Between Test
# Libraries
library(ggplot2)
library(dplyr)
library(tidyr)
library(forcats)
library(hrbrthemes)
library(viridis)
##https://petolau.github.io/Analyzing-double-seasonal-time-series-with-GAM-in-R/
##https://semba-blog.netlify.com/02/22/2019/exploring-time-series-data-in-r/
library(data.table)
library(gapminder)
library(ggstatsplot)
setwd("C:/Users/subas/Syncplicity/MyProjects_IMP/MY_Papers_V2/TRB 2021/EScotter_BayesianRule/")
it01 <- fread("IT_aadtMaster.csv")
names(it01)
## [1] "State" "SD_ID" "Route_ID"
## [4] "S_DFO" "E_DFO" "Seg_Length"
## [7] "Counted_Uncounted_Seg" "Route_Name" "FC"
## [10] "RU" "FC_RU" "URBAN_CODE"
## [13] "Route_Sys" "Paved_Unpaved" "District_ID"
## [16] "County_ID" "County_Name" "Count_ID"
## [19] "Count_Lat" "Count_Long" "AADT_1995"
## [22] "AADT_1996" "AADT_1997" "AADT_1998"
## [25] "AADT_1999" "AADT_2000" "AADT_2001"
## [28] "AADT_2002" "AADT_2003" "AADT_2004"
## [31] "AADT_2005" "AADT_2006" "AADT_2007"
## [34] "AADT_2008" "AADT_2009" "AADT_2010"
## [37] "AADT_2011" "AADT_2012" "AADT_2013"
## [40] "AADT_2014" "AADT_2015" "AADT_2016"
## [43] "AADT_2017" "AADT_2018" "Latest_AADT"
## [46] "Stratum" "Default_AADT" "Tract_Number"
## [49] "BG_Number" "GEOID_US" "GEOID"
## [52] "BG_Area_SqMet" "BG_Area_SqMi" "Agg_Earn"
## [55] "Agg_Inc" "Agg_Rooms" "Workers"
## [58] "Agg_Veh" "Empl" "HU"
## [61] "OHU" "Pop" "C_Pop"
## [64] "C_HU" "WAC" "RAC"
## [67] "WAC_RAC" "Pop_Empl" "Agg_Earn_Den"
## [70] "Agg_Inc_Den" "Agg_Room_Den" "Worker_Den"
## [73] "Agg_Veh_Den" "Empl_Den" "HU_Den"
## [76] "OHU_Den" "Pop_Den" "C_Pop_Den"
## [79] "C_HU_Den" "WAC_Den" "RAC_Den"
## [82] "WAC_RAC_Den" "Pop_Empl_Den" "Dist_IH"
## [85] "Dist_US" "V86"
it01 %>% mutate_if(is.character, as.factor) -> it01
glimpse(it01)
## Observations: 55,970
## Variables: 86
## $ State <fct> MT, MT, MT, MT, MT, MT, MT, MT, MT, MT, MT, M...
## $ SD_ID <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...
## $ Route_ID <fct> C032131S, C002652N, C002650N, C002650N, C0026...
## $ S_DFO <dbl> 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.0...
## $ E_DFO <dbl> 0.949, 0.796, 1.145, 1.145, 0.423, 0.435, 0.4...
## $ Seg_Length <dbl> 0.94872127, 0.79549011, 1.14513904, 1.1451390...
## $ Counted_Uncounted_Seg <fct> Counted, Counted, Counted, Counted, Counted, ...
## $ Route_Name <fct> COTE LN, 1ST ST SE, RAILWAY ST, RAILWAY ST, S...
## $ FC <int> 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 7, 7, 7, 7, 6, ...
## $ RU <fct> U, R, R, R, R, R, R, R, R, R, R, R, R, R, R, ...
## $ FC_RU <fct> 7U, 7R, 7R, 7R, 7R, 7R, 7R, 7R, 6R, 6R, 7R, 7...
## $ URBAN_CODE <int> 57736, 99999, 99999, 99999, 99999, 99999, 999...
## $ Route_Sys <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ Paved_Unpaved <fct> PAVED, PAVED, PAVED, PAVED, PAVED, PAVED, PAV...
## $ District_ID <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ County_ID <int> 63, 35, 35, 35, 35, 35, 35, 35, 61, 61, 61, 6...
## $ County_Name <fct> MISSOULA, GLACIER, GLACIER, GLACIER, GLACIER,...
## $ Count_ID <fct> 32-3A-038, 18-5-018, 18-5-016, 18-5-015, 18-5...
## $ Count_Lat <dbl> 46.89283, 48.63417, 48.63583, 48.63782, 48.63...
## $ Count_Long <dbl> -114.1078, -112.3319, -112.3297, -112.3333, -...
## $ AADT_1995 <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ AADT_1996 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ AADT_1997 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ AADT_1998 <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ AADT_1999 <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ AADT_2000 <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ AADT_2001 <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ AADT_2002 <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ AADT_2003 <int> 2630, NA, NA, NA, NA, NA, NA, 4240, 170, NA, ...
## $ AADT_2004 <int> NA, 980, 3240, 2650, 1040, NA, NA, NA, NA, NA...
## $ AADT_2005 <int> 2810, 820, 2870, 2270, 920, NA, NA, 4090, NA,...
## $ AADT_2006 <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ AADT_2007 <int> 3100, NA, NA, NA, NA, NA, NA, NA, 110, NA, NA...
## $ AADT_2008 <int> NA, NA, NA, 3330, 1020, NA, NA, 3860, NA, NA,...
## $ AADT_2009 <int> 2960, 1360, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ AADT_2010 <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ AADT_2011 <int> 2640, NA, 3250, 3140, 940, NA, NA, 3880, 90, ...
## $ AADT_2012 <int> NA, 410, NA, NA, NA, NA, NA, NA, 100, NA, NA,...
## $ AADT_2013 <int> NA, NA, 3380, 2450, 860, NA, NA, 3150, NA, NA...
## $ AADT_2014 <int> 2720, 460, 2880, 2410, 730, NA, NA, 2700, NA,...
## $ AADT_2015 <int> NA, NA, NA, NA, NA, 3341, 3341, NA, 120, 60, ...
## $ AADT_2016 <int> NA, NA, NA, NA, NA, NA, 2776, NA, NA, NA, NA,...
## $ AADT_2017 <int> 2669, 922, 2893, 2388, 791, NA, NA, 3054, NA,...
## $ AADT_2018 <int> NA, 377, NA, NA, 1381, NA, NA, NA, NA, NA, NA...
## $ Latest_AADT <int> 2669, 377, 2893, 2388, 1381, 3341, 2776, 3054...
## $ Stratum <fct> 7U, 7R, 7R, 7R, 7R, 7R, 7R, 7R, 6R, 6R, 7R, 7...
## $ Default_AADT <dbl> 736, 58, 58, 58, 58, 58, 58, 58, 124, 124, 58...
## $ Tract_Number <int> 202, 976000, 976000, 976000, 976000, 976000, ...
## $ BG_Number <int> 4, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, ...
## $ GEOID_US <fct> 15000US300630002024, 15000US300359760003, 150...
## $ GEOID <int64> 300630002024, 300359760003, 300359760003, 3...
## $ BG_Area_SqMet <int64> 7831315, 1028636, 1028636, 1028636, 1028636...
## $ BG_Area_SqMi <dbl> 3.0236876, 0.3971586, 0.3971586, 0.3971586, 0...
## $ Agg_Earn <int> 32912900, 13060600, 13060600, 13060600, 13060...
## $ Agg_Inc <int> 41450100, 14921700, 14921700, 14921700, 14921...
## $ Agg_Rooms <int> 2474, 1283, 1283, 1283, 1283, 1283, 1283, 128...
## $ Workers <dbl> 522, 170, 170, 170, 170, 170, 170, 170, 391, ...
## $ Agg_Veh <int> 905, 353, 353, 353, 353, 353, 353, 353, 964, ...
## $ Empl <int> 540, 190, 190, 190, 190, 190, 190, 190, 429, ...
## $ HU <int> 426, 265, 265, 265, 265, 265, 265, 265, 869, ...
## $ OHU <int> 426, 194, 194, 194, 194, 194, 194, 194, 468, ...
## $ Pop <int> 1406, 433, 433, 433, 433, 433, 433, 433, 1217...
## $ C_Pop <int> 1375, 654, 654, 654, 654, 654, 654, 654, 1216...
## $ C_HU <int> 492, 343, 343, 343, 343, 343, 343, 343, 812, ...
## $ WAC <int> 112, 826, 826, 826, 826, 826, 826, 826, 414, ...
## $ RAC <int> 730, 258, 258, 258, 258, 258, 258, 258, 344, ...
## $ WAC_RAC <int> 842, 1084, 1084, 1084, 1084, 1084, 1084, 1084...
## $ Pop_Empl <int> 1946, 623, 623, 623, 623, 623, 623, 623, 1646...
## $ Agg_Earn_Den <int64> 10885020, 32885101, 32885101, 32885101, 328...
## $ Agg_Inc_Den <int64> 13708460, 37571138, 37571138, 37571138, 375...
## $ Agg_Room_Den <int> 818, 3230, 3230, 3230, 3230, 3230, 3230, 3230...
## $ Worker_Den <int> 173, 428, 428, 428, 428, 428, 428, 428, 1, 1,...
## $ Agg_Veh_Den <int> 299, 889, 889, 889, 889, 889, 889, 889, 2, 2,...
## $ Empl_Den <int> 179, 478, 478, 478, 478, 478, 478, 478, 1, 1,...
## $ HU_Den <int> 141, 667, 667, 667, 667, 667, 667, 667, 2, 2,...
## $ OHU_Den <int> 141, 488, 488, 488, 488, 488, 488, 488, 1, 1,...
## $ Pop_Den <int> 465, 1090, 1090, 1090, 1090, 1090, 1090, 1090...
## $ C_Pop_Den <int> 455, 1647, 1647, 1647, 1647, 1647, 1647, 1647...
## $ C_HU_Den <int> 163, 864, 864, 864, 864, 864, 864, 864, 2, 2,...
## $ WAC_Den <int> 37, 2080, 2080, 2080, 2080, 2080, 2080, 2080,...
## $ RAC_Den <int> 241, 650, 650, 650, 650, 650, 650, 650, 1, 1,...
## $ WAC_RAC_Den <int> 278, 2729, 2729, 2729, 2729, 2729, 2729, 2729...
## $ Pop_Empl_Den <int> 644, 1569, 1569, 1569, 1569, 1569, 1569, 1569...
## $ Dist_IH <dbl> 5.50023402, 22.78645620, 22.67396564, 22.7340...
## $ Dist_US <dbl> 3.54804381, 0.14351486, 0.08519120, 0.1304387...
## $ V86 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
dat1= subset(it01, FC_RU=="7R")
dat1= filter(it01, FC_RU=="7R")
dim(dat1)
## [1] 26141 86
dat2= dat1[,c("Default_AADT","State")]
dim(dat2)
## [1] 26141 2
dat3=na.omit(dat2)
dim(dat3)
## [1] 6213 2
## State SD_ID Route_ID S_DFO
## GA: 1769 Min. : 2 0 : 3582 Min. : 0.000
## MN: 3908 1st Qu.: 4847 4000171530: 10 1st Qu.: 0.000
## MT: 439 Median : 8294 4000100576: 9 Median : 0.205
## NC:13646 Mean : 9559 4000141481: 9 Mean : 1.331
## NM: 103 3rd Qu.:13920 4000110657: 8 3rd Qu.: 1.879
## PA: 6276 Max. :22852 4000115957: 8 Max. :34.679
## (Other) :22515 NA's :10184
## E_DFO Seg_Length Counted_Uncounted_Seg Route_Name
## Min. : 0.000 Min. : 0.00 :13646 :15518
## 1st Qu.: 0.529 1st Qu.: 0.42 Counted:12495 MAIN ST : 57
## Median : 1.377 Median : 1.05 CR 62 : 45
## Mean : 2.175 Mean : 859.33 RIDGE RD: 35
## 3rd Qu.: 2.915 3rd Qu.: 2.42 CR 101 : 33
## Max. :52.261 Max. :76486.08 (Other) :10452
## NA's :10184 NA's : 1
## FC RU FC_RU URBAN_CODE Route_Sys
## Min. :7 R:26141 6R: 0 Min. : 0 Min. : 3.000
## 1st Qu.:7 U: 0 7R:26141 1st Qu.: 0 1st Qu.: 4.000
## Median :7 7U: 0 Median : 0 Median : 7.000
## Mean :7 Mean :13927 Mean : 6.144
## 3rd Qu.:7 3rd Qu.: 0 3rd Qu.: 7.000
## Max. :7 Max. :99999 Max. :21.000
## NA's :10287 NA's :22233
## Paved_Unpaved District_ID County_ID County_Name
## :20025 Min. : 1.000 Min. : 1.00 ROBESON : 353
## Paved: 5677 1st Qu.: 3.000 1st Qu.: 49.00 RANDOLPH: 346
## PAVED: 439 Median : 4.000 Median : 97.00 SAMPSON : 343
## Mean : 5.506 Mean : 97.82 DUPLIN : 328
## 3rd Qu.: 9.000 3rd Qu.:143.00 MOORE : 289
## Max. :12.000 Max. :321.00 IREDELL : 280
## NA's :18096 (Other) :24202
## Count_ID Count_Lat Count_Long AADT_1995
## :19922 Min. :30.46 Min. :-115.98 Min. : 10
## 099-8029: 3 1st Qu.:40.13 1st Qu.: -93.65 1st Qu.: 60
## 011-8043: 2 Median :41.26 Median : -80.50 Median : 115
## 031-8096: 2 Mean :41.48 Mean : -85.23 Mean : 251
## 035-8023: 2 3rd Qu.:44.77 3rd Qu.: -77.61 3rd Qu.: 280
## 035-8025: 2 Max. :48.97 Max. : -74.72 Max. :4550
## (Other) : 6208 NA's :13646 NA's :13646 NA's :25083
## AADT_1996 AADT_1997 AADT_1998 AADT_1999
## Mode:logical Mode:logical Min. : 10 Min. : 5.0
## NA's:26141 NA's:26141 1st Qu.: 70 1st Qu.: 80.0
## Median : 130 Median : 145.0
## Mean : 244 Mean : 286.2
## 3rd Qu.: 270 3rd Qu.: 335.0
## Max. :3100 Max. :4400.0
## NA's :25673 NA's :25110
## AADT_2000 AADT_2001 AADT_2002 AADT_2003
## Min. : 10.0 Min. : 10.0 Min. : 10.0 Min. : 5.0
## 1st Qu.: 75.0 1st Qu.: 80.0 1st Qu.: 240.0 1st Qu.: 220.0
## Median : 155.0 Median : 135.0 Median : 430.0 Median : 410.0
## Mean : 283.8 Mean : 263.7 Mean : 675.2 Mean : 645.3
## 3rd Qu.: 355.0 3rd Qu.: 290.0 3rd Qu.: 770.0 3rd Qu.: 730.0
## Max. :3850.0 Max. :2300.0 Max. :36000.0 Max. :19000.0
## NA's :25894 NA's :25046 NA's :19592 NA's :18207
## AADT_2004 AADT_2005 AADT_2006 AADT_2007
## Min. : 5 Min. : 5.0 Min. : 15.0 Min. : 10
## 1st Qu.: 240 1st Qu.: 210.0 1st Qu.: 240.0 1st Qu.: 220
## Median : 450 Median : 400.0 Median : 430.0 Median : 410
## Mean : 686 Mean : 629.5 Mean : 666.7 Mean : 637
## 3rd Qu.: 790 3rd Qu.: 730.0 3rd Qu.: 770.0 3rd Qu.: 740
## Max. :31000 Max. :18000.0 Max. :32000.0 Max. :18000
## NA's :19383 NA's :17982 NA's :19332 NA's :18092
## AADT_2008 AADT_2009 AADT_2010 AADT_2011
## Min. : 7.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 216.0 1st Qu.: 140.0 1st Qu.: 210.0 1st Qu.: 180.0
## Median : 400.0 Median : 310.0 Median : 400.0 Median : 350.0
## Mean : 644.8 Mean : 530.8 Mean : 623.1 Mean : 557.6
## 3rd Qu.: 740.0 3rd Qu.: 614.8 3rd Qu.: 720.0 3rd Qu.: 650.0
## Max. :38000.0 Max. :18000.0 Max. :42000.0 Max. :18000.0
## NA's :18756 NA's :15083 NA's :18702 NA's :16806
## AADT_2012 AADT_2013 AADT_2014 AADT_2015
## Min. : 0.0 Min. : 0 Min. : 0.0 Min. : 1.0
## 1st Qu.: 170.0 1st Qu.: 160 1st Qu.: 150.0 1st Qu.: 150.0
## Median : 349.0 Median : 330 Median : 310.0 Median : 310.0
## Mean : 566.8 Mean : 539 Mean : 545.1 Mean : 533.3
## 3rd Qu.: 660.0 3rd Qu.: 630 3rd Qu.: 610.0 3rd Qu.: 616.2
## Max. :33000.0 Max. :16000 Max. :28000.0 Max. :20000.0
## NA's :17260 NA's :17605 NA's :19867 NA's :15647
## AADT_2016 AADT_2017 AADT_2018 Latest_AADT
## Min. : 2.0 Min. : 4.0 Min. : 3.0 Min. : 1.0
## 1st Qu.: 160.0 1st Qu.: 90.0 1st Qu.: 61.0 1st Qu.: 130.0
## Median : 330.0 Median : 190.0 Median : 136.0 Median : 280.0
## Mean : 563.1 Mean : 342.3 Mean : 299.2 Mean : 487.3
## 3rd Qu.: 640.0 3rd Qu.: 400.0 3rd Qu.: 310.0 3rd Qu.: 580.0
## Max. :19000.0 Max. :15100.0 Max. :7845.0 Max. :28000.0
## NA's :16725 NA's :23042 NA's :24267 NA's :11
## Stratum Default_AADT Tract_Number BG_Number
## :19928 Min. : 5 Min. : 100 Min. :1.000
## 703 : 1011 1st Qu.: 45 1st Qu.: 30700 1st Qu.:1.000
## 701 : 758 Median : 60 Median :460200 Median :2.000
## 7R : 439 Mean :135 Mean :488970 Mean :2.151
## 39 : 168 3rd Qu.:265 3rd Qu.:950400 3rd Qu.:3.000
## 77 : 160 Max. :650 Max. :980100 Max. :8.000
## (Other): 3677 NA's :19928
## GEOID_US GEOID BG_Area_SqMet
## 15000US271079602001: 33 Min. :130019501001 Min. : 300737
## 15000US371539702002: 32 1st Qu.:370059501003 1st Qu.: 28685823
## 15000US270510701002: 28 Median :371139704003 Median : 54859325
## 15000US370079202001: 26 Mean :350542104420 Mean : 115672634
## 15000US370079206001: 26 3rd Qu.:371950016002 3rd Qu.: 100102736
## 15000US370079203001: 25 Max. :421330240022 Max. :12869141335
## (Other) :25971
## BG_Area_SqMi Agg_Earn Agg_Inc Agg_Rooms
## Min. : 0.116 Min. : 912700 Min. : 1699800 Min. : 102
## 1st Qu.: 11.076 1st Qu.: 15923250 1st Qu.: 23258300 1st Qu.: 2925
## Median : 21.181 Median : 22546900 Median : 31993500 Median : 3872
## Mean : 44.661 Mean : 27105916 Mean : 37371002 Mean : 4235
## 3rd Qu.: 38.650 3rd Qu.: 32540900 3rd Qu.: 44271800 3rd Qu.: 5052
## Max. :4968.803 Max. :320626700 Max. :397346300 Max. :31653
## NA's :10 NA's :2 NA's :9
## Workers Agg_Veh Empl HU
## Min. : 0.0 Min. : 115 Min. : 0.0 Min. : 0.0
## 1st Qu.: 414.0 1st Qu.: 822 1st Qu.: 451.0 1st Qu.: 485.0
## Median : 574.0 Median :1107 Median : 626.0 Median : 649.0
## Mean : 642.7 Mean :1213 Mean : 701.9 Mean : 703.4
## 3rd Qu.: 787.0 3rd Qu.:1482 3rd Qu.: 860.0 3rd Qu.: 852.0
## Max. :4892.0 Max. :6817 Max. :5725.0 Max. :4179.0
## NA's :1799
## OHU Pop C_Pop C_HU
## Min. : 0.0 Min. : 0 Min. : 0 Min. : 0
## 1st Qu.: 393.0 1st Qu.: 974 1st Qu.:1017 1st Qu.: 486
## Median : 521.0 Median : 1320 Median :1338 Median : 636
## Mean : 565.9 Mean : 1464 Mean :1460 Mean : 690
## 3rd Qu.: 689.0 3rd Qu.: 1785 3rd Qu.:1769 3rd Qu.: 821
## Max. :3552.0 Max. :11220 Max. :9305 Max. :3597
##
## WAC RAC WAC_RAC Pop_Empl
## Min. : 1.0 Min. : 4 Min. : 4 Min. : 0
## 1st Qu.: 76.0 1st Qu.: 399 1st Qu.: 543 1st Qu.: 1434
## Median : 163.0 Median : 537 Median : 766 Median : 1953
## Mean : 313.2 Mean : 595 Mean : 908 Mean : 2166
## 3rd Qu.: 361.0 3rd Qu.: 723 3rd Qu.: 1090 3rd Qu.: 2626
## Max. :14334.0 Max. :4231 Max. :15733 Max. :16945
## NA's :21
## Agg_Earn_Den Agg_Inc_Den Agg_Room_Den Worker_Den
## Min. : 2598 Min. : 6093 Min. : 1.0 Min. : 0.00
## 1st Qu.: 510452 1st Qu.: 750207 1st Qu.: 98.0 1st Qu.: 13.00
## Median : 1137832 Median : 1638896 Median : 191.0 Median : 29.00
## Mean : 2536385 Mean : 3522216 Mean : 397.3 Mean : 61.21
## 3rd Qu.: 2487753 3rd Qu.: 3452413 3rd Qu.: 378.0 3rd Qu.: 61.00
## Max. :109686755 Max. :160292056 Max. :18404.0 Max. :3195.00
## NA's : 10 NA's : 2 NA's :9
## Agg_Veh_Den Empl_Den HU_Den OHU_Den
## Min. : 0.0 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 27.0 1st Qu.: 15.00 1st Qu.: 16.00 1st Qu.: 13.00
## Median : 56.0 Median : 32.00 Median : 32.00 Median : 27.00
## Mean : 108.5 Mean : 67.06 Mean : 66.52 Mean : 55.97
## 3rd Qu.: 112.0 3rd Qu.: 67.00 3rd Qu.: 63.00 3rd Qu.: 53.00
## Max. :4659.0 Max. :3583.00 Max. :3476.00 Max. :2691.00
## NA's :1799
## Pop_Den C_Pop_Den C_HU_Den WAC_Den
## Min. : 0 Min. : 0.0 Min. : 0.0 Min. : 0.00
## 1st Qu.: 32 1st Qu.: 34.0 1st Qu.: 16.0 1st Qu.: 2.00
## Median : 69 Median : 71.0 Median : 32.0 Median : 7.00
## Mean : 140 Mean : 139.5 Mean : 65.5 Mean : 45.98
## 3rd Qu.: 139 3rd Qu.: 139.0 3rd Qu.: 61.0 3rd Qu.: 24.00
## Max. :7303 Max. :5607.0 Max. :2839.0 Max. :5400.00
## NA's :21
## RAC_Den WAC_RAC_Den Pop_Empl_Den Dist_IH
## Min. : 0.0 Min. : 0.0 Min. : 0 Min. : 0.000
## 1st Qu.: 13.0 1st Qu.: 17.0 1st Qu.: 48 1st Qu.: 8.451
## Median : 28.0 Median : 38.0 Median : 102 Median : 18.281
## Mean : 58.2 Mean : 104.2 Mean : 207 Mean : 28.244
## 3rd Qu.: 57.0 3rd Qu.: 86.0 3rd Qu.: 206 3rd Qu.: 35.387
## Max. :2618.0 Max. :5733.0 Max. :10886 Max. :215.189
## NA's :6603
## Dist_US V86
## Min. : 0.000 Mode:logical
## 1st Qu.: 1.208 NA's:26141
## Median : 3.739
## Mean : 6.023
## 3rd Qu.: 7.724
## Max. :158.150
## NA's :6386
##
## GA MN MT NC NM PA
## 1769 3908 439 13646 103 6276
dat4=subset(dat3, State!="PA" |State!="NC")
### FIGURE 1
## without cleaning
ggstatsplot::ggbetweenstats(dat1,
x = State,
y = Default_AADT,
nboot = 10,
messages = FALSE
)
## cleaning
ggstatsplot::ggbetweenstats(dat3,
x = State,
y = Default_AADT,
nboot = 10,
messages = FALSE
)
