Parsing Java Micro-benchmarking Harness data using dplyr – Part 2

I have to add explanations later because I have to determine if the statistical measures calculated are correct or wrong. But this is based on the previous blog post.

Update : I think the measures are correctly plotted.

Types of Error bars used to plot the diagram

The parsing will not work if JMH changes the default format of the output file.

library(stringr)
library(dplyr)
library(ggplot2)

data <- read.table("D:\\jmh\\jmh.txt",sep="\t")

final <-data %>%
	    select(V1) %>%	
		filter(grepl("^Iteration", V1)) %>%  
        mutate(V1 = str_extract(V1, "\\d+\\.\\d*"))

final <- mutate(final,IDX = 1:n())

jc <- final %>%
		filter(IDX < 21)


gc <- final %>%
		filter(IDX > 20)

gc <- mutate(gc,IDX = 1:n())

jc <- data.frame(sapply(jc, function(x) as.numeric(as.character(x))))
gc <- data.frame(sapply(gc, function(x) as.numeric(as.character(x))))


print(summary(jc$V1))
error <- qt(0.995,df=length(jc$V1)-1)*sd(jc$V1)/sqrt(length(jc$V1))
error1 <- mean(jc$V1)-error
error2 <- mean(jc$V1)+error

q <- qplot(geom = "line",jc$IDX,jc$V1, colour='red')+geom_errorbar(aes(x=jc$IDX, ymin=jc$V1-sd(jc$V1), ymax=jc$V1+sd(jc$V1)), width=0.25)+ 
		geom_ribbon(aes(x=jc$IDX, y=jc$V1, ymin=error1, ymax=error2),fill="ivory2",alpha = 0.4)+ 
		xlab('Iterations') + ylab("Java Collections")+theme_bw() 

ggsave("D:\\jmh\\jc.png", width=6, height=6, dpi=100)

#Using error <- qt(0.995,df=length(jc$V1)-1)*sd(jc$V1)/sqrt(length(jc$V1)) 
g <- ggplot(jc, aes(x = IDX, y = V1)) +
		theme_bw() +
		geom_ribbon(aes(ymin = V1 - error, ymax = V1 + error), fill = "gray60",
				alpha = 0.3) +
		geom_line(color = "blue", size = 1) +
		geom_errorbar(aes(ymin = V1 - error, ymax = V1 + error), width = 0.25,
				color = "red") +
		labs(x = "Iterations", y = "Java collections")

ggsave("D:\\jmh\\ggplotjc.png", width=6, height=6, dpi=100)


print(summary(gc$V1))
error <- qt(0.995,df=length(gc$V1)-1)*sd(gc$V1)/sqrt(length(gc$V1))
error1 <- mean(gc$V1)-error
error2 <- mean(gc$V1)+error

q1 <- qplot(geom = "line",gc$IDX,gc$V1, colour='red')+geom_errorbar(aes(x=gc$IDX, ymin=gc$V1-sd(gc$V1), ymax=gc$V1+sd(gc$V1)), width=0.25)+ 
		geom_ribbon(aes(x=gc$IDX, y=gc$V1, ymin=error1, ymax=error2),fill="ivory2",alpha = 0.4)+ 
		xlab('Iterations') + ylab("Goldmansachs Collections")+theme_bw() 


ggsave("D:\\jmh\\gc.png", width=6, height=6, dpi=100)

#Using error <- qt(0.995,df=length(gc$V1)-1)*sd(gc$V1)/sqrt(length(gc$V1)) 
g1 <- ggplot(gc, aes(x = IDX, y = V1)) +
		theme_bw() +
		geom_ribbon(aes(ymin = V1 - error, ymax = V1 + error), fill = "gray60",
				alpha = 0.3) +
		geom_line(color = "blue", size = 1) +
		geom_errorbar(aes(ymin = V1 - error, ymax = V1 + error), width = 0.25,
				color = "red") +
		labs(x = "Iterations", y = "Goldmansachs collections")

ggsave("D:\\jmh\\ggplotgc.png", width=6, height=6, dpi=100)

Suggested by the R user forum to improve the aesthetics of the plot. The Confidence Interval of 99% shown in the plots above is not correct. But the curves and error bars are correct.

g1 <- ggplot(gc, aes(x = IDX, y = V1)) +
		theme_bw() +
		geom_ribbon(aes(ymin = V1 - error, ymax = V1 + error), fill = "gray60",
				alpha = 0.3) +
		geom_line(color = "blue", size = 1) +
		geom_errorbar(aes(ymin = V1 - error, ymax = V1 + error), width = 0.25,
				color = "red") +
		labs(x = "Iterations", y = "Goldmansachs collections")

ggplot creates these two graphs. So instead of qplot code we should use ggplot.

Update : See this