1-OPERATIONS ON MATRICES:

mat1.data <- c(1,2,3,4,5,6,7,8,9)
mat1 <- matrix(mat1.data,nrow=3,ncol=3,byrow=TRUE)
mat1
mat2.data <- c(10,11,12,13,14,15,16,17,18)
mat2 <- matrix(mat2.data,nrow=3)
mat2
mat2[1,2] 
mat2[c(1,3),c(2,3)] 
mat2[2, , drop=FALSE] 
mat2[c(-1),c(-2,-3),drop=FALSE] #first row and second and third column are excluded
mat2[c(TRUE,FALSE,TRUE),c(FALSE,TRUE,TRUE)]
mat2[c(TRUE,FALSE),c(FALSE,TRUE,TRUE)] #the row vector is recycled to 3 elements.
mat1*5
mat1+2
mat1/2
mat_add <- mat1+mat2
mat_add
mat_sub <- mat1-mat2
mat_sub
mat_mul <- mat1*mat2
mat_mul


2-OPERATIONS ON VECTORS:

vec<-c(1,2,3,4,5)
multivec <- vec*2
multivec
vec2<-c(6,7,8,9,10)
vector_add <- vec+vec2 
vector_add
vector_mul <- vec*vec2
vector_mul
vector_sub <- vec2-vec
vector_sub
vector_div <- multivec/vec 
vector_div
vec_seq <- seq(from=1,to=20,length=30)
vec_seq
vec_rep <- rep(c(2,3,4), times=3)
vec_rep
sum(vec_rep)

3-Dataframes

setwd("E:\\Sample")
getwd()
sample = read.csv("k1.csv")
data <- read.csv("k1.csv")
head(sample)
# Create a data frame.
# Get the max salary from data frame.
sal <- max(data$salary)
print(sal)
salaries <-(data$salary)
salaries
names<-(data$name)
names
summary(data$salary)
salarybar<-c(names,salaries, byrow=FALSE)
barplot(data$salary,main="SALARY",
ylab='salary',
xlab ="name",
col="blue",
horiz=FALSE,beside=TRUE)
hist(data$salary)
colnames<-c(data$name)
colnames
rowname<-c(data$sa)




4-SORTING:

df <- data.frame("Serial_number" = 1:5, "Age" = c(20, 21, 17, 23, 19), "Name" = c("raja","ramu", "ravi", "raghu", "rajesh"))
newdataAsc <- df[order(df$Age),]
newdataAsc
newdataDsc <- df[order(-df$Age),]
newdataDsc

5-Mean Median

setwd("E:\\Sample") 
getwd() 
sample2 = read.csv("k5.csv") 
data <- read.csv("k5.csv") 
data 
mean(data$x) 
median(data$x) 
sd(data$x) 
var(data$x) 
#mediam absolute variance 
mad(data$x) 
max(data$x) 
min(data$x) 
sum(data$x) 
length(data$x)



6-FACTORIAL USING RECURSIVE FUNCTION:

rec_fac <- function(x){
  if(x==0 || x==1){
    return(1)
  } else {
    return(x*rec_fac(x-1))
  }
}
var = 6 
var = as.integer(var) 
print(var) 
x<-var 
f<-rec_fac(x) 
t1<-c("factorial",x,"is",f ) 
t1

Program 7 non-linear 



data <- data.frame("store" = c("padi","maduravoyal","anna nagar"), "sales" = c(20, 21, 17), "advt_cost" = c(1,2, 1.5))
store_name<-data$store 
store_name 
car_sales <-data$sales 
car_sales 
store_advt<-data$advt_cost 
store_advt 
data<-data.frame(store_name,car_sales,store_advt) 
data 
scatter.smooth(x=data$car_sales,y=data$store_advt, xlab = "car sales",ylab = "advertisement cost", main="car sales - advertisementcost") 
Linear_model <- lm(car_sales~store_advt,data = sample1) 
print(Linear_model) 
summary(Linear_model)




8-PLOT GRAPH FOR NORMAL DISTRIBUTION:


x <- seq(-5, 5, by = .1)

y <- dnorm(x, mean =0, sd = 1)

plot(x,y)


9-TIMESERIES:

sales_ice <- c(450, 613, 466, 205.7,571.0, 622.0, 851.4, 621.4,875.3, 
979.7, 927.5,14.45)
sales_cooldrink <- c(550, 713, 566, 687.2,110, 120, 72.4, 
814.4,423.5, 98.7, 741.4,345.3)
combined.sales <- matrix(c(sales_ice,sales_cooldrink),nrow = 12)
sales.timeseries <- ts(combined.sales,start = c(2021,1),frequency = 
12)
plot(sales.timeseries, main = "Showing ice cream – cooldrink sales")
print(sales.timeseries)


10-HYPOTHESIS:

x <- sample(c(1:100),size=20,replace=TRUE)
y <- sample(c(1:100),size=20,replace=TRUE)
t1<- c("value of variable x")
t1
x
t2<- c("value of variable y")
y
t3<-c("t test ")
t3
t.test(x,y)
t4<-c("Correlation between x and y")
t4
cor(x,y)
t5<-c("covarience")
t5
cov(x,y)


