Introduction into R

# Introduction into R
## <img src='https://raw.githubusercontent.com/rstudio/hex-stickers/master/PNG/RStudio.png' alt='RStudio' width='100' height='120'>
### Johan Aparicio
### International Center for Tropical Agriculture
### 2019/10/09

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# Recordando...

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# Basic operations

```r
a <- 3+3 
b <- 3*4
c <- 4^2 
```
--

```r
a + b + c
```

```
[1] 34
```

```r
z <- log(200)
w <- exp(4) 
x <- sqrt(9)
```

```r
z  - w  +  x
```

```
[1] -46.29983
```

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# Hello World

En R existen varios tipos de objectos que permiten que el usuario pueda almacenar la información para realizar procedimientos estadísticos y gráficos.

Los principales objetos en R son:

- [Vectores](vectores)
- [Matrices](matrices) 
- [Marcos de datos](marcos de datos) 
- [Listas](listas)

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# Vectors

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# Vectors

Vectors are ordered arrangements in which information of numerical type **([quantitative variable](quantitative variable))**, alphanumeric **([qualitative variable](qualitative variable))** or logical **([TRUE or FALSE](TRUE or FALSE))** can be stored, but not mixtures of these.

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###Numeric ([Quantitative](Quantitative))

```r
a <- 41201 ; b <- 12.3 
```

-----------------

###Character ([Qualitative](Quanlitative))

```r
e <- "Dog" ; f <- "CIAT" ; g <- "RStudio"
```

-----------------

### Boolean  ([Logical](Logical))

```r
TRUE

FALSE
```

```r
r <- 4 > 6 ; w <- 400 == 30
```

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# Class

```r
a <- 41201

class(a)
```

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

-----------------

```r
g <- "RStudio"

class(g)
```

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

-----------------

```r
r <- 4 > 6

class(r)
```

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

---

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# Example

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# Vectors using colon operator

```r
# Creating a sequence from 5 to 13.
v <- 5:13
*print(v)
```

```
[1]  5  6  7  8  9 10 11 12 13
```

```r
# Creating a sequence from 6.6 to 12.6.
v <- 6.6:12.6
print(v)
```

```
[1]  6.6  7.6  8.6  9.6 10.6 11.6 12.6
```

```r
# What happens in this case?
*v <- 3.8:11.4
print(v) 
```

```
[1]  3.8  4.8  5.8  6.8  7.8  8.8  9.8 10.8
```

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##Using sequence (seq.) operator

```r
# Create vector with elements from 5 to 9 incrementing by 0.4.
test <- seq(from = 5, to = 9, by = 0.4)
```

```r
print(test)
```

```
 [1] 5.0 5.4 5.8 6.2 6.6 7.0 7.4 7.8 8.2 8.6 9.0
```

```r
# What happens in this case?
seq( from = 2 , to = 7 , by = 0.7)
```

```
[1] 2.0 2.7 3.4 4.1 4.8 5.5 6.2 6.9
```

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#Using the c( ) function

```r
x <- c(1, 5, 4, 9, 0)
y <- c("Carlos", "Bean" , "Carolina Torres")
z <- c(TRUE, FALSE , FALSE)
```

-----------------

```r
class(x)
```

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

```r
class(y)
```

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

```r
class(z)
```

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

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##Giving different types of data items in a Vector

```r
y <- c(2,3,"ten")
```

Since vector is a collection of similar data type. Therefore, if we try to create vectors like the above example then Coercion takes place.

```r
y
```

```
[1] "2"   "3"   "ten"
```

## Generic order in increasing fashion is:

<center>
Boolean < Numeric < Character
</center>

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```r
# Example 1
x <- c(3,4,5,"ten",TRUE)
```

```r
x
```

```
[1] "3"    "4"    "5"    "ten"  "TRUE"
```

-------------------

```r
# Example 2
p <- c(3,4,5,TRUE)
```

```r
p
```

```
[1] 3 4 5 1
```

-------------------

```r
# Example 3
q <- c("car", FALSE, TRUE)
```

```r
q
```

```
[1] "car"   "FALSE" "TRUE" 
```

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# Summary

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# Creating seq using rep( )

```r
rep( c(0,0,7), times = 4)
```

```
 [1] 0 0 7 0 0 7 0 0 7 0 0 7
```

```r
rep( c(2,4,2), each = 4)
```

```
 [1] 2 2 2 2 4 4 4 4 2 2 2 2
```

```r
rep( c(0,7), times = c(4, 3) )
```

```
[1] 0 0 0 0 7 7 7
```

```r
rep( 1:3, length.out = 9)
```

```
[1] 1 2 3 1 2 3 1 2 3
```

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#Accessing Vector Elements (1)

Elements of a Vector can be accessed using indexing. The **[ ]** brackets are used for indexing. Indexing starts with position [1](1). Giving a negative value in the index drops that element from the result.[TRUE](TRUE), [FALSE](FALSE) can also be used for indexing.

```r
# Accessing vector elements 
# using position.

t <- c("Sun","Mon","Tue")
u <- t[c(2,3)]
```
]

```r
# Accessing vector elements 
# using logical indexing.

t <- c("Sun","Mon","Tue")
v <- t[c(TRUE,FALSE,FALSE)]
```

]

```r
print(u)
```

```
[1] "Mon" "Tue"
```

```r
print(v)
```

```
[1] "Sun"
```

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#Accessing Vector Elements (2)

Elements of a Vector can be accessed using indexing. The **[ ]** brackets are used for indexing. Indexing starts with position [1](1). Giving a negative value in the index drops that element from result.[TRUE](TRUE), [FALSE](FALSE) or 0 and 1 can also be used for indexing.

```r
# Accessing vector elements
# using negative indexing.

t <- c("Sun","Mon","Tue")
x <- t[c(-2,-3)]
```
]

```r
# Accessing vector elements 
# using indexing.

t <- c("Sun","Mon","Tue")
y <- t[c(4,8)]
```

]

```r
print(x)
```

```
[1] "Sun"
```

```r
print(y)
```

```
[1] NA NA
```

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#Accessing Vector Elements (3)

Elements of a Vector can be accessed using names.

```r
I <- c("one"=1,"two"=2,"three"=3)
```

```r
print(I)
```

```
  one   two three 
    1     2     3 
```

```r
*I["one"]
```

```
one 
  1 
```

```r
*I[c("three","two")]
```

```
three   two 
    3     2 
```

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# Vector arithmetic

Two vectors of same length can be added, subtracted, multiplied or divided giving the result as a vector output.

```r
# Create two vectors.
v1 <- c(3, 8, 4, 5, 0, 11)
v2 <- c(4, 11, 0, 8, 1, 2)
```

```r
# Vector addition.
add.result <- v1+v2

# Vector subtraction.
sub.result <- v1-v2
```
]

```r
# Vector multiplication.
multi.result <- v1*v2

# Vector division.
divi.result <- v1/v2
```

]

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# Vector arithmetic

When we execute the code, it produces the following result:

```r
print(add.result)
```

```
[1]  7 19  4 13  1 13
```

```r
print(sub.result)
```

```
[1] -1 -3  4 -3 -1  9
```

```r
print(multi.result)
```

```
[1] 12 88  0 40  0 22
```

```r
print(divi.result)
```

```
[1] 0.7500000 0.7272727       Inf 0.6250000 0.0000000 5.5000000
```

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#Vector Element Recycling

If we apply arithmetic operations to two vectors of unequal length, then the elements of the shorter vector are recycled to complete the operations.

```r
v1 <- c(3,8,4,5,0,11)
v2 <- c(4,11)
# V2 becomes c(4,11,4,11,4,11)
```
--

```r
add.result <- v1+v2
print(add.result)
```

```
[1]  7 19  8 16  4 22
```

```r
sub.result <- v1-v2
print(sub.result)
```

```
[1] -1 -3  0 -6 -4  0
```

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#Vector Element Sorting

Elements in a vector can be sorted using the sort() function.

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#Vector Element Sorting

Elements in a vector can be sorted using the sort() function.

```r
v <- c(3,8,4,5,0,11, -9, 304)

# Sort the elements of the vector.
sort.result <- sort(v)
```
--

```r
# Sort the elements in the reverse order.
revsort.result <- sort(v, decreasing = TRUE)
```
--

```r
# Sorting character vectors.
v <- c("Red","Blue","yellow","violet")
sort.result <- sort(v)
```
--

```r
# Sorting character vectors in reverse order.
revsort.result <- sort(v, decreasing = TRUE)
```

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# R - Matrices

Matrices are the `R` objects in which the elements are arranged in a two-dimensional rectangular layout. They contain elements of the same types. Though we can create a matrix containing only [characters](characters) or only [logical](logical) values, they are not of much use. We use matrices containing [numeric](numeric) elements.

<center>
A Matrix is created using the `matrix()` function.
</center>

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# Syntax

The basic syntax for creating a matrix in R is:

```r
matrix(data, nrow, ncol, byrow, dimnames)
```

Following is the description of the parameters used −

1. **[data](data)** is the input vector which becomes the data elements of the matrix.

1. **[nrow](nrow)** is the number of rows to be created.

1. **[ncol](ncol)** is the number of columns to be created.

1. **[byrow](byrow)** is a logical clue. If `TRUE` then the input vector elements are arranged by row.

1. **[dimname](dimname)** is the names assigned to the rows and columns.

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## Create a matrix taking a vector of numbers as input

```r
# Elements are arranged sequentially by row.
M <- matrix(c(3:14), nrow = 4, byrow = TRUE)
print(M)
```

```
     [,1] [,2] [,3]
[1,]    3    4    5
[2,]    6    7    8
[3,]    9   10   11
[4,]   12   13   14
```

```r
# Elements are arranged sequentially by column.
N <- matrix(c(3:14), nrow = 4, byrow = FALSE)
print(N)
```

```
     [,1] [,2] [,3]
[1,]    3    7   11
[2,]    4    8   12
[3,]    5    9   13
[4,]    6   10   14
```

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#Example

Create a matrix taking a vector of numbers as input.

```r
# Define the column and row names.
rownames = c("row1", "row2", "row3", "row4")
colnames = c("col1", "col2", "col3")

P <- matrix(c(3:14), 
 nrow = 4,
 byrow = TRUE,
 dimnames = list(rownames, colnames))
print(P)
```

```
     col1 col2 col3
row1    3    4    5
row2    6    7    8
row3    9   10   11
row4   12   13   14
```

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# How to extract elements from a matrix?

As in the case of vectors, brackets [,] and inside, separated by a comma, the row number and column number that interest us are used to extract elements stored within a matrix.

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# Example 1

- If we want to extract the value stored in row 4 and column 3.

```r
P[4,3]
```

```
[1] 14
```

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# Example 2

- If we want to retrieve the entire row 2 we use the following code.

```r
P[2,]
```

```
col1 col2 col3 
   6    7    8 
```

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# Example 3

- If we want to retrieve the entire column 3 we use the following code.

```r
P[,3]
```

```
row1 row2 row3 row4 
   5    8   11   14 
```

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# Example 4

If we want to show the original matrix without columns 2 we use the following code.

```r
P[,-2]
```

```
     col1 col3
row1    3    5
row2    6    8
row3    9   11
row4   12   14
```

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# Example 5

If we want to show the original matrix without row 1 and column 3 we use the following code.

```r
P[-1,-3]
```

```
     col1 col2
row2    6    7
row3    9   10
row4   12   13
```

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# Data frame

The data frame is one of the most used objects since it allows grouping vectors with information of **different types** ([numerical](numerical), [alphanumeric](alphanumeric) or [logical](logical)) in the same object, the only restriction is that the vectors must have the **same length**.
The `data.frame()` function is used to create a data frame.

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# data(iris)

This famous (Fisher's or Anderson's) iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.

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# data(iris)

```r
data(iris)

# ?iris
```

<div id="htmlwidget-fbbf009a088c2f1d94e4" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-fbbf009a088c2f1d94e4">{"x":{"filter":"none","fillContainer":false,"data":[["1","2","3","4","5","6","7","8"],[5.1,4.9,4.7,4.6,5,5.4,4.6,5],[3.5,3,3.2,3.1,3.6,3.9,3.4,3.4],[1.4,1.4,1.3,1.5,1.4,1.7,1.4,1.5],[0.2,0.2,0.2,0.2,0.2,0.4,0.3,0.2],["setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa"]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Sepal.Length<\/th>\n <th>Sepal.Width<\/th>\n <th>Petal.Length<\/th>\n <th>Petal.Width<\/th>\n <th>Species<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"pageLength":4,"columnDefs":[{"className":"dt-right","targets":[1,2,3,4]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[4,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>

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# Iris

<center>
<div id="htmlwidget-230a8ff185b4af0ce4d8" style="width:648px;height:504px;" class="plotly html-widget"></div>
<script type="application/json" data-for="htmlwidget-230a8ff185b4af0ce4d8">{"x":{"data":[{"x":[5.1,4.9,4.7,4.6,5,5.4,4.6,5,4.4,4.9,5.4,4.8,4.8,4.3,5.8,5.7,5.4,5.1,5.7,5.1,5.4,5.1,4.6,5.1,4.8,5,5,5.2,5.2,4.7,4.8,5.4,5.2,5.5,4.9,5,5.5,4.9,4.4,5.1,5,4.5,4.4,5,5.1,4.8,5.1,4.6,5.3,5],"y":[3.5,3,3.2,3.1,3.6,3.9,3.4,3.4,2.9,3.1,3.7,3.4,3,3,4,4.4,3.9,3.5,3.8,3.8,3.4,3.7,3.6,3.3,3.4,3,3.4,3.5,3.4,3.2,3.1,3.4,4.1,4.2,3.1,3.2,3.5,3.6,3,3.4,3.5,2.3,3.2,3.5,3.8,3,3.8,3.2,3.7,3.3],"text":["Sepal.Length: 5.1 Sepal.Width: 3.5 Species: setosa","Sepal.Length: 4.9 Sepal.Width: 3.0 Species: setosa","Sepal.Length: 4.7 Sepal.Width: 3.2 Species: setosa","Sepal.Length: 4.6 Sepal.Width: 3.1 Species: setosa","Sepal.Length: 5.0 Sepal.Width: 3.6 Species: setosa","Sepal.Length: 5.4 Sepal.Width: 3.9 Species: setosa","Sepal.Length: 4.6 Sepal.Width: 3.4 Species: setosa","Sepal.Length: 5.0 Sepal.Width: 3.4 Species: setosa","Sepal.Length: 4.4 Sepal.Width: 2.9 Species: setosa","Sepal.Length: 4.9 Sepal.Width: 3.1 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---
background-image: url(logo.svg)
background-size: 120px
background-position: 10% 98%

# Iris

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versicolor"],"type":"scatter","mode":"markers","marker":{"autocolorscale":false,"color":"rgba(0,186,56,1)","opacity":1,"size":5.66929133858268,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(0,186,56,1)"}},"hoveron":"points","name":"versicolor","legendgroup":"versicolor","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[6,5.1,5.9,5.6,5.8,6.6,4.5,6.3,5.8,6.1,5.1,5.3,5.5,5,5.1,5.3,5.5,6.7,6.9,5,5.7,4.9,6.7,4.9,5.7,6,4.8,4.9,5.6,5.8,6.1,6.4,5.6,5.1,5.6,6.1,5.6,5.5,4.8,5.4,5.6,5.1,5.1,5.9,5.7,5.2,5,5.2,5.4,5.1],"y":[2.5,1.9,2.1,1.8,2.2,2.1,1.7,1.8,1.8,2.5,2,1.9,2.1,2,2.4,2.3,1.8,2.2,2.3,1.5,2.3,2,2,1.8,2.1,1.8,1.8,1.8,2.1,1.6,1.9,2,2.2,1.5,1.4,2.3,2.4,1.8,1.8,2.1,2.4,2.3,1.9,2.3,2.5,2.3,1.9,2,2.3,1.8],"text":["Petal.Length: 6.0 Petal.Width: 2.5 Species: virginica","Petal.Length: 5.1 Petal.Width: 1.9 Species: virginica","Petal.Length: 5.9 Petal.Width: 2.1 Species: virginica","Petal.Length: 5.6 Petal.Width: 1.8 Species: 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</center>

---

background-image: url(logo.svg)
background-size: 120px
background-position: 10% 98%

# How to extract elements from a data frame?

To retrieve the variables (columns) stored in a data frame, you can use the [$]($) operator, single square brackets [[]]([]) or double square brackets [[[]]]([[]]). Here are some examples to understand the differences between these options.

- If we want to extract the ["Sepal.Length"]("Sepal.Length") variable from the iris data frame as a vector, we use the following code.

```r
iris$Sepal.Length
```

```r
iris[,1]
```

```r
iris[["Sepal.Length"]]
```

```r
iris[,"Sepal.Length"]
```

---

background-image: url(logo.svg)
background-size: 120px
background-position: 10% 98%

#How to extract subsets of a data frame?

The `subset(x, subset, select)`  function can be used to extract parts of a data frame. The [x](x) parameter is used to indicate the original data frame, the [subset](subset) parameter is used to place the condition and the [select](select) parameter is used to keep only some of the variables in the data frame.

* If we want the iris data frame only with the setosa species we use the following code.

```r
subset(iris, subset=Species == "setosa")
```

```
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa
7          4.6         3.4          1.4         0.3  setosa
8          5.0         3.4          1.5         0.2  setosa
```

---

background-image: url(logo.svg)
background-size: 120px
background-position: 10% 98%

# Logical comparison operators in R

---
background-image: url(logo.svg)
background-size: 120px
background-position: 10% 98%

# Exercises

* Con la base de datos Iris, seleccionar un subconjuto de los datos donde se cumplan las siguientes condiciones:

* Solo seleccionar las flores de especie Versicolor
  
  * Solo seleccionar las flores de especie Versicolor o setosa
  
  * Las flores con Sepal.length mayor que 6 
  
  * Las flores con Petal.width menor o igual a 0.8

---

background-image: url(logo.svg)
background-size: 120px
background-position: 10% 98%

# Creating Lists

Lists are another type of object widely used to store objects of different types. The instruction to create a list is `list()`. Next we are going to create a list that contains [**three**](three) objects:

* a vector with [**5**](5) random numbers called [**mivector**](mivector)
* a 6 × 2 dimension matrix with the first twelve positive integers called [**matrix2**](matrix2) 
* and the third object will be the [**iris**](iris) data frame loaded in the previous slide.

The instructions for creating the required list are shown below.

```r
set.seed(1)
mivector <- rnorm(n=5)
matriz2 <- matrix(data=1:12, ncol=6)

*milista <- list(E1=mivector, E2=matriz2, E3=iris)
```

---
background-image: url(logo.svg)
background-size: 120px
background-position: 10% 98%

# Creating Lists

The `set.seed` function of line number 1 serves to set the seed in such a way that the random numbers generated in the second line with the `rnorm( )` function are always the same.

```r
print(milista)
```

```
$E1
[1] -0.6264538  0.1836433 -0.8356286  1.5952808  0.3295078

$E2
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    1    3    5    7    9   11
[2,]    2    4    6    8   10   12

$E3
    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1            5.1         3.5          1.4         0.2     setosa
2            4.9         3.0          1.4         0.2     setosa
3            4.7         3.2          1.3         0.2     setosa
4            4.6         3.1          1.5         0.2     setosa
5            5.0         3.6          1.4         0.2     setosa
6            5.4         3.9          1.7         0.4     setosa
7            4.6         3.4          1.4         0.3     setosa
8            5.0         3.4          1.5         0.2     setosa
9            4.4         2.9          1.4         0.2     setosa
10           4.9         3.1          1.5         0.1     setosa
11           5.4         3.7          1.5         0.2     setosa
12           4.8         3.4          1.6         0.2     setosa
13           4.8         3.0          1.4         0.1     setosa
14           4.3         3.0          1.1         0.1     setosa
15           5.8         4.0          1.2         0.2     setosa
16           5.7         4.4          1.5         0.4     setosa
17           5.4         3.9          1.3         0.4     setosa
18           5.1         3.5          1.4         0.3     setosa
19           5.7         3.8          1.7         0.3     setosa
20           5.1         3.8          1.5         0.3     setosa
21           5.4         3.4          1.7         0.2     setosa
22           5.1         3.7          1.5         0.4     setosa
23           4.6         3.6          1.0         0.2     setosa
24           5.1         3.3          1.7         0.5     setosa
25           4.8         3.4          1.9         0.2     setosa
26           5.0         3.0          1.6         0.2     setosa
27           5.0         3.4          1.6         0.4     setosa
28           5.2         3.5          1.5         0.2     setosa
29           5.2         3.4          1.4         0.2     setosa
30           4.7         3.2          1.6         0.2     setosa
31           4.8         3.1          1.6         0.2     setosa
32           5.4         3.4          1.5         0.4     setosa
33           5.2         4.1          1.5         0.1     setosa
34           5.5         4.2          1.4         0.2     setosa
35           4.9         3.1          1.5         0.2     setosa
36           5.0         3.2          1.2         0.2     setosa
37           5.5         3.5          1.3         0.2     setosa
38           4.9         3.6          1.4         0.1     setosa
39           4.4         3.0          1.3         0.2     setosa
40           5.1         3.4          1.5         0.2     setosa
41           5.0         3.5          1.3         0.3     setosa
42           4.5         2.3          1.3         0.3     setosa
43           4.4         3.2          1.3         0.2     setosa
44           5.0         3.5          1.6         0.6     setosa
45           5.1         3.8          1.9         0.4     setosa
46           4.8         3.0          1.4         0.3     setosa
47           5.1         3.8          1.6         0.2     setosa
48           4.6         3.2          1.4         0.2     setosa
49           5.3         3.7          1.5         0.2     setosa
50           5.0         3.3          1.4         0.2     setosa
51           7.0         3.2          4.7         1.4 versicolor
52           6.4         3.2          4.5         1.5 versicolor
53           6.9         3.1          4.9         1.5 versicolor
54           5.5         2.3          4.0         1.3 versicolor
55           6.5         2.8          4.6         1.5 versicolor
56           5.7         2.8          4.5         1.3 versicolor
57           6.3         3.3          4.7         1.6 versicolor
58           4.9         2.4          3.3         1.0 versicolor
59           6.6         2.9          4.6         1.3 versicolor
60           5.2         2.7          3.9         1.4 versicolor
61           5.0         2.0          3.5         1.0 versicolor
62           5.9         3.0          4.2         1.5 versicolor
63           6.0         2.2          4.0         1.0 versicolor
64           6.1         2.9          4.7         1.4 versicolor
65           5.6         2.9          3.6         1.3 versicolor
66           6.7         3.1          4.4         1.4 versicolor
67           5.6         3.0          4.5         1.5 versicolor
68           5.8         2.7          4.1         1.0 versicolor
69           6.2         2.2          4.5         1.5 versicolor
70           5.6         2.5          3.9         1.1 versicolor
71           5.9         3.2          4.8         1.8 versicolor
72           6.1         2.8          4.0         1.3 versicolor
73           6.3         2.5          4.9         1.5 versicolor
74           6.1         2.8          4.7         1.2 versicolor
75           6.4         2.9          4.3         1.3 versicolor
76           6.6         3.0          4.4         1.4 versicolor
77           6.8         2.8          4.8         1.4 versicolor
78           6.7         3.0          5.0         1.7 versicolor
79           6.0         2.9          4.5         1.5 versicolor
80           5.7         2.6          3.5         1.0 versicolor
81           5.5         2.4          3.8         1.1 versicolor
82           5.5         2.4          3.7         1.0 versicolor
83           5.8         2.7          3.9         1.2 versicolor
84           6.0         2.7          5.1         1.6 versicolor
85           5.4         3.0          4.5         1.5 versicolor
86           6.0         3.4          4.5         1.6 versicolor
87           6.7         3.1          4.7         1.5 versicolor
88           6.3         2.3          4.4         1.3 versicolor
89           5.6         3.0          4.1         1.3 versicolor
90           5.5         2.5          4.0         1.3 versicolor
91           5.5         2.6          4.4         1.2 versicolor
92           6.1         3.0          4.6         1.4 versicolor
93           5.8         2.6          4.0         1.2 versicolor
94           5.0         2.3          3.3         1.0 versicolor
95           5.6         2.7          4.2         1.3 versicolor
96           5.7         3.0          4.2         1.2 versicolor
97           5.7         2.9          4.2         1.3 versicolor
98           6.2         2.9          4.3         1.3 versicolor
99           5.1         2.5          3.0         1.1 versicolor
100          5.7         2.8          4.1         1.3 versicolor
101          6.3         3.3          6.0         2.5  virginica
102          5.8         2.7          5.1         1.9  virginica
103          7.1         3.0          5.9         2.1  virginica
104          6.3         2.9          5.6         1.8  virginica
105          6.5         3.0          5.8         2.2  virginica
106          7.6         3.0          6.6         2.1  virginica
107          4.9         2.5          4.5         1.7  virginica
108          7.3         2.9          6.3         1.8  virginica
109          6.7         2.5          5.8         1.8  virginica
110          7.2         3.6          6.1         2.5  virginica
111          6.5         3.2          5.1         2.0  virginica
112          6.4         2.7          5.3         1.9  virginica
113          6.8         3.0          5.5         2.1  virginica
114          5.7         2.5          5.0         2.0  virginica
115          5.8         2.8          5.1         2.4  virginica
116          6.4         3.2          5.3         2.3  virginica
117          6.5         3.0          5.5         1.8  virginica
118          7.7         3.8          6.7         2.2  virginica
119          7.7         2.6          6.9         2.3  virginica
120          6.0         2.2          5.0         1.5  virginica
121          6.9         3.2          5.7         2.3  virginica
122          5.6         2.8          4.9         2.0  virginica
123          7.7         2.8          6.7         2.0  virginica
124          6.3         2.7          4.9         1.8  virginica
125          6.7         3.3          5.7         2.1  virginica
126          7.2         3.2          6.0         1.8  virginica
127          6.2         2.8          4.8         1.8  virginica
128          6.1         3.0          4.9         1.8  virginica
129          6.4         2.8          5.6         2.1  virginica
130          7.2         3.0          5.8         1.6  virginica
131          7.4         2.8          6.1         1.9  virginica
132          7.9         3.8          6.4         2.0  virginica
133          6.4         2.8          5.6         2.2  virginica
134          6.3         2.8          5.1         1.5  virginica
135          6.1         2.6          5.6         1.4  virginica
136          7.7         3.0          6.1         2.3  virginica
137          6.3         3.4          5.6         2.4  virginica
138          6.4         3.1          5.5         1.8  virginica
139          6.0         3.0          4.8         1.8  virginica
140          6.9         3.1          5.4         2.1  virginica
141          6.7         3.1          5.6         2.4  virginica
142          6.9         3.1          5.1         2.3  virginica
143          5.8         2.7          5.1         1.9  virginica
144          6.8         3.2          5.9         2.3  virginica
145          6.7         3.3          5.7         2.5  virginica
146          6.7         3.0          5.2         2.3  virginica
147          6.3         2.5          5.0         1.9  virginica
148          6.5         3.0          5.2         2.0  virginica
149          6.2         3.4          5.4         2.3  virginica
150          5.9         3.0          5.1         1.8  virginica
```

---
background-image: url(logo.svg)
background-size: 120px
background-position: 10% 98%

# How to extract items from a list?

To retrieve items stored in a list, use the [$]($) operator, double square brackets [[[]]]([[]]) or single square brackets [[]]([]). Here are some examples to understand how to extract items from a list.

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# How to extract items from a list?

```r
milista$E2
```

```
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    1    3    5    7    9   11
[2,]    2    4    6    8   10   12
```
--

```r
milista[[2]]
```

```
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    1    3    5    7    9   11
[2,]    2    4    6    8   10   12
```
--

```r
milista[2]
```

```
$E2
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    1    3    5    7    9   11
[2,]    2    4    6    8   10   12
```

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# Using class for items in a list

```r
class(milista$E2)
```

```
[1] "matrix"
```

```r
class(milista[[2]])
```

```
[1] "matrix"
```

```r
class(milista[2])
```

```
[1] "list"
```

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# Exercises

1. Cargue la base de datos mtcars ([?mtcars](?mtcars))

2. Identifique la estructura de la base de datos.

3. Realice un resumen general.

4. ¿Qué sucede con la variable vs?
  ¿Realmente es una variable númerica?
  
5. Realice un resumen para los autos con transmisión automática.

6. Guarde en una nueva base de datos los vehículos  con más de 5 carburadores.

7. Almacene en un nuevo vector llamado temp, la variable mpg.

8. Seleccione de temp sólo los valores que superan los 20 mpg

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# Database reading

## In what format to store a database?

Preferably, save the information in `.csv` or `.txt` and then if we import this to R.

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# Database reading (Excel)

Se debe iniciar en la parte superior izquierda, no se deben dejar filas vacías, no se debe colorear, no se deben colocar bordes ni nada, se ingresa la información sin embellecer el contenido. Por último se guarda el archivo en la carpeta deseada y al momento de nombrar el archivo se debe modificar la opción tipo de archivo a csv (delimitado por comas).

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# Database reading

```r
read.table(file, header = FALSE, sep = "", quote = "\"'",
           dec = ".", row.names, col.names)

read.csv(file, header = TRUE, sep = ",", quote = "\"",
         dec = ".", fill = TRUE, comment.char = "", ...)

read.csv2(file, header = TRUE, sep = ";", quote = "\"",
          dec = ",", fill = TRUE, comment.char = "", ...)

read.delim(file, header = TRUE, sep = "\t", quote = "\"",
           dec = ".", fill = TRUE, comment.char = "", ...)

read.delim2(file, header = TRUE, sep = "\t", quote = "\"",
            dec = ",", fill = TRUE, comment.char = "", ...) 
```

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# Thanks!

Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan).

[**https://apariciojohan.github.io/**](https://apariciojohan.github.io/)

Johan Aparicio