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{poorman} <a href='https://nathaneastwood.github.io/tags/poorman/'><img src='man/figures/logo.png' align="right" height="139" /></a>

<blockquote align="center"> I’d seen my father. He was a poor man, and I watched him do astonishing things. - Sidney Poitier </blockquote>

Overview

{poorman} is a grammar of data manipulation, providing dependency free versions of {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.

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Installation

You can install:

• the development version from GitHub with

# install.packages("remotes")
remotes::install_github("nathaneastwood/poorman")

• the latest release from CRAN with

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:

docker run --rm -it nathaneastwood/poorman

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Usage

library(poorman, warn.conflicts = FALSE)
# 
#   I'd seen my father. He was a poor man, and I watched him do astonishing things.
#     - Sidney Poitier

mtcars %>%
  select(mpg, wt, starts_with("c")) %>%
  mutate(kpl = (1.609 * mpg) / 3.785, wt_kg = wt * 453.5924) %>%
  filter(mpg > 28)
#                 mpg    wt cyl carb      kpl    wt_kg
# Fiat 128       32.4 2.200   4    1 13.77321 997.9033
# Honda Civic    30.4 1.615   4    2 12.92301 732.5517
# Toyota Corolla 33.9 1.835   4    1 14.41086 832.3421
# Lotus Europa   30.4 1.513   4    2 12.92301 686.2853

mtcars %>%
  group_by(am, cyl) %>%
  summarise(mean_mpg = mean(mpg), sd_mpg = sd(mpg)) %>%
  ungroup()
#   am cyl mean_mpg    sd_mpg
# 1  0   4 22.90000 1.4525839
# 2  0   6 19.12500 1.6317169
# 3  0   8 15.05000 2.7743959
# 4  1   4 28.07500 4.4838599
# 5  1   6 20.56667 0.7505553
# 6  1   8 15.40000 0.5656854

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Related Work

• {dplyr}
• {bplyr} - imports {magrittr} and {rlang}; it prepends functions with b_*(), e.g. b_select().
• {tbltools} - imports {magrittr} and appends *_data() to each of its functions, e.g. select_data().

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