Data base
Jens Daniel Müller
14 July, 2020
Last updated: 2020-07-14
Checks: 7 0
Knit directory: Cant_eMLR/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | dc1c56e | jens-daniel-mueller | 2020-07-14 | tref calculated |
| html | b1ece68 | jens-daniel-mueller | 2020-07-13 | Build site. |
| Rmd | 8eb1b22 | jens-daniel-mueller | 2020-07-13 | cleaned data base file |
| Rmd | 9e8f7f1 | jens-daniel-mueller | 2020-07-13 | untracked changes |
| html | 79312b2 | jens-daniel-mueller | 2020-07-13 | Build site. |
| html | 090cfeb | jens-daniel-mueller | 2020-07-13 | Build site. |
| Rmd | e6a2ade | jens-daniel-mueller | 2020-07-13 | added Cstar calculation |
| html | ae846e5 | jens-daniel-mueller | 2020-07-09 | Build site. |
| Rmd | 80782a1 | jens-daniel-mueller | 2020-07-09 | Data cleaning, gridding, coverage plots |
| html | f73ba58 | jens-daniel-mueller | 2020-07-08 | Build site. |
| Rmd | 8de2366 | jens-daniel-mueller | 2020-07-08 | Data cleaning, gridding, coverage plots |
| html | 6f0ac0f | jens-daniel-mueller | 2020-07-08 | Build site. |
| Rmd | 9f12d15 | jens-daniel-mueller | 2020-07-08 | Data cleaning, gridding, coverage plots |
| html | 848d7c4 | jens-daniel-mueller | 2020-07-08 | Build site. |
| Rmd | 5de66c3 | jens-daniel-mueller | 2020-07-08 | correct label GLODAPv2_2020 |
| html | 81670f8 | jens-daniel-mueller | 2020-07-08 | Build site. |
| Rmd | 36cbea0 | jens-daniel-mueller | 2020-07-08 | read GLODAPv2_2020 |
library(tidyverse)
library(lubridate)
library(patchwork)1 Read GLODAP master file
- Data source:
GLODAPv2.2020_Merged_Master_File.csvfrom glodap.info
GLODAP <- read_csv(here::here("data", "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))GLODAP_stats <- GLODAP %>%
summarise(total = n(),
tco2 = sum(!is.na(tco2)),
tco2_flag = sum(!is.na(tco2) & tco2f == 2 & tco2qc == 1),
talk = sum(!is.na(talk)),
talk_f_qc = sum(!is.na(talk) & talkf == 2 & talkqc == 1),
phosphate = sum(!is.na(phosphate)),
phosphate_f_qc = sum(!is.na(phosphate) & phosphatef == 2 & phosphateqc == 1))
GLODAP_stats_2 <- GLODAP %>%
summarise(tco2_talk = sum(!is.na(tco2) & !is.na(talk)),
tco2_phosphate = sum(!is.na(tco2) & !is.na(phosphate)),
tco2_talk_phosphate = sum(!is.na(tco2) & !is.na(talk) & !is.na(phosphate)),
tco2_talk_phosphate_f_qc = sum(!is.na(tco2) & !is.na(talk) & !is.na(phosphate) &
tco2f == 2 & tco2qc == 1 &
talkf == 2 & talkqc == 1 &
phosphatef == 2 & phosphateqc == 1),
incl_pH = sum(!is.na(tco2) & !is.na(talk) & !is.na(phosphate) &
tco2f == 2 & tco2qc == 1 &
talkf == 2 & talkqc == 1 &
phosphatef == 2 & phosphateqc == 1 &
!is.na(phts25p0)))
GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_2)
rm(GLODAP_stats_2)
GLODAP_stats_long <- GLODAP_stats %>%
pivot_longer(1:12, names_to = "parameter", values_to = "n")
GLODAP_stats_long %>% write_csv(here::here("data/_summarized_data_files",
"GLODAPv2.2020_stats.csv"))2 Clean data
- subsetted rows with
- paired observations of all relevant parameters
- flag f: 2
- flag qc: 1
GLODAP <- GLODAP %>%
filter(!is.na(tco2))
GLODAP <- GLODAP %>%
filter(tco2f == "2") %>%
select(-tco2f)
GLODAP <- GLODAP %>%
filter(tco2qc == "1") %>%
select(-tco2qc)
###
GLODAP <- GLODAP %>%
filter(!is.na(talk))
GLODAP <- GLODAP %>%
filter(talkf == "2") %>%
select(-talkf)
GLODAP <- GLODAP %>%
filter(talkqc == "1") %>%
select(-talkqc)
###
GLODAP <- GLODAP %>%
filter(!is.na(phosphate))
GLODAP <- GLODAP %>%
filter(phosphatef == "2") %>%
select(-phosphatef)
GLODAP <- GLODAP %>%
filter(phosphateqc == "1") %>%
select(-phosphateqc)
###
GLODAP <- GLODAP %>%
filter(!is.na(temperature))
###
GLODAP <- GLODAP %>%
filter(!is.na(salinity))
GLODAP <- GLODAP %>%
filter(salinityf == "2") %>%
select(-salinityf)
GLODAP <- GLODAP %>%
filter(salinityqc == "1") %>%
select(-salinityqc)
###
GLODAP <- GLODAP %>%
filter(!is.na(silicate))
GLODAP <- GLODAP %>%
filter(silicatef == "2") %>%
select(-silicatef)
GLODAP <- GLODAP %>%
filter(silicateqc == "1") %>%
select(-silicateqc)
###
GLODAP <- GLODAP %>%
filter(!is.na(oxygen))
GLODAP <- GLODAP %>%
filter(oxygenf == "2") %>%
select(-oxygenf)
GLODAP <- GLODAP %>%
filter(oxygenqc == "1") %>%
select(-oxygenqc)
###
GLODAP <- GLODAP %>%
filter(!is.na(aou))
GLODAP <- GLODAP %>%
filter(aouf == "2") %>%
select(-aouf)3 Assign reference eras
Samples before Jan 01, 2000 were assigned as to the “JGOFS/WOCE” era, else to “GO-SHIP” era.
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))GLODAP %>% write_csv(here::here("data/_summarized_data_files",
"GLODAPv2.2020_clean.csv"))4 Overview GLODAPv2-2020
4.1 Nr of observations
GLODAP_stats_long <- read_csv(here::here("data/_summarized_data_files",
"GLODAPv2.2020_stats.csv"))
GLODAP_stats_long %>%
ggplot(aes(parameter, n))+
geom_col()+
coord_flip()
rm(GLODAP_stats_long)4.2 Data coverage
Spatio-temporal distribution of non-flagged, paired talk, tco2, and phosphate data.
GLODAP <- read_csv(here::here("data/_summarized_data_files",
"GLODAPv2.2020_clean.csv"))4.3 Gridding
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)))
GLODAP <- GLODAP %>%
arrange(date)
GLODAP_histogram_year <- GLODAP %>%
group_by(era, year) %>%
tally() %>%
ungroup()
GLODAP_map_era <- GLODAP %>%
group_by(era, lat_grid, lon_grid) %>%
tally() %>%
ungroup()
GLODAP_hovmoeller_year <- GLODAP %>%
group_by(year, lat_grid) %>%
tally() %>%
ungroup()
era_median_year <- GLODAP %>%
group_by(era) %>%
summarise(t_ref = median(year)) %>%
ungroup()4.4 Histograms
GLODAP %>%
ggplot(aes(latitude, fill=era))+
geom_histogram(binwidth = 10, col="black", boundary = 0)+
scale_x_continuous(breaks = seq(-85,90,10))+
scale_fill_viridis_d()+
coord_flip(expand = 0)
GLODAP_histogram_year %>%
ggplot(aes(year, n, fill=era))+
geom_col()+
geom_vline(xintercept = era_median_year$t_ref)+
coord_cartesian(expand = 0)
rm(GLODAP_histogram_year)4.5 Hovmoeller (Lat-time)
GLODAP_hovmoeller_year %>%
ggplot(aes(year, lat_grid, fill=log10(n)))+
geom_vline(xintercept = c(2000, 2013))+
geom_tile()+
scale_fill_viridis_c(option = "magma", direction = -1)
rm(GLODAP_hovmoeller_year)4.6 Maps
mapWorld <- borders("world", colour="gray60", fill="gray60")
GLODAP_map_era <- GLODAP_map_era %>%
mutate(era = factor(era, c("JGOFS/WOCE", "GO-SHIP", "new_era")))
GLODAP_map_era %>%
ggplot(aes(lon_grid, lat_grid, fill=log10(n)))+
mapWorld+
geom_raster()+
scale_fill_viridis_c(option = "magma", direction = -1)+
scale_x_continuous(breaks = seq(-180, 180, 30))+
scale_y_continuous(breaks = seq(-90, 90, 30))+
coord_quickmap(expand = FALSE)+
facet_wrap(~era, ncol=1)
rm(GLODAP_map_era)4.7 Individual cruise sections
cruises <- GLODAP %>%
group_by(cruise) %>%
summarise(date_mean = mean(date, na.rm = TRUE),
n = n()) %>%
ungroup() %>%
arrange(date_mean)
GLODAP <- full_join(GLODAP, cruises)
mapWorld <- borders("world", colour="gray60", fill="gray60")
n <- 0
for (i_cruise in unique(cruises$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_1.0.0 lubridate_1.7.4 forcats_0.5.0 stringr_1.4.0
[5] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1 tidyr_1.0.2
[9] tibble_3.0.1 ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.1
loaded via a namespace (and not attached):
[1] jsonlite_1.6.1 rstudioapi_0.11 generics_0.0.2 magrittr_1.5
[5] farver_2.0.3 gtable_0.3.0 rmarkdown_2.1 vctrs_0.3.0
[9] fs_1.4.0 hms_0.5.3 xml2_1.3.0 pillar_1.4.4
[13] htmltools_0.5.0 haven_2.2.0 later_1.0.0 broom_0.5.5
[17] cellranger_1.1.0 lattice_0.20-41 tidyselect_1.1.0 knitr_1.28
[21] git2r_0.26.1 whisker_0.4 lifecycle_0.2.0 pkgconfig_2.0.3
[25] R6_2.4.1 digest_0.6.25 xfun_0.12 colorspace_1.4-1
[29] rprojroot_1.3-2 stringi_1.4.6 yaml_2.2.1 evaluate_0.14
[33] labeling_0.3 fansi_0.4.1 httr_1.4.1 compiler_3.6.3
[37] here_0.1 cli_2.0.2 withr_2.1.2 backports_1.1.5
[41] munsell_0.5.0 DBI_1.1.0 modelr_0.1.6 Rcpp_1.0.4.6
[45] readxl_1.3.1 maps_3.3.0 dbplyr_1.4.2 ellipsis_0.3.1
[49] assertthat_0.2.1 tools_3.6.3 reprex_0.3.0 viridisLite_0.3.0
[53] httpuv_1.5.2 scales_1.1.0 crayon_1.3.4 glue_1.4.1
[57] rlang_0.4.6 nlme_3.1-145 rvest_0.3.5 promises_1.1.0
[61] grid_3.6.3