#' fars_read Function
#'
#' This is a function that reads the cvs file with the data.
#'
#' @param filename A character with the name of the file you want to study
#' @importFrom readr read_csv
#' @return data.table object with all the data
#' @note An error occur when the file does not exist'
fars_read <- function(filename) {
if(!file.exists(filename))
stop("file '", filename, "' does not exist")
data <- suppressMessages({
readr::read_csv(filename, progress = FALSE)
})
dplyr::tbl_df(data)
}
#' make_filename Functions
#'
#' This is adunction that creates the filename from which you will access the data with the help of the fars read function
#' @param year A character, numeric or integer with the year you are interested in
#' @return A character vector with the name of the file you want to access.
#' @export
make_filename <- function(year) {
year <- as.integer(year)
sprintf("accident_%d.csv.bz2", year)
}
#' fars_read_years Function
#'
#' This is a function that gathers the data cancerning all the years you give as input.
#'
#' @param years A vector with all the years you want to study
#' @importFrom tidyr spread
#' @importFrom dplyr bind_rows group_by summarize %>%
#' @return a data list with objects the data from all the years you are interested in.
#' @note An error occur when there are no data for a year that you asked'
#' @export
fars_read_years <- function(years) {
lapply(years, function(year) {
file <- make_filename(year)
tryCatch({
dat <- fars_read(file)
dplyr::mutate(dat, year = year) %>%
dplyr::select(MONTH, year)
}, error = function(e) {
warning("invalid year: ", year)
return(NULL)
})
})
}
#' fars_summarize_years Function
#'
#' This is a function that gathers the data cancerning all the years you give as input.
#'
#' @param years A vector with all the years you want to study
#' @importFrom tidyr spread
#' @importFrom dplyr summarize bind_rows
#' @return a data list with objects the data from all the years you are interested in.
#' @note An error occur when there are no data for a year that you asked
#' @export
fars_summarize_years <- function(years) {
dat_list <- fars_read_years(years)
dplyr::bind_rows(dat_list) %>%
dplyr::group_by(year, MONTH) %>%
dplyr::summarize(n = n()) %>%
tidyr::spread(year, n)
}
#' fars_map_state Function
#'
#' This is a function that takes as input the state you are interested in and the state you want to study
#' and gives as output a map with dots in all the places were a fatal accident took place.
#'
#' @param state.num code number of a state and the year
#' @param year the year
#' @importFrom maps map
#' @importFrom dplyr filter
#' @return a plot that depicts a map of the state
#' @note A message appears when an invalid state number is given when there are no accidents.
#' @export
fars_map_state <- function(state.num, year) {
filename <- make_filename(year)
data <- fars_read(filename)
state.num <- as.integer(state.num)
if(!(state.num %in% unique(data$STATE)))
stop("invalid STATE number: ", state.num)
data.sub <- dplyr::filter(data, STATE == state.num)
if(nrow(data.sub) == 0L) {
message("no accidents to plot")
return(invisible(NULL))
}
is.na(data.sub$LONGITUD) <- data.sub$LONGITUD > 900
is.na(data.sub$LATITUDE) <- data.sub$LATITUDE > 90
with(data.sub, {
maps::map("state", ylim = range(LATITUDE, na.rm = TRUE),
xlim = range(LONGITUD, na.rm = TRUE))
graphics::points(LONGITUD, LATITUDE, pch = 46)
})
}
#### these are all
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