In this course we will use R Notebooks which blend blocks of R code with regular lecture notes. Below is an example of an R code block to perform a simple operation!

1+1
## [1] 2
# Use the hashtag (#) to write comments, R will ignore code preceeded by hashtags.

# In this these notebooks I will use comments to describe what R is doing. 
# Please read through this notebook, executing the commands! e.g.

print("Hello World") # This tells R to greet the world
## [1] "Hello World"

Today we will be describe some of the basic R functionalities.

Topics:

  1. How to install packages
  2. Interacting with R
  3. Variable Assignment
  4. Types
  5. Logical Operators
  6. Vectors in R

0. Install Packages

To install and run a package in R we need to do two things:

  1. Install the package by running > install.packages(“package_name”)

  2. Load the package into scope by running > library(package_name)

Example: Installing the swirl package

# install.packages("swirl") # Run this command to install swirl, if you have already you don't need to do it again.

library(swirl) # This command tells R you want to use the package swirl.
## 
## | Hi! Type swirl() when you are ready to begin.
# From here follow swirls instructions, download the course R Programming and explore it's lessons!
# You can begin interaction with swirl by typing
# swirl() 
# But more on that later!

1+2. Interacting with R and Variable Assignment

In the following block we interact with R!

# This is another block of R code
# R can perform simple numeric operations like any calculator.

1+1-3 # Addition and Subtraction
## [1] -1
5*6/3 # Mult and Division
## [1] 10
(1+3)^(2+1)/4 # Exponentiation, Standard Order of Operations
## [1] 16
#

x <- 3.37 # The "<-" tells R to assign the 3.37 to a variable named x.
x = 3.37 # You can also use "=" to tell R to assign the 3.37 to a variable named x.
y = 4.63 # We can define mutliple variables, checl up the enviroment tab of your console to see the currently defined variables.
this.is.my.var <- x+y
this.is.my.var
## [1] 8
# this.is.my.var is a variable, not a function of x and y
# Note we can use "."'s in naming our variables
x <- 1
this.is.my.var # Note it remains the same.
## [1] 8
# Some names should not be used since R already knows them
pi
## [1] 3.141593
pi = 3
pi
## [1] 3
rm(pi) # rm() is function built into r that removes variables from memory
pi # note pi is again 3.14...
## [1] 3.141593
# Here are two other special terms that will be useful to know:
exp(1) # R doesn't know e, exp(VAR) returns e^VAR
## [1] 2.718282
NA # NA is a special character, we will return to it when we talk about conditionals.
## [1] NA

3+4. Variables types + Logicals in R

In the previous blcok we saw some operations Numeric Types (Doubles and Integerss). In the next block lets look at the more exotic types.

## Logicals/Booleans
x = TRUE
y = FALSE

# Boolean operators return TRUE/FALSE

# Example 1: '==' is the boolean comparison operator, not assignment
1 == 1
## [1] TRUE
# Example 2: 
(1 == 1) == y # Try to guess what this line will output before running!
## [1] FALSE
# The other two important boolean comparisons are <=, >=, !=
1 >= 3 # <=, >= make numeric comparisons. 
## [1] FALSE
# Fun fact! When TRUE or FALSE are numerically compared, TRUE = 1, FALSE = 0
(1 >= 3) <= TRUE # Try to guess what this will output!
## [1] TRUE
## Characters
x <- "Hello World!"
y <- 'Hello World!'
print(x) # The print function will display the variable inside to the console
## [1] "Hello World!"
print(y)
## [1] "Hello World!"
# Fun fact: In R "strings" are not functions of characters but their own object.
# One useful function is substring(char, int start, int end)
hello = substring(x, 1, 5)  # Here substring is isolating the first 5 letters
world = substring(x, 6, 12) # Here substring is isolating the last six characters
# We can then put the string back together with the paste function
# Syntax: paste(char, char)
z = paste(hello, world)

5. Vectors in R

Pro tip: We can clear our workspace with the command:

CTRL+L

down in the console. We can also clears the variables in the enviroment by typing

rm(list=ls())

# Lets try it, type CTRL+L below in the console.
rm(list=ls()) # Clears the variables in the enviroment

# In this block of code we will introduce vectors (arrays)
x_corr = 1
y_corr = 2
point = c(x_corr, y_corr) # c(OBJ, OBJ, ..., OBJ) concatenates variables or, more generally, other objects
two_points = c(point, point) # Here we are combining two vectors

# We query elements of a vector using brackets 
## NOTE: Functions use paranthesis to take arguments, brackets take in indicies!

# Example
two_points[2]
## [1] 2
two_points[1] # note the first element starts at 1, not 0 like in some languages
## [1] 1
# We can see how long a vector is using the length(vector) function
# Example
length(two_points)
## [1] 4
# *Imporant*: Two numbers separated by a colon are all numbers in between 
1:4 # is a vector of numbers 1,2,3,4
## [1] 1 2 3 4
x = 1:5
# Combined with the brackets, we can use this to subsample the data
x[2:4]
## [1] 2 3 4
# We can concatenate multiple types of variables into an array
many_types = c(1,2,TRUE,"Cool")

# Two really useful functions for creating vectors are seq(from = x, to = y, by = inc) and rep(elt, times = x)

# Example of seq()
seq(from = 0, to = 100, by = 10) # this creates a vector of numbers from 0 to 100 by tens
##  [1]   0  10  20  30  40  50  60  70  80  90 100
# Example of rep()
rep(1:5, times = 3) # repeats 1:5, 3 times 
##  [1] 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
# Another really useful way to build vectors is draw them from a distribution!

# Example of rnorm()
random_sample = rnorm(20, 0, 5) # rnorm(n, mean, std) randomly draws n samples from a normal distribution with mean and std.

# We can also analyze vectors using the mean, sd, and summary function
mean(random_sample)
## [1] -2.621532
sd(random_sample)
## [1] 4.653402
summary(random_sample)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -10.18560  -5.21074  -2.63824  -2.62153   0.01128  10.09517