Syntax and vectors

name <- "Ada"
age <- 28
price <- 29.99

prices <- c(29.99, 59.99, 99.99, 149.99)
print(mean(prices))
print(prices * 1.1)  # applies to every element at once

<- is the traditional assignment operator (= also works). c() combines values into a vector -- R's core data structure, and operations on it are automatically "vectorized" across every element.

There's actually no such thing as a single scalar value in R -- even price <- 29.99 creates a vector of length 1. This is why every operation you do on a "single" number also works transparently on an entire vector: R doesn't have a separate concept of scalar math versus array math the way many languages do.

Indexing is one of the biggest differences from most other languages: R vectors are indexed starting at 1, not 0, and square brackets accept far more than a single position -- a vector of positions, a range, or even a logical (boolean) vector the same length as the data, which is how filtering is typically done:

prices <- c(29.99, 149.99, 59.99, 9.99)

prices[1]              # 29.99 -- first element (not "zeroth")
prices[2:3]             # elements 2 through 3
prices[-1]              # every element EXCEPT the first
prices[prices > 50]    # logical indexing: only elements greater than 50
prices[c(TRUE, FALSE, TRUE, FALSE)] # equivalent, spelled out manually

Recycling is another vectorization quirk worth knowing: if you combine two vectors of different lengths in an arithmetic operation, R "recycles" (repeats) the shorter one to match the longer one's length, issuing a warning if the lengths aren't a clean multiple of each other:

c(1, 2, 3, 4) + c(10, 20)   # [1] 11 22 13 24 -- c(10, 20) recycled to c(10, 20, 10, 20)

This is powerful once you're used to it (no explicit loop needed to apply a two-element pattern across a longer vector) but a common source of silent bugs when the recycling wasn't actually intended -- always double-check vector lengths line up the way you expect before relying on an operation between two vectors.