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‎README.md

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---
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editor_options:
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---
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# D-Lab R SQL Fundamentals Workshop
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[![DataHub](https://img.shields.io/badge/launch-datahub-blue)](DATAHUB_LINK_HERE)
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[![Binder](https://mybinder.org/badge_logo.svg)](BINDER_LINK_HERE)
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[![License: CC BY
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4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
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This repository contains the materials for D-Lab R SQL Fundamentals
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workshop.
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### Prerequisites
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Prior experience with [R
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Fundamentals](https://github.com/dlab-berkeley/R-Fundamentals), [R Data
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Wrangling](https://github.com/dlab-berkeley/R-Data-Wrangling), and [R
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Data
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Visualization](https://github.com/dlab-berkeley/R-Data-Visualization) is
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assumed.
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Check D-Lab's [Learning
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Pathways](https://dlab-berkeley.github.io/dlab-workshops/python_path.html)
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to figure out which of our workshops to take!
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## Workshop Goals
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In this workshop, we provide an introduction to using SQL to query and
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retrieve data from relational databases in R. First, we’ll cover what
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relational databases and SQL are. Then, we’ll use different packages in
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R to navigate relational databases using SQL.
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If you are not familiar with material in [R
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Fundamentals](https://github.com/dlab-berkeley/R-Fundamentals), [R Data
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Wrangling](https://github.com/dlab-berkeley/R-Data-Wrangling), and [R
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Data
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Visualization](https://github.com/dlab-berkeley/R-Data-Visualization),
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we recommend attending those workshops first.
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## Learning Objectives
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After this workshop, you will be able to:
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1. Explain what a relational database is and why we would want to use
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it.
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2. Access and query a database using SQL.
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3. Use `DBI` and `dbplyr` to query data from a relational database.
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This workshop does not cover the following:
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- Read and conduct basic data operations in R. These are covered in [R
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Fundamentals](#0).
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- Wrangle data for basic data analysis using R. These are covered in
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[R Data Wrangling](#0).
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## Installation Instructions
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We will use RStudio to go through the workshop materials, which requires
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installation of both the R language and the RStudio software. Complete
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the following steps:
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1. [Download R](https://cloud.r-project.org/): Follow the links
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according to the operating system that you are running. Download the
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package, and install R onto your compute. You should install the
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most recent version (at least version 4.0).
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2. [Download
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RStudio](https://rstudio.com/products/rstudio/download/#download):
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Install RStudio Desktop. This should be free. Do this after you have
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already installed R. The D-Lab strongly recommends an RStudio
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edition of 2022.02.0+443 "Prairie Trillium" or higher.
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3. [Download these workshop
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materials](https://github.com/dlab-berkeley/R-Data-Visualization):
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1. Click the green "Code" button in the top right of the repository
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information.
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2. Click "Download Zip".
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3. Extract this file to a folder on your computer where you can
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easily access it (we recommend Desktop).
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4. Optional: if you’re familiar with git, you can instead clone this
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repository by opening a terminal and entering [GitCloneCommand].
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## Is R not working on your laptop?
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If you do not have R installed and the materials loaded on your workshop
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by the time it starts, we *strongly* recommend using the UC Berkeley
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Datahub to run the materials for these lessons. You can access the
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DataHub by clicking [this
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link](https://datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fdlab-berkeley%2FR-Data-Visualization&urlpath=rstudio%2F&branch=main).
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The DataHub downloads this repository, along with any necessary
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packages, and allows you to run the materials in an RStudio instance on
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UC Berkeley's servers. No installation is necessary from your end - you
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only need an internet browser and a CalNet ID to log in. By using the
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DataHub, you can save your work and come back to it at any time. When
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you want to return to your saved work, just go straight to
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[DataHub](https://datahub.berkeley.edu), sign in, and you click on the
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`R-SQL-Fundamentals` folder.
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## Run the Code
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Now that you have all the required software and materials, you need to
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run the code:
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Provide instructions on running the code, including how to load relevant
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software (RStudio, Jupyter Notebooks, etc.) and which file to open up.
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See other repositories for examples.
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Additionally, provide instructions on how to run code once it’s open
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(running Jupyter cells, RMarkdown cells, etc.).
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# Additional Resources
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Check out the following resources to learn more about using SQL in R:
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- Errickson, J. 2024. STATS 506. [Computational Methods and Tools in
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Statistics](https://dept.stat.lsa.umich.edu/~jerrick/courses/stat506_f24/07-sql.html)
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- [Data Analysis and Visualization in R for
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Ecologists](https://datacarpentry.github.io/R-ecology-lesson/instructor/05-r-and-databases.html)
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# About the UC Berkeley D-Lab
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D-Lab works with Berkeley faculty, research staff, and students to
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advance data-intensive social science and humanities research. Our goal
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at D-Lab is to provide practical training, staff support, resources, and
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space to enable you to use R for your own research applications. Our
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services cater to all skill levels and no programming, statistical, or
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computer science backgrounds are necessary. We offer these services in
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the form of workshops, one-to-one consulting, and working groups that
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cover a variety of research topics, digital tools, and programming
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languages.
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Visit the [D-Lab homepage](https://dlab.berkeley.edu/) to learn more
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about us. You can view our
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[calendar](https://dlab.berkeley.edu/events/calendar) for upcoming
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events, learn about how to utilize our
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[consulting](https://dlab.berkeley.edu/consulting) and
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[data](https://dlab.berkeley.edu/data) services, and check out upcoming
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[workshops](https://dlab.berkeley.edu/events/workshops).
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# Here are other R workshops offered by the D-Lab:
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## Basic Competency
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- [Fast-R](https://github.com/dlab-berkeley/Fast-R)
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- [R Data Wrangling](https://github.com/dlab-berkeley/R-wrang)
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- [R Functional
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Programming](https://github.com/dlab-berkeley/R-functional-programming)
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- [Geospatial Fundamentals in R with
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sf](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-R-with-sf)
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- [Census Data in
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R](https://github.com/dlab-berkeley/Census-Data-in-R)
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## Intermediate/Advanced Competency
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- [Advanced Data Wrangling in
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R](https://github.com/dlab-berkeley/advanced-data-wrangling-in-R)
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- [Unsupervised Learning in
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R](https://github.com/dlab-berkeley/Unsupervised-Learning-in-R)
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- [R Machine Learning with
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tidymodels](https://github.com/dlab-berkeley/Machine-Learning-with-tidymodels)
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- [Introduction to Deep Learning in
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R](https://github.com/dlab-berkeley/Deep-Learning-in-R)
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- [R Package
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Development](https://github.com/dlab-berkeley/R-package-development)
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# Contributors
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- [Taesoo Song](https://taesoosong.github.io/)

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"state","region","population","median_income","median_rent","poverty_rate","immigrant_share"
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"Alabama","South",5028092,59609,925,0.210627531252712,0.0352433885457943
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"Arizona","West",7172282,72581,1308,0.169381648494447,0.130496820956008
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"Arkansas","South",3018669,56335,868,0.214889701348985,0.0502807694384512
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"California","West",39356104,91905,1856,0.15080918300121,0.265343337846653
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"Colorado","West",5770790,87598,1594,0.106675752977669,0.0948920338463191
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"Connecticut","Northeast",3611317,90213,1374,0.12712434714844,0.150350689236088
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"Delaware","South",993635,79325,1286,0.164121606658186,0.0978457884434425
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"District of Columbia","South",670587,101722,1817,0.211741351427667,0.134353931704611
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"Florida","South",21634529,67917,1444,0.169355489947195,0.211441256705889
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"Georgia","South",10722325,71355,1221,0.180140261288291,0.104395828330143
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"Hawaii","West",1450589,94814,1868,0.118265338449083,0.180181981250375
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"Idaho","West",1854109,70214,1061,0.120639554964057,0.0573919872024784
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"Illinois","Midwest",12757634,78433,1179,0.150571111485779,0.141458910014192
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"Indiana","Midwest",6784403,67173,967,0.153426478315972,0.0557063016451116
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"Iowa","Midwest",3188836,70571,914,0.123868114527156,0.0559881411273581
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"Kansas","Midwest",2935922,69747,986,0.133124087853195,0.0706047367743421
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"Kentucky","South",4502935,60183,902,0.1993576337823,0.0408864440637051
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"Louisiana","South",4640546,57852,996,0.250035987040722,0.0423810043042349
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"Maine","Northeast",1366949,68251,1009,0.12908900682591,0.0379728870645503
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"Maryland","South",6161707,98461,1598,0.113367712696242,0.156715987955935
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"Massachusetts","Northeast",6984205,96505,1588,0.112456236811943,0.17580325892496
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"Michigan","Midwest",10057921,68505,1037,0.172065274370222,0.0690858478606066
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"Minnesota","Midwest",5695292,84313,1178,0.102634483779497,0.0846176104754594
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"Mississippi","South",2958846,52985,896,0.261200580153675,0.0230687910083864
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"Missouri","Midwest",6154422,65920,957,0.158639485074933,0.0419538991638857
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"Montana","West",1091840,66341,974,0.140426691043736,0.0226342687573271
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"Nebraska","Midwest",1958939,71722,987,0.113126537281521,0.0750074402520956
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"Nevada","West",3104817,71646,1382,0.162277393077234,0.190759390972157
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"New Hampshire","Northeast",1379610,90845,1336,0.077681647465579,0.060428671871036
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"New Jersey","Northeast",9249063,97126,1577,0.127899673933075,0.231942954653893
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"New Mexico","West",2112463,58722,966,0.234865601958349,0.0919874099570028
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"New York","Northeast",19994379,81386,1507,0.175133051444859,0.225510179635987
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"North Carolina","South",10470214,66186,1093,0.178155791877651,0.0828966819589361
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"North Dakota","Midwest",776874,73959,912,0.105383412835211,0.0463691666859748
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"Ohio","Midwest",11774683,66990,945,0.17527926908441,0.0486097162870542
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"Oklahoma","South",3970497,61364,934,0.196242871142204,0.0614613233557411
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"Oregon","West",4229374,76632,1373,0.127857808268195,0.0976414003585401
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"Pennsylvania","Northeast",12989208,73170,1110,0.154475236377078,0.0725067302024881
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"Rhode Island","Northeast",1094250,81370,1195,0.136164114940795,0.144172721041809
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"South Carolina","South",5142750,63623,1065,0.195185238715585,0.0525341500170142
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"South Dakota","Midwest",890342,69457,878,0.139200853792906,0.0382920271086841
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"Tennessee","South",6923772,64035,1047,0.183407941636799,0.0542626186997492
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"Texas","South",29243342,73035,1251,0.187910968897132,0.170563781663532
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"Vermont","Northeast",643816,74014,1149,0.104958811468038,0.0438494849460094
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"Virginia","South",8624511,87249,1440,0.120914355942008,0.126338177318111
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"Washington","West",7688549,90325,1592,0.113841281868454,0.148718308226949
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"West Virginia","South",1792967,55217,831,0.215511414496319,0.0161408436407363
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"Wisconsin","Midwest",5882128,72458,992,0.126570951058706,0.0502143101952219
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"Wyoming","West",577929,72495,933,0.117718814280931,0.0341841298844668

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