Assignment 2

Code

Plots

Assignment

Author

Vikrant Sagar R

Published

September 20, 2022

1.Build a Quarto blog on your personal website

2.RunPaul Murrell’s RGraphics basic R programs

Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.

 [1] -2.058285088 -1.607677855  0.720887059 -1.739149773  1.013559530
 [6] -1.102497796  0.756976884  0.414884366 -0.299894137 -2.532673874
[11]  1.301294175 -1.613633334 -1.104632426 -0.092355357  0.310409811
[16] -1.111126625  0.115930664  0.568959799  0.570875910 -0.026314715
[21]  1.228658991  0.235143191  0.817425734  0.346860807  0.891784573
[26] -0.680165963  1.919592712  0.128190218 -0.619151007 -0.575378018
[31] -2.181492903  0.206704774 -0.507813374  0.555124400 -0.528218803
[36]  0.816923719 -0.940774254 -0.871854445 -2.533348948 -0.003356822
[41] -0.134715218  0.440160719  1.224765694 -1.112611588 -1.019808134
[46] -0.589970557 -0.254529408 -1.404282839 -0.031580895  0.572184432

2c. Plotting functions

Histogram is the basic representation of the correlation between two normalized quantitative variables. For the above graph those variables being Population vs Life.Expectency.

--- title: "Assignment 2" author: "Vikrant Sagar R" date: "2022-09-20" categories: [Code, R, Plots, Assignment] draft: false format: html: code-fold: true code-tools: true execute: echo: false --- **1.Build a Quarto blog on your personal website** **2.RunPaul Murrell's RGraphics basic R programs** ```{r} ### Paul Murrell's R examples (selected) ## Start plotting from basics # Note the order #install.packages("ggpubr") ggpubr:: show_point_shapes() plot(pressure, pch=25 ) # Can you change pch? "YES" #pch is used to determine the point shape for the plot text(150, 600, "Pressure (mm Hg)\nversus\nTemperature (Celsius)") # Examples of standard high-level plots # In each case, extra output is also added using low-level # plotting functions. # # Setting the parameter (3 rows by 2 cols) par(mfrow=c(3, 2)) # Scatterplot # Note the incremental additions x <- c(0.5, 2, 4, 8, 12, 16) y1 <- c(1, 1.3, 1.9, 3.4, 3.9, 4.8) y2 <- c(4, .8, .5, .45, .4, .3) # Setting label orientation, margins c(bottom, left, top, right) & text size par(las=1, mar=c(4, 4, 2, 4), cex=.7) #mar is used for defining margins(bottom, left, top, right) plot.new() plot.window(range(x), c(0, 6)) lines(x, y1) #plots x,y1 values lines(x, y2) #plots x,y2 values points(x, y1, pch=16, cex=2) # Try different cex value? It changes the size of the point points(x, y2, pch=21, bg="white", cex=2) # Different background color i.e. white par(col="gray50", fg="gray50", col.axis="gray50") axis(1, at=seq(0, 16, 4)) # What is the first number standing for? Defining 'x' axis axis(2, at=seq(0, 6, 2)) #Defining 'y1' axis axis(4, at=seq(0, 6, 2)) #Defining 'y2' axis box(bty="u") #creates a box structure around the plot mtext("Travel Time (s)", side=1, line=2, cex=0.8) #Naming x-axis mtext("Responses per Travel", side=2, line=2, las=0, cex=0.8) #Naming y1-axis mtext("Responses per Second", side=4, line=2, las=0, cex=0.8) #Naming y2-axis text(4, 5, "Bird 131") #plotting for x=4 & y1=5 and naming as 'Bird 131' par(mar=c(5.1, 4.1, 4.1, 2.1), col="black", fg="black", col.axis="black") # Histogram # Random data Y <- rnorm(50) Y # Make sure no Y exceed [-3.5, 3.5] Y[Y < -3.5 | Y > 3.5] <- NA # Selection/set range x <- seq(-3.5, 3.5, .1) dn <- dnorm(x) par(mar=c(4.5, 4.1, 3.1, 0)) hist(Y, breaks=seq(-3.5, 3.5), ylim=c(0, 0.5), col="gray80", freq=FALSE) lines(x, dnorm(x), lwd=2) #dnorm is the density function par(mar=c(5.1, 4.1, 4.1, 2.1)) # Barplot par(mar=c(2, 3.1, 2, 2.1)) midpts <- barplot(VADeaths, col=gray(0.1 + seq(1, 9, 2)/11), names=rep("", 4)) mtext(sub(" ", "\n", colnames(VADeaths)), at=midpts, side=1, line=0.5, cex=0.5) text(rep(midpts, each=5), apply(VADeaths, 2, cumsum) - VADeaths/2, VADeaths, col=rep(c("white", "black"), times=3:2), cex=0.8) par(mar=c(5.1, 4.1, 4.1, 2.1)) # Boxplot par(mar=c(3, 4.1, 2, 0)) boxplot(len ~ dose, data = ToothGrowth, boxwex = 0.25, at = 1:3 - 0.2, subset= supp == "VC", col="white", xlab="", ylab="tooth length", ylim=c(0,35)) mtext("Vitamin C dose (mg)", side=1, line=2.5, cex=0.8) boxplot(len ~ dose, data = ToothGrowth, add = TRUE, boxwex = 0.25, at = 1:3 + 0.2, subset= supp == "OJ") legend(1.5, 9, c("Ascorbic acid", "Orange juice"), fill = c("white", "gray"), bty="n") par(mar=c(5.1, 4.1, 4.1, 2.1)) # Persp (Perspective Plots) used to view transformation matrix x <- seq(-10, 10, length= 30) y <- x f <- function(x,y) { r <- sqrt(x^2+y^2); 10 * sin(r)/r } z <- outer(x, y, f) z[is.na(z)] <- 1 # 0.5 to include z axis label par(mar=c(0, 0.5, 0, 0), lwd=0.5) persp(x, y, z, theta = 30, phi = 30, expand = 0.5) par(mar=c(5.1, 4.1, 4.1, 2.1), lwd=1) # Piechart par(mar=c(0, 2, 1, 2), xpd=FALSE, cex=0.5) pie.sales <- c(0.12, 0.3, 0.26, 0.16, 0.04, 0.12) names(pie.sales) <- c("Blueberry", "Cherry", "Apple", "Boston Cream", "Other", "Vanilla") pie(pie.sales, col = gray(seq(0.3,1.0,length=6))) # Exercise: Can you generate these charts individually? 'Yes' #Try these functions using another dataset. Be sure to work on the layout and margins ``` **2c. Plotting functions** ```{r} happy_planet <- read.csv("Happy_Planet.csv") View(happy_planet) x<- happy_planet$Ladder.of.Life y<- happy_planet$HPI x<- rnorm(500) y<- x+rnorm(500) #scatter plot plot(x,y, xlab = "Ladder of Life" , ylab= "Happy People") #boxplot boxplot(x,y, xlab = "Ladder of Life" , ylab= "Happy People") ``` **Histogram is the basic representation of the correlation between two normalized quantitative variables. For the above graph those variables being Population vs Life.Expectency.\ **