myRPackage: Assignment for Coursera

#' Data Input
#'
#' @description After checking if a file exist in a given path, the function read the
#'  file and creat a data frame table.
#' @param filename .csv file from which the data will be read
#'
#' @return heck if the file exists, gives error if it does not. if the file exists,
#'  it read the file in a data frame and returns it \code{getwd()}.
#' @export
#'
#' @importFrom dplyr tbl_df %>%
#' @importFrom readr read_csv
#'
#' @examples
#' \dontrun{
#'   fars_read("accident_2010.csv.bz2")
#' }
#'
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)
}

#' Creat a file name from the data source for a given year
#'
#' @param year Only logical, integer, real and character vectors are supported. The
#'  year for which the file will be created
#'
#' @return read pram as integer. and return file name for a given year
#' @export
#'
#' @examples
#' \dontrun{
#' make_filename(2013)
#' make_filename("2013")
#' }
make_filename <- function(year) {
    year <- as.integer(year)
    sprintf("accident_%d.csv.bz2", year)
}

#' Loads FARS data files for given year/s
#'
#' @param years a vector of years for which FARS data file will be loaded
#'
#' @return return NULL for an invalid year. For a valid input, returns list of
#' FARS data \code{years}
#' @export
#'
#' @importFrom dplyr mutate select %>%
#' @importFrom stats setNames
#'
#' @examples
#'   \dontrun{
#'     fars_read_years(c(2013, 2014))
#'     fars_read_years(c("2013", "2014"))
#'   }
fars_read_years <- function(years) {
    lapply(years, function(year) {
        file <- make_filename(year)
        tryCatch({
            dat <- fars_read(file)
            dplyr::mutate_(dat, .dots = setNames(list(~year)), year) %>%
                dplyr::select_(~MONTH, ~year)
            },
            error = function(e) {
            warning("invalid year: ", year)
            return(NULL)
        })
    })
}

#' Monthly number of accident for given years in FARS data
#'
#' @param years a vector of years for which FARS data file will be loaded and number
#'  of accident will be summurized \code{years}
#'
#' @return return NULL for an invalid year. For a valid input, returns list of
#' FARS data in data frame in years and number of accidents. It uses \code{\link{fars_read_years}} function.
#' @export
#'
#' @importFrom dplyr bind_rows group_by summarize %>%
#' @importFrom tidyr spread
#' @importFrom stats setNames
#'
#' @examples
#'   \dontrun{
#'     fars_summarize_years(c(2013, 2014))
#'     fars_summarize_years(c("2013", "2014"))
#'   }
fars_summarize_years <- function(years) {
    dat_list <- fars_read_years(years)
    dplyr::bind_rows(dat_list) %>%
        dplyr::group_by_(~year, ~MONTH) %>%
        dplyr::summarize_(.dots = setNames(~n(), "n")) %>%
        tidyr::spread_(~year, ~n)
}

#' For a given state and given year, it draws the location of accidents
#'
#' @param state.num an integer representing state number for which the location will be
#'        drawn
#' @param year a vector of years for which FARS data file will be loaded
#'
#' @return plots a map of the state with the accidents as dots on the map.
#' It throws an error if \code{"state.num"} is not invalid. If there hasn't been
#' any accident in that year, it produces a message.
#' @export
#'
#' @importFrom dplyr filter %>%
#' @importFrom maps map
#' @importFrom graphics points
#'
#' @examples
#'   \dontrun{
#'     fars_map_state(34, 2013)
#'     fars_map_state(34, 2014)
#'     fars_map_state(8, 2013)
#'   }
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)
    })
}