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library(pacman)
p_load(tidyverse, janitor, styler)1 Introduction
Pipe operators have become one of the most important tools in modern R programming (Wickham, Çetinkaya-Rundel, and Grolemund 2023; R Core Team 2025). They allow you to express a sequence of data transformations in a clear, readable, and intuitive manner.
This document provides a comprehensive guide to the major pipe operators in R, including:
- The Base R pipe
|>(R Core Team 2025) - The magrittr pipes
%>%,%<>%,%$%,%T>%(@ Wickham, Çetinkaya-Rundel, and Grolemund 2023)
All examples use R’s built-in datasets.
2 Why Use Pipes?
Pipes increase code readability and reduce nested function calls. Compare:
Without pipes:
Code
summary(lm(mpg ~ wt + hp, data = mtcars))
Call:
lm(formula = mpg ~ wt + hp, data = mtcars)
Residuals:
Min 1Q Median 3Q Max
-3.941 -1.600 -0.182 1.050 5.854
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 37.22727 1.59879 23.285 < 2e-16 ***
wt -3.87783 0.63273 -6.129 1.12e-06 ***
hp -0.03177 0.00903 -3.519 0.00145 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.593 on 29 degrees of freedom
Multiple R-squared: 0.8268, Adjusted R-squared: 0.8148
F-statistic: 69.21 on 2 and 29 DF, p-value: 9.109e-12With a pipe:
Code
mtcars |>
lm(mpg ~ wt + hp, data = _) |>
summary()
Call:
lm(formula = mpg ~ wt + hp, data = mtcars)
Residuals:
Min 1Q Median 3Q Max
-3.941 -1.600 -0.182 1.050 5.854
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 37.22727 1.59879 23.285 < 2e-16 ***
wt -3.87783 0.63273 -6.129 1.12e-06 ***
hp -0.03177 0.00903 -3.519 0.00145 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.593 on 29 degrees of freedom
Multiple R-squared: 0.8268, Adjusted R-squared: 0.8148
F-statistic: 69.21 on 2 and 29 DF, p-value: 9.109e-123 The Base R Pipe |>
The base pipe |> was introduced in R 4.1.0 (R Core Team 2025). It is written as “|” followed by “> (greater than symbol)” with no space in between (R Core Team 2024).
Base R pipes automatically place the input as the first argument. If you need to pipe into another position, use “_” placeholder:
3.1 Basic Usage
Code
mtcars |>
head() mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1You read the code as follows “mtcars then head()”. In short you could read the piple as “then”.
This is the same as calling:
head(mtcars)3.2 Placeholder _
The Base R pipe placeholder “” is used when the piped object needs to be inserted somewhere other than the first argument of a function. It is important because, like magrittr, the Base R pipe always passes the input into the first argument by default—so the ”” placeholder gives you precise control over where the data should go, enabling more flexible and complex function calls.
for example:
Code
mtcars |>
lm(mpg ~ disp + hp, data = _) |>
summary()
Call:
lm(formula = mpg ~ disp + hp, data = mtcars)
Residuals:
Min 1Q Median 3Q Max
-4.7945 -2.3036 -0.8246 1.8582 6.9363
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.735904 1.331566 23.083 < 2e-16 ***
disp -0.030346 0.007405 -4.098 0.000306 ***
hp -0.024840 0.013385 -1.856 0.073679 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.127 on 29 degrees of freedom
Multiple R-squared: 0.7482, Adjusted R-squared: 0.7309
F-statistic: 43.09 on 2 and 29 DF, p-value: 2.062e-093.3 How to Read Pipes Function Calls
You could think of the pipe (both |> and %>%) as being synonymous with “then”.
Code
mtcars |>
subset(select = c(mpg, hp, wt)) |>
transform(power_ratio = hp / wt) |>
with(plot(mpg, power_ratio))
We could read this code as
take mtcars then subset (select columns mpg, hp, wt) then
transform (power_ration = hp / wt) then
plot(mpg, power_ratio)4 The magrittr Pipe %>%
The %>% pipe from magrittr is widely used in the tidyverse (Bache & Wickham 2014). The %>% pipe allows more flexibility than the Base R pipe (Bache and Wickham 2014):
- Supports placeholders “.” used in a similar manner with the base pipe “_” placeholder.
Code
# Note with %>% we use "." as the placeholder
# We do this when what we pipe appears in another position other than the first position.
mtcars %>%
lm(mpg ~ wt + hp, data = .) %>%
summary()
Call:
lm(formula = mpg ~ wt + hp, data = .)
Residuals:
Min 1Q Median 3Q Max
-3.941 -1.600 -0.182 1.050 5.854
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 37.22727 1.59879 23.285 < 2e-16 ***
wt -3.87783 0.63273 -6.129 1.12e-06 ***
hp -0.03177 0.00903 -3.519 0.00145 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.593 on 29 degrees of freedom
Multiple R-squared: 0.8268, Adjusted R-squared: 0.8148
F-statistic: 69.21 on 2 and 29 DF, p-value: 9.109e-12- Provides chaining-friendly transformations
4.1 Basic Usage
Code
library(dplyr)
iris %>%
group_by(Species) %>%
summarise(across(everything(), mean))# A tibble: 3 × 5
Species Sepal.Length Sepal.Width Petal.Length Petal.Width
<fct> <dbl> <dbl> <dbl> <dbl>
1 setosa 5.01 3.43 1.46 0.246
2 versicolor 5.94 2.77 4.26 1.33
3 virginica 6.59 2.97 5.55 2.03 4.2 Other pipes in Magrittr (Rarely used so you may skip them for now)
5 The Assignment Pipe %<>%
The pipe %<>% updates an object in-place.
Code
library(magrittr)
x <- 1:5
x %<>% mean()6 The Exposition Pipe %$%
This pipe exposes variables so they can be used directly.
Example using mtcars:
Code
mtcars %$% cor(mpg, wt)[1] -0.86765947 The Tee Pipe %T>%
The Tee pipe allows you to insert side-effects (printing, plotting) without interrupting the pipeline.
Example with PlantGrowth:
Code
PlantGrowth %>%
group_by(group) %>%
summarise(avg_weight = mean(weight)) %T>%
print() %>%
mutate(rank = rank(-avg_weight))# A tibble: 3 × 2
group avg_weight
<fct> <dbl>
1 ctrl 5.03
2 trt1 4.66
3 trt2 5.53# A tibble: 3 × 3
group avg_weight rank
<fct> <dbl> <dbl>
1 ctrl 5.03 2
2 trt1 4.66 3
3 trt2 5.53 18 Complex Examples
8.1 mtcars – horsepower efficiency
Code
mtcars %>%
mutate(
hp_per_litre = hp / disp * 1000
) %>%
group_by(cyl) %>%
summarise(
avg_eff = mean(hp_per_litre)
)# A tibble: 3 × 2
cyl avg_eff
<dbl> <dbl>
1 4 808.
2 6 706.
3 8 608.8.2 iris – ggplot2 example
Code
library(ggplot2)
iris %>%
ggplot(aes(Sepal.Length, Petal.Length, color = Species)) +
geom_point()
8.3 Example 3: ToothGrowth – comparing supplements
Code
ToothGrowth %>%
group_by(supp, dose) %>%
summarise(mean_len = mean(len))# A tibble: 6 × 3
# Groups: supp [2]
supp dose mean_len
<fct> <dbl> <dbl>
1 OJ 0.5 13.2
2 OJ 1 22.7
3 OJ 2 26.1
4 VC 0.5 7.98
5 VC 1 16.8
6 VC 2 26.1 9 Performance
- Base pipes may be faster.
- magrittr adds flexibility.
- Avoid extremely long pipelines.
10 Common Pitfalls
Forgetting to load tidyverse when using %>%.
Using pipes inside functions without debugging — isolate steps.
Overusing pipes for very simple tasks.
Not naming intermediate variables when pipelines exceed 6–7 steps.
11 Summary and Best Practices
Use |> for simple, base R workflows.
Use %>% for data wrangling, modeling, and plotting.
Use %<>% for in-place updates.
Use %$% when functions expect several vectors.
Use %T>% for side effects like printing or plotting.
Keep pipelines readable and modular.
Prefer tidyverse pipelines for clean, reproducible analysis.
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utils::sessionInfo()R version 4.5.2 (2025-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26220)
Matrix products: default
LAPACK version 3.12.1
locale:
[1] LC_COLLATE=English_United Kingdom.utf8
[2] LC_CTYPE=English_United Kingdom.utf8
[3] LC_MONETARY=English_United Kingdom.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.utf8
time zone: America/La_Paz
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] styler_1.11.0 janitor_2.2.1 lubridate_1.9.4 forcats_1.0.1
[5] stringr_1.6.0 purrr_1.2.0 readr_2.1.5 tidyr_1.3.1
[9] tibble_3.3.0 tidyverse_2.0.0 pacman_0.5.1 ggplot2_4.0.0
[13] magrittr_2.0.4 dplyr_1.1.4
loaded via a namespace (and not attached):
[1] gtable_0.3.6 jsonlite_2.0.0 compiler_4.5.2 tidyselect_1.2.1
[5] snakecase_0.11.1 scales_1.4.0 yaml_2.3.10 fastmap_1.2.0
[9] R6_2.6.1 labeling_0.4.3 generics_0.1.4 knitr_1.50
[13] htmlwidgets_1.6.4 R.cache_0.17.0 pillar_1.11.1 RColorBrewer_1.1-3
[17] tzdb_0.5.0 R.utils_2.13.0 rlang_1.1.6 utf8_1.2.6
[21] stringi_1.8.7 xfun_0.54 S7_0.2.0 timechange_0.3.0
[25] cli_3.6.5 withr_3.0.2 digest_0.6.38 grid_4.5.2
[29] rstudioapi_0.17.1 hms_1.1.4 lifecycle_1.0.4 R.oo_1.27.1
[33] R.methodsS3_1.8.2 vctrs_0.6.5 evaluate_1.0.5 glue_1.8.0
[37] farver_2.1.2 codetools_0.2-20 rmarkdown_2.30 tools_4.5.2
[41] pkgconfig_2.0.3 htmltools_0.5.8.1 References
Bache, Stefan Milton, and Hadley Wickham. 2014. “Magrittr: A Forward-Pipe Operator for r.” R Package Version 1.5.
R Core Team. 2024. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.
———. 2025. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund. 2023. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 2nd Edition. O’Reilly Media.