R FOR BEGINNERS

background-image: url("https://images.unsplash.com/photo-1513890333407-6f85205e8ef2?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1770&q=80")
background-size: cover

# .big-text.black[ R FOR BEGINNERS]

---
class: middle

.pull-left[
<img src="images/ajaykoli.png" width="70%" style="display: block; margin: auto auto auto 0;" />

Dr Ajay Kumar Koli<br>Head & Educator<br>School of Information & Data Science<br><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="M320 336c0 8.84-7.16 16-16 16h-96c-8.84 0-16-7.16-16-16v-48H0v144c0 25.6 22.4 48 48 48h416c25.6 0 48-22.4 48-48V288H320v48zm144-208h-80V80c0-25.6-22.4-48-48-48H176c-25.6 0-48 22.4-48 48v48H48c-25.6 0-48 22.4-48 48v80h512v-80c0-25.6-22.4-48-48-48zm-144 0H192V96h128v32z"></path></svg> [Nalanda Academy](https://nalanda-academy.org/) - Wardha<br><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="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"></path></svg> [@ajay_kolii](https://twitter.com/ajay_kolii)<br><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="M464 64H48C21.49 64 0 85.49 0 112v288c0 26.51 21.49 48 48 48h416c26.51 0 48-21.49 48-48V112c0-26.51-21.49-48-48-48zm0 48v40.805c-22.422 18.259-58.168 46.651-134.587 106.49-16.841 13.247-50.201 45.072-73.413 44.701-23.208.375-56.579-31.459-73.413-44.701C106.18 199.465 70.425 171.067 48 152.805V112h416zM48 400V214.398c22.914 18.251 55.409 43.862 104.938 82.646 21.857 17.205 60.134 55.186 103.062 54.955 42.717.231 80.509-37.199 103.053-54.947 49.528-38.783 82.032-64.401 104.947-82.653V400H48z"></path></svg> [koliajaykumar@gmail.com](koliajaykumar@gmail.com)
<br><svg viewBox="0 0 496 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M336.5 160C322 70.7 287.8 8 248 8s-74 62.7-88.5 152h177zM152 256c0 22.2 1.2 43.5 3.3 64h185.3c2.1-20.5 3.3-41.8 3.3-64s-1.2-43.5-3.3-64H155.3c-2.1 20.5-3.3 41.8-3.3 64zm324.7-96c-28.6-67.9-86.5-120.4-158-141.6 24.4 33.8 41.2 84.7 50 141.6h108zM177.2 18.4C105.8 39.6 47.8 92.1 19.3 160h108c8.7-56.9 25.5-107.8 49.9-141.6zM487.4 192H372.7c2.1 21 3.3 42.5 3.3 64s-1.2 43-3.3 64h114.6c5.5-20.5 8.6-41.8 8.6-64s-3.1-43.5-8.5-64zM120 256c0-21.5 1.2-43 3.3-64H8.6C3.2 212.5 0 233.8 0 256s3.2 43.5 8.6 64h114.6c-2-21-3.2-42.5-3.2-64zm39.5 96c14.5 89.3 48.7 152 88.5 152s74-62.7 88.5-152h-177zm159.3 141.6c71.4-21.2 129.4-73.7 158-141.6h-108c-8.8 56.9-25.6 107.8-50 141.6zM19.3 352c28.6 67.9 86.5 120.4 158 141.6-24.4-33.8-41.2-84.7-50-141.6h-108z"></path></svg> [https://koliajay.netlify.app/](https://koliajay.netlify.app/)
]

]

---

# Modules:

1. .b[[Basics of R & RStudio](#module1)]

1. [Dynamic Documents using R Markdown](#module2)

1. [Data Visualisation using ggplot2](#module3)

1. [Data Wrangling using dplyr](#module4)

1. [Slide Crafting using xaringan](#module5)

---
name: module1

background-image: url("https://images.unsplash.com/photo-1513949474464-08a0bdb4fa54?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1770&q=80")
background-size: cover

# .white.big-text[R  and<br>RStudio:<br>Basics] 
## .white.black[Session - 1]

---

---

background-image: url("https://github.com/allisonhorst/stats-illustrations/raw/master/rstats-artwork/r_first_then.png")
background-size: contain

---

# 😍 R is FREE

- R is a language and environment for statistical computing and graphics. (R project)

- In August 1993, designed by

.pull-left[
<div class="figure" style="text-align: center">
<img src="https://cdn.auckland.ac.nz/aem/content/auckland/en/science/about-the-faculty/department-of-statistics/ihaka-lecture-series/_jcr_content/leftpar/imagecomponent/image.img.480.low.jpg/1561079330278.jpg" alt="Ross Ihaka &lt;br&gt;(New Zealand Statistician)" width="80%" />
<p class="caption">Ross Ihaka <br>(New Zealand Statistician)</p>
</div>

]

.pull-right[
<div class="figure" style="text-align: center">
<img src="https://www.computerhope.com/people/pictures/robert_gentleman.jpg" alt="Robert Gentleman&lt;br&gt;(Canadian Statistician)" width="50%" />
<p class="caption">Robert Gentleman<br>(Canadian Statistician)</p>
</div>

]

---

# Download R from [CRAN](https://cran.r-project.org/mirrors.html)

---

##- R version

##- R name

##- R licence

##- prompt `>`
]

<img src="images/console.png" width="100%" style="display: block; margin: auto auto auto 0;" />
]

---

# .red[Never Save R "Workspace Image":]

- "put more trust in your script than in your memory"

]

<img src="images/quit.png" width="75%" style="display: block; margin: auto;" />
]

---

# .center[R as a BIG calc]

### .center[What you code]

```r
*1 
```

]

### .center[What you see]

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

]

---

# .center[R as a BIG calc]

### .center[What you code]

```r
1
*1 + 1
```

]

### .center[What you see]

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

]

---

# .center[R as a BIG calc]

### .center[What you code]

```r
1
1 + 1
*34 / 40
```

]

### .center[What you see]

```
## [1] 1
## [1] 2
## [1] 0.85
```

]

---

# .center[R as a BIG calc]

### .center[What you code]

```r
1
1 + 1
34 / 40
*5 < 4
```

]

### .center[What you see]

```
## [1] 1
## [1] 2
## [1] 0.85
## [1] FALSE
```

]

---

# .center[R as a BIG calc]

### .center[What you code]

```r
1
1 + 1
34 / 40
5 < 4
*16 == 16
```

]

### .center[What you see]

```
## [1] 1
## [1] 2
## [1] 0.85
## [1] FALSE
## [1] TRUE
```

]

---

# .center[Plot using R]

```r
*plot(1:100)
```

]
  .panel[.panel-name[Output]

]
]

---

class: inverse, hide-count, center, bottom, hide-logo
background-image: url("images/rainbowr.gif")
background-size: 50%
background-position: 50% 5%

# .bottom.big-text[Functions]

---

# R Function

- "A function, in a programming environment, is a set of instructions. A programmer builds a function to avoid repeating the same task, or reduce complexity."

<br>

```r
*round(9.1565, 2)
```

]
  .panel[.panel-name[Output]

```
## [1] 9.16
```

]
]

---

class: center
background-image: url("images/function-args.png")
background-size: 90%
background-position: 60% 70%

# Structure of R function

---

# .center[Round Function]

```r
round(x = 564.56743, digits = 2)
```

]
  .panel[.panel-name[Output]

```
## [1] 564.57
```

]
]

---

# .center[Round Function]

```r
round(x = 564.56743, digits = 1)
```

]

```
## [1] 564.6
```

]
]

---

# .center[Square Root Function]

```r
*sqrt(x = 9)
```

]

```
## [1] 3
```

]
]

---

# .center[Sequence Function]

```r
*seq.int(from = 10, to = 30, by = 5)
```

]

```
## [1] 10 15 20 25 30
```

]
]

---

background-image: url("images/stormyr.gif")
background-size: 42%
background-position: 50% 5%

# .bottom.big-text[Comments]

---

# R Comment:

- "Humans will be able to read the comments, but your computer will pass over them."<sup>1</sup>

- In R, `#` is used as a commenting symbol

---

# .center[How to add comment?]

```r
# secret code of happiness

(1 + 4) - (3 * 5) / 10

## r does not evaluate this
# all these are comments
# comments are very important
```

]

```
## [1] 3.5
```
         
  ]
  
]

---

# 😼 *That's okay but you promise to...*

- combine plot, text, tables and images in a single file.

- publish my work online or convert into a word, pdf or html file.

- work efficiently with my different projects and save, share and track them.

## .orange[WE NEED A SUPERHERO ... ]

---
class: center, middle, hide-count, hide-logo

background-image: url("https://www.rstudio.com/wp-content/uploads/2018/10/RStudio-Logo-Flat.png")
background-size: contain

---

background-image: url("https://res-5.cloudinary.com/crunchbase-production/image/upload/c_thumb,h_256,w_256,f_auto,g_faces,z_0.7,q_auto:eco/v1398402407/chvmdsmomg96uoz8g3ad.jpg")
background-size: 20%
background-position: 99% 1%

# 😻 About RStudio:

- 2009, Joseph J. Allaire founded RStudio.

- 2011, RStudio IDE for R was launched.

- "RStudio is dedicated to sustainable investment in free and open-source software for data science." (16 Jan 2022)

---
class: center

# R `$\rightleftharpoons$` RStudio

# .big-text[🚗]
]

# .big-text[⚙️]
]

---

# RStudio IDE

---

### RStudio `$\rightarrow$` Tools `$\rightarrow$` Global Options

---

### RStudio `$\rightarrow$` Tools `$\rightarrow$` Global Options

---

background-color: #F5F4F2

---
class: center middle

# R Program

---
class: center middle

# Data Wrangling

---
class: center middle

# Exploratory Data Analysis

---
class: center middle

# Modeling

---
class: center middle

# Data Visualisation

---
class: center middle

# R Program

---

background-image: url("https://pbs.twimg.com/media/FJJ3qyAXwAQSI8s?format=jpg&name=medium")
background-size: 70%
background-position: 70%

.footnote[
[Source](https://www.business-science.io/learning-r/2018/11/04/data-science-r-cheatsheet.html)
]

---

background-image: url("images/space-pkgs.png")
background-position: 110%
background-size: contain

---

# R Packages:

- On 12 Jan 2022, 18698 R packages were available at [CRAN](https://cran.r-project.org/web/packages/)

- "An R package is a collection of functions, data, and documentation that extends the capabilities of base R. Using packages is key to the successful use of R."

- Top downloaded packages [source](https://www.r-pkg.org/downloaded)

---
class: center

# To Download pkgs

---
class: center

# Name of the R package(s)

---
class: center

# Installed R package(s)

---

# R Function to Download Package

```r
*install.packages("tidyverse")
```

# R Function to use Package

```r
*library(tidyverse)
```

---

# About R Packages:

- You need to install package only once like

- 📚 We buy books once and use them again and again

- 💡 Fix the bulb once and use it again and again

- In every R document you need to `call once` the package using function `library()`, for example library(ggplot2).

- Once in a while, you need to update the installed packages as well.

- If you un-install R or RStudio, you will lose all installed packages.

---

# Tools `$\rightarrow$` Check Package Updates

---
class: center

# Select Package(s) to Update

---
class: center

# Click Install Updates

---
class: center

# To Remove Package(s)

---

# 🧠 YOUR TURN

- Call R packages using console
  ]
  
  .panel[.panel-name[Answer]

```r
install.packages(c("xaringan", "palmerpenguins"))

library(xaringan)
library(palmerpenguins)
```
  
  
  
  ]

]

---

class: hide-count, center, middle, hide-logo
background-image: url("images/heartyr.gif")
background-size: contain
background-position: 5%

# .right.big-text[Objects]

---

# R Object

##- "Just a name that you can use to call up stored data"

---

# .center[Create Object]

```r
*salary <- c(20, 30, 40, 50, -10)
*salary
```

]

```
## [1]  20  30  40  50 -10
```
]

]

---

# .center[Create Object]

```r
name <- c("Ram", "Rani", "Ali", "Preeti", "John")
name
```
         
]
  
.panel[.panel-name[Output]

```
## [1] "Ram"    "Rani"   "Ali"    "Preeti"
## [5] "John"
```
]
  
]

---

# .center[Create Object]

```r
age <- c(34, 54, 23, 65, 2 )
age
```
      
  ]
  
  .panel[.panel-name[Output]

```
## [1] 34 54 23 65  2
```
         
  ]
  
]

---

# .center[Create Object]

```r
place <- c("ny", "ber", "dhl", "tko", "lon")
place
```
  ]
  
  .panel[.panel-name[Output]

```
## [1] "ny"  "ber" "dhl" "tko" "lon"
```
         
  ]
  
]

---

# .center[Create Object]

```r
books <- c(4, 0, 3, 24, 5)
books
```
         
         
  ]
  
  .panel[.panel-name[Output]

```
## [1]  4  0  3 24  5
```
         
  ]
  
]

---

# Guidelines to name `objects` in R:

- a name cannot start with a number  
  
--

- a name cannot use some special symbols, like ^, !, $, @, +, -, /, or *:  
  
--

- avoid caps  
  
--

- avoid space  
--

- use dash (like na-me) or underscore (like na_me)  
  
--

- if chronology matters then add date (2020-09-05-file-name)
  
---

# RStudio Environment Window

## 🤔.i.green[how to combine these<br> objects/variables into a data or say tidy data]

---
exclude: true

---
exclude: true

---
exclude: true

class: hide-count
background-image: url("images/tidydata_1_2.jpg")
background-size: contain

---
exclude: true

class: hide-count
background-image: url("images/tidydata_2_1.jpg")
background-size: contain

---
exclude: true

class: hide-count
background-image: url("images/tidydata_2_2.jpg")
background-size: contain

---

# Tidy data 👇 😻😻😻

```
##   age books   name place salary
## 1  34     4    Ram    ny     20
## 2  54     0   Rani   ber     30
## 3  23     3    Ali   dhl     40
## 4  65    24 Preeti   tko     50
## 5   2     5   John   lon    -10
```

---

# .center[How to create a data object?]

```r
*social <- data.frame(age, books, name, place, salary)

social

```
         
    ]
  
  .panel[.panel-name[Output]

```
##   age books   name place salary
## 1  34     4    Ram    ny     20
## 2  54     0   Rani   ber     30
## 3  23     3    Ali   dhl     40
## 4  65    24 Preeti   tko     50
## 5   2     5   John   lon    -10
```
         
  ]
  
]

---

# .center[How to export data as a csv file?]

```r
library(readr)
# to save this data set as a csv file
*write_csv(social, "data/social.csv")
```
         
    ]
  
  .panel[.panel-name[Output]

]
  
]

---

# 🧠 YOUR TURN

## .yellow[Write codes for below dataframe]

```
##         state pop    capital foundation
## 1     Germany  20     Berlin 1870-12-10
## 2      France  19      Paris 1789-07-14
## 3       India  50      Delhi 1947-08-15
## 4      Russia  25     Moscow 1990-06-12
## 5         USA  30 Washington 1776-07-04
## 6 New Zealand   5 Wellington 1840-02-06
```

]

```r
state <- c("Germany", "France", "India", "Russia", "USA", "New Zealand")
pop <- c(20, 19, 50, 25, 30, 5)
capital <- c("Berlin", "Paris", "Delhi", "Moscow", "Washington", "Wellington")
foundation <- c("1870-12-10", "1789-07-14", "1947-08-15", "1990-06-12", "1776-07-04", "1840-02-06")
world <- data.frame(state, pop, capital, foundation)
world
```

]

]
<div class="countdown" id="timer_6253f109" style="top:0;right:0;font-size:3em;" data-warnwhen="0">
<code class="countdown-time"><span class="countdown-digits minutes">10</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
</div>

---

# .center[Get a List of all Objects]

```r
# names of created objects
*objects()
```
         
         
  ]
  
  .panel[.panel-name[Output]

```
##  [1] "age"        "books"      "capital"   
##  [4] "foundation" "name"       "place"     
##  [7] "pop"        "praw"       "salary"    
## [10] "soc"        "social"     "state"     
## [13] "world"
```
         
  ]
  
]

---

background-image: url("images/breakr.gif")
background-size: 50%
background-position: 90%

---
class: center

# Using Console  `>`

## in console type `?your query`
--
<br><br>.green[for example] `?ggplot`

---

# RStudio: pkg Help Docs
  
<img src="images/help-cran.png" width="60%" style="display: block; margin: auto;" />

---
class: center

# [RStudio Community](https://community.rstudio.com/)
<br>
<img src="images/help-rstd.png" width="90%" style="display: block; margin: auto;" />

---
class: center

# [stackoverflow](https://stackoverflow.com/)
<br>
<img src="images/help-stack.png" width="90%" style="display: block; margin: auto;" />

---
class: center

# [GitHub Issues](https://github.com/)

---
class: center

# [Twitter](https://twitter.com/home) #rstats

---

#  🙋🏽‍♀️🙋‍♂️<br>.big-text[Q&A]

---

# Dynamic Documents<br>Using R Markdown
### .orange[Next Module - 2]

---

# Modules:

1. [Basics of R & RStudio](#module1)

1. .b[[Dynamic Documents using R Markdown](#module2)]

1. [Data Visualisation using ggplot2](#module3)

1. [Data Wrangling using dplyr](#module4)

1. [Slide Crafting using xaringan](#module5)

---
name: module2

background-image: url("https://images.unsplash.com/photo-1617063033111-74cf600531d8?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1776&q=80")

background-size: cover

# .white.big-text[Dynamic Documents]
## .white[Session - 2]

.footnote[
.white[Image credits:][John Anvik](https://images.unsplash.com/photo-1617063033111-74cf600531d8?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1776&q=80)
]

---

---

# Open RStudio

---

# Open RStudio

---

# Open RStudio

---

background-image: url("images/project-logo.png")
background-size: contain
background-position: 110%

# .big-text[RStudio<br>Project]

---

background-image: url("images/r-projects.jpg")
background-size: 62%
background-position: 100% 80%

# About RStudio Projects

- "to divide your work into multiple contexts, each with their own:

- working directory, 
  
--
  
  - workspace, 
  
--
  
  - history, and 
  
--
  
  - source documents."

[Source](https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects) & Artwork [Source](https://www.allisonhorst.com/)

]

---

# 🔥 Create RStudio Project in 4 Steps 🔥

---
class: center, middle

# Create RStudio Project in 4 Steps

---
class: center, middle

# Create RStudio Project in 4 Steps

---
class: center, middle

# Create RStudio Project in 4 Steps

---
class: center, middle

# Create RStudio Project in 4 Steps

---

# Open RStudio Project

---

# Open RStudio Project

---

# Open RStudio Project

---

background-image: url("images/logo-rmd.png")
background-size: contain
background-position: right

# .big-text[R<br>Package]

---

# About R Markdown:

- "You bring your data, code, and ideas, and R Markdown renders your content into a polished document that can be used to:

- Do data science interactively within the RStudio IDE,

- Reproduce your analyses,

- Collaborate and share code with others, and

- Communicate your results with others."

---

---
class: center, middle, hide-logo, hide-count

# 🔥How to create R Markdown file?🔥

---

# File `$\rightarrow$` New File `$\rightarrow$` R Markdown

---

# R Markdown

---

# R Markdown

---

# R Markdown

---

# R Markdown

---

# Save your .Rmd file

---

# Name your .Rmd file

---

# Name your .Rmd file

---

# Save your .Rmd file

---

# Saved .Rmd file `$\rightarrow$` in RStudio Project

---

background-image: url("images/script-to-console1.png")
background-size: 70%
background-position: center bottom

# Script file for writing the codes

---

background-image: url("images/script-to-console2.png")
background-size: 70%
background-position: center bottom

# Console to process the codes

---

background-image: url("images/script-to-console3.png")
background-size: 70%
background-position: center bottom

# R Script `$\rightarrow$` Console `$\rightarrow$` Output

---

background-image: url("images/script-to-console4.png")
background-size: 70%
background-position: center bottom

# R Script `$\rightarrow$` Console `$\rightarrow$` Output

---
class: center, middle

.pull-right[
<img src="images/rmd-output.png" width="100%" style="display: block; margin: auto;" />
]

---

background-image: url("images/knit-html.png")
background-size: contain
background-position: center middle

---

# R Markdown has 3 important parts:

##- .purple[YAML]

##- .orange[Code chunk]

##- .blue[Text]

---

background-image: url("images/temp-yaml.png")
background-size: cover
background-position: left

---

background-image: url("images/temp-chunk.png")
background-size: cover
background-position: left

---

background-image: url("images/temp-text.png")
background-size: cover
background-position: left

---

# 🧶 Knit a R Markdown File

- YAML options

- Headings, subheadings, text & maths equations

- Code Chunk

- Include images

- Include tables
  
  - Include plot

- Themes

- Multiple Output Formats
  
- [eBook](https://bookdown.org/yihui/rmarkdown-cookbook/)

---

#  🙋🏽‍♀️🙋‍♂️<br>.big-text[Q&A]

---

# Dynamic Visualisation<br>Using ggplot2
### .orange[Next Module - 3]

---

# Modules:

1. [Basics of R & RStudio](#module1)

1. [Dynamic Documents using R Markdown](#module2)

1. .b[[Data Visualisation using ggplot2](#module3)]

1. [Data Wrangling using dplyr](#module4)

1. [Slide Crafting using xaringan](#module5)

---

background-image: url("https://images.unsplash.com/photo-1617164924207-40e6ee7c3ffe?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1740&q=80")
background-size: cover

# .white.big-text[Data Visualisation]
## .white[Session - 3]

---

---

# Course Progress

---

background-image: url("images/not_normal.png")
background-size: 70%
background-position: right

# .big-text[Data]

---

# Variable types in R:

- `int` stands for integers, like 4, 55, 300.

- `dbl` stands for doubles, or real numbers like 3, 7.45, 1.565, 12.

- `chr` stands for character vectors, or strings like names.

- `dttm` stands for date-times (a date + a time).

- `lgl` stands for logical, vectors that contain only TRUE or FALSE.

- `fct` stands for factors, which R uses to represent **categorical variables** with fixed possible values like occupation: student, professional, government, business.

- `date` stands for dates.

---

background-image: url("images/culmen_depth.png")
background-size: 25%
background-position: 95% 5%

# Data of Palmer Penguins

- It comes with R package `palmerpenguins`

- Name of the data is `penguins`

- To know more about the data `?penguins`

- Included variables are:
  - species, island, bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, sex, year

---

# An Overview of Data

```r
*glimpse(penguins)
```

]

```
## Rows: 344
## Columns: 8
## $ species           <fct> Adelie, Adelie, …
## $ island            <fct> Torgersen, Torge…
## $ bill_length_mm    <dbl> 39.1, 39.5, 40.3…
## $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0…
## $ flipper_length_mm <int> 181, 186, 195, N…
## $ body_mass_g       <int> 3750, 3800, 3250…
## $ sex               <fct> male, female, fe…
## $ year              <int> 2007, 2007, 2007…
```

]
]

---

### An Overview of Data

```r
*summary(penguins)
```

]

```
##       species          island   
##  Adelie   :152   Biscoe   :168  
##  Chinstrap: 68   Dream    :124  
##  Gentoo   :124   Torgersen: 52  
##                                 
##                                 
##                                 
##                                 
##  bill_length_mm  bill_depth_mm  
##  Min.   :32.10   Min.   :13.10  
##  1st Qu.:39.23   1st Qu.:15.60  
##  Median :44.45   Median :17.30  
##  Mean   :43.92   Mean   :17.15  
##  3rd Qu.:48.50   3rd Qu.:18.70  
##  Max.   :59.60   Max.   :21.50  
##  NA's   :2       NA's   :2      
##  flipper_length_mm  body_mass_g  
##  Min.   :172.0     Min.   :2700  
##  1st Qu.:190.0     1st Qu.:3550  
##  Median :197.0     Median :4050  
##  Mean   :200.9     Mean   :4202  
##  3rd Qu.:213.0     3rd Qu.:4750  
##  Max.   :231.0     Max.   :6300  
##  NA's   :2         NA's   :2     
##      sex           year     
##  female:165   Min.   :2007  
##  male  :168   1st Qu.:2007  
##  NA's  : 11   Median :2008  
##               Mean   :2008  
##               3rd Qu.:2009  
##               Max.   :2009  
## 
```

]
]

---

# Packages required:

```r
library(palmerpenguins) # to access penguin data
library(tidyverse) # to use ggplot2 pkg
```

- Packages recommended:

```r
install.packages(c(
  "directlabels", "dplyr", "gameofthrones", "ggforce", "gghighlight", 
  "ggnewscale", "ggplot2", "ggraph", "ggrepel", "ggtext", "ggthemes", 
  "hexbin", "mapproj", "maps", "munsell", "ozmaps", "paletteer", 
  "patchwork", "rmapshaper", "scico", "seriation", "sf", "stars", 
  "tidygraph", "tidyr", "wesanderson" 
))
```

---

background-image: url("images/ggplot-logo.png")
background-size: contain
background-position: 100% 50%

# .big-text[R<br>Package]

---

# ggplot2 by [Hadley Wickham](http://hadley.nz/)
<br>
- "is a system for declaratively creating graphics, based on [The Grammar of Graphics](https://www.springer.com/gp/book/9780387245447)" (book by Late Leland Wilkinson)

.pull-left[
<div class="figure" style="text-align: center">
<img src="https://upload.wikimedia.org/wikipedia/en/b/b5/Leland_Wilkinson.png" alt="Late Leland Wilkinson" width="40%" />
<p class="caption">Late Leland Wilkinson</p>
</div>
]

.pull-right[
<div class="figure" style="text-align: center">
<img src="images/hadley.jpg" alt="Hadley Wickham" width="58%" />
<p class="caption">Hadley Wickham</p>
</div>
]

---
class: hide-count, hide-logo

background-image: url("images/layer7.png")
background-size: contain
background-position: 50% 50%

---

# Key Components for ggplot2 Plot

1. data,

1. aesthetic mapping

1. at least one layer of geom function

---

]

```r
*ggplot(data = penguins)
```

]

]
]

---

]

```r
*ggplot(data = penguins, mapping = aes(x = species))
```

]

]
]

---

]

```r
ggplot(data = penguins, mapping = aes(x = species)) +
* geom_bar()
```

]

]
]

---

```r
ggplot(penguins, aes(x = species)) +
  geom_bar() 
```

]

]
]

---

# 🧠 YOUR TURN

]

```r
ggplot(data = penguins, mapping = aes(x = island)) +
  geom_bar() 
```

]
]

---

# How to export plot to your computer?

---

```r
ggplot(data = penguins, mapping = aes(x = species)) +
  geom_bar()

*ggsave("peng-species.pdf")  # also try jpg/jpeg/png
```

]

```
## Saving 7 x 7 in image
```

]
]

---
class: center, middle

# How to add color to bars?

---

```r
ggplot(data = penguins, mapping = aes(x = species)) +
* geom_bar(fill = "blue")
```

]

]
]

---

```r
ggplot(data = penguins, mapping = aes(x = species)) +
* geom_bar(fill = c("orange", "white", "green"))

# color names should be equal to the factor levels
# in case of factor species levels are three
# Adele, Chinstrap & Gentoo
```

]

]
]

---

# How to add color using palette? 🎨

---

## 🎨 Color Palette

- R package `RColorBrewer` & `wesanderson`

---

```r
library(RColorBrewer)

ggplot(data = penguins,
       mapping = aes(x = species,
*                    fill = species)) +
  geom_bar() +
* scale_fill_brewer(palette = "Dark2")

```

]

]
]

---

# How to remove legend or change its position?

---

```r
ggplot(data = penguins,
       mapping = aes(x = species,
                     fill = species)) + 
  geom_bar() +
  scale_fill_brewer(palette = "Dark2") +
  theme(legend.position = "none") # top, bottom, left

```

]

]
]

---

# How to plot title and axis titles?

---

```r
ggplot(data = penguins,
       mapping = aes(x = species,
                     fill = species)) +
  geom_bar() +
  scale_fill_brewer(palette = "Dark2") +
  theme(legend.position = "none") +
  labs(
    title = "Species of palmer penguins",
    subtitle = "This data is about penguins",
    x = "Species",
    y = "Frequency"
  )

```

]

]
]

---

# How to control size of text?

---

```r
ggplot(data = penguins,
       mapping = aes(x = species,
                     fill = species)) +
  geom_bar() +
  scale_fill_brewer(palette = "Dark2") +
  theme(legend.position = "none",
*       text = element_text(size = 20)) +
  labs(
    title = "Species of palmer penguins",
    subtitle = "This data is about penguins",
    x = "Species",
    y = "Frequency"
  )

```

]

]
]

---

# How to plot two numeric variables?

---

```r
ggplot(data = penguins,
       mapping = aes(x = bill_length_mm,
                     y = bill_depth_mm,
                     color = species)) +
  geom_point() +
  scale_fill_brewer(palette = "Dark2") +
  theme(legend.position = "none",
*       text = element_text(size = 20)) +
  labs(
    title = "Relationship between bill length \n& depth of palmer penguins",
    subtitle = "This data is about penguins",
    x = "Bill length (mm)",
    y = "Bith depth (mm)"
  )

```

]

]
]

---

# How to add themes to ggplot?

---

# ggplot2 themes

<https://ggplot2.tidyverse.org/reference/ggtheme.html>

- theme_gray()

- theme_bw()

- theme_linedraw()

- theme_light()

- theme_dark()

- theme_minimal()

- theme_classic()

- theme_void()

- theme_test()

---

```r
ggplot(data = penguins,
       mapping = aes(x = bill_length_mm,
                     y = bill_depth_mm,
                     color = species)) +
  geom_point() +
  scale_fill_brewer(palette = "Dark2") +
  theme(legend.position = "none",
        text = element_text(size = 20)) + 
  labs(
    title = "Relationship between bill length \n& depth of palmer penguins",
    subtitle = "This data is about penguins",
    x = "Bill length (mm)",
    y = "Bith depth (mm)"
  ) +
* theme_bw()

```

]

]
]

---

```

]

]
]

---

# How to add regression line to ggplot?

---

```r
ggplot(data = penguins,
       mapping = aes(x = flipper_length_mm,
                     y = body_mass_g)) +
  geom_point() +
  theme(legend.position = "none",
        text = element_text(size = 24)) + 
  labs(
    title = "Relationship between bill length \n& depth of palmer penguins",
    subtitle = "This data is about penguins",
    x = "Flipper length (mm)",
    y = "Body mass (gm)"
  ) +
  theme_classic() +
* geom_smooth()

```

]

]
]

---

# More resources

- ggplot2 book https://ggplot2-book.org/

- CÉDRIC SCHERER https://www.cedricscherer.com/

- ggplot2 cook book http://www.cookbook-r.com/

---

#  🙋🏽‍♀️🙋‍♂️<br>.big-text[Q&A]

---

# Dynamic Wrangling<br>Using dplyr
### .orange[Next Module - 4]

---

# Modules:

1. [Basics of R & RStudio](#module1)

1. [Dynamic Documents using R Markdown](#module2)

1. [Data Visualisation using ggplot2](#module3)

1. .b[[Data Wrangling using dplyr](#module4)]

1. [Slide Crafting using xaringan](#module5)

---

background-image: url("https://images.unsplash.com/photo-1591903934817-c02acbfd4665?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1932&q=80")
background-size: cover

# .black.big-text[Data<br>Wrangling]
## .black[Session - 4]

---

---

# Course Progress

---

# What is Data wrangling?

- "data exploration and data manipulation" [(Jesse Mostipak)](https://www.kaggle.com/jessemostipak/dive-into-dplyr-tutorial-1)

- "tidying and transforming" [(Hadley & Garrett)](https://r4ds.had.co.nz/index.html)

---

# "Transforming" data means:

- "narrowing in on observations of interest ...

- creating new variables that are functions of existing variables ... and

- calculating a set of summary statistics."

---

class: hide-count, middle, hide-logo
background-image: url(images/dplyr.svg)
background-size: contain
background-position: right

# .big-text[R<br>Package]

---

# `dplyr` package

- "dplyr is a grammar of data manipulation"

- "providing a consistent set of verbs that help you solve the most common data manipulation challenges:"

- Few important functions:
  - `filter()` 
  - `select()` 
  - `mutate()` 
  - `arrange()` 
  - `summarise()`

---

# `filter()` function:

- Picks cases based on their values.

---

# How to have a data of only Gentoo penguins?

---

```r
# there are three species: Chinstrap, Gentoo, Adelie
penguins %>% 
* filter(species == "Gentoo")
```
]

```
## # A tibble: 124 × 8
##    species island bill_length_mm
##    <fct>   <fct>           <dbl>
##  1 Gentoo  Biscoe           46.1
##  2 Gentoo  Biscoe           50  
##  3 Gentoo  Biscoe           48.7
##  4 Gentoo  Biscoe           50  
##  5 Gentoo  Biscoe           47.6
##  6 Gentoo  Biscoe           46.5
##  7 Gentoo  Biscoe           45.4
##  8 Gentoo  Biscoe           46.7
##  9 Gentoo  Biscoe           43.3
## 10 Gentoo  Biscoe           46.8
## # … with 114 more rows, and 5 more
## #   variables: bill_depth_mm <dbl>,
## #   flipper_length_mm <int>,
## #   body_mass_g <int>, sex <fct>,
## #   year <int>
```

]
]

---

```r
# there are three species: Chinstrap, Gentoo, Adelie

praw <- read_csv("data/gentoo-penguins1.csv")

praw %>% 
  filter(species == "Gentoo") %>% 
  summary() %>% 
  kableExtra::kable()
```
]

<table>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:left;">   species </th>
   <th style="text-align:left;">    island </th>
   <th style="text-align:left;"> bill_length_mm </th>
   <th style="text-align:left;"> bill_depth_mm </th>
   <th style="text-align:left;"> flipper_length_mm </th>
   <th style="text-align:left;">  body_mass_g </th>
   <th style="text-align:left;">     sex </th>
   <th style="text-align:left;">      year </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> Length:124 </td>
   <td style="text-align:left;"> Length:124 </td>
   <td style="text-align:left;"> Min.   :40.90 </td>
   <td style="text-align:left;"> Min.   :13.10 </td>
   <td style="text-align:left;"> Min.   :203.0 </td>
   <td style="text-align:left;"> Min.   :3950 </td>
   <td style="text-align:left;"> Length:124 </td>
   <td style="text-align:left;"> Min.   :2007 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> Class :character </td>
   <td style="text-align:left;"> Class :character </td>
   <td style="text-align:left;"> 1st Qu.:45.30 </td>
   <td style="text-align:left;"> 1st Qu.:14.20 </td>
   <td style="text-align:left;"> 1st Qu.:212.0 </td>
   <td style="text-align:left;"> 1st Qu.:4500 </td>
   <td style="text-align:left;"> Class :character </td>
   <td style="text-align:left;"> 1st Qu.:2007 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> Mode  :character </td>
   <td style="text-align:left;"> Mode  :character </td>
   <td style="text-align:left;"> Median :47.30 </td>
   <td style="text-align:left;"> Median :15.00 </td>
   <td style="text-align:left;"> Median :216.0 </td>
   <td style="text-align:left;"> Median :4925 </td>
   <td style="text-align:left;"> Mode  :character </td>
   <td style="text-align:left;"> Median :2008 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> NA </td>
   <td style="text-align:left;"> NA </td>
   <td style="text-align:left;"> Mean   :47.50 </td>
   <td style="text-align:left;"> Mean   :14.98 </td>
   <td style="text-align:left;"> Mean   :217.2 </td>
   <td style="text-align:left;"> Mean   :4985 </td>
   <td style="text-align:left;"> NA </td>
   <td style="text-align:left;"> Mean   :2008 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> NA </td>
   <td style="text-align:left;"> NA </td>
   <td style="text-align:left;"> 3rd Qu.:49.55 </td>
   <td style="text-align:left;"> 3rd Qu.:15.70 </td>
   <td style="text-align:left;"> 3rd Qu.:221.0 </td>
   <td style="text-align:left;"> 3rd Qu.:5400 </td>
   <td style="text-align:left;"> NA </td>
   <td style="text-align:left;"> 3rd Qu.:2009 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> NA </td>
   <td style="text-align:left;"> NA </td>
   <td style="text-align:left;"> Max.   :59.60 </td>
   <td style="text-align:left;"> Max.   :17.30 </td>
   <td style="text-align:left;"> Max.   :231.0 </td>
   <td style="text-align:left;"> Max.   :6050 </td>
   <td style="text-align:left;"> NA </td>
   <td style="text-align:left;"> Max.   :2009 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> NA </td>
   <td style="text-align:left;"> NA </td>
   <td style="text-align:left;"> NA's   :1 </td>
   <td style="text-align:left;"> NA's   :1 </td>
   <td style="text-align:left;"> NA's   :1 </td>
   <td style="text-align:left;"> NA's   :1 </td>
   <td style="text-align:left;"> NA </td>
   <td style="text-align:left;"> NA </td>
  </tr>
</tbody>
</table>

]
]

---

# How to export data file to your computer?

---

```r
# three species are Chinstrap, Gentoo, Adelie
penguins %>% 
  filter(species == "Gentoo") %>% 
* write_csv("data/gentoo-penguins.csv")

```
]

]
]

---

# ✋ WAIT! What is `%>% `

- this is called **pipe** ( `%>%` = control + shift + m)

- "a powerful tool for clearly expressing a sequence of **multiple operations**"

- interpret/read it as **then**.

```r
penguins %>% 
  filter(species == "Gentoo") %>% 
  summary() %>% 
  kableExtra::kable()
```

---

# Comparison: Relational Operators

`x < y`

`x > y`

`x <= y`

`x >= y`

`x == y` (equal)

`x != y` (not equal)

---
class: center, middle

# How to have a data of penguins with bill length more than 43 mm?

---

```r
penguins %>% 
  filter(bill_length_mm > 43) 
```
]

```
## # A tibble: 188 × 8
##    species island    bill_length_mm
##    <fct>   <fct>              <dbl>
##  1 Adelie  Torgersen           46  
##  2 Adelie  Dream               44.1
##  3 Adelie  Torgersen           45.8
##  4 Adelie  Dream               43.2
##  5 Adelie  Biscoe              43.2
##  6 Adelie  Biscoe              45.6
##  7 Adelie  Torgersen           44.1
##  8 Adelie  Torgersen           43.1
##  9 Gentoo  Biscoe              46.1
## 10 Gentoo  Biscoe              50  
## # … with 178 more rows, and 5 more
## #   variables: bill_depth_mm <dbl>,
## #   flipper_length_mm <int>,
## #   body_mass_g <int>, sex <fct>,
## #   year <int>
```

]
]

---
class: center, middle

# How to have a data of Gentoo penguins with bill length more than 50 mm?

---
.panelset[

```r
penguins %>% 
  filter(species == "Gentoo",
         bill_length_mm > 55)
```
]

```
## # A tibble: 3 × 8
##   species island bill_length_mm
##   <fct>   <fct>           <dbl>
## 1 Gentoo  Biscoe           59.6
## 2 Gentoo  Biscoe           55.9
## 3 Gentoo  Biscoe           55.1
## # … with 5 more variables:
## #   bill_depth_mm <dbl>,
## #   flipper_length_mm <int>,
## #   body_mass_g <int>, sex <fct>,
## #   year <int>
```

]
]

---
class: center, middle

# How to have data of non-Gentoo penguins with bill length more than 45 mm and weight more than 4 kg?

---

```r
penguins %>% 
  filter(species != "Gentoo",
         bill_length_mm > 45,
         body_mass_g > 4000)
```
]

```
## # A tibble: 18 × 8
##    species   island    bill_length_mm
##    <fct>     <fct>              <dbl>
##  1 Adelie    Torgersen           46  
##  2 Adelie    Torgersen           45.8
##  3 Adelie    Biscoe              45.6
##  4 Chinstrap Dream               46  
##  5 Chinstrap Dream               52  
##  6 Chinstrap Dream               50.5
##  7 Chinstrap Dream               49.2
##  8 Chinstrap Dream               52  
##  9 Chinstrap Dream               52.8
## 10 Chinstrap Dream               54.2
## 11 Chinstrap Dream               51  
## 12 Chinstrap Dream               52  
## 13 Chinstrap Dream               53.5
## 14 Chinstrap Dream               50.8
## 15 Chinstrap Dream               49  
## 16 Chinstrap Dream               50.7
## 17 Chinstrap Dream               49.3
## 18 Chinstrap Dream               50.8
## # … with 5 more variables:
## #   bill_depth_mm <dbl>,
## #   flipper_length_mm <int>,
## #   body_mass_g <int>, sex <fct>,
## #   year <int>
```

]
]

---
class: center, middle

# How to have only top or bottom rows from data?

---

```r
penguins %>% 
  filter(species != "Gentoo",
         bill_length_mm > 45,
         body_mass_g > 4000) %>% 
* head()
```
]

```
## # A tibble: 6 × 8
##   species   island    bill_length_mm
##   <fct>     <fct>              <dbl>
## 1 Adelie    Torgersen           46  
## 2 Adelie    Torgersen           45.8
## 3 Adelie    Biscoe              45.6
## 4 Chinstrap Dream               46  
## 5 Chinstrap Dream               52  
## 6 Chinstrap Dream               50.5
## # … with 5 more variables:
## #   bill_depth_mm <dbl>,
## #   flipper_length_mm <int>,
## #   body_mass_g <int>, sex <fct>,
## #   year <int>
```

]
]

---

```r
penguins %>% 
  filter(species != "Gentoo",
         bill_length_mm > 45,
         body_mass_g > 4000) %>% 
* tail(3)
```
]

```
## # A tibble: 3 × 8
##   species   island bill_length_mm
##   <fct>     <fct>           <dbl>
## 1 Chinstrap Dream            50.7
## 2 Chinstrap Dream            49.3
## 3 Chinstrap Dream            50.8
## # … with 5 more variables:
## #   bill_depth_mm <dbl>,
## #   flipper_length_mm <int>,
## #   body_mass_g <int>, sex <fct>,
## #   year <int>
```

]
]

---

# 🧠 YOUR TURN

How many Chinstrap penguins are with bill length more than 45 mm and weight more than 4 kg?
]

```r
penguins %>% 
  filter(species == "Chinstrap",
         bill_length_mm > 45,
         body_mass_g > 4000) %>% 
  head()
```

]

```
## # A tibble: 6 × 8
##   species   island bill_length_mm
##   <fct>     <fct>           <dbl>
## 1 Chinstrap Dream            46  
## 2 Chinstrap Dream            52  
## 3 Chinstrap Dream            50.5
## 4 Chinstrap Dream            49.2
## 5 Chinstrap Dream            52  
## 6 Chinstrap Dream            52.8
## # … with 5 more variables:
## #   bill_depth_mm <dbl>,
## #   flipper_length_mm <int>,
## #   body_mass_g <int>, sex <fct>,
## #   year <int>
```

]
]

---

# `select()` function: Chooses rows based on column values.

---
class: center, middle

# How to have only `species` variable in data?

---

```r
penguins %>% 
* select(species)
```
]

```
## # A tibble: 344 × 1
##    species
##    <fct>  
##  1 Adelie 
##  2 Adelie 
##  3 Adelie 
##  4 Adelie 
##  5 Adelie 
##  6 Adelie 
##  7 Adelie 
##  8 Adelie 
##  9 Adelie 
## 10 Adelie 
## # … with 334 more rows
```

]
]

---
class: center, middle

# How to have a specific range of variables in data?

---

```r
penguins %>% 
  select(species : bill_depth_mm)
```
]

```
## # A tibble: 344 × 4
##    species island    bill_length_mm
##    <fct>   <fct>              <dbl>
##  1 Adelie  Torgersen           39.1
##  2 Adelie  Torgersen           39.5
##  3 Adelie  Torgersen           40.3
##  4 Adelie  Torgersen           NA  
##  5 Adelie  Torgersen           36.7
##  6 Adelie  Torgersen           39.3
##  7 Adelie  Torgersen           38.9
##  8 Adelie  Torgersen           39.2
##  9 Adelie  Torgersen           34.1
## 10 Adelie  Torgersen           42  
## # … with 334 more rows, and 1 more
## #   variable: bill_depth_mm <dbl>
```

]
]

---
class: center, middle

# How to have variables based upon their location in data?

---

```r
penguins %>% 
  select(4:8)
```
]

```
## # A tibble: 344 × 5
##    bill_depth_mm flipper_length_mm
##            <dbl>             <int>
##  1          18.7               181
##  2          17.4               186
##  3          18                 195
##  4          NA                  NA
##  5          19.3               193
##  6          20.6               190
##  7          17.8               181
##  8          19.6               195
##  9          18.1               193
## 10          20.2               190
## # … with 334 more rows, and 3 more
## #   variables: body_mass_g <int>,
## #   sex <fct>, year <int>
```

]
]

---
class: center, middle

# How to have specific variables in data?

---

```r
penguins %>% 
  select(species, body_mass_g, year)
```
]

```
## # A tibble: 344 × 3
##    species body_mass_g  year
##    <fct>         <int> <int>
##  1 Adelie         3750  2007
##  2 Adelie         3800  2007
##  3 Adelie         3250  2007
##  4 Adelie           NA  2007
##  5 Adelie         3450  2007
##  6 Adelie         3650  2007
##  7 Adelie         3625  2007
##  8 Adelie         4675  2007
##  9 Adelie         3475  2007
## 10 Adelie         4250  2007
## # … with 334 more rows
```

]
]

---

```r
penguins %>% 
  select(-c(species, body_mass_g, year))
```
]

```
## # A tibble: 344 × 5
##    island    bill_length_mm bill_depth_mm
##    <fct>              <dbl>         <dbl>
##  1 Torgersen           39.1          18.7
##  2 Torgersen           39.5          17.4
##  3 Torgersen           40.3          18  
##  4 Torgersen           NA            NA  
##  5 Torgersen           36.7          19.3
##  6 Torgersen           39.3          20.6
##  7 Torgersen           38.9          17.8
##  8 Torgersen           39.2          19.6
##  9 Torgersen           34.1          18.1
## 10 Torgersen           42            20.2
## # … with 334 more rows, and 2 more
## #   variables: flipper_length_mm <int>,
## #   sex <fct>
```

]
]

---

# `mutate()` function: Adds new variables that are functions of existing variables

---

# How to convert penguin body mass from grams to kilograms?

---

```r
penguins %>% 
* mutate(body_mass_kg = body_mass_g / 1000)
```
]

```
## # A tibble: 344 × 9
##    species island    bill_length_mm
##    <fct>   <fct>              <dbl>
##  1 Adelie  Torgersen           39.1
##  2 Adelie  Torgersen           39.5
##  3 Adelie  Torgersen           40.3
##  4 Adelie  Torgersen           NA  
##  5 Adelie  Torgersen           36.7
##  6 Adelie  Torgersen           39.3
##  7 Adelie  Torgersen           38.9
##  8 Adelie  Torgersen           39.2
##  9 Adelie  Torgersen           34.1
## 10 Adelie  Torgersen           42  
## # … with 334 more rows, and 6 more
## #   variables: bill_depth_mm <dbl>,
## #   flipper_length_mm <int>,
## #   body_mass_g <int>, sex <fct>,
## #   year <int>, body_mass_kg <dbl>
```

]
]

---

```r
penguins %>% 
  select(body_mass_g) %>%
  mutate(body_mass_kg = body_mass_g / 1000)
```
]

```
## # A tibble: 344 × 2
##    body_mass_g body_mass_kg
##          <int>        <dbl>
##  1        3750         3.75
##  2        3800         3.8 
##  3        3250         3.25
##  4          NA        NA   
##  5        3450         3.45
##  6        3650         3.65
##  7        3625         3.62
##  8        4675         4.68
##  9        3475         3.48
## 10        4250         4.25
## # … with 334 more rows
```

]
]

---

```r
penguins %>% 
  mutate(body_mass_kg = body_mass_g / 1000, 
         bill = bill_length_mm * bill_depth_mm)
```
]

```
## # A tibble: 344 × 10
##    species island    bill_length_mm
##    <fct>   <fct>              <dbl>
##  1 Adelie  Torgersen           39.1
##  2 Adelie  Torgersen           39.5
##  3 Adelie  Torgersen           40.3
##  4 Adelie  Torgersen           NA  
##  5 Adelie  Torgersen           36.7
##  6 Adelie  Torgersen           39.3
##  7 Adelie  Torgersen           38.9
##  8 Adelie  Torgersen           39.2
##  9 Adelie  Torgersen           34.1
## 10 Adelie  Torgersen           42  
## # … with 334 more rows, and 7 more
## #   variables: bill_depth_mm <dbl>,
## #   flipper_length_mm <int>,
## #   body_mass_g <int>, sex <fct>,
## #   year <int>, body_mass_kg <dbl>,
## #   bill <dbl>
```

]
]

---

```r
penguins %>% 
  mutate(body_mass_kg = body_mass_g / 1000,
         bill = bill_length_mm * bill_depth_mm) %>% 
  select(body_mass_kg,
         bill)
```
]

```
## # A tibble: 344 × 2
##    body_mass_kg  bill
##           <dbl> <dbl>
##  1         3.75  731.
##  2         3.8   687.
##  3         3.25  725.
##  4        NA      NA 
##  5         3.45  708.
##  6         3.65  810.
##  7         3.62  692.
##  8         4.68  768.
##  9         3.48  617.
## 10         4.25  848.
## # … with 334 more rows
```

]
]

---

# `arrange()` function: Changes the order of the rows.

---

# How to have data arranged by the ascending order of bill length of penguins?

---

```r
penguins %>% 
* arrange(bill_length_mm)
```
]

```
## # A tibble: 344 × 8
##    species island    bill_length_mm
##    <fct>   <fct>              <dbl>
##  1 Adelie  Dream               32.1
##  2 Adelie  Dream               33.1
##  3 Adelie  Torgersen           33.5
##  4 Adelie  Dream               34  
##  5 Adelie  Torgersen           34.1
##  6 Adelie  Torgersen           34.4
##  7 Adelie  Biscoe              34.5
##  8 Adelie  Torgersen           34.6
##  9 Adelie  Torgersen           34.6
## 10 Adelie  Biscoe              35  
## # … with 334 more rows, and 5 more
## #   variables: bill_depth_mm <dbl>,
## #   flipper_length_mm <int>,
## #   body_mass_g <int>, sex <fct>,
## #   year <int>
```

]
]

---

```r
penguins %>% 
* arrange(desc(bill_length_mm))
```
]

```
## # A tibble: 344 × 8
##    species   island bill_length_mm
##    <fct>     <fct>           <dbl>
##  1 Gentoo    Biscoe           59.6
##  2 Chinstrap Dream            58  
##  3 Gentoo    Biscoe           55.9
##  4 Chinstrap Dream            55.8
##  5 Gentoo    Biscoe           55.1
##  6 Gentoo    Biscoe           54.3
##  7 Chinstrap Dream            54.2
##  8 Chinstrap Dream            53.5
##  9 Gentoo    Biscoe           53.4
## 10 Chinstrap Dream            52.8
## # … with 334 more rows, and 5 more
## #   variables: bill_depth_mm <dbl>,
## #   flipper_length_mm <int>,
## #   body_mass_g <int>, sex <fct>,
## #   year <int>
```

]
]

---

```r
penguins %>% 
  arrange(species)
```
]

```
## # A tibble: 344 × 8
##    species island    bill_length_mm
##    <fct>   <fct>              <dbl>
##  1 Adelie  Torgersen           39.1
##  2 Adelie  Torgersen           39.5
##  3 Adelie  Torgersen           40.3
##  4 Adelie  Torgersen           NA  
##  5 Adelie  Torgersen           36.7
##  6 Adelie  Torgersen           39.3
##  7 Adelie  Torgersen           38.9
##  8 Adelie  Torgersen           39.2
##  9 Adelie  Torgersen           34.1
## 10 Adelie  Torgersen           42  
## # … with 334 more rows, and 5 more
## #   variables: bill_depth_mm <dbl>,
## #   flipper_length_mm <int>,
## #   body_mass_g <int>, sex <fct>,
## #   year <int>
```

]
]

---

# `summarise()` function

---

# `summarise()` function: Chooses rows based on column values.

---

# How to find mean bill length of all penguins?

---

```r
penguins %>% 
* drop_na() %>%
* summarise(mean_bill_length_mm = mean(bill_length_mm))
```
]

```
## # A tibble: 1 × 1
##   mean_bill_length_mm
##                 <dbl>
## 1                44.0
```

]
]

---

# How to find species-wise mean bill length of penguins?

---

```r
penguins %>% 
* drop_na() %>%
* group_by(species) %>%
  summarise(mean_bill_length_mm = mean(bill_length_mm))
```
]

```
## # A tibble: 3 × 2
##   species   mean_bill_length_mm
##   <fct>                   <dbl>
## 1 Adelie                   38.8
## 2 Chinstrap                48.8
## 3 Gentoo                   47.6
```

]
]

---

# How to find species-wise mean bill length of penguins and total number of penguins in each species?

---

```r
penguins %>% 
  drop_na() %>% 
  group_by(species) %>% 
* summarise(mean_bill_length_mm = mean(bill_length_mm), n = n())
```
]

```
## # A tibble: 3 × 3
##   species   mean_bill_length_mm     n
##   <fct>                   <dbl> <int>
## 1 Adelie                   38.8   146
## 2 Chinstrap                48.8    68
## 3 Gentoo                   47.6   119
```

]
]

---

#  🙋🏽‍♀️🙋‍♂️<br>.big-text[Q&A]

---

# Slide Crafting<br>using xaringan
### .orange[Next Module - 5]

---

# Modules:

1. [Basics of R & RStudio](#module1)

1. [Dynamic Documents using R Markdown](#module2)

1. [Data Visualisation using ggplot2](#module3)

1. [Data Wrangling using dplyr](#module4)

1. .b[[Slide Crafting using xaringan](#module5)]

---
name: module5

background-image: url("https://images.unsplash.com/photo-1638366271870-8b3d428f27f8?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1771&q=80")
background-size: cover

# .black.big-text[Slide<br>Crafting]
## .black[Session - 5]

.footnote[
.white[Image credits:][Markus Spiske](https://images.unsplash.com/photo-1638366271870-8b3d428f27f8?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1742&q=80)
]

---

---

background-image: url(images/xaringan-logo.png)
background-size: contain
background-position: right

# .big-text[R<br>Package]

---

# xaringan

- xaringan package to be a Presentation Ninja 🤺

- "for creating slideshows with remark.js through R Markdown"

- [Xie Yihui](https://yihui.org/en/)

---

# Packages required:

```r
library(palmerpenguins) # to access penguin data

library(xaringan)
library(xaringanthemer)
library(xaringanExtra)
```

---
class: center

# File `$\longrightarrow$` New File `$\longrightarrow$` R Markdown

---
class: center

# Template `$\rightarrow$` Ninja Presentation

---
class: center

# Save this Rmd file

---
class: center

<img src="images/xar3.png" width="65%" style="display: block; margin: auto;" />
]

### xaringan output

<img src="images/xar4.png" width="566" style="display: block; margin: auto;" />
]

---
class: center

<img src="images/xar3.png" width="65%" style="display: block; margin: auto;" />
]

### xaringan slide `$\rightarrow$` browser

<img src="images/xar5-browser.png" width="566" style="display: block; margin: auto;" />
]

- We need to click `Inifinite Moon Reader` only to start the slideshow. To see the changes made in the slides just save the document `ctrl + s`

---

# Using xaringan how to:

1. create a new slide

1. hide an existing slide

1. heading, subheadings, points and normal text

1. include images

--
    - as background
--
    - as part of slide
--

1. make plots

1. include tables

1. in-text R output

1. create columns

---

1. Use `---` to create a new slide

1. `exclude:true` To hide an existing slide

1. Slide text sizes:

- `#` for main heading

- `##` for sub-heading

- `####` for sub-sub-heading

* indented `*` for sub-point1
  * indented `*` for sub-point2
  * indented `*` for sub-point3

- `-` for normal text size

---

# To include images using:

###  CSS `background` option:

- `background-image: url("path of the image")` = path of the image

- `background-size: contain, cover, 50%, 70%` = size of the image

- `background-position: left top` = position of the image

---

# To include images using:

###  `knitr` chunk option:

```r
knitr::include_graphics("path of the image")
```

---

# To include plots

```r
library(palmerpenguins)
ggplot(penguins, aes(x = species)) +
  geom_bar()
```

---

# To include tables

```r
library(kableExtra)
library(tidyverse)

penguins %>% 
  drop_na() %>% 
  head() %>% 
  kable() 
```

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> species </th>
   <th style="text-align:left;"> island </th>
   <th style="text-align:right;"> bill_length_mm </th>
   <th style="text-align:right;"> bill_depth_mm </th>
   <th style="text-align:right;"> flipper_length_mm </th>
   <th style="text-align:right;"> body_mass_g </th>
   <th style="text-align:left;"> sex </th>
   <th style="text-align:right;"> year </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;"> 39.1 </td>
   <td style="text-align:right;"> 18.7 </td>
   <td style="text-align:right;"> 181 </td>
   <td style="text-align:right;"> 3750 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;"> 39.5 </td>
   <td style="text-align:right;"> 17.4 </td>
   <td style="text-align:right;"> 186 </td>
   <td style="text-align:right;"> 3800 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;"> 40.3 </td>
   <td style="text-align:right;"> 18.0 </td>
   <td style="text-align:right;"> 195 </td>
   <td style="text-align:right;"> 3250 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;"> 36.7 </td>
   <td style="text-align:right;"> 19.3 </td>
   <td style="text-align:right;"> 193 </td>
   <td style="text-align:right;"> 3450 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;"> 39.3 </td>
   <td style="text-align:right;"> 20.6 </td>
   <td style="text-align:right;"> 190 </td>
   <td style="text-align:right;"> 3650 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Adelie </td>
   <td style="text-align:left;"> Torgersen </td>
   <td style="text-align:right;"> 38.9 </td>
   <td style="text-align:right;"> 17.8 </td>
   <td style="text-align:right;"> 181 </td>
   <td style="text-align:right;"> 3625 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:right;"> 2007 </td>
  </tr>
</tbody>
</table>

---

# In-text R output

- penguins data have a sample of `$n$` = 344 on total 8 variables.

- math expressions

`$a + b = \sigma - \sum{x}_2^2$`

---

# Column division of slide

- a

- b
  - c
]

- apple
- boy
- cat
]

---

# Slide `class`

- class can be assigned to each slide

- it decides how all elements of one particular slide will look like

---
class: center

# Slide `class`

- class can be assigned to each slide

- it decides how all elements of one particular slide will look like

- `class: center`

---

# Slide `class`

- class can be assigned to each slide

- it decides how all elements of one particular slide will look like

- `class: center, middle, inverse, right`

---

# Extend the power of xaringan:

- using R packages like [xaringanExtra](https://pkg.garrickadenbuie.com/xaringanExtra/#/README?id=xaringanextra)

- learn little about [CSS](https://www.w3schools.com/css/)

- use [cheatsheets](https://rstudio.com/resources/cheatsheets/)

---

background-image: url("images/giphy.gif")
background-position: 10%
background-color: #353935
background-size: 35%