Luckily, you guys have R and Rstudio installed, so you don't have to worry about this!

But if you want to install it at home, please follow this guide

That guide can help you install

• R
• Rstudio
• And swirl, a package in which you could do a bunch of exercises as homework!

R is a programming language designed to do data analysis, usually interactive.

R is helpful for..

• Getting that darn excel/stata file into R (importing)
• Turning that very ugly dataset into something to work with (data cleaning)
• Automating your weekly reports (automating tasks)
• Analyzing data (modeling)
• Creating nicely formatted documents (communicating results)
• Building your own commands to do specific things (functions)

• Building very creative graphics

• Among many things…

R is an interactive language. That means that if you type a number, you will get a number.

#Input
10

[1] 10

#Input
5

[1] 5

R is also a calculator

Try typing these operations in R:

• 5 + 5
• 10 - 5
• 10 * 5
• 20 / 10
• (10 * 20) - 5 / 2 + 2
• 2 ^ 3

Before we continue, what type of operations are these?

Answers in next slide!

• Addition
• Subtraction
• Multiplication
• Division
• A combination of all
• Exponentiation

Numbers in R are called numerics.

For example:

is.numeric(10)
is.numeric(10 + 20)
is.numeric(10 / 2)

Having single numbers, like 10, is not very useful.

We want something similar to a column of a dataset, like age or income.

We can do that with c(), which stands for concatenate.

c(32, 34, 18, 22, 65)

[1] 32 34 18 22 65

Read this expression as: concatenate these numbers into a single object.

We can also give it a name, like age.

age <- c(32, 34, 18, 22, 65)

• Why didn't the result get printed?
• Where is this age object at?
• What is formally the age object?

We just created our first variable! The typical SAS/Excel/Stata column.

In R, these objects are called 'vectors'.

Vectors can have several flavours:

• Numerics (we just saw one)
• Logicals
• Characters
• Factors

Suppose these ages belong to certain people. We can create a character vector with their names.

Following this guideline, create it yourself.

• Create a character vector with c()
• Include the names Paul, Maria, Andres, Roberto and Alicia inside
• wrap every name in quotes like this “Paul”, “Maria”, etc… This will make R understand that input as characters.

Answer:

c("Paul", "Maria", "Andres", "Robert", "Alicia")

[1] "Paul"   "Maria"  "Andres" "Robert" "Alicia"

We can also give it a name, like participants.

participants <- c("Paul", "Maria", "Andres", "Robert", "Alicia")

Character vectors are filled by strings, like “Paul” or “Maria”.

Can we do operations with strings?

"Paul" + "Maria"

Error in "Paul" + "Maria": non-numeric argument to binary operator

Makes sense.. we can't add any letters.

Alright, we're set. Concatenate the numeric vector age and participants.

c(age, participants)

 [1] "32"     "34"     "18"     "22"     "65"     "Paul"   "Maria" 
 [8] "Andres" "Robert" "Alicia"

• What's the problem with this result?

Now, which of these people has an age above 20?

age > 20

[1]  TRUE  TRUE FALSE  TRUE  TRUE

• That's a logical vector.

Contrary to character and numeric vectors, logical vectors can only have three values:

• TRUE
• FALSE
• NA (which stands for “Not available”.)

logicals can be created manually or with a logical statement.

c(TRUE, FALSE, TRUE, TRUE)

[1]  TRUE FALSE  TRUE  TRUE

age < 60

[1]  TRUE  TRUE  TRUE  TRUE FALSE

The above expression tests for the logical statement.

For example,

   32    34    18    22    65 
 TRUE  TRUE  TRUE  TRUE FALSE 

You can also write T or F as short abbreviations of TRUE and FALSE.

c(T, T, F, T) == c(TRUE, TRUE, FALSE, TRUE)

[1] TRUE TRUE TRUE TRUE

Which is comparing:

 TRUE  TRUE FALSE  TRUE 
  "T"   "T"   "F"   "T" 

But behind the scenes, TRUE and T are just a 1 and F and FALSE are just a 0.

What is the result of this?

T + 5
TRUE - 5
FALSE + TRUE
T + T - FALSE

Now that you know that.. what would be the class of the following vectors?

c(5, TRUE)
c(5, "FALSE")
c(FALSE, TRUE)
c(1, FALSE)

• numeric: TRUE is coerced to 1
• character: “FALSE” is a string, can't be turned to a number
• logical: both elements are logical!
• numeric: FALSE is coerced to 0

What do we know so far?

• Numeric vectors
• Character vectors
• Logical vectors
• How to assign a name to these vectors
• Vectors can contain only one class of data

What's missing?

Factors

Factors are R's way of storing categorical variables.

Categories such as:

• 'Male' and 'Female' or 'Married' and 'Divorced'
• 'Good', 'Middle' and 'High'

gender <- c("Male", "Female", "Male", "Male", "Female")

# Can be turned into

gender <- factor(gender)

Factors are useful for some specific operations like:

• Changing order of levels for terms in modelling
• Changing order of axis labels in plots
• Among other things..

In many cases you can use characters to do what you would want with factors!

Now, have you noticed that we've been assigning names to things?

age

[1] 32 34 18 22 65

The name age holds all these elements inside. How do we know where all the variables we've created are?

Let's ask R what objects can be listed from our workspace or environment.

ls()

[1] "age"          "elm"          "elm_factor"   "gender"      
[5] "lgl"          "participants"

So far, we have a bunch of variables scattered around our workspace. This is usually no the way to go!

We want to group similar things in the same place.

our_df <- data.frame(name = participants, age = age, gender = gender, age_60 = lgl)
our_df

    name age gender age_60
1   Paul  32   Male   TRUE
2  Maria  34 Female   TRUE
3 Andres  18   Male   TRUE
4 Robert  22   Male   TRUE
5 Alicia  65 Female  FALSE

A data frame is usually the primary structure of analysis in R

It's important that you understand the thing that defines a data frame.

• A data frame has rows and columns, more technically called dimensions.
• Data frames have two dimensions.

dim(our_df)

[1] 5 4

nrow(our_df)

[1] 5

ncol(our_df)

[1] 4

Data frames are very distinctive because they can hold any type of vector.

Matrices cannot!

our_matrix <- matrix(1:20, ncol = 4, nrow = 5)
our_matrix

     [,1] [,2] [,3] [,4]
[1,]    1    6   11   16
[2,]    2    7   12   17
[3,]    3    8   13   18
[4,]    4    9   14   19
[5,]    5   10   15   20

• Matrices are very similar to data frames.
• They have same number of dimensions.
• You can choose rows/columns in similar ways.

Finally, we're missing the secret ingridient the differentiates both matrices and data frames.

Lists

Think of lists as a bag that can store anything.

our_list <- list(names = participants, gender = gender, age = age)
our_list

$names
[1] "Paul"   "Maria"  "Andres" "Robert" "Alicia"

$gender
[1] Male   Female Male   Male   Female
Levels: Female Male

$age
[1] 32 34 18 22 65

This is a bag that has 3 objects.

• A charachter
• A factor
• A numeric

Think outside the box… when I say anything, I mean ANYTHING!

complex_list <- list(df = our_df[1:3, ], matrix = our_df[1:3, ], avg_age = mean(age))
complex_list

$df
    name age gender age_60
1   Paul  32   Male   TRUE
2  Maria  34 Female   TRUE
3 Andres  18   Male   TRUE

$matrix
    name age gender age_60
1   Paul  32   Male   TRUE
2  Maria  34 Female   TRUE
3 Andres  18   Male   TRUE

$avg_age
[1] 34.2

To sum up, these are the 4 types of data structures available in R.

Now I'm gonna rock your world…

A data frame is a list (because it can have any class) with a row and column dimensions.

data.frame(our_list)

   names gender age
1   Paul   Male  32
2  Maria Female  34
3 Andres   Male  18
4 Robert   Male  22
5 Alicia Female  65