[1] Fundamentals of R

STA141A: Fundamentals of Statistical Data Science

Akira Horiguchi

Fundamentals of R

R as a calculator

5 + 10

[1] 15

10 * 3

[1] 30

(5 + 10) * 3

[1] 45

2^5

[1] 32

(5 + 10) * 3 + 2^5

[1] 77

Assignment operator

We often want to assign values to variables in the workspace to access them again.
The assignment operators are <- and =. (The Tidyverse Style Guide prefers <- and the Google R Style Guide is a fork of the Tidyverse’s. But most other programming languages use =.)

x <- 10  # Assigns 10 to x
x + 3   # Result is 13

[1] 13

y <- 3   # Assigns 3 to y
x + y

[1] 13

z <- x + y  # Assigns the result of x + y to z

Objects: Functions vs Data Objects - 1

From a task-oriented perspective, using R and most other programming languages consists of:

Creating or loading data (inputs, material, values)
Calling or evaluating functions (verbs, actions)
Computing and constructing new data (results, outputs, etc.)

You can think of:

Data objects as matter (i.e., material, or values) that are being measured, manipulated, or processed.
Functions as procedures (i.e., actions, operations, or verbs) that measure, manipulate, or process data.

Objects: Functions vs Data Objects - 2

We will talk about functions later on in detail, but we’ll start making use of functions straight away. Here it is useful to review some basic concepts.

A function is ‘called’ by specifying the function’s name and appropriate data objects as its so-called arguments/parameters.
Function arguments are enclosed in (round) parentheses and best specified in a ‘name = value’ notation.
Some arguments are essential for the function, while others are optional.

x <- 5
log(x)  # This is log with base e (i.e. ln(x))

[1] 1.609438

log(x, base = 10)  # Here, base=10 is an optional argument

[1] 0.69897

Basic data types in R

Basic data types:

Numeric (235.22)
Integer (1)
Character / string ("You are here")
Logical / boolean (TRUE, FALSE)
Missing (NA)

Use class() to get data type of a value

x <- 10.5
class(x)

[1] "numeric"

x <- 1000L # R declares integers by putting L after numbers
class(x)

[1] "integer"

x <- "I love programming"
class(x)

[1] "character"

x <- TRUE
class(x)

[1] "logical"

Inspect data

You can inspect data by entering the variable name into the console, or by using str()

x <- 7
x

[1] 7

str(x)

 num 7

y <- 7L
y

[1] 7

str(y)

 int 7

What is the difference between

the output of x and y?
the output of str(x) and str(y)?

NA, NaN

NA (Not Available): signifies a missing value

x <- c(-2,0,1,9,NA)
x+1

[1] -1  1  2 10 NA

mean(x)

[1] NA

mean(x, na.rm=TRUE)

[1] 2

NaN (Not a Number): signifies an undefined numerical value (e.g., 0/0 or sqrt(-3))

y <- sqrt(x)  # square root
y

[1] NaN   0   1   3  NA

mean(y)

[1] NaN

mean(y, na.rm=TRUE)

[1] 1.333333

Most elementary R functions have a specific argument to deal with these values

NULL

NULL: represents the absence of an object or an empty object.

x <- c(4,5)
y <- NULL
z <- c(-3,-1)
c(z, x)

[1] -3 -1  4  5

c(z, y, x)

[1] -3 -1  4  5