Syntax in R

Introduction

This file is an overview of the syntax in R. It provides a basic introduction to the major features.

It aligns with the Datacamp Introduction to R course.

R Syntax

R is case sensitive! So, var3 isn’t the same as VAR3

Core

• Comments start with a #
• Equality uses ==, so 1 == 1 (not 1 = 1)
• Assignment uses variable_name <- 1

Arithmetic

• Basic math in PEMBAS order: 1 + (2 - 3) / 4 * 5
• Remainder/modulo use %%, so 3 %% 2 is 1
• Exponents use ^, so 2 ^ 3 is 2 * 2 * 2

Variables

• Start with a letter, so quarter1 not 1quarter
• snake_case is recommended, so first_person
  • Avoid firstperson, first.person, or FirstPerson

Data Types

Numbers

• Floating-point by default, so 1 is the same as 1.0
• Integers are defined by adding L at the end, so 1L
• No punctuation! Use 1234.56 instead of $1,234.56

Text

• “Double quote” all values
• ‘Single’ quotes are valid, but not recommended.
• Backtick is for field names in dplyr
• “curly quotes” are bad

Logical

• TRUE or FALSE
• T or F are also ok
• True, “TRUE”, and “True” are wrong
• Comparisons
  • Basic are <, <=, >, >=, ==, and != (not equal)
  • And uses &
  • Or is | (vertical pipe)

Vector

• Vectors are the most commonly used data types
• Create as c(1, 2,3) or c("a", "b")
• Individual items use 1-based index
  • vec[1] for first item
  • vec[length(vec)] for last item
• Multiples items use a vector of indexes
  • vec[c(1, 3)] returns the 1st and 3rd
  • vec[1:3] returns the 1st through the 3rd
  • vec[c(T, F, T)] returns the 1st and 3rd
• name(vector) get/set names for each value
  • names(vec) <- c("A", "B", "C") to set
  • vec[c("A")] or vec["A"] to access named values
• Conditional
  • Test vec > 10 to get c(T, F, T)
  • Select with vec[vec > 10]
• Vectors are concat by default, so c(c(1), c(2)) turns into c(1, 2)
• Items in a vector have the same datatype
  • class(vec) to get data type of items
  • Assigned a string value to a numeric vector causes them to turn into strings.
• class(x) returns the class of a variable
• summary(x) gives a quick summary of a numerical vector

Matrix

• A Matrix is a rectangular grid of items
• Create a 3x3 matrix with matrix(1:9, byrow = TRUE, nrow = 3)
• rownames(mat) and colname(mat) are used to get/set names
• cbind and rbind combines vectors/matrixes together by column or row
• colSums and rowSums sums up values
  
• Select values by [rows, column]
  • my_matrix[, 1] gets first column
  • my_matrix[1, ] gets first row
  • my_matrix[1, 1] gets first cell

Factor

• A Factor is an item from a list of allowed options
  • Useful for ordering
  • Create with factor(c('m', 'f', 'm', 'm', 'f'), c('m', 'f') )
    • Values come first
    • Allowed levels come second
    • ordered can be used to indicate that they have < and > levels

Data Frames

• A Data Frame is a named table
  • Older, we will use tupples instead
  • Useful functions include rownames, colnames, names, nrow, ncol
  • Other, summary, str
  • You can access by position, with df[rows, columns]
  • But, it’s better to access named items as df$field_name
  • We will use dplyr instead of learning order or subset

List

• A List can contain values of different types
  • Different from a matrix or vector, which need identical types
  • Different from a data frame, which uses named positions with identical types
• Create as my_list <- list(name1 = 'a', name2 = 1, name3 = 1:10)
• Access via index as my_list[[1]]
  • Using my_list[1] indstead returns a list with a single value
• Access via named as my_list$name1

Further resources

Great introduction to types! https://www.datacamp.com/tutorial/data-types-in-r

Application problem

See problems on GitHub