Summary

You can now select and replace parts of an object by position, name, or condition, while checking the structure of the result.

What you learned

Task	Syntax	Remember
Select vector elements	x[c(1, 3)]	R positions start at 1.
Exclude vector elements	x[-2]	Do not mix positive and negative positions, except zero.
Select by a condition	x[x > 10]	The condition produces a logical vector. Handle missing values explicitly.
Select a matrix or data-frame rectangle	x[rows, columns]	The comma separates the two dimensions; a blank selects all entries in that dimension.
Keep a one-column table	df[, "height", drop = FALSE]	Without drop = FALSE, selecting one column this way can return a vector.
Select one list component	x[[1]] or x[["name"]]	x[1] instead returns a list containing that component.
Select a named component	df$height	Convenient for a known column name; use df[[column_name]] if the name is stored in a variable.

For a data frame, df["height"] selects a column as a data frame; df[1, ] selects a row. For a matrix, a single index such as m[3] counts through columns. Always consider the object’s structure before interpreting brackets.

which() returns positions that meet a condition. %in% tests membership, while match() finds positions of matches. Use &, |, and ! for element-by-element AND, OR, and NOT. Test equality with ==; <- assigns a value.

A short recap in R

observations <- data.frame(
  site = c("A", "B", "C", "D"),
  height_cm = c(12, NA, 18, 9)
)
keep <- !is.na(observations$height_cm) & observations$height_cm >= 12
selected <- observations[keep, c("site", "height_cm"), drop = FALSE]
selected

The result contains sites A and C, with heights 12 and 18 cm. The explicit !is.na() check excludes the missing measurement. A comparison with an NA alone can leave NA entries in a logical index and produce missing rows in the result.

You can also put an indexed expression on the left of <- to replace selected values. Check the selection first so that you change the intended entries. subset() provides another way to select rows and columns; sample() selects a random sample. Use set.seed() when an example’s random selection needs to be reproducible.

Check your understanding

1. Explain the difference between observations[1, ] and observations[, 1].
2. How would you select only site C using its name rather than its row number?
3. Why do my_list[1] and my_list[[1]] sometimes give very different results?

Revisit indexing and subsetting for more examples.

Test