# Chapter 7 R Syntax

Frustration is natural when you start programming in R. R is a stickler for punctuation, and even one character out of place will cause it to complain. But while you should expect to be a little frustrated, take comfort in that it is both typical and temporary: it happens to everyone, and the only way to get over it is to keep trying.

To understand computations in R, two slogans are helpful:

• Everything that exists is an object.
• Everything that happens is a function call.

John Chambers

## 7.1 Variables

### 7.1.1 Arithmetic operators

In its most basic form, R can be used as a simple calculator. Consider the following arithmetic operators:

Operator Description
- subtraction
* multiplication
/ division
^ or ** exponentiation
x %% y modulus (remainder)
1 / 200 * 30
#> [1] 0.15
(59 + 73 + 2) / 3
#> [1] 44.7
5 %% 2
#> [1] 1

But when we do this, none of our results are saved for later use.

### 7.1.2 Assigning Variables

An essential part of programming is creating objects (or variables).1 Variables are names for values.

A variable is created when a value is assigned to it. We do that with <-.

x <- 3

<- is called the assignment operator. It assigns values on the right to objects on the left, like this:

object_name <- value

So, after executing x <- 3, the value of x is 3. The arrow can be read as 3 goes into x.

NB: Do not use = for assignments. It will work in some contexts, but it will cause confusion later. There will be other scenarios where you will use = - we will discuss these later on.

We can use variables in calculations just as if they were values.

x <- 3
x + 5
#> [1] 8

#### Inspect objects to display values.

In R, the contents of an object can be printed by simply executing the object name.

x <- 3
x
#> [1] 3

#### Whitespace makes code easier to read.

Notice that we surrounded <- with spaces. In R, white space is ignored (unlike Python). It is good practice to use spaces, because it makes code easier to read.

experiment<-"current vs. voltage"   # this is bad
experiment <- "current vs. voltage" # this is better

### 7.1.3 Variable Names

Object names can only contain letters, numbers, _ and ..

You want your object names to be descriptive. x is not a good variable name (sorry!). You will also need a convention for multiple words. I recommend snake_case, where you separate lowercase words with _.

i_use_snake_case
otherPeopleUseCamelCase
some.people.use.periods
And_aFew.People_RENOUNCEconvention

Let’s make an assignment using snake_case:

r_rocks <- 2 ^ 3

And let’s try to inspect it:

r_rock
R_rocks
#> Error in eval(expr, envir, enclos): object 'R_rocks' not found

R is case-sensitive!

#### Use the TAB key to autocomplete.

Because typos are the absolute worst, we can use R Studio to help us type. Let’s inspect r_rocks using RStudio’s tab completion facility. Type r_, press TAB, add characters until you have a unique prefix, then press return.

r_rocks
#> [1] 8

### 7.1.4 Challenges

#### Challenge 1: Making and printing variables.

Make 3 variables: name (with your full name), city (where you were born), and year (when you were born).

#### Challenge 2: Swapping values.

Draw a table showing the values of the variables in this program after each statement is executed.

In simple terms, what do the last three lines of this program do?

lowest <- 1.0
highest <- 3.0
temp <- lowest
lowest <- highest
highest <- temp

#### Challenge 3: Predicting values.

What is the final value of position in the program below? Try to predict the value without running the program, then check your prediction.

initial <- "left"
position <- initial
initial <- "right"

#### Challenge 4: Syntax.

Why does the following code fail?

age == 31

And the following?

31 <-  age

## 7.2 Functions

R has a large collection of built-in functions that helps us do things. When we use a function, we say we are calling a function.

function_name(arg1 = val1, arg2 = val2, ...)

Here are some helpful built-in functions:

my_var <- c(1, 5, 2, 4, 5)

sum(my_var)
#> [1] 17
length(my_var)
#> [1] 5
min(my_var)
#> [1] 1
max(my_var)
#> [1] 5
unique(my_var)
#> [1] 1 5 2 4

### 7.2.1 Arguments

An argument is a value that is passed into a function. Every function returns a result.

Let’s try using seq(), which makes regular sequences of numbers. While we are at it, we will learn more helpful features of RStudio.

Type se and hit TAB. A popup shows you possible completions. Specify seq() by typing more (a q) to disambiguate, or by using ↑/↓ arrows to select. Notice the floating tooltip that pops up, reminding you of the function’s arguments and purpose.

Press TAB once more when you have selected the function you want. RStudio will add matching opening (() and closing ()) parentheses for you. Type the arguments 1, 10 and hit return.

seq(1, 10)
#>  [1]  1  2  3  4  5  6  7  8  9 10

How many arguments did we pass into the seq function?

### 7.2.2 Store Function Output

Notice that, when we called the seq function, nothing changed in our environment. That is because we did not save our results to an object. Let’s try it again by assigning a variable:

y <- seq(1, 10)
y
#>  [1]  1  2  3  4  5  6  7  8  9 10

### 7.2.3 Argument Restrictions and Defaults

Let’s use another function, called round:

round(60.123)
#> [1] 60

round must be given at least one argument. Moreover, it must be given things that can be meaningfully rounded.

round()
round('a')

Functions may have default values for some arguments.

By default, round will round off any number to zero decimal places. But we can specify the number of decimal places we want.

round(60.123)
#> [1] 60
round(60.123, digits = 2)
#> [1] 60.1
round(60.123, 2)
#> [1] 60.1

### 7.2.4 Documentation and Help Files

How do we know what kinds of arguments to pass into a function? Every built-in function comes with documentation.

• ? + object opens a help page for that specific object.
• ?? + object searches help pages containing the name of the object.
?mean
??mean

All help files are structured the same way:

• The Arguments section tells us exactly what kind of information we can pass into a function.
• The Value section explains what the output of the function is.
• The Examples section contains real examples of the function in use.

### 7.2.5 Challenges

#### Challenge 1: What Happens When

Explain, in simple terms, the order of operations in the following program: When does the addition happen, when does the subtraction happen, when is each function called, etc.

What is the final value of radiance?

radiance <- 1.0
radiance <- max(2.1, 2.0 + min(radiance, 1.1 * radiance - 0.5))

#### Challenge 2: Why?

Run the following code.

rich <- "gold"
poor <- "tin"
max(rich, poor)

Using the help files for max, explain why it returns the result it does.

## 7.3 Data Types

Every value in a program has a specific type. In R, those types are called “classes”, and there are 4 of them:

• Character (text or “string”).
• Numeric (integer or decimal).
• Integer (just integer).
• Logical (TRUE or FALSE booleans).
Example Type
“a”, “swc” character
2, 15.5 numeric
2 (Must add a L at end to denote integer) integer
TRUE, FALSE logical

### 7.3.1 What Is that Type?

R is dynamically typed, meaning that it “guesses” what class a value is. Every piece of information in R has a type!

Use the built-in function class() to find out what type a value has.

class(3)
#> [1] "numeric"
class(3L)
#> [1] "integer"
class("Three")
#> [1] "character"
class(T)
#> [1] "logical"

This works on variables as well. But remember: the value has the type — the variable is just a label.

three <- 3
class(three)
#> [1] "numeric"

three <- "three"
class(three)
#> [1] "character"

A value’s class determines what the program can do to it.

3 - 1
#> [1] 2
3 - "1"
#> Error in 3 - "1": non-numeric argument to binary operator

### 7.3.2 Coercion

We just learned we cannot subtract numbers and strings. Instead, use as. + name of class as a function to convert a value to a specified type.

3 - as.numeric("1")

This is called coercion. Here is another example:

my_var <- "FALSE"
my_var
#> [1] "FALSE"
as.logical(my_var)
#> [1] FALSE

What difference did you notice?

### 7.3.3 Other Objects

There are a few other “odd ball” types in R:

#### NA are missing values.

Missing values are specified with NA. NA will always be coerced to the correct type if used inside c().

x <- c(NA, 1)
x
#> [1] NA  1
typeof(NA)
#> [1] "logical"
typeof(x)
#> [1] "double"

#### Inf is infinity.

You can have either positive or negative infinity.

1/0
#> [1] Inf
1/Inf
#> [1] 0

#### NaN means “not a number.” It is an undefined value.

0/0
#> [1] NaN

### 7.3.4 Challenges

#### Challenge 1: Making and Coercing Variables

1. Make a variable year and assign it as the year you were born.
2. Coerce that variable to a string and assign it to a new variable year_string.
3. Someone in your class says they were born in 2001. Really? Really. Find out what your age difference is, using only year_string.

#### Challenge 2: Fixing the Code

Change the following code to make the output TRUE:

val_1 <- F
val_2 <- "F"

class(val_1) == class(val_2)
#> [1] FALSE

## 7.4 Boolean Expressions

Boolean expressions are logical statements that are either true or false. For our purposes, we will often use Boolean expressions to compare quantities. For instance, the Boolean expression 1 < 2 is true, whereas the Boolean expression 1 > 2 is false.

When you type a Boolean expression in R, R will output TRUE if the expression is true and FALSE if the expression is false.

### 7.4.1 Boolean Operators

Operator Description
< less than
<= less than or equal to
> greater than
>= greater than or equal to
== exactly equal to
!= not equal to
%in% is an item of a set

Note that we use a double equal sign == to check whether two values are equal. Typing a = b would set the value of a equal to the value of b.

Here are some examples of Boolean expressions in action:

1 < 2
#> [1] TRUE
1 > 2
#> [1] FALSE
1 == 2
#> [1] FALSE

value_1 <- 1
value_1 > 0
#> [1] TRUE

value_2 <- value_1 + 10
value_1 + value_2 <= 12
#> [1] TRUE

### 7.4.2 Logical Operators

In practice, we often use two or more conditions to decide what to do. To combine different conditions, there are three logical operators:

Operator Description
x & y x AND y
x | y x OR y
!x Not x

First, & is similar to AND in English, that is, x & y is true only if both x and y are true. Second, is similar to OR in English, that is, A || B is false only if both x and y are false. Third, ! is similar to NOT in English, that is, !x is true only if x is false.

x <- 10
y <- 20

# check to see if values are between 5 and 15
x > 5 & x < 15
#> [1] TRUE
y > 5 & y < 15
#> [1] FALSE

# more complex chains
(x > 5 & x < 15) & (x > y & y < 15)
#> [1] FALSE
(x > 5 & x < 15) | (x > y & y < 15)
#> [1] TRUE
(x > 5 & x < 15) & !(x > y & y < 15)
#> [1] TRUE

Different operator take different precendence. It is always a good practice to use brackets to control operation ordering.

### 7.4.3 Boolean Vectors

The nice thing about R is that you can use these comparison operators also on vectors. As with many expressions in R, the Boolean expressions discussed above are all vectorized. We will learn more about vectors and vectorization later in this class, but here is a quick example:

x <- c(2.1, 4.2, 3.3, 5.4)
x > 3
#> [1] FALSE  TRUE  TRUE  TRUE

This command tests for every element of the vector if the condition stated by the comparison operator is TRUE or FALSE.

### 7.4.4 Boolean Vectors in Action

Boolean vectors are partially what makes R so magical. Check out the example below and examine each line. We will cover subsetting operations later, but pay attention to the work of boolean expressions and logical operators.

# An example
x <- c(1:10)
x[(x>8) | (x<5)]
#> [1]  1  2  3  4  9 10

# How it works
x <- c(1:10)
x
#>  [1]  1  2  3  4  5  6  7  8  9 10
x > 8
#>  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
x < 5
#>  [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
x > 8 | x < 5
#>  [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE
x[c(T,T,T,T,F,F,F,F,T,T)]
#> [1]  1  2  3  4  9 10

### 7.4.5 Challenges

#### Challenge 1.

In the partially written code below, vector_1 and vector_2 each contain 10 values. Using the fact that Boolean expressions are vectorized, write code that outputs:

1. A vector of length 10 such that element i of this vector equals TRUE if vector_1[i] is less than vector_2[i] and equals FALSE otherwise.
2. The number of times that element i of vector_1 is less than element i of vector_2, using the sum function.
vector_1 <- c(1, 2, 4, 5, 3, 7, 8, 7, 1, 2)
vector_2 <- c(1, 3, 4, 4, 5, 10, 6, 8, 9, 1)

# YOUR CODE HERE

1. Technically, objects and variables are different things, but we will use the two interchangeably for now.↩︎