Temporal data coverage
Pasqualina Vonlanthen & Jens Daniel Müller
26 November, 2021
Last updated: 2021-11-26
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Knit directory: bgc_argo_r_argodata/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | e09c60b | pasqualina-vonlanthendinenna | 2021-11-26 | Build site. |
| html | 1305d6b | pasqualina-vonlanthendinenna | 2021-11-26 | Build site. |
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| html | 358d54e | pasqualina-vonlanthendinenna | 2021-11-05 | Build site. |
| Rmd | 395f3ec | pasqualina-vonlanthendinenna | 2021-11-05 | added total profile count |
| html | a103f60 | pasqualina-vonlanthendinenna | 2021-11-05 | Build site. |
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| Rmd | 4bc1859 | pasqualina-vonlanthendinenna | 2021-10-26 | run with full data |
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Task
Count the number of bgc-argo profiles, and plot their evolution over time.
Load data
Read the files created in loading_data.html:
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")
bgc_metadata <-
read_rds(file = paste0(path_argo_preprocessed, "/bgc_metadata.rds"))basinmask <-
read_csv(paste(path_emlr_utilities,
"basin_mask_WOA18.csv",
sep = ""),
col_types = cols("MLR_basins" = col_character()))
basinmask <- basinmask %>%
filter(MLR_basins == unique(basinmask$MLR_basins)[1]) %>%
select(lon, lat, basin_AIP)
map <-
read_rds(paste(path_emlr_utilities,
"map_landmask_WOA18.rds",
sep = ""))QC flags
QC flags for values (‘flag’ column) are between 1 and 8, where:
- 1 is ‘good’ data
- 2 is ‘probably good’ data,
- 3 is ‘probably bad’ data,
- 4 is ‘bad’ data,
- 5 is ‘value changed’,
- 8 is ‘estimated value’,
- 9 is ‘missing value’.
- (6 and 7 are not used)
Profile QC flags (‘profile_flag’ column) are QC codes attributed to the entire profile, and indicate the number of depth levels (in %) where the value is considered to be good data (QC flags of 1, 2, 5, and 8):
- ‘A’ means 100% of profile levels contain good data,
- ‘B’ means 75-<100% of profile levels contain good data,
- ‘C’ means 50-75% of profile levels contain good data,
- ‘D’ means 25-50% of profile levels contain good data,
- ‘E’ means >0-50% of profile levels contain good data,
- ‘F’ means 0% of profile levels contain good data.
Number of profiles
Per parameter
# count the number of profiles per parameter
bgc_profile_counts <- bgc_metadata %>%
select(platform_number, cycle_number, date,
profile_doxy_qc, profile_ph_in_situ_total_qc, profile_nitrate_qc) %>%
pivot_longer(cols = profile_doxy_qc:profile_nitrate_qc,
names_to = "parameter",
values_to = "profile_flag",
names_prefix = "profile_") %>%
mutate(year = year(date),
month = month(date)) %>%
count(year, month, parameter, profile_flag) %>% # count the number of occurrences of unique flags for each parameter, in each month of each year
filter(!is.na(profile_flag),
profile_flag != "")
# the 'parameter' column contains character strings of either 'doxy_qc', 'ph_in_situ_total_qc', or 'nitrate_qc', with the corresponding profile QC flag in the 'profile_flag' column
# count the total number of profiles for each parameter and each flag:
bgc_profile_counts_total <- bgc_metadata %>%
select(platform_number, cycle_number, date,
profile_doxy_qc, profile_ph_in_situ_total_qc, profile_nitrate_qc) %>%
pivot_longer(cols = profile_doxy_qc:profile_nitrate_qc,
names_to = "parameter",
values_to = "profile_flag",
names_prefix = "profile_") %>%
mutate(year = year(date),
month = month(date)) %>%
count(parameter, profile_flag) %>% # count the number of occurrences of flags for each parameter
filter(!is.na(profile_flag),
profile_flag != "")Total number of profiles
# count the total number of profiles, regardless of QC flag
total_data_count <- bgc_metadata %>%
select(platform_number, cycle_number, date,
profile_doxy_qc, profile_ph_in_situ_total_qc, profile_nitrate_qc) %>%
pivot_longer(cols = profile_doxy_qc:profile_nitrate_qc,
names_to = "parameter",
values_to = "profile_flag",
names_prefix = "profile_") %>%
mutate(year = year(date),
month = month(date)) %>%
count(parameter, profile_flag) %>% # count the number of occurrences of flags for each parameter
filter(!is.na(profile_flag),
profile_flag != "") %>%
group_by(parameter) %>%
summarise(n = sum(n))
knitr::kable(total_data_count, caption = 'total number of profiles', format = 'markdown')| parameter | n |
|---|---|
| doxy_qc | 126986 |
| nitrate_qc | 39347 |
| ph_in_situ_total_qc | 24628 |
# count the number of profiles which have QC flags of A, B, C, D, or E (profiles which contain data that can be used)
usable_data_count <- bgc_metadata %>%
select(platform_number, cycle_number, date,
profile_doxy_qc, profile_ph_in_situ_total_qc, profile_nitrate_qc) %>%
pivot_longer(cols = profile_doxy_qc:profile_nitrate_qc,
names_to = "parameter",
values_to = "profile_flag",
names_prefix = "profile_") %>%
mutate(year = year(date),
month = month(date)) %>%
count(parameter, profile_flag) %>% # count the number of occurrences of flags for each parameter
filter(!is.na(profile_flag),
profile_flag != "",
profile_flag != 'F') %>%
group_by(parameter) %>%
summarise(n = sum(n))
knitr::kable(usable_data_count,
caption = 'total number of profiles with QC flags A, B, C, D, E',
format = 'markdown')| parameter | n |
|---|---|
| doxy_qc | 106942 |
| nitrate_qc | 34540 |
| ph_in_situ_total_qc | 11550 |
# count the number of profiles with QC flag A
A_data_count <- bgc_metadata %>%
select(platform_number, cycle_number, date,
profile_doxy_qc, profile_ph_in_situ_total_qc, profile_nitrate_qc) %>%
pivot_longer(cols = profile_doxy_qc:profile_nitrate_qc,
names_to = "parameter",
values_to = "profile_flag",
names_prefix = "profile_") %>%
mutate(year = year(date),
month = month(date)) %>%
count(parameter, profile_flag) %>% # count the number of occurrences of flags for each parameter
filter(!is.na(profile_flag),
profile_flag != "",
profile_flag == 'A') %>%
group_by(parameter) %>%
summarise(n = sum(n))
knitr::kable(A_data_count,
caption = 'total number of profiles with QC flag A',
format = 'markdown')| parameter | n |
|---|---|
| doxy_qc | 81062 |
| nitrate_qc | 32596 |
| ph_in_situ_total_qc | 9739 |
# count the number of profiles with QC Flag F (not usable data)
F_data_count <- bgc_metadata %>%
select(platform_number, cycle_number, date,
profile_doxy_qc, profile_ph_in_situ_total_qc, profile_nitrate_qc) %>%
pivot_longer(cols = profile_doxy_qc:profile_nitrate_qc,
names_to = "parameter",
values_to = "profile_flag",
names_prefix = "profile_") %>%
mutate(year = year(date),
month = month(date)) %>%
count(parameter, profile_flag) %>% # count the number of occurrences of flags for each parameter
filter(!is.na(profile_flag),
profile_flag != "",
profile_flag == 'F') %>%
group_by(parameter) %>%
summarise(n = sum(n))
knitr::kable(F_data_count,
caption = 'total number of profiles with QC flag F',
format = 'markdown')| parameter | n |
|---|---|
| doxy_qc | 20044 |
| nitrate_qc | 4807 |
| ph_in_situ_total_qc | 13078 |
Plot the evolution of the number of profiles over time
bgc_profile_counts %>%
ggplot(aes(x = month, y = n, col = profile_flag)) +
geom_line() +
geom_point() +
facet_grid(parameter ~ year,
scales = "free_y") +
scale_x_continuous(breaks = seq(1,12,4))+
labs(x = 'month', y = 'number of profiles', title = 'number of profiles per year')# draw separate plots for the separate parameters
bgc_profile_counts %>%
group_split(parameter) %>% # creates a separate flag count for each parameter
map(
~ ggplot(data = .x, # repeats the ggplot for each separate parameter
aes(
x = month, y = n, col = profile_flag
)) +
geom_line() +
geom_point() +
facet_grid(. ~ year,
scales = "free_y") +
labs(title = paste("Parameter: ", unique(.x$parameter)),
x = 'month', y = 'number of profiles') +
scale_x_continuous(breaks = seq(1,12,4))
)[[1]]
[[2]]
[[3]]
ggsave("output/figures/time_series_profiles_per_parameter.png",
width = 7,
height = 4)All flag A
# count the number of profiles which have a QC flag of A for all three BGC parameters
# the if_all(starts_with()) notation allows to filter over a range of columns simultaneously
# this new approach is identical to your previous solution
# except that it filters also the pres, temp, and sal flags
# (plotted below)
bgc_profile_counts_total_A <- bgc_metadata %>%
select(platform_number, cycle_number, date,
profile_doxy_qc, profile_ph_in_situ_total_qc, profile_nitrate_qc) %>%
filter(if_all(starts_with("profile_"), ~. == 'A')) %>%
pivot_longer(cols = profile_doxy_qc:profile_nitrate_qc,
names_to = "parameter",
values_to = "profile_flag",
names_prefix = "profile_") %>%
mutate(year = year(date),
month = month(date)) %>%
distinct(platform_number, cycle_number, year, month) %>%
count(year, month)bgc_profile_counts_total_A %>%
ggplot(aes(x = month, y = n)) +
geom_line() +
geom_point() +
facet_grid(. ~ year,
scales = "free_y") +
scale_x_continuous(breaks = seq(1,12,4)) +
labs(x = 'month', y = 'number of profiles',
title = "Number of profiles",
subtitle = "All three BGC + core parameters (QC flag A)")
| Version | Author | Date |
|---|---|---|
| 7a01367 | pasqualina-vonlanthendinenna | 2021-11-12 |
| bba33bf | pasqualina-vonlanthendinenna | 2021-10-26 |
| cabaa10 | jens-daniel-mueller | 2021-10-22 |
| aa7280d | jens-daniel-mueller | 2021-10-22 |
| b55928d | pasqualina-vonlanthendinenna | 2021-10-21 |
| b8feac2 | pasqualina-vonlanthendinenna | 2021-10-20 |
| 701fffa | pasqualina-vonlanthendinenna | 2021-10-20 |
ggsave("output/figures/time_series_flag_A_profiles.png",
width = 7,
height = 4)By region
# bgc_metadata <- bgc_metadata %>%
# mutate(
# lat = cut(lat, seq(-90, 90, 1), seq(-89.5, 89.5, 1)),
# lat = as.numeric(as.character(lat)),
# lon = cut(lon, seq(20, 380, 1), seq(20.5, 379.5, 1)),
# lon = as.numeric(as.character(lon))
# )
bgc_grid <- bgc_metadata %>%
distinct(lat, lon)
bgc_grid <- inner_join(
basinmask, bgc_grid
)Joining, by = c("lon", "lat")map +
geom_raster(data = basinmask,
aes(lon, lat, fill = basin_AIP)) +
geom_raster(data = bgc_grid,
aes(lon, lat)) +
scale_fill_brewer(palette = "Dark2")Warning: Raster pixels are placed at uneven vertical intervals and will be
shifted. Consider using geom_tile() instead.
rm(bgc_grid)bgc_profile_counts_total_A_region <-
inner_join(bgc_metadata,
basinmask) %>%
select(platform_number, cycle_number, date,
profile_doxy_qc, profile_ph_in_situ_total_qc, profile_nitrate_qc,
basin_AIP) %>%
filter(if_all(starts_with("profile_"), ~. == 'A')) %>%
pivot_longer(cols = profile_doxy_qc:profile_nitrate_qc,
names_to = "parameter",
values_to = "profile_flag",
names_prefix = "profile_") %>%
mutate(year = year(date),
month = month(date)) %>%
distinct(platform_number, cycle_number, year, month, basin_AIP) %>%
count(year, month, basin_AIP)Joining, by = c("lat", "lon")bgc_profile_counts_total_A_region %>%
ggplot(aes(x = month, y = n, col = basin_AIP)) +
geom_line() +
geom_point() +
facet_grid(. ~ year,
scales = "free_y") +
scale_x_continuous(breaks = seq(1,12,2)) +
labs(x = 'month', y = 'number of profiles',
title = "Number of profiles",
subtitle = "All three BGC + core parameters (QC flag A)")
ggsave("output/figures/time_series_profiles_per_region.png",
width = 7,
height = 4)
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2
Matrix products: default
BLAS: /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.7.9 argodata_0.0.0.9000 forcats_0.5.0
[4] stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4
[7] readr_1.4.0 tidyr_1.1.3 tibble_3.1.3
[10] ggplot2_3.3.5 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 assertthat_0.2.1 rprojroot_2.0.2 digest_0.6.27
[5] utf8_1.2.2 R6_2.5.1 cellranger_1.1.0 backports_1.1.10
[9] reprex_0.3.0 evaluate_0.14 highr_0.8 httr_1.4.2
[13] pillar_1.6.2 rlang_0.4.11 readxl_1.3.1 rstudioapi_0.13
[17] whisker_0.4 jquerylib_0.1.4 blob_1.2.1 rmarkdown_2.10
[21] labeling_0.4.2 munsell_0.5.0 broom_0.7.9 compiler_4.0.3
[25] httpuv_1.6.2 modelr_0.1.8 xfun_0.25 pkgconfig_2.0.3
[29] htmltools_0.5.1.1 tidyselect_1.1.0 fansi_0.5.0 crayon_1.4.1
[33] dbplyr_1.4.4 withr_2.4.2 later_1.3.0 grid_4.0.3
[37] jsonlite_1.7.2 gtable_0.3.0 lifecycle_1.0.0 DBI_1.1.1
[41] git2r_0.27.1 magrittr_2.0.1 scales_1.1.1 cli_3.0.1
[45] stringi_1.5.3 farver_2.1.0 fs_1.5.0 promises_1.2.0.1
[49] xml2_1.3.2 bslib_0.2.5.1 ellipsis_0.3.2 generics_0.1.0
[53] vctrs_0.3.8 RColorBrewer_1.1-2 tools_4.0.3 glue_1.4.2
[57] RNetCDF_2.4-2 hms_0.5.3 yaml_2.2.1 colorspace_2.0-2
[61] rvest_0.3.6 knitr_1.33 haven_2.3.1 sass_0.4.0