R/fars_functions_jmaghirang.R

In booradley1981/Course3wk2: Package Course3wk2 for using functions to analyze Fatality Analysis Reporting System [fars] data. This package was created for the Building R Packages Coursera Course.

# fars_functions.jmaghirang.R
# fars functions with documentation
# February 4, 2018
# Jude Maghirang


#' Read fars data file
#'
#' @param filename is a string representing the name of the data file
#' @return data frame named data
#' @return if filename input is not found, returns error message file does not exist
#' @examples \dontrun{ fars_read("accident_2013.csv.bz2") }
#' @importFrom readr read_csv
#' @importFrom dplyr tbl_df

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)
}

#' Build filename
#'
#' @param year is an integer representing year
#' @return string representing a filename
#' @examples \dontrun{ make_filename(2013) }

make_filename <- function(year) {
  year <- as.integer(year)
  sprintf("accident_%d.csv.bz2", year)
}


#' Read individual files (each year)
#'
#' @param years is an integer or list or vector representing year(s)
#' @return data frames for each year-file containing month and year columns
#' @return if year is invalid (no existing file) returns warning
#'
#' @examples \dontrun{
#'  fars_read_years (2013)
#'  fars_read_years (2013:2015)
#'  fars_read_years (c(2013,2015))
#' }
#' @importFrom dplyr mutate
#' @importFrom dplyr select

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 year file
#'
#' @param years is an integer or list or vector representing year(s)
#' @return data frame containing a row for each month and columns with counts for each year
#' @examples \dontrun{
#' fars_summarize_years(2013)
#' fars_summarize_years(2013:2015)
#' fars_summarize_years(c(2013,2015))
#' }
#' @importFrom dplyr bind_rows
#' @importFrom dplyr group_by
#' @importFrom dplyr summarize
#' @importFrom tidyr spread
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)
}

#' Create a state map
#'
#' @param state.num State number code
#' @param year Year of data file
#' @examples \dontrun{
#' fars_map_state(1,2013)
#' fars_map_state(4,2015)
#' }
#' @return plot of map
#' @return if state number or file corresponding to year does not exist
#' @importFrom graphics points
#' @importFrom maps map

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)
  })
}