r

name	R
description	Statistical programming language and environment for statistical computing, data analysis, and graphical representation.
license	GPL v2
compatibility	R 4.0+
audience	Data scientists, statisticians, bioinformaticians, researchers
category	Programming Languages

R

What I do

I am a language and environment for statistical computing and graphics, developed by Ross Ihaka and Robert Gentleman in 1993. I was designed by statisticians for statisticians, excelling at data manipulation, calculation, and graphical display. I offer a vast ecosystem of packages for statistical analysis, machine learning, data visualization, and bioinformatics. I am widely used in academic research, clinical trials, finance, and any domain requiring sophisticated statistical analysis and visualization.

When to use me

Use R when performing statistical analysis and hypothesis testing, data visualization and exploratory data analysis, machine learning and predictive modeling, bioinformatics and genomic analysis, reporting with R Markdown, or when you need specialized statistical packages not available elsewhere.

Core Concepts

• Vectors as Basic Unit: Most operations are vectorized, operating on entire vectors at once.
• Data Frames: Tabular data structure with rows and columns, similar to spreadsheets or database tables.
• Factors: Categorical data with ordered or unordered levels for statistical modeling.
• S3/S4/OOP Systems: Multiple object-oriented systems with S3 being most common and informal.
• Tidyverse: Collection of packages (dplyr, tidyr, ggplot2, readr) for consistent data science workflows.
• Tibbles: Modern reimagining of data frames with better printing and subsetting behavior.
• Pipes ( %>% / |> ): Chain operations together for readable data transformation pipelines.
• Statistical Modeling: Built-in lm(), glm(), and extensive packages for statistical inference.
• Functional Programming: First-class functions, lapply/sapply/map family, and closure-based abstractions.
• Lazy Evaluation: Arguments are evaluated only when needed, enabling efficient computation.

Code Examples

Data Manipulation with Tidyverse:

library(tidyverse)

# Create a tibble
people <- tibble(
  id = 1:5,
  name = c("Alice", "Bob", "Charlie", "Diana", "Eve"),
  age = c(25, 30, 35, 28, 32),
  department = c("Engineering", "Sales", "Engineering", "Marketing", "Sales"),
  salary = c(75000, 60000, 85000  



engineers  people  
  filterdepartment  


name_salary  people  
  selectname salary


people_with_bonus  people 
  mutatebonus  salary  
         total_comp  salary  bonus


avg_salary_by_dept  people 
  group_bydepartment 
  summariseavg_salary  meansalary
            count  n
            .groups  


sorted_by_age  people 
  arrangedescage


departments  tibble
  department    
  budget    


joined  people 
  left_joindepartments by  


long_data  people 
  pivot_longercols  age salary
               names_to  
               values_to  

wide_data  long_data 
  pivot_widernames_from  metric
              values_from  value

Statistical Analysis:

# Generate sample data
set.seed(42)
control <- rnorm(50, mean = 100, sd = 15)
treatment <- rnorm(50, mean = 110, sd = 15)

# Descriptive statistics
summary(control)
sd(control)
mean(control)
median(control)

# Hypothesis testing
t_result <- t.test(control, treatment, var.equal = TRUE)
print(t_result)

# Correlation
x <- 1:100
y <- 2 * x + rnorm(100, 0, 20)
corx y


model  lmy  x
summarymodel


group1      
group2      
group3      

df  data.frame
  value  group1 group2 group3
  group     each  


anova_result  aovvalue  group data  df
summaryanova_result


sample  rnorm mean   sd  
t.testsample conf.level  


observed  matrix    nrow  
chisq.testobserved

Data Visualization with ggplot2:

library(ggplot2)

# Scatter plot
ggplot(mtcars, aes(x = mpg, y = hp, color = factor(cyl))) +
  geom_point(size = 3) +
  labs(title = "MPG vs Horsepower",
       x = "Miles per Gallon",
       y = "Horsepower",
       color = "Cylinders") +
  theme_minimal()

# Box plot
ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
  geom_boxplot(fill = "lightblue") +
  labs(title = 
       x  
       y  


ggplotmtcars aesx  mpg 
  geom_histogrambins   fill   color   
  labstitle  
       x  
       y  


ggplotmtcars aesx  wt y  mpg 
  geom_point 
  geom_smoothmethod   
  facet_wrap factorcyl 
  labstitle  


correlation_matrix  cormtcars
heatmapcorrelation_matrix
        col  heat.colors
        main  

Functions and Control Flow:

# Function with default arguments
calculate_stats <- function(data, na.rm = TRUE) {
  if (!is.numeric(data)) {
    stop("Data must be numeric")
  }
  
  mean_val <- mean(data, na.rm = na.rm)
  sd_val <- sd(data, na.rm = na.rm)
  median_val <- median(data, na.rm = na.rm)
  
  list(
    mean = mean_val,
    sd = sd_val,
    median = median_val,
    n = sum(!is.na(data))
  )
}

# Apply family functions
data  
  a  rnorm  
  b  rnorm  
    rnorm  


results  lapplydata calculate_stats
strresults


librarypurrr
results_map  mapdata  calculate_stats.x
map_dbldata  mean.x na.rm  


grade_student  score 
   score   
    
     score   
    
     score   
    
     score   
    
    
    
  



scores      
grades  ifelsescores   
         ifelsescores   
         ifelsescores   
         ifelsescores    

Best Practices

1. Use Tidyverse Packages: Prefer tidyverse (dplyr, tidyr, ggplot2) over base R for data manipulation and visualization.
2. Use Tibbles Over Data Frames: Tibbles have better printing and subsetting behavior.
3. Use Pipes for Readability: Chain operations with |> or %>% for readable data transformation pipelines.
4. Use Vectorization: Avoid loops; use vectorized operations for better performance and readability.
5. Use Functional Programming: Use map functions from purrr instead of for loops and lapply.
6. Document with Roxygen2: Document functions with roxygen2 comments for automatic help pages.
7. Use testthat for Testing: Write unit tests with testthat package for reproducible code.
8. Use R Projects: Use RStudio projects for self-contained, reproducible analyses.
9. Use renv for Dependency Management: Use renv for project-specific package versions.
10. Follow Style Guide: Use consistent naming (snake_case), spacing, and code organization.