Lirype
Start
dat <- load("../data/lirype_jags_old.RData")Five_years_mean <- as.data.frame(Five_years_mean)
names(Five_years_mean) <- c("Nord-Norge", "Midt-Norge", "Østlandet", "Sørvest-Norge")
head(Five_years_mean)## Nord-Norge Midt-Norge Østlandet Sørvest-Norge
## 1 11.17701 28.19522 22.72931 25.84919
## 2 11.41788 28.35199 22.79699 25.61495
## 3 11.42503 28.43357 22.86084 24.98241
## 4 11.19823 28.64372 22.95188 25.63509
## 5 11.35169 28.40705 22.87486 25.39087
## 6 11.02213 28.38429 22.90484 25.81855Summert for hele Norge
Fjellareal per region:
wgt <- readRDS("../data/fjellareal.rds")
wgt$Fjellareal2 <- wgt$Fjellareal/max(wgt$Fjellareal)
wgt$reg <- c("N", "C", "E", "W", "S")Vekter
(myWgt <- c(wgt$Fjellareal2[1:3], mean(wgt$Fjellareal2[4:5])))## [1] 1.0000000 0.3058141 0.3176748 0.2291373Five_years_mean2 <- as.matrix(Five_years_mean)
Five_years_mean$Norge <- matrixStats::rowWeightedMeans(Five_years_mean2, myWgt)
Five_years_mean$`Nord-Norge_S` <- Five_years_mean$`Nord-Norge`/36
Five_years_mean$`Midt-Norge_S` <- Five_years_mean$`Midt-Norge`/36
Five_years_mean$`Sørvest-Norge_S` <- Five_years_mean$`Sørvest-Norge`/36
Five_years_mean$Østlandet_S <- Five_years_mean$Østlandet/36dat <- data.table::melt(data = Five_years_mean,
measure.vars=names(Five_years_mean),
variable.name="reg",
value.name="val")Hele Norge
norge_s <- NULL
for(i in 1:10000){
temp <- c(
sample(dat$val[dat$reg == "Nord-Norge_S"], 1),
sample(dat$val[dat$reg == "Midt-Norge_S"], 1),
sample(dat$val[dat$reg == "Østlandet_S"], 1),
sample(dat$val[dat$reg == "Sørvest-Norge_S"],1),
sample(dat$val[dat$reg == "Sørvest-Norge_S"],1)
)
norge_s <- c(norge_s, weighted.mean(temp, wgt$Fjellareal2))
}Hurtigløsning. Alle datasett og figurer er laget med 10000 sim.
norge_s <- NULL
for(i in 1:100){
temp <- c(
sample(dat$val[dat$reg == "Nord-Norge_S"], 1),
sample(dat$val[dat$reg == "Midt-Norge_S"], 1),
sample(dat$val[dat$reg == "Østlandet_S"], 1),
sample(dat$val[dat$reg == "Sørvest-Norge_S"],1),
sample(dat$val[dat$reg == "Sørvest-Norge_S"],1)
)
norge_s <- c(norge_s, weighted.mean(temp, wgt$Fjellareal2))
}norge <- data.frame(reg = "Norge_s",
val = norge_s)
dat <- rbind(dat, norge)
table(dat$reg)##
## Nord-Norge Midt-Norge Østlandet Sørvest-Norge Norge
## 30000 30000 30000 30000 30000
## Nord-Norge_S Midt-Norge_S Sørvest-Norge_S Østlandet_S Norge_s
## 30000 30000 30000 30000 100dat2 <- aggregate(data = dat,
val~reg, FUN = mean)
#dat2$sd <- aggregate(data = dat,
# val~reg, FUN = sd)[,2]
temp <- aggregate(data = dat,
val~reg,
FUN = function(x) c(quantile(x, c(0.025, 0.5, 0.975))))
temp <- do.call(data.frame, temp)
dat2 <- cbind(dat2, temp[,-1])Subset
temp1 <- c("Nord-Norge", "Midt-Norge", "Østlandet", "Sørvest-Norge", "Norge")
temp2 <- c("Nord-Norge_S", "Midt-Norge_S", "Østlandet_S", "Sørvest-Norge_S", "Norge_s")
std <- dat2[dat2$reg %in% temp2,]
raw <- dat2[dat2$reg %in% temp1,](ggrype <- ggplot(data = raw, aes(x = factor(reg, levels = temp1), y = val))+
geom_bar(stat="identity", colour="black", size=1.2,
fill="grey80")+
geom_errorbar(aes(ymax = val.2.5., ymin = val.97.5.),
width=0, size=2)+
#geom_violin(data = dat, aes(x = reg, y = val),
# fill="black", trim=F, scale="width",
# fill="grey80")+
theme_bw(base_size = 20)+
ylab(expression(paste("Lirypeindivider km "^"-2")))+
xlab("")+
geom_hline(yintercept = 36, size=1.2, linetype="dashed")+
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
)+
ylim(c(0,40)))
png("../output/indicatorPlots/uskalert/lirype_barplot.png",
units="in", width=6, height=8, res=300)
ggrype
dev.off()(ggrype2 <- ggplot(data = std, aes(x = factor(reg, levels = temp2), y = val))+
geom_bar(stat="identity", colour="black", size=1.2,
fill="grey80")+
geom_errorbar(aes(ymax = val.2.5., ymin = val.97.5.),
width=0, size=2)+
#geom_violin(data = dat, aes(x = reg, y = scal),
# fill="black", trim=F, scale="width",
# fill="grey80")+
theme_bw(base_size = 20)+
ylab("Lirypetetthet skalert\nmot referansetilstand")+
xlab("")+
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
)+
ylim(c(0,1))+
scale_x_discrete(breaks = temp2,
labels = temp1)
)
png("../output/indicatorPlots/skalert/lirype.png",
units="in", width=6, height=8, res=300)
ggrype2
dev.off()Eksport
datExp <- dat[dat$reg %in% temp2,]
datExp$reg <- droplevels(datExp$reg)
datExp$reg <- plyr::revalue(datExp$reg,
c(
"Nord-Norge_S" = "N",
"Midt-Norge_S" = "C",
"Sørvest-Norge_S" = "S",
"Østlandet_S" = "E",
"Norge_s" = "Norge"
))
west <- datExp[datExp$reg=="S",]
west$reg <- "W"
datExp <- rbind(datExp, west)
write.csv(datExp, "../output/indicator_values/lirype.csv", row.names = F)Tidsserie
rm(list = ls())ser <- load("../data/lirype_jags.RData")
ser <- Mean_density_region_year
dim(ser)## [1] 30000 4 13Det er iterasjon X region x år.
Her er matrisen for region 1 med 13 år 2009-2021
dim(ser[,1,])## [1] 30000 13Rekkefølgen på regionene er slik
order <- c("Nord-Norge", "Midt-Norge", "Østlandet", "Sørvest-Norge")
order2 <- c("N", "C", "E", "S")temp <- NULL
df <- NULL
for(i in 1:length(order2)){
temp <- as.data.frame(ser[,i,])
colnames(temp) <- 2009:2021
temp <- temp[1:1000,]
temp <- data.table::melt(data = temp,
variable.name = "year",
value.name="val")
temp$reg <- order2[i]
df <- rbind(df, temp)
}## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variablesunique(df$reg)## [1] "N" "C" "E" "S"Kloner S og W
W <- df[df$reg=="S",]
W$reg <- "W"
df <- rbind(df, W)
table(df$reg, df$year)##
## 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
## C 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000
## E 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000
## N 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000
## S 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000
## W 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000Veiing
Regner ut nasjonale indikatorverdier som et veid (etter total fjelareal) gjennomsnitt av vedien i regionene. Veiingen gjøres ved å samle større antall verdier for de regionene som har mest fjell i seg.
wgt <- readRDS("../data/fjellareal.rds")
wgt$Fjellareal2 <- wgt$Fjellareal/max(wgt$Fjellareal)
wgt$reg <- c("N", "C", "E", "W", "S")
wgt## Region Fjellareal Fjellareal2 reg
## 1 Nord-Norge 59822.74 1.0000000 N
## 2 Midt-Norge 18294.64 0.3058141 C
## 3 Østlandet 19004.18 0.3176748 E
## 4 Vestlandet 20329.63 0.3398311 W
## 5 Sørlandet 7085.62 0.1184436 Sregions <- c("N", "C", "E", "W", "S")
myYears <- 2009:2021
nor <- data.frame(
year = rep(myYears, each = 1000),
val = as.numeric(NA),
reg = rep("Norge", 1000*length(myYears))
)
table(nor$year)##
## 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
## 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000for(n in myYears){
temp <- df[df$year == n,]
temp3 <- NULL
for(i in 1:1000){
temp2 <- c(
sample(temp$val[temp$reg == "N"], 1),
sample(temp$val[temp$reg == "C"], 1),
sample(temp$val[temp$reg == "E"], 1),
sample(temp$val[temp$reg == "W"], 1),
sample(temp$val[temp$reg == "S"], 1)
)
temp3 <- c(temp3, weighted.mean(temp2, wgt$Fjellareal2))
}
nor$val[nor$year==n] <- temp3
}df <- rbind(df, nor)
df$year <- as.numeric(as.character(df$year))eval(parse("indicator_plots2.R", encoding="UTF-8"))png("../output/indicatorPlots/uskalert/lirype.png",
units="in", width=12, height=7, res=300)
# Plot windows par
par(mfrow=c(1,1), mar=c(4.5,
5.5,
0,
2))
indicator_plot2(dataset = df,
yAxisTitle = expression(paste("Lirypeindivider km "^"-2")),
lowYlimit = 0,
upperYlimit = 50,
yStep = 10,
minyear = 2008,
maxyear = 2022,
colours = c("#FFB25B", "#2DCCD3", "#004F71", "#7A9A01", "#93328E", "dark grey"),
legendPosition = "top",
legendInset = 0,
move = 0.1,
horizontal = T,
legendTextSize = 1.25)
dev.off()