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track.R
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track.R
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#' Read RSMC Tokyo-Typhoon Center's best track data
#'
#' @description
#' `r lifecycle::badge("stable")`
#'
#' Tidy formatting best track data and combine each point to line.
#' @details
#' * `read_rsmc_besttrack()`: Read single best track data into [sf][sf::st_sf]
#' contains observation record as point.
#' * `track_combine()`: Combine track data to line by id
#' (such as international_number and storm_name).
#' @param path path to best track data (`.txt`). Give the path as a directory
#' in the user's computer or the URL.
#' @import rlang
#' @importFrom dplyr across arrange bind_rows group_by lead join_by select
#' left_join if_else mutate ungroup rowwise
#' @importFrom forcats fct_inorder
#' @importFrom lubridate ymd_h
#' @importFrom purrr map set_names
#' @importFrom readr read_lines
#' @importFrom sf st_as_sf st_as_sfc st_as_text st_cast st_sf st_union
#' @importFrom stringr str_detect str_split str_subset
#' @importFrom tibble as_tibble
#' @importFrom tidyr extract
#' @importFrom utils read.table
#' @seealso <https://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/RSMC_HP.htm>
#' @rdname track
#' @examples
#' read_rsmc_besttrack(path = system.file("dummy/bst.txt", package = "jmastats"))
#'
#' read_rsmc_besttrack(path = system.file("dummy/bst.txt", package = "jmastats")) |>
#' track_combine()
#' @export
#' @return a `tbl` object
read_rsmc_besttrack <- function(path) {
last_update <- international_number <- storm_name <-
`central_pressure(hPa)` <- `maximum_sustained_wind_speed(knot)` <-
datetime <- latitude <- longitude <- NULL
v8 <- v10 <- NULL
lines <- readr::read_lines(path)
xx <-
lines[stringr::str_detect(lines, "^66666")]
df_header <-
seq.int(length(xx)) |>
purrr::map(
function(x) {
parse_x <- xx[x] |>
stringr::str_split("[[:space:]]", simplify = TRUE) |>
stringr::str_subset(".+")
if (length(parse_x) == 7L) {
parse_x <-
c(parse_x[1:4],
NA_character_,
parse_x[5:6],
NA_character_,
parse_x[7])
} else if (length(parse_x) == 8L) {
parse_x <-
c(parse_x[1:4],
NA_character_,
parse_x[5:8])
}
parse_x |>
purrr::set_names(c("indicator_66666",
"international_number", "nrow",
"tropical_cyclone_number",
"international_number_copy",
"flag_last_data_line",
"DTM", "storm_name", "last_update"))
}
) |>
dplyr::bind_rows() |>
tibble::as_tibble() |>
readr::type_convert(col_types = "dcdcccdcc") |>
dplyr::mutate(last_update = lubridate::ymd(last_update))
if (df_header |>
dplyr::pull(international_number) |>
stringr::str_sub(1, 2) |>
dplyr::n_distinct() == 1) {
df_header <-
df_header |>
dplyr::arrange(international_number) |>
dplyr::mutate(storm_name = forcats::fct_inorder(storm_name))
} else {
df_header <-
df_header |>
dplyr::mutate(storm_name = forcats::fct_inorder(storm_name))
}
data_common_vars <-
c("datetime", "indicator_002", "grade",
"latitude", "longitude",
"central_pressure(hPa)",
"maximum_sustained_wind_speed(knot)")
data_typhoon_vars <-
paste0(c("_direction_of_the_longest_radius_of_",
"_the_longest_radius_of_",
"_the_shortest_radius_of_"),
rep(c("50kt_winds_or_greater",
"30kt_winds_or_greater"), each = 3),
c("", "(nm)", "(nm)"))
xx <-
lines[stringr::str_detect(lines, "^66666", negate = TRUE)]
df_record <-
seq.int(1, length(xx)) |>
purrr::map(
function(x) {
parse_x <- xx[x] |>
stringr::str_split("[[:space:]]", simplify = TRUE) |>
stringr::str_subset(".+")
if (length(parse_x) <= 7L) {
tmp_d <-
as.data.frame(parse_x) |>
t() |>
tibble::as_tibble(.name_repair = "minimal")
if (length(parse_x) == 6L) {
tmp_d |>
purrr::set_names(data_common_vars[-length(data_common_vars)])
} else if (length(parse_x) == 7L) {
tmp_d |>
purrr::set_names(data_common_vars)
}
} else {
tmp_d <-
parse_x |>
as.data.frame() |>
t() |>
tibble::as_tibble(.name_repair = "minimal") |>
purrr::set_names(paste0("v", seq.int(length(parse_x)))) |>
tidyr::extract(v8,
into = c("H", "I"),
regex = "([0-9]{1})([0-9]{4})") |>
tidyr::extract(v10,
into = c("K", "L"),
regex = "([0-9]{1})([0-9]{4})")
if (length(parse_x) == 11L) {
tmp_d |>
purrr::set_names(c(data_common_vars,
data_typhoon_vars))
} else if (length(parse_x) == 12L) {
tmp_d |>
purrr::set_names(c(data_common_vars,
data_typhoon_vars,
"indicator_of_landfall_or_passage"))
}
}
},
.progress = TRUE
) |>
dplyr::bind_rows() |>
dplyr::mutate(
datetime = lubridate::ymd_h(
paste0(
dplyr::if_else(as.numeric(stringr::str_sub(datetime, 1, 2)) <= 19,
"20",
"19"),
datetime), tz = "UTC"),
latitude = as.numeric(latitude) / 10,
longitude = as.numeric(longitude) / 10,
international_number = rep(df_header$international_number,
df_header$nrow)) |>
dplyr::mutate(
dplyr::across(c(`central_pressure(hPa)`,
`maximum_sustained_wind_speed(knot)`),
as.numeric)) |>
sf::st_as_sf(coords = c("longitude", "latitude"), crs = 4326) |>
dplyr::left_join(df_header,
by = dplyr::join_by(international_number)) |>
dplyr::arrange(datetime)
df_record
}
#' @rdname track
#' @param data Import data using [read_rsmc_besttrack]
#' @param group_vars To combine track variables.
#' @param keep_vars Keep variables.
#' @param geometry geometry column name (default `geometry`).
#' @export
track_combine <- function(data, group_vars = c("international_number", "storm_name"),
keep_vars = NULL, geometry = geometry) {
aa <- bb <- NULL
data |>
dplyr::select(group_vars, keep_vars, geometry) |>
dplyr::group_by(!!! rlang::syms(group_vars)) |>
dplyr::mutate(aa = sf::st_as_text(geometry),
bb = dplyr::lead(sf::st_as_text(geometry))) |>
dplyr::ungroup() |>
dplyr::filter(!is.na(bb)) |>
dplyr::rowwise() |>
dplyr::mutate(geometry = sf::st_union(sf::st_as_sfc(aa),
sf::st_as_sfc(bb)) |>
sf::st_cast("LINESTRING")) |>
dplyr::ungroup() |>
dplyr::select(!c(aa, bb)) |>
sf::st_sf(crs = 4326)
}