This project showcases an exploratory data analysis (EDA) using R programming language. The goal is to demonstrate diverse characteristics of a dataset through multiple types of graphs. In this example, we utilize the famous "iris" dataset, which is readily available in R.

Before running the code, ensure that you have the following R packages installed:

• ggplot2
• dplyr
• tidyr
• ggpubr
• corrplot

You can install these packages using the install.packages() function.

• eda_r_analysis.R: Main R script containing the code for data loading, summary statistics, and various graphical representations.

1. Open the R script eda_r_analysis.R.
2. Run the script in your R environment.

1. Pairwise Scatterplots: Visualizing relationships between continuous variables.
2. Boxplots: Displaying the distribution of each variable by species.
3. Density Plots: Illustrating the density distribution of variables by species.
4. Correlation Matrix Plot: Visualizing the correlation matrix.
5. Bar Plot: Showing the distribution of a categorical variable (Species).
6. Violin Plot: Representing the distribution of a continuous variable by species.

Feel free to adapt the code for your specific dataset and analysis requirements.

This project is licensed under the MIT License - see the LICENSE file for details.