Thread by @niketkedia

Continuing the #learningpath series
⭐Next skill in #DataScience industry is R programming (One of the most important and my favorite)
⭐There always been a debate around R v/s Python
⭐Step by step learning path for R programming

A Thread 🧵

1. Introduction to R
⭐Introduction to R
⭐Arithmetic operation
⭐Variable assignment
⭐Basic Data types
⭐Vectors, Matrix, List, Array and Data Frames
⭐Factors, Factor levels, Ordered factors

2. Data Input and Output in R
⭐Intro to import data
⭐Reading csv, txt, flat and excel files
⭐Saving output

3. Basics of R programming
⭐Logical statements
⭐If, else, conditional statements
⭐Loops in R (for, while)
⭐Functions of R
⭐Merging Dataframes

4. R programming commands
⭐Mathematical function
⭐Row and column operations
⭐Working with Dates
⭐Working with String
⭐Aggregation
⭐installing and loading packages
⭐Inspection data
⭐Creating, deleting and manipulating columns

5. Data Manipulation using dplyr package in R
⭐Basics of dplyr
⭐using select(), arrange(), filter(), summarise(), and mutate() commands
⭐Pipe operators
⭐Summarizing data using dplyr
⭐Pivoting
⭐Long to wide format

6. Data Visualization in R
⭐Intro to data visualization using base R
⭐Basics of ggplot2(Grammar of Graphics) package
⭐Creating plots using ggplot2
⭐Labels, layers, theme, facets using ggplot2
⭐Univariate analysis

6.1- Data visualization using ggplot2
⭐Adjusting coordinates
⭐Change themes
⭐Legend
⭐Grid lines and panel background
⭐Plot margin and background
⭐Annotation
⭐Formatting plots
⭐Saving ggplot2 plot

7. Machine learning using R
⭐Linear Regression
⭐Logistic Regression
⭐Clustering
⭐Decision Trees, Support vector machines and Random Forest
⭐Intro to text analytics(natural language processing)

Thanks for reading 😀

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