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SOD_DSV_summary_calcs.R
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SOD_DSV_summary_calcs.R
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library(terra)
library(tidyverse)
library(lubridate)
library(tigris)
library(sf)
# Load the shapefile containing information about blight in North America in 2023
spinf <- vect("Z:/Late_blight/helpful_shapes_rasters/blight_NA_2023.shp")
spinf <- spinf[spinf$Cnry == "United States",]
spinf$CTY <- gsub(" County", "", spinf$CTY) # remove the space in the county name
infx <-read.csv( "Q:/My Drive/P_infestans_data/USAB_NPDN_combined_USETHIS.csv")
infx$yday <- yday(infx$ObsDt)
#infx <- read.csv("Z:/Late_blight/relatedtables/infxco2.csv")
infxco <- infx %>% filter(!ST %in% c("AZ", "HI", "VI", "MP", "GU", "AS", "PR",
"IA", "KS","MN","ND","ID","NE","OR","SD",
"AK","LA","MT","CA","CO","NB","UT","WA","AR",
"MO","NM","OK","WY","NV","MS","DC","TX"))
infxco <- infxco %>% filter(!st_ct %in% c("IN-West Lafayette", "NJ-Agmart", "NY-Cutches",
"NY-Highland", "PA-PennState"))
#infxco <- infxco %>% group_by(year, State, week, County) %>% mutate(n_infx = n()) %>% slice_head()
infxco <- infxco %>% arrange(Year, yday)
infxco$week <- week(infxco$ObsDt)
stls <- unique(infxco$ST)
spinf <- spinf[spinf$ST %in% stls,]
spinf$ObsDt <- as_date(as_datetime(as.numeric(spinf$newdate)))
spinf$yday <- yday(spinf$ObsDt)
spinf$CTY <- gsub("St\\.", "St", spinf$CTY)
spinf$CTY <- gsub("Fond du", "Fond Du", spinf$CTY)
spinf$CTY <- gsub("LaGrange", "Lagrange", spinf$CTY)
spinf$CTY <- gsub("De [K|k]alb", "Dekalb", spinf$CTY)
spinf$CTY <- gsub("'s","s", spinf$CTY)
spinf$st_ct <- paste0(spinf$ST,"-",spinf$CTY)
spinf <- spinf[!spinf$st_ct %in% c("IN-West Lafayette", "NJ-Agmart", "NY-Cutches",
"NY-Highland", "PA-PennState"),]
#co1 <- counties(stls)
# This takes a long time
#co1 <- st_transform(co1, crs="epsg:3857")
# Saved from earlier
co1 <- vect("D:/data/ncsu/shapesothergis/counties3857.shp")
st1 <- states()
st1 <- st1[st1$STUSPS %in% stls,]
st2 <- as.data.frame(st1[,c(3,6)])
st2 <- st2[,1:2]
# Use letter codes instead of FIPS, easier to understand
co1 <- co1[co1$STATEFP %in% st2$STATEFP,]
# Make some names more easiiy interchangable with other data set
co1$NAME <- gsub("St\\.", "St", co1$NAME)
co1$NAME <- gsub("Fond du", "Fond Du", co1$NAME)
co1$NAME <- gsub("LaGrange", "Lagrange", co1$NAME)
co1$NAME <- gsub("De [K|k]alb", "Dekalb", co1$NAME)
co1$NAME <- gsub("'s","s", co1$NAME)
co1 <- co1[co1$GEOID != 24510,] # the REAL Baltimore is 24005
co3 <- as.data.frame(co1)
co3 <- co3[,c(1,2,4, 18, 19)]
co3 <- co3[co3$GEOID != 24510,]
infxco$FIPS <- as.character(co3[match(infxco$st_ct, co3$stco), "GEOID"])
spinf$FIPS <- as.character(co3[match(spinf$st_ct, co3$stco), "GEOID"])
# Or...
co2 <- vect("D:/data/ncsu/googledrop/newareas/infections/infections/county_infections_2009.shp")
co2$FIPS <- paste0(co2$STATEFP,co2$COUNTYFP)
# Take out Baltimore City (it's independent)
co2 <- co2[co2$FIPS != 24510,] # the REAL Baltimore is 24005
# A list of daily dsvs.
l2 <- list.files("Z:/Late_blight/lbdsveconu/", pattern="dsv[0-9]{4}masked.tif", recursive=T, full.names=T)
#l2 <- list.files("Z:/Late_blight/lbdsveconu/", pattern="maskcml.*tif", recursive=T, full.names=T)
l4 <- rast("D:/data/ncsu/googledrop/newareas/DSV/2009/dsv2009masked.tif")
# A list of daily cumulative dsvs.
l3 <- list.files("Q:/My Drive/popblightdata/newareas/DSV/2009/cumulthreshold/rasters/original_singles/",
full.names = T, pattern = "tif$")
l3 <- mixedsort(l3)
l3 <- rast("D:/data/ncsu/googledrop/popblightdata/cudsv2009.tif")
l5 <- rast("Q:/My Drive/temp/dsvs/2009/cumulative/cumuthresh_2009.tif")
rt1 = list()
rtw1 = list()
l1 <- list.files("D:/data/ncsu/lbdsveconu/", pattern = "cumld", full.names = T,
recursive = T)
l1 <- c(l1[1], l1)
# Read in a list of year files with daily DSV values
l2 <- list.files("Z:/Late_blight/lbdsveconu/", pattern = "dsv_[0-9].*tif", full.names = T,
recursive=T)
l2 <- l2[c(1:2,4:14)]
#l2 <- list.files("D:/data/ncsu/lbdsveconu/", pattern = "dsv_[0-9].*tif$", full.names = T,
# recursive = T)
#l2 <- l2[2:length(l2)]
# Create a spatvect for all the points in infxco. Use coordinates from USAB and
# county center coordinates for NPDS.
spinfm <- spinf[, c(1,20,21)]
infxco2 <- merge(infxco, spinfm, by.x="Rec_ID", by.y="RptId", all.x=T)
delFIPS <- infxco2[which(is.na(infxco2$x)), "FIPS"]
delco <- co1[co1$GEOID %in% delFIPS, c(4, 16, 17)]
delco$INTPTLAT <- as.numeric(delco$INTPTLAT)
delco$INTPTLON <- as.numeric(delco$INTPTLON)
infxco3 <- merge(infxco2, delco, by.x="FIPS", by.y="GEOID", all.x=T)
infxco3$x <- if_else(is.na(infxco3$x), infxco3$INTPTLON, infxco3$x)
infxco3$y <- if_else(is.na(infxco3$y), infxco3$INTPTLAT, infxco3$y)
infxco3 <- infxco3 %>% select(2:14,1,15,16)
infpts <- vect(infxco3, geom=c("x","y"), crs="epsg:4326")
infpts <- infpts[order(infpts$Year, infpts$yday),] # Should've used ObsDt
#writeVector(infpts, "Z:/Late_blight/helpful_shapes_rasters/pointsUSABcrdsNPDNctycenters.gpkg")
spinf <- project(spinf, crs(rast(l2[1])))
dr1 <- NULL
infpts <- project(infpts, crs(rast(l2[1])))
extracted_dsv <- data.frame(ID = as.character(),
Date = as.character(),
st_ct = as.character(),
Max_14 = as.numeric(),
Min_14 = as.numeric(),
Mean_14 = as.numeric(),
Median_14 = as.numeric(),
SD_14 = as.numeric(),
Max_30 = as.numeric(),
Min_30 = as.numeric(),
Mean_30 = as.numeric(),
Median_30 = as.numeric(),
SD_30 = as.numeric(),
FIPS = as.character(),
stringsAsFactors = FALSE)
# Run the extracted_dsv chunk before this each time.
#Loop through each row of spinf
for (i in 1:nrow(infpts)){ #nrow(spinf)) {
# Get the date from the current row
date <- as_date(infpts$ObsDt[i])#spinf$ObsDt[i]
print(date)
# Extract the year and day from the date
syear <- year(date)
sday <- yday(date)
# Find the raster file that matches the year and day
dr1 <- rast(l2[grepl(syear, l2)])#paste0(syear,"masked"), l2)])
# Get the dates for the preceding 14 days
dates_14 <- seq(date - 14, date, by = "day")
# Get the dates for the preceding 30 days
dates_30 <- seq(date - 30, date, by = "day")
# Create list of indices for the dates_14
indx14 <- rev(seq(sday, length.out = 14, by = -1))
# Create list of indices for the dates_30
indx30 <- rev(seq(sday, length.out = 30, by = -1))
if(sday < 30){
# Prepend the earlier year to the selected year
dr2 = c(rast(l2[grepl((syear-1), l2)]), dr1)
# Figure out the number of days for prepended year
dayext = nlyr(rast(l2[grepl( (syear-1), l2)]))
# No. of days for selected year
newday1 = seq(as_date(paste0(syear, "-01-01")), length.out = 365, by = 1)
# No. of days for prepended year
newday2 = rev(seq(as_date(paste0(syear, "-01-01")) - days(1), length.out = dayext, by = -1))
# Add dates to the raster
time(dr2) = c(newday2, newday1)
dr1 = dr2
# Adjust the fortnight for prepended year
sdaynew = sday + dayext
dates_14 = rev(seq(sdaynew, length.out=14, by = -1))
#cr1 = c(rast(l1[grepl((syr1-1), l1)]), cr1)
# Adjust the month for prepended year
dates_30 = rev(seq(sdaynew, length.out=30, by = -1))
indx14 = rev(seq(sdaynew, length.out = 14, by = -1))
indx30 = rev(seq(sdaynew, length.out = 30, by = -1))
} else {
dr1 = dr1
time(dr1) = seq(as_date(paste0(syear, "-01-01")), length.out = 365, by = 1)
dates_14 = dates_14
dates_30 = dates_30
#cr1 = cr1
}
# Test if the raster has values for the dates_14 in the location for extract with spinf. If not, use 0 for extract instead of NA.
dsv_14 <- terra::extract(dr1[[indx14]], spinf[i, ])
dsv_14 <- as.numeric(dsv_14)
print(dsv_14)
#if (any(is.na(terra::extract(dr1[[indx30]], spinf[i, ])))) {
# dsv_30 <- rep(0, length(indx30))
#} else {
dsv_30 <- terra::extract(dr1[[indx30]], spinf[i, ])
# #dsv_30[is.na(dsv_30)] <- 0
#}
dsv_30 <- as.numeric(dsv_30)
print(dsv_30)
# Calculate the max, min, mean, median, and sd for each set
sum_14 <- sum(dsv_14[2:15], na.rm = TRUE)
mean_14 <- mean(dsv_14[2:15], na.rm = TRUE)
median_14 <- median(dsv_14[2:15], na.rm = TRUE)
sd_14 <- sd(dsv_14[2:15], na.rm = TRUE)
sum_30 <- sum(dsv_30[2:31], na.rm = TRUE)
mean_30 <- mean(dsv_30[2:31], na.rm = TRUE)
median_30 <- median(dsv_30[2:31], na.rm = TRUE)
sd_30 <- sd(dsv_30[2:31], na.rm = TRUE)
# Add the extracted DSV values to the data frame
extracted_dsv <- rbind(extracted_dsv, data.frame(ID = infpts$Rec_ID[i], #spinf$RptId[i],
Date = date,
st_ct = infpts$st_ct[i], #spinf$st_ct[i],
Sum_14 = round(sum_14, 1),
#Min_14 = round(min_14, 1),
Mean_14 = round(mean_14, 1),
Median_14 = round(median_14, 1),
SD_14 = round(sd_14, 1),
Sum_30 = round(sum_30, 1),
#Min_30 = round(min_30, 1),
Mean_30 = round(mean_30, 1),
Median_30 = round(median_30, 1),
SD_30 = round(sd_30, 1),
FIPS = infpts$FIPS[i], #spinf$FIPS[i],
stringsAsFactors = FALSE))
}
extracted_dsv$Year <- year(extracted_dsv$Date)
extracted_dsv$yday <- yday(extracted_dsv$Date)
write.csv(extracted_dsv, "Z:/Late_blight/lbdsveconu/lb20extracted_nomask_dsv.csv", row.names = F)
---:SOD
sodsv <- vect("Z:/Late_blight/helpful_shapes_rasters/SOD_all_3857.gpkg")
#table(sodsv$YEAR)
#2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
#55 81 57 28 60 143 162 122 102 120 337 207 188 182 88 222
#2017 2018 2019 2020
#256 260 198 81
#1 2 3 4 5 6 7 8 9 10 11
#7 48 49 25 14 125 36 35 7 3 2
1 2 3 4 5 6 7 8 9 10 11
0.02 0.14 0.14 0.07 0.04 0.36 0.10 0.10 0.02 0.01 0.01
odds1 <- c(0.02, 0.14, 0.14, 0.07, 0.04, 0.36, 0.1, 0.1, 0.02, 0.01, 0.01)
alldt <- seq(from = as.Date("2001-02-01"), to=as.Date("2020-12-31"), by=1)
# Create a new data frame with all dates
df_all_dates <- data.frame(Date = alldt, Max_14 = NA,
Min_14 = NA,
Mean_14 = NA,
Median_14 = NA,
SD_14 = NA,
Max_30 = NA,
Min_30 = NA,
Mean_30 = NA,
Median_30 = NA,
SD_30 = NA
)
l3 <- list.files("Z:/Late_blight/SODdsv/", full.names = T, recursive = T, pattern =
"dsvdai.*tif$")
extracted_soddsv <- data.frame(ID = as.character(),
Date = as.character(),
#st_ct = as.character(),
Max_14 = as.numeric(),
Min_14 = as.numeric(),
Mean_14 = as.numeric(),
Median_14 = as.numeric(),
SD_14 = as.numeric(),
Max_30 = as.numeric(),
Min_30 = as.numeric(),
Mean_30 = as.numeric(),
Median_30 = as.numeric(),
SD_30 = as.numeric(),
#FIPS = as.character(),
stringsAsFactors = FALSE)
for (i in 1:nrow(sod2)){ #nrow(spinf)) {
# Get the date from the current row
print(paste0("Index: ",i))
yearsel <- as_date(paste0(sodsv$YEAR[i],"-01-01"))
# Extract the year and day from the date
syear <- year(yearsel)
# Sample for a month in the year. Weights are based on 2021 and 2022 data
sampl_mo = 1:12
sampl_wt = c(0.02, 0.14, 0.14, 0.07, 0.04, 0.34, 0.1, 0.1, 0.02, 0.01, 0.01, 0.01)
pt_sampl_mo = sample(sampl_mo, 1, replace = T, sampl_wt)
print(pt_sampl_mo)
# Sample for a day to make the date complete.
num_days <- days_in_month(as.Date(paste(syear, pt_sampl_mo, "01", sep="-")))
print(num_days)
# Randomly sample a day
sampl_day <- sample(1:num_days, 1)
# Create the date
date <- as_date(paste(syear, pt_sampl_mo, sampl_day, sep="-"))
print(date)
# date as doy
sday <- yday(date)
# Find the raster file that matches the year and day
dr1 <- rast(l3[grepl(syear, l3)])#paste0(syear,"masked"), l3)])
# Get the dates for the preceding 14 days
dates_14 <- seq(date - 14, date, by = "day")
# Get the dates for the preceding 30 days
dates_30 <- seq(date - 30, date, by = "day")
# Create list of indices for the dates_14
indx14 <- rev(seq(sday, length.out = 14, by = -1))
# Create list of indices for the dates_30
indx30 <- rev(seq(sday, length.out = 30, by = -1))
if(sday < 30){
# Prepend the earlier year to the selected year
dr2 = c(rast(l3[grepl((syear-1), l3)]), dr1)
# Figure out the number of days for prepended year
dayext = nlyr(rast(l3[grepl( (syear-1), l3)]))
# No. of days for selected year
newday1 = seq(as_date(paste0(syear, "-01-01")), length.out = 365, by = 1)
# No. of days for prepended year
newday2 = rev(seq(as_date(paste0(syear, "-01-01")) - days(1), length.out = dayext, by = -1))
# Add dates to the raster
time(dr2) = c(newday2, newday1)
dr1 = dr2
# Adjust the fortnight for prepended year
sdaynew = sday + dayext
dates_14 = rev(seq(sdaynew, length.out=14, by = -1))
#cr1 = c(rast(l1[grepl((syr1-1), l1)]), cr1)
# Adjust the month for prepended year
dates_30 = rev(seq(sdaynew, length.out=30, by = -1))
indx14 = rev(seq(sdaynew, length.out = 14, by = -1))
indx30 = rev(seq(sdaynew, length.out = 30, by = -1))
} else {
dr1 = dr1
time(dr1) = seq(as_date(paste0(syear, "-01-01")), length.out = 365, by = 1)
dates_14 = dates_14
dates_30 = dates_30
#cr1 = cr1
}
# Test if the raster has values for the dates_14 in the location for extract with spinf. If not, use 0 for extract instead of NA.
dsv_14 <- terra::extract(dr1[[indx14]], sodsv[i,])
print(dsv_14)
dsv_14 <- as.numeric(dsv_14)
print(dsv_14)
#if (any(is.na(terra::extract(dr1[[indx30]], spinf[i, ])))) {
# dsv_30 <- rep(0, length(indx30))
#} else {
dsv_30 <- terra::extract(dr1[[indx30]], sodsv[i,])
# #dsv_30[is.na(dsv_30)] <- 0
#}
dsv_30 <- as.numeric(dsv_30)
print(dsv_30)
# Calculate the max, min, mean, median, and sd for each set
sum_14 <- sum(dsv_14[2:15], na.rm = TRUE)
mean_14 <- mean(dsv_14[2:15], na.rm = TRUE)
median_14 <- median(dsv_14[2:15], na.rm = TRUE)
sd_14 <- sd(dsv_14[2:15], na.rm = TRUE)
sum_30 <- sum(dsv_30[2:31], na.rm = TRUE)
mean_30 <- mean(dsv_30[2:31], na.rm = TRUE)
median_30 <- median(dsv_30[2:31], na.rm = TRUE)
sd_30 <- sd(dsv_30[2:31], na.rm = TRUE)
# Add the extracted DSV values to the data frame
extracted_dsv <- rbind(extracted_dsv, data.frame(ID = sodsv$SAMPLE__[i], #spinf$RptId[i],
Date = date,
Sum_14 = round(sum_14, 1),
#Min_14 = round(min_14, 1),
Mean_14 = round(mean_14, 1),
Median_14 = round(median_14, 1),
SD_14 = round(sd_14, 1),
Sum_30 = round(sum_30, 1),
#Min_30 = round(min_30, 1),
Mean_30 = round(mean_30, 1),
Median_30 = round(median_30, 1),
SD_30 = round(sd_30, 1),
stringsAsFactors = FALSE))
}
extracted_dsv$Year <- year(extracted_dsv$Date)
extracted_dsv$yday <- yday(extracted_dsv$Date)
write.csv(extracted_dsv, "Z:/Late_blight/SODdsv/sod_extr_dsvsum.csv", row.names = F)
### I've also called this dsv2roc.R