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Installation and Packages
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Install R on MacOS

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R Operators
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Operators are used to perform operations on variables and values.

R code (live class)

Five core principles

variable
data types
data structures (R)
function
control flow
## Ctrl+Enter = run code (cmd + enter on Mac)
## Ctrl+L = clear console

Helper

help(keyword)
?keyword

1. Variables

## ===== Concept 01 - Variables =====

name <- "Tony"
age <- 31
uni <- "Torrens"

rm(uni)

2. Data types

Index in R start at 1
## ===== Concept 02 - Data types =====
## number, character, date, factor(categorical), logical(true, false)

name <- "Tony"
age <- 31
degree <- "Engineer"
quote <- "I'm loving it"
tiktok <- FALSE

## ! negate
!tiktok

## How to create date 101
today <- "2025-10-04"
today <- as.Date(today)

## How to create factor 101
gender <- c("male", "female", "female")
gender <- as.factor(gender)

fruit <- c("orange", "apple", "apple", "lemon")
fruit <- as.factor(fruit)

table(fruit)

## Recap: class(), as.type(), is.type()
is.numeric(1)
is.null("")

3. Data structures

## ===== Concept 03 - Data structures =====
## 1. vector => can use for only 1 datatype (single datatype)
## 2. matrix
## 3. list
## 4. dataframe

## ----- vector example -----
friends <- c("toy", "jenny", "lisa", "jisoo", "rose")

## subset the vector
## [1] position
## [2] condition
## [3] name

ages <- c(37, 28, 25, 30, 31)

ages <- c(toy=37, jenny=28, lisa=25)

## ----- matrix example -----
m1 <- matrix(1:10, ncol = 5, byrow = TRUE)

m2 <- matrix(
c(3,4,5,8,10,12),
ncol = 3,
byrow = TRUE
)

## wrap variable in () to show its value
(m22 <- matrix(
c(13,4,5,8,10,12),
ncol = 3,
byrow = TRUE
))

## Matrix multiplication
m1 <- matrix(1:6, ncol = 3)
m2 <- matrix(c(5,5,6,6,9,10), ncol = 2)

m1 %*% m2

## ----- list example ----- key - value pair
tony <- list(
fname = "Tanapoom",
age = 31,
dob = "06-Aug"
)

tony$fname

## ----- dataframe example -----

id <- 1:5
friends <- c("toy", "lisa", "jisoo", "jenny", "david")
age <- c(37, 25, 30, 22, 28)
city <- c("bangkok", rep("london", 3), "tokyo")

## dataframe
df <- data.frame(id,
friends,
age,
city)

View(df)

## subset by position
df
df[3, ]
df[3, c(2,4)]

df[ , 1:2]
df[4:5, 4]
df[1:3, 2]

df$city
df[df$age < 30, ]
df[df$age < 30, "friends"]

## Use SQL syntax in R
sqldf("select avg(age) from df")

str(df)
summary(df)
ncol(df)

df$city <- as.factor(df$city)
str(df)
summary(df)

## create column
df$reading <- c(T,T,T,F,F)
df
df[df$reading, ]
df[df$reading == F, ]
df[!df$reading, ]

## remove column
df$reading <- NULL
df

## export column
write.csv(df, "friends.csv",
row.names = FALSE)


df <- read.csv("friends.csv")
df

Vectors

A list of the same type of items. To combine the list of items to a vector, use the c() function and separate the items by a comma.
## Vector of characters/strings
fruits <- c("banana", "apple", "orange")

## Print fruits
fruits

[1] "banana" "apple" "orange"

## Sequence generation
seq(from = , to = 100, by = 2)

Factors

Factors are used to categorise data. Examples of factors are:
Demography: Male/Female
Music: Rock, Pop, Classical, Jazz
## Create a factor
music_genre <- factor(c("Jazz", "Rock", "Classic", "Classic", "Pop", "Jazz", "Rock", "Jazz"))

## Print the factor
music_genre

[1] Jazz Rock Classic Classic Pop Jazz Rock Jazz
Levels: Classic Jazz Pop Rock

## ---------
## To only print the levels, use the levels() function:
levels(music_genre)

[1] "Classic" "Jazz" "Pop" "Rock"

Matrices

A matrix is a two-dimensional data set with columns and rows.
A matrix can be created with the matrix() function. Specify the nrow and ncol parameters to get the number of rows and columns.
thismatrix <- matrix(c("apple", "banana", "cherry", "orange"), nrow = 2, ncol = 2)

thismatrix

## Output
[,1] [,2]
[1,] "apple" "cherry"
[2,] "banana" "orange"
c() function is used to concatenate items together.

4. Function


5. Control flow



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