Basic Syntax

We will introduce some basic syntax in R.

• Pound Sign in R
• Simple Algebra
• Assignment Operator
• Parentheses, Brackets & Curly Brackets

Pound Sign in R

# symbol is for adding comments and notes to your code. In any line of your code, anything after it will not be executed.

# Hello, UD students!

Examples of Simple Algebra

• Example of Addition

7+11
7-11
7 - 11

• Example of Multiplication & Division

7*11
7/11
7 / 11

Note: you should find that 7/11 and 7 / 11 generate the same result. This is because the blank spaces in the code is generally ignored.

Assignment Operators: Equal Sign and Arrow in R

a1 = 7
b1 = 11

a2 <- 7
11 -> b2

What’s difference between them?

mean(x = c(1, 8, 4, 9, 13))

x

## Error in eval(expr, envir, enclos): object 'x' not found

mean(x <- c(1, 8, 4, 9, 13))

x

## [1]  1  8  4  9 13

In the first case, \(x\) is an argument in the function mean() while the second case assigns a vector \((1, 8, 3, 9, 13)\) to \(x\) and then finds the mean value of it.

We should use <- as an assignment operator and = for function arguments!

Parentheses, Brackets & Curly Brackets

Parentheses, ( ), are used to call functions; Brackets, [ ], are used to obtain values in a data structure, Curly Brackets, { }, are used to denote a block of code in a function or in a conditional statement.

Here, we give examples about the use of ( ) and [ ]. The use of curly brackets will be introduced later.

w <- c(17, 57, 69, 50, 100, 68, 29, 16, 65, 5, 15, 25) # c() combines objects
median(w) # find the median of w
w[3] # find the value of the third element in w
w[1:2] # find the values of the first and second elements in w
w[2:4] # find the values between the second and the fourth elements in w
w[c(2,5,8)] # only find the values of the second, fifth and eighth elements in w
w[-5] # the fifth element in w is removed
w[w < 50] # only obtain values that satisfy the condition

Note: c() can concatenate more than just vectors. We will talk about this later.

Basic Data Types

Here are some basic data types in R.

• Character
• Factor
• Numeric
• Integer
• Logical
• Complex

We will focus on the use of the first five types.

Character - 1

A character object is used to store text, letters, or words (strings) in R.

x <- "Hello"
y <- "UD students!"
class(x) # we can use class() function to obtain the data type

## [1] "character"

nchar(x) # use nchar() to count the number of characters

## [1] 5

Note: When defining strings, double quotes ” ” and single quotes ’ ’ are interchangeably but double quotes are preferred (and character constants are printed using double quotes), so single quotes are normally only used to delimit character constants containing double quotes R Documentation (2020).

Character - 2

If we want to combine two strings into one string, we can use paste() or paste0() function.

paste(x,y)

## [1] "Hello UD students!"

paste(x,y,sep=",")

## [1] "Hello,UD students!"

paste(x,y,sep=", ")

## [1] "Hello, UD students!"

paste(x, ", ", y)

## [1] "Hello ,  UD students!"

paste0(x,y)

## [1] "HelloUD students!"

These two functions could be very useful.

Character - 3

Here we give one advanced example.

allfiles1 <- paste("file_", 1:5)
allfiles2 <- paste("file_", 1:5, collapse = "_")
allfiles3 <- paste("file", 1:5, sep = "_")
allfiles1

## [1] "file_ 1" "file_ 2" "file_ 3" "file_ 4" "file_ 5"

allfiles2

## [1] "file_ 1_file_ 2_file_ 3_file_ 4_file_ 5"

allfiles3

## [1] "file_1" "file_2" "file_3" "file_4" "file_5"

What is wrong here?

Factor

A factor object is used to store categorical / qualitative variables.

grade <- factor(c("A", "C", "B", "B-", "A", "C+", "D", "A-", "B+", "C-", "B"))
gender <- c("M", "F", "F", "M", "M", "M", "F", "M", "F")
gender <- as.factor(gender)
class(gender)

## [1] "factor"

levels(gender) # use levels() to find all categories in the variable

## [1] "F" "M"

length(grade) # use length() to find the length of vectors 

## [1] 11

• Question 1: Is factor the same as character in R?
  • No, they are not the same
• Question 2: How do we convert factor to character?
  • Use as.character() function

Numeric

A numeric object is used to store numeric data in R.

x1 <- 3
x2 <- c(-3.13, 2.47, 6, -1.5, 4.29, 2.72, 1, 0, 3.85)
class(x1)
class(x2)
sum(x2)
max(x2)
min(x2)
range(x2)
round(x2) # round off the values
ceiling(x2) # round up to the nearest integer
floor(x2) # round down to the nearest integer
summary(x2)

Integer

An integer object is used to store numeric data without decimals.

x2 <- c(-3.13, 2.47, 6, -1.5, 4.29, 2.72, 1, 0, 3.85)
x3 <- as.integer(x2) # only remain the integers
x3

## [1] -3  2  6 -1  4  2  1  0  3

class(x3)

## [1] "integer"

Logical

A logic object contains only two values: TRUE or FALSE.

y1 <- -7
y2 <- 11
y1 > y2

## [1] FALSE

y1 == y2  # check if two objects are the same

## [1] FALSE

y1 <= y2

## [1] TRUE

result <- y1 > y2
class(result)

## [1] "logical"

Create an empty vector

There are some situations that we may want to create an empty vector. Here is a simple example.

x <- c()
y1 <- vector("character", length=3)
y2 <- character(3)
z1 <- vector("numeric", 5)
z2 <- numeric(5)
w <- rep(NA, 2)

Remark for as.character(), as.integer(), as.numeric(), as.factor() functions

We can use these functions to transform the data type.

z1 <- as.integer(c(3, 5.8))
class(z1)

## [1] "integer"

z2 <- as.character(z1) # transform integer object to character
z2

## [1] "3" "5"

as.numeric(3 > 8)  # transform logical object to numeric values

## [1] 0

gender <- factor(c("M", "F", "F", "M", "M", "M", "F", "M", "F"))
as.numeric(gender) # transform levels in the categorical variables to numbers

## [1] 2 1 1 2 2 2 1 2 1

Note: as.numeric() function can transform logical object to numeric values: TRUE: 1 and FALSE: 0.

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