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BioChem_metadata_generation.R
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BioChem_metadata_generation.R
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# Generate BCS files
library(librarian)
shelf(tidyverse, readr, readxl, here, oce, marmap )
# read in dictionary tables ----
# gather from OSC cruise
oscdata <- read_xlsx('osccruise_davisstrait.xlsx')
expos <- read_csv('extdata/expocodes.csv', show_col_types = FALSE)
# dependency tables
bcs_dict <- readxl::read_xlsx(here('extdata', 'biochem_dictionary.xlsx'),
sheet = 'BCS')
bcd_dict <- readxl::read_xlsx(here('extdata', 'biochem_dictionary.xlsx'),
sheet = 'BCD')
bcs_columns <- c('DIS_SAMPLE_KEY_VALUE',
'MISSION_DESCRIPTOR',
'EVENT_COLLECTOR_EVENT_ID',
'EVENT_COLLECTOR_STN_NAME',
'MISSION_NAME',
'MISSION_LEADER',
'MISSION_SDATE',
'MISSION_EDATE',
'MISSION_INSTITUTE',
'MISSION_PLATFORM',
'MISSION_PROTOCOL',
'MISSION_GEOGRAPHIC_REGION',
'MISSION_COLLECTOR_COMMENT1',
'MISSION_COLLECTOR_COMMENT2',
'MISSION_DATA_MANAGER_COMMENT',
'EVENT_SDATE',
'EVENT_EDATE',
'EVENT_STIME',
'EVENT_ETIME',
'EVENT_MIN_LAT',
'EVENT_MAX_LAT',
'EVENT_MIN_LON',
'EVENT_MAX_LON',
'EVENT_UTC_OFFSET',
'EVENT_COLLECTOR_COMMENT1',
'EVENT_COLLECTOR_COMMENT2',
'EVENT_DATA_MANAGER_COMMENT',
'DIS_HEADR_GEAR_SEQ',
'DIS_HEADR_SDATE',
'DIS_HEADR_EDATE',
'DIS_HEADR_STIME',
'DIS_HEADR_ETIME',
'DIS_HEADR_TIME_QC_CODE',
'DIS_HEADR_SLAT',
'DIS_HEADR_ELAT',
'DIS_HEADR_SLON',
'DIS_HEADR_ELON',
'DIS_HEADR_POSITION_QC_CODE',
'DIS_HEADR_START_DEPTH',
'DIS_HEADR_END_DEPTH',
'DIS_HEADR_SOUNDING',
'DIS_HEADR_COLLECTOR_DEPLMT_ID',
'DIS_HEADR_COLLECTOR_SAMPLE_ID',
'DIS_HEADR_COLLECTOR',
'DIS_HEADR_COLLECTOR_COMMENT1',
'DIS_HEADR_DATA_MANAGER_COMMENT',
'DIS_HEADR_RESPONSIBLE_GROUP',
'DIS_HEADR_SHARED_DATA',
'CREATED_BY',
'CREATED_DATE',
'DATA_CENTER_CODE',
'PROCESS_FLAG',
'BATCH_SEQ' )
# read in all original data sources
# data
data_fns <- choose.files(default = '../data', caption = "Select raw data files: ", multi = TRUE)
# loop for all data files
for (i in 1:length(data_fns)) {
raw_data <- read.csv(data_fns[i])
# remove empty rows
# raw_data <- raw_data[-which(is.na(raw_data[,1])), ]
# bcd
bcd_fn <- choose.files(default = '../data/BioChem',
caption = paste('Select BCD file for',
# as.character(unique(raw_data$`Cruise ID`)),
'[', as.character(na.omit(unique(raw_data$year))), ']', ':')
)
bcd <- read.csv(bcd_fn)
# pull columns from bcd ----
bcs <- bcd %>%
dplyr::select(., DIS_SAMPLE_KEY_VALUE,
MISSION_DESCRIPTOR,
EVENT_COLLECTOR_EVENT_ID,
EVENT_COLLECTOR_STN_NAME,
CREATED_BY,
CREATED_DATE,
DATA_CENTER_CODE,
PROCESS_FLAG,
BATCH_SEQ
)
bcs$DIS_HEADR_SDATE <- bcd$DIS_HEADER_SDATE
bcs$DIS_HEADR_EDATE <- bcd$DIS_HEADER_SDATE
bcs$DIS_HEADR_STIME <- bcd$DIS_HEADER_STIME
bcs$DIS_HEADR_ETIME <- bcd$DIS_HEADER_STIME
bcs$DIS_HEADR_SLAT <- bcd$DIS_HEADER_SLAT
bcs$DIS_HEADR_ELAT <- bcd$DIS_HEADER_SLAT
bcs$DIS_HEADR_SLON <- bcd$DIS_HEADER_SLON
bcs$DIS_HEADR_ELON <- bcd$DIS_HEADER_SLON
bcs$DIS_HEADR_START_DEPTH <- bcd$DIS_HEADER_START_DEPTH
bcs$DIS_HEADR_END_DEPTH <- bcd$DIS_HEADER_END_DEPTH
bcs$DIS_HEADR_COLLECTOR_SAMPLE_ID <- bcd$DIS_DETAIL_COLLECTOR_SAMP_ID
# filter to unique dis_sample_key_values
bcs <- bcs %>%
distinct(DIS_SAMPLE_KEY_VALUE, .keep_all = TRUE)
# fill standard values ----
bcs$MISSION_GEOGRAPHIC_REGION <- 'Davis Strait'
bcs$DIS_HEADR_TIME_QC_CODE <- 0
bcs$DIS_HEADR_POSITION_QC_CODE <- 0
bcs$DIS_HEADR_GEAR_SEQ <- '90000171' # CTD + Rosette
bcs$MISSION_INSTITUTE <- 'DFO-BIO'
# pull values from existing tables ----
# mission name
raw_name <- str_trim(unique(raw_data[grep(names(raw_data), pattern = 'cruise', ignore.case = TRUE)])[1,])
bcs$MISSION_NAME <- expos$biochem_name[expos$cruise_name %in% raw_name]
osc_year <- oscdata[oscdata$year == na.omit(unique(raw_data$year)), ]
bcs$MISSION_LEADER <- paste(osc_year$first_name, osc_year$last_name)
bcs$MISSION_SDATE <- format(osc_year$start_date, '%d-%b-%y')
bcs$MISSION_EDATE <- format(osc_year$end_date, '%d-%b-%y')
bcs$MISSION_PLATFORM <- osc_year$platform
bcs$MISSION_PROTOCOL <- osc_year$protocol
bcs$DIS_HEADR_RESPONSIBLE_GROUP <- osc_year$responsible_group
# match sounding by event
event_col <- grep('event', names(raw_data))
names(raw_data)[event_col] <- 'event'
raw_sounding <- raw_data %>%
select(event, sounding) %>%
distinct(event, .keep_all = TRUE)
bcs <- bcs %>%
right_join(., raw_sounding, by = join_by( EVENT_COLLECTOR_EVENT_ID == event), relationship = 'many-to-many') %>%
rename(., DIS_HEADR_SOUNDING = sounding)
remove(raw_sounding)
# TODO if sounding is not available, pull from NOAA bathymetry map
# initialize event_data_manager_comment
bcs$EVENT_DATA_MANAGER_COMMENT <- ''
miss_sound <- na.omit(unique(bcs$EVENT_COLLECTOR_EVENT_ID[is.na(bcs$DIS_HEADR_SOUNDING) |
is.null(bcs$DIS_HEADR_SOUNDING)]))
if (length(miss_sound) > 0 ) {
for (iii in 1:length(miss_sound)) {
miss_sound_loc <- c(bcs$DIS_HEADR_SLAT[bcs$EVENT_COLLECTOR_EVENT_ID == miss_sound[iii]][1],
bcs$DIS_HEADR_SLON[bcs$EVENT_COLLECTOR_EVENT_ID == miss_sound[iii]][1])
bathy_dat <- marmap::getNOAA.bathy(lon1 = miss_sound_loc[2],
lon2 = miss_sound_loc[2]+0.25,
lat1 = miss_sound_loc[1],
lat2 = miss_sound_loc[1]+0.25,
resolution = 1 )
bathy_dat <- as.xyz(bathy_dat)
fill_sounding <- abs(round(mean(bathy_dat$V3), 0))
bcs$DIS_HEADR_SOUNDING[bcs$EVENT_COLLECTOR_EVENT_ID == miss_sound[iii]] <- fill_sounding
bcs$EVENT_DATA_MANAGER_COMMENT[bcs$EVENT_COLLECTOR_EVENT_ID == miss_sound[iii]] <- "Missing sounding filled based on approximate bathymetric data"
}
}
# pull event summary
event_table <- bcd %>%
group_by(EVENT_COLLECTOR_EVENT_ID) %>%
summarize(EVENT_SDATE = min(DIS_HEADER_SDATE),
EVENT_EDATE = max(DIS_HEADER_SDATE),
EVENT_STIME = min(DIS_HEADER_STIME),
EVENT_ETIME = max(DIS_HEADER_STIME),
EVENT_MIN_LAT = min(DIS_HEADER_SLAT),
EVENT_MAX_LAT = max(DIS_HEADER_SLAT),
EVENT_MIN_LON = min(DIS_HEADER_SLON),
EVENT_MAX_LON = max(DIS_HEADER_SLON))
bcs <- bcs %>%
left_join(event_table, by = 'EVENT_COLLECTOR_EVENT_ID')
# write data_manager_comment
# calculation of depth from pressure
bcs$MISSION_DATA_MANAGER_COMMENT <- "Depth calculated from pressure; start depth equals end depth; sounding manually input;"
# gather comment from raw data
event_comments <- raw_data %>%
group_by(event) %>%
reframe(EVENT_COLLECTOR_COMMENT1 = str_c(unique(notes), collapse = ';')) %>%
filter(!is.na(EVENT_COLLECTOR_COMMENT1))
if (nrow(event_comments) > 0) {
bcs <- bcs %>%
left_join(., event_comments, by = join_by( 'EVENT_COLLECTOR_EVENT_ID' == 'event'))
} else {
bcs$EVENT_COLLECTOR_COMMENT1 <- ''
}
# initiate NA columns
na_cols <- bcs_columns[-which(bcs_columns %in% names(bcs))]
if (length(na_cols) > 0) {
bcs <- bcs %>%
mutate(!!!setNames(rep('', length(na_cols)), na_cols))
warning(paste("Empty columns added to BCS file! \n", str_c(na_cols, collapse = '\n')))
}
# format & export ----
bcs_export <- bcs %>%
select(all_of(bcs_columns))
if (length(which(is.na(bcs_export$MISSION_DESCRIPTOR))) > 0 ) {
bcs_export <- bcs_export[-which(is.na(bcs_export$MISSION_DESCRIPTOR)), ]
}
# export
bcs_name <- file.path('../data/BioChem/', paste0(unique(bcs_export$MISSION_DESCRIPTOR), '_BCS.csv'))
write.csv(bcs_export, bcs_name, row.names = FALSE)
}