Data manipulation

In basetable: Fast and Memory-Efficient Base R Table Manipulation

library(basetable)

basetable uses compact verbs with explicit arguments. The same functions work in nested expressions and native R pipelines, always returning a the native engine without modifying the input.

A compact pipeline

mtcars |>
  subset(cyl >= 6, select = c("mpg", "hp", "wt", "cyl")) |>
  transform(power = hp / wt) |>
  orderrows(by = c("cyl", "mpg"), decreasing = c(FALSE, TRUE))

orderrows() accepts one direction per column. This makes a mixed ascending/descending order explicit without a descending-expression mini-language.

orderrows(
  mtcars,
  by = c("cyl", "mpg"),
  decreasing = c(FALSE, TRUE)
) |>
  firstrows(6)

Values from the calling function

Transformation expressions can combine table columns with ordinary values defined by the calling function. Newly created columns are available to later expressions in the same call.

scorecars <- function(data, horsepowerweight = 0.7) {
  data |>
    transform(
      weightedhp = hp * horsepowerweight,
      score = weightedhp / wt
    ) |>
    orderrows("score", decreasing = TRUE)
}

scorecars(mtcars) |>
  pick(c("mpg", "hp", "wt", "score")) |>
  firstrows(5)

Grouped summaries

aggregate(airquality, by = "Month", value = c("Ozone", "Temp"), fun = mean, na.rm = TRUE)

The expression-oriented equivalent stays compact when several summaries use different functions.

summaries(
  airquality,
  ozone = mean(Ozone, na.rm = TRUE),
  temperature = mean(Temp, na.rm = TRUE),
  days = length(Temp),
  by = "Month"
)

Joins

merge(
  data.frame(id = 1:3, x = letters[1:3]),
  data.frame(id = c(2, 3, 4), y = LETTERS[2:4]),
  by = "id",
  all = TRUE
)

Validation in a pipeline

Assertions return the input invisibly when they pass, so a pipeline can fail close to the operation that violated its contract.

cars <- mtcars |>
  transform(car = rownames(mtcars)) |>
  firstcols("car")

assertcomplete(cars, c("car", "mpg", "cyl"))
assertunique(cars, "car")
assertrows(cars, mpg > 0)

Explicit package calls

A few compact names intentionally match base R or other table packages (subset(), merge(), transform(), split()). basetable does not ship dplyr-named verbs such as filter(), select(), or mutate(), so it can be attached alongside dplyr without shadowing its grammar. For the names it does share with other table packages, qualify the verb rather than changing the workflow grammar.

mtcars |>
  basetable::subset(cyl == 6) |>
  basetable::pick(c("mpg", "hp", "wt")) |>
  basetable::transform(power = hp / wt)

basetable documentation built on Sept. 18, 2026, 9:13 a.m.