In Struya/mtrostR: Functions for the analysis of fatalities datasets for US

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(mtrostR)

Database on fatal injuries suffered in motor vehicle traffic crashes is produced by US National Highway Traffic Safety Administration. Data is provided on a yearly frequency. Using functions from the mtrostR package, one can import data from files disseminated by Administration, summarise it and plot fatal injuries by State on a map.

Installing `mtrostR`

Package mtrostR is only available on GitHub. It can be installed like:

# install.packages("devtools")
devtools::install_github("Struya/mtrostR")

Getting started

Construct filename

Using this package function make_filename one can construct the filename string to be used for importing the dataset into R session. The user must only provide a number for a year (i.e. 2013) to be used by the function. Please note that in the package only dataset from 2013 to 2015 are included.

# choose a value for a year
my_year <- 2013

# construct filename to be used for importing dataset into R
my_filename <- make_filename(year = my_year)

Import dataset

Importing fatalities dataset is simple. Using fars_read with filename string one can import a dataset for a chosen year.

# import datast into R using filename string
my_df <- fars_read(my_filename)

my_df

There is also a function fars_read_years that imports datasets for all years at once but returns only two columns, month and year. The user only has to provide a vector of years.

# vector of years 
my_years <- 2013:2015

# import all datasets into R. 
my_list <- fars_read_years(my_years)

my_list

Summarizing datasets

Using fars_read_years to import only columns months and years might be useful, however there is a function that calls it only to import datasets in order to summarise it by year and month. It is named fars_summarise_years.

# vector of years
my_years <- 2013:2015

# count number of fatalities by month and year
count_by_year_and_month_df <- fars_summarize_years(my_years)
count_by_year_and_month_df

Figures

Plotting fatalities on a map is a nice way of getting a feel for spatial distribution of fatalities accross United States. By providing the State ID number and a year, the function fars_map_state will plot spatial distribution on a map of chosen State.

# choice of State
my_state_id <- 1
my_year <- 2015

my_state_id_to_compare <- 10 

# plot spatial distribution for a year
fars_map_state(my_state_id, my_year)
fars_map_state(my_state_id_to_compare, my_year)