Proximity and Homeownership
Candace Jennings
2023-06-19
Setting up the data
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(stats)
library(car)## Loading required package: carData
##
## Attaching package: 'car'
##
## The following object is masked from 'package:dplyr':
##
## recode
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## The following object is masked from 'package:purrr':
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## somelibrary(dplyr)
library(stargazer)##
## Please cite as:
##
## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.3. https://CRAN.R-project.org/package=stargazercities <- read.csv('C:/Users/canda/DEM Dissertation Data/citiesALLYRS.csv')
cities <- cities %>% mutate_if(is.character, as.numeric)## Warning: There were 117 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `cityname = .Primitive("as.double")(cityname)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 116 remaining warnings.cities$Proximity<-as.factor(ifelse(cities$Proximity==1, "In Proximity", "Not in Prox"))
head(cities)## statefp20 placefp20 cityname Proximity Miles FC.name pboo_20 pboo_10
## 1 48 1000 NA Not in Prox NA NA 4.74 4.73
## 2 48 1240 NA In Proximity 3 NA 4.08 2.53
## 3 48 1576 NA Not in Prox NA NA 0.26 0.19
## 4 48 1696 NA Not in Prox NA NA 3.13 3.31
## 5 48 1852 NA Not in Prox NA NA 0.65 0.57
## 6 48 1924 NA Not in Prox NA NA 6.50 7.01
## pboo_00 pboo_90 pboo_80 w_20 b_20 h_20 a_20 o_20 t_20 pw_20
## 1 4.68 NA NA 70391 14221 33634 3674 3262 125182 56.230927
## 2 1.73 NA NA 8001 2893 3807 1535 425 16661 48.022327
## 3 0.00 NA NA 2302 39 17005 36 111 19493 11.809367
## 4 4.42 NA NA 1288 385 13984 232 110 15999 8.050503
## 5 0.48 NA NA 2120 131 15352 133 155 17891 11.849533
## 6 3.58 NA NA 53330 11286 13197 24113 2701 104627 50.971546
## pb_20 ph_20 pa_20 po_20 dwb_20 dwh_20 dwa_20
## 1 11.3602591 26.86808 2.9349267 2.6058059 28.71591 28.876587 25.311729
## 2 17.3639031 22.84977 9.2131329 2.5508673 24.97171 24.299740 9.137611
## 3 0.2000718 87.23644 0.1846817 0.5694352 35.25474 37.839760 57.845837
## 4 2.4064004 87.40546 1.4500906 0.6875430 32.83879 4.459177 21.953308
## 5 0.7322117 85.80851 0.7433906 0.8663574 38.17082 28.835796 10.373103
## 6 10.7868910 12.61338 23.0466328 2.5815516 13.79678 13.915831 27.770782
## dbw_20 dbh_20 dba_20 dhw_20 dhb_20 dha_20 daw_20 dab_20
## 1 28.71591 21.11862 39.04976 28.876587 21.11862 44.49091 25.311729 39.04976
## 2 24.97171 22.51835 18.21161 24.299740 22.51835 19.16062 9.137611 18.21161
## 3 35.25474 48.17920 61.96581 37.839760 48.17920 40.63903 57.845837 61.96581
## 4 32.83879 33.14220 47.37349 4.459177 33.14220 20.41890 21.953308 47.37349
## 5 38.17082 12.03613 37.92114 28.835796 12.03613 29.04787 10.373103 37.92114
## 6 13.79678 10.68244 32.44231 13.915831 10.68244 36.19292 27.770782 32.44231
## dah_20 w_10 b_10 h_10 a_10 o_10 t_10 pw_10 pb_10 ph_10
## 1 44.49091 73016 11726 28666 2526 1129 117063 62.37325 10.0168285 24.48767
## 2 19.16062 6991 1575 3290 1077 123 13056 53.54626 12.0634193 25.19914
## 3 40.63903 2722 36 15528 24 43 18353 14.83136 0.1961532 84.60742
## 4 20.41890 2026 446 13036 306 55 15869 12.76703 2.8105111 82.14758
## 5 29.04787 2545 113 16259 120 67 19104 13.32182 0.5914991 85.10783
## 6 36.19292 54690 7437 9443 11793 883 84246 64.91703 8.8277187 11.20884
## pa_10 po_10 dwb_10 dwh_10 dwa_10 dbw_10 dbh_10 dba_10
## 1 2.1578124 0.9644380 34.83399 32.331924 21.93330 34.83399 25.58607 38.84781
## 2 8.2490807 0.9420956 21.38260 42.693466 14.55660 21.38260 40.37555 21.58695
## 3 0.1307688 0.2342941 55.31675 37.772499 62.52443 55.31675 50.72557 83.33334
## 4 1.9282879 0.3465877 36.01920 5.878032 19.16201 36.01920 33.09196 48.48911
## 5 0.6281407 0.3507119 27.59289 26.033392 23.33170 27.59289 11.17671 46.42331
## 6 13.9982910 1.0481210 17.53230 16.067127 23.47931 17.53230 10.99336 30.91798
## dhw_10 dhb_10 dha_10 daw_10 dab_10 dah_10 w_00 b_00 h_00 a_00
## 1 32.331924 25.58607 44.50213 21.93330 38.84781 44.50213 79712 10540 22548 2038
## 2 42.693466 40.37555 31.37419 14.55660 21.58695 31.37419 7945 1388 3406 1196
## 3 37.772499 50.72557 38.79432 62.52443 83.33334 38.79432 3154 17 11528 12
## 4 5.878032 33.09196 18.63983 19.16201 48.48911 18.63983 4731 811 7875 493
## 5 26.033392 11.17671 44.16447 23.33170 46.42331 44.16447 3824 141 14837 159
## 6 16.067127 10.99336 34.08430 23.47931 30.91798 34.08430 36239 2029 3038 1803
## o_00 t_00 pw_00 pb_00 ph_00 pa_00 po_00 dwb_00
## 1 1092 115930 68.75874 9.0916929 19.449667 1.75795734 0.9419477 37.67316
## 2 231 14166 56.08499 9.7981081 24.043484 8.44274998 1.6306649 38.84475
## 3 49 14760 21.36856 0.1151761 78.102982 0.08130081 0.3319783 23.42496
## 4 69 13979 33.84362 5.8015594 56.334503 3.52671862 0.4935975 28.66123
## 5 49 19010 20.11573 0.7417149 78.048393 0.83640188 0.2577591 27.41735
## 6 445 43554 83.20476 4.6585846 6.975249 4.13968849 1.0217202 13.73801
## dwh_00 dwa_00 dbw_00 dbh_00 dba_00 dhw_00 dhb_00 dha_00
## 1 38.75163 21.95675 37.67316 32.39957 40.093239 38.75163 32.39957 48.42324
## 2 51.26093 29.91368 38.84475 12.41617 8.947934 51.26093 12.41617 21.34724
## 3 18.40219 19.05525 23.42496 11.74057 28.921568 18.40219 11.74057 17.18115
## 4 14.20844 10.35100 28.66123 15.43837 28.574896 14.20844 15.43837 16.15624
## 5 25.87468 17.56499 27.41735 19.75505 34.898968 25.87468 19.75505 38.05710
## 6 17.91144 22.23367 13.73801 14.46472 21.561922 17.91144 14.46472 32.96284
## daw_00 dab_00 dah_00 w_90 b_90 h_90 a_90 t_90 o_90 pw_90
## 1 21.95675 40.093239 48.42324 81077 7270 16549 1296 106654 462 76.01871
## 2 29.91368 8.947934 21.34724 NA NA NA NA NA NA NA
## 3 19.05525 28.921568 17.18115 NA NA NA NA NA NA NA
## 4 10.35100 28.574896 16.15624 7426 408 2911 317 11133 71 66.70260
## 5 17.56499 34.898968 38.05710 5165 155 14364 31 19788 73 26.10168
## 6 22.23367 21.561922 32.96284 16600 590 800 208 18309 111 90.66579
## pb_90 ph_90 pa_90 po_90 dwb_90 dwh_90 dwa_90 dbw_90
## 1 6.816434 15.516530 1.2151443 0.4331764 38.120529 40.886463 23.78641 38.120529
## 2 NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA
## 4 3.664780 26.147490 2.8473907 0.6377437 24.671652 9.945574 29.99929 24.671652
## 5 0.783303 72.589447 0.1566606 0.3689105 41.193501 33.300629 16.18521 41.193501
## 6 3.222459 4.369436 1.1360533 0.6062592 9.219427 15.289162 18.73400 9.219427
## dbh_90 dba_90 dhw_90 dhb_90 dha_90 daw_90 dab_90 dah_90
## 1 34.77698 34.51051 40.886463 34.77698 48.04832 23.78641 34.51051 48.04832
## 2 NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA
## 4 14.72609 46.91965 9.945574 14.72609 35.98111 29.99929 46.91965 35.98111
## 5 34.33053 45.16128 33.300629 34.33053 37.88809 16.18521 45.16128 37.88809
## 6 10.26271 22.92699 15.289162 10.26271 24.45193 18.73400 22.92699 24.45193
## t_80 w_80 b_80 h_80 a_80 o_80 pw_80 pb_80 ph_80 pa_80
## 1 98315 78076 6473 12427 649 690 79.41413 6.5839396 12.63998 0.6601231
## 2 NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA
## 4 12623 10766 269 1404 90 94 85.28876 2.1310306 11.12255 0.7129843
## 5 20961 6504 179 14213 24 41 31.02905 0.8539669 67.80688 0.1144984
## 6 NA NA NA NA NA NA NA NA NA NA
## po_80 dwb_80 dwh_80 dwa_80 dbw_80 dbh_80 dba_80 dhw_80
## 1 0.7018257 48.22921 41.218159 25.76917 48.22921 44.52198 44.96911 41.218159
## 2 NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA
## 4 0.7446724 29.94836 3.628741 9.46873 29.94836 30.73666 35.14250 3.628741
## 5 0.1956014 52.62288 41.008732 48.76996 52.62288 35.88138 46.97393 41.008732
## 6 NA NA NA NA NA NA NA NA
## dhb_80 dha_80 daw_80 dab_80 dah_80
## 1 44.52198 43.98988 25.76917 44.96911 43.98988
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 30.73666 10.12821 9.46873 35.14250 10.12821
## 5 35.88138 29.21453 48.76996 46.97393 29.21453
## 6 NA NA NA NA NAstr(cities)## 'data.frame': 272 obs. of 126 variables:
## $ statefp20: int 48 48 48 48 48 48 48 48 48 48 ...
## $ placefp20: int 1000 1240 1576 1696 1852 1924 2212 2272 3000 3216 ...
## $ cityname : num NA NA NA NA NA NA NA NA NA NA ...
## $ Proximity: Factor w/ 2 levels "In Proximity",..: 2 1 2 2 2 2 1 2 2 2 ...
## $ Miles : num NA 3 NA NA NA NA 13.7 NA NA NA ...
## $ FC.name : num NA NA NA NA NA NA NA NA NA NA ...
## $ pboo_20 : num 4.74 4.08 0.26 3.13 0.65 6.5 0.13 2.73 3.56 1.08 ...
## $ pboo_10 : num 4.73 2.53 0.19 3.31 0.57 7.01 0.19 1.49 3.69 1.53 ...
## $ pboo_00 : num 4.68 1.73 0 4.42 0.48 3.58 0 1.15 3.76 2.69 ...
## $ pboo_90 : logi NA NA NA NA NA NA ...
## $ pboo_80 : logi NA NA NA NA NA NA ...
## $ w_20 : num 70391 8001 2302 1288 2120 ...
## $ b_20 : num 14221 2893 39 385 131 ...
## $ h_20 : num 33634 3807 17005 13984 15352 ...
## $ a_20 : num 3674 1535 36 232 133 ...
## $ o_20 : num 3262 425 111 110 155 ...
## $ t_20 : num 125182 16661 19493 15999 17891 ...
## $ pw_20 : num 56.23 48.02 11.81 8.05 11.85 ...
## $ pb_20 : num 11.36 17.364 0.2 2.406 0.732 ...
## $ ph_20 : num 26.9 22.8 87.2 87.4 85.8 ...
## $ pa_20 : num 2.935 9.213 0.185 1.45 0.743 ...
## $ po_20 : num 2.606 2.551 0.569 0.688 0.866 ...
## $ dwb_20 : num 28.7 25 35.3 32.8 38.2 ...
## $ dwh_20 : num 28.88 24.3 37.84 4.46 28.84 ...
## $ dwa_20 : num 25.31 9.14 57.85 21.95 10.37 ...
## $ dbw_20 : num 28.7 25 35.3 32.8 38.2 ...
## $ dbh_20 : num 21.1 22.5 48.2 33.1 12 ...
## $ dba_20 : num 39 18.2 62 47.4 37.9 ...
## $ dhw_20 : num 28.88 24.3 37.84 4.46 28.84 ...
## $ dhb_20 : num 21.1 22.5 48.2 33.1 12 ...
## $ dha_20 : num 44.5 19.2 40.6 20.4 29 ...
## $ daw_20 : num 25.31 9.14 57.85 21.95 10.37 ...
## $ dab_20 : num 39 18.2 62 47.4 37.9 ...
## $ dah_20 : num 44.5 19.2 40.6 20.4 29 ...
## $ w_10 : num 73016 6991 2722 2026 2545 ...
## $ b_10 : num 11726 1575 36 446 113 ...
## $ h_10 : num 28666 3290 15528 13036 16259 ...
## $ a_10 : num 2526 1077 24 306 120 ...
## $ o_10 : num 1129 123 43 55 67 ...
## $ t_10 : num 117063 13056 18353 15869 19104 ...
## $ pw_10 : num 62.4 53.5 14.8 12.8 13.3 ...
## $ pb_10 : num 10.017 12.063 0.196 2.811 0.591 ...
## $ ph_10 : num 24.5 25.2 84.6 82.1 85.1 ...
## $ pa_10 : num 2.158 8.249 0.131 1.928 0.628 ...
## $ po_10 : num 0.964 0.942 0.234 0.347 0.351 ...
## $ dwb_10 : num 34.8 21.4 55.3 36 27.6 ...
## $ dwh_10 : num 32.33 42.69 37.77 5.88 26.03 ...
## $ dwa_10 : num 21.9 14.6 62.5 19.2 23.3 ...
## $ dbw_10 : num 34.8 21.4 55.3 36 27.6 ...
## $ dbh_10 : num 25.6 40.4 50.7 33.1 11.2 ...
## $ dba_10 : num 38.8 21.6 83.3 48.5 46.4 ...
## $ dhw_10 : num 32.33 42.69 37.77 5.88 26.03 ...
## $ dhb_10 : num 25.6 40.4 50.7 33.1 11.2 ...
## $ dha_10 : num 44.5 31.4 38.8 18.6 44.2 ...
## $ daw_10 : num 21.9 14.6 62.5 19.2 23.3 ...
## $ dab_10 : num 38.8 21.6 83.3 48.5 46.4 ...
## $ dah_10 : num 44.5 31.4 38.8 18.6 44.2 ...
## $ w_00 : num 79712 7945 3154 4731 3824 ...
## $ b_00 : num 10540 1388 17 811 141 ...
## $ h_00 : num 22548 3406 11528 7875 14837 ...
## $ a_00 : num 2038 1196 12 493 159 ...
## $ o_00 : num 1092 231 49 69 49 ...
## $ t_00 : num 115930 14166 14760 13979 19010 ...
## $ pw_00 : num 68.8 56.1 21.4 33.8 20.1 ...
## $ pb_00 : num 9.092 9.798 0.115 5.802 0.742 ...
## $ ph_00 : num 19.4 24 78.1 56.3 78 ...
## $ pa_00 : num 1.758 8.4427 0.0813 3.5267 0.8364 ...
## $ po_00 : num 0.942 1.631 0.332 0.494 0.258 ...
## $ dwb_00 : num 37.7 38.8 23.4 28.7 27.4 ...
## $ dwh_00 : num 38.8 51.3 18.4 14.2 25.9 ...
## $ dwa_00 : num 22 29.9 19.1 10.4 17.6 ...
## $ dbw_00 : num 37.7 38.8 23.4 28.7 27.4 ...
## $ dbh_00 : num 32.4 12.4 11.7 15.4 19.8 ...
## $ dba_00 : num 40.09 8.95 28.92 28.57 34.9 ...
## $ dhw_00 : num 38.8 51.3 18.4 14.2 25.9 ...
## $ dhb_00 : num 32.4 12.4 11.7 15.4 19.8 ...
## $ dha_00 : num 48.4 21.3 17.2 16.2 38.1 ...
## $ daw_00 : num 22 29.9 19.1 10.4 17.6 ...
## $ dab_00 : num 40.09 8.95 28.92 28.57 34.9 ...
## $ dah_00 : num 48.4 21.3 17.2 16.2 38.1 ...
## $ w_90 : num 81077 NA NA 7426 5165 ...
## $ b_90 : num 7270 NA NA 408 155 ...
## $ h_90 : num 16549 NA NA 2911 14364 ...
## $ a_90 : num 1296 NA NA 317 31 ...
## $ t_90 : num 106654 NA NA 11133 19788 ...
## $ o_90 : num 462 NA NA 71 73 ...
## $ pw_90 : num 76 NA NA 66.7 26.1 ...
## $ pb_90 : num 6.816 NA NA 3.665 0.783 ...
## $ ph_90 : num 15.5 NA NA 26.1 72.6 ...
## $ pa_90 : num 1.215 NA NA 2.847 0.157 ...
## $ po_90 : num 0.433 NA NA 0.638 0.369 ...
## $ dwb_90 : num 38.1 NA NA 24.7 41.2 ...
## $ dwh_90 : num 40.89 NA NA 9.95 33.3 ...
## $ dwa_90 : num 23.8 NA NA 30 16.2 ...
## $ dbw_90 : num 38.1 NA NA 24.7 41.2 ...
## $ dbh_90 : num 34.8 NA NA 14.7 34.3 ...
## $ dba_90 : num 34.5 NA NA 46.9 45.2 ...
## $ dhw_90 : num 40.89 NA NA 9.95 33.3 ...
## $ dhb_90 : num 34.8 NA NA 14.7 34.3 ...
## [list output truncated]Descriptive Statistics
stargazer(cities[c("pboo_20","t_20","dbw_20")], header=FALSE, type='text',
title="Descriptive Statistics 2020", digits=2,
covariate.labels=c("Black Homeownership","City Population","B-W Dissimilarity")
)##
## Descriptive Statistics 2020
## =============================================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------------------------
## Black Homeownership 261 8.54 10.61 0.00 67.12
## City Population 261 75,681.66 207,755.30 10,070 2,304,580
## B-W Dissimilarity 261 25.12 12.01 0.00 69.14
## -------------------------------------------------------------stargazer(cities[c("pboo_10","t_10","dbw_10")], header=FALSE, type='text',
title="Descriptive Statistics 2010", digits=2,
covariate.labels=c("Black Homeownership","City Population","B-W Dissimilarity")
)##
## Descriptive Statistics 2010
## =============================================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------------------------
## Black Homeownership 241 8.65 11.07 0.05 65.63
## City Population 241 70,713.28 194,540.90 10,127 2,099,451
## B-W Dissimilarity 241 27.13 14.08 0.00 82.22
## -------------------------------------------------------------stargazer(cities[c("pboo_00","t_00","dbw_00")], header=FALSE, type='text',
title="Descriptive Statistics 2000", digits=2,
covariate.labels=c("Black Homeownership","City Population","B-W Dissimilarity")
)##
## Descriptive Statistics 2000
## =============================================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------------------------
## Black Homeownership 258 7.18 9.34 0.00 61.00
## City Population 205 68,613.65 190,232.90 10,302 1,953,631
## B-W Dissimilarity 205 30.71 16.06 0.00 75.64
## -------------------------------------------------------------sapply(cities, function(x) sum(is.na(x)))## statefp20 placefp20 cityname Proximity Miles FC.name pboo_20 pboo_10
## 0 0 272 0 158 272 11 31
## pboo_00 pboo_90 pboo_80 w_20 b_20 h_20 a_20 o_20
## 14 272 272 11 11 11 11 11
## t_20 pw_20 pb_20 ph_20 pa_20 po_20 dwb_20 dwh_20
## 11 11 11 11 11 11 11 11
## dwa_20 dbw_20 dbh_20 dba_20 dhw_20 dhb_20 dha_20 daw_20
## 11 11 11 11 11 11 11 11
## dab_20 dah_20 w_10 b_10 h_10 a_10 o_10 t_10
## 11 11 31 31 31 31 31 31
## pw_10 pb_10 ph_10 pa_10 po_10 dwb_10 dwh_10 dwa_10
## 31 31 31 31 31 31 31 31
## dbw_10 dbh_10 dba_10 dhw_10 dhb_10 dha_10 daw_10 dab_10
## 31 31 31 31 31 31 31 31
## dah_10 w_00 b_00 h_00 a_00 o_00 t_00 pw_00
## 31 67 67 67 67 67 67 67
## pb_00 ph_00 pa_00 po_00 dwb_00 dwh_00 dwa_00 dbw_00
## 67 67 67 67 67 67 67 67
## dbh_00 dba_00 dhw_00 dhb_00 dha_00 daw_00 dab_00 dah_00
## 67 67 67 67 67 67 67 67
## w_90 b_90 h_90 a_90 t_90 o_90 pw_90 pb_90
## 90 90 90 90 90 90 90 90
## ph_90 pa_90 po_90 dwb_90 dwh_90 dwa_90 dbw_90 dbh_90
## 90 90 90 90 90 90 90 90
## dba_90 dhw_90 dhb_90 dha_90 daw_90 dab_90 dah_90 t_80
## 90 90 90 90 90 90 90 115
## w_80 b_80 h_80 a_80 o_80 pw_80 pb_80 ph_80
## 115 115 115 115 115 115 115 115
## pa_80 po_80 dwb_80 dwh_80 dwa_80 dbw_80 dbh_80 dba_80
## 115 115 115 115 115 115 115 115
## dhw_80 dhb_80 dha_80 daw_80 dab_80 dah_80
## 115 115 115 115 115 115#Cities In Proximity = 1, Cities Not in Proximity = 0
table(cities$Proximity)##
## In Proximity Not in Prox
## 108 164Count of missing values of Percent Black Homeownership for cities 2020 (pboo_20) = 11, 2010 (pboo_10) = 31, 2000 (pboo_00) = 14
Testing the correlation of proximity to a freedom colony and percent Black homeownership.
#In proximity = 1, Not in proximity = 0. Using point biserial correlation. Treat as numeric within dataframe to get Pearsons R.
cor.test(cities$pboo_20, as.numeric(cities$Proximity))##
## Pearson's product-moment correlation
##
## data: cities$pboo_20 and as.numeric(cities$Proximity)
## t = -6.2183, df = 259, p-value = 2.004e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4616335 -0.2499329
## sample estimates:
## cor
## -0.3604154plot(cities$Proximity,cities$pboo_20,pch=20,
xlab='Proximity',ylab='Percent Black Homeowners',
main='Cities by Proximity and Black Homeownership in 2020')
cor.test(cities$pboo_10, as.numeric(cities$Proximity))##
## Pearson's product-moment correlation
##
## data: cities$pboo_10 and as.numeric(cities$Proximity)
## t = -6.0327, df = 239, p-value = 6.091e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4683765 -0.2485787
## sample estimates:
## cor
## -0.3635261plot(cities$Proximity,cities$pboo_10,pch=20,
xlab='Proximity',ylab='Percent Black Homeowners',
main='Cities by Proximity and Black Homeownership in 2010')
cor.test(cities$pboo_00, as.numeric(cities$Proximity))##
## Pearson's product-moment correlation
##
## data: cities$pboo_00 and as.numeric(cities$Proximity)
## t = -6.617, df = 256, p-value = 2.132e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4818092 -0.2727777
## sample estimates:
## cor
## -0.3821715plot(cities$Proximity,cities$pboo_00,pch=20,
xlab='Proximity',ylab='Percent Black Homeowners',
main='Cities by Proximity and Black Homeownership in 2000')
Result - all 3 years show a weak positive association between proximity and percent Black homeownership.
#Plot the correlation between Proximity and homeownership using a different visual
ggplot(cities, aes(pboo_20, Proximity, col = pboo_20)) +
geom_point()## Warning: Removed 11 rows containing missing values (`geom_point()`).
ggplot(cities, aes(pboo_10, Proximity, col = pboo_10)) +
geom_point()## Warning: Removed 31 rows containing missing values (`geom_point()`).
ggplot(cities, aes(pboo_00, Proximity, col = pboo_00)) +
geom_point()## Warning: Removed 14 rows containing missing values (`geom_point()`).