# packages
library(metaDigitise)
library(magrittr)
library(tibble)
library(dplyr)
library(lubridate)
library(dpeatdecomposition)
library(dm)
library(RMariaDB)Connect to database
# connect to database
con <-
RMariaDB::dbConnect(
drv = RMariaDB::MariaDB(),
dbname = "dpeatdecomposition",
default.file = "~/my.cnf"
)
# get database as dm object
dm_dpeatdecomposition <-
dpeatdecomposition::dp_get_dm(con, learn_keys = TRUE)Get most current IDs
id_last <-
list(
id_dataset =
dm_dpeatdecomposition %>%
dm::pull_tbl(datasets) %>%
dplyr::pull(id_dataset) %>%
tail(1),
id_sample =
dm_dpeatdecomposition %>%
dm::pull_tbl(samples) %>%
dplyr::pull(id_sample) %>%
tail(1),
id_measurement =
dm_dpeatdecomposition %>%
dm::pull_tbl(data) %>%
dplyr::pull(id_measurement) %>%
tail(1)
) %>%
purrr::map(function(.x) {
if(length(.x) == 0) {
0L
} else {
.x
}
})Create directories
dir_name <- "d46"
dir_source <- "../raw_data/data/d46"
dir_target <- paste0("../derived_data/", id_last$id_dataset + 1L)
if(!dir.exists(dir_target)) {
dir.create(dir_target)
}datasets <-
tibble::tibble(
id_dataset = id_last$id_dataset + 1L
)citations_to_datasets <-
dplyr::bind_rows(
db_template_tables$citations_to_datasets,
tibble::tibble(
id_dataset = datasets$id_dataset,
id_citation = c("Lieffers.1988")
)
)# mass remaining
samples2 <-
readODS::read_ods(paste0(dir_source, "/derived/Lieffers.1988-Tab2.ods")) %>%
dplyr::rename(
mass_relative_mass = "mass_loss",
mass_relative_mass_error = "mass_remaining_error",
mass_relative_mass_error_type = "mass_remaining_error_type",
mass_relative_mass_sample_size = "mass_remaining_sample_size",
mesh_size_absolute = "mesh_size",
comments_samples = "comment_samples",
sample_treatment = "treatment"
) %>%
dplyr::mutate(
taxon_organ =
dplyr::case_when(
taxon_organ == "whole_plant" ~ "whole plant",
TRUE ~ taxon_organ
),
id_dataset = datasets$id_dataset[[1]],
is_incubated = TRUE,
incubation_environment = "peat",
mass_absolute = NA_real_,
mass_relative_mass = (100 - mass_relative_mass)/100,
mass_relative_mass_error = mass_relative_mass_error/100,
sampling_longitude =
sampling_longitude %>%
sp::char2dms(chd = "°", chm = "'", chs = "''") %>%
as.numeric(),
sampling_latitude =
sampling_latitude %>%
sp::char2dms(chd = "°", chm = "'", chs = "''") %>%
as.numeric(),
sampling_year = lubridate::year(sampling_date),
sampling_month = lubridate::month(sampling_date),
sampling_day = lubridate::day(sampling_date),
incubation_duration =
incubation_duration %>%
lubridate::dyears() %>%
lubridate::time_length(unit = "days"),
experimental_design =
paste0(
as.numeric(as.factor(site_name)), "_",
as.numeric(as.factor(sample_treatment)), "_",
as.numeric(as.factor(distance_to_drainage_ditch))
)
) %>%
dplyr::mutate(
type =
dplyr::case_when(
incubation_duration == 0.0 ~ "samples2",
TRUE ~ "samples3"
)
)
# sample collection
samples1 <-
samples2 %>%
dplyr::filter(!duplicated(paste0(taxon_rank_value, "_", taxon_organ))) %>%
dplyr::mutate(
id_sample = seq_len(nrow(.)) + id_last$id_sample,
id_sample_origin = id_sample,
id_sample_parent = id_sample,
id_sample_incubation_start = NA_integer_,
site_name =
dplyr::case_when(
sample_type == "cellulose" ~ NA_character_,
TRUE ~ site_name
),
site_label = site_name,
sampling_longitude =
dplyr::case_when(
sample_type == "cellulose" ~ NA_real_,
TRUE ~ sampling_longitude
),
sampling_latitude =
dplyr::case_when(
sample_type == "cellulose" ~ NA_real_,
TRUE ~ sampling_latitude
),
sample_treatment = "control",
experimental_design = NA_character_,
is_incubated = FALSE,
incubation_environment = NA_character_,
sample_depth_upper =
dplyr::case_when(
stringr::str_detect(taxon_rank_value, "Sphagnum") ~ 0,
TRUE ~ NA_real_
),
sample_depth_lower =
dplyr::case_when(
stringr::str_detect(taxon_rank_value, "Sphagnum") ~ 10,
TRUE ~ NA_real_
),
sampling_date = NA_real_,
sampling_year = NA_real_,
sampling_month = NA_real_,
sampling_day = NA_real_
)
# add missing idd
samples2 <-
samples2 %>%
dplyr::mutate(
id_sample = seq_len(nrow(.)) + max(samples1$id_sample),
id_sample_origin =
dplyr::left_join(
samples2 %>% dplyr::select(sample_type, taxon_rank_value),
samples1 %>% dplyr::select(sample_type, taxon_rank_value, id_sample),
by = c("sample_type", "taxon_rank_value")
) %>%
dplyr::pull(id_sample),
id_sample_incubation_start =
purrr::map_int(seq_len(nrow(.)), function(i) {
index <- paste0(sample_type, "_", taxon_rank_value, "_", site_name, "_", taxon_organ, "_", sample_depth_upper) == paste0(sample_type, "_", taxon_rank_value, "_", site_name, "_", taxon_organ, "_", sample_depth_upper)[[i]] & experimental_design == experimental_design[[i]] & incubation_duration == 0.0
id_sample[index]
}),
id_sample_parent =
purrr::map_int(seq_len(nrow(.)), function(i) {
index <- paste0(sample_type, "_", taxon_rank_value, "_", site_name, "_", taxon_organ, "_", sample_depth_upper) == paste0(sample_type, "_", taxon_rank_value, "_", site_name, "_", taxon_organ, "_", sample_depth_upper)[[i]] & experimental_design == experimental_design[[i]] & incubation_duration < incubation_duration[[i]]
if(! any(index)) {
id_sample_origin[[i]]
} else {
target_incubation_duration <- max(incubation_duration[index])
index <- index & incubation_duration == target_incubation_duration
id_sample[index]
}
})
) %>%
dplyr::mutate(
sample_type =
dplyr::case_when(
sample_type == "cellulose" ~ sample_type,
TRUE ~ "litter"
)
)
# peat chemistry and physical properties
samples4 <-
readODS::read_ods(paste0(dir_source, "/derived/Lieffers.1988-Tab1.ods")) %>%
dplyr::mutate(
id_dataset = datasets$id_dataset[[1]],
id_sample = seq_len(nrow(.)) + max(samples2$id_sample),
id_sample_origin = id_sample,
id_sample_parent = id_sample,
id_sample_incubation_start = NA_integer_,
sampling_longitude =
sampling_longitude %>%
sp::char2dms(chd = "°", chm = "'", chs = "''") %>%
as.numeric(),
sampling_latitude =
sampling_latitude %>%
sp::char2dms(chd = "°", chm = "'", chs = "''") %>%
as.numeric(),
sampling_year = lubridate::year(sampling_date),
sampling_month = lubridate::month(sampling_date),
sampling_day = lubridate::day(sampling_date),
incubation_duration = 0.0,
is_incubated = FALSE,
experimental_design =
paste0(
as.numeric(as.factor(site_name)), "_",
as.numeric(as.factor(sample_treatment)), "_",
as.numeric(as.factor(distance_to_drainage_ditch))
),
mass_absolute = NA_real_,
dplyr::across(
dplyr::ends_with("_relative_mass"),
function(.x) NA_real_,
.names = "{.col}_absolute"
)
) %>%
dplyr::rename_with(
.cols = dplyr::ends_with("_relative_mass_absolute", ignore.case = FALSE),
.fn = function(.x) {
stringr::str_replace(.x, pattern = "_relative_mass_absolute$", replacement = "_absolute")
}
)
# water table depth
samples5 <-
readRDS(paste0(dir_source, "/raw/caldat/Lieffers.1988-Fig1"))$processed_data %>%
dplyr::select(id, x, y) %>%
dplyr::rename(
water_table_depth = "y"
) %>%
dplyr::mutate(
id_dataset = datasets$id_dataset[[1]],
id_sample = seq_len(nrow(.)) + max(samples4$id_sample),
id_sample_origin = id_sample,
id_sample_parent = id_sample,
id_sample_incubation_start = NA_integer_,
sample_treatment =
id %>%
stringr::str_extract(pattern = "^treatment(control|drained)") %>%
stringr::str_remove("^treatment"),
distance_to_drainage_ditch =
id %>%
stringr::str_extract(pattern = "distancetoditch\\d+\\.?\\d*$") %>%
stringr::str_remove("^distancetoditch") %>%
as.numeric(),
sampling_date =
as.Date(as.Date("1986-06-01") + lubridate::ddays(round(x, 0))),
sample_type = "peat",
sample_depth_upper = 0,
sample_depth_lower = 0,
sampling_year = lubridate::year(sampling_date),
sampling_month = lubridate::month(sampling_date),
sampling_day = lubridate::day(sampling_date),
incubation_duration = 0.0,
is_incubated = FALSE,
site_name = unique(samples2$site_name),
sampling_longitude = unique(samples2$sampling_longitude),
sampling_latitude = unique(samples2$sampling_latitude),
comments_samples = paste0(unique(samples2$comments_samples), " `sampling_day` is only the approximate sampling day."),
experimental_design =
paste0(
as.numeric(as.factor(site_name)), "_",
as.numeric(as.factor(sample_treatment)), "_",
as.numeric(as.factor(distance_to_drainage_ditch))
)
) %>%
dplyr::select(-x, -id)
# temperature
samples6 <-
dplyr::bind_rows(
readRDS(paste0(dir_source, "/raw/caldat/Lieffers.1988-Fig3a"))$processed_data,
readRDS(paste0(dir_source, "/raw/caldat/Lieffers.1988-Fig3b"))$processed_data
) %>%
dplyr::select(id, x, y) %>%
dplyr::rename(
temperature = "y"
) %>%
dplyr::mutate(
id_dataset = datasets$id_dataset[[1]],
id_sample = seq_len(nrow(.)) + max(samples5$id_sample),
id_sample_origin = id_sample,
id_sample_parent = id_sample,
id_sample_incubation_start = NA_integer_,
sample_treatment =
id %>%
stringr::str_extract(pattern = "^treatment(control|drained)") %>%
stringr::str_remove("^treatment"),
distance_to_drainage_ditch =
id %>%
stringr::str_extract(pattern = "distancetoditch\\d+\\.?\\d*") %>%
stringr::str_remove("^distancetoditch") %>%
as.numeric(),
sampling_date =
as.Date(as.Date("1986-05-01") + lubridate::ddays(round(x, 0))),
sample_type = "peat",
sample_depth_upper =
id %>%
stringr::str_extract(pattern = "depth\\d+$") %>%
stringr::str_remove("^depth") %>%
as.numeric(),
sample_depth_lower =
sample_depth_upper,
sampling_year = lubridate::year(sampling_date),
sampling_month = lubridate::month(sampling_date),
sampling_day = lubridate::day(sampling_date),
incubation_duration = 0.0,
is_incubated = FALSE,
site_name = unique(samples2$site_name),
sampling_longitude = unique(samples2$sampling_longitude),
sampling_latitude = unique(samples2$sampling_latitude),
comments_samples = paste0(unique(samples2$comments_samples), " `sampling_day` is only the approximate sampling day."),
experimental_design =
paste0(
as.numeric(as.factor(site_name)), "_",
as.numeric(as.factor(sample_treatment)), "_",
as.numeric(as.factor(distance_to_drainage_ditch))
)
) %>%
dplyr::select(-x, -id)
# water content
samples7 <-
dplyr::bind_rows(
readRDS(paste0(dir_source, "/raw/caldat/Lieffers.1988-Fig2"))$processed_data
) %>%
dplyr::select(-n, -variable) %>%
dplyr::rename(
water_mass_relative_volume = "mean",
water_mass_relative_volume_error = "error"
) %>%
dplyr::mutate(
id_dataset = datasets$id_dataset[[1]],
id_sample = seq_len(nrow(.)) + max(samples6$id_sample),
id_sample_origin = id_sample,
id_sample_parent = id_sample,
id_sample_incubation_start = NA_integer_,
water_mass_relative_volume_error_type = "se",
water_mass_relative_volume_sample_size = 3L,
sample_treatment =
id %>%
stringr::str_extract(pattern = "^treatment(control|drained)") %>%
stringr::str_remove("^treatment"),
distance_to_drainage_ditch =
id %>%
stringr::str_extract(pattern = "distancetoditch\\d+\\.?\\d*") %>%
stringr::str_remove("^distancetoditch") %>%
as.numeric(),
sample_type = "peat",
sample_depth_upper =
id %>%
stringr::str_extract(pattern = "depth\\d+") %>%
stringr::str_remove("^depth") %>%
as.numeric(),
sample_depth_upper =
dplyr::case_when(
sample_depth_upper == 10 ~ 7,
sample_depth_upper == 30 ~ 27
),
sample_depth_lower =
dplyr::case_when(
sample_depth_upper == 7 ~ 12,
sample_depth_upper == 27 ~ 32
),
id_sampling =
id %>%
stringr::str_extract("sampling\\d*$") %>%
stringr::str_remove("^sampling")
) %>%
dplyr::left_join(
readRDS(paste0(dir_source, "/raw/caldat/Lieffers.1988-Fig2_xaxis"))$processed_data %>%
dplyr::select(x) %>%
dplyr::mutate(
sampling_date =
as.Date(as.Date("1986-07-01") + lubridate::ddays(round(x, 0))),
id_sampling =
as.character(seq_len(nrow(.)))
) %>%
dplyr::select(-x),
by = "id_sampling"
) %>%
dplyr::select(-id, -id_sampling) %>%
dplyr::mutate(
sampling_year = lubridate::year(sampling_date),
sampling_month = lubridate::month(sampling_date),
sampling_day = lubridate::day(sampling_date),
incubation_duration = 0.0,
is_incubated = FALSE,
site_name = unique(samples2$site_name),
sampling_longitude = unique(samples2$sampling_longitude),
sampling_latitude = unique(samples2$sampling_latitude),
comments_samples = paste0(unique(samples2$comments_samples), " `sampling_day` is only the approximate sampling day."),
experimental_design =
paste0(
as.numeric(as.factor(site_name)), "_",
as.numeric(as.factor(sample_treatment)), "_",
as.numeric(as.factor(distance_to_drainage_ditch))
),
volume = NA_real_,
water_mass_absolute = NA_real_
)
## combine
samples <-
dplyr::bind_rows(
db_template_tables$samples,
samples1 %>%
dplyr::mutate(
type = "samples1"
) %>%
dplyr::select(-sampling_date),
samples2,
samples4 %>%
dplyr::mutate(
type = "samples4"
),
samples5 %>%
dplyr::mutate(
type = "samples5"
),
samples6 %>%
dplyr::mutate(
type = "samples6"
),
samples7 %>%
dplyr::mutate(
type = "samples7"
)
)samples_to_samples <-
samples %>%
dplyr::filter(! id_sample %in% id_sample_origin) %>%
dplyr::mutate(
transition_description =
dplyr::case_when(
type %in% c("samples2") ~ "translocate",
type %in% c("samples3") ~ "wait",
TRUE ~ NA_character_
)
) %>%
dplyr::select(id_sample_parent, id_sample, transition_description) %>%
dplyr::rename(
id_sample_child = "id_sample"
)d2 <-
samples2 %>%
tidyr::pivot_longer(
cols = dplyr::all_of(c("mass_absolute", "mass_relative_mass", "mesh_size_absolute")),
names_to = "attribute_name",
values_to = "value"
) %>%
dplyr::mutate(
id_measurement = seq_len(nrow(.)) + id_last$id_measurement,
id_measurement_numerator =
purrr::map_int(seq_len(nrow(.)), function(i) {
switch(
attribute_name[[i]],
"mass_relative_mass" = {
id_measurement[id_sample == id_sample[[i]] & attribute_name == "mass_absolute"]
},
NA_integer_
)
}),
id_measurement_denominator =
purrr::map_int(seq_len(nrow(.)), function(i) {
switch(
attribute_name[[i]],
"mass_relative_mass" = {
id_measurement[id_sample == id_sample_incubation_start[[i]] & attribute_name == "mass_absolute"]
},
NA_integer_
)
}),
value_type =
dplyr::case_when(
attribute_name == "mesh_size_absolute" ~ "point",
TRUE ~ "mean"
)
)
d2_sample_size <-
samples2 %>%
tidyr::pivot_longer(
cols = dplyr::ends_with("_sample_size"),
names_to = "attribute_name",
values_to = "sample_size"
) %>%
dplyr::mutate(
attribute_name =
attribute_name %>%
stringr::str_remove(pattern = "_sample_size$")
) %>%
dplyr::select(id_sample, attribute_name, sample_size)
d2_error <-
samples2 %>%
tidyr::pivot_longer(
cols = dplyr::ends_with(c("_error")),
names_to = "attribute_name",
values_to = "error"
) %>%
dplyr::mutate(
attribute_name =
attribute_name %>%
stringr::str_remove(pattern = "_error$")
) %>%
dplyr::select(id_sample, attribute_name, error)
d2_error_type <-
samples2 %>%
tidyr::pivot_longer(
cols = dplyr::ends_with(c("_error_type")),
names_to = "attribute_name",
values_to = "error_type"
) %>%
dplyr::mutate(
attribute_name =
attribute_name %>%
stringr::str_remove(pattern = "_error_type$")
) %>%
dplyr::select(id_sample, attribute_name, error_type)
d2 <-
d2 %>%
dplyr::mutate(
error =
dplyr::left_join(d2, d2_error, by = c("id_sample", "attribute_name")) %>%
dplyr::pull(error),
error_type =
dplyr::left_join(d2, d2_error_type, by = c("id_sample", "attribute_name")) %>%
dplyr::pull(error_type),
sample_size =
dplyr::left_join(d2, d2_sample_size, by = c("id_sample", "attribute_name")) %>%
dplyr::pull(sample_size)
)
# peat chemistry
d4 <-
samples4 %>%
tidyr::pivot_longer(
cols = dplyr::all_of(c("mass_absolute", "N_relative_mass", "P_relative_mass", "K_relative_mass", "Ca_relative_mass", "Mg_relative_mass", "C_relative_mass", "ash_mass_relative_mass", "N_absolute", "P_absolute", "K_absolute", "Ca_absolute", "Mg_absolute", "C_absolute", "ash_mass_absolute")),
names_to = "attribute_name",
values_to = "value"
) %>%
dplyr::mutate(
id_measurement = seq_len(nrow(.)) + max(d2$id_measurement),
id_measurement_numerator =
purrr::map_int(seq_len(nrow(.)), function(i) {
if(attribute_name[[i]] == "mass_relative_mass") {
id_measurement[id_sample == id_sample[[i]] & attribute_name == "mass_absolute"]
} else if (stringr::str_detect(attribute_name[[i]], pattern = "_relative_mass$")) {
id_measurement[id_sample == id_sample[[i]] & attribute_name == stringr::str_replace(attribute_name[[i]], "_relative_mass2?", "_absolute")]
} else {
NA_integer_
}
}),
id_measurement_denominator =
purrr::map_int(seq_len(nrow(.)), function(i) {
if(attribute_name[[i]] == "mass_relative_mass") {
id_measurement[id_sample == id_sample_incubation_start[[i]] & attribute_name == "mass_absolute"]
} else if (stringr::str_detect(attribute_name[[i]], pattern = "_relative_mass$")) {
id_measurement[id_sample == id_sample[[i]] & attribute_name == "mass_absolute"]
} else {
NA_integer_
}
}),
value_type = "mean"
)
# water table depth
d5 <-
samples5 %>%
tidyr::pivot_longer(
cols = dplyr::all_of(c("water_table_depth")),
names_to = "attribute_name",
values_to = "value"
) %>%
dplyr::mutate(
id_measurement = seq_len(nrow(.)) + max(d4$id_measurement),
id_measurement_numerator =
purrr::map_int(seq_len(nrow(.)), function(i) {
if(attribute_name[[i]] == "mass_relative_mass") {
id_measurement[id_sample == id_sample[[i]] & attribute_name == "mass_absolute"]
} else if (stringr::str_detect(attribute_name[[i]], pattern = "_relative_mass$")) {
id_measurement[id_sample == id_sample[[i]] & attribute_name == stringr::str_replace(attribute_name[[i]], "_relative_mass2?", "_absolute")]
} else {
NA_integer_
}
}),
id_measurement_denominator =
purrr::map_int(seq_len(nrow(.)), function(i) {
if(attribute_name[[i]] == "mass_relative_mass") {
id_measurement[id_sample == id_sample_incubation_start[[i]] & attribute_name == "mass_absolute"]
} else if (stringr::str_detect(attribute_name[[i]], pattern = "_relative_mass$")) {
id_measurement[id_sample == id_sample[[i]] & attribute_name == "mass_absolute"]
} else {
NA_integer_
}
}),
value_type = "mean"
)
# temperature
d6 <-
samples6 %>%
dplyr::mutate(
temperature = temperature + 273.15
) %>%
tidyr::pivot_longer(
cols = dplyr::all_of(c("temperature")),
names_to = "attribute_name",
values_to = "value"
) %>%
dplyr::mutate(
id_measurement = seq_len(nrow(.)) + max(d5$id_measurement),
id_measurement_numerator =
purrr::map_int(seq_len(nrow(.)), function(i) {
if(attribute_name[[i]] == "mass_relative_mass") {
id_measurement[id_sample == id_sample[[i]] & attribute_name == "mass_absolute"]
} else if (stringr::str_detect(attribute_name[[i]], pattern = "_relative_mass$")) {
id_measurement[id_sample == id_sample[[i]] & attribute_name == stringr::str_replace(attribute_name[[i]], "_relative_mass2?", "_absolute")]
} else {
NA_integer_
}
}),
id_measurement_denominator =
purrr::map_int(seq_len(nrow(.)), function(i) {
if(attribute_name[[i]] == "mass_relative_mass") {
id_measurement[id_sample == id_sample_incubation_start[[i]] & attribute_name == "mass_absolute"]
} else if (stringr::str_detect(attribute_name[[i]], pattern = "_relative_mass$")) {
id_measurement[id_sample == id_sample[[i]] & attribute_name == "mass_absolute"]
} else {
NA_integer_
}
}),
value_type = "mean"
)
# water content
d7 <-
samples7 %>%
tidyr::pivot_longer(
cols = dplyr::all_of(c("water_mass_absolute", "volume", "water_mass_relative_volume")),
names_to = "attribute_name",
values_to = "value"
) %>%
dplyr::mutate(
id_measurement = seq_len(nrow(.)) + max(d6$id_measurement),
id_measurement_numerator =
purrr::map_int(seq_len(nrow(.)), function(i) {
if(attribute_name[[i]] == "water_mass_relative_volume") {
id_measurement[id_sample == id_sample[[i]] & attribute_name == "water_mass_absolute"]
} else {
NA_integer_
}
}),
id_measurement_denominator =
purrr::map_int(seq_len(nrow(.)), function(i) {
if(attribute_name[[i]] == "water_mass_relative_volume") {
id_measurement[id_sample == id_sample[[i]] & attribute_name == "volume"]
} else {
NA_integer_
}
}),
value_type = "mean"
)
d7_sample_size <-
samples7 %>%
tidyr::pivot_longer(
cols = dplyr::ends_with("_sample_size"),
names_to = "attribute_name",
values_to = "sample_size"
) %>%
dplyr::mutate(
attribute_name =
attribute_name %>%
stringr::str_remove(pattern = "_sample_size$")
) %>%
dplyr::select(id_sample, attribute_name, sample_size)
d7_error <-
samples7 %>%
tidyr::pivot_longer(
cols = dplyr::ends_with(c("_error")),
names_to = "attribute_name",
values_to = "error"
) %>%
dplyr::mutate(
attribute_name =
attribute_name %>%
stringr::str_remove(pattern = "_error$")
) %>%
dplyr::select(id_sample, attribute_name, error)
d7_error_type <-
samples7 %>%
tidyr::pivot_longer(
cols = dplyr::ends_with(c("_error_type")),
names_to = "attribute_name",
values_to = "error_type"
) %>%
dplyr::mutate(
attribute_name =
attribute_name %>%
stringr::str_remove(pattern = "_error_type$")
) %>%
dplyr::select(id_sample, attribute_name, error_type)
d7 <-
d7 %>%
dplyr::mutate(
error =
dplyr::left_join(d7, d7_error, by = c("id_sample", "attribute_name")) %>%
dplyr::pull(error),
error_type =
dplyr::left_join(d7, d7_error_type, by = c("id_sample", "attribute_name")) %>%
dplyr::pull(error_type),
sample_size =
dplyr::left_join(d7, d7_sample_size, by = c("id_sample", "attribute_name")) %>%
dplyr::pull(sample_size)
)
# combine
d <-
dplyr::bind_rows(
db_template_tables$data,
d2,
d4,
d5,
d6,
d7
) %>%
dplyr::select(dplyr::all_of(colnames(db_template_tables$data)))experimental_design_format <-
tibble::tibble(
id_dataset = datasets$id_dataset,
file = paste0(id_last$id_dataset + 1L, "/experimental_design_format.csv"),
experimental_design_description = "`site_name`: Name of the site. `sample_treatment`: Logical value indicating whether the plot has been drained (`TRUE`) or not (`FALSE`). `distance_to_drainage_ditch`: Numerical value representing the distance to the next drainage ditch [m]."
)
# csv file to export
experimental_design_format2 <-
samples %>%
dplyr::filter(! duplicated(experimental_design) & ! is.na(experimental_design)) %>%
dplyr::select(experimental_design, site_name, sample_treatment, distance_to_drainage_ditch)
# export
write.csv(experimental_design_format2, paste0(dir_target, "/experimental_design_format.csv"), row.names = FALSE)# list all tables
dm_insert_in <-
list(
datasets =
datasets %>%
dplyr::select(dplyr::all_of(colnames(dm_dpeatdecomposition$datasets))),
samples =
samples %>%
dplyr::select(dplyr::all_of(colnames(dm_dpeatdecomposition$samples))),
data =
d %>%
dplyr::select(dplyr::all_of(colnames(dm_dpeatdecomposition$data))),
samples_to_samples =
samples_to_samples %>%
dplyr::select(dplyr::all_of(colnames(dm_dpeatdecomposition$samples_to_samples))),
citations_to_datasets =
citations_to_datasets %>%
dplyr::select(dplyr::all_of(colnames(dm_dpeatdecomposition$citations_to_datasets))),
experimental_design_format =
experimental_design_format %>%
dplyr::select(dplyr::all_of(colnames(dm_dpeatdecomposition$experimental_design_format)))
)
# check whether all column names as present in table attributes
all_column_names <-
purrr::map(dm_insert_in, colnames) %>%
unlist() %>%
unique()
if(! all(all_column_names %in% (dm_dpeatdecomposition %>% dm::pull_tbl(attributes) %>% dplyr::pull(attribute_name)))) {
cond <- purrr::map_lgl(all_column_names, function(.x) ! .x %in% (dm_dpeatdecomposition %>% dm::pull_tbl(attributes) %>% dplyr::pull(attribute_name)))
RMariaDB::dbDisconnect(con)
stop(paste0("New `attribute_name`s discovered: ", paste(all_column_names[cond], collapse = ", ")))
}
all_data_attributes <- unique(dm_insert_in$data$attribute_name)
if(! all(all_data_attributes %in% (dm_dpeatdecomposition %>% dm::pull_tbl(attributes) %>% dplyr::pull(attribute_name)))) {
cond <- purrr::map_lgl(all_data_attributes, function(.x) ! .x %in% (dm_dpeatdecomposition %>% dm::pull_tbl(attributes) %>% dplyr::pull(attribute_name)))
stop(paste0("New `attribute_name`s discovered: ", paste(all_data_attributes[cond], collapse = ", ")))
RMariaDB::dbDisconnect(con)
}
# filter empty tables
dm_insert_in_check <-
dm_insert_in[purrr::map_lgl(dm_insert_in, function(x) nrow(x) > 0)] %>%
dm::as_dm() %>%
dp_dm_add_keys(dm_dpeatdecomposition)
# copy into dm_pmird
for(i in seq_along(dm_insert_in)) {
RMariaDB::dbAppendTable(con, name = names(dm_insert_in)[[i]], value = dm_insert_in[[i]])
}
RMariaDB::dbDisconnect(con)