README.md

In anandjage/nacleanr: Easy to use functions that aide in data cleansing

nacleanR

DESCRIPTION

The goal of nacleanR is to provide functions that aide in data cleansing. It comprises of functions to * Probe the percentage of missing values within the variables * Find valid & invalid variables from point of view of percentage of missing data * Remove Variables with Missing Values above user defined limit from a dataset * Calculate age variable in years from an existing calendar year variable in dataset by subtracting year variable from System Date

Installation

Update:Package is currently not available on CRAN Please use GitHub to install development version You can install the released version of nacleanr from CRAN with:

install.packages("nacleanR")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("anandjage/nacleanR")

Example perecnt_na(dataset)

This is a basic example which shows how to find percentage of missing values in the form of NA in each variable:

library(nacleanR)
## basic example code
csv <- system.file("extdata", 'nadata.csv', package = "nacleanR")
sample_data <- read_data(path = csv)
percent_na(sample_data)
#> Length  Width Height Weight 
#>     20     40     60     20

Example invalidcols(dataset,threshold)

This is a basic example which shows variables that contain missing values NA above user defined threshold

nacleanR::invalidcols(data = sample_data,threshold = 50)
#> [1] "Height"

Example validcols(dataset,threshold)

This is a basic example which shows variables that contain missing values NA in the form within the user defined threshold.

nacleanR::validcols(data = sample_data,threshold = 50)
#> [1] "Length" "Width"  "Weight"

Example select_cols(dataset,threshold)

This is a basic example which returns dataset after removing variables that contain missing values above the user defined threshold.

new_data <- nacleanR::select_cols(data = sample_data,threshold = 50)
new_data
#>    Length Width Weight
#> 1       5    NA     17
#> 2       9    NA     19
#> 3       7     6     NA
#> 4       6     7     NA
#> 5      NA     3     10
#> 6       5    NA     23
#> 7      NA    NA     12
#> 8       4     9     13
#> 9       5     4     14
#> 10      7     6     16

Example age_cal(dataset,variable)

Calculates age by subtracting a year vector variable from current system year. Creates a new vector in dataset.

csv = system.file("extdata", 'agedata.csv', package = "nacleanR")
agedata <- read_data(csv)
agedata$ageTodaySinceBuilt <- age_cal(agedata,"YearBuilt")
agedata$ageTodaySinceRenovated <- age_cal(agedata, "YearRenovated")
head(agedata)
#>   YearBuilt YearRenovated ageTodaySinceBuilt ageTodaySinceRenovated
#> 1      1991          2011                 28                      8
#> 2      1993          2003                 26                     16
#> 3      1991          2008                 28                     11
#> 4      1978          2000                 41                     19
#> 5      1983          2007                 36                     12
#> 6      1987          2005                 32                     14

anandjage/nacleanr documentation built on July 13, 2019, 3:06 a.m.