R/sample_summary.R
In OB7-IRD/fishi: Package Fisheries Indicators
Defines functions
sample_summary
Documented in
sample_summary
#' @name sample_summary
#' @title Sample summary
#' @description Give the number of trip, vessel or well sampled for a given year. Need to define your goal in the parameter.
#' @param dataframe {\link[base]{data.frame}} expected. Csv or output of the function {\link[furdeb]{data_extraction}}, which must be done before using the sample_summary() function.
#' @param data_type {\link[base]{character}} expected. 'tunabio' or 'observe'.
#' @param graph_type {\link[base]{character}} expected. 'number' or 'table'. Number by default.
#' @param reported_year {\link[base]{integer}} expected. Write the wanted year of the report.
#' @param start_date {\link[base]{character}} expected. if reported_year is not given. Write the start date of the time range of the report.
#' @param end_date {\link[base]{character}} expected. if reported_year is not given. Write the end date of the time range of the report
#' @param selected_country {\link[base]{integer}} expected. Country code to select the list of boat to count. If NULL give all the vessel for the given year.
#' @param selected_ocean {\link[base]{integer}} expected. Ocean code to select the list of boat to count. If NULL give all the vessel for the given year, works only for 'data_type' == 'observe'
#' @param selected_harbour {\link[base]{integer}} expected. Harbour code to select the list of boat to count. If NULL give all the vessel for the given year, works only for 'data_type' == 'observe'
#' @param selected_variable {\link[base]{character}} expected. Write the variable of the PSU. Can be "trip", "vessel" or "well". "trip" by default.
#' @details
#' The input dataframe frome sql must contain all these columns for the function to work [\href{https://ob7-ird.github.io/fishi/articles/Db_and_csv.html}{see referentials}]:
#' \preformatted{
#' ocean_label | fleet | vessel_type_code | vessel_label | landing_year | departure | port_departure | arrival | port_arrival | total_landing
#' ------------------------------------------------------------------------------------------------------------------------------------------
#' Atlantic | FRA | 6 | VIA AVENIR | 2022 | 2022-01-13 | ABIDJAN | 2022-02-03 | ABIDJAN |
#' Atlantic | FRA | 6 | VIA AVENIR | 2022 | 2022-01-13 | ABIDJAN | 2022-02-03 | ABIDJAN |
#' Atlantic | FRA | 6 | VIA AVENIR | 2022 | 2022-01-16 | ABIDJAN | 2022-10-05 | ABIDJAN |
#' }
#' add these columns to select the country and ocean (optional):
#' \itemize{
#' \item{\code{ country_code}}
#' \item{\code{ ocean_code}}
#' }
#' @return The function return a table.
#' @export
sample_summary <- function(dataframe,
data_type,
graph_type = "number",
reported_year = NULL,
start_date = NULL,
end_date = NULL,
selected_country = NULL,
selected_ocean = NULL,
selected_harbour = NULL,
selected_variable = "trip") {
# 0 - Global variables assignement ----
STATUT <- NULL
NOMBAT <- NULL
NUMBAT <- NULL
PAYS <- NULL
FLOTTE <- NULL
fleet <- NULL
fish_sampling_date <- NULL
landing_date <- NULL
landing_site <- NULL
fish_identifier <- NULL
sampling_year <- NULL
vessel_code <- NULL
vessel_label <- NULL
vessel_well_number <- NULL
well_position <- NULL
country <- NULL
country_code <- NULL
ocean_code <- NULL
port_code <- NULL
landing_year <- NULL
port_label <- NULL
nb_trip <- NULL
nb_well <- NULL
ocean_label <- NULL
vessel_type <- NULL
departure <- NULL
port_departure <- NULL
arrival <- NULL
port_arrival <- NULL
total_landing <- NULL
sample_number <- NULL
number_of_samples <- NULL
# 1 - Arguments verification ----
# data type
if (codama::r_type_checking(r_object = data_type,
type = "character",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = data_type,
type = "character",
output = "message"))
}
# graph type
if (codama::r_type_checking(r_object = graph_type,
type = "character",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = graph_type,
type = "character",
output = "message"))
}
# reported year
if ((! is.null(x = reported_year))
&& codama::r_type_checking(r_object = reported_year,
type = "integer",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = reported_year,
type = "integer",
output = "message"))
}
# start_date
if ((! is.null(x = start_date))
&& codama::r_type_checking(r_object = start_date,
type = "character",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = start_date,
type = "character",
output = "message"))
}
# end_date
if ((! is.null(x = end_date))
&& codama::r_type_checking(r_object = end_date,
type = "character",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = end_date,
type = "character",
output = "message"))
}
# selected country
if ((! is.null(x = selected_country))
&& codama::r_type_checking(r_object = selected_country,
type = "integer",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = selected_country,
type = "integer",
output = "message"))
}
# selected ocean
if ((! is.null(x = selected_ocean))
&& codama::r_type_checking(r_object = selected_ocean,
type = "integer",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = selected_ocean,
type = "integer",
output = "message"))
}
# variable
if (codama::r_type_checking(r_object = selected_variable,
type = "character",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = selected_variable,
type = "character",
output = "message"))
}
# 2 - Data design ----
# TUNABIO ----
if (data_type == "tunabio") {
## Data import -----
tunabio <- vector("list")
tunabio[["biology"]] <- readxl::read_excel(path = dataframe,
sheet = "SPECIMEN",
col_types = c("text",
"text",
"text",
"text",
"date",
"text",
"text",
"text",
"text",
"text",
"numeric",
"numeric",
"numeric",
"numeric",
"text",
"numeric",
"numeric",
"numeric",
"numeric",
"text",
"numeric",
"numeric",
"numeric",
"numeric",
"text",
"numeric",
"numeric",
"numeric",
"numeric",
"numeric",
"numeric",
"text",
"text",
"text",
"numeric",
"numeric",
"numeric",
"numeric",
"numeric",
"numeric",
"numeric",
"text",
"numeric",
"text",
"text",
"text",
"text"),
na = "na")
tunabio[["env"]] <- readxl::read_excel(path = dataframe,
sheet = "ENVIRONMENT",
col_types = c("text",
"text",
"text",
"text",
"text",
"text",
"text",
"text",
"text",
"text",
"text",
"date",
"date",
"date",
"date",
"numeric",
"text",
"text",
"text",
"text",
"text",
"text",
"text",
"text",
"text"),
na = "na")
tunabio[["vessel"]] <- readxl::read_excel(path = dataframe,
sheet = "vessel") %>%
dplyr::filter(STATUT == 1) %>%
dplyr::select(vessel_label = NOMBAT,
boat_code = NUMBAT,
country = PAYS,
fleet = FLOTTE)
## Data manipulation ----
tunabio[["biology"]] <- dplyr::mutate(.data = tunabio[["biology"]],
sampling_year = lubridate::year(fish_sampling_date),
fish_sampling_date = lubridate::date(fish_sampling_date))
### mean fishing date
tunabio[["env"]] <- dplyr::mutate(.data = tunabio[["env"]],
landing_date = lubridate::date(landing_date),
landing_year = lubridate::year(landing_date))
### merge of ENV and BIO data
tunabio[["merged"]] <- dplyr::left_join(x = tunabio[["biology"]],
y = tunabio[["env"]],
by = "fish_identifier",
relationship = "many-to-many") %>%
dplyr::select(fish_identifier,
fish_sampling_date,
sampling_year,
vessel_code,
vessel_label,
landing_date,
landing_year,
port_label = landing_site,
vessel_well_number,
well_position)
### Selection of the time period
if (!is.null(reported_year)) {
sample_filtered <- tunabio[["merged"]] %>%
dplyr::filter(sampling_year == reported_year)
} else if (!is.null(start_date) && !is.null(end_date)) {
sample_filtered <- tunabio[["merged"]] %>%
dplyr::filter(fish_sampling_date >= start_date &
fish_sampling_date <= end_date)
} else {
sample_filtered <- tunabio[["merged"]]
}
## Data analyze ----
if (selected_variable == "trip") {
if (!is.null(selected_country)) {
sample_summarize <- sample_filtered %>%
dplyr::group_by(landing_year,
port_label,
vessel_label,
landing_date) %>%
dplyr::summarise(.groups = "drop") %>%
dplyr::group_by(landing_year,
port_label,
vessel_label) %>%
dplyr::summarise(nb_trip = dplyr::n(),
.groups = "drop") %>%
dplyr::left_join(y = tunabio[["vessel"]],
by = dplyr::join_by(vessel_label)) %>%
dplyr::filter(country == selected_country) %>%
dplyr::select(landing_year,
port_label,
fleet,
country,
vessel_label,
nb_trip)
} else if (is.null(selected_country)) {
sample_summarize <- sample_filtered %>%
dplyr::group_by(landing_year,
port_label,
vessel_label,
landing_date) %>%
dplyr::summarise(.groups = "drop") %>%
dplyr::group_by(landing_year,
port_label,
vessel_label) %>%
dplyr::summarise(nb_trip = dplyr::n(),
.groups = "drop") %>%
dplyr::filter(!is.na(vessel_label)) %>%
dplyr::left_join(y = tunabio[["vessel"]],
by = dplyr::join_by(vessel_label)) %>%
dplyr::select(landing_year,
port_label,
fleet,
country,
vessel_label,
nb_trip)
}
} else if (selected_variable == "well") {
if (!is.null(selected_country)) {
sample_summarize <- sample_filtered %>%
tidyr::separate_longer_delim(cols = c("vessel_well_number",
"well_position"),
delim = ";") %>%
dplyr::group_by(landing_year,
port_label,
vessel_label,
landing_date,
vessel_well_number,
well_position) %>%
dplyr::summarise(.groups = "drop") %>%
dplyr::group_by(landing_year,
port_label,
vessel_label) %>%
dplyr::summarise(nb_well = dplyr::n(),
.groups = "drop") %>%
dplyr::left_join(y = tunabio[["vessel"]],
by = dplyr::join_by(vessel_label)) %>%
dplyr::filter(selected_country == country) %>%
dplyr::select(landing_year,
port_label,
fleet,
country,
vessel_label,
nb_well)
} else if (is.null(selected_country)) {
sample_summarize <- sample_filtered %>%
tidyr::separate_longer_delim(cols = c("vessel_well_number",
"well_position"),
delim = ";") %>%
dplyr::group_by(landing_year,
port_label,
vessel_label,
landing_date,
vessel_well_number,
well_position) %>%
dplyr::summarise(.groups = "drop") %>%
dplyr::group_by(landing_year,
port_label,
vessel_label) %>%
dplyr::summarise(nb_well = dplyr::n(),
.groups = "drop") %>%
dplyr::left_join(y = tunabio[["vessel"]],
by = dplyr::join_by(vessel_label)) %>%
dplyr::select(landing_year,
port_label,
fleet,
country,
vessel_label,
nb_well)
}
} else if (selected_variable == "vessel") {
if (!is.null(selected_country)) {
sample_summarize <- sample_filtered %>%
dplyr::group_by(landing_year,
port_label,
vessel_label) %>%
dplyr::summarise(.groups = "drop") %>%
dplyr::left_join(y = tunabio[["vessel"]],
by = dplyr::join_by(vessel_label)) %>%
dplyr::filter(selected_country == country) %>%
dplyr::select(landing_year,
port_label,
fleet,
country,
vessel_label)
} else if (is.null(selected_country)) {
sample_summarize <- sample_filtered %>%
dplyr::group_by(landing_year,
port_label,
vessel_label) %>%
dplyr::summarise(.groups = "drop") %>%
dplyr::left_join(y = tunabio[["vessel"]],
by = dplyr::join_by(vessel_label)) %>%
dplyr::select(landing_year,
port_label,
fleet,
country,
vessel_label)
}
}
} else if (data_type == "observe") {
# OBSERVE ----
# If is null
# selected ocean
if (is.null(selected_ocean)) {
selected_ocean <- as.integer(1:6)
}
# selected harbour
if (is.null(selected_harbour)) {
selected_harbour <- as.integer(1:999)
}
# selected country
if (is.null(selected_country)) {
selected_country <- as.integer(1:87)
}
# dataframe filter
dataframe <- dataframe %>%
dplyr::mutate(landing_year = lubridate::year(x = landing_date))
dataframe <- dataframe %>%
dplyr::filter(country_code %in% selected_country,
ocean_code %in% selected_ocean,
port_code %in% selected_harbour,
landing_year %in% reported_year)
# Vessel type
dataframe <- dataframe %>%
dplyr::mutate(vessel_type = dplyr::case_when(vessel_type_code %in% c(1, 2, 3) ~ "BB",
vessel_type_code %in% c(4, 5, 6) ~ "PS",
vessel_type_code %in% c(7) ~ "LL",
vessel_type_code %in% c(10) ~ "SV",
TRUE ~ "OTH"))
if (selected_variable == "trip") {
(sample_summarize <- dataframe %>%
dplyr::group_by(ocean_label,
fleet,
vessel_type,
vessel_label,
landing_year,
departure,
port_departure,
arrival,
port_arrival,
total_landing) %>%
dplyr::summarize("number_of_samples" = dplyr::n_distinct(sample_number,
na.rm = TRUE)) %>%
dplyr::group_by(landing_year,
port_arrival,
fleet,
vessel_type,
vessel_label) %>%
dplyr::summarize("nb_trip" = sum(number_of_samples != 0)))
} else if (selected_variable == "well") {
(sample_summarize <- dataframe %>%
dplyr::group_by(ocean_label,
fleet,
vessel_type,
vessel_label,
landing_year,
departure,
port_departure,
arrival,
port_arrival,
vessel_well_number) %>%
dplyr::summarize(.groups = "drop") %>%
dplyr::group_by(landing_year,
fleet,
vessel_type,
vessel_label) %>%
dplyr::summarize("nb_well" = dplyr::n(),
.groups = "drop"))
} else if (selected_variable == "vessel") {
(sample_summarize <- dataframe %>%
dplyr::group_by(ocean_label,
fleet,
vessel_type,
vessel_label,
landing_year,
departure,
port_departure,
arrival,
port_arrival,
total_landing) %>%
dplyr::summarize(.groups = "drop") %>%
dplyr::group_by(landing_year,
fleet,
vessel_type,
vessel_label) %>%
dplyr::summarize(.groups = "drop"))
}
# 3 - Graphic design ----
if (graph_type == "number") {
if (selected_variable == "trip") {
sum(sample_summarize$nb_trip)
} else if (selected_variable == "well") {
sum(sample_summarize$nb_well)
} else if (selected_variable == "vessel") {
length(sample_summarize$vessel_label)
}
} else if (graph_type == "table") {
as.data.frame(sample_summarize)
}
}
}
OB7-IRD/fishi documentation built on Feb. 4, 2025, 2:03 a.m.
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Defines functions sample_summary
Documented in sample_summary
#' @name sample_summary
#' @title Sample summary
#' @description Give the number of trip, vessel or well sampled for a given year. Need to define your goal in the parameter.
#' @param dataframe {\link[base]{data.frame}} expected. Csv or output of the function {\link[furdeb]{data_extraction}}, which must be done before using the sample_summary() function.
#' @param data_type {\link[base]{character}} expected. 'tunabio' or 'observe'.
#' @param graph_type {\link[base]{character}} expected. 'number' or 'table'. Number by default.
#' @param reported_year {\link[base]{integer}} expected. Write the wanted year of the report.
#' @param start_date {\link[base]{character}} expected. if reported_year is not given. Write the start date of the time range of the report.
#' @param end_date {\link[base]{character}} expected. if reported_year is not given. Write the end date of the time range of the report
#' @param selected_country {\link[base]{integer}} expected. Country code to select the list of boat to count. If NULL give all the vessel for the given year.
#' @param selected_ocean {\link[base]{integer}} expected. Ocean code to select the list of boat to count. If NULL give all the vessel for the given year, works only for 'data_type' == 'observe'
#' @param selected_harbour {\link[base]{integer}} expected. Harbour code to select the list of boat to count. If NULL give all the vessel for the given year, works only for 'data_type' == 'observe'
#' @param selected_variable {\link[base]{character}} expected. Write the variable of the PSU. Can be "trip", "vessel" or "well". "trip" by default.
#' @details
#' The input dataframe frome sql must contain all these columns for the function to work [\href{https://ob7-ird.github.io/fishi/articles/Db_and_csv.html}{see referentials}]:
#' \preformatted{
#' ocean_label | fleet | vessel_type_code | vessel_label | landing_year | departure | port_departure | arrival | port_arrival | total_landing
#' ------------------------------------------------------------------------------------------------------------------------------------------
#' Atlantic | FRA | 6 | VIA AVENIR | 2022 | 2022-01-13 | ABIDJAN | 2022-02-03 | ABIDJAN |
#' Atlantic | FRA | 6 | VIA AVENIR | 2022 | 2022-01-13 | ABIDJAN | 2022-02-03 | ABIDJAN |
#' Atlantic | FRA | 6 | VIA AVENIR | 2022 | 2022-01-16 | ABIDJAN | 2022-10-05 | ABIDJAN |
#' }
#' add these columns to select the country and ocean (optional):
#' \itemize{
#' \item{\code{ country_code}}
#' \item{\code{ ocean_code}}
#' }
#' @return The function return a table.
#' @export
sample_summary <- function(dataframe,
data_type,
graph_type = "number",
reported_year = NULL,
start_date = NULL,
end_date = NULL,
selected_country = NULL,
selected_ocean = NULL,
selected_harbour = NULL,
selected_variable = "trip") {
# 0 - Global variables assignement ----
STATUT <- NULL
NOMBAT <- NULL
NUMBAT <- NULL
PAYS <- NULL
FLOTTE <- NULL
fleet <- NULL
fish_sampling_date <- NULL
landing_date <- NULL
landing_site <- NULL
fish_identifier <- NULL
sampling_year <- NULL
vessel_code <- NULL
vessel_label <- NULL
vessel_well_number <- NULL
well_position <- NULL
country <- NULL
country_code <- NULL
ocean_code <- NULL
port_code <- NULL
landing_year <- NULL
port_label <- NULL
nb_trip <- NULL
nb_well <- NULL
ocean_label <- NULL
vessel_type <- NULL
departure <- NULL
port_departure <- NULL
arrival <- NULL
port_arrival <- NULL
total_landing <- NULL
sample_number <- NULL
number_of_samples <- NULL
# 1 - Arguments verification ----
# data type
if (codama::r_type_checking(r_object = data_type,
type = "character",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = data_type,
type = "character",
output = "message"))
}
# graph type
if (codama::r_type_checking(r_object = graph_type,
type = "character",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = graph_type,
type = "character",
output = "message"))
}
# reported year
if ((! is.null(x = reported_year))
&& codama::r_type_checking(r_object = reported_year,
type = "integer",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = reported_year,
type = "integer",
output = "message"))
}
# start_date
if ((! is.null(x = start_date))
&& codama::r_type_checking(r_object = start_date,
type = "character",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = start_date,
type = "character",
output = "message"))
}
# end_date
if ((! is.null(x = end_date))
&& codama::r_type_checking(r_object = end_date,
type = "character",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = end_date,
type = "character",
output = "message"))
}
# selected country
if ((! is.null(x = selected_country))
&& codama::r_type_checking(r_object = selected_country,
type = "integer",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = selected_country,
type = "integer",
output = "message"))
}
# selected ocean
if ((! is.null(x = selected_ocean))
&& codama::r_type_checking(r_object = selected_ocean,
type = "integer",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = selected_ocean,
type = "integer",
output = "message"))
}
# variable
if (codama::r_type_checking(r_object = selected_variable,
type = "character",
output = "logical") != TRUE) {
return(codama::r_type_checking(r_object = selected_variable,
type = "character",
output = "message"))
}
# 2 - Data design ----
# TUNABIO ----
if (data_type == "tunabio") {
## Data import -----
tunabio <- vector("list")
tunabio[["biology"]] <- readxl::read_excel(path = dataframe,
sheet = "SPECIMEN",
col_types = c("text",
"text",
"text",
"text",
"date",
"text",
"text",
"text",
"text",
"text",
"numeric",
"numeric",
"numeric",
"numeric",
"text",
"numeric",
"numeric",
"numeric",
"numeric",
"text",
"numeric",
"numeric",
"numeric",
"numeric",
"text",
"numeric",
"numeric",
"numeric",
"numeric",
"numeric",
"numeric",
"text",
"text",
"text",
"numeric",
"numeric",
"numeric",
"numeric",
"numeric",
"numeric",
"numeric",
"text",
"numeric",
"text",
"text",
"text",
"text"),
na = "na")
tunabio[["env"]] <- readxl::read_excel(path = dataframe,
sheet = "ENVIRONMENT",
col_types = c("text",
"text",
"text",
"text",
"text",
"text",
"text",
"text",
"text",
"text",
"text",
"date",
"date",
"date",
"date",
"numeric",
"text",
"text",
"text",
"text",
"text",
"text",
"text",
"text",
"text"),
na = "na")
tunabio[["vessel"]] <- readxl::read_excel(path = dataframe,
sheet = "vessel") %>%
dplyr::filter(STATUT == 1) %>%
dplyr::select(vessel_label = NOMBAT,
boat_code = NUMBAT,
country = PAYS,
fleet = FLOTTE)
## Data manipulation ----
tunabio[["biology"]] <- dplyr::mutate(.data = tunabio[["biology"]],
sampling_year = lubridate::year(fish_sampling_date),
fish_sampling_date = lubridate::date(fish_sampling_date))
### mean fishing date
tunabio[["env"]] <- dplyr::mutate(.data = tunabio[["env"]],
landing_date = lubridate::date(landing_date),
landing_year = lubridate::year(landing_date))
### merge of ENV and BIO data
tunabio[["merged"]] <- dplyr::left_join(x = tunabio[["biology"]],
y = tunabio[["env"]],
by = "fish_identifier",
relationship = "many-to-many") %>%
dplyr::select(fish_identifier,
fish_sampling_date,
sampling_year,
vessel_code,
vessel_label,
landing_date,
landing_year,
port_label = landing_site,
vessel_well_number,
well_position)
### Selection of the time period
if (!is.null(reported_year)) {
sample_filtered <- tunabio[["merged"]] %>%
dplyr::filter(sampling_year == reported_year)
} else if (!is.null(start_date) && !is.null(end_date)) {
sample_filtered <- tunabio[["merged"]] %>%
dplyr::filter(fish_sampling_date >= start_date &
fish_sampling_date <= end_date)
} else {
sample_filtered <- tunabio[["merged"]]
}
## Data analyze ----
if (selected_variable == "trip") {
if (!is.null(selected_country)) {
sample_summarize <- sample_filtered %>%
dplyr::group_by(landing_year,
port_label,
vessel_label,
landing_date) %>%
dplyr::summarise(.groups = "drop") %>%
dplyr::group_by(landing_year,
port_label,
vessel_label) %>%
dplyr::summarise(nb_trip = dplyr::n(),
.groups = "drop") %>%
dplyr::left_join(y = tunabio[["vessel"]],
by = dplyr::join_by(vessel_label)) %>%
dplyr::filter(country == selected_country) %>%
dplyr::select(landing_year,
port_label,
fleet,
country,
vessel_label,
nb_trip)
} else if (is.null(selected_country)) {
sample_summarize <- sample_filtered %>%
dplyr::group_by(landing_year,
port_label,
vessel_label,
landing_date) %>%
dplyr::summarise(.groups = "drop") %>%
dplyr::group_by(landing_year,
port_label,
vessel_label) %>%
dplyr::summarise(nb_trip = dplyr::n(),
.groups = "drop") %>%
dplyr::filter(!is.na(vessel_label)) %>%
dplyr::left_join(y = tunabio[["vessel"]],
by = dplyr::join_by(vessel_label)) %>%
dplyr::select(landing_year,
port_label,
fleet,
country,
vessel_label,
nb_trip)
}
} else if (selected_variable == "well") {
if (!is.null(selected_country)) {
sample_summarize <- sample_filtered %>%
tidyr::separate_longer_delim(cols = c("vessel_well_number",
"well_position"),
delim = ";") %>%
dplyr::group_by(landing_year,
port_label,
vessel_label,
landing_date,
vessel_well_number,
well_position) %>%
dplyr::summarise(.groups = "drop") %>%
dplyr::group_by(landing_year,
port_label,
vessel_label) %>%
dplyr::summarise(nb_well = dplyr::n(),
.groups = "drop") %>%
dplyr::left_join(y = tunabio[["vessel"]],
by = dplyr::join_by(vessel_label)) %>%
dplyr::filter(selected_country == country) %>%
dplyr::select(landing_year,
port_label,
fleet,
country,
vessel_label,
nb_well)
} else if (is.null(selected_country)) {
sample_summarize <- sample_filtered %>%
tidyr::separate_longer_delim(cols = c("vessel_well_number",
"well_position"),
delim = ";") %>%
dplyr::group_by(landing_year,
port_label,
vessel_label,
landing_date,
vessel_well_number,
well_position) %>%
dplyr::summarise(.groups = "drop") %>%
dplyr::group_by(landing_year,
port_label,
vessel_label) %>%
dplyr::summarise(nb_well = dplyr::n(),
.groups = "drop") %>%
dplyr::left_join(y = tunabio[["vessel"]],
by = dplyr::join_by(vessel_label)) %>%
dplyr::select(landing_year,
port_label,
fleet,
country,
vessel_label,
nb_well)
}
} else if (selected_variable == "vessel") {
if (!is.null(selected_country)) {
sample_summarize <- sample_filtered %>%
dplyr::group_by(landing_year,
port_label,
vessel_label) %>%
dplyr::summarise(.groups = "drop") %>%
dplyr::left_join(y = tunabio[["vessel"]],
by = dplyr::join_by(vessel_label)) %>%
dplyr::filter(selected_country == country) %>%
dplyr::select(landing_year,
port_label,
fleet,
country,
vessel_label)
} else if (is.null(selected_country)) {
sample_summarize <- sample_filtered %>%
dplyr::group_by(landing_year,
port_label,
vessel_label) %>%
dplyr::summarise(.groups = "drop") %>%
dplyr::left_join(y = tunabio[["vessel"]],
by = dplyr::join_by(vessel_label)) %>%
dplyr::select(landing_year,
port_label,
fleet,
country,
vessel_label)
}
}
} else if (data_type == "observe") {
# OBSERVE ----
# If is null
# selected ocean
if (is.null(selected_ocean)) {
selected_ocean <- as.integer(1:6)
}
# selected harbour
if (is.null(selected_harbour)) {
selected_harbour <- as.integer(1:999)
}
# selected country
if (is.null(selected_country)) {
selected_country <- as.integer(1:87)
}
# dataframe filter
dataframe <- dataframe %>%
dplyr::mutate(landing_year = lubridate::year(x = landing_date))
dataframe <- dataframe %>%
dplyr::filter(country_code %in% selected_country,
ocean_code %in% selected_ocean,
port_code %in% selected_harbour,
landing_year %in% reported_year)
# Vessel type
dataframe <- dataframe %>%
dplyr::mutate(vessel_type = dplyr::case_when(vessel_type_code %in% c(1, 2, 3) ~ "BB",
vessel_type_code %in% c(4, 5, 6) ~ "PS",
vessel_type_code %in% c(7) ~ "LL",
vessel_type_code %in% c(10) ~ "SV",
TRUE ~ "OTH"))
if (selected_variable == "trip") {
(sample_summarize <- dataframe %>%
dplyr::group_by(ocean_label,
fleet,
vessel_type,
vessel_label,
landing_year,
departure,
port_departure,
arrival,
port_arrival,
total_landing) %>%
dplyr::summarize("number_of_samples" = dplyr::n_distinct(sample_number,
na.rm = TRUE)) %>%
dplyr::group_by(landing_year,
port_arrival,
fleet,
vessel_type,
vessel_label) %>%
dplyr::summarize("nb_trip" = sum(number_of_samples != 0)))
} else if (selected_variable == "well") {
(sample_summarize <- dataframe %>%
dplyr::group_by(ocean_label,
fleet,
vessel_type,
vessel_label,
landing_year,
departure,
port_departure,
arrival,
port_arrival,
vessel_well_number) %>%
dplyr::summarize(.groups = "drop") %>%
dplyr::group_by(landing_year,
fleet,
vessel_type,
vessel_label) %>%
dplyr::summarize("nb_well" = dplyr::n(),
.groups = "drop"))
} else if (selected_variable == "vessel") {
(sample_summarize <- dataframe %>%
dplyr::group_by(ocean_label,
fleet,
vessel_type,
vessel_label,
landing_year,
departure,
port_departure,
arrival,
port_arrival,
total_landing) %>%
dplyr::summarize(.groups = "drop") %>%
dplyr::group_by(landing_year,
fleet,
vessel_type,
vessel_label) %>%
dplyr::summarize(.groups = "drop"))
}
# 3 - Graphic design ----
if (graph_type == "number") {
if (selected_variable == "trip") {
sum(sample_summarize$nb_trip)
} else if (selected_variable == "well") {
sum(sample_summarize$nb_well)
} else if (selected_variable == "vessel") {
length(sample_summarize$vessel_label)
}
} else if (graph_type == "table") {
as.data.frame(sample_summarize)
}
}
}
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