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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Three R Libraries for Automated EDA

Devashree
Devashree Last Updated : 29 Nov, 2023
6 min read

Introduction

With the increasing use of technology, data accumulation is faster than ever due to connected smart devices. These devices continuously collect and transmit data that can be processed, transformed, and stored for later use. This collected data, known as big data, holds valuable insights which can be harnessed across different industries to solve real-world problems. However, this data can be structured or semi-structured but, sometimes, is highly unstructured in its original format. Raw data processing is often challenging, prone to manual errors, time-consuming, and expensive due to high-end resources. Hence, companies look for automated solutions which can process large amounts of data in significantly less time.

This article was published as a part of the Data Science Blogathon.

Table of contents

What is Exploratory Data Analysis?

The most crucial step in data processing is data exploration using Exploratory Data Analysis (EDA). Different steps are performed to prepare and format the raw data depending on the end goal (regression or classification). This process involves evaluating the available data in all possible directions to identify the correlations between different features and the target. It also involves identifying and handling any inconsistencies in the data, like redundancies, missing values, or outliers. Most organizations employ Python and R programming to process their acquired data. Thus, several open-source packages have been developed to automate the EDA process in these two programming languages. These libraries are helpful for data scientists to perform EDA faster and spend more time on model development tasks. R beginners can also use these libraries as they are easy to use and can help them quickly understand the dataset with a few code lines.

This article will discuss three AutoEDA R libraries with their respective code to perform EDA. These R packages accomplish several EDA tasks for efficiently summarizing data into tables and preparing detailed HTML reports to speed up the process of exploration of a new dataset.

AutoEDA Packages in R

Now, let us explore all three Automated Exploratory Data Analysis packages. We will use the Motor Trend Car Road Tests dataset ‘mtcars’ from the available built-in R datasets. To load and view this dataset, we will use –

#load dataset
df = mtcars
head(df)

dataMaid

The dataMaid package creates a report in different formats, such as PDF, DOCX, or HTML. The generated report checks and neatly simply summarizes the dataset. It is a good tool for checking errors in the dataset.

We will install, import, and run the dataMaid package using the following command:

# Install package
install.packages("dataMaid")
# Import library
library(dataMaid)
# Create report
makeDataReport(df, output = "html", replace = TRUE)

From the above .gif for the .html report generated by the dataMaid package, we can see that all the discrepancies in the dataset are summarized variable by variable in the generated report. Thus, it is easier to understand the data quality and decide on the next steps required for data cleaning.

The dataMaid package documentation can be explored for additional details.

DataExplorer

This is one of the most commonly used AutoEDA packages in R. With DataExplorer, we can simplify the EDA process and report generation. This library automatically scans the dataset for variables, performs data profiling, and provides many useful functions to create various charts on both discrete and continuous features in the dataset.

Let us look at the code we need to install and use the DataExplorer library:

# Installing the package

install.packages("DataExplorer")
# Importing the library
library(DataExplorer)
create_report(mtcars)
data()

The DataExplorer library generates a complete HTML report in the working directory for the EDA on the dataset using the create_report function. This function also accepts additional arguments to customize the EDA report. The report HTML file, when opened with a browser, looks like the following –

From the above .gif image of the report, we can see that the table of contents indicates a comprehensive report covering most of the tasks performed during EDA generated with just one line of code. Here are some sample plots from the report at a glance-

R Libraries

You can also refer to the package documentation on the CRAN-R website for additional details

SmartEDA

The next AutoEDA library is SmartEDA which is a powerful package for automating most EDA tasks through functions for Descriptive statistics, Data visualization, Custom tables, and HTML reports.

Similar to the previous DataExplorer package, the SmartEDA library also allows us to generate a complete HTML report using the ExpReport function. Let us install and import the package as well as run the ExpReport function to perform the EDA, as shown below.

# Install package
install.packages("SmartEDA")
# Import library
library(SmartEDA)
ExpReport(df,op_file='smartEDA.html')

This ExpReport function accepts several arguments to customize the report for Template, op_file,op_dir, label, theme, etc.

Here, we will use the ‘op_file’ function to name the report.html file. This report file is available with the specified name in the working directory and can be opened with a browser. The snippet of the .html report shown below provides information on how well the SmartEDA package has summarized the ‘mtcars’ dataset.

R Libraries

Here are a few sample plots from the report –

R Libraries

From the .html report, we can see that it contains several plots generated with just one line of code and these plots are useful in understanding the dataset better. The documentation for SmartEDA can be found here.

Conclusion

In this article, we explored three different AutoEDA R packages. Overall, these R packages are similar in that they provide a big picture and help to improve the general understanding of the dataset. Note that the summary in these reports may also be done manually. Depending on different purposes, you can choose the appropriate package to apply. E.g., the DataMaid package is good at identifying errors in the data. In contrast, the DataExplorer library gives detailed information on the dataset through plots on feature importance, correlation matrix, missing data plots, etc. On the other hand, the SmartEDA package is an excellent tool for understanding the dataset’s multivariate relationships.

Here are some key takeaways from the article –

  • Manual data processing of big data requires a lot of time, money, and effort.
  • Companies prefer automating data processing tasks for faster model building and deployment.
  • Automated EDA helps expedite the data analysis; hence, several open-source packages in Python and R are available to automate EDA.
  • All three R packages- DataMaid, DataExplorer, and SmartEDA, are highly effective in performing EDA on a given dataset and can be considered for implementation by data scientists in an organization.
  • As these libraries require a few lines of code for performing EDA, even R beginners can try out the libraries mentioned in this article to improve their skills.

I hope you liked the article. Try any of these AutoEDA packages to quickly uncover interesting insights with a few code lines for your next R project.

Frequently Asked Questions

Q1. What are the packages in R for EDA?

A. Common packages for Exploratory Data Analysis (EDA) in R include dplyr, ggplot2, tidyr, and summarytools. These packages offer tools for data manipulation, visualization, and summary statistics.

Q2. What is EDA packages?

A. EDA packages refer to libraries or sets of functions in programming languages like R that facilitate Exploratory Data Analysis. These packages provide tools for visualizing, summarizing, and understanding data patterns.

Q3. What is EDA in R programming?

A. EDA in R programming stands for Exploratory Data Analysis. The data analysis approach involves summarizing and visualizing data to understand its key characteristics, uncover patterns, and identify anomalies.

Q4. Which library is used for EDA?

A. Libraries commonly used for EDA in R include dplyr for data manipulation, ggplot2 for data visualization, tidyr for data tidying, and summarytools for generating summary statistics. These libraries collectively support comprehensive data exploration.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 

blogathondata visualizationEDAR Libraries
Devashree
Devashree

Devashree has an M.Eng degree in Information Technology from Germany and a Data Science background. As an Engineer, she enjoys working with numbers and uncovering hidden insights in diverse datasets from different sectors to build beautiful visualizations to try and solve interesting real-world machine learning problems.

In her spare time, she loves to cook, read & write, discover new Python-Machine Learning libraries or participate in coding competitions.

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