Temporal data coverage
Pasqualina Vonlanthen & Jens Daniel Müller
21 October, 2021
Last updated: 2021-10-21
Checks: 7 0
Knit directory: bgc_argo_r_argodata/
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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_subset <-
read_rds(file = paste0(path_argo_preprocessed, "/bgc_subset.rds"))
bgc_metadata <-
read_rds(file = paste0(path_argo_preprocessed, "/bgc_metadata.rds"))
bgc_data <-
read_rds(file = paste0(path_argo_preprocessed, "/bgc_data.rds"))
bgc_merge <-
read_rds(file = paste0(path_argo_preprocessed, "/bgc_merge.rds"))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
# 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' columnPlot 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(1,12,2))+
labs(x = 'month', y = 'number of profiles', title = 'number of profiles per year')+
theme_bw()# 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(1,12,2))+
theme_bw()
)[[1]]
[[2]]
[[3]]
# count the number of profiles with a QC flag of A for at least one BGC parameter
# (not plotted)
bgc_profile_counts_A <- 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)) %>%
filter(profile_flag == "A") %>%
distinct(platform_number, cycle_number, year, month) %>%
count(year, month)# count the number of profiles which have a QC flag of A for all three BGC parameters
# (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(profile_doxy_qc == 'A' &
profile_ph_in_situ_total_qc == 'A' &
profile_nitrate_qc == '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(1,12,2)) +
labs(x = 'month', y = 'number of profiles', title = 'number of profiles with all three BGC parameters (QC flag A)')+
theme_bw()
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 tools_4.0.3 glue_1.4.2 RNetCDF_2.4-2
[57] hms_0.5.3 yaml_2.2.1 colorspace_2.0-2 rvest_0.3.6
[61] knitr_1.33 haven_2.3.1 sass_0.4.0