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0_data_processing.Rmd
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0_data_processing.Rmd
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---
output: html_document
editor_options:
chunk_output_type: console
---
```{r load libraries}
library(raster)
library(ncdf4)
library(stringr)
library(rgdal)
library(corrplot)
library(viridis)
library(dplyr)
```
#define paths where you have saved the coral data
coral_cover_directory="C:/Users/Shannon/Desktop/Ecoregions/Coral cover/GitHub_code"
cyclone_directory = "D:/Lab Data/Global/Cyclone/cyclone"
historical_sst_directory = "D:/historical_sst/1x1_resolution"
turbidity_directory = "C:/Users/Shannon/Desktop/Ecoregions"
shapefiles_directory = "C:/Users/Shannon/Desktop/Ecoregions/shapefiles"
diversity_data_directory = "C:/Users/Shannon/Desktop/Ecoregions/Coral cover"
output_directory = "C:/Users/Shannon/Desktop/Ecoregions/Coral cover/output"
#future sea surface temperature
future_sst_directory<-"D:/high_res_SST"
#future degree heating weeks
future_dhw_directory<-"D:/future_sst/dhw_yearly_max_rcp45_rcp85"
#set path for the external hard drive where you have downloaded the CoRTAD data
Cortad_directory="D:/Cortad_2020"
#read in the Reef Check csv
setwd(coral_cover_directory)
coral_cover_data <- read.csv(file="raw_Reef_Check_data.csv", header=TRUE, sep=",")
```{r format Reef Check date to match the CoRTAD date format (days after 19811231)}
coral_cover_data=subset(coral_cover_data, !is.na(Latitude.Degrees))
coral_cover_data=subset(coral_cover_data, !is.na(Longitude.Degrees))
coral_cover_data=subset(coral_cover_data, !is.na(Average_coral_cover))
coral_cover_data=subset(coral_cover_data, !is.na(Depth))
coral_cover_data$year<-coral_cover_data$Date_Year
year_split_function<-function(x){
year=as.numeric(str_split(x, "/")[[1]][3])
return(year)
}
coral_cover_data$year<-NA
for(i in 1:nrow(coral_cover_data)){
coral_cover_data$year[i] <- year_split_function(coral_cover_data$Date[i])
}
days_since_19811231<-array(0, dim=dim(coral_cover_data)[1])
for (i in 1:dim(coral_cover_data)[1])
{
date_string<-str_split(coral_cover_data$Date[i], "/")
day_string<-date_string[[1]][2]
day_numeric<-as.numeric(day_string)
month_string<-date_string[[1]][1]
if (month_string=="1"){days_since_19811231_due_to_month_number<-0}
if (month_string=="2"){days_since_19811231_due_to_month_number<-31}
if (month_string=="3"){days_since_19811231_due_to_month_number<-59}
if (month_string=="4"){days_since_19811231_due_to_month_number<-90}
if (month_string=="5"){days_since_19811231_due_to_month_number<-120}
if (month_string=="6"){days_since_19811231_due_to_month_number<-151}
if (month_string=="7"){days_since_19811231_due_to_month_number<-181}
if (month_string=="8"){days_since_19811231_due_to_month_number<-212}
if (month_string=="9"){days_since_19811231_due_to_month_number<-243}
if (month_string=="10"){days_since_19811231_due_to_month_number<-273}
if (month_string=="11"){days_since_19811231_due_to_month_number<-304}
if (month_string=="12"){days_since_19811231_due_to_month_number<-334}
year_string<-date_string[[1]][3]
year_numeric<-as.numeric(year_string)
if( (year_numeric>1984) | (year_numeric==1984 & month_string!="1" & month_string!="2"))
{leap_year_days<-1}
if( (year_numeric>1988) | (year_numeric==1988 & month_string!="1" & month_string!="2"))
{leap_year_days<-2}
if( (year_numeric>1992) | (year_numeric==1992 & month_string!="1" & month_string!="2"))
{leap_year_days<-3}
if( (year_numeric>1996) | (year_numeric==1996 & month_string!="1" & month_string!="2"))
{leap_year_days<-4}
if( (year_numeric>2000) | (year_numeric==2000 & month_string!="1" & month_string!="2"))
{leap_year_days<-5}
if( (year_numeric>2004) | (year_numeric==2004 & month_string!="1" & month_string!="2"))
{leap_year_days<-6}
if( (year_numeric>2008) | (year_numeric==2008 & month_string!="1" & month_string!="2"))
{leap_year_days<-7}
if( (year_numeric>2012) | (year_numeric==2012 & month_string!="1" & month_string!="2"))
{leap_year_days<-8}
if( (year_numeric>2016) | (year_numeric==2016 & month_string!="1" & month_string!="2"))
{leap_year_days<-9}
if( (year_numeric>2020) | (year_numeric==2020 & month_string!="1" & month_string!="2"))
{leap_year_days<-10}
days_since_19811231[i]<-((year_numeric-1982)*365)+days_since_19811231_due_to_month_number+day_numeric+leap_year_days
}
coral_cover_data$days_since_19811231<-days_since_19811231
```
```{r Data formatting: format lat and lon, calculate average coral cover, remove data points that have NA values for lat, lon, average coral cover, depth, or date}
#Remove NA's
coral_cover_data=subset(coral_cover_data, !is.na(Latitude.Degrees))
coral_cover_data=subset(coral_cover_data, !is.na(Longitude.Degrees))
coral_cover_data=subset(coral_cover_data, !is.na(Average_coral_cover))
coral_cover_data=subset(coral_cover_data, !is.na(Depth))
coral_cover_data=subset(coral_cover_data, !is.na(Date))
#note: after removing rows with NA's we are left with 13077 surveys
#Average coral cover in the Reef Check data csv is reported relative to 40 quadrats, so we divide by 40 to get percent
coral_cover_data$Average_coral_cover<-as.numeric(as.character(coral_cover_data$Average_coral_cover))/40
coral_cover_data$Ocean<-coral_cover_data$Ocean_Name
coral_cover_data<- subset(coral_cover_data, select=c(Reef_ID, Latitude.Degrees, Longitude.Degrees, Ocean, Realm, Ecoregion, Country_Name, State_Island_Province, City_Town, City_Town_2, City_Town_3, Depth, Organism.Code, Bleaching_S1, Bleaching_S2, Bleaching_S3, Bleaching_S4, Average_Bleaching, Average_coral_cover, days_since_19811231, year))
number_of_surveys=dim(coral_cover_data)[1]
```
```{r open one NetCDF file and look at the dimensions}
setwd(Cortad_directory)
#FilledSST<-nc_open("outfile_1.nc", write=FALSE, readunlim=TRUE, verbose=FALSE)
FilledSST<-nc_open("cortadv6_outfile.nc", write=FALSE, readunlim=TRUE, verbose=FALSE)
names(FilledSST$var) #prints a list of the variable names
#time_bounds, lat_bounds, lon_bounds, crs, land, NumberGood, AllBad, FilledSST, FilledSSTminimum, FilledSSTmaximum, FilledSSTstandardDeviation, FilledSSTmean
FilledSST_time_bounds<-ncvar_get(FilledSST, varid="time_bnds")
dim(FilledSST_time_bounds) #2 1878
FilledSST_lat_bounds<-ncvar_get(FilledSST, varid="lat_bnds")
dim(FilledSST_lat_bounds) #2 4320
FilledSST_lon_bounds<-ncvar_get(FilledSST, varid="lon_bnds")
dim(FilledSST_lon_bounds) #2 8640
FilledSST_land<-ncvar_get(FilledSST, varid="land")
#0 or 1
dim(FilledSST_land) #8640 4320
```
```{r check the grid cell size}
#checking if the grid is evenly split. it's very close. each step is between .04165649 and .04167175
difference<-array(0, dim=(length(FilledSST_lon_bounds[1,])-1))
for (i in 1:(dim(FilledSST_lon_bounds)[2]-1))
{difference[i]<-FilledSST_lon_bounds[1,(i+1)]-FilledSST_lon_bounds[1,i]}
```
```{r calculate latitude grid cell}
coral_cover_cortad_lat_cell<-array(0, dim=number_of_surveys)
lat_step<--1*(FilledSST_lat_bounds[2,dim(FilledSST_lat_bounds)[2]]-FilledSST_lat_bounds[1,1])/(dim(FilledSST_lat_bounds)[2]+1)
for (i in 1:number_of_surveys)
{
lat_grid_cell<-NA
if(is.na(coral_cover_data$Latitude.Degrees[i]))
{lat_grid_cell<-NA}else{
n_lat_steps<-floor((FilledSST_lat_bounds[1,1]-coral_cover_data$Latitude.Degrees[i])/lat_step+1)
if(FilledSST_lat_bounds[1,n_lat_steps]>=coral_cover_data$Latitude.Degrees[i])
{
if(FilledSST_lat_bounds[2,n_lat_steps]<=coral_cover_data$Latitude.Degrees[i])
{lat_grid_cell<-n_lat_steps}
else
{
repeat{
n_lat_steps=n_lat_steps+1
if(FilledSST_lat_bounds[1,n_lat_steps]>coral_cover_data$Latitude.Degrees[i]){
if(FilledSST_lat_bounds[2,n_lat_steps]<=coral_cover_data$Latitude.Degrees[i])
{break}
}
}
lat_grid_cell<-n_lat_steps
}
}
if(FilledSST_lat_bounds[1,n_lat_steps]<coral_cover_data$Latitude.Degrees[i])
{
repeat{
n_lat_steps=n_lat_steps-1
if(FilledSST_lat_bounds[1,n_lat_steps]>=coral_cover_data$Latitude.Degrees[i])
{
if(FilledSST_lat_bounds[2,n_lat_steps]<=coral_cover_data$Latitude.Degrees[i])
{break}
}
}
lat_grid_cell<-n_lat_steps
}
}
coral_cover_cortad_lat_cell[i]<-lat_grid_cell
}
```
```{r calculate longitude grid cell}
coral_cover_cortad_lon_cell<-array(0, dim=number_of_surveys)
lon_step<-(FilledSST_lon_bounds[1,dim(FilledSST_lon_bounds)[2]]-FilledSST_lon_bounds[1,1])/(dim(FilledSST_lon_bounds)[2]+1)
for (i in 1:length(coral_cover_data$Longitude.Degrees))
{
lon_grid_cell<-NA
if(is.na(coral_cover_data$Longitude.Degrees[i]))
{lon_grid_cell<-NA}else{
n_lon_steps<-floor(-1*(FilledSST_lon_bounds[1,1]-coral_cover_data$Longitude.Degrees[i])/lon_step+1)
if(n_lon_steps>(dim(FilledSST_lon_bounds)[2])){n_lon_steps<-(dim(FilledSST_lon_bounds)[2])}
if(n_lon_steps<1){n_lon_steps<-1}
if(FilledSST_lon_bounds[1,n_lon_steps]<=coral_cover_data$Longitude.Degrees[i])
{
if(FilledSST_lon_bounds[2,n_lon_steps]>coral_cover_data$Longitude.Degrees[i])
{lon_grid_cell<-n_lon_steps}
else
{
repeat{
n_lon_steps=n_lon_steps+1
if(n_lon_steps>(dim(FilledSST_lon_bounds)[2])){break}
if(FilledSST_lon_bounds[1,n_lon_steps]<=coral_cover_data$Longitude.Degrees[i]){
if(FilledSST_lon_bounds[2,n_lon_steps]>coral_cover_data$Longitude.Degrees[i])
{break}
}
}
lon_grid_cell<-n_lon_steps
}
}
if(FilledSST_lon_bounds[1,n_lon_steps]>coral_cover_data$Longitude.Degrees[i])
{
repeat{
n_lon_steps=n_lon_steps-1
if(n_lon_steps==0){break}
if(FilledSST_lon_bounds[1,n_lon_steps]<=coral_cover_data$Longitude.Degrees[i])
{
if(FilledSST_lon_bounds[2,n_lon_steps]>coral_cover_data$Longitude.Degrees[i])
{break}
}
}
lon_grid_cell<-n_lon_steps
}
}
coral_cover_cortad_lon_cell[i]<-lon_grid_cell
}
```
```{r initialize some arrays to fill with the appropriate cortad data}
coral_cover_cortad_temperature_Minimum<-array(NA, dim=number_of_surveys)
coral_cover_cortad_temperature_Maximum<-array(NA, dim=number_of_surveys)
coral_cover_cortad_temperature_Mean<-array(NA, dim=number_of_surveys)
coral_cover_cortad_temperature_standardDeviation<-array(NA, dim=number_of_surveys)
coral_cover_cortad_SSTA_Minimum<-array(NA, dim=number_of_surveys)
coral_cover_cortad_SSTA_Mean<-array(NA, dim=number_of_surveys)
coral_cover_cortad_SSTA_Maximum<-array(NA, dim=number_of_surveys)
coral_cover_cortad_SSTA_StandardDeviation<-array(NA, dim=number_of_surveys)
coral_cover_cortad_SSTA_FrequencyMax<-array(NA, dim=number_of_surveys)
coral_cover_cortad_SSTA_FrequencyMean<-array(NA, dim=number_of_surveys)
coral_cover_cortad_SSTA_FrequencyStandardDeviation<-array(NA, dim=number_of_surveys)
coral_cover_cortad_SSTA_DHWMax<-array(NA, dim=number_of_surveys)
coral_cover_cortad_SSTA_DHWMean<-array(NA, dim=number_of_surveys)
coral_cover_cortad_SSTA_DHWStandardDeviation<-array(NA, dim=number_of_surveys)
coral_cover_cortad_TSA_StandardDeviation<-array(NA, dim=number_of_surveys)
coral_cover_cortad_TSA_Minimum<-array(NA, dim=number_of_surveys)
coral_cover_cortad_TSA_Maximum<-array(NA, dim=number_of_surveys)
coral_cover_cortad_TSA_Mean<-array(NA, dim=number_of_surveys)
coral_cover_cortad_TSA_FrequencyMax<-array(NA, dim=number_of_surveys)
coral_cover_cortad_TSA_FrequencyMean<-array(NA, dim=number_of_surveys)
coral_cover_cortad_TSA_FrequencyStandardDeviation<-array(NA, dim=number_of_surveys)
coral_cover_cortad_TSA_DHWMax<-array(NA, dim=number_of_surveys)
coral_cover_cortad_TSA_DHWMean<-array(NA, dim=number_of_surveys)
coral_cover_cortad_TSA_DHWStandardDeviation<-array(NA, dim=number_of_surveys)
coral_cover_cortad_ClimSST<-array(NA, dim=number_of_surveys)
```
```{r open all the netcdf files you will need}
FilledSST<-nc_open("cortadv6_outfile.nc", write=FALSE, readunlim=TRUE, verbose=FALSE)
names(FilledSST$var) #FilledSST, FilledSSTminimum, FilledSSTmaximum, FilledSSTstandardDeviation, FilledSSTmean
SSTA<-nc_open("cortadv6_SSTA.nc", write=FALSE, readunlim=TRUE, verbose=FALSE)
names(SSTA$var) #SSTA_Minimum, SSTA_Maximum, SSTA_StandardDeviation, SSTA_Mean, SSTA_AbsoluteValueMean, SSTA_Frequency, SSTA_FrequencyMax, SSTA_FrequencyStandardDeviation, SSTA_FrequencyMean, SSTA_DHW, SSTA_DHWMax, SSTA_DHWStandardDeviation, SSTA_DHWMean
TSA<-nc_open("cortadv6_TSA.nc", write=FALSE, readunlim=TRUE, verbose=FALSE)
names(TSA$var) #TSA, TSA_Minimum, TSA_Maximum, TSA_StandardDeviation, TSA_Mean, TSA_Frequency, TSA_FrequencyMax, TSA_FrequencyStandardDeviation, TSA_FrequencyMean, TSA_DHW, TSA_DHWMax, TSA_DHWStandardDeviation, TSA_DHWMean
HarmonicsClimatology<-nc_open("cortadv6_HarmonicsClimatology.nc", write=FALSE, readunlim=TRUE, verbose=FALSE)
names(HarmonicsClimatology$var) #ClimSST, AnnualAmplitudeCoefficient, AnnualPhaseCoefficient, SemiAnnualAmplitudeCoefficient, SemiAnnualPhaseCoefficient, MeanCoefficient, LSqFitFlag
```
```{r calculate day index in the Cortad data}
#SST uses one index because it has a monthly resolution. the other variables use another index because they have a weekly resolution
SST_coral_cover_cortad_day_index<-array(0, dim=number_of_surveys)
SST_max_index_of_CoRTAD<-dim(FilledSST_time_bounds)[2]+1
for (i in 1:number_of_surveys)
{
minimum<- min(abs(days_since_19811231[i]-FilledSST$var$time_bnds$dim[[2]]$vals))
for (j in 1:length(FilledSST$var$time_bnds$dim[[2]]$vals))
{
if (abs(days_since_19811231[i]-FilledSST$var$time_bnds$dim[[2]]$vals[j])==minimum)
{
SST_coral_cover_cortad_day_index[i]<-j
}
}
if (SST_coral_cover_cortad_day_index[i]>SST_max_index_of_CoRTAD){SST_coral_cover_cortad_day_index[i]<-NA}
}
time_bounds<-ncvar_get(SSTA, varid="time_bounds")
dim(time_bounds) #2
#SSTA or TSA
coral_cover_cortad_day_index<-array(0, dim=number_of_surveys)
max_index_of_CoRTAD<-dim(time_bounds)[2]+1
for (i in 1:number_of_surveys)
{
coral_cover_cortad_day_index[i]<-floor((days_since_19811231[i]+time_bounds[1,1])/7)+1
if (coral_cover_cortad_day_index[i]>max_index_of_CoRTAD){coral_cover_cortad_day_index[i]<-NA}
}
```
```{r make the functions for grabbing the correct CoRTAD grid cell}
first_pass_function_Harmonics<-function(netcdf_variable_name, variable_id){
result<-try(ncvar_get(netcdf_variable_name, varid=variable_id, start=c(coral_cover_cortad_lon_cell[i],coral_cover_cortad_lat_cell[i], (coral_cover_cortad_day_index[i]%%52+1)), count=c(1,1,1)), silent=TRUE)
return(result)
}
first_pass_function_3d_mean<-function(netcdf_variable_name, variable_id){
result<-try(mean(ncvar_get(netcdf_variable_name, varid=variable_id, start=c(coral_cover_cortad_lon_cell[i],coral_cover_cortad_lat_cell[i],coral_cover_cortad_day_index[i]-155), count=c(1,1,156))), silent=TRUE)
return(result)
}
first_pass_function_3d_min<-function(netcdf_variable_name, variable_id){
result<-try(min(ncvar_get(netcdf_variable_name, varid=variable_id, start=c(coral_cover_cortad_lon_cell[i],coral_cover_cortad_lat_cell[i],coral_cover_cortad_day_index[i]-155), count=c(1,1,156))), silent=TRUE)
return(result)
}
first_pass_function_3d_max<-function(netcdf_variable_name, variable_id){
result<-try(max(ncvar_get(netcdf_variable_name, varid=variable_id, start=c(coral_cover_cortad_lon_cell[i],coral_cover_cortad_lat_cell[i],coral_cover_cortad_day_index[i]-155), count=c(1,1,156))), silent=TRUE)
return(result)
}
first_pass_function_3d_sd<-function(netcdf_variable_name, variable_id){
result<-try(sd(ncvar_get(netcdf_variable_name, varid=variable_id, start=c(coral_cover_cortad_lon_cell[i],coral_cover_cortad_lat_cell[i],coral_cover_cortad_day_index[i]-155), count=c(1,1,156))), silent=TRUE)
return(result)
}
first_pass_function_3d_SST_mean<-function(netcdf_variable_name, variable_id){
result<-try(mean(ncvar_get(netcdf_variable_name, varid=variable_id, start=c(coral_cover_cortad_lon_cell[i],coral_cover_cortad_lat_cell[i],SST_coral_cover_cortad_day_index[i]-35), count=c(1,1,36))), silent=TRUE)
return(result)
}
first_pass_function_3d_SST_max<-function(netcdf_variable_name, variable_id){
result<-try(max(ncvar_get(netcdf_variable_name, varid=variable_id, start=c(coral_cover_cortad_lon_cell[i],coral_cover_cortad_lat_cell[i],SST_coral_cover_cortad_day_index[i]-35), count=c(1,1,36))), silent=TRUE)
return(result)
}
first_pass_function_3d_SST_min<-function(netcdf_variable_name, variable_id){
result<-try(min(ncvar_get(netcdf_variable_name, varid=variable_id, start=c(coral_cover_cortad_lon_cell[i],coral_cover_cortad_lat_cell[i],SST_coral_cover_cortad_day_index[i]-35), count=c(1,1,36))), silent=TRUE)
return(result)
}
first_pass_function_3d_SST_sd<-function(netcdf_variable_name, variable_id){
result<-try(sd(ncvar_get(netcdf_variable_name, varid=variable_id, start=c(coral_cover_cortad_lon_cell[i],coral_cover_cortad_lat_cell[i],SST_coral_cover_cortad_day_index[i]-35), count=c(1,1,36))), silent=TRUE)
return(result)
}
second_pass_3d_mean<-function(netcdf_variable_name, variable_id){
expand=1
result=NA
repeat{
expanded_grid<-try(ncvar_get(netcdf_variable_name, varid=variable_id, start=c((coral_cover_cortad_lon_cell[i]-expand),(coral_cover_cortad_lat_cell[i]-expand),coral_cover_cortad_day_index[i]-155), count=c((1+2*expand),(1+2*expand),156)), silent=TRUE)
if(sum(is.na(expanded_grid))==((1+2*expand)*(1+2*expand)))
{expand=expand+1
if (expand>=3){break}
}
else{
result<-mean(expanded_grid, na.rm=TRUE)
break}
}
return(result)
}
second_pass_3d_min<-function(netcdf_variable_name, variable_id){
expand=1
result=NA
repeat{
expanded_grid<-try(ncvar_get(netcdf_variable_name, varid=variable_id, start=c((coral_cover_cortad_lon_cell[i]-expand),(coral_cover_cortad_lat_cell[i]-expand),coral_cover_cortad_day_index[i]-155), count=c((1+2*expand),(1+2*expand),156)), silent=TRUE)
if(sum(is.na(expanded_grid))==((1+2*expand)*(1+2*expand)))
{expand=expand+1
if (expand>=3){break}
}
else{
result<-min(expanded_grid, na.rm=TRUE)
break}
}
return(result)
}
second_pass_3d_max<-function(netcdf_variable_name, variable_id){
expand=1
result=NA
repeat{
expanded_grid<-try(ncvar_get(netcdf_variable_name, varid=variable_id, start=c((coral_cover_cortad_lon_cell[i]-expand),(coral_cover_cortad_lat_cell[i]-expand),coral_cover_cortad_day_index[i]-155), count=c((1+2*expand),(1+2*expand),156)), silent=TRUE)
if(sum(is.na(expanded_grid))==((1+2*expand)*(1+2*expand)))
{expand=expand+1
if (expand>=3){break}
}
else{
result<-max(expanded_grid, na.rm=TRUE)
break}
}
return(result)
}
second_pass_3d_sd<-function(netcdf_variable_name, variable_id){
expand=1
result=NA
repeat{
expanded_grid<-try(ncvar_get(netcdf_variable_name, varid=variable_id, start=c((coral_cover_cortad_lon_cell[i]-expand),(coral_cover_cortad_lat_cell[i]-expand),coral_cover_cortad_day_index[i]-155), count=c((1+2*expand),(1+2*expand),156)), silent=TRUE)
if(sum(is.na(expanded_grid))==((1+2*expand)*(1+2*expand)))
{expand=expand+1
if (expand>=3){break}
}
else{
result<-sd(expanded_grid, na.rm=TRUE)
break}
}
return(result)
}
second_pass_3d_SST_mean<-function(netcdf_variable_name, variable_id){
expand=1
result=NA
repeat{
expanded_grid<-try(ncvar_get(netcdf_variable_name, varid=variable_id, start=c((coral_cover_cortad_lon_cell[i]-expand),(coral_cover_cortad_lat_cell[i]-expand),SST_coral_cover_cortad_day_index[i]-35), count=c((1+2*expand),(1+2*expand),36)), silent=TRUE)
if(sum(is.na(expanded_grid))==((1+2*expand)*(1+2*expand)))
{expand=expand+1
if (expand>=3){break}
}
else{
result<-mean(expanded_grid, na.rm=TRUE)
break}
}
return(result)
}
second_pass_3d_SST_min<-function(netcdf_variable_name, variable_id){
expand=1
result=NA
repeat{
expanded_grid<-try(ncvar_get(netcdf_variable_name, varid=variable_id, start=c((coral_cover_cortad_lon_cell[i]-expand),(coral_cover_cortad_lat_cell[i]-expand),SST_coral_cover_cortad_day_index[i]-35), count=c((1+2*expand),(1+2*expand),36)), silent=TRUE)
if(sum(is.na(expanded_grid))==((1+2*expand)*(1+2*expand)))
{expand=expand+1
if (expand>=3){break}
}
else{
result<-min(expanded_grid, na.rm=TRUE)
break}
}
return(result)
}
second_pass_3d_SST_max<-function(netcdf_variable_name, variable_id){
expand=1
result=NA
repeat{
expanded_grid<-try(ncvar_get(netcdf_variable_name, varid=variable_id, start=c((coral_cover_cortad_lon_cell[i]-expand),(coral_cover_cortad_lat_cell[i]-expand),SST_coral_cover_cortad_day_index[i]-35), count=c((1+2*expand),(1+2*expand),36)), silent=TRUE)
if(sum(is.na(expanded_grid))==((1+2*expand)*(1+2*expand)))
{expand=expand+1
if (expand>=3){break}
}
else{
result<-max(expanded_grid, na.rm=TRUE)
break}
}
return(result)
}
second_pass_3d_SST_sd<-function(netcdf_variable_name, variable_id){
expand=1
result=NA
repeat{
expanded_grid<-try(ncvar_get(netcdf_variable_name, varid=variable_id, start=c((coral_cover_cortad_lon_cell[i]-expand),(coral_cover_cortad_lat_cell[i]-expand),SST_coral_cover_cortad_day_index[i]-35), count=c((1+2*expand),(1+2*expand),36)), silent=TRUE)
if(sum(is.na(expanded_grid))==((1+2*expand)*(1+2*expand)))
{expand=expand+1
if (expand>=3){break}
}
else{
result<-sd(expanded_grid, na.rm=TRUE)
break}
}
return(result)
}
second_pass_Harmonics<-function(netcdf_variable_name, variable_id){
expand=1
result=NA
repeat{
expanded_grid<-try(ncvar_get(netcdf_variable_name, varid=variable_id, start=c((coral_cover_cortad_lon_cell[i]-expand),(coral_cover_cortad_lat_cell[i]-expand), (coral_cover_cortad_day_index[i]%%52)+1), count=c((1+2*expand),(1+2*expand),1)), silent=TRUE)
if(sum(is.na(expanded_grid))==((1+2*expand)*(1+2*expand)))
{expand=expand+1
if (expand>=3){break}
}
else{
result<-mean(expanded_grid, na.rm=TRUE)
break}
}
return(result)
}
```
```{r for each Reef Check survey grab the corresponding Cortad variables}
for (i in 1:number_of_surveys)
{
if(!is.na(coral_cover_cortad_day_index[i]))
{
coral_cover_cortad_ClimSST[i]<-first_pass_function_Harmonics(HarmonicsClimatology, "ClimSST")
coral_cover_cortad_temperature_Minimum[i]<-first_pass_function_3d_SST_min(FilledSST, "FilledSST")
coral_cover_cortad_temperature_Maximum[i]<-first_pass_function_3d_SST_max(FilledSST, "FilledSST")
coral_cover_cortad_temperature_Mean[i]<-first_pass_function_3d_SST_mean(FilledSST, "FilledSST")
coral_cover_cortad_temperature_standardDeviation[i]<-first_pass_function_3d_SST_sd(FilledSST, "FilledSST")
coral_cover_cortad_SSTA_StandardDeviation[i]<-first_pass_function_3d_sd(SSTA, "SSTA")
coral_cover_cortad_SSTA_Mean[i]<-first_pass_function_3d_mean(SSTA, "SSTA")
coral_cover_cortad_SSTA_Maximum[i]<-first_pass_function_3d_max(SSTA,"SSTA")
coral_cover_cortad_SSTA_Minimum[i]<-first_pass_function_3d_min(SSTA, "SSTA")
coral_cover_cortad_SSTA_FrequencyStandardDeviation[i]<-first_pass_function_3d_sd(SSTA, "SSTA_Frequency")
coral_cover_cortad_SSTA_FrequencyMax[i]<-first_pass_function_3d_max(SSTA, "SSTA_Frequency")
coral_cover_cortad_SSTA_FrequencyMean[i]<-first_pass_function_3d_mean(SSTA, "SSTA_Frequency")
coral_cover_cortad_SSTA_DHWStandardDeviation[i]<-first_pass_function_3d_sd(SSTA, "SSTA_DHW")
coral_cover_cortad_SSTA_DHWMax[i]<-first_pass_function_3d_max(SSTA, "SSTA_DHW")
coral_cover_cortad_SSTA_DHWMean[i]<-first_pass_function_3d_mean(SSTA, "SSTA_DHW")
coral_cover_cortad_TSA_StandardDeviation[i]<-first_pass_function_3d_sd(TSA, "TSA")
coral_cover_cortad_TSA_Maximum[i]<-first_pass_function_3d_max(TSA, "TSA")
coral_cover_cortad_TSA_Minimum[i]<-first_pass_function_3d_min(TSA, "TSA")
coral_cover_cortad_TSA_Mean[i]<-first_pass_function_3d_mean(TSA, "TSA")
coral_cover_cortad_TSA_FrequencyStandardDeviation[i]<-first_pass_function_3d_sd(TSA, "TSA_Frequency")
coral_cover_cortad_TSA_FrequencyMax[i]<-first_pass_function_3d_max(TSA, "TSA_Frequency")
coral_cover_cortad_TSA_FrequencyMean[i]<-first_pass_function_3d_mean(TSA, "TSA_Frequency")
coral_cover_cortad_TSA_DHWStandardDeviation[i]<-first_pass_function_3d_sd(TSA, "TSA_DHW")
coral_cover_cortad_TSA_DHWMax[i]<-first_pass_function_3d_max(TSA, "TSA_DHW")
coral_cover_cortad_TSA_DHWMean[i]<-first_pass_function_3d_mean(TSA, "TSA_DHW")
}
print(i)
}
setwd(coral_cover_directory)
#write.csv(coral_cover_cortad_ClimSST, file = "ClimSST.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_temperature_Minimum, file = "SST_min.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_temperature_Maximum, file = "SST_max.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_temperature_Mean, file = "SST_mean.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_temperature_standardDeviation, file = "SST_sd.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_SSTA_StandardDeviation, file = "SSTA_sd.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_SSTA_Mean, file = "SSTA_mean.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_SSTA_Maximum, file = "SSTA_max.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_SSTA_Minimum, file = "SSTA_min.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_SSTA_FrequencyStandardDeviation, file = "SSTA_freq_sd.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_SSTA_FrequencyMax, file = "SSTA_freq_max.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_SSTA_FrequencyMean, file = "SSTA_freq_mean.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_SSTA_DHWStandardDeviation, file = "SSTA_dhw_sd.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_SSTA_DHWMax, file = "SSTA_dhw_max.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_SSTA_DHWMean, file = "SSTA_dhw_mean.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_TSA_StandardDeviation, file = "TSA_sd.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_TSA_Maximum, file = "TSA_max.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_TSA_Minimum, file = "TSA_min.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_TSA_Mean, file = "TSA_mean.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_TSA_FrequencyStandardDeviation, file = "TSA_freq_sd.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_TSA_FrequencyMax, file = "TSA_freq_max.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_TSA_FrequencyMean, file = "TSA_freq_mean.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_TSA_DHWStandardDeviation, file = "TSA_dhw_sd.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_TSA_DHWMax, file = "TSA_dhw_max.csv", row.names=FALSE)
#write.csv(coral_cover_cortad_TSA_DHWMean, file = "TSA_dhw_mean.csv", row.names=FALSE)
#coral_cover_cortad_ClimSST<-read.csv(file = "ClimSST.csv")[,1]
#coral_cover_cortad_temperature_Minimum<-read.csv(file = "SST_min.csv")[,1]
#coral_cover_cortad_temperature_Maximum<-read.csv(file = "SST_max.csv")[,1]
#coral_cover_cortad_temperature_Mean<-read.csv(file = "SST_mean.csv")[,1]
#coral_cover_cortad_temperature_standardDeviation<-read.csv(file = "SST_sd.csv")[,1]
#coral_cover_cortad_SSTA_StandardDeviation<-read.csv(file = "SSTA_sd.csv")[,1]
#coral_cover_cortad_SSTA_Mean<-read.csv(file = "SSTA_mean.csv")[,1]
#coral_cover_cortad_SSTA_Maximum<-read.csv(file = "SSTA_max.csv")[,1]
#coral_cover_cortad_SSTA_Minimum<-read.csv(file = "SSTA_min.csv")[,1]
#coral_cover_cortad_SSTA_FrequencyStandardDeviation<-read.csv(file = "SSTA_freq_sd.csv")[,1]
#coral_cover_cortad_SSTA_FrequencyMax<-read.csv(file = "SSTA_freq_max.csv")[,1]
#coral_cover_cortad_SSTA_FrequencyMean<-read.csv(file = "SSTA_freq_mean.csv")[,1]
#coral_cover_cortad_SSTA_DHWStandardDeviation<-read.csv(file = "SSTA_dhw_sd.csv")[,1]
#coral_cover_cortad_SSTA_DHWMax<-read.csv(file = "SSTA_dhw_max.csv")[,1]
#coral_cover_cortad_SSTA_DHWMean<-read.csv(file = "SSTA_dhw_mean.csv")[,1]
#coral_cover_cortad_TSA_StandardDeviation<-read.csv(file = "TSA_sd.csv")[,1]
#coral_cover_cortad_TSA_Maximum<-read.csv(file = "TSA_max.csv")[,1]
#coral_cover_cortad_TSA_Minimum<-read.csv(file = "TSA_min.csv")[,1]
#coral_cover_cortad_TSA_Mean<-read.csv(file = "TSA_mean.csv")[,1]
#coral_cover_cortad_TSA_FrequencyStandardDeviation<-read.csv(file = "TSA_freq_sd.csv")[,1]
#coral_cover_cortad_TSA_FrequencyMax<-read.csv(file = "TSA_freq_max.csv")[,1]
#coral_cover_cortad_TSA_FrequencyMean<-read.csv(file = "TSA_freq_mean.csv")[,1]
#coral_cover_cortad_TSA_DHWStandardDeviation<-read.csv(file = "TSA_dhw_sd.csv")[,1]
#coral_cover_cortad_TSA_DHWMax<-read.csv(file = "TSA_dhw_max.csv")[,1]
#coral_cover_cortad_TSA_DHWMean<-read.csv(file = "TSA_dhw_mean.csv")[,1]
coral_cover_cortad_temperature_standardDeviation<-as.numeric(as.character(coral_cover_cortad_temperature_standardDeviation))
coral_cover_cortad_SSTA_StandardDeviation<-as.numeric(as.character(coral_cover_cortad_SSTA_StandardDeviation))
coral_cover_cortad_SSTA_Mean<-as.numeric(as.character(coral_cover_cortad_SSTA_Mean))
coral_cover_cortad_SSTA_Maximum<-as.numeric(as.character(coral_cover_cortad_SSTA_Maximum))
coral_cover_cortad_SSTA_FrequencyStandardDeviation<-as.numeric(as.character(coral_cover_cortad_SSTA_FrequencyStandardDeviation))
coral_cover_cortad_SSTA_FrequencyMax<-as.numeric(as.character(coral_cover_cortad_SSTA_FrequencyMax))
coral_cover_cortad_SSTA_FrequencyMean<-as.numeric(as.character(coral_cover_cortad_SSTA_FrequencyMean))
coral_cover_cortad_SSTA_DHWStandardDeviation<-as.numeric(as.character(coral_cover_cortad_SSTA_DHWStandardDeviation))
coral_cover_cortad_SSTA_DHWMax<-as.numeric(as.character(coral_cover_cortad_SSTA_DHWMax))
coral_cover_cortad_SSTA_DHWMean<-as.numeric(as.character(coral_cover_cortad_SSTA_DHWMean))
coral_cover_cortad_TSA_StandardDeviation<-as.numeric(as.character(coral_cover_cortad_TSA_StandardDeviation))
coral_cover_cortad_TSA_Maximum<-as.numeric(as.character(coral_cover_cortad_TSA_Maximum))
coral_cover_cortad_TSA_Mean<-as.numeric(as.character(coral_cover_cortad_TSA_Mean))
coral_cover_cortad_TSA_FrequencyStandardDeviation<-as.numeric(as.character(coral_cover_cortad_TSA_FrequencyStandardDeviation))
coral_cover_cortad_TSA_FrequencyMax<-as.numeric(as.character(coral_cover_cortad_TSA_FrequencyMax))
coral_cover_cortad_TSA_FrequencyMean<-as.numeric(as.character(coral_cover_cortad_TSA_FrequencyMean))
coral_cover_cortad_TSA_DHWStandardDeviation<-as.numeric(as.character(coral_cover_cortad_TSA_DHWStandardDeviation))
coral_cover_cortad_TSA_DHWMax<-as.numeric(as.character(coral_cover_cortad_TSA_DHWMax))
coral_cover_cortad_TSA_DHWMean<-as.numeric(as.character(coral_cover_cortad_TSA_DHWMean))
coral_cover_cortad_ClimSST<-as.numeric(as.character(coral_cover_cortad_ClimSST))
coral_cover_cortad_TSA_Minimum<-as.numeric(as.character(coral_cover_cortad_TSA_Minimum))
coral_cover_cortad_SSTA_Minimum<-as.numeric(as.character(coral_cover_cortad_SSTA_Minimum))
coral_cover_cortad_temperature_Minimum<-as.numeric(as.character(coral_cover_cortad_temperature_Minimum))
coral_cover_cortad_temperature_Maximum<-as.numeric(as.character(coral_cover_cortad_temperature_Maximum))
coral_cover_cortad_temperature_Mean<-as.numeric(as.character(coral_cover_cortad_temperature_Mean))
```
```{r write a csv of the data}
setwd(coral_cover_directory)
RC_with_cortad_variables<-cbind(coral_cover_data, coral_cover_cortad_ClimSST, coral_cover_cortad_temperature_Mean, coral_cover_cortad_temperature_Minimum, coral_cover_cortad_temperature_Maximum, coral_cover_cortad_temperature_standardDeviation, coral_cover_cortad_SSTA_StandardDeviation, coral_cover_cortad_SSTA_Mean, coral_cover_cortad_SSTA_Minimum, coral_cover_cortad_SSTA_Maximum, coral_cover_cortad_SSTA_FrequencyStandardDeviation, coral_cover_cortad_SSTA_FrequencyMax, coral_cover_cortad_SSTA_FrequencyMean, coral_cover_cortad_SSTA_DHWStandardDeviation, coral_cover_cortad_SSTA_DHWMax, coral_cover_cortad_SSTA_DHWMean, coral_cover_cortad_TSA_StandardDeviation, coral_cover_cortad_TSA_Minimum, coral_cover_cortad_TSA_Maximum, coral_cover_cortad_TSA_Mean, coral_cover_cortad_TSA_FrequencyStandardDeviation, coral_cover_cortad_TSA_FrequencyMax, coral_cover_cortad_TSA_FrequencyMean, coral_cover_cortad_TSA_DHWStandardDeviation, coral_cover_cortad_TSA_DHWMax, coral_cover_cortad_TSA_DHWMean)
number_of_columns<-dim(coral_cover_data)[2]
colnames(RC_with_cortad_variables)[number_of_columns+1]<-"ClimSST"
colnames(RC_with_cortad_variables)[number_of_columns+2]<-"Temperature_Mean"
colnames(RC_with_cortad_variables)[number_of_columns+3]<-"Temperature_Minimum"
colnames(RC_with_cortad_variables)[number_of_columns+4]<-"Temperature_Maximum"
colnames(RC_with_cortad_variables)[number_of_columns+5]<-"Temperature_Kelvin_Standard_Deviation"
colnames(RC_with_cortad_variables)[number_of_columns+6]<-"SSTA_Standard_Deviation"
colnames(RC_with_cortad_variables)[number_of_columns+7]<-"SSTA_Mean"
colnames(RC_with_cortad_variables)[number_of_columns+8]<-"SSTA_Minimum"
colnames(RC_with_cortad_variables)[number_of_columns+9]<-"SSTA_Maximum"
colnames(RC_with_cortad_variables)[number_of_columns+10]<-"SSTA_Frequency_Standard_Deviation"
colnames(RC_with_cortad_variables)[number_of_columns+11]<-"SSTA_FrequencyMax"
colnames(RC_with_cortad_variables)[number_of_columns+12]<-"SSTA_FrequencyMean"
colnames(RC_with_cortad_variables)[number_of_columns+13]<-"SSTA_DHW_Standard_Deviation"
colnames(RC_with_cortad_variables)[number_of_columns+14]<-"SSTA_DHWMax"
colnames(RC_with_cortad_variables)[number_of_columns+15]<-"SSTA_DHWMean"
colnames(RC_with_cortad_variables)[number_of_columns+16]<-"TSA_Standard_Deviation"
colnames(RC_with_cortad_variables)[number_of_columns+17]<-"TSA_Minimum"
colnames(RC_with_cortad_variables)[number_of_columns+18]<-"TSA_Maximum"
colnames(RC_with_cortad_variables)[number_of_columns+19]<-"TSA_Mean"
colnames(RC_with_cortad_variables)[number_of_columns+20]<-"TSA_Frequency_Standard_Deviation"
colnames(RC_with_cortad_variables)[number_of_columns+21]<-"TSA_FrequencyMax"
colnames(RC_with_cortad_variables)[number_of_columns+22]<-"TSA_FrequencyMean"
colnames(RC_with_cortad_variables)[number_of_columns+23]<-"TSA_DHW_Standard_Deviation"
colnames(RC_with_cortad_variables)[number_of_columns+24]<-"TSA_DHWMax"
colnames(RC_with_cortad_variables)[number_of_columns+25]<-"TSA_DHWMean"
```
#save off a copy of the data up to this point
write.csv(RC_with_cortad_variables, file = "Reef_Check_with_cortad_variables.csv", row.names=FALSE)
coral_cover_data <- read.csv(file="Reef_Check_with_cortad_variables.csv", header=TRUE, sep=",")
```{r close the netcdf files}
setwd(Cortad_directory)
nc_close(FilledSST)
nc_close(HarmonicsClimatology)
nc_close(SSTA)
nc_close(TSA)
```
coral_cover_data$reef <- coral_cover_data$Reef_ID
```{r read in and format turbidity, cyclone, diversity data}
#turbidity data
setwd(turbidity_directory)
turbidity_raster_min <- raster("turbidity_raster_min.nc")
turbidity_raster_min[turbidity_raster_min>1] <- 1
turbidity_raster_max <- raster("turbidity_raster_max.nc")
turbidity_raster_max[turbidity_raster_max>1] <- 1
turbidity_raster_mean <- raster("turbidity_raster_mean.nc")
turbidity_raster_mean[turbidity_raster_mean>1] <- 1
#cyclone data
setwd(cyclone_directory)
cyclone_raster <- raster("cyclone frequency yr-1.nc")
#historical sst data
setwd(historical_sst_directory)
historical_sst_sd <- raster("historical_sst_sd.tif")
#the rotate() function changes how the raster is centered. We need it to match the rest of our data
historical_sst_sd<-rotate(historical_sst_sd)
historical_sst_max <- raster("historical_sst_max.tif")
historical_sst_max<-rotate(historical_sst_max)
#convert from Celcius to Kelvin so that all temperature measurements are on the same scale
historical_sst_max<-historical_sst_max+273.15
historical_sst_mean <- raster("historical_sst_mean.tif")
historical_sst_mean<-rotate(historical_sst_mean)
#convert from Celcius to Kelvin so that all temperature measurements are on the same scale
historical_sst_mean<-historical_sst_mean+273.15
#diversity data
diversity<-read.csv(file=file.path(diversity_data_directory, "coral_diversity_for_coral_cover.csv"), header=TRUE, sep=",")
names(diversity)[1]<-"Ecoregion"
diversity$Region<-diversity$Ecoregion
diversity<-diversity[order(diversity$Ecoregion),]
```
```{r read in and format shapefiles of a world map, and ecoregion boundaries}
wlrd.p <- readOGR(file.path(shapefiles_directory,'TM_WORLD_BORDERS_SIMPL_PC150.shp'))
ECO <- readOGR(file.path(shapefiles_directory,'ecoregion_exportPolygon.shp')) # ecoregions
ecos_list<-c()
for (i in 1:150){
eco_i<-Polygons((Filter(function(f){f@ringDir==1}, ECO@polygons[[i]]@Polygons)), ID=i)
ecos_list<-append(ecos_list, values=eco_i, after = length(ecos_list))
#include a brief pause (Sys.sleep) because if running in Rstudio, it takes a while for the code to run and for the value to be loaded into the global environment. If there is no pause, the next iteration of the loop starts before the previous value is fully saved and loaded into the environment, and there can be errors in the shapefile
Sys.sleep(.2)
}
ecos<-SpatialPolygons(ecos_list)
ecos$ERG<-ECO$ERG
ecos$Ecoregion<-ECO$Ecoregion
ecos@proj4string<-ECO@proj4string
ecos@plotOrder<-ECO@plotOrder
ecos@data<-ECO@data
ECO<-ecos
```
```{r obtain the corresponding temperature, environmental, and human parameters for each reef survey}
#obtain a list of each unique reef site and get the coordinates for the sites
reef_id_df<-data.frame(table(coral_cover_data$Reef_ID))
reef_id_list<-as.character(reef_id_df[,1])
data_points<-cbind(coral_cover_data$Longitude.Degrees, coral_cover_data$Latitude.Degrees)
result <- raster::extract(cyclone_raster, data_points)
coral_cover_data$cyclone<-result
result <- raster::extract(turbidity_raster_mean, data_points)
coral_cover_data$Turbidity_mean<-result
result <- raster::extract(turbidity_raster_max, data_points)
coral_cover_data$Turbidity_max<-result
result <- raster::extract(turbidity_raster_min, data_points)
coral_cover_data$Turbidity_min<-result
result <- raster::extract(historical_sst_mean, data_points)
coral_cover_data$historical_sst_mean<-result
result <- raster::extract(historical_sst_max, data_points)
coral_cover_data$historical_sst_max<-result
result <- raster::extract(historical_sst_sd, data_points)
coral_cover_data$historical_sst_sd<-result
#1990, 2000 are from https://sedac.ciesin.columbia.edu/data/set/popdynamics-global-pop-count-time-series-estimates/data-download#close
human_pop_1990<-raster("D:/human_population/human_population/human_pop_1990/popdynamics-global-pop-count-time-series-estimates_1990.tif")
coral_cover_data$human_pop_1990_vals<-extract(human_pop_1990, data_points, buffer=10000, fun=sum)
coral_cover_data$human_pop_1990_vals[is.na(coral_cover_data$human_pop_1990_vals)]<-0
human_pop_2000<-raster("D:/human_population/human_population/human_pop_2000/popdynamics-global-pop-count-time-series-estimates_2000.tif")
coral_cover_data$human_pop_2000_vals<-extract(human_pop_2000, data_points, buffer=10000, fun=sum)
coral_cover_data$human_pop_2000_vals[is.na(coral_cover_data$human_pop_2000_vals)]<-0
#2010, 2020, 2050, 2100 are from https://sedac.ciesin.columbia.edu/data/set/popdynamics-1-km-downscaled-pop-base-year-projection-ssp-2000-2100-rev01
human_pop_2010<-raster("D:/human_population/human_population/human_pop_2000_to_2100_SSP2/SSP2_1km/ssp2_total_2010.nc4")
coral_cover_data$human_pop_2010_vals<-extract(human_pop_2010, data_points, buffer=10000, fun=sum)
coral_cover_data$human_pop_2010_vals[is.na(coral_cover_data$human_pop_2010_vals)]<-0
human_pop_2020<-raster("D:/human_population/human_population/human_pop_2000_to_2100_SSP2/SSP2_1km/ssp2_total_2020.nc4")
coral_cover_data$human_pop_2020_vals<-extract(human_pop_2020, data_points, buffer=10000, fun=sum)
coral_cover_data$human_pop_2020_vals[is.na(coral_cover_data$human_pop_2020_vals)]<-0
human_pop_2050<-raster("D:/human_population/human_population/human_pop_2000_to_2100_SSP2/SSP2_1km/ssp2_total_2050.nc4")
coral_cover_data$human_pop_2050_vals<-extract(human_pop_2050, data_points, buffer=10000, fun=sum)
coral_cover_data$human_pop_2050_vals[is.na(coral_cover_data$human_pop_2050_vals)]<-0
human_pop_2100<-raster("D:/human_population/human_population/human_pop_2000_to_2100_SSP2/SSP2_1km/ssp2_total_2100.nc4")
coral_cover_data$human_pop_2100_vals<-extract(human_pop_2100, data_points, buffer=10000, fun=sum)
coral_cover_data$human_pop_2100_vals[is.na(coral_cover_data$human_pop_2100_vals)]<-0
calculate_human_pop_function<-function(x, a, b, c, d){
if(x<2000){human_pop <- a+(((b-a)/10)*(x-1990))}
if(x==2000){human_pop <- b}
if(x>2000 & x<2010){human_pop <- b+(((c-b)/10)*(x-2000))}
if(x==2010){human_pop <- c}
if(x>2010){human_pop <- c+(((d-c)/10)*(x-2010))}
return(human_pop)
}
coral_cover_data$human_pop<-NA
for(i in 1:nrow(coral_cover_data)){
coral_cover_data$human_pop[i]<-calculate_human_pop_function(coral_cover_data$year[i], coral_cover_data$human_pop_1990_vals[i], coral_cover_data$human_pop_2000_vals[i], coral_cover_data$human_pop_2010_vals[i], coral_cover_data$human_pop_2020_vals[i])
}
coral_cover_data<-subset(coral_cover_data, select = -c(year, human_pop_1990_vals, human_pop_2000_vals, human_pop_2010_vals, human_pop_2020_vals) )
```
names(coral_cover_data)[names(coral_cover_data)=='Temperature_Mean']<-'SST_Mean'
names(coral_cover_data)[names(coral_cover_data)=='Temperature_Minimum']<-'SST_min'
names(coral_cover_data)[names(coral_cover_data)=='Temperature_Maximum']<-'SST_max'
names(coral_cover_data)[names(coral_cover_data)=='Temperature_Kelvin_Standard_Deviation']<-'SST_stdev'
names(coral_cover_data)[names(coral_cover_data)=='SSTA_Mean']<-'SSTA_Mean'
names(coral_cover_data)[names(coral_cover_data)=='SSTA_Minimum']<-'SSTA_min'
names(coral_cover_data)[names(coral_cover_data)=='SSTA_Maximum']<-'SSTA_max'
names(coral_cover_data)[names(coral_cover_data)=='SSTA_Standard_Deviation']<-'SSTA_stdev'
names(coral_cover_data)[names(coral_cover_data)=='SSTA_DHW_Standard_Deviation']<-'SSTA_dhwstdev'
names(coral_cover_data)[names(coral_cover_data)=='SSTA_DHWMax']<-'SSTA_dhwmax'
names(coral_cover_data)[names(coral_cover_data)=='SSTA_DHWMean']<-'SSTA_dhwmean'
names(coral_cover_data)[names(coral_cover_data)=='SSTA_FrequencyMean']<-'SSTA_freqmean'
names(coral_cover_data)[names(coral_cover_data)=='SSTA_FrequencyMax']<-'SSTA_freqmax'
names(coral_cover_data)[names(coral_cover_data)=='SSTA_Frequency_Standard_Deviation']<-'SSTA_freqstdev'
names(coral_cover_data)[names(coral_cover_data)=='TSA_Mean']<-'TSA_mean'
names(coral_cover_data)[names(coral_cover_data)=='TSA_Maximum']<-'TSA_max'
names(coral_cover_data)[names(coral_cover_data)=='TSA_Minimum']<-'TSA_min'
names(coral_cover_data)[names(coral_cover_data)=='TSA_Standard_Deviation']<-'TSA_stdev'
names(coral_cover_data)[names(coral_cover_data)=='TSA_FrequencyMean']<-'TSA_freqmean'
names(coral_cover_data)[names(coral_cover_data)=='TSA_FrequencyMax']<-'TSA_freqmax'
names(coral_cover_data)[names(coral_cover_data)=='TSA_Frequency_Standard_Deviation']<-'TSA_freqstdev'
names(coral_cover_data)[names(coral_cover_data)=='TSA_DHWMean']<-'TSA_dhwmean'
names(coral_cover_data)[names(coral_cover_data)=='TSA_DHWMax']<-'TSA_dhwmax'
names(coral_cover_data)[names(coral_cover_data)=='TSA_DHW_Standard_Deviation']<-'TSA_dhwstdev'
var_names<-names(coral_cover_data)
```{r get future mean SST, maximum SST, and future ssta_dhw}
setwd(future_sst_directory)
rcp45<-brick("ensemble_rcp45_v2.nc", var="tos")
#rcp45_2050_mean<-mean(rcp45[,,493:528]) #too big to work
rcp45_204701<-raster("ensemble_rcp45_v2.nc", var="tos", band=493)
coral_cover_data$rcp45_204701<-extract(rcp45_204701, data_points)
rcp45_204702<-raster("ensemble_rcp45_v2.nc", var="tos", band=494)
coral_cover_data$rcp45_204702<-extract(rcp45_204702, data_points)
rcp45_204703<-raster("ensemble_rcp45_v2.nc", var="tos", band=495)
coral_cover_data$rcp45_204703<-extract(rcp45_204703, data_points)
rcp45_204704<-raster("ensemble_rcp45_v2.nc", var="tos", band=496)
coral_cover_data$rcp45_204704<-extract(rcp45_204704, data_points)
rcp45_204705<-raster("ensemble_rcp45_v2.nc", var="tos", band=497)
coral_cover_data$rcp45_204705<-extract(rcp45_204705, data_points)
rcp45_204706<-raster("ensemble_rcp45_v2.nc", var="tos", band=498)
coral_cover_data$rcp45_204706<-extract(rcp45_204706, data_points)
rcp45_204707<-raster("ensemble_rcp45_v2.nc", var="tos", band=499)
coral_cover_data$rcp45_204707<-extract(rcp45_204707, data_points)
rcp45_204708<-raster("ensemble_rcp45_v2.nc", var="tos", band=500)
coral_cover_data$rcp45_204708<-extract(rcp45_204708, data_points)
rcp45_204709<-raster("ensemble_rcp45_v2.nc", var="tos", band=501)
coral_cover_data$rcp45_204709<-extract(rcp45_204709, data_points)
rcp45_204710<-raster("ensemble_rcp45_v2.nc", var="tos", band=502)
coral_cover_data$rcp45_204710<-extract(rcp45_204710, data_points)
rcp45_204711<-raster("ensemble_rcp45_v2.nc", var="tos", band=503)
coral_cover_data$rcp45_204711<-extract(rcp45_204711, data_points)
rcp45_204712<-raster("ensemble_rcp45_v2.nc", var="tos", band=504)
coral_cover_data$rcp45_204712<-extract(rcp45_204712, data_points)
rcp45_204801<-raster("ensemble_rcp45_v2.nc", var="tos", band=505)
coral_cover_data$rcp45_204801<-extract(rcp45_204801, data_points)
rcp45_204802<-raster("ensemble_rcp45_v2.nc", var="tos", band=506)
coral_cover_data$rcp45_204802<-extract(rcp45_204802, data_points)
rcp45_204803<-raster("ensemble_rcp45_v2.nc", var="tos", band=507)
coral_cover_data$rcp45_204803<-extract(rcp45_204803, data_points)
rcp45_204804<-raster("ensemble_rcp45_v2.nc", var="tos", band=508)
coral_cover_data$rcp45_204804<-extract(rcp45_204804, data_points)
rcp45_204805<-raster("ensemble_rcp45_v2.nc", var="tos", band=509)
coral_cover_data$rcp45_204805<-extract(rcp45_204805, data_points)
rcp45_204806<-raster("ensemble_rcp45_v2.nc", var="tos", band=510)
coral_cover_data$rcp45_204806<-extract(rcp45_204806, data_points)
rcp45_204807<-raster("ensemble_rcp45_v2.nc", var="tos", band=511)
coral_cover_data$rcp45_204807<-extract(rcp45_204807, data_points)
rcp45_204808<-raster("ensemble_rcp45_v2.nc", var="tos", band=512)
coral_cover_data$rcp45_204808<-extract(rcp45_204808, data_points)
rcp45_204809<-raster("ensemble_rcp45_v2.nc", var="tos", band=513)
coral_cover_data$rcp45_204809<-extract(rcp45_204809, data_points)
rcp45_204810<-raster("ensemble_rcp45_v2.nc", var="tos", band=514)
coral_cover_data$rcp45_204810<-extract(rcp45_204810, data_points)
rcp45_204811<-raster("ensemble_rcp45_v2.nc", var="tos", band=515)
coral_cover_data$rcp45_204811<-extract(rcp45_204811, data_points)
rcp45_204812<-raster("ensemble_rcp45_v2.nc", var="tos", band=516)
coral_cover_data$rcp45_204812<-extract(rcp45_204812, data_points)
rcp45_204901<-raster("ensemble_rcp45_v2.nc", var="tos", band=517)
coral_cover_data$rcp45_204901<-extract(rcp45_204901, data_points)
rcp45_204902<-raster("ensemble_rcp45_v2.nc", var="tos", band=518)
coral_cover_data$rcp45_204902<-extract(rcp45_204902, data_points)
rcp45_204903<-raster("ensemble_rcp45_v2.nc", var="tos", band=519)
coral_cover_data$rcp45_204903<-extract(rcp45_204903, data_points)
rcp45_204904<-raster("ensemble_rcp45_v2.nc", var="tos", band=520)
coral_cover_data$rcp45_204904<-extract(rcp45_204904, data_points)
rcp45_204905<-raster("ensemble_rcp45_v2.nc", var="tos", band=521)
coral_cover_data$rcp45_204905<-extract(rcp45_204905, data_points)
rcp45_204906<-raster("ensemble_rcp45_v2.nc", var="tos", band=522)
coral_cover_data$rcp45_204906<-extract(rcp45_204906, data_points)
rcp45_204907<-raster("ensemble_rcp45_v2.nc", var="tos", band=523)
coral_cover_data$rcp45_204907<-extract(rcp45_204907, data_points)
rcp45_204908<-raster("ensemble_rcp45_v2.nc", var="tos", band=524)
coral_cover_data$rcp45_204908<-extract(rcp45_204908, data_points)
rcp45_204909<-raster("ensemble_rcp45_v2.nc", var="tos", band=525)
coral_cover_data$rcp45_204909<-extract(rcp45_204909, data_points)
rcp45_204910<-raster("ensemble_rcp45_v2.nc", var="tos", band=526)
coral_cover_data$rcp45_204910<-extract(rcp45_204910, data_points)
rcp45_204911<-raster("ensemble_rcp45_v2.nc", var="tos", band=527)
coral_cover_data$rcp45_204911<-extract(rcp45_204911, data_points)
rcp45_204912<-raster("ensemble_rcp45_v2.nc", var="tos", band=528)
coral_cover_data$rcp45_204912<-extract(rcp45_204912, data_points)
#################################################
#2100 uses 2097-2099 [1093:1128]
rcp45_209701<-raster("ensemble_rcp45_v2.nc", var="tos", band=1093)
coral_cover_data$rcp45_209701<-extract(rcp45_209701, data_points)
rcp45_209702<-raster("ensemble_rcp45_v2.nc", var="tos", band=1094)
coral_cover_data$rcp45_209702<-extract(rcp45_209702, data_points)
rcp45_209703<-raster("ensemble_rcp45_v2.nc", var="tos", band=1095)
coral_cover_data$rcp45_209703<-extract(rcp45_209703, data_points)
rcp45_209704<-raster("ensemble_rcp45_v2.nc", var="tos", band=1096)
coral_cover_data$rcp45_209704<-extract(rcp45_209704, data_points)
rcp45_209705<-raster("ensemble_rcp45_v2.nc", var="tos", band=1097)
coral_cover_data$rcp45_209705<-extract(rcp45_209705, data_points)
rcp45_209706<-raster("ensemble_rcp45_v2.nc", var="tos", band=1098)
coral_cover_data$rcp45_209706<-extract(rcp45_209706, data_points)