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revising plots for TAP presentation
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CarissaGervasi-NOAA committed Jul 15, 2024
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Binary file added PPT.pptx
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Binary file modified indicator_plots/ACEindex_plot.png
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Binary file modified indicator_plots/RVC_PR_plot.png
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Binary file added indicator_plots/cruise_plot_TAP.png
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277 changes: 242 additions & 35 deletions indicator_processing/PlotAllIndicators.R

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2 changes: 1 addition & 1 deletion indicator_processing/automated_download/cruise_visitors.R
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Expand Up @@ -112,7 +112,7 @@ class(inddata) <- "indicatordata"
# plot and save ----------------------------------

ind <- inddata
plotIndicatorTimeSeries(ind, coltoplot = 1:2, plotrownum = 2, sublabel = TRUE)
plotIndicatorTimeSeries(ind, coltoplot = 1:2, plotrownum = 2, plotcolnum = 1, trendAnalysis = TRUE, dateformat = "%b%Y", sublabel = TRUE, widadj = 1, hgtadj = 0.7, anom = "none", yposadj = 1)

save(ind, file = "indicator_objects/cruise.RData")

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4 changes: 2 additions & 2 deletions indicator_processing/automated_download/kd490.R
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Expand Up @@ -80,7 +80,7 @@ for (yr in styear:enyear) {

datdata <- paste(sprintf('%02d',pr_dat$mon),pr_dat$year,sep='-')
inddata <- data.frame(cbind(pr_dat$mean, st_dat$mean, sc_dat$mean), row.names = datdata)
labs <- c(rep("Turbidity from ocean color data", 3), rep("Diffuse attenuation coefficient at 490 nm (m^-1)", 3),
labs <- c(rep("Turbidity from ocean color data", 3), rep("Diffuse attenuation coefficient\n at 490 nm (m^-1)", 3),
"Puerto Rico", "St. Thomas", "St. Croix")
indnames <- data.frame(matrix(labs, nrow = 3, byrow = T))

Expand All @@ -91,6 +91,6 @@ class(ind) <- "indicatordata"

save(ind, file = "indicator_objects/turbidity.RData")

plotIndicatorTimeSeries(ind, coltoplot = 1:3, plotrownum = 3, dateformat = "%m-%Y", sublabel = T, trendAnalysis = F, widadj = 1.5)
plotIndicatorTimeSeries(ind, coltoplot = 1:3, plotrownum = 3, dateformat = "%m-%Y", sublabel = T, trendAnalysis = F, widadj = 1.5, yposadj = 1.3)
plotIndicatorTimeSeries(ind, coltoplot = 1:3, plotrownum = 3, dateformat = "%m-%Y", sublabel = T, trendAnalysis = F, widadj = 1.5, anom = "mon")

8 changes: 5 additions & 3 deletions indicator_processing/non_automated/RVC.R
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Expand Up @@ -101,7 +101,7 @@ stx = readRDS("indicator_data/RVC/combined_stx_2001_2023.rds")
spcs = c("OCY CHRY", "LUT ANAL", "BAL VETU", "EPI GUTT", "SPA AURO", "SPA VIRI")



#test = getDomainDensity(prico, species = "OCY CHRY")


# Extract the time series for each species and each area
Expand Down Expand Up @@ -134,11 +134,13 @@ for(j in regions) {

datdata <- species_data$YEAR
inddata <- data.frame(species_data$density)
s <- cbind(datdata, inddata)
vardata <- data.frame(species_data$var)
s1 <- cbind(datdata, inddata)
s2 <- cbind(s1, vardata)

# save -----------------------------------------

save(s, file = paste("indicator_data/RVC/RUVdensity_", j, "_", i, ".RData", sep = ""))
save(s2, file = paste("indicator_data/RVC/RUVdensity_", j, "_", i, ".RData", sep = ""))
}
}

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59 changes: 59 additions & 0 deletions indicator_processing/non_automated/avgLmax.R
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@@ -0,0 +1,59 @@
## Average Lmax per year by island

rm(list = ls())
dev.off()

library(plotTimeSeries)
library(dplyr)

load("indicator_data/fish-dep-indicators/Lmax_PR.RData")
PR_dat = as.data.frame(findat)

load("indicator_data/fish-dep-indicators/Lmax_STT.RData")
STT_dat = as.data.frame(findat)

load("indicator_data/fish-dep-indicators/Lmax_STX.RData")
STX_dat = as.data.frame(findat)


styear = min(PR_dat$V1, STT_dat$yrs, STX_dat$yrs)
enyear = max(PR_dat$V1, STT_dat$yrs, STX_dat$yrs)


yrs = styear:enyear
PR = PR_dat$lmax

STT_dat2 = data.frame(yrs = yrs)
STT_dat2 = STT_dat2 %>%
left_join(STT_dat, by = "yrs")

STT = STT_dat2$lmax


STX_dat2 = data.frame(yrs = yrs)
STX_dat2 = STX_dat2 %>%
left_join(STX_dat, by = "yrs")

STX = STX_dat2$lmax




# save as indicator object ----------------------
datdata <- yrs
inddata <- data.frame(cbind(PR, STT, STX))
labs <- c("Average Lmax" , "length (cm)", "Puerto Rico",
"Average Lmax" , "length (cm)", "St. Thomas and St. John",
"Average Lmax" , "length (cm)", "St. Croix")
indnames <- data.frame(matrix(labs, nrow = 3, byrow = F))
inddata <- list(labels = indnames, indicators = inddata, datelist = datdata)
class(inddata) <- "indicatordata"

# plot and save ----------------------------------

ind <- inddata
plotIndicatorTimeSeries(ind, coltoplot = 1:3, plotrownum = 3, plotcolnum = 1, trendAnalysis = TRUE, dateformat = "%b%Y", sublabel = TRUE, widadj = 1, hgtadj = 0.7, anom = "none", yposadj = 1, sameYscale = TRUE)

save(ind, file = "indicator_objects/avgLmax.RData")

############################### END #############################
4 changes: 2 additions & 2 deletions indicator_processing/non_automated/tier_designation.R
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Expand Up @@ -23,8 +23,8 @@ USVI = c(0,0,0,0,0,0,0,0,0,0,0,0,USVI_perc)
# save as indicator object ----------------------
datdata <- 2011:2023
inddata <- data.frame(cbind(PR, USVI))
labs <- c("Stocks/complexes with Tier 3 designation" , "Percent", "Puerto Rico",
"Stocks/complexes with Tier 3 designation" , "Percent", "USVI")
labs <- c("Stocks/complexes with ACLs derived from stock assessments" , "Percent", "Puerto Rico",
"Stocks/complexes with ACLs derived from stock assessments" , "Percent", "USVI")
indnames <- data.frame(matrix(labs, nrow = 3, byrow = F))
inddata <- list(labels = indnames, indicators = inddata, datelist = datdata)
class(inddata) <- "indicatordata"
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10 changes: 10 additions & 0 deletions test.qmd
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---
title: "Presentation"
format:
pptx:
reference-doc: PPT.pptx
---

# test

## hi

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