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Dr Ajay Kumar Koli
Head & Educator
School of Information & Data Science
Nalanda Academy - Wardha
@ajay_kolii
koliajaykumar@gmail.com
https://koliajay.netlify.app/





Hello! 😊

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Modules:

  1. Basics of R & RStudio

  2. Dynamic Documents using R Markdown

  3. Data Visualisation using ggplot2

  4. Data Wrangling using dplyr

  5. Slide Crafting using xaringan

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Source

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😍 R is FREE

  • R is a language and environment for statistical computing and graphics. (R project)
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😍 R is FREE

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

  • In August 1993, designed by

Ross Ihaka <br>(New Zealand Statistician)

Ross Ihaka
(New Zealand Statistician)

Robert Gentleman<br>(Canadian Statistician)

Robert Gentleman
(Canadian Statistician)

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Download R from CRAN

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R Console

- R version

- R name

- R licence

- prompt >

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Never Save R "Workspace Image":

  • It helps in "freshly minted R sessions".

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

Source

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R as a BIG calc

What you code

1

What you see

## [1] 1
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R as a BIG calc

What you code

1
1 + 1

What you see

## [1] 1
## [1] 2
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R as a BIG calc

What you code

1
1 + 1
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What you see

## [1] 1
## [1] 2
## [1] 0.85
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R as a BIG calc

What you code

1
1 + 1
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5 < 4

What you see

## [1] 1
## [1] 2
## [1] 0.85
## [1] FALSE
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R as a BIG calc

What you code

1
1 + 1
34 / 40
5 < 4
16 == 16

What you see

## [1] 1
## [1] 2
## [1] 0.85
## [1] FALSE
## [1] TRUE
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Plot using R

plot(1:100)

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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."


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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."


round(9.1565, 2)
## [1] 9.16
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Structure of R function

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Round Function

round(x = 564.56743, digits = 2)
## [1] 564.57
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Round Function

round(x = 564.56743, digits = 1)
## [1] 564.6
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Square Root Function

sqrt(x = 9)
## [1] 3
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Sequence Function

seq.int(from = 10, to = 30, by = 5)
## [1] 10 15 20 25 30
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R Comment:

  • "Humans will be able to read the comments, but your computer will pass over them."1
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R Comment:

  • "Humans will be able to read the comments, but your computer will pass over them."1

  • In R, # is used as a commenting symbol

Source

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How to add comment?

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

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😼 That's okay but you promise to...

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

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😼 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.

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😼 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.

WE NEED A SUPERHERO ...

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R \(\rightleftharpoons\) RStudio

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R \(\rightleftharpoons\) RStudio

Imagine RStudio as a stylish car ...

🚗

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R \(\rightleftharpoons\) RStudio

Imagine RStudio as a stylish car ...

🚗

and R as its powerful engine.

⚙️

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RStudio IDE

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RStudio \(\rightarrow\) Tools \(\rightarrow\) Global Options

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RStudio \(\rightarrow\) Tools \(\rightarrow\) Global Options

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Source

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R Program

Source

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Data Wrangling

Source

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Exploratory Data Analysis

Source

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Modeling

Source

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Data Visualisation

Source

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R Program

Source

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R Packages:

  • On 12 Jan 2022, 18698 R packages were available at CRAN
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R Packages:

  • On 12 Jan 2022, 18698 R packages were available at CRAN

  • "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."

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R Packages:

  • On 12 Jan 2022, 18698 R packages were available at CRAN

  • "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

Source

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To Download pkgs

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Name of the R package(s)

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Installed R package(s)

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R Function to Download Package

install.packages("tidyverse")
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R Function to Download Package

install.packages("tidyverse")

R Function to use Package

library(tidyverse)
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About R Packages:

  • You need to install package only once like

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

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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).

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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.

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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.
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Tools \(\rightarrow\) Check Package Updates

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Select Package(s) to Update

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Click Install Updates

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To Remove Package(s)

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R Object

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

Source: RStudio

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Create Object

salary <- c(20, 30, 40, 50, -10)
salary
## [1]  20  30  40  50 -10
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Create Object

name <- c("Ram", "Rani", "Ali", "Preeti", "John")
name
## [1] "Ram"    "Rani"   "Ali"    "Preeti"
## [5] "John"
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Create Object

age <- c(34, 54, 23, 65, 2 )
age
## [1] 34 54 23 65  2
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Create Object

place <- c("ny", "ber", "dhl", "tko", "lon")
place
## [1] "ny"  "ber" "dhl" "tko" "lon"
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Create Object

books <- c(4, 0, 3, 24, 5)
books
## [1]  4  0  3 24  5
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Guidelines to name objects in R:

  • a name cannot start with a number
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Guidelines to name objects in R:

  • a name cannot start with a number

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

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Guidelines to name objects in R:

  • a name cannot start with a number

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

  • avoid caps

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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

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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)
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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)

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RStudio Environment Window

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RStudio Environment Window

🤔how to combine these
objects/variables into a data or say tidy data

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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
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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

Source

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How to create a data object?

social <- data.frame(age, books, name, place, salary)
social
##   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
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How to export data as a csv file?

library(readr)
# to save this data set as a csv file
write_csv(social, "data/social.csv")
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Get a List of all Objects

# names of created objects
objects()
##  [1] "age"        "books"      "capital"   
##  [4] "foundation" "name"       "place"     
##  [7] "pop"        "praw"       "salary"    
## [10] "soc"        "social"     "state"     
## [13] "world"
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Using Console >

in console type ?your query

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Using Console >

in console type ?your query

for example ?ggplot

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RStudio: pkg Help Docs

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RStudio Community


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stackoverflow


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GitHub Issues

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Twitter #rstats

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🙋🏽‍♀️🙋‍♂️
Q&A

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Dynamic Documents
Using R Markdown

Next Module - 2

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Modules:

  1. Basics of R & RStudio

  2. Dynamic Documents using R Markdown

  3. Data Visualisation using ggplot2

  4. Data Wrangling using dplyr

  5. Slide Crafting using xaringan

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Open RStudio

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Open RStudio

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Open RStudio

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About RStudio Projects

  • "to divide your work into multiple contexts, each with their own:
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About RStudio Projects

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

    • working directory,
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About RStudio Projects

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

    • working directory,

    • workspace,

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About RStudio Projects

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

    • working directory,

    • workspace,

    • history, and

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About RStudio Projects

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

    • working directory,

    • workspace,

    • history, and

    • source documents."

Source & Artwork Source

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🔥 Create RStudio Project in 4 Steps 🔥

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Create RStudio Project in 4 Steps

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Create RStudio Project in 4 Steps

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Create RStudio Project in 4 Steps

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Create RStudio Project in 4 Steps

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Open RStudio Project

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Open RStudio Project

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Open RStudio Project

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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:
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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,
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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,

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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

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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."

Source

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File \(\rightarrow\) New File \(\rightarrow\) R Markdown

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R Markdown

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R Markdown

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R Markdown

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R Markdown

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Save your .Rmd file

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Name your .Rmd file

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Name your .Rmd file

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Save your .Rmd file

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Saved .Rmd file \(\rightarrow\) in RStudio Project

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R Markdown has 3 important parts:

- YAML

- Code chunk

- Text

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🧶 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

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🙋🏽‍♀️🙋‍♂️
Q&A

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Dynamic Visualisation
Using ggplot2

Next Module - 3

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Modules:

  1. Basics of R & RStudio

  2. Dynamic Documents using R Markdown

  3. Data Visualisation using ggplot2

  4. Data Wrangling using dplyr

  5. Slide Crafting using xaringan

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Course Progress

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Variable types in R:

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Variable types in R:

  • int stands for integers, like 4, 55, 300.
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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.

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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.

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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).

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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.

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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.

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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.

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An Overview of Data

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…
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An Overview of Data

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  
##
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Packages required:

library(palmerpenguins) # to access penguin data
library(tidyverse) # to use ggplot2 pkg
  • Packages recommended:
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" 
))
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ggplot2 by Hadley Wickham


  • "is a system for declaratively creating graphics, based on The Grammar of Graphics" (book by Late Leland Wilkinson)
Late Leland Wilkinson

Late Leland Wilkinson

Hadley Wickham

Hadley Wickham

Source

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Key Components for ggplot2 Plot

  1. data,

  2. aesthetic mapping

  3. at least one layer of geom function

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ggplot(data = penguins)

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ggplot(data = penguins, mapping = aes(x = species))

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

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

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How to export plot to your computer?

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ggplot(data = penguins, mapping = aes(x = species)) +
  geom_bar() 
ggsave("peng-species.pdf")  # also try jpg/jpeg/png

## Saving 7 x 7 in image
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How to add color to bars?

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

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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

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How to add color using palette? 🎨

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🎨 Color Palette

  • R package RColorBrewer & wesanderson

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

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How to remove legend or change its position?

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

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How to plot title and axis titles?

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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"
  )

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How to control size of text?

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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"
  )

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How to plot two numeric variables?

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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)"
  )

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How to add themes to ggplot?

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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()

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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()

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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_classic()

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How to add regression line to ggplot?

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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()

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More resources

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🙋🏽‍♀️🙋‍♂️
Q&A

158/240

Dynamic Wrangling
Using dplyr

Next Module - 4

159/240

Modules:

  1. Basics of R & RStudio

  2. Dynamic Documents using R Markdown

  3. Data Visualisation using ggplot2

  4. Data Wrangling using dplyr

  5. Slide Crafting using xaringan

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Course Progress

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What is Data wrangling?

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What is Data wrangling?

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What is Data wrangling?

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What is Data wrangling?

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"Transforming" data means:

  • "narrowing in on observations of interest ...
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"Transforming" data means:

  • "narrowing in on observations of interest ...

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

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"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."

Source

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dplyr package

  • "dplyr is a grammar of data manipulation"
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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:"

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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()

Source

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filter() function:

  • Picks cases based on their values.

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How to have a data of only Gentoo penguins?

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# 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>
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# there are three species: Chinstrap, Gentoo, Adelie
praw <- read_csv("data/gentoo-penguins1.csv")
praw %>% 
  filter(species == "Gentoo") %>% 
  summary() %>% 
  kableExtra::kable()
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
Length:124 Length:124 Min. :40.90 Min. :13.10 Min. :203.0 Min. :3950 Length:124 Min. :2007
Class :character Class :character 1st Qu.:45.30 1st Qu.:14.20 1st Qu.:212.0 1st Qu.:4500 Class :character 1st Qu.:2007
Mode :character Mode :character Median :47.30 Median :15.00 Median :216.0 Median :4925 Mode :character Median :2008
NA NA Mean :47.50 Mean :14.98 Mean :217.2 Mean :4985 NA Mean :2008
NA NA 3rd Qu.:49.55 3rd Qu.:15.70 3rd Qu.:221.0 3rd Qu.:5400 NA 3rd Qu.:2009
NA NA Max. :59.60 Max. :17.30 Max. :231.0 Max. :6050 NA Max. :2009
NA NA NA's :1 NA's :1 NA's :1 NA's :1 NA NA
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How to export data file to your computer?

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# three species are Chinstrap, Gentoo, Adelie
penguins %>% 
  filter(species == "Gentoo") %>% 
  write_csv("data/gentoo-penguins.csv")
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✋ WAIT! What is %>%

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✋ WAIT! What is %>%

  • this is called pipe ( %>% = control + shift + m)
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✋ WAIT! What is %>%

  • this is called pipe ( %>% = control + shift + m)

  • "a powerful tool for clearly expressing a sequence of multiple operations"

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✋ 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.

penguins %>% 
  filter(species == "Gentoo") %>% 
  summary() %>% 
  kableExtra::kable()
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Comparison: Relational Operators

x < y

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Comparison: Relational Operators

x < y

x > y

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Comparison: Relational Operators

x < y

x > y

x <= y

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Comparison: Relational Operators

x < y

x > y

x <= y

x >= y

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Comparison: Relational Operators

x < y

x > y

x <= y

x >= y

x == y (equal)

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Comparison: Relational Operators

x < y

x > y

x <= y

x >= y

x == y (equal)

x != y (not equal)

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

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

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

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

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

186/240

How to have only species variable in data?

187/240
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
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How to have a specific range of variables in data?

189/240
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>
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How to have variables based upon their location in data?

191/240
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>
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How to have specific variables in data?

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

196/240

How to convert penguin body mass from grams to kilograms?

197/240
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>
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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
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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>
200/240
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
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arrange() function: Changes the order of the rows.

202/240

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

203/240
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>
204/240
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>
205/240
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>
206/240

summarise() function

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

208/240

How to find mean bill length of all penguins?

209/240
penguins %>% 
  drop_na() %>%
  summarise(mean_bill_length_mm = mean(bill_length_mm))
## # A tibble: 1 × 1
##   mean_bill_length_mm
##                 <dbl>
## 1                44.0
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How to find species-wise mean bill length of penguins?

211/240
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
212/240

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

213/240
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
214/240

🙋🏽‍♀️🙋‍♂️
Q&A

215/240

Slide Crafting
using xaringan

Next Module - 5

216/240

Modules:

  1. Basics of R & RStudio

  2. Dynamic Documents using R Markdown

  3. Data Visualisation using ggplot2

  4. Data Wrangling using dplyr

  5. Slide Crafting using xaringan

217/240
219/240

xaringan

  • xaringan package to be a Presentation Ninja 🤺
221/240

xaringan

  • xaringan package to be a Presentation Ninja 🤺

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

221/240

xaringan

  • xaringan package to be a Presentation Ninja 🤺

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

  • Xie Yihui

Source

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Packages required:

library(palmerpenguins) # to access penguin data
library(xaringan)
library(xaringanthemer)
library(xaringanExtra)
222/240

File \(\longrightarrow\) New File \(\longrightarrow\) R Markdown

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Template \(\rightarrow\) Ninja Presentation

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Save this Rmd file

225/240

Addins \(\rightarrow\) Inifinite Moon Reader

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Addins \(\rightarrow\) Inifinite Moon Reader

xaringan output

226/240

Addins \(\rightarrow\) Inifinite Moon Reader

xaringan slide \(\rightarrow\) browser

227/240

Addins \(\rightarrow\) Inifinite Moon Reader

xaringan slide \(\rightarrow\) browser

  • 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
227/240

Using xaringan how to:

228/240

Using xaringan how to:

  1. create a new slide
228/240

Using xaringan how to:

  1. create a new slide

  2. hide an existing slide

228/240

Using xaringan how to:

  1. create a new slide

  2. hide an existing slide

  3. heading, subheadings, points and normal text

228/240

Using xaringan how to:

  1. create a new slide

  2. hide an existing slide

  3. heading, subheadings, points and normal text

  4. include images

228/240

Using xaringan how to:

  1. create a new slide

  2. hide an existing slide

  3. heading, subheadings, points and normal text

  4. include images

    • as background
228/240

Using xaringan how to:

  1. create a new slide

  2. hide an existing slide

  3. heading, subheadings, points and normal text

  4. include images

    • as background
    • as part of slide
228/240

Using xaringan how to:

  1. create a new slide

  2. hide an existing slide

  3. heading, subheadings, points and normal text

  4. include images

    • as background
    • as part of slide

  5. make plots

228/240

Using xaringan how to:

  1. create a new slide

  2. hide an existing slide

  3. heading, subheadings, points and normal text

  4. include images

    • as background
    • as part of slide

  5. make plots

  6. include tables

228/240

Using xaringan how to:

  1. create a new slide

  2. hide an existing slide

  3. heading, subheadings, points and normal text

  4. include images

    • as background
    • as part of slide

  5. make plots

  6. include tables

  7. in-text R output

228/240

Using xaringan how to:

  1. create a new slide

  2. hide an existing slide

  3. heading, subheadings, points and normal text

  4. include images

    • as background
    • as part of slide

  5. make plots

  6. include tables

  7. in-text R output

  8. create columns

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  1. Use --- to create a new slide
229/240

  1. Use --- to create a new slide

  2. exclude:true To hide an existing slide

229/240

  1. Use --- to create a new slide

  2. exclude:true To hide an existing slide

  3. Slide text sizes:

229/240

  1. Use --- to create a new slide

  2. exclude:true To hide an existing slide

  3. Slide text sizes:

    • # for main heading
229/240

  1. Use --- to create a new slide

  2. exclude:true To hide an existing slide

  3. Slide text sizes:

    • # for main heading

    • ## for sub-heading

229/240

  1. Use --- to create a new slide

  2. exclude:true To hide an existing slide

  3. Slide text sizes:

    • # for main heading

    • ## for sub-heading

    • #### for sub-sub-heading

229/240

  1. Use --- to create a new slide

  2. exclude:true To hide an existing slide

  3. 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
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  1. Use --- to create a new slide

  2. exclude:true To hide an existing slide

  3. 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
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To include images using:

CSS background option:

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To include images using:

CSS background option:

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

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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

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To include images using:

knitr chunk option:

knitr::include_graphics("path of the image")
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To include plots

library(palmerpenguins)
ggplot(penguins, aes(x = species)) +
  geom_bar()
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To include tables

library(kableExtra)
library(tidyverse)
penguins %>% 
  drop_na() %>% 
  head() %>% 
  kable()
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
Adelie Torgersen 39.1 18.7 181 3750 male 2007
Adelie Torgersen 39.5 17.4 186 3800 female 2007
Adelie Torgersen 40.3 18.0 195 3250 female 2007
Adelie Torgersen 36.7 19.3 193 3450 female 2007
Adelie Torgersen 39.3 20.6 190 3650 male 2007
Adelie Torgersen 38.9 17.8 181 3625 female 2007
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In-text R output

  • penguins data have a sample of \(n\) = 344 on total 8 variables.
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In-text R output

  • penguins data have a sample of \(n\) = 344 on total 8 variables.

  • math expressions

\(a + b = \sigma - \sum{x}_2^2\)

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Column division of slide

- left column

  • a

    • b
    • c

- right column

  • apple
  • boy
  • cat
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Slide class

  • class can be assigned to each slide
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Slide class

  • class can be assigned to each slide

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

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Slide class

  • class can be assigned to each slide

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

  • class: center

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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

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Extend the power of xaringan:

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Extend the power of xaringan:

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Extend the power of xaringan:

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Extend the power of xaringan:

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Dr Ajay Kumar Koli
Head & Educator
School of Information & Data Science
Nalanda Academy - Wardha
@ajay_kolii
koliajaykumar@gmail.com
https://koliajay.netlify.app/





Hello! 😊

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