Mastering Data Wrangling

We’ll dive into data cleaning and manipulation using Base R, learning how to subset data with `subset()`, detect and handle missing values using `is.na()` and `na.omit()`, rename columns for better clarity, and filter datasets to focus on key observations. These foundational skills will help you prepare raw data for analysis efficiently.
We’ll shift to the tidyverse’s `dplyr` package, exploring powerful tools like `select()` for choosing columns, `filter()` for row-based conditions, `mutate()` for creating new variables, and `arrange()` for sorting data. We’ll also master the `%>%` pipe operator to chain operations seamlessly, making data wrangling faster and more intuitive. Together, these sessions will equip you with both Base R and tidyverse techniques for effective data manipulation.