GLODAPv2_2020
Jens Daniel Müller
18 July, 2020
Last updated: 2020-07-18
Checks: 7 0
Knit directory: Cant_eMLR/
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| File | Version | Author | Date | Message |
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| Rmd | d3032c7 | jens-daniel-mueller | 2020-07-18 | formating |
| html | 22b588c | jens-daniel-mueller | 2020-07-18 | Build site. |
| html | 3964fd7 | jens-daniel-mueller | 2020-07-17 | Build site. |
| Rmd | 4685ad7 | jens-daniel-mueller | 2020-07-17 | 150m depth limit implemented |
| html | 56c3ed9 | jens-daniel-mueller | 2020-07-14 | Build site. |
| Rmd | aed89af | jens-daniel-mueller | 2020-07-14 | added Rmd for MappedClimatologies |
| html | 74d4abd | jens-daniel-mueller | 2020-07-14 | Build site. |
| html | 1c511ce | jens-daniel-mueller | 2020-07-14 | Build site. |
| Rmd | e03016e | jens-daniel-mueller | 2020-07-14 | split read in per data set |
library(tidyverse)
library(lubridate)
library(patchwork)1 Read master file
- Data source:
GLODAPv2.2020_Merged_Master_File.csvfrom glodap.info
GLODAP <- read_csv(here::here("data/GLODAPv2_2020/Merged_data_product",
"GLODAPv2.2020_Merged_Master_File.csv"),
na = "-9999",
col_types = cols(.default = col_double()))
# relevant columns
GLODAP <- GLODAP %>%
select(cruise:talkqc)
GLODAP <- GLODAP %>%
mutate(date = ymd(paste(year, month, day))) %>%
#decade = as.factor(floor(year / 10) * 10)) %>%
relocate(date)
GLODAP <- GLODAP %>%
select(-c(month:minute,
maxsampdepth, pressure,
theta, sigma0:gamma,
nitrate:nitritef))2 Data cleaning
2.1 Define subsetting criteria
flag_f <- 2
flag_qc <- 1
min_depth <- 150Only samples with all relevant parameters determined were selected. The following subsetting parameters were defined:
flag_f: 2
flag_qc: 1
min_depth: 150
summary statistics were calculated during cleaning process
2.2 Subset rows
GLODAP_stats <- GLODAP %>%
summarise(total = n())
GLODAP <- GLODAP %>%
filter(!is.na(tco2))
GLODAP <- GLODAP %>%
filter(tco2f == flag_f) %>%
select(-tco2f)
GLODAP <- GLODAP %>%
filter(tco2qc == flag_qc) %>%
select(-tco2qc)
GLODAP_stats_temp <- GLODAP %>%
summarise(tco2 = n())
GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)
##
GLODAP <- GLODAP %>%
filter(!is.na(talk))
GLODAP <- GLODAP %>%
filter(talkf == flag_f) %>%
select(-talkf)
GLODAP <- GLODAP %>%
filter(talkqc == flag_qc) %>%
select(-talkqc)
##
GLODAP <- GLODAP %>%
filter(!is.na(phosphate))
GLODAP <- GLODAP %>%
filter(phosphatef == flag_f) %>%
select(-phosphatef)
GLODAP <- GLODAP %>%
filter(phosphateqc == flag_qc) %>%
select(-phosphateqc)
GLODAP_stats_temp <- GLODAP %>%
summarise(C_star_variables = n())
GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)
##
GLODAP <- GLODAP %>%
filter(!is.na(temperature))
##
GLODAP <- GLODAP %>%
filter(!is.na(salinity))
GLODAP <- GLODAP %>%
filter(salinityf == flag_f) %>%
select(-salinityf)
GLODAP <- GLODAP %>%
filter(salinityqc == flag_qc) %>%
select(-salinityqc)
##
GLODAP <- GLODAP %>%
filter(!is.na(silicate))
GLODAP <- GLODAP %>%
filter(silicatef == flag_f) %>%
select(-silicatef)
GLODAP <- GLODAP %>%
filter(silicateqc == flag_qc) %>%
select(-silicateqc)
##
GLODAP <- GLODAP %>%
filter(!is.na(oxygen))
GLODAP <- GLODAP %>%
filter(oxygenf == flag_f) %>%
select(-oxygenf)
GLODAP <- GLODAP %>%
filter(oxygenqc == flag_qc) %>%
select(-oxygenqc)
##
GLODAP <- GLODAP %>%
filter(!is.na(aou))
GLODAP <- GLODAP %>%
filter(aouf == flag_f) %>%
select(-aouf)
GLODAP_stats_temp <- GLODAP %>%
summarise(eMLR_variables = n())
GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)
##
GLODAP <- GLODAP %>%
filter(depth >= min_depth)
GLODAP_stats_temp <- GLODAP %>%
summarise(eMLR_variables_150 = n())
GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)
GLODAP_stats_long <- GLODAP_stats %>%
pivot_longer(1:length(GLODAP_stats), names_to = "parameter", values_to = "n")
GLODAP_stats_long %>% write_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
"GLODAPv2.2020_stats.csv"))
rm(GLODAP_stats_long, GLODAP_stats)2.3 Assign reference eras
Samples were assigned to following eras:
JGOFS/WOCE 1981 - 1999
GO-SHIP 2000 - 2012
new_era 2013 - now
GLODAP <- GLODAP %>%
filter(year >= 1981) %>%
mutate(era = "JGOFS/WOCE",
era = if_else(year >= 2000, "GO-SHIP", era),
era = if_else(year >= 2013, "new_era", era))2.4 Write summary file
GLODAP %>% write_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
"GLODAPv2.2020_clean.csv"))3 Overview plots
Open clean data file.
GLODAP <- read_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
"GLODAPv2.2020_clean.csv"))3.1 Assign spatial grid
For the following plots, observations were gridded spatially to 5°x5° intervals.
GLODAP <- GLODAP %>%
mutate(lat_grid = cut(latitude, seq(-90, 90, 5), seq(-87.5, 87.5, 5)),
lat_grid = as.numeric(as.character(lat_grid)),
lon_grid = cut(longitude, seq(-180, 180, 5), seq(-177.5, 177.5, 5)),
lon_grid = as.numeric(as.character(lon_grid))) %>%
arrange(date)3.2 Cleaning stats
GLODAP_stats_long <- read_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
"GLODAPv2.2020_stats.csv"))
GLODAP_stats_long <- GLODAP_stats_long %>%
mutate(parameter = fct_reorder(parameter, n))
GLODAP_stats_long %>%
ggplot(aes(parameter, n/1000))+
geom_col()+
coord_flip()+
labs(y="n (1000)")+
theme(axis.title.y = element_blank())
rm(GLODAP_stats_long)3.3 Histogram latitudes
GLODAP_histogram_lat <- GLODAP %>%
group_by(era, lat_grid) %>%
tally() %>%
ungroup()
GLODAP_histogram_lat %>%
ggplot(aes(lat_grid, n, fill=era))+
geom_col()+
scale_x_continuous(breaks = seq(-87.5,90,10))+
scale_fill_viridis_d()+
coord_flip(expand = 0)
rm(GLODAP_histogram_lat)3.4 Histogram year
GLODAP_histogram_year <- GLODAP %>%
group_by(era, year) %>%
tally() %>%
ungroup()
era_median_year <- GLODAP %>%
group_by(era) %>%
summarise(t_ref = median(year)) %>%
ungroup()
GLODAP_histogram_year %>%
ggplot(aes(year, n, fill=era))+
geom_col()+
geom_vline(data = era_median_year, aes(xintercept = t_ref))+
coord_cartesian(expand = 0)
rm(GLODAP_histogram_year,
era_median_year)3.5 Hovmoeller (lat vs year)
GLODAP_hovmoeller_year <- GLODAP %>%
group_by(year, lat_grid) %>%
tally() %>%
ungroup()
GLODAP_hovmoeller_year %>%
ggplot(aes(year, lat_grid, fill=log10(n)))+
geom_tile()+
geom_vline(xintercept = c(1999.5, 2012.5))+
scale_fill_viridis_c(option = "magma", direction = -1)
rm(GLODAP_hovmoeller_year)3.6 Maps by era
mapWorld <- borders("world", colour="gray60", fill="gray60")
GLODAP_map_era <- GLODAP %>%
group_by(era, lat_grid, lon_grid) %>%
tally() %>%
ungroup()
GLODAP_map_era <- GLODAP_map_era %>%
mutate(era = factor(era, c("JGOFS/WOCE", "GO-SHIP", "new_era")))
GLODAP_map_era %>%
ggplot(aes(lon_grid, lat_grid, fill=log10(n)))+
mapWorld+
geom_raster()+
scale_fill_viridis_c(option = "magma", direction = -1)+
scale_x_continuous(breaks = seq(-180, 180, 30))+
scale_y_continuous(breaks = seq(-90, 90, 30))+
coord_quickmap(expand = FALSE)+
facet_wrap(~era, ncol=1)
rm(GLODAP_map_era,
mapWorld)4 Individual cruise sections
Zonal and meridional section plots are produce
cruises <- GLODAP %>%
group_by(cruise) %>%
summarise(date_mean = mean(date, na.rm = TRUE),
n = n()) %>%
ungroup() %>%
arrange(date_mean)
GLODAP <- full_join(GLODAP, cruises)
mapWorld <- borders("world", colour="gray60", fill="gray60")
n <- 0
for (i_cruise in unique(cruises$cruise)) {
#i_cruise <- unique(cruises$cruise)[1]
n <- n+1
print(n)
GLODAP_cruise <- GLODAP %>%
filter(cruise == i_cruise) %>%
arrange(date)
cruises_cruise <- cruises %>%
filter(cruise == i_cruise)
map <- GLODAP_cruise %>%
ggplot(aes(longitude, latitude))+
mapWorld+
geom_point(aes(col=date))+
geom_path()+
coord_quickmap(expand = FALSE)+
scale_color_viridis_c(trans = "date")+
labs(title = paste("Mean date:", cruises_cruise$date_mean,
"| cruise:", cruises_cruise$cruise,
"| n(samples):", cruises_cruise$n))
lon_section <- GLODAP_cruise %>%
ggplot(aes(longitude, depth))+
scale_y_reverse()+
scale_color_viridis_c()
lon_tco2 <- lon_section+
geom_point(aes(col=tco2))
lon_talk <- lon_section+
geom_point(aes(col=talk))
lon_phosphate <- lon_section+
geom_point(aes(col=phosphate))
lat_section <- GLODAP_cruise %>%
ggplot(aes(latitude, depth))+
scale_y_reverse()+
scale_color_viridis_c()
lat_tco2 <- lat_section+
geom_point(aes(col=tco2))
lat_talk <- lat_section+
geom_point(aes(col=talk))
lat_phosphate <- lat_section+
geom_point(aes(col=phosphate))
map /
((lat_tco2 / lat_talk / lat_phosphate) |
(lon_tco2 / lon_talk / lon_phosphate))
ggsave(here::here("output/plots/data/all_cruises_clean",
paste("GLODAP_cruise_date",
cruises_cruise$date_mean,
"n",
cruises_cruise$n,
"cruise",
cruises_cruise$cruise,
".png",
sep = "_")),
width = 9, height = 9)
rm(map,
lon_section, lat_section,
lat_tco2, lat_talk, lat_phosphate, lon_tco2, lon_talk, lon_phosphate,
GLODAP_cruise, cruises_cruise)
}
# library("rnaturalearth")
# library("rnaturalearthdata")
# library("sf")
#
# world <- ne_countries(scale = "small", returnclass = "sf")
# class(world)
#
# GLODAP_map <- GLODAP %>%
# group_by(lat_grid, lon_grid) %>%
# tally() %>%
# ungroup()
#
# ggplot() +
# geom_raster(data = GLODAP_map, aes(lon_grid, lat_grid))+
# geom_sf(data = world)+
# coord_sf(crs = "+proj=robin +lat_0=0 +lon_0=0 +x0=0 +y0=0")
# https://gist.github.com/clauswilke/783e1a8ee3233775c9c3b8bfe531e28a
# https://twitter.com/clauswilke/status/1066024436208406529
# https://www.r-spatial.org/r/2018/10/25/ggplot2-sf.html5 Open tasks
- remove marginal seas and data north of 65°N
- move A16 cruises from new_era to GO-SHIP era
6 Questions
- exclude coastal area in general?
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: i386-w64-mingw32/i386 (32-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_Germany.1252 LC_CTYPE=English_Germany.1252
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=English_Germany.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] patchwork_1.0.1 lubridate_1.7.9 forcats_0.5.0 stringr_1.4.0
[5] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0
[9] tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] jsonlite_1.7.0 rstudioapi_0.11 generics_0.0.2 magrittr_1.5
[5] farver_2.0.3 gtable_0.3.0 rmarkdown_2.3 vctrs_0.3.1
[9] fs_1.4.2 hms_0.5.3 xml2_1.3.2 pillar_1.4.6
[13] htmltools_0.5.0 haven_2.3.1 later_1.1.0.1 broom_0.7.0
[17] cellranger_1.1.0 tidyselect_1.1.0 knitr_1.29 git2r_0.27.1
[21] whisker_0.4 lifecycle_0.2.0 pkgconfig_2.0.3 R6_2.4.1
[25] digest_0.6.25 xfun_0.15 colorspace_1.4-1 rprojroot_1.3-2
[29] stringi_1.4.6 yaml_2.2.1 evaluate_0.14 labeling_0.3
[33] fansi_0.4.1 httr_1.4.1 compiler_3.6.3 here_0.1
[37] cli_2.0.2 withr_2.2.0 backports_1.1.5 munsell_0.5.0
[41] DBI_1.1.0 modelr_0.1.8 Rcpp_1.0.5 readxl_1.3.1
[45] maps_3.3.0 dbplyr_1.4.4 ellipsis_0.3.1 assertthat_0.2.1
[49] blob_1.2.1 tools_3.6.3 reprex_0.3.0 viridisLite_0.3.0
[53] httpuv_1.5.4 scales_1.1.1 crayon_1.3.4 glue_1.4.1
[57] rlang_0.4.7 rvest_0.3.5 promises_1.1.1 grid_3.6.3