Last updated: 2020-03-18
Checks: 7 0
Knit directory: Baltic_Productivity/
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Ignored: data/ARGO/
Ignored: data/Finnmaid/
Ignored: data/GETM/
Ignored: data/OSTIA/
Ignored: data/_merged_data_files/
Ignored: data/_merged_data_files_2019V1/
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Ignored: data/_summarized_data_files_2019V1/
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library(tidyverse)
library(ncdf4)
library(vroom) #'rlang' needs to be installed
library(lubridate)
library(geosphere)
library(dygraphs)
library(xts)
library(here)
library(metR)# route
select_route <- "E"
# variable names in 2d and 3d GETM files
var <- "SSS_east"
# latitude limits
low_lat <- 57.5
high_lat <- 58.5The mean salinity was calculated across all measurments made between march - september in the NGS subregion.
filesList_2d <- list.files(path= "data", pattern = "Finnmaid.E.2d.20", recursive = TRUE)nc <- nc_open(paste("data/Finnmaid/", "FM_all_2019_on_standard_tracks.nc", sep = ""))
# read required vectors from netcdf file
route <- ncvar_get(nc, "route")
route <- unlist(strsplit(route, ""))
date_time <- ncvar_get(nc, "time")
latitude_east <- ncvar_get(nc, "latitude_east")
array <- ncvar_get(nc, var) # store the data in a 2-dimensional array
#dim(array) # should have 2 dimensions: 544 coordinate, 2089 time steps
fillvalue <- ncatt_get(nc, var, "_FillValue")
array[array == fillvalue$value] <- NA
rm(fillvalue)
#i <- 5
for (i in seq(1,length(route),1)){
if(route[i] == select_route) {
slice <- array[i,]
value <- mean(slice[latitude_east > low_lat & latitude_east < high_lat], na.rm = TRUE)
sd <- sd(slice[latitude_east > low_lat & latitude_east < high_lat], na.rm = TRUE)
date <- ymd("2000-01-01") + date_time[i]
temp <- bind_cols(date = date, var=var, value = value, sd = sd)
if (exists("timeseries", inherits = FALSE)){
timeseries <- bind_rows(timeseries, temp)
} else{timeseries <- temp}
rm(temp, value, date, sd)
}
}
nc_close(nc)
fm_sss__ngs <- timeseries %>%
mutate(sss = value,
year = year(date),
month = month(date))
fm_sss_ngs_monthlymean <- fm_sss__ngs %>%
filter(month >=3 , month <=9) %>%
summarise(sss_mean = mean(sss, na.rm = TRUE))
rm(array,fm_sss__ngs,nc, timeseries, date_time, filesList_2d, high_lat, i, latitude_east, low_lat, route, select_route, slice, var )The mean salinity between March and September for the NGS subregion for all years is 6.73.
In the following section, we create a dygraph with two y-axis, combining the pCO2 measurments from VOS Finnmaid with the MLD5 calculations of the GETM Model.
gt_mld_fm_pco2_ngs <-
vroom::vroom(here::here("data/_merged_data_files/", file = "gt_mld_fm_pco2_ngs.csv"))
gt_mld_fm_pco2_ngs <- gt_mld_fm_pco2_ngs %>%
mutate(as.POSIXct(date))
pco2 <- xts(cbind(gt_mld_fm_pco2_ngs$value_pCO2, gt_mld_fm_pco2_ngs$min_pCO2, gt_mld_fm_pco2_ngs$max_pCO2), order.by = gt_mld_fm_pco2_ngs$date)
names(pco2) <- c("pCO2 Finnmaid", "lwr", "upr")
mld5 <- xts(gt_mld_fm_pco2_ngs$value_mld5, order.by = gt_mld_fm_pco2_ngs$date)
plotdata <- cbind(pco2,mld5)
dygraph(plotdata) %>%
dySeries("mld5", axis = 'y2') %>%
dySeries(c("lwr","pCO2.Finnmaid", "upr")) %>%
dyRangeSelector(dateWindow = c("2014-01-01", "2016-12-31")) %>%
dyOptions(drawPoints = TRUE, pointSize = 1.5, connectSeparatedPoints=TRUE, strokeWidth=0.5,
drawAxesAtZero=TRUE)rm(pco2, mld5, plotdata, fm_sss_ngs_monthlymean, gt_mld_fm_pco2_ngs)
sessionInfo()R version 3.5.0 (2018-04-23)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] metR_0.5.0 here_0.1 xts_0.11-2 zoo_1.8-6
[5] dygraphs_1.1.1.6 geosphere_1.5-10 lubridate_1.7.4 vroom_1.2.0
[9] ncdf4_1.17 forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3
[13] purrr_0.3.3 readr_1.3.1 tidyr_1.0.0 tibble_2.1.3
[17] ggplot2_3.3.0 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] nlme_3.1-137 bitops_1.0-6 fs_1.3.1
[4] bit64_0.9-7 httr_1.4.1 rprojroot_1.3-2
[7] tools_3.5.0 backports_1.1.5 utf8_1.1.4
[10] R6_2.4.0 DBI_1.0.0 colorspace_1.4-1
[13] withr_2.1.2 sp_1.3-2 tidyselect_0.2.5
[16] gridExtra_2.3 bit_1.1-14 compiler_3.5.0
[19] git2r_0.26.1 cli_1.1.0 rvest_0.3.5
[22] xml2_1.2.2 scales_1.0.0 checkmate_1.9.4
[25] digest_0.6.22 foreign_0.8-70 rmarkdown_2.0
[28] pkgconfig_2.0.3 htmltools_0.4.0 dbplyr_1.4.2
[31] maps_3.3.0 htmlwidgets_1.5.1 rlang_0.4.5
[34] readxl_1.3.1 rstudioapi_0.10 generics_0.0.2
[37] jsonlite_1.6 RCurl_1.95-4.12 magrittr_1.5
[40] Formula_1.2-3 dotCall64_1.0-0 Matrix_1.2-14
[43] fansi_0.4.0 Rcpp_1.0.2 munsell_0.5.0
[46] lifecycle_0.1.0 stringi_1.4.3 yaml_2.2.0
[49] plyr_1.8.4 grid_3.5.0 maptools_0.9-8
[52] formula.tools_1.7.1 parallel_3.5.0 promises_1.1.0
[55] crayon_1.3.4 lattice_0.20-35 haven_2.2.0
[58] hms_0.5.2 zeallot_0.1.0 knitr_1.26
[61] pillar_1.4.2 reprex_0.3.0 glue_1.3.1
[64] evaluate_0.14 data.table_1.12.6 modelr_0.1.5
[67] operator.tools_1.6.3 vctrs_0.2.0 spam_2.3-0.2
[70] httpuv_1.5.2 cellranger_1.1.0 gtable_0.3.0
[73] assertthat_0.2.1 xfun_0.10 broom_0.5.3
[76] later_1.0.0 memoise_1.1.0 fields_9.9
[79] workflowr_1.6.0