library(haven)

HBS_2015_HH_short <- read_sav("~/HBS database inspecting/HBS_2015_HH - short.sav")
setwd("C:/Users/Amra/Documents/HBS database inspecting")

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

hbs_total.income <- HBS_2015_HH_short  %>% select(contains(c( "SifraDom","Entity","Canton","naziv_Opcine","RurUrb","ncomp","finweight","HH_Monthly", "COIC", "_S2", "_S6", "_DA_","_S11", "_S12")))
#_S2 sam stavila radi izracuna potrosnje na stanovanje od ukupnog incoma i od labor incoma

DISPOSABLE INCOME - TOTAL HOUSEHOLD

_S11 odnosi se samo na pitanja da li je odredjeni clan domacinstva koristio penziju ili ne a ne i na iznos

Npr kod QA01_YN_09_S12 QA01 se odnosi na pitanje za prvu zaradu, YN se odnosi na Yes No tj, da li je bilo _01_se odnosi na prvog clana domacinstva a _S12 na modul income.

FORMIRAM INCOME ZA SVAKOG CLANA DOMACINSTVA (SVE OSIM SOCIAL CONTRIBUTIONS AND BENEFITS))

2nd household

Mjesecno sva primanja za drugog householda

3rd household

Mjesecno sva primanja za treci householda

4th household

Mjesecno sva primanja za cetvrtog clana householda

mjesecno all wage for 5th household

Mjesecno sva primanja za petog householda

Mjesecno all wage for 6th household

Mjesecno sva primanja za sesti householda

mjesecno all wage for 7th household

Mjesecno sva primanja za sedmog householda

mjesecno all wage for 8th household

Mjesecno sva primanja za osmog householda

mjesecno all wage for 9th household

Mjesecno sva primanja za devetog householda

###Formiraj varijablu all income per hh no soc. uzima income za QA01_01 - QA23_09 for nine members of households

hbs_total.income$Inc_HH_all_mj <- rowSums (hbs_total.income[,c("QA_01_all_mj","QA_02_all_mj","QA_03_all_mj", "QA_04_all_mj", "QA_05_all_mj", "QA_06_all_mj", "QA_07_all_mj", "QA_08_all_mj", "QA_09_all_mj")],na.rm = T)
hbs_total.income$Inc_HH_all_mj <- as.numeric(hbs_total.income$Inc_HH_all_mj)
summary(hbs_total.income$Inc_HH_all_mj)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0     0.0   208.3   558.8   850.0 18350.0

str(hbs_total.income$Inc_HH_all_mj)

##  num [1:7702] 0 125 0 225 350 100 0 800 0 0 ...

plot(density(hbs_total.income$Inc_HH_all_mj))

Social contributions and other social benefits

U koji ulayi, dodatci, penzije, ..i slicno

QB01_Y1_01_S12 Were you eligible for old-age pension in the last 12 months? QB01_IN_01_S12 What was the last monthly amount you received?
QB01_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB02_Y1_01_S12 Were you eligible for family survivor pension in the last 12 months? Have you received pension in last 12 months?
QB02_IN_01_S12 What was the last monthly amount you received?
QB02_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB03_Y1_01_S12 Were you eligible for work disability pension in the last 12 months?
QB03_Y2_01_S12 Have you received pension in last 12 months?
QB03_IN_01_S12 What was the last monthly amount you received?
QB03_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB04_Y1_01_S12 Were you eligible for pension from abroad in the last 12 months?
QB04_Y2_01_S12 Have you received pension in last 12 months?
QB04_IN_01_S12 What was the last monthly amount you received?
QB04_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB05_Y1_01_S12 Were you eligible for veterans’ pension in the last 12 months? QB05_Y2_01_S12 Have you received pension in last 12 months?
QB05_IN_01_S12 What was the last monthly amount you received?
QB05_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB06_Y1_01_S12 Were you eligible for personal disability benefit in the last 12 months?
QB06_Y2_01_S12 Have you received pension in last 12 months?
QB06_IN_01_S12 What was the last monthly amount you received?
QB06_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB07_Y1_01_S12 Were you eligible for long-term care and support benefit in the last 12 months?
QB07_Y2_01_S12 Have you received pension in last 12 months?
QB07_IN_01_S12 What was the last monthly amount you received?
QB07_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB08_Y1_01_S12 Were you eligible for orthopaedic benefit in the last 12 months?
QB08_Y2_01_S12 Have you received pension in last 12 months?
QB08_IN_01_S12 What was the last monthly amount you received?
QB08_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB09_Y1_01_S12 Were you eligible for survivor dependent benefit in the last 12 months? QB09_Y2_01_S12 Have you received pension in last 12 months?
QB09_IN_01_S12 What was the last monthly amount you received?
QB09_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB10_Y1_01_S12 Were you eligible for veteran’s allowance in the last 12 months?
QB10_Y2_01_S12 Have you received pension in last 12 months?
QB10_IN_01_S12 What was the last monthly amount you received?
QB10_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB11_Y1_01_S12 Were you eligible for personal disability benefit in the last 12 months?
QB11_Y2_01_S12 Have you received pension in last 12 months?
QB11_IN_01_S12 What was the last monthly amount you received?
QB11_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB12_Y1_01_S12 Were you eligible for long-term care and support benefit in the last 12 months? QB12_Y2_01_S12 Have you received pension in last 12 months?
QB12_IN_01_S12 What was the last monthly amount you received?
QB12_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB13_Y1_01_S12 Were you eligible for orthopaedic benefit in the last 12 months?
QB13_Y2_01_S12 Have you received pension in last 12 months?
QB13_IN_01_S12 What was the last monthly amount you received?
QB13_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB14_Y1_01_S12 Were you eligible for survivor dependent benefit in the last 12 months? QB14_Y2_01_S12 Have you received pension in last 12 months?
QB14_IN_01_S12 What was the last monthly amount you received?
QB14_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB15_Y1_01_S12 Were you eligible for monthly personal cash benefit in the last 12 months?
QB15_Y2_01_S12 Have you received pension in last 12 months?
QB15_IN_01_S12 What was the last monthly amount you received?
QB15_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB16_Y1_01_S12 Were you eligible for allowance for family members unable to work in the last 12 months?
QB16_Y2_01_S12 Have you received pension in last 12 months?
QB16_IN_01_S12 What was the last monthly amount you received?
QB16_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB17_Y1_01_S12 Were you eligible for allowance for single parents in the last 12 months?
QB17_Y2_01_S12 Have you received pension in last 12 months?
QB17_IN_01_S12 What was the last monthly amount you received?
QB17_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB18_Y1_01_S12 Were you eligible for personal disability benefit (non-war PWDs) in the last 12 months?
QB18_Y2_01_S12 Have you received pension in last 12 months?
QB18_IN_01_S12 What was the last monthly amount you received?
QB18_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB19_Y1_01_S12 Were you eligible for long-term care and support benefit in the last 12 months?
QB19_Y2_01_S12 Have you received pension in last 12 months?
QB19_IN_01_S12 What was the last monthly amount you received?
QB19_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB20_Y1_01_S12 Were you eligible for orthopaedic benefit in the last 12 months?
QB20_Y2_01_S12 Have you received pension in last 12 months?
QB20_IN_01_S12 What was the last monthly amount you received?
QB20_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB21_Y1_01_S12 Were you eligible for child-care allowance in the last 12 months?
QB21_Y2_01_S12 Have you received pension in last 12 months?
QB21_IN_01_S12 What was the last monthly amount you received?
QB21_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB22_Y1_01_S12 Were you eligible for maternity benefit in the last 12 months?
QB22_Y2_01_S12 Have you received pension in last 12 months?
QB22_IN_01_S12 What was the last monthly amount you received?
QB22_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB23_Y1_01_S12 Were you eligible for baby packages in the last 12 months?
QB23_Y2_01_S12 Have you received benefit in last 12 months?
QB23_IN_01_S12 What was the last monthly amount you received?
QB23_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB24_Y1_01_S12 Were you eligible for cash benefit for unemployed in the last 12 months?
QB24_Y2_01_S12 Have you received benefit in last 12 months?
QB24_IN_01_S12 What was the last monthly amount you received?
QB24_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB25_Y1_01_S12 Were you eligible for demobilised soldier’s unemployment benefit in the last 12 months?
QB25_Y2_01_S12 Have you received benefit in last 12 months?
QB25_IN_01_S12 What was the last monthly amount you received?
QB25_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB26_Y1_01_S12 Were you eligible for permanent financial assistance in the last 12 months?
QB26_Y2_01_S12 Have you received pension in last 12 months?
QB26_IN_01_S12 What was the last monthly amount you received?
QB26_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB27_Y1_01_S12 Were you eligible for temporary, one-off and other financial assistance (government) n in the last 12 months?
QB27_Y2_01_S12 Have you received pension in last 12 months?
QB27_IN_01_S12 What was the last monthly amount you received?
QB27_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB28_Y1_01_S12 Were you eligible for subsidies for accommodation (rent), heating and funerals in the last 12 months?
QB28_Y2_01_S12 Have you received subsidies in last 12 months?
QB28_IN_01_S12 What was the last monthly amount you received?
QB28_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB29_Y1_01_S12 Were you eligible for assistance for gaining qualifications in the last 12 months?
QB29_Y2_01_S12 Have you received assistance in last 12 months?
QB29_IN_01_S12 What was the last monthly amount you received?
QB29_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB30_Y1_01_S12 Were you eligible for scholarship in the last 12 months?
QB30_Y2_01_S12 Have you received scholarship in last 12 months? { QB30_IN_01_S12 What was the last monthly amount you received?
QB30_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB31_Y1_01_S12 Were you eligible for other pension in the last 12 months?
QB31_Y2_01_S12 Have you received pension in last 12 months?
QB31_IN_01_S12 What was the last monthly amount you received?
QB31_NM_01_S12 What is the number of pensions/benefits you have received over the last 12 months?
QB31_DS_01_S12 Describe other pension

SADA FORMIRAM SOC.BENEFIT ZA SVAKOG CLANA DOMACINSTVA ZA INCOME

mjesecno all soc. benefit for 3rd household

Mjesecno svi soc benefiti za treceg householda

mjesecno all soc. benefit for 4th household

Mjesecno svi soc benefiti za cetvrtog householda

mjesecno all soc. benefit for 5th household

Mjesecno svi soc benefiti za petog householda

mjesecno all soc. benefit for 6th household

Mjesecno svi soc benefiti za sestog householda

mjesecno all soc. benefit for 7th household

Mjesecno svi soc benefiti za sedmog householda

mjesecno all soc. benefit for 8th household

Mjesecno svi soc benefiti za osmog householda

mjesecno all soc. benefit for 9th household

Mjesecno svi soc benefiti za devetog householda

mjesecno all soc. benefit for 10th household

Mjesecno svi soc benefiti za desetog householda

###Formiraj varijablu all income per hh no soc.

0 pretvoriti u NA

#Prvo za Income
hbs_total.income$Inc_HH_all_mj <- ifelse(hbs_total.income$Inc_HH_all_mj==0,NA,paste(hbs_total.income$Inc_HH_all_mj))
summary(hbs_total.income$Inc_HH_all_mj)

##    Length     Class      Mode 
##      7702 character character

hbs_total.income$Inc_HH_all_mj <- as.numeric(hbs_total.income$Inc_HH_all_mj)
summary(hbs_total.income$Inc_HH_all_mj)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
##     0.083   349.167   700.000   931.852  1233.333 18350.000      3083

##3083 Na-ova
#Pa za contributions 
hbs_total.income$Soc.Ben_HH_all_mj <- ifelse(hbs_total.income$Soc.Ben_HH_all_mj==0,NA,paste(hbs_total.income$Soc.Ben_HH_all_mj))
summary(hbs_total.income$Soc.Ben_HH_all_mj)

##    Length     Class      Mode 
##      7702 character character

hbs_total.income$Soc.Ben_HH_all_mj <- as.numeric(hbs_total.income$Soc.Ben_HH_all_mj)
summary(hbs_total.income$Soc.Ben_HH_all_mj)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##    0.333  270.000  330.000  435.042  510.000 5000.000     3233

# 3233 NAs

Prije formiranja varijable total.revenu brisem percentiles za Inc_HH_all_mj i Soc.Ben_HH_all_mj

Ovo radimo iz razloga sto kad sam brisale percentile na kraju dobila sam da su mi u nekim opstinama wages vece od total.revenue i to nema smisla. Pa cu probati ovako. Napomena: Mozda se to desilo i radi toga sto su bile 0 umjesto NA. Probala sam to sve da uradim i ni to mi nije pomoglo.. dobila sam jos vecu korist za wages.. tako da taj nacin ne moze

Formiraj varijablu - total.revenue -

hbs_total.income$total.revenue <- rowSums(hbs_total.income[,c("Inc_HH_all_mj","Soc.Ben_HH_all_mj")],na.rm = T)
hbs_total.income$total.revenue <- as.numeric(hbs_total.income$total.revenue)
summary(hbs_total.income$total.revenue)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0   320.0   580.0   811.3  1079.8 18350.0

plot(density(hbs_total.income$total.revenue))

hbs_total.income1 <- hbs_total.income #kloniaj bazu

summary(hbs_total.income1$HH_Monthly_Consumption_Equilized) #pregled ukupne potrosnje domacinstava

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##    39.29   451.58   642.10   756.04   923.65 13409.12

summary(hbs_total.income1$Inc_HH_all_mj) #pregled ukupnog income domacinstava (no soc. contributions, soc.benefits, scholarships..etc)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
##     0.083   349.167   700.000   931.852  1233.333 18350.000      3083

summary(hbs_total.income1$Soc.Ben_HH_all_mj) #pregled total soc. contributions, soc.benefits, scholarships..etc

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##    0.333  270.000  330.000  435.042  510.000 5000.000     3233

summary(hbs_total.income1$total.revenue) #pregled ukupnih primanja domacinstava

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0   320.0   580.0   811.3  1079.8 18350.0

nule prebaciti u NA kod total.revenue

hbs_total.income1$total.revenue <- ifelse(hbs_total.income1$total.revenue==0,NA,paste(hbs_total.income1$total.revenue))
summary(hbs_total.income1$total.revenue)

##    Length     Class      Mode 
##      7702 character character

hbs_total.income1$total.revenue <- as.numeric(hbs_total.income1$total.revenue)
summary(hbs_total.income1$total.revenue)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
##     0.083   345.000   641.667   889.330  1150.000 18350.000       676

#676 Na-ova

WAGE

1.IZRACUNAM SAMO WAGES (KOJI SAM KORISITILA U WAGE INDEX)

hbs_total.income1$wage.hh.mj <- rowSums(hbs_total.income1[,c("QA01_01_mj","QA02_01_mj","QA03_01_mj","QA04_01_mj","QA05_01_mj","QA06_01_mj", "QA07_01_mj", "QA08_01_mj", "QA09_01_mj", "QA10_01_mj", "QA11_01_mj","QA12_01_mj","QA13_01_mj", "QA14_01_mj", "QA15_01_mj", "QA23_01_mj", "QA01_02_mj","QA02_02_mj","QA03_02_mj","QA04_02_mj","QA05_02_mj","QA06_02_mj", "QA07_02_mj", "QA08_02_mj", "QA09_02_mj", "QA10_02_mj", "QA11_02_mj","QA12_02_mj","QA13_02_mj", "QA14_02_mj", "QA15_02_mj", "QA23_02_mj","QA01_03_mj","QA02_03_mj","QA03_03_mj","QA04_03_mj","QA05_03_mj","QA06_03_mj", "QA07_03_mj", "QA08_03_mj", "QA09_03_mj", "QA10_03_mj", "QA11_03_mj","QA12_03_mj","QA13_03_mj", "QA14_03_mj", "QA15_03_mj", "QA23_03_mj", "QA01_04_mj","QA02_04_mj","QA03_04_mj","QA04_04_mj","QA05_04_mj","QA06_04_mj", "QA07_04_mj", "QA08_04_mj", "QA09_04_mj", "QA10_04_mj", "QA11_04_mj","QA12_04_mj","QA13_04_mj", "QA14_04_mj", "QA15_04_mj", "QA23_04_mj","QA01_05_mj","QA02_05_mj","QA03_05_mj","QA04_05_mj","QA05_05_mj","QA06_05_mj", "QA07_05_mj", "QA08_05_mj", "QA09_05_mj", "QA10_05_mj", "QA11_05_mj","QA12_05_mj","QA13_05_mj", "QA14_05_mj", "QA15_05_mj", "QA23_05_mj","QA01_06_mj","QA02_06_mj","QA03_06_mj","QA04_06_mj","QA05_06_mj","QA06_06_mj", "QA07_06_mj", "QA08_06_mj", "QA09_06_mj", "QA10_06_mj", "QA11_06_mj","QA12_06_mj","QA13_06_mj", "QA14_06_mj", "QA15_06_mj", "QA23_06_mj","QA01_07_mj","QA02_07_mj","QA03_07_mj","QA04_07_mj","QA05_07_mj","QA06_07_mj", "QA07_07_mj", "QA08_07_mj", "QA09_07_mj", "QA10_07_mj", "QA11_07_mj","QA12_07_mj","QA13_07_mj", "QA14_07_mj", "QA15_07_mj", "QA23_07_mj","QA01_08_mj","QA02_08_mj","QA03_08_mj","QA04_08_mj","QA05_08_mj","QA06_08_mj", "QA07_08_mj", "QA08_08_mj", "QA09_08_mj", "QA10_08_mj", "QA11_08_mj","QA12_08_mj","QA13_08_mj", "QA14_08_mj", "QA15_08_mj", "QA23_08_mj","QA01_09_mj","QA02_09_mj","QA03_09_mj","QA04_09_mj","QA05_09_mj","QA06_09_mj", "QA07_09_mj", "QA08_09_mj", "QA09_09_mj", "QA10_09_mj", "QA11_09_mj","QA12_09_mj","QA13_09_mj", "QA14_09_mj", "QA15_09_mj", "QA23_09_mj")],na.rm = T)

summary(hbs_total.income1$wage.hh.mj)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0     0.0   125.0   535.1   830.0 18350.0

Nule prebaci u NA kod wage

hbs_total.income1$wage.hh.mj <- ifelse (hbs_total.income1$wage.hh.mj==0, NA,paste(hbs_total.income1$wage.hh.mj))
summary(hbs_total.income1$wage.hh.mj)

##    Length     Class      Mode 
##      7702 character character

hbs_total.income1$wage.hh.mj <- as.numeric(hbs_total.income1$wage.hh.mj)
summary(hbs_total.income1$wage.hh.mj)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
##     0.083   416.667   800.000   993.416  1300.000 18350.000      3553

# 3.553 Na-ova

Vidi po Opsitinama koliko je Naova u wages

tapply (hbs_total.income1$wage.hh.mj, hbs_total.income1$naziv_Opcine,summary, na.rm=T)

## $B.Grahovo
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     350     600     730    1173    1650    2535      10 
## 
## $B.Krupa
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    25.0   300.0   465.0   708.9   941.5  2500.0      14 
## 
## $B.Petrovac
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   20.83  100.00  416.67  632.83 1000.00 2083.33       5 
## 
## $Banovici
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    50.0   475.0   991.7   910.6  1271.2  1975.0      10 
## 
## $`Banja Luka`
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##    0.083  600.000  826.250 1130.024 1400.000 5000.000      202 
## 
## $Berkovici
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   625.0   768.8   912.5   912.5  1056.2  1200.0       8 
## 
## $Bihac
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    10.0   250.0   679.2   861.2  1103.8  4580.0      49 
## 
## $Bijeljina
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##      25     320     673    1021    1280    8333      51 
## 
## $Bileca
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   66.67  242.50  570.00  797.17 1267.50 1800.00       8 
## 
## $Bratunac
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   100.0   440.0   675.0   892.5  1290.0  2270.0       8 
## 
## $Brcko
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    25.0   355.0   633.3   886.3  1068.3  7200.0     185 
## 
## $Breza
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   66.67 1010.00 1100.00 1188.21 1450.00 2060.00      10 
## 
## $Brod
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    25.0   127.1   604.2   668.9  1026.7  1929.2       8 
## 
## $Bugojno
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   59.17  350.00  633.33  815.09 1113.33 2500.00      28 
## 
## $Busovaca
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   125.0   389.2   950.0   942.5  1217.5  2200.0      28 
## 
## $Buzim
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    25.0   174.0   400.0   511.5   697.5  1391.7       8 
## 
## $C.Sarajevo
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   133.3   860.0  1200.0  2124.6  2890.0 18350.0      54 
## 
## $Cajnice
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.67  416.67  600.00  704.49  670.00 1970.00       6 
## 
## $Capljina
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   233.3   777.1  1102.1  1198.7  1600.0  3550.0      18 
## 
## $Cazin
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   33.33  300.00  525.00  726.63  932.92 3500.00      31 
## 
## $Celic
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     100     100     250     500     550    1500      12 
## 
## $Celinac
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    75.0   166.7   450.0   533.6   600.0  1865.3      26 
## 
## $Citluk
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   58.33  350.00 1300.00 1455.32 1950.00 4020.00      11 
## 
## $`D-Samac`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     600    1012    1175    1105    1268    1470       8 
## 
## $D.Vakuf
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    50.0   387.5   526.7   654.0   887.5  1777.2      12 
## 
## $Derventa
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    15.0   156.2   400.0   558.6   904.2  1812.5      14 
## 
## $Doboj
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    12.5   250.0   612.5   822.5  1233.8  3400.0      56 
## 
## $`Doboj-Istok`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   305.0   355.0   660.0   801.7  1218.3  1500.0       9 
## 
## $`Doboj-Jug`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   291.7   588.3   820.0   772.6   960.0  1200.0       3 
## 
## $Dobretici
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##      NA      NA      NA     NaN      NA      NA       1 
## 
## $`Donji Zabar`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   600.0   650.0   700.0   766.7   850.0  1000.0       1 
## 
## $Drvar
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   150.0   400.0   550.0   792.5   937.5  2500.0      11 
## 
## $Foca
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##      NA      NA      NA     NaN      NA      NA       2 
## 
## $`Foca - RS`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   33.33  283.33  850.00  903.05 1470.00 2050.00      25 
## 
## $Fojnica
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   400.0   500.0   716.7   813.4  1000.0  1804.0      13 
## 
## $G.Vakuf
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   175.0   408.3   724.0   782.3  1062.5  1875.0       8 
## 
## $Gacko
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   400.0   557.5  1000.0  1217.6  1875.0  2650.0      13 
## 
## $Glamoc
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   200.0   537.5   715.0   648.0   800.0  1100.0       8 
## 
## $Gorazde
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   250.0   583.1   800.0   982.2  1260.4  2268.0      20 
## 
## $Gracanica
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.67  358.33  556.00  676.21  783.50 2075.00      67 
## 
## $`Grad Mostar`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   41.67  753.75 1158.33 1439.67 1832.50 5453.00     152 
## 
## $Gradacac
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   66.67  494.58  950.00  910.47 1277.08 2166.67      14 
## 
## $Gradiska
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   108.0   360.0   650.0   639.9   918.8  1170.0      46 
## 
## $Grude
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   275.0   843.8  1275.0  1496.9  1895.0  3800.0      14 
## 
## $Hadzici
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    10.0   100.0   350.0   490.2   758.0  1450.0      12 
## 
## $`Han Pijesak`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   175.0   475.0   750.0   800.7  1085.0  1560.0       5 
## 
## $I.Ilidza
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   350.0   450.0   790.0   924.6  1140.0  2200.0      11 
## 
## $I.N.Sarajevo
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##      NA      NA      NA     NaN      NA      NA       5 
## 
## $`I.Stari Grad`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##      NA      NA      NA     NaN      NA      NA       1 
## 
## $Ilidza
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   229.2   740.0  1251.5  1446.3  1704.4  5890.7      25 
## 
## $Ilijas
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   116.7   471.7   650.0   733.4   962.5  1604.2      25 
## 
## $Jablanica
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   100.0   287.5   517.5   508.8   738.8   900.0       3 
## 
## $Jajce
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    50.0   335.0   725.0   887.3  1162.5  2494.0      31 
## 
## $Jezero
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     100     100     100     100     100     100       1 
## 
## $K.Dubica
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   308.3   407.5   450.0   800.6  1095.0  1940.0      60 
## 
## $Kakanj
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    12.5   375.0   762.5   951.4  1258.3  5000.0      86 
## 
## $Kalesija
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    55.0   278.5   495.0   734.6   999.5  2647.0      13 
## 
## $Kalinovik
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   796.7  1065.0  1333.3  1333.3  1601.7  1870.0 
## 
## $Kiseljak
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     100     575     885    1009    1425    2000      32 
## 
## $Kladanj
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   41.67  262.50  569.33  651.41  875.00 1780.00       4 
## 
## $Kljuc
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    50.0   675.0   920.0   857.7  1212.5  1522.5      26 
## 
## $Knezevo
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    37.5   325.6   620.8   708.1  1079.4  1570.0      16 
## 
## $Konjic
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    50.0   450.0   753.3  1059.5  1633.3  3200.0      14 
## 
## $Kostajnica
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   200.0   300.0   465.0   465.0   517.5   880.0      10 
## 
## $`Kotor Varos`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.67  289.17  580.00  565.01  822.50 1170.00      19 
## 
## $Kresevo
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   233.3   775.0  1066.7  1219.6  1770.0  2400.0      11 
## 
## $`Krupa na Uni`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   733.3   733.3   733.3   733.3   733.3   733.3       3 
## 
## $Kupres
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   166.7   605.0  1326.7  1409.8  1833.3  3550.0       6 
## 
## $Laktasi
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     140     500     850    1023    1472    2080      52 
## 
## $Livno
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   291.7   765.0  1150.0  1391.7  1758.3  3300.0      27 
## 
## $Lopare
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    25.0   300.0   585.0   796.7  1116.2  2500.0      10 
## 
## $Lukavac
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   86.67  475.00  840.00  896.32 1337.00 2500.00      63 
## 
## $Ljubinje
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   66.67   66.67   66.67   66.67   66.67   66.67       1 
## 
## $Ljubuski
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##    33.33  1400.00  2000.00  2210.92  2500.00 12000.00       21 
## 
## $Maglaj
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   41.67  337.50  675.00  794.25 1000.00 3100.00      35 
## 
## $Milici
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   133.3   325.0   490.0   896.2  1000.0  3000.0      10 
## 
## $Modrica
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   41.67  210.42  701.67  788.40 1050.00 2725.00      18 
## 
## $Mrkonjic
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   400.0   675.0   800.0  1025.0   937.5  3050.0      12 
## 
## $N.G.Sarajevo
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    25.0   727.6  1178.2  1360.3  1921.0  5750.0     120 
## 
## $N.Gorazde
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    44.5   249.0   605.8   825.0   787.4  3703.3       2 
## 
## $N.Sarajevo
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    20.0   832.5  1593.0  1841.2  2516.7  5409.9      58 
## 
## $N.Travnik
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   100.0   451.2   800.0   838.5  1055.0  3550.0      14 
## 
## $Neum
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   180.0   347.5   515.0   515.0   682.5   850.0       6 
## 
## $Nevesinje
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   370.0   400.0   500.0   825.5  1175.0  2000.0      15 
## 
## $`Novi Grad`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   33.33  352.50  600.00  863.64 1225.00 5000.00      28 
## 
## $Odzak
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     755    1138    1625    1766    2142    3300      12 
## 
## $Olovo
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   96.67  285.42  888.33 1135.83 1497.50 3400.00       5 
## 
## $Orasje
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   83.33  800.00 1800.00 1803.65 2625.00 4440.00      32 
## 
## $Osmaci
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##      75     125     175     175     225     275       2 
## 
## $`Ostra Luka`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   166.7   322.9   416.7   428.8   522.5   715.0      12 
## 
## $Pale
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   275.0   450.0   715.0   911.2  1010.0  2500.0      24 
## 
## $`Pale-FBiH`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##      NA      NA      NA     NaN      NA      NA       1 
## 
## $Pelagicevo
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   33.33  575.00  800.00 1080.56 1775.00 2250.00       7 
## 
## $Petrovo
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   116.7   379.2   618.3   572.7   811.9   937.5       5 
## 
## $Posusje
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   466.7   775.0  1000.0  1481.8  2155.0  3800.0       8 
## 
## $Prijedor
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   23.33  500.00  765.42  812.03 1034.79 1961.67     140 
## 
## $Prnjavor
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.67  150.00  600.00  839.69 1073.33 5500.00      38 
## 
## $Prozor
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   125.0   701.5   806.2  1170.7  1476.7  3650.0      11 
## 
## $Ravno
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    1225    1225    1225    1225    1225    1225       2 
## 
## $Ribnik
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   450.0   608.3   700.0   889.4   850.0  1736.7      19 
## 
## $Rogatica
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    50.0   587.5  1400.0  1457.7  1710.0  5390.0      21 
## 
## $Rudo
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##    8.333  421.250  789.167  820.754  877.500 2791.667        5 
## 
## $S.Brijeg
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   333.3  1225.0  1666.2  1796.9  2475.0  4000.0      18 
## 
## $S.G.Sarajevo
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   300.0   691.7  1346.3  1419.1  1860.2  4706.7      30 
## 
## $Samac
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   83.33  525.00  771.67 1017.80 1270.00 3333.33      31 
## 
## $`Sanski Most`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   83.33  366.67  787.50 1120.51 1375.00 8250.00      46 
## 
## $Sapna
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   66.67  566.67 1066.67 1066.67 1566.67 2066.67       1 
## 
## $Sekovici
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   33.33   43.75  163.75  251.81  375.62  700.00       6 
## 
## $Sipovo
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   395.0   696.2   997.5   997.5  1298.8  1600.0       2 
## 
## $Sokolac
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    70.0   565.0   670.0   914.1  1265.0  2200.0      14 
## 
## $Srbac
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   233.3   437.5   903.3   927.5  1295.0  1980.0      21 
## 
## $Srebrenica
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     400     515     790    1155    1940    2280      13 
## 
## $Srebrenik
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   350.0   541.7   616.7   731.5   850.0  1250.0      99 
## 
## $Stolac
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     200    1309    1663    1686    2042    3367       9 
## 
## $Teocak
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   58.33  421.88  826.33  874.92 1135.83 2107.50       3 
## 
## $Tesanj
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    50.0   400.0   600.0   727.5   930.0  2300.0      28 
## 
## $Teslic
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    60.0   394.2   733.3   757.9  1012.5  1687.5      23 
## 
## $Tomislavgrad
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     150     740    1087    1480    2100    4000      18 
## 
## $Travnik
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##      50     800    1010    1117    1300    3122      39 
## 
## $Trebinje
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   133.3   420.0   600.0   813.7   900.0  2800.0      25 
## 
## $Trnovo
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   100.0   306.2   659.8   804.9  1158.5  1800.0       1 
## 
## $Tuzla
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    25.0   351.7   750.0   844.7  1122.5  3600.0      94 
## 
## $Ugljevik
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    50.0   147.5   800.0   792.0  1050.0  2633.3       6 
## 
## $Usora
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##      50     150     250     590    1100    1400       5 
## 
## $V.Kladusa
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   66.67  329.58  625.00  752.87  920.83 2696.67      21 
## 
## $Vares
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     400     560     660     900     860    2020      13 
## 
## $Visegrad
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   66.67  541.67  866.67  982.67 1268.33 2800.00      11 
## 
## $Visoko
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   100.0   400.0   620.0   812.1  1150.0  2460.0      47 
## 
## $Vitez
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   83.33  512.50  730.00  953.35 1239.00 4213.33      18 
## 
## $Vlasenica
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   300.0   550.0   721.7   843.9  1075.0  2333.3      13 
## 
## $Vogosca
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   120.0   453.2  1099.0  1177.5  1542.1  3490.0      18 
## 
## $Vukosavlje
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   41.67  104.17  166.67  337.50  541.67  900.00       1 
## 
## $Zavidovici
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    62.5   412.5   737.5   809.9  1000.0  2600.0      42 
## 
## $Zenica
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    25.0   386.2   786.2   831.9  1095.6  2595.0     163 
## 
## $Zepce
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     400     500     850    1203    1875    3000      14 
## 
## $Zivinice
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   33.17  295.83  623.33  695.76  939.58 2625.00      66 
## 
## $Zvornik
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##    16.67   233.33   525.00   821.48   882.50 12958.33       52

#npr brcko, n.sarajevo..itd imaju veliki broj Naova

Napravi df. sa ommited Nas i vidi koliko ima unosa po municipality

hbs_total.income.naomit <- hbs_total.income1[,c("wage.hh.mj","naziv_Opcine")]

hbs_total.income.naomit <- na.omit(hbs_total.income.naomit)
dim(hbs_total.income.naomit) #dakle 4149 unosa sto odgovara da ima oko 3000 NAova

## [1] 4149    2

#vidi unose po municipality

hbs_total.income.naomit %>% group_by(naziv_Opcine) %>% summarise (mean(wage.hh.mj), n=n()) %>% arrange (naziv_Opcine) %>% filter(n<10)

## # A tibble: 34 × 3
##    naziv_Opcine `mean(wage.hh.mj)`     n
##    <chr>                     <dbl> <int>
##  1 B.Grahovo                 1173      5
##  2 Berkovici                  912.     2
##  3 Celic                      500      5
##  4 D-Samac                   1105      4
##  5 Doboj-Istok                802.     7
##  6 Doboj-Jug                  773.     7
##  7 Donji Zabar                767.     3
##  8 Han Pijesak                801.     7
##  9 Jablanica                  509.     4
## 10 Jezero                     100      1
## # ℹ 24 more rows

Imam dosta ovih za koje imam unose a ima manje od 10 unosa i u indeksu cak

2.HOUSING EXPEND as a share of total income

amr.st.AM - je varijabla koju sam kreirala a u kojoj sam objedinila i realizirane troskove domacinstva na rentu i na parking mjesto i garazu te na procijenu domacinstva koliko bi kostala njihova stambena jedinica ako bi je rentali.

Nule prebaci u NA kod rentanja

hbs_total.income1$Rent.mnt.AM <- ifelse (hbs_total.income1$Rent.mnt.AM==0, NA, paste (hbs_total.income1$Rent.mnt.AM))
summary(hbs_total.income1$Rent.mnt.AM)

##    Length     Class      Mode 
##      7702 character character

hbs_total.income1$Rent.mnt.AM <- as.numeric(hbs_total.income1$Rent.mnt.AM)
summary(hbs_total.income1$Rent.mnt.AM)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    50.0   100.0   150.0   199.6   250.0  2000.0

#nema Naova

#TRANSPORT EXPENDITURE

vars.trans.cost <- hbs_total.income1 %>% select(
"mj_Q02_01_AM_S6",
"mj_Q02_02_AM_S6",
"mj_Q02_03_AM_S6",
"mj_Q02_04_AM_S6",
"mj_Q02_05_AM_S6",
"mj_Q02_06_AM_S6",
"mj_Q02_07_AM_S6",
"mj_Q02_08_AM_S6",
"mj_Q02_09_AM_S6",
"mj_Q02_10_AM_S6",
"mj_Q04_02_AM_S6",
"mj_Q04_03_AM_S6",
"mj_Q04_04_AM_S6",
"mj_DA_S_04",
"mj_DA_S_05",
"mj_DA_S_06",
"mj_DA_S_07") %>% names()
#"mj_Q02_11_AM_S6" polaganje vozackog ispita cu izbaciti

trans.cost <- rowSums (hbs_total.income1[, c(vars.trans.cost)], na.rm=T)
table(trans.cost==0) #2408

## 
## FALSE  TRUE 
##  5294  2408

hbs_total.income1$mj.trans.AM <- trans.cost
hbs_total.income1$mj.trans.AM <- as.numeric (hbs_total.income1$mj.trans.AM)

Analiza varijable

boxplot(hbs_total.income1$HH_Monthly_Consumption)

boxplot (hbs_total.income1$total.revenue)

boxplot(hbs_total.income1$wage.hh.mj)

boxplot (hbs_total.income1$Rent.mnt.AM)

boxplot(hbs_total.income1$mj.trans.AM)

which.max(hbs_total.income1$HH_Monthly_Consumption_Equilized)

## [1] 4921

hbs_total.income1[4921,c("HH_Monthly_Consumption_Equilized", "total.revenue")] #ogromna razlika u potrosnji i incomeu

## # A tibble: 1 × 2
##   HH_Monthly_Consumption_Equilized total.revenue
##                              <dbl>         <dbl>
## 1                           13409.          4250

Radi velike razlike u incomu i potrosnji formiracu varijablu APSOLUTNE RAZLIKE razlike izmedju consumption i revenue i to ce mi biti outlier

hbs_total.income1$diff.con.rev <- hbs_total.income1$HH_Monthly_Consumption_Equilized - hbs_total.income1$total.revenue
summary(hbs_total.income1$diff.con.rev) #Ovdje dakle imam da neko trosi skoro 10000 vise nego sto su prihodi sto nema smisla

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
## -16753.03   -396.96     -0.38   -138.97    244.44   9159.12       676

u jednoj iteraciji sam brisala i samo upper kod diff.con.rev. Ostalo mi je manje za stotinjak obzervacija ali opet sam imala veoma sitnu potrosnju npr 0.86 feninga sot je nekako i nemoguce. Ipak vecu logiku ima da brisem upper percentile odnosa consumpti/income

Share.con.rev

hbs_total.income1$shar.coneq.rev <- hbs_total.income1$HH_Monthly_Consumption_Equilized/ hbs_total.income1$total.revenue
summary(hbs_total.income1$shar.coneq.rev)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
##     0.039     0.597     0.999     3.634     1.648 10194.600       676

which.max(hbs_total.income1$shar.coneq.rev)

## [1] 351

hbs_total.income1[351, c("HH_Monthly_Consumption_Equilized", "total.revenue","Rent.mnt.AM", "mj.trans.AM")]

## # A tibble: 1 × 4
##   HH_Monthly_Consumption_Equilized total.revenue Rent.mnt.AM mj.trans.AM
##                              <dbl>         <dbl>       <dbl>       <dbl>
## 1                             850.        0.0833         200        312.

Share trans.rev

hbs_total.income1$shar.trans.rev <- hbs_total.income1$mj.trans.AM/ hbs_total.income1$total.revenue
summary(hbs_total.income1$shar.trans.rev)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##    0.000    0.000    0.038    0.642    0.099 3740.000      676

Share rent.rev

hbs_total.income1$shar.rent.rev <- hbs_total.income1$Rent.mnt.AM/ hbs_total.income1$total.revenue
summary(hbs_total.income1$shar.trans.rev)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##    0.000    0.000    0.038    0.642    0.099 3740.000      676

#brisati upper pecrcentile consuption ali nakon revenua, zato sto u revenue imam NA i sl.

Brisati percentile kod ukupnih prihoda

Ovo zato sto opet imam znacano niske ali i visoke iznose prihoda

summary(hbs_total.income1$total.revenue)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
##     0.083   345.000   641.667   889.330  1150.000 18350.000       676

# Bottom 1% and upper 99%is removed and new db is formed
hbs_total.income2 <- hbs_total.income1 %>% 
  filter(total.revenue > quantile (hbs_total.income1$total.revenue , 0.01, na.rm = T) &
         total.revenue  < quantile(hbs_total.income1$total.revenue , 0.99, na.rm = T))
hbs_total.income2$total.revenue  <- as.numeric(hbs_total.income2$total.revenue)
table(is.na(hbs_total.income2$total.revenue))

## 
## FALSE 
##  6864

summary (hbs_total.income2$total.revenue) ##veci su wage nego total.revenue zato sto ima mnogo domacinstava u total.revenue a Na-ovi su u wage i oni u prosjeku vuku manje

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   50.83  350.00  644.00  854.65 1139.69 3741.67

summary(hbs_total.income2$HH_Monthly_Consumption_Equilized)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   39.29  455.11  636.77  743.80  907.05 5609.42

summary(hbs_total.income2$shar.coneq.rev) #share 84puta

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##  0.08494  0.60140  0.99554  1.35876  1.61719 84.14130

dim (hbs_total.income2) #6864

## [1] 6864 3061

Brisi percentile kod consuption_equilized

summary(hbs_total.income2$HH_Monthly_Consumption_Equilized)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   39.29  455.11  636.77  743.80  907.05 5609.42

hbs_total.income3 <- hbs_total.income2 %>% 
  filter(HH_Monthly_Consumption_Equilized > quantile (hbs_total.income2$HH_Monthly_Consumption_Equilized , 0.01, na.rm = T) &
         HH_Monthly_Consumption_Equilized  < quantile(hbs_total.income2$HH_Monthly_Consumption_Equilized , 0.99, na.rm = T))
hbs_total.income3$HH_Monthly_Consumption_Equilized  <- as.numeric(hbs_total.income3$HH_Monthly_Consumption_Equilized)
table(is.na(hbs_total.income3$HH_Monthly_Consumption_Equilized))

## 
## FALSE 
##  6726

summary (hbs_total.income3$HH_Monthly_Consumption_Equilized)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   166.5   459.3   636.8   726.8   900.6  2383.6

summary(hbs_total.income3$shar.coneq.rev)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##  0.08494  0.60238  0.99554  1.33297  1.60900 29.98080

dim(hbs_total.income3) #6726 obs u ovoj fazi samo 300 manje

## [1] 6726 3061

#brisati share.coneq.rev

hbs_total.income4 <- hbs_total.income3 %>% filter (shar.coneq.rev  < quantile(hbs_total.income3$shar.coneq.rev , 0.99, na.rm = T)) 

summary (hbs_total.income4$shar.con.rev)

## Warning: Unknown or uninitialised column: `shar.con.rev`.

## Length  Class   Mode 
##      0   NULL   NULL

which.max(hbs_total.income4$shar.con.rev)

## Warning: Unknown or uninitialised column: `shar.con.rev`.

## integer(0)

hbs_total.income4[633, c("HH_Monthly_Consumption_Equilized", "total.revenue","Rent.mnt.AM", "mj.trans.AM")]

## # A tibble: 1 × 4
##   HH_Monthly_Consumption_Equilized total.revenue Rent.mnt.AM mj.trans.AM
##                              <dbl>         <dbl>       <dbl>       <dbl>
## 1                             825.           385         150        38.3

Total shares

Zanemari jer ubraja i one gdje ima vrlo malo observacija.. sto nije tacno.. mada gleda ukupnu drzavu pa mozda i moze

summary(hbs_total.income4$HH_Monthly_Consumption_Equilized/hbs_total.income4$total.revenue)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.08494 0.60028 0.98542 1.24094 1.58386 6.37743

which.max(hbs_total.income4$HH_Monthly_Consumption_Equilized/hbs_total.income4$total.revenue)

## [1] 5570

hbs_total.income4[620, c("HH_Monthly_Consumption_Equilized", "total.revenue","Rent.mnt.AM", "mj.trans.AM")]

## # A tibble: 1 × 4
##   HH_Monthly_Consumption_Equilized total.revenue Rent.mnt.AM mj.trans.AM
##                              <dbl>         <dbl>       <dbl>       <dbl>
## 1                             368.          192.          50        21.4

#vidi da li je ista observacija po varijabli diff.con.rev

plot (density(hbs_total.income4$HH_Monthly_Consumption_Equilized/hbs_total.income4$total.revenue))

summary(hbs_total.income4$Rent.mnt.AM/hbs_total.income4$total.revenue)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.01428 0.14493 0.24540 0.34143 0.42553 4.80000

summary (hbs_total.income4$mj.trans.AM/hbs_total.income4$total.revenue)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.00000 0.03848 0.08566 0.09524 2.95455

Izbaciti low frequency opstine

hbs_total.income4 %>% select(naziv_Opcine, total.revenue) %>%  group_by(naziv_Opcine)%>% summarise(n=n()) %>% arrange(n) %>% print (n=30)

## # A tibble: 136 × 2
##    naziv_Opcine     n
##    <chr>        <int>
##  1 Dobretici        1
##  2 I.Stari Grad     1
##  3 Pale-FBiH        1
##  4 Ravno            1
##  5 Foca             2
##  6 Kalinovik        2
##  7 Ljubinje         2
##  8 Sapna            2
##  9 Sipovo           3
## 10 Donji Zabar      4
## 11 Krupa na Uni     4
## 12 Osmaci           4
## 13 Trnovo           4
## 14 I.N.Sarajevo     5
## 15 D-Samac          6
## 16 Celic            7
## 17 Jablanica        7
## 18 Vukosavlje       7
## 19 Berkovici        8
## 20 Neum             8
## 21 Usora            8
## 22 Kostajnica       9
## 23 Odzak            9
## 24 Petrovo          9
## 25 Doboj-Jug       10
## 26 Teocak          10
## 27 Pelagicevo      11
## 28 Sekovici        11
## 29 Han Pijesak     12
## 30 N.Gorazde       12
## # ℹ 106 more rows

hbs_total.income4 <- hbs_total.income4[!hbs_total.income4$naziv_Opcine %in% c ("Pale-FBiH", "I.Stari Grad", "Dobretici", "Ljubinje", "Kalinovik", "Jezero", "Foca", "Sapna", "Ravno", "Sipovo", "Osmaci", "Krupa na Uni", "D-Samac", "Celic","Donji Zabar", "Trnovo", "I.N.Sarajevo", "Vukosavlje", "Jablanica", "Neum", "Petrovo", "Usora", "Berkovici", "Odzak"),]

Ponovi summary

summary(hbs_total.income4$HH_Monthly_Consumption_Equilized/hbs_total.income4$total.revenue)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.08494 0.59888 0.98355 1.23791 1.58158 6.37743

#izgleda da bih trebala brisati percentile i kode consumpitona a i renta..pa cu opet vjerovatno morati brisati i opstine

summary(hbs_total.income4$Rent.mnt.AM/hbs_total.income4$total.revenue)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.01428 0.14493 0.24590 0.34189 0.42614 4.80000

summary (hbs_total.income4$mj.trans.AM/hbs_total.income4$total.revenue)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.00000 0.03864 0.08561 0.09524 2.95455

Grupisi ove shareve u tabelu

shares <- hbs_total.income4 %>% 
  select(c(naziv_Opcine, total.revenue, HH_Monthly_Consumption, HH_Monthly_Consumption_Equilized, mj.trans.AM, wage.hh.mj, Rent.mnt.AM, Inc_HH_all_mj,Soc.Ben_HH_all_mj,shar.coneq.rev,shar.rent.rev,shar.trans.rev)) %>%
  group_by(naziv_Opcine) %>% 
  summarise (mean.rev = 
               round(mean(total.revenue, na.rm = T),3), 
             mean.con.eq = mean(HH_Monthly_Consumption_Equilized, na.rm = T), 
             mean.wage = mean(wage.hh.mj, na.rm = T), 
             mean.rent = mean(Rent.mnt.AM, na.rm = T), 
             mean.trans = mean(mj.trans.AM, na.rm = T), 
             mean.inc = mean(Inc_HH_all_mj, na.rm = T), 
             mean.soc = mean(Soc.Ben_HH_all_mj, na.rm = T), 
             Freq = n(),
             share.coneq.rev.from.db = mean (shar.coneq.rev, na.rm=T),
             share.con.eq.rev = mean.con.eq/mean.rev,
             share.rent.rev.from.db = mean (shar.rent.rev, na.rm=T),
             share.rent.rev = mean.rent/mean.rev,
             share.trans.rev.from.db = mean (shar.trans.rev, na.rm=T),
             share.trans.rev = mean.trans/mean.rev,
             share.wag.rev = mean.wage/mean.rev,
             share.con.eq.wag = mean.con.eq/mean.wage) %>%
  arrange (desc(Freq))
shares

## # A tibble: 113 × 17
##    naziv_Opcine mean.rev mean.con.eq mean.wage mean.rent mean.trans mean.inc
##    <chr>           <dbl>       <dbl>     <dbl>     <dbl>      <dbl>    <dbl>
##  1 Brcko            800.        712.      851.      165.       49.6     733.
##  2 Zenica           750.        677.      828.      223.       30.0     763.
##  3 Banja Luka       887.        782.     1033.      248.       78.0    1036.
##  4 N.G.Sarajevo    1094.        838.     1278.      325.       74.0    1172.
##  5 Grad Mostar     1044.        823.     1302.      234.       67.0    1249.
##  6 Bijeljina        960.        648.      912.      175.       66.7     877.
##  7 Prijedor         536.        777.      823.      268.       32.4     823.
##  8 Doboj            814.        747.      812.      161.       58.5     701.
##  9 Tuzla            782.        801.      850.      205.       74.5     825.
## 10 Travnik         1106.        692.     1128.      197.       95.5    1034.
## # ℹ 103 more rows
## # ℹ 10 more variables: mean.soc <dbl>, Freq <int>,
## #   share.coneq.rev.from.db <dbl>, share.con.eq.rev <dbl>,
## #   share.rent.rev.from.db <dbl>, share.rent.rev <dbl>,
## #   share.trans.rev.from.db <dbl>, share.trans.rev <dbl>, share.wag.rev <dbl>,
## #   share.con.eq.wag <dbl>

#spasiti a u excel

library(openxlsx)
wb = createWorkbook()
sh = addWorksheet(wb, "SHARES+TRANSPORT") #ovo je naziv sheeta

library(expss)

## Loading required package: maditr

## 
## Use magrittr pipe '%>%' to chain several operations:
##              mtcars %>%
##                  let(mpg_hp = mpg/hp) %>%
##                  take(mean(mpg_hp), by = am)
##

## 
## Attaching package: 'maditr'

## The following objects are masked from 'package:dplyr':
## 
##     between, coalesce, first, last

## 
## Use 'expss_output_rnotebook()' to display tables inside R Notebooks.
##  To return to the console output, use 'expss_output_default()'.

## 
## Attaching package: 'expss'

## The following objects are masked from 'package:tidyselect':
## 
##     contains, where

## The following objects are masked from 'package:dplyr':
## 
##     compute, contains, na_if, recode, vars, where

## The following objects are masked from 'package:haven':
## 
##     is.labelled, read_spss

xl_write(shares, wb, sh) #shares je naziv objekta koji hocu spasiti

saveWorkbook(wb, "shares.hbs.16.12.xlsx", overwrite = T)

Income average per municipality

Amra Fetahovic

2023-11-28

DISPOSABLE INCOME - TOTAL HOUSEHOLD

WAGE

1.IZRACUNAM SAMO WAGES (KOJI SAM KORISITILA U WAGE INDEX)

Nule prebaci u NA kod wage

Vidi po Opsitinama koliko je Naova u wages

Napravi df. sa ommited Nas i vidi koliko ima unosa po municipality

Brisi percentile kod consuption_equilized

Total shares

Izbaciti low frequency opstine

Ponovi summary

Grupisi ove shareve u tabelu

Income average per municipality

Amra Fetahovic

2023-11-28

DISPOSABLE INCOME - TOTAL HOUSEHOLD

FORMIRAM INCOME ZA SVAKOG CLANA DOMACINSTVA (SVE OSIM SOCIAL CONTRIBUTIONS AND BENEFITS))

1st household INCOME except social benefit

2nd household

3rd household

4th household

mjesecno all wage for 5th household

Mjesecno all wage for 6th household

mjesecno all wage for 7th household

mjesecno all wage for 8th household

mjesecno all wage for 9th household

Social contributions and other social benefits

SADA FORMIRAM SOC.BENEFIT ZA SVAKOG CLANA DOMACINSTVA ZA INCOME

mjesecno all income for 1st household except social benefit

mjesecno all income for 2nd household except social benefit

mjesecno all soc. benefit for 3rd household

mjesecno all soc. benefit for 4th household

mjesecno all soc. benefit for 5th household

mjesecno all soc. benefit for 6th household

mjesecno all soc. benefit for 7th household

mjesecno all soc. benefit for 8th household

mjesecno all soc. benefit for 9th household

mjesecno all soc. benefit for 10th household

0 pretvoriti u NA

Prije formiranja varijable total.revenu brisem percentiles za Inc_HH_all_mj i Soc.Ben_HH_all_mj

Formiraj varijablu - total.revenue -

nule prebaciti u NA kod total.revenue

WAGE

1.IZRACUNAM SAMO WAGES (KOJI SAM KORISITILA U WAGE INDEX)

Nule prebaci u NA kod wage

Vidi po Opsitinama koliko je Naova u wages

Napravi df. sa ommited Nas i vidi koliko ima unosa po municipality

2.HOUSING EXPEND as a share of total income

Nule prebaci u NA kod rentanja

Analiza varijable

Sada formiraj share varijable za bazu

Share.con.rev

Share trans.rev

Share rent.rev

Brisati percentile kod ukupnih prihoda

Brisi percentile kod consuption_equilized

Total shares

Izbaciti low frequency opstine

Ponovi summary

Grupisi ove shareve u tabelu