R Pipe Operator

The pipe operator passes the output of one expression as the input to the next. It eliminates deeply nested function calls and makes multi-step transformations read in a natural, left-to-right sequence — the same order you think through the steps.

The Problem Pipes Solve

# Without pipe — deeply nested, read inside-out:
result <- round(mean(sqrt(abs(c(-4, 9, -16, 25, -1)))), 2)

# With native pipe |> — reads left to right:
result <- c(-4, 9, -16, 25, -1) |> abs() |> sqrt() |> mean() |> round(2)

# Step by step:
# c(-4,9,-16,25,-1) → abs → sqrt → mean → round
# [4,9,16,25,1]  →  [2,3,4,5,1]  → 3  → 3

Two Pipe Operators in R

Pipe        Package         Available Since    Notes
──────────────────────────────────────────────────────────────
|>          Base R          R 4.1.0 (2021)     No package needed
%>%         magrittr/dplyr  Always             More features

Native Pipe |> (R 4.1+)

library(dplyr)

c(1, 4, 9, 16, 25) |>
  sqrt() |>
  sum()
# 15

# With data frames
employees |>
  filter(dept == "IT") |>
  arrange(desc(salary)) |>
  select(name, salary)

Magrittr Pipe %>%

library(magrittr)  # or just load dplyr

c(1, 4, 9, 16, 25) %>%
  sqrt() %>%
  sum()
# 15

Placeholder in Pipe Chains

# Native pipe placeholder: _ (R 4.2+)
mtcars |> lm(mpg ~ wt, data=_) |> summary()

# Magrittr placeholder: .
c(1,2,3) %>% paste("item", .)
# "item 1" "item 2" "item 3"

Real Analysis Pipeline

raw_sales <- data.frame(
  date    = c("2024-01-15","2024-01-20","2024-02-10","2024-02-25","2024-03-05"),
  region  = c("North","South","North","South","North"),
  product = c("A","B","A","A","B"),
  revenue = c(1200, NA, 1500, 1100, 800)
)

summary_report <- raw_sales |>
  filter(!is.na(revenue)) |>                     # remove NAs
  mutate(date = as.Date(date),
         month = format(date, "%B")) |>           # extract month
  group_by(region, month) |>
  summarise(total_revenue = sum(revenue),
            avg_revenue   = mean(revenue),
            orders        = n(),
            .groups = "drop") |>
  arrange(region, desc(total_revenue))

print(summary_report)

Pipe vs Step-by-Step Assignment

# Without pipe (intermediate objects pollute environment):
step1 <- filter(raw_sales, !is.na(revenue))
step2 <- mutate(step1, month = format(as.Date(date), "%B"))
step3 <- group_by(step2, region, month)
result <- summarise(step3, total=sum(revenue), .groups="drop")

# With pipe (clean, no intermediate variables):
result <- raw_sales |>
  filter(!is.na(revenue)) |>
  mutate(month = format(as.Date(date), "%B")) |>
  group_by(region, month) |>
  summarise(total=sum(revenue), .groups="drop")

Enable |> in RStudio

Go to Tools → Global Options → Code → Editing and check "Use native pipe operator |>". Now the Ctrl+Shift+M shortcut inserts |> instead of %>%.

The pipe operator does not add new functionality — it just changes how code is written. The difference it makes to readability is enormous. Every modern R workflow uses pipes to chain data operations into clear, readable pipelines that a new reader can understand almost immediately.