README.md

In allenbaron/JABmisc: Utility Functions for Data Analysis

JABmisc

The goal of JABmisc is to improve data analysis workflows by:

1. simplifying initial data exploration
2. providing functions to fill gaps in data manipulation/munging/wrangling

Installation

JABmisc currently only exists in a development version. You can install this version from GitHub with:

# install.packages("devtools")
devtools::install_github("allenbaron/JABmisc")

Examples

To get a quick look at a tabular dataset (after loading) use describe_cols() or the full-functioning audit_df(). Here’s an example with the airquality dataset:

library(JABmisc)
describe_cols(airquality)
#> # A tibble: 6 x 6
#>   col    type  missing_n missing_percent unique_n unique_as_string              
#>   <chr>  <chr>     <int>           <dbl>    <int> <chr>                         
#> 1 Ozone  int          37            24.2       67 41 | 36 | 12 | 18 | 28 | 23 |~
#> 2 Solar~ int           7             4.6      117 190 | 118 | 149 | 313 | 299 |~
#> 3 Wind   dbl           0             0         31 7.4 | 8 | 12.6 | 11.5 | 14.3 ~
#> 4 Temp   int           0             0         40 67 | 72 | 74 | 62 | 56 | 66 |~
#> 5 Month  int           0             0          5 5 | 6 | 7 | 8 | 9             
#> 6 Day    int           0             0         31 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8~

Other available functions in this package:

• filter lists by name or value (filter_list())
• perform numeric formatting (round_up(), format_num())
• or, are TOTALLY miscellaneous and provide functionality to help in random, odd use-cases where I’ve needed them (e.g. find_consensus_path())

allenbaron/JABmisc documentation built on July 4, 2021, 9:56 a.m.