Data Manipulation with Tidyverse

1. Core Principles of the tidyverse

• 1. Tidy Data
  • Definition of tidy data
  • Importance of tidy data in data analysis
• 2. The Grammar of Data Manipulation
  • Introduction to the concept
  • Overview of the pipe operator %>%

2. Key Packages in the tidyverse

• 1. ggplot2: Data Visualization
  • Introduction to ggplot2
  • Basic syntax and structure
  • Examples of creating different types of plots
  • Customization options
• 2. dplyr: Data Manipulation
  • Introduction to dplyr
  • Key functions: filter(), select(), mutate(), arrange(), summarize()
  • Grouped operations with group_by()
• 3. tidyr: Data Tidying
  • Introduction to tidyr
  • Key functions: gather(), spread(), separate(), unite()
  • Working with missing values
  • Working with variables (local, global)
• 4. readr: Data Import
  • Introduction to readr
  • Reading different types of data files (csv, tsv, etc.)
  • Handling issues like missing values, column types
• 5. purrr: Functional Programming
  • Introduction to purrr
  • Key functions: map(), reduce(), walk()
  • Benefits of functional programming in data science
• 6. tibble: Modern Data Frames
  • Introduction to tibble
  • Differences from base R data frames
  • Tibble vs. data.frame vs. data.table
  • Key features and functions