session3.Rmd

---
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'
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```{r setup, include=FALSE}
options(htmltools.dir.version = FALSE)
knitr::opts_chunk$set(
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if (!require(remotes)) install.packages("remotes")
if (!require(fontawesome)) remotes::install_github("rstudio/fontawesome")
```

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library(xaringanthemer)
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# 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, echo = TRUE, eval = FALSE}
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, echo = TRUE}
data("women")
```
]

.pull-right[
```{r, echo = TRUE}
women
```
]

---

# 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, echo = TRUE}
## Get class of women dataset
class(women)
```

---

# 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, echo = TRUE}
## Get number of rows of women dataset
nrow(women)
```

---

# Using functions - number of records in the data

## Bonus question:

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

--

```{r, echo = TRUE}
ncol(women)
```

---

# 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, echo = TRUE}
## Get variable names of women dataset
names(women)
```

---

# Using functions - variable names of a dataset

## Bonus questions:

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

--

```{r, echo = TRUE}
## Get class of variable names of women dataset
class(names(women))
```

--

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

--

```{r, echo = TRUE}
## Get length of the variable names of women dataset
length(names(women))
```

---

# 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, echo = TRUE}
str(women)
```

--

* 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, echo = TRUE}
women$height
```

--

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

--

```{r, echo = TRUE}
women$weight
```

---

# Using functions - accessing the variables of a dataset

## Using the indexing method - `[ ]`

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

```{r, echo = TRUE}
women[ , "height"]

women[ , 1]

women[[1]]
```

---

# Using functions - accessing the variables of a dataset

## Using the indexing method - `[ ]`

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

--

```{r, echo = TRUE}
women[ , "weight"]

women[ , 2]

women[[2]]
```

---

# Using functions - accessing other values of a dataset

## Bonus question:

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

--

```{r, echo = TRUE}
women[3, "height"]
```

--

```{r, echo = TRUE}
women[ , "height"][3]
```

---

# Using functions - accessing other values of a dataset

## Bonus question:

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

```{r, echo = TRUE}
women[3, ]["height"]
```

--

```{r, echo = TRUE}
women[3, ][["height"]]
```

--

```{r, echo = TRUE}
women$height[3]
```

---

# Using functions - some basic statistical functions

```{r, echo = FALSE}
function_name <- c("mean()", "median()", "var()", "sd()", "scale()")
function_description <- c(
  "Get the mean value of a set of numbers",
  "Get the median value of a set of numbers",
  "Get the estimated variance of the population from which you sampled",
  "Get the standard deviation of the population from which you sampled",
  "Get the z-scores for a set of numbers"
)

data.frame(function_name, function_description) |>
  knitr::kable(
    row.names = FALSE,
    col.names = c("Function", "Description")
  ) |>
  kableExtra::kable_styling(
    bootstrap_options = "striped",
    full_width = FALSE,
    position = "center"
  )
```

---

# 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, echo = TRUE}
mean(women$height)
```

```{r, echo = TRUE}
median(women$weight)
```

---

# 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:

.pull-left[
```{r, echo = TRUE, eval = FALSE}
read.table(
  file = "data/women.csv", 
  header = TRUE, sep = ","
)
```
]

.pull-right[
```{r, echo = FALSE, eval = TRUE}
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:

.pull-left[
```{r, echo = TRUE, eval = FALSE}
read.csv(file = "data/women.csv")
```
]

.pull-right[
```{r, echo = FALSE, eval = TRUE}
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`

.pull-left[
```{r, echo = TRUE, eval = TRUE}
women_csv <- read.csv("data/women.csv")
```
]

.pull-right[
```{r, echo = FALSE, eval = TRUE}
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, echo = TRUE, eval = FALSE}
install.packages("openxlsx")
```

--

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

```{r, echo = TRUE}
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:

.pull-left[
```{r, echo = TRUE, eval = FALSE}
read.xlsx(
  xlsxFile = "data/women.xlsx",
  sheet = 1
)
```
]

.pull-right[
```{r, echo = FALSE, eval = TRUE}
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`

.pull-left[
```{r, echo = TRUE, eval = TRUE}
women_xlsx <- read.xlsx(
  xlsxFile = "data/women.xlsx",
  sheet = 1
)
```
]

.pull-right[
```{r, echo = TRUE}
women_xlsx
```
]

---

class: inverse, center, middle

# Coding challenge

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

---

class: inverse, center, middle

# Questions?

---

class: inverse, center, middle

# 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)