Jupyter Notebook: Python or R—Or Both?
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
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
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
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
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
Monthly Summary of Selected R Trends, Activities and Insights – October 2018
In October, the spike in activities observed in September across the R ecosystem was maintained. In the following article, a summary of selected R trends, activities, and insights in October, 2018, are presented as the R language keeps trending. Data for the trends and activities summarized... Read more
How Tidyverse Guides R Programmers Through Data Science Workflows
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
Build a Multi-Class Support Vector Machine in R
Support Vector Machines (SVMs) are quite popular in the data science community. Data scientists often use SVMs for classification tasks, and they tend to perform well in a variety of problem domains. An SVM performs classification tasks by constructing hyperplanes in a multidimensional space that separates... Read more
Monthly Summary of Selected Trends, Activities, and Insights for R – September 2018
In September, there was a serious spike in activities across the R ecosystem. This article examines a summary of selected R trends, activities, and insights in September. Data for the trends and activities summarized here were obtained from popular websites used by the R community such as... Read more
Sentiment Analysis in R Made Simple
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