## Project Status

This package is currently maintained by Craig Hutton, a Data Scientist working with the Research Branch of the British Columbia Ministry of Social Development & Poverty Reduction.

## Why elucidate?

elucidate provides a collection of convenience functions to make exploratory data analysis in R easier and more accessible for researchers to (among other things):

• Interrogate data in search of row duplicates with dupes() and anomalous values with the counts* set of functions.

• Describe data with the describe* set of functions for obtaining summary statistics, bootstrapping confidence intervals, and detecting missing values.

• Quickly visualise and interact with data representations with the plot_* set of functions.

Inspired by tidyverse naming conventions, the core functions of elucidate are organized into sets that begin with a common root (e.g. describe*, plot_*), since this enables the user to see them all as suggestions as you are coding in R studio.

Drawing from similar inspiration, many elucidate functions are also designed to accept a data object as the 1st argument and return a data or plotting object (e.g. ggplot2 or plotly) so they are compatible with the pipe operator from the magrittr package for easy integration into data processing pipelines.

For a comprehensive introduction to the package see the vignette via vignette("elucidate").

## Installation

You can install the development version of elucidate from this repository with:

# use the remotes package to install from a github repository

install.packages("remotes") #only run this 1st if you haven't installed remotes before

remotes::install_github("bcgov/elucidate")

The authors of elucidate acknowledge and express their gratitude to the authors of the tidyverse packages, data.table, and the functions of other dependency packages which were used to build elucidate, since without their effort and ingenuity elucidate would mostly have remained a collection of ideas instead of functions.

## Usage

dupes() can tell you how many rows are duplicated based on one or more variables (default is all of them).

library(elucidate)

#list any number of variables to use when searching for duplicates after the
#data argument
dupes(pdata, d)
#> Duplicated rows detected! 12000 of 12000 rows in the input data have multiple copies.
#> # A tibble: 12,000 x 11
#>       id d          g     high_low even     y1    y2    x1    x2    x3 n_copies
#>    <int> <date>     <fct> <chr>    <lgl> <dbl> <dbl> <int> <int> <int>    <int>
#>  1     1 2019-01-01 e     high     FALSE  144. 112.     86   199   261     1000
#>  2     2 2019-01-01 c     low      TRUE   262.  91.8    88   104   224     1000
#>  3     3 2019-01-01 d     high     FALSE  224. 120.     29   192   224     1000
#>  4     4 2019-01-01 c     high     TRUE   262. 102.     77   151   254     1000
#>  5     5 2019-01-01 a     low      FALSE  167.  91.6    75   186   258     1000
#>  6     6 2019-01-01 a     low      TRUE   163.  83.6    39   152   293     1000
#>  7     7 2019-01-01 d     low      FALSE  247.  98.9    88   177   202     1000
#>  8     8 2019-01-01 b     high     TRUE   189. 102.     79   163   217     1000
#>  9     9 2019-01-01 e     high     FALSE  156. 110.      4   136   220     1000
#> 10    10 2019-01-01 c     low      TRUE   248.  95.9    63   113   213     1000
#> # ... with 11,990 more rows
#in this case we search for duplicated based on the "d" (date) column        

describe() a single variable in a data frame or a vector of values.

#set random generator seed for reproducibility
set.seed(1234)

#using a numeric vector as input
describe(data = rnorm(1:1000, 100, 5))
#> # A tibble: 1 x 14
#>   cases     n    na  p_na  mean    sd    se    p0   p25   p50   p75  p100   skew
#>   <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
#> 1  1000  1000     0     0  99.9  4.99 0.158  83.0  96.6  99.8  103.  116. -0.005
#> # ... with 1 more variable: kurt <dbl>

describe_all() all variables in a data frame.

describe_all(pdata)
#> $date #> variable cases n na p_na n_unique start end #> 1: d 12000 12000 0 0 12 2008-01-01 2019-01-01 #> #>$factor
#>    variable cases     n na p_na n_unique ordered
#> 1:        g 12000 12000  0    0        5   FALSE
#>                              counts_tb
#> 1: a_2592, b_2460, ..., e_2352, c_2220
#>
#> $character #> variable cases n na p_na n_unique min_chars max_chars #> 1: high_low 12000 12000 0 0 2 3 4 #> counts_tb #> 1: high_6045, low_5955 #> #>$logical
#>    variable cases     n na p_na n_TRUE n_FALSE p_TRUE
#> 1:     even 12000 12000  0    0   6000    6000    0.5
#>
#> \$numeric
#>    variable cases     n na p_na    mean      sd    se      p0     p25     p50
#> 1:       id 12000 12000  0    0 500.500 288.687 2.635   1.000 250.750 500.500
#> 2:       y1 12000 12000  0    0 153.705  42.724 0.390  69.224 120.941 144.782
#> 3:       y2 12000 12000  0    0 100.092  10.120 0.092  59.963  93.351 100.128
#> 4:       x1 12000 12000  0    0  50.495  28.937 0.264   1.000  25.000  50.000
#> 5:       x2 12000 12000  0    0 150.651  28.820 0.263 101.000 126.000 150.000
#> 6:       x3 12000 12000  0    0 250.497  28.865 0.264 201.000 225.000 251.000
#>        p75     p100   skew   kurt
#> 1: 750.250 1000.000  0.000 -1.200
#> 2: 181.022  289.235  0.739 -0.181
#> 3: 106.906  142.181 -0.032  0.059
#> 4:  75.000  100.000  0.008 -1.208
#> 5: 176.000  200.000  0.002 -1.209
#> 6: 276.000  300.000  0.006 -1.214

Use plot_var() to produce a class-appropriate ggplot2 graph of a single variable in a data frame or a vector of values.

plot_var(data = rnorm(1:1000, 100, 5)) 

#in this case we get a density plot with a normal density curve added for
#reference (dashed line).

To generate class-appropriate ggplot2 graphs for all variables in a data frame and combine them into a multiple-panel figure with the patchwork package, use plot_var_all(). You can also limit the graphing to a subset of columns with the “cols” argument, which accepts a character vector of column names.

plot_var_all(pdata, cols = c("y1", "y2", "g", "even"))

#density plots for numeric variables and bar graphs for categorical variables

These examples only highlight a few of the many things elucidate can do. You can learn more from these additional resources:

• A blog post, which was written to help researchers and science trainees learn how to use the package for common exploratory data analysis tasks.

• The introductory vignette (vignette("elucidate")), which provides annotated code examples that demonstrate most of the package’s functionality.

## Reporting an Issue

To report bugs/issues or request feature changes, open an issue for the package GitHub repo. If raising an issue, please provide a reproducible example (reprex) of the problem you’re encountering.

## Requesting Features and/or Changes

To suggest changes or code improvements, please submit a pull request.