Chapter 10 2019 files
TO04
mclim_TO04_CN01_2019<-read_csv('./data/2019/TO04-MM-CN01.csv')## Parsed with column specification:
## cols(
## .default = col_double(),
## date = col_character(),
## nodeID = col_character(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )## See spec(...) for full column specifications.#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_TO04_CN01_2019)## [1] "date" "nodeID" "temp03" "temp12"
## [5] "temp15" "temp0" "temp06" "temp09"
## [9] "addrtemp1" "addrtemp2" "addrtemp3" "addrtemp4"
## [13] "addrtemp5" "addrtemp6" "windspeed1" "windspeed2"
## [17] "windspeed3" "windspeed4" "winddirection1" "winddirection2"
## [21] "winddirection3" "winddirection4" "solar"mclim_TO04_CN01_2019$time <- as_datetime(mclim_TO04_CN01_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_TO04_CN01_2019$time <-as.POSIXct(mclim_TO04_CN01_2019$time)
#Add hour month and day
## 11-05-2018
mclim_TO04_CN01_2019$hr <- hour(mclim_TO04_CN01_2019$time)
mclim_TO04_CN01_2019$minute <- minute(mclim_TO04_CN01_2019$time)
mclim_TO04_CN01_2019$day <- day(mclim_TO04_CN01_2019$time)
mclim_TO04_CN01_2019$month<- month(mclim_TO04_CN01_2019$time)
mclim_TO04_CN01_2019$doy<- date(mclim_TO04_CN01_2019$time)
#Roughly one feet apart
mclim_TO04_CN01_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_TO04_CN01_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in temp at TO04 - Open") + xlab("Date") +
ylab("Temp (degc)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
mclim_TO04_CN01_2019 %>% filter(month %in% c(8,9) ) %>%
ggplot() +
geom_line( aes(time,temp0,color = '0')) +
geom_line( aes(time,temp06,color = '0.6')) +
geom_line( aes(time,temp15,color = '1.5')) +
theme_minimal() +
#facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in temp at TO04 - Open") + xlab("Date") +
ylab("Temperature (deg C") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
filter to04 2019
mclim_TO04_CN01_2019_trimmed <- mclim_TO04_CN01_2019 %>% filter(time > '2019-08-08' & time < '2019-09-29' & temp12 < 85)
mclim_TO04_CN01_2019_trimmed %>% filter(month %in% c(8,9) ) %>%
ggplot() +
geom_line( aes(time,temp0,color = '0')) +
geom_line( aes(time,temp06,color = '0.6')) +
geom_line( aes(time,temp15,color = '1.5')) +
theme_minimal() +
facet_grid(. ~ doy, scales = "free") +
ggtitle("Variation in temp at TO04 - Open") + xlab("Date") +
ylab("Temperature (deg C") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
TO04 TN01
mclim_TO04_TN01_2019<-read_csv('./data/2019/TO04-MM-TN01.csv')## Parsed with column specification:
## cols(
## date = col_character(),
## temp09 = col_double(),
## temp03 = col_double(),
## temp0 = col_double(),
## temp15 = col_double(),
## temp06 = col_double(),
## temp12 = col_double(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character(),
## X14 = col_character()
## )#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_TO04_TN01_2019)## [1] "date" "temp09" "temp03" "temp0" "temp15"
## [6] "temp06" "temp12" "addrtemp1" "addrtemp2" "addrtemp3"
## [11] "addrtemp4" "addrtemp5" "addrtemp6" "X14"mclim_TO04_TN01_2019$time <- as_datetime(mclim_TO04_TN01_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_TO04_TN01_2019$time <-as.POSIXct(mclim_TO04_TN01_2019$time)
#Add hour month and day
## 11-05-2018
mclim_TO04_TN01_2019$hr <- hour(mclim_TO04_TN01_2019$time)
mclim_TO04_TN01_2019$minute <- minute(mclim_TO04_TN01_2019$time)
mclim_TO04_TN01_2019$day <- day(mclim_TO04_TN01_2019$time)
mclim_TO04_TN01_2019$month<- month(mclim_TO04_TN01_2019$time)
mclim_TO04_TN01_2019$doy<- date(mclim_TO04_TN01_2019$time)
#Roughly one feet apart
mclim_TO04_TN01_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_TO04_TN01_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in Temp at TO04 - TN01") + xlab("Date") +
ylab("Temp (deg C)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
TO04 TN02
mclim_TO04_TN02_2019<-read_csv('./data/2019/TO04-MM-TN02.csv')## Parsed with column specification:
## cols(
## date = col_character(),
## temp03 = col_double(),
## temp12 = col_double(),
## temp0 = col_double(),
## temp06 = col_double(),
## temp15 = col_double(),
## temp09 = col_double(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_TO04_TN02_2019)## [1] "date" "temp03" "temp12" "temp0" "temp06"
## [6] "temp15" "temp09" "addrtemp1" "addrtemp2" "addrtemp3"
## [11] "addrtemp4" "addrtemp5" "addrtemp6"mclim_TO04_TN02_2019$time <- as_datetime(mclim_TO04_TN02_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_TO04_TN02_2019$time <-as.POSIXct(mclim_TO04_TN02_2019$time)
#Add hour month and day
## 11-05-2018
mclim_TO04_TN02_2019$hr <- hour(mclim_TO04_TN02_2019$time)
mclim_TO04_TN02_2019$minute <- minute(mclim_TO04_TN02_2019$time)
mclim_TO04_TN02_2019$day <- day(mclim_TO04_TN02_2019$time)
mclim_TO04_TN02_2019$month<- month(mclim_TO04_TN02_2019$time)
mclim_TO04_TN02_2019$doy<- date(mclim_TO04_TN02_2019$time)
#Roughly one feet apart
mclim_TO04_TN02_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_TO04_TN02_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in temp at TO04 - TN02") + xlab("Date") +
ylab("Temp (deg C)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
TO04 TN03
mclim_TO04_TN03_2019<-read_csv('./data/2019/TO04-MM-TN03.csv')## Parsed with column specification:
## cols(
## date = col_character(),
## temp03 = col_double(),
## temp15 = col_double(),
## temp06 = col_double(),
## temp12 = col_double(),
## temp0 = col_double(),
## temp09 = col_double(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_TO04_TN03_2019)## [1] "date" "temp03" "temp15" "temp06" "temp12"
## [6] "temp0" "temp09" "addrtemp1" "addrtemp2" "addrtemp3"
## [11] "addrtemp4" "addrtemp5" "addrtemp6"mclim_TO04_TN03_2019$time <- as_datetime(mclim_TO04_TN03_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_TO04_TN03_2019$time <-as.POSIXct(mclim_TO04_TN03_2019$time)
#Add hour month and day
## 11-05-2018
mclim_TO04_TN03_2019$hr <- hour(mclim_TO04_TN03_2019$time)
mclim_TO04_TN03_2019$minute <- minute(mclim_TO04_TN03_2019$time)
mclim_TO04_TN03_2019$day <- day(mclim_TO04_TN03_2019$time)
mclim_TO04_TN03_2019$month<- month(mclim_TO04_TN03_2019$time)
mclim_TO04_TN03_2019$doy<- date(mclim_TO04_TN03_2019$time)
#Roughly one feet apart
mclim_TO04_TN03_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_TO04_TN03_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in temp at TO04 - TN03") + xlab("Date") +
ylab("Temp (deg C)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
mclim_AG05_CN01_2019<-read_csv('./data/2019/AG05-MM-CN01.csv')## Parsed with column specification:
## cols(
## .default = col_double(),
## date = col_character(),
## nodeID = col_character(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )## See spec(...) for full column specifications.#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AG05_CN01_2019)## [1] "date" "nodeID" "temp15" "temp03"
## [5] "temp0" "temp06" "temp09" "temp12"
## [9] "addrtemp1" "addrtemp2" "addrtemp3" "addrtemp4"
## [13] "addrtemp5" "addrtemp6" "windspeed1" "windspeed2"
## [17] "windspeed3" "windspeed4" "winddirection1" "winddirection2"
## [21] "winddirection3" "winddirection4" "solar"mclim_AG05_CN01_2019$time <- as_datetime(mclim_AG05_CN01_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AG05_CN01_2019$time <-as.POSIXct(mclim_AG05_CN01_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AG05_CN01_2019$hr <- hour(mclim_AG05_CN01_2019$time)
mclim_AG05_CN01_2019$minute <- minute(mclim_AG05_CN01_2019$time)
mclim_AG05_CN01_2019$day <- day(mclim_AG05_CN01_2019$time)
mclim_AG05_CN01_2019$month<- month(mclim_AG05_CN01_2019$time)
mclim_AG05_CN01_2019$doy<- date(mclim_AG05_CN01_2019$time)
#Roughly one feet apart
mclim_AG05_CN01_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AG05_CN01_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,windspeed2,color = '1.2')) +
geom_line( aes(time,windspeed3,color = '0.9')) +
geom_line( aes(time,windspeed4,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in wind at AG05 - Open") + xlab("Date") +
ylab("Windspeed (k/h)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
AG05 TN01
mclim_AG05_TN01_2019<-read_csv('./data/2019/AG05-MM-TN01.csv')## Parsed with column specification:
## cols(
## date = col_character(),
## temp0 = col_double(),
## temp15 = col_double(),
## temp09 = col_double(),
## temp06 = col_double(),
## temp03 = col_double(),
## temp12 = col_double(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AG05_TN01_2019)## [1] "date" "temp0" "temp15" "temp09" "temp06"
## [6] "temp03" "temp12" "addrtemp1" "addrtemp2" "addrtemp3"
## [11] "addrtemp4" "addrtemp5" "addrtemp6"mclim_AG05_TN01_2019$time <- as_datetime(mclim_AG05_TN01_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AG05_TN01_2019$time <-as.POSIXct(mclim_AG05_TN01_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AG05_TN01_2019$hr <- hour(mclim_AG05_TN01_2019$time)
mclim_AG05_TN01_2019$minute <- minute(mclim_AG05_TN01_2019$time)
mclim_AG05_TN01_2019$day <- day(mclim_AG05_TN01_2019$time)
mclim_AG05_TN01_2019$month<- month(mclim_AG05_TN01_2019$time)
mclim_AG05_TN01_2019$doy<- date(mclim_AG05_TN01_2019$time)
#Roughly one feet apart
mclim_AG05_TN01_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AG05_TN01_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in Temp at AG05 - TN01") + xlab("Date") +
ylab("Temp (deg C)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
AG05 TN02
mclim_AG05_TN02_2019<-read_csv('./data/2019/AG05-MM-TN02.csv')## Parsed with column specification:
## cols(
## date = col_character(),
## temp15 = col_double(),
## temp09 = col_double(),
## temp03 = col_double(),
## temp12 = col_double(),
## temp0 = col_double(),
## temp06 = col_double(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AG05_TN02_2019)## [1] "date" "temp15" "temp09" "temp03" "temp12"
## [6] "temp0" "temp06" "addrtemp1" "addrtemp2" "addrtemp3"
## [11] "addrtemp4" "addrtemp5" "addrtemp6"mclim_AG05_TN02_2019$time <- as_datetime(mclim_AG05_TN02_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AG05_TN02_2019$time <-as.POSIXct(mclim_AG05_TN02_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AG05_TN02_2019$hr <- hour(mclim_AG05_TN02_2019$time)
mclim_AG05_TN02_2019$minute <- minute(mclim_AG05_TN02_2019$time)
mclim_AG05_TN02_2019$day <- day(mclim_AG05_TN02_2019$time)
mclim_AG05_TN02_2019$month<- month(mclim_AG05_TN02_2019$time)
mclim_AG05_TN02_2019$doy<- date(mclim_AG05_TN02_2019$time)
#Roughly one feet apart
mclim_AG05_TN02_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AG05_TN02_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in Temp at AG05 - TN02") + xlab("Date") +
ylab("Temp (deg C)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
AG05 TN03
mclim_AG05_TN03_2019<-read_csv('./data/2019/AG05-MM-TN03.csv')## Parsed with column specification:
## cols(
## date = col_character(),
## temp0 = col_double(),
## temp15 = col_double(),
## temp12 = col_double(),
## temp03 = col_double(),
## temp06 = col_double(),
## temp09 = col_double(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AG05_TN03_2019)## [1] "date" "temp0" "temp15" "temp12" "temp03"
## [6] "temp06" "temp09" "addrtemp1" "addrtemp2" "addrtemp3"
## [11] "addrtemp4" "addrtemp5" "addrtemp6"mclim_AG05_TN03_2019$time <- as_datetime(mclim_AG05_TN03_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AG05_TN03_2019$time <-as.POSIXct(mclim_AG05_TN03_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AG05_TN03_2019$hr <- hour(mclim_AG05_TN03_2019$time)
mclim_AG05_TN03_2019$minute <- minute(mclim_AG05_TN03_2019$time)
mclim_AG05_TN03_2019$day <- day(mclim_AG05_TN03_2019$time)
mclim_AG05_TN03_2019$month<- month(mclim_AG05_TN03_2019$time)
mclim_AG05_TN03_2019$doy<- date(mclim_AG05_TN03_2019$time)
#Roughly one feet apart
mclim_AG05_TN03_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AG05_TN03_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in Temp at AG05 - TN03") + xlab("Date") +
ylab("Temp (deg C)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
mclim_AM16_CN01_2019<-read_csv('./data/2019/AM16-MM-CN01.csv')## Parsed with column specification:
## cols(
## .default = col_double(),
## date = col_character(),
## nodeID = col_character(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )## See spec(...) for full column specifications.#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AM16_CN01_2019)## [1] "date" "nodeID" "temp03" "temp12"
## [5] "temp09" "temp06" "temp0" "temp15"
## [9] "addrtemp1" "addrtemp2" "addrtemp3" "addrtemp4"
## [13] "addrtemp5" "addrtemp6" "windspeed1" "windspeed2"
## [17] "windspeed3" "windspeed4" "winddirection1" "winddirection2"
## [21] "winddirection3" "winddirection4" "solar"mclim_AM16_CN01_2019$time <- as_datetime(mclim_AM16_CN01_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AM16_CN01_2019$time <-as.POSIXct(mclim_AM16_CN01_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AM16_CN01_2019$hr <- hour(mclim_AM16_CN01_2019$time)
mclim_AM16_CN01_2019$minute <- minute(mclim_AM16_CN01_2019$time)
mclim_AM16_CN01_2019$day <- day(mclim_AM16_CN01_2019$time)
mclim_AM16_CN01_2019$month<- month(mclim_AM16_CN01_2019$time)
mclim_AM16_CN01_2019$doy<- date(mclim_AM16_CN01_2019$time)
#Roughly one feet apart
mclim_AM16_CN01_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AM16_CN01_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,windspeed2,color = '1.2')) +
geom_line( aes(time,windspeed3,color = '0.9')) +
geom_line( aes(time,windspeed4,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in wind at AM16 - Open") + xlab("Date") +
ylab("Windspeed (k/h)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
AM 16 TN01
mclim_AM16_TN01_2019<-read_csv('./data/2019/AM16-MM-TN01.csv')## Parsed with column specification:
## cols(
## date = col_character(),
## temp06 = col_double(),
## temp0 = col_double(),
## temp15 = col_double(),
## temp09 = col_double(),
## temp12 = col_double(),
## temp03 = col_double(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AM16_TN01_2019)## [1] "date" "temp06" "temp0" "temp15" "temp09"
## [6] "temp12" "temp03" "addrtemp1" "addrtemp2" "addrtemp3"
## [11] "addrtemp4" "addrtemp5" "addrtemp6"mclim_AM16_TN01_2019$time <- as_datetime(mclim_AM16_TN01_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AM16_TN01_2019$time <-as.POSIXct(mclim_AM16_TN01_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AM16_TN01_2019$hr <- hour(mclim_AM16_TN01_2019$time)
mclim_AM16_TN01_2019$minute <- minute(mclim_AM16_TN01_2019$time)
mclim_AM16_TN01_2019$day <- day(mclim_AM16_TN01_2019$time)
mclim_AM16_TN01_2019$month<- month(mclim_AM16_TN01_2019$time)
mclim_AM16_TN01_2019$doy<- date(mclim_AM16_TN01_2019$time)
#Roughly one feet apart
mclim_AM16_TN01_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AM16_TN01_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in Temp at AM16 - TN01") + xlab("Date") +
ylab("Temp (deg C)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
AM16 TN02
mclim_AM16_TN02_2019<-read_csv('./data/2019/AM16-MM-TN02.csv')## Parsed with column specification:
## cols(
## date = col_character(),
## temp0 = col_double(),
## temp06 = col_double(),
## temp15 = col_double(),
## temp09 = col_double(),
## temp12 = col_double(),
## temp03 = col_double(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AM16_TN02_2019)## [1] "date" "temp0" "temp06" "temp15" "temp09"
## [6] "temp12" "temp03" "addrtemp1" "addrtemp2" "addrtemp3"
## [11] "addrtemp4" "addrtemp5" "addrtemp6"mclim_AM16_TN02_2019$time <- as_datetime(mclim_AM16_TN02_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AM16_TN02_2019$time <-as.POSIXct(mclim_AM16_TN02_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AM16_TN02_2019$hr <- hour(mclim_AM16_TN02_2019$time)
mclim_AM16_TN02_2019$minute <- minute(mclim_AM16_TN02_2019$time)
mclim_AM16_TN02_2019$day <- day(mclim_AM16_TN02_2019$time)
mclim_AM16_TN02_2019$month<- month(mclim_AM16_TN02_2019$time)
mclim_AM16_TN02_2019$doy<- date(mclim_AM16_TN02_2019$time)
#Roughly one feet apart
mclim_AM16_TN02_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AM16_TN02_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in Temp at AM16 - TN02") + xlab("Date") +
ylab("Temp (deg C)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
AM16 TN03
mclim_AM16_TN03_2019<-read_csv('./data/2019/AM16-MM-TN03.csv')## Parsed with column specification:
## cols(
## date = col_character(),
## temp15 = col_double(),
## temp12 = col_double(),
## temp09 = col_double(),
## temp15_1 = col_double(),
## temp03 = col_double(),
## temp0 = col_double(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AM16_TN03_2019)## [1] "date" "temp15" "temp12" "temp09" "temp15_1"
## [6] "temp03" "temp0" "addrtemp1" "addrtemp2" "addrtemp3"
## [11] "addrtemp4" "addrtemp5" "addrtemp6"mclim_AM16_TN03_2019$time <- as_datetime(mclim_AM16_TN03_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AM16_TN03_2019$time <-as.POSIXct(mclim_AM16_TN03_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AM16_TN03_2019$hr <- hour(mclim_AM16_TN03_2019$time)
mclim_AM16_TN03_2019$minute <- minute(mclim_AM16_TN03_2019$time)
mclim_AM16_TN03_2019$day <- day(mclim_AM16_TN03_2019$time)
mclim_AM16_TN03_2019$month<- month(mclim_AM16_TN03_2019$time)
mclim_AM16_TN03_2019$doy<- date(mclim_AM16_TN03_2019$time)
#Roughly one feet apart
mclim_AM16_TN03_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AM16_TN03_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in Temp at AM16 - TN03") + xlab("Date") +
ylab("Temp (deg C)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
mclim_AE10_CN01_2019<-read_csv('./data/2019/AE10-MM-CN01.csv')## Parsed with column specification:
## cols(
## .default = col_double(),
## date = col_character(),
## nodeID = col_character(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )## See spec(...) for full column specifications.#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AE10_CN01_2019)## [1] "date" "nodeID" "temp09" "temp15"
## [5] "temp12" "temp03" "temp06" "temp0"
## [9] "addrtemp1" "addrtemp2" "addrtemp3" "addrtemp4"
## [13] "addrtemp5" "addrtemp6" "windspeed1" "windspeed2"
## [17] "windspeed3" "windspeed4" "winddirection1" "winddirection2"
## [21] "winddirection3" "winddirection4" "solar"mclim_AE10_CN01_2019$time <- as_datetime(mclim_AE10_CN01_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AE10_CN01_2019$time <-as.POSIXct(mclim_AE10_CN01_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AE10_CN01_2019$hr <- hour(mclim_AE10_CN01_2019$time)
mclim_AE10_CN01_2019$minute <- minute(mclim_AE10_CN01_2019$time)
mclim_AE10_CN01_2019$day <- day(mclim_AE10_CN01_2019$time)
mclim_AE10_CN01_2019$month<- month(mclim_AE10_CN01_2019$time)
mclim_AE10_CN01_2019$doy<- date(mclim_AE10_CN01_2019$time)
#Roughly one feet apart
mclim_AE10_CN01_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AE10_CN01_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,windspeed2,color = '1.2')) +
geom_line( aes(time,windspeed3,color = '0.9')) +
geom_line( aes(time,windspeed4,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in wind at AE10 - Open") + xlab("Date") +
ylab("Windspeed (k/h)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
AE 10TN01
mclim_AE10_TN01_2019<-read_csv('./data/2019/AE10-MM-TN01.csv')## Parsed with column specification:
## cols(
## date = col_character(),
## temp06 = col_double(),
## temp0 = col_double(),
## temp03 = col_double(),
## temp09 = col_double(),
## temp12 = col_double(),
## temp15 = col_double(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AE10_TN01_2019)## [1] "date" "temp06" "temp0" "temp03" "temp09"
## [6] "temp12" "temp15" "addrtemp1" "addrtemp2" "addrtemp3"
## [11] "addrtemp4" "addrtemp5" "addrtemp6"mclim_AE10_TN01_2019$time <- as_datetime(mclim_AE10_TN01_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AE10_TN01_2019$time <-as.POSIXct(mclim_AE10_TN01_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AE10_TN01_2019$hr <- hour(mclim_AE10_TN01_2019$time)
mclim_AE10_TN01_2019$minute <- minute(mclim_AE10_TN01_2019$time)
mclim_AE10_TN01_2019$day <- day(mclim_AE10_TN01_2019$time)
mclim_AE10_TN01_2019$month<- month(mclim_AE10_TN01_2019$time)
mclim_AE10_TN01_2019$doy<- date(mclim_AE10_TN01_2019$time)
#Roughly one feet apart
mclim_AE10_TN01_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AE10_TN01_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in Temp at AE10 - TN01") + xlab("Date") +
ylab("Temp (deg C)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
AE 10TN02
mclim_AE10_TN02_2019<-read_csv('./data/2019/AE10-MM-TN02.csv')## Parsed with column specification:
## cols(
## date = col_character(),
## temp0 = col_double(),
## temp09 = col_double(),
## temp12 = col_double(),
## temp06 = col_double(),
## temp03 = col_double(),
## temp15 = col_double(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AE10_TN02_2019)## [1] "date" "temp0" "temp09" "temp12" "temp06"
## [6] "temp03" "temp15" "addrtemp1" "addrtemp2" "addrtemp3"
## [11] "addrtemp4" "addrtemp5" "addrtemp6"mclim_AE10_TN02_2019$time <- as_datetime(mclim_AE10_TN02_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AE10_TN02_2019$time <-as.POSIXct(mclim_AE10_TN02_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AE10_TN02_2019$hr <- hour(mclim_AE10_TN02_2019$time)
mclim_AE10_TN02_2019$minute <- minute(mclim_AE10_TN02_2019$time)
mclim_AE10_TN02_2019$day <- day(mclim_AE10_TN02_2019$time)
mclim_AE10_TN02_2019$month<- month(mclim_AE10_TN02_2019$time)
mclim_AE10_TN02_2019$doy<- date(mclim_AE10_TN02_2019$time)
#Roughly one feet apart
mclim_AE10_TN02_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AE10_TN02_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in Temp at AE10 - TN02") + xlab("Date") +
ylab("Temp (deg C)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
AE 10TN03
mclim_AE10_TN03_2019<-read_csv('./data/2019/AE10-MM-TN03.csv')## Parsed with column specification:
## cols(
## date = col_character(),
## temp06 = col_double(),
## temp12 = col_double(),
## temp09 = col_double(),
## temp03 = col_double(),
## temp15 = col_double(),
## temp0 = col_double(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AE10_TN03_2019)## [1] "date" "temp06" "temp12" "temp09" "temp03"
## [6] "temp15" "temp0" "addrtemp1" "addrtemp2" "addrtemp3"
## [11] "addrtemp4" "addrtemp5" "addrtemp6"mclim_AE10_TN03_2019$time <- as_datetime(mclim_AE10_TN03_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AE10_TN03_2019$time <-as.POSIXct(mclim_AE10_TN03_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AE10_TN03_2019$hr <- hour(mclim_AE10_TN03_2019$time)
mclim_AE10_TN03_2019$minute <- minute(mclim_AE10_TN03_2019$time)
mclim_AE10_TN03_2019$day <- day(mclim_AE10_TN03_2019$time)
mclim_AE10_TN03_2019$month<- month(mclim_AE10_TN03_2019$time)
mclim_AE10_TN03_2019$doy<- date(mclim_AE10_TN03_2019$time)
#Roughly one feet apart
mclim_AE10_TN03_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AE10_TN03_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in Temp at AE10 - TN03") + xlab("Date") +
ylab("Temp (deg C)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
mclim_AV06_CN01_2019<-read_csv('./data/2019/AV06-MM-CN01.csv')## Parsed with column specification:
## cols(
## .default = col_double(),
## date = col_character(),
## nodeID = col_character(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )## See spec(...) for full column specifications.#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AV06_CN01_2019)## [1] "date" "nodeID" "temp0" "temp15"
## [5] "temp06" "temp12" "temp09" "temp03"
## [9] "addrtemp1" "addrtemp2" "addrtemp3" "addrtemp4"
## [13] "addrtemp5" "addrtemp6" "windspeed1" "windspeed2"
## [17] "windspeed3" "windspeed4" "winddirection1" "winddirection2"
## [21] "winddirection3" "winddirection4" "solar"mclim_AV06_CN01_2019$time <- as_datetime(mclim_AV06_CN01_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AV06_CN01_2019$time <-as.POSIXct(mclim_AV06_CN01_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AV06_CN01_2019$hr <- hour(mclim_AV06_CN01_2019$time)
mclim_AV06_CN01_2019$minute <- minute(mclim_AV06_CN01_2019$time)
mclim_AV06_CN01_2019$day <- day(mclim_AV06_CN01_2019$time)
mclim_AV06_CN01_2019$month<- month(mclim_AV06_CN01_2019$time)
mclim_AV06_CN01_2019$doy<- date(mclim_AV06_CN01_2019$time)
#Roughly one feet apart
mclim_AV06_CN01_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AV06_CN01_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,windspeed2,color = '1.2')) +
geom_line( aes(time,windspeed3,color = '0.9')) +
geom_line( aes(time,windspeed4,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in wind at AV06 - Open") + xlab("Date") +
ylab("Windspeed (k/h)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
AV06TN01
mclim_AV06_TN01_2019<-read_csv('./data/2019/AV06-MM-TN01.csv')## Parsed with column specification:
## cols(
## date = col_character(),
## temp0 = col_double(),
## temp09 = col_double(),
## temp12 = col_double(),
## temp06 = col_double(),
## temp15 = col_double(),
## temp03 = col_double(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AV06_TN01_2019)## [1] "date" "temp0" "temp09" "temp12" "temp06"
## [6] "temp15" "temp03" "addrtemp1" "addrtemp2" "addrtemp3"
## [11] "addrtemp4" "addrtemp5" "addrtemp6"mclim_AV06_TN01_2019$time <- as_datetime(mclim_AV06_TN01_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AV06_TN01_2019$time <-as.POSIXct(mclim_AV06_TN01_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AV06_TN01_2019$hr <- hour(mclim_AV06_TN01_2019$time)
mclim_AV06_TN01_2019$minute <- minute(mclim_AV06_TN01_2019$time)
mclim_AV06_TN01_2019$day <- day(mclim_AV06_TN01_2019$time)
mclim_AV06_TN01_2019$month<- month(mclim_AV06_TN01_2019$time)
mclim_AV06_TN01_2019$doy<- date(mclim_AV06_TN01_2019$time)
#Roughly one feet apart
mclim_AV06_TN01_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AV06_TN01_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in Temp at AV06 - TN01") + xlab("Date") +
ylab("Temp (deg C)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
AV06TN02
mclim_AV06_TN02_2019<-read_csv('./data/2019/AV06-MM-TN02.csv')## Parsed with column specification:
## cols(
## date = col_character(),
## temp03 = col_double(),
## temp09 = col_double(),
## temp15 = col_double(),
## temp12 = col_double(),
## temp0 = col_double(),
## temp06 = col_double(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AV06_TN02_2019)## [1] "date" "temp03" "temp09" "temp15" "temp12"
## [6] "temp0" "temp06" "addrtemp1" "addrtemp2" "addrtemp3"
## [11] "addrtemp4" "addrtemp5" "addrtemp6"mclim_AV06_TN02_2019$time <- as_datetime(mclim_AV06_TN02_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AV06_TN02_2019$time <-as.POSIXct(mclim_AV06_TN02_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AV06_TN02_2019$hr <- hour(mclim_AV06_TN02_2019$time)
mclim_AV06_TN02_2019$minute <- minute(mclim_AV06_TN02_2019$time)
mclim_AV06_TN02_2019$day <- day(mclim_AV06_TN02_2019$time)
mclim_AV06_TN02_2019$month<- month(mclim_AV06_TN02_2019$time)
mclim_AV06_TN02_2019$doy<- date(mclim_AV06_TN02_2019$time)
#Roughly one feet apart
mclim_AV06_TN02_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AV06_TN02_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in Temp at AV06 - TN02") + xlab("Date") +
ylab("Temp (deg C)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
AV06TN03
mclim_AV06_TN03_2019<-read_csv('./data/2019/AV06-MM-TN03.csv')## Parsed with column specification:
## cols(
## date = col_character(),
## temp03 = col_double(),
## temp0 = col_double(),
## temp06 = col_double(),
## temp12 = col_double(),
## temp15 = col_double(),
## temp09 = col_double(),
## addrtemp1 = col_character(),
## addrtemp2 = col_character(),
## addrtemp3 = col_character(),
## addrtemp4 = col_character(),
## addrtemp5 = col_character(),
## addrtemp6 = col_character()
## )#mclim_AE10_CN01_2018<- read.csv('./data/DATALOG-AE10-CN01.CSV',stringsAsFactors = T)
names(mclim_AV06_TN03_2019)## [1] "date" "temp03" "temp0" "temp06" "temp12"
## [6] "temp15" "temp09" "addrtemp1" "addrtemp2" "addrtemp3"
## [11] "addrtemp4" "addrtemp5" "addrtemp6"mclim_AV06_TN03_2019$time <- as_datetime(mclim_AV06_TN03_2019$date, format="%m/%d/%Y %H:%M:%S",tz="America/Los_Angeles")
mclim_AV06_TN03_2019$time <-as.POSIXct(mclim_AV06_TN03_2019$time)
#Add hour month and day
## 11-05-2018
mclim_AV06_TN03_2019$hr <- hour(mclim_AV06_TN03_2019$time)
mclim_AV06_TN03_2019$minute <- minute(mclim_AV06_TN03_2019$time)
mclim_AV06_TN03_2019$day <- day(mclim_AV06_TN03_2019$time)
mclim_AV06_TN03_2019$month<- month(mclim_AV06_TN03_2019$time)
mclim_AV06_TN03_2019$doy<- date(mclim_AV06_TN03_2019$time)
#Roughly one feet apart
mclim_AV06_TN03_2019$height <- 0
#therm1,28782877910A0217 - 0.6
#therm2,284CCE7791090235 - 1.2
#therm3,286C7A7791080217 - 0.9
#therm4,280A2577910A020D - 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='2862367791040281' ,]$height <- 1.2
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='286E9D77910802F2' ,]$height <- 0.6
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='28A91B7791060263' ,]$height <- 0.3
# mclim_AE10_CN01_2018[mclim_AE10_CN01_2018$nodeid=='280704779106023F' ,]$height <- 0.9
## Day plot , Representive day
mclim_AV06_TN03_2019 %>% filter(month %in% c(8) & day(time) %in% c(5:10) & hr %in% c(10,11,12,13)) %>%
ggplot() +
geom_line( aes(time,temp12,color = '1.2')) +
geom_line( aes(time,temp09,color = '0.9')) +
geom_line( aes(time,temp0,color = '0.3')) +
theme_minimal() +
facet_grid(. ~ day, scales = "free") +
ggtitle("Variation in Temp at AV06 - TN03") + xlab("Date") +
ylab("Temp (deg C)") + labs(colour = "Height(m)",
subtitle="9/14/2019",caption="Data Source : http://nanoclimate.org") 
create file for Samantha - 8/8 thru 8/10
mclim_AV06_CN01_2019 %>% filter (temp0 <85 ) %>%
#filter (time >'2019-08-08 18:00:21' &
# doy < '2019-08-11' ) %>%
dplyr::select(nodeID,time,temp0,temp03,temp06,temp09,temp12,temp15) %>%
ggplot() + geom_line(aes(time,temp0, color="0")) +
geom_line(aes(time,temp03, color="0.3")) +
geom_line(aes(time,temp06, color="0.6")) +
geom_line(aes(time,temp09, color="0.9")) +
geom_line(aes(time,temp12, color="1.2")) +
geom_line(aes(time,temp15, color="1.5")) +
theme_minimal(base_size = 18) + theme_cleveland() +
labs(x="Date",y=" (°C)", color="Height (m)", subtitle = "AV06")
#mclim_AV06_CN01_2019 %>% filter (temp0 <85 ) %>%
# filter (time >'2019-08-08 18:00:21' &
# doy < '2019-08-11' ) %>%
# dplyr::select(nodeID,time,temp0,temp03,temp06,temp09,temp12,temp15) %>%write_csv("av06-sapflow_temps-2019.csv")
#ggsave("av06-sapflow_temps.png",dpi=300, dev='png', height=8, width=12, units="in")mclim_TO04_CN01_2019 %>% filter (temp0 <85 ) %>%
filter (time >'2019-08-08 18:00:21' &
doy < '2019-08-11' ) %>%
dplyr::select(nodeID,time,temp0,temp03,temp06,temp09,temp12,temp15) %>%
ggplot() + geom_line(aes(time,temp0, color="0")) +
geom_line(aes(time,temp03, color="0.3")) +
geom_line(aes(time,temp06, color="0.6")) +
geom_line(aes(time,temp09, color="0.9")) +
geom_line(aes(time,temp12, color="1.2")) +
geom_line(aes(time,temp15, color="1.5")) +
theme_minimal(base_size = 18) + theme_cleveland() +
labs(x="Date",y=" (°C)", color="Height (m)", subtitle = "TO04")
mclim_TO04_CN01_2019 %>% filter (temp0 <85 ) %>%
filter (time >'2019-08-08 18:00:21' &
doy < '2019-08-11' ) %>%
dplyr::select(nodeID,time,temp0,temp03,temp06,temp09,temp12,temp15) %>%write_csv("to04-sapflow_temps-2019.csv")
ggsave("too04-sapflow_temps.png",dpi=300, dev='png', height=8, width=12, units="in")Create 2019 data set
that correspond to TO04 (650m), AG05 (950m), AV06 (1060m), AM16 (1200m) and AE10 (1450m)
mclim_2019 <- rbind(mclim_AE10_CN01_2019,mclim_AV06_CN01_2019,
mclim_AG05_CN01_2019,mclim_AM16_CN01_2019,
mclim_TO04_CN01_2019)
mclim_2019$elevation <- 0
mclim_2019[str_detect(mclim_2019$nodeID,"TO-04") ,]$elevation <- 650
mclim_2019[str_detect(mclim_2019$nodeID,"AG-05") ,]$elevation <- 950
mclim_2019[str_detect(mclim_2019$nodeID,"AV-06") ,]$elevation <- 1060
mclim_2019[str_detect(mclim_2019$nodeID,"AM-16") ,]$elevation <- 1200
mclim_2019[str_detect(mclim_2019$nodeID,"AE-10") ,]$elevation <- 1450mclim_2019 %>% filter(elevation > 0) %>% group_by(elevation,doy, temp12) %>%
dplyr::summarise(mt12 = mean(temp12)) %>%
ggplot(aes(doy,mt12,group=as.factor(elevation),color=as.factor(elevation))) +
geom_point() +
geom_smooth(span = 0.3)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
mclim_2019 %>% filter(elevation > 0) %>% group_by(elevation,doy, temp12) %>%
dplyr::summarise(mt12 = mean(temp12)) %>%
ggplot(aes(as.factor(elevation),mt12)) +
geom_col()
library(ggpubr)
elevation_comparisons = list( c("650", "950"), c("1060", "950"),
c("1060", "1200"),c("650","1450"),c("1200","1450") )
# New facet label names for Stand
mclim_2019 %>% filter(elevation > 0 & temp12 < 50) %>% group_by(elevation, temp12) %>%
ggboxplot( x = "elevation", y = "temp12",
color = "elevation", palette = "jco")+
stat_compare_means(comparisons = elevation_comparisons)+
stat_compare_means(label.y = 45.0) +
labs(x="Elevation",y="Mean temperature") + theme_minimal(base_size = 24) 
#ggsave(filename = "figs/Rev2-Species-comparisons-by-elevation-Phi2.png",dpi = 300, width = 8, height = 8, units = "in")John, Aji. 2018. Time Seriously. 1st ed. Seattle, Washington: Chapman; Hall/CRC. http://ajijohn.com.