## Map indeces and do correlations 29.09.2024 as agreed with ismir
#this document contains:
#1. enhanced maps of all indexes
#2. decomposition regression
#3. maps that represend scaled standardized index
##to do with this (10.10.)
#1. decide which version of quality of life should we take
#2. rerun graphs of QoLI with adequate name
#3. rerun regression output through chat gpt for interpretation
#Open libraries
library(ggplot2)
library(ggmap)
## ℹ Google's Terms of Service: <https://mapsplatform.google.com>
## Stadia Maps' Terms of Service: <https://stadiamaps.com/terms-of-service/>
## OpenStreetMap's Tile Usage Policy: <https://operations.osmfoundation.org/policies/tiles/>
## ℹ Please cite ggmap if you use it! Use `citation("ggmap")` for details.
### Maping with sf and ggplot
library(sf)
## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
library(tmap)
## Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
## remotes::install_github('r-tmap/tmap')
library(maps)
library(raster)
## Loading required package: sp
library(dplyr)
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:raster':
##
## intersect, select, union
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(spData)
## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
## repos='https://nowosad.github.io/drat/', type='source')`
#library(spDataLarge) samo mi ova nije instalirana, probacu bez nje
### Import map
## Import map
amraGIS.nova <- st_read ("C:/Users/Amra/Documents/HBS database inspecting/geoBoundaries-BIH-ADM2-all/geoBoundaries-BIH-ADM2_simplified.shp")
## Reading layer `geoBoundaries-BIH-ADM2_simplified' from data source
## `C:\Users\Amra\Documents\HBS database inspecting\geoBoundaries-BIH-ADM2-all\geoBoundaries-BIH-ADM2_simplified.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 142 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 15.73111 ymin: 42.54831 xmax: 19.62407 ymax: 45.27963
## Geodetic CRS: WGS 84
amraGIS.nova$shapeName <- c("Brcko", "Vukosavlje","Banja Luka", "Bihac", "Bijeljina", "Doboj", "Jajce", "Mostar", "Prijedor", "Trebinje",
"Zenica", "Banovici", "Berkovici" , "Bileca","Gradiska", "Kostajnica",
"Bosanska Krupa" , "Kozarska Dubica", "Brod", "Novi Grad", "Bosanski Petrovac",
"Samac", "Bosansko Grahovo", "Breza" , "Bugojno" , "Busovaca", "Buzim" , "Cajnice",
"Capljina", "Cazin", "Celic", "Celinac", "Sarajevo - Centar", "Citluk",
"Derventa", "Doboj Istok", "Doboj Jug", "Dobretici", "Domaljevac-Samac",
"Donji Vakuf", "Donji Zabar", "Drvar", "Istocni Drvar", "Istocna Ilidza",
"Istocni Mostar", "Istocno Novo Sarajevo", "Istocni Stari Grad", "Foca (RS)",
"Fojnica", "Gacko", "Glamoc","Gorazde", "Gornji Vakuf-Uskoplje", "Gracanica",
"Gradacac", "Grude", "Hadzici", "Han Pijesak", "Ilidza", "Ilijas", "Jablanica",
"Jezero", "Kakanj", "Kalesija", "Kalinovik", "Kiseljak", "Kladanj", "Kljuc",
"Konjic", "Kotor Varos", "Kresevo", "Krupa na Uni", "Kupres (RS)",
"Kupres (FBiH)", "Laktasi", "Livno", "Ljubinje", "Ljubuski", "Lopare", "Lukavac",
"Maglaj", "Milici", "Modrica", "Mrkonjic Grad" ,"Neum", "Nevesinje", "Novo Gorazde",
"Sarajevo - Novo Sarajevo", "Sarajevo - Novi Grad", "Novi Travnik", "Odzak",
"Sarajevo - Stari Grad", "Olovo", "Orasje", "Osmaci", "Ostra Luka", "Pale (RS)",
"Pale (FBiH)", "Pelagicevo", "Petrovac", "Petrovo", "Posusje", "Prnjavor",
"Prozor-Rama", "Ravno", "Ribnik", "Rogatica", "Rudo", "Sanski Most", "Sapna",
"Sekovici", "Sipovo", "Siroki Brijeg", "Knezevo", "Sokolac", "Srbac", "Srebrenica",
"Srebrenik", "Stolac", "Teocak", "Tesanj", "Teslic", "Tomislavgrad", "Travnik",
"Trnovo (FBiH)", "Tuzla", "Ugljevik", "Usora", "Foca (FBiH)", "Vares",
"Velika Kladusa", "Visegrad", "Visoko", "Vitez", "Vlasenica", "Vogosca",
"Zavidovici", "Zepce", "Zivinice", "Zvornik", "Bratunac", "Trnovo (RS)")
table(amraGIS.nova$shapeName)
##
## Banovici Banja Luka Berkovici
## 1 1 1
## Bihac Bijeljina Bileca
## 1 1 1
## Bosanska Krupa Bosanski Petrovac Bosansko Grahovo
## 1 1 1
## Bratunac Brcko Breza
## 1 1 1
## Brod Bugojno Busovaca
## 1 1 1
## Buzim Cajnice Capljina
## 1 1 1
## Cazin Celic Celinac
## 1 1 1
## Citluk Derventa Doboj
## 1 1 1
## Doboj Istok Doboj Jug Dobretici
## 1 1 1
## Domaljevac-Samac Donji Vakuf Donji Zabar
## 1 1 1
## Drvar Foca (FBiH) Foca (RS)
## 1 1 1
## Fojnica Gacko Glamoc
## 1 1 1
## Gorazde Gornji Vakuf-Uskoplje Gracanica
## 1 1 1
## Gradacac Gradiska Grude
## 1 1 1
## Hadzici Han Pijesak Ilidza
## 1 1 1
## Ilijas Istocna Ilidza Istocni Drvar
## 1 1 1
## Istocni Mostar Istocni Stari Grad Istocno Novo Sarajevo
## 1 1 1
## Jablanica Jajce Jezero
## 1 1 1
## Kakanj Kalesija Kalinovik
## 1 1 1
## Kiseljak Kladanj Kljuc
## 1 1 1
## Knezevo Konjic Kostajnica
## 1 1 1
## Kotor Varos Kozarska Dubica Kresevo
## 1 1 1
## Krupa na Uni Kupres (FBiH) Kupres (RS)
## 1 1 1
## Laktasi Livno Lopare
## 1 1 1
## Lukavac Ljubinje Ljubuski
## 1 1 1
## Maglaj Milici Modrica
## 1 1 1
## Mostar Mrkonjic Grad Neum
## 1 1 1
## Nevesinje Novi Grad Novi Travnik
## 1 1 1
## Novo Gorazde Odzak Olovo
## 1 1 1
## Orasje Osmaci Ostra Luka
## 1 1 1
## Pale (FBiH) Pale (RS) Pelagicevo
## 1 1 1
## Petrovac Petrovo Posusje
## 1 1 1
## Prijedor Prnjavor Prozor-Rama
## 1 1 1
## Ravno Ribnik Rogatica
## 1 1 1
## Rudo Samac Sanski Most
## 1 1 1
## Sapna Sarajevo - Centar Sarajevo - Novi Grad
## 1 1 1
## Sarajevo - Novo Sarajevo Sarajevo - Stari Grad Sekovici
## 1 1 1
## Sipovo Siroki Brijeg Sokolac
## 1 1 1
## Srbac Srebrenica Srebrenik
## 1 1 1
## Stolac Teocak Tesanj
## 1 1 1
## Teslic Tomislavgrad Travnik
## 1 1 1
## Trebinje Trnovo (FBiH) Trnovo (RS)
## 1 1 1
## Tuzla Ugljevik Usora
## 1 1 1
## Vares Velika Kladusa Visegrad
## 1 1 1
## Visoko Vitez Vlasenica
## 1 1 1
## Vogosca Vukosavlje Zavidovici
## 1 1 1
## Zenica Zepce Zivinice
## 1 1 1
## Zvornik
## 1
#Import montery indexes
library(readxl)
Monetary_Indexes_for_mapping_2_7_2024 <- read_excel("C:/Users/Amra/OneDrive - Direkcija za ekonomsko planiranje/Prijava teme doktorske disertacije/Pisanje disertacije/Monetary Indexes for mapping_2.7.2024.xlsx",
sheet = "QoLponders and sd_maps")
Indexes_QoL <- Monetary_Indexes_for_mapping_2_7_2024
names (Indexes_QoL)
## [1] "Munic1"
## [2] "Region"
## [3] "Entity"
## [4] "FBiH"
## [5] "QoLIv1_onlyempl"
## [6] "QoLIv2_allactivy"
## [7] "QoLIv3-Stat_adj wage"
## [8] "WI _onlyempl_HPI"
## [9] "WI _allempl_HPI"
## [10] "Stand.wi.ponder_onlyempl"
## [11] "Stand.wi.ponder_all activ"
## [12] "Stand.HPI"
## [13] "Stand.TI"
## [14] "emplrate2014"
## [15] "Average.totalpop2013_14"
## [16] "Average.totalpop2023_24"
## [17] "Munic"
## [18] "Povrsina_km2"
## [19] "pop.per.km2_2013_2014"
## [20] "pop.per.km2_2023_2024"
## [21] "Rate.change.pop.per.km2"
## [22] "Prim.enr.change.per.Pop2013"
## [23] "Rate.change(prim.enr.per.capita.2023_2013)"
## [24] "rate.change.prim.enrolment"
## [25] "share_65"
## [26] "Share univ.degre"
## [27] "Share no primary education"
## [28] "Urban.Share"
## [29] "Diversity Index-ethnicity"
## [30] "DI_ethnic_region"
## [31] "Ratio_Munic.region"
## [32] "DI_ethnicity_country"
## [33] "Diversity Index-religion"
## [34] "Diversity Index-mother language"
## [35] "War_intesity"
## [36] "Gender.gap.per.working age_2013"
## [37] "Gender.em.rate.gap(f-m)"
## [38] "majority m=r(0=not)"
## [39] "majority m=r(0=not,2=nomajority)"
## [40] "Nearest_munic_other_entity"
## [41] "route_m"
## [42] "Route_drive_minutes"
## [43] "rtrn_abrd"
## [44] "share_rtrn_abroad"
## [45] "Scaled_Stand.wi.ponder_onlyempl"
## [46] "Scaled_Stand.wi.ponder_all activ"
## [47] "Scaled_Stand.HPI"
## [48] "Scaled_Stand.TI"
## [49] "Scaled_Stand.Adj.wage_ponder2"
str(Indexes_QoL)
## tibble [142 × 49] (S3: tbl_df/tbl/data.frame)
## $ Munic1 : chr [1:142] "Banovici" "Banja Luka" "Berkovici" "Bihac" ...
## $ Region : chr [1:142] "TK" "RS" "RS" "USK" ...
## $ Entity : chr [1:142] "FBiH" "RS" "RS" "FBiH" ...
## $ FBiH : num [1:142] 1 0 0 1 0 0 1 1 1 0 ...
## $ QoLIv1_onlyempl : num [1:142] 0.681 0.99 -0.301 -0.538 -0.118 ...
## $ QoLIv2_allactivy : num [1:142] 0.507 1.023 -0.185 -0.559 -0.168 ...
## $ QoLIv3-Stat_adj wage : num [1:142] 0.486 1.29 -0.109 0.134 0.199 ...
## $ WI _onlyempl_HPI : num [1:142] 0.824 1 -0.348 -0.39 -0.225 ...
## $ WI _allempl_HPI : num [1:142] 0.65 1.033 -0.232 -0.411 -0.275 ...
## $ Stand.wi.ponder_onlyempl : num [1:142] 0.752 1.5612 -0.4845 -0.0475 -0.163 ...
## $ Stand.wi.ponder_all activ : num [1:142] 0.5689 1.5955 -0.3624 -0.0692 -0.2158 ...
## $ Stand.HPI : num [1:142] -0.549 2.415 -0.562 1.727 0.351 ...
## $ Stand.TI : num [1:142] 0.9549 0.0665 -0.3098 0.9825 -0.7166 ...
## $ emplrate2014 : num [1:142] 0.302 0.49 0.199 0.293 0.279 ...
## $ Average.totalpop2013_14 : num [1:142] 22813 180509 2040 56300 103955 ...
## $ Average.totalpop2023_24 : num [1:142] 22049 185431 1738 54766 103050 ...
## $ Munic : chr [1:142] "Banovici" "Banja Luka" "Berkovici" "Bihac" ...
## $ Povrsina_km2 : num [1:142] 185 1239 264 900 734 ...
## $ pop.per.km2_2013_2014 : num [1:142] 123.31 145.7 7.73 62.56 141.61 ...
## $ pop.per.km2_2023_2024 : num [1:142] 119.18 149.67 6.58 60.85 140.38 ...
## $ Rate.change.pop.per.km2 : num [1:142] 0.03465 -0.02654 0.17348 0.02802 0.00878 ...
## $ Prim.enr.change.per.Pop2013 : num [1:142] -0.00213 0.00669 -0.04903 -0.03068 -0.00868 ...
## $ Rate.change(prim.enr.per.capita.2023_2013): num [1:142] 0.01 0.0484 -0.2987 -0.308 -0.0907 ...
## $ rate.change.prim.enrolment : num [1:142] -0.0238 0.077 -0.4024 -0.3268 -0.0986 ...
## $ share_65 : num [1:142] 0.1673 0.0927 0.1216 0.1258 0.2365 ...
## $ Share univ.degre : num [1:142] 0.0667 0.1692 0.0573 0.113 0.0912 ...
## $ Share no primary education : num [1:142] 0.1326 0.0783 0.1921 0.115 0.1623 ...
## $ Urban.Share : num [1:142] 0.28244 0.750981 0.000001 0.705462 0.392499 ...
## $ Diversity Index-ethnicity : num [1:142] 0.882 0.805 0.85 0.78 0.741 ...
## $ DI_ethnic_region : num [1:142] 0.781 0.685 0.685 0.813 0.685 ...
## $ Ratio_Munic.region : num [1:142] 1.129 1.176 1.241 0.959 1.082 ...
## $ DI_ethnicity_country : num [1:142] 0.37 0.37 0.37 0.37 0.37 ...
## $ Diversity Index-religion : num [1:142] 0.913 0.797 0.85 0.802 0.737 ...
## $ Diversity Index-mother language : num [1:142] 0.959 0.845 0.851 0.871 0.758 ...
## $ War_intesity : num [1:142] 0.01234 0.00906 0.01017 0.02259 0.01111 ...
## $ Gender.gap.per.working age_2013 : num [1:142] -0.1694 -0.0163 -0.0155 -0.0358 -0.0352 ...
## $ Gender.em.rate.gap(f-m) : num [1:142] -33.117 -5.374 -0.243 -8.477 -6.755 ...
## $ majority m=r(0=not) : num [1:142] 1 1 1 1 1 1 1 0 0 1 ...
## $ majority m=r(0=not,2=nomajority) : num [1:142] 1 1 1 1 1 1 1 0 0 1 ...
## $ Nearest_munic_other_entity : chr [1:142] "Lopare" "Jajce" "Stolac" "Novi Grad" ...
## $ route_m : num [1:142] 56000 72000 20000 79000 32000 59000 35000 11300 65000 72000 ...
## $ Route_drive_minutes : num [1:142] 74 79 21 86 43 58 39 12 78 81 ...
## $ rtrn_abrd : num [1:142] 910 11452 50 4232 10972 ...
## $ share_rtrn_abroad : num [1:142] 0.04 0.0619 0.0237 0.0752 0.1019 ...
## $ Scaled_Stand.wi.ponder_onlyempl : num [1:142] -0.51 -0.32 -0.801 -0.698 -0.725 ...
## $ Scaled_Stand.wi.ponder_all activ : num [1:142] -0.551 -0.309 -0.77 -0.701 -0.735 ...
## $ Scaled_Stand.HPI : num [1:142] -0.463 0.549 -0.468 0.314 -0.156 ...
## $ Scaled_Stand.TI : num [1:142] -0.444 -0.715 -0.83 -0.436 -0.954 ...
## $ Scaled_Stand.Adj.wage_ponder2 : num [1:142] -0.657 -0.39 -0.823 -0.634 -0.732 ...
summary(Indexes_QoL)
## Munic1 Region Entity FBiH
## Length:142 Length:142 Length:142 Min. :0.0000
## Class :character Class :character Class :character 1st Qu.:0.0000
## Mode :character Mode :character Mode :character Median :1.0000
## Mean :0.5532
## 3rd Qu.:1.0000
## Max. :1.0000
## NA's :1
## QoLIv1_onlyempl QoLIv2_allactivy QoLIv3-Stat_adj wage WI _onlyempl_HPI
## Min. :-1.83034 Min. :-1.87287 Min. :-1.53894 Min. :-1.37141
## 1st Qu.:-0.44319 1st Qu.:-0.48733 1st Qu.:-0.40159 1st Qu.:-0.50450
## Median :-0.07984 Median :-0.09638 Median :-0.09818 Median :-0.09722
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.: 0.33814 3rd Qu.: 0.35832 3rd Qu.: 0.24660 3rd Qu.: 0.25943
## Max. : 6.15275 Max. : 6.15910 Max. : 7.69973 Max. : 6.06975
##
## WI _allempl_HPI Stand.wi.ponder_onlyempl Stand.wi.ponder_all activ
## Min. :-1.3528 Min. :-1.3320 Min. :-1.3417
## 1st Qu.:-0.4982 1st Qu.:-0.5874 1st Qu.:-0.5810
## Median :-0.1278 Median :-0.1651 Median :-0.1665
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.2949 3rd Qu.: 0.3137 3rd Qu.: 0.3309
## Max. : 6.0761 Max. : 7.1753 Max. : 7.1820
##
## Stand.HPI Stand.TI emplrate2014 Average.totalpop2013_14
## Min. :-2.1201 Min. :-0.8687 Min. :-0.6683 Min. : 69.5
## 1st Qu.:-0.6519 1st Qu.:-0.6512 1st Qu.: 0.1710 1st Qu.: 6687.1
## Median :-0.1488 Median :-0.3098 Median : 0.2305 Median : 16324.0
## Mean : 0.0000 Mean : 0.0000 Mean : 0.2377 Mean : 24545.3
## 3rd Qu.: 0.4697 3rd Qu.: 0.3271 3rd Qu.: 0.2997 3rd Qu.: 31123.8
## Max. : 3.7341 Max. : 5.6921 Max. : 1.0861 Max. :180508.5
##
## Average.totalpop2023_24 Munic Povrsina_km2
## Min. : 105.5 Length:142 Min. : 9.9
## 1st Qu.: 5728.5 Class :character 1st Qu.: 150.7
## Median : 14967.2 Mode :character Median : 295.9
## Mean : 23606.7 Mean : 359.7
## 3rd Qu.: 29688.2 3rd Qu.: 499.8
## Max. :185430.5 Max. :1238.9
##
## pop.per.km2_2013_2014 pop.per.km2_2023_2024 Rate.change.pop.per.km2
## Min. : 0.924 Min. : 1.403 Min. :-0.43239
## 1st Qu.: 22.480 1st Qu.: 19.855 1st Qu.: 0.02735
## Median : 56.010 Median : 50.716 Median : 0.06566
## Mean : 161.557 Mean : 157.393 Mean : 0.06494
## 3rd Qu.: 117.208 3rd Qu.: 107.195 3rd Qu.: 0.09986
## Max. :6542.828 Max. :6331.515 Max. : 0.38818
##
## Prim.enr.change.per.Pop2013 Rate.change(prim.enr.per.capita.2023_2013)
## Min. :-0.053695 Min. :-0.49690
## 1st Qu.:-0.023823 1st Qu.:-0.25547
## Median :-0.017315 Median :-0.15726
## Mean :-0.016511 Mean :-0.16319
## 3rd Qu.:-0.008582 3rd Qu.:-0.07007
## Max. : 0.027677 Max. : 0.25851
##
## rate.change.prim.enrolment share_65 Share univ.degre
## Min. :-0.5591 Min. :0.07416 Min. :0.01060
## 1st Qu.:-0.3127 1st Qu.:0.12702 1st Qu.:0.05223
## Median :-0.1981 Median :0.16126 Median :0.06452
## Mean :-0.2109 Mean :0.21152 Mean :0.07696
## 3rd Qu.:-0.1148 3rd Qu.:0.19843 3rd Qu.:0.08904
## Max. : 0.3127 Max. :6.37641 Max. :0.32029
##
## Share no primary education Urban.Share Diversity Index-ethnicity
## Min. :0.03777 Min. :0.000001 Min. :0.3418
## 1st Qu.:0.13050 1st Qu.:0.114391 1st Qu.:0.5991
## Median :0.16047 Median :0.263319 Median :0.8044
## Mean :0.16541 Mean :0.297650 Mean :0.7561
## 3rd Qu.:0.19236 3rd Qu.:0.435802 3rd Qu.:0.9091
## Max. :0.44853 Max. :0.993834 Max. :0.9963
##
## DI_ethnic_region Ratio_Munic.region DI_ethnicity_country
## Min. :0.3418 Min. :0.5146 Min. :0.3699
## 1st Qu.:0.6846 1st Qu.:0.9616 1st Qu.:0.3699
## Median :0.6846 Median :1.1334 Median :0.3699
## Mean :0.6760 Mean :1.1340 Mean :0.3699
## 3rd Qu.:0.7025 3rd Qu.:1.3013 3rd Qu.:0.3699
## Max. :0.9755 Max. :2.1325 Max. :0.3699
##
## Diversity Index-religion Diversity Index-mother language War_intesity
## Min. :0.2257 Min. :0.3459 Min. :0.001459
## 1st Qu.:0.6024 1st Qu.:0.6174 1st Qu.:0.012094
## Median :0.8099 Median :0.8470 Median :0.018531
## Mean :0.7592 Mean :0.7826 Mean :0.023415
## 3rd Qu.:0.9145 3rd Qu.:0.9360 3rd Qu.:0.025094
## Max. :0.9951 Max. :0.9962 Max. :0.197540
##
## Gender.gap.per.working age_2013 Gender.em.rate.gap(f-m) majority m=r(0=not)
## Min. :-0.92683 Min. :-168.142 Min. :0.0000
## 1st Qu.:-0.07076 1st Qu.: -13.423 1st Qu.:1.0000
## Median :-0.03887 Median : -8.030 Median :1.0000
## Mean :-0.06206 Mean : -10.432 Mean :0.8732
## 3rd Qu.:-0.02606 3rd Qu.: -4.135 3rd Qu.:1.0000
## Max. : 0.02588 Max. : 4.069 Max. :1.0000
##
## majority m=r(0=not,2=nomajority) Nearest_munic_other_entity route_m
## Min. :0.0000 Length:142 Min. : 4600
## 1st Qu.:1.0000 Class :character 1st Qu.: 15000
## Median :1.0000 Mode :character Median : 31500
## Mean :0.9437 Mean : 34917
## 3rd Qu.:1.0000 3rd Qu.: 50250
## Max. :2.0000 Max. :140000
##
## Route_drive_minutes rtrn_abrd share_rtrn_abroad
## Min. : 7.00 Min. : 12 Min. :0.009905
## 1st Qu.: 20.00 1st Qu.: 634 1st Qu.:0.053424
## Median : 38.00 Median : 1774 Median :0.099612
## Mean : 41.75 Mean : 3179 Mean :0.147327
## 3rd Qu.: 57.75 3rd Qu.: 3770 3rd Qu.:0.185676
## Max. :150.00 Max. :25470 Max. :0.683855
##
## Scaled_Stand.wi.ponder_onlyempl Scaled_Stand.wi.ponder_all activ
## Min. :-1.0000 Min. :-1.0000
## 1st Qu.:-0.8250 1st Qu.:-0.8212
## Median :-0.7257 Median :-0.7237
## Mean :-0.6869 Mean :-0.6846
## 3rd Qu.:-0.6131 3rd Qu.:-0.6068
## Max. : 1.0000 Max. : 1.0039
##
## Scaled_Stand.HPI Scaled_Stand.TI Scaled_Stand.Adj.wage_ponder2
## Min. :-1.0000 Min. :-1.0000 Min. :-1.0000
## 1st Qu.:-0.4984 1st Qu.:-0.9337 1st Qu.:-0.8726
## Median :-0.3265 Median :-0.8296 Median :-0.8022
## Mean :-0.2757 Mean :-0.7352 Mean :-0.7662
## 3rd Qu.:-0.1152 3rd Qu.:-0.6354 3rd Qu.:-0.7097
## Max. : 1.0000 Max. : 1.0000 Max. : 1.0000
##
Indexes_QoL$hpi_wage1 <- Indexes_QoL$Stand.HPI/Indexes_QoL$Stand.wi.ponder_onlyempl
Indexes_QoL$hpi_wage2 <- Indexes_QoL$Stand.HPI/Indexes_QoL$`Stand.wi.ponder_all activ`
Indexes_QoL %>% group_by(Munic1) %>% dplyr::select(c(Munic1, Stand.HPI, Stand.wi.ponder_onlyempl, hpi_wage1)) %>% arrange(desc(hpi_wage1)) #ovo sam uradi nakon Ismirovog maila od 3.4.
## # A tibble: 142 × 4
## # Groups: Munic1 [142]
## Munic1 Stand.HPI Stand.wi.ponder_onlyempl hpi_wage1
## <chr> <dbl> <dbl> <dbl>
## 1 Kostajnica -0.695 -0.00587 118.
## 2 Vares -2.12 -0.172 12.4
## 3 Livno -0.744 -0.0752 9.89
## 4 Visegrad -1.23 -0.136 9.09
## 5 Brod -1.21 -0.243 5.00
## 6 Kupres -0.731 -0.164 4.47
## 7 Hadzici 1.01 0.245 4.15
## 8 K.Dubica -0.969 -0.240 4.04
## 9 Bosanski Petrovac -1.68 -0.436 3.85
## 10 Milici -1.12 -0.310 3.61
## # ℹ 132 more rows
Indexes_QoL %>% group_by(Munic1) %>% dplyr::select(c(Munic1, Stand.HPI, Stand.wi.ponder_onlyempl, hpi_wage1)) %>% arrange(hpi_wage1) #ovo sam uradi nakon Ismirovog maila od 3.4.
## # A tibble: 142 × 4
## # Groups: Munic1 [142]
## Munic1 Stand.HPI Stand.wi.ponder_onlyempl hpi_wage1
## <chr> <dbl> <dbl> <dbl>
## 1 Fojnica 0.679 -0.00166 -409.
## 2 Foca - RS 0.124 -0.00197 -62.8
## 3 Sokolac -1.30 0.0336 -38.8
## 4 Bihac 1.73 -0.0475 -36.4
## 5 Kakanj 0.440 -0.0164 -26.7
## 6 Olovo 0.674 -0.0838 -8.05
## 7 Glamoc -0.737 0.0978 -7.54
## 8 Ostra Luka 0.493 -0.0743 -6.63
## 9 Vogosca 1.10 -0.167 -6.60
## 10 Srebrenica -0.957 0.165 -5.79
## # ℹ 132 more rows
Indexes_QoL %>% group_by(Munic1) %>% dplyr::select(c(Munic1, Stand.HPI, `Stand.wi.ponder_all activ`,hpi_wage2)) %>% arrange(hpi_wage2) #bottom 10
## # A tibble: 142 × 4
## # Groups: Munic1 [142]
## Munic1 Stand.HPI `Stand.wi.ponder_all activ` hpi_wage2
## <chr> <dbl> <dbl> <dbl>
## 1 Kostajnica -0.695 0.000551 -1261.
## 2 Livno -0.744 0.0182 -40.8
## 3 Bihac 1.73 -0.0692 -25.0
## 4 Visegrad -1.23 0.0657 -18.8
## 5 Rogatica -0.806 0.0567 -14.2
## 6 Vogosca 1.10 -0.149 -7.38
## 7 Kakanj 0.440 -0.0639 -6.88
## 8 Sokolac -1.30 0.235 -5.55
## 9 Glamoc -0.737 0.140 -5.28
## 10 Srebrenica -0.957 0.187 -5.12
## # ℹ 132 more rows
Indexes_QoL %>% group_by(Munic1) %>% dplyr::select(c(Munic1, Stand.HPI, `Stand.wi.ponder_all activ`,hpi_wage2)) %>% arrange(desc(hpi_wage2)) #top10
## # A tibble: 142 × 4
## # Groups: Munic1 [142]
## Munic1 Stand.HPI `Stand.wi.ponder_all activ` hpi_wage2
## <chr> <dbl> <dbl> <dbl>
## 1 Petrovac -0.238 -0.00444 53.6
## 2 Fojnica 0.679 0.0302 22.5
## 3 Vares -2.12 -0.173 12.3
## 4 Olovo 0.674 0.0653 10.3
## 5 Gradiska -0.417 -0.0648 6.44
## 6 K.Dubica -0.969 -0.176 5.51
## 7 Kupres -0.731 -0.160 4.56
## 8 Bosanski Petrovac -1.68 -0.393 4.27
## 9 Konjic 1.29 0.373 3.45
## 10 Milici -1.12 -0.347 3.21
## # ℹ 132 more rows
#Merge files
map.Indexes_QoL <- merge (Indexes_QoL, amraGIS.nova, by.x="Munic", by.y = "shapeName", all=T)
dim(map.Indexes_QoL)
## [1] 142 56
#142 muncicip
unique(map.Indexes_QoL$Munic)
## [1] "Banovici" "Banja Luka"
## [3] "Berkovici" "Bihac"
## [5] "Bijeljina" "Bileca"
## [7] "Bosanska Krupa" "Bosanski Petrovac"
## [9] "Bosansko Grahovo" "Bratunac"
## [11] "Brcko" "Breza"
## [13] "Brod" "Bugojno"
## [15] "Busovaca" "Buzim"
## [17] "Cajnice" "Capljina"
## [19] "Cazin" "Celic"
## [21] "Celinac" "Citluk"
## [23] "Derventa" "Doboj"
## [25] "Doboj Istok" "Doboj Jug"
## [27] "Dobretici" "Domaljevac-Samac"
## [29] "Donji Vakuf" "Donji Zabar"
## [31] "Drvar" "Foca (FBiH)"
## [33] "Foca (RS)" "Fojnica"
## [35] "Gacko" "Glamoc"
## [37] "Gorazde" "Gornji Vakuf-Uskoplje"
## [39] "Gracanica" "Gradacac"
## [41] "Gradiska" "Grude"
## [43] "Hadzici" "Han Pijesak"
## [45] "Ilidza" "Ilijas"
## [47] "Istocna Ilidza" "Istocni Drvar"
## [49] "Istocni Mostar" "Istocni Stari Grad"
## [51] "Istocno Novo Sarajevo" "Jablanica"
## [53] "Jajce" "Jezero"
## [55] "Kakanj" "Kalesija"
## [57] "Kalinovik" "Kiseljak"
## [59] "Kladanj" "Kljuc"
## [61] "Knezevo" "Konjic"
## [63] "Kostajnica" "Kotor Varos"
## [65] "Kozarska Dubica" "Kresevo"
## [67] "Krupa na Uni" "Kupres (FBiH)"
## [69] "Kupres (RS)" "Laktasi"
## [71] "Livno" "Lopare"
## [73] "Lukavac" "Ljubinje"
## [75] "Ljubuski" "Maglaj"
## [77] "Milici" "Modrica"
## [79] "Mostar" "Mrkonjic Grad"
## [81] "Neum" "Nevesinje"
## [83] "Novi Grad" "Novi Travnik"
## [85] "Novo Gorazde" "Odzak"
## [87] "Olovo" "Orasje"
## [89] "Osmaci" "Ostra Luka"
## [91] "Pale (FBiH)" "Pale (RS)"
## [93] "Pelagicevo" "Petrovac"
## [95] "Petrovo" "Posusje"
## [97] "Prijedor" "Prnjavor"
## [99] "Prozor-Rama" "Ravno"
## [101] "Ribnik" "Rogatica"
## [103] "Rudo" "Samac"
## [105] "Sanski Most" "Sapna"
## [107] "Sarajevo - Centar" "Sarajevo - Novi Grad"
## [109] "Sarajevo - Novo Sarajevo" "Sarajevo - Stari Grad"
## [111] "Sekovici" "Sipovo"
## [113] "Siroki Brijeg" "Sokolac"
## [115] "Srbac" "Srebrenica"
## [117] "Srebrenik" "Stolac"
## [119] "Teocak" "Tesanj"
## [121] "Teslic" "Tomislavgrad"
## [123] "Travnik" "Trebinje"
## [125] "Trnovo (FBiH)" "Trnovo (RS)"
## [127] "Tuzla" "Ugljevik"
## [129] "Usora" "Vares"
## [131] "Velika Kladusa" "Visegrad"
## [133] "Visoko" "Vitez"
## [135] "Vlasenica" "Vogosca"
## [137] "Vukosavlje" "Zavidovici"
## [139] "Zenica" "Zepce"
## [141] "Zivinice" "Zvornik"
head(map.Indexes_QoL)
## Munic Munic1 Region Entity FBiH QoLIv1_onlyempl QoLIv2_allactivy
## 1 Banovici Banovici TK FBiH 1 0.6810219 0.5070688
## 2 Banja Luka Banja Luka RS RS 0 0.9901483 1.0227138
## 3 Berkovici Berkovici RS RS 0 -0.3014406 -0.1854586
## 4 Bihac Bihac USK FBiH 1 -0.5378713 -0.5585231
## 5 Bijeljina Bijeljina RS RS 0 -0.1175975 -0.1677032
## 6 Bileca Bileca RS RS 0 -0.2158090 -0.2041717
## QoLIv3-Stat_adj wage WI _onlyempl_HPI WI _allempl_HPI
## 1 0.4856811 0.8242549 0.6503018
## 2 1.2898245 1.0001255 1.0326910
## 3 -0.1087296 -0.3479101 -0.2319281
## 4 0.1344120 -0.3904959 -0.4111478
## 5 0.1991883 -0.2250897 -0.2751954
## 6 -0.2990825 -0.3028159 -0.2911786
## Stand.wi.ponder_onlyempl Stand.wi.ponder_all activ Stand.HPI Stand.TI
## 1 0.75202133 0.56891276 -0.5491733 0.95488664
## 2 1.56122940 1.59550887 2.4152123 0.06651418
## 3 -0.48447993 -0.36239360 -0.5617290 -0.30979662
## 4 -0.04745998 -0.06919877 1.7270446 0.98250230
## 5 -0.16304739 -0.21579026 0.3509732 -0.71661445
## 6 -0.24203780 -0.22978801 0.3644000 -0.58004624
## emplrate2014 Average.totalpop2013_14 Average.totalpop2023_24 Povrsina_km2
## 1 0.3019166 22813.0 22049.0 185.0
## 2 0.4897254 180508.5 185430.5 1238.9
## 3 0.1993007 2039.5 1738.0 264.0
## 4 0.2925893 56300.0 54765.5 900.0
## 5 0.2787604 103954.5 103050.0 734.1
## 6 0.2235159 10577.0 9712.0 636.8
## pop.per.km2_2013_2014 pop.per.km2_2023_2024 Rate.change.pop.per.km2
## 1 123.313514 119.183784 0.034650098
## 2 145.700622 149.673501 -0.026543638
## 3 7.725379 6.583333 0.173475259
## 4 62.555556 60.850556 0.028019465
## 5 141.608092 140.375971 0.008777293
## 6 16.609611 15.251256 0.089065074
## Prim.enr.change.per.Pop2013 Rate.change(prim.enr.per.capita.2023_2013)
## 1 -0.002125981 0.01003370
## 2 0.006689436 0.04841641
## 3 -0.049031625 -0.29874819
## 4 -0.030683837 -0.30797592
## 5 -0.008681683 -0.09071145
## 6 -0.012054458 -0.05625758
## rate.change.prim.enrolment share_65 Share univ.degre
## 1 -0.02379200 0.16729994 0.06668757
## 2 0.07700402 0.09272306 0.16918758
## 3 -0.40241449 0.12161891 0.05733945
## 4 -0.32683757 0.12582402 0.11303522
## 5 -0.09862310 0.23647433 0.09124274
## 6 -0.13343799 0.20643729 0.08931091
## Share no primary education Urban.Share Diversity Index-ethnicity
## 1 0.13259120 0.2824397 0.8815236
## 2 0.07833322 0.7509809 0.8051077
## 3 0.19208716 0.0000010 0.8495796
## 4 0.11503902 0.7054620 0.7797104
## 5 0.16225047 0.3924987 0.7409461
## 6 0.10851872 0.7047284 0.9704710
## DI_ethnic_region Ratio_Munic.region DI_ethnicity_country
## 1 0.7808587 1.1289156 0.3698706
## 2 0.6846210 1.1759904 0.3698706
## 3 0.6846210 1.2409489 0.3698706
## 4 0.8126933 0.9594153 0.3698706
## 5 0.6846210 1.0822720 0.3698706
## 6 0.6846210 1.4175304 0.3698706
## Diversity Index-religion Diversity Index-mother language War_intesity
## 1 0.9125563 0.9590498 0.012335464
## 2 0.7974721 0.8445417 0.009055046
## 3 0.8495635 0.8513013 0.010170762
## 4 0.8023645 0.8711129 0.022592320
## 5 0.7365319 0.7583539 0.011114777
## 6 0.9784840 0.9865463 0.007603132
## Gender.gap.per.working age_2013 Gender.em.rate.gap(f-m) majority m=r(0=not)
## 1 -0.16943017 -33.1165018 1
## 2 -0.01625329 -5.3737500 1
## 3 -0.01547721 -0.2425079 1
## 4 -0.03577110 -8.4770704 1
## 5 -0.03521284 -6.7547016 1
## 6 -0.00427716 1.0138717 1
## majority m=r(0=not,2=nomajority) Nearest_munic_other_entity route_m
## 1 1 Lopare 56000
## 2 1 Jajce 72000
## 3 1 Stolac 20000
## 4 1 Novi Grad 79000
## 5 1 Teocak 32000
## 6 1 Stolac 59000
## Route_drive_minutes rtrn_abrd share_rtrn_abroad
## 1 74 910 0.03995960
## 2 79 11452 0.06188865
## 3 21 50 0.02365184
## 4 86 4232 0.07522085
## 5 43 10972 0.10186139
## 6 58 118 0.01091885
## Scaled_Stand.wi.ponder_onlyempl Scaled_Stand.wi.ponder_all activ
## 1 -0.5100734 -0.5508298
## 2 -0.3198349 -0.3094849
## 3 -0.8007654 -0.7697727
## 4 -0.6980253 -0.7008449
## 5 -0.7251990 -0.7353074
## 6 -0.7437691 -0.7385982
## Scaled_Stand.HPI Scaled_Stand.TI Scaled_Stand.Adj.wage_ponder2 hpi_wage1
## 1 -0.4633220 -0.4440848 -0.6566048 -0.730263
## 2 0.5494186 -0.7148975 -0.3897219 1.546994
## 3 -0.4676114 -0.8296127 -0.8227248 1.159447
## 4 0.3143158 -0.4356664 -0.6337726 -36.389494
## 5 -0.1557996 -0.9536276 -0.7320362 -2.152584
## 6 -0.1512125 -0.9119959 -0.8323682 -1.505550
## hpi_wage2 shapeISO shapeID shapeGroup shapeType
## 1 -0.9653032 <NA> 47562260B73001299105468 BIH ADM2
## 2 1.5137567 <NA> 47562260B25307751810097 BIH ADM2
## 3 1.5500523 <NA> 47562260B7976823747309 BIH ADM2
## 4 -24.9577369 <NA> 47562260B48918375665015 BIH ADM2
## 5 -1.6264553 <NA> 47562260B7913122040730 BIH ADM2
## 6 -1.5858096 <NA> 47562260B94562227670273 BIH ADM2
## geometry
## 1 MULTIPOLYGON (((18.54929 44...
## 2 MULTIPOLYGON (((16.7992 44....
## 3 MULTIPOLYGON (((17.95274 43...
## 4 MULTIPOLYGON (((16.20963 44...
## 5 MULTIPOLYGON (((18.92213 44...
## 6 MULTIPOLYGON (((18.46525 42...
tail(map.Indexes_QoL)
## Munic Munic1 Region Entity FBiH QoLIv1_onlyempl QoLIv2_allactivy
## 137 Vukosavlje Vukosavlje RS RS 0 -0.79137486 -0.83263273
## 138 Zavidovici Zavidovici ZDK FBiH 1 -0.54346084 -0.51642276
## 139 Zenica Zenica ZDK FBiH 1 0.38177088 0.40847972
## 140 Zepce Zepce ZDK FBiH 1 -0.07608545 -0.07283633
## 141 Zivinice Zivinice TK FBiH 1 -0.78374385 -0.74124899
## 142 Zvornik Zvornik RS RS 0 -0.44370783 -0.44339250
## QoLIv3-Stat_adj wage WI _onlyempl_HPI WI _allempl_HPI
## 137 -0.63993892 -0.8549628 -0.8962207
## 138 -0.44389332 -0.6613086 -0.6342705
## 139 0.57934701 0.2731529 0.2998617
## 140 -0.26724029 -0.1257509 -0.1225018
## 141 -0.61482809 -0.6162448 -0.5737499
## 142 0.07216474 -0.5003781 -0.5000627
## Stand.wi.ponder_onlyempl Stand.wi.ponder_all activ Stand.HPI Stand.TI
## 137 -1.1562524 -1.1996817 -1.2173848 -0.4239195
## 138 -0.8152546 -0.7867935 -0.5659168 -0.7856515
## 139 0.3982271 0.4263417 0.5258143 -0.7241200
## 140 -0.3330547 -0.3296346 -0.9532556 -0.3311029
## 141 -0.5549578 -0.5102263 0.4451746 1.1166603
## 142 -0.7272235 -0.7268916 -0.9524214 -0.3778015
## emplrate2014 Average.totalpop2013_14 Average.totalpop2023_24 Povrsina_km2
## 137 0.08313211 4360.0 4159.0 73.7
## 138 0.14045507 35915.5 34403.0 556.4
## 139 0.31311735 110578.0 107540.0 558.5
## 140 0.20988673 30206.5 29404.0 282.3
## 141 0.19017826 57773.0 57636.0 291.0
## 142 0.20805562 54248.0 52157.5 374.4
## pop.per.km2_2013_2014 pop.per.km2_2023_2024 Rate.change.pop.per.km2
## 137 59.15875 56.43148 0.048328925
## 138 64.54978 61.83142 0.043964189
## 139 197.99105 192.55148 0.028249954
## 140 107.00142 104.15870 0.027292205
## 141 198.53265 198.06186 0.002376987
## 142 144.89316 139.30956 0.040080525
## Prim.enr.change.per.Pop2013 Rate.change(prim.enr.per.capita.2023_2013)
## 137 -0.016972477 -0.19447652
## 138 -0.019531957 -0.16057174
## 139 -0.006447937 -0.04364083
## 140 -0.025375333 -0.24108526
## 141 -0.011579804 -0.11766064
## 142 -0.013853045 -0.14646375
## rate.change.prim.enrolment share_65 Share univ.degre
## 137 -0.23161189 0.09471573 0.02674741
## 138 -0.19592236 0.13648111 0.05131548
## 139 -0.06991567 0.16677554 0.08820744
## 140 -0.26124744 0.12549150 0.05524039
## 141 -0.11975298 0.15829048 0.05755771
## 142 -0.17935561 0.19437231 0.06515265
## Share no primary education Urban.Share Diversity Index-ethnicity
## 137 0.2692405 0.0000010 0.3522997
## 138 0.1631732 0.2271313 0.8291437
## 139 0.1213915 0.6375482 0.7130138
## 140 0.1460471 0.1806810 0.4978777
## 141 0.1746161 0.2797022 0.8467879
## 142 0.1639756 0.1953412 0.5434690
## DI_ethnic_region Ratio_Munic.region DI_ethnicity_country
## 137 0.6846210 0.5145909 0.3698706
## 138 0.6902386 1.2012422 0.3698706
## 139 0.6902386 1.0329962 0.3698706
## 140 0.6902386 0.7213124 0.3698706
## 141 0.7808587 1.0844316 0.3698706
## 142 0.6846210 0.7938246 0.3698706
## Diversity Index-religion Diversity Index-mother language War_intesity
## 137 0.3758664 0.3679370 0.009848283
## 138 0.8572523 0.9018098 0.015919110
## 139 0.7298300 0.8321021 0.009806414
## 140 0.4900191 0.5021360 0.014543238
## 141 0.8786719 0.9094565 0.006753920
## 142 0.5407449 0.5461451 0.048416262
## Gender.gap.per.working age_2013 Gender.em.rate.gap(f-m) majority m=r(0=not)
## 137 0.004824259 1.577774 0
## 138 -0.032428500 -6.404286 1
## 139 -0.077966230 -15.252711 1
## 140 -0.034660292 -5.638754 0
## 141 -0.087441245 -17.374274 1
## 142 -0.036479067 -6.466666 1
## majority m=r(0=not,2=nomajority) Nearest_munic_other_entity route_m
## 137 0 Odzak 16000
## 138 1 Teslic 46000
## 139 1 Teslic 67000
## 140 0 Teslic 36000
## 141 1 Sekovici 36000
## 142 1 Sapna 20000
## Route_drive_minutes rtrn_abrd share_rtrn_abroad
## 137 13 2302 0.49325048
## 138 46 2696 0.07491386
## 139 87 4322 0.03905551
## 140 55 1833 0.06065720
## 141 62 3318 0.05743963
## 142 24 8043 0.13665557
## Scaled_Stand.wi.ponder_onlyempl Scaled_Stand.wi.ponder_all activ
## 137 -0.9586939 -0.9666127
## 138 -0.8785279 -0.8695459
## 139 -0.5932477 -0.5843471
## 140 -0.7651665 -0.7620713
## 141 -0.8173342 -0.8045271
## 142 -0.8578325 -0.8554634
## Scaled_Stand.HPI Scaled_Stand.TI Scaled_Stand.Adj.wage_ponder2 hpi_wage1
## 137 -0.6916070 -0.8644020 -0.9662135 1.0528711
## 138 -0.4690421 -0.9746729 -0.9087555 0.6941595
## 139 -0.0960676 -0.9559156 -0.6446071 1.3203881
## 140 -0.6013710 -0.8361077 -0.8734107 2.8621589
## 141 -0.1236170 -0.3947695 -0.8418893 -0.8021775
## 142 -0.6010860 -0.8503434 -0.8031789 1.3096681
## hpi_wage2 shapeISO shapeID shapeGroup shapeType
## 137 1.0147565 <NA> 47562260B11760408730406 BIH ADM2
## 138 0.7192698 <NA> 47562260B38480252250756 BIH ADM2
## 139 1.2333168 <NA> 47562260B71839486239008 BIH ADM2
## 140 2.8918552 <NA> 47562260B65722901280803 BIH ADM2
## 141 -0.8725043 <NA> 47562260B75753554056783 BIH ADM2
## 142 1.3102661 <NA> 47562260B26020173487367 BIH ADM2
## geometry
## 137 MULTIPOLYGON (((19.11012 44...
## 138 MULTIPOLYGON (((18.08808 44...
## 139 MULTIPOLYGON (((18.07031 44...
## 140 MULTIPOLYGON (((18.07031 44...
## 141 MULTIPOLYGON (((18.57037 44...
## 142 MULTIPOLYGON (((19.17619 44...
##**************
##CORRELATION
##**************
#correlation between indeces
cor.test(map.Indexes_QoL$Stand.HPI, map.Indexes_QoL$Stand.wi.ponder_onlyempl) #0.5885 !kod ismira ovo je 0.76
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$Stand.HPI and map.Indexes_QoL$Stand.wi.ponder_onlyempl
## t = 8.6184, df = 140, p-value = 1.298e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4695763 0.6868725
## sample estimates:
## cor
## 0.5887616
cor.test(map.Indexes_QoL$Stand.HPI, map.Indexes_QoL$`Stand.wi.ponder_all activ`) #0.5867
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$Stand.HPI and map.Indexes_QoL$`Stand.wi.ponder_all activ`
## t = 8.5742, df = 140, p-value = 1.67e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4672180 0.6852735
## sample estimates:
## cor
## 0.5867843
#r = 0.20 – 0.39 is considered a weak relationship. r = 0.40 – 0.59 is considered a moderate relationship. r = 0.60 – 0.79 is considered a strong relationship. r = 0.80 – 1 is considered a very strong relationship
#************
#MAP INDECES
#************
##MAP TRANSPORT
map.Indexes_QoL <- mutate(map.Indexes_QoL, cut.Scaled_Stand.TI1 = cut_width(Scaled_Stand.TI, width = 0.2))
t = ggplot() +
geom_sf(data = map.Indexes_QoL, aes(geometry=geometry, fill = cut.Scaled_Stand.TI1),
color = "lightgray") +
scale_fill_viridis_d() +
scale_x_continuous(breaks = c(18, 48))
t1 <- t +
ggtitle ("Transport index") +
theme(plot.title = element_text(color="darkgray", size=12, face="bold")) +
guides(fill=guide_legend(title=""))
t1 #ovo je scaled standardizovani index. tako sam nasla i vdijela da je to radio i Ismir, dakle ide od-1 pa do 1 i ljepse ispadne na slici.
#to se zove scaliranje. formula je, ako zelim da ide od -1 do 1 slijedeca (2*(vrijednost-minimum)/(maximum-minimum)-1)
# Plot with enhancements
# Load necessary libraries
library(ggplot2)
library(dplyr)
library(viridis)
## Loading required package: viridisLite
##
## Attaching package: 'viridis'
##
## The following object is masked from 'package:maps':
##
## unemp
library(sf)
# Create the cut for the Scaled Transport Index
map.Indexes_QoL <- mutate(map.Indexes_QoL,
cut.Scaled_Stand.TI1 = cut_width(Scaled_Stand.TI, width = 0.2))
# Identify the municipalities with the highest and lowest levels
highest_muni <- map.Indexes_QoL %>%
filter(Scaled_Stand.TI == max(Scaled_Stand.TI, na.rm = TRUE)) %>%
dplyr::select(Munic1, Scaled_Stand.TI, geometry)
lowest_muni <- map.Indexes_QoL %>%
filter(Scaled_Stand.TI == min(Scaled_Stand.TI, na.rm = TRUE)) %>%
dplyr::select(Munic1, Scaled_Stand.TI, geometry)
# Create the base plot
t <- ggplot(data = map.Indexes_QoL) +
geom_sf(aes(geometry = geometry, fill = cut.Scaled_Stand.TI1),
color = "lightgray", size = 0.1) +
scale_fill_viridis_d(option = "A") + # Use a different option for color
scale_x_continuous(breaks = c(18, 48)) +
labs(title = "Transport Index",
fill = "") +
theme_minimal(base_size = 12) + # Use a minimal theme
theme(plot.title = element_text(color = "darkgray", size = 14, face = "bold"),
axis.title.x = element_blank(), # Remove x-axis label
axis.title.y = element_blank(), # Remove y-axis label
plot.caption = element_text(color = "darkred", size = 10, face = "italic"),
legend.position = "right")
# Add lines from the highest and lowest municipalities to the caption
highest_coords <- st_coordinates(st_centroid(highest_muni$geometry))
lowest_coords <- st_coordinates(st_centroid(lowest_muni$geometry))
# Define a longer line length
line_length <- 1.2 # Adjusted length of the line segments
# Adding the lines with municipality names
t <- t +
geom_segment(aes(x = highest_coords[1], y = highest_coords[2],
xend = highest_coords[1], yend = highest_coords[2] - line_length),
color = "darkorange", linetype = "solid", size = 0.7) + # Use a distinct color
annotate("text", x = highest_coords[1], y = highest_coords[2] - line_length - 0.1,
label = highest_muni$Munic1, vjust = 0, hjust = 0.5,
color = "darkorange", size = 4) +
geom_segment(aes(x = lowest_coords[1], y = lowest_coords[2],
xend = lowest_coords[1], yend = lowest_coords[2] + line_length),
color = "steelblue", linetype = "solid", size = 0.7) + # Use a distinct color
annotate("text", x = lowest_coords[1], y = lowest_coords[2] + line_length + 0.1,
label = lowest_muni$Munic1, vjust = 0, hjust = 0.5,
color = "steelblue", size = 4)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Print the plot
print(t)
## Warning in geom_segment(aes(x = highest_coords[1], y = highest_coords[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
## Warning in geom_segment(aes(x = lowest_coords[1], y = lowest_coords[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
# Print the plot
print(t)
## Warning in geom_segment(aes(x = highest_coords[1], y = highest_coords[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
## All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
# MAP WAGE all.activity
# radim samo all activity jer mi je on do sada naj logicniji
map.Indexes_QoL <- mutate(map.Indexes_QoL, cut.Scaled.Stand.wi.all.activity = cut_width(`Scaled_Stand.wi.ponder_all activ`, width = 0.2))
w = ggplot() +
geom_sf(data = map.Indexes_QoL, aes(geometry=geometry, fill = cut.Scaled.Stand.wi.all.activity),
color = "lightgray") +
theme(legend.title=element_blank()) + scale_fill_viridis_d() +
scale_x_continuous(breaks = c(18, 48))
w1 <- w +
ggtitle ("Wage index- all activity") +
theme(plot.title = element_text(color="darkgray", size=12, face="bold")) +
guides(fill=guide_legend())
w1
#MORE INCANCE GRAPH
# Load necessary libraries
library(ggplot2)
library(dplyr)
library(viridis)
library(sf)
# Create the cut for the Wage Index
map.Indexes_QoL <- mutate(map.Indexes_QoL,
cut.Scaled.Stand.wi.all.activity = cut_width(`Scaled_Stand.wi.ponder_all activ`, width = 0.2))
# Identify the municipalities with the highest and lowest wage index levels
highest_wage_muni <- map.Indexes_QoL %>%
filter(`Scaled_Stand.wi.ponder_all activ` == max(`Scaled_Stand.wi.ponder_all activ`, na.rm = TRUE)) %>%
dplyr::select(Munic1, `Scaled_Stand.wi.ponder_all activ`, geometry)
lowest_wage_muni <- map.Indexes_QoL %>%
filter(`Scaled_Stand.wi.ponder_all activ` == min(`Scaled_Stand.wi.ponder_all activ`, na.rm = TRUE)) %>%
dplyr::select(Munic1, `Scaled_Stand.wi.ponder_all activ`, geometry)
# Create the base plot with improved color palette
w <- ggplot(data = map.Indexes_QoL) +
geom_sf(aes(geometry = geometry, fill = cut.Scaled.Stand.wi.all.activity),
color = "white", size = 0.1) + # Use white borders for clarity
scale_fill_viridis_d(option = "B") + # A subtle color palette
scale_x_continuous(breaks = NULL) + # Remove x-axis ticks
scale_y_continuous(breaks = NULL) + # Remove y-axis ticks
labs(title = "Wage Index - All Activity",
fill = "") +
theme_minimal(base_size = 14) + # Use a minimal theme
theme(plot.title = element_text(color = "black", size = 16, face = "bold"),
axis.title.x = element_blank(), # Remove x-axis label
axis.title.y = element_blank(), # Remove y-axis label
plot.caption = element_text(color = "darkgray", size = 10, face = "italic"),
legend.position = "right",
panel.grid.major = element_blank(), # Remove major grid lines
panel.grid.minor = element_blank(), # Remove minor grid lines
panel.background = element_blank()) # Clear background
# Add lines from the highest and lowest wage index municipalities
highest_coords_wage <- st_coordinates(st_centroid(highest_wage_muni$geometry))
lowest_coords_wage <- st_coordinates(st_centroid(lowest_wage_muni$geometry))
# Define a suitable line length for wage index plot
line_length_wage <- 1.5 # Length of the line segments
# Adding the lines with municipality names for wage index
w1 <- w +
geom_segment(aes(x = highest_coords_wage[1], y = highest_coords_wage[2],
xend = highest_coords_wage[1], yend = highest_coords_wage[2] - line_length_wage),
color = "darkorange", linetype = "solid", size = 0.7) + # Use a distinct color
annotate("text", x = highest_coords_wage[1], y = highest_coords_wage[2] - line_length_wage - 0.1,
label = highest_wage_muni$Munic1, vjust = 0, hjust = 0.5,
color = "darkorange", size = 4) +
geom_segment(aes(x = lowest_coords_wage[1], y = lowest_coords_wage[2],
xend = lowest_coords_wage[1], yend = lowest_coords_wage[2] + line_length_wage),
color = "steelblue", linetype = "solid", size = 0.7) + # Use a distinct color
annotate("text", x = lowest_coords_wage[1], y = lowest_coords_wage[2] + line_length_wage + 0.1,
label = lowest_wage_muni$Munic1, vjust = 0, hjust = 0.5,
color = "steelblue", size = 4)
# Print the plot
print(w1)
## Warning in geom_segment(aes(x = highest_coords_wage[1], y = highest_coords_wage[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
## Warning in geom_segment(aes(x = lowest_coords_wage[1], y = lowest_coords_wage[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
# MAP HOUSING
map.Indexes_QoL <- mutate(map.Indexes_QoL, cut.Scaled_Stand.HPI = cut_width(Scaled_Stand.HPI, width = 0.2))
h = ggplot() +
geom_sf(data = map.Indexes_QoL, aes(geometry=geometry, fill = cut.Scaled_Stand.HPI),
color = "lightgray") +
theme(legend.title=element_blank()) +
scale_fill_viridis_d() +
scale_x_continuous(breaks = c(18, 48))
h1 <- h +
ggtitle ("Housing index") +
theme(plot.title = element_text(color="darkgray", size=12, face="bold")) +
guides(fill=guide_legend())
h1
#enhanced mao on housing index
# Load necessary libraries
##library(ggplot2)
##library(dplyr)
##library(viridis)
##library(sf)
# Create the cut for the Housing Price Index (HPI)
map.Indexes_QoL <- mutate(map.Indexes_QoL,
cut.Scaled_Stand.HPI = cut_width(Scaled_Stand.HPI, width = 0.2))
# Identify the municipalities with the highest and lowest HPI
highest_HPI_muni <- map.Indexes_QoL %>%
filter(Scaled_Stand.HPI == max(Scaled_Stand.HPI, na.rm = TRUE)) %>%
dplyr::select(Munic1, Scaled_Stand.HPI, geometry)
lowest_HPI_muni <- map.Indexes_QoL %>%
filter(Scaled_Stand.HPI == min(Scaled_Stand.HPI, na.rm = TRUE)) %>%
dplyr::select(Munic1, Scaled_Stand.HPI, geometry)
# Create the base plot with improved color palette for the housing index
h <- ggplot(data = map.Indexes_QoL) +
geom_sf(aes(geometry = geometry, fill = cut.Scaled_Stand.HPI),
color = "white", size = 0.1) + # Use white borders for clarity
scale_fill_viridis_d(option = "B") + # Subtle color palette
scale_x_continuous(breaks = NULL) + # Remove x-axis ticks
scale_y_continuous(breaks = NULL) + # Remove y-axis ticks
labs(title = "Housing Index",
fill = "") +
theme_minimal(base_size = 14) + # Minimal theme
theme(plot.title = element_text(color = "black", size = 16, face = "bold"),
axis.title.x = element_blank(), # Remove x-axis label
axis.title.y = element_blank(), # Remove y-axis label
plot.caption = element_text(color = "darkgray", size = 10, face = "italic"),
legend.position = "right",
panel.grid.major = element_blank(), # Remove major grid lines
panel.grid.minor = element_blank(), # Remove minor grid lines
panel.background = element_blank()) # Clear background
# Add lines from the highest and lowest HPI municipalities
highest_coords_HPI <- st_coordinates(st_centroid(highest_HPI_muni$geometry))
lowest_coords_HPI <- st_coordinates(st_centroid(lowest_HPI_muni$geometry))
# Define a suitable line length for the housing index plot
line_length_HPI <- 1.5 # Length of the line segments
# Adding the lines with municipality names for HPI
h1 <- h +
geom_segment(aes(x = highest_coords_HPI[1], y = highest_coords_HPI[2],
xend = highest_coords_HPI[1], yend = highest_coords_HPI[2] - line_length_HPI),
color = "darkorange", linetype = "solid", size = 0.7) + # Use distinct color
annotate("text", x = highest_coords_HPI[1], y = highest_coords_HPI[2] - line_length_HPI - 0.1,
label = highest_HPI_muni$Munic1, vjust = 0, hjust = 0.5,
color = "darkorange", size = 4) +
geom_segment(aes(x = lowest_coords_HPI[1], y = lowest_coords_HPI[2],
xend = lowest_coords_HPI[1], yend = lowest_coords_HPI[2] + line_length_HPI),
color = "steelblue", linetype = "solid", size = 0.7) + # Use distinct color
annotate("text", x = lowest_coords_HPI[1], y = lowest_coords_HPI[2] + line_length_HPI + 0.1,
label = lowest_HPI_muni$Munic1, vjust = 0, hjust = 0.5,
color = "steelblue", size = 4)
# Print the plot
print(h1)
## Warning in geom_segment(aes(x = highest_coords_HPI[1], y = highest_coords_HPI[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
## Warning in geom_segment(aes(x = lowest_coords_HPI[1], y = lowest_coords_HPI[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
# MAP EMPLOYMENT RATE
map.Indexes_QoL <- mutate(map.Indexes_QoL, cut.emplrate2014 = cut_width(emplrate2014, width = 0.1))
emp.rate = ggplot() +
geom_sf(data = map.Indexes_QoL,
aes(geometry = geometry, fill = cut.emplrate2014),
color = "lightgray") + # Set border color to light gray
theme(legend.title = element_blank()) +
scale_fill_viridis_d() +
scale_x_continuous(breaks = c(18, 48))
emp.rate1 <- emp.rate + labs (title = "Employment rate 2014")+
theme(plot.title = element_text(color="darkgray", size=12, face="bold"))
emp.rate1
library(gridExtra)
##
## Attaching package: 'gridExtra'
##
## The following object is masked from 'package:dplyr':
##
## combine
grid.arrange(t1, w1, h1, emp.rate1)
## Warning in geom_segment(aes(x = highest_coords_wage[1], y = highest_coords_wage[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
## Warning in geom_segment(aes(x = lowest_coords_wage[1], y = lowest_coords_wage[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
## Warning in geom_segment(aes(x = highest_coords_HPI[1], y = highest_coords_HPI[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
## Warning in geom_segment(aes(x = lowest_coords_HPI[1], y = lowest_coords_HPI[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
# MAP QOLI ONLY EMPLOYED
map.Indexes_QoL <- mutate(map.Indexes_QoL, cut.QoLIv1_onlyempl = cut_width(QoLIv1_onlyempl, width = 0.3))
QoLIv1 = ggplot() +
geom_sf(data = map.Indexes_QoL, aes(geometry=geometry, fill = cut.QoLIv1_onlyempl),color="lightgray") +
theme(legend.title=element_blank()) +
scale_fill_viridis_d() +
scale_x_continuous(breaks = c(18, 48))
QoLIv1 <- QoLIv1 + labs (title = "QoLIv1_onlyempl")+
theme(plot.title = element_text(color="darkgray", size=12, face="bold"))
QoLIv1
## enhanced QoLiv1
# Create the cut for QoLIv1_onlyempl
map.Indexes_QoL <- mutate(map.Indexes_QoL,
cut.QoLIv1_onlyempl = cut_width(QoLIv1_onlyempl, width = 0.3))
# Identify the municipalities with the highest and lowest QoLIv1_onlyempl
highest_QoLIv1_muni <- map.Indexes_QoL %>%
filter(QoLIv1_onlyempl == max(QoLIv1_onlyempl, na.rm = TRUE)) %>%
dplyr::select(Munic1, QoLIv1_onlyempl, geometry)
lowest_QoLIv1_muni <- map.Indexes_QoL %>%
filter(QoLIv1_onlyempl == min(QoLIv1_onlyempl, na.rm = TRUE)) %>%
dplyr::select(Munic1, QoLIv1_onlyempl, geometry)
# Create the base plot for QoLIv1_onlyempl
QoLIv1 <- ggplot(data = map.Indexes_QoL) +
geom_sf(aes(geometry = geometry, fill = cut.QoLIv1_onlyempl),
color = "white", size = 0.1) + # White borders for clarity
scale_fill_viridis_d(option = "B") + # Subtle color palette
scale_x_continuous(breaks = NULL) + # Remove x-axis ticks
scale_y_continuous(breaks = NULL) + # Remove y-axis ticks
labs(title = "QoLIv1 - Only Employment",
fill = "") +
theme_minimal(base_size = 14) + # Minimalist theme
theme(plot.title = element_text(color = "black", size = 16, face = "bold"),
axis.title.x = element_blank(), # Remove x-axis label
axis.title.y = element_blank(), # Remove y-axis label
plot.caption = element_text(color = "darkgray", size = 10, face = "italic"),
legend.position = "right",
panel.grid.major = element_blank(), # Remove major grid lines
panel.grid.minor = element_blank(), # Remove minor grid lines
panel.background = element_blank()) # Clear background
# Add lines from the highest and lowest QoLIv1_onlyempl municipalities
highest_coords_QoLIv1 <- st_coordinates(st_centroid(highest_QoLIv1_muni$geometry))
lowest_coords_QoLIv1 <- st_coordinates(st_centroid(lowest_QoLIv1_muni$geometry))
# Define a suitable line length for QoLIv1 plot
line_length_QoLIv1 <- 1.5 # Length of the line segments
# Adding the lines with municipality names for QoLIv1_onlyempl
QoLIv1_plot <- QoLIv1 +
geom_segment(aes(x = highest_coords_QoLIv1[1], y = highest_coords_QoLIv1[2],
xend = highest_coords_QoLIv1[1], yend = highest_coords_QoLIv1[2] - line_length_QoLIv1),
color = "darkorange", linetype = "solid", size = 0.7) + # Use distinct color
annotate("text", x = highest_coords_QoLIv1[1], y = highest_coords_QoLIv1[2] - line_length_QoLIv1 - 0.1,
label = highest_QoLIv1_muni$Munic1, vjust = 0, hjust = 0.5,
color = "darkorange", size = 4) +
geom_segment(aes(x = lowest_coords_QoLIv1[1], y = lowest_coords_QoLIv1[2],
xend = lowest_coords_QoLIv1[1], yend = lowest_coords_QoLIv1[2] + line_length_QoLIv1),
color = "steelblue", linetype = "solid", size = 0.7) + # Use distinct color
annotate("text", x = lowest_coords_QoLIv1[1], y = lowest_coords_QoLIv1[2] + line_length_QoLIv1 + 0.1,
label = lowest_QoLIv1_muni$Munic1, vjust = 0, hjust = 0.5,
color = "steelblue", size = 4)
# Print the plot
print(QoLIv1_plot)
## Warning in geom_segment(aes(x = highest_coords_QoLIv1[1], y = highest_coords_QoLIv1[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
## Warning in geom_segment(aes(x = lowest_coords_QoLIv1[1], y = lowest_coords_QoLIv1[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
## MAp QoLIV2
map.Indexes_QoL <- mutate(map.Indexes_QoL, cut.QoLIv2_allactivy = cut_width(QoLIv2_allactivy, width = 0.3))
QoLIv2 = ggplot() +
geom_sf(data = map.Indexes_QoL, aes(geometry=geometry, fill = cut.QoLIv2_allactivy),color="lightgray") +
theme(legend.title=element_blank()) +
scale_fill_viridis_d() +
scale_x_continuous(breaks = c(18, 48))
QoLIv2 <- QoLIv2 + labs (title = "QoLIv2_allactivy")+
theme(plot.title = element_text(color="darkgray", size=12, face="bold"))
QoLIv2
##enhanced QoLIv2
# Load necessary libraries
library(gridExtra)
# Create the cut for QoLIv2_allactivy
map.Indexes_QoL <- mutate(map.Indexes_QoL,
cut.QoLIv2_allactivy = cut_width(QoLIv2_allactivy, width = 0.3))
# Identify the municipalities with the highest and lowest QoLIv2_allactivy
highest_QoLIv2_muni <- map.Indexes_QoL %>%
filter(QoLIv2_allactivy == max(QoLIv2_allactivy, na.rm = TRUE)) %>%
dplyr::select(Munic1, QoLIv2_allactivy, geometry)
lowest_QoLIv2_muni <- map.Indexes_QoL %>%
filter(QoLIv2_allactivy == min(QoLIv2_allactivy, na.rm = TRUE)) %>%
dplyr::select(Munic1, QoLIv2_allactivy, geometry)
# Create the base plot for QoLIv2_allactivy
QoLIv2 <- ggplot(data = map.Indexes_QoL) +
geom_sf(aes(geometry = geometry, fill = cut.QoLIv2_allactivy),
color = "white", size = 0.1) + # White borders for clarity
scale_fill_viridis_d(option = "B") + # Subtle color palette
scale_x_continuous(breaks = NULL) + # Remove x-axis ticks
scale_y_continuous(breaks = NULL) + # Remove y-axis ticks
labs(title = "QoLIv2 - All Activity",
fill = "") +
theme_minimal(base_size = 14) + # Minimalist theme
theme(plot.title = element_text(color = "black", size = 16, face = "bold"),
axis.title.x = element_blank(), # Remove x-axis label
axis.title.y = element_blank(), # Remove y-axis label
plot.caption = element_text(color = "darkgray", size = 10, face = "italic"),
legend.position = "right",
panel.grid.major = element_blank(), # Remove major grid lines
panel.grid.minor = element_blank(), # Remove minor grid lines
panel.background = element_blank()) # Clear background
# Add lines from the highest and lowest QoLIv2_allactivy municipalities
highest_coords_QoLIv2 <- st_coordinates(st_centroid(highest_QoLIv2_muni$geometry))
lowest_coords_QoLIv2 <- st_coordinates(st_centroid(lowest_QoLIv2_muni$geometry))
# Define a suitable line length for QoLIv2 plot
line_length_QoLIv2 <- 1.5 # Length of the line segments
# Adding the lines with municipality names for QoLIv2_allactivy
QoLIv2_plot <- QoLIv2 +
geom_segment(aes(x = highest_coords_QoLIv2[1], y = highest_coords_QoLIv2[2],
xend = highest_coords_QoLIv2[1], yend = highest_coords_QoLIv2[2] - line_length_QoLIv2),
color = "darkorange", linetype = "solid", size = 0.7) + # Use distinct color
annotate("text", x = highest_coords_QoLIv2[1], y = highest_coords_QoLIv2[2] - line_length_QoLIv2 - 0.1,
label = highest_QoLIv2_muni$Munic1, vjust = 0, hjust = 0.5,
color = "darkorange", size = 4) +
geom_segment(aes(x = lowest_coords_QoLIv2[1], y = lowest_coords_QoLIv2[2],
xend = lowest_coords_QoLIv2[1], yend = lowest_coords_QoLIv2[2] + line_length_QoLIv2),
color = "steelblue", linetype = "solid", size = 0.7) + # Use distinct color
annotate("text", x = lowest_coords_QoLIv2[1], y = lowest_coords_QoLIv2[2] + line_length_QoLIv2 + 0.1,
label = lowest_QoLIv2_muni$Munic1, vjust = 0, hjust = 0.5,
color = "steelblue", size = 4)
# Print the QoLIv2 plot
print(QoLIv2_plot)
## Warning in geom_segment(aes(x = highest_coords_QoLIv2[1], y = highest_coords_QoLIv2[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
## Warning in geom_segment(aes(x = lowest_coords_QoLIv2[1], y = lowest_coords_QoLIv2[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
# Combine the previous QoLIv1 plot with the new QoLIv2 plot in a grid layout
# Remove the legend from QoLIv2 plot
QoLIv2_plot_no_legend <- QoLIv2_plot + theme(legend.position = "none")
# Use grid.arrange to place the plots side by side
grid.arrange(QoLIv1_plot, QoLIv2_plot_no_legend, ncol = 2)
## Warning in geom_segment(aes(x = highest_coords_QoLIv1[1], y = highest_coords_QoLIv1[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
## Warning in geom_segment(aes(x = lowest_coords_QoLIv1[1], y = lowest_coords_QoLIv1[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
## Warning in geom_segment(aes(x = highest_coords_QoLIv2[1], y = highest_coords_QoLIv2[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
## Warning in geom_segment(aes(x = lowest_coords_QoLIv2[1], y = lowest_coords_QoLIv2[2], : All aesthetics have length 1, but the data has 142 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
#graph correlation QoLI AND population change
cor.test (map.Indexes_QoL$QoLIv1_onlyempl, map.Indexes_QoL$Rate.change.pop.per.km2) #no.corr
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and map.Indexes_QoL$Rate.change.pop.per.km2
## t = -0.90358, df = 140, p-value = 0.3678
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.23788784 0.08970808
## sample estimates:
## cor
## -0.07614441
cor.test (map.Indexes_QoL$QoLIv2_allactivy, map.Indexes_QoL$Rate.change.pop.per.km2) #no.corr
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and map.Indexes_QoL$Rate.change.pop.per.km2
## t = -0.87247, df = 140, p-value = 0.3844
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.23541295 0.09230808
## sample estimates:
## cor
## -0.07353738
ggplot(map.Indexes_QoL, aes(x = QoLIv1_onlyempl, y = Rate.change.pop.per.km2)) +
geom_point(size=1)+
geom_text(aes(label = ifelse(Rate.change.pop.per.km2>0.2|Rate.change.pop.per.km2 < c(-0.2),paste0(Munic1)," " )),size=3) +
geom_text(aes(label = ifelse(QoLIv1_onlyempl>5|QoLIv1_onlyempl < c(-5),paste0(Munic1)," ")),size=3) +
ggtitle("Corr plot Qoliv3",subtitle = "no.cor")
#Centar i Novo Sarajevo odstupajau. Cudno je da imaju visok QoL a nisku korelaciju.
#Problem moze biti sa stvarnim brojem stanovnika. pa je ipak mozda bolje uzimati upise u skole
#drop Centar i Novo Sarajevo
map.Indexes_QoL.drop <- map.Indexes_QoL %>% filter(as.character (Munic1) != "N.Sarajevo" & Munic1!= "C.Sarajevo")
cor.test(map.Indexes_QoL.drop$QoLIv2_allactivy, map.Indexes_QoL.drop$Rate.change.pop.per.km2)#jos uvijek bez signif.
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL.drop$QoLIv2_allactivy and map.Indexes_QoL.drop$Rate.change.pop.per.km2
## t = -0.9457, df = 138, p-value = 0.346
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.24291307 0.08681541
## sample estimates:
## cor
## -0.08024361
cor.test(map.Indexes_QoL.drop$QoLIv1_onlyempl, map.Indexes_QoL.drop$Rate.change.pop.per.km2)#bez signif.
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL.drop$QoLIv1_onlyempl and map.Indexes_QoL.drop$Rate.change.pop.per.km2
## t = -0.98543, df = 138, p-value = 0.3261
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.24608225 0.08346917
## sample estimates:
## cor
## -0.08359167
##CORELATION QOL INDECES AND PRIMARY SCHOOL ENROLEMENT
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, map.Indexes_QoL$`Rate.change(prim.enr.per.capita.2023_2013)`)#ovo je ok jer dakle sa rastom qol raste upis
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and map.Indexes_QoL$`Rate.change(prim.enr.per.capita.2023_2013)`
## t = 5.2306, df = 140, p-value = 6.031e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2566923 0.5335165
## sample estimates:
## cor
## 0.4043231
# 0.4043231
ggplot(map.Indexes_QoL, aes(x = QoLIv1_onlyempl, y = `Rate.change(prim.enr.per.capita.2023_2013)`)) +
geom_point(size=1) +
geom_text(aes(label = ifelse(`Rate.change(prim.enr.per.capita.2023_2013)`>0.3 & QoLIv1_onlyempl < -0.1,paste0(Munic1)," " )),size=3) +
geom_text(aes(label = ifelse(`Rate.change(prim.enr.per.capita.2023_2013)`< -0.3 & QoLIv1_onlyempl > 0.1,paste0(Munic1)," ")),size=3) +
ggtitle("Corr plot QoLIv1_onlyempl",subtitle = "cor=0.4043231")
cor.test(map.Indexes_QoL$QoLIv2_allactivy, map.Indexes_QoL$`Rate.change(prim.enr.per.capita.2023_2013)`)#
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and map.Indexes_QoL$`Rate.change(prim.enr.per.capita.2023_2013)`
## t = 4.8999, df = 140, p-value = 2.613e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2325387 0.5148858
## sample estimates:
## cor
## 0.3826099
#0.3826099
ggplot(map.Indexes_QoL, aes(x = QoLIv2_allactivy, y = `Rate.change(prim.enr.per.capita.2023_2013)`)) +
geom_point(size=1) +
geom_text(aes(label = ifelse(`Rate.change(prim.enr.per.capita.2023_2013)`>0 & QoLIv2_allactivy < -0.2,paste0(Munic1)," " )),size=3) +
geom_text(aes(label = ifelse(`Rate.change(prim.enr.per.capita.2023_2013)`< -0.1 &QoLIv2_allactivy > 0.2,paste0(Munic1)," ")),size=3) +
ggtitle("Corr plot QoLIv2_allactivy",subtitle = "cor=0.3826099")
###pozitivna je korelacije i umjereno moderate
##KORELACIJE QOLI AND PRIMARY ENROLMENT SHARE OF TOT POP
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, map.Indexes_QoL$rate.change.prim.enrolment)#
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and map.Indexes_QoL$rate.change.prim.enrolment
## t = 5.5172, df = 140, p-value = 1.615e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2771744 0.5491075
## sample estimates:
## cor
## 0.4226062
#0.4226062
cor.test(map.Indexes_QoL$QoLIv2_allactivy, map.Indexes_QoL$rate.change.prim.enrolment)#
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and map.Indexes_QoL$rate.change.prim.enrolment
## t = 5.1998, df = 140, p-value = 6.932e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2544618 0.5318072
## sample estimates:
## cor
## 0.4023249
# 0.4023249
#KORALACIJA
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, map.Indexes_QoL$Rate.change.pop.per.km2)#
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and map.Indexes_QoL$Rate.change.pop.per.km2
## t = -0.90358, df = 140, p-value = 0.3678
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.23788784 0.08970808
## sample estimates:
## cor
## -0.07614441
#no sing
#SHARE UNIVERSITY DEGREE
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, map.Indexes_QoL$`Share univ.degre`)#sad je ovo normalno dakle sa rastom qol raste gustina
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and map.Indexes_QoL$`Share univ.degre`
## t = 10.367, df = 140, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5544657 0.7430658
## sample estimates:
## cor
## 0.6590025
#0.659
cor.test(map.Indexes_QoL$QoLIv2_allactivy, map.Indexes_QoL$`Share univ.degre`)#sad je ovo normalno dakle sa rastom qol raste gustina
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and map.Indexes_QoL$`Share univ.degre`
## t = 10.779, df = 140, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5721755 0.7544630
## sample estimates:
## cor
## 0.6734301
#0.673
#CORRELATION OF SHARE NO PRIMARY
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, map.Indexes_QoL$`Share no primary education`)#sad je ovo normalno dakle sa rastom qol raste gustina
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and map.Indexes_QoL$`Share no primary education`
## t = -4.5749, df = 140, p-value = 1.04e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4959012 -0.2082794
## sample estimates:
## cor
## -0.3606336
#-0.361
cor.test(map.Indexes_QoL$QoLIv2_allactivy, map.Indexes_QoL$`Share no primary education`)#sad je ovo normalno dakle sa rastom qol raste gustina
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and map.Indexes_QoL$`Share no primary education`
## t = -4.728, df = 140, p-value = 5.467e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5049263 -0.2197681
## sample estimates:
## cor
## -0.3710622
#-0.371
#CORRELATION SHARE URBAN
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, map.Indexes_QoL$Urban.Share)#
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and map.Indexes_QoL$Urban.Share
## t = 6.4361, df = 140, p-value = 1.817e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3398571 0.5956739
## sample estimates:
## cor
## 0.4778335
#0.4778335
cor.test(map.Indexes_QoL$QoLIv2_allactivy, map.Indexes_QoL$Urban.Share)#
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and map.Indexes_QoL$Urban.Share
## t = 6.4156, df = 140, p-value = 2.017e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3385089 0.5946901
## sample estimates:
## cor
## 0.476657
#0.476657
#CORRELATION WAR INTENSITY
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, map.Indexes_QoL$War_intesity)#
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and map.Indexes_QoL$War_intesity
## t = -0.10566, df = 140, p-value = 0.916
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1734016 0.1560274
## sample estimates:
## cor
## -0.008929368
#NO sin
cor.test(map.Indexes_QoL$QoLIv2_allactivy, map.Indexes_QoL$War_intesity)#
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and map.Indexes_QoL$War_intesity
## t = -0.0074211, df = 140, p-value = 0.9941
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1653374 0.1641170
## sample estimates:
## cor
## -0.0006271957
#no. sing
#CORRELATION WAR INTENSITY
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, map.Indexes_QoL$War_intesity)#
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and map.Indexes_QoL$War_intesity
## t = -0.10566, df = 140, p-value = 0.916
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1734016 0.1560274
## sample estimates:
## cor
## -0.008929368
#NO sin
cor.test(map.Indexes_QoL$QoLIv2_allactivy, map.Indexes_QoL$War_intesity)#
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and map.Indexes_QoL$War_intesity
## t = -0.0074211, df = 140, p-value = 0.9941
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1653374 0.1641170
## sample estimates:
## cor
## -0.0006271957
#no. sing
#CORRELATION WAR INTENSITY
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, map.Indexes_QoL$share_rtrn_abroad)#
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and map.Indexes_QoL$share_rtrn_abroad
## t = -2.5383, df = 140, p-value = 0.01223
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3619713 -0.0466348
## sample estimates:
## cor
## -0.2097508
#-0.2097508
cor.test(map.Indexes_QoL$QoLIv2_allactivy, map.Indexes_QoL$share_rtrn_abroad)#
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and map.Indexes_QoL$share_rtrn_abroad
## t = -2.4541, df = 140, p-value = 0.01535
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3559110 -0.0396912
## sample estimates:
## cor
## -0.2030906
#-0.2030906
#CORRELATION OF DIVERSITY
##Ethnicity
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, map.Indexes_QoL$`Diversity Index-ethnicity`) #Nema korelacije
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and map.Indexes_QoL$`Diversity Index-ethnicity`
## t = 0.14227, df = 140, p-value = 0.8871
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1530069 0.1764013
## sample estimates:
## cor
## 0.01202341
#no.sing
cor.test(map.Indexes_QoL$QoLIv2_allactivy, map.Indexes_QoL$`Diversity Index-ethnicity`)#Nema korelacije
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and map.Indexes_QoL$`Diversity Index-ethnicity`
## t = 0.3665, df = 140, p-value = 0.7145
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1344525 0.1946948
## sample estimates:
## cor
## 0.03096047
#no.sing
#CORRELATION OF DIVERSITY
##Ethnicity RATIO
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, map.Indexes_QoL$Ratio_Munic.region) #Nema korelacije
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and map.Indexes_QoL$Ratio_Munic.region
## t = -0.47483, df = 140, p-value = 0.6356
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2034812 0.1254580
## sample estimates:
## cor
## -0.04009795
#no.sing
cor.test(map.Indexes_QoL$QoLIv2_allactivy, map.Indexes_QoL$Ratio_Munic.region)#Nema korelacije
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and map.Indexes_QoL$Ratio_Munic.region
## t = -0.24818, df = 140, p-value = 0.8044
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1850587 0.1442549
## sample estimates:
## cor
## -0.02097071
#no.sing
#Ethnicity yes and no municipality-region version 1
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, map.Indexes_QoL$`majority m=r(0=not)`) #Nema korelacije
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and map.Indexes_QoL$`majority m=r(0=not)`
## t = 0.14524, df = 140, p-value = 0.8847
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1527618 0.1766444
## sample estimates:
## cor
## 0.01227432
#no.sing
cor.test(map.Indexes_QoL$QoLIv2_allactivy, map.Indexes_QoL$`majority m=r(0=not)`)#Nema korelacije
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and map.Indexes_QoL$`majority m=r(0=not)`
## t = 0.15977, df = 140, p-value = 0.8733
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1515627 0.1778334
## sample estimates:
## cor
## 0.01350161
#no.sing
#Ethnicity yes and no municipality-region version 2
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, map.Indexes_QoL$`majority m=r(0=not,2=nomajority)`) #Nema korelacije
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and map.Indexes_QoL$`majority m=r(0=not,2=nomajority)`
## t = 0.30755, df = 140, p-value = 0.7589
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1393397 0.1898984
## sample estimates:
## cor
## 0.02598394
#no.sing
cor.test(map.Indexes_QoL$QoLIv2_allactivy, map.Indexes_QoL$`majority m=r(0=not,2=nomajority)`)#Nema korelacije
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and map.Indexes_QoL$`majority m=r(0=not,2=nomajority)`
## t = 0.34119, df = 140, p-value = 0.7335
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1365517 0.1926366
## sample estimates:
## cor
## 0.02882397
#no.sing
##Language
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, map.Indexes_QoL$`Diversity Index-mother language`) #Nema korelacije
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and map.Indexes_QoL$`Diversity Index-mother language`
## t = 0.92041, df = 140, p-value = 0.3589
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.0883008 0.2392256
## sample estimates:
## cor
## 0.07755455
#no.sing
cor.test(map.Indexes_QoL$QoLIv2_allactivy, map.Indexes_QoL$`Diversity Index-mother language`)#Nema korelacije
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and map.Indexes_QoL$`Diversity Index-mother language`
## t = 1.0817, df = 140, p-value = 0.2812
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07480522 0.25199198
## sample estimates:
## cor
## 0.09104394
#no.sing
#share of 65+
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, as.numeric(map.Indexes_QoL$share_65)) #no corelation
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and as.numeric(map.Indexes_QoL$share_65)
## t = -0.047444, df = 140, p-value = 0.9622
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1686256 0.1608238
## sample estimates:
## cor
## -0.004009699
#no sing
cor.test(map.Indexes_QoL$QoLIv2_allactivy, as.numeric (map.Indexes_QoL$share_65))#no corelation
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and as.numeric(map.Indexes_QoL$share_65)
## t = -0.070957, df = 140, p-value = 0.9435
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1705557 0.1588873
## sample estimates:
## cor
## -0.005996895
#no sing
#GENDER GAP ACROSS MUNICIPALITIES
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, as.numeric(map.Indexes_QoL$`Gender.em.rate.gap(f-m)`)) #negative cor
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and as.numeric(map.Indexes_QoL$`Gender.em.rate.gap(f-m)`)
## t = -2.5923, df = 140, p-value = 0.01055
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.36584128 -0.05108482
## sample estimates:
## cor
## -0.2140112
#-0.2140112
cor.test(map.Indexes_QoL$QoLIv2_allactivy, as.numeric (map.Indexes_QoL$`Gender.em.rate.gap(f-m)`))#no corelation
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and as.numeric(map.Indexes_QoL$`Gender.em.rate.gap(f-m)`)
## t = -2.5172, df = 140, p-value = 0.01296
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.36045587 -0.04489559
## sample estimates:
## cor
## -0.208084
#-0.208084
##CORELATION DISTANCE FROM ANOTHER ENTITY
#DISTANCE QOLI
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, as.numeric (log(map.Indexes_QoL$route_m))) #no corelation
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and as.numeric(log(map.Indexes_QoL$route_m))
## t = -0.93631, df = 140, p-value = 0.3507
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2404882 0.0869714
## sample estimates:
## cor
## -0.07888604
cor.test(map.Indexes_QoL$QoLIv2_allactivy, as.numeric (log(map.Indexes_QoL$route_m))) #no corelation
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and as.numeric(log(map.Indexes_QoL$route_m))
## t = -0.98228, df = 140, p-value = 0.3277
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.24413318 0.08312711
## sample estimates:
## cor
## -0.08273305
#QoLI and drive
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, as.numeric (log(map.Indexes_QoL$Route_drive_minutes))) #no corelation
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and as.numeric(log(map.Indexes_QoL$Route_drive_minutes))
## t = -0.26259, df = 140, p-value = 0.7933
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1862339 0.1430629
## sample estimates:
## cor
## -0.02218725
cor.test(map.Indexes_QoL$QoLIv2_allactivy, as.numeric (log(map.Indexes_QoL$Route_drive_minutes))) #no corelation
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and as.numeric(log(map.Indexes_QoL$Route_drive_minutes))
## t = -0.27342, df = 140, p-value = 0.7849
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1871171 0.1421663
## sample estimates:
## cor
## -0.02310194
#corr density
cor.test(map.Indexes_QoL$QoLIv1_onlyempl, as.numeric (log(map.Indexes_QoL$pop.per.km2_2013_2014))) #yes positive
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv1_onlyempl and as.numeric(log(map.Indexes_QoL$pop.per.km2_2013_2014))
## t = 2.6057, df = 140, p-value = 0.01016
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05219233 0.36680273
## sample estimates:
## cor
## 0.2150705
#0.2150705
cor.test(map.Indexes_QoL$QoLIv2_allactivy, as.numeric (log(map.Indexes_QoL$pop.per.km2_2013_2014))) #yes positive
##
## Pearson's product-moment correlation
##
## data: map.Indexes_QoL$QoLIv2_allactivy and as.numeric(log(map.Indexes_QoL$pop.per.km2_2013_2014))
## t = 2.4615, df = 140, p-value = 0.01505
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.04029601 0.35643998
## sample estimates:
## cor
## 0.2036713
#0.2036713
##regression with amenities
library(dplyr)
#socio-econimic
QoLiv1_decomp1 <- lm(QoLIv1_onlyempl ~
as.numeric(`Rate.change(prim.enr.per.capita.2023_2013)`) + #marginalni rast upisa u osnovne skole
as.numeric(pop.per.km2_2013_2014) +
as.numeric(`Share univ.degre`) +
as.numeric(Urban.Share)+
`share_65`+
as.numeric(`Gender.em.rate.gap(f-m)`),
data=map.Indexes_QoL)
summary (QoLiv1_decomp1)
##
## Call:
## lm(formula = QoLIv1_onlyempl ~ as.numeric(`Rate.change(prim.enr.per.capita.2023_2013)`) +
## as.numeric(pop.per.km2_2013_2014) + as.numeric(`Share univ.degre`) +
## as.numeric(Urban.Share) + share_65 + as.numeric(`Gender.em.rate.gap(f-m)`),
## data = map.Indexes_QoL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.31427 -0.32914 -0.02541 0.34437 2.93523
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -8.640e-01 1.533e-01
## as.numeric(`Rate.change(prim.enr.per.capita.2023_2013)`) 1.099e+00 3.607e-01
## as.numeric(pop.per.km2_2013_2014) -6.311e-05 1.073e-04
## as.numeric(`Share univ.degre`) 1.094e+01 1.715e+00
## as.numeric(Urban.Share) 3.068e-01 2.770e-01
## share_65 9.171e-03 1.003e-01
## as.numeric(`Gender.em.rate.gap(f-m)`) -1.138e-02 3.516e-03
## t value Pr(>|t|)
## (Intercept) -5.638 9.67e-08 ***
## as.numeric(`Rate.change(prim.enr.per.capita.2023_2013)`) 3.046 0.00279 **
## as.numeric(pop.per.km2_2013_2014) -0.588 0.55727
## as.numeric(`Share univ.degre`) 6.375 2.68e-09 ***
## as.numeric(Urban.Share) 1.108 0.26992
## share_65 0.091 0.92729
## as.numeric(`Gender.em.rate.gap(f-m)`) -3.236 0.00153 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6234 on 135 degrees of freedom
## Multiple R-squared: 0.5124, Adjusted R-squared: 0.4907
## F-statistic: 23.65 on 6 and 135 DF, p-value: < 2.2e-16
QoLiv1_decomp1a <- lm(QoLIv1_onlyempl ~
as.numeric(`Prim.enr.change.per.Pop2013`) + #diff u odnsou na br.stanovnika
as.numeric(`Share univ.degre`) +
as.numeric(Urban.Share)+
as.numeric(`Gender.em.rate.gap(f-m)`),
data=map.Indexes_QoL)
summary (QoLiv1_decomp1a)
##
## Call:
## lm(formula = QoLIv1_onlyempl ~ as.numeric(Prim.enr.change.per.Pop2013) +
## as.numeric(`Share univ.degre`) + as.numeric(Urban.Share) +
## as.numeric(`Gender.em.rate.gap(f-m)`), data = map.Indexes_QoL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.98020 -0.36717 -0.01115 0.31362 3.06432
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.902745 0.172826 -5.223 6.39e-07
## as.numeric(Prim.enr.change.per.Pop2013) 7.838352 4.726904 1.658 0.09956
## as.numeric(`Share univ.degre`) 10.356066 1.599279 6.475 1.56e-09
## as.numeric(Urban.Share) 0.439600 0.275709 1.594 0.11314
## as.numeric(`Gender.em.rate.gap(f-m)`) -0.010002 0.003606 -2.774 0.00631
##
## (Intercept) ***
## as.numeric(Prim.enr.change.per.Pop2013) .
## as.numeric(`Share univ.degre`) ***
## as.numeric(Urban.Share)
## as.numeric(`Gender.em.rate.gap(f-m)`) **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6337 on 137 degrees of freedom
## Multiple R-squared: 0.4886, Adjusted R-squared: 0.4736
## F-statistic: 32.72 on 4 and 137 DF, p-value: < 2.2e-16
#diversity
QoLiv1_decomp2 <- lm(QoLIv1_onlyempl ~
Ratio_Munic.region+
factor (FBiH) +
factor (FBiH)*Ratio_Munic.region +
share_rtrn_abroad +
War_intesity +
`majority m=r(0=not)`+
log(route_m)+
log(Route_drive_minutes)+
Urban.Share,
data=map.Indexes_QoL)
summary(QoLiv1_decomp2)
##
## Call:
## lm(formula = QoLIv1_onlyempl ~ Ratio_Munic.region + factor(FBiH) +
## factor(FBiH) * Ratio_Munic.region + share_rtrn_abroad + War_intesity +
## `majority m=r(0=not)` + log(route_m) + log(Route_drive_minutes) +
## Urban.Share, data = map.Indexes_QoL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4692 -0.4366 -0.0692 0.3260 4.8451
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.924212 1.358622 0.680 0.4975
## Ratio_Munic.region 0.387236 0.403297 0.960 0.3387
## factor(FBiH)1 0.683338 0.603026 1.133 0.2592
## share_rtrn_abroad -1.123418 0.561753 -2.000 0.0476 *
## War_intesity 0.510158 3.073887 0.166 0.8684
## `majority m=r(0=not)` -0.189218 0.215540 -0.878 0.3816
## log(route_m) -0.146181 0.174516 -0.838 0.4038
## log(Route_drive_minutes) 0.004061 0.212155 0.019 0.9848
## Urban.Share 1.566165 0.267602 5.853 3.67e-08 ***
## Ratio_Munic.region:factor(FBiH)1 -0.639717 0.523914 -1.221 0.2243
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7666 on 131 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2842, Adjusted R-squared: 0.235
## F-statistic: 5.778 on 9 and 131 DF, p-value: 9.531e-07
## decomposition all acitivity
#socio-econimic
QoLiv2_decomp1 <- lm(QoLIv2_allactivy ~
as.numeric(`Rate.change(prim.enr.per.capita.2023_2013)`) + #marginalni rast upisa u osnovne skole
as.numeric(pop.per.km2_2013_2014) +
as.numeric(`Share univ.degre`) +
as.numeric(Urban.Share)+
`share_65`+
as.numeric(`Gender.em.rate.gap(f-m)`),
data=map.Indexes_QoL)
summary (QoLiv2_decomp1)
##
## Call:
## lm(formula = QoLIv2_allactivy ~ as.numeric(`Rate.change(prim.enr.per.capita.2023_2013)`) +
## as.numeric(pop.per.km2_2013_2014) + as.numeric(`Share univ.degre`) +
## as.numeric(Urban.Share) + share_65 + as.numeric(`Gender.em.rate.gap(f-m)`),
## data = map.Indexes_QoL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.29657 -0.33773 -0.00948 0.35018 2.86086
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -9.331e-01 1.522e-01
## as.numeric(`Rate.change(prim.enr.per.capita.2023_2013)`) 9.492e-01 3.583e-01
## as.numeric(pop.per.km2_2013_2014) -9.464e-05 1.066e-04
## as.numeric(`Share univ.degre`) 1.175e+01 1.704e+00
## as.numeric(Urban.Share) 2.823e-01 2.751e-01
## share_65 6.600e-03 9.964e-02
## as.numeric(`Gender.em.rate.gap(f-m)`) -1.087e-02 3.492e-03
## t value Pr(>|t|)
## (Intercept) -6.130 9.09e-09 ***
## as.numeric(`Rate.change(prim.enr.per.capita.2023_2013)`) 2.649 0.00904 **
## as.numeric(pop.per.km2_2013_2014) -0.888 0.37600
## as.numeric(`Share univ.degre`) 6.898 1.86e-10 ***
## as.numeric(Urban.Share) 1.026 0.30670
## share_65 0.066 0.94729
## as.numeric(`Gender.em.rate.gap(f-m)`) -3.114 0.00226 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6192 on 135 degrees of freedom
## Multiple R-squared: 0.5191, Adjusted R-squared: 0.4977
## F-statistic: 24.29 on 6 and 135 DF, p-value: < 2.2e-16
QoLiv2_decomp1a <- lm(QoLIv2_allactivy ~
as.numeric(`Prim.enr.change.per.Pop2013`) + #diff u odnsou na br.stanovnika
as.numeric(`Share univ.degre`) +
as.numeric(Urban.Share)+
as.numeric(`Gender.em.rate.gap(f-m)`),
data=map.Indexes_QoL)
summary (QoLiv2_decomp1a)
##
## Call:
## lm(formula = QoLIv2_allactivy ~ as.numeric(Prim.enr.change.per.Pop2013) +
## as.numeric(`Share univ.degre`) + as.numeric(Urban.Share) +
## as.numeric(`Gender.em.rate.gap(f-m)`), data = map.Indexes_QoL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.98301 -0.37272 -0.00993 0.31673 2.98594
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.965779 0.171169 -5.642 9.26e-08
## as.numeric(Prim.enr.change.per.Pop2013) 6.147047 4.681580 1.313 0.19137
## as.numeric(`Share univ.degre`) 11.013655 1.583945 6.953 1.33e-10
## as.numeric(Urban.Share) 0.397992 0.273065 1.457 0.14727
## as.numeric(`Gender.em.rate.gap(f-m)`) -0.009704 0.003571 -2.717 0.00743
##
## (Intercept) ***
## as.numeric(Prim.enr.change.per.Pop2013)
## as.numeric(`Share univ.degre`) ***
## as.numeric(Urban.Share)
## as.numeric(`Gender.em.rate.gap(f-m)`) **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6277 on 137 degrees of freedom
## Multiple R-squared: 0.4985, Adjusted R-squared: 0.4839
## F-statistic: 34.05 on 4 and 137 DF, p-value: < 2.2e-16
#diversity
QoLiv2_decomp2 <- lm(QoLIv2_allactivy ~
Ratio_Munic.region+
factor (FBiH) +
factor (FBiH)*Ratio_Munic.region +
share_rtrn_abroad +
War_intesity +
`majority m=r(0=not)`+
log(route_m)+
log(Route_drive_minutes)+
Urban.Share,
data=map.Indexes_QoL)
summary(QoLiv2_decomp2)
##
## Call:
## lm(formula = QoLIv2_allactivy ~ Ratio_Munic.region + factor(FBiH) +
## factor(FBiH) * Ratio_Munic.region + share_rtrn_abroad + War_intesity +
## `majority m=r(0=not)` + log(route_m) + log(Route_drive_minutes) +
## Urban.Share, data = map.Indexes_QoL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4495 -0.4521 -0.0695 0.3444 4.8756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.82854 1.35711 0.611 0.5426
## Ratio_Munic.region 0.54562 0.40285 1.354 0.1779
## factor(FBiH)1 0.83758 0.60235 1.391 0.1667
## share_rtrn_abroad -1.02341 0.56113 -1.824 0.0705 .
## War_intesity 1.07526 3.07046 0.350 0.7268
## `majority m=r(0=not)` -0.18569 0.21530 -0.862 0.3900
## log(route_m) -0.16350 0.17432 -0.938 0.3500
## log(Route_drive_minutes) 0.02545 0.21192 0.120 0.9046
## Urban.Share 1.55100 0.26730 5.802 4.66e-08 ***
## Ratio_Munic.region:factor(FBiH)1 -0.78738 0.52333 -1.505 0.1348
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7657 on 131 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2861, Adjusted R-squared: 0.237
## F-statistic: 5.833 on 9 and 131 DF, p-value: 8.18e-07
##density charts of numeric variable
plot(density(map.Indexes_QoL$Average.totalpop2013_14))
plot(density(map.Indexes_QoL$`Rate.change(prim.enr.per.capita.2023_2013)`))
plot(density(log(map.Indexes_QoL$Average.totalpop2013_14)))
summary(map.Indexes_QoL)
## Munic Munic1 Region Entity
## Length:142 Length:142 Length:142 Length:142
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## FBiH QoLIv1_onlyempl QoLIv2_allactivy QoLIv3-Stat_adj wage
## Min. :0.0000 Min. :-1.83034 Min. :-1.87287 Min. :-1.53894
## 1st Qu.:0.0000 1st Qu.:-0.44319 1st Qu.:-0.48733 1st Qu.:-0.40159
## Median :1.0000 Median :-0.07984 Median :-0.09638 Median :-0.09818
## Mean :0.5532 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:1.0000 3rd Qu.: 0.33814 3rd Qu.: 0.35832 3rd Qu.: 0.24660
## Max. :1.0000 Max. : 6.15275 Max. : 6.15910 Max. : 7.69973
## NA's :1
## WI _onlyempl_HPI WI _allempl_HPI Stand.wi.ponder_onlyempl
## Min. :-1.37141 Min. :-1.3528 Min. :-1.3320
## 1st Qu.:-0.50450 1st Qu.:-0.4982 1st Qu.:-0.5874
## Median :-0.09722 Median :-0.1278 Median :-0.1651
## Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.25943 3rd Qu.: 0.2949 3rd Qu.: 0.3137
## Max. : 6.06975 Max. : 6.0761 Max. : 7.1753
##
## Stand.wi.ponder_all activ Stand.HPI Stand.TI
## Min. :-1.3417 Min. :-2.1201 Min. :-0.8687
## 1st Qu.:-0.5810 1st Qu.:-0.6519 1st Qu.:-0.6512
## Median :-0.1665 Median :-0.1488 Median :-0.3098
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.3309 3rd Qu.: 0.4697 3rd Qu.: 0.3271
## Max. : 7.1820 Max. : 3.7341 Max. : 5.6921
##
## emplrate2014 Average.totalpop2013_14 Average.totalpop2023_24
## Min. :-0.6683 Min. : 69.5 Min. : 105.5
## 1st Qu.: 0.1710 1st Qu.: 6687.1 1st Qu.: 5728.5
## Median : 0.2305 Median : 16324.0 Median : 14967.2
## Mean : 0.2377 Mean : 24545.3 Mean : 23606.7
## 3rd Qu.: 0.2997 3rd Qu.: 31123.8 3rd Qu.: 29688.2
## Max. : 1.0861 Max. :180508.5 Max. :185430.5
##
## Povrsina_km2 pop.per.km2_2013_2014 pop.per.km2_2023_2024
## Min. : 9.9 Min. : 0.924 Min. : 1.403
## 1st Qu.: 150.7 1st Qu.: 22.480 1st Qu.: 19.855
## Median : 295.9 Median : 56.010 Median : 50.716
## Mean : 359.7 Mean : 161.557 Mean : 157.393
## 3rd Qu.: 499.8 3rd Qu.: 117.208 3rd Qu.: 107.195
## Max. :1238.9 Max. :6542.828 Max. :6331.515
##
## Rate.change.pop.per.km2 Prim.enr.change.per.Pop2013
## Min. :-0.43239 Min. :-0.053695
## 1st Qu.: 0.02735 1st Qu.:-0.023823
## Median : 0.06566 Median :-0.017315
## Mean : 0.06494 Mean :-0.016511
## 3rd Qu.: 0.09986 3rd Qu.:-0.008582
## Max. : 0.38818 Max. : 0.027677
##
## Rate.change(prim.enr.per.capita.2023_2013) rate.change.prim.enrolment
## Min. :-0.49690 Min. :-0.5591
## 1st Qu.:-0.25547 1st Qu.:-0.3127
## Median :-0.15726 Median :-0.1981
## Mean :-0.16319 Mean :-0.2109
## 3rd Qu.:-0.07007 3rd Qu.:-0.1148
## Max. : 0.25851 Max. : 0.3127
##
## share_65 Share univ.degre Share no primary education
## Min. :0.07416 Min. :0.01060 Min. :0.03777
## 1st Qu.:0.12702 1st Qu.:0.05223 1st Qu.:0.13050
## Median :0.16126 Median :0.06452 Median :0.16047
## Mean :0.21152 Mean :0.07696 Mean :0.16541
## 3rd Qu.:0.19843 3rd Qu.:0.08904 3rd Qu.:0.19236
## Max. :6.37641 Max. :0.32029 Max. :0.44853
##
## Urban.Share Diversity Index-ethnicity DI_ethnic_region
## Min. :0.000001 Min. :0.3418 Min. :0.3418
## 1st Qu.:0.114391 1st Qu.:0.5991 1st Qu.:0.6846
## Median :0.263319 Median :0.8044 Median :0.6846
## Mean :0.297650 Mean :0.7561 Mean :0.6760
## 3rd Qu.:0.435802 3rd Qu.:0.9091 3rd Qu.:0.7025
## Max. :0.993834 Max. :0.9963 Max. :0.9755
##
## Ratio_Munic.region DI_ethnicity_country Diversity Index-religion
## Min. :0.5146 Min. :0.3699 Min. :0.2257
## 1st Qu.:0.9616 1st Qu.:0.3699 1st Qu.:0.6024
## Median :1.1334 Median :0.3699 Median :0.8099
## Mean :1.1340 Mean :0.3699 Mean :0.7592
## 3rd Qu.:1.3013 3rd Qu.:0.3699 3rd Qu.:0.9145
## Max. :2.1325 Max. :0.3699 Max. :0.9951
##
## Diversity Index-mother language War_intesity
## Min. :0.3459 Min. :0.001459
## 1st Qu.:0.6174 1st Qu.:0.012094
## Median :0.8470 Median :0.018531
## Mean :0.7826 Mean :0.023415
## 3rd Qu.:0.9360 3rd Qu.:0.025094
## Max. :0.9962 Max. :0.197540
##
## Gender.gap.per.working age_2013 Gender.em.rate.gap(f-m) majority m=r(0=not)
## Min. :-0.92683 Min. :-168.142 Min. :0.0000
## 1st Qu.:-0.07076 1st Qu.: -13.423 1st Qu.:1.0000
## Median :-0.03887 Median : -8.030 Median :1.0000
## Mean :-0.06206 Mean : -10.432 Mean :0.8732
## 3rd Qu.:-0.02606 3rd Qu.: -4.135 3rd Qu.:1.0000
## Max. : 0.02588 Max. : 4.069 Max. :1.0000
##
## majority m=r(0=not,2=nomajority) Nearest_munic_other_entity route_m
## Min. :0.0000 Length:142 Min. : 4600
## 1st Qu.:1.0000 Class :character 1st Qu.: 15000
## Median :1.0000 Mode :character Median : 31500
## Mean :0.9437 Mean : 34917
## 3rd Qu.:1.0000 3rd Qu.: 50250
## Max. :2.0000 Max. :140000
##
## Route_drive_minutes rtrn_abrd share_rtrn_abroad
## Min. : 7.00 Min. : 12 Min. :0.009905
## 1st Qu.: 20.00 1st Qu.: 634 1st Qu.:0.053424
## Median : 38.00 Median : 1774 Median :0.099612
## Mean : 41.75 Mean : 3179 Mean :0.147327
## 3rd Qu.: 57.75 3rd Qu.: 3770 3rd Qu.:0.185676
## Max. :150.00 Max. :25470 Max. :0.683855
##
## Scaled_Stand.wi.ponder_onlyempl Scaled_Stand.wi.ponder_all activ
## Min. :-1.0000 Min. :-1.0000
## 1st Qu.:-0.8250 1st Qu.:-0.8212
## Median :-0.7257 Median :-0.7237
## Mean :-0.6869 Mean :-0.6846
## 3rd Qu.:-0.6131 3rd Qu.:-0.6068
## Max. : 1.0000 Max. : 1.0039
##
## Scaled_Stand.HPI Scaled_Stand.TI Scaled_Stand.Adj.wage_ponder2
## Min. :-1.0000 Min. :-1.0000 Min. :-1.0000
## 1st Qu.:-0.4984 1st Qu.:-0.9337 1st Qu.:-0.8726
## Median :-0.3265 Median :-0.8296 Median :-0.8022
## Mean :-0.2757 Mean :-0.7352 Mean :-0.7662
## 3rd Qu.:-0.1152 3rd Qu.:-0.6354 3rd Qu.:-0.7097
## Max. : 1.0000 Max. : 1.0000 Max. : 1.0000
##
## hpi_wage1 hpi_wage2 shapeISO shapeID
## Min. :-408.9546 Min. :-1260.9845 Length:142 Length:142
## 1st Qu.: -0.7481 1st Qu.: -0.7933 Class :character Class :character
## Median : 0.4229 Median : 0.3799 Mode :character Mode :character
## Mean : -2.7334 Mean : -8.6714
## 3rd Qu.: 1.4637 3rd Qu.: 1.4483
## Max. : 118.4704 Max. : 53.6216
##
## shapeGroup shapeType geometry
## Length:142 Length:142 MULTIPOLYGON :142
## Class :character Class :character epsg:4326 : 0
## Mode :character Mode :character +proj=long...: 0
##
##
##
##
## cut.Scaled_Stand.TI1 cut.Scaled.Stand.wi.all.activity cut.Scaled_Stand.HPI
## [-1.1,-0.9]:52 (-0.9,-0.7]:66 (-0.5,-0.3]:44
## (-0.9,-0.7]:48 (-0.7,-0.5]:40 (-0.3,-0.1]:29
## (-0.7,-0.5]:21 [-1.1,-0.9]:14 (-0.7,-0.5]:28
## (-0.5,-0.3]:11 (-0.5,-0.3]:14 (-0.1,0.1] :16
## (-0.1,0.1] : 5 (-0.3,-0.1]: 6 (0.1,0.3] :10
## (-0.3,-0.1]: 2 (-0.1,0.1] : 1 (-0.9,-0.7]: 4
## (Other) : 3 (Other) : 1 (Other) :11
## cut.emplrate2014 cut.QoLIv1_onlyempl cut.QoLIv2_allactivy
## (0.15,0.25] :51 (-0.15,0.15] :31 (-0.45,-0.15]:28
## (0.25,0.35] :44 (-0.45,-0.15]:25 (-0.15,0.15] :27
## (0.05,0.15] :25 (0.15,0.45] :22 (0.15,0.45] :20
## (0.35,0.45] :14 (-0.75,-0.45]:14 (-0.75,-0.45]:18
## (0.45,0.55] : 3 (-1.05,-0.75]:11 (0.45,0.75] :13
## (-0.05,0.05]: 2 (0.45,0.75] :10 (-1.05,-0.75]:10
## (Other) : 3 (Other) :29 (Other) :26