BASH intro Mark Down

M<- c(1.1,2,3.5,3.9,4.2)
M

## [1] 1.1 2.0 3.5 3.9 4.2

N<- c(2,2.2,-1.3,0,0.2)
N

## [1]  2.0  2.2 -1.3  0.0  0.2

plot(M,N)

MN<- cbind(M,N)
MN

##        M    N
## [1,] 1.1  2.0
## [2,] 2.0  2.2
## [3,] 3.5 -1.3
## [4,] 3.9  0.0
## [5,] 4.2  0.2

plot(MN)
plot(M,N,type="b")

plot(M,N,type="l")

plot(M,N,type="p")

plot(M,N,type="b",main="Rainfall againt Malaria cases",xlab="rainfall",ylab="malaria cases")

plot(M,N,type="b",main="Rainfall againt Malaria cases",xlab="rainfall",ylab="malaria cases",col= "violet")

plot(M,N,type="b",main="Rainfall againt Malaria cases",xlab="rainfall",ylab="malaria cases",col= "violet",cex=2.1)

plot(M,N,type="b",main="Rainfall againt Malaria cases",xlab="rainfall",ylab="malaria cases",col= "violet",cex=2.1,pch=8.0)

plot(M,N,type="b",main="Rainfall againt Malaria cases",xlab="rainfall",ylab="malaria cases",col= "violet",cex=2.1,pch=8.0,lty=3)

plot(M,N,type="b",main="Rainfall againt Malaria cases",xlab="rainfall",ylab="malaria cases",col= "violet",cex=2.1,pch=8.0,lty=3,lwd=2.5,xlim= c(-2,5),ylim=c(-10,5.0))

plot(M,N,type="n",main="",xlim= c(5,20),ylim=c(-15,15))
abline(h=c(-5,5),col="red",lty=2,lwd=2)

######functions####
I<- function(C,S,beta,alpha){
  C+S-(alpha/beta)*log(S)
  
}
C<- 1
beta<-0.01
alpha<-1/5
S<- seq(1, 100,50)
plot(I(C,S,alpha,beta),S, type="l",lwd=2,lty=2,col="blue")

I <- function(S,beta,C,alpha){
  C-S+(alpha/beta)*log(S)
}
C<- 100
beta<-0.01
alpha<-1/5
S<- seq(1,100,1)
plot(S,I(S,beta,C,alpha), type="p",lwd=2, col="blue", xlab="S", ylab="I")

y<- function(x){
  x^2 - 4*x +3
}
x<- seq(-10,10,1)
plot(y,x,  type="l",lwd=1,xlab="x-axis", ylab="y-axis")

I <- function(S, beta, alpha, C){
  C - S + (alpha/beta)*log(S)
}
beta <- 0.01
alpha <- 1/5
C <- 100
S <- seq(1,100,1)
plot(S,I(S,beta,alpha,C), xlab = "x-axis", ylab = "y-axis", col = "red", lwd = 2)

I <- function(S, beta, alpha, C){
  C - S + (alpha/beta)*log(S)
}
beta <- 0.01
alpha <- 1/5
C <- 100
S <- seq(1,100,1)
plot(S,I(S,beta,alpha,C), xlab = "x-axis", ylab = "y-axis", col = "red", lwd = 2)

y<- function(x){ifelse(x<0,x^2,sqrt(x))}
x<- seq(-5,20,0.5)
plot(x,y(x),col="purple",xlab="x-axis",ylab="y-axis",pch=5,lwd=2,lty=4,type ="l")

## Warning in sqrt(x): NaNs produced

y<- function(x){ifelse(x<0,x^2,sqrt(x))}
x<- seq(-5,20,0.5)
plot(x,y(x),col="purple",xlab="x-axis",ylab="y-axis",pch=5,lwd=2,lty=4,type ="l",main="piecewise function")

## Warning in sqrt(x): NaNs produced

y<-  function(x){
  (exp(-0.1*x))*(sin(2*x))
  }
x<- seq(0,20,0.1)
plot(x,y(x),col="green",xlab="x-axis",ylab="y-axis",pch=4,lwd=2,lty=4,type ="l",main="Dampened sine wave")

y<- function(x){1/(1 + exp(-x))}
x<- seq(-10,10,0.1)
plot(x,y(x),col="yellow",xlab="x-axis",ylab="y-axis",pch=8,lwd=6,lty=4,type ="l",main="Logistic function")

####parametric cycle#####
x<- function(t){r*cos(t)}
y<- function(t){r*sin(t)}
t<- seq(0,2*pi,0.01)
r<- 1
plot(x(t),y(t),col="pink",xlab="x-axis",ylab="y-axis",lwd=6,lty=1,type ="l",main="parametric cycle")

#####Lissajous curve#####
x<- function(t){sin(3*t)}
y<- function(t){sin(4*t)}
t<- seq(0,2*pi,0.01)
r<- 1
plot(x(t),y(t),col="magenta",xlab="x-axis",ylab="y-axis",lwd=6,lty=1,type ="l",main="Lissajous Curve")

#####(Normal Distribution Curve)####
y<- function(x){(1/sqrt(2*pi))*exp((-(x^2)/2))}
x<- seq(-4,4,0.1)
plot(x,y(x),col="blue",xlab="x-axis",ylab="y-axis",pch=2,type="p",lwd=2,lty=2,main="guassian function")