Exploratory Data Analysis (EDA) for Journal Submissions

Exploratory Data Analysis (EDA) for Journal Submissions

Key points Exploratory data analysis (EDA) is crucial in any data analysis project. It involves exploring, summarizing, and visualizing your data to gain insights, identify patterns, and detect outliers. EDA can also help you formulate hypotheses, choose appropriate statistical tests , and communicate your findings effectively. In this article, I will explain how I perform EDA in R using tidyverse packages, a collection of tools for data manipulation, visualization, and modeling, and my article in Impact Factor Journal. I will use a generated dataset for this tutorial that contains information about 1000 students from different countries, their academic performance, and their satisfaction with their university. You will learn how to Load and view the data in R, Summarize the data using descriptive statistics, Visualize the data using charts and graphs, Identify missing values and outliers, Transform and filter the data, Perform hypothesis testing and correlation analysis, Generate an EDA re…