Introduction to Data Exploration and Visualization

Data Exploration or Exploratory Data Analysis (EDA) is an approach to analyzing data in which you investigate and summarize the data’s main characteristics, aiming to understand the underlying patterns and relationships between variables. EDA helps you identify potential data problems, discover outliers, and generate ideas for further analysis.

Here are below basic steps for data exploration in R:

  • Import data: The first step in data exploration is to import your data into R. You can import data from various sources such as Excel, CSV, or databases.

  • Check the structure of data: Use the str() function to check the structure of your data. This function will give you information about the dimensions of your data and the types of variables in your data.

  • check data distribution and visualize the data distribution using histograms, density plot and qqplots. Perform normality test to determine whether a given data sample is derived from a normally distributed population.

  • Check for missing values: Use the is.na() function to check for missing values in your data. If you have missing values, you can use the na.omit() function to remove the missing values from your data.

  • Summarize or descriptive statisics of the data: Start by summarizing the main characteristics of the data, such as the mean, median, standard deviation, and range of each variable.

  • box plot to identify outliers, which are observations significantly different from the rest of the data.

  • Perform statistical tests: Use the t.test() function to perform statistical tests on your data. You can use this function to test whether the means of two groups are significantly different.

  • Explore and visualize the relationships between two variables using scatter plot and correlation analysis.

  • Look for patterns and trends: Search for patterns and trends in the data that can help you to generate new hypotheses or insights. You can use clustering and principal component analysis (PCA) techniques in R.

Overall, EDA is an important first step in any data analysis project, as it helps you to understand the data and generate hypotheses that can guide further analysis. R provides many powerful tools for EDA, making it a popular language for data scientists and analysts.