README.md

In UBC-MDS/instaeda_R: InstaEDA: Quick and Easy Way to Clean Data and Build Exploratory Data Analysis Plots

instaeda

Quick and easy way to clean data and build exploratory data analysis plots.

This idea came up as we have been building data projects for quite some time now in the UBC MDS program. We noticed that there are some repetitive activities that occur when we first explore the data. This project will help you take a given raw data set an conduct some data cleansing and plotting with a minimal amount of code.

The main components of this package are:

• Data Checking

  • Plot basic summary information for a given data frame. An output ggplot will be returned with summary metrics including the basic description of the input data such as the distribution of the numeric columns, factor columns, complete rows and missing observations.

• Data Cleansing

  • Update missing values in a data frame. This function uses custom Imputation of missing values using mean, median and random. In addition, you can shuffle the data using a parameter.

• Exploratory Visualization

  • Generate a numerical correlation plot using your preferred correlation method.
  • Generate basic distribution plots by column type.

There are a myriad of packages that provide similar functionality in the R ecosystem. A few of the packages include:

• dlookr
• SmartEDA

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("UBC-MDS/instaeda_R")

Documentation

The package vingette or long form documentation is available in the link below:

https://jufu.github.io/instaeda/

Example

This is a basic example of usage for functions found inside the instaeda package.

library(instaeda)

plot_info(example_dataframe)
plot_corr(example_dataframe)
plot_basic_distributions(example_dataframe)
divide_and_fill(example_dataframe)

UBC-MDS/instaeda_R documentation built on March 29, 2021, 7:55 a.m.