explore_feature_map: Returns a cumulative faceted plot on pairwise feature...

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

The plot consists of NxN mini-charts where N is number of features. Main diagonal shows feature distribution. Pairwise Pearson correlations are shown above main diagonal. Pairwise feature joint distributions are shown below main diagonal.

Usage

explore_feature_map(df)

Arguments

df	A data.frame: The target dataframe to explore

Details

Non-numeric features will be skipped. All passed features should not include any missing data, otherwise an error will be raised

Value

plot

Note

Raises: Invalid dataframe specified

Raises: Dataframe is empty

Raises: No numeric columns present in the dataframe

Raises: Features must not include missing data

Examples

explore_feature_map(iris)

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