validate_limits: Use codebook to check continuous variables for validity

In petebaker/codebookr: Aids cleaning, checking and formatting datasets using codebook metadata

Description

Uses a codebook which is an S3 class codebook, possibly read in by read_codebook, to check for data within valid ranges.

Usage

validate_limits(x, code_book, column_names = NULL)

Arguments

x	tibble to which codebook is applied
code_book	codebook containing names of continuous variables and limits for checking data are within range
column_names	character vector of column names for checking data in range. Default: All continuous variables defined in codebook.

Details

REPEAT: Often, when analysing data, data dictionaries or code books are provided with data files. Rather than a word doc or pdf files, the format required here is in a very specific format stored as a csv file. Once read in, attributes such as factor labels/levels and variable labels can be added to the data.frame and/or also used to check factor labels and variable names are consistent with the code book. Note that while various methods may be available which attempt to convert word docs or pdf's to a spreadsheet and/or csv file, extreme care should be taken as these are far from perfect.

Value

object of type class tibble containing data out of limits

Author(s)

Peter Baker [e-mail]

Examples

file.copy(system.file('demoFiles', 'data1_codebook.csv',
                      package='codebookr'), 'data1_codebook.csv')
file.copy(system.file('demoFiles', 'data1-yr21.csv',
                      package='codebookr'), 'data1-yr21.csv')
data1_codebook <- read_codebook("data1_codebook.csv",
         column_names = list(variable_levels = "Factor.Levels",
                             variable_original = "Old.Variable",
                             min = "Min", max = "Max"))
data1 <- readr::read_csv('data1-yr21.csv')
data1
non_valid <- validate_limits(data1, data1_codebook)

petebaker/codebookr documentation built on May 6, 2019, 12:05 a.m.