# Chapter 4 Data Structures

There are different data types that are commonly used in R among which the most important ones are the following:

**Numeric**(or double): these are used to store real numbers. Examples: -4, 12.4532, 6.**Integer**: examples: 2L, 12L.**Logical**(or boolean): examples:`TRUE`

,`FALSE`

.**Character**: examples:`"a"`

,`"Bonjour"`

.

In R there are five types of data structures in which elements can be stored. A data structure is said to *homogeneous* if it only contains elements of the same type (for example it only contains character or only numeric values) and *heterogenous* if it contains elements of more than one type. The five types of data structures are commonly summarized in a table similar to the one below (see e.g. Wickham 2014a):

Dimension | Homogenous | Heterogeneous |
---|---|---|

1 | Vector | List |

2 | Matrix | Data Frame |

n | Array |

Consider a simple data set of the top five male single tennis players presented below:

Name | Date of Birth | Born | Country | ATP Ranking | Prize Money | Win Percentage | Grand Slam Wins |
---|---|---|---|---|---|---|---|

Andy Murray | 15 May 1987 | Glasgow, Scotland | Great Britain | 1 | 60,449,649 | 78.07 | 9 |

Rafael Nadal | 3 June 1986 | Manacor, Spain | Spain | 2 | 85,920,132 | 82.48 | 15 |

Stan Wawrinka | 28 March 1985 | Lausanne, Switzerland | Switzerland | 3 | 30,577,981 | 63.96 | 5 |

Novak Djokovic | 22 May 1987 | Belgrade, Serbia | Serbia | 4 | 109,447,408 | 82.77 | 12 |

Roger Federer | 8 August 1981 | Basel, Switzerland | Switzerland | 5 | 104,445,185 | 81.80 | 18 |

Notice that this data set contains a variety of data types; in the next sections we will use this data to illustrate how to work with five common data structures.

## 4.1 Vectors

A vector has three important properties:

- The
**Type**of objects contained in the vector. The function`typeof()`

returns a description of the type of objects in a vector. - The
**Length**of a vector indicates the number of elements in the vector. This information can be obtained using the function`length()`

. **Attributes**are metadata attached to a vector. The functions`attr()`

and`attributes()`

can be used to store and retrieve attributes (more details can be found in Section 4.1.4).

`c()`

is a generic function that combines arguments to form a vector. All arguments are coerced to a common type (which is the type of the returned value) and all attributes except names are removed. For example, consider the number of grand slams won by the five players considered in the eighth column of Table 4.2:

`grand_slam_win <- c(9, 15, 5, 12, 18)`

To display the values stored in `grand_slam_win`

we could simply enter the following in the R console:

`grand_slam_win`

`## [1] 9 15 5 12 18`

Alternatively, we could have created and displayed the value by using `()`

around the definition of the object itself as follows:

`(grand_slam_win <- c(9, 15, 5, 12, 18))`

`## [1] 9 15 5 12 18`

Various forms of “nested concatenation” can be used to define vectors. For example, we could also define `grand_slam_win`

as

`(grand_slam_win <- c(9, c(15, 5, c(12, c(18)))))`

`## [1] 9 15 5 12 18`

This approach is often used to assemble vectors in various ways.

It is also possible to define vectors with characters, for example we could define a vector with the player names as follows:

```
(players <- c("Andy Murray", "Rafael Nadal", "Stan Wawrinka",
"Novak Djokovic", "Roger Federer"))
```

```
## [1] "Andy Murray" "Rafael Nadal" "Stan Wawrinka" "Novak Djokovic"
## [5] "Roger Federer"
```

### 4.1.1 Type

We can evaluate the kind or type of elements that are stored in a vector using the function `typeof()`

. For example, for the vectors we just created we obtain:

`typeof(grand_slam_win)`

`## [1] "double"`

`typeof(players)`

`## [1] "character"`

This is a little surprising since all the elements in `grand_slam_win`

are integers and it would seem natural to expect this as an output of the function `typeof()`

. This is because R considers any number as a “double” by default, except when adding the suffix `L`

after an integer. For example:

`typeof(1)`

`## [1] "double"`

`typeof(1L)`

`## [1] "integer"`

Therefore, we could express `grand_slam_win`

as follows:

`(grand_slam_win_int <- c(9L, 15L, 5L, 12L, 18L))`

`## [1] 9 15 5 12 18`

`typeof(grand_slam_win_int)`

`## [1] "integer"`

In general, the difference between the two is relatively unimportant.

### 4.1.2 Coercion

As indicated earlier, a vector has a homogeneous data structure meaning that it can only contain a single type among all the data types. Therefore, when more than one data type is provided, R will *coerce* the data into a “shared” type. To identify this “shared” type we can use this simple rule:

which simply means that if a vector has more than one data type, the “shared” type will be that of the “largest” type according to the progression shown above. For example:

```
# Logical + numeric
(mix_logic_int <- c(TRUE, 12, 0.5))
```

`## [1] 1.0 12.0 0.5`

`typeof(mix_logic_int)`

`## [1] "double"`

```
# Numeric + character
(mix_int_char <- c(14.3, "Hi"))
```

`## [1] "14.3" "Hi"`

`typeof(mix_int_char)`

`## [1] "character"`

### 4.1.3 Subsetting

Naturally, it is possible to “subset” the values of in our vector in many ways. Essentially, there are four main ways to subset a vector. Here we’ll only discuss the first three:

**Positive Index**: We can*access*or*subset*the \(i\)-th element of a vector by simply using`grand_slam_win[i]`

where \(i\) is a positive number between 1 and the length of the vector.

```
# Accessing the first element
grand_slam_win[1]
```

`## [1] 9`

```
# Accessing the third and first value
grand_slam_win[c(3, 1)]
```

`## [1] 5 9`

```
# Duplicated indices yield duplicated values
grand_slam_win[c(1, 1, 2, 2, 3, 4)]
```

`## [1] 9 9 15 15 5 12`

**Negative Index**: We*remove*elements in a vector using negative indices:

```
# Removing the second observation
grand_slam_win[-2]
```

`## [1] 9 5 12 18`

```
# Removing the first and fourth observations
grand_slam_win[c(-1, -4)]
```

`## [1] 15 5 18`

**Logical Indices**: Another useful approach is based on*logical*operators:

```
# Access the first and fourth observations
grand_slam_win[c(TRUE, FALSE, FALSE, TRUE, FALSE)]
```

`## [1] 9 12`

`grand_slam_win[c(-1, 2)]`

would produce an error message.
### 4.1.4 Attributes

Let’s suppose that we conduct an experiment under specific conditions, say a date and place that are stored as attributes of the object containing the results of this experiment. Indeed, objects can have arbitrary additional attributes that are used to store metadata on the object of interest. For example:

```
attr(grand_slam_win, "date") <- "07-15-2017"
attr(grand_slam_win, "type") <- "Men, Singles"
```

To display the vector with its attributes

`grand_slam_win`

```
## [1] 9 15 5 12 18
## attr(,"date")
## [1] "07-15-2017"
## attr(,"type")
## [1] "Men, Singles"
```

To only display the attributes we can use

`attributes(grand_slam_win)`

```
## $date
## [1] "07-15-2017"
##
## $type
## [1] "Men, Singles"
```

It is also possible to extract a specific attribute

`attr(grand_slam_win, "date")`

`## [1] "07-15-2017"`

### 4.1.5 Adding Labels

In some cases, it is useful to characterize vector elements with labels. For example, we could define the vector `grand_slam_win`

and associate the name of each corresponding athlete as a label, i.e.

```
(grand_slam_win <- c("Andy Murray" = 9, "Rafael Nadal" = 15,
"Stan Wawrinka" = 5, "Novak Djokovic" = 12,
"Roger Federer" = 18))
```

```
## Andy Murray Rafael Nadal Stan Wawrinka Novak Djokovic Roger Federer
## 9 15 5 12 18
```

The main advantage of this approach is that the number of grand slams won can now be referred to by the player’s name. For example:

`grand_slam_win["Andy Murray"]`

```
## Andy Murray
## 9
```

`grand_slam_win[c("Andy Murray","Roger Federer")]`

```
## Andy Murray Roger Federer
## 9 18
```

All labels (athlete names in this case) can be obtained with the function `names()`

, i.e.

`names(grand_slam_win)`

```
## [1] "Andy Murray" "Rafael Nadal" "Stan Wawrinka" "Novak Djokovic"
## [5] "Roger Federer"
```

### 4.1.6 Working with Dates

When working with dates it is useful to treat them as real dates rather than character strings that *look* like dates (to a human) but don’t otherwise *behave* like dates. For example, consider a vector of three dates: `c("03-21-2015", "12-13-2011", "06-27-2008")`

.

The `sort()`

function returns the elements of a vector in ascending order, but since these dates are actually just character strings that *look* like dates (to a human), R sorts them in alphanumeric order (for characters) rather than chronological order (for dates):

```
# The `sort()` function sorts elements in a vector in ascending order
sort(c("03-21-2015", "12-31-2011", "06-27-2008", "01-01-2012"))
```

`## [1] "01-01-2012" "03-21-2015" "06-27-2008" "12-31-2011"`

Converting the character strings to “yyyy-mm-dd” would solve our sorting problem, but perhaps we also want to calculate the number of days between two events that are several months or years apart.

The `as.Date()`

function is one straight-forward method for converting character strings into dates that can be used as such. The typical syntax is of the form:

`as.Dates(<vector of dates>, format = <your format>)`

Considering the dates of birth presented in Table 4.2 we can save them in an appropriate format using:

```
(players_dob <- as.Date(c("15 May 1987", "3 Jun 1986", "28 Mar 1985",
"22 May 1987", "8 Aug 1981"),
format = "%d %b %Y"))
```

`## [1] "1987-05-15" "1986-06-03" "1985-03-28" "1987-05-22" "1981-08-08"`

Note the syntax of `format = "%d %b %Y"`

. The following table shows common format elements for use with the `as.Date()`

function:

Symbol | Meaning | Example |
---|---|---|

%d | day as a number (0-31) | 01-31 |

%a | abbreviated weekday | Mon |

%A | unabbreviated weekday | Monday |

%m | month (00-12) | 00-12 |

%b | abbreviated month | Jan |

%B | unabbreviated month | January |

%y | 2-digit year | 07 |

%Y | 4-digit year | 2007 |

There are many advantages to using the `as.Date()`

format (in addition to proper sorting). For example, the subtraction between two dates becomes more meaningful and yields the difference in days between them. As an example, the number of days between Rafael Nadal’s and Andy Murray’s dates of birth can be obtained as

`players_dob[1] - players_dob[2]`

`## Time difference of 346 days`

In addition, subsetting becomes also more intuitive and, for example, to find the players born after 1 January 1986 we can simply run:

`players[players_dob > "1986-01-01"]`

`## [1] "Andy Murray" "Rafael Nadal" "Novak Djokovic"`

There are many other reasons for using this format (or other date formats). A more detailed discussion on this topic can, for example, be found in Cole Beck’s notes.

### 4.1.7 Useful Functions with Vectors

The reason for extracting or creating vectors often lies in the need to collect information from them. For this purpose, a series of useful functions are available that allow to extract information or arrange the vector elements in a certain way which can be of interest to the user. Among the most commonly used functions we can find the following ones

`length()`

`sum()`

`mean()`

`order()`

and `sort()`

whose name is self-explanatory in most cases. For example we have

`length(grand_slam_win)`

`## [1] 5`

`sum(grand_slam_win)`

`## [1] 59`

`mean(grand_slam_win)`

`## [1] 11.8`

To sort the players by number of grand slam wins, we could use the function `order()`

which returns the *position* of the elements of a vector sorted in an ascending order,

`order(grand_slam_win)`

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

Therefore, we can sort the players in ascending order of wins as follows

`players[order(grand_slam_win)]`

```
## [1] "Stan Wawrinka" "Andy Murray" "Novak Djokovic" "Rafael Nadal"
## [5] "Roger Federer"
```

which implies that Roger Federer won most grand slams. Another related function is `sort()`

which simply sorts the elements of a vector in an ascending manner. For example,

`sort(grand_slam_win)`

```
## Stan Wawrinka Andy Murray Novak Djokovic Rafael Nadal Roger Federer
## 5 9 12 15 18
```

which is compact version of

`grand_slam_win[order(grand_slam_win)]`

```
## Stan Wawrinka Andy Murray Novak Djokovic Rafael Nadal Roger Federer
## 5 9 12 15 18
```

It is also possible to use the functions `sort()`

and `order()`

with characters and dates. For example, to sort the players’ names alphabetically (by first name) we can use:

`sort(players)`

```
## [1] "Andy Murray" "Novak Djokovic" "Rafael Nadal" "Roger Federer"
## [5] "Stan Wawrinka"
```

Similarly, we can sort players by age (oldest first)

`players[order(players_dob)]`

```
## [1] "Roger Federer" "Stan Wawrinka" "Rafael Nadal" "Andy Murray"
## [5] "Novak Djokovic"
```

or in an reversed manner (oldest last):

`players[order(players_dob, decreasing = TRUE)]`

```
## [1] "Novak Djokovic" "Andy Murray" "Rafael Nadal" "Stan Wawrinka"
## [5] "Roger Federer"
```

There are of course many other useful functions that allow to deal with vectors which we will not mention in this section but can be found in a variety of references (see e.g. Wickham 2014a).

### 4.1.8 Creating sequences

When using R for statistical programming and data analysis it is very common to create sequences of numbers. Here are three common ways used to create such sequences:

`from:to`

: This method is quite intuitive and very compact. For example:

`1:3`

`## [1] 1 2 3`

`(x <- 3:1)`

`## [1] 3 2 1`

`(y <- -1:-4)`

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

`(z <- 1.3:3)`

`## [1] 1.3 2.3`

`seq_len(n)`

: This function provides a simple way to generate a sequence from 1 to an arbitrary number`n`

. In general,`1:n`

and`seq_len(n)`

are equivalent with the notable exceptions where`n = 0`

and`n < 0`

. The reason for these exceptions will become clear in Section 5.2.3.1. Let’s see a few examples:

```
n <- 3
1:n
```

`## [1] 1 2 3`

`seq_len(n)`

`## [1] 1 2 3`

```
n <- 0
1:n
```

`## [1] 1 0`

`seq_len(n)`

`## integer(0)`

`seq(a, b, by/length.out = d)`

: This function can be used to create more “complex” sequences. It can either be used to create a sequence from`a`

to`b`

by increments of`d`

(using the option`by`

) or of a total length of`d`

(using the option`length.out`

). A few examples:

`(x <- seq(1, 2.8, by = 0.4))`

`## [1] 1.0 1.4 1.8 2.2 2.6`

`(y <- seq(1, 2.8, length.out = 6))`

`## [1] 1.00 1.36 1.72 2.08 2.44 2.80`

This can be combined with the `rep()`

function to create vectors with repeated values or sequences, for example:

`rep(c(1,2), times = 3, each = 1)`

`## [1] 1 2 1 2 1 2`

`rep(c(1,2), times = 1, each = 3)`

`## [1] 1 1 1 2 2 2`

`rep(c(1,2), times = 2, each = 2)`

`## [1] 1 1 2 2 1 1 2 2`

where the option `times`

allows to specify how many times the object needs to be repeated and `each`

regulates how many times each element in the object is repeated.

It is also possible to generate sequences of dates using the function `seq()`

. For example, to generate a sequence of 10 dates between the dates of birth of Andy Murray and Rafael Nadal we can use

`seq(from = players_dob[1], to = players_dob[2], length.out = 10)`

```
## [1] "1987-05-15" "1987-04-06" "1987-02-27" "1987-01-19" "1986-12-12"
## [6] "1986-11-03" "1986-09-26" "1986-08-18" "1986-07-11" "1986-06-03"
```

Similarly, we can create a sequence between the two dates by increments of one week (backwards)

`seq(players_dob[1], players_dob[2], by = "-1 week")`

```
## [1] "1987-05-15" "1987-05-08" "1987-05-01" "1987-04-24" "1987-04-17"
## [6] "1987-04-10" "1987-04-03" "1987-03-27" "1987-03-20" "1987-03-13"
## [11] "1987-03-06" "1987-02-27" "1987-02-20" "1987-02-13" "1987-02-06"
## [16] "1987-01-30" "1987-01-23" "1987-01-16" "1987-01-09" "1987-01-02"
## [21] "1986-12-26" "1986-12-19" "1986-12-12" "1986-12-05" "1986-11-28"
## [26] "1986-11-21" "1986-11-14" "1986-11-07" "1986-10-31" "1986-10-24"
## [31] "1986-10-17" "1986-10-10" "1986-10-03" "1986-09-26" "1986-09-19"
## [36] "1986-09-12" "1986-09-05" "1986-08-29" "1986-08-22" "1986-08-15"
## [41] "1986-08-08" "1986-08-01" "1986-07-25" "1986-07-18" "1986-07-11"
## [46] "1986-07-04" "1986-06-27" "1986-06-20" "1986-06-13" "1986-06-06"
```

or by increments of one month (forwards)

`seq(players_dob[2], players_dob[1], by = "1 month")`

```
## [1] "1986-06-03" "1986-07-03" "1986-08-03" "1986-09-03" "1986-10-03"
## [6] "1986-11-03" "1986-12-03" "1987-01-03" "1987-02-03" "1987-03-03"
## [11] "1987-04-03" "1987-05-03"
```

### 4.1.9 Example: Apple Stock Price

Suppose that someone is interested in analyzing the behavior of Apple’s stock price over the last three months. The first thing needed to perform such analysis is to recover (automatically) today’s date. In R, this can be obtained as follows

`(today <- Sys.Date())`

`## [1] "2019-04-18"`

Once this is done, we can obtain the date which is exactly three months ago by using

`(three_months_ago <- seq(today, length = 2, by = "-3 months")[2])`

`## [1] "2019-01-18"`

With this information, we can now download Apple’s stock price and represent these stocks through a candlestick chart which summarizes information on daily opening and closing prices as well as minimum and maximum prices. These charts are often used with the hope of detecting trading patterns over a given period of time.

```
library(quantmod)
getSymbols("AAPL", from = three_months_ago, to = today)
candleChart(AAPL, theme = 'white', type = 'candles')
```

Using the price contained in the object we downloaded (i.e. `AAPL`

), we can compute Apple’s arithmetic returns which are defined as follows

where \(R_t\) are the returns at time *t* and \(S_t\) is the stock price. This is implemented in the function `ClCl()`

within the `quantmod`

package. For example, we can create a vector of returns as follows

`AAPL_returns <- na.omit(ClCl(AAPL))`

where `na.omit()`

is used to remove missing values in the stock prices vector since, if we have \(n+1\) stock prices, we will only have \(n\) returns and therefore the first return cannot be computed. We can now compute the mean and median of the returns over the considered period.

`mean(AAPL_returns)`

`## [1] 0.0001369265`

`median(AAPL_returns)`

`## [1] 0.003158065`

However, a statistic that is of particular interest to financial operators is the Excess Kurtosis which, for a random variable that we denote as \(X\), can be defined as

\[\begin{equation} \text{Kurt} = \frac{{\mathbb{E}}\left[\left(X - \mathbb{E}[X]\right)^4\right]}{\left({\mathbb{E}}\left[\left(X - \mathbb{E}[X]\right)^2\right]\right)^2} - 3. \end{equation}\]The reason for defining this statistic as *Excess* Kurtosis lies in the fact that the standardized kurtosis is compared to that of a Gaussian distribution (whose kurtosis is equal to 3) which has exponentially decaying tails. Consequently, if the Excess Kurtosis is positive, this implies that the distribution has heavier tails than a Gaussian and therefore has higher probabilities of extreme events occurring. To understand why the Excess Kurtosis is equal to 0 for a Gaussian distribution click on the button below:

Assuming \(X\) to be Gaussian, we have \(X \sim \mathcal{N}(\mu, \sigma^2)\). Then, we define \(Z\) as \(Z \sim \mathcal{N}(0,1)\) and \(Y\) as \(Y = Z^2 \sim \chi^2_1\). Remember that a random variable following a \(\chi^2_1\) distribution has the following properties: \(\text{Var}[Y] = 2\) and \(\mathbb{E}[Y] = 1\). Using these definitions and properties, we obtain:

\[\begin{equation} \begin{aligned} \text{Kurt} &= \frac{{\mathbb{E}}\left[\left(X - \mathbb{E}[X]\right)^4\right]}{\left({\mathbb{E}}\left[\left(X - \mathbb{E}[X]\right)^2\right]\right)^2} - 3 = \frac{{\mathbb{E}}\left[\left(X - \mathbb{E}[X]\right)^4\right]}{\left[\text{Var} \left(X\right)\right]^2} - 3 = \frac{{\mathbb{E}}\left[\left(X - \mathbb{E}[X]\right)^4\right]}{\sigma^4} - 3\\ &= {\mathbb{E}}\left[\left(\frac{X - \mathbb{E}[X]}{\sigma}\right)^4\right] - 3 = \mathbb{E}\left[Z^4\right] - 3 = \text{Var}[Z^2] + \mathbb{E}^2\left[Z^2\right] - 3\\ &= \text{Var}[Y] + \mathbb{E}^2\left[Y\right] - 3 = 0. \end{aligned} \end{equation}\] This implies that the theoretical value of the excess Kurtosis for a normally distributed random variable is \(0\).Given this statistic, it is useful to compute this on the observed data and for this purpose a common estimator of the excess Kurtosis is

\[\begin{equation} k = \frac{\frac{1}{n} \sum_{t = 1}^{n} \left(R_t -\bar{R}\right)^4}{\left(\frac{1}{n} \sum_{t = 1}^{n} \left(R_t -\bar{R}\right)^2 \right)^2} - 3, \end{equation}\]where \(\bar{R}\) denotes the sample average of the returns, i.e.

\[\begin{equation*} \bar{R} = \frac{1}{n} \sum_{i = 1}^n R_i. \end{equation*}\]In R, this can simply be done as follows:

```
mu <- mean(AAPL_returns)
(k <- mean((AAPL_returns - mu)^4)/(mean((AAPL_returns - mu)^2))^2 - 3)
```

`## [1] 1.724814`

Therefore, we observe an estimated Excess Kurtosis of 1.72 which is quite high and tends to indicate that the returns have heavier tails than the normal distribution. In Chapter 5, we will revisit this example and investigate if there is **enough evidence** to conclude that Apple’s stock has Excess Kurtosis larger than zero.

## 4.2 Matrices

Matrices are a common data structure in R which have two dimensions to store multiple vectors of the same length combined as a unified object. The `matrix()`

function can be used to create a matrix from a vector:

`(mat <- matrix(1:12, ncol = 4, nrow = 3))`

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

Notice that the first argument to the function is a vector (in this case the sequence 1 to 12) which is then transformed into a matrix with four columns (`ncol = 4`

) and three rows (`nrow = 3`

).

By default, the vectors are transformed into matrices by placing the elements by column (i.e. top to the bottom of ech column in sequence until all columns are full). If you wish to fill the matrix by row, all you need to do is specify the argument `byrow = T`

.

```
# Compare with the matrix above
matrix(1:12, ncol = 4, nrow = 3, byrow = T)
```

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

It is often the case that we already have equi-dimensional vectors available and we wish to bundle them together as matrix. In these cases, two useful functions are `cbind()`

to combine vectors as vertical **c**olumns side-by-side, and `rbind()`

to combine vectors as horizontal **r**ows. An example of `cbind()`

is shown here:

```
players <- c("Andy Murray", "Rafael Nadal", "Stan Wawrinka",
"Novak Djokovic", "Roger Federer")
grand_slam_win <- c(9, 15, 5, 12, 18)
win_percentage <- c(78.07, 82.48, 63.96, 82.77, 81.80)
(mat <- cbind(grand_slam_win, win_percentage))
```

```
## grand_slam_win win_percentage
## [1,] 9 78.07
## [2,] 15 82.48
## [3,] 5 63.96
## [4,] 12 82.77
## [5,] 18 81.80
```

The result in this case is a \(5 \times 2\) matrix (with `rbind()`

it would have been a \(2 \times 5\) matrix). Once the matrix is defined, we can assign names to its rows and columns by using `rownames()`

and `colnames()`

, respectively. Of course, the number of names must match the corresponding matrix dimension. In the following example, each row corresponds to a specific player (thereby using the `players`

vector) and each column corresponds to a specific statistic of the players.

```
rownames(mat) <- players
colnames(mat) <- c("GS win", "Win rate")
mat
```

```
## GS win Win rate
## Andy Murray 9 78.07
## Rafael Nadal 15 82.48
## Stan Wawrinka 5 63.96
## Novak Djokovic 12 82.77
## Roger Federer 18 81.80
```

### 4.2.1 Subsetting

As with vectors, it is possible to subset the elements of a matrix. Since matrices are two-dimensional data structures, it is necessary to specify the position of the elements of interest in both dimensions. For this purpose we can use `[ , ]`

immediately following the named matrix. Note the use of `,`

within the square brackets in order to specify both row and column position of desired elements within the matrix (e.g. `matrixName[row, column]`

). Consider the following examples:

```
# Subset players "Stan Wawrinka" and "Roger Federer" in matrix named "mat"
mat[c("Stan Wawrinka", "Roger Federer"), ]
```

```
## GS win Win rate
## Stan Wawrinka 5 63.96
## Roger Federer 18 81.80
```

```
# Subset includes row 1 and 3 for all columns
mat[c(1, 3), ]
```

```
## GS win Win rate
## Andy Murray 9 78.07
## Stan Wawrinka 5 63.96
```

```
# Subset includes all rows for column 2
mat[ , 2]
```

```
## Andy Murray Rafael Nadal Stan Wawrinka Novak Djokovic Roger Federer
## 78.07 82.48 63.96 82.77 81.80
```

```
# Subset includes rows 1, 2, & 3 for column 1
mat[1:3, 1]
```

```
## Andy Murray Rafael Nadal Stan Wawrinka
## 9 15 5
```

It can be noticed that, when a space is left blank before or after the comma, this means that respectively all the rows or all the columns are considered.

### 4.2.2 Matrix Operators in R

As with vectors, there are some useful functions that can be used with matrices. A first example is the function `dim()`

that allows to determine the dimension of a matrix. For example, consider the following \(4 \times 2\) matrix

which can be created in R as follows:

`(A <- matrix(1:8, 4, 2))`

```
## [,1] [,2]
## [1,] 1 5
## [2,] 2 6
## [3,] 3 7
## [4,] 4 8
```

Therefore, we expect `dim(A)`

to return the vector `c(4, 2)`

. Indeed, we have

`dim(A)`

`## [1] 4 2`

Next, we consider the function `t()`

that allows transpose a matrix. For example, \(\mathbf{A}^T\) is equal to:

which is a \(2 \times 4\) matrix. In R, we achieve this as follows

`(At <- t(A))`

```
## [,1] [,2] [,3] [,4]
## [1,] 1 2 3 4
## [2,] 5 6 7 8
```

`dim(At)`

`## [1] 2 4`

Aside from playing with matrix dimensions, matrix algebraic operations have specific commands. For example, the operator `%*%`

is used in R to denote matrix multiplication while, as opposed to scalar objects, the regular product operator `*`

performs the element by element product (or Hadamard product) when applied to matrices. For example, consider the following matrix product:

which can be done in R as follows:

`(B <- At %*% A)`

```
## [,1] [,2]
## [1,] 30 70
## [2,] 70 174
```

Other common matrix operations include finding the determinant of a matrix and finding its inverse. These are often used, for example, when computing the likelihood function for a variable following a Gaussian distribution or when simulating time series or spatial data. The functions that perform these operations are `det()`

and `solve()`

that respectively find the determinant and the inverse of a matrix (which necessarily has to be square). The function `det()`

can be used to compute the determinant of a square matrix. In the case of a \(2 \times 2\) matrix, there exists a simple solution for the determinant which is

Consider the matrix \(\mathbf{B}\), we have

\[\begin{equation*} \text{det} \left( \mathbf{B}\right) = 30 \cdot 174 - 70^2 = 320. \end{equation*}\]In R, we can simply do

`det(B)`

`## [1] 320`

The function `solve()`

is also an important function when working with matrices as it allows to inverse a matrix. It is worth remembering that a square matrix that is not invertible (i.e. \(\mathbf{A}^{-1}\) doesn’t exist) is called *singular* and the determinant offers a way to “check” if this is the case for a given matrix. Indeed, a square matrix is singular if and only if its determinant is 0. Therefore, in the case of \(\mathbf{B}\), we should be able to compute its inverse. As for the determinant, there exists a formula to compute the inverse of \(2 \times 2\) matrices, i.e.

Considering the matrix \(\mathbf{B}\), we obtain

\[\begin{equation*} \mathbf{B}^{-1} = \left[ \begin{matrix} 30 & 70\\ 70 & 174 \end{matrix} \right]^{-1} = \frac{1}{320}\left[ \begin{matrix} \phantom{-}174 & -70\\ -70 & \phantom{-}30 \end{matrix} \right] = \left[ \begin{matrix} \phantom{-}0.54375 & -0.21875\\ -0.21875 & \phantom{-}0.09375 \end{matrix} \right] \end{equation*}\]`(B_inv <- solve(B))`

```
## [,1] [,2]
## [1,] 0.54375 -0.21875
## [2,] -0.21875 0.09375
```

Finally, we can verify that

\[\begin{equation*} \mathbf{G} = \mathbf{B} \mathbf{B}^{-1}, \end{equation*}\]should be equal to the identity matrix,

`(G <- B %*% B_inv)`

```
## [,1] [,2]
## [1,] 1 -8.881784e-16
## [2,] 0 1.000000e+00
```

The result is of course extremely close but \(\mathbf{G}\) is not exactly equal to the identity matrix due to rounding and other numerical errors.

Another function of interest is the function `diag()`

that can be used to extract the diagonal of a matrix. For example, we have

which can be done in R as follows:

`diag(B)`

`## [1] 30 174`

Therefore, the function `diag()`

computes the trace of matrix (i.e. the sum of the diagonal elements). For example,

or in R:

`sum(diag(B))`

`## [1] 204`

Another use of the function `diag()`

is to create diagonal matrices. Indeed, if the argument of this function is a vector, its behavior is the following:

Therefore, this provides a simple way of creating an identity matrix by combining the functions `diag()`

and `rep()`

(discussed in the previous section) as follows:

```
n <- 4
(ident <- diag(rep(1, n)))
```

```
## [,1] [,2] [,3] [,4]
## [1,] 1 0 0 0
## [2,] 0 1 0 0
## [3,] 0 0 1 0
## [4,] 0 0 0 1
```

### 4.2.3 Example: Summary Statistics with Matrix Notation

A simple example of the operations we discussed in the previous section is given by many common statistics that can be re-expressed using matrix notation. As an example, we will consider three common statistics that are the sample mean, variance and covariance. Let us consider the following two samples of size \(n\)

\[\begin{equation*} \begin{aligned} \mathbf{x} &= \left[x_1 \;\; x_2 \; \;\cdots \;\; x_n\right]^T\\ \mathbf{y} &= \left[y_1 \;\;\; y_2 \; \;\;\cdots \;\;\; y_n\right]^T. \end{aligned} \end{equation*}\]The sample mean of \(\mathbf{x}\) is

\[\begin{equation*} \bar{x} = \frac{1}{n} \sum_{i = 1}^{n} x_i, \end{equation*}\]and its sample variance is

\[\begin{equation*} s_x^2 = \frac{1}{n} \sum_{i = 1}^n \left(x_i - \bar{x}\right)^2. \end{equation*}\]The sample covariance between \(\mathbf{x}\) and \(\mathbf{y}\) is

\[\begin{equation*} s_{x,y} = \frac{1}{n} \sum_{i = 1}^n \left(X_i - \bar{x}\right) \left(Y_i - \bar{y}\right), \end{equation*}\]where \(\bar{y}\) denotes the sample mean of \(\mathbf{y}\).

Consider the sample mean, this statistic can be expressed in matrix notation as follows

\[\begin{equation*} \bar{x} = \frac{1}{n} \sum_{i = 1}^{n} x_i = \frac{1}{n} \mathbf{x}^T \mathbf{1}, \end{equation*}\]where \(\mathbf{1}\) is a column vector of \(n\) ones.

\[\begin{equation*} \begin{aligned} s_x^2 &= \frac{1}{n} \sum_{i = 1}^n \left(x_i - \bar{x}\right)^2 = \frac{1}{n} \sum_{i = 1}^n x_i^2 - \bar{x}^2 = \frac{1}{n} \mathbf{x}^T \mathbf{x} - \bar{x}^2\\ &= \frac{1}{n} \mathbf{x}^T \mathbf{x} - \left(\frac{1}{n} \mathbf{x}^T \mathbf{1}\right)^2 = \frac{1}{n} \left(\mathbf{x}^T \mathbf{x} - \frac{1}{n} \mathbf{x}^T \mathbf{1} \mathbf{1}^T \mathbf{x}\right)\\ &= \frac{1}{n}\mathbf{x}^T \left( \mathbf{I} - \frac{1}{n} \mathbf{1} \mathbf{1}^T \right) \mathbf{x} = \frac{1}{n}\mathbf{x}^T \mathbf{H} \mathbf{x}, \end{aligned} \end{equation*}\]where \(\mathbf{H} = \mathbf{I} - \frac{1}{n} \mathbf{1} \mathbf{1}^T\). This matrix is often called the *centering* matrix. Similarly, for the sample covariance we obtain

In the code below we verify the validity of these results through a simple simulated example where we compare the values of the three statistics based on the different formulas discussed above.

```
# Sample size
n <- 100
# Simulate random numbers from a zero mean normal distribution with
# variance equal to 4.
x <- rnorm(n, 0, sqrt(4))
# Simulate random numbers from normal distribution with mean 3 and
# variance equal to 1.
y <- rnorm(n, 3, 1)
# Note that x and y are independent.
# Sample mean
one <- rep(1, n)
x_bar <- 1/n*sum(x)
x_bar_mat <- 1/n*t(x)%*%one
# Sample variance of x
H <- diag(rep(1, n)) - 1/n * one %*% t(one)
s_x <- 1/n * sum((x - x_bar)^2)
s_x_mat <- 1/n*t(x) %*% H %*% x
# Sample covariance
y_bar <- 1/n*sum(y)
s_xy <- 1/n*sum((x - x_bar)*(y - y_bar))
s_xy_mat <- 1/n*t(x) %*% H %*% y
```

To compare, let’s construct a matrix of all the results that we calculated.

```
cp_matrix <- matrix(c(x_bar, x_bar_mat, s_x, s_x_mat, s_xy, s_xy_mat), ncol = 2, byrow = T)
row.names(cp_matrix) <- c("Sample Mean", "Sample Variance", "Sample Covariance")
colnames(cp_matrix) <- c("Scalar", "Matrix")
cp_matrix
```

```
## Scalar Matrix
## Sample Mean -0.095715724 -0.095715724
## Sample Variance 3.485078494 3.485078494
## Sample Covariance -0.007969419 -0.007969419
```

Therefore, using the previously obtained results we can construct the following *empirical* covariance matrix

In R, this can be done as

```
# Sample variance of y
s_y_mat <- 1/n*t(y) %*% H %*% y
# Covariance matrix
(V <- matrix(c(s_x_mat, s_xy_mat, s_xy_mat, s_y_mat), 2, 2))
```

```
## [,1] [,2]
## [1,] 3.485078494 -0.007969419
## [2,] -0.007969419 0.956116661
```

This result can now be compared to

`cov(cbind(x, y))`

```
## x y
## x 3.520281307 -0.008049918
## y -0.008049918 0.965774405
```

We can see that the results are slightly different from what we expected. This is because the calculation of `cov()`

within the default R `stats`

package is based on an unbiased estimator which is not the one we used. To obtain the same result, we can go back to our estimation by calculating via the below method

`(n-1)/n*cov(cbind(x, y))`

```
## x y
## x 3.485078494 -0.007969419
## y -0.007969419 0.956116661
```

### 4.2.4 Example: Portfolio Optimization

Suppose that you are interested in investing your money in two stocks, say Apple and Netflix. However, you are wondering how much of each stock you should buy. To make it simple let us assume that you will invest \(\omega W\) in Apple (AAPL) and \((1-\omega) W\) in Netflix (NFLX), where \(W\) denotes the amount of money you would like to invest, and \(\omega \in [0, \, 1]\) dictates the proportion allocated to each investment. Let \(R_A\) and \(R_N\) denote, respectively, the daily return (see Equation (4.1) if you don’t remember what returns are) of Apple and Netflix. To make things simple we assume that the returns \(R_A\) and \(R_N\) are jointly normally distributed so we can write

\[ \mathbf{R} \stackrel{iid}{\sim} \mathcal{N} \left(\boldsymbol{\mu}, \boldsymbol{\Sigma}\right), \]

where

\[ \mathbf{R} = \left[ \begin{matrix} R_A\\ R_N \end{matrix} \right], \;\;\;\; \boldsymbol{\mu} = \left[ \begin{matrix} \mu_A\\ \mu_N \end{matrix} \right], \;\; \text{and}\;\; \boldsymbol{\Sigma} = \left[ \begin{matrix} \sigma_A^2 & \sigma_{AN}\\ \sigma_{AN} & \sigma_N^2 \end{matrix} \right]. \]

Using these assumptions, the classical portfolio optimization problem, which would allow you to determine a “good” value of \(\omega\), can be stated as follows: Given our initial wealth \(W\), the investment problem is to decide how much should be invested in our first asset (Apple) and in our second asset (Netflix). As mentioned earlier, we assume that \(\omega \in [0,\,1]\), implying that it is only possible to buy the two assets (i.e. *short positions* are not allowed). Therefore, to solve our investment problem we must choose the value of \(\omega\) which would maximize your personal *utility*. Informally speaking, the utility represents a measurement of the “usefulness” that is obtained from your investment. To consider a simple case, we will assume that you are a particularly *risk averse* individual (i.e. you are looking for an investment that is as sure and safe as possible) and as such you want to pick the value of \(\omega\) providing you with the smallest investment risk. We can then determine the optimal value of \(\omega\) as follows. First, we construct a new random variable, say \(Y\), which denotes the return of your portfolio. Since you invest \(\omega W\) in Apple and \((1 - \omega) W\) in Netflix, it is easy to see that

\[Y = \left[\omega R_A + (1 - \omega) R_N \right]W.\]

In general the risk and variance of an investment are two different things but in our case we assume that the return \(\mathbf{R}\) is normally distributed and in this case the variance can perfectly characterize the risk of our investment. Therefore, we can define the risk of our investment as the following function of \(\omega\)

\[f(\omega) = \text{Risk}(Y) = \text{Var} (Y) = \left[\omega^2 \sigma_A^2 + (1 - \omega)^2 \sigma_N^2 + 2 \omega (1 - \omega) \sigma_{AN}\right]W^2.\]

The function \(f(\omega)\) is minimized for the value \(\omega^*\) which is given by

\[\begin{equation} \omega^* = \frac{\sigma^2_N - \sigma_{AN}}{\sigma^2_A + \sigma^2_N - 2\sigma_{AN}}. \tag{4.2} \end{equation}\]If you are interested in understanding how Equation (4.2) was obtained, click on the button below:

To obtain \(\omega^*\) we first differentiate the function \(f(\omega)\) with respect to \(\omega\), which gives

\[\begin{equation} \frac{\partial}{\partial \omega} \, f(\omega) = \left[2 \omega \sigma_A^2 - 2 (1 - \omega) \sigma_N^2 + 2 (1 - 2\omega) \sigma_{AN}\right]W^2 \end{equation}\]Then, we define \(\omega^*\) as

\[\begin{equation} \omega^* \; : \frac{\partial}{\partial \omega} \, f(\omega) = 0. \end{equation}\]Therefore, we obtain

\[\begin{equation} \omega^* \left(\sigma_A^2 + \sigma_N^2 - 2 \sigma_{AN}\right) = \sigma_N^2 - \sigma_{AN}, \end{equation}\]which simplifies to

\[\begin{equation} \omega^* = \frac{\sigma_N^2 - \sigma_{AN}}{\sigma_A^2 + \sigma_N^2 - 2 \sigma_{AN}}. \end{equation}\]Finally, we verify that our result is a minimum by considering the second derivative, i.e.

\[\begin{equation} \frac{\partial^2}{\partial \omega^2} \, f(\omega) = 2W\left[\sigma_A^2 + \sigma_N^2 - 2\sigma_{AN} \sigma_{AN}\right]. \end{equation}\]Since \(W\) is strictly positive, all that remains to conclude our derivation is to verify that \(\sigma_A^2 + \sigma_N^2 - 2\sigma_{AN} \sigma_{AN} \leq 0\). In fact, this is a well known inequality which is a direct consequence of the Tchebychev inequality. However, here is a simpler argument to understand why this is the case. Indeed, it is obvious that \(\text{Var}(R_A - R_N) \leq 0\) and we also have \(\text{Var}(R_A - R_N) = \sigma_A^2 + \sigma_N^2 - 2\sigma_{AN}\). Thus, we obtain

\[\text{Var}(R_A - R_N) = \sigma_A^2 + \sigma_N^2 - 2\sigma_{AN} \leq 0,\]

which verifies that our result is a minimum.Using (4.2), we obtain that the expected value and variance of our investment are given by

\[ \begin{aligned} \text{Expected Value Investment} &=\left[\omega^* \mu_A + (1 - \omega^*) \mu_N\right] W,\\ \text{Variance Investment} &= \left[(\omega^*)^2 \sigma_A^2 + (1 - \omega^*)^2 \sigma_N^2 + 2 \omega^* (1 - \omega^*) \sigma_{AN}\right]W^2. \end{aligned} \]

In practice, the vector \(\boldsymbol{\mu}\) and the matrix \(\boldsymbol{\Sigma}\) are unknown and must be estimated using historical data. In the code below, we compute the optimal value of \(\omega\) based on (4.2) by using the last five years of historical data for the two stocks considered. For simplicity we set \(W\) to one but note that \(W\) plays no role in determining \(\omega^*\).

```
# Load quantmod
library(quantmod)
# Download data
today <- Sys.Date()
five_years_ago <- seq(today, length = 2, by = "-5 year")[2]
getSymbols("AAPL", from = five_years_ago, to = today)
getSymbols("NFLX", from = five_years_ago, to = today)
# Compute returns
Ra <- na.omit(ClCl(AAPL))
Rn <- na.omit(ClCl(NFLX))
# Estimation of mu and Sigma
Sigma <- cov(cbind(Ra, Rn))
mu <- c(mean(Ra), mean(Rn))
# Compute omega^*
omega_star <- (Sigma[2, 2] - Sigma[1, 2])/(Sigma[1, 1] + Sigma[2, 2] - 2*Sigma[1, 2])
# Compute investment expected value and variance
mu_investment <- omega_star*mu[1] + (1 - omega_star)*mu[2]
var_investment <- omega_star^2*Sigma[1,1] + (1 - omega_star)^2*Sigma[2,2] +
2*omega_star*(1 - omega_star)*Sigma[1,2]
```

From this code, we obtain \(\omega^* \approx\) 86.03%. In the table below we compare the empirical expected values and variances of the stocks as well as those of our investment:

```
investment_summary <- matrix(NA, 2, 3)
dimnames(investment_summary)[[1]] <- c("Expected value", "Variance")
dimnames(investment_summary)[[2]] <- c("Apple", "Netflix", "Investment")
investment_summary[1, ] <- c(mu, mu_investment)
investment_summary[2, ] <- c(diag(Sigma), var_investment)
knitr::kable(investment_summary)
```

Apple | Netflix | Investment | |
---|---|---|---|

Expected value | 0.0009275 | 0.0018499 | 0.0010563 |

Variance | 0.0002109 | 0.0007084 | 0.0001975 |

In Table 4.4 and the plot below we can observe that Netflix has high risk and high return. Apple seems like a safer investment in terms of risk but lower in expected return. Our portfolio optimization investment, however, seems to be in a good area, meaning that it has low risk and a return that is between the two stocks mentioned above. Finally, we provide a graphical representation of the obtained results in the figure below.

## 4.3 Array

Arrays allow to construct **multidimensional** objects. Indeed, vectors and matrices are respectively objects in one and two dimensions while arrays can in be in an arbitrary \(n\) dimension. Essentially, matrices are a special case of arrays which is only in two dimensions. Matrices are commonly used in Statistics while arrays are much rarer but nevertheless worth being aware of. To consider a simple using arrays we will revisit an example presented in the previous section where we constructed a matrix containing the number of Grand Slam won and the win rate of the five players considered in Table 4.2. This matrix was constructed as follows:

```
players <- c("Andy Murray", "Rafael Nadal", "Stan Wawrinka",
"Novak Djokovic", "Roger Federer")
grand_slam_win <- c(9, 15, 5, 12, 18)
win_percentage <- c(78.07, 82.48, 63.96, 82.77, 81.80)
mat <- cbind(grand_slam_win, win_percentage)
rownames(mat) <- players
colnames(mat) <- c("GS win", "Win rate")
mat
```

```
## GS win Win rate
## Andy Murray 9 78.07
## Rafael Nadal 15 82.48
## Stan Wawrinka 5 63.96
## Novak Djokovic 12 82.77
## Roger Federer 18 81.80
```

The data used to construct this matrix we collected in mid-July 2017 and suppose that we now would like to add to this existing the object the updated numbers from mid-September 2017. This can be done by constructing an array as shown in the example below.

```
# Constuct "empty" array
my_array <- array(NA, dim = c(5, 2, 2))
dimnames(my_array)[[1]] <- players
dimnames(my_array)[[2]] <- c("GS win", "Win rate")
dimnames(my_array)[[3]] <- c("07-15-2017", "09-13-2017")
# Construct matrix
my_array[,,1] <- mat
my_array[,,2] <- cbind(c(9, 16, 5, 12, 19),
c(78.07, 82.49, 63.96, 82.77, 81.80))
my_array
```

```
## , , 07-15-2017
##
## GS win Win rate
## Andy Murray 9 78.07
## Rafael Nadal 15 82.48
## Stan Wawrinka 5 63.96
## Novak Djokovic 12 82.77
## Roger Federer 18 81.80
##
## , , 09-13-2017
##
## GS win Win rate
## Andy Murray 9 78.07
## Rafael Nadal 16 82.49
## Stan Wawrinka 5 63.96
## Novak Djokovic 12 82.77
## Roger Federer 19 81.80
```

Like what we experimented with matrices, we can extract and manipulate information.

`length(my_array) # length is 20, i.e. 5x2x2 = 20`

`## [1] 20`

`dim(my_array) # dimensions are 5x2x2`

`## [1] 5 2 2`

`is.array(my_array) # yes it is an array!`

`## [1] TRUE`

Subsetting the elements of an array works similarly to what we already discussed with matrices. Below are a few examples:

`my_array[,,1]`

```
## GS win Win rate
## Andy Murray 9 78.07
## Rafael Nadal 15 82.48
## Stan Wawrinka 5 63.96
## Novak Djokovic 12 82.77
## Roger Federer 18 81.80
```

`my_array[1,,2]`

```
## GS win Win rate
## 9.00 78.07
```

`my_array[,1,]`

```
## 07-15-2017 09-13-2017
## Andy Murray 9 9
## Rafael Nadal 15 16
## Stan Wawrinka 5 5
## Novak Djokovic 12 12
## Roger Federer 18 19
```

Finally, we can assess if some our five won any Grand Slam between mid-July and mid-September 2017 by using the code below:

`players[my_array[,1,2] > my_array[,1,1]]`

`## [1] "Rafael Nadal" "Roger Federer"`

## 4.4 List

A list is one of the commonly used **heterogeneous** data structures, which depicts a generic “vector” containing other object types. For example, we can create a list containing numeric character and logical vectors as well as matrices. Here we can create a list that contains different element types. (numeric, character, logical, matrix)

```
# List elements
num_vec <- c(188, 140)
char_vec <- c("Height", "Weight", "Length")
logic_vec <- rep(TRUE, 8)
my_mat <- matrix(0, nrow = 5, ncol = 5)
# List initialization
(my_list <- list(num_vec, char_vec, logic_vec, my_mat))
```

```
## [[1]]
## [1] 188 140
##
## [[2]]
## [1] "Height" "Weight" "Length"
##
## [[3]]
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##
## [[4]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0 0 0 0 0
## [2,] 0 0 0 0 0
## [3,] 0 0 0 0 0
## [4,] 0 0 0 0 0
## [5,] 0 0 0 0 0
```

Alternatively, it is possible to add named labels to the elements of your list. For example,

```
# List initialization with custom names
(my_list <- list(number = num_vec, character = char_vec,
logic = logic_vec, matrix = my_mat))
```

```
## $number
## [1] 188 140
##
## $character
## [1] "Height" "Weight" "Length"
##
## $logic
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##
## $matrix
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0 0 0 0 0
## [2,] 0 0 0 0 0
## [3,] 0 0 0 0 0
## [4,] 0 0 0 0 0
## [5,] 0 0 0 0 0
```

Subsetting is very similar to what we have already discussed. Indeed, we have

```
# Extract the first and third element
my_list[c(1, 3)]
```

```
## $number
## [1] 188 140
##
## $logic
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
```

```
# Compare these two subsets
my_list[1]
```

```
## $number
## [1] 188 140
```

`my_list[[1]]`

`## [1] 188 140`

```
# Using labels to subset
my_list$number
```

`## [1] 188 140`

`my_list$matrix`

```
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0 0 0 0 0
## [2,] 0 0 0 0 0
## [3,] 0 0 0 0 0
## [4,] 0 0 0 0 0
## [5,] 0 0 0 0 0
```

`my_list$matrix[,3] # Extract third column of matrix `

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

It is interesting to notice the difference between `my_list[1]`

and `my_list[[1]]`

. To understand this difference suppose that we are interested in retrieving the second element of the vector `num_vec`

which is stored in list `my_list`

. In general most people use of the two ways presented below:

```
# These mean the same thing!
my_list[[1]][2]
```

`## [1] 140`

`my_list$number[2]`

`## [1] 140`

Simply including one bracket like `my_list[1]`

will return the first element of the list, but will retain the `list`

structure. `my_list`

is a list, and `my_list[1]`

is also a list. In other words, one bracket retains the class information, while using two brackets simplifies the list into a numeric vector.

```
# type of object
typeof(my_list)
```

`## [1] "list"`

`typeof(my_list[1])`

`## [1] "list"`

Therefore, `my_list[1][2]`

**does not work** and if you wish to use `my_list[1]`

you will have to a syntax similar to the ones presented below:

`my_list[1][[1]][2]`

`## [1] 140`

`my_list[1]$number[2]`

`## [1] 140`

Most people generally uses `[[`

to select any single element, whereas `[`

returns a list of the selected elements.

## 4.5 Dataframe

A data frame is a **heterogeneous** data structure used for storing data tables. A data frame is the most common way of storing data in R, and it has a 2D structure and shares properties of both the matrix and the list. The table contains lists of equal-length, same-type vectors, and most datasets will have a data frame format.

We can create a data frame using data.frame()

```
### Creation
players <- c("Andy Murray", "Rafael Nadal", "Stan Wawrinka",
"Novak Djokovic", "Roger Federer")
grand_slam_win <- c(9, 15, 5, 12, 18)
date_of_birth <- c("15 May 1987", "3 June 1986", "28 March 1985",
"22 May 1987", "8 August 1981")
country <- c("Great Britain", "Spain", "Switzerland",
"Serbia", "Switzerland")
ATP_ranking <- c(1, 2, 3, 4, 5)
prize_money <- c(60449649, 85920132, 30577981, 109447408, 104445185)
tennis <- data.frame(date_of_birth, grand_slam_win, country,
ATP_ranking, prize_money)
dimnames(tennis)[[1]] <- players
tennis
```

```
## date_of_birth grand_slam_win country ATP_ranking
## Andy Murray 15 May 1987 9 Great Britain 1
## Rafael Nadal 3 June 1986 15 Spain 2
## Stan Wawrinka 28 March 1985 5 Switzerland 3
## Novak Djokovic 22 May 1987 12 Serbia 4
## Roger Federer 8 August 1981 18 Switzerland 5
## prize_money
## Andy Murray 60449649
## Rafael Nadal 85920132
## Stan Wawrinka 30577981
## Novak Djokovic 109447408
## Roger Federer 104445185
```

Notice that each column has its own type. We can check if we have achieved our goal by using:

`is.data.frame(tennis)`

`## [1] TRUE`

### 4.5.1 Combination

Data frames can also be combined. Let say we want to add some information to the table above (e.g. the player’s height) and if he is right-handed or left-handed.

We can do so by using `cbind()`

- column bind and `rbind()`

- row bind:

```
height <- c(1.90, 1.85, 1.83, 1.88, 1.85)
hand <- c("R","L","R","R","R")
(tennis <- cbind(tennis, data.frame(height, hand)))
```

```
## date_of_birth grand_slam_win country ATP_ranking
## Andy Murray 15 May 1987 9 Great Britain 1
## Rafael Nadal 3 June 1986 15 Spain 2
## Stan Wawrinka 28 March 1985 5 Switzerland 3
## Novak Djokovic 22 May 1987 12 Serbia 4
## Roger Federer 8 August 1981 18 Switzerland 5
## prize_money height hand
## Andy Murray 60449649 1.90 R
## Rafael Nadal 85920132 1.85 L
## Stan Wawrinka 30577981 1.83 R
## Novak Djokovic 109447408 1.88 R
## Roger Federer 104445185 1.85 R
```

### 4.5.2 Subsetting

Like for vectors, it is also possible to subset the values that we have stored in our data frames. Since data frames possess the characteristics of both lists and matrices: if you subset with a single vector, they behave like lists; if you subset with two vectors, they behave like matrices.

Let us say we want only want to know the country and date of birth of the players.

```
# There are two ways to select columns from a data frame
# Like a list:
tennis[c("country", "date_of_birth")]
```

```
## country date_of_birth
## Andy Murray Great Britain 15 May 1987
## Rafael Nadal Spain 3 June 1986
## Stan Wawrinka Switzerland 28 March 1985
## Novak Djokovic Serbia 22 May 1987
## Roger Federer Switzerland 8 August 1981
```

```
# Like a matrix
tennis[, c("country", "date_of_birth")]
```

```
## country date_of_birth
## Andy Murray Great Britain 15 May 1987
## Rafael Nadal Spain 3 June 1986
## Stan Wawrinka Switzerland 28 March 1985
## Novak Djokovic Serbia 22 May 1987
## Roger Federer Switzerland 8 August 1981
```

We can extract column names of dataframes by using `names`

.

`names(tennis)`

```
## [1] "date_of_birth" "grand_slam_win" "country" "ATP_ranking"
## [5] "prize_money" "height" "hand"
```

```
# To access a single element, let say the date of birth,
# you can also use:
tennis$date_of_birth
```

```
## [1] 15 May 1987 3 June 1986 28 March 1985 22 May 1987 8 August 1981
## 5 Levels: 15 May 1987 22 May 1987 28 March 1985 ... 8 August 1981
```

We can change the ordering of the columns outputted by simply changing the order within the `c()`

subsetting.

```
# Note the difference between
tennis[, c(1, 3)]
```

```
## date_of_birth country
## Andy Murray 15 May 1987 Great Britain
## Rafael Nadal 3 June 1986 Spain
## Stan Wawrinka 28 March 1985 Switzerland
## Novak Djokovic 22 May 1987 Serbia
## Roger Federer 8 August 1981 Switzerland
```

```
# and
tennis[, c(3, 1)]
```

```
## country date_of_birth
## Andy Murray Great Britain 15 May 1987
## Rafael Nadal Spain 3 June 1986
## Stan Wawrinka Switzerland 28 March 1985
## Novak Djokovic Serbia 22 May 1987
## Roger Federer Switzerland 8 August 1981
```

Let us say we want only want to know right-handed players. We can subset `rows`

using similar syntax as above.

```
# We will talk more about == in the next chapter
# This represents all players with hand equal to "R"
tennis[hand=="R",]
```

```
## date_of_birth grand_slam_win country ATP_ranking
## Andy Murray 15 May 1987 9 Great Britain 1
## Stan Wawrinka 28 March 1985 5 Switzerland 3
## Novak Djokovic 22 May 1987 12 Serbia 4
## Roger Federer 8 August 1981 18 Switzerland 5
## prize_money height hand
## Andy Murray 60449649 1.90 R
## Stan Wawrinka 30577981 1.83 R
## Novak Djokovic 109447408 1.88 R
## Roger Federer 104445185 1.85 R
```

```
# Note what this returns
hand=="R"
```

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

Often data are better view when it is sorted. The function `order`

helps do this, which can be utilized with subsetting to output a custom sorted dataframe based on a column(s).

Below we will order the tennis players by the number of grand slam wins. Set `decreasing=TRUE`

to order the rows in descreasing order.

```
# increasing order
tennis[order(tennis[,"grand_slam_win"]),]
```

```
## date_of_birth grand_slam_win country ATP_ranking
## Stan Wawrinka 28 March 1985 5 Switzerland 3
## Andy Murray 15 May 1987 9 Great Britain 1
## Novak Djokovic 22 May 1987 12 Serbia 4
## Rafael Nadal 3 June 1986 15 Spain 2
## Roger Federer 8 August 1981 18 Switzerland 5
## prize_money height hand
## Stan Wawrinka 30577981 1.83 R
## Andy Murray 60449649 1.90 R
## Novak Djokovic 109447408 1.88 R
## Rafael Nadal 85920132 1.85 L
## Roger Federer 104445185 1.85 R
```

```
# descreasing order
tennis[order(tennis[,"grand_slam_win"], decreasing = TRUE),]
```

```
## date_of_birth grand_slam_win country ATP_ranking
## Roger Federer 8 August 1981 18 Switzerland 5
## Rafael Nadal 3 June 1986 15 Spain 2
## Novak Djokovic 22 May 1987 12 Serbia 4
## Andy Murray 15 May 1987 9 Great Britain 1
## Stan Wawrinka 28 March 1985 5 Switzerland 3
## prize_money height hand
## Roger Federer 104445185 1.85 R
## Rafael Nadal 85920132 1.85 L
## Novak Djokovic 109447408 1.88 R
## Andy Murray 60449649 1.90 R
## Stan Wawrinka 30577981 1.83 R
```

### 4.5.3 Example: Maps

Dataframe objects can be optimized for different applications, as many packages use dataframes as input or output objects. For example, we will use a package, `ggmap`

to map simple cities. We will talk more about the functionality of the package later. We can extract the latitude - longitude information of the specific cities of the tennis players using `geocode`

within `ggmap`

.

```
library(ggmap)
birth_place <- c("Glasgow, Scotland", "Manacor, Spain", "Lausanne, Switzerland", "Belgrade, Serbia", "Basel, Switzerland")
birth_coord <- geocode(birth_place, source = "google")
```

Furthermore, we can create new columns of dataframes using vectors that we created before.

```
birth_coord$Players <- players
birth_coord$GS <- grand_slam_win
birth_coord
```

```
## # A tibble: 5 x 4
## lon lat Players GS
## <dbl> <dbl> <chr> <dbl>
## 1 -4.25 55.9 Andy Murray 9
## 2 3.21 39.6 Rafael Nadal 15
## 3 6.63 46.5 Stan Wawrinka 5
## 4 20.4 44.8 Novak Djokovic 12
## 5 7.59 47.6 Roger Federer 18
```

`is.data.frame(birth_coord)`

`## [1] TRUE`

Let’s represent this information graphically. We haven’t seen how to make graph yet so don’t worry too much about the details of how this graph is made.

```
library(mapproj)
map <- get_map(location = 'Switzerland', zoom = 4)
ggmap(map) + geom_point(data = birth_coord,
aes(lon, lat, col = Players, size = GS)) +
scale_size(name="Grand Slam Wins") +
xlab("Longitude") + ylab("Latitude")
```

As we see, we can produce various output using dataframe objects.

### A Basic Probability and Statistics with `R`

Wickham, Hadley. 2014a. *Advanced R*. CRC Press. http://adv-r.had.co.nz/.