing model that gave them a competitive advantage over their market. Beyond retail, predictive analytics models are used extensively in financial services, pharmaceuticals, life sciences, and a host of other verticals
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What is R? (Oracle R Enterprise) - 0 views
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R has been receiving a lot of attention recently, although it’s been around for over 15 years. R is an open-source language and environment for statistical computing and data visualization, supporting data manipulation and transformations, as well as sophisticated graphical displays
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Corporate data analysts and statisticians often know R and use it in their daily work, either writing their own R functionality,
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It’s a powerful, extensible environment, and as noted above, it has a wide range of statistics and data visualization capabilities. It’s easy to install and use, and it’s free
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n contrast, statisticians and data analysts typically don’t know SQL and are not familiar with database tasks. R provides statisticians and data analysts access a wide range of analytical capabilities in a natural statistical language
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this chews up memory faster.
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Qué es R y su aplicación al mundo empresarial - 0 views
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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
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Podría medir cosas como cambios en el sentimiento del consumidor hacia su marca mientras dura la campaña
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la capacidad para crear rápidamente exhibiciones visuales dinámicas de los datos, se ha tornado valiosa en el mundo empresarial a medida que las empresas comienzan a afrontar el desafío de los Grandes datos.
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la fortaleza de R en un contexto empresarial es su capacidad para modificarse y adaptarse a sus necesidades
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en los 5.000 paquetes de R, es probable que encuentre cualquier método que necesite en la comunidad de usuarios de R.
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¿Qué es R? R es un lenguaje de programación y entorno de software de fuente abierta para computación y gráficos estadísticos.
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Actualmente R es utilizado por más del 47% de los profesionales de extracción de datos, aumentando constantemente el número de personas que lo utilizan debido principalmente a su capacidad de crear representaciones visuales de forma rápida de una gran cantidad de datos. Además otra de sus ventajas es que R es una fuente abierta, lo que quiere decir que si no encuentra la grafica que necesita en los paquetes de datos de R es posible que la encuentre en la comunidad de usuarios de R.
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R You Ready for R? (1/2) - 0 views
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Data Analysts Captivated by R’s Power
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R is also the name of a popular programming language used by a growing number of data analysts inside corporations and academia.
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“It allows statisticians to do very intricate and complicated analyses without knowing the blood and guts of computing systems.”
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. I.B.M., Hewlett-Packard and Dell make billions of dollars a year selling servers that run the open-source Linux operating system
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can help illuminate data trends more clearly than is possible by entering information into rows and columns
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is that statisticians, engineers and scientists can improve the software’s code or write variations for specific tasks
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advanced algorithms, colored and textured graphs and mining techniques to dig deeper into databases.
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R immediately gained a following with people who saw the possibilities in customizing the free software.
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“The diversity and excitement around what all of these people are doing is great,” Mr. Chambers said.
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Big Data Analytics with Revolution R Enterprise for IBM PureData System for Analytics -... - 0 views
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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
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can deliver 10-100x performance improvements at a fraction of the cost compared to traditional analytics vendors.
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R You Ready for R?(2/2) - 0 views
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R You Ready for R?
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the software is open source, meaning people can pick it up for free and make their own changes to the code
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“It’s very hard to displace the industry standard in those types of cases,” said Max Kuhn, associate director of nonclinical statistics at Pfizer.
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Revolution offers ways to speed up the software on certain applications and to run it on large computers
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R - Nada es Gratis - 0 views
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Según la perspectiva de Jesús Fernández Villa-Verde, compara con otros programas como el Excel, citamos al autor: Todas estas razones me llevan a concluir que R es la mejor manera de efectuar análisis estadístico. Mientras que emplear Excel puede funcionar en unos cuantos casos. Eso entre otra cosas que nos comenta es que el lenguaje R tiene una mayor facilidad al utilizarlo y es mas completo, ya que tiene una gran cantidad de distinto paquetes, los cuales ayudan a la mejora del programa. También nos habla de las relaciones que tiene con los programas como C++ o matlab, lo que ayuda al fácil manejo del programa.
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R aplicado a la psicología - 0 views
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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
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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.
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R integrated throughout the enterprise analytics stack - 0 views
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and as new graduates trained in R have migrated to the workplace the demand for R in corporations has become more and more intense.
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Let's take a look at some of the applications within the analytics stack which now provide integration with R.
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Oracle announced last year a forthcoming connection between R and Oracle
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As you can see, for organizations who need to create advanced analytics applications, R is integrated throughout the analytics stack: for data access, for presentation of results, and of course for the statistical analysis process itself. This degree of integration by so many companies is indicative of the level of demand for R throughout the enterprise. As the leading provider of commercial software and support for R, Revolution Analytics supports R users througout the organization,
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Aplicaciones basadas en R - 0 views
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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
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100 Mensajes más leídos de R - 0 views
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Top 100 R posts of 2012
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Reproducible R blogging – make it to blog from R to WordPress and blogger (via knitr, RStudio, etc.)
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Introduction to R Programming Online Course: (Day 1 of 16; Part 1 of 8) - YouTube - 0 views
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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
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What is Open Source R - 0 views
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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.
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Draw on the talents of data scientists worldwide As a thriving open-source project, R is supported by a community of more than 2 million users and thousands of developers worldwide. Whether you’re using R to optimize portfolios, analyze genomic sequences, or to predict component failure times, experts in every domain have made resources, applications and code available for free, online.
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