The May 2026 TIOBE index shows Python and R emerging as the dominant forces in statistical programming, while legacy tools like MATLAB, SAS, and SPSS tumble, new entrants such as Stan and Zig gain traction, and the overall market undergoes a pronounced consolidation toward a few ecosystems.
This tutorial explains how to automatically locate trend change points in large collections of time series by combining grid search with piecewise regression, covering the underlying theory, cost functions, implementation in R and Python, and practical constraints to avoid over‑fitting.
This article shows how to turn change‑point detection into an optimization problem by exhaustively searching knot configurations with grid search, selecting the best model using a penalised likelihood criterion (BIC), and applying piecewise regression to automatically locate trend breakpoints, illustrated with R and Python code on California natural‑gas consumption data.
This article compares Python and R for machine learning projects, outlining each language’s strengths, weaknesses, typical use cases, and offering guidance on when to use Python for data preprocessing and R for modeling, while also highlighting community adoption and library support.
This guide compares the strengths and weaknesses of common data analysis tools—from basic spreadsheet software and multi‑dimensional tables to professional statistical packages, BI platforms, and programming languages like R and Python—helping readers select the most suitable solution for their needs.
This article surveys the most suitable programming languages for artificial intelligence, detailing why Python, R, Java, LISP, Prolog, C++, Haskell, JavaScript, and Julia each excel in AI development, and provides practical FAQs for developers choosing the right language.
This tutorial explains how to merge multiple graphs into a single figure using the patchwork package in R and the patchworklib library in Python, providing installation steps, code examples for arranging plots side‑by‑side and in grids, and visual results.
Positron, the new beta IDE from Posit built on Visual Studio Code, offers a ready‑to‑use, cross‑platform environment for R and Python with integrated data exploration tools, seamless language switching, and easy extension management via OpenVSX, while still being an early‑stage project.
This article surveys the most suitable programming languages for artificial intelligence, examining Python, R, Java, LISP, Prolog, C++, Haskell, JavaScript, and Julia, and outlines their strengths, ecosystem support, and use cases to help developers choose the right tool for AI projects.
This article explains how to query, analyze, and visualize time‑series data stored in MongoDB using the aggregation framework, MongoDB Compass, read‑only views, the BI connector with SQL tools, as well as integrations with Spark and R for advanced analytics.
Choosing the right programming language is crucial for AI development, and this article examines popular options—Python, Julia, R, and Java—highlighting their strengths, libraries, and suitability, while also pointing out outdated languages like COBOL, FORTRAN, and Pascal that offer little value for modern AI projects.
This article explains logistic regression for binary outcomes, demonstrates its implementation in R with the TitanicSurvival dataset, and interprets the model coefficients showing how gender, age, and passenger class significantly affect survival probability.
This article explains the theory of multiple linear regression, demonstrates how to fit such a model in R using the birthwt dataset with the lm() function, and interprets the output, diagnostic plots, and handling of categorical variables.
This article explains the theory behind simple linear regression, demonstrates how to generate random data and fit a model using R's lm() function, and interprets the statistical output including coefficients, significance tests, and goodness‑of‑fit measures.
This article explains the principles of one‑way, two‑way and multi‑factor ANOVA, demonstrates how to perform them in R using aov(), lm() and anova(), and shows post‑hoc comparisons with TukeyHSD as well as correlation testing with cor() and cor.test().
This article explains the theory behind hypothesis testing, demonstrates one‑sample and two‑sample t‑tests using R’s t.test() function with agricultural yield examples, and shows how to interpret one‑sided versus two‑sided test results for decision making.
This article explains how various continuous and discrete probability distributions—such as normal, binomial, Poisson, and negative binomial—are used in real‑world contexts, introduces R’s naming conventions for distribution functions, and provides code examples for computing densities, CDFs, quantiles, and random samples.
Before statistical analysis in R, you need to preprocess data by sorting vectors with sort(), rank(), order() or arrange(), merging datasets horizontally with merge() or cbind() and vertically with rbind(), and handling missing values using NA, NaN, na.rm, and na.omit functions.
This guide explains how to install, update, load, and use R packages—including CRAN and Bioconductor sources—by demonstrating key commands such as install.packages(), BiocManager::install(), library(), and help(), while also covering library paths and package management best practices.
This guide explains why data visualization matters, how to create and save graphics in R, adjust graphical parameters, and use core plotting functions such as barplot, pie, hist, boxplot, and scatter plots to produce clear, informative charts.
This guide introduces R's case‑sensitive syntax, workspace management, variable handling, input/output methods, commenting styles, and core data structures such as vectors, matrices, arrays, data frames, factors, and lists, providing essential commands and functions for each concept.
This article introduces major economic and financial data analysis tools—including Python with its scientific libraries, R, Stata, Matlab, EViews, SAS, and SPSS—detailing their origins, key features, typical applications, and where to find more information.
This article introduces the k‑means clustering algorithm and its major variants—k‑means++, k‑medoids, k‑medians, k‑modes, and k‑prototype—explaining their characteristics, step‑by‑step procedures, and provides a practical R implementation with code examples for data analysis.
This article highlights the critical role of visual communication in data analysis and presents a curated collection of books covering Python, R, Excel, Tableau, and other tools, offering readers practical guidance to create compelling charts and improve storytelling with data.
This article reviews the most suitable programming languages for artificial intelligence, explaining why Python, R, Java, LISP, Prolog, C++, Haskell, JavaScript, and Julia excel in AI development and outlining their key advantages and typical applications.
This article examines how Python has surged to become the leading language for data science, while R's popularity has declined, citing TIOBE rankings, industry surveys, academic adoption, and expert opinions to assess whether R is truly on the brink of obsolescence.
This article presents a detailed infographic comparison of R and Python from a data‑science perspective, outlining their histories, ecosystems, usability, community support, and advantages in data analysis to help readers decide which language better fits their projects.
This step‑by‑step guide shows beginners how to download and install the R programming language and its RStudio IDE on both Windows and macOS, covering package selection, installation options, shortcut creation, verification of a successful setup, and common troubleshooting tips.
Learn how response time (RT), queries per second (QPS), concurrent user count, and optimal thread settings interrelate, using a Disney park analogy to illustrate calculation methods and performance tuning strategies for web applications.
Meituan’s in‑store dining team demonstrates how R’s open‑source packages, powerful data manipulation, rich visualization libraries, and reproducible reporting can be engineered into scalable, parallelized workflows that turn secondary data processing into fast, interactive dashboards and analytics, proving R’s enterprise‑grade performance and adoption.
This article demonstrates how to use the Python itchat library to extract personal WeChat friend data, then analyzes gender ratios, city distribution, and signature word clouds with R and Python visualisation tools, offering a practical guide for personal social network analytics.
This article demonstrates a step‑by‑step workflow for constructing a churn prediction model using R in Discover, covering data loading, preprocessing, feature extraction, labeling, random‑forest training, prediction, and evaluation to help businesses proactively retain high‑value customers.
This guide explains the fundamentals of association‑rule mining, introduces support, confidence and lift metrics, and demonstrates step‑by‑step how to use the discoverR R package with Apriori and FP‑Growth algorithms to extract and visualize recommendation rules from the classic groceries dataset.
This tutorial explains why R programmers should add Python to their toolkit, compares core data types and structures between the two languages, introduces essential Python libraries for data analysis, and walks through a practical Boston housing dataset example to solidify the concepts.
This article compares R, Python, Scala, and Java for big‑data projects, outlining each language’s strengths and weaknesses, and offers guidance on selecting the most suitable language based on project requirements, team expertise, and production needs.
This article presents a step‑by‑step machine‑learning analysis of the UCI Phishing Websites dataset in R, loading the data, training boosted logistic regression, SVM, tree‑bagging, and random‑forest models, comparing their accuracies, and identifying the most important predictive features for phishing detection.
This article explains three practical approaches—Streaming APIs, the Rhipe package, and RHadoop—to integrate R with Hadoop, enabling R to process petabyte-scale datasets, compares their setup complexity, capabilities, and trade‑offs, and highlights key conclusions for choosing the right method.
This article warns against abandoning machine learning near the finish line and offers a concise cheat‑sheet of the ten most commonly used algorithms, complete with ready‑to‑run Python and R code examples to help practitioners accelerate model development.
This article explains how feature engineering and principal component analysis (PCA) can be applied to a two‑feature binary classification problem in R, illustrating data exploration, model evaluation with ROC AUC, and the impact of dimensionality reduction on predictive performance.
This article explains how support vector machines work, how to handle non‑linear and multi‑class problems, and provides a complete R implementation using the e1071 package, including linear and radial kernels, model evaluation, parameter tuning, and visualisation with grid plots.
