Graphical representation of data. Experiment :1 Bar Graph Experiment :2 Pie Graph Experiment :3 Histograms Experiment :4 Boxplot

df=data.frame(Name=c("John","Bill","Maria","Tom","Emma"),age=c(23,41,32,55,40))

df

write.csv(df,"people.csv",row.names=FALSE)

#Built-in Data

data(mtcars)

head(mtcars,6)

#BAR Graph

x=c("Punjab","Harayana","U.P","Gujarat","Bihar","Karnataka")

y=c(728,943,1469,2903,2153,2276)

barplot(y,names.arg=x,col="red",main="Yield of rice in Kg. Per Acre in various states of india",xlab="States",ylab="Yield")

#Multiple Bar Graph

clg=c("A","B","C","D")

clgA=c(120,260,500)

clgB=c(100,180,650)

clgC=c(140,300,850)

clgD=c(750,900,300)

d=data.frame(clgA,clgB,clgC,clgD)

d1=as.matrix(d)

barplot(d1,beside=T,names.arg=clg,xlab="College",ylab="No.Of Students",col=c("orange","white","green"),border="black")

legend("topleft",legend=c("ARTS","SCIENCE","COMMERSE"),fill=c("orange","white","green"),border="black")

#subdivided_VERTICAL

barplot(d1, beside = F, names.arg = clg, xlab = "College", ylab = "No. of students",col = c("Green","red","yellow"))

legend("topleft",legend = c("ARTS","SCIENCE","COMMERSE"), fill =c("green","red","yellow"),border = "black")

#SUBDIVIDED HORIZONTAL

barplot(d1, beside = F, horiz = T, names.arg = clg, xlab = "College", ylab = "No. of students",col = c("Green","red","yellow"))

legend("bottomright",inset = c(0, -0.01),legend =c("ARTS","SCIENCE","COMMERSE"), fill=c("green","red","yellow"),border = "black",cex = 0.4)

#PIE_CHART

item =c('Food','Clothing','Recreation','Electric','Movie','Rent')

exp = c(87, 24, 11, 13, 25, 20)

pie(exp, main='Expenditure', labels = item, radius =1, col = rainbow(length(exp)))

#PIE SLICE PER%

x =c(21,62,10,53)

labels= c("London","NewYork","Singapore","Mumbai")

piepercent= round(100*x/sum(x),1)

png(file ="city_percentage_legends.jpg")

pie(x, labels =piepercent, main ="City pie chart",col= rainbow(length(x)))

legend("topright", c("London","NewYork", "Singapore","Mumbai"), cex=0.8,

fill= rainbow(length(x)))

dev.off()

#HISTOGRAM

x=seq(150,175,5)

f=c(6,11,14,9,3,2)

y=rep(x,f)

t=seq(147.5,177.5,5)

hist(y,breaks=t,col=1:6)

#PIE_CHART

item =c('Food','Clothing','Recreation','Electric','Movie','Rent')

exp = c(87, 24, 11, 13, 25, 20)

pie(exp, main='Expenditure', labels = item, radius =1, col = rainbow(length(exp)))

#PIE SLICE PER%

x =c(21,62,10,53)

labels= c("London","NewYork","Singapore","Mumbai")

piepercent= round(100*x/sum(x),1)

png(file ="city_percentage_legends2.jpg")

pie(x, labels =piepercent, main ="City pie chart",col= rainbow(length(x)))

legend("topright", c("London","NewYork", "Singapore","Mumbai"), cex=0.8,

       fill= rainbow(length(x)))

dev.off()

#HISTOGRAM Ungrouped

x=seq(150,175,5)

f=c(6,11,14,9,3,2)

y=rep(x,f)

t=seq(147.5,177.5,5)

hist(y,breaks=t,col=1:6)

#HISTOGRAM Grouped Data

midx=seq(12.5,112.5,25)

f=c(5,8,13,11,3)

climit=seq(0,125,25)

y=rep(midx,f)

hist(y,breaks=climit,col=6:1)

#Box Plot

input=mtcars[,c('mpg','cyl')]

print(head(input))

boxplot(mpg ~cyl,data=mtcars,xlab = "Number Of Cylinders",

        ylab="Miles Per Gallon",main="Mileage Data",col=c("red","green","yellow"))

#Box Plot

data("iris")

iris

boxplot(Sepal.Length~Sepal.Width,data=iris,xlab="Sepal Width",

        ylab="Sepal Length",main="Iris data",col=c("red","green","yellow"))