R Programming Roadmap for Data Analysts

R Programming Roadmap for Data Analysts
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|– Fundamentals
| |– Basics of Programming
| | |– Introduction to R
| | |– Setting Up Development Environment (RStudio)
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| |– Syntax and Structure
| | |– Basic Syntax
| | |– Variables and Data Types
| | |– Operators and Expressions
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|– Control Structures
| |– Conditional Statements
| | |– If-Else Statements
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| |– Loops
| | |– For Loop
| | |– While Loop
| | |– Repeat Loop
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| |– Exception Handling
| | |– Try-Catch Block
| | |– Warnings and Errors
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|– Functions and Scope
| |– Defining Functions
| | |– Function Syntax
| | |– Parameters and Arguments
| | |– Return Statement
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| |– Scope
| | |– Global and Local Scope
| | |– Environments
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|– Data Structures
| |– Vectors
| | |– Creating Vectors
| | |– Vectorized Operations
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| |– Lists
| | |– Creating and Manipulating Lists
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| |– Matrices
| | |– Creating Matrices
| | |– Matrix Operations
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| |– Data Frames
| | |– Creating Data Frames
| | |– Manipulating Data Frames
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| |– Factors
| | |– Creating and Using Factors
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|– Data Manipulation
| |– dplyr
| | |– Select, Filter, Arrange, Mutate, Summarize
| | |– Piping (%>%)
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| |– tidyr
| | |– Gather and Spread
| | |– Separate and Unite
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|– Data Visualization
| |– Base R Graphics
| | |– Plot, Hist, Boxplot, Barplot
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| |– ggplot2
| | |– Grammar of Graphics
| | |– Creating Plots (Scatter, Line, Bar, Histogram)
| | |– Customizing Plots (Themes, Labels, Legends)
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|– Statistical Analysis
| |– Descriptive Statistics
| | |– Mean, Median, Mode
| | |– Standard Deviation, Variance
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| |– Inferential Statistics
| | |– Hypothesis Testing (t-tests, ANOVA)
| | |– Correlation and Regression Analysis
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|– Advanced R
| |– Date and Time
| | |– Working with Dates and Times
| | |– lubridate Package
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| |– String Manipulation
| | |– Stringr Package
| | |– Regular Expressions
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|– Programming Concepts
| |– Apply Family of Functions
| | |– lapply, sapply, tapply, vapply
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| |– Debugging
| | |– Debugging Tools (browser, debug, trace)
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| |– Object-Oriented Programming (OOP)
| | |– S3 and S4 Systems
| | |– Reference Classes (R5)
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|– Libraries and Packages
| |– CRAN and Bioconductor
| | |– Installing and Using Packages
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| |– Popular Packages
| | |– Data Manipulation (dplyr, tidyr)
| | |– Data Visualization (ggplot2, lattice)
| | |– Machine Learning (caret, randomForest)
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|– Reporting and Documentation
| |– RMarkdown
| | |– Creating RMarkdown Documents
| | |– Including Code Chunks
| | |– Generating Reports (HTML, PDF, Word)
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|– Deployment and Reproducibility
| |– Version Control with Git
| | |– Integrating RStudio with GitHub
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| |– Reproducible Research
| | |– Workflow Practices
| | |– Using renv for Package Management
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|– Working with Big Data
| |– Data.table Package
| | |– Efficient Data Manipulation
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| |– SparkR
| | |– Using Apache Spark with R
| | |– Handling Large Datasets