r

# 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:** ```r 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, 65000, 70000) ) # Filter rows engineers <- people |> filter(department == "Engineering") # Select columns name_salary <- people |> select(name, salary) # Create new columns people_with_bonus <- people |> mutate(bonus = salary * 0.1, total_comp = salary + bonus) # Summarize data avg_salary_by_dept <- people |> group_by(department) |> summarise(avg_salary = mean(salary), count = n(), .groups = "drop") # Arrange rows sorted_by_age <- people |> arrange(desc(age)) # Join data departments <- tibble( department = c("Engineering", "Sales", "Marketing"), budget = c(500000, 300000, 200000) ) joined <- people |> left_join(departments, by = "department") # Reshape data long_data <- people |> pivot_longer(cols = c(age, salary), names_to = "metric", values_to = "value") wide_data <- long_data |> pivot_wider(names_from = metric, values_from = value) ``` **Statistical Analysis:** ```r # 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) cor(x, y) # Linear regression model <- lm(y ~ x) summary(model) # ANOVA group1 <- c(5, 6, 7, 8, 9) group2 <- c(3, 4, 5, 6, 7) group3 <- c(2, 3, 4, 5, 6) df <- data.frame( value = c(group1, group2, group3), group = rep(c("A", "B", "C"), each = 5) ) anova_result <- aov(value ~ group, data = df) summary(anova_result) # Confidence intervals sample <- rnorm(30, mean = 50, sd = 10) t.test(sample, conf.level = 0.95) # Chi-square test observed <- matrix(c(50, 30, 25, 45), nrow = 2) chisq.test(observed) ``` **Data Visualization with ggplot2:** ```r 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 = "MPG Distribution by Cylinders", x = "Number of Cylinders", y = "Miles per Gallon") # Histogram ggplot(mtcars, aes(x = mpg)) + geom_histogram(bins = 10, fill = "steelblue", color = "black") + labs(title = "MPG Distribution", x = "Miles per Gallon", y = "Count") # Faceted plot ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point() + geom_smooth(method = "lm") + facet_wrap(~ factor(cyl)) + labs(title = "MPG vs Weight by Cylinders") # Heatmap correlation_matrix <- cor(mtcars) heatmap(correlation_matrix, col = heat.colors(256), main = "Correlation Matrix") ``` **Functions and Control Flow:** ```r # 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 <- list( a = rnorm(100, 0, 1), b = rnorm(100, 1, 1), c = rnorm(100, 2, 1) ) results <- lapply(data, calculate_stats) str(results) # Map functions from purrr library(purrr) results_map <- map(data, ~ calculate_stats(.x)) map_dbl(data, ~ mean(.x, na.rm = TRUE)) # Conditional execution grade_student <- function(score) { if (score >= 90) { "A" } else if (score >= 80) { "B" } else if (score >= 70) { "C" } else if (score >= 60) { "D" } else { "F" } } # Vectorized conditional scores <- c(95, 85, 75, 65, 55) grades <- ifelse(scores >= 90, "A", ifelse(scores >= 80, "B", ifelse(scores >= 70, "C", ifelse(scores >= 60, "D", "F")))) ``` ## 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.