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 <- 1450
mclim_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.