JaredData1
output: html_document: default pdf_document: default
By GaryFH - 04/19/2017
Synopsis
Two tests were conducted to observe the bandwidth over time and temperature over time.
Start by preparing the working environment
library(dplyr)## Warning: package 'dplyr' was built under R version 3.3.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 3.3.3Download data
Two tests each with three csv files noted fioA, fioB & smart_temp - the files included in the Github repo. Removed bad temp data (ie temps over 200 deg C)
t1fioA<-read.csv("t1fioA.csv",stringsAsFactors = FALSE)
t1fioB<-read.csv("t1fioB.csv",stringsAsFactors = FALSE)
t2fioA<-read.csv("t2fioA.csv",stringsAsFactors = FALSE)
t2fioB<-read.csv("t2fioB.csv",stringsAsFactors = FALSE)
t1smart<-read.csv("t1smart_temp.csv", stringsAsFactors=FALSE)
t2smart<-read.csv("t2smart_temp.csv", stringsAsFactors=FALSE)
t1a1<-tbl_df(t1fioA)
t1b1<-tbl_df(t1fioB)
t1s1<-tbl_df(t1smart)
t1s1<-filter(t1s1,X.dev.nvme0.FUM27053002F187A < 200)
t2a1<-tbl_df(t2fioA)
t2b1<-tbl_df(t2fioB)
t2s1<-tbl_df(t2smart)
t2s1<-filter(t2s1,X.dev.nvme0.FUM27053002F187A < 200)Work with test1 first
Look at fioA.csv, fiob.csv, smart_temp.csv
summary(t1a1)## Time bandwidth
## Min. :1.492e+09 Min. : 0.0
## 1st Qu.:1.492e+09 1st Qu.: 9.0
## Median :1.492e+09 Median : 10.0
## Mean :1.492e+09 Mean : 370.5
## 3rd Qu.:1.492e+09 3rd Qu.: 937.0
## Max. :1.492e+09 Max. :9993.0 summary(t1b1)## Time bandwidth
## Min. :1.492e+09 Min. : 0.0
## 1st Qu.:1.492e+09 1st Qu.: 9.0
## Median :1.492e+09 Median : 10.0
## Mean :1.492e+09 Mean : 360.3
## 3rd Qu.:1.492e+09 3rd Qu.: 930.0
## Max. :1.492e+09 Max. :9993.0 summary(t1s1)## Time X.dev.nvme0.FUM27053002F187A
## Length:4984 Min. :35.00
## Class :character 1st Qu.:56.00
## Mode :character Median :77.00
## Mean :71.38
## 3rd Qu.:82.00
## Max. :83.00Test1 Remove non common time stamps from bandwidth data
t1a2a<-filter(t1a1,Time>=1492156719)
t1a2b<-mutate(t1a2a,bandwidth2=bandwidth)
t1a2<-select(t1a2b,Time,bandwidth2)Test1 Merge bandwidth data (fioA and fioB) based on time stamp
t1a<-full_join(t1a2,t1b1,by="Time")
t1b<-mutate(t1a,totalbandwidth=bandwidth+bandwidth)
t1c<-select(t1b,Time,totalbandwidth)Test1 Remove NA and rename temperature data and arrange dataframe
s1a<-mutate(t1s1,temp=X.dev.nvme0.FUM27053002F187A)
s1b<-filter(s1a,temp,!is.na(temp))
s1b$Time<-as.integer(s1b$Time)## Warning: NAs introduced by coercion to integer range s1<-select(s1b,Time,temp)Test1 Merge tempature data with bandwidth data based on time stamp
test1unfilteredB<-full_join(s1,t1c,by="Time")
test1unfilteredC<-filter(test1unfilteredB,temp,!is.na(temp))
test1unfilteredD<-filter(test1unfilteredC,Time,!is.na(Time))
test1unfiltered<-arrange(test1unfilteredD,Time)Test1 Pretty up Timestamp & correct bandwidth
test1unfiltered$Time<-as.numeric(test1unfiltered$Time)
test1unfiltered1b<-mutate(test1unfiltered,Time=Time - 1492156656)
t1<-mutate(test1unfiltered1b,totalbandwidth=
ifelse(totalbandwidth<4000,totalbandwidth,totalbandwidth/1000))Test1: Plot time vrs temp
g1<-ggplot(t1,aes(x=Time,y=temp))
Test1_plot1<-g1+geom_point(shape=1,color="orange") + geom_smooth()
Test1_plot1 ## `geom_smooth()` using method = 'gam'
Test1: Plot time vrs bandwidth
t2<-filter(t1,totalbandwidth,!is.na(totalbandwidth))
g2<-ggplot(t2,aes(x=Time,y=totalbandwidth))
Test1_plot2<-g2+geom_point(shape=1,color="magenta") + geom_smooth()
Test1_plot2 ## `geom_smooth()` using method = 'gam'
Test1: Plot bandwidth vrs temp (just for fun)
t2<-filter(t1,totalbandwidth,!is.na(totalbandwidth))
g3<-ggplot(t2,aes(x=temp,y=totalbandwidth))
Test1_plot3<-g3+geom_point(shape=1,color="red") + geom_smooth()
Test1_plot3 ## `geom_smooth()` using method = 'gam'
Next - work with Test2
Look at test2 fioA.csv, fiob.csv, smart_temp.csv
summary(t2a1)## Time bandwidth
## Min. :1.492e+09 Min. : 9.0
## 1st Qu.:1.492e+09 1st Qu.: 813.0
## Median :1.492e+09 Median :1041.0
## Mean :1.492e+09 Mean : 848.4
## 3rd Qu.:1.492e+09 3rd Qu.:1096.0
## Max. :1.492e+09 Max. :1126.0 summary(t2b1)## Time bandwidth
## Min. :1.492e+09 Min. : 9.0
## 1st Qu.:1.492e+09 1st Qu.: 815.0
## Median :1.492e+09 Median :1053.0
## Mean :1.492e+09 Mean : 871.1
## 3rd Qu.:1.492e+09 3rd Qu.:1096.0
## Max. :1.492e+09 Max. :9993.0 summary(t2s1)## Time X.dev.nvme0.FUM27053002F187A
## Min. :1.492e+09 Min. :56.00
## 1st Qu.:1.492e+09 1st Qu.:56.00
## Median :1.492e+09 Median :56.00
## Mean :1.492e+09 Mean :58.21
## 3rd Qu.:1.492e+09 3rd Qu.:56.00
## Max. :1.492e+09 Max. :72.00Test2 Remove non common time stamps from bandwidth data
t2a2a<-filter(t2a1,Time>=1492163556)
t2a2b<-mutate(t2a2a,bandwidth2=bandwidth)
t2a2<-select(t2a2b,Time,bandwidth2)Test2 Merge bandwidth data (fioA and fioB) based on time stamp
t2a<-full_join(t2a2,t2b1,by="Time")
t2b<-mutate(t2a,totalbandwidth=bandwidth+bandwidth)
t2c<-select(t2b,Time,totalbandwidth)Test2 Remove NA and rename tempature data and arrange dataframe
s2a<-mutate(t2s1,temp=X.dev.nvme0.FUM27053002F187A)
s2b<-filter(s2a,temp,!is.na(temp))
s2b$Time<-as.integer(s2b$Time)
s2<-select(s2b,Time,temp)Test2 Merge tempature data with bandwidth data based on time stamp
test2unfilteredB<-full_join(s2,t2c,by="Time")
test2unfilteredC<-filter(test2unfilteredB,temp,!is.na(temp))
test2unfilteredD<-filter(test2unfilteredC,Time,!is.na(Time))
test2unfiltered<-arrange(test2unfilteredD,Time)Test2 Pretty up Timestamp & correct bandwidth
test2unfiltered$Time<-as.numeric(test2unfiltered$Time)
test2unfiltered2b<-mutate(test2unfiltered,Time=Time - 1492163513)
t2<-mutate(test2unfiltered2b,totalbandwidth=
ifelse(totalbandwidth<4000,totalbandwidth,totalbandwidth/1000))Test2: Plot time vrs temp
g21<-ggplot(t2,aes(x=Time,y=temp))
Test2_plot1<-g21+geom_point(shape=1,color="orange") + geom_smooth()
Test2_plot1 ## `geom_smooth()` using method = 'gam'
Test1: Plot time vrs bandwidth
t2b<-filter(t2,totalbandwidth,!is.na(totalbandwidth))
g22<-ggplot(t2b,aes(x=Time,y=totalbandwidth))
Test2_plot2<-g22+geom_point(shape=1,color="magenta") + geom_smooth()
Test2_plot2 ## `geom_smooth()` using method = 'gam'
Test1: Plot bandwidth vrs temp (just for fun)
t2b<-filter(t2,totalbandwidth,!is.na(totalbandwidth))
g23<-ggplot(t2b,aes(x=temp,y=totalbandwidth))
Test2_plot3<-g23+geom_point(shape=1,color="red") + geom_smooth()
Test2_plot3 ## `geom_smooth()` using method = 'gam'