---
title: "ID Correction and Autobot"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{ID Correction and Autobot}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(ohcleandat)
```

The data cleaning and validation pipeline provides a way to specify rules that can then be applied to data in order to produce a validation log for manual corrections. However, in some cases particularly with the use of ID columns there are certain automatic corrections that can be made due to formatting errors.

For instance missing prefixes, incorrect case, or non-standard formatting of columns where there should be a predictable and fixed format. In this case, we wish to provide an automated cleaning step that makes these corrections to the data, but also produces a validation log for our records. This is done in two steps.

The first is applying the automatic corrections through the use of an `id_check()` function (or family of checking functions). These operate on the semi-clean data set to produce a new proposed column with the automated corrections. These functions are designed and implemented by users based on their requirements. 

Once these corrections are made, both the original ID column and the new corrected ID column are provided to an `autobot()` function in the pipeline. The `autobot()` function compares these records and keeps only those where the original in the new column are different - indicating that some form of automatic correction has been made.

A validation log is generated in the exact same format as other validation logs, however a key change here is that this validation log does not require the manual overview of a reviewer. The proposed changes are automatically accepted by the autobot. The reason for producing the log is to persist changes and have a record of how IDs have changed due to automatic corrections. 

## Example  

Below is an example of a (fake) farm_id identifier. We can see the ID checker functions have corrected an 'O' to '0' in record 2. Case correction has taken place, and records that do not conform the the required pattern post corrections, are set to NA for manual review. 

```
# A tibble: 6 × 2
  farm_id    farm_id_new
  <chr>      <chr>      
1 123ABC0007 NA         
2 1O3ABC010  103ABC010  
3 143abc010  143ABC010  
4 13DEFH005  NA         
5 243DLF803  243DLF803  
6 243DPF911  243DPF911 
```

```
> ohcleandat::autobot(data = test, old_col = "farm_id", new_col = "farm_id_new", key = "farm_id")
# A tibble: 2 × 8
  entry     field   issue                               old_value is_valid new_val  user_initials comments
  <chr>     <chr>   <chr>                               <chr>     <chr>    <chr>    <chr>         <chr>   
1 1O3ABC010 farm_id Automated field format check failed 1O3ABC010 FALSE    103ABC0… autobot       ""      
2 143abc010 farm_id Automated field format check failed 143abc010 FALSE    143ABC0… autobot       ""    
```