In Yailama/assignment4:

Suppose your data is stored in tables by year, with the name pattern "accident_2011.csv.bz2", "accident_2012.csv.bz2", "accident_2013.csv.bz2" etc. Use function \code{fars_read_years} to read all these files you need by assigning a vector of years.

fars_read_years(years=seq(from=2011, to=2015))

If you prefer the other way to import data, you can just create a file name with the same pattern, using

filename<-make_filename(year=2012)

You can also read .csv file with other name by using:

fars_read(file.choose())

To not only import files, but also get summary information of how many observation occurs for each year, use \code{fars_summarize_years} with the same input, as \code{fars_read_years} function:

You can also read .csv file with other name by using:

fars_summarize_years(years=c(2011,2012,2013))

The result should look like this:

| MONTH| 2013| 2014| 2015| |-----:|----:|----:|----:| | 1| 2230| 2168| 2368| | 2| 1952| 1893| 1968| | 3| 2356| 2245| 2385| | 4| 2300| 2308| 2430| | 5| 2532| 2596| 2847| | 6| 2692| 2583| 2765| | 7| 2660| 2696| 2998| | 8| 2899| 2800| 3016| | 9| 2741| 2618| 2865| | 10| 2768| 2831| 3019| | 11| 2615| 2714| 2724| | 12| 2457| 2604| 2781|

Finally, get plot of the result, use \code{fars_map_state} with specifyed state number and year.

fars_map_state(1, 2015)

"He who gives up [code] safety for [code] speed deserves neither." (via)

Yailama/assignment4 documentation built on May 10, 2019, midnight