Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/moodymudskipper/nakedpipe
Pipe Into a Sequence of Calls Without Repeating the Pipe Symbol.
https://github.com/moodymudskipper/nakedpipe
Last synced: 3 months ago
JSON representation
Pipe Into a Sequence of Calls Without Repeating the Pipe Symbol.
- Host: GitHub
- URL: https://github.com/moodymudskipper/nakedpipe
- Owner: moodymudskipper
- Created: 2020-02-17T16:33:58.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-08-01T14:22:59.000Z (3 months ago)
- Last Synced: 2024-08-01T16:05:08.147Z (3 months ago)
- Language: R
- Size: 322 KB
- Stars: 69
- Watchers: 4
- Forks: 7
- Open Issues: 6
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
Awesome Lists containing this project
- jimsghstars - moodymudskipper/nakedpipe - Pipe Into a Sequence of Calls Without Repeating the Pipe Symbol. (R)
README
---
output: github_document
---
[](https://www.tidyverse.org/lifecycle/#experimental)
[](https://travis-ci.org/moodymudskipper/nakedpipe)
[](https://codecov.io/gh/moodymudskipper/nakedpipe?branch=master)
# nakedpipe 
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
Pipe into a sequence of calls without repeating the pipe symbol.
This is inspired by Stefan Bache and Hadley Wickham's *magrittr* pipe and behaves
mostly consistently.
*nakedpipe* calls are more compact, and are intended to be more readable,
though it's expected that they will look surprising to new users.
The syntax allowed the development of many additional features that cannot be
implemented as ergonomically with *magrittr*.
An instant translation addin between *magrittr* and *nakedpipe* is included.
It's not yet on *CRAN* so you should install with :
``` r
remotes::install_github("moodymudskipper/nakedpipe")
```
## General principles
A basic *{nakedpipe}* call looks a lot like a *{magrittr}* pipe chain, except that the
piping symbol is not repeated, and that we surround the calls with `{}`
*{magrittr}* syntax :
```{r}
library(magrittr)
cars %>%
subset(speed < 6) %>%
transform(time = dist/speed)
```
*{nakedpipe}* syntax :
```{r}
library(nakedpipe)
cars %.% {
subset(speed < 6)
transform(time = dist/speed)
}
```
The dot insertion rules are identical to the ones used by *{magrittr}*, and likewise
if we surround a step in `{}`, no dot will be inserted.
It plays well with left to right assignment:
```{r}
cars %.% {
subset(speed < 6)
transform(time = dist/speed)
} -> res
```
Additional features include :
* Side effects, using `~~`, similar to ```magrittr::`%T>%```
* Temporary assignments and assignments to the calling environment
* Shorthands for most common data manipulation operations, namely `subset()`,
`transform()` and grouped transformations.
* Conditional steps using `if`
* Possibility to use *{data.table}* syntax for one step
* Additional pipes to debug, assign in place, print or clock each step...
## Side effects and assignments
Use `~~` for side effects:
```{r}
cars %.% {
subset(speed < 6)
~~ message("nrow:", nrow(.))
transform(time = dist/speed)
}
```
This include assignments :
```{r}
cars %.% {
subset(speed < 6)
~~ cars_h <- . # or ~~ . -> cars_h
transform(time = dist/speed)
}
cars_h
```
To assign to a temp variable, use a dotted name:
```{r}
cars %.% {
~~ .n <- 6
subset(speed < .n)
transform(time = dist/speed)
}
exists(".n")
```
## Data manipulation shorthands
For the very common `subset()` and `transform()` operations, shorthands are
available, so that for our first example we could simply write:
```{r}
cars %.% {
speed < 6 # any call to < > <= >= == != %in% & | is interpreted as a subset call
time = dist/speed # any call to = is interpreted as a transform call
}
```
## Conditional steps
Use `if` for conditional step. if the condition is not TRUE and there is no
`else` clause the data is unchanged:
```{r}
cars %.% {
subset(speed < 6)
if(ncol(.) < 5) transform(time = dist/speed)
}
cars %.% {
subset(speed < 6)
if(ncol(.) > 5) transform(time = dist/speed)
}
```
## Use *data.table* syntax
We can use *data.table* syntax for one step by using `.dt[...]`, the output
will be of the same class of the input (the temporary conversion to *data.table*
is invisible):
```{r}
cars %.% {
speed < 8
time = dist/speed
.dt[, .(mmean_time = mean(time)), by = speed]
}
```
We can chain *data.table* brackets too:
```{r}
cars %.% {
.dt[speed < 8][, time := dist/speed][,.(mmean_time = mean(time)), by = speed]
}
```
## Additional pipes
Assign in place using `%<.%`
```{r}
cars_copy <- cars
cars_copy %<.% {
head(2)
~~ message("nrow:", nrow(.))
transform(time = dist/speed)
}
cars_copy
```
Clock each step using `%L.%`
```{r}
cars %L.% {
head(2)
~~ Sys.sleep(1)
transform(time = dist/speed)
}
```
`print()` the output of each step using `%P.%`
```{r}
cars %P.% {
head(2)
transform(time = dist/speed)
}
```
`View()` the output of each step using `%V.%`
```{r, eval = FALSE}
cars %V.% {
head(2)
transform(time = dist/speed)
}
```
`%..%` is faster at the cost of using explicit dots
```{r}
cars %..% {
head(.,2)
transform(.,time = dist/speed)
}
```
It is better suited for programming and doesn't support side effect notation
but you can do :
```{r}
cars %..% {
head(.,2)
{message("nrow:", nrow(.)); .}
transform(.,time = dist/speed)
}
```
Create a function using `%F.%` on `.`
```{r}
fun <- . %F.% {
head(.,2)
transform(.,time = dist/speed)
}
fun(cars)
```
Apply a sequence of calls on all elements using `%lapply.%`
```{r}
replicate(2, cars, simplify = FALSE) %lapply.% {
head(.,2)
transform(.,time = dist/speed)
}
```
See `?"%.%"` and `?"%lapply.%"` to see all available pipes (including variants of
the above).
## Debugging
The `%D.%` pipe allows you to step through the calls one by one.
```{r, eval = FALSE}
# Debug the pipe using `%D.%`
cars %D.% {
head(2)
transform(time = dist/speed)
}
```
You could also inster a browser() call as a side effect at a chosen step.
```{r, eval = FALSE}
# Debug the pipe using `%D.%`
cars %D.% {
head(2)
~~ browser()
transform(time = dist/speed)
}
```
## *ggplot2*
It's a little known trick that you can use *magrittr*'s pipe with *ggplot2* if you pipe
to the `+` symbol. It is convenient if you want to use the ggplot object
as the input of another function without intermediate variables of bracket
overload :
```{r, eval = FALSE}
library(ggplot2)
path <- tempfile()
cars %>%
head() %>%
ggplot(aes(speed, dist)) %>%
+ geom_point() %>%
+ ggtitle("head(cars)") %>%
saveRDS(path)
# rather than
plt <- cars %>%
head() %>%
ggplot(aes(speed, dist)) +
geom_point() +
ggtitle("head(cars)")
saveRDS(plt, path)
```
The former case above shows operators on both sides, which looks a bit complicated,
the latter requires a temporary variable and we must look at the end of the
previous line to know what kind of piping was done.
In both cases additionally if I chose to comment out the `ggtitle("head(cars)")` line, I should
also comment the last operator at the end of the previous line.
With *nakedpipe* we can write :
```{r, eval = FALSE}
cars %.% {
head()
ggplot(aes(speed, dist))
+ geom_point()
+ ggtitle("head(cars)")
saveRDS(path)
}
```
`+` signs are neatly alligned, it's obvious where the *ggplot* chain starts and
ends, and trivial to pipe it to another instruction or to comment a line.
## Conversion to magrittr syntax and back
We provide an addin to ease the conversion.
## Running partial selection
It's easy with standard pipes to run only the first steps of a pipe chain, by
selecting them and running selected code. With *{nakedpipe}* the closing `}` is
missing if we do the same. The addin "nakedpipe run incomplete call" allows one
to run the selection after adding the closing `}`.
## Benchmark
We're a bit faster than *{magrittr} 1.5*, if you want to be even faster use `%..%` with
explicit dots. Note that *magrittr*'s upcoming version is much faster than both,
though keep in mind these are micro seconds and that the fastest
solution is always not to use pipes at all. See below the benchmark using
`{magrittr} 2.0`.
```{r}
library(magrittr)
bench::mark(iterations = 10000,
`%>%` = cars %>%
identity %>%
identity() %>%
identity(.) %>%
{identity(.)},
`%.%` = cars %.% {
identity
identity()
identity(.)
{identity(.)}
},
`%..%` = cars %..% {
identity(.)
identity(.)
identity(.)
{identity(.)}
},
`base` = {
. <- cars
. <- identity(.)
. <- identity(.)
. <- identity(.)
. <- identity(.)
}
)
```
## Snippets
Runing `setup_nakedpipe_snippets()` will open RStudio's snippet file so you
can add our suggested snippets there. Follow the instructions and you'll be able to type :
```{r, eval = FALSE}
cars . # + 2 time the key
```
and display :
```{r, eval = FALSE}
cars %.% {
# with the cursor conveniently placed here
}
```
(or type `..` to get the `%..%` equivalent)
## Aknowledgements and similar efforts
*{nakedpipe}* is heavily inspired by *{magrittr}* and follows the same dot insertion
rules.
The functions from `*{dplyr}*` and the *tidyverse* in general had a big
influence on `*{nakedpipe}*`.
*{data.table}* is the package behind the `.dt[...]` syntax described above.
Alternative pipes are available on *CRAN*, at the time of writing and to my knowledge,
in packages *wrapr* and *pipeR*. The latter includes a function `pipeline()` that
allows piping a sequence of calls in a similar fashion as *nakedpipe*.