README.md

In ashlarnder/grouping: Data Grouping and Numerical Summarizing

grouping

The goal of ‘grouping’ is to group your data set by a variable and then summarize the remaining numerical variables based on a defined summary function. See the function documentation details for further details.

Installation

The package ‘grouping’ is not yet on CRAN, but you can download it from this repository using the following R command:

devtools::install_github("ashlarnder/grouping")

Example

This is a simple example which shows you the primary use of the function:

library(grouping)
group_summary(palmerpenguins::penguins, island, mean)
#> # A tibble: 3 × 6
#>   island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g  year
#>   <fct>              <dbl>         <dbl>             <dbl>       <dbl> <dbl>
#> 1 Biscoe              45.3          15.9              210.       4716. 2008.
#> 2 Dream               44.2          18.3              193.       3713. 2008.
#> 3 Torgersen           39.0          18.4              191.       3706. 2008.

This function requires three inputs in order to run: (1) the data set you are analyzing, (2) the variable you want to groupby, and (3) the summary function you wish to use.

This package is a huge time-saver in repetitive data analysis. When working with large, unruly data sets and when working across multiple different data sets, summarizing is a key function that is used. This package puts summarizing data at the tip of your finger. It allows you to apply a cleaner and quicker data analysis. You can even simultaneously apply two summary functions to the same data set.

group_summary(mtcars, cyl, c(mean,sd))
#> # A tibble: 3 × 21
#>     cyl mpg_1 mpg_2 disp_1 disp_2  hp_1  hp_2 drat_1 drat_2  wt_1  wt_2 qsec_1
#>   <dbl> <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl>  <dbl>
#> 1     4  26.7  4.51   105.   26.9  82.6  20.9   4.07  0.365  2.29 0.570   19.1
#> 2     6  19.7  1.45   183.   41.6 122.   24.3   3.59  0.476  3.12 0.356   18.0
#> 3     8  15.1  2.56   353.   67.8 209.   51.0   3.23  0.372  4.00 0.759   16.8
#> # … with 9 more variables: qsec_2 <dbl>, vs_1 <dbl>, vs_2 <dbl>, am_1 <dbl>,
#> #   am_2 <dbl>, gear_1 <dbl>, gear_2 <dbl>, carb_1 <dbl>, carb_2 <dbl>

There is also a check step inherently included in this function. Sometimes you forget which data set you are using, or sometimes your data is input as the wrong data class. This function will let you know if there is no numerical data for the function to be applied to, by throwing an informative error saying “This does not contain numerical data”. See the function documentation for more info.

This is pretty basic, so you should be able to apply it no problem!

ashlarnder/grouping documentation built on Dec. 19, 2021, 5:36 a.m.