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README.Rmd
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README.Rmd
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---
title: "README"
author:
- Jamaal Green, University of Pennsylvania; Dillon Mahmoudi, University of Maryland Baltimore County; Liming Wang, Portland State University
output: github_document
compact-title: false
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
eval = FALSE
)
```
# lehdr
## Build Status
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[](https://cran.r-project.org/package=lehdr)
[](https://github.com/jamgreen/lehdr/actions)
[](https://cran.r-project.org/package=lehdr)
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**lehdr** (pronounced: _lee dur_ like a metric _litre_) is an R package that allows users to interface with the [Longitudinal and Employer-Household Dynamics (LEHD)](https://lehd.ces.census.gov/) Origin-Destination Employment Statistics (LODES) dataset returned as dataframes. The package is continually in development and can be installed via CRAN.
## Installation
You can install the released version of lehdr from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("lehdr")
```
And the development version from [GitHub](https://github.com/) with:
```{r manualinstall, eval=FALSE}
install.packages("devtools", repos = "http://cran.us.r-project.org")
devtools::install_github("jamgreen/lehdr")
```
## Usage
After loading the library, this first example pulls the Oregon (`state = "or"`) 2014 (`year = 2014`), origin-destination (`lodes_type = "od"`), all jobs including private primary, secondary, and Federal (`job_type = "JT01"`), all jobs across ages, earnings, and industry (`segment = "S000"`), aggregated at the Census Tract level rather than the default Census Block (`agg_geo = "tract"`).
```{r usage1, eval=FALSE}
library(lehdr)
grab_lodes(state = "or", year = 2014, lodes_type = "od", job_type = "JT01",
segment = "S000", state_part = "main", agg_geo = "tract")
```
The package can be used to retrieve multiple states and years at the same time by creating a vector or list. This second example pulls the Oregon AND Rhode Island (`state = c("or", "ri")`) for 2013 and 2014 (`year = c(2013, 2014)` or `year = 2013:2014`).
```{r usage2, eval=FALSE}
grab_lodes(state = c("or", "ri"), year = c(2013, 2014), lodes_type = "od", job_type = "JT01",
segment = "S000", state_part = "main", agg_geo = "tract")
```
Not all years are available for each state. To see all options for `lodes_type`, `job_type`, and `segment` and the availability for each state/year, please see the most recent LEHD Technical Document at https://lehd.ces.census.gov/data/lodes/LODES7/.
Other common uses might include retrieving Residential or Work Area Characteristics (`lodes_type = "rac"` or `lodes_type = "wac"` respectively), low income jobs (`segment = "SE01"`) or good producing jobs (`segment = "SI01"`). Other common geographies might include retrieving data at the Census Block level (`agg_geo = "block"`, not necessary as it is default).
## Why lehdr?
The LODES dataset is frequently used by transportation and economic development planners, regional economists, disaster managers and other public servants in order to have a fine grained understanding of the distribution of employment. Such data is integral for regional travel demand models that help to dictate transportation policy options, regional economists and economic development planners interested in the spatial distribution of particular kinds of work use the data to weigh different industrial or workforce policy options. Finally, as a census product, the LODES data can be joined to census Decennial or American Community Survey data to help visualize the interactions between different population groups and work. In short, the LODES dataset is the only source of detailed geographic information on employment for the country and should be more widely available for researchers and analysts who work on regional development issues.
## Future Development
Currently, **lehdr** is designed to grab the LODES flat files (origin-destination, workplace, and residential association files) and includes an option to aggregate results to the census tract level for analysts who find the fuzzing at the block level too great.
Next steps include exploring integration of the package with the [**sf**]( https://CRAN.R-project.org/package=sf) and [**tigris**]( https://CRAN.R-project.org/package=tigris) packages to allow for easier mapping of LODES data.
## Acknowledgements
This package would not exist in its current format without the inspiration of [Bob Rudis's](https://rud.is/b/) [lodes package](https://github.com/hrbrmstr/lodes)