library(raster)
library(rgdal)
library(data.table)
library(gdalUtils)
library(sprawl)
library(stringr)
library(magrittr)
library(xts)
library(ggplot2)
in_mod <- "/home/lb/Temp/quality_phenorice/IND/QUALITY_ts_input_2013_137_2015_009.dat"
inmodrast <- stack(in_mod)
mod_proj <- "+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs"
in_ext <- extent(inmodrast)
in_fishnet <- "/home/lb/Temp/quality_phenorice/IND/mask_fishnet.tif"
in_maskfile <- "/home/lb/Temp/quality_phenorice/IND/mask_quality.tif"
NAvalue(inmodrast) <- 255
years <- str_split(names(inmodrast), "_", simplify = TRUE)[,3]
temp <- str_split(names(inmodrast), "_", simplify = TRUE)[,4]
doys <- str_split(temp,".dat", simplify = TRUE)[,1]
dates <- doytodate(doys = as.numeric(doys), year = as.numeric(years))
inmodrast <- setZ(inmodrast, dates, name = 'time')
inmodcropped <- crop (inmodrast, extent(raster(in_fishnet)))
data_quality <- fastzonal(inmodcropped, in_fishnet, verbose = T, out_format = 'dframe')
in_mask <- raster(in_maskfile)
rice_cells <- which(getValues(in_mask) == 1)
sel_cells <- which(as.numeric(names(data_quality)[2:length(names(data_quality))]) %in% rice_cells)
sel_data <- data_quality[,c(1,sel_cells)]
seldata_melt <- melt(sel_data, id.vars = "date")
# p <- ggplot(seldata_melt, aes(x = date, y = value, group = date)) +
# geom_boxplot() +
# theme_bw()
# p
#
# seldata_avg <- data.frame(date = sel_data[,1], value = rowSums(sel_data[(2:dim(sel_data)[2])])/(dim(sel_data)[2]-1))
#
# p <- ggplot(seldata_avg, aes ( x = date, y = value))
# p <- p + geom_point() + theme_bw()
# p
sel_data_phl = sel_data
seldata_melt_phl = seldata_melt
seldata_avg_phl = seldata_avg
sel_data_phl$country = "PHL"
seldata_melt_phl$country = "PHL"
seldata_avg_phl$country = "PHL"
sel_data_ita = sel_data
seldata_melt_ita = seldata_melt
seldata_avg_ita = seldata_avg
sel_data_ita$country = "ITA"
seldata_melt_ita$country = "ITA"
seldata_avg_ita$country = "ITA"
sel_data_ind = sel_data
seldata_melt_ind = seldata_melt
seldata_avg_ind = seldata_avg
sel_data_ind$country = "IND"
seldata_melt_ind$country = "IND"
seldata_avg_ind$country = "IND"
sel_datatot = rbind(sel_data_phl, sel_data_ind, sel_data_ita)
sel_datamelttot = rbind(seldata_melt_phl, seldata_melt_ind, seldata_melt_ita)
sel_dataavgttot = rbind(seldata_avg_phl, seldata_avg_ind, seldata_avg_ita)
p <- ggplot(sel_datamelttot, aes(x = date, y = value, group = date))
p <- p + geom_boxplot() + facet_wrap(~country)
p
percs$value = as.factor(percs$value)
p <- ggplot(percs, aes(x = date, y = freq, group = value))
p <- p + geom_line(aes(colour = value)) + theme_bw() + facet_wrap(~country)
p <- p + scale_x_date(date_breaks = "3 month") +
scale_colour_manual(values = c("green","blue","red")) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
p
percs = sel_datamelttot %>%
group_by(date, country, value) %>%
summarise (n = n()) %>%
mutate(freq = 100*n / sum(n))
data_ita = subset(percs, country =="ITA" & value == "2")
p2 = p1 + geom_line(data = subset(percs, country =="ITA" & value == "2"), aes(x = date + 365, y = freq/100, group = value),color = "black", lty = 'dashed')
#p2 = p2 + geom_line(data = subset(percs, country =="ITA" & value == "1"), aes(x = date + 365, y = freq/100, group = value),color = "blue", lty = 'dashed')
#:p2 = p2 + geom_line(data = subset(percs, country =="ITA" & value == "0"), aes(x = date + 365, y = freq/100, group = value),color = "green", lty = 'dashed')
p2 = p2
data_phl = subset(percs, country =="PHL" & value == "2")
p8 = p7 + geom_line(data = subset(percs, country =="PHL" & value == "2"),
aes(x = date +365, y = freq/100, group = value),color = "black", lty = 'dashed')
#p2 = p2 + geom_line(data = subset(percs, country =="ITA" & value == "1"), aes(x = date + 365, y = freq/100, group = value),color = "blue", lty = 'dashed')
#:p2 = p2 + geom_line(data = subset(percs, country =="ITA" & value == "0"), aes(x = date + 365, y = freq/100, group = value),color = "green", lty = 'dashed')
p8
p9 = p6 + geom_line(data = subset(percs, country =="IND" & value == "2"),
aes(x = date, y = freq/100, group = value),color = "black", lty = 'dashed')
p9
scale_colour_manual(values = c("green","blue","red"))
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