Day 1: R Fundamentals & Data Types | R Programming in 5 Days

R's Vector-First Design

In R, everything is a vector. A single number is a vector of length 1. Operations apply element-wise: c(1,2,3) * 2 returns c(2,4,6). This vectorized design means most operations require no explicit loops — they work on entire vectors at once, which is both concise and fast (the underlying C code handles the loop). The <- operator assigns; = works too but <- is idiomatic R.

Core Data Types

Atomic types: numeric (double), integer (1L), character ('hello'), logical (TRUE/FALSE), complex, and raw. Data structures: vector (1D, same type), matrix (2D, same type), list (1D, mixed types), data frame (2D, columns can be different types), and factor (categorical variable with levels). The is.*() family tests types; as.*() coerces. NA represents missing values; NULL represents the absence of an object.

Subsetting

R has three subsetting operators: [ (returns same type, multiple elements), [[ (returns single element, drops container), and $ (named element, same as [[name]]). Subsetting with a logical vector extracts matching elements: x[x > 5] returns values greater than 5. Negative indexing excludes: x[-1] drops the first element. Data frame columns: df$col, df[,'col'], df[[1]].

# Vectors are the foundation
x <- c(1, 4, 9, 16, 25)
cat('Square roots:', sqrt(x), '\n')

# Vectorized operations (no loop needed)
temps_c <- c(0, 20, 37, 100)
temps_f <- temps_c * 9/5 + 32
cat('Fahrenheit:', temps_f, '\n')  # 32 68 98.6 212

# Data frame basics
df <- data.frame(
  name  = c('Alice', 'Bob', 'Carol', 'Dave'),
  score = c(92, 78, 95, 85),
  grade = c('A', 'C', 'A', 'B'),
  stringsAsFactors = FALSE
)

# Subsetting
df[df$score >= 90, ]           # rows where score >= 90
df[df$grade == 'A', 'name']   # names of A students
subset(df, score > 80, select = c(name, score))

# Summary statistics
cat('Mean:', mean(df$score), '\n')
cat('SD:  ', sd(df$score), '\n')
summary(df)

💡

Use <- for assignment, not =. While = works, <- is the universal R convention and makes the code's intent clear to any R programmer reading it. RStudio shortcut: Alt+- types <-.

📝 Day 1 Exercise

Explore Your First Dataset

Install R from r-project.org and RStudio from posit.co
Load the built-in mtcars dataset: data(mtcars); head(mtcars)
Find the mean mpg, median hp, and standard deviation of wt
Subset to only cars with mpg > 20 and more than 4 cylinders
Use table() to count how many cars have each number of cylinders

Day 1 Summary

Everything in R is a vector — even a single number
Operations apply element-wise to entire vectors without explicit loops
Data frames are the primary tabular data structure — columns can be different types
Subsetting: [ returns same type; [[ returns single element; $ accesses named columns
NA represents missing values; use is.na() and na.rm=TRUE to handle them

Challenge

Load the built-in 'iris' dataset. Compute the mean and SD of all four numeric columns grouped by Species. Identify which species has the largest average petal length and which has the smallest variation in sepal width.

Day 2: Data Manipulation with dplyr & tidyr

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