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read_hurdat2.R
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read_hurdat2.R
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#' Read HURDAT2 dataset
#'
#' @param con a connection object or a character string. This is the file path or URL for the HURDAT2 dataset to read.
#' @param interp_wind whether missing wind speeds should be linearly interpolated.
#'
#' @return Hurricane observations dataframe
#' @export
#'
#' @importFrom rlang .data
read_hurdat2 <- function(con, interp_wind = FALSE) {
# Read and split raw data ----------------------------------
hurr_tracks <- readLines(con)
hurr_tracks <- lapply(hurr_tracks, stringr::str_split, pattern = ",", simplify = TRUE)
# Clean the raw data ---------------------------------------
# Split the hurr_tracks into meta and observation lists
hurr_lengths <- sapply(hurr_tracks, length)
hurr_meta <- hurr_tracks[hurr_lengths == 4]
hurr_obs_raw <- hurr_tracks[hurr_lengths == 21]
# Create and clean meta data frame
hurr_meta <- lapply(hurr_meta, tibble::as_tibble)
hurr_meta <- dplyr::bind_rows(hurr_meta)
hurr_meta <- hurr_meta %>%
dplyr::select(-.data$V4) %>%
dplyr::rename(storm_id = .data$V1, storm_name = .data$V2, n_obs = .data$V3)
storm_ids <- rep(hurr_meta$storm_id, times = hurr_meta$n_obs)
# Create and clean obs data frame
hurr_obs <- lapply(hurr_obs_raw, tibble::as_tibble) %>%
dplyr::bind_rows() %>%
dplyr::mutate(storm_id = storm_ids) %>%
dplyr::select(.data$storm_id, dplyr::everything(), -.data$V21) %>%
dplyr::rename(
date = .data$V1, time = .data$V2, record_id = .data$V3, status = .data$V4,
lat = .data$V5, long = .data$V6, max_wind = .data$V7, min_pressure = .data$V8,
wind_34kt_radii_ne = .data$V9, wind_34kt_radii_se = .data$V10,
wind_34kt_radii_sw = .data$V11, wind_34kt_radii_nw = .data$V12,
wind_50kt_radii_ne = .data$V13, wind_50kt_radii_se = .data$V14,
wind_50kt_radii_sw = .data$V15, wind_50kt_radii_nw = .data$V16,
wind_64kt_radii_ne = .data$V17, wind_64kt_radii_se = .data$V18,
wind_64kt_radii_sw = .data$V19, wind_64kt_radii_nw = .data$V20
) %>%
# Add storm_name
dplyr::left_join(
hurr_meta,
by = "storm_id"
)
# Clean variables ------------------------------------------
# Trim whitespaces and some cleanup
hurr_obs <- hurr_obs %>%
dplyr::mutate_all(stringr::str_trim) %>%
# Replace record_id NAs
dplyr::mutate(record_id = replace(.data$record_id, .data$record_id == "", NA)) %>%
# Clean numeric values and replace NAs
dplyr::mutate_at(
.vars = dplyr::vars(
.data$max_wind, .data$min_pressure,
.data$wind_34kt_radii_ne, .data$wind_34kt_radii_se,
.data$wind_34kt_radii_sw, .data$wind_34kt_radii_nw,
.data$wind_50kt_radii_ne, .data$wind_50kt_radii_se,
.data$wind_50kt_radii_sw, .data$wind_50kt_radii_nw,
.data$wind_64kt_radii_ne, .data$wind_64kt_radii_se,
.data$wind_64kt_radii_sw, .data$wind_64kt_radii_nw
),
.funs = function(x) {
ifelse(x %in% c("-99", "-999"), NA, as.numeric(x))
}
) %>%
dplyr::mutate(
n_obs = as.numeric(.data$n_obs),
record_id = as.factor(.data$record_id)
)
# Change date and time & unite them
hurr_obs <- hurr_obs %>%
tidyr::unite(
col = "datetime",
.data$date, .data$time
) %>%
dplyr::mutate(
datetime = lubridate::ymd_hm(.data$datetime),
storm_year = lubridate::year(.data$datetime)
)
# Meaningful status names
hurr_obs <- hurr_obs %>%
dplyr::mutate(
status = factor(.data$status,
levels = c("TD", "TS", "HU", "EX", "SD", "SS", "LO", "WV", "DB"),
labels = c(
"Tropical depression", "Tropical storm", "Hurricane",
"Extratropical cyclone", "Subtropical depression", "Subtropical storm",
"Other low", "Tropical wave", "Disturbance"
)
)
)
# Morph coordinates
morph_long <- function(long) {
long <- ifelse(
stringr::str_extract(long, "[A-Z]") == "W",
-as.numeric(stringr::str_extract(long, "[^A-Z]+")),
as.numeric(stringr::str_extract(long, "[^A-Z]+"))
)
return(long)
}
morph_lat <- function(lat) {
lat <- ifelse(
stringr::str_extract(lat, "[A-Z]") == "S",
-as.numeric(stringr::str_extract(lat, "[^A-Z]+")),
as.numeric(stringr::str_extract(lat, "[^A-Z]+"))
)
return(lat)
}
# Split the numeric coordinates from their directions
hurr_obs <- hurr_obs %>%
dplyr::mutate(
lat_num = as.numeric(stringr::str_extract(.data$lat, "[^A-Z]+")),
lat_hem = stringr::str_extract(.data$lat, "[A-Z]"),
lat = morph_lat(.data$lat),
long_num = as.numeric(stringr::str_extract(.data$long, "[^A-Z]+")),
long_hem = stringr::str_extract(.data$long, "[A-Z]"),
long = morph_long(.data$long)
)
# Clean non-standard data ----------------------------------
# Ignore data outside the delta_t = 6 hours
# hurr_obs <- hurr_obs %>%
# dplyr::filter(
# lubridate::hour(.data$datetime) %in% c(0, 6, 12, 18),
# lubridate::minute(.data$datetime) == 0
# ) %>%
# dplyr::group_by(.data$storm_id) %>%
# dplyr::mutate(n_obs = dplyr::n())
# Interpolate missing wind speeds --------------------------
if (interp_wind) {
hurr_obs <- hurr_obs %>%
dplyr::group_by(.data$storm_id) %>%
dplyr::mutate(
max_wind = ifelse(
.data$n_obs - sum(is.na(.data$max_wind)) >= 2,
stats::approx(.data$datetime, .data$max_wind, .data$datetime)$y,
.data$max_wind
)
) %>%
dplyr::ungroup()
}
# Add useful info to data frame ----------------------------
# Add category 5 hurricanes boolean
hurr_obs <- hurr_obs %>%
dplyr::group_by(.data$storm_id) %>%
dplyr::mutate(
cat_5 = ifelse(
.data$n_obs - sum(is.na(.data$max_wind)) > 0,
max(.data$max_wind, na.rm = TRUE) >= 137,
NA
)
) %>%
dplyr::ungroup()
# Rearrange hurr_obs data frame columns
hurr_obs <- hurr_obs %>%
dplyr::select(
.data$storm_id, .data$storm_name, .data$n_obs,
.data$storm_year, .data$datetime,
.data$status, .data$record_id,
.data$lat, .data$lat_num, .data$lat_hem,
.data$long, .data$long_num, .data$long_hem,
.data$max_wind, .data$cat_5,
dplyr::everything()
)
return(hurr_obs)
}