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fixing readme
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README.Rmd

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@@ -16,7 +16,6 @@ knitr::opts_chunk$set(
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[![R build status](https://github.com/data-cleaning/errorlocate/workflows/R-CMD-check/badge.svg)](https://github.com/data-cleaning/errorlocate/actions)
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[![CRAN](http://www.r-pkg.org/badges/version/errorlocate)](https://CRAN.R-project.org/package=errorlocate)
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[![Downloads](http://cranlogs.r-pkg.org/badges/errorlocate)](http://www.r-pkg.org/pkg/errorlocate)
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[![status](https://tinyverse.netlify.com/badge/errorlocate)](https://CRAN.R-project.org/package=errorlocate)
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[![Codecov test coverage](https://codecov.io/gh/data-cleaning/errorlocate/branch/master/graph/badge.svg)](https://codecov.io/gh/data-cleaning/errorlocate?branch=master)
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[![Mentioned in Awesome Official Statistics ](https://awesome.re/mentioned-badge.svg)](http://www.awesomeofficialstatistics.org)
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README.md

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@@ -5,7 +5,6 @@
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status](https://github.com/data-cleaning/errorlocate/workflows/R-CMD-check/badge.svg)](https://github.com/data-cleaning/errorlocate/actions)
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[![CRAN](http://www.r-pkg.org/badges/version/errorlocate)](https://CRAN.R-project.org/package=errorlocate)
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[![Downloads](http://cranlogs.r-pkg.org/badges/errorlocate)](http://www.r-pkg.org/pkg/errorlocate)
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[![status](https://tinyverse.netlify.com/badge/errorlocate)](https://CRAN.R-project.org/package=errorlocate)
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[![Codecov test
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coverage](https://codecov.io/gh/data-cleaning/errorlocate/branch/master/graph/badge.svg)](https://codecov.io/gh/data-cleaning/errorlocate?branch=master)
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[![Mentioned in Awesome Official
@@ -21,10 +20,10 @@ formulate data validation rules to which the data must comply.
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For example:
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- “age cannot be negative”: `age >= 0`.
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- “if a person is married, he must be older then 16 years”:
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`if (married ==TRUE) age > 16`.
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- “Profit is turnover minus cost”: `profit == turnover - cost`.
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- “age cannot be negative”: `age >= 0`.
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- “if a person is married, he must be older then 16 years”:
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`if (married ==TRUE) age > 16`.
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- “Profit is turnover minus cost”: `profit == turnover - cost`.
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While `validate` can check if a record is valid or not, it does not
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identify which of the variables are responsible for the invalidation.
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er <- errors_removed(data_no_error)
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print(er)
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#> call: locate_errors(data, x, ref, ..., cl = cl)
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#> call: locate_errors(data, x, ref, ..., cl = cl, Ncpus = Ncpus)
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#> located 1 error(s).
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#> located 0 missing value(s).
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#> Use 'summary', 'values', '$errors' or '$weight', to explore and retrieve the errors.

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