## Efficient Processing With Apply() Exercises

September 8, 2016
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The apply() function is an alternative to writing loops, via applying a function to columns, rows, or individual values of an array or matrix. The structure of the apply() function is: apply(X, MARGIN, FUN, ...) The matrix variable used for the exercises is: dataset1 <- cbind(observationA = 16:8, observationB = c(20:19, 6:12)) Answers to the

## Make Easy Heatmaps to Visualize your Turnaround Times

The Problem In two previous posts, I discussed visualizing your turnaround times (TATs). These posts are here and here. One other nice way to visualize your TAT is by means of a heatmap. In particular, we would like to look at the TAT for every hour of the week in a single figure. This manner … Continue...

## The start of satRdays

September 8, 2016
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This post was originally shared on the R Consortium blog.Almost 200 people from 19 countries registered for the first satRday conference which was held last Saturday, September 3rd, in Budapest. The final count showed that nearly 170 R users spent 12 hours at the conference venue attending workshops, regular and lighting talks, social events and...

## Application possibilities of data science in laser technology

September 8, 2016
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Speaker of the Kenntnis-Tage 2016: Julia Gleixner | TRUMPF Laser GmbH The solid-state lasers of TRUMPF Laser are used for machining materials in various areas – from welding car bodies to cutting stents to drilling minute holes for the production of solar cells. These are just some of the diverse application possibilities. In any …

## Effect-Size Calculation for Meta-Analysis in R #rstats

September 8, 2016
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When conducting meta-analysis, you most likely have to calculate or convert effects sizes into an effect size with common measure. There are various tools to do this – one easy to use tool is the Practical Meta-Analysis Effect Size Calculator from David B. Wilson. This online-tool is now implemented as an R-package: esc: Effect Size

## New in Magick 0.3

September 8, 2016
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A new version of the ropensci magick package has been released to CRAN. Magick is a package for Advanced Image-Processing in R. It wraps the ImageMagick STL which is perhaps the most comprehensive open-source image processing library available today. Our original announcement has more details. New features This new version now includes a beautiful vignette which gives an...

## What Every R Package Must (REALLY) Contain? An Example on the eRum2016 Package

September 7, 2016
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The R package development is a complex process of creating (mostly) a useful software, that will (probably) be used by other users. This means the provided tool should be resistant, immune, well tested and properly documented. Developers from many dif...

## HW Checker: R, AppScripts, & Google Forms

September 7, 2016
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googleformr For the past few months I’ve been toying with the idea of using googleformr to empower data science-oriented instructors to use data science in their workflow. In this 2-part series of posts, I’ll show how to AppScripts and R to turn...

## rtide: a R package for predicting tide heights (US locations only currently)

September 6, 2016
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Joe Thorley at Poisson Consulting has released a new R package, rtide, (on which I am listed as a co-author) that provides the ability to predict future (and past) tide heights for 637 different tide stations in the United States and associated territories. The underlying data, consisting of tide harmonic constituents, are collected and released

## Annual Mean Temperature Trends – 12 Airports

September 6, 2016
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This animated gif  shows changes in annual mean temperature at 12 East Coast USA airports that had continuous daily data for the 1950 – 2015 period. The data was retrieved from Weather Underground using the R weatherData package . 11 … Continue reading →

## Classification in Spark 2.0: “Input validation failed” and other wondrous tales

September 6, 2016
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Spark 2.0 has been released since last July but, despite the numerous improvements and new features, several annoyances still remain and can cause headaches, especially in the Spark machine learning APIs. Today we’ll have a look at some of them, inspired by a recent answer of mine in a Stack Overflow question (the question was about Spark 1.6 but,...

## Tidying computational biology models with biobroom: a case study in tidy analysis

September 6, 2016
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Previously in this series: Cleaning and visualizing genomic data: a case study in tidy analysis Modeling gene expression with broom: a case study in tidy analysis In previous posts, I’ve examined the benefits of the tidy data framework in cleaning, visualizing, and modeling in exploratory data analysis on a molecular biology experiment. We’re using Brauer et al...

## Examining Data Exercises

September 6, 2016
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One of the first steps of data analysis is the descriptive analysis; this helps to understand how the data is distributed and provides important information for further steps. This set of exercises will include functions useful for one variable descriptive analysis, including graphs. Before proceeding, it might be helpful to look over the help pages

## Sharing thoughts on satRdays R Conference, Budapest 2016 #satRdays

September 6, 2016
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First satRdays in Budapest September 03, 2016 event is completed. This one day, community driven event with regional for very affordable prices, good for networking, getting latest from R community event is over. And it was a blast! Great time, nice atmosphere, lots of interesting people and where there is a good energy, there is … Continue...

## Chaotic Galaxies

September 6, 2016
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$Chaotic Galaxies$

Tell me, which side of the earth does this nose come from? Ha! (ALF) Reading about strange attractors I came across with this book, where I discovered a way to generate two dimensional chaotic maps. The generic equation is pretty simple: I used it to generate these chaotic galaxies: Changing the vector of parameters you … Continue...

September 6, 2016
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We released googleVis version 0.6.1 on CRAN last week. The update fixes issues with setting certain options, following the switch from RJSONIO to jsonlite. Screen shot of some of the Google ChartsNew to googleVis? The package provides an interface betw...

## How to add pbapply to R packages?

September 6, 2016
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As of today, there are 20 R packages that reverse depend/import/suggest (3/14/3) the pbapply package. Current and future package developers who decide to incorporate the progress bar using pbapply might want to customize the type and style of the progress bar in their packages to better suit the needs of certain functions or to create a distinctive look. Here is a quick guide to help...

## Showing a different approach to making statistical tests

September 5, 2016
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In this post, I will talk about an alternative way to choose quantiles (and more broadly, decision boundaries) for statistical tests, the ones you choose in order to have a 95% confidence interval (5% of type-I error). I will then show that this idea can be used to combine tests. I will use some illustrations in R to make...

## astroABC: ABC SMC sampler for cosmological parameter estimation

September 5, 2016
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“…the chosen statistic needs to be a so-called sufficient statistic in that any information about the parameter of interest which is contained in the data, is also contained in the summary statistic.” Elise Jenningsa and Maeve Madigan arXived a paper on a new Python code they developed for implementing ABC-SMC, towards astronomy or rather cosmology

## Analyze pull requests and Travis builds using Rperform

September 5, 2016
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Always code as if the guy who ends up maintaining your code will be a violent psychopath who knows where you live. – Martin Golding In previous posts, I had discussed how Rperform can be used to obtain and visualize package performance data. However, real-world software development is a collaborative process. Thus, automating performance testing for your package is...

## Last few days for EARL London Tickets

September 5, 2016
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Last few days to purchase your tickets for the UK’s biggest R Conference this year! On September 13th-15th at The Tower Hotel, London, the conference has 1 day of workshops which are selling up fast and 2 days of conference with … Continue reading →

## Software engineering data sets

September 5, 2016
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The pretty pictures from my empirical software engineering book are now online, along with the 210 data sets and R code (330M). Plotting the number of data sets in each year shows that empirical software engineering has really taken off in the last 10 years (code+data). Around dozen or so confidential data sets are not

## Matlab goes deep [learning]

September 5, 2016
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A most interesting link I got when reading Le Monde, about MatLab proposing deep learning tools…Filed under: Books, pictures, R, Statistics, University life Tagged: deep learning, Le Monde, Matlab

## R Programming – Pitfalls to avoid (Part 1)

September 5, 2016
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As we continue to program in R, all of us would have inevitably encountered multiple errors or bugs in our code. Not all programming errors are created equal – Many of the errors we encounter are pretty straight-forward to deal with, with clear, unambiguous error messages that a little googling (or reading the help documentation) Related Post

## MinechaRts #1 (Minecraft + R + Edgar Anderson’s Iris Data)

September 5, 2016
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How to use R to draw 3D scatterplots in Minecraft? Let’s see. Minecraft is a game about placing blocks and going on adventures (source). Blocks are usually placed by players but there are add-ons that allow to add/modify/remove blocks through external API. And this feature is being used in educational materials that show how to … Czytaj...

September 4, 2016
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The seventh update in the 0.12.* series of Rcpp just arrived on the CRAN network for GNU R as well as in Debian. This 0.12.7 release follows the 0.12.0 release from late July, the 0.12.1 release in September, the 0.12.2 release in November, the 0.12.3 release in January, the 0.12.4...

## conditional sampling

September 4, 2016
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An interesting question about stratified sampling came up on X validated last week, namely how to optimise a Monte Carlo estimate based on two subsequent simulations, one, X, from a marginal and one or several Y from the corresponding conditional given X, when the costs of producing those two simulations significantly differ. When looking at

## Announcing R Course Finder

September 4, 2016
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Did you ever feel knowing exactly what you wanted, but couldn’t find it in a mountain of information? We know how you feel… As the mountain of R courses and tutorials continues to get bigger, we need smart tools to quickly find those that meet our needs best. So, we built the R Course Finder

## Mirror, mirror on the wall

September 4, 2016
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Introduction Saving your R dataframe to a .csv can be useful; being able to view the data all at once can help to see the bigger picture. Often though, multiple dataframes, all pieces of the same project, need to be viewed this way and related back to...