1-10_Indexing_Subsetting_answers.qmd

# Indexing and Subsetting - 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

## Indexing and Subsetting

Sometimes we want to obtain a subset of the data sets before investigating the descriptive statistics and performing the statistical analysis.

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

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

Using the **heart** data set:\

-   Select the first row.\
-   Select the first column.\
-   Select the column `surgery`.

#### Solution 1

```{r ind1-solution, solution = TRUE}
heart[1, ]
heart[, 1]
heart["surgery"]
heart[["surgery"]]
heart[, "surgery"]
```
:::

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

Create a matrix that takes the values 1:4 and has 2 rows and 2 columns. You can name this object `mat`. Select the second row of all columns.

#### Solution 2

```{r ind2-solution, solution = TRUE}
mat <- matrix(1:4, 2, 2)
mat[2, ]
```
:::

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

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. Give the name `ar1` to the this array. Select the 2nd row of all columns from each matrix.

#### Solution 3

```{r ind3-solution, solution = TRUE}
ar1 <- array(data = 1:8, dim = c(2, 2, 2))
ar1[2, , ]
```
:::

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

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

Using the **retinopathy** data set:\

-   Select the `futime` for all `adult` patients.\
-   Select all the variables for patients that received treatment.\

#### Solution 1

```{r sub1-solution, solution = TRUE}
retinopathy$futime[retinopathy$type == "adult"]
retinopathy[retinopathy$trt == 1, ]
```
:::

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

Using the **retinopathy** data set:\

-   Select the `age` for patients that have `futime` more than 20.\
-   Select the `age` for patients that have `futime` more than 20 and are adults.\
-   Select patients that have no missing values in `age`.

#### Solution 2

```{r sub2-solution, solution = TRUE}
retinopathy$age[retinopathy$futime > 20]
retinopathy$age[retinopathy$futime > 20 & retinopathy$type == "adult"]
retinopathy[!is.na(retinopathy$age), ]
```
:::

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

Using the **retinopathy** data set:\

-   Select only the rows of the left eye.
-   Select only the rows of adult patients.

#### Solution 3

```{r sub3-solution, solution = TRUE}
retinopathy[retinopathy$eye == "left", ]
retinopathy[retinopathy$type == "adult", ]
```
:::