https://github.com/tomaztk/R_Data_manipulation

R Data manipulation with Tidyverse
https://github.com/tomaztk/R_Data_manipulation

Last synced: 3 months ago
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R Data manipulation with Tidyverse

• Host: GitHub
• URL: https://github.com/tomaztk/R_Data_manipulation
• Owner: tomaztk
• Created: 2024-08-23T17:46:37.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-10-25T04:12:31.000Z (6 months ago)
• Last Synced: 2024-10-25T15:46:48.296Z (6 months ago)
• Language: R
• Size: 39.1 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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