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# assign value 3 to variable radius
radius <- 3

# assign value 3.14 to variable pi
pi <- 3.14

# compute the area of the circle and assign it to 
# a variable of the same name
area <- pi * radius * radius

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# create a numeric vector using c()
num_vect <- c(1, 2.3, 5)
num_vect

# test the data structure
is.vector(num_vect)

# test the data type
class(num_vect)

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# vector coercion
# case 1: create a vector of different data types
vect1 <- c(1.5, 1L, 'one', TRUE)
vect1

# test data type
class(vect1)

# case 2: create a vector of different data types excluding character
vect2 <- c(1.5, 1L, TRUE)

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# syntax
args(matrix)

# numeric matrix of 3 rows filled by columns
mat <- matrix(data = 1:9, nrow = 3, byrow = FALSE)
mat

# error in specifying rows/columns
mat1 <- matrix(data = 1:9, nrow = 2)
mat

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# example 1: list containing vector and matrix
vect1 <- c('Jack', 'Jill', 'John')   # character vector
mat <- matrix(sample(12), nrow = 3)  # 3 x 4 matrix
list1 <- list(vect1, mat)            # list of 2 objects
list1

# example 2: coerce vector using as.list function
# numeric vector
vect1 <- 1:5                        

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# import numpy
import numpy as np 

# create numpy array from lists
np1 = np.array([1, 3, 5, 7, 9])
np1

# use the range function to create array
np2 = np.array(range(6))
np2

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import numpy as np

# select array elements
np1 = np.arange(0, 10)
np1

# select 3rd and 5th elements
np1[2]
np1[4]

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import numpy as np

# one dimensional array
np1 = np.arange(0, 12)
np1

# reshape() method
# modify to 3 x 4 two dimensional array
np1.reshape(3, 4)

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# install library
install.packages('tibble')
library(tibble)

# create tibble
tibble(x = letters,
       y = 1:26,
       z = sample(100, 26))

# tibble features

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# install 
install.packages('readr')
install.packages('haven')

# library
library(readr)
library(haven)

# read_csv
read_csv('hsb2.csv')