Language lab · Optional: statistics

📊 R

Concept → trace → write code → run tests → explain why → improve → mini-project. Start in the browser if you like; set up your own computer when you are ready.

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📖 Topics with real code (13)

Every verified output below was produced by running the code with R version 4.4.1 (2024-06-14) -- "Race for Your Life" on 6 Nov 2026. Open a topic to edit the code and run it right here in your browser.

🌱 Beginner (5)

Vectors and data types

R works on vectors: even one number is a vector of length 1. Types include numeric, integer (5L), character and logical. class() shows the type; as.numeric() converts.

age <- 15L
height <- 1.62
student <- TRUE
name <- "Asha"
cat(name, age, height, student, "\n")
cat(class(age), class(height), class(student), class(name), "\n")
marks <- c(72, 95, 64, 88)
cat(as.numeric("88") + 2, length(marks), marks * 2, "\n")

✅ Verified output

Asha 15 1.62 TRUE 
integer numeric logical character 
90 4 144 190 128 176

Arithmetic and logic on vectors

Operators work element by element. %/% is integer division and %% the remainder. Comparisons give logical vectors used for filtering.

a <- 17; b <- 5
cat(a + b, a - b, a * b, a / b, a %/% b, a %% b, a^2, "\n")
x <- c(3, 8, 1, 9, 4)
cat(x > 4, "\n")
cat(x[x > 4], sum(x > 4), "\n")
cat(TRUE && FALSE, TRUE || FALSE, !TRUE, "\n")

✅ Verified output

22 12 85 3.4 3 2 289 
FALSE TRUE FALSE TRUE FALSE 
8 9 2 
FALSE TRUE FALSE

Conditionals and ifelse

if/else for single values; ifelse() works on whole vectors at once, which is how R code usually avoids loops.

marks <- 78
grade <- if (marks >= 90) "A+" else if (marks >= 75) "A" else if (marks >= 35) "Pass" else "Fail"
cat("Grade:", grade, "\n")
all_marks <- c(92, 78, 30, 55)
print(ifelse(all_marks >= 35, "pass", "fail"))
switch("sat", sat = , sun = cat("Weekend\n"), cat("School day\n"))

✅ Verified output

Grade: A 
[1] "pass" "pass" "fail" "pass"
Weekend

Loops and seq

for walks a vector; seq and seq_along make sequences. Prefer vector operations; loops are fine for small jobs.

for (i in 1:5) {
  if (i == 3) next
  cat("for", i, "\n")
}
n <- 10
while (n > 0) { if (n < 4) break; cat("while", n, "\n"); n <- n - 3 }
print(seq(0, 1, by = 0.25))
print(outer(1:3, 1:3))

✅ Verified output

for 1 
for 2 
for 4 
for 5 
while 10 
while 7 
while 4 
[1] 0.00 0.25 0.50 0.75 1.00
     [,1] [,2] [,3]
[1,]    1    2    3
[2,]    2    4    6
[3,]    3    6    9

Functions

function() creates a function; the last value is returned. Arguments can have defaults and be named.

area <- function(length, width = 1) length * width
stats <- function(x) c(min = min(x), max = max(x), mean = mean(x))
cat(area(5, 3), area(7), area(width = 2, length = 9), "\n")
print(stats(c(4, 9, 2, 7)))

✅ Verified output

15 7 18 
 min  max mean 
 2.0  9.0  5.5

🌿 Intermediate (4)

Data frames

A data frame is a table: columns of equal length. Select rows with conditions, add columns, and summarise.

df <- data.frame(name = c("Asha", "Ravi", "Meena", "Imran"), class = c(8, 9, 8, 9), marks = c(88, 95, 72, 64))
df$grade <- ifelse(df$marks >= 75, "A", "B")
print(df[df$marks > 70, ])
print(aggregate(marks ~ class, data = df, FUN = mean))
cat("rows:", nrow(df), "columns:", ncol(df), "\n")

✅ Verified output

   name class marks grade
1  Asha     8    88     A
2  Ravi     9    95     A
3 Meena     8    72     B
  class marks
1     8  80.0
2     9  79.5
rows: 4 columns: 4

Descriptive statistics

mean, median, sd, quantile and summary describe data; table() counts categories.

set.seed(42)
heights <- round(rnorm(200, mean = 160, sd = 8), 1)
print(round(c(mean = mean(heights), median = median(heights), sd = sd(heights)), 2))
print(quantile(heights, c(0.1, 0.5, 0.9)))
print(table(cut(heights, breaks = c(130, 150, 160, 170, 190))))

✅ Verified output

  mean median     sd 
159.78 159.85   7.80 
   10%    50%    90% 
149.57 159.85 170.31 

(130,150] (150,160] (160,170] (170,190] 
       21        83        75        21

Lists and apply functions

A list holds mixed items. lapply/sapply apply a function to each element; split + sapply gives group summaries.

student <- list(name = "Asha", marks = c(88, 92, 79), passed = TRUE)
str(student)
print(sapply(list(a = 1:5, b = c(10, 20)), mean))
scores <- c(88, 95, 72, 64, 81); cls <- c("8A", "8B", "8A", "8B", "8A")
print(sapply(split(scores, cls), max))

✅ Verified output

List of 3
 $ name  : chr "Asha"
 $ marks : num [1:3] 88 92 79
 $ passed: logi TRUE
 a  b 
 3 15 
8A 8B 
88 95

Strings

paste, sprintf, toupper, gsub, strsplit and nchar handle text; grepl finds patterns.

t <- trimws("  GayaGnan learning  ")
cat(toupper(t), "|", sub("learning", "labs", t), "|", nchar(t), "\n")
print(strsplit(t, " ")[[1]])
cat(sprintf("Total: %.2f for %d items", 49.5 * 3, 3L), "\n")
print(grepl("^G", c("Gaya", "Ravi", "Gnan")))

✅ Verified output

GAYAGNAN LEARNING | GayaGnan labs | 17 
[1] "GayaGnan" "learning"
Total: 148.50 for 3 items 
[1]  TRUE FALSE  TRUE

🌳 Advanced (4)

Linear regression

lm() fits a straight line y = a + b·x by least squares; coef() gives the intercept and slope, and R² measures fit.

study_hours <- c(1, 2, 3, 4, 5, 6, 7, 8)
marks <- c(52, 55, 61, 64, 70, 73, 79, 84)
fit <- lm(marks ~ study_hours)
print(round(coef(fit), 3))
cat("R squared:", round(summary(fit)$r.squared, 4), "\n")
cat("predicted marks for 9 hours:", round(predict(fit, data.frame(study_hours = 9)), 1), "\n")

✅ Verified output

(Intercept) study_hours 
     46.571       4.595 
R squared: 0.9948 
predicted marks for 9 hours: 87.9

Hypothesis testing

t.test compares the means of two groups; a small p-value suggests the difference is unlikely to be chance.

set.seed(7)
group_a <- rnorm(30, mean = 72, sd = 8)
group_b <- rnorm(30, mean = 78, sd = 8)
res <- t.test(group_a, group_b)
cat("means:", round(mean(group_a), 1), round(mean(group_b), 1), "\n")
cat("t =", round(res$statistic, 3), " p =", signif(res$p.value, 3), "\n")
cat(if (res$p.value < 0.05) "significant at 5 %\n" else "not significant\n")

✅ Verified output

means: 75.2 78.3 
t = -1.55  p = 0.127 
not significant

Matrices and linear algebra

matrix() builds matrices; %*% multiplies, t() transposes, solve() inverts or solves systems, eigen() finds eigenvalues.

A <- matrix(c(2, 1, 1, 3), nrow = 2)
b <- c(5, 10)
x <- solve(A, b)
print(x)
print(A %*% x)
print(round(eigen(A)$values, 4))

✅ Verified output

[1] 1 3
     [,1]
[1,]    5
[2,]   10
[1] 3.618 1.382

Probability and simulation

sample and runif simulate experiments; the law of large numbers makes the simulated probability approach the exact one.

set.seed(1)
rolls <- sample(1:6, 60000, replace = TRUE)
print(round(table(rolls) / length(rolls), 3))
two_dice <- sample(1:6, 1e5, TRUE) + sample(1:6, 1e5, TRUE)
cat("P(sum = 7) simulated:", mean(two_dice == 7), " exact:", round(6 / 36, 4), "\n")

✅ Verified output

rolls
    1     2     3     4     5     6 
0.166 0.164 0.165 0.170 0.168 0.167 
P(sum = 7) simulated: 0.16736  exact: 0.1667

🛠️ Set up and verify

Install

winget install RProject.R

brew install r

sudo apt install r-base

Editor: Visual Studio Code with R (REditorSupport) or RStudio.
Create hello.R
```
cat('Hello, GayaGnan!\n')
```
Run it and check the version
```
Rscript hello.R
R --version
```
Health check: paste what your terminal printed.

Install one language at a time. These commands only install or print versions; nothing here deletes files. Versions change: check the official page and note the version you installed.

🧭 The 14-lab path

00 · Setup and verify — install the runtime and editor, run hello world, print the version, fix PATH
01 · Syntax and trace — follow each line and the value of every variable
02 · Logic — conditionals and decision trees
03 · Loops — trace tables and loop challenges
04 · Functions — break problems down and test each piece
05 · Data structures — lists, maps, stacks, queues and trees you can see
06 · Debugging — bug cases with a hint ladder
07 · Algorithms — search, sort and Big-O comparison
08 · Files and data — read and write fixture files
09 · API or database — talk to a mock API or a sandbox database
10 · Testing — write tests and read failing assertions
11 · Security — secure coding and dependency hygiene for this language
12 · Project — one portfolio mini-project
13 · Assessment — quiz, coding test, rubric and an explain-your-answer prompt

📚 Modules

vectors
data frames
functions
tidy data
plots
statistical summaries
reproducible scripts
tests

🏗️ Mini-projects

Synthetic course-performance analysis
Descriptive analytics report

Projects use synthetic data only.