explore_missing: Shows exactly where there is missing data and how much data...

In UBC-MDS/xplrrr: This package makes exploratory data analysis (EDA) simple and seamless. EDA is a crucial phase in the data science workflow as it allows us get a fist glimpse of the data. It is important to identify statistical characteristics of the data so that we can properly set up the rest of the analysis

Description

explore_missing will identify missing observations within data. It will return 1 of 2 tables: (location) 1 table of the exact location in the dataframe where there is missing data or (count) another table showing how many observations are missing and the proportion of how much data is missing for each feature. Note: this function cannot detect entries of empty strings as missing.

Usage

explore_missing(data, num_rows = 0, type = "location")

Arguments

data	A data.frame: The target dataframe to explore
num_rows	integer: The number of rows above and below the missing value to output
type	character: The desired type of output (location or count)

Value

dataframe: The resultant dataframes

Examples

explore_missing(data.frame(a = c(1,2), b = c(NA, 3)))

UBC-MDS/xplrrr documentation built on April 2, 2020, 4 a.m.