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title	author	output_format
State of the Ecosystem: Indicator Processing & Visualization	Sean Hardison	html_document

Introduction

The purpose of this file is to document State of the Ecosystem (SOE) indicator data processing. To update existing data sets, set the save_clean parameter in the Rmarkdown set-up chunk to TRUE. Raw data for these indicators are available in the file directory extdata, and libraries required to process indicator data sets are shown below.

knitr::opts_chunk$set(echo = TRUE,                                     fig.align='center')

#Required libraries
library(dplyr)
library(stringr)
library(tidyr)
library(ggplot2)
library(lubridate)
library(here)
library(zoo)
library(kableExtra)
library(sf)
library(rgdal)
library(raster)
library(sp)
library(gridExtra)

#Data directories
raw.dir <- here("inst","extdata") #raw data directory
clean.dir <- here("data") #output directory for cleaned data
gis.dir <- here("inst","extdata","gis")
sample.dir <- here("inst","extdata","sample")

#CRS
crs <- "+proj=longlat +datum=NAD83 +no_defs +ellps=GRS80 +towgs84=0,0,0"

#Write output to file
save_clean <- FALSE

#Execute spatial processing (must be TRUE to write clean data to file). If FALSE, will load sample data from file for plotting example
spatial_processing <- FALSE

Data sets

Surface winds {.tabset .tabset-fade}

These data are sourced from the NCEP North American Regional Reanalysis (NARR), extending from January 1979 to September 2018.

Variables in "NCEP NARR surface wind; TKE; HLCY, monthly, 1979-2018, V1.csv"

Variable	Name	Units
Wind speed	uwnd	m sec^-1^
Wind direction	vwnd	°
Turbulent kinetic energy	tke	J kg^-1^
Storm relative helicity	hlcy	m^2^sec^-2^

Variables included in these data are surface wind speed and direction (uwnd and vwnd respectively), surface turbulent kinetic energy (TKE), and storm relative helicity (HLCY). An indicator for total wind speed is calculated below as $$\textrm{TWS} = \sqrt{u^2 + v^2}$$. Data are visualized seasonally (Fall = October, November, December; Winter = January, February, March; Spring = April, May, June; Summer = July, August, September).

Filename: NCEP NARR surface wind; TKE; HLCY, monthly, 1979-2018, V1.csv
Contributor: Vincent Saba, ([email protected])

Processing

# Read in raw data
d <- read.csv(file.path(raw.dir,"NCEP NARR surface wind; TKE; HLCY, monthly, 1979-2018, V1.csv"))

# Processing steps for all wind speed data
wind_clean1 <- d  %>% gather(., Var, Value, GB.uwnd:MAB.tke) %>% #convert wide to long
  dplyr::rename(Time = Month.Year) %>% #rename time variable
  separate(Var, c("EPU","Var"),"\\.") %>% #separate out EPU from variable names
  mutate(Time = as.yearmon(as.Date(.$Time,Time, format = "%d-%b-%y"), "%m/%Y"), #Convert to date format
         Units = plyr::mapvalues(Var, from = unique(Var), to = c(rep("J/kg",2),"m^2/sec^2","J/kg")), #add units
         Time, season = plyr::mapvalues(month(Time), from = seq(1,12,1), #Get season
                                         to = c(rep("winter",3),
                                                rep("spring",3),
                                                rep("summer",3),
                                                rep("fall",3)))) 

# Calculate total wind speed from u and v components
total_wind_speed <- wind_clean1 %>% 
  filter(Var == "uwnd"| Var == "vwnd") %>% #select variables
  spread(., Var, Value) %>% #convert to wide for calculating tws
  mutate(`total wind speed` = sqrt(uwnd^2 + vwnd^2)) %>%  #tws
  dplyr::select(-uwnd, -vwnd) %>% #start processing back to SOE format
  gather(.,Var, Value, `total wind speed`) #convert to long

wind_clean <- rbind(wind_clean1, total_wind_speed)
wind_clean <- wind_clean %>% unite(., Var, c(Var, season), sep = " ") #merge season into Var column

if (save_clean){
save(wind_clean, file =
       file.path(clean.dir, "wind_clean.Rdata"))
}

MAB Total Wind Speed

mab <- wind_clean %>%
  filter(str_detect(Var, "total wind speed"), EPU == "MAB") %>% #filter
  mutate(Var = word(Var, -1)) %>% 
  mutate(Time = year(Time)) %>% #find year
  group_by(Time, Var) %>% #group by season
  dplyr::summarise(Value = mean(Value)) %>% #find mean
  ggplot()+ #plot
  geom_line(aes(x = Time, y = Value))+
  facet_wrap(.~Var, nrow = 2)+
  ylab("Wind Speed (m/s)") +
  ggtitle("MAB Total Wind Speed") +
  theme_bw()+
  theme(strip.background = element_blank()) 

mab

GB Total Wind Speed

gb <- wind_clean %>%
  filter(str_detect(Var, "total wind speed"), EPU == "GB") %>% #filter
  mutate(Var = word(Var, -1)) %>% 
  mutate(Time = year(Time)) %>% #find year
  group_by(Time, Var) %>% #group by season
  dplyr::summarise(Value = mean(Value)) %>% #find mean
  
  ggplot()+ #plot
  geom_line(aes(x = Time, y = Value))+
  facet_wrap(.~Var, nrow = 2)+
  ylab("Wind Speed (m/s)") +
  ggtitle("GB Total Wind Speed") +
  theme_bw()+
  theme(strip.background = element_blank()) 

gb

GOM Total Wind Speed

gom <- wind_clean %>%
  filter(str_detect(Var, "total wind speed"), EPU == "GOM") %>% #filter
  mutate(Var = word(Var, -1)) %>% 
  mutate(Time = year(Time)) %>% #find year
  group_by(Time, Var) %>% #group by season
  dplyr::summarise(Value = mean(Value)) %>% #find mean
  
  ggplot()+ #plot
  geom_line(aes(x = Time, y = Value))+
  facet_wrap(.~Var, nrow = 2)+
  ylab("Wind Speed (m/s)") +
  ggtitle("GOM Total Wind Speed") +
  theme_bw()+
  theme(strip.background = element_blank()) 

gom

MAB Helicity

mab <- wind_clean %>%
  filter(str_detect(Var, "hcly"), EPU == "MAB") %>% #filter
  mutate(Var = word(Var, -1)) %>% 
  mutate(Time = year(Time)) %>% #find year
  group_by(Time, Var) %>% #group by season
  dplyr::summarise(Value = mean(Value)) %>% #find mean
  ggplot()+ #plot
  geom_line(aes(x = Time, y = Value))+
  facet_wrap(.~Var, nrow = 2)+
  ylab(expression("Relative Helicity (m"^2*" sec"^-2*")")) +
  ggtitle("MAB Storm Relative Helicity ") +
  theme_bw()+
  theme(strip.background = element_blank()) 

mab

GB Helicity

gb <- wind_clean %>%
  filter(str_detect(Var, "hcly"), EPU == "GB") %>% #filter
  mutate(Var = word(Var, -1)) %>% 
  mutate(Time = year(Time)) %>% #find year
  group_by(Time, Var) %>% #group by season
  dplyr::summarise(Value = mean(Value)) %>% #find mean
  ggplot()+ #plot
  geom_line(aes(x = Time, y = Value))+
  facet_wrap(.~Var, nrow = 2)+
  ylab(expression("Relative Helicity (m"^2*" sec"^-2*")")) +
  ggtitle("GB Storm Relative Helicity ") +
  theme_bw()+
  theme(strip.background = element_blank()) 

gb

GOM Helicity

gom <- wind_clean %>%
  filter(str_detect(Var, "hcly"), EPU == "GOM") %>% #filter
  mutate(Var = word(Var, -1)) %>% 
  mutate(Time = year(Time)) %>% #find year
  group_by(Time, Var) %>% #group by season
  dplyr::summarise(Value = mean(Value)) %>% #find mean
  ggplot()+ #plot
  geom_line(aes(x = Time, y = Value))+
  facet_wrap(.~Var, nrow = 2)+
  ylab(expression("Relative Helicity (m"^2*" sec"^-2*")")) +
  ggtitle("GOM Storm Relative Helicity ") +
  theme_bw()+
  theme(strip.background = element_blank()) 

gom

MAB TKE

mab <- wind_clean %>%
  filter(str_detect(Var, "tke"), EPU == "MAB") %>% #filter
  mutate(Var = word(Var, -1)) %>% 
  mutate(Time = year(Time)) %>% #find year
  group_by(Time, Var) %>% #group by season
  dplyr::summarise(Value = mean(Value)) %>% #find mean
  ggplot()+ #plot
  geom_line(aes(x = Time, y = Value))+
  facet_wrap(.~Var, nrow = 2)+
  ylab(expression("Total Kinetic Energy (J kg"^-1*")")) +
  ggtitle("MAB Total Kinetic Energy ") +
  theme_bw()+
  theme(strip.background = element_blank()) 

mab

GB TKE

gb <- wind_clean %>%
  filter(str_detect(Var, "tke"), EPU == "GB") %>% #filter
  mutate(Var = word(Var, -1)) %>% 
  mutate(Time = year(Time)) %>% #find year
  group_by(Time, Var) %>% #group by season
  dplyr::summarise(Value = mean(Value)) %>% #find mean
  ggplot()+ #plot
  geom_line(aes(x = Time, y = Value))+
  facet_wrap(.~Var, nrow = 2)+
  ylab(expression("Total Kinetic Energy (J kg"^-1*")")) +
  ggtitle("GB Total Kinetic Energy ") +
  theme_bw()+
  theme(strip.background = element_blank()) 

gb

GOM TKE

gom <- wind_clean %>%
  filter(str_detect(Var, "tke"), EPU == "GOM") %>% #filter
  mutate(Var = word(Var, -1)) %>%
  mutate(Time = year(Time)) %>% #find year
  group_by(Time, Var) %>% #group by season
  dplyr::summarise(Value = mean(Value)) %>% #find mean
  ggplot()+ #plot
  geom_line(aes(x = Time, y = Value))+
  facet_wrap(.~Var, nrow = 2)+
  ylab(expression("Total Kinetic Energy (J kg"^-1*")")) +
  ggtitle("GOM Total Kinetic Energy ") +
  theme_bw()+
  theme(strip.background = element_blank())

gom

Slopewater proportions {.tabset .tabset-fade}

Slopewater proportions give the percent total of water type observed in the deep Northeast Channel (150-200 m depth).

Variables in "slopewater_proportions.csv"

Variable	Names	Units
Warm Slope Water proportion	WSW	unitless
Labrador Shelf Slope Water proportion	LSLW	unitless

Raw data fields correspond to year, water mass flavor (WSW = Warm Slope Water, LSLW = Labrador Slope Water), and proportion of total expressed as a percentage.

Filename: slopewater_proportions.csv
Contributor: Paula Fratantoni ([email protected])

Processing

d <- read.csv(file.path(raw.dir,"slopewater_proportions.csv"))

slopewater <- d %>%
  dplyr::rename(Time = year, Var = water.mass.flavor, Value = prop) %>% 
  mutate(EPU = "all", Units = "unitless", Var2 = "proportion ne channel") %>% 
  unite(.,Var,c(Var,Var2), sep = " ")
  
if (save_clean){
save(slopewater, file =
       file.path(clean.dir, "slopewater_proportions.Rdata"))
}

Visualization

slopewater %>% 
  mutate(Var, Var = plyr::mapvalues(Var, from = c("WSW proportion ne channel",
                                                  "LSLW proportion ne channel"),
                                    to = c("WSW","LSLW"))) %>% 
  dplyr::rename(Flavor  = Var) %>% 
ggplot() +
  geom_line(aes(x = Time, y = Value, color = Flavor))+
  geom_point(aes(x = Time, y = Value, color = Flavor)) +
  ylab("Percent of Total Slopewater") +
  ggtitle("Slopewater Proportions in NE Channel")+
  theme_bw()+
  theme(strip.background = element_blank())

Ocean temperature anomaly {.tabset .tabset-fade}

These data include in situ regional time series of both surface and bottom water temperature anomalies on the Northeast Continental Shelf. Raw data is split into four files by EPU (SS, GOM, GB, and MAB).

Variables in "Eco{EPU}_core_Ttopbot.csv"

Variable	Names	Units
SST anomaly	Tsfc_anom	?C
Reference SST (1981-2010)	Tsfc_ref	?C
Bottom temp. anomaly	Tbot_anom	?C
Reference BT (1981-2010)	Tbot_ref	?C

Filenames: EcoSS_core_Ttopbot.csv, EcoGoM_core_Ttopbot.csv, EcoGB_core_Ttopbot.csv, EcoMAB_core_Ttopbot.csv
Contributor: Paula Fratantoni ([email protected])

Processing

ss <- read.csv(file.path(raw.dir,"EcoSS_core_Ttopbot.csv")) %>% mutate(EPU = "SS")
gom <- read.csv(file.path(raw.dir,"EcoGoM_core_Ttopbot.csv")) %>% mutate(EPU = "GOM")
gb <- read.csv(file.path(raw.dir,"EcoGB_core_Ttopbot.csv")) %>% mutate(EPU = "GB")
mab <- read.csv(file.path(raw.dir,"EcoMAB_core_Ttopbot.csv")) %>% mutate(EPU = "MAB")

ocean_temp_insitu <- rbind(ss, gom, gb, mab) %>% #bind all
  dplyr::rename(Time = decimal.year, Var = variable.name, Value = temperature) %>% #rename
  mutate(Units = "degreesC", Time = as.Date(format(date_decimal(Time), "%Y-%b-%d"), "%Y-%b-%d"),
         Var, Var = plyr::mapvalues(Var, from = c("Tsfc_anom",
                             "Tsfc_ref",
                             "Tbot_anom",
                             "Tbot_ref"),
                             to = c("sst anomaly in situ",
                                "reference sst in situ (1981-2010)",
                                "bottom temp anomaly in situ",
                                "reference bt in situ (1981-2010)"))) #Rename variables

if (save_clean){
save(ocean_temp_insitu, file =
       file.path(clean.dir, "ocean_temp_insitu.Rdata"))
}

Visualization

one <- ocean_temp_insitu %>%
  filter(Var == "sst anomaly in situ") %>% 
  group_by(Time = year(Time), EPU) %>% 
  dplyr::summarise(Value = mean(Value)) %>% 
ggplot2::ggplot() +
  geom_line(aes(x = Time, y = Value)) +
  geom_point(aes(x = Time, y = Value), size = 1) +
  facet_grid(.~EPU) +
  ylab(expression("Temp. Anomaly ("*degree*"C)")) +
  ggtitle("SST") +
  theme_bw()+
  theme(strip.background = element_blank())

two <- ocean_temp_insitu %>%
 filter(Var == "bottom temp anomaly in situ") %>%
 group_by(Time = year(Time), EPU) %>%
 dplyr::summarise(Value = mean(Value)) %>% 
ggplot2::ggplot() +
  geom_line(aes(x = Time, y = Value)) +
  geom_point(aes(x = Time, y = Value), size = 1) +
  facet_grid(.~EPU) +
  ylab(expression("Temp. Anomaly ("*degree*"C)")) +
  ggtitle("Bottom temperature") +
  theme_bw()+
  theme(strip.background = element_blank())

grid.arrange(one, two)

Ocean salinity anomaly {.tabset .tabset-fade}

These data include in situ regional time series of both surface and bottom salinity anomalies on the Northeast Continental Shelf. Raw data is split into four files by EPU (SS, GOM, GB, and MAB).

Variables in "Eco{EPU}_core_Stopbot.csv"

Variable	Names	Units
Surface salinity anomaly	Ssfc_anom	PSU
Reference surface salinity (1981-2010)	Ssfc_ref	PSU
Bottom salinity anomaly	Sbot_anom	PSU
Reference bottom salinity (1981-2010)	Sbot_ref	PSU

Filenames: EcoSS_core_Stopbot.csv, EcoGoM_core_Stopbot.csv, EcoGB_core_Stopbot.csv, EcoMAB_core_Stopbot.csv
Contributor: Paula Fratantoni ([email protected])

Processing

ss <- read.csv(file.path(raw.dir,"EcoSS_core_Stopbot.csv")) %>% mutate(EPU = "SS")
gom <- read.csv(file.path(raw.dir,"EcoGoM_core_Stopbot.csv")) %>% mutate(EPU = "GOM")
gb <- read.csv(file.path(raw.dir,"EcoGB_core_Stopbot.csv")) %>% mutate(EPU = "GB")
mab <- read.csv(file.path(raw.dir,"EcoMAB_core_Stopbot.csv")) %>% mutate(EPU = "MAB")

ocean_sal_insitu <- rbind(ss, gom, gb, mab) %>% #bind all
  dplyr::rename(Time = decimal.year, Var = variable.name, Value = salinity) %>% #rename
  mutate(Units = "PSU", Time = as.Date(format(date_decimal(Time), "%Y-%b-%d"), "%Y-%b-%d"),
         Var, Var = plyr::mapvalues(Var, from = c("Ssfc_anom",
                             "Ssfc_ref",
                             "Sbot_anom",
                             "Sbot_ref"),
                     to = c("surface salinity anomaly in situ",
                        "reference surface salinity in situ (1981-2010)",
                        "bottom salinity anomaly in situ",
                        "reference bottom salinity in situ (1981-2010)")))
if (save_clean){
save(ocean_sal_insitu, file =
       file.path(clean.dir, "ocean_sal_insitu.Rdata"))
}

#surface salinity

#bottom salinity

Visualization

one <- ocean_sal_insitu %>%
  filter(Var == "surface salinity anomaly in situ") %>% 
  group_by(Time = year(Time), EPU) %>% 
  dplyr::summarise(Value = mean(Value)) %>% 
ggplot2::ggplot() +
  geom_line(aes(x = Time, y = Value)) +
  geom_point(aes(x = Time, y = Value), size = 1) +
  facet_grid(.~EPU) +
  ylab("Salinity Anomaly (PSU)") +
  ggtitle("Surface salinity") +
  theme_bw()+
  theme(strip.background = element_blank())

two <- ocean_sal_insitu %>%
 filter(Var == "bottom salinity anomaly in situ") %>%
 group_by(Time = year(Time), EPU) %>%
 dplyr::summarise(Value = mean(Value)) %>% 
ggplot2::ggplot() +
  geom_line(aes(x = Time, y = Value)) +
  geom_point(aes(x = Time, y = Value), size = 1) +
  facet_grid(.~EPU) +
  ylab("Salinity Anomaly (PSU)") +
  ggtitle("Bottom salinity") +
  theme_bw()+
  theme(strip.background = element_blank())

grid.arrange(one, two)

Stratification {.tabset .tabset-fade}

These data are time series of average stratification (0-50 m depth) by EPU.

Filename: Strat50.csv Contributor: Paula Fratantoni ([email protected])

Variables in "Strat50.csv"

Variable	Names	Units
stratification	stratification	kg m ^-3^

Processing

strat <- read.csv(file.path(raw.dir, "Strat50.csv"), stringsAsFactors = FALSE)

stratification <- strat %>% 
  dplyr::rename(Time = time, Var = var, Value = stratification) %>% 
  separate(., Var, c("Var","EPU"), sep = "_") %>% 
  mutate(Var = "stratification (0-50 m)",
         Units = "kg m^-3") 

if (save_clean){
  save(stratification, file = file.path(clean.dir, "stratification.Rdata"))
}

Visualization

ggplot(data =stratification)+
  geom_line(aes(x = Time, y = Value)) +
  geom_point(aes(x = Time, y = Value)) +
  facet_grid(.~EPU) +
  theme_bw() +
  ylab(expression("Stratification (kg m"^-3*")")) +
  theme(strip.background = element_blank())

EcoMon Nutrient Data {.tabset .tabset-fade}

These data include nutrient data sampled on EcoMon cruises between 11/3/2009 - 10/19/2016.

Filename: EcoMon Nutrient Data Through June 2018.csv
Contributor: Chris Melrose ([email protected])

Processing

d <- read.csv(file.path(raw.dir,"EcoMon Nutrient Data Through June 2018.csv"), stringsAsFactors = FALSE)

#Create data frame for mapping units to variable names
mapping <- data.frame(Units = as.character(d[1,]),
                      Var = as.character(names(d)))
mapping[mapping$Units == "" | mapping$Units == "NA",]$Units <- NA

#remove row with units
d <- slice(d,-1)

d1 <- d %>% 
  mutate(Time = Date_UTC) %>% #create Time variable
  dplyr::select(-Date_UTC,-Time_UTC) %>% #remove time, date
  gather(., Var, Value, -Latitude, -Longitude, -Time) %>% #turn wide to long while retaining lat/lon
  filter(!is.na(Value)) %>% #remove NA
  left_join(., mapping, by = c("Var")) %>% #join units 
  mutate(Longitude = as.numeric(Longitude),
         Latitude = as.numeric(Latitude),
         Time = mdy(Time)) %>% 
  filter(Latitude > 32, Latitude<50)


#Sanity check
# t1 <- d1[d1$Var == "CTDOXYMOL" ,]$Value
# t <-  d %>% slice(.,-1)
# t <- as.character(t$CTDOXYMOL)
# all(t == t1)

#Read in EPU shapefile
epu <- readOGR(file.path(gis.dir, "EPU_Extended.shp"), verbose = F) 
epu <- as(epu, "sf") #convert to sf object

if(spatial_processing){

  #Test maps
  #All EPUs
  #ggplot() + geom_sf(data = epu)
  
  #Scotian shelf
  # ss <- epu %>% filter(EPU == "SS")
  # ggplot() + geom_sf(data = ss)
  
  #get latitude and longitude for creating SpatialPointsDataFrame
  lat <-  as.numeric(d$Latitude)
  lon <- as.numeric(d$Longitude)
  coords <- data.frame(lon = lon,lat = lat)
  
  #create spdf
  spdf <- SpatialPointsDataFrame(coords = coords, data = coords,
                                 proj4string = CRS(crs))
  #convert to sf
  coords_sf <- st_as_sf(spdf) 
  
  #get intersection for mapping EPUs back to nutrient data
  epu_intersect <- st_intersection(epu, coords_sf)
  #plot(epu_intersect[epu_intersect$EPU == "MAB",])
  
  #Map back to nutrient data frame
  epu_df <- data.frame(Longitude = epu_intersect$lon,
                       Latitude = epu_intersect$lat,
                       EPU = epu_intersect$EPU)
  #join
  NE_LME_nutrients <- d1 %>% 
    left_join(.,epu_df, by = c("Latitude","Longitude"))
  
  #Select data for plotting 
  Nitr <- NE_LME_nutrients %>% filter(Var == "NITRIT.NITRAT")
  
  #Back to SOE format
  NE_LME_nutrients <- NE_LME_nutrients %>%
    dplyr::select(-Latitude, -Longitude) %>% 
    mutate(Value = as.numeric(Value))
  
  if (save_clean){
    save(NE_LME_nutrients,file = file.path(clean.dir, "EcoMon_nutrients.Rdata"))
  }

} else {
  load(file.path(sample.dir,"sample_nutrients.Rdata"))
  load(file.path(clean.dir,"EcoMon_nutrients.Rdata"))
}

QA

#Confirm transformation
ggplot() + 
  geom_sf(data = epu) +
  geom_point(data = Nitr, aes(x = Longitude, y = Latitude, color = EPU)) +
  ggtitle("Mapping EcoMon Nutrient Data to EPU") +
  theme_bw()

Visualization

N <- NE_LME_nutrients %>% 
  filter(Var == "NITRIT.NITRAT", Value > 0, !is.na(EPU)) %>% 
  group_by(EPU, Time = year(Time)) %>% 
  dplyr::summarise(Value = mean(Value, na.rm = TRUE)) %>% 
  ggplot() +
  geom_line(aes(x = Time, y = Value, color = EPU)) +
  ylab(expression("Nitrit+Nitrat (micromoles kg"^-1*")"))+
  ggtitle("Nitrit+Nitrat")+
  theme_bw()+
  theme(strip.background = element_blank())+
  guides(colour=FALSE)

P <- NE_LME_nutrients %>% 
  filter(Var == "PHSPHT", Value > 0, !is.na(EPU)) %>% 
  group_by(EPU, Time = year(Time)) %>% 
  dplyr::summarise(Value = mean(Value, na.rm = TRUE)) %>% 
  ggplot() +
  geom_line(aes(x = Time, y = Value, color = EPU)) +
  ylab(expression("Phosphate (micromoles kg"^-1*")"))+
  ggtitle("Phosphate")+
  theme_bw()+
  theme(strip.background = element_blank())+
  guides(colour=FALSE)

ammon <- NE_LME_nutrients %>% 
  filter(Var == "AMMONIA", Value > 0, !is.na(EPU)) %>% 
  group_by(EPU, Time = year(Time)) %>% 
  dplyr::summarise(Value = mean(Value, na.rm = TRUE)) %>% 
  ggplot() +
  geom_line(aes(x = Time, y = Value, color = EPU)) +
  ylab(expression("Ammonia (micromoles kg"^-1*")"))+
  ggtitle("Ammonia")+
  theme_bw()+
  theme(strip.background = element_blank())+
  theme(strip.background = element_blank(),legend.position = c(0.85, 0.6),
        legend.key = element_rect(color="transparent"),
        legend.background = element_rect(fill="transparent"))

silcat <- NE_LME_nutrients %>% 
  filter(Var == "SILCAT", Value > 0, !is.na(EPU)) %>% 
  group_by(EPU, Time = year(Time)) %>% 
  dplyr::summarise(Value = mean(Value, na.rm = TRUE)) %>% 
  ggplot() +
  geom_line(aes(x = Time, y = Value, color = EPU)) +
  ylab(expression("Silicates (micromoles kg"^-1*")"))+
    ggtitle("Silicates")+
  theme_bw()+
  guides(color= FALSE)

cowplot::plot_grid(N, P, ammon , silcat)

kevinushey/repro.md

Introduction

Data sets

Surface winds {.tabset .tabset-fade}

Processing

MAB Total Wind Speed

GB Total Wind Speed

GOM Total Wind Speed

MAB Helicity

GB Helicity

GOM Helicity

MAB TKE

GB TKE

GOM TKE

Slopewater proportions {.tabset .tabset-fade}

Processing

Visualization

Ocean temperature anomaly {.tabset .tabset-fade}

Processing

Visualization

Ocean salinity anomaly {.tabset .tabset-fade}

Processing

Visualization

Stratification {.tabset .tabset-fade}

Processing

Visualization

EcoMon Nutrient Data {.tabset .tabset-fade}

Processing

QA

Visualization