Overview of R and its applications in data analytics

Installation and setup of R, RStudio, and necessary libraries

Understanding basic R syntax (variables, data types, and comments)

Introduction to data structures: Vectors, Lists, Matrices, Arrays, Data frames, and Factors.

Perform basic arithmetic, logical, and vectorized operations in R

Use functions and control structures (loops, if-else statements)

Import data from CSV, Excel, and text files using read.csv and readxl

Export data to CSV and Excel formats for further use.

Handle missing data using functions like is.na(), na.omit(), and na.rm()

Apply data imputation techniques to fill missing values

Manipulate data using dplyr functions (select(), filter(), mutate(), arrange(), summarize())

Join datasets with left_join(), right_join(), inner_join(), and full_join()

Reshape data with tidyr using unite() and separate()

Work with nested and hierarchical data structures

Perform exploratory data analysis with descriptive statistics (mean, median, mode, standard deviation)

Identify and visualize relationships in data using correlation analysis and ggplot2.