# 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 <- "d14"
dir_source <- "../raw_data/data/d14"
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("Farrish.1988")
)
)## mass remaining
samples2 <-
readODS::read_ods(paste0(dir_source, "/derived/Farrish.1988-Tab1_4_5.ods")) %>%
dplyr::mutate(
sampling_longitude = -93.459632, #---note: no exact location given in the paper, approximate location of the Marcell experimental forest
sampling_latitude = 47.519077, #---note: no exact location given in the paper, approximate location of the Marcell experimental forest
comments_samples = "No exact location given in the paper, approximate location of the Marcell experimental forest. Sampling dates are unknwon."
) %>%
dplyr::rename(
mass_relative_mass = "mass_remaining",
sample_microhabitat = "microtopography",
incubation_duration = "incubation_time"
) %>%
dplyr::mutate(
id_dataset = datasets$id_dataset[[1]],
incubation_duration = 365,
mesh_size_absolute = 550/1000,
mass_absolute = NA_real_,
treatment = "control",
is_incubated = TRUE,
incubation_enironment = "peat",
experimental_design = {
site <- as.numeric(as.factor(site_name))
microhabitat <- as.numeric(as.factor(sample_microhabitat))
microhabitat <- ifelse(is.na(microhabitat), 1, microhabitat)
paste0(site, "_", microhabitat)
}
)
# initial mass
samples1 <-
samples2 %>%
dplyr::mutate(
mass_relative_mass = 1,
incubation_duration = 0,
id_sample = seq_len(nrow(.)) + id_last$id_sample,
id_sample_origin = id_sample,
id_sample_parent = id_sample,
id_sample_incubation_start = id_sample
)
samples2 <-
samples2 %>%
dplyr::mutate(
id_sample = seq_len(nrow(.)) + max(samples1$id_sample),
id_sample_origin = samples1$id_sample,
id_sample_parent = samples1$id_sample,
id_sample_incubation_start = samples1$id_sample
)
# water table depth
samples3 <-
samples2 %>%
dplyr::filter(!duplicated(experimental_design)) %>%
dplyr::mutate(
id_sample = seq_len(nrow(.)) + max(samples2$id_sample),
id_sample_origin = id_sample,
id_sample_parent = id_sample,
sample_type = "peat",
sample_type2 = NA_character_,
sample_depth_upper = 0,
sample_depth_lower = 0,
incubation_duration = 0,
is_incubated = FALSE,
incubation_enironment = NA_character_,
water_table_depth =
dplyr::case_when(
site_name == "S-3 fen" ~ 10,
site_name == "S-2 bog" & sample_microhabitat == "hollow" ~ 11,
site_name == "S-2 bog" & sample_microhabitat == "hummock" ~ 11 + 33, #---note: from text
),
comments_samples = "No exact location given in the paper, approximate location of the Marcell experimental forest. Sampling dates are unknwon."
) %>%
dplyr::select(-mesh_size_absolute, -mass_relative_mass, -mass_absolute)
## combine
samples <-
dplyr::bind_rows(
db_template_tables$samples,
samples1 %>%
dplyr::mutate(
type = "samples1"
),
samples2 %>%
dplyr::mutate(
type = "samples2"
),
samples3 %>%
dplyr::mutate(
type = "samples3"
)
)samples_to_samples <-
samples %>%
dplyr::filter(! id_sample %in% id_sample_origin) %>%
dplyr::mutate(
transition_description =
dplyr::case_when(
type %in% c("samples2") ~ "wait",
TRUE ~ NA_character_
)
) %>%
dplyr::select(id_sample_parent, id_sample, transition_description) %>%
dplyr::rename(
id_sample_child = "id_sample"
)d1 <-
samples1 %>%
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$id_measurement,
id_measurement_numerator =
purrr::map_int(seq_len(nrow(.)), function(i) {
switch(
attribute_name[[i]],
"mass_relative_mass" = {
id_measurement[id_sample == id_sample[[i]] & attribute_name == "mass_absolute"]
},
NA_integer_
)
}),
id_measurement_denominator =
purrr::map_int(seq_len(nrow(.)), function(i) {
switch(
attribute_name[[i]],
"mass_relative_mass" = {
id_measurement[id_sample == id_sample[[i]] & attribute_name == "mass_absolute"]
},
NA_integer_
)
}),
value_type =
dplyr::case_when(
attribute_name == "mesh_size_absolute" ~ "point",
TRUE ~ "mean"
),
sample_size = NA_integer_
)
d2 <-
samples2 %>%
tidyr::pivot_longer(
cols = dplyr::all_of(c("mass_absolute", "mass_relative_mass", "mesh_size_absolute")),
names_to = "attribute_name",
values_to = "value"
) %>%
dplyr::mutate(
id_measurement = seq_len(nrow(.)) + max(d1$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" = {
d1$id_measurement[d1$id_sample == id_sample_origin[[i]] & d1$attribute_name == "mass_absolute"]
},
NA_integer_
)
}),
value_type =
dplyr::case_when(
attribute_name == "mesh_size_absolute" ~ "point",
TRUE ~ "mean"
),
sample_size = NA_integer_
)
d3 <-
samples3 %>%
tidyr::pivot_longer(
cols = dplyr::all_of(c("water_table_depth")),
names_to = "attribute_name",
values_to = "value"
) %>%
dplyr::mutate(
id_measurement = seq_len(nrow(.)) + max(d2$id_measurement),
value_type = "mean",
sample_size =
dplyr::case_when(
site_name == "S-3 fen" ~ 47L,
site_name == "S-2 bog" ~ 41L
),
comments_measurement =
dplyr::case_when(
attribute_name == "water_table_depth" & sample_microhabitat != "hummock" ~ "Approximate position of the water table depth defined as sulfide horizon.",
attribute_name == "water_table_depth" & sample_microhabitat == "hummock" ~ "Approximate position of the water table depth defined as sulfide horizon. Computed by adding the average height of hummocks to the value for hollows.",
TRUE ~ NA_character_
)
)
# combine
d <-
dplyr::bind_rows(
db_template_tables$data,
d1,
d2,
d3
) %>%
dplyr::select(dplyr::all_of(colnames(db_template_tables$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_microhabitat`: Name of the microhabitat type."
)
# csv file to export
experimental_design_format2 <-
samples %>%
dplyr::filter(! is.na(experimental_design)) %>%
dplyr::filter(! duplicated(experimental_design)) %>%
dplyr::select(experimental_design, site_name, sample_microhabitat)
# 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)))) {
RMariaDB::dbDisconnect(con)
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 = ", ")))
}
# 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)