R Programming

Module 1: Introduction to R

• Overview of R environment and RStudio installation.
• Basic syntax, data types (numeric, character, logical), and operators.
• Workspace management and help functions.

Module 2: Data Structures - Vectors

• Creating vectors with c() and seq().
• Vector operations: arithmetic, indexing, and slicing.
• Common functions: length(), mean(), sum().

Module 3: Data Structures - Matrices

• Matrix creation with matrix() and rbind/cbind.
• Matrix indexing, transposition, and operations.
• Applications in linear algebra basics.

Module 4: Data Structures - Lists

• List creation, naming, and extraction.
• Nested lists and lapply/sapply functions.
• Combining lists with unlist().

Module 5: Data Structures - Data Frames

• Creating data frames and reading CSV files.
• Column and row operations with $ and [].
• Summary statistics and data inspection.

Module 6: Control Structures

• Conditional statements: if-else, ifelse().
• Loops: for, while, repeat, and break/next.
• Vectorized alternatives for efficiency.

Module 7: Functions

• Writing custom functions with arguments.
• Default parameters, scoping, and lazy evaluation.
• Anonymous functions and closures.

Module 8: Base Graphics

• Plot types: scatter, line, histogram, boxplot.
• Customizing plots with colors, labels, and legends.
• Saving graphics to files.

Module 9: Statistical Analysis

• Descriptive statistics: mean, median, variance.
• Hypothesis testing: t-test, chi-square.
• Correlation and basic regression.

Module 10: Data Manipulation with dplyr

• Select, filter, mutate, arrange functions.
• Pipe operator (%>%) for chaining.
• Group_by and summarize for aggregation.

Module 11: Data Import/Export

• Reading/writing CSV, Excel, JSON.
• Handling missing values with na.omit().
• Web scraping basics with read.table().

Module 12: Probability Distributions

• Binomial, Poisson, Normal distributions.
• Density, distribution, quantile functions (d/p/q/r).
• Simulations and random sampling.

Module 13: ggplot2 Visualization

• Grammar of graphics principles.
• Layers: geom_point, geom_bar, themes.
• Faceting and scales customization.

Module 14: Linear Regression

• lm() model fitting and summary().
• Residual analysis and diagnostics.
• Multiple regression with interactions.

Module 15: Time Series Analysis

• Date handling with as.Date() and POSIXct.
• ts() objects and decomposition.
• ARIMA basics and forecasting.

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