\(~~~~~~\)Why you need to learn R\(~~~~~~\)

Somsak Chanaim

International College of Digital Innovation, Chiang Mai University

April 8, 2025

Tools

\(~\)

\[~\] posit: Cheatsheet

Why R is good?

R is the most popular language in the world of Data Analytics/Science.

• It helps analyze data that is both structured and unstructured.

• It was made R the standard language for performing statistical operations.

• R allows various features that differentiate it from other Data Analytics/Science languages.

• R is a very good program for data visualization.

The most popular R packages

tidyverse package

Why R is essential for Data Science?

R plays a vital role in Data Science, you will be benefited with following operations in R.

• We can run a code without any compiler: R is an interpreted language, so that we can run code without any compiler.

• Many calculations are done with vectors: R is a vector language, so anyone can add functions to a single Vector without putting it in a loop.

• Statistical language: R is used in economics, business, finance, and statistics etc..

Why is R Good for Business?

R will also be a great help in business.

• The primary reason is that R is open-source. Therefore it can be modified and redistributed as per the user’s need.

• R has a tool to create a dynamic report (rmarkdown and quarto).

Open-source

R is an open-source language, you can modify various functions in R and make your packages. Since R is issued under the General Public Licence (GNU), there are no restrictions on its usage.

Popularity

R is one of the most popular programming languages in the industry. With the emergence of data science, the need for R in the industries became evident.

https://techvidvan.com/tutorials/career-in-r-programming/

Robust Visualization Library

R comprises libraries like the ggplot2, plotly, and highcharter that offer graphical aesthetic plots to its users.

Quantmod

The quantmod package in R is a widely used package for quantitative financial modeling and trading analysis.

Web-Apps

R provides the package with the ability to build aesthetic web applications. For example, using the R Shiny package, we can develop interactive dashboards straight from the console of R IDE(recommend RStudio).

• MY Shiny: Quantmod

• MY Shiny: Mathematics Function Plot

Interactive Histogram

viewof N = Inputs.range([1000, 10000], {step: 100, label: "N"})
viewof myColor = Inputs.color({ label: "Choose a color", value: "#ff0000" })
viewof myText = Inputs.text({ label: "Enter text", placeholder: "Type title" })

viewof Choices = Inputs.radio([
  "✔️ Yes", 
  "❌ No"
], { label: "เลือกตัวเลือก:", value: 
  "❌ No" })


viewof clicks = Inputs.button("Click to Random")

Interactive Dashboard

viewof x_var = Inputs.select(["gdpPercap", "lifeExp", "pop"], {label: "X-axis", value: "gdpPercap"})

viewof y_var = Inputs.select(["gdpPercap", "lifeExp", "pop"], {label: "Y-axis", value: "lifeExp"})

viewof continent_filter = Inputs.checkbox(
  ["Africa", "Americas", "Asia", "Europe", "Oceania"], 
  { label: "Continents", value: ["Africa", "Americas", "Asia", "Europe", "Oceania"] }
)

viewof year_slider = Inputs.range([1952, 2007], {label: "Year", step: 5, value: 2007})

viewof legend_positions = Inputs.radio(["right", "left","top","bottom","none"], {label: "Legend", value: "right"})

viewof selected_theme = Inputs.select(
  ["theme_gray", 
   "theme_bw", 
   "theme_linedraw", 
   "theme_light", 
   "theme_minimal", 
   "theme_classic", 
   "theme_void", 
   "theme_dark", 
   "theme_excel", 
   "theme_fivethirtyeight", 
   "theme_gdocs", 
   "theme_hc", 
   "theme_pander", 
   "theme_solarized", 
   "theme_tufte", 
   "theme_wsj"], 
  { label: "Theme", value: "theme_gray" } // ค่าเริ่มต้นเป็น theme_gray
)

Data manipualate with dplyr

viewof filter_species = Inputs.checkbox(["setosa", "versicolor", "virginica"], { label: "Filter by Species", value: ["setosa", "versicolor", "virginica"] })

// เลือกตัวแปรที่ต้องการ select
viewof select_vars = Inputs.checkbox(["Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species"], { label: "Select Variables", value: ["Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species"] })

// เพิ่มตัวแปรใหม่ด้วย mutate
viewof mutate_option = Inputs.radio(["Sepal.Area", "Petal.Area", "None"], { label: "Mutate New Variable", value: "None" })

// เลือกตัวแปรสำหรับ arrange
viewof arrange_var = Inputs.select(["Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species"], { label: "Arrange by", value: "Sepal.Length" })

// กำหนดทิศทาง arrange
viewof arrange_desc = Inputs.toggle({ label: "Descending Order?" })

Advantages of R Programming

There are several benefits and some limitations of the R programming language.

Pros of R Language

• R is the most comprehensive statistical analysis package, as new technology and ideas often appear first in R.

• R is open-source. We can run R anywhere at any time and even sell it under the conditions of the license.

• It is cross-platform and runs on many operating systems. It is best for GNU/Linux, Windows, and MAC OS.

• Everyone is welcome to provide bug fixes, code enhancements, and new packages.

Disadvantages of R Programming

Cons of R Language

• The quality of some packages in R is less than perfect.

• There is no customer support for R language if something does not work.

• R commands are hardly concerned with memory management.

The Learning Curve of R Programming

https://www.linkedin.com/pulse/things-try-when-your-r-code-fails-angela-cao

Install R

Download R

View slides in full screen

Install RStudio

\(~\)Download RStudio

Download RStudio

Install quarto

Download quarto

Please select current release

View slides in full screen

Test the R program on these slides

Test the commands

# commands 1
str(cars)

# commands 2
plot(cars)

# commands 3
cor(cars)

03:00