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cToF <- function(c) { c * 9/5 + 32 }
fToC <- function(f) { (f - 32) * 5/9 }
cat('25°C =', cToF(25), '°F\n')
cat('77°F =', fToC(77), '°C\n')Coding works best on desktop or with an external keyboard.
Temperature Converter - R Typing CST Test
Temperature Converter — R Code
Converts Celsius to Fahrenheit and Fahrenheit to Celsius.
cToF <- function(c) { c * 9/5 + 32 }
fToC <- function(f) { (f - 32) * 5/9 }
cat('25°C =', cToF(25), '°F\n')
cat('77°F =', fToC(77), '°C\n')R Language Guide
R is a high-level, interpreted programming language and environment specifically designed for statistical computing, data analysis, and graphical representation. It provides a rich ecosystem of packages and functions for statistical modeling, data visualization, and reproducible research.
Primary Use Cases
- ▸Statistical modeling and hypothesis testing
- ▸Data visualization and reporting
- ▸Machine learning and predictive analytics
- ▸Bioinformatics and genomic data analysis
- ▸Financial and econometric analysis
Notable Features
- ▸Extensive statistical functions and packages
- ▸High-quality graphics with ggplot2 and base plotting
- ▸Vectorized operations for efficient computation
- ▸Data frame and tibble structures for data manipulation
- ▸CRAN repository with thousands of contributed packages
Origin & Creator
Created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, in 1993 as an open-source alternative to S and S-PLUS.
Industrial Note
R is heavily used in statistical research, data science, machine learning experiments, and academic publications where reproducible data analysis is critical.
Quick Explain
- ▸R allows statisticians and data scientists to perform complex data analyses efficiently.
- ▸It integrates statistical techniques, machine learning algorithms, and high-quality graphics in a single platform.
- ▸Widely used in academia, research, and industry for data science, bioinformatics, finance, and social sciences.
Core Features
- ▸Interpreted language with REPL interface
- ▸Functional programming paradigm
- ▸Rich data structures (vectors, matrices, lists, data frames)
- ▸Advanced statistical and machine learning libraries
- ▸Integration with C, C++, and Python for performance
Learning Path
- ▸Learn R syntax and basic data structures
- ▸Practice data manipulation with dplyr
- ▸Explore visualization with ggplot2
- ▸Study statistical modeling and machine learning
- ▸Build reproducible reports and Shiny apps
Practical Examples
- ▸Plotting a histogram with ggplot2
- ▸Performing linear regression on a dataset
- ▸Clustering with k-means
- ▸Building a Shiny interactive dashboard
- ▸Analyzing genomic or financial datasets
Comparisons
- ▸Stronger in statistics than Python, though Python has broader general-purpose use
- ▸R excels at data visualization and reporting
- ▸CRAN offers thousands of specialized packages
- ▸RStudio IDE provides excellent data science workflow
- ▸Less performant for large-scale computational tasks than compiled languages
Strengths
- ▸Excellent for statistical analysis and data visualization
- ▸Vast ecosystem of specialized packages
- ▸Strong community support for data science
- ▸Open-source with extensive documentation
- ▸Highly reproducible workflows using R Markdown
Limitations
- ▸Slower than compiled languages for large datasets
- ▸Memory-intensive with very large data
- ▸Steeper learning curve for programming beginners
- ▸Less suited for general-purpose software development
- ▸Graphical performance can lag behind modern GUI frameworks
When NOT to Use
- ▸High-performance computing without optimized packages
- ▸General-purpose software development
- ▸Mobile or embedded application development
- ▸Projects requiring strict multithreading without external tools
- ▸Applications outside data-centric use cases
Cheat Sheet
- ▸x <- 10 - variable assignment
- ▸c(1,2,3) - vector creation
- ▸data.frame() - create data frame
- ▸lm(y ~ x, data=df) - linear regression
- ▸library(ggplot2) - load package
FAQ
- ▸Is R free?
- ▸Yes - open-source under GPL license.
- ▸Can R run on Windows, macOS, and Linux?
- ▸Yes, it is cross-platform.
- ▸Is R suitable for machine learning?
- ▸Yes, with packages like caret, mlr, and tidymodels.
- ▸Can R integrate with Python?
- ▸Yes, using the reticulate package.
- ▸Is R good for big data?
- ▸Yes, with proper packages (data.table, SparkR), but memory limitations exist.
30-Day Skill Plan
- ▸Week 1: R basics and vectors
- ▸Week 2: Data frames, lists, and matrices
- ▸Week 3: Functions, control flow, and packages
- ▸Week 4: Visualization and reporting
- ▸Week 5: Statistical analysis and real datasets
Final Summary
- ▸R is a specialized language for statistical computing and data analysis.
- ▸It has strong visualization and reproducible reporting capabilities.
- ▸CRAN and community packages make it extensible for diverse use cases.
- ▸Ideal for researchers, statisticians, and data scientists.
- ▸Open-source and widely supported in academia and industry.
Project Structure
- ▸data/ - raw and processed datasets
- ▸scripts/ - R scripts or notebooks
- ▸plots/ - output visualizations
- ▸docs/ - reports and R Markdown files
- ▸libs/ - custom or third-party packages
Monetization
- ▸Data science consulting
- ▸Financial modeling and forecasting
- ▸Bioinformatics pipelines
- ▸Statistical reporting services
- ▸R/Shiny-based analytics products
Productivity Tips
- ▸Use RStudio keyboard shortcuts
- ▸Leverage R Markdown for documentation
- ▸Reuse functions and packages
- ▸Automate repetitive analyses
- ▸Regularly update packages and R version
Basic Concepts
- ▸Variables and basic data types (numeric, character, logical)
- ▸Vectors, matrices, lists, and data frames
- ▸Functions and functional programming
- ▸Control flow: loops, if-else, apply functions
- ▸Statistical modeling and plotting basics
Official Docs
- ▸R Project official website
- ▸R manuals and reference guides
- ▸CRAN package documentation