R for Data Science: Learn About R Programming With These Articles

Comparing Point-and-Click Front Ends for R

R Tools & Languages point and click Rposted by Bob Muenchen August 23, 2019

For an updated version of this post, see:http://r4stats.com/articles/software-reviews/r-gui-comparison/. Now that I’ve completed seven detailed reviews of Graphical User Interfaces (GUIs) for R, let’s compare them. It’s easy enough to count their features and plot them, so let’s start there. I’m basing the counts on the number of menu... Read more

Some Details on Running xgboost

R Tools & Languages R XGBoostposted by John Mount August 21, 2019

While reading Dr. Nina Zumel’s excellent note on bias in common ensemble methods, I ran the examples to see the effects she described (and I think it is very important that she is establishing the issue, prior to discussing mitigation). [Related Article: When Less is More: A Brief Story About Feature... Read more

Hierarchical Bayesian Models in R

Modeling R Statistics Tools & Languages bayesian hierarchical bayesian Hierarchical Bayesian Models Statisticsposted by Nathaniel Jermain August 19, 2019

Hierarchical approaches to statistical modeling are integral to a data scientist’s skill set because hierarchical data is incredibly common. In this article, we’ll go through the advantages of employing hierarchical Bayesian models and go through an exercise building one in R. If you’re unfamiliar with Bayesian... Read more

Factors in R

R Tools & Languages factoring factors R toolsposted by Steve Miller August 9, 2019

The factor is a foundational data type in R. Factors are generally used to represent categorical variables, which may be intrinsically unordered (nominal) or ordered (ordinal). While the underlying data is often character, factors can be built on numerics as well. Factor variables are stored as integers pointing to unique... Read more

Deep Learning in R with Keras

Conferences Deep Learning Modeling R Tools & Languages Kerasposted by Daniel Gutierrez, ODSC August 8, 2019

The primary professional hat I wear is as a data science consultant working with machine learning in a variety of problem domains. Due to my academic past in computer science and applied statistics, my development environment of choice today is typically R. Lately however, Python is... Read more

Jupyter Notebook: Python or R—Or Both?

Python R Tools & Languages Python Rposted by Steve Miller June 13, 2019

I was analytically betwixt and between a few weeks ago. Most of my Jupyter Notebook work is done in either Python or R. Indeed, I like to self-demonstrate the power of each platform by recoding R work in Python and vice-versa. I must have a dozen... Read more

Validating Type I and II Errors in A/B Tests in R

Modeling R Tools & Languages R Tools and Languagesposted by Roland Stevenson April 25, 2019

In the below work, we will intentionally leave out statistics theory and attempt to develop an intuitive sense of what type I(false-positive) and type II(false-negative) errors represent when comparing metrics in A/B tests. One of the problems plaguing the analysis of A/B tests today is known... Read more

Introduction to R Shiny

Data Visualization Featured Post R Tools & Languages Conferences Data Visualization R R Shinyposted by Alyssa Columbus March 7, 2019

Alyssa is a speaker for ODSC East 2019 this April 30 to May 3! Attend her talk “Data Visualization with R Shiny.” What is R Shiny? Shiny is an R package that enables you to build interactive web apps using both the statistical power of R... Read more

Activities and Insights for R: Monthly Summary of Selected Trends – December 2018

R Tools & Languages R Central R Trendsposted by R Central February 6, 2019

In December, activities across the R ecosystem reduced from levels observed in November. This was notable in StackOverflow, meetup events, and in the downloads of R packages. The December holidays likely caused this general reduction in activities. However, the first two weeks in December saw great... Read more

Monthly Summary of Selected Trends, Activities, and Insights for R – November 2018

Modeling R Tools & Languages R R Central R Trends Researchposted by R Central December 28, 2018

In November, activities continued to increase beyond the numbers recorded since July across the R ecosystem. This was most notable in events and in the downloads of R packages. Total package downloads from a single CRAN mirror and in one single year hit half-billion this November... Read more