An Introduction to [ comment ] and RStudio for Educational Researchers

class: center, middle, inverse, title-slide

.title[
# An Introduction to <svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;"> [ comment ] <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z" /></svg>and RStudio for Educational Researchers
]
.subtitle[
## <br>Descriptive and Inferential Statistics:<br>Creating and Manipulating Datasets in <svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;"> [ comment ] <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z" /></svg>
]
.author[
### Jorge Sinval
]
.date[
### 2025-11-18
]

---

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# 1.2 Measurement Instruments

In Social and Human Sciences, information is collected in different ways:

**1.Direct observation**

Participant observation, Focus Groups

**2.Sociodemographic questionnaires**

With closed, semi-closed and/or open questions

**3. Psychometric instruments**

Assess attitudinal states (Likert, Guttman, Thurstone, etc... scales)

SIJS (job satisfaction), UWES (work engagement)

---

# 1.2 Measurement Instruments

**4. Psychometric tests**

Assess aptitudes (knowledge, competence, skills, abilities and skills)

WISC (intelligence quotient)

**5. Psychophysiological assessments**

Measurements of physical and biological parameters.

Additionally, several other types of data can be collected, e.g.: financial, meteorological , spatial, etc...

**After collecting data, it is necessary to create a dataset…**

---

# 1.3 Measurement Scales

### **Qualitative**

Variables organized into classes or categories. These classes can be represented by numbers which, however, **do not have a true numerical meaning**.

### **Quantitative**

Variables that assume values represented by numbers with **true numerical meaning**. These can be:

- Discrete: Assume a numberable set (infinite or finite) of values (e.g. number of children); and,

- Continuous: They assume any value in a given real range (e.g. height).

---

# 1.3 Measurement Scales

According to Stevens (1946)<sup>📜</sup> variables can still be classified according to the scale used in their measurement. This classification is organized into 4 levels/scales:

- **Nominal** (e.g. sex, country)

Numbers (or other symbols) represent qualitative, mutually exclusive, **non-hierarchical** classes or categories.

.footnote[
📜 Stevens, S.S. (1946). On the theory of scales of measurement. _Science, 103_(2684), 677-680. https://doi.org/10.1126/science.103.2684,677]

- **Ordinal** (e.g. academic level, socioeconomic level)

Numbers distinguish qualitative, mutually exclusive, and **hierarchical classes**.

---

# 1.3 Measurement Scales

- **Interval** (e.g. temperature in degrees Celsius)

Quantitative, discrete, or continuous values with fixed distances (ranges) between scale values, but .orange[without] absolute zero<sup>0️⃣</sup>.

- **Ratio** (e.g. age, height, mass)

Quantitative, discrete, or continuous values with fixed distances (ranges) between scale values, but .orange[with] absolute zero<sup>0️⃣</sup>.

.footnote[
0️⃣ Absolute zero: the origin of the scale always corresponds to the total absence of the characteristic under study, and it is represented by the value of zero.]

---

# 1.4 Create dataset

Sociodemographic questionnaire

---

# 1.4 Create dataset

Psychometric instrument<sup>📜</sup>

.footnote[
<sup>📜</sup> Sinval, J., & Marôco, J. (2020). Short Index of Job Satisfaction: Validity evidence from Portugal and Brazil. _PLoS ONE, 15_(4), 1–21. [https://doi.org/10.1371/journal.pone.0231474](https://doi.org/10.1371/journal.pone.0231474)]

---

# 1.4 Create dataset

### Names for variables

### Naming conventions for a variable

### How to create variables in <svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;">  [ comment ]  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg>?

### How to use the created variables?

### Components of a Variable

---

# 1.4 Create dataset

**Creation of the Coding Sheet**

Document on paper or directly in <svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;">  [ comment ]  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg>, with variable names, categories and numeric coding of categories (if required).

---

# 1.4 Create dataset

### Names for variables

**Some rules for variable names:**

- Start with a letter. Do **not start** with numbers, dollar sign (`$`) or underscore (`_`).

- After the first letter it can contain numbers, underscore or period. Do **not use** a hyphen (`-`).

- Avoid using built-in function names (e.g. `c`<sup>⚠️</sup>).

- Variables are case sensitive; `average` and `Average` are different variables.

- Use names that are descriptive. Generally, nouns are used for variable names and verbs for function names.

.footnote[<sup>⚠️</sup> `c` is used to name the function `c()` which is used to combine values into a vector or list. It is one of the most important functions.
]

---

# 1.4 Create dataset

**Naming conventions**:

---

# 1.4 Create dataset

**Use a <sup>💡</sup>... convention and be consistent with it**:

- All lowercase: e.g. `anyvariable`
- Separated by dots: e.g. `any.variable`
- snake_case<sup>🐍</sup>: e.g. `any_variable`
- camelCase<sup>🐫</sup> lowercase: e.g. `anyVariable`
- CamelCase<sup>🐫</sup> uppercase: e.g. `AnyVariable`

The use of snake_case<sup>🐍</sup> gathers the preference of several prominent <svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;">  [ comment ]  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg>s.

.footnote[
💡 Bååth, R. (2012). The state of naming conventions in R. _The R Journal, 4_(2), 74–75. https://doi.org/10.32614/RJ-2012-018

🐫 _CamelCase_ is the English name for the practice of writing compound words or sentences, where each word is capitalized and joined without spaces.

🐍 _snake_case_ is the English name for the practice of writing compound words or sentences, where words are separated with an underscore.]

---

# 1.4 Create dataset

### Data types (five basic or "atomic" object types<sup>⚠️</sup>):

#### `numeric`/`double` (real numbers) `$\mathbb{R}$`

#### `integer`(integers) `$\mathbb{Z}$`

#### `character` (string)

#### `logical` (TRUE/FALSE)

#### `complex`(complex numbers) `$\mathbb{C}$`

.footnote[
⚠️ There is also the `raw` object type, its use is rare in data analysis, as such it will not be covered.]

---

# 1.4 Create dataset

A variable has 3 components:

- Name

- assignment operator (.orange[**`<-`**]<sup>⚠️,🤓</sup> or `=`)<sup>💡</sup>

.footnote[

⚠️ It is also possible to assign a value to an object with `->`... 👀 but its use is rare 😬

💡 It is **good practice** to use `<-` for object assignment, and `=` for function parameters

🤓 the .kbd[Alt]+.kbd[-] key combination automatically inserts the `<-` operator
]

- variable value

---

# 1.4 Create dataset

The value of the number of students in the classroom can be stored by creating a variable and assigning the value to it. In this case, a variable called `students` can be created, and the value `$5$` can be assigned via the assignment operator `<-`.

```r
*students <- 5
students
```

```
## [1] 5
```

.footnote[
⚠️ Do not insert spaces between the two assignment operator symbols, if there are spaces: `< -` the _R_ will interpret the code very differently. 😐
]

---

# 1.4 Create dataset

Once created, variables can be used in calculations. The relative frequency (in proportion) of attending students taking in consideration the `class` size can be calculated (`$N = 10$`).

```r
#assign the value 5 to the students variable
students <- 5

#assign the value 10 to the class variable
class <- 10

#compute the relative frequency of students in the classroom
fi_students <- students/class

#call the fi_students object, and check the relative frequency of students in the classroom
fi_students
```

```
## [1] 0.5
```

It can be described verbally by saying that the variable receives a value of 0.5.

---

# 1.4 Create dataset

### Components of a Variable

1 - Name -> `students`

2 - Address (memory) -> `0x6d8d8c8`

3 - Value -> `5`

4 - Data type -> `numeric`

---

# 1.4 Create dataset
### Components of a Variable: Attributes

Objects in <svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;">  [ comment ]  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg> can have attributes (i.e. metadata) that help describe the object.

Examples of attributes:

- `names`, `dimnames`

- `dimensions` (e.g. `matrix`, `array`)

- `class` (e.g. `integer`, `numeric`)

- `length`

- other user defined attributes

An object's attributes (if any) can be accessed with the `attributes()` function. Not all R objects contain attributes; in this case, the `attributes()` function produces `NULL`.
---

# 1.4 Create dataset

### Data types: `numeric`/`double`

In <svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;">  [ comment ]  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg>, numbers are usually represented by the `numeric` data type (i.e. `double` -> double precision floating point number<sup>🤔</sup>).

.footnote[
🤔 stores a floating point number with space to indicate larger numbers, either integer or decimal part.]

The `1` that the "naked eye" can consider as an integer, internally can be a `numeric` object (thus `1.00`).

To explicitly indicate an integer (i.e. `integer`) it is necessary to add the suffix `L`.

---

# 1.4 Create dataset

Thus, inserting `1` gives a `numeric` object; explicitly inserting `1L` gives an `integer` object.

There is also a special number `Inf`, which represents infinity.

Allows representing entities like `1/0`. `Inf` can be used in calculations like `1/Inf`.

The value `NaN` represents an undefined value (i.e. "not a number"; e.g. `0/0`.

`NaN` can also be considered a missing value.

---

# 1.4 Create dataset

### Data types: `numeric`/`double`

```r
# create to objects
number_1 <- 3.5
number_2 <- 3

# verify the data type
class(number_1)
```

```
## [1] "numeric"
```

```r
class(number_2)
```

```
## [1] "numeric"
```

---

# 1.4 Create dataset

### Data types: `numeric`/`double`

```r
# verify if the data type is numeric/double
is.numeric(number_1)
```

```
## [1] TRUE
```

```r
is.numeric(number_2)
```

```
## [1] TRUE
```

---

# 1.4 Create dataset

### Data types: `numeric`/`double`

Integers are also treated as `numeric/double`. Two functions to retain:

`class()` -> returns the class or type of the object

`is.numeric()` -> tests if the variable is of `numeric`/`double` type

---

# 1.4 Create dataset

### Data types: `integer`

Unless otherwise noted, integers are treated as `numeric` or `double` (by default). It is possible to create variables of type `integer` and convert other data types to `integer`.

**Exercise**

1 - Create a variable `number_1` and set the value `5`.

2 - Check the data type of the `number_1` object through the `class()` function.

3 - Create a second variable `number_2`, use the function `as.integer()` and set the value `5`.

4 - Check the data type of the `number_2` object through the `class()` function.

5 - Use the `is.integer()` function to check the data type of `number_1` and `number_2`.

---

# 1.4 Create dataset

### Data types: `integer`

```r
# create the object and assign it an integer value
number_1 <- 5
#check the data type
class(number_1)
```

```
## [1] "numeric"
```

```r
# create another object using as.integer()
*number_2 <- as.integer(5)
# check data type
class(number_2)
```

```
*## [1] "integer"
```

---

# 1.4 Create dataset

### Data types: `integer`

```r
# use is.integer to check if the data type is integer
is.integer(number_1)
```

```
## [1] FALSE
```

```r
is.integer(number_2)
```

```
## [1] TRUE
```

---

# 1.4 Create dataset

### Data types: `character`

Letters, words, and words groups are represented by the `character` data type. All `character` data must be enclosed in 'single' (`'`) or "double" (`"`) quotes. Any value enclosed in quotes will be treated as `character`.

**Exercise**

1 - Create 2 variables to store someone's first (i.e. `first_name`), and last (i.e. `last_name`) names.
  
2 - Check the data type.

3 - Use the `is.character()` function to check the data type.

---

# 1.4 Create dataset

### Data types: `character`

```r
# first name
first_name <- "Jorge"
# last name
last_name <- 'Sinval'
# check data type
class(first_name)
```

```
## [1] "character"
```

```r
class(last_name)
```

```
## [1] "character"
```

---

# 1.4 Create dataset

### Data types: `character`

```r
# use is.character() to verify the data type
is.character(first_name)
```

```
## [1] TRUE
```

```r
is.character(last_name)
```

```
## [1] TRUE
```

---

# 1.4 Create dataset

### Data types: `character`

It is possible to transform any data type into `character` through the `as.character()` function.

```r
# create different types of variables
age <- as.integer(30) # integer
score <- 9.8 # numeric/double
attending_today <- TRUE # logical
```

---

# 1.4 Create dataset

### Data types: `character`

```r
as.character(age)
```

```
## [1] "30"
```

```r
as.character(score)
```

```
## [1] "9.8"
```

```r
as.character(attending_today)
```

```
## [1] "TRUE"
```

---

# 1.4 Create dataset

### Data types: `logical`

The data type `logical` can only take two values, either `TRUE` (also `T`) or `FALSE` (also `F`). This type of data is created when comparing two objects in <svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;">  [ comment ]  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg> using logical or comparison operators.

**Exercise**

1 - Create two objects `x` and `y`.
 
2 - Assign the values `TRUE` and `FALSE`, respectively.

3 - Use `is.logical()` to check the data type.

---

# 1.4 Create dataset

### Data types: `logical`

```r
# create the x and y objects
x <- TRUE
y <- FALSE
# check data type
class(x)
```

```
## [1] "logical"
```

```r
is.logical(y)
```

```
## [1] TRUE
```

.footnote[🤔 - Use `as.logical()` to turn other data types into `logical`.]

---

# 1.4 Create dataset

### Data types: `logical`

The result of comparison operators is always `logical`.

**Exercise**

1 - Create two numeric objects.

2 - Compare these two objects with the comparison operators (ie `<`,`>`,`<=`,`>=`, `!=`, `==`)<sup>⚠️</sup>

.footnote[

⚠️ in <svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;">  [ comment ]  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg> to test:`<=` one should use `$<=$`, `>=` one should use `$>=$`,`!=` one should use `$!=$`, `==` one should use `$==$`.
]

---

# 1.4 Create dataset

### Data types: `logical`

```r
# create two numeric objects
x <- 3
y <- 4
# compare x with y
x > y
```

```
## [1] FALSE
```

```r
x < y
```

```
## [1] TRUE
```

---

# 1.4 Create dataset

### Data types: `logical`

```r
# store the result in an object
z <- x > y
class(z)
```

```
## [1] "logical"
```

---

# 1.4 Create dataset

### Data types: `logical`

`TRUE` is represented by all numbers except `0`.

`FALSE` is represented only and only by `0`.

```r
# TRUE and FALSE are represent by 1, and 0, respectively
as.logical(1)
```

```
## [1] TRUE
```

```r
as.logical(0)
```

```
## [1] FALSE
```

---

# 1.4 Create dataset

### Data types: `logical`

```r
# using numbers
as.numeric(TRUE)
```

```
## [1] 1
```

```r
as.numeric(FALSE)
```

```
## [1] 0
```

---

# 1.4 Create dataset

### Data types: `logical`

```r
# with different numbers
as.logical(-2, -1.5, -1, 0, 1, 2)
```

```
## [1] TRUE
```

---

# 1.4 Create dataset

### Data types: `logical`

Use `as.logical()` to force other data types to `logical`.

```r
# create different types of variables
age <- as.integer(30) # integer
score <- 9.8 # numeric/double
attending_today <- TRUE # logical
```

---

# 1.4 Create dataset

### Data types: `logical`

```r
as.logical(age)
```

```
## [1] TRUE
```

```r
as.logical(score)
```

```
## [1] TRUE
```

```r
as.logical(attending_today)
```

```
## [1] TRUE
```

---

# 1.4 Create dataset

### Data types: `complex`

Complex numbers with real and imaginary parts.

```r
w <- 1+5i
class(w)
```

```
## [1] "complex"
```

---

# 1.4 Create dataset: Geral

## Data structures

.footnote[📚 Grolemund, G. (2014). _Hands-on programming with R: Write your own functions and simulations_. O’Reilly. https://doi.org/10.1017/CBO9781107415324.004]

---

# 1.4 Create dataset

### Data structures: `vectors`

The most basic structure of <svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;">  [ comment ]  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg> object is a `vector`, which can **only** contain elements of the same class.

**But**, like any good rule, there is an exception, lists (`list`). A list is represented as a vector, but it can contain objects of different classes.

```r
a_object <- c(1,2,5,3,6,-2,4) # numeric vector
b_object <- c("one","two","three") # character vector
c_object <- c(TRUE,TRUE,TRUE,FALSE,TRUE,FALSE) #logical vector
```

💡

.footnote[
💡 The `c()` function groups elements into a vector.]

---

# 1.4 Create dataset

### Data structures: `matrix`

All columns of an `matrix` must have the same data type (`numeric`, `character`, etc.) and the same length. The general format is:

```r
my_matrix <- matrix(1:20, #vector (in this case a numeric vector)
                    nrow = 5, #number of rows
                    ncol = 4, #number of columns
                    byrow = FALSE, #fill the matrix by row or by column
                    dimnames=list(
                      c("row_1", "row_2", "row_3", "row_4", "row_5"), #row names
                      c("column_1", "column_2", "column_3", "column_4"))) #column names
```

---

# 1.4 Create dataset

### Data structures: `matrix`

Generating a simpler example...

```r
# generate a numeric matrix  5 x 4
y <- matrix(1:20,
            nrow = 5,
            ncol = 4)
#print the matrix
y
```

```
##      [,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
```

---

# 1.4 Create dataset

### Data structures: `matrix`

```r
# another example
cells <- c(1.32, 43.23)
rows_names <- c("row_1", "row_2")
columns_names <- c("column_1", "column_2")
my_matrix <- matrix(cells,
                    nrow = 2,
                    ncol = 2,
                    byrow = TRUE,
                    dimnames=list(rows_names,
                                  columns_names))
```

---

# 1.4 Create dataset

### Data structures: `matrix`

_Et voilà_ 😄:

```r
my_matrix
```

```
##       column_1 column_2
## row_1     1.32    43.23
## row_2     1.32    43.23
```

---

# 1.4 Create dataset

### Data structures: `arrays`

The data structure `array` are similar to `matrix`, but can have more than two dimensions.

```r
my_array <- array(0, dim = c(2, 2, 3))
```

---

# 1.4 Create dataset

### Data structures: `arrays`

```r
my_array
```

```
## , , 1
## 
##      [,1] [,2]
## [1,]    0    0
## [2,]    0    0
## 
## , , 2
## 
##      [,1] [,2]
## [1,]    0    0
## [2,]    0    0
## 
## , , 3
## 
##      [,1] [,2]
## [1,]    0    0
## [2,]    0    0
```

---

# 1.4 Create dataset

### Data structures: `data.frame`

A `data.frame` is more general than an `matrix`, as different columns can have different data types (`numeric`, `character`, etc.). Identical to the _IBM SPSS Statistics_ dataset files

```r
d_object <- c(1,2,3,4)
e_object <- c("red", "blue", "green", NA)
f_object <- c(TRUE,TRUE,TRUE,FALSE)
my_data <- data.frame(d_object,
                      e_object,
                      f_object)
# variable names
names(my_data) <- c("id","color","approved")
```

---

# 1.4 Create dataset

### Data structures: `data.frame`

There are several ways to identify the elements of a `data.frame`.

```r
my_data[1:2] # columns 1, and 2 of the data.frame
```

```
## data.frame [4, 2] 
## id    dbl 1 2 3 4
## color chr red blue green NA
```

---

# 1.4 Create dataset

### Data structures: `data.frame`

```r
# the columns id, and approved of the data.frame
my_data[c("id","approved")] 
```

```
## data.frame [4, 2] 
## id       dbl 1 2 3 4
## approved lgl TRUE TRUE TRUE FALSE
```

```r
# the variable color of the data.frame
my_data$color
```

```
## [1] "red"   "blue"  "green" NA
```

---

# 1.4 Create dataset

### Data structures: `list`

A `list` is an ordered set of objects ("components"). A `list` allows you to gather a variety of objects of different types and structures (possibly unrelated) under one object.

```r
# example of a list with 4 components
# a string, a numeric vector, a matrix and a numberic/double value

#a numeric vector
a_object <- c(1,2,5,3,6,-2,4)

#a matrix
y <- matrix(1:20,
            nrow = 5,
            ncol = 4)

w <- list(name = "Sinval",
           my_numbers = a_object,
           my_array = y,
           age = 32.9)
```

---

# 1.4 Create dataset

### Data structures: `list`

Calling the `w` list:

```
## $name
## [1] "Sinval"
## 
## $my_numbers
## [1]  1  2  5  3  6 -2  4
## 
## $my_array
##      [,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
## 
## $age
## [1] 32.9
```

---

# 1.4 Create dataset

### Data structures: `list`

The elements of a list are identified with the `[[]]` convention.

```r
w[[2]] # 2nd component of the list
```

```
## [1]  1  2  5  3  6 -2  4
```

```r
w[["my_numbers"]] # component named "my_numbers" in the list
```

```
## [1]  1  2  5  3  6 -2  4
```

---

# 1.4 Create dataset

### Data structures: `factors`

Create a nominal variable (`character`), turn it into a factor.

```r
# variable sex with 20 times "m" and 30 "f"
sex <- c(rep("male",20), rep("female", 30))
sex <- factor(sex)

# R stores sex with 20 x "1" and 30 x "2"
#associates 1=female", 2="male" internally (alphabetically)
# R now treats the sex variable as nominal
summary(sex)
```

```
## female   male 
##     30     20
```

---

# 1.4 Create dataset

### Data structures: `factors`

A factor is an `vector` that can only contain predefined values, and is used to store categorical data. The factor stores nominal values as a `vector` of **integers** (`integer`) in the range `[1,...,k]` (where `k` is the number of unique values in the nominal variable).

It is obtained an inner vector with a string of characters (the original values) mapped to these integers is obtained.

---

# 1.4 Create dataset

### Data structures: `factors`

An ordered factor is used to represent an ordinal variable.

```r
# "categories" variable encoded as "large", "medium", "small'
categories <-c(rep("large",2),
               rep("medium", 1),
               rep("small", 2))
# recode categories to 1,2,3 and
# associate # 1="large", 2="medium", 3="small"
*categories <- ordered (categories)
categories
```

```
*## [1] large  large  medium small  small 
*## Levels: large < medium < small
```

---

# 1.4 Create dataset

### Data structures: `factors`

```r
#you can also use the `factor` function
categories <- factor(categories,
                     levels = c("small","medium","large"),
                     ordered = TRUE)
# R now treats the categories variable as ordinal
categories
```

```
## [1] large  large  medium small  small 
## Levels: small < medium < large
```

---

# 1.4 Creating data frames

### Data structures: `factors`

You can use the options in the `factor()` and `ordered()` functions to control the mapping of integers to words (overriding alphabetical order).

You can also use `factor` to create labels (`labels`) for the values.

```r
# the "color" variable is coded with 1, 2 or 3

color <- c(1,2,3,3,2,1)
# assign the following value labels
# 1="red", 2="blue", 3="green"
color <- factor(color,
                levels = c(1,2,3),
                labels = c("red", "blue", "green"))
```

---

# 1.4 Create dataset

### Data structures: `factors`

```r
# the variable "categories" encoded with 1,3,5
categories <- c(1,3,5,5,3,1)
# we want to assign the lables of values 1="low", 3="medium", 5="high"
categories <- ordered(categories,
                      levels = c(1,3,5),
                      labels = c("low", "medium", "high"))
```

.footnote[
🤓 - `factor`, and `ordered` they are used in the same way, with the same arguments. The first creates factors (ordered or not; `ordered=TRUE/FALSE`) and the second creates ordered factors.]

---

# 1.4 Create dataset

### Summary <sup>📚</sup>

- Variables are the blocks on which the construction of a programming language is based.

- Variables have a reference to the memory locations that store information/data.

- The assignment operator `<-` assigns values to a variable.

- A variable has the following components: name, address (memory), value and data type.

- There are rules that must be followed when assigning names to variables.

.footnote[
<sup>📚</sup> Wickham, H. (2015). _Advanced R_. CRC Press. https://adv-r.hadley.nz/
]

---

# 1.4 Create dataset

### Summary

- The basic data types of R are: `character`, `numeric`, `integer`, `complex`, and `logical`.

- <svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;">  [ comment ]  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg>'s basic data structures include `vector`, `list`, `matrix`, `data.frame` and `factor`. Some of these structures require all elements to be of the same data type (e.g. `vector`, `matrix`) while others allow multiple data types (e.g. `list`, `data.frame`).

- Objects can have attributes such as name, dimension and class.

---

# 1.4 Create dataset

**<svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;">  [ comment ]  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg> does not use nominal, ordinal, interval, and ratio terminology for data types.**

--
In <svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;">  [ comment ]  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg>, **nominal variables** can be coded as:

`factor`

```r
factor_colors <- factor("red", "green", "blue")
class(factor_colors)
```

```
## [1] "factor"
```

or as `character`.

```r
character_colors <- c("red", "green", "blue")
class(character_colors)
```

```
## [1] "character"
```

---

# 1.4 Create dataset

`factor`

```r
categories_ord <- factor(c("bad", "good", "more or less"),
                  levels = c("bad", "more or less","good"),
                  ordered = TRUE)
class(categories_ord)
```

```
## [1] "ordered" "factor"
```

```r
categories_ord
```

```
## [1] bad          good         more or less
## Levels: bad < more or less < good
```

---

# 1.4 Create dataset

or as `ordered`

```r
ordered_ranked <- ordered(c(3,1,2),
                          levels = c(1,2,3),
                          labels = c("-", "+/-", "+"))
class(ordered_ranked)
```

```
## [1] "ordered" "factor"
```

```r
ordered_ranked
```

```
## [1] +   -   +/-
## Levels: - < +/- < +
```

---

# 1.4 Create dataset

The **quantitative interval or ratio** variables can be coded as variables such as `numeric` or `integer`.

If an `L` is placed after the number (i.e. X`L`), <svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;">  [ comment ]  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg> will create an `integer`

```r
num_classes_int <- c(1L, 2L, 3L, 4L, 5L)
class(num_classes_int)
```

```
## [1] "integer"
```

or via the `as.integer` function

```r
num_classes_int <- as.integer(c(1, 2, 3, 4, 5))
class(num_classes_int)
```

```
## [1] "integer"
```

---

# 1.4 Create dataset

Otherwise, it will always consider the `numeric` data type

```r
num_classes <- c(1, 2, 3, 4, 5)

class(num_classes)
```

```
## [1] "numeric"
```

---

# 1.4 Create dataset

Some useful functions:

- `head()` - display the first 6 lines

- `tail()` - display the last 6 lines

- `dim()` - displays the dimension of an object (i.e. `data.frame`: number of rows, and number of columns)

- `nrow()` - number of rows
--

_Continue..._

---

# 1.4 Create dataset

Some useful functions (continued):

- `ncol()` - number of columns

- `str()` - displays the structure of an object: name, type and brief preview of data in each column

- `names()` or `colnames()` - both show the names of of an object

- `sapply(our_data_frame, class)` - display the data class in each database column

---

# 1.5 Packages

Some packages come installed with <svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;">  [ comment ]  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg> (`r-base`)  (eg `base`, `stats`, `utils`).They provide functions for some essential tasks (e.g. import/export data, descriptive statistics, tables, and statistical tests). There are loads of additional packages that can be **installed for free**.

Some essential functions for **packages**:

- See which packages are installed: `library()`

- View which packages are loaded in the environment: `search()`

- Install: `install.packages("package_name")`

- Load: `library(package_name)`

![:scale 75%](data:image/png;base64,#assets/img/hex_packages_sample.png)

---
class: center, bottom, inverse

# More info

Slides created with the <svg viewBox="0 0 581 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#384CB7;">  [ comment ]  <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg> package [`xaringan`](https://github.com/yihui/xaringan).

Practice is the best strategy for learning.

_In God we trust, all others bring data_

Edwards Deming

THE END

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class: center, bottom, inverse

![:scale 50%](data:image/png;base64,#assets/gif/the_end.gif)