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https://github.com/nathaneastwood/poorman

A poor man's dependency free grammar of data manipulation
https://github.com/nathaneastwood/poorman

base-r data-manipulation grammar r

Last synced: about 2 months ago
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A poor man's dependency free grammar of data manipulation

Lists

README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

options(poorman.summarise.inform = FALSE)

```






# {poorman} 



[![CRAN status](https://www.r-pkg.org/badges/version/poorman)](https://cran.r-project.org/package=poorman)

[![Dependencies](https://tinyverse.netlify.com/badge/poorman)](https://cran.r-project.org/package=poorman)

![CRAN downloads](https://cranlogs.r-pkg.org/badges/poorman)

[![R-CMD-check](https://github.com/nathaneastwood/poorman/actions/workflows/check-standard.yaml/badge.svg)](https://github.com/nathaneastwood/poorman/actions/workflows/check-standard.yaml)

[![codecov](https://codecov.io/gh/nathaneastwood/poorman/branch/master/graph/badge.svg?token=YPQSSEEHZJ)](https://app.codecov.io/gh/nathaneastwood/poorman)





  I'd seen my father. He was a poor man, and I watched him do astonishing things.

    - Sidney Poitier




## Overview



{poorman} is a grammar of data manipulation, providing dependency free versions of [{dplyr}](https://github.com/tidyverse/dplyr) verbs that help you solve the most common data manipulation challenges:



* `select()` picks variables based on their names.

* `mutate()` adds new variables that are functions of existing variables.

* `filter()` picks cases based on their values.

* `summarise()` reduces multiple values down to a single summary.

* `arrange()` changes the ordering of the rows.



{poorman} attempts to replicate the {dplyr} API exactly such that your {dplyr} code will still run even if you use {poorman} in its place. In addition to replicating {dplyr} functionality, {poorman} implements other functionality from the wider {tidyverse} such as select helpers and the pipe, `%>%`.



For more details on the functionality available within {poorman}, check out the {poorman} series of blog posts [here](https://nathaneastwood.github.io/tags/poorman/).



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## Installation



You can install:



* the development version from [GitHub](https://github.com/nathaneastwood/poorman) with



```{r installation, eval = FALSE}

# install.packages("remotes")

remotes::install_github("nathaneastwood/poorman")

```



* the latest release from CRAN with



```{r cran, eval = FALSE}

install.packages("poorman")

```



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## Docker



If you'd like to try out the latest version of the package on CRAN using Docker, you can run the latest image with:



```{bash, eval = FALSE}

docker run --rm -it nathaneastwood/poorman

```



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## Usage



```{r usage}

library(poorman, warn.conflicts = FALSE)



mtcars %>%

  select(mpg, wt, starts_with("c")) %>%

  mutate(kpl = (1.609 * mpg) / 3.785, wt_kg = wt * 453.5924) %>%

  filter(mpg > 28)



mtcars %>%

  group_by(am, cyl) %>%

  summarise(mean_mpg = mean(mpg), sd_mpg = sd(mpg)) %>%

  ungroup()

```



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## Related Work



* [{dplyr}](https://github.com/tidyverse/dplyr)

* [{bplyr}](https://github.com/yonicd/bplyr) - imports {magrittr} and {rlang}; it prepends functions with `b_*()`, e.g. `b_select()`.

* [{tbltools}](https://github.com/mkearney/tbltools) - imports {magrittr} and appends `*_data()` to each of its functions, e.g. `select_data()`.



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