big_data_analsis.R

# Plot the data

# Imports
library(reshape2)
library(ggplot2)

# Load the data
# Change path to file
# data_path <- '../parallel/analysis.csv'  # You can use this instead
data_path <- '/home/reynaldo/Documents/School/Spring2017/HPC/project/parallel_mcmc/parallel/complete_big_analysis.csv'
perf_data <- read.csv(file=data_path, header=TRUE, sep=",")
melted_data <- melt(perf_data, id=c("trial", "block", "thread"))
five_hundred_trials <- melted_data[which(melted_data$trial == 500), ]
five_hundred_trials$thread = as.factor(five_hundred_trials$thread)


# Find which combination of blocks and threads works best
ours_five_hundred_trials <- five_hundred_trials[which(five_hundred_trials$variable == "our_time"), ]
ours_five_hundred_trials <- ours_five_hundred_trials[which(!is.na(ours_five_hundred_trials$thread)), ]

line_base <- ggplot(ours_five_hundred_trials, aes(x=block, y=value, group=thread, shape=thread, color=thread)) +
  geom_line() +
  geom_point()
line_base <- line_base +  xlab("Blocks") + ylab("Time (seconds)") + labs(colour = "Num Threads", shape="Num Threads")
line_base <- line_base + ggtitle("Blocks vs Time vs Threads") + theme(plot.title = element_text(hjust = 0.5))
line_base