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Basics

R is a powerful language used for data analysis, statistical computing, and graphical representation. This guide will introduce the basic syntax, data structures, control structures, and functions in R, providing a solid foundation for beginners.

Basic Syntax​

Elements of a Program​

  • Data Structures: Information carriers (vectors, matrices, data frames, lists)
  • Algorithms: Steps to complete tasks

Data Structures​

Basic Data Types​

  1. Numeric: Includes integers and floating-point numbers
  2. Character: Represents textual information
  3. Logical: Boolean values (TRUE, FALSE)

Vectors​

A vector is a sequence of elements of the same type. A scalar is a vector of length 1.

Creating Numeric Vectors​

c(1.70, 1.72, 1.80, 1.66, 1.65, 1.88)

Checking Data Types​

typeof(3.14)
class(3.14)

Operations on Vectors​

heights <- c(1.70, 1.72, 1.80, 1.66, 1.65, 1.88)
mean(heights)
sd(heights)

Character Vectors​

c("Male", "Female", "Female", "Male")

Factors​

Factors are used to represent categorical data with levels.

sex <- factor(c("Male", "Female", "Female", "Male"))
levels(sex)

Logical Vectors​

heights > 1.7
heights[heights > 1.7]

Arrays and Matrices​

2D and higher-dimensional arrays.

matrix(1:12, nrow = 4, ncol = 3)

Data Frames​

Data frames can hold different types of data.

df <- data.frame(
  sex = c("F", "M", "M", "F"),
  age = c(17, 29, 20, 33),
  heights = c(1.66, 1.84, 1.83, 1.56)
)
str(df)

Lists​

Lists can hold different types of elements.

l <- list(
  sex = c("F", "M"),
  age = c(17, 29, 20),
  heights = c(1.66, 1.84, 1.83, 1.56)
)
l$sex

Control Structures​

Conditional Statements​

If-else​

age <- 16
if (age > 18) {
  message("You are an adult!")
} else {
  message("You are a child!")
}

Switch​

ch <- "b"
switch(EXPR = ch, a = 1, b = 2:3)

Looping Structures​

For Loop​

for (i in 1:10) {
  print(i)
}

While Loop​

v <- 10
while(v > 2) {
  print(v)
  v <- v - 1.1
}

Repeat Loop​

i <- 1
repeat {
  print(i)
  i <- i * 2
  if (i > 100) break
}

Functions and Functional Programming​

Creating Functions​

customMean <- function(x) {
  s <- i <- 0
  for (j in x) {
    s <- s + j
    i <- i + 1
  }
  return(s / i)
}

Scope​

Variables within functions have local scope.

a <- 3
Sum <- function(b) {
  a + b
}
Sum(10)

Passing Functions​

Functions can be passed as arguments.

f <- function(x, fun) {
  fun(x)
}
f(1:10, mean)

Packages​

Installing Packages​

install.packages("ggplot2")
BiocManager::install("maftools")
remotes::install_github("tidyverse/ggplot2")

Loading Packages​

library(ggplot2)

Practical Example: ROC Curve Calculation and Plotting​

Background and Goal​

Implement a function to compute ROC curve and AUC for binary classification.

Implementation​

Define a function to calculate true positive rate, false positive rate, and plot the ROC curve.

Explanation​

Discuss the steps, code logic, and how it integrates various R concepts.

Common Problems and Solutions​

Complex Numbers​

Complex numbers can be represented using i for the imaginary part.

1 + 2i

Differences between = and <-​

The <- operator is preferred for assignment, especially in function calls.

x <- NULL
customMean(x <- 1:100)
x

Using :: and :::​

:: accesses exported functions, while ::: accesses internal functions of a package.

xfun:::base_pkgs()

Factor Reordering​

Convert factors to characters before adding new levels.

sex <- factor(c(as.character(sex), "M", "M"))