In-place updates with data.table's := operator: a practical guide

Problem: Repeatedly updating large tables in R

When you need to add or change columns for many groups in a data set with millions of rows, the usual base-R or tidyverse approaches copy the whole object each time, which can consume a lot of memory and slow down your script.

Thesis: data.table's := operator updates columns in place, avoiding copies and giving speed gains for grouped updates.

How := works

The := operator is part of the data.table package. It assigns a value to a column (or creates a new one) directly inside the existing data.table object, so no full copy is made. When you combine it with a key set by setkey() or a by expression, data.table locates each group via an internal binary search and applies the expression only to those rows.

Worked example: Adding a speed column to flight data

# Install and load the package (run once)
# install.packages("data.table")
library(data.table)

# Example data: nycflights13::flights (if you have it)
# install.packages("nycflights13")
library(nycflights13)
DT <- as.data.table(flights)

# Set a key for the origin column to speed up grouped updates
setkey(DT, origin)

# Compute average speed per origin and store it in a new column, in place
DT[, avg_speed := mean(distance / air_time * 60, na.rm = TRUE), by = origin]

# Check the first few rows
head(DT[, .(origin, avg_speed)])

Because := modifies DT by reference, any other variable that points to the same object will see the new column immediately.

Trade‑off: side effects and learning curve

The main caution is that the update is in place. If you have another reference, e.g. DT2 <- DT, changing DT also changes DT2. To avoid accidental overwrites, copy explicitly when you need an unchanged version: DT_copy <- copy(DT). The syntax also differs from base R and dplyr, so teams unfamiliar with data.table may need a short ramp‑up period.

Actionable closing: verify that no copy was made

You can confirm the in‑place behavior with tracemem():

tracemem(DT)
DT[, avg_speed := mean(distance / air_time * 60, na.rm = TRUE), by = origin]
# No output means no copy was created.

If you see a memory address printed, a copy occurred. For a quick performance check, compare system.time() of the := version against a dplyr::mutate() equivalent on the same data.

By using := with a keyed data.table, you can perform grouped updates that stay memory‑efficient and run faster, especially when the same table is updated many times in a loop or pipeline.