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https://github.com/mkearney/dapr
☝🏼👉🏼👇🏼👈🏼 Dependency-free purrr-like apply/map/iterate functions
https://github.com/mkearney/dapr
for-loops functional-programming iterator r r-package rstats
Last synced: about 2 months ago
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☝🏼👉🏼👇🏼👈🏼 Dependency-free purrr-like apply/map/iterate functions
- Host: GitHub
- URL: https://github.com/mkearney/dapr
- Owner: mkearney
- License: gpl-3.0
- Created: 2018-10-19T21:56:59.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-06-28T18:49:36.000Z (about 5 years ago)
- Last Synced: 2024-02-09T02:08:48.827Z (5 months ago)
- Topics: for-loops, functional-programming, iterator, r, r-package, rstats
- Language: R
- Homepage: https://dapr.mikewk.com
- Size: 998 KB
- Stars: 52
- Watchers: 4
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE.md
Lists
- jimsghstars - mkearney/dapr - ☝🏼👉🏼👇🏼👈🏼 Dependency-free purrr-like apply/map/iterate functions (R)
- awesome-stars - mkearney/dapr - ☝🏼👉🏼👇🏼👈🏼 Dependency-free purrr-like apply/map/iterate functions (R)
README
---
output: github_document
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, comment = "#>")
library(dapr)
```
# dapr 
[](https://travis-ci.org/mkearney/dapr)
[](https://cran.r-project.org/package=dapr)
[](https://codecov.io/gh/mkearney/dapr?branch=master)
[](https://doi.org/10.5281/zenodo.2528504)
[](https://www.tidyverse.org/lifecycle/#experimental)
Dependency-free purrr-like apply/map/iterate functions
## Installation
Install the development version from Github with:
``` r
## install remotes pkg if not already
if (!requireNamespace("remotes", quietly = TRUE)) {
install.packages("remotes")
}
## install from github
remotes::install_github("mkearney/dapr")
```
## {dapr} vs. {base} & {purrr}?
**{dapr}** provides the ease and consistency of [**{purrr}**](https://purrr.tidyverse.org),
(see also: simple benchmark results plot below) including use of `~` and `.x`,
without all the dependencies. In other words, use **{dapr}** when you want a
purrr-like experience but you need a lightweight solution.
## Use
Function names use the convention `*ap()` where **`*`** is the first letter of output data type.
+ vap for **vectors**
+ lap for **lists**
+ dap for **data frames**
Common inputs:
+ `.data` Input object–numeric, character, list, data frame, etc.–over which elements will be iterated. If matrix or data frame, each column will be treated as the elements which are to be iterated over.
+ `.f` Function to apply to each element of input object. This can be written as a single function name e.g., `mean`, a formula-like function call where `.x` is assumed to be the iterated over element of input data e.g., `~ mean(.x)`, or an in-line function definition e.g., `function(x) mean(x)`.
### Vectors
Functions that apply expressions to input data objects and return atomic vectors
e.g., numeric (double), character, logical.
+ **`vap_dbl()`** Iterate and return **numeric** vector.
+ **`vap_int()`** Iterate and return **integer** vector.
+ **`vap_lgl()`** Iterate and return **logical** vector.
+ **`vap_chr()`** Iterate and return **character** vector.
```{r}
## create data
set.seed(2018)
d <- replicate(5, rnorm(10), simplify = FALSE)
e <- replicate(5, sample(letters, 10), simplify = FALSE)
## numeric
vap_dbl(d, ~ mean(.x))
## integer
vap_int(d, length)
## logical
vap_lgl(d, ~ max(.x) > 3)
## character
vap_chr(e, paste, collapse = "")
```
### Lists
Function(s) that apply expressions to input data objects and return lists.
+ **`lap()`** Iterate and return a **list** vector.
```{r}
## list of strings
lap(e[1:2], ~ paste0(.x, "."))
```
+ **`ilap()`** Iterate over sequence length `.i` (instead of `.x`) and return a **list** vector.
```{r}
## list of strings
ilap(1:4, ~ paste0(letters[.i], rev(LETTERS)[.i]))
```
### Data frames
Functions that apply expressions to input data objects and return data frames.
+ **`dap*()`** Iterate and return a **data frame**
- **`dapc()`** Iterate over **columns**
- **`dapr()`** Iterate over **rows**
+ **`dap*_if()`** Conditionally iterate
- **`dapc_if()`** Conditionally iterate over **columns**
- **`dapr_if()`** Conditionally iterate over **rows**
```{r}
## some data
d <- data.frame(
a = letters[1:3],
b = rnorm(3),
c = rnorm(3),
stringsAsFactors = FALSE
)
## column explicit (same as dap)
dapc(d[-1], ~ round(.x, 2))
## rows
dapr(d[-1], round, 3)
## conditional COLUMNS
dapc_if(d, is.numeric, ~ round(.x, 4))
## conditional ROWS
dapr_if(d[-1], ~ sum(.x) >= -.7, ~ round(.x, 0))
```