library(data.table)
library(tidyverse)
library(read.dbc)
library(foreign)
library(dplyr)
require("stringr")
require(summarytools)
library(rapportools)
source("http://pcwww.liv.ac.uk/~william/R/crosstab.r")library(tibble)
library(readxl)load("/cloud/project/binomial/bases finais/base_analise_ha_v2.RData")
load("/cloud/project/binomial/bases finais/bahia_pop_long.RData")library("tidyverse")
# seleciona municipios com pelo menos 1 caso de arbovirose
incid_arboviroses_ano<-base_analise_ha_v2%>%dplyr::select(mun,ano,dg_conf,chik_conf,zika_conf,populacao,semiarido)%>% filter(dg_conf>0 &chik_conf>0 & zika_conf>0)
mun_coc<-incid_arboviroses_ano%>%dplyr::select(mun)%>%
group_by(mun)%>%
count()
names(mun_coc)<-c("mun","freq")
mun_coc<-mun_coc%>%
dplyr::filter(freq==4)municipios_todosanos<-mun_coc$mun
municipios_todosanos[1] "BARREIRAS" "EUNAPOLIS" "FEIRA DE SANTANA"
[4] "ILHEUS" "RIBEIRA DO POMBAL" "SALVADOR"
[7] "TEIXEIRA DE FREITAS"library("patchwork")
library("expss")
bahia_pop_long%>%dplyr::filter(mun%in%municipios_todosanos)%>%
dplyr::filter(ano==2016)%>%
dplyr::select(macrorregiao,regiao.de.saude,mun, semiarido,IDH,popporte)%>%
sort_asc(macrorregiao, regiao.de.saude, mun,semiarido,semiarido,IDH,popporte)%>%
unique()%>%
knitr::kable(.,"simple")| macrorregiao | regiao.de.saude | mun | semiarido | IDH | popporte | |
|---|---|---|---|---|---|---|
| 3 | Centro-Leste | Feira de Santana | FEIRA DE SANTANA | 1 | ALTO | Grande Porte |
| 2 | Extremo Sul | Porto Seguro | EUNAPOLIS | 0 | ALTO | Grande Porte |
| 7 | Extremo Sul | Teixeira de Freitas | TEIXEIRA DE FREITAS | 0 | ALTO | Grande Porte |
| 6 | Leste | Salvador | SALVADOR | 0 | MUITO ALTO | Metrópole |
| 5 | Nordeste | Ribeira do Pombal | RIBEIRA DO POMBAL | 1 | MEDIO | Pequeno Porte II |
| 1 | Oeste | Barreiras | BARREIRAS | 1 | ALTO | Grande Porte |
| 4 | Sul | Ilheus | ILHEUS | 0 | ALTO | Grande Porte |
bahia_pop_long%>%dplyr::filter(mun%in%municipios_todosanos)%>%
dplyr::filter(ano==2016)%>%
dplyr::select(macrorregiao,regiao.de.saude,mun, pop, semiarido,bioma_2014,newtipology,IDH,popporte)%>%
dplyr::mutate(newtipology = case_when(
newtipology== 1 ~ 'intermediarioadjacente',
newtipology== 2 ~ 'intermediarioremoto',
newtipology== 3 ~ 'ruraladjacente',
newtipology== 4 ~ 'ruralremoto',
newtipology== 5 ~ 'Urbano'))%>%
dplyr::mutate(bioma_2014 = case_when(
bioma_2014== 1 ~ 'Floresta',
bioma_2014== 2 ~ 'Natural não florestal',
bioma_2014== 3 ~ 'Agropecuária',
bioma_2014== 4 ~ 'Area não vegetada',
bioma_2014== 5 ~ 'Corpos d’água'))%>%
dplyr::mutate(semiarido = case_when(
semiarido== 0 ~ 'regiao nao seca',
semiarido== 1 ~ 'regiao arida'))%>%
# sort_asc(macrorregiao, regiao.de.saude, mun,semiarido,IDH,popporte)%>%
group_by(semiarido)%>%
dfSummary(style='multiline', graph.col = FALSE)Data Frame Summary
bahia_pop_long
Group: semiarido = regiao arida
Dimensions: 3 x 9
Duplicates: 0
------------------------------------------------------------------------------------------------------------------------------------
No Variable Label Stats / Values Freqs (% of Valid) Valid Missing
---- ----------------- --------------------------------------- --------------------------- -------------------- ---------- ---------
1 macrorregiao 1. Centro-Leste 1 (33.3%) 3 0
[character] 2. Nordeste 1 (33.3%) (100.0%) (0.0%)
3. Oeste 1 (33.3%)
2 regiao.de.saude 1. Barreiras 1 (33.3%) 3 0
[character] 2. Feira de Santana 1 (33.3%) (100.0%) (0.0%)
3. Ribeira do Pombal 1 (33.3%)
3 mun 1. BARREIRAS 1 (33.3%) 3 0
[character] 2. FEIRA DE SANTANA 1 (33.3%) (100.0%) (0.0%)
3. RIBEIRA DO POMBAL 1 (33.3%)
4 pop Mean (sd) : 181.2 (256.8) 19.79 : 1 (33.3%) 3 0
[numeric] min < med < max: 46.52 : 1 (33.3%) (100.0%) (0.0%)
19.8 < 46.5 < 477.3 477.33 : 1 (33.3%)
IQR (CV) : 228.8 (1.4)
6 bioma_2014 1. Agropecuária 2 (66.7%) 3 0
[character] 2. Floresta 1 (33.3%) (100.0%) (0.0%)
7 newtipology 1. Urbano 3 (100.0%) 3 0
[character] (100.0%) (0.0%)
8 IDH 1. ALTO 2 (66.7%) 3 0
[character] 2. MEDIO 1 (33.3%) (100.0%) (0.0%)
9 popporte Porte da Populacao segundo Censo IBGE 1. Grande Porte 2 (66.7%) 3 0
[character] 2010 2. Pequeno Porte II 1 (33.3%) (100.0%) (0.0%)
------------------------------------------------------------------------------------------------------------------------------------
Group: semiarido = regiao nao seca
Dimensions: 4 x 9
Duplicates: 0
-------------------------------------------------------------------------------------------------------------------------------------
No Variable Label Stats / Values Freqs (% of Valid) Valid Missing
---- ----------------- --------------------------------------- --------------------------- --------------------- ---------- ---------
1 macrorregiao 1. Extremo Sul 2 (50.0%) 4 0
[character] 2. Leste 1 (25.0%) (100.0%) (0.0%)
3. Sul 1 (25.0%)
2 regiao.de.saude 1. Ilheus 1 (25.0%) 4 0
[character] 2. Porto Seguro 1 (25.0%) (100.0%) (0.0%)
3. Salvador 1 (25.0%)
4. Teixeira de Freitas 1 (25.0%)
3 mun 1. EUNAPOLIS 1 (25.0%) 4 0
[character] 2. ILHEUS 1 (25.0%) (100.0%) (0.0%)
3. SALVADOR 1 (25.0%)
4. TEIXEIRA DE FREITAS 1 (25.0%)
4 pop Mean (sd) : 1141 (2062.5) 80.14 : 1 (25.0%) 4 0
[numeric] min < med < max: 112.18 : 1 (25.0%) (100.0%) (0.0%)
80.1 < 124.6 < 4234.6 137.11 : 1 (25.0%)
IQR (CV) : 1057.3 (1.8) 4234.59 : 1 (25.0%)
6 bioma_2014 1. Agropecuária 2 (50.0%) 4 0
[character] 2. Corpos d’água 1 (25.0%) (100.0%) (0.0%)
3. Floresta 1 (25.0%)
7 newtipology 1. Urbano 4 (100.0%) 4 0
[character] (100.0%) (0.0%)
8 IDH 1. ALTO 3 (75.0%) 4 0
[character] 2. MUITO ALTO 1 (25.0%) (100.0%) (0.0%)
9 popporte Porte da Populacao segundo Censo IBGE 1. Grande Porte 3 (75.0%) 4 0
[character] 2010 2. Metrópole 1 (25.0%) (100.0%) (0.0%)
-------------------------------------------------------------------------------------------------------------------------------------dg<-base_analise_ha_v2%>%dplyr::select(mun,ano,dg_conf)%>%
dplyr::filter(dg_conf>0)%>%
group_by(ano)
dg_mun_2016<-dg$mun[dg$ano==2016]
dg_mun_2017<-dg$mun[dg$ano==2017]
dg_mun_2018<-dg$mun[dg$ano==2018]
dg_mun_2019<-dg$mun[dg$ano==2019]uni_16_17<-intersect(dg_mun_2016,dg_mun_2017)
uni_161718<-intersect(uni_16_17,dg_mun_2018)
dg_uni_16171819<-intersect(uni_161718,dg_mun_2019)
dg_uni_16171819 [1] "ABAIRA" "ALAGOINHAS"
[3] "AMERICA DOURADA" "BARREIRAS"
[5] "BELMONTE" "BOM JESUS DA LAPA"
[7] "BRUMADO" "BURITIRAMA"
[9] "CAETITE" "CAMACARI"
[11] "CORRENTINA" "CRISTOPOLIS"
[13] "CRUZ DAS ALMAS" "DIAS D AVILA"
[15] "EUNAPOLIS" "FEIRA DE SANTANA"
[17] "FLORESTA AZUL" "IBICARAI"
[19] "IBIPITANGA" "ILHEUS"
[21] "IRAQUARA" "IRECE"
[23] "ITABUNA" "ITAMARAJU"
[25] "ITANHEM" "ITIUBA"
[27] "ITORORO" "JABORANDI"
[29] "JACOBINA" "JAGUAQUARA"
[31] "JEQUIE" "JUAZEIRO"
[33] "LAURO DE FREITAS" "LUIS EDUARDO MAGALHAES"
[35] "MACAUBAS" "MEDEIROS NETO"
[37] "MORRO DO CHAPEU" "MUCURI"
[39] "MURITIBA" "NOVO HORIZONTE"
[41] "OLIVEIRA DOS BREJINHOS" "PARAMIRIM"
[43] "PARATINGA" "PAULO AFONSO"
[45] "PORTO SEGURO" "PRESIDENTE TANCREDO NEVES"
[47] "REMANSO" "RIACHO DE SANTANA"
[49] "RIBEIRA DO POMBAL" "SALVADOR"
[51] "SANTA MARIA DA VITORIA" "SANTO ANTONIO DE JESUS"
[53] "SANTO ESTEVAO" "SAO DESIDERIO"
[55] "SAO DOMINGOS" "SAO FELIX DO CORIBE"
[57] "SEABRA" "SENHOR DO BONFIM"
[59] "SERRINHA" "SERROLANDIA"
[61] "SIMOES FILHO" "SITIO DO MATO"
[63] "TANQUINHO" "TEIXEIRA DE FREITAS"
[65] "UIBAI" "UNA"
[67] "VARZEA NOVA" "VERA CRUZ"
[69] "VITORIA DA CONQUISTA" "WANDERLEY" ck<-base_analise_ha_v2%>%dplyr::select(mun,ano,chik_conf)%>%
dplyr::filter(chik_conf>0)%>%
group_by(ano)
ck_mun_2016<-ck$mun[ck$ano==2016]
ck_mun_2017<-ck$mun[ck$ano==2017]
ck_mun_2018<-ck$mun[ck$ano==2018]
ck_mun_2019<-ck$mun[ck$ano==2019]uni_16_17<-intersect(ck_mun_2016,ck_mun_2017)
uni_161718<-intersect(uni_16_17,ck_mun_2018)
ck_uni_16171819<-intersect(uni_161718,ck_mun_2019)
ck_uni_16171819 [1] "BARREIRAS" "CANDEIAS" "CARAVELAS"
[4] "EUNAPOLIS" "FEIRA DE SANTANA" "ILHEUS"
[7] "IRECE" "ITABUNA" "ITAMARAJU"
[10] "ITANHEM" "MACAUBAS" "PORTO SEGURO"
[13] "RIBEIRA DO POMBAL" "SALVADOR" "SAO DESIDERIO"
[16] "SENHOR DO BONFIM" "TEIXEIRA DE FREITAS" "VITORIA DA CONQUISTA"zk<-base_analise_ha_v2%>%dplyr::select(mun,ano,zika_conf)%>%
dplyr::filter(zika_conf>0)%>%
group_by(ano)
zk_mun_2016<-zk$mun[zk$ano==2016]
zk_mun_2017<-zk$mun[zk$ano==2017]
zk_mun_2018<-zk$mun[zk$ano==2018]
zk_mun_2019<-zk$mun[zk$ano==2019]uni_16_17<-intersect(ck_mun_2016,zk_mun_2017)
uni_161718<-intersect(uni_16_17,zk_mun_2018)
zk_uni_16171819<-intersect(uni_161718,zk_mun_2019)
zk_uni_16171819 [1] "BARREIRAS" "EUNAPOLIS" "FEIRA DE SANTANA"
[4] "ILHEUS" "ITABELA" "LAURO DE FREITAS"
[7] "PRADO" "REMANSO" "RIBEIRA DO POMBAL"
[10] "SALVADOR" "TEIXEIRA DE FREITAS"dg_uni_chik<-intersect(dg_uni_16171819,ck_uni_16171819)
dg_uni_chik [1] "BARREIRAS" "EUNAPOLIS" "FEIRA DE SANTANA"
[4] "ILHEUS" "IRECE" "ITABUNA"
[7] "ITAMARAJU" "ITANHEM" "MACAUBAS"
[10] "PORTO SEGURO" "RIBEIRA DO POMBAL" "SALVADOR"
[13] "SAO DESIDERIO" "SENHOR DO BONFIM" "TEIXEIRA DE FREITAS"
[16] "VITORIA DA CONQUISTA"#library("prob")
dg_chik<-setdiff(dg_uni_chik,municipios_todosanos)
dg_chik[1] "IRECE" "ITABUNA" "ITAMARAJU"
[4] "ITANHEM" "MACAUBAS" "PORTO SEGURO"
[7] "SAO DESIDERIO" "SENHOR DO BONFIM" "VITORIA DA CONQUISTA"#dg_chik ### caracteristica dos municipios
bahia_pop_long%>%dplyr::filter(mun%in%dg_chik)%>%
dplyr::filter(ano==2016)%>%
dplyr::select(macrorregiao,regiao.de.saude,mun, semiarido,IDH,popporte)%>%
sort_asc(macrorregiao, regiao.de.saude, mun,semiarido,semiarido,IDH,popporte)%>%
unique()%>%
knitr::kable(.,"simple")| macrorregiao | regiao.de.saude | mun | semiarido | IDH | popporte | |
|---|---|---|---|---|---|---|
| 1 | Centro-Norte | Irece | IRECE | 1 | ALTO | Médio Porte |
| 6 | Extremo Sul | Porto Seguro | PORTO SEGURO | 0 | ALTO | Grande Porte |
| 3 | Extremo Sul | Teixeira de Freitas | ITAMARAJU | 0 | ALTO | Médio Porte |
| 4 | Extremo Sul | Teixeira de Freitas | ITANHEM | 0 | ALTO | Pequeno Porte II |
| 8 | Norte | Senhor do Bonfim | SENHOR DO BONFIM | 1 | ALTO | Médio Porte |
| 7 | Oeste | Barreiras | SAO DESIDERIO | 0 | MEDIO | Pequeno Porte II |
| 5 | Sudoeste | Brumado | MACAUBAS | 1 | MEDIO | Pequeno Porte II |
| 9 | Sudoeste | Vitoria da Conquista | VITORIA DA CONQUISTA | 1 | ALTO | Grande Porte |
| 2 | Sul | Itabuna | ITABUNA | 0 | ALTO | Grande Porte |
bahia_pop_long%>%dplyr::filter(mun%in%dg_chik)%>%
dplyr::filter(ano==2016)%>%
dplyr::select(macrorregiao,regiao.de.saude,mun, pop, semiarido,bioma_2014,newtipology,IDH,popporte)%>%
dplyr::mutate(newtipology = case_when(
newtipology== 1 ~ 'intermediarioadjacente',
newtipology== 2 ~ 'intermediarioremoto',
newtipology== 3 ~ 'ruraladjacente',
newtipology== 4 ~ 'ruralremoto',
newtipology== 5 ~ 'Urbano'))%>%
dplyr::mutate(bioma_2014 = case_when(
bioma_2014== 1 ~ 'Floresta',
bioma_2014== 2 ~ 'Natural não florestal',
bioma_2014== 3 ~ 'Agropecuária',
bioma_2014== 4 ~ 'Area não vegetada',
bioma_2014== 5 ~ 'Corpos d’água'))%>%
dplyr::mutate(semiarido = case_when(
semiarido== 0 ~ 'regiao nao seca',
semiarido== 1 ~ 'regiao arida'))%>%
# sort_asc(macrorregiao, regiao.de.saude, mun,semiarido,IDH,popporte)%>%
group_by(semiarido)%>%
dfSummary(style='multiline', graph.col = FALSE)Data Frame Summary
bahia_pop_long
Group: semiarido = regiao arida
Dimensions: 4 x 9
Duplicates: 0
----------------------------------------------------------------------------------------------------------------------------------
No Variable Label Stats / Values Freqs (% of Valid) Valid Missing
---- ----------------- --------------------------------------- ------------------------- -------------------- ---------- ---------
1 macrorregiao 1. Centro-Norte 1 (25.0%) 4 0
[character] 2. Norte 1 (25.0%) (100.0%) (0.0%)
3. Sudoeste 2 (50.0%)
2 regiao.de.saude 1. Brumado 1 (25.0%) 4 0
[character] 2. Irece 1 (25.0%) (100.0%) (0.0%)
3. Senhor do Bonfim 1 (25.0%)
4. Vitoria da Conquista 1 (25.0%)
3 mun 1. IRECE 1 (25.0%) 4 0
[character] 2. MACAUBAS 1 (25.0%) (100.0%) (0.0%)
3. SENHOR DO BONFIM 1 (25.0%)
4. VITORIA DA CONQUISTA 1 (25.0%)
4 pop Mean (sd) : 112 (87.7) 20.59 : 1 (25.0%) 4 0
[numeric] min < med < max: 93.38 : 1 (25.0%) (100.0%) (0.0%)
20.6 < 97.8 < 231.6 102.32 : 1 (25.0%)
IQR (CV) : 59.5 (0.8) 231.58 : 1 (25.0%)
6 bioma_2014 1. Agropecuária 3 (75.0%) 4 0
[character] 2. Floresta 1 (25.0%) (100.0%) (0.0%)
7 newtipology 1. ruralremoto 1 (25.0%) 4 0
[character] 2. Urbano 3 (75.0%) (100.0%) (0.0%)
8 IDH 1. ALTO 3 (75.0%) 4 0
[character] 2. MEDIO 1 (25.0%) (100.0%) (0.0%)
9 popporte Porte da Populacao segundo Censo IBGE 1. Grande Porte 1 (25.0%) 4 0
[character] 2010 2. Médio Porte 2 (50.0%) (100.0%) (0.0%)
3. Pequeno Porte II 1 (25.0%)
----------------------------------------------------------------------------------------------------------------------------------
Group: semiarido = regiao nao seca
Dimensions: 5 x 9
Duplicates: 0
------------------------------------------------------------------------------------------------------------------------------------
No Variable Label Stats / Values Freqs (% of Valid) Valid Missing
---- ----------------- --------------------------------------- --------------------------- -------------------- ---------- ---------
1 macrorregiao 1. Extremo Sul 3 (60.0%) 5 0
[character] 2. Oeste 1 (20.0%) (100.0%) (0.0%)
3. Sul 1 (20.0%)
2 regiao.de.saude 1. Barreiras 1 (20.0%) 5 0
[character] 2. Itabuna 1 (20.0%) (100.0%) (0.0%)
3. Porto Seguro 1 (20.0%)
4. Teixeira de Freitas 2 (40.0%)
3 mun 1. ITABUNA 1 (20.0%) 5 0
[character] 2. ITAMARAJU 1 (20.0%) (100.0%) (0.0%)
3. ITANHEM 1 (20.0%)
4. PORTO SEGURO 1 (20.0%)
5. SAO DESIDERIO 1 (20.0%)
4 pop Mean (sd) : 131.9 (234.6) 2.19 : 1 (20.0%) 5 0
[numeric] min < med < max: 14.74 : 1 (20.0%) (100.0%) (0.0%)
2.2 < 28.5 < 549.5 28.52 : 1 (20.0%)
IQR (CV) : 49.8 (1.8) 64.51 : 1 (20.0%)
549.55 : 1 (20.0%)
6 bioma_2014 1. Agropecuária 3 (60.0%) 5 0
[character] 2. Floresta 2 (40.0%) (100.0%) (0.0%)
7 newtipology 1. intermediarioadjacente 1 (20.0%) 5 0
[character] 2. ruraladjacente 1 (20.0%) (100.0%) (0.0%)
3. Urbano 3 (60.0%)
8 IDH 1. ALTO 4 (80.0%) 5 0
[character] 2. MEDIO 1 (20.0%) (100.0%) (0.0%)
9 popporte Porte da Populacao segundo Censo IBGE 1. Grande Porte 2 (40.0%) 5 0
[character] 2010 2. Médio Porte 1 (20.0%) (100.0%) (0.0%)
3. Pequeno Porte II 2 (40.0%)
------------------------------------------------------------------------------------------------------------------------------------chik_notdg<-setdiff(ck_uni_16171819,dg_uni_16171819)
chik_notdg[1] "CANDEIAS" "CARAVELAS"bahia_pop_long%>%dplyr::filter(mun%in%chik_notdg)%>%
dplyr::filter(ano==2016)%>%
dplyr::select(macrorregiao,regiao.de.saude,mun, semiarido,IDH,popporte)%>%
sort_asc(macrorregiao, regiao.de.saude, mun,semiarido,semiarido,IDH,popporte)%>%
unique()%>%
knitr::kable(.,"simple")| macrorregiao | regiao.de.saude | mun | semiarido | IDH | popporte | |
|---|---|---|---|---|---|---|
| 2 | Extremo Sul | Teixeira de Freitas | CARAVELAS | 0 | MEDIO | Pequeno Porte II |
| 1 | Leste | Salvador | CANDEIAS | 0 | ALTO | Médio Porte |
bahia_pop_long%>%dplyr::filter(mun%in%chik_notdg)%>%
dplyr::filter(ano==2016)%>%
dplyr::select(macrorregiao,regiao.de.saude,mun, pop, semiarido,bioma_2014,newtipology,IDH,popporte)%>%
dplyr::mutate(newtipology = case_when(
newtipology== 1 ~ 'intermediarioadjacente',
newtipology== 2 ~ 'intermediarioremoto',
newtipology== 3 ~ 'ruraladjacente',
newtipology== 4 ~ 'ruralremoto',
newtipology== 5 ~ 'Urbano'))%>%
dplyr::mutate(bioma_2014 = case_when(
bioma_2014== 1 ~ 'Floresta',
bioma_2014== 2 ~ 'Natural não florestal',
bioma_2014== 3 ~ 'Agropecuária',
bioma_2014== 4 ~ 'Area não vegetada',
bioma_2014== 5 ~ 'Corpos d’água'))%>%
dplyr::mutate(semiarido = case_when(
semiarido== 0 ~ 'regiao nao seca',
semiarido== 1 ~ 'regiao arida'))%>%
# sort_asc(macrorregiao, regiao.de.saude, mun,semiarido,IDH,popporte)%>%
group_by(semiarido)%>%
dfSummary(style='multiline', graph.col = FALSE)Data Frame Summary
bahia_pop_long
Group: semiarido = regiao nao seca
Dimensions: 2 x 9
Duplicates: 0
---------------------------------------------------------------------------------------------------------------------------------
No Variable Label Stats / Values Freqs (% of Valid) Valid Missing
---- ----------------- --------------------------------------- ------------------------ -------------------- ---------- ---------
1 macrorregiao 1. Extremo Sul 1 (50.0%) 2 0
[character] 2. Leste 1 (50.0%) (100.0%) (0.0%)
2 regiao.de.saude 1. Salvador 1 (50.0%) 2 0
[character] 2. Teixeira de Freitas 1 (50.0%) (100.0%) (0.0%)
3 mun 1. CANDEIAS 1 (50.0%) 2 0
[character] 2. CARAVELAS 1 (50.0%) (100.0%) (0.0%)
4 pop Min : 9.5 9.52 : 1 (50.0%) 2 0
[numeric] Mean : 182.1 354.77 : 1 (50.0%) (100.0%) (0.0%)
Max : 354.8
6 bioma_2014 1. Agropecuária 1 (50.0%) 2 0
[character] 2. Floresta 1 (50.0%) (100.0%) (0.0%)
7 newtipology 1. ruraladjacente 1 (50.0%) 2 0
[character] 2. Urbano 1 (50.0%) (100.0%) (0.0%)
8 IDH 1. ALTO 1 (50.0%) 2 0
[character] 2. MEDIO 1 (50.0%) (100.0%) (0.0%)
9 popporte Porte da Populacao segundo Censo IBGE 1. Médio Porte 1 (50.0%) 2 0
[character] 2010 2. Pequeno Porte II 1 (50.0%) (100.0%) (0.0%)
---------------------------------------------------------------------------------------------------------------------------------dg_uni_zk<-intersect(dg_uni_16171819,zk_uni_16171819)
dg_uni_zk[1] "BARREIRAS" "EUNAPOLIS" "FEIRA DE SANTANA"
[4] "ILHEUS" "LAURO DE FREITAS" "REMANSO"
[7] "RIBEIRA DO POMBAL" "SALVADOR" "TEIXEIRA DE FREITAS"#library("prob")
dg_zk<-setdiff(dg_uni_zk,municipios_todosanos)
dg_zk[1] "LAURO DE FREITAS" "REMANSO" bahia_pop_long%>%dplyr::filter(mun%in%dg_zk)%>%
dplyr::filter(ano==2016)%>%
dplyr::select(macrorregiao,regiao.de.saude,mun, semiarido,IDH,popporte,newtipology,bioma_2014)%>%
dplyr::mutate(newtipology = case_when(
newtipology== 1 ~ 'intermediarioadjacente',
newtipology== 2 ~ 'intermediarioremoto',
newtipology== 3 ~ 'ruraladjacente',
newtipology== 4 ~ 'ruralremoto',
newtipology== 5 ~ 'Urbano'))%>%
dplyr::mutate(bioma_2014 = case_when(
bioma_2014== 1 ~ 'Floresta',
bioma_2014== 2 ~ 'Natural não florestal',
bioma_2014== 3 ~ 'Agropecuária',
bioma_2014== 4 ~ 'Area não vegetada',
bioma_2014== 5 ~ 'Corpos d’água'))%>%
dplyr::mutate(semiarido = case_when(
semiarido== 0 ~ 'regiao nao seca',
semiarido== 1 ~ 'regiao arida'))%>%
sort_asc(macrorregiao, regiao.de.saude, mun,semiarido,semiarido,IDH,popporte)%>%
unique()%>%
knitr::kable(.,"simple")| macrorregiao | regiao.de.saude | mun | semiarido | IDH | popporte | newtipology | bioma_2014 |
|---|---|---|---|---|---|---|---|
| Leste | Salvador | LAURO DE FREITAS | regiao nao seca | MUITO ALTO | Grande Porte | Urbano | Area não vegetada |
| Norte | Juazeiro | REMANSO | regiao arida | MEDIO | Pequeno Porte II | intermediarioadjacente | Floresta |
bahia_pop_long%>%dplyr::filter(mun%in%dg_zk)%>%
dplyr::filter(ano==2016)%>%
dplyr::select(macrorregiao,regiao.de.saude,mun, pop, semiarido,bioma_2014,newtipology,IDH,popporte)%>%
dplyr::mutate(newtipology = case_when(
newtipology== 1 ~ 'intermediarioadjacente',
newtipology== 2 ~ 'intermediarioremoto',
newtipology== 3 ~ 'ruraladjacente',
newtipology== 4 ~ 'ruralremoto',
newtipology== 5 ~ 'Urbano'))%>%
dplyr::mutate(bioma_2014 = case_when(
bioma_2014== 1 ~ 'Floresta',
bioma_2014== 2 ~ 'Natural não florestal',
bioma_2014== 3 ~ 'Agropecuária',
bioma_2014== 4 ~ 'Area não vegetada',
bioma_2014== 5 ~ 'Corpos d’água'))%>%
dplyr::mutate(semiarido = case_when(
semiarido== 0 ~ 'regiao nao seca',
semiarido== 1 ~ 'regiao arida'))%>%
# sort_asc(macrorregiao, regiao.de.saude, mun,semiarido,IDH,popporte)%>%
group_by(semiarido)%>%
dfSummary(style='multiline', graph.col = FALSE)Data Frame Summary
bahia_pop_long
Group: semiarido = regiao arida
Dimensions: 1 x 9
Duplicates: 0
------------------------------------------------------------------------------------------------------------------------------------
No Variable Label Stats / Values Freqs (% of Valid) Valid Missing
---- ----------------- --------------------------------------- --------------------------- -------------------- ---------- ---------
1 macrorregiao 1. Norte 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
2 regiao.de.saude 1. Juazeiro 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
3 mun 1. REMANSO 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
4 pop 1 distinct value 9.07 : 1 (100.0%) 1 0
[numeric] (100.0%) (0.0%)
6 bioma_2014 1. Floresta 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
7 newtipology 1. intermediarioadjacente 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
8 IDH 1. MEDIO 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
9 popporte Porte da Populacao segundo Censo IBGE 1. Pequeno Porte II 1 (100.0%) 1 0
[character] 2010 (100.0%) (0.0%)
------------------------------------------------------------------------------------------------------------------------------------
Group: semiarido = regiao nao seca
Dimensions: 1 x 9
Duplicates: 0
---------------------------------------------------------------------------------------------------------------------------------
No Variable Label Stats / Values Freqs (% of Valid) Valid Missing
---- ----------------- --------------------------------------- ---------------------- ---------------------- ---------- ---------
1 macrorregiao 1. Leste 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
2 regiao.de.saude 1. Salvador 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
3 mun 1. LAURO DE FREITAS 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
4 pop 1 distinct value 3375.43 : 1 (100.0%) 1 0
[numeric] (100.0%) (0.0%)
6 bioma_2014 1. Area não vegetada 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
7 newtipology 1. Urbano 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
8 IDH 1. MUITO ALTO 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
9 popporte Porte da Populacao segundo Censo IBGE 1. Grande Porte 1 (100.0%) 1 0
[character] 2010 (100.0%) (0.0%)
---------------------------------------------------------------------------------------------------------------------------------zk_notdg<-setdiff(zk_uni_16171819,dg_uni_16171819)
zk_notdg[1] "ITABELA" "PRADO" bahia_pop_long%>%dplyr::filter(mun%in%zk_notdg)%>%
dplyr::filter(ano==2016)%>%
dplyr::select(macrorregiao,regiao.de.saude,mun, semiarido,IDH,popporte,newtipology,bioma_2014)%>%
dplyr::mutate(newtipology = case_when(
newtipology== 1 ~ 'intermediarioadjacente',
newtipology== 2 ~ 'intermediarioremoto',
newtipology== 3 ~ 'ruraladjacente',
newtipology== 4 ~ 'ruralremoto',
newtipology== 5 ~ 'Urbano'))%>%
dplyr::mutate(bioma_2014 = case_when(
bioma_2014== 1 ~ 'Floresta',
bioma_2014== 2 ~ 'Natural não florestal',
bioma_2014== 3 ~ 'Agropecuária',
bioma_2014== 4 ~ 'Area não vegetada',
bioma_2014== 5 ~ 'Corpos d’água'))%>%
dplyr::mutate(semiarido = case_when(
semiarido== 0 ~ 'regiao nao seca',
semiarido== 1 ~ 'regiao arida'))%>%
sort_asc(macrorregiao, regiao.de.saude, mun,semiarido,semiarido,IDH,popporte)%>%
unique()%>%
knitr::kable(.,"simple")| macrorregiao | regiao.de.saude | mun | semiarido | IDH | popporte | newtipology | bioma_2014 |
|---|---|---|---|---|---|---|---|
| Extremo Sul | Porto Seguro | ITABELA | regiao nao seca | MEDIO | Pequeno Porte II | Urbano | Agropecuária |
| Extremo Sul | Teixeira de Freitas | PRADO | regiao nao seca | MEDIO | Pequeno Porte II | ruraladjacente | Agropecuária |
bahia_pop_long%>%dplyr::filter(mun%in%zk_notdg)%>%
dplyr::filter(ano==2016)%>%
dplyr::select(macrorregiao,regiao.de.saude,mun, pop, semiarido,bioma_2014,newtipology,IDH,popporte)%>%
dplyr::mutate(newtipology = case_when(
newtipology== 1 ~ 'intermediarioadjacente',
newtipology== 2 ~ 'intermediarioremoto',
newtipology== 3 ~ 'ruraladjacente',
newtipology== 4 ~ 'ruralremoto',
newtipology== 5 ~ 'Urbano'))%>%
dplyr::mutate(bioma_2014 = case_when(
bioma_2014== 1 ~ 'Floresta',
bioma_2014== 2 ~ 'Natural não florestal',
bioma_2014== 3 ~ 'Agropecuária',
bioma_2014== 4 ~ 'Area não vegetada',
bioma_2014== 5 ~ 'Corpos d’água'))%>%
dplyr::mutate(semiarido = case_when(
semiarido== 0 ~ 'regiao nao seca',
semiarido== 1 ~ 'regiao arida'))%>%
sort_asc(macrorregiao, regiao.de.saude, mun,semiarido,IDH,popporte)%>%
group_by(semiarido)%>%
dfSummary(style='multiline', graph.col = FALSE)Data Frame Summary
bahia_pop_long
Group: semiarido = regiao nao seca
Dimensions: 2 x 9
Duplicates: 0
-----------------------------------------------------------------------------------------
No Variable Stats / Values Freqs (% of Valid) Valid Missing
---- ----------------- ------------------------ -------------------- ---------- ---------
1 macrorregiao 1. Extremo Sul 2 (100.0%) 2 0
[character] (100.0%) (0.0%)
2 regiao.de.saude 1. Porto Seguro 1 (50.0%) 2 0
[character] 2. Teixeira de Freitas 1 (50.0%) (100.0%) (0.0%)
3 mun 1. ITABELA 1 (50.0%) 2 0
[character] 2. PRADO 1 (50.0%) (100.0%) (0.0%)
4 pop Min : 17.3 17.34 : 1 (50.0%) 2 0
[numeric] Mean : 25.6 33.78 : 1 (50.0%) (100.0%) (0.0%)
Max : 33.8
6 bioma_2014 1. Agropecuária 2 (100.0%) 2 0
[character] (100.0%) (0.0%)
7 newtipology 1. ruraladjacente 1 (50.0%) 2 0
[character] 2. Urbano 1 (50.0%) (100.0%) (0.0%)
8 IDH 1. MEDIO 2 (100.0%) 2 0
[character] (100.0%) (0.0%)
9 popporte 1. Pequeno Porte II 2 (100.0%) 2 0
[character] (100.0%) (0.0%)
-----------------------------------------------------------------------------------------ck_uni_zk<-intersect(ck_uni_16171819,zk_uni_16171819)
ck_uni_zk[1] "BARREIRAS" "EUNAPOLIS" "FEIRA DE SANTANA"
[4] "ILHEUS" "RIBEIRA DO POMBAL" "SALVADOR"
[7] "TEIXEIRA DE FREITAS"#library("prob")
ck_zk<-setdiff(ck_uni_zk,municipios_todosanos)
ck_zkcharacter(0)zk_notchik<-setdiff(zk_uni_16171819,ck_uni_16171819)
zk_notchik[1] "ITABELA" "LAURO DE FREITAS" "PRADO" "REMANSO" bahia_pop_long%>%dplyr::filter(mun%in%zk_notchik)%>%
dplyr::filter(ano==2016)%>%
dplyr::select(macrorregiao,regiao.de.saude,mun, semiarido,IDH,popporte,newtipology,bioma_2014)%>%
dplyr::mutate(newtipology = case_when(
newtipology== 1 ~ 'intermediarioadjacente',
newtipology== 2 ~ 'intermediarioremoto',
newtipology== 3 ~ 'ruraladjacente',
newtipology== 4 ~ 'ruralremoto',
newtipology== 5 ~ 'Urbano'))%>%
dplyr::mutate(bioma_2014 = case_when(
bioma_2014== 1 ~ 'Floresta',
bioma_2014== 2 ~ 'Natural não florestal',
bioma_2014== 3 ~ 'Agropecuária',
bioma_2014== 4 ~ 'Area não vegetada',
bioma_2014== 5 ~ 'Corpos d’água'))%>%
dplyr::mutate(semiarido = case_when(
semiarido== 0 ~ 'regiao nao seca',
semiarido== 1 ~ 'regiao arida'))%>%
sort_asc(macrorregiao, regiao.de.saude, mun,semiarido,semiarido,IDH,popporte)%>%
unique()%>%
knitr::kable(.,"simple")| macrorregiao | regiao.de.saude | mun | semiarido | IDH | popporte | newtipology | bioma_2014 | |
|---|---|---|---|---|---|---|---|---|
| 1 | Extremo Sul | Porto Seguro | ITABELA | regiao nao seca | MEDIO | Pequeno Porte II | Urbano | Agropecuária |
| 3 | Extremo Sul | Teixeira de Freitas | PRADO | regiao nao seca | MEDIO | Pequeno Porte II | ruraladjacente | Agropecuária |
| 2 | Leste | Salvador | LAURO DE FREITAS | regiao nao seca | MUITO ALTO | Grande Porte | Urbano | Area não vegetada |
| 4 | Norte | Juazeiro | REMANSO | regiao arida | MEDIO | Pequeno Porte II | intermediarioadjacente | Floresta |
bahia_pop_long%>%dplyr::filter(mun%in%zk_notchik)%>%
dplyr::filter(ano==2016)%>%
dplyr::select(macrorregiao,regiao.de.saude,mun, pop, semiarido,bioma_2014,newtipology,IDH,popporte)%>%
dplyr::mutate(newtipology = case_when(
newtipology== 1 ~ 'intermediarioadjacente',
newtipology== 2 ~ 'intermediarioremoto',
newtipology== 3 ~ 'ruraladjacente',
newtipology== 4 ~ 'ruralremoto',
newtipology== 5 ~ 'Urbano'))%>%
dplyr::mutate(bioma_2014 = case_when(
bioma_2014== 1 ~ 'Floresta',
bioma_2014== 2 ~ 'Natural não florestal',
bioma_2014== 3 ~ 'Agropecuária',
bioma_2014== 4 ~ 'Area não vegetada',
bioma_2014== 5 ~ 'Corpos d’água'))%>%
dplyr::mutate(semiarido = case_when(
semiarido== 0 ~ 'regiao nao seca',
semiarido== 1 ~ 'regiao arida'))%>%
sort_asc(macrorregiao, regiao.de.saude, mun,semiarido,semiarido,IDH,popporte)%>%
group_by(semiarido)%>%
dfSummary(style='multiline', graph.col = FALSE)Data Frame Summary
bahia_pop_long
Group: semiarido = regiao arida
Dimensions: 1 x 9
Duplicates: 0
--------------------------------------------------------------------------------------------
No Variable Stats / Values Freqs (% of Valid) Valid Missing
---- ----------------- --------------------------- -------------------- ---------- ---------
1 macrorregiao 1. Norte 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
2 regiao.de.saude 1. Juazeiro 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
3 mun 1. REMANSO 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
4 pop 1 distinct value 9.07 : 1 (100.0%) 1 0
[numeric] (100.0%) (0.0%)
6 bioma_2014 1. Floresta 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
7 newtipology 1. intermediarioadjacente 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
8 IDH 1. MEDIO 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
9 popporte 1. Pequeno Porte II 1 (100.0%) 1 0
[character] (100.0%) (0.0%)
--------------------------------------------------------------------------------------------
Group: semiarido = regiao nao seca
Dimensions: 3 x 9
Duplicates: 0
-----------------------------------------------------------------------------------------------
No Variable Stats / Values Freqs (% of Valid) Valid Missing
---- ----------------- ----------------------------- --------------------- ---------- ---------
1 macrorregiao 1. Extremo Sul 2 (66.7%) 3 0
[character] 2. Leste 1 (33.3%) (100.0%) (0.0%)
2 regiao.de.saude 1. Porto Seguro 1 (33.3%) 3 0
[character] 2. Salvador 1 (33.3%) (100.0%) (0.0%)
3. Teixeira de Freitas 1 (33.3%)
3 mun 1. ITABELA 1 (33.3%) 3 0
[character] 2. LAURO DE FREITAS 1 (33.3%) (100.0%) (0.0%)
3. PRADO 1 (33.3%)
4 pop Mean (sd) : 1142.2 (1934.1) 17.34 : 1 (33.3%) 3 0
[numeric] min < med < max: 33.78 : 1 (33.3%) (100.0%) (0.0%)
17.3 < 33.8 < 3375.4 3375.43 : 1 (33.3%)
IQR (CV) : 1679 (1.7)
6 bioma_2014 1. Agropecuária 2 (66.7%) 3 0
[character] 2. Area não vegetada 1 (33.3%) (100.0%) (0.0%)
7 newtipology 1. ruraladjacente 1 (33.3%) 3 0
[character] 2. Urbano 2 (66.7%) (100.0%) (0.0%)
8 IDH 1. MEDIO 2 (66.7%) 3 0
[character] 2. MUITO ALTO 1 (33.3%) (100.0%) (0.0%)
9 popporte 1. Grande Porte 1 (33.3%) 3 0
[character] 2. Pequeno Porte II 2 (66.7%) (100.0%) (0.0%)
-----------------------------------------------------------------------------------------------bahia_pop_long%>%dplyr::filter(macrorregiao== "Extremo Sul")%>%
dplyr::filter(ano==2016)%>%
dplyr::select(macrorregiao,regiao.de.saude,mun, semiarido,IDH,popporte,newtipology,bioma_2014)%>%
dplyr::mutate(newtipology = case_when(
newtipology== 1 ~ 'intermediarioadjacente',
newtipology== 2 ~ 'intermediarioremoto',
newtipology== 3 ~ 'ruraladjacente',
newtipology== 4 ~ 'ruralremoto',
newtipology== 5 ~ 'Urbano'))%>%
dplyr::mutate(bioma_2014 = case_when(
bioma_2014== 1 ~ 'Floresta',
bioma_2014== 2 ~ 'Natural não florestal',
bioma_2014== 3 ~ 'Agropecuária',
bioma_2014== 4 ~ 'Area não vegetada',
bioma_2014== 5 ~ 'Corpos d’água'))%>%
dplyr::mutate(semiarido = case_when(
semiarido== 0 ~ 'regiao nao seca',
semiarido== 1 ~ 'regiao arida'))%>%
sort_asc(macrorregiao, regiao.de.saude, mun,semiarido,semiarido,IDH)%>%
group_by(semiarido)%>%
unique()%>%
dfSummary(style='multiline', graph.col = FALSE)Data Frame Summary
bahia_pop_long
Group: semiarido = regiao nao seca
Dimensions: 21 x 8
Duplicates: 0
--------------------------------------------------------------------------------------------
No Variable Stats / Values Freqs (% of Valid) Valid Missing
---- ----------------- --------------------------- -------------------- ---------- ---------
1 macrorregiao 1. Extremo Sul 21 (100.0%) 21 0
[character] (100.0%) (0.0%)
2 regiao.de.saude 1. Porto Seguro 8 (38.1%) 21 0
[character] 2. Teixeira de Freitas 13 (61.9%) (100.0%) (0.0%)
3 mun 1. ALCOBACA 1 ( 4.8%) 21 0
[character] 2. BELMONTE 1 ( 4.8%) (100.0%) (0.0%)
3. CARAVELAS 1 ( 4.8%)
4. EUNAPOLIS 1 ( 4.8%)
5. GUARATINGA 1 ( 4.8%)
6. IBIRAPUA 1 ( 4.8%)
7. ITABELA 1 ( 4.8%)
8. ITAGIMIRIM 1 ( 4.8%)
9. ITAMARAJU 1 ( 4.8%)
10. ITANHEM 1 ( 4.8%)
[ 11 others ] 11 (52.4%)
5 IDH 1. ALTO 10 (47.6%) 21 0
[character] 2. MEDIO 11 (52.4%) (100.0%) (0.0%)
6 popporte 1. Grande Porte 3 (14.3%) 21 0
[character] 2. Médio Porte 1 ( 4.8%) (100.0%) (0.0%)
3. Pequeno Porte I 6 (28.6%)
4. Pequeno Porte II 11 (52.4%)
7 newtipology 1. intermediarioadjacente 6 (28.6%) 21 0
[character] 2. ruraladjacente 9 (42.9%) (100.0%) (0.0%)
3. Urbano 6 (28.6%)
8 bioma_2014 1. Agropecuária 15 (71.4%) 21 0
[character] 2. Floresta 6 (28.6%) (100.0%) (0.0%)
--------------------------------------------------------------------------------------------bahia_pop_long%>%dplyr::filter(macrorregiao== "Sul")%>%
dplyr::filter(ano==2016)%>%
dplyr::select(macrorregiao,regiao.de.saude,mun, semiarido,IDH,popporte,newtipology,bioma_2014)%>%
dplyr::mutate(newtipology = case_when(
newtipology== 1 ~ 'intermediarioadjacente',
newtipology== 2 ~ 'intermediarioremoto',
newtipology== 3 ~ 'ruraladjacente',
newtipology== 4 ~ 'ruralremoto',
newtipology== 5 ~ 'Urbano'))%>%
dplyr::mutate(bioma_2014 = case_when(
bioma_2014== 1 ~ 'Floresta',
bioma_2014== 2 ~ 'Natural não florestal',
bioma_2014== 3 ~ 'Agropecuária',
bioma_2014== 4 ~ 'Area não vegetada',
bioma_2014== 5 ~ 'Corpos d’água'))%>%
dplyr::mutate(semiarido = case_when(
semiarido== 0 ~ 'regiao nao seca',
semiarido== 1 ~ 'regiao arida'))%>%
sort_asc(macrorregiao, regiao.de.saude, mun,semiarido,IDH)%>%
group_by(semiarido)%>%
unique()%>%
dfSummary(style='multiline', graph.col = FALSE)Data Frame Summary
bahia_pop_long
Group: semiarido = regiao arida
Dimensions: 17 x 8
Duplicates: 0
--------------------------------------------------------------------------------------------
No Variable Stats / Values Freqs (% of Valid) Valid Missing
---- ----------------- --------------------------- -------------------- ---------- ---------
1 macrorregiao 1. Sul 17 (100.0%) 17 0
[character] (100.0%) (0.0%)
2 regiao.de.saude 1. Jequie 17 (100.0%) 17 0
[character] (100.0%) (0.0%)
3 mun 1. BOA NOVA 1 ( 5.9%) 17 0
[character] 2. BREJOES 1 ( 5.9%) (100.0%) (0.0%)
3. CRAVOLANDIA 1 ( 5.9%)
4. IRAJUBA 1 ( 5.9%)
5. IRAMAIA 1 ( 5.9%)
6. ITAGI 1 ( 5.9%)
7. ITAQUARA 1 ( 5.9%)
8. ITIRUCU 1 ( 5.9%)
9. JAGUAQUARA 1 ( 5.9%)
10. JEQUIE 1 ( 5.9%)
[ 7 others ] 7 (41.2%)
5 IDH 1. ALTO 3 (17.6%) 17 0
[character] 2. MEDIO 14 (82.4%) (100.0%) (0.0%)
6 popporte 1. Grande Porte 1 ( 5.9%) 17 0
[character] 2. Médio Porte 1 ( 5.9%) (100.0%) (0.0%)
3. Pequeno Porte I 14 (82.4%)
4. Pequeno Porte II 1 ( 5.9%)
7 newtipology 1. intermediarioadjacente 4 (23.5%) 17 0
[character] 2. ruraladjacente 11 (64.7%) (100.0%) (0.0%)
3. Urbano 2 (11.8%)
8 bioma_2014 1. Agropecuária 10 (58.8%) 17 0
[character] 2. Floresta 7 (41.2%) (100.0%) (0.0%)
--------------------------------------------------------------------------------------------
Group: semiarido = regiao nao seca
Dimensions: 51 x 8
Duplicates: 0
--------------------------------------------------------------------------------------------
No Variable Stats / Values Freqs (% of Valid) Valid Missing
---- ----------------- --------------------------- -------------------- ---------- ---------
1 macrorregiao 1. Sul 51 (100.0%) 51 0
[character] (100.0%) (0.0%)
2 regiao.de.saude 1. Ilheus 8 (15.7%) 51 0
[character] 2. Itabuna 22 (43.1%) (100.0%) (0.0%)
3. Jequie 9 (17.6%)
4. Valenca 12 (23.5%)
3 mun 1. AIQUARA 1 ( 2.0%) 51 0
[character] 2. ALMADINA 1 ( 2.0%) (100.0%) (0.0%)
3. APUAREMA 1 ( 2.0%)
4. ARATACA 1 ( 2.0%)
5. AURELINO LEAL 1 ( 2.0%)
6. BARRA DO ROCHA 1 ( 2.0%)
7. BARRO PRETO 1 ( 2.0%)
8. BUERAREMA 1 ( 2.0%)
9. CAIRU 1 ( 2.0%)
10. CAMACAN 1 ( 2.0%)
[ 41 others ] 41 (80.4%)
5 IDH 1. ALTO 11 (21.6%) 51 0
[character] 2. MEDIO 40 (78.4%) (100.0%) (0.0%)
6 popporte 1. Grande Porte 2 ( 3.9%) 51 0
[character] 2. Médio Porte 1 ( 2.0%) (100.0%) (0.0%)
3. Pequeno Porte I 33 (64.7%)
4. Pequeno Porte II 15 (29.4%)
7 newtipology 1. intermediarioadjacente 16 (31.4%) 51 0
[character] 2. ruraladjacente 28 (54.9%) (100.0%) (0.0%)
3. Urbano 7 (13.7%)
8 bioma_2014 1. Agropecuária 13 (25.5%) 51 0
[character] 2. Floresta 38 (74.5%) (100.0%) (0.0%)
--------------------------------------------------------------------------------------------bahia_pop_long%>%dplyr::filter(macrorregiao== "Centro-Leste")%>%
dplyr::filter(ano==2016)%>%
dplyr::select(macrorregiao,regiao.de.saude,mun, semiarido,IDH,popporte,newtipology,bioma_2014)%>%
dplyr::mutate(newtipology = case_when(
newtipology== 1 ~ 'intermediarioadjacente',
newtipology== 2 ~ 'intermediarioremoto',
newtipology== 3 ~ 'ruraladjacente',
newtipology== 4 ~ 'ruralremoto',
newtipology== 5 ~ 'Urbano'))%>%
dplyr::mutate(bioma_2014 = case_when(
bioma_2014== 1 ~ 'Floresta',
bioma_2014== 2 ~ 'Natural não florestal',
bioma_2014== 3 ~ 'Agropecuária',
bioma_2014== 4 ~ 'Area não vegetada',
bioma_2014== 5 ~ 'Corpos d’água'))%>%
dplyr::mutate(semiarido = case_when(
semiarido== 0 ~ 'regiao nao seca',
semiarido== 1 ~ 'regiao arida'))%>%
sort_asc(macrorregiao, regiao.de.saude, mun,semiarido,IDH)%>%
unique()%>%
group_by(semiarido)%>%
dfSummary(style='multiline', graph.col = FALSE)Data Frame Summary
bahia_pop_long
Group: semiarido = regiao arida
Dimensions: 65 x 8
Duplicates: 0
--------------------------------------------------------------------------------------------
No Variable Stats / Values Freqs (% of Valid) Valid Missing
---- ----------------- --------------------------- -------------------- ---------- ---------
1 macrorregiao 1. Centro-Leste 65 (100.0%) 65 0
[character] (100.0%) (0.0%)
2 regiao.de.saude 1. Feira de Santana 21 (32.3%) 65 0
[character] 2. Itaberaba 14 (21.5%) (100.0%) (0.0%)
3. Seabra 11 (16.9%)
4. Serrinha 19 (29.2%)
3 mun 1. ABAIRA 1 ( 1.5%) 65 0
[character] 2. AGUA FRIA 1 ( 1.5%) (100.0%) (0.0%)
3. ANDARAI 1 ( 1.5%)
4. ANGUERA 1 ( 1.5%)
5. ANTONIO CARDOSO 1 ( 1.5%)
6. ARACI 1 ( 1.5%)
7. BAIXA GRANDE 1 ( 1.5%)
8. BARROCAS 1 ( 1.5%)
9. BIRITINGA 1 ( 1.5%)
10. BOA VISTA DO TUPIM 1 ( 1.5%)
[ 55 others ] 55 (84.6%)
5 IDH 1. ALTO 11 (16.9%) 65 0
[character] 2. BAIXO 3 ( 4.6%) (100.0%) (0.0%)
3. MEDIO 51 (78.5%)
6 popporte 1. Grande Porte 1 ( 1.5%) 65 0
[character] 2. Médio Porte 8 (12.3%) (100.0%) (0.0%)
3. Pequeno Porte I 41 (63.1%)
4. Pequeno Porte II 15 (23.1%)
7 newtipology 1. intermediarioadjacente 8 (12.3%) 65 0
[character] 2. ruraladjacente 48 (73.8%) (100.0%) (0.0%)
3. ruralremoto 5 ( 7.7%)
4. Urbano 4 ( 6.2%)
8 bioma_2014 1. Agropecuária 45 (69.2%) 65 0
[character] 2. Floresta 20 (30.8%) (100.0%) (0.0%)
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Group: semiarido = regiao nao seca
Dimensions: 7 x 8
Duplicates: 0
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No Variable Stats / Values Freqs (% of Valid) Valid Missing
---- ----------------- --------------------------- -------------------- ---------- ---------
1 macrorregiao 1. Centro-Leste 7 (100.0%) 7 0
[character] (100.0%) (0.0%)
2 regiao.de.saude 1. Feira de Santana 7 (100.0%) 7 0
[character] (100.0%) (0.0%)
3 mun 1. AMELIA RODRIGUES 1 (14.3%) 7 0
[character] 2. CONCEICAO DO JACUIPE 1 (14.3%) (100.0%) (0.0%)
3. CORACAO DE MARIA 1 (14.3%)
4. IRARA 1 (14.3%)
5. SAO GONCALO DOS CAMPOS 1 (14.3%)
6. TEODORO SAMPAIO 1 (14.3%)
7. TERRA NOVA 1 (14.3%)
5 IDH 1. ALTO 3 (42.9%) 7 0
[character] 2. MEDIO 4 (57.1%) (100.0%) (0.0%)
6 popporte 1. Pequeno Porte I 2 (28.6%) 7 0
[character] 2. Pequeno Porte II 5 (71.4%) (100.0%) (0.0%)
7 newtipology 1. intermediarioadjacente 2 (28.6%) 7 0
[character] 2. ruraladjacente 4 (57.1%) (100.0%) (0.0%)
3. Urbano 1 (14.3%)
8 bioma_2014 1. Agropecuária 7 (100.0%) 7 0
[character] (100.0%) (0.0%)
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