# 1 R Basics

“8/27/2020 | Last Compiled: 2022-04-26”

## 1.1 Reading and walkthrough video

Vokey & Allen,^{11} Chapter 1, available online here: http://people.uleth.ca/~vokey/pdf/thinking.pdf.

## 1.2 Overview

The labs for this course are designed to give students exposure to the free and open-source statistical programming language R. The assumption is that students may have zero prior experience with scripting, coding, or computer programming. Over the semester we will use R as a tool for data-analysis, and as a tool to sharpen understanding of statistical concepts.

Before starting this lab follow the getting started instructions to install R, R-studio, create a Github.com account, download github Desktop; and, make sure that you test the github pipeline. Throughout the semester you will be posting your assignments on Github.com, and submitting links to your repositories on blackboard.

## 1.3 Problem 1: Summing 1 to 100

Vokey & Allen^{12} tell the story of a teacher giving challenging students to add up all of the numbers from 1 to 100. This was supposed to stump one of the students, a young Gauss. Apparently, it did not, and Gauss very quickly wrote down the sum 5050.

Gauss didn’t have R, but we do, so we can solve the problem very quickly too:

- Use R to find the sum of the sequence of numbers from 1 to 100:

```
sum(1:100)
#> [1] 5050
```

Using `sum()`

, it is simple and fast to solve Gauss’s problem in R. You could easily find other sums by changing the 1 or the 100

There are many details going on behind the scenes in R that allow the `sum()`

function do its work. One detail is how to get R to create a sequence of numbers, another is how to take an action like adding up all the numbers in the sequence. Two major concepts here are *variables* for storing information (like sequences of numbers), and *functions* that take actions to transform an input (a number sequence) to a desired output (a sum of the number sequence).

## 1.4 R Basics Background

### 1.4.1 Creating sequences of numbers in R

There are multiple ways to create number sequences in R. A sequence of integers can be generated with `x:y`

, where x is the starting value, and y is the ending value.

```
1:5
#> [1] 1 2 3 4 5
1:10
#> [1] 1 2 3 4 5 6 7 8 9 10
5:-5
#> [1] 5 4 3 2 1 0 -1 -2 -3 -4 -5
```

### 1.4.2 seq()

Sequences incremented by any constant value can be created using the `seq()`

function. Look up “help” for any R function by typing `?name_of_function`

into the console

`?seq`

R comes pre-packaged with many functions like `seq()`

, and you can write your own functions, and download libraries of functions that other people have written to extend the base functionality of R. We will look more closely at functions throughout the semester.

An R function usually has three components. It will 1) receive some kind of input, 2) “do something”, and 3) return some kind of output. In R, you use functions by writing the name of the function and parentheses `name()`

. If the function takes inputs, then you define the inputs inside the parentheses `name(x=1)`

. Functions can have multiple inputs, that are separated by commas.

Let’s take a look at using the `seq()`

function to generate sequences of numbers.

```
#lines beginning with # are comments and not run
#seq(from, to)
seq(from = 1, to = 5)
#> [1] 1 2 3 4 5
seq(1, 5)
#> [1] 1 2 3 4 5
#seq(from, to, by= )
seq(from = 1, to = 5, by = 2)
#> [1] 1 3 5
seq(1, 5, .5)
#> [1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
seq(1, 10, 2)
#> [1] 1 3 5 7 9
#seq(from, to, length.out= )
seq(from = 1, to = 2, length.out =5)
#> [1] 1.00 1.25 1.50 1.75 2.00
seq(5)
#> [1] 1 2 3 4 5
```

## 1.5 Problem 2: Summing any constant series

Now that we have seen the `sum()`

and `seq()`

functions, you should be able to use them to find the sum of any constant series.

For example, find the sum of the series 100 to 200, going up by five.

It is also possible to write the analytic formula in R, and compare results, remember that:

\(X_1 + X_2 + \ldots + X_n = (\frac{X_n-X_1}{c}+1)(\frac{X_1+X_n}{2})\)

Where \(X_1\) is the starting value, \(X_n\) is the ending value and \(c\) is the constant step value.

Here is an example writing this formula in R. We create variables with the names `X1`

, `Xn`

, and `step`

, and assign (`<-`

) them any value we want. Then we compute the formula. This should give the same value as the previous example, because it is the same sequence.

```
X1 <- 100
Xn <- 200
step <- 5
(((Xn - X1)/step) + 1) * ((X1 + Xn)/2)
#> [1] 3150
( ( (Xn-X1)/step ) + 1 ) * ( (X1+Xn)/2 )
#> [1] 3150
```

## 1.6 Vectors

### 1.6.1 The Gaussian trick

Remember Gauss added up the numbers from 1 to 100 by imagining two number lines:

```
1:100
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#> [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#> [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
#> [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
#> [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
#> [91] 91 92 93 94 95 96 97 98 99 100
100:1
#> [1] 100 99 98 97 96 95 94 93 92 91 90 89 88 87 86 85 84 83
#> [19] 82 81 80 79 78 77 76 75 74 73 72 71 70 69 68 67 66 65
#> [37] 64 63 62 61 60 59 58 57 56 55 54 53 52 51 50 49 48 47
#> [55] 46 45 44 43 42 41 40 39 38 37 36 35 34 33 32 31 30 29
#> [73] 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11
#> [91] 10 9 8 7 6 5 4 3 2 1
```

He noted that the sum of the columns always added up to 101 (e.g., 1+100 = 101, 2+99 = 101, etc.). It is possible to demonstrate this in R because we can directly add both number lines:

```
1:100 + 100:1
#> [1] 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101
#> [19] 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101
#> [37] 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101
#> [55] 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101
#> [73] 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101
#> [91] 101 101 101 101 101 101 101 101 101 101
```

Each time we created a sequence of numbers (e.g., from 1 to 100) we were creating an object called a `numeric vector`

in R. Vectors can store multiple numbers.

Vectors are a kind of variable in R. Vectors can have names, they can be saved, and they can be manipulated. Let’s take a quick look at vectors:

`a`

is the name of the new vector. `<-`

is called the assignment operator. `1:5`

creates a vector of length 5, containing the sequence of numbers 1 to 5. In plain language, we “assign” the object on the right (the 1:5), to the “name” `a`

written on the left side of the `<-`

. In other words, we put the numbers 1 to 5 into something called `a`

.

```
#creates a variable a
a <- 1:5
```

When you create a variable like the `a`

above, its name will appear in the global environment (top right environment tab). When the variable is created for the first time (by executing the code in the console), it becomes registered or saved in your computer’s memory for the current R session.

If you want to double-check that a variable exists in memory, enter its name into the console, and press enter:

```
a
#> [1] 1 2 3 4 5
```

You can clear (remove) a variable using `rm()`

. And, you can clear the entire global environment using `Session > Clear Workspace...`

from the RStudio menu.

`rm(a)`

You can also check the `class`

of a variable in R using the `class()`

function. Let’s create two vectors and check their classes. The `a`

vector contains an integer class, and `b`

vector contains a `numeric`

class because there are decimal values.

### 1.6.2 c()

It is also possible to create vectors that contain characters, rather than numbers. To illustrate an example of a `character vector`

, we introduce one more basic R function `c()`

, short for “combine”.

`?c`

Vectors are like trains, they have slots (train cars) that can contain things, like passengers, or oil or coal. Trains can have any number of train cars, just like a vector can have any length (number of slots).

Importantly, train cars need to be connected together to form a whole train. Similarly, in R, to make a vector you need to concatenate or connect individual slots of a vector together. This is what the `c()`

function is all about. It combines individual units together to form a vector.

To use `c()`

, insert individual items separated by commas between the parentheses. To insert characters, wrap each character (or string of characters) between quotations.

```
letters <- c("a","b","c")
numbers <- c(1,2,3)
numbers_as_chars <- c("1","2","3")
words <- c("this","is","a","vector","of","strings")
```

The elements between the commas are like individual train cars, and the `c()`

function connects the elements together into a single entity comprised of multiple units, like a train, called a vector.

### 1.6.3 length()

Just like a train has a specific number of cars, a vector has a specific number of slots. This is called the length of the vector. R has a `length()`

function that reports the length of a vector.

### 1.6.4 More on combining

The `c()`

function is flexible because it can combine all sorts of elements, including vectors or variables.

Remember that vectors have different classes depending on what kind of elements are inside the vector. This is important, because R requires that **all of the elements** in a vector have the **same** class.

```
class(c(1,2,3))
#> [1] "numeric"
class(c("A","B","C"))
#> [1] "character"
class(c(TRUE,FALSE,TRUE))
#> [1] "logical"
```

It is possible to combine vectors that start with different classes, but R may give an error, or it will convert one class into another. To go back to the train car analogy, R doesn’t like trains that have different kinds of cars…it wants the whole train to be passenger cars, or the whole train to be oil tankers.

```
# the numbers are converted to characters
c(1,2,3,"a","b","c")
#> [1] "1" "2" "3" "a" "b" "c"
```

### 1.6.5 Indexing a vector

Vector indexing iallows elements to be inspected and changed. This is like a train, where you could inspect the contents of cars 3 to 5, or unload car 7 and put something else in it.

Square bracket `[]`

notation indexes a vector, with `variable_name[x]`

; where, `x`

is another vector specifying the indexed slots.

`a <- c(1,6,3,2,8,9) # make a vector`

The following examples use `[]`

to index specific elements of the vector `a`

. The outcome is that the elements specified inside the square brackets are printed to the console.

```
a[1] # first element
#> [1] 1
a[2] # second element
#> [1] 6
a[1:3] # 1st to 3rd elements
#> [1] 1 6 3
a[c(1,5)] # elements 1 and 5
#> [1] 1 8
```

It is also possible to assign new values to specific elements of a vector:

```
# assign 100 to the first slot of a
a[1] <- 100
a
#> [1] 100 6 3 2 8 9
# assign the value 1 to slots 5 to 6 of a
a[5:6] <- 1
a
#> [1] 100 6 3 2 1 1
```

### 1.6.6 Growing a vector

In upcoming labs we will use vectors to store information. Sometimes you know in advance how many slots you need for your vector, and other times you might not know, and instead you could decide to build your vector one slot at a time.

Below I begin with an empty (NULL) vector. I use the `c()`

command, but I don’t combine anything together. This like starting a train with no cars at all.

```
a <- c()
a
#> NULL
```

We can add a slot to this vector by combining a new element to the existing variable. Here we combine `a`

with 1, and then assign the result back into `a`

, replacing it’s original NULL value.

```
a <- c(a,1)
a
#> [1] 1
```

If we keep doing this, we will keep adding 1s, to the end of `a`

.

```
a <- c(a,1)
a
#> [1] 1 1
a <- c(a,1) # c(1,1,1)
a
#> [1] 1 1 1
a <- c(a,1)
a
#> [1] 1 1 1 1
a <- c(a,1)
a
#> [1] 1 1 1 1 1
```

Consider this alternative method for growing a vector:

```
a <- c()
a
#> NULL
a[1] <- 1
a
#> [1] 1
a[2] <- 1
a
#> [1] 1 1
a[3] <- 1
a
#> [1] 1 1 1
a[10] <- 1
a
#> [1] 1 1 1 NA NA NA NA NA NA 1
```

## 1.7 Problem 3: Writing a sum function in R

We have already used R to solve the problems in Chapter in 1 of Vokey & Allen (2018). We can create sequences of numbers, we can create custom vectors of numbers, and we can use the `sum()`

function to find the sum. However, we haven’t discussed **how** the `sum()`

function actually works. How does R know how to find the sum?

This is an example of writing your own sum function in R. This example involves understanding `for`

loops and writing custom functions, both of which are explained in the next sections.

```
my_sum <- function(x) {
sum <- 0
for(i in x) sum <- sum + i
return(sum)
}
my_sum(1:100)
#> [1] 5050
```

## 1.8 Algorithms

To understand how functions like `sum()`

work in R, we need to understand the more general concept of an algorithm. I’ll define algorithm as a recipe, or series of steps/actions that result in a particular outcome. With a scripting language like R, it is possible to define algorithms that are infallible. That is, given an input, they **always** apply all of the steps and arrive at the **same answer** demanded by the algorithm.

When you sum up a series of numbers in your head, say the numbers 1 to 5, you are probably applying a simple algorithm that could be describe like this:

- Take the first number and add it to the second (1+2 = 3)
- Take the sum (3) and add it to the next number (3+3 = 6)
- Repeat step 2 until there are no more numbers in the series

- 6+4 = 10
- 10+5 = 15

- report the final sum (15)

```
sum(1:5)
#> [1] 15
```

Consider how this could look in R. This is one example of producing an algorithm in R. Everytime we run the below script, we always end up with a sum of 15, because the answer is demanded by the series of steps that I wrote down.

```
a <- 1:5
a
#> [1] 1 2 3 4 5
the_sum <- a[1]+a[2]
the_sum
#> [1] 3
the_sum <- the_sum + a[3]
the_sum
#> [1] 6
the_sum <- the_sum + a[4]
the_sum
#> [1] 10
the_sum <- the_sum + a[5]
the_sum
#> [1] 15
```

## 1.9 Loops

The above example shows an algorithm written by hand in R. It would be tiresome to write out a sum for a sequence with many more numbers. Fortunately, there are ways to **automate** repetitive processes in R. A common method for repeating commands in R is to use a `for`

loop.

Check R help for on Control Flow `?Control`

.

`for(){}`

`for(loop control){do something each iteration}`

The basic syntax for loops is as follows:

```
for(iterator in vector){
#do something
}
```

Loop control is defined in between the parentheses. The name of the iterator is placed on the left of `in`

(can be assigned any name you want, does not need to be declared in advance). During the execution of the loop, the iterator takes on the values inside the vector which is placed on the right side of `in`

. Specifically, the following is happening.

Loop steps: 1. iterator <- vector[1] 2. iterator <- vector[2] 3. iterator <- vector[3] 4. etc.

The loop will automatically stop once it reaches the last item in the vector. The loop can be stopped before that using the `break`

command.

```
# Make a loop do something 5 times
# i is the iterator
# 1:5 creates a vector with 5 numbers in it, 1, 2, 3, 4, 5
# the loop will run 5 times, because there are five things to assign to i
for(i in 1:5){
print("hello")
}
#> [1] "hello"
#> [1] "hello"
#> [1] "hello"
#> [1] "hello"
#> [1] "hello"
```

```
# show the value of i each step of the loop
for(i in 1:5){
print(i)
}
#> [1] 1
#> [1] 2
#> [1] 3
#> [1] 4
#> [1] 5
```

```
# define the vector to loop over in advance
my_sequence <- 1:5
for(i in my_sequence){
print(i)
}
#> [1] 1
#> [1] 2
#> [1] 3
#> [1] 4
#> [1] 5
```

```
# Reminder that i becomes the next value in the vector
# your vector can have any order
my_sequence <- c(1,5,2,3,4)
for(i in my_sequence){
print(i)
}
#> [1] 1
#> [1] 5
#> [1] 2
#> [1] 3
#> [1] 4
```

```
# index vector does not need to be numbers
my_things <- c("A","B","C","D")
for(i in my_things){
print(i)
}
#> [1] "A"
#> [1] "B"
#> [1] "C"
#> [1] "D"
```

### 1.9.1 Breaking a loop

`break`

stops a loop. Used with logical statements to define the conditions necessary to cause the break.

```
for(i in 1:10){
if(i <= 5){
print(i)
} else {
break
}
}
#> [1] 1
#> [1] 2
#> [1] 3
#> [1] 4
#> [1] 5
```

### 1.9.2 While loops

While loops run until a logical condition is met. Here there is no iterator, just a logic statement that needs to be met.

This one prints i while i is less than 6. As soon as i becomes “not less than 6”, then the loop stops. Critically, inside the loop, the value of i increases each iteration.

```
i <- 1 # create an variable
while (i < 6) {
print(i)
i = i+1 #add one each step of the loop
}
#> [1] 1
#> [1] 2
#> [1] 3
#> [1] 4
#> [1] 5
```

### 1.9.3 Repeat loops

Similar to while, but let’s do things until a condition is met.

```
i<-0
repeat{
i<-i+1
print(i)
if(i==5){
break
}
}
#> [1] 1
#> [1] 2
#> [1] 3
#> [1] 4
#> [1] 5
```

### 1.9.4 Examples

Braces are not needed on one line

```
for(i in 1:5) print(i)
#> [1] 1
#> [1] 2
#> [1] 3
#> [1] 4
#> [1] 5
```

Using the value of the iterator to assign in values systematically to another variable.

```
# put 1 into the first five positions of x
x <- c() # create empty vector
for(i in 1:5){
x[i] <- 1 # assign 1 to the ith slot in x
}
x
#> [1] 1 1 1 1 1
# put the numbers 1-5 in the first 5 positions of x
x <-c()
for(i in 1:5){
x[i] <- i
}
x
#> [1] 1 2 3 4 5
```

Using a loop to add up numbers in a vector.

```
a <- c(10,20,30,40,50) #some numbers
the_sum <- 0 # initialize a variable that will keep track of the sum
for (i in a) {
the_sum <- the_sum + i
}
the_sum
#> [1] 150
sum(a) #check against the sum function
#> [1] 150
```

The above example shows the use of a `for`

loop to compute the sum of the numbers in the vector `a`

. This is an example of using a loop in an algorithm. At the end we checked out custom script against the `sum()`

function, and we see that we arrived at the same answer.

A final task for this lab is take a look at R functions, and learn how to write our own basic functions like `sum()`

.

## 1.10 Functions

Functions are re-useable algorithms. For example, rather re-writing all of the code necessary to compute a sum everytime we want to find a sum, we instead store the necessary code inside a named variable called `sum()`

, then we “call” the function by writing its name and providing inputs.

It is fairly straightforward to write your own custom functions in R, and learning how to write your own functions is an excellent method to improve your understanding of R fundamentals.

### 1.10.1 function syntax

This is the general syntax for writing functions:

```
function_name <- function(input1,input2){
#code here
return(something)
}
```

### 1.10.2 Example functions

This function has no input between the `()`

. Whenever you run this function, it will simply return whatever is placed inside the `return`

statement.

```
# define the function
print_hello_world <- function(){
return(print("hello world"))
}
# use the function
print_hello_world()
#> [1] "hello world"
```

This function simply takes an input, and then returns the input without modifying it.

```
return_input <- function(input){
return(input)
}
# the variable input is assigned a 1
# then we return(input), which will result in a 1
# because the function internally assigns 1 to the input
return_input(1)
#> [1] 1
a <- "something"
return_input(a)
#> [1] "something"
```

This function takes an input, then creates an internal variable called temp and assigns input+1. Then the contents of temp is returned. Note there, is no checking of the input, so it will return an erro if you input a character (can’t add one to a character in R)

```
add_one <- function(input){
temp <- input+1
return(temp)
}
add_one(1)
#> [1] 2
add_one("a") #this will cause an error
#> Error in input + 1: non-numeric argument to binary operator
```

This function adds some input checking. We only add one if the input is a numeric type. Otherwise, we use `stop()`

to return an error message to the console

```
add_one <- function(input){
if(class(input) == "numeric"){
temp <- input+1
return(temp)
} else {
return(stop("input must be numeric"))
}
}
add_one(1)
#> [1] 2
add_one("a")
#> Error in add_one("a"): input must be numeric
```

A function with three inputs

```
add_multiply <- function(input, x_plus, x_times){
temp <- (input+x_plus)*x_times
return(temp)
}
# input is 1
# x_plus <- 2
# x_times <- 3
# will return (1+2)*3 = 9
add_multiply(1,2,3)
#> [1] 9
```

## 1.11 Lab 1 Generalization Assignment

Follow the instructions below to complete the assignment for lab 1 and hand it in by the due date on blackboard. For the first lab I have taken the extra step of pretending I was student in this course, and completed the first lab myself. This next video shows how I would complete the lab if I was a student. It is important that you try to solve the problems on your own, but please use this video as a resource to help you if you get stuck.

### 1.11.1 Instructions

In general, labs will present a discussion of problems and issues with example code like above, and then students will be tasked with completing generalization assignments, showing that they can work with the concepts and tools independently.

Your assignment instructions are the following:

- Make a new R project (initialized as a git repository) called “StatsLab1’.
- Create a new R Markdown document called “Lab1.Rmd”
- Upload your StatsLab1 R project to Github.com using Github Desktop
- Use Lab1.Rmd to show your work attempting to solve the following generalization problems. Commit your work regularly so that it appears on your Github repository.
- Submit your github repository link for Lab 1 on blackboard.
- There are six problems to solve, each worth 1 point.

Refer to the getting started videos for examples of creating a new R project and uploading it to Github. If you have problems with these steps and they have not been resolved in our first class, then please email me about them, or create an issue on the course github page https://github.com/CrumpLab/psyc7709Lab/issues

### 1.11.2 Problems

- Compute the sum of the sequence 100 to 1000, going up by a constant value of 100 (100,200,300,400,500,600,700,800,900,1000).
- Compute the sum of these numbers (1,3,2,4,3,5,4,3,4,5,6,5,6,7,6,5,6,5,4,3,4,5)
- Write a custom sequence generator function
**using a for loop**that generates a sequence from a starting integer value to an ending integer value in steps of 1. Demonstrate that it can produce the sequence 1 to 10. - Write a custom function to implement the following general equation to find the sum of any constant series:

\(X_1 + X_2 + \ldots + X_n = (\frac{X_n-X_1}{c}+1)(\frac{X_1+X_n}{2})\)

Demonstrate that your function correctly produces the sum for the series below:

```
seq(10,100,10)
#> [1] 10 20 30 40 50 60 70 80 90 100
```

- Write a custom function that generates a constant series between any start and end values, with any constant, and finds the sum. Have your function output both the sequence and the sum. For this problem, feel free to use the existing
`seq()`

and`sum()`

functions in your custom function. Demonstrate the function correctly prints out the above sequence (10 to 100 in steps of 10), and its sum. - Use the
`sum()`

and the`length()`

functions to calculate the mean (average) of the numbers`x = c(1,2,3,4,5)`

.