# 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 <- "d53"
dir_source <- "../raw_data/data/d53"
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("Bengtsson.2017", "Bengtsson.2016")
)
)# mass remaining
samples3 <-
read.csv(paste0(dir_source, "/raw/bengtsson_etal_2016_traits.csv")) %>%
dplyr::select(-c(6:7, 11, 16:ncol(.))) %>%
dplyr::rename(
sample_label = "sample",
site_name = "site",
sampling_latitude = "latitude",
sampling_longitude = "longitude",
taxon_rank_value = "species.name",
site_type = "vegetation.type",
origin_sample_microhabitat = "microtopographical.position"
) %>%
tidyr::pivot_longer(
cols = dplyr::starts_with("loss"),
values_to = "mass_remaining",
names_to = "name"
) %>%
dplyr::mutate(
mass_absolute = NA_real_,
sample_treatment =
dplyr::case_when(
stringr::str_detect(name, "losslab") ~ "laboratory incubation",
stringr::str_detect(name, "lossfield") ~ "control"
),
id_dataset = datasets$id_dataset[[1]],
is_incubated = TRUE,
origin_plot_label = sample_label,
origin_site_name = site_name,
origin_sampling_longitude = sampling_longitude,
origin_sampling_latitude = sampling_latitude,
site_name =
dplyr::case_when(
sample_treatment == "control" ~ origin_site_name,
TRUE ~ NA_character_
),
plot_label =
dplyr::case_when(
sample_treatment == "control" ~ origin_plot_label,
TRUE ~ NA_character_
),
sampling_longitude =
dplyr::case_when(
sample_treatment == "control" ~ origin_sampling_longitude,
TRUE ~ NA_real_
),
sampling_latitude =
dplyr::case_when(
sample_treatment == "control" ~ origin_sampling_latitude,
TRUE ~ NA_real_
),
sample_depth_upper =
dplyr::case_when(
sample_treatment == "control" ~ 5,
TRUE ~ NA_real_
),
sample_depth_lower =
dplyr::case_when(
sample_treatment == "control" ~ 5,
TRUE ~ NA_real_
),
incubation_environment =
dplyr::case_when(
sample_treatment == "control" ~ "peat",
TRUE ~ "container"
),
mesh_size_absolute = 0.3,
mass_relative_mass = 1 - mass_remaining/100,
sampling_date = dplyr::case_when(
stringr::str_detect(name, "losslab1") ~ as.Date("2012-09-10") + lubridate::dmonths(7),
stringr::str_detect(name, "losslab2b") ~ as.Date("2012-09-10") + lubridate::dmonths(14),
stringr::str_detect(name, "lossfield") ~ as.Date("2012-07-9") + lubridate::dmonths(14),
TRUE ~ NA_real_
) %>%
as.Date(),
taxon_rank_value =
taxon_rank_value %>%
stringr::str_replace(pattern = "_", replacement = " "),
taxon_rank_name = "species",
shade =
dplyr::case_when(
shade == "1open" ~ "open",
shade == "2semi" ~ "semi-open",
shade == "3dark" ~ "shaded"
),
sampling_year = lubridate::year(sampling_date),
sampling_month = lubridate::month(sampling_date),
sampling_day = lubridate::day(sampling_date),
incubation_duration =
lubridate::time_length(sampling_date - as.Date("2012-09-10"), unit = "days"),
sample_type = "litter",
taxon_organ = "shoots",
origin_sample_depth_upper = 1,
origin_sample_depth_lower = 4,
dplyr::across(dplyr::any_of(c("site_type", "sample_microhabitat")), tolower),
comments_samples = "Coordinates are only the approximate location of the study site, but not sampling points. Each sample corresponds to one separate patch (plot, identified by `plot_label`) on the peatlands from which samples were taken. Samples were collected at the same patches as samples from @Bengtsson.2018, but are not the same samples. I assumed that the capitulum of moss shoots had a length of 1 cm.",
experimental_design =
paste0(
as.numeric(as.factor(sample_treatment)), "_",
as.numeric(as.factor(origin_site_name)), "_",
as.numeric(as.factor(site_name)), "_",
as.numeric(as.factor(origin_plot_label)), "_",
as.numeric(as.factor(plot_label))
)
) %>%
dplyr::filter(name != "losslab2a")
# initial mass
samples2 <-
samples3 %>%
dplyr::filter(name != "losslab2b") %>%
dplyr::mutate(
mass_relative_mass = 1.0,
sampling_date = dplyr::case_when(
stringr::str_detect(name, "losslab1") ~ as.Date("2012-09-10"),
stringr::str_detect(name, "lossfield") ~ as.Date("2012-07-9"),
TRUE ~ NA_real_
),
sampling_year = lubridate::year(sampling_date),
sampling_month = lubridate::month(sampling_date),
sampling_day = lubridate::day(sampling_date),
incubation_duration = 0
)
# litter collection
samples1 <-
samples2 %>%
dplyr::filter(! stringr::str_detect(name, "losslab")) %>%
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_,
is_incubated = FALSE,
sampling_date =
dplyr::case_when(
site_name == "Kulflyten" ~ "2012-06-12",
site_name == "Glon" ~ "2012-07-01"
) %>%
as.Date(),
sampling_year = lubridate::year(sampling_date),
sampling_month = lubridate::month(sampling_date),
sampling_day = lubridate::day(sampling_date),
incubation_environment = NA_character_,
site_name = origin_site_name,
sampling_longitude = origin_sampling_longitude,
sampling_latitude = origin_sampling_latitude,
sample_depth_upper = origin_sample_depth_upper,
sample_depth_lower = origin_sample_depth_lower,
plot_label = origin_plot_label,
sample_treatment = "control",
experimental_design =
paste0(
as.numeric(as.factor(sample_treatment)), "_",
as.numeric(as.factor(origin_site_name)), "_",
as.numeric(as.factor(site_name)), "_",
as.numeric(as.factor(origin_plot_label)), "_",
as.numeric(as.factor(plot_label))
)
)
# add missing ids
samples2 <-
dplyr::bind_rows(
samples2 %>%
dplyr::mutate(
type = "samples2"
),
samples3 %>%
dplyr::mutate(
type = "samples3"
)
)
samples2 <-
samples2 %>%
dplyr::mutate(
id_sample = seq_len(nrow(.)) + max(samples1$id_sample),
id_sample_origin =
dplyr::left_join(
samples2 %>%
dplyr::select(origin_site_name, origin_plot_label, taxon_rank_value) %>%
dplyr::rename(
site_name = "origin_site_name",
plot_label = "origin_plot_label"
),
samples1 %>% dplyr::select(site_name, plot_label, taxon_rank_value, id_sample),
by = c("site_name", "plot_label", "taxon_rank_value")
) %>%
dplyr::pull(id_sample),
id_sample_incubation_start =
purrr::map_int(seq_len(nrow(.)), function(i) {
index <- paste0(taxon_rank_value, "_", site_name, "_", taxon_organ) == paste0(taxon_rank_value, "_", site_name, "_", taxon_organ)[[i]] & experimental_design == experimental_design[[i]] & incubation_duration == 0.0
id_sample[index]
}),
id_sample_parent =
purrr::map_int(seq_len(nrow(.)), function(i) {
index <- paste0(taxon_rank_value, "_", site_name, "_", taxon_organ) == paste0(taxon_rank_value, "_", site_name, "_", taxon_organ)[[i]] & experimental_design == experimental_design[[i]] & incubation_duration < incubation_duration[[i]]
if(! any(index)) {
id_sample_origin[[i]]
} else {
target_incubation_duration <- max(incubation_duration[index])
index <- index & incubation_duration == target_incubation_duration
id_sample[index]
}
})
)
## water table depth
samples4 <-
read.csv(paste0(dir_source, "/raw/bengtsson_etal_2016_traits.csv")) %>%
dplyr::select(-c(5:7, 11:22, 24:ncol(.))) %>%
dplyr::rename(
sample_label = "sample",
site_name = "site",
sampling_latitude = "latitude",
sampling_longitude = "longitude",
site_type = "vegetation.type",
sample_microhabitat = "microtopographical.position",
water_table_depth = "HWT2012"
) %>%
dplyr::mutate(
incubation_duration = 0.0,
id_dataset = datasets$id_dataset[[1]],
id_sample = seq_len(nrow(.)) + max(samples2$id_sample),
id_sample_origin = id_sample,
id_sample_parent = id_sample,
id_sample_incubation_start = NA_integer_,
is_incubated = FALSE,
incubation_environment = NA_character_,
water_table_depth =
water_table_depth %>%
units::set_units("mm") %>%
units::set_units("cm") %>%
as.numeric(),
plot_label = sample_label,
sample_type = "peat",
sample_depth_upper = 0,
sample_depth_lower = 0,
sampling_year = 2012,
sampling_month = 6,
sampling_day = NA_integer_,
shade =
dplyr::case_when(
shade == "1open" ~ "open",
shade == "2semi" ~ "semi-open",
shade == "3dark" ~ "shaded"
),
dplyr::across(dplyr::any_of(c("site_type", "sample_microhabitat")), tolower),
comments_samples = "Coordinates are only the approximate location of the study site, but not sampling points. Each sample corresponds to one separate patch (plot, identified by `plot_label`) on the peatlands from which samples were taken.",
sample_treatment = "control",
origin_site_name = site_name,
origin_plot_label = plot_label,
experimental_design =
paste0(
as.numeric(as.factor(sample_treatment)), "_",
as.numeric(as.factor(origin_site_name)), "_",
as.numeric(as.factor(site_name)), "_",
as.numeric(as.factor(origin_plot_label)), "_",
as.numeric(as.factor(plot_label))
)
)
## combine
samples <-
dplyr::bind_rows(
db_template_tables$samples,
samples1 %>%
dplyr::mutate(
type = "samples1"
),
samples2,
samples4 %>%
dplyr::mutate(
type = "samples4"
)
)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::any_of(c("mass_absolute", "mass_relative_mass", "mesh_size_absolute", paste0(PeriodicTable:::periodicTable$symb, "_relative_mass"), paste0(PeriodicTable:::periodicTable$symb, "_absolute"))),
names_to = "attribute_name",
values_to = "value"
) %>%
dplyr::mutate(
id_measurement = seq_len(nrow(.)) + id_last$id_measurement,
id_measurement_numerator =
purrr::map_int(seq_len(nrow(.)), function(i) {
if(attribute_name[[i]] == "mass_relative_mass") {
id_measurement[id_sample == id_sample[[i]] & attribute_name == "mass_absolute"]
} else if (stringr::str_detect(attribute_name[[i]], pattern = "_relative_mass$")) {
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$")) {
id_measurement[id_sample == id_sample[[i]] & attribute_name == "mass_absolute"]
} else {
NA_integer_
}
}),
value_type = "point",
sample_size = 1L
)
d4 <-
samples4 %>%
tidyr::pivot_longer(
cols = dplyr::any_of(c("water_table_depth")),
names_to = "attribute_name",
values_to = "value"
) %>%
dplyr::mutate(
id_measurement = seq_len(nrow(.)) + max(d2$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",
sample_size = NA_integer_
)
# combine
d <-
dplyr::bind_rows(
db_template_tables$data,
d2,
d4
) %>%
dplyr::select(dplyr::all_of(colnames(db_template_tables$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 = "`sample_treatment`: Character value. One of: `laboratory incubation`: Incubated in containers in a laboratory with a nutrient solution (see @Bengtsson.2016). `control`: Incubated in the plots where the samples were harvested. `origin_site_name`: Character value: Site where the samples grew. `site_name`: Character value. Site where the sample was collected (for samples which were not incubated, this is the same as `origin_site_name`). `origin_plot_label`: Character value. Plot label where the samples grew. `plot_label`: Character value. Plot where the sample was collected (for samples which were not incubated, this is the same as `origin_plot_label`). `site_type`: Character type describing the peatland type. `shade`: Character value describing how much the floow is shaded by trees/shrubs."
)
# csv file to export
experimental_design_format2 <-
samples %>%
dplyr::filter(! is.na(experimental_design)) %>%
dplyr::filter(! duplicated(experimental_design)) %>%
dplyr::select(experimental_design, sample_treatment, origin_site_name, site_name, plot_label, origin_plot_label, site_type, shade)
# 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)