knitr::opts_chunk$set(echo = TRUE)
## preparation library(aceecostats) library(raster) library(feather) library(dplyr) library(ggplot2) ## local path to required cache files datapath <- "/mnt/acebulk" library(ggplot2) library(tidyr) ##db file library(dplyr) db <- src_sqlite("/mnt/acebulk/habitat_assessment_output.sqlite3") epoch <- ISOdatetime(1970, 1, 1, 0, 0, 0, tz = "GMT") sst_density_tab <- tbl(db, "sst_density_tab") %>% collect(n = Inf) sst_sparkline_tab <- tbl(db, "sst_sparkline_tab") %>% collect(n = Inf) %>% mutate(season_year = season_year + epoch, season = aes_season(season_year)) library(tidyr) ## loop the plots uzones <- unique(sst_density_tab$Zone) useasons <- c("Summer", "Winter") iseason <- izone <- 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 <- sst_sparkline_tab %>% filter(Zone == uzones[izone], season == useasons[iseason]) %>% gather(measure, sst, -season, -SectorName, -Zone, -season_year) ## subset the density data density_data <- sst_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 = sst, group = measure, colour = measure)) + geom_line() + facet_wrap(~SectorName) gdens_min <- ggplot(density_data, aes(x = min, weights = area, group = decade, colour = decade)) + geom_density() + facet_wrap(~SectorName+ Zone) gdens_max <- ggplot(density_data, aes(x = max, weights = area, group = decade, colour = decade)) + geom_density() + facet_wrap(~SectorName+ Zone) op <- options(warn = -1) if (nrow(spark_data) > 1) print(gspark) if (nrow(density_data) > 1) print(gdens_min + ggtitle(sprintf("%s", useasons[iseason]))) if (nrow(density_data) > 1) print(gdens_max + ggtitle(sprintf("%s", useasons[iseason]))) par(op) } }
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