In data-cleaning/dcmodifydb: Modifying Rules on a DataBase

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(dcmodify)
library(dcmodifydb)

Introduction

The goal of dcmodifydb is to apply modification rules specified with dcmodify on a database table, allowing for documented, reproducable data cleaning adjustments in a database.

This vignette describes the supported syntax that can be used with dcmodify and dcmodifydb.

For common error scenario's see vignette("scenarios", package="dcmodifydb")

Syntax

if rule

m <- modifier( if (age < 12) income = 0)

The following statements are equivalent. It is wise to choose a syntax that is familiar to the persons specifying the correction rules.

```{eval=FALSE} if (age < 12) income = 0 # R noobs if (age < 12) income <- 0 # a bit more R-y income[age < 12] <- 0 # very R-y

### `else`

Each `if` rule may be followed with an `else` or `else if`

```r
m <- modifier(if (age > 67) {retired = TRUE} else {retired = FALSE})

The following statements are equivalent. It is wise to choose a syntax that is familiar to the persons specifying the correction rules.

```{eval=FALSE} if (age > 67) {retired = TRUE} else {retired = FALSE} # R noobs if (age > 67) {retired <- TRUE} else {retired <- FALSE} # R-y retired <- if (age > 67) TRUE else FALSE # very R-y retired <- age > 67 # very R-y

### multiple assignments

```r
m <- modifier( 
  if (age > 67) {
    retired = TRUE
    salary = 0 
  }
)

else if

data-cleaning/dcmodifydb documentation built on June 23, 2022, 10:34 p.m.