Lecture Video

Introduction

Now that we have learned about the importance of utilizing reproducible tools for data-oriented projects, we will spend the next few weeks putting these tools into practice by managing a project of our own from start-to-finish.

The project that we will be completing will take the form of a web-based report on the most socially vulnerable areas in the United States broadly as well as within specific US Census Divisions and whether or not we believe that the New Market and Low Income Housing Tax Credit programs which are aimed at community development have had an impact on reducing social vulnerability from 2010 to 2020. As a team, you will summarize your findings and each individual team member will provide specifics on a census division of their choosing.

For Lab 02 and Lab 03 we will be focusing on building a customized version of the CDC’s Social Vulnerability Index for our report and visualizing the indices across our region of interest.

Before we explore the data, let’s gain an overview of the CDC Social Vulnerability Index (CDC SVI):

Census Geography

As detailed in the video above, US Census data is divided into a variety of geographies, starting with census blocks as the smallest units of analysis that are then aggregated up to state, divisional, and regional level data.

Recall from the previous section that we have two datasets for this course where we’ve pulled US Census Data to compare across 2010 and 2020. In order to do this, we pulled Census-Tract level data for 2010 and Census Block Group level data for 2020.

Why two different levels of data?

Well, as explained in the video, the boundaries of Census Tracts change every decade. Thus, we cannot directly compare 2010 and 2020 Census Tracts.

Look at the example below from GIS software company Esri:

[This] area represented by a single San Diego County Census Tract from the 2010 Census is now represented by seven (7) new Census Tracts in 2020. Additionally, only a portion of new Census Tract 133.16 covers area originally contained in Census Tract 133.1.

Alt-text: Visual map of San Diego County Census Tract Boundary Changes from 2010 to 2020

When comparing population changes for an area across a span of time, we want to be sure that we are actually comparing the same geographic boundaries. Thus, in order to see how people in an area defined in 2010 are functioning in 2020, we must convert our 2020 to data to ensure that it falls within the same boundaries as those specified in 2010.

To accomplish this, we can tighten our geographic boundaries in 2020 to pull data on Census Block Groups. Remember from the video, Census Block Groups (CBGs) are small chunks of geography that represent neighborhoods and fall within the bounds of Census Tracts. We can then utilize methodologies employed by GIS and population experts to calculate what percentage of these Census Block Groups fall within a broader boundary and create a more accurate comparison across time.

Thus to create the data for this project, CBG-level data for 2020 was pulled from the US Census Bureau. This data was then weighted using a crosswalk file from the IPUMS National Historical Geographic Information System (NHGIS) to determine which blocks belong to the 2010 census tracts. Once the 2020 Census Block Groups data was assigned to 2010 Census Tracts, it was aggregated to find counts within the geographic boundaries of the 2010 Census Tract data.

Because of this crosswalk, we are now able to equitably compare 2010 and 2020 changes within a reasonable margin of error. We can also aggregate our 2010 and 2020 data even further to find calculations on county, state, divisional, regional, and national-level issues as highlighted in the map below:

Alt-text: Map of US Census Regions and Divisions, Source: U.S. Energy Information Administration

Recall from Lab 01 that to complete your work for the course, each member of your group will select a different division of interest and conduct your analyses on that specific division.

Then as a group, you will compare your findings across the different divisions and highlight whether you believe the Tax Credit Programs were effective for all of your divisions, some of them, none of them, or in certain divisions versus others.

Note

All of the tutorials throughout the course will use the Middle Atlantic Division for examples. Thus, students can not pick this division. However, all other divisions are available for selection with the limitation that each member of a group must select a unique division within their group. This selection should have been made during week 1. If you do not have a division for some reason, check with your teammates before beginning this lab.

Additional Readings

Now that we have a general overview of the CDC’s SVI Program and the intricacies of US Census Geography, feel free to review the following resources for more details.

Once you feel comfortable with the material, we can then embark on creating the reusable functions we will cite throughout the course to create our SVI indices and examine our division of interest.

CDC Social Vulnerability Index:

Overview:

https://www.atsdr.cdc.gov/placeandhealth/svi/index.html

CDC SVI Calculation Methodology:

2010 Data Dictionary: https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/pdf/SVI-2010-Documentation-H.pdf
2020 Data Dictionary: https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/pdf/SVI2020Documentation_08.05.22.pdf

US Census Data:

US Census Regions and Divisions: - https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf

US Census Geography: - https://learn.arcgis.com/en/related-concepts/united-states-census-geography.htm

Census API Documentation: - https://www.census.gov/data/developers/data-sets/acs-5year.html

Census API Guide: - https://www.census.gov/data/developers/guidance/api-user-guide.html

Census GEOIDs Documentation: - https://www.census.gov/programs-surveys/geography/guidance/geo-identifiers.html

Census Regions: - https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf

NHGIS Data Crosswalk 2020 Block Group to 2010 Census Tract: - https://www.nhgis.org/geographic-crosswalks

Functions in R and Related Topics:

Functions in R:

https://www.datamentor.io/r-programming/function

String/Character Manipulation in the tidyverse:

https://stringr.tidyverse.org/articles/stringr.html

String/Character Manipulation with stringi:

https://stringi.gagolewski.com/index.html

Grepl/Regular Expressions in R:

If/Else Statements:

https://www.datamentor.io/r-programming/if-else-statement

For Loop:

https://www.dataquest.io/blog/for-loop-in-r/

Injection Operator with environment variables in R:

https://rlang.r-lib.org/reference/topic-data-mask-ambiguity.html#injecting-env-variables-with-

Library

To begin this week’s lab we need to load up our R libraries:

Code

# Load packages
library(here)        # relative filepaths for reproducibility
library(tidyverse)   # data wrangling
library(stringi)     # string wrangling
library(kableExtra)  # table formatting
library(tidycensus)  # census data

API Key Import

Next, as we learned last week in our introduction to reproducibility for data project management, we can import constants and functions from separate .R files.

Remember that your API key is a password, so all results of this chunk should be set to ‘hide’.

If for some reason you have not yet requested an API key, visit https://api.census.gov/data/key_signup.html

Code

# Load API key, assign to TidyCensus Package
source(here::here("analysis/password.R"))
census_api_key(census_api_key)

Project Data Steps

Recall that last week we created a R file called project_data_steps_[your initials].R (for example, my file labeled with my intials would be project_data_steps_CS.R).

In this file, you should have a library section with the libraries above and a variable called author that has your name assigned to it:

Code

#
# Author:     Courtney Stowers
# Date:       December 11, 2023
# Purpose:    Create custom functions to process data for SVI Tax Credit Project
#

# Library ----
library(here)
library(tidyverse)
library(stringi)
library(kableExtra)
library(tidycensus)


# Variables ----
author <- "Courtney Stowers"
census_division <- "Middle Atlantic Division"

Import

Next, we can import our author variable from our project_data_steps.R file to ensure that it works. Note this is different from the source() function used for our API key because it imports specific items from the file instead of the complete file.

Recall that we should use the here::here() function for relative file paths. We also use double colons :: to indicate both the specific library and function name we want to use to avoid any overriding.

Code

import::here( "author",
             # notice the use of here::here() that points to the .R file
             # where all these R objects are created
             .from = here::here("analysis/project_data_steps.R"),
             .character_only = TRUE)

Data

Once we have our functions loaded, we can load in the data sets we will need and preview them:

Code

#Load US Census region data
census_regions <- readxl::read_excel(here::here("data/raw/Census_Data_SVI/census_regions.xlsx"))

# Load SVI data sets
svi_2010 <- readRDS(here::here("data/raw/Census_Data_SVI/svi_2010_trt10.rds"))
svi_2020 <- readRDS(here::here("data/raw/Census_Data_SVI/svi_2020_trt10.rds"))

Code

# Load FIPS codes from TidyCensus
fips <- tidycensus::fips_codes
fips %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

state	state_code	state_name	county_code	county
AL	01	Alabama	001	Autauga County
AL	01	Alabama	003	Baldwin County
AL	01	Alabama	005	Barbour County
AL	01	Alabama	007	Bibb County
AL	01	Alabama	009	Blount County
AL	01	Alabama	011	Bullock County

Code

census_regions %>% colnames()

[1] "Region_Number"   "Region"          "Division_Number" "Division"       
[5] "State"

Code

colnames(census_regions) <- colnames(census_regions) %>% tolower()

census_regions %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

region_number	region	division_number	division	state
4	West Region	9	Pacific Division	AK
3	South Region	6	East South Central Division	AL
3	South Region	7	West South Central Division	AR
4	West Region	8	Mountain Division	AZ
4	West Region	9	Pacific Division	CA
4	West Region	8	Mountain Division	CO

Code

# Join FIPS codes to regions data
fips_census_regions <- left_join(fips, census_regions, join_by("state" == "state"))
fips_census_regions %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

state	state_code	state_name	county_code	county	region_number	region	division_number	division
AL	01	Alabama	001	Autauga County	3	South Region	6	East South Central Division
AL	01	Alabama	003	Baldwin County	3	South Region	6	East South Central Division
AL	01	Alabama	005	Barbour County	3	South Region	6	East South Central Division
AL	01	Alabama	007	Bibb County	3	South Region	6	East South Central Division
AL	01	Alabama	009	Blount County	3	South Region	6	East South Central Division
AL	01	Alabama	011	Bullock County	3	South Region	6	East South Central Division

Code

svi_2010 %>% colnames()

 [1] "GEOID_2010_trt"         "E_TOTPOP_10"            "E_HU_10"               
 [4] "E_HH_10"                "E_POV150_10"            "ET_POVSTATUS_10"       
 [7] "EP_POV150_10"           "EPL_POV150_10"          "E_UNEMP_10"            
[10] "ET_EMPSTATUS_10"        "EP_UNEMP_10"            "EPL_UNEMP_10"          
[13] "E_HBURD_OWN_10"         "ET_HOUSINGCOST_OWN_10"  "EP_HBURD_OWN_10"       
[16] "EPL_HBURD_OWN_10"       "E_HBURD_RENT_10"        "ET_HOUSINGCOST_RENT_10"
[19] "EP_HBURD_RENT_10"       "EPL_HBURD_RENT_10"      "E_HBURD_10"            
[22] "ET_HOUSINGCOST_10"      "EP_HBURD_10"            "EPL_HBURD_10"          
[25] "E_NOHSDP_10"            "ET_EDSTATUS_10"         "EP_NOHSDP_10"          
[28] "EPL_NOHSDP_10"          "E_UNINSUR_12"           "ET_INSURSTATUS_12"     
[31] "EP_UNINSUR_12"          "EPL_UNINSUR_12"         "E_AGE65_10"            
[34] "EP_AGE65_10"            "EPL_AGE65_10"           "E_AGE17_10"            
[37] "EP_AGE17_10"            "EPL_AGE17_10"           "E_DISABL_12"           
[40] "ET_DISABLSTATUS_12"     "EP_DISABL_12"           "EPL_DISABL_12"         
[43] "E_SNGPNT_10"            "ET_FAMILIES_10"         "EP_SNGPNT_10"          
[46] "EPL_SNGPNT_10"          "E_LIMENG_10"            "ET_POPAGE5UP_10"       
[49] "EP_LIMENG_10"           "EPL_LIMENG_10"          "E_MINRTY_10"           
[52] "ET_POPETHRACE_10"       "EP_MINRTY_10"           "EPL_MINRTY_10"         
[55] "E_STRHU_10"             "E_MUNIT_10"             "EP_MUNIT_10"           
[58] "EPL_MUNIT_10"           "E_MOBILE_10"            "EP_MOBILE_10"          
[61] "EPL_MOBILE_10"          "E_CROWD_10"             "ET_OCCUPANTS_10"       
[64] "EP_CROWD_10"            "EPL_CROWD_10"           "E_NOVEH_10"            
[67] "ET_KNOWNVEH_10"         "EP_NOVEH_10"            "EPL_NOVEH_10"          
[70] "E_GROUPQ_10"            "ET_HHTYPE_10"           "EP_GROUPQ_10"          
[73] "EPL_GROUPQ_10"

Code

svi_2010 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	ET_HHTYPE_10	EPL_GROUPQ_10
01001020100	1809	771	696	297	1809	16.41791	NA	36	889	4.049494	NA	127	598	21.23746	NA	47	98	47.95918	NA	174	696	25.00000	NA	196	1242	15.780998	NA	186	1759	10.574190	NA	222	12.271973	NA	445	24.59923	NA	298	1335	22.32210	NA	27	545	4.954128	NA	36	1705	2.1114370	NA	385	1809	21.282477	NA	771	0	0.0000000	NA	92	11.9325551	NA	0	696	0.0000000	NA	50	696	7.183908	NA	1809	NA
01001020200	2020	816	730	495	1992	24.84940	NA	68	834	8.153477	NA	49	439	11.16173	NA	105	291	36.08247	NA	154	730	21.09589	NA	339	1265	26.798419	NA	313	2012	15.556660	NA	204	10.099010	NA	597	29.55446	NA	359	1515	23.69637	NA	132	456	28.947368	NA	15	1890	0.7936508	NA	1243	2020	61.534653	NA	816	0	0.0000000	NA	34	4.1666667	NA	13	730	1.7808219	NA	115	730	15.753425	NA	2020	NA
01001020300	3543	1403	1287	656	3533	18.56779	NA	93	1552	5.992268	NA	273	957	28.52665	NA	178	330	53.93939	NA	451	1287	35.04274	NA	346	2260	15.309734	NA	252	3102	8.123791	NA	487	13.745413	NA	998	28.16822	NA	371	2224	16.68165	NA	126	913	13.800657	NA	0	3365	0.0000000	NA	637	3543	17.979114	NA	1403	10	0.7127584	NA	2	0.1425517	NA	0	1287	0.0000000	NA	101	1287	7.847708	NA	3543	NA
01001020400	4840	1957	1839	501	4840	10.35124	NA	101	2129	4.744011	NA	310	1549	20.01291	NA	89	290	30.68966	NA	399	1839	21.69657	NA	274	3280	8.353658	NA	399	4293	9.294200	NA	955	19.731405	NA	1195	24.69008	NA	625	3328	18.78005	NA	152	1374	11.062591	NA	10	4537	0.2204100	NA	297	4840	6.136364	NA	1957	33	1.6862545	NA	25	1.2774655	NA	14	1839	0.7612833	NA	19	1839	1.033170	NA	4840	NA
01001020500	9938	3969	3741	1096	9938	11.02838	NA	188	4937	3.807981	NA	426	2406	17.70574	NA	528	1335	39.55056	NA	954	3741	25.50120	NA	293	5983	4.897209	NA	740	10110	7.319486	NA	837	8.422218	NA	3012	30.30791	NA	759	7155	10.60797	NA	476	2529	18.821669	NA	78	9297	0.8389803	NA	1970	9938	19.822902	NA	3969	306	7.7097506	NA	0	0.0000000	NA	7	3741	0.1871157	NA	223	3741	5.960973	NA	9938	NA
01001020600	3402	1456	1308	735	3402	21.60494	NA	134	1720	7.790698	NA	242	1032	23.44961	NA	62	276	22.46377	NA	304	1308	23.24159	NA	301	2151	13.993491	NA	355	3445	10.304790	NA	386	11.346267	NA	931	27.36626	NA	440	2439	18.04018	NA	143	924	15.476190	NA	4	3254	0.1229256	NA	723	3402	21.252205	NA	1456	18	1.2362637	NA	433	29.7390110	NA	16	1308	1.2232416	NA	28	1308	2.140673	NA	3402	NA

Code

svi_2010 %>% nrow()

[1] 73057

We can then repeat these steps for our 2020 data:

Code

svi_2020 %>% colnames()

 [1] "GEOID_2010_trt"         "E_TOTPOP_20"            "E_HU_20"               
 [4] "E_HH_20"                "E_POV150_20"            "ET_POVSTATUS_20"       
 [7] "EP_POV150_20"           "EPL_POV150_20"          "E_UNEMP_20"            
[10] "ET_EMPSTATUS_20"        "EP_UNEMP_20"            "EPL_UNEMP_20"          
[13] "E_HBURD_OWN_20"         "ET_HOUSINGCOST_OWN_20"  "EP_HBURD_OWN_20"       
[16] "EPL_HBURD_OWN_20"       "E_HBURD_RENT_20"        "ET_HOUSINGCOST_RENT_20"
[19] "EP_HBURD_RENT_20"       "EPL_HBURD_RENT_20"      "E_HBURD_20"            
[22] "ET_HOUSINGCOST_20"      "EP_HBURD_20"            "EPL_HBURD_20"          
[25] "E_NOHSDP_20"            "ET_EDSTATUS_20"         "EP_NOHSDP_20"          
[28] "EPL_NOHSDP_20"          "E_UNINSUR_20"           "ET_INSURSTATUS_20"     
[31] "EP_UNINSUR_20"          "EPL_UNINSUR_20"         "E_AGE65_20"            
[34] "EP_AGE65_20"            "EPL_AGE65_20"           "E_AGE17_20"            
[37] "EP_AGE17_20"            "EPL_AGE17_20"           "E_DISABL_20"           
[40] "ET_DISABLSTATUS_20"     "EP_DISABL_20"           "EPL_DISABL_20"         
[43] "E_SNGPNT_20"            "ET_FAMILIES_20"         "EP_SNGPNT_20"          
[46] "EPL_SNGPNT_20"          "E_LIMENG_20"            "ET_POPAGE5UP_20"       
[49] "EP_LIMENG_20"           "EPL_LIMENG_20"          "E_MINRTY_20"           
[52] "ET_POPETHRACE_20"       "EP_MINRTY_20"           "EPL_MINRTY_20"         
[55] "E_STRHU_20"             "E_MUNIT_20"             "EP_MUNIT_20"           
[58] "EPL_MUNIT_20"           "E_MOBILE_20"            "EP_MOBILE_20"          
[61] "EPL_MOBILE_20"          "E_CROWD_20"             "ET_OCCUPANTS_20"       
[64] "EP_CROWD_20"            "EPL_CROWD_20"           "E_NOVEH_20"            
[67] "ET_KNOWNVEH_20"         "EP_NOVEH_20"            "EPL_NOVEH_20"          
[70] "E_GROUPQ_20"            "ET_HHTYPE_20"           "EP_GROUPQ_20"          
[73] "EPL_GROUPQ_20"

Code

svi_2020 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20
01001020100	1941	710	693	352	1941	18.13498	NA	18	852	2.112676	NA	81	507	15.976331	NA	63	186	33.87097	NA	144	693	20.77922	NA	187	1309	14.285714	NA	187	1941	9.634209	NA	295	15.19835	NA	415	21.38073	NA	391	1526	25.62254	NA	58	555	10.45045	NA	0	1843	0.0000000	NA	437	1941	22.51417	NA	710	0	0.0000000	NA	88	12.3943662	NA	0	693	0.0000000	NA	10	693	1.443001	NA	0	1941	0.000000	NA
01001020200	1757	720	573	384	1511	25.41363	NA	29	717	4.044630	NA	33	392	8.418367	NA	116	181	64.08840	NA	149	573	26.00349	NA	139	1313	10.586443	NA	91	1533	5.936073	NA	284	16.16392	NA	325	18.49744	NA	164	1208	13.57616	NA	42	359	11.69916	NA	0	1651	0.0000000	NA	1116	1757	63.51736	NA	720	3	0.4166667	NA	5	0.6944444	NA	9	573	1.5706806	NA	57	573	9.947644	NA	212	1757	12.066022	NA
01001020300	3694	1464	1351	842	3694	22.79372	NA	53	1994	2.657974	NA	117	967	12.099276	NA	147	384	38.28125	NA	264	1351	19.54108	NA	317	2477	12.797739	NA	127	3673	3.457664	NA	464	12.56091	NA	929	25.14889	NA	473	2744	17.23761	NA	263	975	26.97436	NA	128	3586	3.5694367	NA	1331	3694	36.03140	NA	1464	26	1.7759563	NA	14	0.9562842	NA	35	1351	2.5906736	NA	42	1351	3.108808	NA	0	3694	0.000000	NA
01001020400	3539	1741	1636	503	3539	14.21305	NA	39	1658	2.352232	NA	219	1290	16.976744	NA	74	346	21.38728	NA	293	1636	17.90954	NA	173	2775	6.234234	NA	169	3529	4.788892	NA	969	27.38062	NA	510	14.41085	NA	670	3019	22.19278	NA	148	1137	13.01671	NA	89	3409	2.6107363	NA	454	3539	12.82848	NA	1741	143	8.2136703	NA	0	0.0000000	NA	10	1636	0.6112469	NA	72	1636	4.400978	NA	0	3539	0.000000	NA
01001020500	10674	4504	4424	1626	10509	15.47245	NA	81	5048	1.604596	NA	321	2299	13.962592	NA	711	2125	33.45882	NA	1032	4424	23.32731	NA	531	6816	7.790493	NA	301	10046	2.996217	NA	1613	15.11149	NA	2765	25.90407	NA	1124	7281	15.43744	NA	342	2912	11.74451	NA	52	9920	0.5241935	NA	2603	10674	24.38636	NA	4504	703	15.6083481	NA	29	0.6438721	NA	37	4424	0.8363472	NA	207	4424	4.679023	NA	176	10674	1.648866	NA
01001020600	3536	1464	1330	1279	3523	36.30429	NA	34	1223	2.780049	NA	321	1111	28.892889	NA	67	219	30.59361	NA	388	1330	29.17293	NA	306	2380	12.857143	NA	415	3496	11.870709	NA	547	15.46946	NA	982	27.77149	NA	729	2514	28.99761	NA	95	880	10.79545	NA	0	3394	0.0000000	NA	985	3536	27.85633	NA	1464	0	0.0000000	NA	364	24.8633880	NA	0	1330	0.0000000	NA	17	1330	1.278196	NA	0	3536	0.000000	NA

Code

svi_2020 %>% nrow()

[1] 73057

Function Creation

Once we have our data loaded, we can now begin creating our functions to generate our SVI percentile ranks. For our study, we will want to be able to generate SVI percentile ranks on 5 different Census geographic levels:

National (All 50 states and D.C. in the United States)
Regional (Refer back to US Census regions in map above)
Divisional (Refer back to US Census divisions in map above)
State
County

As we discussed in week one, when embarking on a large, reproducible data project it is important to always consider ways that we can write code that can be reused multiple times and for various purposes. Thus, when we create functions, we want to conscientiously consider what information should be a hard coded value in our code and what information should be a variable (meaning, something that we can easily change without rewriting our code).

Fortunately the R language has a lot of flexibility to allow for us to create functions with variable components. A few key elements to allow for this are the following:

String/character manipulation
Regular expression (Regex/grepl)
If Statements
For loops
Injection Operators

While you should be familiar with the basics of function creation, string/character manipulation, regular expressions, if statements and for loops from previous courses in the PEDA program, Injection Operators may be an entirely new concept. No worries! We will learn how to utilize these tools together in the following step-by-step code. You can also refer back to the Additional Readings.

We can work directly in RMarkdown to create our functions and then copy them over to our project_data_steps.R file so we can re-import our functions later.

FIPS Region Assignment

In order to assign our data to their respective divisions/regions, we need to have a function that will create FIPS codes columns and allow us to link our Census Data with our fips_census_regions data set.

We can look back at the US Census Bureau’s GeoIdentifier structure to understand how to breakdown the GEOID into state, county, and tract identifiers.

We can then translate this to a function called fips_region_assignment:

Code

fips_region_assignment <- function(df) {

  
  # Create columns with fips codes for df
  df <- df %>% mutate(FIPS_st = substr(GEOID_2010_trt, 1, 2),
                                  FIPS_county = substr(GEOID_2010_trt, 3, 5),
                                  FIPS_tract = substr(GEOID_2010_trt, 6, 11)) %>%
    relocate(c(FIPS_st, FIPS_county, FIPS_tract),.after = GEOID_2010_trt)
  
  
  # Join region data 
  df <- left_join(df, fips_census_regions, join_by("FIPS_st" == "state_code",
                                                               "FIPS_county" == "county_code")) %>%
    relocate(c(state, state_name, county, region_number, region, division_number, division),.after = FIPS_tract)

  # Output data frame  
  return(df)
}

2010:

Code

svi_2010 <- fips_region_assignment(svi_2010)

svi_2010 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	ET_HHTYPE_10	EPL_GROUPQ_10
01001020100	01	001	020100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1809	771	696	297	1809	16.41791	NA	36	889	4.049494	NA	127	598	21.23746	NA	47	98	47.95918	NA	174	696	25.00000	NA	196	1242	15.780998	NA	186	1759	10.574190	NA	222	12.271973	NA	445	24.59923	NA	298	1335	22.32210	NA	27	545	4.954128	NA	36	1705	2.1114370	NA	385	1809	21.282477	NA	771	0	0.0000000	NA	92	11.9325551	NA	0	696	0.0000000	NA	50	696	7.183908	NA	1809	NA
01001020200	01	001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2020	816	730	495	1992	24.84940	NA	68	834	8.153477	NA	49	439	11.16173	NA	105	291	36.08247	NA	154	730	21.09589	NA	339	1265	26.798419	NA	313	2012	15.556660	NA	204	10.099010	NA	597	29.55446	NA	359	1515	23.69637	NA	132	456	28.947368	NA	15	1890	0.7936508	NA	1243	2020	61.534653	NA	816	0	0.0000000	NA	34	4.1666667	NA	13	730	1.7808219	NA	115	730	15.753425	NA	2020	NA
01001020300	01	001	020300	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3543	1403	1287	656	3533	18.56779	NA	93	1552	5.992268	NA	273	957	28.52665	NA	178	330	53.93939	NA	451	1287	35.04274	NA	346	2260	15.309734	NA	252	3102	8.123791	NA	487	13.745413	NA	998	28.16822	NA	371	2224	16.68165	NA	126	913	13.800657	NA	0	3365	0.0000000	NA	637	3543	17.979114	NA	1403	10	0.7127584	NA	2	0.1425517	NA	0	1287	0.0000000	NA	101	1287	7.847708	NA	3543	NA
01001020400	01	001	020400	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	4840	1957	1839	501	4840	10.35124	NA	101	2129	4.744011	NA	310	1549	20.01291	NA	89	290	30.68966	NA	399	1839	21.69657	NA	274	3280	8.353658	NA	399	4293	9.294200	NA	955	19.731405	NA	1195	24.69008	NA	625	3328	18.78005	NA	152	1374	11.062591	NA	10	4537	0.2204100	NA	297	4840	6.136364	NA	1957	33	1.6862545	NA	25	1.2774655	NA	14	1839	0.7612833	NA	19	1839	1.033170	NA	4840	NA
01001020500	01	001	020500	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	9938	3969	3741	1096	9938	11.02838	NA	188	4937	3.807981	NA	426	2406	17.70574	NA	528	1335	39.55056	NA	954	3741	25.50120	NA	293	5983	4.897209	NA	740	10110	7.319486	NA	837	8.422218	NA	3012	30.30791	NA	759	7155	10.60797	NA	476	2529	18.821669	NA	78	9297	0.8389803	NA	1970	9938	19.822902	NA	3969	306	7.7097506	NA	0	0.0000000	NA	7	3741	0.1871157	NA	223	3741	5.960973	NA	9938	NA
01001020600	01	001	020600	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3402	1456	1308	735	3402	21.60494	NA	134	1720	7.790698	NA	242	1032	23.44961	NA	62	276	22.46377	NA	304	1308	23.24159	NA	301	2151	13.993491	NA	355	3445	10.304790	NA	386	11.346267	NA	931	27.36626	NA	440	2439	18.04018	NA	143	924	15.476190	NA	4	3254	0.1229256	NA	723	3402	21.252205	NA	1456	18	1.2362637	NA	433	29.7390110	NA	16	1308	1.2232416	NA	28	1308	2.140673	NA	3402	NA

2020:

Code

svi_2020 <- fips_region_assignment(svi_2020)

svi_2020 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20
01001020100	01	001	020100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1941	710	693	352	1941	18.13498	NA	18	852	2.112676	NA	81	507	15.976331	NA	63	186	33.87097	NA	144	693	20.77922	NA	187	1309	14.285714	NA	187	1941	9.634209	NA	295	15.19835	NA	415	21.38073	NA	391	1526	25.62254	NA	58	555	10.45045	NA	0	1843	0.0000000	NA	437	1941	22.51417	NA	710	0	0.0000000	NA	88	12.3943662	NA	0	693	0.0000000	NA	10	693	1.443001	NA	0	1941	0.000000	NA
01001020200	01	001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1757	720	573	384	1511	25.41363	NA	29	717	4.044630	NA	33	392	8.418367	NA	116	181	64.08840	NA	149	573	26.00349	NA	139	1313	10.586443	NA	91	1533	5.936073	NA	284	16.16392	NA	325	18.49744	NA	164	1208	13.57616	NA	42	359	11.69916	NA	0	1651	0.0000000	NA	1116	1757	63.51736	NA	720	3	0.4166667	NA	5	0.6944444	NA	9	573	1.5706806	NA	57	573	9.947644	NA	212	1757	12.066022	NA
01001020300	01	001	020300	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3694	1464	1351	842	3694	22.79372	NA	53	1994	2.657974	NA	117	967	12.099276	NA	147	384	38.28125	NA	264	1351	19.54108	NA	317	2477	12.797739	NA	127	3673	3.457664	NA	464	12.56091	NA	929	25.14889	NA	473	2744	17.23761	NA	263	975	26.97436	NA	128	3586	3.5694367	NA	1331	3694	36.03140	NA	1464	26	1.7759563	NA	14	0.9562842	NA	35	1351	2.5906736	NA	42	1351	3.108808	NA	0	3694	0.000000	NA
01001020400	01	001	020400	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3539	1741	1636	503	3539	14.21305	NA	39	1658	2.352232	NA	219	1290	16.976744	NA	74	346	21.38728	NA	293	1636	17.90954	NA	173	2775	6.234234	NA	169	3529	4.788892	NA	969	27.38062	NA	510	14.41085	NA	670	3019	22.19278	NA	148	1137	13.01671	NA	89	3409	2.6107363	NA	454	3539	12.82848	NA	1741	143	8.2136703	NA	0	0.0000000	NA	10	1636	0.6112469	NA	72	1636	4.400978	NA	0	3539	0.000000	NA
01001020500	01	001	020500	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	10674	4504	4424	1626	10509	15.47245	NA	81	5048	1.604596	NA	321	2299	13.962592	NA	711	2125	33.45882	NA	1032	4424	23.32731	NA	531	6816	7.790493	NA	301	10046	2.996217	NA	1613	15.11149	NA	2765	25.90407	NA	1124	7281	15.43744	NA	342	2912	11.74451	NA	52	9920	0.5241935	NA	2603	10674	24.38636	NA	4504	703	15.6083481	NA	29	0.6438721	NA	37	4424	0.8363472	NA	207	4424	4.679023	NA	176	10674	1.648866	NA
01001020600	01	001	020600	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3536	1464	1330	1279	3523	36.30429	NA	34	1223	2.780049	NA	321	1111	28.892889	NA	67	219	30.59361	NA	388	1330	29.17293	NA	306	2380	12.857143	NA	415	3496	11.870709	NA	547	15.46946	NA	982	27.77149	NA	729	2514	28.99761	NA	95	880	10.79545	NA	0	3394	0.0000000	NA	985	3536	27.85633	NA	1464	0	0.0000000	NA	364	24.8633880	NA	0	1330	0.0000000	NA	17	1330	1.278196	NA	0	3536	0.000000	NA

SVI Percentile Ranking

First, notice that we are going to create a function called rank_variables and we’re going to list 4 separate variables:

df (our input data frame)
rank_by (an indicator of if we want our rankings to be national, regional, divisional, state, or county)
location (the exact location we want to rank)
state_abbr

Next, if we examine the code, we can find that we want to start by creating a list of column names that are relevant for ranking our SVI variables: the columns that start with EPL (indicated by the Caret symbol ^ in regex). As we noticed in our data above, these columns are currently empty and thus must be filled with the appropriate percentile rankings.

Once we create this list, we now can segment out our code using if statements to indicate how we want to calculate the percentile rankings for our data at different levels of measurement.

For example, on the regional level, we will filter our data to our regional location of interest. After that, we will go through a for loop to find each EPL_ column names in our data set, select the corresponding estimated percentage (EP) column, rank it, and then keep 4 significant digits (consistent with the CDC’s methodology).

In order to do this, we can use a for loop to cycle through all the column names and then the injection operator !! (called bang-bang) to allow us to insert the variable values epct and epl into our code and reuse it for each column name in the for loop. At the end of the loop, we merge our ranking back to our main data frame for our area of interest.

For county, we take these a step closer and require a state_abbr to complete our county ranking. This is because unlike our regional, divisional, and state levels of data which have unique names for all values, there are multiple states that have counties with the same names across the United States (If you’d like to see a fun example of this, type Jefferson County in Google and see how many county websites across different states appear!).

Finally, since we set the default value of rank_by to national, if we run the function with just a data set without specifying any other parameters, we will return all of our data with national rankings.

Code

rank_variables <- function(df, rank_by="national", location=NULL, state_abbr=NULL) {
  
  # Search column names for our estimated percentile columns
  epl_cols <- colnames(df) %>% str_detect("^EPL_")
  # Select these from all column names
  epl_cols_names <- colnames(df)[epl_cols]
  
    
  if (tolower(rank_by) == "regional") {
    # Create estimated percentile rankings on a regional level, with 4 significant digits
    location <- tolower(location)
    for (epl in epl_cols_names) {
      epct <- str_replace(epl, "EPL_", "EP_")
      df2 <- df %>% 
        filter(tolower(region) == location) %>%
        filter(!is.na(!!as.name(epct))) %>%
        mutate(!!as.name(epl) := rank(!!as.name(epct))/length(!!as.name(epct))) %>%
        mutate(!!as.name(epl) := signif(!!as.name(epl), 4)) %>% select(GEOID_2010_trt,
                                                                       !!as.name(epl))
      df3 <- df  %>% 
        filter(tolower(region) == location) %>%
        select(-quote(!!as.name(epl)))
      
      df <- left_join(df3, df2, join_by("GEOID_2010_trt" == "GEOID_2010_trt")) %>%
        relocate(c(!!as.name(epl)),.after = !!as.name(epct))
    }
  } else if (tolower(rank_by) == "divisional") {
    # Create estimated percentile rankings on a divisional level, with 4 significant digits
    location <- tolower(location)
    for (epl in epl_cols_names) {
      epct <- str_replace(epl, "EPL_", "EP_")
      df2 <- df %>% 
        filter(tolower(division) == location) %>%
        filter(!is.na(!!as.name(epct))) %>%
        mutate(!!as.name(epl) := rank(!!as.name(epct))/length(!!as.name(epct))) %>%
        mutate(!!as.name(epl) := signif(!!as.name(epl), 4)) %>% select(GEOID_2010_trt,
                                                                       !!as.name(epl))
      df3 <- df  %>% 
        filter(tolower(division) == location) %>% 
        select(-quote(!!as.name(epl)))
      
      df <- left_join(df3, df2, join_by("GEOID_2010_trt" == "GEOID_2010_trt")) %>%
        relocate(c(!!as.name(epl)),.after = !!as.name(epct))
    }
  } else if (tolower(rank_by) == "state") {
    # Create estimated percentile rankings on a state level, with 4 significant digits
    location <- tolower(location)
    for (epl in epl_cols_names) {
      epct <- str_replace(epl, "EPL_", "EP_")
      df2 <- df %>% 
        filter(tolower(state) == location) %>%
        filter(!is.na(!!as.name(epct))) %>%
        mutate(!!as.name(epl) := rank(!!as.name(epct))/length(!!as.name(epct))) %>%
        mutate(!!as.name(epl) := signif(!!as.name(epl), 4)) %>% select(GEOID_2010_trt,
                                                                       !!as.name(epl))
      df3 <- df  %>% 
        filter(tolower(state) == location) %>% 
        select(-quote(!!as.name(epl)))
      
      df <- left_join(df3, df2, join_by("GEOID_2010_trt" == "GEOID_2010_trt")) %>%
        relocate(c(!!as.name(epl)),.after = !!as.name(epct))
    }
  } else if (tolower(rank_by) == "county") {
    # Create estimated percentile rankings on a county level, require a state selection as 
    # county names are not unique outside of states, with 4 significant digits
    if (is.null(state_abbr)) {
      print("Enter state abbreviation in function and re-try")
      df <- NULL
    } else {
            location <- tolower(location)
            state_abbr <- tolower(state_abbr)
            for (epl in epl_cols_names) {
              epct <- str_replace(epl, "EPL_", "EP_")
              df2 <- df %>% 
                filter(tolower(state) == state_abbr) %>% 
                filter(tolower(county) == location) %>% 
                filter(!is.na(!!as.name(epct))) %>%
                mutate(!!as.name(epl) := rank(!!as.name(epct))/length(!!as.name(epct))) %>%
                mutate(!!as.name(epl) := signif(!!as.name(epl), 4)) %>% select(GEOID_2010_trt,
                                                                               !!as.name(epl))
              df3 <- df  %>% 
                filter(tolower(state) == state_abbr) %>% 
                filter(tolower(county) == location) %>% 
                select(-quote(!!as.name(epl)))
              
              df <- left_join(df3, df2, join_by("GEOID_2010_trt" == "GEOID_2010_trt")) %>%
                relocate(c(!!as.name(epl)),.after = !!as.name(epct))
                                
              
            }
    }
  } else {
    # If no lower level region is selected, create percentiles on a national level, with 4 significant digits
    for (epl in epl_cols_names) {
      epct <- str_replace(epl, "EPL_", "EP_")
      df2 <- df %>%
        filter(!is.na(!!as.name(epct))) %>%
        mutate(!!as.name(epl) := rank(!!as.name(epct))/length(!!as.name(epct))) %>%
        mutate(!!as.name(epl) := signif(!!as.name(epl), 4)) %>% select(GEOID_2010_trt,
                                                                               !!as.name(epl))
      df3 <- df  %>% 
        select(-quote(!!as.name(epl)))
      
      df <- left_join(df3, df2, join_by("GEOID_2010_trt" == "GEOID_2010_trt")) %>%
        relocate(c(!!as.name(epl)),.after = !!as.name(epct))
    }
  }
  # Output ranked df
  return(df)
}

SVI Percentile Ranking Function Output

National

Recall for national data we only need to enter our data set:

Code

# National
svi_2010_national <- rank_variables(svi_2010)
svi_2010_national %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	ET_HHTYPE_10	EPL_GROUPQ_10
01001020100	01	001	020100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1809	771	696	297	1809	16.41791	0.3871	36	889	4.049494	0.1790	127	598	21.23746	0.20770	47	98	47.95918	0.5767	174	696	25.00000	0.18790	196	1242	15.780998	0.6093	186	1759	10.574190	0.3790	222	12.271973	0.4876	445	24.59923	0.5473	298	1335	22.32210	0.8454	27	545	4.954128	0.09275	36	1705	2.1114370	0.59040	385	1809	21.282477	0.4524	771	0	0.0000000	0.1224	92	11.9325551	0.8005	0	696	0.0000000	0.1238	50	696	7.183908	0.6134	1809	0.364
01001020200	01	001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2020	816	730	495	1992	24.84940	0.5954	68	834	8.153477	0.5754	49	439	11.16173	0.02067	105	291	36.08247	0.3019	154	730	21.09589	0.09312	339	1265	26.798419	0.8392	313	2012	15.556660	0.6000	204	10.099010	0.3419	597	29.55446	0.8192	359	1515	23.69637	0.8791	132	456	28.947368	0.83510	15	1890	0.7936508	0.40130	1243	2020	61.534653	0.7781	816	0	0.0000000	0.1224	34	4.1666667	0.6664	13	730	1.7808219	0.5406	115	730	15.753425	0.8382	2020	0.364
01001020300	01	001	020300	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3543	1403	1287	656	3533	18.56779	0.4443	93	1552	5.992268	0.3724	273	957	28.52665	0.45780	178	330	53.93939	0.7152	451	1287	35.04274	0.49930	346	2260	15.309734	0.5950	252	3102	8.123791	0.2596	487	13.745413	0.5868	998	28.16822	0.7606	371	2224	16.68165	0.6266	126	913	13.800657	0.46350	0	3365	0.0000000	0.09298	637	3543	17.979114	0.4049	1403	10	0.7127584	0.3015	2	0.1425517	0.4407	0	1287	0.0000000	0.1238	101	1287	7.847708	0.6443	3543	0.364
01001020400	01	001	020400	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	4840	1957	1839	501	4840	10.35124	0.2177	101	2129	4.744011	0.2447	310	1549	20.01291	0.17080	89	290	30.68966	0.2044	399	1839	21.69657	0.10540	274	3280	8.353658	0.3205	399	4293	9.294200	0.3171	955	19.731405	0.8643	1195	24.69008	0.5530	625	3328	18.78005	0.7233	152	1374	11.062591	0.34710	10	4537	0.2204100	0.22560	297	4840	6.136364	0.1647	1957	33	1.6862545	0.3843	25	1.2774655	0.5516	14	1839	0.7612833	0.3564	19	1839	1.033170	0.1127	4840	0.364
01001020500	01	001	020500	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	9938	3969	3741	1096	9938	11.02838	0.2364	188	4937	3.807981	0.1577	426	2406	17.70574	0.11050	528	1335	39.55056	0.3753	954	3741	25.50120	0.20140	293	5983	4.897209	0.1655	740	10110	7.319486	0.2211	837	8.422218	0.2408	3012	30.30791	0.8455	759	7155	10.60797	0.2668	476	2529	18.821669	0.63540	78	9297	0.8389803	0.41110	1970	9938	19.822902	0.4330	3969	306	7.7097506	0.6153	0	0.0000000	0.2198	7	3741	0.1871157	0.2535	223	3741	5.960973	0.5483	9938	0.364
01001020600	01	001	020600	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3402	1456	1308	735	3402	21.60494	0.5199	134	1720	7.790698	0.5436	242	1032	23.44961	0.28010	62	276	22.46377	0.1035	304	1308	23.24159	0.14070	301	2151	13.993491	0.5510	355	3445	10.304790	0.3656	386	11.346267	0.4232	931	27.36626	0.7200	440	2439	18.04018	0.6912	143	924	15.476190	0.52900	4	3254	0.1229256	0.19840	723	3402	21.252205	0.4519	1456	18	1.2362637	0.3507	433	29.7390110	0.9468	16	1308	1.2232416	0.4493	28	1308	2.140673	0.2298	3402	0.364

Code

# National
svi_2020_national <- rank_variables(svi_2020)
svi_2020_national %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20
01001020100	01	001	020100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1941	710	693	352	1941	18.13498	0.4630	18	852	2.112676	0.15070	81	507	15.976331	0.26320	63	186	33.87097	0.2913	144	693	20.77922	0.2230	187	1309	14.285714	0.6928	187	1941	9.634209	0.6617	295	15.19835	0.4601	415	21.38073	0.4681	391	1526	25.62254	0.9011	58	555	10.45045	0.3451	0	1843	0.0000000	0.09479	437	1941	22.51417	0.3902	710	0	0.0000000	0.1079	88	12.3943662	0.8263	0	693	0.0000000	0.09796	10	693	1.443001	0.1643	0	1941	0.000000	0.1831
01001020200	01	001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1757	720	573	384	1511	25.41363	0.6427	29	717	4.044630	0.41320	33	392	8.418367	0.03542	116	181	64.08840	0.9086	149	573	26.00349	0.4041	139	1313	10.586443	0.5601	91	1533	5.936073	0.4343	284	16.16392	0.5169	325	18.49744	0.2851	164	1208	13.57616	0.4127	42	359	11.69916	0.3998	0	1651	0.0000000	0.09479	1116	1757	63.51736	0.7591	720	3	0.4166667	0.2470	5	0.6944444	0.5106	9	573	1.5706806	0.46880	57	573	9.947644	0.7317	212	1757	12.066022	0.9549
01001020300	01	001	020300	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3694	1464	1351	842	3694	22.79372	0.5833	53	1994	2.657974	0.22050	117	967	12.099276	0.11370	147	384	38.28125	0.3856	264	1351	19.54108	0.1827	317	2477	12.797739	0.6460	127	3673	3.457664	0.2308	464	12.56091	0.3088	929	25.14889	0.7080	473	2744	17.23761	0.6211	263	975	26.97436	0.8234	128	3586	3.5694367	0.70770	1331	3694	36.03140	0.5515	1464	26	1.7759563	0.3675	14	0.9562842	0.5389	35	1351	2.5906736	0.60550	42	1351	3.108808	0.3415	0	3694	0.000000	0.1831
01001020400	01	001	020400	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3539	1741	1636	503	3539	14.21305	0.3472	39	1658	2.352232	0.17990	219	1290	16.976744	0.30880	74	346	21.38728	0.1037	293	1636	17.90954	0.1333	173	2775	6.234234	0.3351	169	3529	4.788892	0.3448	969	27.38062	0.9225	510	14.41085	0.1208	670	3019	22.19278	0.8194	148	1137	13.01671	0.4541	89	3409	2.6107363	0.64690	454	3539	12.82848	0.2364	1741	143	8.2136703	0.6028	0	0.0000000	0.2186	10	1636	0.6112469	0.28340	72	1636	4.400978	0.4538	0	3539	0.000000	0.1831
01001020500	01	001	020500	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	10674	4504	4424	1626	10509	15.47245	0.3851	81	5048	1.604596	0.09431	321	2299	13.962592	0.17970	711	2125	33.45882	0.2836	1032	4424	23.32731	0.3109	531	6816	7.790493	0.4251	301	10046	2.996217	0.1894	1613	15.11149	0.4553	2765	25.90407	0.7494	1124	7281	15.43744	0.5253	342	2912	11.74451	0.4019	52	9920	0.5241935	0.35230	2603	10674	24.38636	0.4160	4504	703	15.6083481	0.7378	29	0.6438721	0.5037	37	4424	0.8363472	0.33420	207	4424	4.679023	0.4754	176	10674	1.648866	0.7598
01001020600	01	001	020600	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3536	1464	1330	1279	3523	36.30429	0.8215	34	1223	2.780049	0.23780	321	1111	28.892889	0.75870	67	219	30.59361	0.2305	388	1330	29.17293	0.5075	306	2380	12.857143	0.6480	415	3496	11.870709	0.7535	547	15.46946	0.4760	982	27.77149	0.8327	729	2514	28.99761	0.9488	95	880	10.79545	0.3601	0	3394	0.0000000	0.09479	985	3536	27.85633	0.4608	1464	0	0.0000000	0.1079	364	24.8633880	0.9300	0	1330	0.0000000	0.09796	17	1330	1.278196	0.1463	0	3536	0.000000	0.1831

Regional

Recall for regional data we need to enter our rank_by variables and location variable:

Code

# Regional
svi_2010_regional <- rank_variables(svi_2010, rank_by = "regional", location = "Northeast Region")
svi_2010_regional %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10
09001010101	09	001	010101	CT	Connecticut	Fairfield County	1	Northeast Region	1	New England Division	4053	1608	1416	242	3851	6.284082	0.16010	52	1511	3.441429	0.1265	429	1240	34.59677	0.5194	24	176	13.63636	0.03745	453	1416	31.99153	0.29440	234	2778	8.423326	0.35790	150	3851	3.895092	0.17230	959	23.66149	0.9384	1115	27.51049	0.8155	252	2898	8.695652	0.1725	32	1041	3.073967	0.05352	96	3821	2.5124313	0.6127	175	4053	4.317789	0.1678	1608	46	2.8606965	0.38610	0	0.0000000	0.3204	11	1416	0.7768362	0.4311	43	1416	3.036723	0.22200	202	4053	4.983962	0.8725
09001010102	09	001	010102	CT	Connecticut	Fairfield County	1	Northeast Region	1	New England Division	4833	1538	1430	205	4815	4.257529	0.08309	78	1799	4.335742	0.2178	478	1352	35.35503	0.5437	37	78	47.43590	0.54680	515	1430	36.01399	0.42400	66	3159	2.089269	0.04865	156	4544	3.433099	0.14220	750	15.51831	0.6469	1419	29.36065	0.8840	290	3345	8.669656	0.1708	28	1235	2.267206	0.03553	131	4538	2.8867342	0.6395	1176	4833	24.332713	0.5919	1538	0	0.0000000	0.09502	0	0.0000000	0.3204	0	1430	0.0000000	0.1525	101	1430	7.062937	0.46710	237	4833	4.903786	0.8706
09001010201	09	001	010201	CT	Connecticut	Fairfield County	1	Northeast Region	1	New England Division	3550	1091	948	108	3346	3.227735	0.05048	53	950	5.578947	0.3654	246	903	27.24252	0.2833	8	45	17.77778	0.05548	254	948	26.79325	0.14380	99	2117	4.676429	0.15780	49	3217	1.523158	0.04121	672	18.92958	0.8255	1282	36.11268	0.9778	244	2146	11.369991	0.3628	71	894	7.941834	0.26130	25	3422	0.7305669	0.3653	300	3550	8.450704	0.3187	1091	0	0.0000000	0.09502	0	0.0000000	0.3204	0	948	0.0000000	0.1525	0	948	0.000000	0.01141	204	3550	5.746479	0.8862
09001010202	09	001	010202	CT	Connecticut	Fairfield County	1	Northeast Region	1	New England Division	4839	1928	1729	215	4826	4.455035	0.08982	79	2116	3.733459	0.1528	335	1413	23.70842	0.1771	64	316	20.25316	0.06942	399	1729	23.07692	0.06555	149	3374	4.416123	0.14510	92	5430	1.694291	0.04860	693	14.32114	0.5654	1304	26.94772	0.7899	332	3969	8.364827	0.1531	71	1323	5.366591	0.13510	123	4514	2.7248560	0.6284	837	4839	17.296962	0.5062	1928	9	0.4668050	0.20820	10	0.5186722	0.6707	0	1729	0.0000000	0.1525	10	1729	0.578369	0.03255	0	4839	0.000000	0.3433
09001010300	09	001	010300	CT	Connecticut	Fairfield County	1	Northeast Region	1	New England Division	3847	1632	1421	218	3847	5.666753	0.13480	106	1463	7.245386	0.5540	398	1125	35.37778	0.5447	116	296	39.18919	0.34300	514	1421	36.17171	0.42800	101	2762	3.656770	0.10950	215	3910	5.498721	0.28470	732	19.02781	0.8296	961	24.98050	0.6777	219	2898	7.556936	0.1061	24	1076	2.230483	0.03478	16	3668	0.4362050	0.2769	536	3847	13.932935	0.4482	1632	8	0.4901961	0.21090	0	0.0000000	0.3204	0	1421	0.0000000	0.1525	26	1421	1.829697	0.11950	0	3847	0.000000	0.3433
09001010400	09	001	010400	CT	Connecticut	Fairfield County	1	Northeast Region	1	New England Division	5181	2250	2108	365	5181	7.044972	0.18980	102	2379	4.287516	0.2118	764	1600	47.75000	0.8269	275	508	54.13386	0.70890	1039	2108	49.28842	0.79620	301	3682	8.174905	0.34450	473	5252	9.006093	0.52990	1035	19.97684	0.8601	1333	25.72862	0.7257	661	4151	15.923874	0.6668	197	1357	14.517318	0.55450	67	4767	1.4054961	0.4895	595	5181	11.484269	0.3994	2250	123	5.4666667	0.49320	10	0.4444444	0.6594	26	2108	1.2333966	0.5267	63	2108	2.988615	0.21810	0	5181	0.000000	0.3433

Code

# Regional
svi_2020_regional <- rank_variables(svi_2020, rank_by = "regional", location = "Northeast Region")
svi_2020_regional %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20
09001010101	09	001	010101	CT	Connecticut	Fairfield County	1	Northeast Region	1	New England Division	4192	1591	1347	241	3922	6.144824	0.144600	76	1947	3.903441	0.3806	554	1108	50.00000	0.9397	86	239	35.98326	0.29250	640	1347	47.51299	0.8415	93	3061	3.038223	0.14930	7	3922	0.1784804	0.014040	1238	29.53244	0.9500	908	21.66031	0.6033	259	3014.000	8.593232	0.15340	65	1033.000	6.2923524	0.20440	63	4022	1.5663849	0.50770	453	4192.000	10.80630	0.2732	1591	25	1.5713388	0.29150	0	0.000000	0.3172	25	1347	1.8559762	0.5608	33	1347.000	2.4498886	0.17820	275	4192	6.5601145	0.9107
09001010102	09	001	010102	CT	Connecticut	Fairfield County	1	Northeast Region	1	New England Division	4419	1602	1362	60	4419	1.357773	0.007698	67	2032	3.297244	0.2916	363	1231	29.48822	0.6387	29	131	22.13740	0.08811	392	1362	28.78120	0.3840	33	2827	1.167315	0.03804	4	4419	0.0905182	0.011050	828	18.73727	0.6218	1208	27.33650	0.8876	239	3211.000	7.443164	0.09654	8	1255.000	0.6374502	0.01594	17	4186	0.4061156	0.27980	539	4419.000	12.19733	0.3037	1602	0	0.0000000	0.08313	0	0.000000	0.3172	9	1362	0.6607930	0.3515	17	1362.000	1.2481645	0.08452	20	4419	0.4525911	0.5565
09001010201	09	001	010201	CT	Connecticut	Fairfield County	1	Northeast Region	1	New England Division	3429	1255	1119	181	3277	5.523345	0.120100	76	1201	6.328060	0.6691	255	978	26.07362	0.5312	24	141	17.02128	0.05272	279	1119	24.93298	0.2590	44	2207	1.993656	0.07981	0	3277	0.0000000	0.004705	741	21.60980	0.7671	964	28.11315	0.9053	158	2313.000	6.830955	0.07420	21	871.000	2.4110218	0.04849	10	3218	0.3107520	0.24640	561	3429.000	16.36045	0.3846	1255	24	1.9123506	0.31520	0	0.000000	0.3172	0	1119	0.0000000	0.1244	24	1119.000	2.1447721	0.15360	251	3429	7.3199183	0.9178
09001010202	09	001	010202	CT	Connecticut	Fairfield County	1	Northeast Region	1	New England Division	5262	1917	1811	647	5245	12.335558	0.390800	169	2620	6.450382	0.6798	346	1294	26.73879	0.5524	272	517	52.61122	0.68900	618	1811	34.12479	0.5544	175	3433	5.097582	0.29890	83	5263	1.5770473	0.158300	883	16.78069	0.5027	1550	29.45648	0.9311	383	3713.163	10.314657	0.26320	136	1483.018	9.1704909	0.34670	157	5042	3.1138437	0.64660	1339	5261.892	25.44712	0.5154	1917	7	0.3651539	0.18520	0	0.000000	0.3172	0	1811	0.0000000	0.1244	42	1811.390	2.3186617	0.16720	8	5262	0.1520334	0.3711
09001010300	09	001	010300	CT	Connecticut	Fairfield County	1	Northeast Region	1	New England Division	3815	1664	1457	89	3810	2.335958	0.020850	81	1839	4.404568	0.4541	293	1038	28.22736	0.6002	75	419	17.89976	0.05786	368	1457	25.25738	0.2693	35	2698	1.297257	0.04371	38	3809	0.9976372	0.083710	742	19.44954	0.6605	881	23.09305	0.6986	124	2928.576	4.234140	0.01599	31	1164.582	2.6618993	0.05612	0	3612	0.0000000	0.07348	502	3815.450	13.15703	0.3245	1664	51	3.0649038	0.37610	13	0.781250	0.7117	13	1457	0.8922443	0.4053	13	1456.744	0.8924009	0.05999	58	3815	1.5203145	0.7278
09001010400	09	001	010400	CT	Connecticut	Fairfield County	1	Northeast Region	1	New England Division	5846	2131	1978	352	5846	6.021211	0.139600	171	3470	4.927954	0.5229	259	1285	20.15564	0.3169	339	693	48.91775	0.59620	598	1978	30.23256	0.4314	169	4282	3.946754	0.21520	342	5846	5.8501540	0.656600	948	16.21622	0.4662	1144	19.56894	0.4424	590	4702.000	12.547852	0.41400	187	1561.000	11.9795003	0.47280	72	5567	1.2933357	0.47110	946	5846.000	16.18200	0.3817	2131	92	4.3172220	0.42850	23	1.079306	0.7367	76	1978	3.8422649	0.7360	15	1978.000	0.7583418	0.04981	23	5846	0.3934314	0.5304

Divisional

For divisional we need to enter our divisional variable and our location:

Code

# Divisional
svi_2010_divisional <- rank_variables(svi_2010, rank_by = "divisional", location = "Middle Atlantic Division")
svi_2010_divisional %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10
34001000100	34	001	000100	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	2907	1088	983	1127	2907	38.76849	0.8482	144	1433	10.048849	0.7544	280	435	64.36782	0.9529	204	548	37.22628	0.2998	484	983	49.23703	0.7813	468	1759	26.60603	0.8634	532	2543	20.92017	0.8978	250	8.599931	0.1777	944	32.47334	0.94170	186	1851	10.04862	0.2706	266	678	39.233038	0.8981	177	2611	6.779012	0.7778	1928	2907	66.32267	0.7743	1088	113	10.386029	0.6229	9	0.8272059	0.7223	80	983	8.138352	0.8657	265	983	26.95829	0.7354	0	2907	0.000000	0.3512
34001000200	34	001	000200	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	3189	2217	1473	519	3189	16.27469	0.4806	109	1558	6.996149	0.5179	573	955	60.00000	0.9323	199	518	38.41699	0.3261	772	1473	52.41005	0.8418	405	2579	15.70376	0.6491	484	3547	13.64533	0.7154	847	26.560050	0.9629	436	13.67200	0.08181	608	3005	20.23295	0.8466	42	857	4.900817	0.1204	422	3072	13.736979	0.8799	1792	3189	56.19316	0.7390	2217	901	40.640505	0.8693	0	0.0000000	0.3251	48	1473	3.258656	0.7064	250	1473	16.97217	0.6444	0	3189	0.000000	0.3512
34001000300	34	001	000300	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	3997	1823	1357	1401	3968	35.30746	0.8164	382	2238	17.068811	0.9376	176	329	53.49544	0.8855	604	1028	58.75486	0.7947	780	1357	57.47973	0.9165	920	2677	34.36683	0.9346	1351	4149	32.56206	0.9811	314	7.855892	0.1437	937	23.44258	0.55900	319	3054	10.44532	0.3000	187	782	23.913044	0.7498	1080	3671	29.419777	0.9742	3357	3997	83.98799	0.8419	1823	363	19.912233	0.7535	0	0.0000000	0.3251	150	1357	11.053795	0.9136	651	1357	47.97347	0.8585	0	3997	0.000000	0.3512
34001000400	34	001	000400	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	2902	2683	1401	1172	2902	40.38594	0.8615	190	1389	13.678906	0.8811	364	707	51.48515	0.8627	507	694	73.05476	0.9503	871	1401	62.16988	0.9572	481	1981	24.28067	0.8339	674	3204	21.03620	0.8998	434	14.955203	0.6083	596	20.53756	0.33980	426	2607	16.34062	0.6886	111	652	17.024540	0.6204	215	2736	7.858187	0.8008	1792	2902	61.75052	0.7584	2683	2049	76.369735	0.9401	0	0.0000000	0.3251	69	1401	4.925053	0.7847	511	1401	36.47395	0.7992	72	2902	2.481048	0.8114
34001000500	34	001	000500	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	3483	1241	1027	1938	3483	55.64169	0.9533	124	1630	7.607362	0.5830	227	446	50.89686	0.8549	478	581	82.27194	0.9799	705	1027	68.64654	0.9863	733	2077	35.29129	0.9396	727	3258	22.31430	0.9149	377	10.824002	0.3081	1055	30.28998	0.90140	268	2401	11.16202	0.3549	209	763	27.391874	0.7940	911	3077	29.606760	0.9746	3036	3483	87.16624	0.8550	1241	52	4.190169	0.4505	4	0.3223207	0.6567	113	1027	11.002921	0.9128	422	1027	41.09056	0.8250	0	3483	0.000000	0.3512
34001001100	34	001	001100	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	2204	1204	1204	1185	2204	53.76588	0.9457	219	927	23.624596	0.9830	97	172	56.39535	0.9094	462	1032	44.76744	0.4746	559	1204	46.42857	0.7197	346	1440	24.02778	0.8306	469	1942	24.15036	0.9360	363	16.470054	0.7020	578	26.22505	0.74410	442	1558	28.36970	0.9675	247	396	62.373737	0.9898	104	2051	5.070697	0.7260	2118	2204	96.09800	0.9204	1204	570	47.342193	0.8858	0	0.0000000	0.3251	14	1204	1.162791	0.4877	817	1204	67.85714	0.9413	0	2204	0.000000	0.3512

Code

# Divisional
svi_2020_divisional <- rank_variables(svi_2020, rank_by = "divisional", location = "Middle Atlantic Division")
svi_2020_divisional %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20
34001000100	34	001	000100	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	2157	941	784	1182	2157	54.79833	0.9571	242	1058	22.873346	0.9922	215	342	62.86550	0.9780	316	442	71.49321	0.9481	531	784	67.72959	0.9893	396	1274	31.08320	0.9497	266	2157	12.331943	0.9041	185	8.576727	0.09430	552	25.59110	0.8128	297	1605	18.504673	0.74880	83	510	16.27451	0.6090	251	2020	12.425743	0.8710	1852	2157	85.85999	0.8476	941	118	12.5398512	0.6385	0	0.0000000	0.3216	67	784	8.545918	0.8657	212	784	27.04082	0.7502	0	2157	0.0000000	0.1517
34001000200	34	001	000200	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	3510	2046	1353	1021	3510	29.08832	0.7682	121	1852	6.533477	0.6717	343	696	49.28161	0.9273	416	657	63.31811	0.8696	759	1353	56.09756	0.9321	553	2338	23.65269	0.8871	354	3510	10.085470	0.8530	643	18.319088	0.60310	1002	28.54701	0.9055	450	2508	17.942584	0.72330	237	786	30.15267	0.8539	534	3375	15.822222	0.9062	2534	3510	72.19373	0.7818	2046	906	44.2815249	0.8690	0	0.0000000	0.3216	119	1353	8.795270	0.8711	324	1353	23.94678	0.7255	0	3510	0.0000000	0.1517
34001000300	34	001	000300	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	3801	1640	1226	1857	3801	48.85556	0.9333	226	1800	12.555556	0.9267	111	280	39.64286	0.8339	608	946	64.27061	0.8842	719	1226	58.64600	0.9528	650	2275	28.57143	0.9337	1027	3801	27.019206	0.9914	380	9.997369	0.14040	1223	32.17574	0.9607	219	2578	8.494957	0.15680	268	909	29.48295	0.8456	940	3400	27.647059	0.9728	3318	3801	87.29282	0.8579	1640	262	15.9756098	0.6917	0	0.0000000	0.3216	124	1226	10.114192	0.8955	477	1226	38.90701	0.8258	0	3801	0.0000000	0.1517
34001000400	34	001	000400	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	3178	2264	1390	1508	3176	47.48111	0.9246	172	1804	9.534368	0.8460	205	468	43.80342	0.8858	622	922	67.46204	0.9192	827	1390	59.49640	0.9587	364	2076	17.53372	0.8013	476	3178	14.977974	0.9390	483	15.198238	0.41220	539	16.96035	0.2484	319	2639	12.087912	0.38790	101	565	17.87611	0.6539	583	3022	19.291860	0.9349	2186	3178	68.78540	0.7658	2264	1609	71.0689046	0.9266	15	0.6625442	0.7078	226	1390	16.258993	0.9567	599	1390	43.09353	0.8474	20	3178	0.6293266	0.6292
34001000500	34	001	000500	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	3385	1185	945	1682	3364	50.00000	0.9391	72	1577	4.565631	0.4586	185	468	39.52991	0.8332	362	477	75.89099	0.9703	547	945	57.88360	0.9477	592	1983	29.85376	0.9422	738	3385	21.802068	0.9817	240	7.090103	0.05988	1129	33.35303	0.9689	135	2256	5.984043	0.04817	110	717	15.34170	0.5822	721	3076	23.439532	0.9569	3029	3385	89.48301	0.8727	1185	9	0.7594937	0.2382	0	0.0000000	0.3216	103	945	10.899471	0.9072	263	945	27.83069	0.7560	0	3385	0.0000000	0.1517
34001001100	34	001	001100	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	1950	1267	1096	1131	1950	58.00000	0.9678	66	706	9.348442	0.8395	42	101	41.58416	0.8612	309	995	31.05528	0.1959	351	1096	32.02555	0.4782	510	1379	36.98332	0.9763	155	1950	7.948718	0.7660	392	20.102564	0.69880	447	22.92308	0.6712	570	1503	37.924152	0.99200	143	374	38.23529	0.9167	109	1841	5.920695	0.7464	1909	1950	97.89744	0.9529	1267	479	37.8058406	0.8464	0	0.0000000	0.3216	33	1096	3.010949	0.6446	743	1096	67.79197	0.9414	0	1950	0.0000000	0.1517

State

For state we need to enter state and our state of interest:

Code

# State
svi_2010_state <- rank_variables(svi_2010, rank_by = "state", location = "PA")
svi_2010_state %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	ET_HHTYPE_10	EPL_GROUPQ_10
42001030101	42	001	030101	PA	Pennsylvania	Adams County	1	Northeast Region	2	Middle Atlantic Division	2597	993	915	230	2597	8.856373	0.1950	51	1399	3.645461	0.14870	279	840	33.21429	0.7852	27	75	36.00000	0.3706	306	915	33.44262	0.6063	185	1784	10.36996	0.4372	172	2580	6.666667	0.3133	280	10.781671	0.1947	694	26.72314	0.8216	281	1963	14.31482	0.4013	36	808	4.455445	0.09445	16	2481	0.6449012	0.5528	120	2597	4.620716	0.3091	993	0	0.000000	0.1312	55	5.538771	0.7558	5	915	0.5464481	0.4989	9	915	0.9836066	0.06514	2597	0.3334
42001030102	42	001	030102	PA	Pennsylvania	Adams County	1	Northeast Region	2	Middle Atlantic Division	5709	2415	2220	675	5709	11.823437	0.2920	236	3176	7.430731	0.59030	595	1894	31.41499	0.7163	131	326	40.18405	0.4772	726	2220	32.70270	0.5744	645	3990	16.16541	0.7281	326	5717	5.702291	0.2461	703	12.313890	0.2850	1332	23.33158	0.6116	674	4490	15.01114	0.4460	185	1727	10.712218	0.41920	95	5461	1.7396081	0.7700	370	5709	6.480995	0.3994	2415	0	0.000000	0.1312	261	10.807453	0.8497	61	2220	2.7477477	0.8804	62	2220	2.7927928	0.21860	5709	0.3334
42001030200	42	001	030200	PA	Pennsylvania	Adams County	1	Northeast Region	2	Middle Atlantic Division	5656	2180	2111	983	5588	17.591267	0.4964	161	3170	5.078864	0.30640	481	1677	28.68217	0.6106	154	434	35.48387	0.3586	635	2111	30.08053	0.4666	771	3785	20.36988	0.8475	877	5471	16.029976	0.8743	664	11.739745	0.2453	1428	25.24752	0.7469	630	4135	15.23579	0.4586	247	1594	15.495608	0.61690	193	5363	3.5987321	0.8791	866	5656	15.311174	0.6466	2180	0	0.000000	0.1312	302	13.853211	0.8982	70	2111	3.3159640	0.9126	85	2111	4.0265277	0.31290	5656	0.3334
42001030300	42	001	030300	PA	Pennsylvania	Adams County	1	Northeast Region	2	Middle Atlantic Division	3932	1561	1438	522	3913	13.340148	0.3424	103	2127	4.842501	0.27670	357	1097	32.54330	0.7606	89	341	26.09971	0.1637	446	1438	31.01530	0.5089	452	2560	17.65625	0.7828	634	4125	15.369697	0.8549	380	9.664293	0.1431	1059	26.93286	0.8306	369	3011	12.25506	0.2787	176	1125	15.644444	0.62220	190	3617	5.2529721	0.9178	673	3932	17.115972	0.6756	1561	0	0.000000	0.1312	90	5.765535	0.7592	67	1438	4.6592490	0.9511	6	1438	0.4172462	0.03038	3932	0.3334
42001030400	42	001	030400	PA	Pennsylvania	Adams County	1	Northeast Region	2	Middle Atlantic Division	5661	2359	2218	765	5658	13.520679	0.3512	74	3122	2.370275	0.05383	577	1766	32.67271	0.7653	221	452	48.89381	0.6877	798	2218	35.97836	0.6846	520	3886	13.38137	0.5988	697	5692	12.245256	0.7295	688	12.153330	0.2731	1423	25.13690	0.7381	694	4338	15.99816	0.5052	320	1633	19.595836	0.71700	29	5276	0.5496588	0.5116	644	5661	11.376082	0.5712	2359	77	3.264095	0.5131	224	9.495549	0.8303	22	2218	0.9918846	0.6427	141	2218	6.3570784	0.47170	5661	0.3334
42001030500	42	001	030500	PA	Pennsylvania	Adams County	1	Northeast Region	2	Middle Atlantic Division	3676	1585	1541	568	3670	15.476839	0.4235	96	1952	4.918033	0.28480	275	1214	22.65239	0.2979	142	327	43.42508	0.5586	417	1541	27.06035	0.3301	477	2622	18.19222	0.7978	565	3711	15.225007	0.8483	631	17.165397	0.6422	737	20.04897	0.3312	566	3026	18.70456	0.6753	116	1013	11.451135	0.45280	87	3464	2.5115473	0.8356	500	3676	13.601741	0.6162	1585	39	2.460568	0.4703	160	10.094637	0.8406	22	1541	1.4276444	0.7285	59	1541	3.8286827	0.29970	3676	0.3334

Code

# State
svi_2020_state <- rank_variables(svi_2020, rank_by = "state", location = "PA")
svi_2020_state %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20
42001030101	42	001	030101	PA	Pennsylvania	Adams County	1	Northeast Region	2	Middle Atlantic Division	2651	1160	1058	246	2651	9.279517	0.2243	12	1433	0.837404	0.02914	200	940	21.27660	0.6030	33	118	27.96610	0.20760	233	1058	22.02268	0.3198	167	1930	8.65285	0.5666	182	2652	6.862745	0.7413	521	19.65296	0.5860	601	22.67069	0.6889	262	2050.964	12.77448	0.2721	33	760.4212	4.339700	0.09897	42	2495	1.683367	0.7392	397	2650.619	14.977633	0.5194	1160	0	0.000000	0.1113	50	4.310345	0.7433	21	1058	1.984877	0.7410	8	1057.939	0.7561874	0.04917	0	2651	0.0000000	0.1546
42001030102	42	001	030102	PA	Pennsylvania	Adams County	1	Northeast Region	2	Middle Atlantic Division	5873	2397	2211	591	5837	10.125064	0.2547	138	3182	4.336895	0.46080	296	2028	14.59566	0.2304	75	183	40.98361	0.51510	371	2211	16.77974	0.1063	455	4273	10.64826	0.6904	337	5873	5.738124	0.6508	1256	21.38600	0.6876	1286	21.89682	0.6345	751	4587.000	16.37236	0.4975	130	1733.0000	7.501443	0.24150	0	5729	0.000000	0.1334	280	5873.000	4.767581	0.1981	2397	0	0.000000	0.1113	341	14.226116	0.9282	25	2211	1.130710	0.6043	33	2211.000	1.4925373	0.10300	0	5873	0.0000000	0.1546
42001030200	42	001	030200	PA	Pennsylvania	Adams County	1	Northeast Region	2	Middle Atlantic Division	5824	2189	2044	1054	5816	18.122421	0.5288	170	2876	5.910988	0.64130	341	1574	21.66455	0.6237	206	470	43.82979	0.59050	547	2044	26.76125	0.5394	718	4007	17.91864	0.8949	756	5808	13.016529	0.9439	976	16.75824	0.4081	1432	24.58791	0.7930	609	4376.036	13.91670	0.3447	130	1572.5788	8.266676	0.28590	266	5495	4.840764	0.8937	1357	5824.381	23.298614	0.6566	2189	0	0.000000	0.1113	393	17.953403	0.9602	55	2044	2.690802	0.8172	59	2044.061	2.8864106	0.22300	7	5824	0.1201923	0.3637
42001030300	42	001	030300	PA	Pennsylvania	Adams County	1	Northeast Region	2	Middle Atlantic Division	4306	1763	1620	324	4298	7.538390	0.1620	57	2528	2.254747	0.16040	245	1358	18.04124	0.4282	53	262	20.22901	0.09642	298	1620	18.39506	0.1703	359	3137	11.44405	0.7264	310	4299	7.210979	0.7670	666	15.46679	0.3333	824	19.13609	0.4109	473	3475.000	13.61151	0.3262	20	1173.0000	1.705030	0.02443	151	4119	3.665938	0.8624	813	4306.000	18.880632	0.5894	1763	0	0.000000	0.1113	182	10.323313	0.8665	59	1620	3.641975	0.8786	32	1620.000	1.9753086	0.14030	87	4306	2.0204366	0.7477
42001030400	42	001	030400	PA	Pennsylvania	Adams County	1	Northeast Region	2	Middle Atlantic Division	5798	2614	2271	967	5697	16.973846	0.4937	69	2979	2.316213	0.16920	444	1839	24.14356	0.7325	138	432	31.94444	0.28360	582	2271	25.62748	0.4917	696	4198	16.57932	0.8705	549	5697	9.636651	0.8697	1196	20.62780	0.6426	1192	20.55881	0.5253	871	4505.000	19.33407	0.6741	220	1721.0000	12.783266	0.49800	135	5437	2.482987	0.8093	513	5798.000	8.847879	0.3531	2614	61	2.333588	0.4313	283	10.826320	0.8752	91	2271	4.007045	0.8990	210	2271.000	9.2470277	0.60570	139	5798	2.3973784	0.7745
42001030500	42	001	030500	PA	Pennsylvania	Adams County	1	Northeast Region	2	Middle Atlantic Division	3879	1675	1531	771	3879	19.876257	0.5852	110	2120	5.188679	0.56360	189	1173	16.11253	0.3112	161	358	44.97207	0.61870	350	1531	22.86088	0.3590	418	2743	15.23879	0.8458	263	3879	6.780098	0.7376	815	21.01057	0.6679	796	20.52075	0.5235	513	3083.000	16.63964	0.5125	137	1139.0000	12.028095	0.46350	131	3667	3.572402	0.8587	831	3879.000	21.423047	0.6300	1675	28	1.671642	0.3824	147	8.776119	0.8404	23	1531	1.502286	0.6736	35	1531.000	2.2860875	0.16500	12	3879	0.3093581	0.5109

County

And then finally recall for county we need to add state abbreviation:

Code

# Note that county without state returns an error
rank_variables(svi_2010, rank_by = "county", location = "Bucks County")

[1] "Enter state abbreviation in function and re-try"

NULL

Code

# County
svi_2010_county <- rank_variables(svi_2010, rank_by = "county", location = "Bucks County", state_abbr = "PA")
svi_2010_county %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10
42017100102	42	017	100102	PA	Pennsylvania	Bucks County	1	Northeast Region	2	Middle Atlantic Division	2520	1559	1255	501	2484	20.16908	0.9437	110	1281	8.587041	0.8732	179	415	43.13253	0.9366	387	840	46.07143	0.4648	566	1255	45.09960	0.9014	233	1952	11.936475	0.7606	209	2770	7.545126	0.6549	455	18.055556	0.8169	429	17.02381	0.08451	466	2391	19.48975	0.8873	102	575	17.739130	0.8803	38	2373	1.6013485	0.6972	361	2520	14.32540	0.7606	1559	366	23.476588	0.9014	25	1.603592	0.8310	29	1255	2.310757	0.9296	41	1255	3.266932	0.5986	0	2520	0.0000000	0.3275
42017100103	42	017	100103	PA	Pennsylvania	Bucks County	1	Northeast Region	2	Middle Atlantic Division	2677	1134	1009	303	2136	14.18539	0.7535	64	1204	5.315615	0.4225	219	570	38.42105	0.8310	172	439	39.17995	0.3521	391	1009	38.75124	0.6972	202	1568	12.882653	0.7958	190	2161	8.792226	0.7535	400	14.942099	0.6549	709	26.48487	0.78870	388	1880	20.63830	0.9648	52	571	9.106830	0.5070	22	2545	0.8644401	0.4648	972	2677	36.30930	0.9366	1134	185	16.313933	0.8310	0	0.000000	0.3451	0	1009	0.000000	0.2535	78	1009	7.730426	0.8662	541	2677	20.2091894	0.9789
42017100104	42	017	100104	PA	Pennsylvania	Bucks County	1	Northeast Region	2	Middle Atlantic Division	4613	2052	1830	1091	4613	23.65055	0.9648	158	2492	6.340289	0.5634	211	611	34.53355	0.5845	672	1219	55.12715	0.7254	883	1830	48.25137	0.9577	447	3271	13.665546	0.8239	469	4441	10.560685	0.8521	590	12.789941	0.4296	788	17.08216	0.09859	637	3631	17.54338	0.8028	135	1175	11.489362	0.6761	365	4357	8.3773238	0.9859	1846	4613	40.01734	0.9718	2052	549	26.754386	0.9225	0	0.000000	0.3451	0	1830	0.000000	0.2535	270	1830	14.754098	0.9648	0	4613	0.0000000	0.3275
42017100105	42	017	100105	PA	Pennsylvania	Bucks County	1	Northeast Region	2	Middle Atlantic Division	3457	1324	1242	444	3457	12.84351	0.7042	225	1719	13.089005	0.9859	334	982	34.01222	0.5352	139	260	53.46154	0.7042	473	1242	38.08374	0.6620	452	2418	18.693135	0.9507	385	3593	10.715280	0.8732	528	15.273358	0.6901	752	21.75296	0.42250	490	2896	16.91989	0.7606	57	886	6.433409	0.3099	150	3288	4.5620438	0.9437	458	3457	13.24848	0.7254	1324	21	1.586103	0.3873	0	0.000000	0.3451	50	1242	4.025765	0.9718	61	1242	4.911433	0.7113	34	3457	0.9835117	0.7535
42017100201	42	017	100201	PA	Pennsylvania	Bucks County	1	Northeast Region	2	Middle Atlantic Division	4127	1789	1605	576	4127	13.95687	0.7394	159	2197	7.237142	0.7042	521	1376	37.86337	0.8169	89	229	38.86463	0.3451	610	1605	38.00623	0.6479	457	2990	15.284281	0.8944	557	4264	13.062852	0.9577	601	14.562636	0.5915	839	20.32954	0.28170	429	3380	12.69231	0.6197	62	1165	5.321888	0.2183	135	3825	3.5294118	0.8803	887	4127	21.49261	0.8521	1789	0	0.000000	0.1444	244	13.638904	0.9648	37	1605	2.305296	0.9225	76	1605	4.735202	0.6901	0	4127	0.0000000	0.3275
42017100206	42	017	100206	PA	Pennsylvania	Bucks County	1	Northeast Region	2	Middle Atlantic Division	4815	2086	1828	860	4786	17.96908	0.8944	194	2661	7.290492	0.7254	426	1103	38.62194	0.8451	370	725	51.03448	0.6338	796	1828	43.54486	0.8592	237	3392	6.987028	0.5000	439	4765	9.213012	0.7887	452	9.387331	0.1620	1097	22.78297	0.50700	421	3746	11.23865	0.4507	180	1392	12.931035	0.7500	50	4570	1.0940919	0.5634	1095	4815	22.74143	0.8803	2086	189	9.060403	0.7042	25	1.198466	0.7887	0	1828	0.000000	0.2535	53	1828	2.899344	0.5563	34	4815	0.7061267	0.7113

Code

# County
svi_2020_county <- rank_variables(svi_2020, rank_by = "county", location = "Bucks County", state = "PA")
svi_2020_county %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20
42017100102	42	017	100102	PA	Pennsylvania	Bucks County	1	Northeast Region	2	Middle Atlantic Division	2595	1375	1223	547	2595	21.078998	0.9366	162	1515	10.693069	0.9648	125	348	35.91954	0.9577	337	875	38.51429	0.3873	462	1223	37.77596	0.8803	179	2043	8.761625	0.8028	148	2595	5.703276	0.7465	545	21.001927	0.6690	311	11.98459	0.05634	342	2284	14.973730	0.7465	104	711	14.627285	0.7254	25	2493	1.002808	0.4648	480	2595	18.49711	0.6831	1375	422	30.69091	0.9366	25	1.818182	0.8451	58	1223	4.742437	0.9366	86	1223	7.031889	0.7324	0	2595	0.0000000	0.2042
42017100103	42	017	100103	PA	Pennsylvania	Bucks County	1	Northeast Region	2	Middle Atlantic Division	2375	1029	952	479	2375	20.168421	0.8803	50	1383	3.615329	0.4789	76	449	16.92650	0.1479	143	503	28.42942	0.2183	219	952	23.00420	0.2113	148	1688	8.767772	0.8099	143	2375	6.021053	0.7676	221	9.305263	0.0493	561	23.62105	0.79580	490	1814	27.012128	0.9930	197	612	32.189543	0.9789	71	2199	3.228740	0.8521	662	2375	27.87368	0.8592	1029	225	21.86589	0.8451	0	0.000000	0.3310	67	952	7.037815	0.9718	99	952	10.399160	0.8944	10	2375	0.4210526	0.6549
42017100104	42	017	100104	PA	Pennsylvania	Bucks County	1	Northeast Region	2	Middle Atlantic Division	5242	2034	1982	1733	5238	33.085147	0.9930	156	2950	5.288136	0.6972	130	611	21.27660	0.3239	721	1371	52.58935	0.7183	851	1982	42.93643	0.9648	610	3491	17.473503	1.0000	821	5238	15.673921	1.0000	609	11.617703	0.1479	1102	21.02251	0.60560	837	4138	20.227163	0.9296	236	1373	17.188638	0.7887	745	4776	15.598828	1.0000	3082	5242	58.79435	1.0000	2034	600	29.49853	0.9225	0	0.000000	0.3310	161	1982	8.123108	0.9930	444	1982	22.401615	1.0000	4	5242	0.0763068	0.4085
42017100105	42	017	100105	PA	Pennsylvania	Bucks County	1	Northeast Region	2	Middle Atlantic Division	3022	1259	1199	397	2942	13.494222	0.7746	137	1985	6.901763	0.8662	312	953	32.73872	0.9225	74	246	30.08130	0.2606	386	1199	32.19349	0.6972	95	2276	4.173990	0.4507	167	2957	5.647616	0.7324	420	13.898081	0.2817	468	15.48643	0.18310	241	2543	9.476996	0.2958	51	677	7.533235	0.4507	60	2882	2.081888	0.7465	362	3022	11.97882	0.4366	1259	163	12.94678	0.7042	0	0.000000	0.3310	0	1199	0.000000	0.2254	106	1199	8.840701	0.8451	176	3022	5.8239576	0.9366
42017100201	42	017	100201	PA	Pennsylvania	Bucks County	1	Northeast Region	2	Middle Atlantic Division	5061	1903	1836	444	4940	8.987854	0.5634	160	2803	5.708170	0.7676	513	1683	30.48128	0.8732	75	153	49.01961	0.6408	588	1836	32.02614	0.6761	440	3602	12.215436	0.9437	321	5061	6.342620	0.7958	645	12.744517	0.1901	935	18.47461	0.35920	503	4126	12.190984	0.5634	171	1277	13.390760	0.6690	280	4838	5.787515	0.9366	1638	5061	32.36515	0.9085	1903	0	0.00000	0.1232	211	11.087756	0.9718	29	1836	1.579521	0.7465	58	1836	3.159041	0.4507	5	5061	0.0987947	0.4296
42017100206	42	017	100206	PA	Pennsylvania	Bucks County	1	Northeast Region	2	Middle Atlantic Division	5333	2093	2011	449	5323	8.435093	0.5070	239	3326	7.185809	0.8873	304	1095	27.76256	0.7042	490	916	53.49345	0.7535	794	2011	39.48284	0.9085	499	3895	12.811296	0.9648	452	5302	8.525085	0.8944	646	12.113257	0.1620	942	17.66360	0.28170	682	4360	15.642202	0.7817	202	1549	13.040671	0.6620	276	5098	5.413888	0.9296	1970	5333	36.93981	0.9225	2093	555	26.51696	0.8873	0	0.000000	0.3310	91	2011	4.525112	0.9225	138	2011	6.862258	0.7254	0	5333	0.0000000	0.2042

SVI Ranks Function

Now that we have our datasets created with our EPL columns filled, we can filter to look at various tracts of interest to see their demographics. For example if we look at Norfolk Naval Shipyard we can see the uniqueness of the area where almost all of its inhabitants live in group quarters (as would be expected due to the fact that residents of this area live in Military Quarters):

Code

# Norfolk Naval Shipyard example
# https://data.indystar.com/census/total-population/total-population-change/census-tract-9801-portsmouth-city-virginia/140-51740980100/
svi_2020_national %>% filter(GEOID_2010_trt == "51740980100") %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20
51740980100	51	740	980100	VA	Virginia	Portsmouth city	3	South Region	5	South Atlantic Division	661	0	0	0	0	NaN	NA	0	14	0	0.004873	0	0	NaN	NA	0	0	NaN	NA	0	0	NaN	NA	0	99	0	0.003598	0	0	NaN	NA	0	0	0.001609	0	0	0.001926	0	0	NaN	NA	0	0	NaN	NA	0	661	0	0.09479	403	661	60.96823	0.744	0	0	NaN	NA	0	NaN	NA	0	0	NaN	NA	0	0	NaN	NA	661	661	100	0.9988

We can now take this data a step further and create our actual SVI rankings with a second function called svi_theme_variables():

Once again, we will want to employ our grepl/regex functions to find our columns of interest for each category. We can refer back to our CDC Social Vulnerability Index chart at the top of the lab to refresh our memories on which variables belong to each grouping.

We can also note that our only variable needed here is the df:

Code

svi_theme_variables <- function(df) {

  # Sum of Theme1: Socioeconomic Status Percentiles
  SES_cols <- colnames(df) %>% str_detect("EPL_POV|EPL_UNEMP|EPL_HBURD_[0-9]|EPL_NOHSDP|EPL_UNINSUR")
  
  SES_select <- colnames(df)[SES_cols]
  
  SES <- paste0(SES_select)
  
  df2 <- df %>% select(GEOID_2010_trt, all_of(SES)) %>% mutate(SPL_THEME1 = rowSums(across(where(is.numeric)))) %>% select(GEOID_2010_trt, SPL_THEME1)
  
  df <- left_join(df, df2, join_by("GEOID_2010_trt" == "GEOID_2010_trt"))
  
  # Ranking for Theme1: Socioeconomic Status Percentiles with 4 significant digits
  
  df <- df %>% 
    mutate(RPL_THEME1 = rank(SPL_THEME1)/length(SPL_THEME1)) %>%
    mutate(RPL_THEME1 = signif(RPL_THEME1, 4))
  
  # Sum of Theme2: Household Characteristics
  
  HHchar_cols <- colnames(df) %>% str_detect("EPL_AGE65|EPL_AGE17|EPL_DISABL|EPL_SNGPNT|EPL_LIMENG")
  
  HHchar_select <- colnames(df)[HHchar_cols]
  
  HHchar <- paste0(HHchar_select)
  
  df2 <- df %>% select(GEOID_2010_trt, all_of(HHchar)) %>% mutate(SPL_THEME2 = rowSums(across(where(is.numeric)))) %>% select(GEOID_2010_trt, SPL_THEME2)
  
  df <- left_join(df, df2, join_by("GEOID_2010_trt" == "GEOID_2010_trt"))
  
  # Ranking for Theme2: Household Characteristics
  
  df <- df %>% 
    mutate(RPL_THEME2 = rank(SPL_THEME2)/length(SPL_THEME2)) %>%
    mutate(RPL_THEME2 = signif(RPL_THEME2, 4))
  
  
  # Sum of Theme3: Racial & Ethnic Minority Status
  
  REM_cols <- colnames(df) %>% str_detect("EPL_MINRTY")
  
  REM_select <- colnames(df)[REM_cols]
  
  REM <- paste0(REM_select)
  
  df2 <- df %>% select(GEOID_2010_trt, all_of(REM)) %>% mutate(SPL_THEME3 = rowSums(across(where(is.numeric)))) %>% select(GEOID_2010_trt, SPL_THEME3)
  
  df <- left_join(df, df2, join_by("GEOID_2010_trt" == "GEOID_2010_trt"))
  
  # Ranking for Theme3: Racial & Ethnic Minority Status
  
  df <- df %>% 
    mutate(RPL_THEME3 = rank(SPL_THEME3)/length(SPL_THEME3)) %>%
    mutate(RPL_THEME3 = signif(RPL_THEME3, 4))
  
  
  # Sum of Theme4: Housing Type & Transportation
  
  HTT_cols <- colnames(df) %>% str_detect("EPL_MUNIT|EPL_MOBILE|EPL_CROWD|EPL_NOVEH|EPL_GROUPQ")
  
  HTT_select <- colnames(df)[HTT_cols]
  
  HTT <- paste0(HTT_select)
  
  df2 <- df %>% select(GEOID_2010_trt, all_of(HTT)) %>% mutate(SPL_THEME4 = rowSums(across(where(is.numeric)))) %>% select(GEOID_2010_trt, SPL_THEME4)
  
  df <- left_join(df, df2, join_by("GEOID_2010_trt" == "GEOID_2010_trt"))
  
  # Ranking for Theme4: Housing Type & Transportation
  
  df <- df %>% 
    mutate(RPL_THEME4 = rank(SPL_THEME4)/length(SPL_THEME4)) %>%
    mutate(RPL_THEME4 = signif(RPL_THEME4, 4))
  
    # Sum for All Themes
  SPL_cols <- colnames(df) %>% str_detect("SPL_THEME")
  
  SPL_select <- colnames(df)[SPL_cols]
  
  SPL <- paste0(SPL_select)
  
  df2 <- df %>% select(GEOID_2010_trt, all_of(SPL)) %>% mutate(SPL_THEMES = rowSums(across(where(is.numeric)))) %>% select(GEOID_2010_trt, SPL_THEMES)
  
  df <- left_join(df, df2, join_by("GEOID_2010_trt" == "GEOID_2010_trt"))
  
  # Ranking for All These
  
  df <- df %>% 
    mutate(RPL_THEMES = rank(SPL_THEMES)/length(SPL_THEMES)) %>%
    mutate(RPL_THEMES = signif(RPL_THEMES, 4))
  
  # Output data
  return(df)

}

We can take a peek at the output of this function here:

Code

svi_2020_national <- svi_theme_variables(svi_2020_national)
svi_2020_national %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	SPL_THEME1	RPL_THEME1	SPL_THEME2	RPL_THEME2	SPL_THEME3	RPL_THEME3	SPL_THEME4	RPL_THEME4	SPL_THEMES	RPL_THEMES
01001020100	01	001	020100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1941	710	693	352	1941	18.13498	0.4630	18	852	2.112676	0.15070	81	507	15.976331	0.26320	63	186	33.87097	0.2913	144	693	20.77922	0.2230	187	1309	14.285714	0.6928	187	1941	9.634209	0.6617	295	15.19835	0.4601	415	21.38073	0.4681	391	1526	25.62254	0.9011	58	555	10.45045	0.3451	0	1843	0.0000000	0.09479	437	1941	22.51417	0.3902	710	0	0.0000000	0.1079	88	12.3943662	0.8263	0	693	0.0000000	0.09796	10	693	1.443001	0.1643	0	1941	0.000000	0.1831	2.19120	0.4084	2.26919	0.3503	0.3902	0.3869	1.37956	0.07216	6.23015	0.2314
01001020200	01	001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1757	720	573	384	1511	25.41363	0.6427	29	717	4.044630	0.41320	33	392	8.418367	0.03542	116	181	64.08840	0.9086	149	573	26.00349	0.4041	139	1313	10.586443	0.5601	91	1533	5.936073	0.4343	284	16.16392	0.5169	325	18.49744	0.2851	164	1208	13.57616	0.4127	42	359	11.69916	0.3998	0	1651	0.0000000	0.09479	1116	1757	63.51736	0.7591	720	3	0.4166667	0.2470	5	0.6944444	0.5106	9	573	1.5706806	0.46880	57	573	9.947644	0.7317	212	1757	12.066022	0.9549	2.45440	0.4888	1.70929	0.1025	0.7591	0.7527	2.91300	0.68620	7.83579	0.4802
01001020300	01	001	020300	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3694	1464	1351	842	3694	22.79372	0.5833	53	1994	2.657974	0.22050	117	967	12.099276	0.11370	147	384	38.28125	0.3856	264	1351	19.54108	0.1827	317	2477	12.797739	0.6460	127	3673	3.457664	0.2308	464	12.56091	0.3088	929	25.14889	0.7080	473	2744	17.23761	0.6211	263	975	26.97436	0.8234	128	3586	3.5694367	0.70770	1331	3694	36.03140	0.5515	1464	26	1.7759563	0.3675	14	0.9562842	0.5389	35	1351	2.5906736	0.60550	42	1351	3.108808	0.3415	0	3694	0.000000	0.1831	1.86330	0.3063	3.16900	0.8380	0.5515	0.5468	2.03650	0.26830	7.62030	0.4460
01001020400	01	001	020400	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3539	1741	1636	503	3539	14.21305	0.3472	39	1658	2.352232	0.17990	219	1290	16.976744	0.30880	74	346	21.38728	0.1037	293	1636	17.90954	0.1333	173	2775	6.234234	0.3351	169	3529	4.788892	0.3448	969	27.38062	0.9225	510	14.41085	0.1208	670	3019	22.19278	0.8194	148	1137	13.01671	0.4541	89	3409	2.6107363	0.64690	454	3539	12.82848	0.2364	1741	143	8.2136703	0.6028	0	0.0000000	0.2186	10	1636	0.6112469	0.28340	72	1636	4.400978	0.4538	0	3539	0.000000	0.1831	1.34030	0.1575	2.96370	0.7496	0.2364	0.2344	1.74170	0.16270	6.28210	0.2389
01001020500	01	001	020500	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	10674	4504	4424	1626	10509	15.47245	0.3851	81	5048	1.604596	0.09431	321	2299	13.962592	0.17970	711	2125	33.45882	0.2836	1032	4424	23.32731	0.3109	531	6816	7.790493	0.4251	301	10046	2.996217	0.1894	1613	15.11149	0.4553	2765	25.90407	0.7494	1124	7281	15.43744	0.5253	342	2912	11.74451	0.4019	52	9920	0.5241935	0.35230	2603	10674	24.38636	0.4160	4504	703	15.6083481	0.7378	29	0.6438721	0.5037	37	4424	0.8363472	0.33420	207	4424	4.679023	0.4754	176	10674	1.648866	0.7598	1.40481	0.1743	2.48420	0.4802	0.4160	0.4125	2.81090	0.63730	7.11591	0.3654
01001020600	01	001	020600	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3536	1464	1330	1279	3523	36.30429	0.8215	34	1223	2.780049	0.23780	321	1111	28.892889	0.75870	67	219	30.59361	0.2305	388	1330	29.17293	0.5075	306	2380	12.857143	0.6480	415	3496	11.870709	0.7535	547	15.46946	0.4760	982	27.77149	0.8327	729	2514	28.99761	0.9488	95	880	10.79545	0.3601	0	3394	0.0000000	0.09479	985	3536	27.85633	0.4608	1464	0	0.0000000	0.1079	364	24.8633880	0.9300	0	1330	0.0000000	0.09796	17	1330	1.278196	0.1463	0	3536	0.000000	0.1831	2.96830	0.6434	2.71239	0.6156	0.4608	0.4569	1.46526	0.08976	7.60675	0.4440

SVI Flags Function

Once we finally have our SVI categories created, we can create our SVI flags. We will want to have two input variables for this function: the dataframe and our percentile for interest.

Specifically, we want our function to create a flag for 1 if estimated percentiles (indicated by EPL_ prefix) are greater than a particular threshold of interest (example .90 or .75) and 0 if they are not. We will then create a count of flags by theme and total count of flags across all themes.

To accomplish this we will repeat techniques from our previous functions to employ grepl/regex, for loops, and the bang-bang injection operator:

Code

svi_theme_flags <- function(df, percentile) {

  # Search column names for our estimated percentile columns
  epl_cols <- colnames(df) %>% str_detect("^EPL_")
  # Select these from all column names
  epl_cols_names <- colnames(df)[epl_cols]
  
  for (epl in epl_cols_names) {
      eflg <- str_replace(epl, "EPL_", "F_")
      df <- df %>%
        mutate(!!as.name(eflg) := if_else(!!as.name(epl) > percentile, 1, 0)) %>%
        relocate(c(!!as.name(eflg)),.after = !!as.name(epl))
  }
  
  # Find flags by category, sum
  
  # Sum of Theme1 Flags: Socioeconomic Status
  SES_cols <- colnames(df) %>% str_detect("F_POV|F_UNEMP|F_HBURD_[0-9]|F_NOHSDP|F_UNINSUR")
  
  SES_select <- colnames(df)[SES_cols]
  
  SES <- paste0(SES_select)
  
  df2 <- df %>% select(GEOID_2010_trt, all_of(SES)) %>% mutate(F_THEME1 = rowSums(across(where(is.numeric)))) %>% select(GEOID_2010_trt, F_THEME1)
  
  df <- left_join(df, df2, join_by("GEOID_2010_trt" == "GEOID_2010_trt")) %>%
        relocate(c(F_THEME1),.after = RPL_THEME1) 
  
  
  # Sum of Theme2 Flags: Household Characteristics
  
  HHchar_cols <- colnames(df) %>% str_detect("F_AGE65|F_AGE17|F_DISABL|F_SNGPNT|F_LIMENG")
  
  HHchar_select <- colnames(df)[HHchar_cols]
  
  HHchar <- paste0(HHchar_select)
  
  df2 <- df %>% select(GEOID_2010_trt, all_of(HHchar)) %>% mutate(F_THEME2 = rowSums(across(where(is.numeric)))) %>% select(GEOID_2010_trt, F_THEME2)
  
  df <- left_join(df, df2, join_by("GEOID_2010_trt" == "GEOID_2010_trt"))  %>%
        relocate(c(F_THEME2),.after = RPL_THEME2) 
  
  
  # Sum of Theme3 Flags: Household Characteristics
  
  REM_cols <- colnames(df) %>% str_detect("F_MINRTY")
  
  REM_select <- colnames(df)[REM_cols]
  
  REM <- paste0(REM_select)
  
  df2 <- df %>% select(GEOID_2010_trt, all_of(REM)) %>% mutate(F_THEME3 = rowSums(across(where(is.numeric)))) %>% select(GEOID_2010_trt, F_THEME3)
  
  df <- left_join(df, df2, join_by("GEOID_2010_trt" == "GEOID_2010_trt"))  %>%
        relocate(c(F_THEME3),.after = RPL_THEME3) 
  
  # Sum of Theme4 Flags: Housing Type & Transportation
  
  HTT_cols <- colnames(df) %>% str_detect("F_MUNIT|F_MOBILE|F_CROWD|F_NOVEH|F_GROUPQ")
  
  HTT_select <- colnames(df)[HTT_cols]
  
  HTT <- paste0(HTT_select)
  
  df2 <- df %>% select(GEOID_2010_trt, all_of(HTT)) %>% mutate(F_THEME4 = rowSums(across(where(is.numeric)))) %>% select(GEOID_2010_trt, F_THEME4) 
  
  df <- left_join(df, df2, join_by("GEOID_2010_trt" == "GEOID_2010_trt")) %>%
        relocate(c(F_THEME4),.after = RPL_THEME4) 
  
  # Sum of all theme flags
  
  THEME_cols <- colnames(df) %>% str_detect("F_THEME")
  
  THEME_select <- colnames(df)[THEME_cols]
  
  THEME <- paste0(THEME_select)
  
  df2 <- df %>% select(GEOID_2010_trt, all_of(THEME)) %>% mutate(F_TOTAL = rowSums(across(where(is.numeric)))) %>% select(GEOID_2010_trt, F_TOTAL)
  
  df <- left_join(df, df2, join_by("GEOID_2010_trt" == "GEOID_2010_trt"))

  # Return data
  return(df)
}

Code

svi_2020_national <- svi_theme_flags(svi_2020_national, .90)
svi_2020_national %>% arrange(desc(F_TOTAL)) %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1	RPL_THEME1	F_THEME1	SPL_THEME2	RPL_THEME2	F_THEME2	SPL_THEME3	RPL_THEME3	F_THEME3	SPL_THEME4	RPL_THEME4	F_THEME4	SPL_THEMES	RPL_THEMES	F_TOTAL
48141000404	48	141	000404	TX	Texas	El Paso County	3	South Region	7	West South Central Division	3380	1648	1221	2390	3380	70.71006	0.9928	1	210	1268	16.56151	0.9697	1	12	154	7.792208	0.02796	0	705	1067	66.07310	0.9281	1	717	1221	58.72236	0.9772	1	745	1955	38.10742	0.9703	1	1234	3370	36.61721	0.9940	1	369	10.917160	0.22390	1070	31.65680	0.9356	1	664	2300	28.86957	0.9477	1	365	761	47.96321	0.9741	1	649	3025	21.45455	0.9659	1	3222	3380	95.32544	0.9478	1	1648	701	42.53641	0.9214	1	0.2186	130	1221	10.647011	0.9224	1	344	1221	28.17363	0.9344	1	0	3380	0.0000000	0.1831	0	4.9040	0.9886	5	4.04720	0.9848	4	0.9478	0.9398	1	3.1799	0.8000	3	13.07890	0.9859	13
06037224420	06	037	224420	CA	California	Los Angeles County	4	West Region	9	Pacific Division	2651	943	883	1562	2389	65.38301	0.9868	1	232	1430	16.22378	0.9672	1	13	57	22.807018	0.56500	0	584	826	70.70218	0.9614	1	597	883	67.61042	0.9949	1	894	1588	56.29723	0.9975	1	605	2651	22.82158	0.9514	1	243	9.166352	0.14720	543	20.48284	0.4073	0	211	1889	11.16993	0.2666	0	207	571	36.25219	0.9217	1	870	2539	34.26546	0.9937	1	2523	2651	95.17163	0.9465	1	943	540	57.26405	0.9549	1	0.2186	344	883	38.958097	0.9984	1	302	883	34.20159	0.9522	1	253	2651	9.5435685	0.9459	1	4.8978	0.9886	5	2.73650	0.6297	2	0.9465	0.9385	1	4.0700	0.9818	4	12.65080	0.9801	12
12086001401	12	086	001401	FL	Florida	Miami-Dade County	3	South Region	5	South Atlantic Division	6606	2427	2203	4080	6606	61.76203	0.9802	1	314	2673	11.74710	0.9138	1	134	300	44.666667	0.95870	1	1111	1903	58.38150	0.8314	0	1245	2203	56.51384	0.9682	1	1363	4012	33.97308	0.9551	1	1313	6606	19.87587	0.9252	1	732	11.080836	0.23160	1985	30.04844	0.9023	1	670	4621	14.49903	0.4697	0	606	1267	47.82952	0.9736	1	854	5756	14.83669	0.9273	1	6592	6606	99.78807	0.9954	1	2427	965	39.76102	0.9125	1	0.2186	378	2203	17.158420	0.9692	1	777	2203	35.27009	0.9546	1	40	6606	0.6055101	0.6241	0	4.7425	0.9867	5	3.50450	0.9317	3	0.9954	0.9870	1	3.6790	0.9403	3	12.92140	0.9845	12
27053005902	27	053	005902	MN	Minnesota	Hennepin County	2	Midwest Region	4	West North Central Division	3896	1173	1087	2343	3739	62.66381	0.9824	1	226	1842	12.26927	0.9232	1	21	71	29.577465	0.77560	0	521	1016	51.27953	0.6904	0	542	1087	49.86201	0.9223	1	719	1932	37.21532	0.9675	1	608	3896	15.60575	0.8581	0	87	2.233059	0.01142	1442	37.01232	0.9852	1	640	2440	26.22951	0.9114	1	274	583	46.99828	0.9714	1	444	3462	12.82496	0.9096	1	2911	3896	74.71766	0.8205	0	1173	721	61.46633	0.9616	1	0.2186	226	1087	20.791168	0.9809	1	323	1087	29.71481	0.9398	1	579	3896	14.8613963	0.9619	1	4.6535	0.9834	4	3.78902	0.9713	4	0.8205	0.8136	0	4.0628	0.9815	4	13.32582	0.9873	12
34001002400	34	001	002400	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	2614	1726	1217	1579	2612	60.45176	0.9773	1	290	1171	24.76516	0.9931	1	69	127	54.330709	0.98500	1	538	1090	49.35780	0.6469	0	607	1217	49.87675	0.9224	1	697	1998	34.88488	0.9590	1	551	2614	21.07881	0.9374	1	516	19.739862	0.70560	503	19.24254	0.3280	0	576	2111	27.28565	0.9280	1	257	567	45.32628	0.9667	1	556	2368	23.47973	0.9732	1	2029	2614	77.62050	0.8359	0	1726	1166	67.55504	0.9693	1	0.2186	115	1217	9.449466	0.9062	1	673	1217	55.29992	0.9828	1	223	2614	8.5309870	0.9407	1	4.7892	0.9877	5	3.90150	0.9792	3	0.8359	0.8288	0	4.0176	0.9793	4	13.54420	0.9879	12
34039039300	34	039	039300	NJ	New Jersey	Union County	1	Northeast Region	2	Middle Atlantic Division	6145	1835	1727	3492	6096	57.28346	0.9684	1	350	3006	11.64338	0.9114	1	58	140	41.428571	0.94020	1	829	1587	52.23692	0.7127	0	887	1727	51.36074	0.9352	1	1367	3639	37.56527	0.9686	1	2771	6145	45.09357	0.9987	1	400	6.509357	0.06257	2006	32.64443	0.9503	1	443	4139	10.70307	0.2400	0	476	1178	40.40747	0.9470	1	1979	5714	34.63423	0.9941	1	5955	6145	96.90806	0.9624	1	1835	815	44.41417	0.9271	1	0.2186	391	1727	22.640417	0.9849	1	748	1727	43.31210	0.9692	1	114	6145	1.8551668	0.7766	0	4.7823	0.9877	5	3.19397	0.8468	3	0.9624	0.9543	1	3.8764	0.9680	3	12.81507	0.9830	12

Data Analysis Questions

We can now utilize our created functions to analyze our data and determine the most vulnerable areas in our dataset based on the SVI.

Before we begin our analysis, place all of your newly created functions into your project_data_steps.R file. Import them, and run code to answer questions about national data and your division of interest:

Code

import::here( "author",
              "census_division",
              "fips_region_assignment",
              "rank_variables",
              "svi_theme_variables",
              "svi_theme_flags",
             # notice the use of here::here() that points to the .R file
             # where all these R objects are created
             .from = here::here("analysis/project_data_steps_[your initials].R"),
             .character_only = TRUE)

Once you have loaded your data and processed it with your functions, answer the following questions in your RMarkdown file:

Note, remember for SVI: 0 is least vulnerable, 1 is most vulnerable

From a data science project management standpoint, how do you believe that functions are most useful? Can you think of scenarios in your current work or previous data projects where it would have been helpful to implement functions? Can you think of scenarios where functions are not the most useful strategy for processing data?
Describe the CDC SVI index. What is it and how do we intend to use it for our project? (Type your response)
Nationally, what are the most & least vulnerable tracts by THEME1, THEME2, THEME3, THEME4, OVERALL for 2020 and 2010. (Show code/data output to display your findings)
For your division, what are the most & least vulnerable tracts by THEME1, THEME2, THEME3, THEME4, OVERALL. Do they belong to certain divisions/states? (Show code/data output to display your findings)
For the most vulnerable state in your division, what are the most & least vulnerable tracts by THEME1, THEME2, THEME3, THEME4, OVERALL? Do they belong to certain counties? (Show code/data output to display your findings)
OPTIONAL CHALLENGE QUESTION: Do you think it’s possible to create a single function that calls in all of the individual functions we created? If time permits, draft an example function called load_svi_data() and show the inputs/outputs for the function.

Lab Submission Instructions

Congratulations! You’ve reached the end of the Lab-02 Tutorial!

You are now ready to complete your lab and submit it on Canvas.

The following checklist will ensure that you’re on track: