R Archives

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

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

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

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

How Tidyverse Guides R Programmers Through Data Science Workflows

R Tools & Languages Workflow R tidyverseposted by Daniel Gutierrez, ODSC November 19, 2018

Whenever someone asks me how to get into data science using R, I invariably recommend checking out the tidyverse package. Tidyverse is a great launch pad for a language like R because it offers order and consistency. I studied programming language design as a CS undergrad.... Read more

Sentiment Analysis in R Made Simple

Modeling NLP/Text Analytics R Natural Language Processing R sentiment analysisposted by Daniel Gutierrez, ODSC October 24, 2018

Sentiment analysis is located at the heart of natural language processing, text mining/analytics, and computational linguistics. It refers to any measurement technique by which subjective information is extracted from textual documents. In other words, it extracts the polarity of the expressed sentiment in a range spanning... Read more

Feature Engineering with Forward and Backward Elimination

Modeling Feature Engineering Rposted by Daniel Gutierrez, ODSC October 15, 2018

Sometimes when you fit models to test their predictive accuracy, you find that you’re dealing with too many predictors (feature variables). You can draw upon your domain knowledge, or that of an available domain expert, to reduce predictors until you only have those that will offer... Read more

Snakes in a Package: Combining Python and R with Reticulate

Python R Tools & Languages Python Rposted by Adnan Fiaz September 18, 2018

When I first started working as a data scientist (or something like it) I was told to program in C++ and Java. Then R came along and it was liberating; my ability to do data analysis increased substantially. As my applications grew in size and complexity,... Read more