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15+ Resources to Get Started with R

Career Insights Featured Post R Tools & Languagesposted by Elizabeth Wallace, ODSC January 3, 2020

R is the second most sought after language in data science behind Python, so gaining mastery of R is a prerequisite to a thriving career in the field. Whether you’re an experienced developer or a newbie considering a career move, here are some excellent resources so you can get... Read more

Quantifying R Package Dependency Risk

R Tools & Languages dependency riskposted by John Mount December 26, 2019

We recently commented on excess package dependencies as representing risk in the R package ecosystem. The question remains: how much risk? Is low dependency a mere talisman, or is there evidence it is a good practice (or at least correlates with other good practices)? [Related Article: Data-Driven Exploration of the R User Community... Read more

Machine Learning 101: Predicting Drug Use Using Logistic Regression In R

Machine Learning Modeling R Tools & Languages Logistic Regressionposted by Leihua Ye December 24, 2019

Executive Summary Generalized Linear Models (GLM) Three types of link function: Logit, Probit, and Complementary log-log (cloglog) Building a logistic regression to predict drug use and compare these three types of GLM In Machine Learning 101 courses, stats professors introduce GLM right after linear regression as the next stepping... Read more

Image Compression In 10 Lines of R Code

Modeling R Tools & Languages image compressingposted by Leihua Ye December 9, 2019

Principal Component Analysis (PCA) is a powerful Machine Learning tool. As an unsupervised learning technique, it excels in dimension reduction and feature extraction However, do you know we can use PCA to compress images? In this post, I’ll walk through the process and explain how PCA can compress images... Read more

A Quick Look Into Bootstrapping

Machine Learning Modeling R Statistics Tools & Languages bootstrapping Statisticsposted by Leihua Ye December 3, 2019

Executive Summary As a resampling method, bootstrapping allows us to generate statistical inferences about the population from a single sample. Learn to bootstrap in R. Bootstrapping lies the foundation for several machine learning methods (e.g., Bagging. I’ll explain Bagging in a follow-up post). [Related Article: Discovering 135 Nights of... Read more

Balancing Interpretability and Predictive Power with Cubist Models in R

Machine Learning Modeling R Tools & Languages Cubist Modelsposted by Jacey Heuer November 18, 2019

Machine learning models are powerful tools that do well in their purpose of prediction. In many business applications, the power of these models is quite beneficial. With any application of a machine learning model, the process to choosing which model involves determining the model that performs best across a... Read more

Using Keras and TensorFlow in R

R Tools & Languages Keras R TensorFlowposted by Jacey Heuer November 12, 2019

Keras and Tensorflow are two very powerful packages that are normally accessed via python. Since the packages were developed for python they may have the illusion of being out of reach for R users. However, this is not the case as the Keras and Tensorflow packages may be set... Read more

What is “Tidy Data”?

R Tools & Languages R tidy data tutorialsposted by John Mount November 8, 2019

I would like to write a bit on the meaning and history of the phrase “tidy data.” Hadley Wickham has been promoting the term “tidy data.” For example in an eponymous paper, he wrote: In tidy data: Each variable... Read more

Discovering 135 Nights of Sleep with Data, Anomaly Detection, and Time Series

Modeling Python R Statistics Tools & Languages anomaly detection Time Seriesposted by Juan De Dios Santos November 4, 2019

In this article, I look at data from 135 nights of sleep and use anomaly detection and time series data to understand the results. Three things are certain in life: death, taxes, and sleeping. Here, we’ll talk about the latest. Every night*, us humans, after a long day of... Read more

Using an Embedding Matrix on Tabular Data in R

Modeling R Tools & Languages embedding matrix TensorFlowposted by Jacey Heuer October 22, 2019

How would you tackle the prospects of representing a categorical feature, with 100’s of levels, in a model? A first approach may be to create a one-hot encoded matrix representing each level of the feature. The result would be a large and sparse matrix where the majority of the... Read more