#' ----1-----
#' fars_read: Reads a csv file.
#'
#' @description The function reads a csv file defined by \code{filename} argument and returns
#' a tibble. If the path is incorrect the function will end and return an error.
#'
#' @param filename Path to the csv file.
#'
#' @return The function returns a tibble based on the specified csv file.
#'
#' @examples
#'
#' \dontrun{
#' tb_accident_2015 <- fars_read("./data/accident_2015.csv.bz2")
#' }
#'
#' @import readr
#' @import dplyr
#'
#' @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)
}
#' ----2-----
#' make_filename: Creates a filename.
#'
#' @description The function creates a filename for a .csv.bz2 file based on the \code{year}
#' argument in a form "accident_<year>.csv.bz2". It requires a numerical or
#' integer input otherwise ends and returns an error.
#'
#' @param year A numerical input which is defined as integer indicating a year of a data set.
#'
#' @return Returns a character variable in a format "accident_<year>.csv.bz2".
#'
#' @examples
#' \dontrun{
#' makefilename(2016)
#' }
make_filename <- function(year) {
year <- as.integer(year)
sprintf("accident_%d.csv.bz2", year)
}
#' ----3-----
#' fars_read_years: Reads month and year from accident files
#'
#' @description The function accepts a vector of years and returns a list of data
#' frames with month and year columns based on data in "accident_<year>.csv.bz2
#' files.
#'
#' @param years A vector or list of years in numeric or integer format.
#'
#' @return Returns a list of tibbles with the same number of rows
#' as the data in "accident_<year>.csv.bz2" files and two columns - month &
#' year. Returns NULL and a warning if the file does not exist.
#'
#'
#' @import dplyr
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)
})
})
}
#' ----4-----
#' fars_summarize_years: Counts number of accidents per month & year.
#'
#' @description Based on the user-selected years, the function summarises the number of accidents
#' in the US on a monthly basis. The accident files need to be in the working
#' directory.
#'
#' @param years A vector or list of years (numeric or integer) that will be
#' searched in the data
#'
#' @return Returns a tibble in a wide format (pivot) with months in rows and defined
#' years in columns, using as values the number of accidents. Returns a warning for
#' every input year that does not exist in the datasets.
#'
#' @examples
#' \dontrun{
#' fars_summarize_years(2015:2016)
#' }
#'
#' @import dplyr
#' @import tidyr
#'
#' @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")
}
#' ----5-----
#' fars_map_state: Creates a visualisation of the accidents on a US state map.
#'
#' @description The function accepts a state ID and year and returns a map visualisation of the accidents.
#' The state ID has to be an integer or numerical.
#'
#' @param state.num The ID of a US state as specified in the FARS data pack.
#'
#' @param year The selected year the visualisation will be based on.
#'
#' @return Returns a graph of the accidents based on the \code{state.num} and
#' \code{year} inputs. Returns an error if the state and/ or year do not exist in the
#' data set.
#'
#'
#' @import dplyr
#' @import maps
#' @import graphics
#'
#' @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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