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- library(reshape)
- library(lattice)
- #
- # Categorical example
- #
- data1 <- read.csv(file="plots/categorical.csv")
- # Reshape the data into long format
- data1 <- melt(data1,measure.var=c("Systems.biology","Functional.genomics","Non.coding.RNA"))
- # Barplot of number of cups of tea per day
- plot1 <- bwplot(value ~ variable, data = data1)
- plot1$ylab <- "Cups of Tea per day"
- #
- # Continuous example
- #
- data2 <- read.csv(file="/Users/mike/Desktop/plots/continuous.csv")
- # Plot data as a scatter plot
- plot2 <- xyplot(productivity ~ distance, data=data2)
- plot2$xlab <- "Distance to tea making area (feet)"
- plot2$ylab <- "Weekly productivity (hours)"
- # Add a custom panel with a loess trend
- custom_panel_loess <- function(x,y,...){
- panel.xyplot(x,y,...)
- panel.loess(x,y)
- }
- plot2$panel <- custom_panel_loess
- #
- # Factored catergorical data
- #
- data3<- read.csv(file="/Users/mike/Desktop/plots/categorical_categorical.csv")
- # Split data by seasons
- winter <- data3[,1:3]
- summer <- data3[,4:6]
- # Convert to long format
- winter <- melt(winter,measure.var=c("SB","FG","ncRNA"))
- summer <- melt(summer,measure.var=c("SB.1","FG.1","ncRNA.1"))
- # Add the name of the season as an extra column
- summer <- cbind(summer,season="summer")
- winter <- cbind(winter,season="winter")
- # Convert back to a single data set
- data3 <- rbind(winter,summer)
- # Rename the variables for consistency
- levels(data3$variable)[4:6] <- levels(data3$variable)[1:3]
- # Print a plot of the data
- plot3 <- bwplot(value ~ variable | season, data=data3)
- plot3$ylab <- "Cups of Tea per day"
- print(plot3)
- #
- # Factored continuous data
- #
- data4 <- read.csv("/Users/mike/Desktop/plots/continuous_categorical.csv")
- # Name each of the sets of data
- water <- cbind(data4[,1:2],drink="water")
- tea <- cbind(data4[,3:4],drink="tea")
- hipflask <- cbind(data4[,5:6],drink="hipflask")
- # Name the columns and bind the data into a single dataset
- col.names <- c("volume","productivity")
- names(water)[1:2] <- col.names
- names(tea)[1:2] <- col.names
- names(hipflask)[1:2] <- col.names
- data4 <- rbind(water,tea,hipflask)
- # Plot the factored data
- plot4 <- xyplot(productivity ~ volume | drink, data=data4)
- plot4$xlab <- "Average volume ingested (litres / day)"
- plot4$ylab <- "Weekly productivity (hours)"
- custom_panel <- function(x,y,...){
- # Add a custom panel
- panel.xyplot(x,y,...)
- panel.loess(x,y,col="red",lty=2)
- }
- plot4$panel <- custom_panel
- print(plot4)
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