GLODAPv2_2020
Jens Daniel Müller
22 July, 2020
Last updated: 2020-07-22
Checks: 7 0
Knit directory: Cant_eMLR/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20200707) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version ab4b731. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rproj.user/
Ignored: data/GLODAPv2_2016b_MappedClimatologies/
Ignored: data/GLODAPv2_2020/
Ignored: data/World_Ocean_Atlas_2018/
Ignored: data/pCO2_atmosphere/
Ignored: dump/
Ignored: output/plots/
Unstaged changes:
Modified: code/Workflowr_project_managment.R
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/read_GLODAPv2_2020.Rmd) and HTML (docs/read_GLODAPv2_2020.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | bb9c002 | jens-daniel-mueller | 2020-07-21 | Build site. |
| Rmd | d2ed0f8 | jens-daniel-mueller | 2020-07-21 | harmonied lat lon labeling |
| html | b47adc2 | jens-daniel-mueller | 2020-07-20 | Build site. |
| Rmd | 366d7d5 | jens-daniel-mueller | 2020-07-20 | update plots |
| html | 25812b9 | jens-daniel-mueller | 2020-07-20 | Build site. |
| Rmd | e45a882 | jens-daniel-mueller | 2020-07-20 | basins included, higher grid resolution for maps |
| html | 6a6431e | jens-daniel-mueller | 2020-07-20 | Build site. |
| Rmd | 50be8de | jens-daniel-mueller | 2020-07-20 | formating |
| html | b9769e8 | jens-daniel-mueller | 2020-07-20 | Build site. |
| Rmd | 82120fe | jens-daniel-mueller | 2020-07-20 | nitrate, gamma included, 65N removed |
| html | fe55909 | jens-daniel-mueller | 2020-07-19 | Build site. |
| Rmd | 66540a9 | jens-daniel-mueller | 2020-07-19 | formating and color scale |
| html | 61b3390 | jens-daniel-mueller | 2020-07-18 | Build site. |
| 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:sigma4,
nitrite:nitritef))2 Data preparation
2.1 Flags and missing data
flag_f <- 2
flag_qc <- 1Only samples with all relevant parameters determined were selected. The following flagging criteria were defined:
- flag f: 2
- flag qc: 1
Summary statistics were calculated during cleaning process
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 <- GLODAP %>%
filter(!is.na(nitrate))
GLODAP <- GLODAP %>%
filter(nitratef == flag_f) %>%
select(-nitratef)
GLODAP <- GLODAP %>%
filter(nitrateqc == flag_qc) %>%
select(-nitrateqc)
##
GLODAP <- GLODAP %>%
filter(!is.na(depth))
GLODAP <- GLODAP %>%
filter(!is.na(gamma))
##
GLODAP_stats_temp <- GLODAP %>%
summarise(eMLR_variables = n())
GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)rm(flag_f, flag_qc)2.2 Spatial boundaries
min_depth <- 150
max_lat <- 65Only observations with:
- minimum depth: 150m
- maximum latitude: 65°N
were taken into consideration.
GLODAP <- GLODAP %>%
filter(depth >= min_depth)
GLODAP_stats_temp <- GLODAP %>%
summarise(eMLR_variables_150m = n())
GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)
##
GLODAP <- GLODAP %>%
filter(latitude <= max_lat)
GLODAP_stats_temp <- GLODAP %>%
summarise(eMLR_variables_150m_65N = 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)
rm(min_depth, max_lat)rm(min_depth, max_lat)2.3 Reference eras
start <- 1981
JGOFS <- 1999
GOSHIP <- 2012Samples were assigned to following eras:
- JGOFS/WOCE: 1981 - 1999
- GO-SHIP: 2000 - 2012
- new_era: > 2013
GLODAP <- GLODAP %>%
filter(year >= start) %>%
mutate(era = "JGOFS/WOCE",
era = if_else(year > JGOFS, "GO-SHIP", era),
era = if_else(year > GOSHIP, "new_era", era))# rm(start, JGOFS, GOSHIP)2.4 Basin mask
basinmask <- read_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
"basin_mask_WOA18.csv"))GLODAP <- GLODAP %>%
mutate(lat = cut(latitude, seq(-90, 90, 1), seq(-89.5, 89.5, 1)),
lat = as.numeric(as.character(lat)),
lon = cut(longitude, seq(-180, 180, 1), seq(-179.5, 179.5, 1)),
lon = as.numeric(as.character(lon))) %>%
arrange(date) %>%
select(-c(latitude,longitude))GLODAP <- inner_join(GLODAP, basinmask)
rm(basinmask)2.5 Write summary file
GLODAP %>% write_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
"GLODAPv2.2020_clean.csv"))
rm(GLODAP)3 Overview plots
3.1 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)3.2 Assign coarse spatial grid
For the following plots, the cleaned data set was re-opened and observations were gridded spatially to 5°x5° intervals.
GLODAP <- read_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
"GLODAPv2.2020_clean.csv"))GLODAP <- GLODAP %>%
mutate(lat_grid = cut(lat, seq(-90, 90, 5), seq(-87.5, 87.5, 5)),
lat_grid = as.numeric(as.character(lat_grid)),
lon_grid = cut(lon, seq(-180, 180, 5), seq(-177.5, 177.5, 5)),
lon_grid = as.numeric(as.character(lon_grid))) %>%
arrange(date)3.3 Histogram lats
GLODAP_histogram_lat <- GLODAP %>%
group_by(era, lat_grid, basin) %>%
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_brewer(palette = "Dark2")+
facet_wrap(~basin)+
coord_flip(expand = 0)
rm(GLODAP_histogram_lat)3.4 Histogram year
GLODAP_histogram_year <- GLODAP %>%
group_by(year, basin) %>%
tally() %>%
ungroup()
era_median_year <- GLODAP %>%
group_by(era) %>%
summarise(t_ref = median(year)) %>%
ungroup()
GLODAP_histogram_year %>%
ggplot()+
geom_vline(xintercept = c(JGOFS+0.5, GOSHIP+0.5))+
geom_col(aes(year, n, fill=basin))+
geom_point(data = era_median_year, aes(t_ref,0, shape="Median year"), size=2, fill="white")+
scale_fill_brewer(palette = "Dark2")+
scale_shape_manual(values = 24)+
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, basin) %>%
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)+
facet_wrap(~basin, ncol=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, lon) %>%
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, lat, fill=log10(n)))+
mapWorld+
geom_tile()+
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 for each cruise individually and stored locally.
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(lon, lat))+
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$n))
lon_section <- GLODAP_cruise %>%
ggplot(aes(lon, depth))+
scale_y_reverse()+
scale_color_viridis_c()
lon_tco2 <- lon_section+
geom_point(aes(col=tco2))
lon_talk <- lon_section+
geom_point(aes(col=talk))
lon_phosphate <- lon_section+
geom_point(aes(col=phosphate))
lat_section <- GLODAP_cruise %>%
ggplot(aes(lat, depth))+
scale_y_reverse()+
scale_color_viridis_c()
lat_tco2 <- lat_section+
geom_point(aes(col=tco2))
lat_talk <- lat_section+
geom_point(aes(col=talk))
lat_phosphate <- lat_section+
geom_point(aes(col=phosphate))
map /
((lat_tco2 / lat_talk / lat_phosphate) |
(lon_tco2 / lon_talk / lon_phosphate))
ggsave(here::here("output/plots/data/all_cruises_clean",
paste("GLODAP_cruise_date",
cruises_cruise$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
- 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] RColorBrewer_1.1-2 jsonlite_1.7.0 rstudioapi_0.11 generics_0.0.2
[5] magrittr_1.5 farver_2.0.3 gtable_0.3.0 rmarkdown_2.3
[9] vctrs_0.3.1 fs_1.4.2 hms_0.5.3 xml2_1.3.2
[13] pillar_1.4.6 htmltools_0.5.0 haven_2.3.1 later_1.1.0.1
[17] broom_0.7.0 cellranger_1.1.0 tidyselect_1.1.0 knitr_1.29
[21] git2r_0.27.1 whisker_0.4 lifecycle_0.2.0 pkgconfig_2.0.3
[25] R6_2.4.1 digest_0.6.25 xfun_0.15 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_2.2.0 backports_1.1.5
[41] munsell_0.5.0 DBI_1.1.0 modelr_0.1.8 Rcpp_1.0.5
[45] readxl_1.3.1 maps_3.3.0 dbplyr_1.4.4 ellipsis_0.3.1
[49] assertthat_0.2.1 blob_1.2.1 tools_3.6.3 reprex_0.3.0
[53] viridisLite_0.3.0 httpuv_1.5.4 scales_1.1.1 crayon_1.3.4
[57] glue_1.4.1 rlang_0.4.7 rvest_0.3.5 promises_1.1.1
[61] grid_3.6.3