In vanita1/A1B2:

'@note

'fars_functions : Package for reading, summarising and create visual representation

'for data from the Fatality Analysis Reporting System (FARS).

'@note

'fars_read function reads the given file and convert resulting data frame

'to a table using tbl_df function from dplyr

'@details

': Function Checks the existence of given file. In case file is not found, a

' message is generated. While reading the file,suppresses the progress

' messages to have a clean output

'@examples - fars_read("accident_2015.csv.bz2")

'@importFrom read_csv readr

'@importFrom tbl_df dplyr

'@parameter - filename : a character string with path of the file

'@return - Tibble object with csv file data

'@export

'

filename = "accident_2013.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)
}

'@note make_filename function creates a custom file name based on year argument value

'@examples - make_filename(2015)

'@parameter - year- Numeric e.g. 2015

'@return - character string having custom file name e.g.accident_2015.csv.bz2

'@export

'

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

#'@note -fars_read_years function to iteratively read the files based on numeric year #'vector #'@examples - fars_read_years(c(2015,1980,2010)) #'mportFrom select dplyr #'@importFrom mutate dplyr #'@importFrom %/% magrittr #'@parameter - a numeric vector contains years e.g. c(2015,2001,1980) #'@return - a list of tibbles each containing year and month columns. If files does not exist, returns NULL #'@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)
                })
        })
}

#'@note -fars_summarize_years combines multiple year files by rows and then group on year and month #' Further it calculates summary of the resulting data frame and spreads across year

#'@parameter - a numeric vector contains years e.g. c(2015,2001,1980)

#'@return A wide format tibble. One column for year and a MONTH column. Each #' value is the count of observations in a year-month.

#'@examples - fars_read("accident_2015.csv.bz2")

#'@importFrom dplyr bind_rows #'@importFrom dplyr group_by #'@importFrom dplyr 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)
}

#'@note -fars_map_state- draw a map for a given year and state #'@details #? Function to check if there is data for a state in a particular year file. If data #'is present and plot longitude against latitude for observations where #' longitude is > 900 and latitude is >90 #' Add Points to a Plot #'@parameter-a vector containing statenumber and year or state and year e.g. fars_map_state(20,2015)

#'@return NULL

#'@importFrom maps map #'@importFrom graphics points

#'@export #'@examples fars_map_state(20,2015)

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)
        })
}