@Rushali_07

##Boxplot using notch Program: # Plot the chart. boxplot(mpg ~ cyl, data = mtcars, xlab = "Number of Cylinders", ylab = "Miles Per Gallon", main = "Mileage Data", notch = TRUE, varwidth = TRUE, col = c("green", "red", "blue"), names = c("High", "Medium", "Low"))

##Pie charts Program: data("chickwts") # main is used to create # an heading for the chart d = table(chickwts$feed) pie(d[order(d, decreasing=TRUE)], clockwise=TRUE, main="Pie Chart of feeds from chichwits", ) data(chickwts)

library (ggplot2) ##Polar Plot #mtcars %>% #dplyr::group_by(cyl) %>% #dplyr::summarize(mpg = median(mpg)) %>% #ggplot(aes(x = cyl, y = mpg)) + geom_col(aes(fill =cyl), color = NA) + labs(x = "", y = "Median mpg") + coord_polar() ##Bump Chart ggplot(mtcars, aes(x = hp, y = mpg, group = cyl))+ geom_line(aes(color = cyl), size = 2) + geom_point(aes(color = cyl), size = 4) + scale_y_reverse(breaks = 1:nrow(mtcars)) ##Pairplot with ggpairs #library(GGally)

library (ggplot2) ##Scatter Plot data(iris) ggplot(iris, aes(x=Sepal.Length, y=Petal.Length))+geom_point() ##geometry of data ggplot(iris, aes(x=Sepal.Length, y=Petal.Length, col=Species, shape=Species))+geom_point() ## smooth line ggplot(iris, aes(x=Sepal.Length, y=Petal.Length, col=Species))+geom_point() +geom_smooth() ggplot(iris, aes(x=Sepal.Length, y=Petal.Length, col=Species))+geom_point(color = "blue") + geom_smooth(color = "red") data(mtcars)

#Slice Percentage & Chart Legend Program: # Create data for the graph. geeks <- c(23, 56, 20, 63) labels <- c("Mumbai", "Pune", "Chennai", "Bangalore") piepercent<- round(100 * geeks / sum(geeks), 1) # Plot the chart. pie(geeks, labels = piepercent, main = "City pie chart", col = rainbow(length(geeks))) legend("topright", c("Mumbai", "Pune", "Chennai", "Bangalore"), cex = 0.5, fill = rainbow(length(geeks)))

#Create the data for the chart. v <- c(17, 25, 38, 13, 41) t <- c(22, 19, 36, 19, 23) m <- c(25, 14, 16, 34, 29) # Plot the bar chart. plot(v, type = "o", col = "red", xlab = "Month", ylab = "Article Written ", main = "Article Written chart") lines(t, type = "o", col = "blue") lines(m, type = "o", col = "green")

##Horizontal Bar Plot for ##Ozone concentration in air barplot(airquality$Ozone, main = 'Ozone Concenteration in air', xlab = 'ozone levels', horiz = TRUE) # Vertical Bar Plot for # Ozone concentration in air barplot(airquality$Ozone, main = 'Ozone Concenteration in air', xlab = 'ozone levels', col ='blue', horiz = FALSE) # Histogram for Maximum Daily Temperature

##log(-1) try(log("not a number"),silent=TRUE) print("errors can't stop me") ##Use tryCatch to handle errors an.error.occured <- FALSE tryCatch( { result <- log("not a number"); print(res) } , error = function(e) {an.error.occured <<- TRUE}) print(an.error.occured) ##tryCatch can handle all conditions 1/2 tryCatch( { result <- log(-1); print(result) }

library("ggplot2") data(airquality$Ozone) head(airquality$Ozone) #Ozone concentration in air barplot(airquality$Ozone, main='Ozone concentration in air', xlab='ozone levels',horiz=FALSE) barplot(airquality$Ozone, main='Ozone concentration in air', xlab='ozone levels',horiz=TRUE)

input <- mtcars[,c("mpg","disp","hp","wt")] # Create the relationship model. model <- lm(mpg~disp+hp+wt, data = input) # Show the model. input <- mtcars[,c("mpg","disp","hp","wt")] # Create the relationship model. model <- lm(mpg~disp+hp+wt, data = input) # Show the model. print(model)

x<-c(3,5,7,9,11,15) y<-c(9,12,16,14,15,66) print(lm(y~x)) plot(x,y,main="the linear regression",abline(lm(y~x)))

library(tibble) (df1 <- tibble( g = c(1, 2, 3), data = list( tibble(x = 1, y = 2), tibble(x = 4:5, y = 6:7), tibble(x = 10) ) ))

df <- data.frame( id = c(10,11,12,13), name = c('sai,ram,deepika,sahithi'), gender = c('M','M','F','F'), dob = as.Date(c('1990-10-02','1981-3-24','1987-6-14','1985-8-16')), state = c('CA','NY',NA,NA), row.names=c('r1','r2','r3','r4') )

show databases; +--------------------+ | Database | +--------------------+ | information_schema | | company | | fycsdemo1 | | fycsdemo56 | | fyds | | joins |

show databases; +--------------------+ | Database | +--------------------+ | information_schema | | fycsdemo1 | | fyds | | joins | | logindb | | logindb22 |

show databases; +--------------------+ | Database | +--------------------+ | information_schema | | fycsdemo1 | | fyds | | joins | | logindb | | logindb22 |

# create matrix with 4 columns and 4 rows data= matrix(c(1:16), ncol=4, byrow=TRUE) # specify the column names and row names of matrix colnames(data) = c('col1','col2','col3','col4')

library(tibble) tibble(x=1:5,y=1:5,z=x^2+y) tibble(a="Rushali",b="Galande") tibble() tibble(mtcars) tibble(iris) tibble(head(iris)) options(tibble.print_min=20) tibble(mtcars,options(tibble.print_min=20)) tibble(iris,options(tibble.print_min=30))

library (ggplot2) library (dplyr) data(mtcars) dim(mtcars) head(mtcars) mtcars%>%dplyr::group_by(cyl)%>% dplyr::summarize(mpg=median(mpg))%>%ggplot(aes(x=cyl,y=mpg))+geom_col(aes(fill=cyl),color=NA)+labs(x="",y="Median mpg")+coord_polar() ggplot(mtcars,aes(x=hp,y=mpg,group=cyl))+geom_line(aes(color=cyl),size=2)+geom_point(aes(color=cyl),size=4)+scale_y_reverse(breaks=1:nrow(mtcars))

library (ggplot2) library(dplyr) data(mtcars) ggplot(mtcars,aes(mpg,hp))+geom_density_2d_filled(show.legend=FALSE)+coord_cartesian(expand=FALSE)+labs(x="mpg") ggplot(mtcars,aes(x=mpg,y=hp))+geom_point()+geom_density_2d()