1 Basic of R Program & Basic Mathematical Operations

Code:

print('Hello R Program')

x <- 10

y <- 20

z <- x + y

z

print(z)

18 + 4

18 - 4

18 * 4

4 - 18


4.5 + 2

4.5 - 2

4.5 * 2


10 / 2

4.5 / 3


2 ** 3  # Exponential

2 ^ 3   # Exponential

3.5 ** 2

3.5 ^ 2

10.0 ** 2

10.0 ^ 2

3.0 ** 3

3.0 ^ 3

2 ** 1.5

2 ^ 1.5

9 ** 0.5

9 ^ 0.5

9 ^ .5


9 ^ 1/2

9 ^ (1/2)


sqrt(9)


# operators with in %% are special operators 

8 / 3

8 %/% 3     # Integer Division

-8 %/% 3    # Integer Division

8 %/% -3    # Integer Division


14 %% 4     # Modulus 

14.75 %% 4  # Modulus 

10 %% 3.5   # Modulus 



TRUE + TRUE

TRUE + FALSE

TRUE * TRUE

TRUE * FALSE


# Not Possible

'ghg' + 3

'5' + 3

3 + '5'

'hk' * 3


Data Type and variable 

age = 24

age <- 24

marks <- 78

marks1 <- 78

subject_java <- 85

subject.java <- 85

subject_1 <- 75

subject.1 <- 75

#1subject <- 75

#_subject <- 75

#$subject <- 75


x = 10

typeof(x)

mode(x)

storage.mode(x)

class(x)


typeof(10.25)

mode(10.25)

storage.mode(10.25)

class(10.25)


typeof(TRUE)

mode(TRUE)

storage.mode(TRUE)

class(TRUE)


typeof('Hello')

mode('Hello')

storage.mode('Hello')

class('Hello')


x <- 10.25
x
class(x)

y <- 10
y
class(y)

is.integer(x)
is.integer(y)

y <- as.integer(10)
y
is.integer(y)
class(y)

y <- 10L
is.integer(y)

y <- as.integer(10.25)
y
is.integer(y)

y <- as.integer('5.75')
y
is.integer(y)

y <- as.integer('John')
y
is.integer(y)

z <- 3 + 4i
z
class(z)

#Logical
x <- TRUE
x
class(x)

x <- T
x
class(x)

#Character
x <- 'Jack'
x
class(x)

x <- '3.75'
x
class(x)

x <- as.character(3.75)
x
class(x)

Variable Assignment

age = 24
age <- 24         # Left Assignemnt
assign('age',24)
18 -> age         # Right Assignemnt

z <- y <- x <- 10
x
y
z

x = pi
x
x = letters
x
x = LETTERS
x
x = month.name
x
x = month.abb
x

Relational Operators 
x <- 10
y <- 20

x < y
x <= y
x > y
x >= y
x == y
x != y

x = 'Hello'
y = 'Program'
x < y
x <= y
x > y
x >= y
x == y
x != y

x = TRUE
y = FALSE
x < y
x <= y
x > y
x >= y
x == y
x != y


200 < 'Hello'
'Hello' > 20
10 < TRUE
FALSE > 20
'Hello' > TRUE
TRUE > 'Hello'

10 < 20 < 30

Logical Operators 

x <- TRUE
y <- TRUE

x & y

TRUE & TRUE
TRUE & FALSE
FALSE & TRUE
FALSE & FALSE

TRUE && TRUE  # Not Vectorized
TRUE && FALSE
FALSE && TRUE
FALSE && FALSE

TRUE | TRUE
TRUE | FALSE
FALSE | TRUE
FALSE | FALSE

TRUE || TRUE   # Not Vectorized
TRUE || FALSE
FALSE || TRUE
FALSE || FALSE

!TRUE
!FALSE

10 & TRUE
FALSE & 20
10 | TRUE
FALSE | 20
10 | TRUE
FALSE | 20
!10
!0
!-10

#Cannot Use
'Hello' & 'Program'
'Hello' | 'Program'
!'Hello'
10 & 'Hello'
'Hello' & 20
'Hello' & TRUE
TRUE & 'Hello'

Sequence in R

x <- 1:10
x

x <- 5:1
x

x <- 2 * 1:10
x

x <- 10
1:x-1
1:(x-1)

x <- seq(1,10)
x
x <- seq(10)
x

x <- seq(from=5, to=10)
x

x <- seq(to=10)
x

x <- seq(1,10,2)
x
x <- seq(10,1,-2)
x

x <- seq(1,10,by=2)
x
x <- seq(from=1,to=10,by=2)  #cannot use 0 for by
x
x <- seq(from=1.0,to=2.0,by=0.2)
x

x <- seq(1,10,length=4)

Repetition

x <- rep(1,times=5)
x
x <- rep('A', times=3)
x

y <- 1:3
x <- rep(y,times=3)
x

x <- rep(1,each=5)
x

x <- rep(c(1,2),times=5)
x

x <- rep(c(1,2),each=5)
x




Comments

Post a Comment

Popular posts from this blog

Probability: Binomial Distribution

 Binomial Distribution The binomial distribution is a discrete probability distribution that describes the number of successes in a fixed number of independent trials, where each trial has a constant probability of success. The binomial distribution has two parameters: n, the number of trials, and p, the probability of success in each trial. The probability of getting exactly k successes in n trials is given by the formula: P(X=k) = (n choose k) * p^k * (1-p)^(n-k) where (n choose k) is the binomial coefficient, which is equal to n! / (k! * (n-k)!) and represents the number of ways to choose k successes out of n trials. The mean or expected value of a binomial distribution is given by E(X) = n * p, and the variance is given by Var(X) = n * p * (1-p). The binomial distribution is widely used in statistics and probability theory, particularly in fields such as genetics, engineering, and finance, where it is used to model the probability of success or failure in a series of trials. Co...
Read more

Probability: Normal Distribution

 Normal Distribution DataSet: https://drive.google.com/file/d/1eggLsBKx_RIK8_m0CHLHu2zAtZlrR3aG/view?usp=share_link Generate a Normal Distribution We can use the rnorm() function in R to generate random values from a normal distribution with a specified mean and standard deviation. The syntax of the function is: rnorm (n, mean = 0 , sd = 1 ) where n is the number of values to generate, mean is the mean of the distribution, and sd is the standard deviation of the distribution. For example, to generate 1000 random values from a normal distribution with a mean of 0 and a standard deviation of 1, we can use the following code: set .seed( 123 ) # set a seed for reproducibility x <- rnorm( 1000 , mean = 0 , sd = 1 ) The set.seed() function is used to set a seed value for the random number generator, which ensures that the same set of random numbers will be generated each time the code is run. This can be useful for reproducibility. Plot a Histogram of the Distribution We can us...
Read more

Hypothesis testing

Drive:  https://drive.google.com/drive/folders/1XSJoE-3rCDDJ6S4mRb8rYCgw5CQ_PYxP?usp=share_link Problem Statement Paired dataset: Suppose we have a dataset of 20 students who took a math test before and after attending a tutoring program. We want to test if the tutoring program had a significant effect on the students' math scores. Our null hypothesis is that there is no significant difference between the mean scores before and after the tutoring program, and our alternative hypothesis is that there is a significant difference. Data is stored in a file called "math_scores.csv" with two columns: "pre_tutoring_scores" and "post_tutoring_scores".  Load the data: math_scores <- read.csv("math_scores.csv") Calculate the sample mean and standard deviation for both pre-tutoring and post-tutoring scores: pre_tutoring_mean <- mean(math_scores$pre_tutoring_scores) pre_tutoring_sd <- sd(math_scores$pre_tutoring_scores) post_tutoring_mean <- ...
Read more