Process of Data Preparation

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######2.1.Casos #####
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covidcl_comunas <- 
  readr::read_csv("https://raw.githubusercontent.com/jorgeperezrojas/covid19-data/master/csv/long_confirmados_comunas_interpolado.csv", 
                  col_types = cols(codigo_region = col_character(), 
                                   codigo_comuna = col_character(), 
                                   fecha = col_date(format = "%m/%d/%Y")))

library(readr)
covidcl_comunas_minciencia <- 
  readr::read_csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto1/Covid-19_std.csv", 
                  col_types = cols(Fecha = col_date(format = "%Y-%m-%d")))%>%
  dplyr::rename("codigo_region"="Codigo region",
                "codigo_comuna"="Codigo comuna",
                "positivos_minciencia"="Casos confirmados")
#
esto=1
if(esto==0){
  print(paste0("aquí vemos que hay alrededor de 25 fechas, para una mayor cantidad de días que debiesen existir"," (n=",
               as.Date(today)-min(covidcl_comunas_minciencia$Fecha),")"))
  covidcl_comunas_minciencia%>%
    group_by(codigo_comuna)%>%
    summarise("Número de días distintos por comuna"=n())
}

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######2.2.Selección #####
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#formatear cada comuna con un número de días desde el inicio del primer caso en todo chile
#
covidcl_comunas<- covidcl_comunas %>% 
  dplyr::mutate(date_format=lubridate::parse_date_time(as.character(fecha), '%Y-%m-%d'))%>%
  dplyr::mutate(fech_num=as.numeric(as.Date(date_format)))%>%
  # filter(comuna %in% 
  #           c('Viña del Mar', 'Valparaíso', 'San Antonio', 'Iquique', 'Alto Hospicio',"Maipú", "Puente Alto", "Santiago", "San Bernardo", "El Bosque"))%>%
  arrange(codigo_region,codigo_comuna,date_format) %>% 
  group_by(codigo_comuna)%>%
  dplyr::mutate(ndays_zero=row_number())
#860 valores

#formatear cada comuna con un número de días desde el inicio del primer caso, de cada comuna
#
covidcl_comunas_descarte<-
  covidcl_comunas%>%
  group_by(codigo_comuna)%>%
  dplyr::filter(positivos_acumulados>0)%>%
  arrange(codigo_comuna,date_format) %>% 
  dplyr::mutate(ndays=row_number())%>%
  dplyr::select(codigo_comuna,date_format,ndays)

#hacer el join del inicio por cada comuna, con el resto de la base de datos.
covidcl_comunas<-
  covidcl_comunas %>% 
  dplyr::left_join(covidcl_comunas_descarte, by=c("codigo_comuna"="codigo_comuna","date_format"="date_format"))


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######2.3.Hacinamiento #####
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#https://redatam-ine.ine.cl/manuales/Manual-Usuario.pdf
# https://www.censo2017.cl/descargas/home/sintesis-de-resultados-censo2017.pdf

#Porcentaje de viviendas con hacinamiento:
#Corresponde al porcentaje de viviendas particulares ocupadas con moradores presentes que tienen igual o más de 2,5 personas por pieza de uso exclusivo como dormitorio. Este cálculo se realiza 
#considerando a todas las personas censadas en la vivienda y las piezas declaradas exclusivamente como dormitorio.

#VARIABLES INVOLUCRADAS
#En esta BD sólo es a nivel de viviendax
# - ID_ZONA_LOC= Identificador único de zona/localidad (1-16053)
# - NVIV= Número de la vivienda (1-4296)

# - P02= Ocupación de la vivienda, sólo con moradores presentes (1)
# - VIV_PART= Total de viviendas particulares: Corresponde al total de viviendas particulares efectivamente censadas
# - PERSONAS= Número total de personas: Corresponde al total de personas efectivamente censada
# - CANT_PER= Cantidad de persona (0-9999) 
# - P04= Número de piezas usadas exclusivamente como dormitorio /(0-6) /6 o más piezas
# P04 182.798 3,32%

# Reenumeración de variables desde base de datos de viviendas colectivas y personas en tránsito a base de datos de viviendas particulares
# P11
# P04COMUNA, SE RECONVIERTE P11COMUNA
#(1) Se excluyen las viviendas donde no se declaró información sobre las piezas de uso exclusivo para dormir.
#98 No aplica, 99 Missing

censo_2017_df <- readr::read_delim("https://dl.dropboxusercontent.com/s/bi1fqiq1d86n191/Microdato_Censo2017-Viviendas.csv?dl=0", 
                                   ";", escape_double = FALSE, trim_ws = TRUE,
                                   col_types = cols(`COMUNA` = col_character()))

censo_2017_df_HAC <-
  censo_2017_df%>%
  dplyr::filter(!P01 %in% c(8,9,10))%>% #Se descartan viviendas colectivas (Vivienda colectiva, Operativo personas en tránsito (no es vivienda), Operativo calle (no es vivienda))
  dplyr::filter(P02==1)%>%# sólo con moradores presentes (1) #baja a 5,521,482 desde 6,499,574
  dplyr::select(REGION, PROVINCIA, COMUNA, ID_ZONA_LOC,NVIV,CANT_PER, P02,P04)%>%
  #  dplyr::mutate(P04=if_else(as.numeric(P04)>6,NA_real_,as.numeric(P04)))%>% 
  # janitor::tabyl(P04) %>% #responden 0 piezas los siguientes:  17,413 0.003153682
  dplyr::mutate(HAC=as.numeric(CANT_PER)/as.numeric(P04))%>%
  dplyr::mutate(HAC=replace(HAC, is.na(HAC), 5))%>%
  dplyr::mutate(HAC=replace(HAC,HAC== Inf, 5))

#https://www.censo2017.cl/wp-content/uploads/2018/05/presentacion_de_la_segunda_entrega_de_resultados_censo2017.pdf  
#3,1 en el período al 2017. Se condice

#Viviendas Particulares Ocupadas con Moradores Presentes 5.508.441
censo_2017_df_cond_hac <-
  censo_2017_df_HAC%>%
  dplyr::filter(P04!=98|P04!=99)%>%
  #dplyr::filter(CANT_PER>0)%>% #lo decidí sacar para ver si rescataba más casos
  #dplyr::filter(P04>0)%>% #pruebo, porque puede que no tengan.  También, se
  #consideran hacinadas aquellas personas o familias que viven en viviendas sin habitaciones de uso exclusivo como dormitorio
  dplyr::mutate(cond_hac=if_else(HAC>2.4,1,0,1))%>%
  dplyr::mutate(nombre_region=dplyr::recode(as.character(REGION),
                                            "2"="Antofagasta",
                                            "9"="Araucanía",
                                            "15"="Arica y Parinacota",
                                            "3"="Atacama",
                                            "11"="Aysén",
                                            "8"="Biobío",
                                            "4"="Coquimbo",
                                            "10"="Los Lagos",
                                            "14"="Los Ríos",
                                            "12"="Magallanes",
                                            "7"="Maule",
                                            "13"="Metropolitana",
                                            "16"="Ñuble",
                                            "6"="O'Higgins",
                                            "1"="Tarapacá",
                                            "5"="Valparaíso"))%>%
  dplyr::arrange(COMUNA)%>%
  dplyr::group_by(COMUNA,cond_hac)%>%
  dplyr::summarise(n = n()) %>%
  dplyr::mutate(freq = n / sum(n))%>%
  tidyr::pivot_wider(names_from = cond_hac, values_from = c(n,freq))%>%
  dplyr::select(-freq_0)%>%
  dplyr::rename("perc_overcrowding"="freq_1")

#http://observatoriodoc.colabora.minvu.cl/Documentos%20compartidos/Metodolog%C3%ADa%20de%20c%C3%A1lculo%20del%20D%C3%A9ficit%20Habitacional%20Cuantitativo%20ajustada%20al%20Censo%202017.pdf
#debbería llegar a 7,3 nacional  
#Tabla 8: Índice de Hacinamiento (>2,4)
#Sin hacinamiento 4.936.302
#Hacinamiento medio 347.213
#Hacinamiento crítico 42.128
#hacinados, debisen ser 347213+42128= 389,341
#table(censo_2017_df_cond_hac$cond_hac)
#llegué al número= 389,341 , pero no al porcentaeje

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######2.4.Cuarentenas #####
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cuarentenas<- readr::read_csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto29/Cuarentenas-Totales.csv",
                              col_types = cols(`Fecha de Inicio` = col_datetime(format = "%Y-%m-%d %H:%M:%S"), 
                                               `Código CUT Comuna` = col_character()))%>%
  dplyr::rename("fecha_inicio"="Fecha de Inicio",
                "fecha_termino"="Fecha de Término",
                "codigo_comuna"="Código CUT Comuna")%>%
  dplyr::mutate(fech_ini_num=as.numeric(as.Date(fecha_inicio)))%>%
  dplyr::mutate(fech_ter_num=as.numeric(as.Date(fecha_termino)))%>%
  dplyr::select(-Detalle, -`Superficie en m2`,-`Perímetro en m`)%>%
  dplyr::mutate(Alcance_2=Alcance)%>%
  tidyr::pivot_wider(names_from = Alcance, values_from = Alcance)%>%
  dplyr::select(-fecha_inicio,-fecha_termino,-Nombre)%>%
  dplyr::rename("area_urb"="Área Urbana Completa","especifico"="Sector Específico","completa"="Comuna completa")

#!!!!
#MATCHEAR POR POBLACIÓN POR COMUNA!!!!
#https://github.com/MinCiencia/Datos-COVID19/blob/master/output/producto1/Covid-19_std.csv
#!!!

## veo si hay superposiciones entre las medidas aplicadas por día (digamos, días continuos, o en medio de una hora determinada en un mismo día)
covidcl_comuna_superp <-janitor::clean_names(sqldf("SELECT *
                             FROM cuarentenas AS x  
                             LEFT JOIN cuarentenas AS y 
                             ON x.codigo_comuna == y.codigo_comuna AND 
                             x.fech_ini_num >= y.fech_ini_num AND x.fech_ter_num <= y.fech_ter_num AND x.ID != y.ID"))%>%
  dplyr::filter(!is.na(fech_ini_num_2))

if (nrow(covidcl_comuna_superp)>0) {print("hay problemas, una comuna tiene distintas cuarentenas, pero aplicadas en un mismo día")}

#janitor::tabyl(ID)
require(sqldf)
#genero un match por intervalos, desde que inicia la cuarentena, hasta que termina
covidcl_comuna_cuarentena <- janitor::clean_names(sqldf("SELECT *
                 FROM covidcl_comunas AS x  
                 LEFT JOIN cuarentenas AS y 
                 ON x.codigo_comuna == y.codigo_comuna AND 
                 fech_num >= y.fech_ini_num AND fech_num <= y.fech_ter_num"))

covidcl_comuna_cuarentena %>%
  dplyr::select(-codigo_comuna_2)%>%
  dplyr::mutate(date_format=as.Date(fecha, "%m/%d/%y"))%>%
  #:#;#;#;#;#;#;#;#
  #veo si hay superposición de casos
  group_by(codigo_comuna, date_format)%>%
  dplyr::mutate(dup_dates_by_communes=n(),
                n_dist_alcance=n_distinct(alcance_2,na.rm=T), # ver si genero distintos escenarios en caso que sean distintas.
                order_dup_cases=row_number(), #para ordenar cuál saco
                discard= dplyr::if_else(dup_dates_by_communes>1&  
                                          n_dist_alcance>1& 
                                          order_dup_cases==1,1,0,0))%>%
  
  dplyr::filter(discard!=1)%>%
  dplyr::mutate(discard=dplyr::if_else(dup_dates_by_communes>1&
                                         order_dup_cases==1,1,0,0))%>%
  dplyr::filter(discard!=1)%>%
  #dplyr::filter(dup_dates_by_communes>1)%>% #para ver si siguen
  dplyr::ungroup()%>%
  #dplyr::filter(codigo_comuna %in% c("13101", "13201", "13401")) #para ver lso efectos en esas comunas en las que encontré problemas
  #LO QUE HAGO ES ADELANTAR LA SIGUIENTE MEDIDA, ABSORVIENDO EL DÍA Q SUPERPONE. ES DECIR, ME QUEDO CON LA MÁS RECIENTE.
  #:#;#;#;#;#;#;#;#
  dplyr::arrange(codigo_comuna, date_format)%>%
  group_by(codigo_comuna)%>%
  dplyr::mutate(positivos=positivos_acumulados-dplyr::lag(positivos_acumulados))%>%
  janitor::clean_names()%>%
  dplyr::mutate(ndays = tidyr::replace_na(ndays, 0))%>% #cuidado con esto
  dplyr::mutate(positivos= ifelse(positivos<0,0,positivos))%>% # los casos na's, 
  dplyr::mutate(positivos = tidyr::replace_na(positivos, 0))%>% #cuidado con esto
  dplyr::ungroup()%>%
  dplyr::left_join(censo_2017_df_cond_hac,by=c("codigo_comuna"="COMUNA"))%>%
  dplyr::mutate(alcance_2= replace(alcance_2, is.na(alcance_2), "Sin Cuarentena"))%>%
  dplyr::mutate(alcance_2= factor(alcance_2))%>%
  dplyr::mutate(casos_cuarentenas=ifelse(alcance_2!="Sin Cuarentena",positivos_acumulados,100000))%>%
  #dplyr::mutate(completa=factor(if_else(completa=="Comuna completa"|area_urb=="Área Urbana Completa",1,0,0)))%>%
  dplyr::mutate(completa=factor(if_else(completa=="Comuna completa",1,0,0)))%>%
  dplyr::mutate(comuna= stringr::str_replace(comuna, "á", "a"))%>%
  dplyr::mutate(comuna= stringr::str_replace(comuna, "é", "e"))%>%
  dplyr::mutate(comuna= stringr::str_replace(comuna, "í", "i"))%>%
  dplyr::mutate(comuna= stringr::str_replace(comuna, "ó", "o"))%>%
  dplyr::mutate(comuna= stringr::str_replace(comuna, "ú", "u"))%>%
  dplyr::mutate(comuna= stringr::str_replace(comuna, "ñ", "n"))%>%
  dplyr::mutate(comuna= stringr::str_replace(comuna, "Ñ", "N"))%>%
  dplyr::mutate(comuna= stringr::str_replace(comuna, "Á", "A"))%>%
  #dplyr::filter(date_format<=lubridate::parse_date_time("2020-06-15", 'Ymd'))%>%
  dplyr::mutate(alcance_3= stringr::str_replace(alcance_2, "Área Urbana Completa", "Complete Urb. Area"))%>%
  dplyr::mutate(alcance_3= stringr::str_replace(alcance_3, "Comuna completa", "Complete Commune"))%>%
  dplyr::mutate(alcance_3= stringr::str_replace(alcance_3, "Sector Específico", "Specific Sector"))%>%
  dplyr::mutate(alcance_3= stringr::str_replace(alcance_3, "Sin Cuarentena", "No Quarantine"))%>%
  dplyr::mutate(text= paste("Commune:", comuna,"<br> Cum.cases:",positivos_acumulados,"<br> Status:",alcance_3,"<br> Date:",date_format))%>%
  assign("covidcl_comuna_cuarentena_censo",.,envir=.GlobalEnv)

mostrar="no"
    if(mostrar=="si"){
linea_base_uah_2018%>%
  dplyr::anti_join(covidcl_comuna_cuarentena_censo_edad_sexo_ens_clear_def_mov_dths_tidy,c("comuna2"="comuna"))%>%
  distinct(comuna2)%>% copiar_nombres()
    }

comunas_cuarentena_completa_a_la_fecha<-
  covidcl_comuna_cuarentena_censo%>%
  dplyr::filter(completa==1)%>%
  dplyr::filter(date_format<=lubridate::parse_date_time(as.character(today), 'Ymd'))%>%
  distinct(comuna)

#ver casos con fechas 
fechas_sin_info_casos<-covidcl_comuna_cuarentena_censo %>% dplyr::filter(is.na(positivos))%>% 
  group_by(date_format)%>% summarise(n())

covidcl_comuna_cuarentena_censo%>%
  dplyr::mutate(con_cuar_hist= ifelse(comuna %in% as.character(unlist(comunas_cuarentena_completa_a_la_fecha)),1,0))%>%
  dplyr::group_by(codigo_comuna)%>%
  dplyr::mutate(cambios=if_else(completa==lag(completa),0,1,0))%>% 
  dplyr::mutate(cum_cambios=cumsum(cambios))%>% 
  dplyr::ungroup()%>%
  assign("covidcl_comuna_cuarentena_censo",.,envir=.GlobalEnv)

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######2.5.Edad Sexo CENSO#####
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#buscar los códigos, me parece q están en agrupado
#https://datosabiertos.ine.cl/dataviews/250576/censo-2017-microdatos-de-viviendas-segun-region-provincia-y-comuna/

template <- tempfile(fileext = ".xlsx")
httr::GET(url = "https://dl.dropbox.com/s/qgpjnoyx5kj82k0/Cantidad-de-Personas-por-Sexo-y-Edad.xlsx?dl=0",
httr::write_disk(template))
## Response [https://dl.dropboxusercontent.com/s/qgpjnoyx5kj82k0/Cantidad-de-Personas-por-Sexo-y-Edad.xlsx?dl=0]
##   Date: 2020-07-28 22:26
##   Status: 200
##   Content-Type: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
##   Size: 566 kB
## <ON DISK>  C:\Users\andre\AppData\Local\Temp\RtmpOccRHt\file6e0459bc5b53.xlsx
censo_sexo_edad <- readxl::read_excel(template, 
                                      sheet = "COMUNAS", col_types = c("skip", 
                                                                       "text", "text", "text", "text", "text", 
                                                                       "text", "text", "numeric", "numeric", 
                                                                       "numeric"))%>%
  dplyr::filter(`NOMBRE REGIÓN`!="PAÍS")%>%
  dplyr::rename("cod_comuna"=`Código Comuna`)%>%
  dplyr::filter(Edad %in% c("75 a 79","80 a 84","85 a 89","90 a 94", "95 a 99", "100 o más","Total Comunal"))

censo_sexo_edad_final<-
  censo_sexo_edad%>%
  dplyr::group_by(cod_comuna)%>% 
  #::summarize(pop_75_mas= sum(Edad!="Total Comunal"))
  dplyr::mutate(pop_75_mas= sum(TOTAL[Edad!="Total Comunal"])/TOTAL[Edad=="Total Comunal"])%>%
  dplyr::mutate(pop_85_mas= sum(TOTAL[!Edad %in% c("75 a 79","80 a 84","Total Comunal")])/TOTAL[Edad=="Total Comunal"])%>%
  dplyr::mutate(perc_mujeres= Mujeres/TOTAL)

      #censo_sexo_edad_final %>% 
      #  dplyr::filter(Edad=="Total Comunal") %>% 
      #  dplyr::select("NOMBRE REGIÓN","NOMBRE COMUNA", "cod_comuna") %>%
      #  data.frame()%>%
      #  copiar_nombres()

covidcl_comuna_cuarentena_censo_edad_sexo<-
              covidcl_comuna_cuarentena_censo %>%
                dplyr::left_join(dplyr::select(censo_sexo_edad_final, 6:13)%>% 
                             dplyr::filter(Edad=="Total Comunal"), by=c("codigo_comuna"="cod_comuna"))

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######2.6.ENS#####
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ENS_2016_2017_1_comuna <- haven::read_sav("https://dl.dropbox.com/s/pzu4yb2i0k1z3wa/Base%20de%20datos%20Encuesta%20Nacional%20de%20Salud%202016-2017%28ENS%29.Formulario%201_2_EX.MINSAL_EPI.%20%282%29_CIDI_SF_Comuna.sav?dl=0")%>%
  dplyr::mutate(diabetes= ifelse(Glucosa>=126|di3==1,1,0),diabetes2= ifelse(Glucosa>=126|m2p6==1,1,0))%>%
  dplyr::mutate(alta_dep_tabaco=ifelse(ta5==1|ta5==2|ta5==3,1,0))%>%
  dplyr::mutate(obesidad=ifelse(IMC>=30,1,0))

#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;
#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;
#
#Se entiende por “sospecha de Diabetes Mellitus” a la medición de glicemia en ayuno con resultados mayores o iguales a 126mg/dl.
#
#SOSPECHA DE DIABETES MELLITUS: actualmente sólo llego a un 12% sumando la opc 3, me queda afuera el 0,3%
#OJO, YO ESTOY VIENDO EL AUTORREPORTADO
# sin los pesos,tengo en la opc 1 un 14,2.
#no afecta en nada el conservar el estrato o el conglomerado o no. 
#debo llegar a un 12,3%
#1- SÍ 2- NO 3- NO RECUERDO
#
#Fumadores
#1 5 MINUTOS O MENOS
#2    6 A 30 MINUTOS
#3   31 A 60 MINUTOS
#4 MÁS DE 60 MINUTOS
#
#Categorías realizadas a partir de cálculo de índice de masa
#####Obesidad: IMC 30-39,9
#####Obesidad mórbida: IMC ≥ 40
#
#Se define “alta dependencia al tabaco” aquel encuestado que declara fumar antes de los 60 minutos después de despertar.
#Alta dependencia al tabaco (% de fumadores actuales).
#Fumadores #1 5 MINUTOS O MENOS #2    6 A 30 MINUTOS #3   31 A 60 MINUTOS #4 MÁS DE 60 MINUTOS

#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;
#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;#;

# 6233 1161, filas y variables
#"Fexp_F1p_Corr" "Fexp_F2p_Corr" "Fexp_F1F2p_Corr" "Fexp_EX1p_Corr" "Fexp_F1F2EX1p_Corr" "Fexp_EX2p_Corr" "Fexp_F1F2EX2p_Corr" 

write.table(data.frame(Hmisc::label(ENS_2016_2017_1_comuna)),"ENS_16_17_comuna.csv", sep="\t", quote = T)

#NO FUNCIONA PORQUE HAY VALORES PERDIDOS EN WEIGHTS

#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#
#
# sólo el con comuna tiene información de comunas
#
#
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_

library(survey)

#esto es para definir el diseño de la encuesta de acuerdo a ponderadores, conglomerados y estratos.
ENS_2016_2017_1_comuna_dsgn <-
         svydesign(
           #ids=~1,
           ids = ~Conglomerado, 
           data = ENS_2016_2017_1_comuna, 
           strata = ~Estrato,
           weights = ~Fexp_F1p_Corr,#Fexp_F1p_Corr. Ponderador Postestratificado 120 celdas, Truncado Abs. p99,5%
           nest= T) #podría sacarlo. no estoy seguro

#OTROS PONDERADORES
#####120 CELDAS= Fexp_F1p_Corr Fexp_F2p_Corr Fexp_F1F2p_Corr Fexp_EX1p_Corr Fexp_F1F2EX1p_Corr 
#####14  CELDAS= Fexp_EX2p_Corr Fexp_F1F2EX2p_Corr Fexp_EX3p_Corr Fexp_F1F2EX3p_Corr
no_tocar=5
if (no_tocar!=5){
  for (i in c("Fexp_F1p_Corr", "Fexp_F2p_Corr", "Fexp_F1F2p_Corr", "Fexp_EX1p_Corr", "Fexp_F1F2EX1p_Corr",
              "Fexp_EX2p_Corr", "Fexp_F1F2EX2p_Corr", "Fexp_EX3p_Corr", "Fexp_F1F2EX3p_Corr" )){
    assign(paste0("ENS_2016_2017_1_",i),
           svydesign(
             #ids=~1,
             ids = ~Conglomerado, 
             data = ENS_2016_2017_1_comuna, 
             strata = ~Estrato,
             weights = formula(paste0("~",i)),#Fexp_F1p_Corr. Ponderador Postestratificado 120 celdas, Truncado Abs. p99,5%
             nest= T), envir=.GlobalEnv)
  }
}
#_#_#_#_#_#_#_#_#_#_#_#_
invisible(
  c("Estimaciones")
  )

invisb=1
if (invisb==10){
    #_#_#_#_#_#_#_#_#_#_#_#_
    #NO PONDERADO
    prop.table(table(ENS_2016_2017_1_comuna$diabetes))
    prop.table(table(ENS_2016_2017_1_comuna$diabetes2))
    prop.table(table(ENS_2016_2017_1_comuna$alta_dep_tabaco))
    prop.table(table(ENS_2016_2017_1_comuna$obesidad))
    
    #Por sexo
    prop.table(table(ENS_2016_2017_1_comuna$diabetes,ENS_2016_2017_1_comuna$Sexo),2)
    prop.table(table(ENS_2016_2017_1_comuna$diabetes2,ENS_2016_2017_1_comuna$Sexo),2)
    prop.table(table(ENS_2016_2017_1_comuna$alta_dep_tabaco,ENS_2016_2017_1_comuna$Sexo))
    prop.table(table(ENS_2016_2017_1_comuna$obesidad,ENS_2016_2017_1_comuna$Sexo))
    
    #_#_#_#_#_#_
    #PONDERADO
    round(prop.table(svytable(~di3,ENS_2016_2017_1_comuna_dsgn)),3)
    round(prop.table(svytable(~m2p6,ENS_2016_2017_1_comuna_dsgn)),3)
    round(prop.table(svytable(~diabetes,ENS_2016_2017_1_comuna_dsgn)),3)
    round(prop.table(svytable(~diabetes2,ENS_2016_2017_1_comuna_dsgn)),3)
    round(prop.table(svytable(~alta_dep_tabaco,ENS_2016_2017_1_comuna_dsgn)),3)
    round(prop.table(svytable(~obesidad,ENS_2016_2017_1_comuna_dsgn)),3)
    
    #SEXO
    round(prop.table(svytable(~diabetes+Sexo,ENS_2016_2017_1_comuna_dsgn),2),3)
    # 1 HOMBRE #xta2  MUJER
    round(prop.table(svytable(~diabetes2+Sexo,ENS_2016_2017_1_comuna_dsgn),2),3)
    round(prop.table(svytable(~alta_dep_tabaco+Sexo,ENS_2016_2017_1_comuna_dsgn),2),3)
    round(prop.table(svytable(~obesidad+Sexo,ENS_2016_2017_1_comuna_dsgn),2),3)

    #por ej., Chiguallante Chiguayante está escrito mal en una.
    #ME PARECE QUE NO HAY N PARA ANDAR SACANDO RESULTADOS POR COMUNA
    copiar_nombres(head(arrange(data.frame(table(ENS_2016_2017_1_comuna$Comuna)),Freq),30))
    copiar_nombres(tail(arrange(data.frame(table(ENS_2016_2017_1_comuna$Comuna)),Freq),30))
    #NO SE VE REPRESENTATIVA A NIVEL COMUNAS
    #m2p6
}

#Genero bases de datos por comuna con proporciones
comunas_diabetes <-data.frame(round(prop.table(svytable(~diabetes+Comuna,ENS_2016_2017_1_comuna_dsgn),2),3))
comunas_diabetes2 <-data.frame(round(prop.table(svytable(~diabetes2+Comuna,ENS_2016_2017_1_comuna_dsgn),2),3))
comunas_tabaco<- data.frame(round(prop.table(svytable(~alta_dep_tabaco+Comuna,ENS_2016_2017_1_comuna_dsgn),2),3))
comunas_obesidad<- data.frame(round(prop.table(svytable(~obesidad+Comuna,ENS_2016_2017_1_comuna_dsgn),2),3))


template <- tempfile(fileext = ".xlsx")
httr::GET(url = "https://dl.dropbox.com/s/61ektijkh8cge9c/HOMOLOGACION_COMUNAS.xlsx?dl=0",
          httr::write_disk(template))
## Response [https://dl.dropboxusercontent.com/s/61ektijkh8cge9c/HOMOLOGACION_COMUNAS.xlsx?dl=0]
##   Date: 2020-07-28 22:27
##   Status: 200
##   Content-Type: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
##   Size: 14.4 kB
## <ON DISK>  C:\Users\andre\AppData\Local\Temp\RtmpOccRHt\file6e046cfb64b3.xlsx
HOMOLOGACION_COMUNAS <- readxl::read_excel(template)%>% 
                        dplyr::distinct(cod_comuna,.keep_all=T)%>%
                        data.frame()

comunas_diabetes_cod_comunas<-
  comunas_diabetes%>%
  dplyr::left_join(HOMOLOGACION_COMUNAS,by=c("Comuna"="Comuna"))%>%
  dplyr::select(cod_comuna, everything())%>%
  dplyr::group_by(cod_comuna,diabetes)%>%
  dplyr::mutate(rn=row_number())%>%
  dplyr::filter(rn==1)%>%
  dplyr::ungroup()%>%
  dplyr::filter(diabetes==1)%>%
  dplyr::select(1,4)%>%
  dplyr::mutate(cod_comuna=as.character(cod_comuna))%>%
  dplyr::rename("perc_diabetes"="Freq")

comunas_diabetes2_cod_comunas<-
  comunas_diabetes2%>%
  dplyr::left_join(HOMOLOGACION_COMUNAS,by=c("Comuna"="Comuna"))%>%
  dplyr::select(cod_comuna, everything())%>%
  dplyr::group_by(cod_comuna,diabetes2)%>%
  dplyr::mutate(rn=row_number())%>%
  dplyr::filter(rn==1)%>%
  dplyr::ungroup()%>%
  dplyr::filter(diabetes2==1)%>%
  dplyr::select(1,4)%>%
  dplyr::mutate(cod_comuna=as.character(cod_comuna))%>%
  dplyr::rename("perc_diabetes2"="Freq")

comunas_tabaco_cod_comunas<-
  comunas_tabaco%>%
  dplyr::left_join(HOMOLOGACION_COMUNAS,by=c("Comuna"="Comuna"))%>%
  dplyr::select(cod_comuna, everything())%>%
  dplyr::group_by(cod_comuna,alta_dep_tabaco)%>%
  dplyr::mutate(rn=row_number())%>%
  dplyr::filter(rn==1)%>%
  dplyr::ungroup()%>%
  dplyr::filter(alta_dep_tabaco==1)%>%
  dplyr::select(1,4)%>%
  dplyr::mutate(cod_comuna=as.character(cod_comuna))%>%
  dplyr::rename("perc_tabaco"="Freq")

comunas_obesidad_cod_comunas <-
  comunas_obesidad%>%
  dplyr::left_join(HOMOLOGACION_COMUNAS,by=c("Comuna"="Comuna"))%>%
  dplyr::select(cod_comuna, everything())%>%
  dplyr::group_by(cod_comuna,obesidad)%>%
  dplyr::mutate(rn=row_number())%>%
  dplyr::filter(rn==1)%>%
  dplyr::ungroup()%>%
  dplyr::filter(obesidad==1)%>%
  dplyr::select(1,4)%>%
  dplyr::mutate(cod_comuna=as.character(cod_comuna))%>%
  dplyr::rename("perc_obes"="Freq")

#JOIN de las bases.
covidcl_comuna_cuarentena_censo_edad_sexo_ens<-
  covidcl_comuna_cuarentena_censo_edad_sexo%>%
  dplyr::left_join(comunas_diabetes_cod_comunas,by=c("codigo_comuna"="cod_comuna"))%>%
  dplyr::left_join(comunas_diabetes2_cod_comunas,by=c("codigo_comuna"="cod_comuna"))%>%
  dplyr::left_join(comunas_tabaco_cod_comunas,by=c("codigo_comuna"="cod_comuna"))%>%
  dplyr::left_join(comunas_obesidad_cod_comunas,by=c("codigo_comuna"="cod_comuna"))%>%
  dplyr::select(-Edad,-fecha, -n_0,-n_1,-especifico,-area_urb,-discard,-alcance_2)%>%
  janitor::clean_names()

##Clearances

## New names:
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * `` -> ...6
## * `` -> ...7
## * ...
## New names:
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * `` -> ...6
## * `` -> ...9
## * ...
## New names:
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * `` -> ...6
## * `` -> ...7
## * ...

Deaths

defun_deis <-readr::read_csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto50/DefuncionesDEISPorComuna_std.csv",
                             col_types = cols(Fecha = col_date(format = "%Y-%m-%d")))%>% clean_names()
covidcl_comuna_cuarentena_censo_edad_sexo_ens_clear_def<-
covidcl_comuna_cuarentena_censo_edad_sexo_ens_clear%>%
  dplyr::left_join(dplyr::select(defun_deis,fecha, codigo_comuna,defunciones), by=c("codigo_comuna"="codigo_comuna","date_format"="fecha"))%>%
  dplyr::rename("defunciones_deis"="defunciones")

Mobility

feriados<- readr::read_csv("https://docs.google.com/spreadsheets/d/1Kd8SLHFLohGKCuJHa3cxbhkTH9691BCd_1ryCKUM_UA/export?format=csv&id=1Kd8SLHFLohGKCuJHa3cxbhkTH9691BCd_1ryCKUM_UA&gid=208597553",skip=2)%>%
    dplyr::mutate(`Día`=gsub(" de ", " ", `Día`))
#agrego el día viernes
feriados<-feriados%>% rbind(feriados[6,])%>%
    dplyr::mutate(`Día`=case_when(row_number()==19~"Viernes, 22 Mayo",TRUE~as.character(`Día`)))%>%
    dplyr::mutate(date_format=lubridate::parse_date_time(`Día`, '%A, %d %B'))

#https://github.com/patrick-eng/its.analysis

#defino el día de hoy
if (format(Sys.time(),"%H")<12) {
  today <- format(Sys.time()-3600*24, "%Y-%m-%d")
} else {
  today <- format(Sys.time(), "%Y-%m-%d")
}

# 
movilidad_minciencia <- readr::read_csv("https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto33/IndiceDeMovilidad_std.csv", col_types = cols("Codigo comuna"=col_character()))%>%
  dplyr::mutate(date_format=lubridate::parse_date_time(`Fecha`, 'Ymd'))%>%
  tidyr::spread(variable, value)%>%
  dplyr::rename("codigo_comuna"="Codigo comuna")%>% clean_names()

#_#_#_#_
#Incluyo movilidad

covidcl_comuna_cuarentena_censo_edad_sexo_ens_clear_def_mov<-
covidcl_comuna_cuarentena_censo_edad_sexo_ens_clear_def%>%
    #sacando fines de semana
                dplyr::mutate(weekday=weekdays(date_format))%>%
                dplyr::mutate(fds_feriados= dplyr::case_when(weekday %in% c("sábado", "domingo")~1,
                                                      date_format %in% unlist(feriados[,"date_format"])~1,
                                                      TRUE~0))%>%
                dplyr::select(-weekday)%>%
    dplyr::left_join(dplyr::select(movilidad_minciencia,codigo_comuna,fecha,im,im_externo,im_interno), by=c("codigo_comuna"="codigo_comuna","date_format"="fecha"))%>%
        dplyr::mutate(new_normal=case_when(date_format>=lubridate::parse_date_time("2020-04-19", 'Ymd')&date_format<=lubridate::parse_date_time("2020-04-26", 'Ymd')~1,TRUE~0))%>%
      dplyr::mutate(quarantine=case_when(date_format>lubridate::parse_date_time("2020-05-09", 'Ymd')&
                                           date_format<=lubridate::parse_date_time(as.character(today), 'Ymd')~1,TRUE~0))%>%
        dplyr::mutate(quarantine_date=case_when(date_format>lubridate::parse_date_time("2020-05-16", 'Ymd')~1,TRUE~0))%>%
   #para aproximar variables.
    dplyr::arrange(codigo_comuna,date_format)%>%
    group_by(codigo_comuna)%>%
    dplyr::mutate(roll_muerto = zoo::rollmean(defunciones_deis, 7, align = "right",na.pad = TRUE))%>%
    dplyr::mutate(roll_confirmado = zoo::rollmean(positivos, 7, align = "right",na.pad = TRUE))%>%
    dplyr::mutate(roll_im = zoo::rollmean(im, 7, align = "right",na.pad = TRUE))%>%
    dplyr::mutate(roll_sc_serv_esencial = zoo::rollmean(sc_serv_esencial, 7, align = "right",na.pad = TRUE))


#!!!!
#MATCHEAR POR POBLACIÓN POR COMUNA!!!!
#https://github.com/MinCiencia/Datos-COVID19/blob/master/output/producto1/Covid-19_std.csv
#!!!
url <- "https://dl.dropbox.com/s/8brlex6789stb8q/icvu.xlsx?dl=0"
destfile <- "icvu.xlsx"
curl::curl_download(url, destfile)
## Error in curl::curl_download(url, destfile): HTTP error 404.
icvu <- readxl::read_excel(destfile)
defunc_comuna_regciv<-read_csv("https://github.com/MinCiencia/Datos-COVID19/raw/master/output/producto32/2020-Defunciones_std.csv")%>% dplyr::mutate(date_format=as.Date(Fecha, "%m/%d/%y"))%>% janitor::clean_names() %>% dplyr::select(codigo_comuna, defunciones, date_format)%>%
  dplyr::mutate(codigo_comuna=as.character(codigo_comuna))

covidcl_comuna_cuarentena_censo_edad_sexo_ens_clear_def_mov%>%
  dplyr::left_join(defunc_comuna_regciv,c("codigo_comuna"="codigo_comuna","date_format"="date_format"))%>%
  dplyr::left_join(icvu,c("comuna"="comuna"))%>%
  dplyr::rename("defunciones_regciv"="defunciones")%>%
    assign("covidcl_comuna_cuarentena_censo_edad_sexo_ens_clear_def_mov_dths",.,envir=.GlobalEnv)

#ver casos con fechas 
fechas_sin_info_casos<-
  covidcl_comuna_cuarentena_censo_edad_sexo_ens_clear_def_mov_dths%>% 
  dplyr::filter(is.na(positivos))%>% 
  group_by(date_format)%>% 
  summarise(n())
#covidcl_comuna_did %>% dplyr::filter(!is.na(positivos))%>% group_by(date_format)%>% summarise(n())%>% View()

covidcl_comuna_cuarentena_censo_edad_sexo_ens_clear_def_mov_dths%>%
            dplyr::mutate(defunciones_regciv = replace_na(defunciones_regciv, 0))%>%
            dplyr::mutate(defunciones_deis = replace_na(defunciones_deis, 0))%>%
            dplyr::arrange(codigo_comuna,date_format)%>%
            dplyr::mutate(categoria_icvu2019=factor(categoria_icvu2019))%>%
            dplyr::group_by(codigo_comuna)%>%
            dplyr::mutate(defunciones_regciv_acum = cumsum(defunciones_regciv))%>%
            dplyr::mutate(defunciones_deis_acum = cumsum(defunciones_deis))%>%
          #  dplyr::filter(!date_format %in% as.Date(unlist(fechas_sin_info_casos[1])))%>%
            dplyr::mutate(roll_muerto_regciv = zoo::rollmean(defunciones_regciv, 7, align = "right",na.pad = TRUE))%>%
          assign("covidcl_comuna_cuarentena_censo_edad_sexo_ens_clear_def_mov_dths_tidy",.,envir=.GlobalEnv)
#http://cead.spd.gov.cl/estudios-y-encuestas/
#“LEVANTAMIENTO DE LÍNEA BASE DEL ESTADO DE GESTIÓN MUNICIPAL EN SEGURIDAD #Indice #IndiceREC
#nivel de gestión
#informe 2018 - aplicado en 2017
#Levantar una línea base del estado de gestión municipal en seguridad, para obtener información detallada acerca de la capacidad de gestión municipal en temáticas relativas a la prevención del delito y la violencia. 
#REGIÓN Nº COMUNAS QUE NO RESPONDIERON
# Juan Fernández, Isla de Pascua, Nogales, El Quisco
#3 Santa Cruz, Marchihue, Placilla
# 1 Galvarino
#Calbuco, Los Muermos, Chonchi, Dalcahue, Queilén, Puerto Octay, Puyehue, Chaitén, Curaco de Vélez
# Los Lagos, Lanco, Panguipulli
# 1 Laguna Blanca
# 5 Cerro Navia, San José de Maipo, Alhué, María Pinto, Isla de Maipo

Primer estudio de medición de capacidades de gestión municipal en seguridad

Busca levantar una línea base del estado de gestión municipal en seguridad, para obtener información detallada acerca de la capacidad de gestión municipal en temáticas relativas a la prevención del delito y la violencia. informe 2018 - aplicado en 2017.


linea_base_uah_2018 <- haven::read_sav("https://dl.dropbox.com/s/rcrhw8bbjbltjvi/Base%20Levantamiento%20L%C3%ADnea%20Base%20319%20Final%20%2Bndice.sav?dl=0")%>% clean_names()%>% dplyr::select(comuna2,indice,indice_rec)%>%
  dplyr::mutate(comuna2= stringr::str_replace(comuna2, "á", "a"))%>%
  dplyr::mutate(comuna2= stringr::str_replace(comuna2, "é", "e"))%>%
  dplyr::mutate(comuna2= stringr::str_replace(comuna2, "í", "i"))%>%
  dplyr::mutate(comuna2= stringr::str_replace(comuna2, "ó", "o"))%>%
  dplyr::mutate(comuna2= stringr::str_replace(comuna2, "ú", "u"))%>%
  dplyr::mutate(comuna2= stringr::str_replace(comuna2, "ñ", "n"))%>%
  dplyr::mutate(comuna2= stringr::str_replace(comuna2, "Ñ", "N"))%>%
  dplyr::mutate(comuna2= stringr::str_replace(comuna2, "Llay Llay", "Llaillay"))%>%
  dplyr::mutate(comuna2= stringr::str_replace(comuna2, "OHiggins", "O'Higgins"))%>%
  dplyr::mutate(comuna2= stringr::str_replace(comuna2, "Á", "A"))
  #no lo hice antes, pero no o puedo modificar al principio, porque hice una tabla de homologacion de comunas

covidcl_comuna_cuarentena_censo_edad_sexo_ens_clear_def_mov_dths_tidy%>%
  dplyr::left_join(linea_base_uah_2018,c("comuna"="comuna2"))%>%
  dplyr::mutate(indice_rec=as.factor(indice_rec))%>%
    assign("covidcl_comuna_cuarentena_censo_edad_sexo_ens_clear_def_mov_dths_tidy_lb",.,envir=.GlobalEnv)

mostrar="no"
    if(mostrar=="si"){
linea_base_uah_2018%>%
  dplyr::anti_join(covidcl_comuna_cuarentena_censo_edad_sexo_ens_clear_def_mov_dths_tidy,c("comuna2"="comuna"))%>%
  distinct(comuna2)%>% copiar_nombres()
    }

Codebook Deployment

## Warning in detect_scales(codebook_data): alcance items found, but no aggregate
## Warning in detect_scales(codebook_data): perc_diabetes was not perfectly
## correlated with the mean of its items. This can e.g. happen with reverse items.
## Please check this, as it likely will mean that the reliability computations go
## awry.
## 1 perc_diabetes items connected to scale
## Warning in detect_scales(codebook_data): categoria_icvu items found, but no
## aggregate
require(polycor)
## Loading required package: polycor
require(ggcorrplot)
## Loading required package: ggcorrplot
codebook_data_hetcor<-codebook_data%>% ungroup()%>%
  dplyr::select(positivos_acumulados, ndays_zero, positivos, perc_overcrowding, hombres, mujeres, total, pop_75_mas, pop_85_mas, perc_mujeres, perc_diabetes, perc_diabetes2, perc_tabaco, perc_obes, pti_as_est_salud, pti_as_funeral_fam, pti_cam_dom, pti_citacion_ley, pti_compras_ins, pti_nino_padres, pti_alim_adult_mayor, pti_pago_serv_bas, pti_paseo_mascota, pti_alim_ins_peni, pti_alim_org_pub, pti_retorno_resid, pti_ret_menor, pti_salida_autista, pti_traslado_cuida, pti_traslado_pad, pti_traslado_nin_padres, si_compra_farm, si_mudanza, si_tram_funerario, si_trat_medico, sc_serv_esencial, defunciones_deis, im, im_externo, im_interno, roll_muerto, roll_confirmado, roll_im, roll_sc_serv_esencial, defunciones_regciv, categoria_icvu2019, defunciones_regciv_acum, defunciones_deis_acum, roll_muerto_regciv,indice,indice_rec)%>% data.frame()

#Computes a heterogenous correlation matrix, consisting of Pearson product-moment correlations between numeric variables, polyserial correlations between numeric and ordinal variables, and polychoric correlations between 
hetcor_mat<-hetcor(codebook_data_hetcor, ML = T, std.err = T,use="pairwise.complete.obs",  bins=4, pd=TRUE)
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 20, 27
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 26, 27
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 20, 29
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 26, 29
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 20, 30
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 26, 30
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 27, 31
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 29, 31
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 30, 31
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 20, 32
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 27, 32
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 29, 32
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 30, 32
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 31, 32
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 20, 33
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 26, 33
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 31, 33
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : no
## cases for pair 32, 33
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : could not compute polyserial correlation between variables 46 and 3
##     Message: Error in optim(c(rho, cuts), f, control = control, hessian = std.err) : 
##   la función no se puede evaluar en los parámetros iniciales
## Warning in hetcor.data.frame(codebook_data_hetcor, ML = T, std.err = T, : could not compute polyserial correlation between variables 51 and 3
##     Message: Error in optim(c(rho, cuts), f, control = control, hessian = std.err) : 
##   la función no se puede evaluar en los parámetros iniciales
## Warning in polychor(x, y, ML = ML, std.err = std.err): 1 row with zero marginal
## removed
#mix_cor<- psych::mixedCor(data=monthly_dataset_comp_wise_dt,
          #c=2:20, 
         #p=1, 
         #d=21:27, c(")
#         smooth=TRUE,
#         correct=.5,
#         global=TRUE,
#         ncat=8,
#         use="pairwise",
#         method="kendall",
#         weight=NULL)

# Getting the correlation matrix of the dataset.
# mix_cor$rho

ggcorrplot::ggcorrplot(hetcor_mat$correlations,
          ggtheme = ggplot2::theme_gray,
          colors = c("#E46726", "white", "#6D9EC1"), tl.cex=8)
Figure 5.Heterogeneous Correlation Matrix

Figure 5.Heterogeneous Correlation Matrix 

#owid_covid_data
#iso_code
#location

#land_area_skm
## Warning in gzfile(file, "wb"): cannot open compressed
## file 'C:/Users/CISS Fondecyt/Dropbox/CGCCovid19CL/ags/
## covidcl_comuna_cuarentena_censo_edad_sexo_ens.RDataTmp', probable reason 'No
## such file or directory'
## Error in gzfile(file, "wb"): no se puede abrir la conexión
## Warning in file.remove(outfile): no fue posible abrir el
## archivo 'C:/Users/CISS Fondecyt/Dropbox/CGCCovid19CL/ags/
## covidcl_comuna_cuarentena_censo_edad_sexo_ens.RDataTmp', motivo 'No such file or
## directory'
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18362)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] ggcorrplot_0.1.3       polycor_0.7-10         readxl_1.3.1          
##  [4] survey_4.0             survival_3.1-12        Matrix_1.2-18         
##  [7] sqldf_0.4-11           RSQLite_2.2.0          gsubfn_0.7            
## [10] proto_1.0.0            geojson_0.3.4          sjPlot_2.8.4          
## [13] textreg_0.1.5          RCurl_1.98-1.2         RJSONIO_1.3-1.4       
## [16] jsonlite_1.6.1         estimatr_0.22.0        rjson_0.2.20          
## [19] janitor_2.0.1          panelView_1.1.2        zoo_1.8-8             
## [22] its.analysis_1.4.1     forecast_8.12          lattice_0.20-41       
## [25] ggseas_0.5.4           ggplottimeseries_0.1.0 DT_0.13               
## [28] data.table_1.12.8      gifski_0.8.6           gapminder_0.3.0       
## [31] gganimate_1.0.5        forcats_0.5.0          stringr_1.4.0         
## [34] purrr_0.3.4            readr_1.3.1            tidyr_1.1.0           
## [37] tibble_3.0.1           tidyverse_1.3.0        coronavirus_0.3.0     
## [40] htmlwidgets_1.5.1      lubridate_1.7.9        plotly_4.9.2.1        
## [43] ggplot2_3.3.1          dplyr_1.0.0            future_1.17.0         
## [46] codebook_0.9.2        
## 
## loaded via a namespace (and not attached):
##   [1] tidyselect_1.1.0    lme4_1.1-23         munsell_0.5.0      
##   [4] jqr_1.1.0           codetools_0.2-16    effectsize_0.3.1   
##   [7] units_0.6-7         seasonal_1.7.1      chron_2.3-55       
##  [10] statmod_1.4.34      withr_2.2.0         colorspace_1.4-1   
##  [13] highr_0.8           NLP_0.2-0           knitr_1.28         
##  [16] rstudioapi_0.11     TTR_0.23-6          listenv_0.8.0      
##  [19] labeling_0.3        emmeans_1.4.7       slam_0.1-47        
##  [22] bit64_0.9-7         farver_2.0.3        coda_0.19-3        
##  [25] vctrs_0.3.1         generics_0.0.2      TH.data_1.0-10     
##  [28] xfun_0.14           R6_2.4.1            bitops_1.0-6       
##  [31] assertthat_0.2.1    scales_1.1.1        multcomp_1.4-13    
##  [34] nnet_7.3-14         texreg_1.37.5       gtable_0.3.0       
##  [37] globals_0.12.5      sandwich_2.5-1      timeDate_3043.102  
##  [40] rlang_0.4.6         splines_4.0.2       lazyeval_0.2.2     
##  [43] acepack_1.4.1       checkmate_2.0.0     broom_0.5.6        
##  [46] reshape2_1.4.4      yaml_2.2.1          modelr_0.1.8       
##  [49] backports_1.1.7     quantmod_0.4.17     Hmisc_4.4-0        
##  [52] tools_4.0.2         tcltk_4.0.2         ellipsis_0.3.1     
##  [55] RColorBrewer_1.1-2  plyr_1.8.6          Rcpp_1.0.4.6       
##  [58] base64enc_0.1-3     progress_1.2.2      classInt_0.4-3     
##  [61] prettyunits_1.1.1   rpart_4.1-15        fracdiff_1.5-1     
##  [64] haven_2.3.1         cluster_2.1.0       fs_1.4.1           
##  [67] magrittr_1.5        openxlsx_4.1.5      lmtest_0.9-37      
##  [70] reprex_0.3.0        x13binary_1.1.39-2  mvtnorm_1.1-1      
##  [73] sjmisc_2.8.5        hms_0.5.3           evaluate_0.14      
##  [76] xtable_1.8-4        rio_0.5.16          sjstats_0.18.0     
##  [79] jpeg_0.1-8.1        gridExtra_2.3       ggeffects_0.15.0   
##  [82] compiler_4.0.2      KernSmooth_2.23-17  crayon_1.3.4       
##  [85] minqa_1.2.4         htmltools_0.4.0     Formula_1.2-3      
##  [88] DBI_1.1.0           tweenr_1.0.1        sjlabelled_1.1.5   
##  [91] dbplyr_1.4.4        MASS_7.3-51.6       sf_0.9-4           
##  [94] boot_1.3-25         mitools_2.4         cli_2.0.2          
##  [97] quadprog_1.5-8      parallel_4.0.2      insight_0.8.5      
## [100] pkgconfig_2.0.3     foreign_0.8-80      sp_1.4-2           
## [103] xml2_1.3.2          estimability_1.3    rvest_0.3.5        
## [106] snakecase_0.11.0    digest_0.6.25       parameters_0.8.0   
## [109] tm_0.7-7            rmarkdown_2.2       cellranger_1.1.0   
## [112] htmlTable_1.13.3    curl_4.3            urca_1.3-0         
## [115] nloptr_1.2.2.1      lifecycle_0.2.0     nlme_3.1-148       
## [118] tseries_0.10-47     viridisLite_0.3.0   fansi_0.4.1        
## [121] labelled_2.5.0      pillar_1.4.4        httr_1.4.1         
## [124] glue_1.4.1          xts_0.12-0          zip_2.0.4          
## [127] bayestestR_0.7.0    png_0.1-7           pander_0.6.3       
## [130] bit_1.1-15.2        class_7.3-17        stringi_1.4.6      
## [133] performance_0.4.7   blob_1.2.1          latticeExtra_0.6-29
## [136] memoise_1.1.0       e1071_1.7-3
## Adding missing grouping variables: `codigo_comuna`
## Warning in doTryCatch(return(expr), name, parentenv, handler): Reliability CIs
## could not be computed for perc_diabetes
## Warning in doTryCatch(return(expr), name, parentenv, handler): Package "ufs"
## needed to compute reliabilites.
## Warning in value[[3L]](cond): Reliability could not be computed for
## perc_diabetes
## Warning in value[[3L]](cond): Package "ufs" needed to compute reliabilites.
## Adding missing grouping variables: `codigo_comuna`
## Adding missing grouping variables: `codigo_comuna`

Metadata

Description

Dataset name: Join of CENSUS, Quarantines and ENSs Variables

(ags)

Metadata for search engines

  • Date published: 2020-07-28
x
codigo_region
region
codigo_comuna
comuna
positivos_acumulados
date_format
fech_num
ndays_zero
ndays
id
estado
fech_ini_num
fech_ter_num
completa
dup_dates_by_communes
n_dist_alcance
order_dup_cases
positivos
perc_overcrowding
casos_cuarentenas
alcance_3
text
con_cuar_hist
cambios
cum_cambios
hombres
mujeres
total
pop_75_mas
pop_85_mas
perc_mujeres
perc_diabetes
perc_diabetes2
perc_tabaco
perc_obes
pti_as_est_salud
pti_as_funeral_fam
pti_cam_dom
pti_citacion_ley
pti_compras_ins
pti_nino_padres
pti_alim_adult_mayor
pti_pago_serv_bas
pti_paseo_mascota
pti_alim_ins_peni
pti_alim_org_pub
pti_retorno_resid
pti_ret_menor
pti_salida_autista
pti_traslado_cuida
pti_traslado_pad
pti_traslado_nin_padres
si_compra_farm
si_mudanza
si_tram_funerario
si_trat_medico
sc_serv_esencial
defunciones_deis
fds_feriados
im
im_externo
im_interno
new_normal
quarantine
quarantine_date
roll_muerto
roll_confirmado
roll_im
roll_sc_serv_esencial
defunciones_regciv
categoria_icvu2019
defunciones_regciv_acum
defunciones_deis_acum
roll_muerto_regciv
indice
indice_rec

#Variables

codigo_region

Code of Region (Github.com/jorgeperezrojas/covid19-data)

Distribution

Distribution of values for codigo_region

Distribution of values for codigo_region

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
codigo_region Code of Region (Github.com/jorgeperezrojas/covid19-data) character 0 1 16 0 1 2 0

region

Name of the Region (Github.com/jorgeperezrojas/covid19-data)

Distribution

Distribution of values for region

Distribution of values for region

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
region Name of the Region (Github.com/jorgeperezrojas/covid19-data) character 0 1 16 0 5 18 0

codigo_comuna

Code of the Commune (Github.com/jorgeperezrojas/covid19-data)

Distribution

Distribution of values for codigo_comuna

Distribution of values for codigo_comuna

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
codigo_comuna Code of the Commune (Github.com/jorgeperezrojas/covid19-data) character 0 1 346 0 4 5 0

comuna

Name of the Commune (Github.com/jorgeperezrojas/covid19-data)

Distribution

Distribution of values for comuna

Distribution of values for comuna

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
comuna Name of the Commune (Github.com/jorgeperezrojas/covid19-data) character 0 1 346 0 4 20 0

positivos_acumulados

Cumulative Cases (Github.com/jorgeperezrojas/covid19-data)

Distribution

Distribution of values for positivos_acumulados

Distribution of values for positivos_acumulados

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
positivos_acumulados Cumulative Cases (Github.com/jorgeperezrojas/covid19-data) numeric 0 1 0 14 21893 341.727 1306.226 ▇▁▁▁▁

date_format

Date (Date format) (Github.com/jorgeperezrojas/covid19-data)

Distribution

## 132  unique, categorical values, so not shown.

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique min median max
date_format Date (Date format) (Github.com/jorgeperezrojas/covid19-data) Date 0 1 132 2020-03-15 2020-05-19 2020-07-24

fech_num

Date (Numeric) (Github.com/jorgeperezrojas/covid19-data)

Distribution

Distribution of values for fech_num

Distribution of values for fech_num

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
fech_num Date (Numeric) (Github.com/jorgeperezrojas/covid19-data) numeric 0 1 18336 18402 18467 18401.5 38.10444 ▇▇▇▇▇

ndays_zero

Days since the first Confirmed Case (Github.com/jorgeperezrojas/covid19-data)

Distribution

Distribution of values for ndays_zero

Distribution of values for ndays_zero

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
ndays_zero Days since the first Confirmed Case (Github.com/jorgeperezrojas/covid19-data) numeric 0 1 1 66 132 66.5 38.10444 ▇▇▇▇▇

ndays

Days since the first Confirmed Case by Commune (Github.com/jorgeperezrojas/covid19-data)

Distribution

Distribution of values for ndays

Distribution of values for ndays

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
ndays Days since the first Confirmed Case by Commune (Github.com/jorgeperezrojas/covid19-data) numeric 0 1 0 37 132 43.0337 38.15172 ▇▃▃▃▂

id

ID number of Quarantine (Github.com/MinCiencia/Datos-COVID19/DP29)

Distribution

Distribution of values for id

Distribution of values for id

41064 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
id ID number of Quarantine (Github.com/MinCiencia/Datos-COVID19/DP29) numeric 41064 0.1008933 1 53 100 52.84549 24.34184 ▃▅▇▇▃

estado

State of Quarantine (Active/Historical (Github.com/MinCiencia/Datos-COVID19/DP29)

Distribution

Distribution of values for estado

Distribution of values for estado

41064 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
estado State of Quarantine (Active/Historical (Github.com/MinCiencia/Datos-COVID19/DP29) character 41064 0.1008933 2 0 6 9 0

fech_ini_num

Date of Onset Of Quaratine (Github.com/MinCiencia/Datos-COVID19/DP29)

Distribution

Distribution of values for fech_ini_num

Distribution of values for fech_ini_num

41064 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
fech_ini_num Date of Onset Of Quaratine (Github.com/MinCiencia/Datos-COVID19/DP29) numeric 41064 0.1008933 18333 18390 18457 18393.81 22.63796 ▁▂▇▂▁

fech_ter_num

Date of Ending Of Quaratine (Github.com/MinCiencia/Datos-COVID19/DP29)

Distribution

Distribution of values for fech_ter_num

Distribution of values for fech_ter_num

41064 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
fech_ter_num Date of Ending Of Quaratine (Github.com/MinCiencia/Datos-COVID19/DP29) numeric 41064 0.1008933 18348 18474 18474 18460.73 33.01946 ▁▁▁▁▇

completa

Scope of Quarantine: Complete Quarantine (Github.com/MinCiencia/Datos-COVID19/DP29)

Distribution

Distribution of values for completa

Distribution of values for completa

0 missing values.

Summary statistics

name label data_type ordered value_labels n_missing complete_rate n_unique top_counts
completa Scope of Quarantine: Complete Quarantine (Github.com/MinCiencia/Datos-COVID19/DP29) factor FALSE 1. 0,
2. 1		 0 1 2 0: 41918, 1: 3754

dup_dates_by_communes

Duplicated Dates by Commune (discarded the entry with the absence or less strict measure) (Github.com/MinCiencia/Datos-COVID19/DP29)

Distribution

Distribution of values for dup_dates_by_communes

Distribution of values for dup_dates_by_communes

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
dup_dates_by_communes Duplicated Dates by Commune (discarded the entry with the absence or less strict measure) (Github.com/MinCiencia/Datos-COVID19/DP29) numeric 0 1 1 1 2 1.000241 0.0155176 ▇▁▁▁▁

n_dist_alcance

No. of distinct Scopes of Quarantine by Date & Commune (Github.com/MinCiencia/Datos-COVID19/DP29)

Distribution

Distribution of values for n_dist_alcance

Distribution of values for n_dist_alcance

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
n_dist_alcance No. of distinct Scopes of Quarantine by Date & Commune (Github.com/MinCiencia/Datos-COVID19/DP29) numeric 0 1 0 0 2 0.1010904 0.3021047 ▇▁▁▁▁

order_dup_cases

Communes and Days that overlapped different quarantine conditions (Github.com/MinCiencia/Datos-COVID19/DP29)

Distribution

Distribution of values for order_dup_cases

Distribution of values for order_dup_cases

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
order_dup_cases Communes and Days that overlapped different quarantine conditions (Github.com/MinCiencia/Datos-COVID19/DP29) numeric 0 1 1 1 2 1.000241 0.0155176 ▇▁▁▁▁

positivos

Daily Cases by Commune (Github.com/MinCiencia/Datos-COVID19/DP29)

Distribution

Distribution of values for positivos

Distribution of values for positivos

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
positivos Daily Cases by Commune (Github.com/MinCiencia/Datos-COVID19/DP29) numeric 0 1 0 0 2560 7.956932 35.73664 ▇▁▁▁▁

perc_overcrowding

Percentage of Overcrowding (CENSUS_17)

Distribution

Distribution of values for perc_overcrowding

Distribution of values for perc_overcrowding

132 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
perc_overcrowding Percentage of Overcrowding (CENSUS_17) numeric 132 0.9971098 0.0079 0.07 0.25 0.0731053 0.0281673 ▂▇▁▁▁

casos_cuarentenas

Cumulative Cases, if in a determined date had quarantene; if not, replaced cases with a value of 100,000 (out of the scope of plots) (Github.com/jorgeperezrojas/covid19-data)

Distribution

Distribution of values for casos_cuarentenas

Distribution of values for casos_cuarentenas

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
casos_cuarentenas Cumulative Cases, if in a determined date had quarantene; if not, replaced cases with a value of 100,000 (out of the scope of plots) (Github.com/jorgeperezrojas/covid19-data) numeric 0 1 0 1e+05 1e+05 90192.19 29295.78 ▁▁▁▁▇

alcance_3

Scope of Quarantine (Urban Area, Specific, etc.) (Github.com/MinCiencia/Datos-COVID19/DP29)

Distribution

Distribution of values for alcance_3

Distribution of values for alcance_3

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
alcance_3 Scope of Quarantine (Urban Area, Specific, etc.) (Github.com/MinCiencia/Datos-COVID19/DP29) character 0 1 4 0 13 18 0

text

Compound text label with more important information (Source)

Distribution

Distribution of values for text

Distribution of values for text

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
text Compound text label with more important information (Source) character 0 1 45672 0 80 101 0

con_cuar_hist

Commune had Historic Quarantenes (Github.com/MinCiencia/Datos-COVID19/DP29)

Distribution

Distribution of values for con_cuar_hist

Distribution of values for con_cuar_hist

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
con_cuar_hist Commune had Historic Quarantenes (Github.com/MinCiencia/Datos-COVID19/DP29) numeric 0 1 0 0 1 0.1676301 0.3735415 ▇▁▁▁▂

cambios

Changes in Quarantine Conditions by Communes (Github.com/MinCiencia/Datos-COVID19/DP29)

Distribution

Distribution of values for cambios

Distribution of values for cambios

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
cambios Changes in Quarantine Conditions by Communes (Github.com/MinCiencia/Datos-COVID19/DP29) numeric 0 1 0 0 1 0.0015984 0.0399479 ▇▁▁▁▁

cum_cambios

Cumulative changes in Quarantine Conditions by Communes (Github.com/MinCiencia/Datos-COVID19/DP29)

Distribution

Distribution of values for cum_cambios

Distribution of values for cum_cambios

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
cum_cambios Cumulative changes in Quarantine Conditions by Communes (Github.com/MinCiencia/Datos-COVID19/DP29) numeric 0 1 0 0 3 0.1187161 0.4250257 ▇▁▁▁▁

hombres

No. of Men (CENSUS_17)

Distribution

Distribution of values for hombres

Distribution of values for hombres

2772 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
hombres No. of Men (CENSUS_17) numeric 2772 0.9393064 126 9861 275147 25752.01 38894.32 ▇▁▁▁▁

mujeres

No. of Women (CENSUS_17)

Distribution

Distribution of values for mujeres

Distribution of values for mujeres

2772 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
mujeres No. of Women (CENSUS_17) numeric 2772 0.9393064 12 9824 292959 26843.05 41310.4 ▇▁▁▁▁

total

Total Population by Commune (CENSUS_17)

Distribution

Distribution of values for total

Distribution of values for total

2772 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
total Total Population by Commune (CENSUS_17) numeric 2772 0.9393064 138 19597 568106 52595.06 80171.41 ▇▁▁▁▁

pop_75_mas

Percentage of People with Ages >=75 by Communes (CENSUS_17)

Distribution

Distribution of values for pop_75_mas

Distribution of values for pop_75_mas

2772 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pop_75_mas Percentage of People with Ages >=75 by Communes (CENSUS_17) numeric 2772 0.9393064 0 0.053 0.1 0.0519971 0.0169104 ▁▂▇▃▁

pop_85_mas

Percentage of People with Ages >=85 by Communes (CENSUS_17)

Distribution

Distribution of values for pop_85_mas

Distribution of values for pop_85_mas

2772 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pop_85_mas Percentage of People with Ages >=85 by Communes (CENSUS_17) numeric 2772 0.9393064 0 0.014 0.031 0.0138902 0.0053006 ▂▅▇▂▁

perc_mujeres

Percentage of Women by Communes (CENSUS_17)

Distribution

Distribution of values for perc_mujeres

Distribution of values for perc_mujeres

2772 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
perc_mujeres Percentage of Women by Communes (CENSUS_17) numeric 2772 0.9393064 0.087 0.5 0.55 0.4885134 0.0569679 ▁▁▁▁▇

Scale: perc_diabetes

Overview

Reliability: Not computed.

Missing: 25344.

Distribution of scale perc_diabetes

Distribution of scale perc_diabetes

Reliability details

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist codigo_comuna
codigo_comuna Code of the Commune (Github.com/jorgeperezrojas/covid19-data) NA NA NA NA NA NA NA NA NA NA
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.170 0.170 0.170 0.170 0 ▁▁▇▁▁ 10101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10102
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10103
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10104
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.164 0.164 0.164 0.164 0 ▁▁▇▁▁ 10105
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10106
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10107
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10108
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.438 0.438 0.438 0.438 0 ▁▁▇▁▁ 10109
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.204 0.204 0.204 0.204 0 ▁▁▇▁▁ 10201
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.013 0.013 0.013 0.013 0 ▁▁▇▁▁ 10202
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.304 0.304 0.304 0.304 0 ▁▁▇▁▁ 10203
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10204
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10205
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10206
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.163 0.163 0.163 0.163 0 ▁▁▇▁▁ 10207
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10208
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10209
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10210
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.055 0.055 0.055 0.055 0 ▁▁▇▁▁ 10301
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10302
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.126 0.126 0.126 0.126 0 ▁▁▇▁▁ 10303
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10304
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10305
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10306
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10307
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10401
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10402
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10403
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 10404
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.127 0.127 0.127 0.127 0 ▁▁▇▁▁ 1101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.103 0.103 0.103 0.103 0 ▁▁▇▁▁ 1107
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.098 0.098 0.098 0.098 0 ▁▁▇▁▁ 11101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 11102
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.102 0.102 0.102 0.102 0 ▁▁▇▁▁ 11201
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 11202
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 11203
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 11301
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 11302
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 11303
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 11401
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 11402
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.159 0.159 0.159 0.159 0 ▁▁▇▁▁ 12101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 12102
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 12103
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 12104
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 12201
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 12202
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.056 0.056 0.056 0.056 0 ▁▁▇▁▁ 12301
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 12302
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 12303
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.152 0.152 0.152 0.152 0 ▁▁▇▁▁ 12401
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 12402
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.056 0.056 0.056 0.056 0 ▁▁▇▁▁ 13101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.523 0.523 0.523 0.523 0 ▁▁▇▁▁ 13102
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.068 0.068 0.068 0.068 0 ▁▁▇▁▁ 13103
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.059 0.059 0.059 0.059 0 ▁▁▇▁▁ 13104
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.155 0.155 0.155 0.155 0 ▁▁▇▁▁ 13105
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.070 0.070 0.070 0.070 0 ▁▁▇▁▁ 13106
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.053 0.053 0.053 0.053 0 ▁▁▇▁▁ 13107
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.025 0.025 0.025 0.025 0 ▁▁▇▁▁ 13108
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.068 0.068 0.068 0.068 0 ▁▁▇▁▁ 13109
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.168 0.168 0.168 0.168 0 ▁▁▇▁▁ 13110
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.293 0.293 0.293 0.293 0 ▁▁▇▁▁ 13111
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.099 0.099 0.099 0.099 0 ▁▁▇▁▁ 13112
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.000 0.000 0.000 0.000 0 ▁▁▇▁▁ 13113
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.186 0.186 0.186 0.186 0 ▁▁▇▁▁ 13114
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.082 0.082 0.082 0.082 0 ▁▁▇▁▁ 13115
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.124 0.124 0.124 0.124 0 ▁▁▇▁▁ 13116
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.160 0.160 0.160 0.160 0 ▁▁▇▁▁ 13117
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.062 0.062 0.062 0.062 0 ▁▁▇▁▁ 13118
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.145 0.145 0.145 0.145 0 ▁▁▇▁▁ 13119
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.026 0.026 0.026 0.026 0 ▁▁▇▁▁ 13120
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.212 0.212 0.212 0.212 0 ▁▁▇▁▁ 13121
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.150 0.150 0.150 0.150 0 ▁▁▇▁▁ 13122
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.001 0.001 0.001 0.001 0 ▁▁▇▁▁ 13123
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.059 0.059 0.059 0.059 0 ▁▁▇▁▁ 13124
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.015 0.015 0.015 0.015 0 ▁▁▇▁▁ 13125
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.194 0.194 0.194 0.194 0 ▁▁▇▁▁ 13126
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.013 0.013 0.013 0.013 0 ▁▁▇▁▁ 13127
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.076 0.076 0.076 0.076 0 ▁▁▇▁▁ 13128
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.000 0.000 0.000 0.000 0 ▁▁▇▁▁ 13129
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.049 0.049 0.049 0.049 0 ▁▁▇▁▁ 13130
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.000 0.000 0.000 0.000 0 ▁▁▇▁▁ 13131
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.000 0.000 0.000 0.000 0 ▁▁▇▁▁ 13132
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.126 0.126 0.126 0.126 0 ▁▁▇▁▁ 13201
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 13202
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 13203
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.009 0.009 0.009 0.009 0 ▁▁▇▁▁ 13301
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.332 0.332 0.332 0.332 0 ▁▁▇▁▁ 13302
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 13303
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.035 0.035 0.035 0.035 0 ▁▁▇▁▁ 13401
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.000 0.000 0.000 0.000 0 ▁▁▇▁▁ 13402
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.091 0.091 0.091 0.091 0 ▁▁▇▁▁ 13403
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.054 0.054 0.054 0.054 0 ▁▁▇▁▁ 13404
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.236 0.236 0.236 0.236 0 ▁▁▇▁▁ 13501
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 13502
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 13503
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 13504
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 13505
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.157 0.157 0.157 0.157 0 ▁▁▇▁▁ 13601
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.157 0.157 0.157 0.157 0 ▁▁▇▁▁ 13602
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.000 0.000 0.000 0.000 0 ▁▁▇▁▁ 13603
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.733 0.733 0.733 0.733 0 ▁▁▇▁▁ 13604
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.000 0.000 0.000 0.000 0 ▁▁▇▁▁ 13605
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.106 0.106 0.106 0.106 0 ▁▁▇▁▁ 1401
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 1402
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 1403
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 1404
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.070 0.070 0.070 0.070 0 ▁▁▇▁▁ 1405
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.149 0.149 0.149 0.149 0 ▁▁▇▁▁ 14101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 14102
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 14103
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.191 0.191 0.191 0.191 0 ▁▁▇▁▁ 14104
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 14105
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 14106
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.148 0.148 0.148 0.148 0 ▁▁▇▁▁ 14107
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 14108
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.103 0.103 0.103 0.103 0 ▁▁▇▁▁ 14201
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 14202
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.217 0.217 0.217 0.217 0 ▁▁▇▁▁ 14203
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.059 0.059 0.059 0.059 0 ▁▁▇▁▁ 14204
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.150 0.150 0.150 0.150 0 ▁▁▇▁▁ 15101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 15102
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 15201
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 15202
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16102
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16103
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16104
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16105
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16106
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16107
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16108
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16109
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16201
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16202
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16203
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16204
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16205
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16206
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16207
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16301
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16302
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16303
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16304
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 16305
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.131 0.131 0.131 0.131 0 ▁▁▇▁▁ 2101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 2102
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 2103
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 2104
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.149 0.149 0.149 0.149 0 ▁▁▇▁▁ 2201
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 2202
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.010 0.010 0.010 0.010 0 ▁▁▇▁▁ 2203
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 2301
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 2302
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.144 0.144 0.144 0.144 0 ▁▁▇▁▁ 3101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 3102
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.187 0.187 0.187 0.187 0 ▁▁▇▁▁ 3103
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 3201
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 3202
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.178 0.178 0.178 0.178 0 ▁▁▇▁▁ 3301
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.044 0.044 0.044 0.044 0 ▁▁▇▁▁ 3302
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 3303
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.031 0.031 0.031 0.031 0 ▁▁▇▁▁ 3304
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.162 0.162 0.162 0.162 0 ▁▁▇▁▁ 4101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.218 0.218 0.218 0.218 0 ▁▁▇▁▁ 4102
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 4103
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 4104
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 4105
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.187 0.187 0.187 0.187 0 ▁▁▇▁▁ 4106
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.204 0.204 0.204 0.204 0 ▁▁▇▁▁ 4201
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 4202
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 4203
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 4204
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.116 0.116 0.116 0.116 0 ▁▁▇▁▁ 4301
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 4302
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.041 0.041 0.041 0.041 0 ▁▁▇▁▁ 4303
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.174 0.174 0.174 0.174 0 ▁▁▇▁▁ 4304
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 4305
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.113 0.113 0.113 0.113 0 ▁▁▇▁▁ 5101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.184 0.184 0.184 0.184 0 ▁▁▇▁▁ 5102
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.221 0.221 0.221 0.221 0 ▁▁▇▁▁ 5103
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5104
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5105
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5107
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.121 0.121 0.121 0.121 0 ▁▁▇▁▁ 5109
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5201
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.009 0.009 0.009 0.009 0 ▁▁▇▁▁ 5301
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5302
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5303
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.042 0.042 0.042 0.042 0 ▁▁▇▁▁ 5304
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5401
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.126 0.126 0.126 0.126 0 ▁▁▇▁▁ 5402
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5403
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5404
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5405
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.093 0.093 0.093 0.093 0 ▁▁▇▁▁ 5501
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.115 0.115 0.115 0.115 0 ▁▁▇▁▁ 5502
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.053 0.053 0.053 0.053 0 ▁▁▇▁▁ 5503
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5504
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5506
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.060 0.060 0.060 0.060 0 ▁▁▇▁▁ 5601
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.031 0.031 0.031 0.031 0 ▁▁▇▁▁ 5602
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5603
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5604
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5605
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5606
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.122 0.122 0.122 0.122 0 ▁▁▇▁▁ 5701
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5702
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.085 0.085 0.085 0.085 0 ▁▁▇▁▁ 5703
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5704
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.112 0.112 0.112 0.112 0 ▁▁▇▁▁ 5705
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5706
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.082 0.082 0.082 0.082 0 ▁▁▇▁▁ 5801
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.125 0.125 0.125 0.125 0 ▁▁▇▁▁ 5802
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 5803
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.244 0.244 0.244 0.244 0 ▁▁▇▁▁ 5804
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.040 0.040 0.040 0.040 0 ▁▁▇▁▁ 6101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6102
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6103
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6104
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.009 0.009 0.009 0.009 0 ▁▁▇▁▁ 6105
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6106
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.145 0.145 0.145 0.145 0 ▁▁▇▁▁ 6107
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.034 0.034 0.034 0.034 0 ▁▁▇▁▁ 6108
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6109
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.176 0.176 0.176 0.176 0 ▁▁▇▁▁ 6110
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6111
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6112
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6113
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.107 0.107 0.107 0.107 0 ▁▁▇▁▁ 6114
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.046 0.046 0.046 0.046 0 ▁▁▇▁▁ 6115
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6116
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.255 0.255 0.255 0.255 0 ▁▁▇▁▁ 6117
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6201
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6202
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6203
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6204
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6205
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6206
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.226 0.226 0.226 0.226 0 ▁▁▇▁▁ 6301
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6302
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.075 0.075 0.075 0.075 0 ▁▁▇▁▁ 6303
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6304
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.138 0.138 0.138 0.138 0 ▁▁▇▁▁ 6305
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6306
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6307
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6308
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 6309
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.058 0.058 0.058 0.058 0 ▁▁▇▁▁ 6310
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.122 0.122 0.122 0.122 0 ▁▁▇▁▁ 7101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.239 0.239 0.239 0.239 0 ▁▁▇▁▁ 7102
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7103
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.244 0.244 0.244 0.244 0 ▁▁▇▁▁ 7104
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.304 0.304 0.304 0.304 0 ▁▁▇▁▁ 7105
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7106
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7107
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7108
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.194 0.194 0.194 0.194 0 ▁▁▇▁▁ 7109
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7110
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.312 0.312 0.312 0.312 0 ▁▁▇▁▁ 7201
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7202
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7203
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.135 0.135 0.135 0.135 0 ▁▁▇▁▁ 7301
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7302
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7303
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.409 0.409 0.409 0.409 0 ▁▁▇▁▁ 7304
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7305
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7306
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.113 0.113 0.113 0.113 0 ▁▁▇▁▁ 7307
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7308
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7309
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.062 0.062 0.062 0.062 0 ▁▁▇▁▁ 7401
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.020 0.020 0.020 0.020 0 ▁▁▇▁▁ 7402
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7403
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.150 0.150 0.150 0.150 0 ▁▁▇▁▁ 7404
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.370 0.370 0.370 0.370 0 ▁▁▇▁▁ 7405
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7406
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7407
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 7408
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.223 0.223 0.223 0.223 0 ▁▁▇▁▁ 8101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.058 0.058 0.058 0.058 0 ▁▁▇▁▁ 8102
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.000 0.000 0.000 0.000 0 ▁▁▇▁▁ 8103
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8104
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8105
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.067 0.067 0.067 0.067 0 ▁▁▇▁▁ 8106
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.189 0.189 0.189 0.189 0 ▁▁▇▁▁ 8107
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.176 0.176 0.176 0.176 0 ▁▁▇▁▁ 8108
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.260 0.260 0.260 0.260 0 ▁▁▇▁▁ 8109
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.211 0.211 0.211 0.211 0 ▁▁▇▁▁ 8110
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.321 0.321 0.321 0.321 0 ▁▁▇▁▁ 8111
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.076 0.076 0.076 0.076 0 ▁▁▇▁▁ 8112
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8201
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8202
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8203
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8204
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.124 0.124 0.124 0.124 0 ▁▁▇▁▁ 8205
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.178 0.178 0.178 0.178 0 ▁▁▇▁▁ 8206
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8207
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.180 0.180 0.180 0.180 0 ▁▁▇▁▁ 8301
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8302
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8303
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8304
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8305
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8306
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.268 0.268 0.268 0.268 0 ▁▁▇▁▁ 8307
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8308
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.206 0.206 0.206 0.206 0 ▁▁▇▁▁ 8309
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8310
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8311
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8312
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8313
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 8314
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.086 0.086 0.086 0.086 0 ▁▁▇▁▁ 9101
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9102
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9103
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9104
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.277 0.277 0.277 0.277 0 ▁▁▇▁▁ 9105
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.114 0.114 0.114 0.114 0 ▁▁▇▁▁ 9106
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9107
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9108
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9109
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9110
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.140 0.140 0.140 0.140 0 ▁▁▇▁▁ 9111
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.224 0.224 0.224 0.224 0 ▁▁▇▁▁ 9112
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9113
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9114
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9115
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.304 0.304 0.304 0.304 0 ▁▁▇▁▁ 9116
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9117
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.077 0.077 0.077 0.077 0 ▁▁▇▁▁ 9118
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9119
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.000 0.000 0.000 0.000 0 ▁▁▇▁▁ 9120
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9121
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.028 0.028 0.028 0.028 0 ▁▁▇▁▁ 9201
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.204 0.204 0.204 0.204 0 ▁▁▇▁▁ 9202
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9203
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9204
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9205
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9206
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9207
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9208
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 0 1 0.110 0.110 0.110 0.110 0 ▁▁▇▁▁ 9209
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9210
perc_diabetes2 Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS) numeric 132 0 NA NA NA NaN NA 9211

perc_tabaco

Percentage of High Dependency to Tobacco (1st daily cigarrete<=60 min) by Communes (ENS)

Distribution

Distribution of values for perc_tabaco

Distribution of values for perc_tabaco

25476 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
perc_tabaco Percentage of High Dependency to Tobacco (1st daily cigarrete<=60 min) by Communes (ENS) numeric 25476 0.4421965 0 0.15 1 0.2320719 0.2569429 ▇▃▂▁▁

perc_obes

Percentage of Obesity (IMC >=30) by Communes (ENS)

Distribution

Distribution of values for perc_obes

Distribution of values for perc_obes

25344 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
perc_obes Percentage of Obesity (IMC >=30) by Communes (ENS) numeric 25344 0.4450867 0 0.35 1 0.3731818 0.166812 ▂▇▆▁▁

pti_as_est_salud

Clearence of access to health centres (Permisos)

Distribution

Distribution of values for pti_as_est_salud

Distribution of values for pti_as_est_salud

11418 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_as_est_salud Clearence of access to health centres (Permisos) numeric 11418 0.75 0 3 8184 134.4879 533.7834 ▇▁▁▁▁

pti_as_funeral_fam

Clearene of attendance to a funeral of a close relative (Permisos)

Distribution

Distribution of values for pti_as_funeral_fam

Distribution of values for pti_as_funeral_fam

11072 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_as_funeral_fam Clearene of attendance to a funeral of a close relative (Permisos) numeric 11072 0.7575758 0 0 177 3.126041 11.06278 ▇▁▁▁▁

pti_cam_dom

Clearence of residence (Permisos)

Distribution

Distribution of values for pti_cam_dom

Distribution of values for pti_cam_dom

20760 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_cam_dom Clearence of residence (Permisos) numeric 20760 0.5454545 0 0 1107 8.67895 33.26204 ▇▁▁▁▁

pti_citacion_ley

Clearence of writ of summons under the law (Permisos)

Distribution

Distribution of values for pti_citacion_ley

Distribution of values for pti_citacion_ley

11418 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_citacion_ley Clearence of writ of summons under the law (Permisos) numeric 11418 0.75 0 0 394 3.749956 14.90928 ▇▁▁▁▁

pti_compras_ins

Clearence of consumables purchase (Permisos)

Distribution

Distribution of values for pti_compras_ins

Distribution of values for pti_compras_ins

11418 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_compras_ins Clearence of consumables purchase (Permisos) numeric 11418 0.75 0 3 58729 882.0174 3570.523 ▇▁▁▁▁

pti_nino_padres

Clearence of exercise a right to have some ongoing contact with one’s parents (Permisos)

Distribution

Distribution of values for pti_nino_padres

Distribution of values for pti_nino_padres

34946 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_nino_padres Clearence of exercise a right to have some ongoing contact with one’s parents (Permisos) numeric 34946 0.2348485 0 0 230 3.031046 12.42907 ▇▁▁▁▁

pti_alim_adult_mayor

Clearence of food delivery to senior citizens (Permisos)

Distribution

Distribution of values for pti_alim_adult_mayor

Distribution of values for pti_alim_adult_mayor

13840 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_alim_adult_mayor Clearence of food delivery to senior citizens (Permisos) numeric 13840 0.6969697 0 0 7991 80.37839 330.3135 ▇▁▁▁▁

pti_pago_serv_bas

Clearence of payment of utility (Permisos)

Distribution

Distribution of values for pti_pago_serv_bas

Distribution of values for pti_pago_serv_bas

12110 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_pago_serv_bas Clearence of payment of utility (Permisos) numeric 12110 0.7348485 0 0 11328 153.2877 684.9856 ▇▁▁▁▁

pti_paseo_mascota

Clearence of walking their pets (Permisos)

Distribution

Distribution of values for pti_paseo_mascota

Distribution of values for pti_paseo_mascota

11418 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_paseo_mascota Clearence of walking their pets (Permisos) numeric 11418 0.75 0 0 12544 77.05894 502.3284 ▇▁▁▁▁

pti_alim_ins_peni

Clearence of food provision en prison complex (Permisos)

Distribution

Distribution of values for pti_alim_ins_peni

Distribution of values for pti_alim_ins_peni

13840 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_alim_ins_peni Clearence of food provision en prison complex (Permisos) numeric 13840 0.6969697 0 0 190 2.95071 11.48748 ▇▁▁▁▁

pti_alim_org_pub

Clearence of food withdrawal from public agencies (Permisos)

Distribution

Distribution of values for pti_alim_org_pub

Distribution of values for pti_alim_org_pub

21452 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_alim_org_pub Clearence of food withdrawal from public agencies (Permisos) numeric 21452 0.530303 0 0 4506 26.75615 158.9743 ▇▁▁▁▁

pti_retorno_resid

Clearence of return to normal residence (Permisos)

Distribution

Distribution of values for pti_retorno_resid

Distribution of values for pti_retorno_resid

26296 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_retorno_resid Clearence of return to normal residence (Permisos) numeric 26296 0.4242424 0 7 1001 23.56534 55.26125 ▇▁▁▁▁

pti_ret_menor

Permiso retorno del menor o adolescente al hogar tutelar (Permisos)

Distribution

Distribution of values for pti_ret_menor

Distribution of values for pti_ret_menor

30102 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_ret_menor Permiso retorno del menor o adolescente al hogar tutelar (Permisos) numeric 30102 0.3409091 0 0 831 11.18812 41.37223 ▇▁▁▁▁

pti_salida_autista

Clearence of affected by some disorders of the autistic spectrum (Permisos)

Distribution

Distribution of values for pti_salida_autista

Distribution of values for pti_salida_autista

11418 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_salida_autista Clearence of affected by some disorders of the autistic spectrum (Permisos) numeric 11418 0.75 0 0 253 3.434869 15.54845 ▇▁▁▁▁

pti_traslado_cuida

Clearence of movement of adult carers, mental or physical disability (Permisos)

Distribution

Distribution of values for pti_traslado_cuida

Distribution of values for pti_traslado_cuida

39444 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_traslado_cuida Clearence of movement of adult carers, mental or physical disability (Permisos) numeric 39444 0.1363636 0 0 551 7.527457 29.66856 ▇▁▁▁▁

pti_traslado_pad

Clearence of movement of parents’ to health centres (Permisos)

Distribution

Distribution of values for pti_traslado_pad

Distribution of values for pti_traslado_pad

33216 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_traslado_pad Clearence of movement of parents’ to health centres (Permisos) numeric 33216 0.2727273 0 0 75 1.835742 6.178373 ▇▁▁▁▁

pti_traslado_nin_padres

Clearence of movement of children/adolescents between both parents’ homes (Permisos)

Distribution

Distribution of values for pti_traslado_nin_padres

Distribution of values for pti_traslado_nin_padres

34946 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
pti_traslado_nin_padres Clearence of movement of children/adolescents between both parents’ homes (Permisos) numeric 34946 0.2348485 0 0 385 3.611132 15.8029 ▇▁▁▁▁

si_compra_farm

Laissez-passer of pharmacy purchases beacuse of health emergency (Permisos)

Distribution

Distribution of values for si_compra_farm

Distribution of values for si_compra_farm

44646 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
si_compra_farm Laissez-passer of pharmacy purchases beacuse of health emergency (Permisos) numeric 44646 0.0224645 0 0 6 0.1013645 0.4813174 ▇▁▁▁▁

si_mudanza

Laissez-passer of change of movers/house removals (Permisos)

Distribution

Distribution of values for si_mudanza

Distribution of values for si_mudanza

39516 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
si_mudanza Laissez-passer of change of movers/house removals (Permisos) numeric 39516 0.1347872 0 0 482 4.79386 16.16908 ▇▁▁▁▁

si_tram_funerario

Laissez-passer of funerary procedures of close relatives (Permisos)

Distribution

Distribution of values for si_tram_funerario

Distribution of values for si_tram_funerario

10788 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
si_tram_funerario Laissez-passer of funerary procedures of close relatives (Permisos) numeric 10788 0.763794 0 0 25 0.412338 1.333857 ▇▁▁▁▁

si_trat_medico

Laissez-passer of medical treatments (Permisos)

Distribution

Distribution of values for si_trat_medico

Distribution of values for si_trat_medico

10788 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
si_trat_medico Laissez-passer of medical treatments (Permisos) numeric 10788 0.763794 0 0 264 5.229303 16.11607 ▇▁▁▁▁

sc_serv_esencial

Collective laissez-passer of essential services (Permisos)

Distribution

Distribution of values for sc_serv_esencial

Distribution of values for sc_serv_esencial

14186 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
sc_serv_esencial Collective laissez-passer of essential services (Permisos) numeric 14186 0.6893939 0 5 7766 58.98507 211.7182 ▇▁▁▁▁

defunciones_deis

Defunciones Reportadas Deis (Github.com/MinCiencia/Datos-COVID19/DP50)

Distribution

Distribution of values for defunciones_deis

Distribution of values for defunciones_deis

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
defunciones_deis Defunciones Reportadas Deis (Github.com/MinCiencia/Datos-COVID19/DP50) numeric 0 1 0 0 18 0.1562883 0.8147662 ▇▁▁▁▁

fds_feriados

Fines de semana y Feriados

Distribution

Distribution of values for fds_feriados

Distribution of values for fds_feriados

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
fds_feriados Fines de semana y Feriados numeric 0 1 0 0 1 0.280303 0.4491522 ▇▁▁▁▃

im

Índice de Movilidad para la nueva normalidad post Covid-19 (Github.com/MinCiencia/Datos-COVID19/DP33)

Distribution

Distribution of values for im

Distribution of values for im

1984 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
im Índice de Movilidad para la nueva normalidad post Covid-19 (Github.com/MinCiencia/Datos-COVID19/DP33) numeric 1984 0.9565598 0 4.9 30 5.150064 2.90921 ▇▃▁▁▁

im_externo

Índice de Movilidad desde la comuna a otra (Github.com/MinCiencia/Datos-COVID19/DP33)

Distribution

Distribution of values for im_externo

Distribution of values for im_externo

1984 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
im_externo Índice de Movilidad desde la comuna a otra (Github.com/MinCiencia/Datos-COVID19/DP33) numeric 1984 0.9565598 0 2.5 29 3.043974 2.605722 ▇▁▁▁▁

im_interno

Índice de Movilidad desde otra a la comuna señalada (Github.com/MinCiencia/Datos-COVID19/DP33)

Distribution

Distribution of values for im_interno

Distribution of values for im_interno

1984 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
im_interno Índice de Movilidad desde otra a la comuna señalada (Github.com/MinCiencia/Datos-COVID19/DP33) numeric 1984 0.9565598 0 1.9 12 2.106089 1.588956 ▇▅▁▁▁

new_normal

Periodo Nueva normalidad. Desde el 19 al 26 de Abril

Distribution

Distribution of values for new_normal

Distribution of values for new_normal

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
new_normal Periodo Nueva normalidad. Desde el 19 al 26 de Abril numeric 0 1 0 0 1 0.0606061 0.2386089 ▇▁▁▁▁

quarantine

Periodo Cuarentena Simplificado. Desde el 16 de mayo al día de hoy

Distribution

Distribution of values for quarantine

Distribution of values for quarantine

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
quarantine Periodo Cuarentena Simplificado. Desde el 16 de mayo al día de hoy numeric 0 1 0 1 1 0.5757576 0.4942329 ▆▁▁▁▇

quarantine_date

Periodo Cuarentena Simplificada. Desde el 16 de mayo al día de hoy

Distribution

Distribution of values for quarantine_date

Distribution of values for quarantine_date

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
quarantine_date Periodo Cuarentena Simplificada. Desde el 16 de mayo al día de hoy numeric 0 1 0 1 1 0.5227273 0.4994887 ▇▁▁▁▇

roll_muerto

Media movil de defunciones DEIS en 7 días

Distribution

Distribution of values for roll_muerto

Distribution of values for roll_muerto

29712 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
roll_muerto Media movil de defunciones DEIS en 7 días numeric 29712 0.3494482 0 0 12 0.4398586 1.169518 ▇▁▁▁▁

roll_confirmado

Media movil de casos confirmados

Distribution

Distribution of values for roll_confirmado

Distribution of values for roll_confirmado

2076 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
roll_confirmado Media movil de casos confirmados numeric 2076 0.9545455 0 0.43 747 8.15255 28.88001 ▇▁▁▁▁

roll_im

Media movil Índice de Movilidad

Distribution

Distribution of values for roll_im

Distribution of values for roll_im

4048 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
roll_im Media movil Índice de Movilidad numeric 4048 0.911368 0 4.9 27 5.118705 2.797931 ▇▆▁▁▁

roll_sc_serv_esencial

Media movil Salvoconductos de Servicios Escenciales

Distribution

Distribution of values for roll_sc_serv_esencial

Distribution of values for roll_sc_serv_esencial

16262 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
roll_sc_serv_esencial Media movil Salvoconductos de Servicios Escenciales numeric 16262 0.6439394 0 7.9 3901 61.60877 180.2207 ▇▁▁▁▁

defunciones_regciv

Defunciones Registro Civil (Github.com/MinCiencia/Datos-COVID19/DP32)

Distribution

Distribution of values for defunciones_regciv

Distribution of values for defunciones_regciv

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
defunciones_regciv Defunciones Registro Civil (Github.com/MinCiencia/Datos-COVID19/DP32) numeric 0 1 0 0 51 1.125088 2.981681 ▇▁▁▁▁

categoria_icvu2019

Categoría Rangos (Superior, >58; Promedio, 50,6-58; Inferior<50,6) Comunas Índice de Calidad de Vida Urbana 2019

Distribution

Distribution of values for categoria_icvu2019

Distribution of values for categoria_icvu2019

35772 missing values.

Summary statistics

name label data_type ordered value_labels n_missing complete_rate n_unique top_counts
categoria_icvu2019 Categoría Rangos (Superior, >58; Promedio, 50,6-58; Inferior<50,6) Comunas Índice de Calidad de Vida Urbana 2019 factor FALSE 1. medio,
2. bueno,
3. malo		 35772 0.216763 3 mal: 4224, med: 3828, bue: 1848

defunciones_regciv_acum

Defunciones Registro Civil Acumularadas (Github.com/MinCiencia/Datos-COVID19/DP32)

Distribution

Distribution of values for defunciones_regciv_acum

Distribution of values for defunciones_regciv_acum

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
defunciones_regciv_acum Defunciones Registro Civil Acumularadas (Github.com/MinCiencia/Datos-COVID19/DP32) numeric 0 1 0 14 2849 67.02711 171.5364 ▇▁▁▁▁

defunciones_deis_acum

Defunciones Deis Acumuladas (Github.com/MinCiencia/Datos-COVID19/DP50)

Distribution

Distribution of values for defunciones_deis_acum

Distribution of values for defunciones_deis_acum

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
defunciones_deis_acum Defunciones Deis Acumuladas (Github.com/MinCiencia/Datos-COVID19/DP50) numeric 0 1 0 0 513 6.969062 33.24114 ▇▁▁▁▁

roll_muerto_regciv

Media movil de defunciones Registro Civil 7 días (Github.com/MinCiencia/Datos-COVID19/DP32)

Distribution

Distribution of values for roll_muerto_regciv

Distribution of values for roll_muerto_regciv

2076 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
roll_muerto_regciv Media movil de defunciones Registro Civil 7 días (Github.com/MinCiencia/Datos-COVID19/DP32) numeric 2076 0.9545455 0 0.29 36 1.130838 2.559027 ▇▁▁▁▁

indice

Índice gestión comunal seguridad (1=Muy bajo-5=Muy alto) (Linea Base UAH)

Distribution

Distribution of values for indice

Distribution of values for indice

3564 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist format.spss display_width
indice Índice gestión comunal seguridad (1=Muy bajo-5=Muy alto) (Linea Base UAH) numeric 3564 0.9219653 4.8 55 90 53.67005 19.81015 ▂▅▇▇▆ F8.2 0

indice_rec

Niv. de gestión comunal seguridad (1=Muy bajo-5=Muy alto) (Linea Base UAH)

Distribution

Distribution of values for indice_rec

Distribution of values for indice_rec

3564 missing values.

Summary statistics

name label data_type ordered value_labels n_missing complete_rate n_unique top_counts
indice_rec Niv. de gestión comunal seguridad (1=Muy bajo-5=Muy alto) (Linea Base UAH) factor FALSE 1. 5,
2. 4,
3. 1,
4. 3,
5. 2		 3564 0.9219653 5 3: 8580, 5: 8448, 1: 8448, 4: 8316

Missingness report

Codebook table

## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html

JSON-LD metadata The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "Join of CENSUS, Quarantines and ENSs Variables",
  "description": "(ags)\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "datePublished": "2020-07-28",
  "keywords": ["codigo_region", "region", "codigo_comuna", "comuna", "positivos_acumulados", "date_format", "fech_num", "ndays_zero", "ndays", "id", "estado", "fech_ini_num", "fech_ter_num", "completa", "dup_dates_by_communes", "n_dist_alcance", "order_dup_cases", "positivos", "perc_overcrowding", "casos_cuarentenas", "alcance_3", "text", "con_cuar_hist", "cambios", "cum_cambios", "hombres", "mujeres", "total", "pop_75_mas", "pop_85_mas", "perc_mujeres", "perc_diabetes", "perc_diabetes2", "perc_tabaco", "perc_obes", "pti_as_est_salud", "pti_as_funeral_fam", "pti_cam_dom", "pti_citacion_ley", "pti_compras_ins", "pti_nino_padres", "pti_alim_adult_mayor", "pti_pago_serv_bas", "pti_paseo_mascota", "pti_alim_ins_peni", "pti_alim_org_pub", "pti_retorno_resid", "pti_ret_menor", "pti_salida_autista", "pti_traslado_cuida", "pti_traslado_pad", "pti_traslado_nin_padres", "si_compra_farm", "si_mudanza", "si_tram_funerario", "si_trat_medico", "sc_serv_esencial", "defunciones_deis", "fds_feriados", "im", "im_externo", "im_interno", "new_normal", "quarantine", "quarantine_date", "roll_muerto", "roll_confirmado", "roll_im", "roll_sc_serv_esencial", "defunciones_regciv", "categoria_icvu2019", "defunciones_regciv_acum", "defunciones_deis_acum", "roll_muerto_regciv", "indice", "indice_rec"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "codigo_region",
      "description": "Code of Region (Github.com/jorgeperezrojas/covid19-data)",
      "@type": "propertyValue"
    },
    {
      "name": "region",
      "description": "Name of the Region (Github.com/jorgeperezrojas/covid19-data)",
      "@type": "propertyValue"
    },
    {
      "name": "codigo_comuna",
      "description": "Code of the Commune (Github.com/jorgeperezrojas/covid19-data)",
      "@type": "propertyValue"
    },
    {
      "name": "comuna",
      "description": "Name of the Commune (Github.com/jorgeperezrojas/covid19-data)",
      "@type": "propertyValue"
    },
    {
      "name": "positivos_acumulados",
      "description": "Cumulative Cases (Github.com/jorgeperezrojas/covid19-data)",
      "@type": "propertyValue"
    },
    {
      "name": "date_format",
      "description": "Date (Date format) (Github.com/jorgeperezrojas/covid19-data)",
      "@type": "propertyValue"
    },
    {
      "name": "fech_num",
      "description": "Date (Numeric) (Github.com/jorgeperezrojas/covid19-data)",
      "@type": "propertyValue"
    },
    {
      "name": "ndays_zero",
      "description": "Days since the first Confirmed Case (Github.com/jorgeperezrojas/covid19-data)",
      "@type": "propertyValue"
    },
    {
      "name": "ndays",
      "description": "Days since the first Confirmed Case by Commune (Github.com/jorgeperezrojas/covid19-data)",
      "@type": "propertyValue"
    },
    {
      "name": "id",
      "description": "ID number of Quarantine (Github.com/MinCiencia/Datos-COVID19/DP29)",
      "@type": "propertyValue"
    },
    {
      "name": "estado",
      "description": "State of Quarantine (Active/Historical (Github.com/MinCiencia/Datos-COVID19/DP29)",
      "@type": "propertyValue"
    },
    {
      "name": "fech_ini_num",
      "description": "Date of Onset Of Quaratine (Github.com/MinCiencia/Datos-COVID19/DP29)",
      "@type": "propertyValue"
    },
    {
      "name": "fech_ter_num",
      "description": "Date of Ending Of Quaratine (Github.com/MinCiencia/Datos-COVID19/DP29)",
      "@type": "propertyValue"
    },
    {
      "name": "completa",
      "description": "Scope of Quarantine: Complete Quarantine (Github.com/MinCiencia/Datos-COVID19/DP29)",
      "value": "1. 0,\n2. 1",
      "@type": "propertyValue"
    },
    {
      "name": "dup_dates_by_communes",
      "description": "Duplicated Dates by Commune (discarded the entry with the absence or less strict measure) (Github.com/MinCiencia/Datos-COVID19/DP29)",
      "@type": "propertyValue"
    },
    {
      "name": "n_dist_alcance",
      "description": "No. of distinct Scopes of Quarantine by Date & Commune (Github.com/MinCiencia/Datos-COVID19/DP29)",
      "@type": "propertyValue"
    },
    {
      "name": "order_dup_cases",
      "description": "Communes and Days that overlapped different quarantine conditions (Github.com/MinCiencia/Datos-COVID19/DP29)",
      "@type": "propertyValue"
    },
    {
      "name": "positivos",
      "description": "Daily Cases by Commune (Github.com/MinCiencia/Datos-COVID19/DP29)",
      "@type": "propertyValue"
    },
    {
      "name": "perc_overcrowding",
      "description": "Percentage of Overcrowding (CENSUS_17)",
      "@type": "propertyValue"
    },
    {
      "name": "casos_cuarentenas",
      "description": "Cumulative Cases, if in a determined date had quarantene; if not, replaced cases with a value of 100,000 (out of the scope of plots) (Github.com/jorgeperezrojas/covid19-data)",
      "@type": "propertyValue"
    },
    {
      "name": "alcance_3",
      "description": "Scope of Quarantine (Urban Area, Specific, etc.) (Github.com/MinCiencia/Datos-COVID19/DP29)",
      "@type": "propertyValue"
    },
    {
      "name": "text",
      "description": "Compound text label with more important information (Source)",
      "@type": "propertyValue"
    },
    {
      "name": "con_cuar_hist",
      "description": "Commune had Historic Quarantenes (Github.com/MinCiencia/Datos-COVID19/DP29)",
      "@type": "propertyValue"
    },
    {
      "name": "cambios",
      "description": "Changes in Quarantine Conditions by Communes (Github.com/MinCiencia/Datos-COVID19/DP29)",
      "@type": "propertyValue"
    },
    {
      "name": "cum_cambios",
      "description": "Cumulative changes in Quarantine Conditions by Communes (Github.com/MinCiencia/Datos-COVID19/DP29)",
      "@type": "propertyValue"
    },
    {
      "name": "hombres",
      "description": "No. of Men (CENSUS_17)",
      "@type": "propertyValue"
    },
    {
      "name": "mujeres",
      "description": "No. of Women (CENSUS_17)",
      "@type": "propertyValue"
    },
    {
      "name": "total",
      "description": "Total Population by Commune (CENSUS_17)",
      "@type": "propertyValue"
    },
    {
      "name": "pop_75_mas",
      "description": "Percentage of People with Ages >=75 by Communes (CENSUS_17)",
      "@type": "propertyValue"
    },
    {
      "name": "pop_85_mas",
      "description": "Percentage of People with Ages >=85 by Communes (CENSUS_17)",
      "@type": "propertyValue"
    },
    {
      "name": "perc_mujeres",
      "description": "Percentage of Women by Communes (CENSUS_17)",
      "@type": "propertyValue"
    },
    {
      "name": "perc_diabetes",
      "description": "aggregate of 1 perc_diabetes items",
      "@type": "propertyValue"
    },
    {
      "name": "perc_diabetes2",
      "description": "Percentage of Diabetes Prevalence by Communes (Doctor|Glucose=126mg/dl) (ENS)",
      "@type": "propertyValue"
    },
    {
      "name": "perc_tabaco",
      "description": "Percentage of High Dependency to Tobacco (1st daily cigarrete<=60 min) by Communes (ENS)",
      "@type": "propertyValue"
    },
    {
      "name": "perc_obes",
      "description": "Percentage of Obesity (IMC >=30) by Communes (ENS)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_as_est_salud",
      "description": "Clearence of access to health centres (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_as_funeral_fam",
      "description": "Clearene of attendance to a funeral of a close relative (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_cam_dom",
      "description": "Clearence of residence (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_citacion_ley",
      "description": "Clearence of writ of summons under the law (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_compras_ins",
      "description": "Clearence of consumables purchase (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_nino_padres",
      "description": "Clearence of exercise a right to have some ongoing contact with one's parents (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_alim_adult_mayor",
      "description": "Clearence of food delivery to senior citizens (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_pago_serv_bas",
      "description": "Clearence of payment of utility (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_paseo_mascota",
      "description": "Clearence of walking their pets (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_alim_ins_peni",
      "description": "Clearence of food provision en prison complex (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_alim_org_pub",
      "description": "Clearence of food withdrawal  from public agencies (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_retorno_resid",
      "description": "Clearence of return to normal residence (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_ret_menor",
      "description": "Permiso retorno del menor o adolescente al hogar tutelar (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_salida_autista",
      "description": "Clearence of  affected by some disorders of the autistic spectrum  (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_traslado_cuida",
      "description": "Clearence of movement of adult carers, mental or physical disability (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_traslado_pad",
      "description": "Clearence of movement of parents' to health centres (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "pti_traslado_nin_padres",
      "description": "Clearence of movement of children/adolescents  between both parents' homes (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "si_compra_farm",
      "description": "Laissez-passer of pharmacy purchases beacuse of health emergency (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "si_mudanza",
      "description": "Laissez-passer of change of movers/house removals (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "si_tram_funerario",
      "description": "Laissez-passer of funerary procedures of close relatives (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "si_trat_medico",
      "description": "Laissez-passer of medical treatments (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "sc_serv_esencial",
      "description": "Collective laissez-passer of essential services (Permisos)",
      "@type": "propertyValue"
    },
    {
      "name": "defunciones_deis",
      "description": "Defunciones Reportadas Deis (Github.com/MinCiencia/Datos-COVID19/DP50)",
      "@type": "propertyValue"
    },
    {
      "name": "fds_feriados",
      "description": "Fines de semana y Feriados",
      "@type": "propertyValue"
    },
    {
      "name": "im",
      "description": "Índice de Movilidad para la nueva normalidad post Covid-19 (Github.com/MinCiencia/Datos-COVID19/DP33)",
      "@type": "propertyValue"
    },
    {
      "name": "im_externo",
      "description": "Índice de Movilidad desde la comuna a otra (Github.com/MinCiencia/Datos-COVID19/DP33)",
      "@type": "propertyValue"
    },
    {
      "name": "im_interno",
      "description": "Índice de Movilidad desde otra a la comuna señalada (Github.com/MinCiencia/Datos-COVID19/DP33)",
      "@type": "propertyValue"
    },
    {
      "name": "new_normal",
      "description": "Periodo Nueva normalidad. Desde el 19 al 26 de Abril",
      "@type": "propertyValue"
    },
    {
      "name": "quarantine",
      "description": "Periodo Cuarentena Simplificado. Desde el 16 de mayo al día de hoy",
      "@type": "propertyValue"
    },
    {
      "name": "quarantine_date",
      "description": "Periodo Cuarentena Simplificada. Desde el 16 de mayo al día de hoy",
      "@type": "propertyValue"
    },
    {
      "name": "roll_muerto",
      "description": "Media movil de defunciones DEIS en 7 días",
      "@type": "propertyValue"
    },
    {
      "name": "roll_confirmado",
      "description": "Media movil de casos confirmados",
      "@type": "propertyValue"
    },
    {
      "name": "roll_im",
      "description": "Media movil Índice de Movilidad",
      "@type": "propertyValue"
    },
    {
      "name": "roll_sc_serv_esencial",
      "description": "Media movil Salvoconductos de Servicios Escenciales",
      "@type": "propertyValue"
    },
    {
      "name": "defunciones_regciv",
      "description": "Defunciones Registro Civil (Github.com/MinCiencia/Datos-COVID19/DP32)",
      "@type": "propertyValue"
    },
    {
      "name": "categoria_icvu2019",
      "description": "Categoría Rangos (Superior, >58; Promedio, 50,6-58; Inferior<50,6) Comunas Índice de Calidad de Vida Urbana 2019",
      "value": "1. medio,\n2. bueno,\n3. malo",
      "@type": "propertyValue"
    },
    {
      "name": "defunciones_regciv_acum",
      "description": "Defunciones Registro Civil Acumularadas (Github.com/MinCiencia/Datos-COVID19/DP32)",
      "@type": "propertyValue"
    },
    {
      "name": "defunciones_deis_acum",
      "description": "Defunciones Deis Acumuladas (Github.com/MinCiencia/Datos-COVID19/DP50)",
      "@type": "propertyValue"
    },
    {
      "name": "roll_muerto_regciv",
      "description": "Media movil de defunciones Registro Civil 7 días (Github.com/MinCiencia/Datos-COVID19/DP32)",
      "@type": "propertyValue"
    },
    {
      "name": "indice",
      "description": "Índice gestión comunal seguridad (1=Muy bajo-5=Muy alto) (Linea Base UAH)",
      "@type": "propertyValue"
    },
    {
      "name": "indice_rec",
      "description": "Niv. de gestión comunal seguridad (1=Muy bajo-5=Muy alto) (Linea Base UAH)",
      "value": "1. 5,\n2. 4,\n3. 1,\n4. 3,\n5. 2",
      "@type": "propertyValue"
    }
  ]
}`