M.Tech-2022

why analysis?

To predict hidden pattern
Useful to take decisions
To help organizations harness their data and use it to identify new opportunities.

Examination of data
To understand their relationships

# Variables in R
x=12
x
y<- "Hello"
y
TRUE ->B
B
############################
#Operators
#Arithmetic operators
2+3 #add
3-1 #sub
2*3 #multi
6/2 #division
2^3 #power
5%%2 # Remainder
7%/%3 #quotient
#########################
#assignment
x=5
x<-15
x<<-30
25->x
x
#######################
#Relational Operators
# >
2>3 #greater
2<3 #less
2==2 #equal
2!=3 #not equal
2>=3
2<=3
############################
#Logical operators
#AND Operator “&”
# & takes two logical values and returns TRUE only if both values are TRUE themselves
x <- 12
x > 5 & x < 15

# OR operator "|"
#works similarly, but the difference is that only at least one of the logical values should be equal to TRUE for the entire OR operation to evaluate to TRUE
TRUE | TRUE
TRUE | FALSE
FALSE | FALSE
FALSE | TRUE
y <- 4 #16
y < 5 | y > 15

# NOT Operator “!”
#!TRUE evaluates to FALSE, while !FALSE evaluates to TRUE
!TRUE
!FALSE

x=TRUE 
y=FALSE
x&y #returns x if x is false, y otherwise
x&x
y&x

x | y # returns y if x is false
################################
#special operators
# ":" Colon operator
x<- 2:8 
x
y<-5 #9
#used to identify if an element (e.g., a number) belongs to a vector or dataframe
y %in% x
###############################

#Data Type
# Vector
#vectors is a sequence of data elements of the same basic type
#vector_name<-c(list of value: range)
vt1<-c(1,2,3,4)
vtr2=c(1,2,3,4,5)
vtr3<-c(1:12)
#index
#vector_name[index]
vtr3[2]
vtr4<-c(1,5,6,3)
#sort
sort_vtr4<-sort(vtr4)
vtr_mix<-c("hi",1,3,"hey")
class(vtr_mix)
class(vtr4)
vtr<-c(2,3)
sum(c(vtr))
mean(c(vtr))
prod(c(vtr))
##################################
#Lists
#Contain elements of different types like-numbers,strings, vectors and another list inside it

n=c(2,3,5)
n
s=c("aa","bb")
x=list(n,s)
class(x)
list1<-list("JK",12,3,TRUE)
list2<-list("R",14,3,FALSE)
list2
list3<-merge(list1,list2)
list3
class(list3)
list2[2]
#################################
#Vector-1D
#list under one name
#ARRAY
#Array multidimensional data
#can values having similar kinds of data
#array()
vtra<-c(2,3,4,5,6,7)
vtrb<-c(2,3,4,5,6,7,8,9,0,1)
array_1<-array(vtra)
array_2<-array(vtrb)
#using rang
arrayc <- array(1:15,c(4,3))
array_1[4]
multi_array<-array(1:24,c(3,4,2))
multi_array[1,3,1]
dim(multi_array)
a<-array(1:12,c(2,3,2))
dim(a)
dimnames(a)<-list(c("one","Two"),
                  c("a","b","c"),
                  c("A","B"))
################################
#Create a matrix
#special case of two-dimensional arrays.

mat1<-matrix(c(1:9),nrow = 3,ncol =3 )
mat1
mat1<-matrix(c(1:9),nrow = 3)
class(mat1)
mat1<-matrix(c(1:9),nrow=3, byrow = TRUE,
             dimnames=list(c("x","y","z"),
             c("A","B","C")))

mat1[1,2]
#select row and colm
mat1
mat1[c(1,2),c(2,3)]
mat1[1,] #1st row
mat1[,2] #2nd colm
mat1[-1,] #select all the rows except 1st
#modify
mat1[2,2]<-100
#######################################
rm(list=ls())
##matrix(data, nrow,ncol, dimnames)
mat1<-matrix(c(1:9), nrow=3,ncol=3)
mat1
mat2<-matrix(c(1:12), nrow = 3, byrow=T)
mat2
#########################################

#Matrix with row and column name & 
#filling by row wise
mat5<-matrix(c(1:9),nrow = 3, byrow = TRUE,
             dimnames = list(c("x","y","z"),
                             c("a","b","c")))
mat5
colnames(mat5)
rownames(mat5)
#change row name and col name
rownames(mat5)<-c("a1","a2","a3")
colnames(mat5)<-c("x1","x2","x3")
mat5
vtr1<-c(1,2,3,4,5,6,7,8,9,10)
mat6<-matrix(c(vtr1),nrow = 5, byrow = TRUE)
mat6
##################################
## Access matrix element ##
mat6[2,2]
mat3<-matrix(c(1:9),nrow=3,byrow = T)
mat3
a<-mat3[c(1,2),c(2,3)]
mat3[2,2]
#modify single element in matrix
mat3[2,2]<-100
mat3[mat3<5]<-0 
mat3
#matrix operation
mat_trans<-matrix(1:15, nrow=5)
#transpose
t(mat_trans)
#addition
p<-matrix(c(1:12), nrow = 4, byrow = T)
q<-matrix(c(12:23), nrow = 4, byrow = T)
p
q
#addition
p+q
p-q
p*q
p/q
##########################
##Factors##
#Factor is a data structure which are used to categorize the data and store it as levels.

#create a vector
m_status<-c("single","married","divorced","single")
m_factor<-factor(m_status)
class(m_factor)
gender<-factor(c("male","female","female","male","male"))
gender
#ordered factor
mstatus<-factor(m_status,levels=c("single","married","divorced"),
                ordered=T)
levels(mstatus)
min(mstatus)
## generate level ##
a<-gl(4,4, labels = c("hi","hello","hey","aa"))
a
#access factor component
m_factor[1]
## modify factor component
m_factor[1]<-"divorced"
#add new level
levels(m_factor)<-c(levels(m_factor),"widow")
m_factor
m_factor[1]<-"widow"
################################################
#Data frame
#a table or a two-dimensional array-like structure in which each column contains values of one variable and each row contains one set of values from each column.

data_name<-c("a","b","c","d","e")
data_id<-c(1:5)
rollno<-c(11,22,33,44,55)
#create
data_f<-data.frame(data_name,data_id,rollno)
dim(data_f)
str(data_f)
#extract a specific colum
data_f$data_name
data_extr<-data.frame(data_f$data_name,data_f$data_id)
#add one column
data_f
age<-c(12,23,34,45,56)
data_f1<-data.frame(data_f,age)
#display specific rows
data_f1[2,1:2]
#####################
data_f3<-data.frame(
  id=c(11:15),
  name=c("aa","bb","cc","dd","ee"),
  rollno=c(1,2,3,4,5),
  ad_date=as.Date(c("2021-01-01",
                    "2021-01-02",
                    "2021-01-03",
                    "2021-01-04",
                    "2021-01-05")),
  stringsAsFactors=F
)
data_f3
#Automatic string to factor conversion 

str(data_f3)
#stingsAsFactors
data_f3$dept<-c("biochem","micro","env","phy","chem")
data_f3
data_f4<-data.frame(
  id=c(16,17),
  name=c("qq","ww"),
  rollno=c(6,7),
  ad_date=as.Date(c("2021-01-06",
                      "2021-01-07")),
  dept=c("nano","eco"),
  stringsAsFactors=F)
#############################
data_f3
data_f4
data_bind<-rbind(data_f3,data_f4)
##deleting component##
data_bind$dept<-NULL
data_bind
#######################
#loops
a<-6
if(a>5){
  print("a is greater than 5")
}else if (a==5){
  print("a is equal to 5 ")
}else{
  print("a is less than 5")
}
#################################
# read user input
name<-readline(prompt = "Enter the name: ")
age<-readline(prompt = "Enter the age: ")
if(age<=10){
  print("kids")
}else{
  print("adult")
}
#####################################
#switch statement
vtr<-c(1,2,3,4,5)
option<-readline(prompt = "Enter the option: ")
switch (option,
        "mean"=print(mean(vtr)),
        "median"=print(median(vtr)),
        "min"=print(min(vtr))
)
``
#################################
# loops repeat
```R
x=2
repeat{
  x=x^2
  print(x)
  if(x>15)
    break
}
#########################
#while
x=2
while(x<100){
  x=x^2
  print(x)
}
########################
#for
###############
for (i in 1:15) {
  if((i%%2)==0){
    next
  }
  print(i)
}

Data visualization

x<-c(1:10)
y<-c(11,30,16,21,25,26,31,18,33,27)
length(y)
#plot graph
# scatter plot
plot(x,y)
#pie
pie(x,y)
name_data<-letters[1:10]
pie(x,name_data)
#barplot
barplot(x)
#boxplot
boxplot(x)
#hist
hist(y)
?par
#par()<-accomodate several graph
#oma<- all side line space 
par(mfrow=c(2,3), oma=c(2,2,2,2))

#plot 1
#pch<- plotting character
plot(x,y, pch=6)
#dev.off()

#plot 2
plot(x,y, pch=6, type="o")

#plot3 #color
plot(x,y, pch=6, type="o",
    col="red",
    xlab="height",
    ylab="weight")

#plot 4 
plot(x,y, pch=6, type="o",
     col="red",
     xlab="height",
     ylab="weight",
     sub="H/W")
#plot 5
plot(x,y, pch=6, type="o",
     col="red",
     xlab="height",
     ylab="weight",
     sub="H/W", cex.axis=1.5,
     cex.lab=1.0)

#plot 6 las-orient
plot(x,y, pch=6, type="o",
     col="green",
     xlab="height",
     ylab="weight",
     sub="H/W", cex.axis=1.5,
     cex.lab=1.0, las=2)

Data Handeling

getwd()
setwd("/cloud/project/data")
sepsis<-read.csv(file="Sepsis.csv", header=T)
head(sepsis,2)
colnames(sepsis)
x<-read.
delim("clipboard")

sepsis$Shock<-factor(sepsis$Shock,
                     label=c("no","yes"))
sepsis$Alcoholism <-factor(sepsis$Alcoholism, 
                           labels=c("no","yes"))
sepsis$Infarction <-factor(sepsis$Infarction, 
                           labels=c("no","yes"))
head(sepsis)
sepsis<-sepsis[-1]

Linear-Regression_R

linear regression:

1. Use least-squares to fit a line to the data
1. Calculate R^2
1. Calculate a p-value for R^2

Linear Regression: Process of finding a line that best fits the data points available on the plot

Y = a + bX
Y = Y-intercept + Slope.X
SS(mean) = (data - mean)^2
Variation around the mean = (data-mean)^2 / n
Var(mean) = SS(mean) / n
predict a quantitative response of Y on the basis of a single predictor variable X.
It assumes that there is approximately a linear relationship between X and Y