#' Read a FARS file
#'
#' This function reads a file, to be precise a file from Fatality Analysis Reporting System.
#' We can pass a file name to read (using the \code{filename} argument).
#'
#' @param filename A character.
#'
#' @return This function returns a dataframe table.
#'
#' @note This function will throws an error if the filename doesn't exist.
#'
#' @examples
#' \dontrun{
#' fars_read("fars_data.csv")
#' fars_read(filename = "fars_data.csv")
#' fars_read("data/accident_2013.csv")
#' }
#'
#'
#' @importFrom readr read_csv
#'
#' @export
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 a file name for the given year.
#'
#' This function creates a file name in desired pattern by taking a \code{year} as input.
#'
#' @param year A character.
#'
#' @return This function will return the file name in desired pattern for the given \code{year}.
#'
#' @examples
#' \dontrun{
#' make_filename("2013")
#' make_filename(year = "2014")
#' }
#'
#' @export
make_filename <- function(year) {
year <- as.integer(year)
sprintf("accident_%d.csv.bz2", year)
}
#'
#' Data for the given years.
#'
#' This function takes a vector of years, and for each year in the vector it uses \code{make_filename(year)} to make a file name pattern
#' for the given year and \code{fars_read(filename)} to read the file.
#'
#' @param years A vector of years.
#'
#' @return A list of dataframes for each year in the vector of years if that year exist,
#' or it will return a NULL for invalid year.
#'
#' @note This function will throw a warning if the entered year is invalid.
#'
#' @examples
#' \dontrun{
#' fars_read_years(c("2013", "2014"))
#' yearvector <- c("2013", "2014")
#' fars_read_years(yearvector)
#' }
#'
#' @importFrom dplyr mutate select
#'
#' @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)
})
})
}
#'
#' Summarize the data by years.
#'
#' This function takes a vector of \code{years} as input. Then reads the list generated by \code{fars_read_years(years)} by calling it.
#' Then summarize the data and spreads the data in multiple column.
#'
#' @inheritParams fars_read_years
#'
#' @return summarized data that spread across multiple columns.
#'
#' @examples
#' \dontrun{
#' fars_summarize_years(c("2013", "2014"))
#' fars_summarize_years(yearvector)
#' }
#'
#' @importFrom dplyr bind_rows group_by summarize
#' @importFrom tidyr spread
#'
#' @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)
}
#'
#' Map with plotted accidents.
#'
#' This function will take \code{state.num} and \code{year} as input and returns a map with accident plotted in it.
#' It will throw an error if the \code{state.num} is an invalid'.
#'
#' @param state.num A character that represents the state number.
#' @inheritParams make_filename
#'
#' @return a map with accidents plotted in it. It will also returns a message that there are no accidents to plot
#' if there no records of accident for the given state.
#'
#' @note This function will throws an error if \code{state.num} is invalid.
#'
#' @examples
#' \dontrun{
#' fars_map_state("1", "2013")
#' fars_map_state(state.num = "1", year = "2013")
#' }
#'
#' @importFrom dplyr filter
#' @importFrom maps map
#' @importFrom graphics points
#'
#' @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)
})
}
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