R Data Structure

# changed the names to president names, and also salary and application
president_name <- c("Trump", "Biden", "Washington")
salary <- c(400000, 400000, 25000)  # Approximate annual salaries (in USD)
application_status <- c(TRUE, TRUE, TRUE)  # Assuming all applied successfully

# access the second name in the president_name vector
president_name[2]

[1] "Biden"

# include items in the range 2 to 3
salary[2:3]

[1] 400000  25000

# exclude item 2 using the minus sign
salary[-2]

[1] 400000  25000

# use a vector to indicate whether to include item
salary[c(TRUE, TRUE, FALSE)]

[1] 4e+05 4e+05

# add ordered factor, in this case we used government_expenditure
government_expenditure <- factor(c("LOW", "MODERATE", "HIGH"),
                   levels = c("LOW", "MODERATE", "HIGH"),
                   ordered = TRUE)

# check for government_expenditure greater than moderate
government_expenditure > "MODERATE"

[1] FALSE FALSE  TRUE

#in which of the cases are the level grater than model, here it is our answer:

#retrive he name of the first president
president_name[1]

[1] "Trump"

salary[1]

[1] 4e+05

application_status[1]

[1] TRUE

government_expenditure[1]

[1] LOW
Levels: LOW < MODERATE < HIGH

# create list for presidency
subject1 <- list(fullname = president_name[1], 
                 salary = salary[1],
                 application_status = application_status[1],
                 government_expenditure = government_expenditure[1])

# display the presidency
subject1

$fullname
[1] "Trump"

$salary
[1] 4e+05

$application_status
[1] TRUE

$government_expenditure
[1] LOW
Levels: LOW < MODERATE < HIGH

#lets create a presidency data set
presidency_dataset <- data.frame(president_name, salary, application_status, government_expenditure, stringsAsFactors = FALSE)
                     
# display the data frame
presidency_dataset

#how to access elements in the dataset
# get a single column
presidency_dataset$president_name

[1] "Trump"      "Biden"      "Washington"

# get several columns by specifying a vector of names
presidency_dataset[c("salary", "application_status")]

# this is the same as above, extracting temperature and flu_status
presidency_dataset[2:3]

# accessing by row and column
presidency_dataset[2, 2]

[1] 4e+05

# accessing several rows and several columns using vectors
presidency_dataset[c(1, 2), c(2, 3)]

# column 1, all rows
presidency_dataset[, 1]

[1] "Trump"      "Biden"      "Washington"

# row 1, all columns
presidency_dataset[1, ]

# all rows and all columns
presidency_dataset[ , ]

# the following are equivalent
presidency_dataset[c(1, 3), c("president_name", "application_status")]

presidency_dataset[-2, c(-1, -3, -5, -6)]

Metrices

# Create a matrix named 'm1' using the matrix() function
# The c(1, 2, 3, 4) creates a vector containing the elements 1, 2, 3, and 4
# The nrow=2 specifies that the matrix should have 2 rows
m1<-matrix(c(1,2,3,4),nrow=2)
m1

     [,1] [,2]
[1,]    1    3
[2,]    2    4

m2<-matrix(c(1,2,3,4),ncol = 2)
m2

     [,1] [,2]
[1,]    1    3
[2,]    2    4

m1[1,1]

[1] 1

m2[2,1]

[1] 2

#creating a 2x3 matrix
m3 <- matrix(c(1,2,3,4,5,6), nrow=2)
m3

     [,1] [,2] [,3]
[1,]    1    3    5
[2,]    2    4    6

m4 <- matrix (c(2,3,7,9,11,10i),ncol = 2)
m4

     [,1]   [,2]
[1,] 2+0i  9+ 0i
[2,] 3+0i 11+ 0i
[3,] 7+0i  0+10i

#retrive the first row
m4[1,]

[1] 2+0i 9+0i

m4[,2]

[1]  9+ 0i 11+ 0i  0+10i