#' Import CSV into a data frame tbl.
#'
#' This is a simple function that, by default, is useful for reading the fars data
#' the fars is a data set from the US National Highway Traffic Safety Administration's Fatality Analysis
#' Reporting System, which is a nationwide census providing the American public yearly data regarding
#' fatal injuries suffered in motor vehicle traffic crashes
#'
#' @param filename the name of the file which the data are to be read from
#'
#' @return A data frame (tbl) containing a representation of the data in the file.
#'
#' @examples
#' \dontrun{
#' fars_read("accident_2013.csv")
#' }
#'
#' @importFrom "readr" "read_csv"
#'
#' @importFrom "dplyr" "tbl_df"
#'
#' @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)
}
#' fars file name creation
#'
#' Making the file name of a fars data according to the year of occurence
#'
#' @param year an integer indicates the year of the fars file
#'
#' @return a string which indicates the file name of the fars data
#'
#' @examples
#' \dontrun{
#' make_filename(2013)
#' }
#'
#' @export
make_filename <- function(year) {
year <- as.integer(year)
sprintf("accident_%d.csv.bz2", year)
}
#' reading fars data according to years
#'
#' This function read the fars data of a specific year and select only month and year of the accident
#'
#' @param years a vector of integer indicates the year of the fars file
#'
#' @return a list of data frame(tibble), each component of the list contains a tibble with two columns
#' of MONTH and year of the accident
#'
#' @examples
#' \dontrun{
#' fars_read_years(c(2013, 2014))
#' }
#'
#' @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)
})
})
}
#' fars data summary
#'
#' This function read the fars data of a specific year and summarize the data in a tibble,
#' this data frame has 12 rows for month, and one column for each year. In each cell you can
#' find the number of the accident in that YEAR, MONTH.
#'
#' @param years a vector of integer indicates the year of the fars file
#'
#' @return a data frame(tibble) with 12 rows and one column for each year
#'
#' @examples
#' \dontrun{
#' fars_summarize_years(c(2013, 2014))
#' }
#'
#' @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)
}
#' plotting the location of accidents
#'
#' This function read the fars data of a specific year and state then and then try to plot the
#' location of the accident in that state and year.
#'
#' @param state.num an integer which indicates one state of US
#' @param year an integer indicates the year of the fars file
#'
#' @return NULL, just plot the points of the fars data accident
#'
#' @importFrom "dplyr" "filter"
#'
#' @importFrom "maps" "map"
#'
#' @importFrom "graphics" "points"
#'
#' @examples
#' \dontrun{
#' fars_summarize_years(33 , 2013)
#' }
#'
#' @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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