Introduction to R Programming

R Programming:

R is a free, open-source language and environment for:
  ✅ Statistical computing
  ✅ Data analysis
  ✅ Machine learning
  ✅ Data visualization
  ✅ Bioinformatics
  ✅ Financial modeling

Created by: Ross Ihaka and Robert Gentleman (1993)
Based on:   S programming language (Bell Labs, 1976)
Governed by: R Core Team + R Foundation

Key fact: R has 19,000+ packages on CRAN (Comprehensive R Archive Network)

R vs Other Languages:

+─────────────────────+────────────────────────+──────────────────────+
│ Feature             │ R                      │ Python               │
+─────────────────────+────────────────────────+──────────────────────+
│ Primary strength    │ Statistics, research   │ General ML/AI        │
│ Learning curve      │ Moderate (stats-heavy) │ Moderate (code-heavy)│
│ Visualization       │ ggplot2 (best-in-class)│ Matplotlib/Seaborn   │
│ Statistical tests   │ Built-in (400+ tests)  │ SciPy/Statsmodels    │
│ Academic research   │ Dominant               │ Growing              │
│ Bioinformatics      │ Dominant (Bioconductor)│ Limited              │
│ Finance/quant       │ Strong                 │ Strong               │
│ Report generation   │ R Markdown + knitr     │ Jupyter              │
+─────────────────────+────────────────────────+──────────────────────+

R is preferred when: statistical rigor matters, research reproducibility
is required, or you're working in academic/healthcare/pharma settings.

R ECOSYSTEM:

Base R ──────── Core language + built-in functions
   │
   ├── CRAN ──── 19,000+ community packages
   │     ├── tidyverse   (dplyr, ggplot2, tidyr, purrr)
   │     ├── data.table  (blazing fast data manipulation)
   │     ├── caret       (machine learning framework)
   │     ├── forecast    (time series)
   │     ├── shiny       (interactive web apps)
   │     └── Bioconductor(genomics/bioinformatics)
   │
   ├── RStudio ── IDE for R (most popular)
   │
   └── R Markdown── Reproducible research reports

The tidyverse (by Hadley Wickham) is R's most important package collection:
  readr   → data import
  dplyr   → data manipulation
  tidyr   → data reshaping
  ggplot2 → data visualization
  purrr   → functional programming
  stringr → string manipulation

Industry          Application                     R Tools Used
──────────────────────────────────────────────────────────────────
Healthcare        Clinical trial analysis         survival, ggplot2
Pharma            Drug efficacy modeling          nlme, lme4
Finance           Quantitative modeling           quantmod, TTR
Academia          Statistical research            base R, lme4
Genomics          Gene expression analysis        Bioconductor
Social Science    Survey analysis                 lavaan, psych
Marketing         Customer segmentation           cluster, mclust
Insurance         Actuarial modeling              actuar
Government        Economic data analysis          ggplot2, dplyr
Manufacturing     Quality control (SPC)           qcc

# Mini Project: Student Performance Analysis in R

# Sample dataset
students <- data.frame(
  name    = c("Alice", "Bob", "Carol", "David", "Eve", "Frank"),
  math    = c(92, 78, 85, 60, 95, 72),
  science = c(88, 82, 90, 55, 97, 65),
  english = c(76, 88, 84, 70, 89, 80),
  stringsAsFactors = FALSE
)

# Calculate averages
students$average <- rowMeans(students[, c("math", "science", "english")])

# Grade assignment
students$grade <- ifelse(students$average >= 90, "A",
                   ifelse(students$average >= 80, "B",
                   ifelse(students$average >= 70, "C",
                   ifelse(students$average >= 60, "D", "F"))))

# Summary statistics
cat("=== STUDENT PERFORMANCE REPORT ===\n\n")
cat("Class Statistics:\n")
cat("  Mean Math:    ", mean(students$math),    "\n")
cat("  Mean Science: ", mean(students$science), "\n")
cat("  Mean English: ", mean(students$english), "\n")
cat("  Class Average:", round(mean(students$average), 2), "\n\n")

cat("Individual Results:\n")
print(students[, c("name", "average", "grade")])

cat("\nTop Performer:", students$name[which.max(students$average)], "\n")
cat("Needs Support: ", students$name[which.min(students$average)], "\n")

# Grade distribution
cat("\nGrade Distribution:\n")
print(table(students$grade))

=== STUDENT PERFORMANCE REPORT ===

Class Statistics:
  Mean Math:     80.33
  Mean Science:  79.5
  Mean English:  81.17
  Class Average: 80.33

Individual Results:
   name  average grade
1 Alice   85.333     B
2   Bob   82.667     B
3 Carol   86.333     B
4 David   61.667     D
5   Eve   93.667     A
6 Frank   72.333     C

Top Performer: Eve
Needs Support:  David

Grade Distribution:
A B C D
1 3 1 1

R-related Job Roles:
  Data Analyst          → $65,000 - $95,000/year
  Statistician          → $75,000 - $115,000/year
  Data Scientist        → $95,000 - $140,000/year
  Quantitative Analyst  → $100,000 - $160,000/year
  Biostatistician       → $80,000 - $130,000/year
  Research Scientist    → $85,000 - $130,000/year

Top companies using R:
  Google, Facebook, Airbnb, Pfizer, Johnson & Johnson,
  Merck, Goldman Sachs, McKinsey, WHO, FDA