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R

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Python Programming Learning Circle
Python Programming Learning Circle
Jan 3, 2025 · Artificial Intelligence

Python vs R for Machine Learning: Advantages, Disadvantages, and Choosing the Right Language

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.

AIPythonR
0 likes · 6 min read
Python vs R for Machine Learning: Advantages, Disadvantages, and Choosing the Right Language
Python Programming Learning Circle
Python Programming Learning Circle
Aug 14, 2024 · Fundamentals

Combining Multiple Plots in R and Python Using patchwork and patchworklib

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.

Data VisualizationPlottingPython
0 likes · 4 min read
Combining Multiple Plots in R and Python Using patchwork and patchworklib
Architects Research Society
Architects Research Society
Apr 14, 2023 · Databases

Querying, Analyzing, and Presenting Time Series Data in MongoDB

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.

AggregationBI ConnectorMongoDB
0 likes · 14 min read
Querying, Analyzing, and Presenting Time Series Data in MongoDB
Model Perspective
Model Perspective
Dec 4, 2022 · Fundamentals

How Logistic Regression Predicts Titanic Survival: A Step-by-Step R Guide

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.

RTitanic datasetbinary classification
0 likes · 5 min read
How Logistic Regression Predicts Titanic Survival: A Step-by-Step R Guide
Model Perspective
Model Perspective
Dec 3, 2022 · Fundamentals

How to Perform Multiple Linear Regression in R with the Birthweight Dataset

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.

Rbirthweight datasetlm function
0 likes · 6 min read
How to Perform Multiple Linear Regression in R with the Birthweight Dataset
Model Perspective
Model Perspective
Dec 2, 2022 · Fundamentals

Master Linear Regression in R: From Random Data to Insightful Models

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.

Rdata analysislinear regression
0 likes · 4 min read
Master Linear Regression in R: From Random Data to Insightful Models
Model Perspective
Model Perspective
Dec 1, 2022 · Fundamentals

Mastering One‑Way and Multi‑Factor ANOVA in R: From Theory to Code

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().

ANOVARTukey test
0 likes · 10 min read
Mastering One‑Way and Multi‑Factor ANOVA in R: From Theory to Code
Model Perspective
Model Perspective
Nov 30, 2022 · Fundamentals

How to Perform One‑Sample and Two‑Sample t‑Tests in R

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.

Rdata analysishypothesis testing
0 likes · 5 min read
How to Perform One‑Sample and Two‑Sample t‑Tests in R
Model Perspective
Model Perspective
Nov 29, 2022 · Fundamentals

Mastering Probability Distributions in R: From Normal to Poisson

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.

Rdata analysisdistributions
0 likes · 5 min read
Mastering Probability Distributions in R: From Normal to Poisson
Model Perspective
Model Perspective
Nov 28, 2022 · Fundamentals

Master R Data Preprocessing: Sorting, Merging, and Handling Missing Values

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.

RSortingdata preprocessing
0 likes · 4 min read
Master R Data Preprocessing: Sorting, Merging, and Handling Missing Values
Model Perspective
Model Perspective
Nov 27, 2022 · Fundamentals

Mastering R Packages: Installation, Loading, and Usage Made Simple

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.

BioconductorInstallationR
0 likes · 4 min read
Mastering R Packages: Installation, Loading, and Usage Made Simple
Model Perspective
Model Perspective
Nov 26, 2022 · Fundamentals

Master R Data Visualization: Essential Techniques for Clear Graphs

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.

Data VisualizationPlottingR
0 likes · 11 min read
Master R Data Visualization: Essential Techniques for Clear Graphs
Model Perspective
Model Perspective
Nov 25, 2022 · Fundamentals

Master R Basics: Core Syntax, Workspace, and Data Types Explained

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.

Data TypesRfundamentals
0 likes · 10 min read
Master R Basics: Core Syntax, Workspace, and Data Types Explained
Model Perspective
Model Perspective
Oct 23, 2022 · Fundamentals

Essential Economic & Financial Data Analysis Tools: Python, R, Stata, and More

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.

PythonRdata analysis
0 likes · 9 min read
Essential Economic & Financial Data Analysis Tools: Python, R, Stata, and More
NetEase LeiHuo UX Big Data Technology
NetEase LeiHuo UX Big Data Technology
Feb 24, 2022 · Artificial Intelligence

An Introduction to k-means and Its Variants with Practical R Implementation

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.

ClusteringData MiningR
0 likes · 11 min read
An Introduction to k-means and Its Variants with Practical R Implementation
Architects Research Society
Architects Research Society
Oct 28, 2016 · Artificial Intelligence

Phishing Website Detection Using Machine Learning Models in R

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.

RSVMcaret
0 likes · 11 min read
Phishing Website Detection Using Machine Learning Models in R
Art of Distributed System Architecture Design
Art of Distributed System Architecture Design
Oct 6, 2015 · Artificial Intelligence

Feature Engineering and PCA for Binary Classification in R

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.

Feature EngineeringPCAR
0 likes · 9 min read
Feature Engineering and PCA for Binary Classification in R
Qunar Tech Salon
Qunar Tech Salon
Mar 28, 2015 · Artificial Intelligence

Support Vector Machines in R: Theory, Implementation, and Parameter Tuning

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.

Grid PlotRRadial Kernel
0 likes · 9 min read
Support Vector Machines in R: Theory, Implementation, and Parameter Tuning