R Language Data Analytics Mastery: 250 Real-World Problems Solved

Stop Wasting Time on Data Cleaning and Start Solving Real Business Problems. Data science isn't just about building models; 80% of the work is wrestling with messy, real-world data. This book bridges the gap between textbook theory and on-the-job reality, providing you with 250 immediately usable solutions for the most common data wrangling and analysis challenges. Whether you're a data analyst, scientist, or business intelligence professional, you’ll learn how to transform chaotic raw data into clean, production-ready datasets using R, dplyr, tidyr, ggplot2, and more. What’s Inside: This book is structured as a practical cookbook. Each problem is presented with a realistic scenario, a messy "Raw Data Table," a concise R solution, and a clear "Analysis Table Output." You will master every stage of the data science workflow: Data Cleaning: Handle missing values (mean, median, KNN, LOCF), remove duplicates, detect and treat outliers (IQR, Z-score, winsorizing), and standardize messy strings and dates. Data Transformation: Master aggregations, pivoting (long/wide), merging/joining datasets, and creating complex conditional logic. Time Series & Dates: Calculate rolling windows, decompose seasonal trends, find business days, and handle tricky date formats (including Excel dates and leap years). Statistical Testing: Go beyond p-values with clear examples of t-tests, ANOVA, chi-square, Wilcoxon, and power analysis. Regression Modeling: Implement linear, logistic, Poisson, ridge, lasso, GAM, and survival analysis models. Clustering & Dimension Reduction: Apply K-means, PCA, t-SNE, UMAP, DBSCAN, and hierarchical clustering to real customer and product data. Advanced Visualization: Create publication-ready plots with ggplot2, including scatter plots, boxplots with jitter, heatmaps, dumbbell charts, word clouds, and even animated plots with gganimate. Feature Engineering & Automation: Build polynomial features, use target encoding, automate variable selection (Boruta), and create end-to-end ML pipelines. Why This Book is Different: Problem-First Approach: Each solution starts with a messy, real-world table (e.g., "Employee survey with missing NAs," "Stock price with a 795% outlier"), mirroring the data you actually see at work. Complete Solutions: Every problem includes a raw data table, ready-to-run R code, and a final clean analysis table so you can see exactly what changed. Production-Ready Code: Code is written with best practices in mind, using dplyr, tidyr, data.table, and other modern R packages. Instant Application: Use the code snippets as templates to solve your own problems immediately. Stop googling how to handle duplicate transaction IDs or interpolate missing hourly sensor data. Get the solutions you need to clean your data, build better models, and deliver insights faster. Scroll up and click "Buy Now" to take your data analysis skills to the next level.