Creating and manipulating objects, and extending R using packages

.title[
# Creating and manipulating objects, and extending R using packages
]
.subtitle[
## Learning the basics of R - Part 2
]
.author[
### Ernest Guevarra
]
.date[
### 25 October 2024
]

---

# Outline

1. Base functions in R

* What is a function?    
    * Basic function syntax
    
2. Extending R using packages

* What are packages?    
    * How to install packages
    * Loading packages to the environment
    
3. Accessing and reading data into R

---

# What is a function?

* A set of statements organized together to perform a specific task.

* R has a large number of in-built functions.

* In R, a function is an object so the R interpreter is able to pass control to the function, along with arguments that may be necessary for the function to accomplish the actions.

* The function in turn performs its task and returns control to the interpreter as well as any result which may be stored in other objects.

---

# Functions in R

## Base functions

* Term we use for built-in functions in R.

* These functions cover a wide range of purposes, use cases, and applications one of which is for statistical analysis (probably the most common built-in functions in R)

* Everything we do in R is almost always mediated/made possible by using functions

## Basic function syntax

``` r
function_name(argument1, argument2, ...)
```

---

# Using functions - accessing R builtin dataset

.pull-left[
* First let us use some sample/toy data. R has built-in datasets for teaching/testing purposes. We will continue on the BMI theme from yesterday by accessing the `women` built-in dataset in R. This dataset is of average height (inches) and weight (lbs) of women age 30-39 years old.

* We access this data using the `data()` function as follows:

``` r
data("women")
```
]

``` r
women
```

```
##    height weight
## 1      58    115
## 2      59    117
## 3      60    120
## 4      61    123
## 5      62    126
## 6      63    129
## 7      64    132
## 8      65    135
## 9      66    139
## 10     67    142
## 11     68    146
## 12     69    150
## 13     70    154
## 14     71    159
## 15     72    164
```
]

---

# Using functions - exploring data structure

* Being able to understand the **data structure** of a dataset helps us make good decisions on how to work with data or how to analyse data.

* There are several R functions that gives us the characteristics and structure of a dataset such as:

* The shape of the data    
    * The number of records in the data    
    * The variables of the data    
    * The number of variables in the data    
    * The values of variables in the data

---

# Using functions - describing the shape of the data

* We use the `class()` function to know the **class** attribute of an R object.

* Knowing the **class** of an R object give us information on what kind of object it is and how we can work with it in R

## Task:

* Using the `women` dataset that we just loaded, apply the `class()` function:

``` r
## Get class of women dataset
class(women)
```

```
## [1] "data.frame"
```

---

# Using functions - number of records in the data

* We often need to know how many records are in the dataset that we are working on.

* This is useful for various statistical analysis that we perform on data.

* The function `nrow()` gives us the number of rows of a `data.frame` R object

## Task:

* Using the `women` dataset, apply the `nrow()` function to get the number of rows:

``` r
## Get number of rows of women dataset
nrow(women)
```

```
## [1] 15
```

---

# Using functions - number of records in the data

## Bonus question:

* How many columns does the `women` dataset have?

``` r
ncol(women)
```

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

---

# Using functions - variable names of a dataset

* We often need to know the variables of the dataset that we are working on.

* This is useful for various statistical analysis that we perform on data.

* The function `names()` gives us the variable names of a `data.frame` R object

## Task:

* Using the `women` dataset, apply the `names()` function to get the variable names:

``` r
## Get variable names of women dataset
names(women)
```

```
## [1] "height" "weight"
```

---

# Using functions - variable names of a dataset

## Bonus questions:

* Can you describe the shape and structure of the output of `names(women)`?

``` r
## Get class of variable names of women dataset
class(names(women))
```

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

* Can you get how *LONG* (how many variable names) the output of `names(women)` is?

``` r
## Get length of the variable names of women dataset
length(names(women))
```

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

---

# Using functions - describing the structure of a dataset

* Another approach to get a full description of the structure of a dataset object in R is by using the `str` function

``` r
str(women)
```

```
## 'data.frame':	15 obs. of  2 variables:
##  $ height: num  58 59 60 61 62 63 64 65 66 67 ...
##  $ weight: num  115 117 120 123 126 129 132 135 139 142 ...
```

* The output of using `str()` function is comprehensive.

* It gives us the class of the object    
    * It gives us the number of records/observations    
    * It gives us the number of variables    
    * It gives us the names of the variables    
    * It gives us the class of each of the variables
    * It gives us a glimpse of the values of each of the variables
    
---

# Using functions - accessing the variables of a dataset

* When working with `data.frame` objects, we often need to use/access only a specific variable in that `data.frame` object

* Knowing how to access a specific variable in a `data.frame` object is one of the most important skill in R

* There are several ways to access a specific variable in a `data.frame` object

---

# Using functions - accessing the variables of a dataset

## Using the `$` operator

* Access the **`height`** variable using the **`$`** operator

``` r
women$height
```

```
##  [1] 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
```

* Now try to access the **`weight`** variable using the **`$`** operator

``` r
women$weight
```

```
##  [1] 115 117 120 123 126 129 132 135 139 142 146 150 154 159 164
```

---

# Using functions - accessing the variables of a dataset

## Using the indexing method - `[ ]`

* Access the **`height`** variable using **`[ ]`**

``` r
women[ , "height"]
```

```
##  [1] 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
```

``` r
women[ , 1]
```

```
##  [1] 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
```

``` r
women[[1]]
```

```
##  [1] 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
```

---

# Using functions - accessing the variables of a dataset

## Using the indexing method - `[ ]`

* Now try to access the **`weight`** variable using **`[ ]`**

``` r
women[ , "weight"]
```

```
##  [1] 115 117 120 123 126 129 132 135 139 142 146 150 154 159 164
```

``` r
women[ , 2]
```

```
##  [1] 115 117 120 123 126 129 132 135 139 142 146 150 154 159 164
```

``` r
women[[2]]
```

```
##  [1] 115 117 120 123 126 129 132 135 139 142 146 150 154 159 164
```

---

# Using functions - accessing other values of a dataset

## Bonus question:

* Access the **`height`** value for the **third** row/record of the dataset

``` r
women[3, "height"]
```

```
## [1] 60
```

``` r
women[ , "height"][3]
```

```
## [1] 60
```

---

# Using functions - accessing other values of a dataset

## Bonus question:

* Access the **`height`** value for the **third** row/record of the dataset

``` r
women[3, ]["height"]
```

```
##   height
## 3     60
```

``` r
women[3, ][["height"]]
```

```
## [1] 60
```

``` r
women$height[3]
```

```
## [1] 60
```

---

# Using functions - some basic statistical functions

<table class="table table-striped" style="width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Function </th>
   <th style="text-align:left;"> Description </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> mean() </td>
   <td style="text-align:left;"> Get the mean value of a set of numbers </td>
  </tr>
  <tr>
   <td style="text-align:left;"> median() </td>
   <td style="text-align:left;"> Get the median value of a set of numbers </td>
  </tr>
  <tr>
   <td style="text-align:left;"> var() </td>
   <td style="text-align:left;"> Get the estimated variance of the population from which you sampled </td>
  </tr>
  <tr>
   <td style="text-align:left;"> sd() </td>
   <td style="text-align:left;"> Get the standard deviation of the population from which you sampled </td>
  </tr>
  <tr>
   <td style="text-align:left;"> scale() </td>
   <td style="text-align:left;"> Get the z-scores for a set of numbers </td>
  </tr>
</tbody>
</table>

---

# Using functions - application of some basic statistical functions

## 1. Get the mean height in the `women` dataset

## 2. Get the median weight in the `women` dataset

``` r
mean(women$height)
```

```
## [1] 65
```

``` r
median(women$weight)
```

```
## [1] 135
```

---

# Extending R using packages

* There are times that we need functions that are not built-in to R but are available through external **R packages**

* **R packages** are collections of functions and data sets developed by the community.

* **R packages** increase the power of R by improving existing base R functionalities, or by adding new ones.

* For this project, majority of the statistical tools/functions we need are already built-in to R.

* However, most of the tools we need for data access and loading, data manipulation, data processing, creating reports, reproducibility, and automation will require us to extend R using these additional **R packages**

---

# Extending R using packages

* We usually have our data in different files and these files can be in different file formats.

* Depending on the file format of your data, different functions are used to read these files into R.

* Base (built-in) functions in R have a limited types of data that it can read.

* We often need to install additional **R packages** to read other types of data e.g., `.XLSX`, `.dta`, `.sav`, etc.

---

# Extending R using packages

* Using `read.table()` base function in R to read a `text` type of data file such as a *comma-separated value* or `CSV` file:

``` r
read.table(
  file = "data/women.csv", 
  header = TRUE, sep = ","
)
```
]

---

# Extending R using packages

* Using `read.csv()` base function in R to read a `text` type of data file such as a *comma-separated value* or `CSV` file:

``` r
read.csv(file = "data/women.csv")
```
]

---

# Extending R using packages

* We should assign this data to an object. Let us call this object `women_csv`

``` r
women_csv <- read.csv("data/women.csv")
```
]

---

# Extending R using packages

* Using the R package `openxlsx` to read a Microsoft Excel or `.XLSX` type of data file

* We first need to install the `openxlsx` package

``` r
install.packages("openxlsx")
```

* We then need to load the package into the current working environment. We use the `library()` function for this:

``` r
library("openxlsx")
```

---

# Extending R using packages

* Using the R package `openxlsx` to read a Microsoft Exel or `.XLSX` type of data file

* We are now ready to use the function `read.xlsx()` from the `openxlsx` package to read the `women.xlsx` file:

``` r
read.xlsx(
  xlsxFile = "data/women.xlsx",
  sheet = 1
)
```
]

---

# Extending R using packages

* We should assign this data to an object. Let us call this object `women_xlsx`

``` r
women_xlsx <- read.xlsx(
  xlsxFile = "data/women.xlsx",
  sheet = 1
)
```
]

``` r
women_xlsx
```

---

# Coding challenge

Check your email and look for the message from GitHub Classroom with a link to your next coding exercise.

---

# Questions?

---

# Thank you!

Slides can be viewed at https://oxford-ihtm.io/open-reproducible-science/session3.html

PDF version of slides can be downloaded at https://oxford-ihtm.io/open-reproducible-science/pdf/session3-r-basics-part2.pdf

R scripts for slides available [here](https://github.com/OxfordIHTM/open-reproducible-science/blob/main/session3.Rmd)