## preparation
library(aceecostats)
library(raster)
library(feather)
library(dplyr)
library(ggplot2)
## local path to required cache files
datapath <- "/mnt/acebulk"
library(ggplot2)
library(tidyr)
##db file
library(dplyr)
db <- src_sqlite("/mnt/acebulk/habitat_assessment_output.sqlite3")
epoch <- as.Date(ISOdatetime(1970, 1, 1, 0, 0, 0, tz = "GMT"))
ice_density_tab <- tbl(db, "ice_minmaxmean_density_tab") %>% collect(n = Inf)
ice_sparkline_tab <- tbl(db, "ice_minmaxmean_sparkline_tab") %>% collect(n = Inf) %>%
mutate(season_year = season_year + epoch, season = aes_season(season_year))
library(tidyr)
## loop the plots
pdf("inst/workflow/graphics/ice_minmaxmean_density_sparklines000.pdf")
#uzones <- unique(ice_density_tab$Zone)
useasons <- unique(ice_density_tab$season)
izone <- 2
iseason <- 1
#for (izone in seq_along(uzones)) {
for (iseason in seq_along(useasons)) {
## reshape the sparkline data to key/col on min/max
spark_data <- ice_sparkline_tab %>% filter(Zone == uzones[izone], season == useasons[iseason]) %>%
gather(measure, ice, -season, -SectorName, -Zone, -season_year)
## subset the density data
density_data <- ice_density_tab %>% filter(Zone == uzones[izone], season == useasons[iseason])
## create the three gg objects for sparkline, min-sst, max-sst
gspark <- ggplot(spark_data, aes(x = season_year, y = ice, group = measure, colour = measure)) +
geom_line() + facet_wrap(~SectorName)
gdens <- ggplot(density_data %>% filter(meanval > 15), aes(x = meanval, weights = area, group = decade, colour = decade)) +
geom_density() + facet_wrap(~SectorName)
print(gspark)
print(gdens + ggtitle(sprintf("%s", useasons[iseason])))
#print(gdens_max + ggtitle(sprintf("%s", useasons[iseason])))
}
#}
dev.off()
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