In SAS, every DATA step prints a log that tells you exactly what happened:
NOTE: There were 120000 observations read from WORK.SALES.
NOTE: 7153 observations were deleted.
NOTE: The data set WORK.SALES has 112847 observations and 11 variables.R’s dplyr pipelines are silent by default.
logrittr fills that gap with %>=%, a pipe
operator that logs at each step:
No function masking, no dependencies beyond cli and
stringr for coloring and formatting in console.
Tip: Fira Code users: with ligatures enabled,
%>=%renders as a single wide arrow, close from a regular pipe.
install.packages("logrittr", repos = "https://guillaumepressiat.r-universe.dev")
# alternatively
# remotes::install_github("GuillaumePressiat/logrittr")
library(logrittr)
library(dplyr)
iris %>=%
as_tibble() %>=%
filter(Sepal.Length < 5) %>=%
mutate(rn = row_number()) %>=%
group_by(Species) %>=%
summarise(n = n_distinct(rn))── iris [rows: 150 cols: 5] ────────────────────────────────────────────
ℹ as_tibble() rows: 150 +0 cols: 5 +0 [ 0.0 ms]
ℹ filter(Sepal.Length < 5) rows: 22 -128 cols: 5 +0 [ 1.0 ms]
ℹ mutate(rn = row_number()) rows: 22 +0 cols: 6 +1 [ 1.0 ms]
added: rn
ℹ group_by(Species) rows: 22 +0 cols: 6 +0 [ 1.0 ms]
ℹ summarise(n = n_distinct(rn)) rows: 3 -19 cols: 2 -4 [ 1.0 ms]
dropped: Sepal.Length, Sepal.Width, Petal.Length, and 2 other
added: n%>=% is fully composable with |> and
%>%. Use it only where you want visibility, and fall
back to the native pipe for the rest.
When %>=% appears inside an argument (e.g. inside
semi_join()), the nested steps are automatically indented
with a > prefix so they are visually distinct from the
main pipeline:
iris %>=%
as_tibble() %>=%
filter(Sepal.Length < 5) %>=%
mutate(rn = row_number()) %>=%
semi_join(
iris %>% as_tibble() %>=%
filter(Species == "setosa"),
by = "Species"
) %>=%
group_by(Species) %>=%
summarise(n = n_distinct(rn))── iris [rows: 150 cols: 5] ─────────────────────────────────────
ℹ as_tibble() rows: 150 +0 cols: 5 +0 [ 1.0 ms]
ℹ filter(Sepal.Length < 5) rows: 22 -128 cols: 5 +0 [ 1.0 ms]
ℹ mutate(rn = row_number()) rows: 22 +0 cols: 6 +1 [ 1.0 ms]
added: rn
ℹ > filter(Species == "setosa") rows: 50 -100 cols: 5 +0 [ 2.0 ms]
ℹ semi_join(iris %>% as_tibble() %>=% filter(Species == rows: 20 -2 cols: 6 +0 [ 32.0 ms]
"setosa"), by = "Species")
ℹ group_by(Species) rows: 20 +0 cols: 6 +0 [ 1.0 ms]
ℹ summarise(n = n_distinct(rn)) rows: 1 -19 cols: 2 -4 [ 1.0 ms]
dropped: Sepal.Length, Sepal.Width, Petal.Length, and 2 others
added: nAll display options are controlled via
logrittr_options():
logrittr_options()
#> $wrap_width
#> [1] 52
#> $big_mark
#> [1] " "
#> $lang
#> [1] "en"
#> $max_cols
#> [1] 5Switch to French with lang = "fr" (the metrics line uses
lignes instead of rows):
logrittr_options(lang = "fr")
iris %>=%
select(Species, Sepal.Length, Sepal.Width) %>=%
filter(Sepal.Length > 5)── iris [lignes: 150 cols: 5] ─────────────────────────────────────────
ℹ select(Species, Sepal.Length, Sepal.Width) lignes: 150 +0 cols: 3 -2 [ 3.0 ms]
dropped: Petal.Length, Petal.Width
ℹ filter(Sepal.Length > 5) lignes: 118 -32 cols: 3 +0 [ 1.0 ms]
logrittr_options(lang = "en", big_mark = ",")
big <- data.frame(x = seq_len(1e6), y = rnorm(1e6))
big %>=% filter(x > 500000)── big [rows: 1,000,000 cols: 2] ────────────────────────────────────────────
ℹ filter(x > 5e+05) rows: 500,000 -500000 cols: 2 +0 [ 11.0 ms]or underscore
── big [rows: 1_000_000 cols: 2] ────────────────────────────────────────────
ℹ filter(x > 5e+05) rows: 500_000 -500000 cols: 2 +0 [ 11.0 ms]When a select or join adds or drops many
columns, only the first max_cols names are shown to keep
the log readable:
logrittr_options(max_cols = 2, lang = "en")
iris %>=%
as_tibble() %>=%
select(Species, Sepal.Length)── iris [rows: 150 cols: 5] ───────────────────────────────────────────────
ℹ as_tibble() rows: 150 +0 cols: 5 +0 [ 0.0 ms]
ℹ select(Species, Sepal.Length) rows: 150 +0 cols: 2 -3 [ 1.0 ms]
dropped: Sepal.Width, Petal.Length, and 1 otherUse max_cols = Inf to always display all names:
logrittr_options(max_cols = Inf)logrittr_options() invisibly returns the previous
values, which makes it easy to restore the state after a temporary
change:
old <- logrittr_options(lang = "fr", big_mark = ",")
# ... work ...
do.call(logrittr_options, old) # restore previous stateIf you already use the lumberjack package,
logrittr_logger plugs directly into its
%L>% pipe. The same console output as
%>=% is produced, and you keep access to all lumberjack
features (run_file(), custom loggers, etc.).
library(lumberjack)
library(dplyr)
iris %L>%
start_log(log = logrittr_logger$new(), label = "Iris Example") %L>%
as_tibble() %L>%
filter(Sepal.Length < 5) %L>%
mutate(rn = row_number()) %L>%
group_by(Species) %L>%
summarise(n = n_distinct(rn)) %L>%
dump_log(stop = TRUE)── Iris Example [rows: 150 cols: 5] ───────────────────────────────────────
ℹ as_tibble() rows: 150 +0 cols: 5 +0 [ NA ms]
ℹ filter(Sepal.Length < 5) rows: 22 -128 cols: 5 +0 [ 47.0 ms]
ℹ mutate(rn = row_number()) rows: 22 +0 cols: 6 +1 [ 5.0 ms]
added: rn
ℹ group_by(Species) rows: 22 +0 cols: 6 +0 [ 11.0 ms]
ℹ summarise(n = n_distinct(rn)) rows: 3 -19 cols: 2 -4 [ 4.0 ms]
dropped: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, rn
added: n
✔ Log from Iris Example step written to ~/Documents/GitHub/logrittr/Iris Example_simple.csvThe first step always shows NA ms because lumberjack
does not provide a start time – elapsed is measured as the interval
between consecutive steps.