Intro to R (part 1): The basics
Aug 12, 2022
·
4 min read
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”
