Machine Learning with R Tutorial

R- Lists

• Lists are the R objects which contain components of various sorts like – numbers, strings, vectors and anotherlist inside it.
• A rundown can likewise contain a framework or a capacity as its components.
• List is made utilizing list() work.

Making a List:

Create a rundown containing strings, numbers, vectors and a consistent qualities.

Example:1

a=list(10,"raja",100.5)
print(a[0])
print(a[1])
print(a[2])
print(a[3])
print(a[4])

yield:

> print(a[1])
[[1]]
[1] 10
> print(a[2])
[[1]]
[1] "raja"
> print(a[3])
[[1]]
[1] 100.5
> print(a[4])
[[1]]
Invalid

Example:2

list_data <-list("Red", "Green", c(21,32,11), TRUE, 51.23, 119.1)
print(list_data)

yield:

[[1]] [1] “Red”
[[2]] [1] “Green”
[[3]] [1] 21 32 11
[[4]] [1] TRUE
[[5]] [1] 51.23
[[6]] [1] 119.1

Naming List Elements:

The list components can be given names and they can be gotten to utilizing these names.

Example:1

# Create a rundown containing a vector, a lattice and a rundown.
list_data <-list(c("Jan","Feb","Mar"), matrix(c(3,9,5,1,- 2,8), nrow = 2),
list("green",12.3))
# Give names to the components in the rundown.
names(list_data) <-c("First", "Second", "Third")
# Show the rundown.
print(list_data)

yield:

$`First`
[1] "Jan" "Feb" "Blemish"
$Second
[,1] [,2] [,3]
[1,] 3 5 - 2
[2,] 9 1 8
$Third
$Third[[1]]
[1] "green"
$Third[[2]]
[1] 12.3

Getting to List Elements:

Elements of the rundown can be gotten to by the file of the component in the rundown. In instance of named records it can likewise be gotten to utilizing the names.

Example:1

# Create a rundown containing a vector, a grid and a rundown.
list_data <-list(c("Jan","Feb","Mar"), matrix(c(3,9,5,1,- 2,8), nrow = 2),
list("green",12.3))
# Give names to the components in the rundown.
names(list_data) <-c("First", "Second", "Third") # Show the rundown. print(list_data) # Access
the primary component of the rundown. print(list_data[1]) # Access the thrid component. As it is likewise a rundown, every one of its components will be printed. print(list_data[3]) yield: ======= $`First` [1] "Jan" "Feb" "Blemish" $Second [,1] [,2] [,3] [1,] 3 5 - 2 [2,] 9 1 8 $Third $Third[[1]] [1] "green" $Third[[2]] [1] 12.3 >
> # Access the primary component of the rundown.
> print(list_data[1])
$`First`
[1] "Jan" "Feb" "Blemish"
>
> # Access the thrid component. As it is likewise a rundown, every one of its components will be printed.
> print(list_data[3])
$`Third`
$`Third`[[1]]
[1] "green"
$`Third`[[2]]
[1] 12.3

Controlling List Elements:- –

• We can include, erase and refresh list components as demonstrated as follows.
• We can include and erase components just toward the finish of a rundown.
• But we can refresh any component.

Example:1

# Create a rundown containing a vector, a framework and a rundown.
list_data <-list(c("Jan","Feb","Mar"), matrix(c(3,9,5,1,- 2,8), nrow = 2),list("green",12.3))
# Give names to the components in the rundown.
names(list_data) <-c("First", "Second", "Third")
# Add component toward the finish of the rundown.
list_data[4] <-"New component"
print(list_data[4])

yield:

[[1]] [1] “New component”
Example:2

# Create a rundown containing a vector, a framework and a rundown.
list_data <-list(c("Jan","Feb","Mar"), matrix(c(3,9,5,1,- 2,8), nrow = 2),list("green",12.3))
# Give names to the components in the rundown.
names(list_data) <-c("First", "Second", "Third")
# Remove the last component.
list_data[4] <-NULL
# Print the fourth Element.
print(list_data[4])

yield:

list_data[4] <-NULL

$

Invalid

Consolidating Lists:

You can consolidate numerous rundowns into one rundown by putting every one of the rundowns inside one rundown() work.

Example:1

# Create two records.
list1 <-list(1,2,3)
list2 <-list("Sun","Mon","Tue")
# Merge the two records.
merged.list <-c(list1,list2)
# Print the consolidated rundown.
print(merged.list)

yield:

[[1]] [1] 1
[[2]] [1] 2
[3]] [1] 3
[[4]] [1] “Sun”
[[5]] [1] “Mon”
[[6]] [1] “Tue”

Changing over List to Vector:

• A rundown can be changed over to a vector with the goal that the components of the vector can be utilized for further control.
• All the number juggling tasks on vectors can be connected after the rundown is changed over into vectors.
• To do this change, we utilize the unlist() work. It accepts the rundown as information and produces a vector.

Example:1

# Create records.
list1 <-list(1:5)
print(list1)
list2 <-list(10:14)
print(list2)
# Convert the rundowns to vectors.
v1 <-unlist(list1)
v2 <-unlist(list2)
print(v1)
print(v2)
# Now include the vectors
result <-v1+v2
print(result)

yield:

[[1]] [1] 10 11 12 13 14
[1] 1 2 3 4 5
[1] 10 11 12 13 14
[1] 11 13 15 17 19

rmatrices

R- Matrices

• Matrices are the R questions in which the components are orchestrated in a two-dimensional rectangular design.
• Matrix can just have homogenous component type.
• Matrix can be made utilizing grid() work.

Punctuation:

matrix(data, nrow, ncol, byrow, dimnames)

Following is the portrayal of the parameters utilized:

• data is the information vector which turns into the information components of the grid.
• nrow is the quantity of lines to be made.
• ncol is the quantity of sections to be made.
• byrow is a coherent hint. On the off chance that TRUE, the information vector components are organized by line.
• dimname is the names appointed to the lines and segments.

Example:1

# Elements are organized successively by line.
M <-matrix(c(1:12), nrow = 4, byrow = TRUE)
print(M)

yield:

[,1] [,2] [,3][1,] 1 2 3
[2,] 4 5 6
[3,] 7 8 9
[4,] 10 11 12

Example:2

# Elements are masterminded successively by column.
M <-matrix(c(1:12), nrow = 4, byrow = FALSE)
print(M)

yield:

[,1] [,2] [,3] [1,] 1 5 9
[2,] 2 6 10
[3,] 3 7 11
[4,] 4 8 12

Example:3

# Elements are masterminded consecutively by line.
M <-matrix(c(1:12), nrow = 4, byrow = TRUE)
print(M)
# Define the section and column names.
row_names = c("row1", "row2", "row3", "row4")
col_names = c("col1", "col2", "col3")
P <-matrix(c(3:14), nrow = 4, byrow = TRUE, dimnames = list(row_names, col_names))
print(P)

yield:

col1 col2 col3

row1 3 4 5

row2 6 7 8

row3 9 10 11

row4 12 13 14

Getting to Elements of a Matrix:

Elements of a network can be gotten to by utilizing the section and column record of the component.

Example:1

# Elements are orchestrated consecutively by line.
M <-matrix(c(1:12), nrow = 4, byrow = TRUE)
print(M)
# Define the segment and column names.
row_names = c("row1", "row2", "row3", "row4")
col_names = c("col1", "col2", "col3")
P <-matrix(c(3:14), nrow = 4, byrow = TRUE, dimnames = list(row_names, col_names))
print(P)
# Access the component at third section and first line.
print(P[1,3])

yield:

col1 col2 col3

row1 3 4 5

row2 6 7 8

row3 9 10 11

row4 12 13 14

[1] 5

Example:2

# Elements are organized consecutively by column.
M <-matrix(c(1:12), nrow = 4, byrow = TRUE)
print(M)
# Define the segment and line names.
row_names = c("row1", "row2", "row3", "row4")
col_names = c("col1", "col2", "col3")
P <-matrix(c(3:14), nrow = 4, byrow = TRUE, dimnames = list(row_names, col_names))
print(P)
# Access the component at second segment and fourth line.
print(P[4,2])

yield:

col1 col2 col3

row1 3 4 5

row2 6 7 8

row3 9 10 11

row4 12 13 14

[1] 13

Example:3

# Elements are masterminded consecutively by line.
M <-matrix(c(1:12), nrow = 4, byrow = TRUE)
print(M)
# Define the section and column names.
row_names = c("row1", "row2", "row3", "row4")
col_names = c("col1", "col2", "col3")
P <-matrix(c(3:14), nrow = 4, byrow = TRUE, dimnames = list(row_names, col_names))
print(P)
# Access just the second column.
print(P[2,])

yield:

col1 col2 col3

row1 3 4 5

row2 6 7 8

row3 9 10 11

row4 12 13 14

col1 col2 col3

6 7 8

Example:4

# Elements are organized successively by line.
M <-matrix(c(1:12), nrow = 4, byrow = TRUE)
print(M)
# Define the section and line names.
row_names = c("row1", "row2", "row3", "row4")
col_names = c("col1", "col2", "col3")
P <-matrix(c(3:14), nrow = 4, byrow = TRUE, dimnames = list(row_names, col_names))
print(P)
# Access just the third section.
print(P[,3])

yield:

col1 col2 col3

row1 3 4 5

row2 6 7 8

row3 9 10 11

row4 12 13 14

row1 row2 row3 row4

5 8 11 14

Grid Computations:

• Various numerical tasks are performed on the grids utilizing the R administrators.
• The aftereffect of the task is additionally a grid.
• The measurements (number of lines and segments) ought to be same for the grids engaged with the activity.

Example:1

# Create two 2x3 grids.
matrix1 <-matrix(c(3, 9, - 1, 4, 2, 6), nrow = 2)
print(matrix1)
matrix2 <-matrix(c(5, 2, 0, 9, 3, 4), nrow = 2)
print(matrix2)
# Add the grids.
result <-matrix1 + matrix2
cat("Result of addition","\n")
print(result)

yield:

[,1] [,2] [,3] [1,] 3 – 1 2
[2,] 9 4 6[,1] [,2] [,3] 
[1,] 5 0 3
[2,] 2 9 4

Consequence of expansion

[,1] [,2] [,3] [1,] 8 – 1 5
[2,] 11 13 10

Example:2

# Create two 2x3 lattices.
matrix1 <-matrix(c(3, 9, - 1, 4, 2, 6), nrow = 2)
print(matrix1)
matrix2 <-matrix(c(5, 2, 0, 9, 3, 4), nrow = 2)
print(matrix2)
# Add the lattices.
result <-matrix1 - matrix2
cat("Result of addition","\n")
print(result)

yield:

[,1] [,2] [,3] [1,] 3 – 1 2
[2,] 9 4 6
[,1] [,2] [,3] [1,] 5 0 3
[2,] 2 9 4

Consequence of expansion

[,1] [,2] [,3] [1,] – 2 – 1 – 1
[2,] 7 – 5 2

Example:3

# Create two 2x3 lattices.
matrix1 <-matrix(c(3, 9, - 1, 4, 2, 6), nrow = 2)
print(matrix1)
matrix2 <-matrix(c(5, 2, 0, 9, 3, 4), nrow = 2)
print(matrix2)
# Add the lattices.
result <-matrix1 * matrix2
cat("Result of addition","\n")
print(result)

yield:

[,1] [,2] [,3] [1,] 3 – 1 2
[2,] 9 4 6
[,1] [,2] [,3] [1,] 5 0 3
[2,] 2 9 4

Consequence of expansion

[,1] [,2] [,3] [1,] 15 0 6
[2,] 18 36 24

Example:4

# Create two 2x3 lattices.
matrix1 <-matrix(c(3, 9, - 1, 4, 2, 6), nrow = 2)
print(matrix1)
matrix2 <-matrix(c(5, 2, 0, 9, 3, 4), nrow = 2)
print(matrix2)
# Add the lattices.
result <-matrix1/matrix2
cat("Result of addition","\n")
print(result)

yield:

[,1] [,2] [,3] [1,] 3 – 1 2
[2,] 9 4 6
[,1] [,2] [,3] [1,] 5 0 3
[2,] 2 9 4

Consequence of expansion

[,1] [,2] [,3] [1,] 0.6 – Inf 0.6666667
[2,] 4.5 0.4444444 1.5000000

# Create two vectors of various lengths.
vector1 <-c(1,2,3)
vector2 <-c(4,5,6,7,8,9)
# Take these vectors as contribution to the exhibit.
result <-array(c(vector1,vector2),dim = c(3,3,1))
print(result)

, 1

[,1] [,2] [,3] [1,] 1 4 7[2,] 2 5 8[3,] 3 6 9

# Create two vectors of various lengths.
vector1 <-c(1,2,3)
vector2 <-c(4,5,6,7,8,9)
# Take these vectors as contribution to the exhibit.
result <-array(c(vector1,vector2),dim = c(3,3,2))
print(result)

, 1

[,1] [,2] [,3] [1,] 1 4 7

[2,] 2 5 8

[3,] 3 6 9

, 2

[,1] [,2] [,3] [1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9

# Create two vectors of various lengths.
vector1 <-c(1,2,3)
vector2 <-c(4,5,6,7,8,9)
row.names <-c("ROW1","ROW2","ROW3")
# Take these vectors as contribution to the cluster.
result <-array(c(vector1,vector2),dim = c(3,3,2),dimnames = list(row.names))
print(result)

, 1

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

ROW1 1 4 7

ROW2 2 5 8

ROW3 3 6 9

, 2

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

ROW1 1 4 7

ROW2 2 5 8

ROW3 3 6 9

# Create two vectors of various lengths.
vector1 <-c(1,2,3)
vector2 <-c(4,5,6,7,8,9)
row.names <-c("ROW1","ROW2","ROW3")
column.names <-c("COL1","COL2","COL3")
# Take these vectors as contribution to the cluster.
result <-array(c(vector1,vector2),dim = c(3,3,2),dimnames = list(row.names,column.names))
print(result)

, 1
COL1 COL2 COL3
ROW1 1 4 7
ROW2 2 5 8
ROW3 3 6 9

, 2
COL1 COL2 COL3
ROW1 1 4 7
ROW2 2 5 8
ROW3 3 6 9

# Create two vectors of various lengths.
vector1 <-c(1,2,3)
vector2 <-c(4,5,6,7,8,9)
row.names <-c("ROW1","ROW2","ROW3")
column.names <-c("COL1","COL2","COL3")
matrix.names <-c("Matrix1","Matrix2")
# Take these vectors as contribution to the exhibit.
result <-array(c(vector1,vector2),dim = c(3,3,2),dimnames = list(row.names,column.names,matrix.ames))
print(result)

, Matrix1
COL1 COL2 COL3
ROW1 1 4 7
ROW2 2 5 8
ROW3 3 6 9

, Matrix2
COL1 COL2 COL3
ROW1 1 4 7
ROW2 2 5 8
ROW3 3 6 9

# Create two vectors of various lengths.
vector1 <-c(1,2,3)
vector2 <-c(4,5,6,7,8,9)
row.names <-c("ROW1","ROW2","ROW3")
column.names <-c("COL1","COL2","COL3")
matrix.names <-c("Matrix1","Matrix2")
# Take these vectors as contribution to the exhibit.
result <-array(c(vector1,vector2),dim = c(3,3,2),dimnames = list(row.names,column.names,matrix.names))
print(result)
# Print the third column of the second grid of the cluster.
print(result[3,,2])

, Matrix1

COL1 COL2 COL3
ROW1 1 4 7
ROW2 2 5 8
ROW3 3 6 9

, Matrix2

COL1 COL2 COL3
ROW1 1 4 7
ROW2 2 5 8
ROW3 3 6 9
COL1 COL2 COL3
3 6 9

# Create two vectors of various lengths.
vector1 <-c(1,2,3)
vector2 <-c(4,5,6,7,8,9)
row.names <-c("ROW1","ROW2","ROW3")
column.names <-c("COL1","COL2","COL3")
matrix.names <-c("Matrix1","Matrix2")
# Take these vectors as contribution to the exhibit.
result <-array(c(vector1,vector2),dim = c(3,3,2),dimnames = list(row.names,column.names,matrix.names))
print(result)
# Print the component in the first line and third section of the first framework.
print(result[1,3,1])

, Matrix1

COL1 COL2 COL3
ROW1 1 4 7
ROW2 2 5 8
ROW3 3 6 9

, Matrix2

COL1 COL2 COL3
ROW1 1 4 7
ROW2 2 5 8
ROW3 3 6 9
[1] 7

# Create two vectors of various lengths.
vector1 <-c(1,2,3)
vector2 <-c(4,5,6,7,8,9)
row.names <-c("ROW1","ROW2","ROW3")
column.names <-c("COL1","COL2","COL3")
matrix.names <-c("Matrix1","Matrix2")
# Take these vectors as contribution to the cluster.
result <-array(c(vector1,vector2),dim = c(3,3,2),dimnames = list(row.names,column.names,matrix.names))
print(result)
# Print the second Matrix.
print(result[,,2])

, Matrix1

COL1 COL2 COL3
ROW1 1 4 7
ROW2 2 5 8
ROW3 3 6 9

, Matrix2

COL1 COL2 COL3
ROW1 1 4 7
ROW2 2 5 8
ROW3 3 6 9
COL1 COL2 COL3
ROW1 1 4 7
ROW2 2 5 8
ROW3 3 6 9

# Create two vectors of various lengths.
vector1 <-c(1,2,3)
vector2 <-c(4,5,6,7,8,9)
# Take these vectors as contribution to the exhibit.
array1 <-array(c(vector1,vector2),dim = c(3,3,2))
# Create two vectors of various lengths.
vector3 <-c(9,1,0)
vector4 <-c(6,0,11,3,14,1,2,6,9)
array2 <-array(c(vector1,vector2),dim = c(3,3,2))
# make grids from these clusters.
matrix1 <-array1[,,2]
matrix2 <-array2[,,2]
# Add the grids.
result <-matrix1+matrix2
print(result)

[,1] [,2] [,3] [1,] 2 8 14
[2,] 4 10 16
[3,] 6 12 18

# Create two vectors of various lengths.
vector1 <-c(1,2,3)
vector2 <-c(4,5,6,7,8,9)
# Take these vectors as contribution to the cluster.
new.array <-array(c(vector1,vector2),dim = c(3,3,2))
print(new.array)
# Use apply to figure the entirety of the lines over every one of the frameworks.
result <-apply(new.array, c(1), whole)
print(result)

, 1

[,1] [,2] [,3] [1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9

, 2

[,1] [,2] [,3] [1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
[1] 24 30 36

function_name <-function(arg_1, arg_2, ...)
{
Capacity body
}

# Create a grouping of numbers from 1 to 12.
print(seq(1,12))

[1] 1 2 3 4 5 6 7 8 9 10 11 12

# Find mean of numbers from 10 to 25.
print(mean(10:25))

[1] 17.5

# Find whole of numbers frm 1 to 10.
print(sum(1:10))

[1] 55

# Find factorial of number 5
print(factorial(5))

[1] 120

# Create a capacity to print whole of two numbers
sum=function()
{
a=5
b=4
result=a+b
print(result)
}
Calling a Function whole without an Argument
entirety()

[1] 9

# Create a capacity to print entirety of two numbers
sum=function(a,b)
{
result=a+b
print(result)
}
# Call the capacity entirety providing 10,20 as a contention.
sum(10,20)

30

new.function <-function(a)
{
for(i in 1:a)
{
b <-i^2
print(b)
}
}
# Call the capacity new.function providing 6 as a contention.
new.function(6)

[1] 1
[1] 4
[1] 9
[1] 16
[1] 25
[1] 36

# Create a capacity with contentions.
new.function <-function(a,b,c) {
result <-a * b + c
print(result)
}
# Call the capacity by position of contentions.
new.function(1,2,3)

5

# Create a capacity with contentions.
new.function <-function(a,b,c) {
result <-a * b + c
print(result)
}
# Call the capacity by names of the contentions.
new.function(a = 1, b = 4, c = 2)

[1] 6

# Create a capacity with contentions.
new.function <-function(a = 5, b = 6) {
result <-a * b
print(result)
}
# Call the capacity without giving any contention.
new.function()

[1] 30

# Create a capacity with contentions.
new.function <-function(a = 5, b = 6) {
result <-a * b
print(result)
}
# Call the capacity with giving new estimations of the contention.
new.function(9,5)

45

# Create a capacity with contentions.
new.function <-function(a, b) {
print(a^2)
print(a)
print(b)
}
# Evaluate the capacity without providing one of the contentions.
new.function(3)

[1] 9[1] 3

# Create a vector as information.
information <-c("East","West","East","North","North","East","West","West","West","East","North")
print(data)
print(is.factor(data))
# Apply the factor work.
factor_data <-factor(data)
print(factor_data)
print(is.factor(factor_data))

# Create the vectors for information outline.
Name <-c("Raja","Rani","Suresh","Karthik","Manju")
Age <-c(30,41,24,53,17)
Pay <-c(5000,6000,2000,7000,2500)
Sex <-c("male","female","male","male","female")
# Create the information outline.
input_data <-data.frame(Name,Age,Salary,Gender)
print(input_data)
# Test if the sex section is a factor.
print(is.factor(input_data$Gender))
# Print the sexual orientation section so observe the dimensions.
print(input_data$Gender)

Name Age Salary Gender

1 Raja 30 5000 male
2 Rani 41 6000 female
3 Suresh 24 2000 male
4 Karthik 53 7000 male
5 Manju 17 2500 female
FALSE
Levels: female male

Name <-c("Raja","Rani","Suresh","Karthik","Manju")
# Create the variables
factor_data <-factor(Name)
print(factor_data)
# Apply the factor work with required request of the dimension.
new_order_data <-factor(factor_data,levels = c("Suresh","Raja","Manju","Rani","Karthik"))
print(new_order_data)

[1] Raja Rani Suresh Karthik Manju

[1] Raja Rani Suresh Karthik Manju

v <-gl(3, 4, names = c("Python", "Java","Ruby"))
print(v)

[1] Python Java Ruby[12] Ruby

# Create the vectors for information outline.
information <-data.frame(Name=c("Raja","Rani","Suresh","Karthik","Manju"),
Age = c(30,41,24,53,17),
Salary=c(5000,6000,2000,7000,2500),
Gender=c("male","female","male","male","female"),
stringsAsFactors = FALSE
)
# Print the information outline.
print(data)

Name Age Salary Gender

1 Raja 30 5000 male
2 Rani 41 6000 female
3 Suresh 24 2000 male
4 Karthik 53 7000 male
5 Manju 17 2500 female

# Create the vectors for information outline.
information <-data.frame(Name=c("Raja","Rani","Suresh","Karthik","Manju"),
Age = c(30,41,24,53,17),
Salary=c(5000,6000,2000,7000,2500),
Gender=c("male","female","male","male","female"),
stringsAsFactors = FALSE
)
# Get the structure of the information outline.
str(data)

‘data.frame’: 5 obs. of 4 factors:
$ Name : chr “Raja” “Rani” “Suresh” “Karthik” …
$ Age : num 30 41 24 53 17
$ Salary: num 5000 6000 2000 7000 2500
$ Gender: chr “male” “female” “male” “male” …

# Create the vectors for information outline.
information <-data.frame(Name=c("Raja","Rani","Suresh","Karthik","Manju"),
Age = c(30,41,24,53,17),
Salary=c(5000,6000,2000,7000,2500),
Gender=c("male","female","male","male","female"),
stringsAsFactors = FALSE
)
# Print the outline.
print(summary(data))

Name Age Salary Gender

Length:5 Min. :17 Min. :2000 Length:5
Class :character first Qu.:24 first Qu.:2500 Class :character
Mode :character Median :30 Median :5000 Mode :character
Mean :33 Mean :4500
third Qu.:41 third Qu.:6000
Max. :53 Max. :7000

# Create the vectors for information outline.
information <-data.frame(Name=c("Raja","Rani","Suresh","Karthik","Manju"),
Age = c(30,41,24,53,17),
Salary=c(5000,6000,2000,7000,2500),
Gender=c("male","female","male","male","female"),
stringsAsFactors = FALSE
)
# Extract Specific sections.
result <-data.frame(data$Name,data$Salary)
print(result)

data.Name data.Salary

1 Raja 5000
2 Rani 6000
3 Suresh 2000
4 Karthik 7000
5 Manju 2500

# Create the vectors for information outline.
information <-data.frame(Name=c("Raja","Rani","Suresh","Karthik","Manju"),
Age = c(30,41,24,53,17),
Salary=c(5000,6000,2000,7000,2500),
Gender=c("male","female","male","male","female"),
stringsAsFactors = FALSE
)
# Extract initial two columns.
result <-data[1:2,]
print(result)

Name Age Salary Gender

1 Raja 30 5000 male
2 Rani 41 6000 female

# Create the vectors for information outline.
information <-data.frame(Name=c("Raja","Rani","Suresh","Karthik","Manju"),
Age = c(30,41,24,53,17),
Salary=c(5000,6000,2000,7000,2500),
Gender=c("male","female","male","male","female"),
stringsAsFactors = FALSE
)
# Extract third and fifth line with second and fourth segment.
result <-data[c(2,3),c(2,4)]
print(result)

Age Gender
2 41 female
3 24 male

# Create the vectors for information outline.
information <-data.frame(Name=c("Raja","Rani","Suresh","Karthik","Manju"),
Age = c(30,41,24,53,17),
Salary=c(5000,6000,2000,7000,2500),
Gender=c("male","female","male","male","female"),
stringsAsFactors = FALSE
)
# Add the "dept" coulmn.
data$dept <-c("IT","Operations","IT","HR","Finance")
v <-information
print(v)

Name Age Salary Gender dept

1 Raja 30 5000 male IT
2 Rani 41 6000 female Operations
3 Suresh 24 2000 male IT
4 Karthik 53 7000 male HR
5 Manju 17 2500 female Finance

# Create the vectors for information outline.
data1 <-data.frame(Name=c("Raja","Rani","Suresh","Karthik","Manju"),
Age = c(30,41,24,53,17),
Salary=c(5000,6000,2000,7000,2500),
Gender=c("male","female","male","male","female"),
stringsAsFactors = FALSE
)
data2 <-data.frame(Name=c("Murugan","Priya"),
Age = c(34,29),
Salary=c(10000,20000),
Gender=c("male","female"),
stringsAsFactors = FALSE
)
# Bind the two information outlines.
final.data <-rbind(data1,data2)
print(final.data)

Name Age Salary Gender
1 Raja 30 5000 male
2 Rani 41 6000 female
3 Suresh 24 2000 male
4 Karthik 53 7000 male
5 Manju 17 2500 female
6 Murugan 34 10000 male
7 Priya 29 20000 female

#Get library areas that contain R Packages
.libPaths

[1] "C:/Users/LENIN/Documents/R/win-library/3.5"
[2] "C:/Program Files/R/R-3.5.0/library"

Bundles in library ‘C:/Program Files/R/R-3.5.0/library’:
base The R Base Package
boot Bootstrap Functions (Originally by Angelo Canty for S)
class Functions for Classification
bunch “Discovering Groups in Data”: Cluster Analysis
Broadened Rousseeuw et al.
codetools Code Analysis Tools for R
compiler The R Compiler Package
datasets The R Datasets Package
outside Read Data Stored by ‘Minitab’, ‘S’, ‘SAS’,
‘SPSS’, ‘Stata’, ‘Systat’, ‘Weka’, ‘dBase’, …
designs The R Graphics Package
grDevices The R Graphics Devices and Support for Colors
what’s more, Fonts
framework The Grid Graphics Package
KernSmooth Functions for Kernel Smoothing Supporting Wand and Jones (1995)
cross section Trellis Graphics for R
MASS Support Functions and Datasets for Venables and Ripley’s MASS
Framework Sparse and Dense Matrix Classes and Methods
techniques Formal Methods and Classes
mgcv Mixed GAM Computation Vehicle with Automatic Smoothness Estimation
nlme Linear and Nonlinear Mixed Effects Models
nnet Feed-Forward Neural Networks and Multinomial
Log-Linear Models
parallel Support for Parallel calculation in R
rpart Recursive Partitioning and Regression Trees
spatial Functions for Kriging and Point Pattern Analysis
splines Regression Spline Functions and Classes
details The R Stats Package
stats4 Statistical Functions utilizing S4 Classes
survival Analysis
tcltk Tcl/Tk Interface
instruments Tools for Package Development
interpretations The R Translations Package
utils The R Utils Package

#Get all bundles Currently stacked in the R Environment
look()

[1] “.GlobalEnv” “tools:rstudio” “package:stats”
[4] “package:graphics” “package:grDevices” “package:utils”
[7] “package:datasets” “package:methods” “Autoloads”
[10] “package:base”

install.packages("Package Name")

# Install the bundle named "XML".
install.packages("XML")

# Install the bundle named "xlsx".
install.packages("xlsx")

# Install the bundle named "XML"
install.packages("D:/XML_3.98-1.3.zip", repos = NULL, type = "source")

# Install the bundle named "xlsx"
install.packages("D:/xlsx_2.14-1.3.zip", repos = NULL, type = "source")

library("package Name", lib.loc = "way to library")

#Load the bundle named "XML"
library("XML")

#Load the bundle named "xlsx"
library("xlsx")

#Create vector objects
Name<-c("Raja","Suresh","Priya","Potrivel")
Age<-c(30,34,29,25)
#Combine above vectors into one information outline
data.frame1<-cbind(Name,Age)
#print a data.frame1
print(data.frame1)
#create another information outline with comparable sections
data.frame2=data.frame(Name=c("John","Sindhuja"),
Age=c(27,24)
)
#Print the data.frame2
print(data.frame2)
#Combine lines from both the information outlines
new.data_frame<-rbind(data.frame2,data.frame1)
#Print new.data_frame
print(new.data_frame)

[1,] Raja 30[2,] Suresh 34[3,] Priya 29[4,] Potrivel 25

#Create vector objects
Name<-c("Raja","Suresh","Priya","Potrivel")
Age<-c(30,34,29,25)
#Combine above vectors into one information outline
data.frame1<-cbind(Name,Age)
#print a data.frame1
print(data.frame1)
#create another information outline with comparable segments
data.frame2=data.frame(Name=c("John","Raja","Rani","Babu"),
Age=c(27,24,34,67)
)
#Merge the two information outlines
join.data_frame<-merge(data.frame1,data.frame2,by=("Name"))
#Print the join.data_frame
print(join.data_frame)

#Merge the two information outlines
join.data_frame<-merge(data.frame1,data.frame2,by=("Name"),all.x=TRUE)
join.data_frame<-merge(data.frame1,data.frame2,by=("Name"),all.y=FALSE)

# Get and print current working catalog.
print(getwd())

"C:/Users/LENIN/Documents"

# Set current working catalog.
setwd("/Package/R")

# Get and print current working catalog.
print(getwd())

"C:/Users/LENIN/Documents/Package/R/"

#To read A CSV document utilize:
read.csv("filename",,stringsAsFactors = FALSE)
#To compose a CSV document utilize
write.csv(value,"filename.csv")

data=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(data)

data=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(data)
print(is.data.frame(data))
print(ncol(data))
print(nrow(data))

[1] TRUE[1] 6[1] 10

data=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(data)
# Get the maximum pay from information outline.
sal <-max(data$Salary)
print(sal)

[1] 50000

data=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(data)
# Get the maximum pay from information outline.
sal <-max(data$Salary)
# Get the individual detail having max pay.
retval <-subset(data, Salary == max(Salary))
print(retval)

data=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(data)
#Get every one of the general population working in IT division
retval <-subset( information, dept == "IT")
print(retval)

data=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(data)
#Get the people in IT division whose pay is more noteworthy than 15000
data <-subset(data, Salary > 15000 and Department == "IT")
print(info)

data=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(data)
information <-subset(data, Salary > 15000 and Department == "IT")
print(info)
# Write separated information into another document.
write.csv(info,"C:\\Users\\LENIN\\Desktop\\raja sir\\output.csv")
newdata <-read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\output.csv")
print(newdata)

install.packages("xlsx")

# Verify the bundle is introduced.
any(grepl("xlsx",installed.packages()))
# Load the library into R workspace.
library("xlsx")
At the point when the content is run we get the accompanying yield.
yield:

[1] TRUE

#To read A xlsx document utilize:
read.xlsx("filename",sheetIndex = 1)
#To compose a CSV document utilize
write.xlsx(value,"filename.xlsx")

data=read.xlsx("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.xlsx")
print(data)

data=read.xlsx("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.xlsx")
print(data)
print(is.data.frame(data))
print(ncol(data))
print(nrow(data))

[1] TRUE
[1] 6
[1] 10

data=read.xlsx("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.xlsx")
print(data)
# Get the maximum pay from information outline.
sal <-max(data$Salary)
print(sal)

[1] 50000

data=read.xlsx("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.xlsx")
print(data)
# Get the maximum pay from information outline.
sal <-max(data$Salary)
# Get the individual detail having max compensation.
retval <-subset(data, Salary == max(Salary))
print(retval)

data=read.xlsx("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.xlsx")
print(data)
#Get every one of the general population working in IT division
retval <-subset( information, dept == "IT")
print(retval)

data=read.xlsx("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.xlsx")
print(data)
#Get the people in IT division whose compensation is more noteworthy than 15000
data <-subset(data, Salary > 15000 and Department == "IT")
print(info)

data=read.xlsx("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.xlsx")
print(data)
information <-subset(data, Salary > 15000 and Department == "IT")
print(info)
# Write separated information into another document.
write.xlsx(info,"C:\\Users\\LENIN\\Desktop\\raja sir\\output.xlsx)
newdata <-read.xlsx("C:\\Users\\LENIN\\Desktop\\raja sir\\output.xlsx")
print(newdata)

install.packages("XML")

1
Raja
33
90000
12
IT
2
Rani
36
50000
12
operations
3
Suresh
35
70000
12
Finance

# Load the bundle required to peruse XML records.
library("XML")
# Also stack the other required bundle.
library("methods")
# Give the info document name to the capacity.
result <-xmlParse(file="C:\\Users\\LENIN\\Desktop\\R\\DATA\\data.xml")
# Print the outcome.
print(result)

1
Raja
33
90000
12
IT
2
Rani
36
50000
12
operations
3
Suresh
35
70000
12
Finance

# Load the bundle required to peruse XML records.
library("XML")
# Also stack the other required bundle.
library("methods")
# Give the info document name to the capacity.
result <-xmlParse(file="C:\\Users\\LENIN\\Desktop\\R\\DATA\\data.xml")
# Print the outcome.
print(result)
# Exract the root hub frame the xml document.
rootnode <-xmlRoot(result)
# Find number of hubs in the root.
rootsize <-xmlSize(rootnode)
# Print the outcome.
print(rootsize)

[1] 3

Model:
# Load the bundle required to peruse XML records.
library("XML")
# Also stack the other required bundle.
library("methods")
# Give the info record name to the capacity.
result <-xmlParse(file="C:\\Users\\LENIN\\Desktop\\R\\DATA\\data.xml")
# Print the outcome.
print(result)
# Exract the root hub frame the xml record.
rootnode <-xmlRoot(result)
# Print the outcome.
print(rootnode[1])

$`EMPLOYEE`
1
Raja
33
90000
12
IT
attr(,”class”)
[1] “XMLInternalNodeList” “XMLNodeList”

# Load the bundle required to peruse XML documents.
library("XML")
# Also stack the other required bundle.
library("methods")
# Give the information record name to the capacity.
result <-xmlParse(file="C:\\Users\\LENIN\\Desktop\\R\\DATA\\data.xml")
# Print the outcome.
print(result)
# Exract the root hub frame the xml document.
rootnode <-xmlRoot(result)
# Get the primary component of the principal hub.
print(rootnode[[1]][[1]])
# Get the fifth component of the principal hub.
print(rootnode[[1]][[5]])
# Get the second component of the third hub.
print(rootnode[[3]][[2]])

1
12
Suresh

# Load the bundle required to peruse XML documents.
library("XML")
# Also stack the other required bundle.
library("methods")
# Give the info document name to the capacity.
xmldataframe = xmlToDataFrame("C:\\Users\\LENIN\\Desktop\\R\\DATA\\data.xml")
# Print the outcome.
print(xmldataframe)

ID NAME AGE SALARY EXPERIENCE DEPARTMENT
1 Raja 33 90000 12 IT
2 Rani 36 50000 12 tasks
3 Suresh 35 70000 12 Finance

# Create information for the chart.
Age <-c(33, 36, 25, 29)
names <-c("Raja", "Rani", "Priya", "Suresh")
# Give the outline document a name.
png(file = "details.jpg")
# Plot the outline.
pie(Age,labels)
# Save the document.
dev.off()

# Create information for the diagram.
Age <-c(33, 36, 25, 29)
marks <-c("Raja", "Rani", "Priya", "Suresh")
# Give the diagram record a name.
png(file = "details.jpg")
# Plot the diagram with title and rainbow shading bed.
pie(Age, marks, fundamental = "Representative Details", col = rainbow(length(Age)))
# Save the record.
dev.off()

# Get the library.
library(plotrix)
# Create information for the diagram.
Age <-c(33, 36, 25, 29)
Names <-c("Raja", "Rani", "Priya", "Suresh")
# Give the diagram record a name.
png(file = "details.jpg")
# Plot the diagram with title and rainbow shading bed.
pie3D(Age, labels=Names,explode = 0.1, fundamental = "Representative Details", col = rainbow(length(Age)))
# Save the record.
dev.off()

# Create the information for the graph
Pay <-c(3000,12000,50000,35000,41000)
# Give the graph record a name
png(file = "barchart.png")
# Plot the bar graph
barplot(Salary)
# Save the document
dev.off()

# Create the information for the graph
Pay <-c(3000,12000,50000,35000)
Names <-c("Raja", "Rani", "Priya", "Suresh")
# Give the graph document a name
png(file = "barchart.png")
# Plot the bar graph
barplot(Salary,names.arg=Names)
# Save the document
dev.off()

# Create the information for the graph
Pay <-c(3000,12000,50000,35000)
Names <-c("Raja", "Rani", "Priya", "Suresh")
# Give the graph document a name
png(file = "barchart.png")
# Plot the bar graph
barplot(Salary,names.arg=Names,xlab="Employee Name")
# Save the document
dev.off()

# Create the information for the graph
Pay <-c(3000,12000,50000,35000)
Names <-c("Raja", "Rani", "Priya", "Suresh")
# Give the graph document a name
png(file = "barchart.png")
# Plot the bar graph
barplot(Salary,names.arg=Names,xlab="Employee Name",ylab="Monthly Salary")
# Save the record
dev.off()

# Create the information for the diagram
Compensation <-c(3000,12000,50000,35000)
Names <-c("Raja", "Rani", "Priya", "Suresh")
# Give the diagram record a name
png(file = "barchart.png")
# Plot the bar diagram
barplot(Salary,names.arg=Names,xlab="Employee Name",ylab="Monthly Salary",col="green")
# Save the record
dev.off()

# Create the information for the diagram
Compensation <-c(3000,12000,50000,35000)
Names <-c("Raja", "Rani", "Priya", "Suresh")
# Give the diagram record a name
png(file = "barchart.png")
# Plot the bar diagram
barplot(Salary,names.arg=Names,xlab="Employee Name",ylab="Monthly Salary",col="green",main="Employee Details diagram")
# Save the record
dev.off()

# Create the information for the diagram
Compensation <-c(3000,12000,50000,35000)
Names <-c("Raja", "Rani", "Priya", "Suresh")
# Give the diagram record a name
png(file = "barchart.png")
# Plot the bar diagram
barplot(Salary,names.arg=Names,xlab="Employee Name",ylab="Monthly Salary",col="green",main="Employee Details chart",border="red")
# Save the record
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the diagram record a name.
png(file = "boxplot.png")
# Plot the diagram.
boxplot(Salary ~ Age, information = dataset)
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the diagram record a name.
png(file = "boxplot.png")
# Plot the diagram.
boxplot(Salary ~ Age, information = dataset,xlab = "Representative Age")
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the diagram record a name.
png(file = "boxplot.png")
# Plot the diagram.
boxplot(Salary ~ Age, information = dataset,xlab = "Representative Age",ylab = "Worker Salary")
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the diagram record a name.
png(file = "boxplot.png")
# Plot the diagram.
boxplot(Salary ~ Age, information = dataset,xlab = "Representative Age",ylab = "Worker Salary", fundamental = "Representative Details")
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the outline document a name.
png(file = "boxplot.png")
# Plot the outline.
boxplot(Salary ~ Age, information = dataset,xlab = "Worker Age",
ylab = "Worker Salary", primary = "Representative Details",
score = TRUE,varwidth = TRUE,col = c("green","yellow","purple","red"),
names = c("High","Medium","Low","Average"))
# Save the document.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the graph record a name.
png(file = "histogram.png")
# Create the histogram.
hist(dataset$Age,xlab = "Representative Age")
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the graph record a name.
png(file = "histogram.png")
# Create the histogram.
hist(dataset$Age,xlab = "Representative Age",col = "green")
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the graph record a name.
png(file = "histogram.png")
# Create the histogram.
hist(dataset$Age,xlab = "Representative Age",col = "green",border = "blue")
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the outline document a name.
png(file = "histogram.png")
# Create the histogram.
hist(dataset$Age,xlab = "Representative Age",col = "green",border = "blue",xlim = c(0,40))
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the graph record a name.
png(file = "histogram.png")
# Create the histogram.
hist(dataset$Age,xlab = "Representative Age",col = "green",border = "blue",xlim = c(0,40),ylim = c(0,6))
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the graph record a name.
png(file = "histogram.png")
# Create the histogram.
hist(dataset$Age,xlab = "Representative Age",col = "green",border = "blue",xlim = c(0,40),ylim = c(0,6),breaks =10)
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the outline record a name.
png(file = "line_chart.jpg")
# Plot the line outline.
plot(dataset$Age,type = "o")
# Save the document.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the diagram record a name.
png(file = "line_chart.jpg")
# Plot the line diagram.
plot(dataset$Age,type = "o",col = "red", xlab = "Sequence",ylab = "Representative Age",main = "Worker Details")
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the diagram record a name.
png(file = "line_chart.jpg")
# Plot the line diagram.
plot(dataset$Age,type = "o", xlab = "Succession")
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the diagram record a name.
png(file = "line_chart.jpg")
# Plot the line diagram.
plot(dataset$Age,type = "o", xlab = "Sequence",ylab = "Representative Age")
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the diagram record a name.
png(file = "line_chart.jpg")
# Plot the line diagram.
plot(dataset$Age,type = "o", xlab = "Sequence",ylab = "Representative Age",main = "Worker Details")
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the graph record a name.
png(file = "line_chart.jpg")
# Plot the line graph.
plot(dataset$Age,type = "o",col = "red", xlab = "Sequence",ylab = "Worker Age",main = "Representative Details")
lines(dataset$Salary, type = "o", col = "blue")
# Save the document.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the graph record a name.
png(file = "scatterplot.png")
# The beneath content will make a scatterplot diagram for the connection among Salary and Age.
plot(x = dataset$Salary,y = dataset$Age)
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the graph record a name.
png(file = "scatterplot.png")
# The beneath content will make a scatterplot diagram for the connection among Salary and Age.
plot(x = dataset$Salary,y = dataset$Age,xlab = "Compensation")
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the graph record a name.
png(file = "scatterplot.png")
# The beneath content will make a scatterplot diagram for the connection among Salary and Age.
plot(x = dataset$Salary,y = dataset$Age,xlab = "Salary",ylab = "Age")
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the graph record a name.
png(file = "scatterplot.png")
# The beneath content will make a scatterplot diagram for the connection among Salary and Age.
plot(x = dataset$Salary,y = dataset$Age,xlab = "Salary",ylab = "Age",xlim = c(10000,90000))
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the graph record a name.
png(file = "scatterplot.png")
# The beneath content will make a scatterplot diagram for the connection among Salary and Age.
plot(x = dataset$Salary,y = dataset$Age,xlab = "Salary",ylab = "Age",xlim = c(10000,90000),ylim = c(20,45),)
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the graph record a name.
png(file = "scatterplot.png")
# The beneath content will make a scatterplot diagram for the connection among Salary and Age.
plot(x = dataset$Salary,y = dataset$Age,xlab = "Salary",ylab = "Age",xlim = c(10000,90000),ylim = c(20,45),main = "Representative Details")
# Save the record.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the outline record a name.
png(file = "scatterplot.png")
# Plot the grids between 3 factors giving 9 plots.
# One variable with 3 others and aggregate 3 factors.
pairs(~Salary+Age+Experience,data = dataset,main = "Scatterplot Matrix")
# Save the record.
dev.off()

# Create a vector.
x <-c(10,20,30,40)
# Find Mean.
result.mean <-mean(x)
print(result.mean)

# Create a vector.
x <-c(10,20,30,40)
# Find Mean.
result.mean <-mean(x,trim = 0.1)
print(result.mean)

# Create a vector.
x <-c(10,20,30,40,NA)
# Find Mean.
result.mean <-mean(x)
print(result.mean)

# Create a vector.
x <-c(10,20,30,40,NA)
# Find mean dropping NA esteems.
result.mean <-mean(x,na.rm = TRUE)
print(result.mean)

# Create a vector.
x <-c(10,20,30,40)
# Find the middle.
median.result <-median(x)
print(median.result)

# Create the capacity.
getmode <-function(data)
{
uniq <-unique(data)
uniq[which.max(tabulate(match(data, uniq)))]
}
# Create the vector with numbers.
information <-c(2,1,2,3,1,2,3,4,1,5,5,3,2,3)
# Calculate the mode utilizing the client work.
result <-getmode(data)
print(result)

# Create the capacity.
getmode <-function(data)
{
uniq <-unique(data)
uniq[which.max(tabulate(match(data, uniq)))]
}
# Create the vector with characters.
charv <-c("raja","raja","priya","raja","sathish")
# Calculate the mode utilizing the client work.
result <-getmode(charv)
print(result)

#Create Relationship Model and get the Coefficients
dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Apply the lm() work.
connection <-lm(dataset$Salary~dataset$Age)
print(relation)

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Apply the lm() work.
connection <-lm(dataset$Salary~dataset$Age) print(summary(relation)) yield: ======= Call: lm(formula = dataset$Salary ~ dataset$Age) Residuals: Min 1Q Median 3Q Max - 24620.0 - 5428.0 - 832.1 6524.0 16996.2 Coefficients: Gauge Std. Blunder t esteem Pr(>|t|)
(Block) - 161163 29156 - 5.528 0.000252 ***
dataset$Age 7096 1043 6.801 4.73e-05 ***
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' 1
Remaining standard mistake: 11910 on 10 degrees of opportunity
Various R-squared: 0.8222, Adjusted R-squared: 0.8045
F-measurement: 46.26 on 1 and 10 DF, p-esteem: 4.733e-05
anticipate() Function

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Apply the lm() work.
connection <-lm(Salary~Age,data=dataset)
# Find Salary of an Employee with Age 28.
a <-data.frame("Age"=28)
result <-predict(relation,a)
print(result)

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
# Give the outline record a name.
png(file = "linearregression.png")
# Plot the outline.
plot(dataset$Salary,dataset$Age,col = "blue",main = "Age and Salary Regression",
cex = 1.3,pch = 16,xlab = "Age",ylab = "Pay")
# Save the document.
dev.off()

dataset=read.csv("C:\\Users\\LENIN\\Desktop\\raja sir\\raja.csv")
print(dataset)
str(dataset)
head(dataset)
plot(Salary~Age,data=dataset)
cor(dataset$Salary,dataset$Age)
ls()
m1=lm(formula=Salary~Age,data=dataset)
m1
abline(m1,col="red",lty=2,lwd=1)
m1
#Salary=Intercept+size*Age
predict(m1,data.frame("Age"=34),interval ="predict" )

# Create an arrangement of numbers between - 10 and 10 increasing by 0.1.
x <-seq(- 10, 10, by = 0.1)
# Choose the mean as 2.5 and standard deviation as 0.5.
y <-dnorm(x, mean = 2.5, sd = 0.5)
# Give the graph document a name.
png(file = "dnorm.png")
plot(x,y)
# Save the document.
dev.off()

# Create an arrangement of numbers between - 10 and 10 increasing by 0.2.
x <-seq(- 10,10,by = .2)
# Choose the mean as 2.5 and standard deviation as 2.
y <-pnorm(x, mean = 2.5, sd = 2)
# Give the diagram record a name.
png(file = "pnorm.png")
# Plot the diagram.
plot(x,y)
# Save the record.
dev.off()

# Create an arrangement of likelihood esteems augmenting by 0.02.
x <-seq(0, 1, by = 0.02)
# Choose the mean as 2 and standard deviation as 3.
y <-qnorm(x, mean = 2, sd = 1)
# Give the outline record a name.
png(file = "qnorm.png")
# Plot the chart.
plot(x,y)
# Save the document.
dev.off()

# Create an example of 50 numbers which are regularly conveyed.
y <-rnorm(50)
# Give the diagram document a name.
png(file = "rnorm.png")
# Plot the histogram for this example.
hist(y, fundamental = "Typical DIstribution")
# Save the record.
dev.off()