standardise_variables: Standardise the Numeric Variables of a given Dataset
In oislen/BuenaVista: Functions for Everyday Data Science Tasks

Description Usage Arguments Value See Also Examples

This function standardises numeric variables of a given data. There are three methods; range standardisation, normalisation standardisation, and median absolute deviation standardisation. Range standardisation standardises the numeric variables to a specified range, the default is [0, 1]. Normalisation standardisation standardises the numeric variables to have mean 0 and standard deviation 1. Median Absolute Devation standardisation standardises the numeric variables to have median 0 and median absolute deviation 1.

1 2	standardise_variables(dataset, method = c("range", "norm", "MAD"), lower_bound = 0, upper_bound = 1, file_name = NULL, directory = NULL)

`dataset`	A dataset to be standardised, the dataset can have mixed types.
`method`	A charactor object denoting the method of standardisation used. One of three possible options; "range", "norm", "MAD".
`lower_bound`	The lower bound of the range standardisation, default is 0.
`upper_bound`	The upper bound of the range standardisation, default is 1.
`file_name`	A character object indicating the file name when saving the data frame. The default is NULL. The name must include the .csv suffixs.
`directory`	A character object specifying the directory where the data frame is to be saved as a .csv file.

Outputs the standardised dataset as data frame.

remove_variables, derive_variables, extract_variables, impute_variables, transform_variables

# Example Data
x1 <- rnorm(n = 60, mean = 50, sd = 10)
x2 <- rpois(n = 60, lambda = 50)
x3 <- sample(x = 1:10, size = 60, replace = TRUE)
# Standardise the Numeric Variables
standardise_x(dataset = x1, method = "range")
standardise_x(dataset = iris, method = "range", lower_bound = 10, upper_bound = 100)
standardise_x(dataset = x2, method = "norm")
standardise_x(dataset = x3, method = "MAD")