slide-script.knit

background-image: url(images/aims.jpg), url(images/unesco.jpg)
background-position: 50% 100%, 100% 0%
background-size: 60%, 30%

# Artificial Intelligence and Coding Training

## RStudio, R packages, and R project

### A typical data science workflow in R

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---
class: about-me-slide, inverse, middle, center

## About the trainer

### Murera Gisa

#### Data Scientist, ML Engineer

[<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"></path></svg> @myblog](https://mgisa.github.io/myblog)
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???

---
class: middle

.w-100.lh-copy[

Goal:

> The goal of this training is to expose trainees to different instructional strategies using latest emerging technologies such as Artificial Intelligence and Coding.

> Coding section: Equip with trainees the new Software (R) for implementing some of AI applications (Predictive Machine Learning and Natural Language Processing (ChatBot).

> Installing, Testing and scripting in R through the packages ecosystems built-in R

]

---
class: inverse, middle
name: toc

# Table of content

.w-100.lh-copy[

- [AI and Applications](#beg0)

- [R and RStudio](#beg1)

- [R packages and library](#beg2)

- [RStudio project](#beg3)

- [Reading and writing data in R](#beg4)

- [Coding and Cases Demonstration](#beg5)

- [Project1: Predictive Machine Learning](#beg6)

- [Project2: NLP Intelligent ChatBot](#beg7)

]

---

# AI and Applications

---

---

# Data, Tools and Techniques

---

.w-100.lh-copy[

Objectives:

> Understand the world data revolution and explosion,

> Understand Big Data, tools and Techniques,

> Navigate web browser, download and install R and Rstudio,

> Set a data analysis working directory and interactive project,

> Import data, clean it, aggregate and plot it,

> Learn how to use the grammar of graphics, literature programming, and Data tabulating (`tidyverse`).

]

---
class: middle

## World Data Explosion

.w-100.lh-copy[
The world data comes in various ways,types, shapes, forms and sizes.
]

.w-100.lh-copy[
In `\(2020\)`, the world data was estimated at `\(45\)` Zettabytes ( `\(45\)` Millions GBs).
]
--

.w-100.lh-copy[
The amount of data generated daily is expected to reach `\(465\)` exabytes ( `\(465\)` Billions GBs) by `\(2025\)`.
]

.w-100.lh-copy[

It will keep to increase and requires an extremely huge elastic cloud servers to store and save them.
]

.w-100.lh-copy[
Therefore AI and Data Science is going to be a relevant tech to serve the world with such huge data.
]

---

## Data Science

.w-100.lh-copy[
It is acknowledged that all sectors are bombarded by huge amount of data(__Big Data__).
]

.w-100.lh-copy[
That data means nothing till it will be analysed and interpreted to support policy making. 
]

.w-100.lh-copy[
This requires science, tools and techniques (Data Science) to deal with such big data to find:
* hidden patterns, 
* derive meaningful and insigthful information, and 
* make business and policy decisions.
  ]
  
--

.w-100.lh-copy[
The data scientist are at forefront of the latest tech revolution and find the hidden information from the big data.
]

.w-100.lh-copy[
* Example of data-driven projects: Chatbot, Sentiments and Emotions, Automatic data Reporting and dashboard,Robotics, MLOps, Etc...
]

---

## Data Science Skills

---
class: middle

## Tools and Software

.w-100.lh-copy[
There existing different tools and software to analyze the data
* Python
* R
* STATA
* SPSS
* Julia
* Others
]

.w-100.lh-copy[
Intentionally, in this training we be using learn and use R programming.
]

.w-100.lh-copy[
R is user-friendly and easy software tool to efficiently analyse any kind of data data
]

.w-100.lh-copy[
R is a free and open-source software developed by __Ross Ihaka__ and __Robert Gentleman__ in `\(1993\)`.
]

.w-100.lh-copy[
R is designed to perform a very complex statistical analysis and display results using visual graphics and tables.
]

.w-100.lh-copy[
The R programming is written in C, Fortran and R itself and allow the integration with Python,C,C++, and JavaScript.
]

---

## What is R programming

.w-100.lh-copy[
R is a statistical programming language for data cleaning, analysis, visualization, statistical modelling.
]

---
class: middle

## What about RStudio?

.w-100.lh-copy[
RStudio is an integrated development environment (IDE) for R
programming. 
]

.w-100.1h-copy[
R Studio makes programming easier and friendly in R.
]

---
class: middle

## Installation and Testing

* Download R [HERE](https://cran.r-project.org/bin/windows/base/)
--

* Download RStudio a.k.a Posit [HERE]( https://www.rstudio.com/products/rstudio/download/)
--

* Install both R and Rstudio by running the downloaded executable files from your PC Downloads folder.

---

# R packages and library

---

## R packages and library

---

.w-100.lh-copy[
A package is a collection of R functions that extends basic R functionality (`base::functions`).
]

.w-100.lh-copy[
A package can contain a set of functions relating to a specific topic or tasks.
]

.w-100.lh-copy[
For example, data wrangling packages include `tidyr`, `janitor`, etc.
]

.w-100.lh-copy[
The location where the packages are stored is called a **library**. If there is a particular package that you need, you can install the package from the Comprehensive R Archive Network (**CRAN**) by using:
]
--

```r
install.packages("pkg_name")
```
--

For example:

```r
install.packages("tidyverse")
```
--
.w-100.lh-copy[
Please note that the package name must be put on double quotes (**" "**) or a single quote (**' '**).
]

---

.w-100.lh-copy[
Other packages that are not yet on `CRAN` can also be installed from an external repository such as **GitHub** or **GitLab** by using `devtools` or `remotes` packages.
]

For example, package `fakir` is not yet on `CRAN`. 
--
 To install `fakir` from the `GitHub` repository,

--
use

```r
devtools::install_github("ThinkR-open/fakir")
```
--

```r
remotes::install_github("ThinkR-open/fakir")
```

.w-100.lh-copy[
You can also use `devtools` or `remotes` to install development version of a package. 
]

```r
remotes::install_github("datalorax/equatiomatic")
```

---
layout: false

## Import or load a package

.w-100.lh-copy[
Before you can use any installed package, you will need to import or load them by using the command:
]

```r
library(pkg_name)
```
--

.w-100.lh-copy[
which makes that package functions available for you in the R session or environment.
]

For example:

```r
library(tidyverse)

library(janitor)

library(ralger)
```

---
background-image: url(images/package.png)
background-size: contain
background-position: 60% 60%

### Think of R package as this:

.w-100.lh-copy[
You only need to install a package once, but you need to reload it every time you start a new session.
]
---
class: middle

## R Library

.w-100.lh-copy[
Library is a directory where the packages are stored. You can have multiple libraries on your hard disk.
]

To see which libraries are available (which paths are searched for packages), use:

```r
.libPaths()
```

```
[1] "C:/Users/jmurera/AppData/Local/Programs/R/R-4.2.0/library"
```
---
class: middle

## Remove installed packages

Remove installed packages/bundles and updates index information as
necessary.

```r
remove.packages("pkg_name")
```
---

## Use a function from an external package without loading it

.w-100.lh-copy[
There are two ways to make use of a function in a package. You can
load the package with `library(pkg_name)` and then use any of its `functions`. For example:

```r
library(install.load)

install_load(c("tidyverse", "janitor", "ralger"))
```

]

.w-100.lh-copy[
Or you can use the `::` operator to attach a function to a library i.e. `mypackage::myfunction()`. For example:

```r
install.load::install_load(c("tidyverse", "janitor", "ralger"))
```

]

.w-100.lh-copy[
It is often common to see people using `mypackage::myfunction()` so that the reader of a script can know which function belongs to a particular package.
]

---
class: middle

### Example 1

```r
library(janitor)

first_5_iris <- head(iris, 5)

clean_names(first_5_iris)
```

<table>
 <thead>
  <tr>
   <th style="text-align:right;"> sepal_length </th>
   <th style="text-align:right;"> sepal_width </th>
   <th style="text-align:right;"> petal_length </th>
   <th style="text-align:right;"> petal_width </th>
   <th style="text-align:left;"> species </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> 5.1 </td>
   <td style="text-align:right;"> 3.5 </td>
   <td style="text-align:right;"> 1.4 </td>
   <td style="text-align:right;"> 0.2 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 4.9 </td>
   <td style="text-align:right;"> 3.0 </td>
   <td style="text-align:right;"> 1.4 </td>
   <td style="text-align:right;"> 0.2 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 4.7 </td>
   <td style="text-align:right;"> 3.2 </td>
   <td style="text-align:right;"> 1.3 </td>
   <td style="text-align:right;"> 0.2 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 4.6 </td>
   <td style="text-align:right;"> 3.1 </td>
   <td style="text-align:right;"> 1.5 </td>
   <td style="text-align:right;"> 0.2 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 5.0 </td>
   <td style="text-align:right;"> 3.6 </td>
   <td style="text-align:right;"> 1.4 </td>
   <td style="text-align:right;"> 0.2 </td>
   <td style="text-align:left;"> setosa </td>
  </tr>
</tbody>
</table>

---
class: middle

### Example 2

```r
first_5_iris <- head(iris, 5)

janitor::clean_names(first_5_iris)
```

---

# RStudio project

.w-100.lh-copy[
Data Analysis Reproducibility with R and RStudio Project.
]

---
layout: true

## Where Does Your Analysis Live?

---

.w-100.lh-copy[
The working directory is where R looks for files that you ask it to load, and where it will put any files that you ask it to save.
]
--

RStudio shows your current working directory at the top of the console:

<img src="images/console.png" width="1563" />
--

<br>

and you can also print this out by using:

```r
getwd()
```

```
[1] "C:/Users/jmurera/Desktop/Downloads_docs/CONSULTANCY/MoE_PRES/UNESCO_TTP"
```
---
class: middle

.w-100.lh-copy[
If you have specific directory that you want to use as your working
directory, in `R` you can do that with the command `setwd()` e.g.
`setwd("/path/to/my/data_analysis")`
]
--

.w-100.lh-copy[
or by using the keyboard shortcut  `Ctrl+Shift+H` and choose that
specific directory (Folder).
]

---
layout: false

## Paths and Directories

- .w-100.lh-copy[**Absolute paths**: This looks different in every computer. In Windows they start with a drive letter (e.g., `C:`). In my R working directory I have C:/Users/jmurera/Desktop/Downloads_docs/CONSULTANCY/MoE_PRES/UNESCO_TTP as absolute path.
]
--

.w-100.lh-copy[
You should never use *absolute paths* in your scripts, because they hinder sharing and no one else will have exactly the same directory configuration as you.
]

- .w-100.lh-copy[**Relative paths**: With the help of function `here::here()` or `R project` or `getwd()`
we can have a relative path like `data/datafile.csv` that
allows for file sharing and collaboration.
]

---

## RStudio Projects
.w-100.lh-copy[
For a typical data science workflow, you should use Rstudio project.
R experts keep all the files associated with a project together—like data
folder, R scripts folder, analytical results folder, figures folder. This is such
a wise and common practice.
]

---

## Creating a new R project

Click `File → New Project`, then choose Existing Directory:

---

Browse for that specific directory (Folder).

---
class: middle

Hurray! We are in the `RStudio project`.

---
class: middle

<img src="images/rproj.png" width="2045" />
Henceforth, you will click `.Rproj` to open RStudio project.

---

# Reading and writing data in R

<img src="images/export.png" width="60%" height="50%" />
---
layout: true
## Reading and writing data in R

---
.w-100.lh-copy[
Creating a dataframe from scratch is so tedious. In the data science world, data will be available for you on a spreadsheet such as MS-Excel. Our job as a data scientist is to import those datasets into R using any data import packages such as `readr` (.csv), `readxl` (.xlsx), `haven` (.sav, .dta), `rio` (any data file format), or `ralger` (web data).
]

--
.pull-left[

<img src="images/import.png" width="100%" height="100%" />
]

.w-100.lh-copy[
Please note that `readr`, `readxl`, and `haven` are part of `tidyverse` set of packages. You can see all the packages in the tidyverse by using:

```r
tidyverse::tidyverse_packages()
```

]
]
---

- `readr` package:
  - `read_csv()` import a `.csv` file to R 
  
  - `write_csv()` export a dataframe as `.csv` file out of R

- `readxl` package:

- `read_xlsx()` import a `.xlsx` file to R

- `writexl` package:
  - `write_xlsx()` export a  dataframe as`.xlsx` file out of R

- `haven` package:

- `read_sav()` import a `.sav` file to R

- `write_sav()` export a dataframe as `.sav` file out of R 
  
  - `read_dta()` import a `.dta` file to R

- `write_dta()` export a dataframe as `.dta` file out of R  
  
---

- `rio` package
  
  - `import()` import any file format to R
  
  - `export()` export a dataframe as any file format out of R
  
For more information on `rio` package, please visit this [resource](https://www.rdocumentation.org/packages/rio/versions/0.5.26).

### RStudio Project

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To import and export data in R, we will make use of the `RStudio project`, to automatically set up the working directory, and utilize the relative path for the data file path.
]

---
layout: false

### Lab session

<img src="images/lab.png" width="2320" />
---
layout: false
class: middle

## Summary

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Data science workflow can be done in Rstudio project. This enables you to organize your files i.e. keep data files, the script, save the outputs and by using only relative path.
]
--

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Everything you need is in one place, and cleanly separated from all other projects you are working on.
]

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You can comfortably install any R packages be it on the `CRAN` or `GitHub` and load them to the R environment.
]

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Now, you can import any file format to R and also export it out.
]

---
class: center, middle, inverse
name: beg5

# Coding and Cases Demonstration

---
class: middle

.w-100.lh-copy[

Objectives:

> Predictive ML scripting (`randomforest`),

> Intelligent ML output interpretation and Reporting (`modelStudio`),

> ML output saving and management (`modelDown`)

]

---
class: center, middle

### Project1: Predictive Machine Learning

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We will be predicting the universal desire for happiness 
]

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Using the data sourced from world happiness report
]

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Identify and quantify the factors affecting the well-being and happiness
]

---

### GROUP DISCUSSION
 
__Why are some people happier than others, even if they live in the same country under more or less similar circumstances??__

---
class: center, inverse, middle

## ML meaning and types

---

## Inserting the side R ML Project (AI modelStudio)

---
class: center, middle, inverse

## Project2: Natural Language Processing (CHATBOT)

---

.w-100.lh-copy[

Objectives:

> Understand NLP and its philosophy,

> Applications of NLP,

> Scripting a __CHATBOT__ and chatting with such intelligent Bot.

]

---

### What is NLP or Computation Linguistics??

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A sub-field of AI, Machine Learning and linguistics,
]

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It supports computers understand, manipulate, and interpret human language,
]

.w-100.lh-copy[
The ability of a computer program to understand human language in different forms (written or spoken).
]

---
class:center

## What NLP Used For??

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Text Classification: Assigning tags to texts to put them in categories (sentiment analysis). Hence predict what user will text or say in future.
]

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Text Mining and Extraction: it involves sentiments, emotions and perceptions analysis
]

--
.w-100.lh-copy[
Machine translation and Natural Language Generation
]

---
class: center, inverse, middle

## NLP and Applications

---
class: inverse, middle
name: beg6

# Inserting a side Project (Intelligent Bot)

---
class: center, middle, inverse

# The end

**Thank you**