#' Read in a file.
#' This function will check to see if a file exists based on the file path.
#' @param filename The name of the file to read.
#' @param filepath a character string with the directory of the package's data files.
#' @return This file returns the data in the csv file as a tibble.
#' @examples
#' \dontrun{
#' #' fars_read('accident_2013.csv.bz2') #only if in the same directory as data
#' fileName <- system.file("extdata","accident_2013.csv.bz2",package="fars") #when using provided data in package
#' fars_read(fileName)
#' }
#' @export
#' @import readr dplyr
fars_read <- function(filename,filepath = system.file("extdata",filename,package="fars")) {
if(!file.exists(filepath))
stop("file '", filename, "' does not exist")
data <- suppressMessages({
readr::read_csv(filepath, progress = FALSE)
})
dplyr::tbl_df(data)
}
#' Make a file name
#' This function will make a filename string.
#' @param year The year value to paste in the file string
#' @return This function returns a string denoting the file name with the form "accident_[year].csv.bz2"
#' @examples
#' \dontrun{
#' make_filename(2013)
#' make_filename('2013')
#' }
#' @export
make_filename <- function(year) {
year <- as.integer(year)
sprintf("accident_%d.csv.bz2", year)
}
#' Read multiple years of data
#' This function will take a vector of years and return a list of data frames.
#' @param years A vector of years.
#' @return If the years are valid, the function will return a data frame. Otherwise the function will return null.
#' @examples
#' \dontrun{
#' fars_read_years(c(2013,2014,2015))
#' example_years <- 2013:2015
#' fars_read_years(example_years)
#' }
#' @export
#' @importFrom magrittr "%>%"
#' @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)
})
})
}
#' Read multiple years of data and summarize.
#' This function will take a vector of years and return a summary of the data by month and year.
#' @param years A vector of years.
#' @return If the years are valid, the function will return a summary of the total events by month and year. Otherwise the function will return null.
#' @examples
#' \dontrun{
#' fars_summarize_years(c(2013,2014,2015))
#' example_years <- 2013:2015
#' fars_summarize_years(example_years)
#' }
#' @export
#' @import dplyr tidyr
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 map of the events
#' This function will read in a state (by number index) and a year and return a map of the event locations.
#' @param state.num State number index in the fars data
#' @param year Four digit year
#' @return a map of the event locations for the inputted state.
#' @examples
#' \dontrun{
#' fars_map_state(1,2013) #Print events in Alabama for 2013
#' }
#' @export
#' @import maps graphics
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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