Agenda

1. Why R for digital sociology?
2. RStudio & R scripts: the basics and syntax
3. How to write codes? (The logistic of coding)
4. R Programming
5. Hands-on: from data to a plot

Why R?

• Open source and free — runs everywhere
• Purpose-built for statistics and data analysis
  • Comparing with Python
• Packages for digital sociology: dplyr, ggplot2, igraph, quanteda, httr…
  • Public Package (CRAN)
• Great for reproducible research (scripts > clicks)

Before Coding

• I am also new to R programing
• Learn-by-doing
• Know "Input" and Read "Output" - My personal experience
• AI is a helpful tool (Encouraged) - The point is AI can help us writing code but you need to know what are they writing for you.

RStudio basics (1)

The four panes

1. Source (scripts) - Rmd file vs. R file
2. Console
3. Environment
4. Files / Plots / Help

Run the current line with ⌘ + Enter or click Run

RStudio basics (2)

Tips

• Create an R Project per course

• Use relative paths: read_csv("data/x.csv")

  • the difference between Path and Relative Path

• You can use the provided .Rmd file to follow the tutorial.

1. Learn R basics and syntax

1. Input
2. Comment
   • Comments can be used to explain R code, and to make it more readable.
   • It can also be used to prevent execution when testing alternative code.
   • Comments starts with a #. When executing code, R will ignore anything that starts with #.

Work with R variables and data types

1. Variable
   • Variables are containers for storing data values.
2. Data Types
   • In programming, data type is an important concept.
   • Variables can store data of different types, and different types can do different things.
   • In R, variables do not need to be declared with any particular type, and can even change type after they have been set

Work with R variables and data types

Basic Data Types

• numeric - (10.5, 55, 787) - e.g., age, grade, income
• integer - (1L, 55L, 100L, where the letter "L" declares this as an integer) - e.g., educational level
• complex - (9 + 3i, where "i" is the imaginary part) - e.g., Common in mathmatics
• character (a.k.a. string more common) - ("k", "R is exciting", "FALSE", "11.5") - e.g., tweets..
• logical (a.k.a. boolean more common) - (TRUE or FALSE) - e.g., gender..

Work with R variables and data types

Type Conversion

• as.numeric()
• as.integer()
• as.complex()

Why we need convert types?

• In R, you can use operators to perform common mathematical operations on numbers.

Work with R variables and data types

Operators

• Addition, Subtraction, Multiplication, Division, Exponent, Modulus (Remainder from division), Integer Division - for number-related variables

Logistic variable

• In programming, you often need to know if an expression is true or false.
• When you compare two values, the expression is evaluated and R returns the logical answer

2. R Operators and Control structures (if, while, for)

R Operators

R divides the operators in the following groups:

• Arithmetic operators - numeric values (e.g., + - * / )
• Assignment operators - assign values to variables (<-)
• Comparison operators - compare two values (==, !=, >, <)
• Logical operators - combine conditional statements (e.g., &, |, !)
• Miscellaneous operators - manipulate data (e.g., : )

Control Structures

The if Statement

• An "if statement" is written with the if keyword, and it is used to specify a block of code to be executed if a condition is TRUE.
• If, else if, else..
• or

Control Structures

While Loop

• With the while loop we can execute a set of statements as long as a condition is TRUE
• With the break statement, we can stop the loop even if the while condition is TRUE:

For Loop

• A for loop is used for iterating over a sequence.

3. Create and use functions in R

A function is a block of code which only runs when it is called.

• Why we need a function?

You can pass data, known as parameters, into a function.

• To create a function, use the function() keyword

A function can return data as a result.

• return something

4. Work with data structures

Why we need structures?

R provides many built-in data structures. Each is used to handle data in different ways:

• Vectors ; Same type
• Lists; No
• Matrices (2D); Same type
• Arrays (Multiple dimensions); Same type
• Data Frames; No

5. Make plots and visualize data (line, scatter, pie, bar)

plot() function can solve most visualizaton issue.

• ggplot2() - another package

You can use AI to help enhance your images. - Generally, it is better than human.

6. Perform basic statistics (mean, median, mode)

Statistics is the science of analyzing, reviewing and conclude data.

The R language was developed by two statisticians.

• It has many built-in functionalities (Variance,Standard Devation, Probability distributions, etc.)
• In addition to libraries for the exact purpose of statistical analysis. (Regression models, advanced techniques.. we will use them in following tutorials.)

Hands-on

Data → table → plot (5 -10 mins)

Hands-on: from data to a plot

1. Task:
   • Visualize the relationship between vehicle weight and fuel consumption in the mtcars dataset;
2. The code is at the end of Titorial_1.Rmd file (Hands-on: from data to a plot part)

Do: the hands-on exercise end-to-end on your own machine