### Install errorlocate Beta Version with drat

Source: https://github.com/data-cleaning/errorlocate/blob/master/README.md

Install beta versions of the errorlocate package using the drat repository.

```r
drat::addRepo("data-cleaning")
install.packages("errorlocate")
```

--------------------------------

### Install errorlocate from CRAN

Source: https://github.com/data-cleaning/errorlocate/blob/master/README.md

Install the stable version of the errorlocate package from CRAN.

```r
install.packages("errorlocate")
```

--------------------------------

### Install Latest Development Version from GitHub

Source: https://github.com/data-cleaning/errorlocate/blob/master/README.md

Install the most recent development version of the errorlocate package directly from GitHub using devtools.

```r
devtools::install_github("data-cleaning/errorlocate")
```

--------------------------------

### Basic Usage of errorlocate

Source: https://github.com/data-cleaning/errorlocate/blob/master/README.md

Demonstrates how to use the errorlocate package to define validation rules, identify errors in data, and replace erroneous values with NA.

```r
library(errorlocate)
#> Loading required package: validate
rules <- validator(
    profit == turnover - cost,
    cost >= 0.6 * turnover,
    turnover >= 0,
    cost >= 0 # is implied
)

data <- data.frame(profit=750, cost=125, turnover=200)

data_no_error <- replace_errors(data, rules)

# faulty data was replaced with NA
print(data_no_error)
#>   profit cost turnover
#> 1     NA  125      200

er <- errors_removed(data_no_error)

print(er)
#> call:  locate_errors(data, x, ref, ..., cl = cl, Ncpus = Ncpus) 
#> located  1  error(s).
#> located  0  missing value(s).
#> Use 'summary', 'values', '$errors' or '$weight', to explore and retrieve the errors.

summary(er)
#> Variable:
#>       name errors missing
#> 1   profit      1       0
#> 2     cost      0       0
#> 3 turnover      0       0
#> Errors per record:
#>   errors records
#> 1      1       1

er$errors
#>      profit  cost turnover
#> [1,]   TRUE FALSE    FALSE
```

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