R Data Analytics Mastery: 150 Real-World Problems Solved

This book is a practical, code-driven guide for data professionals (analysts, scientists, and engineers) who need to clean, transform, and prepare real-world messy data for production environments. It focuses on solving specific, recurring data problems using R (with dplyr, tidyr, lubridate, stringr, zoo, etc.) rather than abstract theory. The book is organized into 150 standalone problems, grouped into 10 major chapters, each addressing a common data quality or transformation challenge. Part 1: Core Data Cleaning (Chapters 1–4) Missing Data (15 problems) – median/mean imputation, MICE, KNN, longitudinal data, pattern analysis. Duplicate Records (15 problems) – exact/fuzzy duplicates, priority-based dedup, time-window dedup, streaming data. Outlier Detection (15 problems) – Z‑score, IQR, MAD, DBSCAN, Isolation Forest, Grubbs’ test, robust regression. Date/Time Standardization (15 problems) – multiple formats, time zones, date arithmetic, age calculation, rolling windows. Part 2: Advanced Transformation (Chapters 5–7) String Cleaning (15 problems) – whitespace, case standardization, regex, fuzzy matching, HTML tag removal, phone/email/address parsing. Aggregations & Group Ops (15 problems) – conditional aggregations, rolling/cumulative sums, weighted means, window functions (rank, lag/lead), data.table performance. Pivot Tables & Reshaping (15 problems) – long ↔ wide, cross-tabulation, dynamic pivots, hierarchical columns, missing value handling, subtotals. Part 3: Data Integration & Analytics (Chapters 8–10) Merging & Joining (15 problems) – all join types (inner, left, anti, semi), fuzzy joins, inequality joins, date‑range joins, ETL pipelines. Time Series & Rolling Windows (15 problems) – moving averages, decomposition, stationarity testing, ARIMA/SARIMA, exponential smoothing, change point detection, anomaly detection. Conditional Logic & Recoding (15 problems) – ifelse, case_when, binning, factor recoding, lookup tables, risk scoring, business rules engine. Key Features Real raw data tables are shown before and after cleaning. Complete R code for every problem – ready to copy, adapt, and run. Analysis tables display the output clearly. Best practices for production pipelines (validation, logging, reusability). Covers both basic techniques (mean imputation) and advanced methods (MICE, Isolation Forest, SARIMA, Prophet). Target Audience Data analysts / scientists who work with imperfect, real‑world datasets. R users who want a cookbook of solutions for common data wrangling tasks. Anyone preparing for roles that require production‑ready ETL and data quality skills. This is an exceptionally practical, problem‑oriented reference. Instead of explaining concepts in isolation, it shows exactly how to clean a specific messy dataset and move it to a production‑ready state. Ideal for R users who need a solution fast – and want to learn best practices along the way.