Intro to R (part 1): The basics

Fri, 12 Aug 2022 00:00:00 +0000

Welcome to R!

In this tutorial, we will learn the basics of R coding, including:

Using functions
Help functions
Vectors
Sequences
Data frames

R is a nice calculator. Kind of like a fancy graphing calculator. An advantage of using R over a calculator:

Can save our steps and use them later or share them.
Can use other peoples code.
Has a keyboard! Faster! Stronger!

Similar to pressing “enter” on a calculator to compute something, you can press “ctrl+enter” on a line in your script (or terminal) to execute a function, inspect an object, or simply calculate a formula.

Keyboard controls:

Arithmetic operators

5

5 / 2

+, /, -, * are arithmetic operators. There are some others, %%, %/% and ^.

5 %/% 2  # The integer part.
5 %% 2   # The remainder part, aka the modulus.
5 ^ 2    # How you take powers of numbers.

Variables:

x <- 5 # alternatively, use "=" instead of arrows; e.g., x = 5
y <- 2
3 * x + y
3 * (x + y)   # use parentheses to avoid excusing your dear aunt Sally.

Parenthesis nesting can get out of hand quickly, so it can be helpful

to turn on ‘rainbow parenthesis’ in RStudio:

Tools > Global Options > Code > Display > Rainbow Parenthesis

z <- (x * (x + y)) ^ y
z

Variable contents are in the “environment” pane in RStudio.

Functions

The exponential function, e to the power of x.

The inputs ‘x’ is called an ‘argument’ of the function.

exp(x = 1)

The help functions!

Definitely one of the best parts of R. Don’t try this in Python!

help(exp)

Important: Check out the examples at the bottom of the page, or try

example(exp)

?exp
?weibull   # General help, for things that are currently in use.
??weibull  # Global help. Does partial matching.

Help on tricky stuff:

?%%        # Help what's a modulus!

Try putting the thing you can’t search for help on in backticks, ``.

?`%%`
?`??`

When you run across something you don’t recognize, try to ? it.

It’s faster than searching online and has less ads. 👍

Vectors

c() concatenates these together to make a vector.

v1 <- c(5, 8, 0)
v1

The parts of a vector are called ’elements’. You extract them with brackets.

R starts numbering the elements with 1.

v1[1]

We can get more than one element at a time.

v1[c(1, 2)]

We can call them in any order, and any number of times.

v1[c(2, 1, 1)]

R works well with vectors: if you put a vector into most functions, you get a vector back. (This is called vectorization.)

exp(x = v1)
round(x = exp(x = v1), digits = 2)

Sequences:

seq(from = 1, to = 10, by = 1)

the fast way:

1:10
5:10
count2 <- seq(from = 2, to = 10, by = 2)  # To count by twos we need seq.
count2

How many elements are in the vector?

length(count2)

Letter vectors

letters
LETTERS
20:22
letters[20:22]

Data frames

Data frames can hold different data types in different columns.

Each column is a vector, possibly with its own data type.
The columns are bound together to make an object called a data frame.
One restriction is that all the columns need to be of equal length.
You also can only store the basic data types in a data frame, like logical, character and numeric, but still there are many possibilities.

yowza <- data.frame(nums = 1:3, lets = letters[1:3]) yowza

We can access the elements in the dataframe, by row and/or column numbers, or by column names.

yowza[1, 1]   # Elements of a dataframe are indexed from the top left.  
yowza[1, ]    # The whole first row
yowza[ , 1]   # The whole first column

yowza[ , "lets"]
yowza$lets
yowza$nums

Notice that the lets and nums maintain their own data types.

The “lets” are still characters.

class(yowza$lets)
class(yowza$nums)

special thing about dataframes is… it holds more than one type of data,

Constraint: all columns are the same length.

Matrices and Recycling rule

mat = matrix(data = 1:100, nrow = 10)
mat
mat[10, ]
mat[ , c(9, 10)]

matrix(data = 0, nrow = 10, ncol = 10)        # A matrix of zeroes.  
matrix(data = c(1, 2), nrow = 10, ncol = 10)  # Recycling rule!
matrix(data = c(1, 2), nrow = 10, ncol = 10, byrow = T)
matrix(data = c(1, 2, 3), nrow = 10, ncol = 10, byrow = T)

100 not divisible by 3!

Another handy thing, which you might want to do someday.

Suppose we want to create a dataframe with every combination of several variables.

expando <- expand.grid(nums = 1:3, lets = letters[1:5])
expando

Three level expando:

fruit <- expand.grid(nums = 1:3, lets = letters[1:2], fruit = c("pear", "orange"))
fruit
fruit$fruit
fruit$lets

See R intro part 2 for a tutorial of more advanced R features using data from “GapMinder”

Data Frames | Alex Baecher