1100 search results for "SQL"

RevoScaleR package dependencies with graph visualization

March 15, 2017
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RevoScaleR package dependencies  with graph visualization

MRAN currently holds 7520 R Packages. We can see this with usage of following command (stipulating that you are using MRAN R version. ): library(tools) df_ap <- data.frame(available.packages()) head(df_ap) With importing package tools, we get many useful functions to find additional information on packages. Function package.dependencies() parses and check dependencies of a package in current … Continue...

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Three advanced online R courses in Statistics.com that are starting soon (and a discount code for them)

March 15, 2017
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Three advanced online R courses in Statistics.com that are starting soon (and a discount code for them)

Three courses in R are starting tomorrow and two next week on Statistics.com. You may use the code “1qR-blogger-17″ when registering (offering a reduced rate of $399 instead of,  around, $589 a course).  List of R courses: Mar 17:  SQL and R – Introduction to Database Queries, taught by Ben Baumer, (Director of the Statistical & Data Sciences Program...

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Benchmarking rxNeuralNet for OCR

March 13, 2017
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Benchmarking rxNeuralNet for OCR

The MicrosoftML package introduced with Microsoft R Server 9.0 added several new functions for high-performance machine learning, including rxNeuralNet. Tomaz Kastrun recently applied rxNeuralNet to the MNIST database of handwritten digits to compare its performance with two other machine learning packages, h2o and xgboost. The results are summarized in the chart below: In addition to having the best performance...

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Release mongolite 1.0

March 13, 2017
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Release mongolite 1.0

After 2.5 years of development, version 1.0 of the mongolite package has been released to CRAN. The package is now stable, well documented, and will soon be submitted for peer review to be onboarded in the rOpenSci suite. MongoDB in R and mongolite I started working on mongolite in September 2014, and it was first announced at the rOpenSci

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Madrid R User Group, A Brief History

March 10, 2017
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by Carlos Ortega (Editors note: A Spanish verison of the post follows the English text) In the first meeting we were 5, now we are consistently over 60. It was not difficult for us to start up the group of users of R of Madrid. Gregorio Serrano, Carlos Gil Bellosta, Pedro Concejero and I started

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RDocumentation: Scoring and Ranking

March 10, 2017
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RDocumentation: Scoring and Ranking

One of the core features of RDocumentation.org is its search functionality. From the start, we wanted to have a super simple search bar that finds what you are looking for, without a complex form asking for a package name, function name, versions or an...

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Madrid R User Group, A Brief History

March 9, 2017
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(Editors note: A Spanish verison of the post follows the English text) In the first meeting we were 5, now we are consistently over 60. It was not difficult for us to start up the group of users of R of Madrid. Gregorio Serrano, Carlos Gil Bellost...

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Madrid R User Group, A Brief History

March 9, 2017
By

(Editors note: A Spanish verison of the post follows the English text) In the first meeting we were 5, now we are consistently over 60. It was not difficult for us to start up the group of users of R of Madrid. Gregorio Serrano, Carlos Gil Bellosta, Pedro Concejero and I started...

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In case you missed it: February 2017 roundup

March 7, 2017
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In case you missed them, here are some articles from February of particular interest to R users. Public policy researchers use R to predict neighbourhoods in US cities subject to gentrification. The ggraph package provides a grammar-of-graphics framework for visualizing directed and undirected graphs. Facebook has open-sourced the "prophet" package they use for forecasting time series at scale. A...

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replyr: Get a Grip on Big Data in R

March 5, 2017
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replyr: Get a Grip on Big Data in R

replyr is an R package that contains extensions, adaptions, and work-arounds to make remote R dplyr data sources (including big data systems such as Spark) behave more like local data. This allows the analyst to more easily develop and debug procedures that simultaneously work on a variety of data services (in-memory data.frame, SQLite, PostgreSQL, and … Continue...

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