library(nanotime)

suppressMessages(library(data.table))

suppressMessages(library(ggplot2))

set.seed(42)

N <- 300

rainyday <- ISOdatetime(2016,9,28,8,30,0) # made up

shinyday <- ISOdatetime(2016,9,21,8,30,0) # made up too

rdsent <- nanotime(rainyday) + cumsum(10*rpois(N, lambda=4)) # random sent time

sdsent <- nanotime(shinyday) + cumsum(10*rpois(N, lambda=4)) # identical sent process for both

rdrecv <- rdsent + 10*rlnorm(N, 0.30, 0.25) # postulate higher mean and sd

sdrecv <- sdsent + 10*rlnorm(N, 0.10, 0.20) # for rainy than shiny

raw <- data.table(rdsent, rdrecv, sdsent, sdrecv)

raw[, `:=`(rainy=as.numeric(rdrecv-rdsent),

shiny=as.numeric(sdrecv-sdsent))]

## melt into long format

plotdata <- melt(raw[,.(rainy,shiny)], measure.vars=1:2, variable.name="day", value.name="time")

## and plot

ggplot(plotdata, aes(day, time)) + geom_violin(aes(fill=day)) + coord_flip() +

ylab("Message Time in Nanoseconds") + xlab("Weather Conditions") +

ggtitle("Nanosecond Delay", "Under Different Weather Conditions")

tfile <- tempfile(pattern="raw", fileext=".csv")

fwrite(raw, file=tfile)

cooked <- fread(tfile)

## csv files are not 'typed' so need to recover types explicitly

cooked[, `:=`(rdsent=nanotime(rdsent),

rdrecv=nanotime(rdrecv),

sdsent=nanotime(sdsent),

sdrecv=nanotime(sdrecv))]

## now saved and restrored data are identical

all.equal(raw, cooked)