„One function to rule them all“ – visualization of regression models in #rstats w/ #sjPlot

October 23, 2017
By
„One function to rule them all“ –  visualization of regression models in #rstats w/ #sjPlot

I’m pleased to announce the latest update from my sjPlot-package on CRAN. Beside some bug fixes and minor new features, the major update is a new function, plot_model(), which is both an enhancement and replacement of sjp.lm(), sjp.glm(), sjp.lmer(), sjp.glmer() and sjp.int(). The latter functions will become deprecated in the next updates and removed somewhen

Read more »

9th MilanoR meeting on November 20th: call for presentations!

October 23, 2017
By
9th MilanoR meeting on November 20th: call for presentations!

MilanoR Staff is happy to announce the 9th MilanoR Meeting! The meeting will take place on November 20th, from 7pm to about 9:30 pm, in Mikamai (close to the Pasteur metro station) . This time we want to focus on a specific topic: data visualization with R. Do you feel you have something to...

Read more »

Economic time series data quiz as a shiny app for mobile phones

October 22, 2017
By
Economic time series data quiz as a shiny app for mobile phones

Nowadays, a lot of interesting time series data is freely available that allows us to compare important economic, social and environmental trends across countries. I feel that one can learn a lot by surfing through the data sections on the websites of...

Read more »

Exploring Uncertainty with Bayesian ML

October 22, 2017
By
Exploring Uncertainty with Bayesian ML

Introduction: Bayesian machine learning techniques allow us to obtain a posterior density for individual predictions instead of just the mean. This additional information allows us to understand and explore the uncertainty involved. However not all un...

Read more »

The SeaClass R Package

October 22, 2017
By
The SeaClass R Package

The SeaClass R Package The Operations Technology and Advanced Analytics Group (OTAAG) at Seagate Technology has decided to share an internal project that helps accelerate development of classification models. The interactive SeaClass tool is contained in an R-based package built using shiny and other CRAN packages commonly used for binary classification. The package is free to use and develop further,...

Read more »

Who knew likelihood functions could be so pretty?

October 22, 2017
By
Who knew likelihood functions could be so pretty?

I just released a new iteration of simstudy (version 0.1.6), which fixes a bug or two and adds several spline related routines (available on CRAN). The previous post focused on using spline curves to generate data, so I won’t repeat myself here. And, apropos of nothing really - I thought I’d take the opportunity to do a simple simulation...

Read more »

Demo Week: class(Monday) <- tidyquant

Demo Week: class(Monday) <- tidyquant

We’ve got an exciting week ahead of us at Business Science: we’re launching our first ever Business Science Demo Week. Every day this week we are demoing an R package: tidyquant (Monday), timetk (Tuesday), sweep (Wednesday), tibbletime (Thursday) and h2o (Friday)! That’s five packages in five days! We’ll give you intel on what you need to know about these...

Read more »

linl 0.0.1: linl is not Letter

October 22, 2017
By
linl 0.0.1: linl is not Letter

Aaron Wolen and I are pleased to announce the availability of the initial 0.0.1 release of our new linl package on the CRAN network. It provides a simple-yet-powerful Markdown---and RMarkdown---wrapper the venerable LaTeX letter class. Aaron had done...

Read more »

A Call to Tweets (& Blog Posts)!

October 22, 2017
By
A Call to Tweets (& Blog Posts)!

Way back in July of 2009, the first version of the twitteR package was published by Geoff Jentry in CRAN. Since then it has seen 28 updates, finally breaking the 0.x.y barrier into 1.x.y territory in March of 2013 and receiving it’s last update in July of 2015. For a very long time, the twitteR... Continue reading →

Read more »

Fragment – DIT4C – Docker Base Containers for Edu Remote Computing Labs

October 22, 2017
By
Fragment – DIT4C – Docker Base Containers for Edu Remote Computing Labs

What’s an effective way of helping a student run a desktop application when their own computer won’t run the application, for whatever reason, locally? Virtualised software, running remotely, provides one solution. So here’s an example of a project that looks at doing just that:  DIT4C (“Data Intensive Tools for the Cloud”), ‘a platform for hosting data analysis

Read more »

Automatic Time-Series Forecasting with Prophet

October 21, 2017
By
Automatic Time-Series Forecasting with Prophet

Seasonality and Trends Time-series analysis is a battle on multiple fronts by definition. One has to deal with (dynamic) trends, seasonality effects, and good old noise. A general formula can be given as y = level + trend + seasonality + noise However, the relationships between these factors can be realized in many, and sometimes quite complex, ways. It is easy to...

Read more »

Markets Performance after Election: Day 239

October 21, 2017
By
Markets Performance after Election: Day 239

When I wrote the original post, I wasn’t planning on writing a follow-up. Certainly not the week after. But what a difference a week can make in a dynamic system like the US stock market. While re-running the computations testing the latest version of RStudio, I noticed something surprising – President Trump’s rally has advanced The post Markets Performance...

Read more »

Rick and Morty and Tidy Data Principles (Part 2)

October 21, 2017
By
Rick and Morty and Tidy Data Principles (Part 2)

Motivation The first part left an open door to analyze Rick and Morty contents using tf-idf, bag-of-words or some other NLP techniques. Here I'm also taking a lot of ideas from Julia Silge's blog. Note: If some images appear too small on your screen you can open them in a new tab to show them in their original size. Term Frequency The...

Read more »

Linear models: Pushing down and speeding up

October 20, 2017
By
Linear models: Pushing down and speeding up

Let’s start with a few observations about linear models. The usual discussion posits a target variable y of length N and a design matrix X with p columns (of full rank, say) and N rows. The linear model equation can be written and the least squ...

Read more »

RTutor: Pollution Reduction by Wind Energy

October 20, 2017
By
RTutor: Pollution Reduction by Wind Energy

How much pollution reduction do we get from a MWh electricity produced by wind or solar power? In principle, this corresponds to the avoided pollution of a fossil fuel plant that reduces its output due to the higher production of renewable electricity....

Read more »

An Updated History of R

October 20, 2017
By

Here's a refresher on the history of the R project: 1992: R development begins as a research project in Auckland, NZ by Robert Gentleman and Ross Ihaka 1993: First binary versions of R published at Statlib 1995: R first distributed as open-source software, under GPL2 license 1997: R core group formed 1997: CRAN founded (by Kurt...

Read more »

Install Useful Eclipse Plugins in Bio7 for R, Data Science and Programming

October 20, 2017
By
Install Useful Eclipse Plugins in Bio7 for R, Data Science and Programming

20.10.2017 Beside a massive of amount of R packages and ImageJ plugins Bio7 can be extended with Eclipse plugins useful for data science and programming. Some of them could also be very useful for R related developments (e.g., to develop R packages or distribute Shiny apps). Installation of Eclipse Plugins One  way to install Eclipse

Read more »

Practical Machine Learning with R and Python – Part 3

October 20, 2017
By
Practical Machine Learning with R and Python – Part 3

In this post ‘Practical Machine Learning with R and Python – Part 3’,  I discuss ‘Feature Selection’ methods. This post is a continuation of my 2 earlier posts Practical Machine Learning with R and Python – Part 1 Practical Machine Learning with R and Python – Part 2 While applying Machine Learning techniques, the data … Continue reading Practical...

Read more »

Qualitative Research in R

October 20, 2017
By
Qualitative Research in R

In the last two posts, I’ve focused purely on statistical topics – one-way ANOVA and dealing with multicollinearity in R. In this post, I’ll deviate from the pure statistical topics and will try to highlight some aspects of qualitative research. More specifically, I’ll show you the procedure of analyzing text mining and visualizing the text Related Post Multi-Dimensional Reduction and...

Read more »

Gold-Mining – Week 7 (2017)

October 19, 2017
By

Week76 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold! The post Gold-Mining – Week 7 (2017) appeared first on Fantasy Football Analytics.

Read more »

Highlight the Pipe. Highlight.js

October 19, 2017
By

Practical advices about customizing code highlighting on web pages with highlight.js. Prologue While creating this site I had to encounter the topic of highlighting code on web pages. I decided to do that with the help of highlight.js functionality. After picking a style with R in mind, I arrived to the...

Read more »

The R manuals in bookdown format

October 19, 2017
By

While there are hundreds of excellent books and websites devoted to R, the canonical source of truth regarding the R system remains the R manuals. You can find the manuals at your local CRAN mirror and on your laptop as part of the R distribution (try Help __ Manuals in RGui, or Help __ R Help in RStudio to...

Read more »

County-Level Choropleth in Plotly and R

October 19, 2017
By

At Plotly, we are commonly asked about thematic maps — especially county-level choropleth maps. This style of map provides a visual illustration of variation across a geographic area. Some pertinent uses are population density, economic measurements, crime statistics, and election results. With Plotly, there are multiple ways to bring county-level choropleths to life. For example, using ggplotly

Read more »

First steps with MRF smooths

October 19, 2017
By
First steps with MRF smooths

One of the specialist smoother types in the mgcv package is the Markov Random Field (MRF) smooth. This smoother essentially allows you to model spatial data with an intrinsic Gaussian Markov random field (GMRF). GRMFs are often used for spatial data measured over discrete spatial regions. MRFs are quite flexible as you can think about them as representing an...

Read more »

First steps with MRF smooths

October 19, 2017
By
First steps with MRF smooths

One of the specialist smoother types in the mgcv package is the Markov Random Field (MFR) smooth. This smoother essentially allows you to model spatial data with an intrinsic Gaussian Markov random field (GMRF). GRMFs are often used for spatial data measured over discrete spatial regions. MRFs are quite flexible as you can think about them as representing an...

Read more »

Quickly Install R on Ubuntu 17.10

October 19, 2017
By

Motivation On a previous post I explained how to install R on Ubuntu 16.04 without further complications. Now here are the equivalent steps on Ubuntu 17.10. The presented script also installs Java and common packages as I wrote this for a fresh install...

Read more »

Ensemble learning for time series forecasting in R

October 18, 2017
By
Ensemble learning for time series forecasting in R

Ensemble learning methods are widely used nowadays for its predictive performance improvement. Ensemble learning combines multiple predictions (forecasts) from one or multiple methods to overcome accuracy of simple prediction and to avoid possible over...

Read more »

Is it faster to take a bike or taxi in NYC?

October 18, 2017
By
Is it faster to take a bike or taxi in NYC?

Taxis are plentiful and convenient in New York City, but the city is also served by a wide network of commuter bicycles (Citi Bikes). If you need to get from, say, the West Village to the Garment District, are you better off time-wise hailing a cab, or heading over to the nearest Citi Bike station? Data scientist Todd W....

Read more »

Writing Julia functions in R with examples

October 17, 2017
By
Writing Julia functions in R with examples

By Gabriel Vasconcelos The Julia programming language is growing fast and its efficiency and speed is now well-known. Even-though I think R is the best language for Data Science, sometimes we just need more. Modelling is an important part of … Continue reading →

Read more »

Search R-bloggers


Sponsors

Mango solutions





Zero Inflated Models and Generalized Linear Mixed Models with R



Quantide: statistical consulting and training

ODSC2 west

ODSC1_jobs

datasociety

http://www.eoda.de

max kuhn

CRC R books series







Six Sigma Online Training



mljar.com

datazar.com



Contact us if you wish to help support R-bloggers, and place your banner here.