R/clean_data.R

In nobuhiroK/MSDRCapstone:

130#' Read NOAA significant earthquke data
#'
#' @importFrom readr read_delim
#' @param path  defalut path set to inst/extdata/"signif.txt"
#' @param url  set TRUE to download data from NOAA website
#'
#' @return a tibble containing 47 vaiables
#'
#'
#' @examples
#' dat <- read_signif(url = TRUE)
#'
#' @export
read_signif <- function(path = system.file("extdata", "signif.txt", package = "MSDRCapstone"), url =FALSE) {
  if (url ==  TRUE){
    signif_dat <-read_delim(url("https://www.ngdc.noaa.gov/nndc/struts/results?type_0=Exact&query_0=$ID&t=101650&s=13&d=189&dfn=signif.txt"),  delim = '\t')
  }else{
    signif_dat <- read_delim(path,  delim = '\t')
  }
}


#' Clean the row signif_dat
#'
#' @importFrom dplyr mutate
#' @importFrom magrittr %>%
#' @importFrom lubridate make_date
#' @importFrom tools toTitleCase
#'
#'
#' @description
#'     A date column created by uniting the year, month, day and converting it to the Date class
#'     LATITUDE and LONGITUDE columns converted to numeric class
#'
#'
#' @param
#' a tibble of NOAA signif data with 47 variables, default for signif.txt
#' @return
#' a tibble containing 48 vaiables
#'
#'
#' @examples
#' clean_dat <- eq_clean_data()
#'
#' @export
eq_clean_data <- function(dat = read_signif()) {
  dt_clean_month_day_date_long_lat <- dat %>%
    mutate(MONTH = ifelse(is.na(MONTH), 1, MONTH)) %>%
    mutate(DAY = ifelse(is.na(DAY), 1, DAY)) %>%
    mutate(DATE = make_date(YEAR, MONTH, DAY)) %>%
    mutate(LATITUDE = as.numeric(LATITUDE)) %>%
    mutate(LONGITUDE = as.numeric(LONGITUDE))

}

#' cleans the LOCATION_NAME
#' @importFrom dplyr mutate
#' @importFrom dplyr select
#' @importFrom dplyr rowwise
#' @importFrom tidyr separate
#' @importFrom stringi stri_trans_totitle
#' @importFrom magrittr %>%
#'
#'
#' @description
#' stripping out the country name (including the colon)
#' and converts names to title case (as opposed to all caps)
#'
#'
#' @param dat, default for cleaned signif.txt from eq_clean_data()
#'
#' @return
#' a cleand tibble of 48 variables
#'
#'
#' @examples
#' clean_location <- eq_location_clean()
#'
#' @export
eq_location_clean <- function(dat = eq_clean_data()) {
  clean_location <- dat %>%
    separate(LOCATION_NAME, c("first", "second"), sep = ":") %>%
    select(-first) %>%
    rowwise() %>%
    mutate(LOCATION_NAME = ifelse(is.na(second), "Unidentified",
                                  stri_trans_totitle(tolower(second)))) %>%
    select(-second)

}