Mini Assignment 4
KP
2022-10-09
Urban Analytics Mini Assignment 4 10/9/22
Popular and attractive points of interest (or POIs) are important aspects of placemaking, but also have social and economic benefits for neighborhoods. They are often characterized as being destinations within walking distance to residential areas, which can arguably create more walkable, accessible spaces. In fact, the Walk Score of a location is calculated based on how many POIs are found within walking distance from a given location. That being said, we anecdotally know that their distribution is not spatially uniform; popular, attractive POIs are more likely to be found in more advantaged neighborhoods.Therefore, more advantaged neighborhoods may enjoy more benefits from having attractive POIs nearby than their counterparts.
The purpose of this assignment is to try and establish relationships between the attractiveness of POIs in a neighborhood and being advantaged as a neighborhood.To do this, we will create a series of visualizations and discuss our findings.
#As always, let's start off by calling the libraries we need.
library(knitr)
library(ggplot2)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ✔ purrr 0.3.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(dplyr)
library(tidyr)
library(leaflet)
library(here)## here() starts at C:/Users/kwells65/OneDrive - Georgia Institute of Technology/Assignmentslibrary(tidycensus)
library(ggpubr)#Step One: Reading Our Data into a Data Frame
This time, we were given our data in a csv file, so the function we uses is different than what we’ve been using for rds files. Because the file type is different (obviously).
yelp_poi <- read.csv(file="coffee.csv")
#Usually, when I'm writing a script that'll be shared, I like to include the whole filepath so other people can see how/where it's stored--especially if
#it is located on a shared network, but I won't do that here. Also, if people are using the same dataset, it's best to work with a copy of that data.
#Let's check it!
yelp_poi## X GEOID county hhincome pct_pov review_count
## 1 1 13063040202 Clayton County 33276 0.201342282 57.00000
## 2 2 13063040308 Clayton County 28422 0.210718002 13.00000
## 3 3 13063040407 Clayton County 49271 0.108255067 29.33333
## 4 4 13063040408 Clayton County 44551 0.180956612 20.00000
## 5 5 13063040410 Clayton County 49719 0.114680192 41.00000
## 6 6 13063040411 Clayton County 57924 0.090689421 18.00000
## 7 7 13063040412 Clayton County 46530 0.176958408 72.00000
## 8 8 13063040414 Clayton County 40313 0.263539652 31.00000
## 9 9 13063040417 Clayton County 40231 0.263494810 17.00000
## 10 10 13063040510 Clayton County 49265 0.297548387 5.00000
## 11 11 13063040512 Clayton County 41016 0.158646889 36.00000
## 12 12 13063040513 Clayton County 63980 0.141615986 14.00000
## 13 13 13063040523 Clayton County 41688 0.163203753 4.00000
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## 15 15 13063040606 Clayton County 45800 0.097895089 18.00000
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## 17 17 13063040609 Clayton County 58750 0.063359235 34.00000
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## 19 19 13063040612 Clayton County 34517 0.300434713 50.00000
## 20 20 13063040617 Clayton County 33359 0.273890143 43.00000
## 21 21 13063040622 Clayton County 51672 0.267144942 1.00000
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## 306 306 13135050214 Gwinnett County 92990 0.071979143 58.00000
## 307 307 13135050215 Gwinnett County 64306 0.112143103 170.20000
## 308 308 13135050216 Gwinnett County 83193 0.059155957 42.25000
## 309 309 13135050217 Gwinnett County 91451 0.091704922 37.00000
## 310 310 13135050218 Gwinnett County 68529 0.121836506 93.00000
## 311 311 13135050219 Gwinnett County 67188 0.143997797 65.33333
## 312 312 13135050220 Gwinnett County 57845 0.121972444 45.60000
## 313 313 13135050306 Gwinnett County 47011 0.158614113 97.00000
## 314 314 13135050308 Gwinnett County 151875 0.030012300 67.50000
## 315 315 13135050309 Gwinnett County 108786 0.051783917 31.50000
## 316 316 13135050310 Gwinnett County 70102 0.088690677 28.66667
## 317 317 13135050311 Gwinnett County 104302 0.074547391 69.50000
## 318 318 13135050314 Gwinnett County 49054 0.156933553 90.00000
## 319 319 13135050317 Gwinnett County 41711 0.204978410 115.00000
## 320 320 13135050320 Gwinnett County 42500 0.182611672 2.00000
## 321 321 13135050321 Gwinnett County 143875 0.025298231 44.00000
## 322 322 13135050417 Gwinnett County 35882 0.243439986 74.00000
## 323 323 13135050418 Gwinnett County 37970 0.271336884 1.00000
## 324 324 13135050419 Gwinnett County 51930 0.196823380 46.00000
## 325 325 13135050421 Gwinnett County 50511 0.255689424 21.50000
## 326 326 13135050424 Gwinnett County 47539 0.177172061 46.33333
## 327 327 13135050426 Gwinnett County 80320 0.121603363 14.00000
## 328 328 13135050430 Gwinnett County 72937 0.114052584 116.00000
## 329 329 13135050431 Gwinnett County 45816 0.238570039 7.00000
## 330 330 13135050432 Gwinnett County 61027 0.124642481 42.00000
## 331 331 13135050436 Gwinnett County 47200 0.207990735 53.00000
## 332 332 13135050511 Gwinnett County 65399 0.146013597 21.00000
## 333 333 13135050520 Gwinnett County 41512 0.223203995 100.00000
## 334 334 13135050521 Gwinnett County 58897 0.205907461 15.33333
## 335 335 13135050523 Gwinnett County 60363 0.156860306 17.16667
## 336 336 13135050524 Gwinnett County 53594 0.113174695 28.00000
## 337 337 13135050525 Gwinnett County 89475 0.066462769 80.00000
## 338 338 13135050529 Gwinnett County 67361 0.103052559 48.33333
## 339 339 13135050533 Gwinnett County 57518 0.182967399 25.00000
## 340 340 13135050536 Gwinnett County 57426 0.121276975 34.75000
## 341 341 13135050540 Gwinnett County 70506 0.126936258 52.00000
## 342 342 13135050542 Gwinnett County 52640 0.168833616 25.60000
## 343 343 13135050543 Gwinnett County 66901 0.056317401 31.00000
## 344 344 13135050544 Gwinnett County 85293 0.099962078 22.66667
## 345 345 13135050545 Gwinnett County 65249 0.206413210 203.00000
## 346 346 13135050547 Gwinnett County 77736 0.060423916 9.00000
## 347 347 13135050549 Gwinnett County 80490 0.031511840 4.00000
## 348 348 13135050605 Gwinnett County 84466 0.074659700 13.50000
## 349 349 13135050606 Gwinnett County 90338 0.020723946 47.85714
## 350 350 13135050607 Gwinnett County 95308 0.033763708 47.00000
## 351 351 13135050609 Gwinnett County 98038 0.051116056 50.75000
## 352 352 13135050610 Gwinnett County 118623 0.048983692 102.00000
## 353 353 13135050712 Gwinnett County 98886 0.051265661 84.50000
## 354 354 13135050714 Gwinnett County 89444 0.102542933 5.00000
## 355 355 13135050715 Gwinnett County 79971 0.130639731 9.00000
## 356 356 13135050718 Gwinnett County 97043 0.040620988 20.00000
## 357 357 13135050719 Gwinnett County 53875 0.117544775 51.00000
## 358 358 13135050720 Gwinnett County 70899 0.066253781 48.33333
## 359 359 13135050722 Gwinnett County 64496 0.127813907 56.00000
## 360 360 13135050723 Gwinnett County 86563 0.110698825 3.00000
## 361 361 13135050724 Gwinnett County 86588 0.054831705 64.00000
## 362 362 13135050727 Gwinnett County 99072 0.056036866 20.00000
## 363 363 13135050731 Gwinnett County 65704 0.082941797 49.00000
## avg_rating race.tot avg_price pct_white hhincome_log review_count_log
## 1 2 2850 1 0.0750877193 10.412892 4.0604430
## 2 3 4262 1 0.2606757391 10.255271 1.9756219
## 3 2 4046 1 0.2051408799 10.805294 3.3208370
## 4 4 8489 1 0.1686888915 10.704614 3.0445224
## 5 1 7166 1 0.1936924365 10.814344 3.7376696
## 6 2 13311 1 0.1651265870 10.967060 2.9444390
## 7 3 7920 1 0.1438131313 10.748067 3.9617836
## 8 2 2068 1 0.3002901354 10.604677 3.4652474
## 9 2 5850 1 0.1070085470 10.602642 2.8903718
## 10 4 3892 1 0.0300616650 10.805172 1.7917595
## 11 2 5552 1 0.0662824207 10.621961 3.3060205
## 12 2 4604 1 0.1190269331 11.066482 2.7080502
## 13 2 2984 1 0.1109249330 10.638208 1.6094379
## 14 2 7061 1 0.1005523297 10.903089 2.9957323
## 15 1 3894 1 0.4537750385 10.732258 2.9444390
## 16 1 13393 1 0.1219293661 11.128101 4.0253517
## 17 2 6698 1 0.1782621678 10.981217 3.5553481
## 18 2 6850 1 0.2143065693 10.621449 3.2958369
## 19 4 7938 1 0.2091206853 10.449497 3.9318256
## 20 2 1329 1 0.6794582393 10.415383 3.7841896
## 21 4 8224 1 0.1996595331 10.852865 0.6931472
## 22 2 7908 1 0.8777187658 11.405663 3.0699423
## 23 3 9369 2 0.5976091365 11.406153 3.0600990
## 24 2 3883 1 0.7839299511 10.861477 4.0775374
## 25 4 7059 NaN 0.5394531803 11.071875 3.8918203
## 26 4 7662 2 0.6850691725 11.608327 2.3874164
## 27 3 7654 2 0.8369480010 11.412873 2.7917482
## 28 5 9696 NaN 0.7140057756 11.231530 3.1354942
## 29 3 10652 1 0.7974089373 11.707497 2.2033596
## 30 3 1663 1 0.6903187011 11.427890 2.8173948
## 31 3 6239 1 0.5896778330 11.245948 3.4141760
## 32 4 11024 1 0.5109760522 11.353625 2.8244871
## 33 4 8236 2 0.6868625546 11.092595 3.2381739
## 34 3 7363 2 0.6026076328 10.823412 3.2556881
## 35 4 6819 2 0.7072884587 11.123993 2.3674882
## 36 3 13473 1 0.6095895495 11.280401 3.4019518
## 37 4 8329 2 0.8127026054 11.604702 4.5108595
## 38 2 6630 1 0.7276018100 11.685365 4.3174881
## 39 2 4948 1 0.7550525465 11.569580 3.6635616
## 40 4 7819 1 0.8730016626 11.788737 4.6249728
## 41 4 8212 1 0.7496346810 11.273424 5.7776523
## 42 2 6804 1 0.7452968842 11.162616 3.5047045
## 43 3 8998 1 0.6465881307 11.455487 3.4110987
## 44 2 7928 1 0.7836781029 11.600955 3.4657359
## 45 2 7680 2 0.8522135417 11.919550 2.5058403
## 46 4 4603 1 0.8109928308 11.502350 2.9444390
## 47 4 4442 1 0.8464655561 11.957913 3.0868931
## 48 3 6752 1 0.8316054502 11.821042 3.8219810
## 49 2 4863 1 0.7947768867 11.581350 3.6375862
## 50 2 6396 1 0.7148217636 11.748440 3.7647032
## 51 3 3199 NaN 0.7852453892 11.803025 2.0794415
## 52 4 3389 2 0.8093832989 11.727843 4.0904405
## 53 4 7165 1 0.8840195394 12.103741 3.9118230
## 54 4 4564 1 0.8724802805 12.038825 2.8518912
## 55 4 6362 1 0.6035837787 11.232444 5.3324270
## 56 4 3987 1 0.9470780035 11.987668 2.6235120
## 57 2 4625 1 0.8501621622 11.722303 3.9120230
## 58 4 3850 2 0.2488311688 10.803974 3.8066625
## 59 3 5602 1 0.2631203142 11.180051 3.8929243
## 60 2 6449 1 0.5577608932 10.808030 3.9702919
## 61 5 2862 NaN 0.6526904263 11.259155 2.7725887
## 62 3 4656 2 0.6911512027 11.127307 3.4755786
## 63 3 2206 1 0.3889392566 10.571419 4.5849675
## 64 2 5269 1 0.4516986145 10.738655 2.9444390
## 65 4 5554 2 0.4751530429 10.826456 3.4657359
## 66 3 7314 NaN 0.7214930271 11.479131 1.0397208
## 67 2 9189 1 0.4564152791 11.112463 3.1921354
## 68 4 6709 2 0.8267998211 11.437059 4.4193798
## 69 2 6135 1 0.5367563162 10.900713 2.3124864
## 70 2 8349 1 0.6452269733 11.272954 3.7135721
## 71 2 7332 1 0.3323786143 10.672855 3.2462043
## 72 3 8700 2 0.3924137931 11.145782 3.7376696
## 73 4 6146 NaN 0.4248291572 10.987037 2.4849066
## 74 3 8628 2 0.4514371813 11.241012 4.3567088
## 75 4 3241 1 0.6568960197 11.099892 5.0751738
## 76 4 3745 2 0.5543391188 11.363555 6.4815771
## 77 2 7511 2 0.3233923579 10.939870 3.3220602
## 78 2 4690 1 0.4191897655 11.227015 3.2783892
## 79 2 6169 1 0.5397957530 11.349771 3.8501476
## 80 4 6588 NaN 0.4676684882 11.384183 3.1354942
## 81 3 10395 1 0.4319384319 11.279757 3.4611440
## 82 3 4714 2 0.5178192618 11.277039 3.1984648
## 83 3 6010 NaN 0.4462562396 11.206686 1.3540251
## 84 3 4558 2 0.5028521281 11.318612 3.7250895
## 85 4 2330 1 0.9072961373 12.122745 3.9951191
## 86 2 4566 1 0.4472185721 11.275100 3.2188758
## 87 5 8613 NaN 0.1991176129 10.551664 1.3862944
## 88 2 10519 1 0.3764616408 11.371569 3.9827728
## 89 4 10815 NaN 0.3853906611 11.673138 2.8332133
## 90 2 8152 1 0.3447006869 10.977466 2.4952163
## 91 2 8309 1 0.2574317006 10.722672 3.6109179
## 92 4 6956 1 0.3966359977 10.967940 5.0751738
## 93 4 9443 1 0.4014614000 11.090416 4.7957905
## 94 2 5414 1 0.4863317325 10.941429 3.9120230
## 95 5 11568 NaN 0.3496715076 11.309499 1.0986123
## 96 4 4476 2 0.8918677391 11.902424 5.2017222
## 97 4 2701 2 0.8504257682 11.572260 4.6473867
## 98 3 3161 1 0.4950964885 11.092915 3.0739212
## 99 4 2634 1 0.6545178436 11.466117 5.3565863
## 100 4 2509 2 0.6448784376 11.458174 7.1906760
## 101 4 4411 2 0.4096576740 11.211699 4.8160362
## 102 4 5927 1 0.5231989202 11.179241 4.5312456
## 103 2 6533 1 0.7821827644 11.612897 4.4977070
## 104 5 3002 2 0.3907395070 10.248920 3.3080286
## 105 3 4237 2 0.6780741090 11.224336 3.6377462
## 106 3 6793 2 0.7247166201 11.298519 3.2958538
## 107 2 3526 1 0.9064095292 11.945182 3.8918203
## 108 4 6206 2 0.8623912343 12.056929 5.0226237
## 109 3 6550 1 0.3563358779 11.516499 3.1075175
## 110 4 7974 2 0.5185603210 11.371800 4.7291884
## 111 2 6071 1 0.4547850437 11.006490 3.6860590
## 112 4 3817 2 0.4451139638 10.751007 4.9522741
## 113 5 5869 2 0.5605724996 10.676347 3.5577516
## 114 2 5073 1 0.5730337079 10.822335 3.5383269
## 115 2 4185 1 0.5445639188 10.827191 2.4181410
## 116 2 5443 1 0.8160940658 11.050271 2.6735538
## 117 2 3764 1 0.8286397450 11.659352 2.4849066
## 118 2 4156 1 0.4227622714 10.882415 3.3322045
## 119 3 3687 1 0.7700027122 11.624798 5.0562661
## 120 4 4297 2 0.7349313475 11.191990 2.9736857
## 121 3 3938 1 0.6924834942 10.957643 3.9179873
## 122 4 5357 1 0.6647377263 11.260842 4.6486478
## 123 2 4943 2 0.6819745094 11.103979 3.9571261
## 124 4 6236 1 0.5566067992 11.320117 4.8751973
## 125 2 6045 1 0.7301902399 11.562125 3.0553932
## 126 3 4879 1 0.5607706497 10.661697 3.2712173
## 127 3 7536 1 0.4985403397 11.239488 2.7764798
## 128 4 6732 1 0.4578134284 11.130581 3.1560394
## 129 3 6785 2 0.7665438467 11.506374 3.6058987
## 130 3 5788 2 0.5794747754 11.219212 3.3166592
## 131 5 5572 2 0.1475233309 10.746304 4.8903491
## 132 3 9022 2 0.2139215252 10.916142 1.0986123
## 133 2 5443 1 0.0883703840 10.498470 3.4965076
## 134 2 6006 NaN 0.1605061605 10.652897 0.6931472
## 135 4 4559 1 0.7245009871 11.107180 4.8202816
## 136 5 5395 1 0.1395736793 10.578369 1.0986123
## 137 5 4098 1 0.0934602245 10.577095 3.3423059
## 138 4 5572 2 0.0664034458 10.622595 1.7917595
## 139 2 3689 1 0.2314990512 10.695235 2.9444390
## 140 4 2130 2 0.5342723005 11.108575 3.1513095
## 141 3 6061 1 0.7068140571 11.209507 4.4946046
## 142 2 4371 1 0.7073896134 11.398143 3.5553481
## 143 4 6698 2 0.5043296506 10.376393 2.4846170
## 144 4 3987 NaN 0.8918986707 12.069915 2.7725887
## 145 4 7102 1 0.6964235427 11.414849 5.3822491
## 146 3 6600 2 0.6771212121 11.316193 4.0839597
## 147 4 5413 2 0.7009052282 11.822863 5.5956985
## 148 4 2115 1 0.8486997636 11.326969 4.7004804
## 149 2 3390 1 0.3530973451 10.595909 3.5553481
## 150 5 2263 NaN 0.1201944322 11.050557 1.0986123
## 151 5 4877 NaN 0.0469550953 10.302129 2.3978953
## 152 2 2780 1 0.3248201439 10.610340 3.5263605
## 153 2 8059 1 0.0399553294 10.425016 3.2958369
## 154 4 3480 NaN 0.0166666667 10.819578 1.9459101
## 155 1 3608 NaN 0.0263303769 11.002816 2.5649494
## 156 2 6165 1 0.0167072182 10.782284 2.0794415
## 157 5 7087 NaN 0.0688584733 10.792366 2.0126758
## 158 1 10967 1 0.0142244917 10.617417 3.5263605
## 159 2 10571 1 0.0503263646 11.193560 3.1780538
## 160 2 8865 1 0.0214326001 11.143744 3.3672958
## 161 2 4428 1 0.0103884372 10.438284 3.9512437
## 162 2 6008 1 0.0239680426 10.862646 3.2580965
## 163 1 6603 1 0.0622444343 11.149830 3.6635616
## 164 2 16529 1 0.0615282231 10.850598 3.8501476
## 165 4 6057 1 0.0657090969 11.207242 2.8332133
## 166 2 10151 1 0.0141857945 10.581978 2.0635672
## 167 2 3361 1 0.0502826540 10.373804 3.8066625
## 168 2 4268 1 0.0051546392 10.758371 3.3041161
## 169 5 6054 2 0.1508093822 10.773148 2.5375869
## 170 4 5410 2 0.8996303142 12.034133 3.6192484
## 171 4 6175 2 0.8961943320 11.970483 3.7273600
## 172 4 2047 1 0.8651685393 11.485215 3.0345319
## 173 4 5219 1 0.6625790381 11.409230 2.1886503
## 174 4 7952 1 0.4934607646 10.891094 4.7247435
## 175 4 3339 2 0.2952979934 11.339964 1.9459101
## 176 3 2904 2 0.5688705234 11.550761 2.8659419
## 177 2 7305 1 0.4576317591 10.343418 2.9957323
## 178 3 5193 1 0.7818216830 11.603095 3.0949981
## 179 4 4527 1 0.7472940137 11.295839 5.5490761
## 180 4 5206 2 0.7172493277 11.441999 4.1540325
## 181 4 4421 1 0.6704365528 11.241090 3.9315204
## 182 3 2345 1 0.8652452026 11.340677 4.6117270
## 183 4 4617 1 0.8286766298 11.104957 4.9558271
## 184 4 2237 1 0.8480107287 11.266923 4.2508540
## 185 4 4911 NaN 0.6340867440 11.459103 2.5649494
## 186 3 5064 1 0.4194312796 11.045574 2.7724679
## 187 2 2409 1 0.3354088834 11.136456 3.1392607
## 188 4 1672 2 0.0107655502 10.422906 1.4978661
## 189 5 5231 2 0.3265150067 10.409552 2.6216013
## 190 4 2425 1 0.4997938144 11.173529 4.5053455
## 191 4 3630 2 0.8011019284 11.636337 4.7544704
## 192 4 2051 2 0.6981960020 11.039074 4.6116777
## 193 3 2074 1 0.3582449373 10.894867 3.3431709
## 194 4 1254 1 0.1076555024 10.881099 3.4339872
## 195 2 2214 1 0.1287262873 10.900768 3.6170886
## 196 3 2477 1 0.0867985466 9.848133 4.0421271
## 197 4 2835 2 0.5400352734 11.346458 2.6010089
## 198 3 2586 2 0.5769528229 11.232947 4.6406768
## 199 3 5170 1 0.7750483559 11.624190 4.4311810
## 200 4 3861 2 0.6632996633 11.535792 3.4881740
## 201 4 2846 1 0.0769501054 9.990307 3.8066625
## 202 4 1479 NaN 0.2028397566 10.661697 1.6472141
## 203 4 1725 2 0.0243478261 10.532363 3.0931043
## 204 1 1492 NaN 0.0851206434 10.357521 0.6931472
## 205 5 3843 NaN 0.2047879261 10.823232 2.1972246
## 206 5 1755 2 0.1903133903 10.697837 3.9120230
## 207 4 2651 2 0.0211241041 10.724610 2.9957323
## 208 1 2136 1 0.0402621723 10.207031 3.4011974
## 209 2 3348 1 0.1132019116 10.174087 2.3025851
## 210 2 4463 2 0.0163567107 10.702390 3.8118210
## 211 1 4580 1 0.0000000000 9.433084 2.8332133
## 212 4 5529 1 0.0434074878 10.688849 3.7135721
## 213 2 6647 1 0.0279825485 10.208359 2.4708212
## 214 2 5430 1 0.6523020258 11.546312 3.4657359
## 215 4 8007 1 0.6028475084 11.190542 3.6423548
## 216 4 2250 NaN 0.3982222222 11.238331 1.7917595
## 217 2 5341 2 0.8240029957 11.618690 3.6827399
## 218 4 5630 NaN 0.6879218472 11.283500 0.8958797
## 219 4 3344 2 0.6495215311 10.992958 4.0392990
## 220 3 7359 1 0.6583774969 11.177341 3.5105420
## 221 3 5298 2 0.9360135900 11.572495 4.5373749
## 222 2 5537 1 0.4975618566 11.226549 2.8541477
## 223 4 5268 1 0.4671602126 11.055309 3.7612001
## 224 4 3867 2 0.6793379881 11.362835 4.0768187
## 225 3 2631 1 0.8354237932 11.584278 4.6151205
## 226 4 5514 2 0.7566195140 11.540424 3.1671892
## 227 3 4830 2 0.7372670807 11.329639 4.3783397
## 228 2 4746 2 0.8916982722 11.739144 3.3766087
## 229 2 4553 2 0.8021084999 12.017288 2.0794415
## 230 3 5213 1 0.9629771725 11.934985 3.3517833
## 231 5 4923 2 0.7460897827 11.591801 3.5740839
## 232 3 8716 2 0.8509637448 11.892676 2.8651622
## 233 4 8275 2 0.7135951662 11.208260 3.8114451
## 234 4 5771 1 0.4704557269 11.028304 5.2832037
## 235 2 2770 NaN 0.7880866426 11.757423 1.3862944
## 236 2 6351 1 0.3989922847 10.940437 4.3241107
## 237 4 2095 2 0.8377088305 11.412674 4.6695427
## 238 4 6383 1 0.3725520915 11.414165 1.0986123
## 239 4 5607 1 0.9058319957 11.550587 4.7024700
## 240 2 5618 2 0.8666785333 12.167254 3.9971475
## 241 2 4029 1 0.7182923802 11.184866 4.1588831
## 242 4 2864 2 0.9210893855 11.868920 3.8286414
## 243 4 5645 2 0.6572187777 11.117807 3.2313680
## 244 2 21010 1 0.0649214660 11.233820 3.4011974
## 245 2 10840 1 0.0003690037 11.176991 3.8481063
## 246 3 8551 1 0.4336334932 10.839385 3.6919947
## 247 2 7495 1 0.0595063376 10.691854 3.6888795
## 248 2 3359 1 0.0541827925 10.993463 3.5263605
## 249 2 15147 1 0.1447811448 10.780226 3.7273061
## 250 2 6460 1 0.0467492260 10.602244 3.9512437
## 251 2 14560 1 0.1021978022 10.786304 3.4339872
## 252 2 5930 1 0.1099494098 10.931194 2.7080502
## 253 1 4298 NaN 0.0111679851 10.458521 0.6931472
## 254 5 2584 2 0.0874613003 10.492773 0.6931472
## 255 3 6577 1 0.4277026000 10.711993 4.1684582
## 256 2 3036 1 0.2183794466 10.067136 3.3231953
## 257 4 2365 2 0.3737843552 11.007800 6.0330862
## 258 3 3683 2 0.3182188433 10.706027 3.3218949
## 259 1 5230 1 0.0250478011 10.510995 4.4067192
## 260 3 2337 2 0.0654685494 10.422192 3.0474252
## 261 4 7783 1 0.6672234357 11.351477 3.0303972
## 262 4 6745 2 0.7568569311 11.671356 2.8378622
## 263 2 7659 1 0.7216346782 11.351864 3.4339872
## 264 3 9520 2 0.7532563025 11.421161 4.0642730
## 265 4 7683 1 0.5733437459 11.294757 3.7169118
## 266 4 5641 2 0.7854990250 11.603013 4.2331604
## 267 5 6194 2 0.9578624475 11.988103 4.8029694
## 268 3 5919 1 0.8308835952 11.731000 3.9465068
## 269 3 4280 1 0.5843457944 10.826476 3.9061448
## 270 4 3795 2 0.7478260870 11.168221 4.6921468
## 271 2 7950 1 0.7742138365 11.747697 3.2580965
## 272 4 11325 2 0.7880794702 12.036233 1.9944920
## 273 4 13727 2 0.9298462883 12.372261 3.3910960
## 274 2 4377 1 0.8549234636 12.025438 3.4613219
## 275 4 5969 2 0.8944546825 12.015372 2.5619820
## 276 5 5918 2 0.7886110172 11.648103 4.1758036
## 277 4 8707 1 0.6363845182 11.136485 2.6847805
## 278 2 3376 1 0.7138625592 11.924446 2.3025851
## 279 4 7137 2 0.5313156789 11.608926 4.3353861
## 280 4 15411 1 0.4794627214 11.214439 3.3339159
## 281 4 10672 2 0.7497188906 11.990854 3.6736414
## 282 4 6372 2 0.5291902072 11.553933 3.4016714
## 283 4 7891 2 0.7058674439 11.878506 5.4121640
## 284 2 4599 1 0.5642530985 11.718858 4.2341065
## 285 3 7869 1 0.4788410217 11.448494 2.6618151
## 286 4 10206 2 0.5949441505 11.860218 2.8062406
## 287 4 6962 NaN 0.6265440965 11.968462 1.5890269
## 288 4 11005 1 0.3680145388 11.915674 4.3436353
## 289 4 12517 2 0.4702404730 11.706838 3.4914812
## 290 4 8542 2 0.5859283540 11.714813 4.0254104
## 291 3 9546 1 0.5612822124 11.539655 3.5013694
## 292 4 3517 2 0.3542792152 10.154596 2.8878949
## 293 4 2949 NaN 0.3567310953 10.862799 3.7135721
## 294 4 5108 1 0.4610415035 10.892936 3.6926155
## 295 2 11749 1 0.6203081113 11.102865 3.5434382
## 296 2 8379 2 0.7189402077 11.541357 3.2083661
## 297 2 10054 1 0.7499502685 11.768513 3.4339872
## 298 4 8273 2 0.5259277167 11.167346 5.1416636
## 299 3 4673 1 0.4504600899 11.122280 4.4464856
## 300 1 8013 1 0.5063022588 11.086778 4.2766661
## 301 4 9073 2 0.4238950733 10.883354 4.3151078
## 302 3 8495 1 0.4917010006 11.271300 4.0413694
## 303 4 5623 1 0.2646274231 10.711748 3.7466186
## 304 2 13564 2 0.7455028015 11.842999 3.5375929
## 305 4 13617 2 0.4958507748 11.640448 3.5564735
## 306 4 8822 2 0.4715484017 11.440355 3.9033482
## 307 4 2907 1 0.3398692810 11.071564 4.7694702
## 308 3 10971 1 0.4497311093 11.329039 3.6270204
## 309 4 9388 NaN 0.3955048999 11.423668 3.6375862
## 310 4 4488 1 0.4418449198 11.135158 4.5432948
## 311 4 3662 2 0.4330966685 11.115399 3.9686849
## 312 4 6957 1 0.4205835849 10.965695 3.6450572
## 313 3 3170 1 0.4148264984 10.758350 3.9215320
## 314 3 4065 2 0.6851168512 11.930879 4.1163530
## 315 4 8946 2 0.5516431925 11.597230 3.4608291
## 316 2 5942 2 0.6164591047 11.157849 3.0125904
## 317 3 2830 1 0.5335689046 11.555142 3.4569783
## 318 4 9711 1 0.3961486974 10.800881 4.5108595
## 319 1 3937 1 0.2809245618 10.638760 4.7535902
## 320 5 5856 NaN 0.3570696721 10.657495 1.0986123
## 321 4 4862 1 0.8584944467 11.876770 3.8066625
## 322 2 5907 1 0.3341797867 10.488270 4.3174881
## 323 1 7323 NaN 0.2854021576 10.544815 0.6931472
## 324 4 7885 2 0.3138871275 10.857844 3.8501476
## 325 3 7470 1 0.2641231593 10.830144 2.6448213
## 326 2 8218 1 0.3652956924 10.769516 3.7543681
## 327 2 6661 2 0.6680678577 11.293898 2.7080502
## 328 3 5540 1 0.5052346570 11.197488 3.4204322
## 329 2 8224 NaN 0.2031857977 10.732607 2.0794415
## 330 2 6729 1 0.4397384455 11.019236 3.7612001
## 331 2 8635 1 0.2186450492 10.762361 3.8888963
## 332 3 12944 1 0.3458745365 11.088415 2.7681620
## 333 3 5390 1 0.4246753247 10.633979 4.1616386
## 334 3 5879 2 0.4927708794 10.983715 2.6380841
## 335 2 8125 1 0.4384000000 11.008297 2.3673117
## 336 2 6952 1 0.5087744534 10.889379 3.3672958
## 337 4 8523 1 0.5554382260 11.401826 4.0207668
## 338 4 4357 1 0.6047739270 11.117970 3.6494609
## 339 4 9018 NaN 0.2756708805 10.960027 3.2580965
## 340 3 5607 1 0.3902265026 10.958427 3.1082164
## 341 4 7295 2 0.4976010966 11.163595 3.2679811
## 342 3 5307 2 0.2345958168 10.871422 2.9554048
## 343 2 7511 1 0.3827719345 11.111119 3.4657359
## 344 2 13185 1 0.4302616610 11.353965 3.0349933
## 345 4 15629 1 0.4023290038 11.086119 5.3181200
## 346 4 9483 2 0.6072972688 11.261202 2.3025851
## 347 3 5490 NaN 0.5921675774 11.296012 1.6094379
## 348 2 14579 NaN 0.6639687221 11.344223 2.3937459
## 349 3 26399 1 0.6425243380 11.411424 3.0446588
## 350 3 18598 1 0.6018926766 11.464974 3.6681158
## 351 3 14829 1 0.7148829995 11.493212 3.8820112
## 352 2 16924 2 0.7543134011 11.683790 4.6347290
## 353 3 7822 2 0.6709281514 11.501824 4.2001049
## 354 2 6070 NaN 0.4988467875 11.401480 1.7917595
## 355 4 5952 NaN 0.6013104839 11.289544 2.3025851
## 356 2 8722 1 0.5536574180 11.483013 2.9648901
## 357 2 5751 1 0.3870631195 10.894607 3.9512437
## 358 2 6948 2 0.6263672999 11.169153 3.3633500
## 359 2 3998 1 0.3306653327 11.074514 4.0430513
## 360 2 8095 NaN 0.2144533663 11.368743 1.3862944
## 361 3 18893 1 0.4572593024 11.369032 4.0634069
## 362 2 10850 NaN 0.5639631336 11.503703 3.0445224
## 363 2 10440 1 0.3903256705 11.093067 3.9120230
## pct_pov_log yelp_n
## 1 -1.5542763 1
## 2 -1.5108694 2
## 3 -2.1349114 3
## 4 -1.6557090 1
## 5 -2.0820033 1
## 6 -2.2957145 1
## 7 -1.6768691 3
## 8 -1.2963087 2
## 9 -1.2964726 1
## 10 -1.1791228 1
## 11 -1.7799482 2
## 12 -1.8864044 1
## 13 -1.7532866 1
## 14 -1.4848268 1
## 15 -2.2265959 1
## 16 -2.1025552 1
## 17 -2.6123869 1
## 18 -1.3044789 1
## 19 -1.1697817 1
## 20 -1.2591679 1
## 21 -1.2832147 1
## 22 -2.0456576 2
## 23 -3.4491771 5
## 24 -1.5491860 1
## 25 -2.4757450 1
## 26 -2.5527479 4
## 27 -2.9576881 2
## 28 -2.6661037 1
## 29 -2.9184369 2
## 30 -2.4331758 2
## 31 -1.9650487 3
## 32 -2.2820784 2
## 33 -1.8212730 6
## 34 -1.5186011 6
## 35 -1.9989076 5
## 36 -2.8559540 4
## 37 -3.2184857 1
## 38 -3.0946175 1
## 39 -2.7686192 1
## 40 -3.6021282 1
## 41 -2.5014282 1
## 42 -2.4891062 2
## 43 -2.6561133 2
## 44 -2.6750244 1
## 45 -3.0408371 3
## 46 -2.6262310 1
## 47 -3.2477556 2
## 48 -2.9638984 2
## 49 -3.8779604 1
## 50 -3.1390750 2
## 51 -3.6398958 1
## 52 -2.3891767 2
## 53 -3.8293534 2
## 54 -3.2557317 2
## 55 -2.4326286 2
## 56 -3.7924302 2
## 57 -3.1511075 1
## 58 -2.1499197 1
## 59 -2.2333423 9
## 60 -1.5984243 1
## 61 -2.5233065 1
## 62 -2.7910050 3
## 63 -1.3187120 1
## 64 -1.2377673 1
## 65 -1.2844619 1
## 66 -2.2827233 2
## 67 -2.3677957 4
## 68 -2.6103752 12
## 69 -1.6905941 2
## 70 -2.2817356 1
## 71 -1.3202361 3
## 72 -1.8754625 1
## 73 -1.3915839 1
## 74 -2.8812028 1
## 75 -2.0428950 1
## 76 -2.4805370 1
## 77 -1.8431635 3
## 78 -1.8976924 2
## 79 -2.3822428 1
## 80 -2.5607929 1
## 81 -2.4509797 7
## 82 -2.9290022 2
## 83 -2.6006369 2
## 84 -3.2050218 3
## 85 -3.2728834 2
## 86 -2.5103534 1
## 87 -1.3418404 1
## 88 -2.8406341 2
## 89 -3.4849335 1
## 90 -1.7317431 2
## 91 -1.4992177 1
## 92 -2.9819251 1
## 93 -2.0562914 1
## 94 -1.8727428 1
## 95 -2.2350040 1
## 96 -2.3878785 2
## 97 -2.5509893 2
## 98 -1.7846164 4
## 99 -2.2560721 1
## 100 -2.7151097 1
## 101 -1.6304845 2
## 102 -2.5331744 4
## 103 -2.6000486 3
## 104 -0.9042536 4
## 105 -1.9375454 7
## 106 -1.9758743 3
## 107 -3.9895401 1
## 108 -3.9671593 2
## 109 -2.4130477 7
## 110 -3.9357536 5
## 111 -2.1189925 2
## 112 -1.4370250 7
## 113 -1.3101064 5
## 114 -1.4018053 2
## 115 -1.4967388 2
## 116 -1.6499021 2
## 117 -2.8321831 1
## 118 -1.3287339 1
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## 120 -2.4388264 3
## 121 -1.5966251 2
## 122 -1.9804122 5
## 123 -2.2245270 2
## 124 -2.1486727 1
## 125 -2.4234625 3
## 126 -1.4177689 4
## 127 -2.7578337 2
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## 129 -2.8842057 3
## 130 -2.5532051 2
## 131 -1.8690589 1
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## 133 -1.2455667 1
## 134 -1.7325486 1
## 135 -2.1433585 1
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## 138 -0.9362664 1
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## 141 -1.7548269 6
## 142 -2.0601787 1
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## 144 -3.0711415 1
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## 153 -1.0123480 1
## 154 -2.4156380 1
## 155 -1.8408462 1
## 156 -1.6485970 2
## 157 -1.5295039 2
## 158 -1.4368133 1
## 159 -2.5807991 1
## 160 -2.0600039 1
## 161 -1.5017630 1
## 162 -2.0303246 1
## 163 -3.0431543 1
## 164 -2.3549149 1
## 165 -2.0570402 1
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## 167 -1.1889030 1
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## 169 -1.4586375 2
## 170 -3.5519343 2
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## 172 -3.0514582 6
## 173 -2.1088158 6
## 174 -0.8361869 7
## 175 -1.8667022 1
## 176 -1.6947951 6
## 177 -0.8981737 2
## 178 -2.6203579 5
## 179 -2.3215896 1
## 180 -2.5243370 12
## 181 -1.5960004 11
## 182 -3.0737514 2
## 183 -2.0427658 1
## 184 -2.8272899 6
## 185 -2.5483039 1
## 186 -1.2407649 6
## 187 -0.8761265 2
## 188 -1.0411514 2
## 189 -1.1408627 3
## 190 -1.8187970 4
## 191 -2.9122625 4
## 192 -2.1589711 2
## 193 -0.6114355 5
## 194 -1.0469521 1
## 195 -1.3632806 2
## 196 -0.8303001 2
## 197 -1.3603894 3
## 198 -2.7004152 3
## 199 -1.8711568 3
## 200 -2.1307649 2
## 201 -0.8578484 1
## 202 -1.2657490 3
## 203 -1.3656396 2
## 204 -0.9553828 1
## 205 -1.3624867 1
## 206 -1.2741473 1
## 207 -1.4983245 1
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## 209 -1.0549148 1
## 210 -1.5774326 2
## 211 -0.2522756 1
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## 216 -2.2697953 1
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## 220 -1.9000777 2
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## 222 -1.7001470 4
## 223 -1.6781054 1
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## 225 -2.7892421 1
## 226 -2.4383850 9
## 227 -2.1812656 3
## 228 -3.2454426 3
## 229 -3.1478278 1
## 230 -3.0132257 3
## 231 -2.5426545 4
## 232 -2.9465954 5
## 233 -2.4934018 7
## 234 -2.4802020 1
## 235 -3.0681986 1
## 236 -1.5117759 2
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## 238 -2.4554285 2
## 239 -2.6993402 7
## 240 -3.0254212 2
## 241 -3.1601600 1
## 242 -3.0928339 1
## 243 -1.4163610 4
## 244 -2.7498858 1
## 245 -2.0717956 2
## 246 -1.8169146 2
## 247 -1.4744112 1
## 248 -2.1507597 1
## 249 -1.8562594 3
## 250 -1.4936521 1
## 251 -1.6788969 1
## 252 -1.9088366 1
## 253 -1.3970735 1
## 254 -1.5438357 1
## 255 -1.3082189 5
## 256 -0.6272127 2
## 257 -2.1939108 1
## 258 -1.4211101 2
## 259 -1.6631493 1
## 260 -1.4224020 3
## 261 -2.8373197 6
## 262 -3.0562364 3
## 263 -2.2887242 1
## 264 -2.8694529 4
## 265 -2.3071118 3
## 266 -2.7324364 2
## 267 -3.6187373 4
## 268 -2.5180081 3
## 269 -1.3134957 3
## 270 -2.4005635 2
## 271 -2.5362442 1
## 272 -3.7428486 2
## 273 -3.4484091 2
## 274 -3.2555820 2
## 275 -3.3915129 2
## 276 -3.8967632 6
## 277 -2.9659206 7
## 278 -2.8975633 1
## 279 -2.1572160 2
## 280 -2.4319371 6
## 281 -3.4413871 5
## 282 -2.5155787 6
## 283 -3.1048005 2
## 284 -3.0778605 1
## 285 -3.0634461 5
## 286 -2.9045278 6
## 287 -2.3336879 2
## 288 -3.0052275 3
## 289 -3.7958835 6
## 290 -3.3345117 6
## 291 -2.5965583 3
## 292 -0.9804692 19
## 293 -1.6171400 1
## 294 -1.7164238 2
## 295 -2.2899332 8
## 296 -2.7647854 2
## 297 -3.3677276 1
## 298 -2.2422748 1
## 299 -1.9783398 3
## 300 -2.5983108 1
## 301 -1.6801426 6
## 302 -2.5465473 6
## 303 -2.0031407 11
## 304 -3.0780846 3
## 305 -2.7986491 3
## 306 -2.5012904 2
## 307 -2.1025619 10
## 308 -2.6713911 4
## 309 -2.2856796 1
## 310 -2.0261927 1
## 311 -1.8708170 3
## 312 -2.0251621 5
## 313 -1.7801425 2
## 314 -3.2185684 2
## 315 -2.7841122 2
## 316 -2.3157648 3
## 317 -2.4704431 4
## 318 -1.7901594 1
## 319 -1.5372177 1
## 320 -1.6470792 1
## 321 -3.3439224 1
## 322 -1.3726282 1
## 323 -1.2682025 2
## 324 -1.5758901 1
## 325 -1.3254272 4
## 326 -1.6757270 3
## 327 -2.0279627 1
## 328 -2.0870497 5
## 329 -1.3920306 1
## 330 -2.0051323 1
## 331 -1.5233027 2
## 332 -1.8578121 6
## 333 -1.4558417 3
## 334 -1.5329054 3
## 335 -1.7905983 6
## 336 -2.0941516 1
## 337 -2.5709513 3
## 338 -2.1799024 3
## 339 -1.6452340 1
## 340 -2.0304459 4
## 341 -1.9882397 3
## 342 -1.7212994 5
## 343 -2.7133030 1
## 344 -2.2076197 3
## 345 -1.5305657 1
## 346 -2.6532224 1
## 347 -3.1817766 1
## 348 -2.4691156 2
## 349 -3.4827129 7
## 350 -3.1289504 5
## 351 -2.7949807 4
## 352 -2.8304943 1
## 353 -2.7925358 2
## 354 -2.1844205 1
## 355 -1.9615538 1
## 356 -2.9833890 3
## 357 -2.0592878 1
## 358 -2.5736883 3
## 359 -1.9818510 1
## 360 -2.1144569 1
## 361 -2.7359605 2
## 362 -2.7175421 1
## 363 -2.3757818 1#Step Two: Make a Basic Boxplot
Now we’re ready to start visualizing our data.
ggplot(yelp_poi) +
geom_boxplot(mapping = aes(x = avg_rating, y = hhincome, group=avg_rating), color = "yellow")+
labs(x="Average Rating", y="Median Annual Household Income($)", title="Does Neighborhood Income Have a Relationship With Popular, Attractive Coffee Shops?")+
ggdark::dark_theme_gray()## Inverted geom defaults of fill and color/colour.
## To change them back, use invert_geom_defaults().
From what we can see here, we can see that there does appear to be a tacit relationship between the income of a neighborhood and the average rating of coffee shops.This is most apparent in the fact that shops with an average rating of 1 are only found in lower-income neighborhoods. As the ratings increase, the Median annual income appears to as well.However, it also appears that there are some excellent coffee shops with an average rating of 5 in lower-income neighborhoods as well. Moreover, with the exception of a few outliers, I think it’s interesting that there are comparatively fewer coffee-shops in the neighborhoods in the highest income brackets. Why would that be? Is it because they are exclusve and/or more suburban in character and therefore less walkable? Also, I would say that there’s a lot of variation in the data as far as income goes (I think this is particularly the case in coffee shops with an avverage rating of 2,3 and 4). That suggests to me that there might be other factors besides income alone that contributes to this.
#Step Three: Make a Series of Boxplots By County
ggplot(data = yelp_poi %>%
separate(col = "county", into=c("county"),sep=", ") %>%
drop_na(county)) +
geom_boxplot(mapping = aes(x = avg_rating, y = hhincome, group=avg_rating), color = "yellow") +
scale_y_continuous(name = "Median Annual Household Income ($)") +
scale_x_discrete(name = "Average Yelp Rating") +
facet_wrap(~county)+
ggdark::dark_theme_gray()
The first thing I noticed here was that practically all of Clayton County’s coffee shops are in lower-income neighborhoods regardless of rating and there is very little variation in the data. Is this because all the more walkable places are in lower-income neighborhoods while middle and higher-incomes are suburban? Does the county have a lower MHHI than the others? There still aren’t any cafes with a rating of 1 in higher-income neighborhoods and with a few exceptions, there are still few cafes at all in the highest income levels and coffee shops with an average rating of 5 still tend to skew towards lower-income neighborhoods.
#Step Four: Make a Scatterplot for Review Count and Household Income by County
As I mentioned in the last assignment, popularity can also be indicated by review count; a place with a high number of positive reviews is likely a beloved neighborhood staple. However, the data needs to be normalized so a logarithmic analysis was performed on the review count variable before the dataset was turned over to me to get it as close to a normal distribution as possible. Adding demographics can also give us more context.
ggplot(data = yelp_poi %>%
separate(col = "county", into=c("county"),sep=", ") %>%
drop_na(county)) +
geom_point(mapping = aes(x = review_count_log, y = hhincome, color = pct_white), size = 2.5, alpha=0.75) +
labs(x = "Review Count (log)",
y = "Median Annual Household Income ($)",
color = "Proportion of Residents That Self-identify as White",
title = "Scatterplot: Review Count vs. Household Income") +
scale_color_gradient(low="midnightblue", high="orange") +
facet_wrap(~county) +
ggdark::dark_theme_gray()
The first thing I noticed, once again, was Clayton County; it–or at least the neighborhoods with coffee shops–appears to be a majority-minority area. It also does not appear to have a high review count. Is there a digital divide in these areas due to income inequality restricting access to the internet?* Do the people of Clayton just prefer to make better use of their time? We just don’t know.
The relationship between review count and race and review count and income looks like it’s all over the place to me, although the places with higher review counts look like they’re in middle-income areas. Another thing I noticed is that almost all the higher-income neighborhoods are majority white, meaning that any disparity between POIs, walkability and other social and economic benefits associated with them could raise equity concerns along racial and socioeconomic lines.
*While most people have internet on their phones, Yelp’s mobile UI might not be as easy to use and apps use data which might not be ideal if you are not on an unlimited plan.
#Step Five: Make a Scatterplot for Review Count and Neighborhood Characteristics
#Concatenate and then prepare your data
yelp_poi_concat <- yelp_poi[, c(1,2,3,4,5,8,10,6,7,9,11,12,13)]
yelp_poi_longer <- yelp_poi_concat %>%
pivot_longer(
cols = hhincome:pct_white,
names_to = "charts",
values_to = "values")
#Now we can visualize the graph series
ggplot(data = yelp_poi_longer %>%
separate(col = "charts", into=c("charts"),sep=", ") %>%
drop_na(charts)) +
geom_point(mapping = aes(x = review_count_log, y = values, color = county), size=1) +
geom_smooth(mapping = aes(x = review_count_log, y = values, color = county), method='lm', se = FALSE) +
labs(x = "Review Count Logged",
y = "Values",
color = "County",
title = "Scatterplot between logged review count & neighborhood characteristics",
subtitle = "Using Yelp data in Five Counties Around Atlanta, GA") +
facet_wrap(~charts, scales = "free_y", labeller = labeller(charts = c("hhincome" = "Median Annual Household Income ($)",
"pct_pov" = "Percent Residents Under Poverty",
"pct_white" = "Percent White Resident",
"race.tot" = "Total Population"))) +
ggdark::dark_theme_gray()## `geom_smooth()` using formula 'y ~ x'
#This line of code adds the correlation analysis result to the plot. I could not for the life of me get this to work with the facet wrap.
#I was fighting for my life on stackoverflow for 3 hours, but still no dice. I'll go by the correllation coefficients posted on the example chart in the assignment.
ggpubr::stat_cor(method = "pearson", label.x = 0.7, label.y = 1.5)## geom_text: parse = TRUE, na.rm = FALSE
## stat_cor: label.x.npc = left, label.y.npc = top, label.x = 0.7, label.y = 1.5, label.sep = , , method = pearson, alternative = two.sided, output.type = expression, digits = 2, r.digits = 2, p.digits = 2, r.accuracy = NULL, p.accuracy = NULL, cor.coef.name = R, na.rm = FALSE
## position_identityAs expected, the neighborhoods with cafes in Clayton are majority-minority, lower income and have higher poverty rates. Another thing that I thought was interesting was that middle-incoe and majority-white neighborhoods (particularly in Fulton and DeKalb) really seem to like writing Yelp reviews. It would be interesting to look at how other communities engage with and give feedback to their local businesses. It would have been nice if we could have calculated population density since that might also tell us something about walkability. As it stands, I’m not sure that just a straight population variable tells me very much. There also appears to be a very slight negative correllation between review count and poverty rates.
Indeed, I’d argue that all the correlation coefficients tell us that there is very little correlation between review count and these values.However, with the exception of population, the p-values suggest to me that although the correlation is weak, they are still significant. I take this to mean that while there’s some relationship there, these variables are not the driving force when it comes to review counts.
Perhaps we would see a stronger relationship if we looked at another POI characteristic like rating, price, or even proximity to other POIs. In general, only looking at coffee shops is probably only telling us part of the story. A neighborhood’s character might not place a lot of emphasis on cafes. Perhaps residents in some places value BBQ restaurants, wine bars, gyms, parks, or retail experiences over a coffee shop. It’d be interesting to establish a range of POIs and do this analysis again.