1-7_Common_Objects_answers.qmd

# Common Objects - Answers {.unnumbered}

```{r setup, include=FALSE}
htmltools::tagList(rmarkdown::html_dependency_font_awesome())
```

```{r, echo = FALSE, purl = FALSE}
knitr::knit_hooks$set(purl = knitr::hook_purl)

options(purl = FALSE)

knitr::opts_chunk$set(purl = FALSE)
```

```{r, include = FALSE}
knitr::opts_hooks$set(eval = function(opt) {
  if (any(opt$exercise))
    opt$eval <- opt$include <- FALSE
  
  opt
})


static <- TRUE

options(width = 100)

```

```{r packages, include = FALSE}
library(kableExtra)
library(knitr)
```

```{r load_data, context="data", include=FALSE}
library(survival)
```

## Preface {data-progressive="FALSE"}

Open Rstudio to do the practicals. Note that tasks with \* are optional.

### R packages

In this practical, a number of R packages are used. The packages used (with versions that were used to generate the solutions) are:

-   `survival` (version: `r packageVersion("survival")`)

*`r R.version.string`*

### Dataset {.tabset .tabset-fade .tabset-pills}

For this practical, we will use the **heart** and **retinopathy** data sets from the `survival` package. More details about the data sets can be found in:

https://stat.ethz.ch/R-manual/R-devel/library/survival/html/heart.html

https://stat.ethz.ch/R-manual/R-devel/library/survival/html/retinopathy.html

Always check your data.

::: {.panel-tabset .nav-pills}
#### Task 1

Explore the **heart** and **retinopathy** data sets - print the first six and last six rows.

#### Hint 1

Use the functions head(...) and tail(...) to investigate the data set. Replace the dots with the name of the data set.

#### Solution 1

```{r data-solution, solution = TRUE}
library(survival)
head(heart)
tail(heart)
head(retinopathy)
tail(retinopathy)
```
:::

## Common R Objects

It is important to distinguish the different object in R.

### Vectors {.tabset .tabset-fade .tabset-pills}

Let's investigate some vectors.

::: {.panel-tabset .nav-pills}
#### Task 1

View the vectors `event` and `age` from the **heart** data set.

#### *Hint 1*

Use the dollar sign to select the variables.

#### Solution 1

```{r vec-solution, solution = TRUE}
heart$event
heart$age
```
:::

::: {.panel-tabset .nav-pills}
#### Task 2

View the vectors `eye` and `risk` from the **retinopathy** data set.

#### *Hint 2*

Use the dollar sign to select the variables.

#### Solution 2

```{r vec2-solution, solution = TRUE}
retinopathy$eye
retinopathy$risk
```
:::

::: {.panel-tabset .nav-pills}
#### Task 3

Create a numerical vector that consists of the values: 34, 24, 19, 23, 16. Give the name `numbers` to this new vector.

#### *Hint 3*

Use the c(...) function. Replace the dots with the numbers.

#### Solution 3

```{r vec3-solution, solution = TRUE}
numbers <- c(34, 24, 19, 23, 16)
```
:::

::: {.panel-tabset .nav-pills}
#### Task 4

Create a numerical vector that takes the integer values from 1 until 200. Give the name `numbers_2` to this new vector.

#### *Hint 4*

Use the c(...) function. Replace the dots with the numbers.

#### Solution 4

```{r vec4-solution, solution = TRUE}
numbers_2 <- c(1:200)
```
:::

::: {.panel-tabset .nav-pills}
#### Task 5 \*

Create a categorical vector that consists of the values: yes, yes, no, no, no, yes. Give the name `treatment` to this new vector.

#### *Hint 5* \*

Use the c(...) function. Replace the dots with the categories.

#### Solution 5 \*

```{r vec5-solution, solution = TRUE}
treatment <- c("yes", "yes", "no", "no", "no", "yes")
```
:::

### Matrices and Data Frames {.tabset .tabset-fade .tabset-pills}

Let's investigate some matrices and data frames.

::: {.panel-tabset .nav-pills}
#### Task 1

Create a matrix using the vectors `id` and `age` from the **heart** data set. This matrix should have 2 columns where each column represents each variable.

#### *Hint 1*

Use the function matrix(...).

#### Solution 1

```{r mat-solution-1, solution = TRUE}
matrix(c(heart$id, heart$age), , 2) 
```
:::

::: {.panel-tabset .nav-pills}
#### Task 2

Create a data frame using the vectors `id`, `type` and `trt` from the **retinopathy** data set. This data frame should have 3 columns, where each column represents each variable.

#### *Hint 2*

Use the function data.frame(...).

#### Solution 2

```{r dfc-solution, solution = TRUE}
data.frame(id = retinopathy$id, type = retinopathy$type, trt = retinopathy$trt) 
```
:::

### Arrays {.tabset .tabset-fade .tabset-pills}

Let's investigate some arrays.

::: {.panel-tabset .nav-pills}
#### Task 1

Create an array that consists of 2 matrices. Matrix 1 will consist of the values 1:4 and matrix 2 will consist of the values 5:8. Both matrices will have 2 columns and 2 rows.

#### *Hint 1*

Use the function array(...).

#### Solution 1

```{r ar-solution-1, solution = TRUE}
array(data = 1:8, dim = c(2, 2, 2))
```
:::

::: {.panel-tabset .nav-pills}
#### Task 2 \*

Give the name `ar1` to the previous array. Furthermore, investigate the argument dimnames and change the names of the rows, columns and matrices.

#### *Hint 2* \*

Use the function array(...). Check the help page for the dimnames argument. Note that this must be in a list format.

#### Solution 2 \*

```{r ar-solution-2, solution = TRUE}
ar1 <- array(data = 1:8, dim = c(2, 2, 2), 
             dimnames = list(c("Row1", "Row2"), c("Col1", "Col2"), c("Mat1", "Mat2")))
```
:::

### Lists {.tabset .tabset-fade .tabset-pills}

Let's investigate some lists.

::: {.panel-tabset .nav-pills}
#### Task 1

Create a list using the vectors `stop` from the **heart** data set and `id`, `risk` from the **retinopathy** data set. Give the names `stop_heart`, `id_reti` and `risk_reti`.

#### *Hint 1*

Use the function list(...).

#### Solution 1

```{r list-solution, solution = TRUE}
list(stop_heart = heart$stop, id_reti = retinopathy$id, risk_reti = retinopathy$risk)
```
:::

::: {.panel-tabset .nav-pills}
#### Task 2 \*

Create a list using the vectors `numbers`, `numbers_2` and `treatment`. These variables can be taken from the exercise called `Vectors`. Give the names: `numbers`, `many_numbers` and `treatment`.

#### *Hint 2* \*

Use the function list(...).

#### Solution 2 \*

```{r list2-solution, solution = TRUE}
list(numbers = numbers, many_numbers = numbers_2, treatment = treatment)
```
:::