Compares three methods for building JSON strings in R, discussing the pros and cons of each approach for developers.
A practical guide to creating a minimal, functional R package from scratch to streamline code reuse and save development time.
Explains the advantages of vectorization in R for writing faster, simpler, and safer code by examining how R works internally.
A guide to using the RSiteCatalyst R package to access Adobe Analytics real-time reporting API for monitoring metrics like orders and revenue.
RSiteCatalyst v1.3 adds regex search, Realtime API support, and configurable request timing for the Adobe Analytics R package.
A tutorial on manually creating dummy variables in R to handle categorical data with many levels, addressing a common randomForest package error.
A tutorial on creating customizable heat maps in R using the gplots and RColorBrewer packages for data visualization.
A tutorial on creating customizable heat maps in R using the gplots package, covering installation, data preparation, and plot customization.
RSiteCatalyst v1.2 is released on CRAN with bug fixes, dependency removal, and improved numeric type handling for the Adobe Analytics API.
A technical guide on using K-Means clustering in R to analyze and segment search keywords for understanding user intent in digital analytics.
A benchmark comparison of Julia, Python, R, and pqR on a Project Euler problem, exploring performance gains from JIT compilation.
RSiteCatalyst 1.1 released with new API features, faster calls, and extended timeout for Adobe Analytics data in R.
Explains how to parallelize QR decomposition for linear models on big data using R's biglm package and incremental merging.
A guide to printing R help files directly in the console or within knitr documents using a custom function from the tools package.
A statistics professor details his hardware and software setup, including Mac laptops, R, LaTeX, and plans to learn JavaScript.
A summary of upcoming technical talks on statistical computing, rare DNA variant analysis, and handling large datasets with R and SQL.
A summary of Robert Hijmans' talk on spatial data analysis in R, covering packages like raster, dismo, and geosphere for geostatistics and visualization.
Introduces proftable(), an improved R profiling function that displays detailed call stacks and line numbers to identify performance bottlenecks.
A practical guide for R users on optimizing code for speed, balancing performance with readability and real-world time constraints.
