knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
The goal of nhts2017
is to make it considerably easier for R users to
interact with NHTS 2017 datasets. The package contains the four datasets:
nhts_households
nhts_persons
nhts_vehicles
nhts_trips
From ORNL website:
To recognize the valuable role of National Household Travel Survey (NHTS) data in the transportation research process and to facilitate repeatability of the research, users of NHTS data are asked to formally acknowledge the data source. Where possible, this acknowledgment should take place in the form of a formal citation, such as when writing a research report, planning document, on-line article, and other publications. The citation can be formatted as follows:
U.S. Department of Transportation, Federal Highway Administration, 2017 National Household Travel Survey. URL: http://nhts.ornl.gov.
You can install the development version from GitHub with:
# install.packages("devtools") devtools::install_github("byu-transpolab/nhts2017")
Each of the datasets is a properly data-typed tibble
, derived from the
SPSS
files distributed by Oak Ridge National Laboratory.
The variables have attribute labels that appear in RStudio's data set viewer,
and factor variables have correct labels appended.
For instance, to count the number of households completing records for each day, we can simply do
library(nhts2017) library(tidyverse) nhts_households %>% group_by(travday) %>% summarise( count = n(), weighted = sum(wthhfin) )
In one departure from the NHTS public data files, the datasets are tidy
in that
each field appears only once in the dataset. E.g., the msasize
variable
--- indicating the size of the metropolitan area each household resides in ---
is only appended to the nhts_households
tibble rather than to all four tibbles.
Joining is trivial, however.
nhts_trips %>% left_join(nhts_households, by = "houseid") %>% group_by(msasize) %>% summarise( mean_trip_length = weighted.mean(trpmiles, wttrdfin) )
Additionally, the strttime
and endtime
fields on the trips data have been
converted from four-character strings (e.g. 1310
for 1:10 PM) into R datetime
objects. This required setting a date, which was arbitrarily chosen to be October
10, 2017.
ggplot(nhts_trips, aes(x = strttime)) + geom_histogram()
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.