Data frames

courses <- data.frame(
  title = c("HTML", "SQL", "Python"),
  price = c(29.99, 59.99, 29.99),
  level = c("beginner", "intermediate", "beginner")
)

print(courses[courses$level == "beginner", ])
print(mean(courses$price))

A data frame is a table -- rows and named columns, R's equivalent of a spreadsheet or a SQL result set, and the structure almost all real R analysis is built around.

A data frame is really a list of equal-length vectors, one per column, each of which can hold a different type -- so a "price" column can be numeric while a "title" column is character data, all within the same table. There are several ways to inspect and slice one:

str(courses)          # shows column names, types, and a preview of values
nrow(courses)          # number of rows
ncol(courses)          # number of columns
head(courses, 2)       # first 2 rows
courses$title           # the title column, as a plain vector
courses[["price"]]      # equivalent way to pull a single column
courses[1, ]             # the first row, all columns
courses[, "price"]       # the price column, all rows -- note the comma placement

The comma inside [ , ] matters a lot: everything before it selects rows, everything after selects columns. courses[courses$level == "beginner", ] reads as "give me the rows where level equals beginner, and all columns" -- leaving the part after the comma blank means "all columns."

Adding a new column is as simple as assigning to a name that doesn't exist yet, and it's automatically vectorized across every row:

courses$discounted_price <- courses$price * 0.8
courses$is_premium <- courses$price > 50

merge() joins two data frames together on a shared column, similar to a SQL JOIN:

enrollments <- data.frame(title = c("HTML", "SQL"), students = c(120, 80))
merge(courses, enrollments, by = "title")  # inner join on the title column