Jobs for R users – from all over the world (2017-05-08)

May 8, 2017
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Jobs for R users – from all over the world (2017-05-08)

To post your R job on the next post Just visit this link and post a new R job to the R community. You can post a job for free (and there are also “featured job” options available for extra exposure). Current R jobs Job seekers: please follow the links below to learn more and apply for your R job of interest: Featured Jobs Full-Time Data Analyst,...

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Machine Learning Pipelines for R

May 8, 2017
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Machine Learning Pipelines for R

Building machine learning and statistical models often requires pre- and post-transformation of the input and/or response variables, prior to training (or fitting) the models. For example, a model may require training on the logarithm of the response and input variables. As a consequence, fitting and then generating predictions from these models requires repeated application of … Continue...

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From Points to (Messy) Lines

May 8, 2017
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From Points to (Messy) Lines

A week or so ago, I came up with a new chart type – race concordance charts – for looking at a motor circuit race from the on-track perspective of a particular driver. Here are a couple of examples from the 2017 F1 Grand Prix: The gap is the time to the car on track

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Machine Learning. Regression Trees and Model Trees (Predicting Wine Quality)

We will develop a forecasting example using model trees and regression trees algorithms. The exercise was originally published in "Machine Learning in R" by Brett Lantz, PACKT publishing 2015 (open source community experience destilled).The example we will develop is about predicting...

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Installing Packages without Internet

May 8, 2017
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Graham Parsons At Mango we’re often giving R training in locations where a reliable WiFi connection is not always guaranteed, so if we need trainees to download packages from CRAN it can be a show-stopper. Here are a couple of … Continue reading →

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Graphical Presentation of Missing Data; VIM Package

May 8, 2017
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Graphical Presentation of Missing Data; VIM Package

Missing data is a problem that challenge data analysis methodologically and computationally in medical research. Patients of the clinical trials and cohort studies may drop out of the study, and therefore, generate missing data. The missing data could be at random when participants who drop out of study are not different from those who remained Related Post

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Installing packages without the internet

May 8, 2017
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Graham Parsons At Mango we’re often giving R training in locations where a reliable WiFi connection is not always guaranteed, so if we need trainees to download packages from CRAN it can be a show-stopper. Here are a couple of code snippets that are useful to download packages from CRAN onto a USB stick when you have a good connection...

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Trading Strategy: 52-Weeks High Effect in Stocks

May 8, 2017
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Trading Strategy: 52-Weeks High Effect in Stocks

By Milind Paradkar In today’s algorithmic trading having a trading edge is one of the most critical elements. It’s plain simple. If you don’t have an edge, don’t trade! Hence, as a quant, one is always on a look out for good trading ideas. One of the good resources for trading strategies that have been... The post Trading...

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ggplot2 style plotting in Python

May 8, 2017
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ggplot2 style plotting in Python

R is my language of choice for data science but a good data scientist should have some knowledge of all of the great tools available to them. Recently, I have been gleefully using Python for machine learning problems (specifically pandas and the wonderful scikit-learn). However, for all its greatness, I couldn’t help but feel it… Continue reading...

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R Quick Tip: parameter re-use within rmarkdown YAML

May 8, 2017
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Ever wondered how to make an rmarkdown title dynamic? Maybe, wanted to use a parameter in multiple locations? Maybe wanted to pass through a publication date? Advanced use of YAML headers can help! Normally, when we write rmarkdown, we might The post R Quick Tip: parameter re-use within rmarkdown YAML appeared first on Locke Data. Locke...

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GSoC 2017 : Integrating biodiversity data curation functionality

May 7, 2017
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GSoC 2017 : Integrating biodiversity data curation functionality

By Thiloshon Nagarajah URL of project idea page: https://github.com/rstats-gsoc/gsoc2017/wiki/Integrating-biodiversity-data-curation-functionality Introduction The importance of data in the biodiversity research has been repeatedly stressed in the recent times and various organizations have come together and followed each other to provide data for advancing biodiversity research. But, that is exactly where the main hiccup of biodiversity research lies. Since

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Turning kindle notes into a tidy data

May 7, 2017
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It is my dream to do everything with R. And we aRe almost there. We can write blogs in blogdown or bookdown, write reports in RMarkdown (thank you Yihui Xie!) create interactive webpages with Shiny (thank you Winston Chang). Control our lifx light...

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Shiny Application Layouts Exercises (Part-5)

May 7, 2017
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Shiny Application Layouts Exercises (Part-5)

Shiny Application Layouts-Vertical Layout In the fifth part of our series we will apply the kmeans() function to the iris dataset to create a shiny application. The difference is that now we will display its result vertically. This part can be useful for you in two ways. First of all, you can see different ways Related exercise sets:

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Know your data structures!

May 7, 2017
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Know your data structures!

Just a few days ago I stated the following on Twitter: Just reduced the runtime of an algorithm from 9 hours to 3 min. by using a different data structure… Know you data structures 🙂 #rstats — Verena Haunschmid (@ExpectAPatronum) May 1, 2017 Since my tweet has been liked and shared a lot, I thought … Continue...

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Plot the Vote: Making U.S. Senate & House Cartograms in R

May 7, 2017
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Plot the Vote: Making U.S. Senate & House Cartograms in R

Political machinations are a tad insane in the U.S. these days & I regularly hit up @ProPublica & @GovTrack sites (& sub to the GovTrack e-mail updates) as I try to be an informed citizen, especially since I’ve got a Senator and Representative who seem to be in the sway of 🍊. I’ve always appreciated... Continue reading...

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RInside 0.2.14

A new release 0.2.14 of RInside is now on CRAN and in Debian. RInside provides a set of convenience classes which facilitate embedding of R inside of C++ applications and programs, using the classes and functions provided by Rcpp. It has been nearly two years since the last release, and a number of nice extensions,...

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dplyr in Context

May 6, 2017
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dplyr in Context

Introduction Beginning R users often come to the false impression that the popular packages dplyr and tidyr are both all of R and sui generis inventions (in that they might be unprecedented and there might no other reasonable way to get the same effects in R). These packages and their conventions are high-value, but they … Continue...

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Evolution of ice hockey players’ height: IIHF world championships 2001-2016

May 6, 2017
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Evolution of ice hockey players’ height: IIHF world championships 2001-2016

The 2017 Ice Hockey World Championship has started. Thus I want to share a small research on the height of ice hockey players that I did almost a year ago and published in Russian.

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R Weekly Bulletin Vol – VII

May 6, 2017
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R Weekly Bulletin Vol – VII

This week’s R bulletin will cover topics like how to create a sequence of dates, how to add a number to a date and time variable and converting a date in American format to a standard format. Hope you like this R weekly bulletin. Enjoy reading! Shortcut Keys 1. Run current line/selection – Ctrl+Enter 2.... The post R...

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MIT Step by Step Instructions for Creating Your Own R Package.

We will mention the steps to create your own R package using RStudio and we will provide the link to download the complete MIT guide:"Instructions for Creating Your Own R Package", In Song kim, Phil Martin, Nina McMurry. February 23, 2016 .1. Start by ...

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Shiny Application Layouts Exercises (Part-4)

May 6, 2017
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Shiny Application Layouts Exercises (Part-4)

Shiny Application Layouts – Navbar Page Features In the fourth part of our series we will see some of the most important features that a Navbar page provides in order to enhance the appearance of our shiny application. We will use the “Cars” app which we built in Shiny Application Layouts-Navbar Page. This part can Related exercise sets:

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x13binary 1.1.39-1

The US Census Bureau released a new build 1.1.39 of their X-13ARIMA-SEATS program, released as binary and source. So Christoph and went to work and updated our x13binary package on CRAN. The x13binary package takes the pain out of installing X-13ARIM...

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Scrapeover Friday — a.k.a. Another R Scraping Makeover

May 5, 2017
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I caught a glimpse of a tweet by @dataandme on Friday: Using R & rvest to explore Malaysian property mkt: "Web Scraping: The Sequel, Propwall.my" https://t.co/daZOOJJfPN #rstats #rvest pic.twitter.com/u6QMhm4M3e— Mara Averick (@dataandme) May 5, 2017 Mara is — without a doubt — the best data science promoter in the Twitterverse. She seems to have her... Continue reading...

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A note on AIC scores for quasi-families in #rstats

May 5, 2017
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A note on AIC scores for quasi-families in #rstats

A summary note on recent set of #rstats discoveries in estimating AIC scores to better understand a quasipoisson family in GLMS relative to treating data as poisson. Conceptual GLM workflow rules/guidelines Data are best untransformed. Fit better model to data. Select your data structure to match purpose with statistical model. Use logic and understanding of data not

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R Questions Tag Pairs on Stackoverflow

May 5, 2017
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Months ago, I passed by R Questions from Stack Overflow published on Kaggle. I was interested in tag pairs in particular, i.e. which tags appear together in R questions, so I worked on this simple kernel.

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10 reasons to attend EARL

May 5, 2017
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10 reasons to attend EARL

The reasons someone might attend a conferences differ from person to person so we crowdsourced the top 10 reasons you should attend EARL San Francisco. 1. Learn something new. This is a pretty obvious one and we’ve got an excellent … Continue reading →

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Forecasting: ARIMAX Model Exercises (Part-5)

May 5, 2017
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Forecasting: ARIMAX Model Exercises (Part-5)

The standard ARIMA (autoregressive integrated moving average) model allows to make forecasts based only on the past values of the forecast variable. The model assumes that future values of a variable linearly depend on its past values, as well as on the values of past (stochastic) shocks. The ARIMAX model is an extended version of Related exercise sets:

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Modelling individual party vote from the 2014 New Zealand election study

Modelling individual party vote from the 2014 New Zealand election study

Someone asked on Twitter about the characteristics of New Zealand First voters. While some crude conclusions can be drawn from examining votes by location cast and then comparing that with census data, we really need individual level data to answer the...

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10 reasons to attend EARL in San Francisco

May 5, 2017
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10 reasons to attend EARL in San Francisco

...

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