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Task1 library(ggplot2) df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3), D=rep(c("D0.5", "D1", "D2"),2), L=c(6.8, 15, 33, 4.2, 10, 29.5)) head(df2) p<-ggplot(df2, aes(x=D, y=L, group=supp)) + geom_line(aes(color=supp))+ geom_point(aes(color=supp))+ggtitle("R2-24") p + scale_color_grey() + theme_classic()
#Task2 options(scipen=999) library(ggplot2) theme_set(theme_bw()) data("midwest", package = "ggplot2") gg <- ggplot(midwest, aes(x=area, y=poptotal)) + geom_point(aes(col=state, size=popdensity)) + geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) + labs(subtitle="Area Vs Population", y="Population", x="Area", title="R2-24", caption = "Source: midwest") plot(gg)
setwd("D:/R-LMT/10/10") library(reshape2) library(ggplot2) library(plyr) library(scales) data_heatmap<- read.csv("task3.csv") data_m <- melt(data_heatmap, id.vars=c("Name")) data_m <- ddply(data_m, .(variable), transform, label = rescale(value)) ggplot(data_m, aes(x=variable,y=Name)) + geom_tile(aes(fill=label)) + scale_fill_gradient(low = "yellow", high = "red") + xlab("Type") + theme_bw() + ggtitle("R2-24")
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