1)Comandos básicos en R
1.1)comentarios en R
Usamos el símbolo de # dentro de R para que no corra la instrucción y se vea como un comentario:
#Esto es un comentario.1.2)Correr instrucciones
Para correr la instrucción dentro de R debemos presionar las teclas “Ctrl + enter” al mismo tiempo para que se corra lo que queremos. Cabe aclarar que este documento se esta realizando dentro de un “R Markdown” en html, por lo que ingresamos los “Chunks” para que R funcione como tal.
2+2 #Toda la línea = situado en la línea## [1] 45-3## [1] 23-9 #Color azul en consola = Todo bien## [1] -63*8 # * es multiplicación "escalar"## [1] 241.3)Asignación de nombres a variables, tablas, etc.
El signo mayor que y un guion nos ayudan a nombrar cualquier objeto: <- Operador de asignación <-
xyy<-5
yyz<-12
www<-xyy+yyz
sin(0.5)## [1] 0.4794255log(xyy)## [1] 1.6094381.4)Encontrar ayuda de funciones
Para encontrar ayuda aplicamos un signo de interrogación (?) antes de la función y el software nos abrirá la información necesaria.
#?rnorm
#?apropos("sequ")
secu<-seq(0.5,7.5,0.5)1.5)Unir elementos
La función “c”, sirve para unir elementos en uno solo:
#vector_med<-rnorm(100,mean=5,sd=3)
# [] Posiciones
# () Arg de función, u operaciones
vect1<-15
vect2<-18
vect1^vect2## [1] 1.477892e+21vect3<-c(vect1,vect2,20,24) #c es de concatenar = unir/juntar en un elemento
vect4<-seq(from=10,to=40,by=10) #longitud 41.6) Repetición
La función “rep” repite elementos de vectores y listas, en los paréntesis debo poner qué quiero repetir y las veces.
vect5<-rep(5,6) #rep =repetir
vect6<-rep(vect4,4)
vect7<-rep(vect4,each=4)
length(vect7)## [1] 16dim_vec<-length(vect4)1.7) Nomenclaturas y significado de colores en R
las #sentencias = azul los errores o warnings = rojo Al final se ven los resultados
2)operaciones vectoriales y matriciales
2.1)Indagar posiciones y extraer elementos de vectores
x<-10
x## [1] 10x[1]## [1] 10y<-"arturo"
y[1]## [1] "arturo"list<-seq(11,33)
list## [1] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33sample(list,1)## [1] 16x1<-seq(12,27)
x1## [1] 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27z<-5*x1
z## [1] 60 65 70 75 80 85 90 95 100 105 110 115 120 125 130 13550+(60*7**2+5)## [1] 299550+(60*7**(2+5))## [1] 49412630t<-seq(0,15)
t## [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15t>10## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [13] TRUE TRUE TRUE TRUEestatura<-rnorm(10,165,5)
example("min")##
## min> require(stats); require(graphics)
##
## min> min(5:1, pi) #-> one number
## [1] 1
##
## min> pmin(5:1, pi) #-> 5 numbers
## [1] 3.141593 3.141593 3.000000 2.000000 1.000000
##
## min> x <- sort(rnorm(100)); cH <- 1.35
##
## min> pmin(cH, quantile(x)) # no names
## [1] -2.4753102 -0.8454696 -0.1701317 0.6346208 1.3500000
##
## min> pmin(quantile(x), cH) # has names
## 0% 25% 50% 75% 100%
## -2.4753102 -0.8454696 -0.1701317 0.6346208 1.3500000
##
## min> plot(x, pmin(cH, pmax(-cH, x)), type = "b", main = "Huber's function")
##
## min> cut01 <- function(x) pmax(pmin(x, 1), 0)
##
## min> curve( x^2 - 1/4, -1.4, 1.5, col = 2)
##
## min> curve(cut01(x^2 - 1/4), col = "blue", add = TRUE, n = 500)
##
## min> ## pmax(), pmin() preserve attributes of *first* argument
## min> D <- diag(x = (3:1)/4) ; n0 <- numeric()
##
## min> stopifnot(identical(D, cut01(D) ),
## min+ identical(n0, cut01(n0)),
## min+ identical(n0, cut01(NULL)),
## min+ identical(n0, pmax(3:1, n0, 2)),
## min+ identical(n0, pmax(n0, 4)))summary(estatura)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 159.8 164.7 168.3 167.0 169.7 171.9estatura<-rnorm(10,175,5)
summary(estatura)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 167.9 170.6 174.0 174.9 179.0 185.1#a<-catetoadyacente
#b<-catetoopuesto
#c<-hipotenusa
#a^2+b^2=c
CO<-3
CA<-5
sqrt(CO^2+CA^2)## [1] 5.830952sqrt## function (x) .Primitive("sqrt")hip_tr <- function(a,
b
){
c<- sqrt(a^2 + b^2)
print(c)
}
hip_tr(3,
4)## [1] 52.2)clase: 23-08-2021: Operaciones “punto” por vector
Multiplico un escalar por cada uno de los elementos de un vector.
5*vect3## [1] 75 90 100 120vect1*vect3## [1] 225 270 300 360*2.3) Función Matrix
Para hacer un matriz utilizamos la función “matrix”. Entre paréntesis van mis valores, el número de columnas y si el arreglo es o no por filas.
practi<-c(5,7,3,9,7,12,6,7)
practi_mat<-matrix(practi,ncol = 4, byrow=TRUE) #Matrix
practi_mat2<-matrix(practi,nrow=4,byrow = TRUE)
practi_mat## [,1] [,2] [,3] [,4]
## [1,] 5 7 3 9
## [2,] 7 12 6 7dim(practi_mat)## [1] 2 4dim(practi_mat2)## [1] 4 2practi_mat3<-matrix(c(11,13,2,5,8,4,7,12,21,21,6,8),ncol = 4, byrow=TRUE) #Matrix
practi_mat3<-matrix(c(11,13,2,5,8,4,7,12,21,21,6,8),
ncol=3,byrow=TRUE) #Matrix
3*practi_mat3## [,1] [,2] [,3]
## [1,] 33 39 6
## [2,] 15 24 12
## [3,] 21 36 63
## [4,] 63 18 242.4) indicadores de posición (matriz y vector) []
# dim(practi_mat3)
# practi_mat3[1,4] #Escalar
# practi_mat3[,3] #Todos los renglones
# practi_mat3[2,] #todos las columnas
# practi_mat3[1:2,] #Fila 1 y 2, todas las columnas
# matri2<-practi_mat3[-1,-1] #Fila 1 y 2, todas las columnas2.5) Álgebra de matrices
# t(practi_mat3)
# inv_practi_mat4<-solve(practi_mat4) #MAtriz cuadrada n x n, inversa
# practi_mat4<-practi_mat3[-4,]
#%*% #cuando hablamos de matrices
# practi_mat3%*%matri22.6) Matriz por su inversa = identidad
# Sistemas de ecuaciones
# solve()
#5x-3y+2z=1
#-2x+2y-z=5
#4x+2y-4z=-3
# coeficientes<-matrix(c(5,-3,2,-2,2,-1,4,2,-4),byrow = TRUE,ncol = 3)
# respuestas<-c(1,5,-3)
#?solve()
#solve(coeficientes,respuestas) #resuelve la matriz
#ainv<-solve(coeficientes) #inversa de dicha matriz
#solucion<-solve(coeficientes,respuestas) #asignamos el nombre solucion
#length(solucion)
###
#intentemos x=b*a^(-1)
# 1x3 3x3
#respuestas%*%ainv
#ainv%*%respuestas
#suma, resta
#
#(coeficientes + practi_mat4)%*%solve(coeficientes + practi_mat4)
#t(coeficientes)
#diag(coeficientes)
#det(coeficientes)
#
#cbind(coeficientes,coeficientes[,1:2]) #pegar por columnas
#rbind(coeficientes,coeficientes[1:2,]) #r = row = renglón = fila
#vari1<-c("M","H")
#vari2<-c("M","H","M","H","H","M","M","M","M","H","H","H","M")
#length(vari2)
#table(vari2) #Frecuencias
#vari3<-c("1","0","1","0","0","1","1","1","1","0","0","0","1")
#table(vari3) #tabla de frecuencias
#table(vari2,vari3)
#factor(vari3) #Funcion "factor" = factores
#sum(as.numeric(vari3))
#vari3[5]<-"cero"
#vari3[7]<-"uno"
#as.numeric(vari3)
#is.na(as.numeric(vari3))3) Base de datos
Para importar una base de datos tenemos dos opciones: ir a las opciones de R y darle apretar en “importar base de datos” ó primero instalar la librería readxl y luego llarmarla:
#install.packages("readxl") #comprar el foco e instalar el foco
#(readxl) # Encender
#install.packages("kernlab")#comprar el foco e instalar el foco
#library(kernlab) #Encender
#library(readxl) # Importar datos
#datos10 <- read_excel("C:/Users/ARRA/Desktop/METPOL 1er SEMESTRE/Programación/datos_ags_estado_2020.xlsx")
#View(datos_ags_estado_2020)
#View(datos_ags_estado_2020)3.1)Características
Las funciones “is.matrix”, “is.list”, “is.data.frame” nos pueden ser útiles para saber qué tipo de objeto necesitamos manipular, otras funciones utiles:
#head <- #para que nos muestre la parte superior de la tabla que estamos utilizando
#tail <- #para que nos muestre la parte inferior de la tabla que estamos utilizando
#dim <- #para saber las dimensiones o tamaño del objeto a manipular3.2) Personalización
Es posible cambiar el nombre de las columnas y filas con las funciones “colnames” y “rownames”, respectivamente. También podemos mediante la función “as.numeric” cambiar el texto en mi base de datos por números, y si necesito cambiar números a textos usamos “as.character”
#colnames,
#rownames,
#as.numeric
#as.character4)Otras funciones para el manejo de base de datos
"Apply aplica una función a todos los elementos de una matriz. La estructura de esta función es la siguiente.
#apply(X, MARGIN, FUN)apply tiene tres argumentos: • X: Una matriz o un objeto que pueda coercionarse a una matriz, generalmente, un data frame. • MARGIN: La dimensión (margen) que agrupará los elementos de la matriz X, para aplicarles una función. Son identificadas con números, 1 son renglones y 2 son colummnas. • FUN: La función que aplicaremos a la matriz X en su dimención MARGIN"
Tomado de: https://bookdown.org/jboscomendoza/r-principiantes4/apply.html
4.1) “Aggregate”
“La función Aggregate en R, divide los datos en subconjuntos, calcula estadísticas de resumen para cada subconjunto y devuelve el resultado en un grupo por formulario.” Tomado de: https://n9.cl/fm38y
#rango(iris3[,1,1])
#apply(iris3[,,1],2,mean) #Setosa
#apply(iris3[,,1],2, rango)
#apply(iris3[,,1],1,rango)
#aggregate(iris)
#aggregate(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)~Species,
#data=iris,mean)
#aggregate(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)~Species,
#data=iris,rango)4.2) “Which”
Nos permite confirmar observaciones que cumplan con una determinada condición
#grupo1<-which(hour(bosque$datetime22)>=3 & hour(bosque$datetime22)<6)
#grupo2<-which(hour(bosque$datetime22)>=22 & hour(bosque$datetime22)<=23)4.3) FUNCIONES PERSONALIZADAS FUNCIONES DEL USUARIO
#rango<-function(x){max(x)-min(x)}
#rango(iris3[,1,1])
#apply(iris3[,,1],2,mean) #Setosa
#apply(iris3[,,1],2, rango)
#apply(iris3[,,1],1,rango)
#aggregate(iris)
#aggregate(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)~Species,
#data=iris,mean)
#aggregate(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)~Species,
#data=iris,rango)
# * ayuda de fuciones muy genéricas
#?"function"
#?"*"
#?mean
# 2) coeficiente de variabilidad = sigma/abs(media)
#coef_var<-function(x){sd(x)/abs(mean(x))}
#apply(iris3[,,1],2,mean) #Setosa
#apply(iris3[,,1],2,coef_var)
#aggregate(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)~Species,
#data=iris,coef_var)
# 3) Media cortada: descartar los mayor y menor
#iris[,1,1]
#max(iris3[,1,1])
#min(iris3[,1,1])
#descarte<-c(which(iris3[,3,1]==max(iris3[,3,1])),
#which(iris3[,3,1]==min(iris3[,3,1])))4.4) Función as.date y strptime
Con esa función podemos manipular una base de datos y hacer modificaciones a nuestro interés
#fallecidos<-read.csv("C:\\Users\\ARRA\\Desktop\\METPOL 1er SEMESTRE\\Programac\\rstudiotrab\\fallecidos.csv")
#covid_oaxaca<-read.csv("C:\\Users\\ARRA\\Desktop\\METPOL 1er SEMESTRE\\Programac\\rstudiotrab\\covid_oaxaca.csv")
#View(covid_oaxaca)
#funcionas<-as.Date(covid_oaxaca$FECHA_INGRESO,format="%d/%m/%Y")
#funcionas2<-as.Date(covid_oaxaca$FECHA_INGRESO,format="%d/%m/%Y")
#funcionas3<-as.Date(covid_oaxaca$FECHA_SINTOMAS,format="%d/%m/%Y")
#funcionas2<-as.Date(covid_oaxaca$FECHA_INGRESO,formart="%Y-%m-%d")
#library(lubridate)
#install.packages("lubridate")
#library(lubridate)
#day(funcionas2)
#table(month(funcionas2))
#hist(as.numeric(funcionas2-funcionas3))strptime Para manejar objetos que no solo tengas fechas sino horas
#strptime #Handling time and dates at the same time
#library(lubridate)
#bosque<-read.csv("C:\\Users\\ARRA\\Desktop\\Datos bosque Harvard.csv")
#dim(bosque)
#getwd()#cuál es nuestro directorio de trabajo?
#setwd() #Cambiar el directorio de trabajo
#bosque$datetime[1]
#fechax<-substr(bosque$datetime[1],1,10)
#horax<-substr(bosque$datetime[1],12,16)
#hora_fechx<-paste0(fechax,"",horax)
#hora_fechaxx<-strptime(hora_fechx, format="%Y-%m-%d %H:%M")
#hour(hora_fechaxx)
#minute(hora_fechaxx)
#year(hora_fechaxx)
#month(hora_fechaxx)Gráfico y tablas relacionadas
datos_covid20<-read.csv("C:\\Users\\ARRA\\Desktop\\basededatos\\COVID2020_5estados.csv")
library(readxl)
catalo_enti<-read_xlsx("C:\\Users\\ARRA\\Desktop\\basededatos\\Catalogos.xlsx", sheet = "Catálogo de ENTIDADES")
tail(catalo_enti)## # A tibble: 6 x 3
## CLAVE_ENTIDAD ENTIDAD_FEDERATIVA ABREVIATURA
## <chr> <chr> <chr>
## 1 31 YUCATÁN YN
## 2 32 ZACATECAS ZS
## 3 36 ESTADOS UNIDOS MEXICANOS EUM
## 4 97 NO APLICA NA
## 5 98 SE IGNORA SI
## 6 99 NO ESPECIFICADO NEcatalo_sexo<-read_excel("C:\\Users\\ARRA\\Desktop\\basededatos\\Catalogos.xlsx",sheet = "Catálogo SEXO")
#datos_covid20$ENTIDAD_UM
#catalo_enti$CLAVE_ENTIDAD
catalo_enti$CLAVE_ENTIDAD<-as.numeric(catalo_enti$CLAVE_ENTIDAD)
catalo_muni<-read_excel("C:\\Users\\ARRA\\Desktop\\basededatos\\Catalogos.xlsx",sheet = "Catálogo MUNICIPIOS")
#EMPAREJAR TABLAS
empate_estadoUM<-merge(datos_covid20,catalo_enti,by.x="ENTIDAD_UM", by.y="CLAVE_ENTIDAD")
dim(empate_estadoUM)## [1] 149564 41dim(datos_covid20) ##TABLAS, SU DIMENSIÓN## [1] 149564 39empate_estadoUM<-merge(datos_covid20,catalo_enti,by.x="ENTIDAD_UM",by.y="CLAVE_ENTIDAD",sort=FALSE)
head(merge(x=empate_estadoUM,y=catalo_sexo,by.x="SEXO",by.y="CLAVE",sort=FALSE))## SEXO ENTIDAD_UM X FECHA_ACTUALIZACION ID_REGISTRO ORIGEN SECTOR
## 1 1 3 5 28/10/2020 016eda 2 12
## 2 1 3 2092469 28/10/2020 2191c4 2 12
## 3 1 3 815907 28/10/2020 0230b3 2 12
## 4 1 2 1000386 28/10/2020 413aae 2 4
## 5 1 3 2174118 28/10/2020 246614 2 12
## 6 1 3 815953 28/10/2020 1a3b55 1 4
## ENTIDAD_NAC ENTIDAD_RES MUNICIPIO_RES TIPO_PACIENTE FECHA_INGRESO
## 1 14 3 8 1 30/03/2020
## 2 3 3 3 1 22/10/2020
## 3 25 3 8 1 02/09/2020
## 4 21 2 4 1 24/09/2020
## 5 9 3 8 1 24/10/2020
## 6 20 3 3 1 15/07/2020
## FECHA_SINTOMAS FECHA_DEF INTUBADO NEUMONIA EDAD NACIONALIDAD EMBARAZO
## 1 23/03/2020 9999-99-99 97 2 29 1 2
## 2 21/10/2020 9999-99-99 97 2 31 1 2
## 3 28/08/2020 9999-99-99 97 2 40 1 2
## 4 20/09/2020 9999-99-99 97 2 50 1 2
## 5 20/10/2020 9999-99-99 97 2 32 1 2
## 6 13/07/2020 9999-99-99 97 2 49 1 2
## HABLA_LENGUA_INDIG INDIGENA DIABETES EPOC ASMA INMUSUPR HIPERTENSION OTRA_COM
## 1 2 2 2 2 2 2 2 2
## 2 2 2 2 2 2 2 2 2
## 3 2 2 2 2 2 2 2 2
## 4 2 2 2 2 2 2 2 2
## 5 2 2 2 2 2 2 2 2
## 6 2 2 2 2 2 2 2 2
## CARDIOVASCULAR OBESIDAD RENAL_CRONICA TABAQUISMO OTRO_CASO TOMA_MUESTRA
## 1 2 2 2 2 1 1
## 2 2 2 2 2 1 1
## 3 2 2 2 2 2 1
## 4 2 2 2 2 1 1
## 5 2 2 2 2 1 1
## 6 2 2 2 2 1 1
## RESULTADO_LAB CLASIFICACION_FINAL MIGRANTE PAIS_NACIONALIDAD PAIS_ORIGEN UCI
## 1 1 3 99 México 97 97
## 2 1 3 99 México 97 97
## 3 1 3 99 México 97 97
## 4 1 3 99 México 97 97
## 5 2 7 99 México 97 97
## 6 1 3 99 México 97 97
## ENTIDAD_FEDERATIVA ABREVIATURA DESCRIPCIÓN
## 1 BAJA CALIFORNIA SUR BS MUJER
## 2 BAJA CALIFORNIA SUR BS MUJER
## 3 BAJA CALIFORNIA SUR BS MUJER
## 4 BAJA CALIFORNIA BC MUJER
## 5 BAJA CALIFORNIA SUR BS MUJER
## 6 BAJA CALIFORNIA SUR BS MUJERempate_estadoUM2<-merge(x=empate_estadoUM,y=catalo_sexo,by.x="SEXO",by.y="CLAVE",sort=FALSE)
dim(empate_estadoUM2)## [1] 149564 4219 de octubre 2021
library(readxl)
#datos_covid20<-read.csv("C:\\Users\\ARRA\\Desktop\\basededatos\\COVID2020_5estados.csv")
#catalo_enti<-read_xlsx("C:\\Users\\ARRA\\Desktop\\basededatos\\Catalogos.xlsx", sheet = "Catálogo de ENTIDADES")
#catalo_enti$CLAVE_ENTIDAD<-as.numeric(catalo_enti$CLAVE_ENTIDAD)#tabla_entid<-merge(datos_covid20,catalo_enti,by.x="ENTIDAD_RES",
#by.y="ENTIDAD_FEDERATIVA",all.x=TRUE,sort = FALSE)
#<-merge(datos_covid20,catalo_enti,by.x="ENTIDAD_RES",
#by.y="ENTIDAD_FEDERATIVA",all.x=FALSE,sort = FALSE)que pasa si invierto los argumentos, es decir la x & y
#merge(x=catalo_enti,y=datos_covid20,by.x="CLAVE_ENTIDAD",
#by.y="ENTIDAD_UM",all.x=FALSE,sort = FALSE)Casos acumulados de estos cuatros estados (en su conjunto)
#datos_covid20<-FECHA_INGRESO<-as.Date(datos_covid20$FECHA_INGRESO,)
#datos_covid20$unos<-rep(1,nrow(datos_covid20))
#aggregate(unos~FECHA_INGRESO,data=datos_covid20,sum)ordenar los datos
#datos_covid20$FECHA_INGRESO<-as.Date(datos_covid20$FECHA_INGRESO, format = "%d/%m/%Y")
#ts_ingreso<-aggregate(unos~FECHA_INGRESO,data=datos_covid20,sum)
#ts_ingreso$FECHA_INGRESO<-as.Date(ts_ingreso$FECHA_INGRESO, format = "%d/%m/%Y")
#plot(ts_ingreso)
#?par
#?plotcon “main” le damos nombre a la gráfica y los ejex x & y con xlab y ylab respectivamente
#plot(ts_ingreso, main="Primer Gráfico", xlab="Fecha", ylab="Casos nuevos diarios")
#plot(ts_ingreso, main="Primer Gráfico", xlab="Fecha", ylab="Casos nuevos diarios")
#plot(ts_ingreso,main="Primer gráfico",xlab="Fecha",ylab="Casos nuevos diarios",
#type="l")cambiar color, ancho de la letra del título, ancho de la línea
#plot(ts_ingreso,main="Primer gráfico",xlab="Fecha",ylab="Casos nuevos diarios",
#type="l",col="orange",lwd=2)##Gráfico de barras por mes
#library(lubridate)
#$mes<-month(ts_ingreso$FECHA_INGRESO)
#ts_ingreso2<-aggregate(unos~mes, FUN=sum,data = ts_ingreso)gráfica de barras por meses
#barplot(ts_ingreso2$unos)ponerle nombre de meses a las barras
#barplot(ts_ingreso2$unos,
#names.arg = c("ENERO","FEBRERO","MARZO","ABRIL",
# "MAYO","JUNIO","JULIO","AGOSTO","SEPT","OCTUBRE"),
#horiz=FALSE)que aparezca una tabla con el nombre de las barras
#barplot(ts_ingreso2$unos,
#names.arg = c("ENERO","FEBRERO","MARZO","ABRIL",
# "MAYO","JUNIO","JULIO","AGOSTO","SEPT","OCTUBRE"),
#horiz=FALSE, legend.text = c("ENERO","FEBRERO","MARZO","ABRIL",
# "MAYO","JUNIO","JULIO","AGOSTO","SEPT","OCTUBRE"),)darle color a las barras
#barplot(ts_ingreso2$unos,
#names.arg = c("ENERO","FEBRERO","MARZO","ABRIL",
# "MAYO","JUNIO","JULIO","AGOSTO","SEPT","OCTUBRE"),
#horiz=FALSE,legend.text= c("ENERO","FEBRERO","MARZO","ABRIL",
# "MAYO","JUNIO","JULIO","AGOSTO","SEPT","OCTUBRE"),
#col= rainbow(10))colores hcl.colors(10)
#barplot(ts_ingreso2$unos,
#names.arg = c("ENERO","FEBRERO","MARZO","ABRIL",
#"MAYO","JUNIO","JULIO","AGOSTO","SEPT","OCTUBRE"),
#horiz=FALSE,legend.text= c("ENERO","FEBRERO","MARZO","ABRIL",
#"MAYO","JUNIO","JULIO","AGOSTO","SEPT","OCTUBRE"),
#col= hcl.colors(10,))
#?hcl.colorscolores heat.colrs(10) el 10 es por el número de barras
#barplot(ts_ingreso2$unos,
#names.arg = c("ENERO","FEBRERO","MARZO","ABRIL",
# "MAYO","JUNIO","JULIO","AGOSTO","SEPT","OCTUBRE"),
#horiz=FALSE,legend.text= c("ENERO","FEBRERO","MARZO","ABRIL",
# "MAYO","JUNIO","JULIO","AGOSTO","SEPT","OCTUBRE"),
#col= heat.colors(10))##como hacer boxplots
#boxplot(ts_ingreso$unos)
#sexo_fecha<-aggregate(unos~FECHA_INGRESO+SEXO,data=datos_covid20,sum)
#boxplot(unos~SEXO,data = sexo_fecha)###boxplot por mes
#datos_covid20$FECHA_INGRESO<-as.Date(datos_covid20$FECHA_INGRESO,format="%Y-%m-%d")
#datos_covid20$mes<-month(datos_covid20$FECHA_INGRESO)
#sexo_fecha<-aggregate(unos~FECHA_INGRESO+SEXO, data = datos_covid20)
#boxplot(unos~SEXO, data = sexo_fecha, horizontal = TRUE)
#sexo_fecha_mes<-aggregate(unos~FECHA_INGRESO+SEXO+mes,data=datos_covid20,sum)
#boxplot(unos~SEXO+mes,data=sexo_fecha_mes,horizontal = TRUE)
#boxplot(unos~mes+SEXO,data=sexo_fecha_mes,horizontal = TRUE)###Boxplot comparando hombre y mujer
#tabla_gen<-data.frame(Codigo=c(1,2),Genero=c("M","H"))
#datos_covid200<-merge(datos_covid20,tabla_gen,by.x="SEXO",
#by.y="Codigo")
#sexo_fecha_mes2<-aggregate(unos~FECHA_INGRESO+Genero+mes,data=datos_covid200,sum)
#boxplot(unos~mes+Genero,data=sexo_fecha_mes2,horizontal = FALSE)
#boxplot(unos~Genero+mes,data=sexo_fecha_mes2,horizontal = FALSE)CLASE MARTES 26.10.21
Vectorizar nuestro nombre
##########clase 26 de Octubre######
####################################
#vectorizar el nombre (quitar espacios) (se necesita un loop?)
nombresss<-"ALBAR UGALDE HERNANDEZ"
LETTERS## [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S"
## [20] "T" "U" "V" "W" "X" "Y" "Z"nombresss<-gsub(" ","",nombresss)
strsplit(nombresss, NULL)## [[1]]
## [1] "A" "L" "B" "A" "R" "U" "G" "A" "L" "D" "E" "H" "E" "R" "N" "A" "N" "D" "E"
## [20] "Z"###una opción acortada###
match(unlist(strsplit("ALBARUGALDEHERNANDEZ", split="")), LETTERS)## [1] 1 12 2 1 18 21 7 1 12 4 5 8 5 18 14 1 14 4 5 26#####otra forma###
funcion_asignadora_numeros_letras <- function(x){
## Quitamos los espacios
nombre_se <- gsub(" ",
"",
x,
)
## Lo hacemos mayusculas
nombre_mayus <- toupper(nombre_se)
## Separamos las letras en objetos individuales
nombre_vectorizado <- data.frame(strsplit(nombre_mayus,split = ""))
## Renombramos para trabajar más fácil
nombre_vectorizado$letras <- nombre_vectorizado[,1]
## Creamos un objeto data.frame con letras y sus correspondientes valores
letras_numeros <- data.frame(letras = LETTERS,
numeros = c(1:26))
## Unimos el nombre vectorizado con la base de datos y le pedimos que solo retorne las observaciones con equivalencias en la
## Data frame de la izquierda
letras_numeros_nombre <- inner_join(nombre_vectorizado,letras_numeros)
## Hacemos un vector con los números
numeros_nombre <- letras_numeros_nombre$numeros
## Garantizamos que regrese los números del vector entrante
return(numeros_nombre)
}UTILIZAR LA FUNCIÓN FOR Y && ###?“for”?“&&”
########
#UTILIZAR LA FUNCIÓN FOR Y && ###?"for"?"&&"
######
vecto_nom<-c("ALBAR","UGALDE","HERNANDEZ")
for (i in 1:3) {
print(vecto_nom[i])}## [1] "ALBAR"
## [1] "UGALDE"
## [1] "HERNANDEZ"palabra<-"METPOL"
strsplit(palabra,"") ####con la funcion "strsplit"## [[1]]
## [1] "M" "E" "T" "P" "O" "L"#convertimos la palabra en vector
unlist(strsplit(palabra,"")) #con "unlist" seperamos las## [1] "M" "E" "T" "P" "O" "L"#letras en vectores
palabra2<-unlist(strsplit(palabra,""))
which(LETTERS==palabra2[1]) #con "which==LETTERS" podemos## [1] 13#cambiar cada vector, es decir cada letra en número
which(LETTERS==palabra2[2]) #para cada letra## [1] 5which(LETTERS==palabra2[3])## [1] 20which(LETTERS==palabra2[4]) ## [1] 16which(LETTERS==palabra2[5]) ## [1] 15which(LETTERS==palabra2[6]) ## [1] 12###otra forma de convertir METPOL en números
vector_palabra<-rep(NA,6)
for(k in 1:6){
print(which(LETTERS==palabra2[k]))
}## [1] 13
## [1] 5
## [1] 20
## [1] 16
## [1] 15
## [1] 12#ahora para sustituir los valores y "vector_palabra"
#convierta el vector directamente cada palabra y uno por separado
for(k in 1:6){
vector_palabra[k]<-which(LETTERS==palabra2[k])
}
vector_palabra## [1] 13 5 20 16 15 12#ejercicios
# 1) Suma acumulada del 1 al 100 (loops)
# 2) Multiplicación de todos los números
# 3) rnorm(100) ¿Mayor o menor que 0? Etiquetarlos
# 4) Raiz cuadrada y le resta 1 a 50 números
# 5) Aplicar la función de Coeficiente de variabilidad a
# 1000 conjuntos de distribución normal estándar de longitud 50 números
#1) Sumatoria
numeross<-c(0,seq(1,100))
cumsum(numeross) #para hacer la suma acumulada "cumsum"## [1] 0 1 3 6 10 15 21 28 36 45 55 66 78 91 105
## [16] 120 136 153 171 190 210 231 253 276 300 325 351 378 406 435
## [31] 465 496 528 561 595 630 666 703 741 780 820 861 903 946 990
## [46] 1035 1081 1128 1176 1225 1275 1326 1378 1431 1485 1540 1596 1653 1711 1770
## [61] 1830 1891 1953 2016 2080 2145 2211 2278 2346 2415 2485 2556 2628 2701 2775
## [76] 2850 2926 3003 3081 3160 3240 3321 3403 3486 3570 3655 3741 3828 3916 4005
## [91] 4095 4186 4278 4371 4465 4560 4656 4753 4851 4950 5050#formula: n(n+1)/2 100(101)/2
sumatoria<-rep(0,100) #A sumar
#numeross<-seq(1,100))#serie de datos
sumatoria[1]<-numeross[2]#Valor igual para la posición 1
for(j in 2:101){
sumatoria[j]<-sumatoria[j-1]+numeross[j+1] }
sumatoria[2]<-sumatoria[1]+numeross[2] #Para el caso 2
sumatoria[3]<-sumatoria[2]+numeross[3] #Para el caso 3
sumatoria[4]<-sumatoria[3]+numeross[4] #Para el caso 5
sumatoria[j]<-sumatoria[j-1]+numeross[j] #para el caso j
#Ciclo
for(j in 2:100){
sumatoria[j]<-sumatoria[j-1]+numeross[j]}
#########
#Multiplicación acumulada del 1 al 10
valores_mult<-seq(1,10)
vector_multacu<-rep(NA,10)
vector_multacu[1]<-valores_mult[1]
vector_multacu[2]<-vector_multacu[1]*valores_mult[2]
vector_multacu[3]<-vector_multacu[2]*valores_mult[3]
vector_multacu[4]<-vector_multacu[3]*valores_mult[4]
vector_multacu[5]<-vector_multacu[4]*valores_mult[5]
for(k in 2:10) {
vector_multacu[k]<-vector_multacu[k-1]*valores_mult[k]
}
prod(1:10) #funcion para realizar la multiplicación acumulada## [1] 3628800cumprod(1:10) #otra funcion para realizar la multiplicación acumulada## [1] 1 2 6 24 120 720 5040 40320 362880
## [10] 3628800# 3) rnorm(100) ¿Mayor o menor que 0? Etiquetarlos
#generar 100 con rnorm()
nume_aleato<-rnorm(100) #Normal (0,1)
etiqueta_alea<-rep(NA,100)
etiqueta_alea[which(nume_aleato>0)]<-"POSITIVO"
etiqueta_alea[which(nume_aleato<0)]<-"NEGATIVO"
table(etiqueta_alea)## etiqueta_alea
## NEGATIVO POSITIVO
## 43 57#IFs como funciona "if" y "else"
if(nume_aleato[1]>0){"POSITIVO"}else{"NEGATIVO"}## [1] "POSITIVO"#para cada valor
if(nume_aleato[1]>0){etiqueta_alea[1]<-"POSITIVO"}else{etiqueta_alea[1]<-"NEGATIVO"}
if(nume_aleato[2]>0){etiqueta_alea[2]<-"POSITIVO"}else{etiqueta_alea[2]<-"NEGATIVO"}
if(nume_aleato[7]>0){etiqueta_alea[7]<-"POSITIVO"}else{etiqueta_alea[7]<-"NEGATIVO"}
#para todos los valores
for(k in 1:100){
if(nume_aleato[k]>0){etiqueta_alea[k]<-"POSITIVO"}else{etiqueta_alea[k]<-"NEGATIVO"} }
table(etiqueta_alea)## etiqueta_alea
## NEGATIVO POSITIVO
## 43 57#sqrt(valor positivo)
raiz_cua<-rep(NA,100)
for(k in 1:100){
if(nume_aleato[k]>0){raiz_cua[k]<-sqrt(nume_aleato[k])}else{raiz_cua[k]<-"NÚMERO IMAGINARIO"} }
as.numeric(raiz_cua) #hace NA los imaginarios## Warning: NAs introducidos por coerción## [1] 0.96282549 1.16084839 NA 1.33366863 NA 0.99259198
## [7] NA 0.64883558 NA NA NA 0.69051728
## [13] NA 1.44704958 0.72727366 NA 0.18394834 NA
## [19] 1.12146329 NA 1.26010909 0.75274096 NA NA
## [25] 1.08987800 1.33081299 1.17761081 NA 0.10481704 1.27678460
## [31] 1.25707772 NA 0.71138853 0.88322226 NA 1.19647692
## [37] 0.80701266 0.86795021 1.15293038 0.80398760 1.32564147 0.41825526
## [43] NA NA 0.18614563 0.82628201 NA 0.32231727
## [49] NA NA 0.64633001 NA 0.68993298 NA
## [55] 0.82700701 0.80932126 0.59732853 1.15840256 1.12112427 0.43869289
## [61] 1.04689192 NA NA 1.37830663 0.24380091 1.27497176
## [67] 1.08187358 NA NA NA NA NA
## [73] 0.74500690 NA 0.47780887 NA NA 0.60527057
## [79] 1.43336075 NA 0.99539832 1.32085231 NA 0.09460943
## [85] 0.21351455 NA NA 0.43079358 NA 0.45841931
## [91] NA 0.53298804 NA 0.92156889 NA NA
## [97] 1.39296551 NA 0.63891387 NAdata.frame(nume_aleato,etiqueta_alea,as.numeric(raiz_cua) )## Warning in data.frame(nume_aleato, etiqueta_alea, as.numeric(raiz_cua)): NAs
## introducidos por coerción## nume_aleato etiqueta_alea as.numeric.raiz_cua.
## 1 0.927032926 POSITIVO 0.96282549
## 2 1.347568985 POSITIVO 1.16084839
## 3 -1.397446434 NEGATIVO NA
## 4 1.778672007 POSITIVO 1.33366863
## 5 -2.098788524 NEGATIVO NA
## 6 0.985238832 POSITIVO 0.99259198
## 7 -0.256717470 NEGATIVO NA
## 8 0.420987616 POSITIVO 0.64883558
## 9 -0.064748805 NEGATIVO NA
## 10 -2.334933301 NEGATIVO NA
## 11 -0.533583152 NEGATIVO NA
## 12 0.476814109 POSITIVO 0.69051728
## 13 -0.631706548 NEGATIVO NA
## 14 2.093952476 POSITIVO 1.44704958
## 15 0.528926977 POSITIVO 0.72727366
## 16 -0.684574563 NEGATIVO NA
## 17 0.033836990 POSITIVO 0.18394834
## 18 -1.229304246 NEGATIVO NA
## 19 1.257679912 POSITIVO 1.12146329
## 20 -0.304614852 NEGATIVO NA
## 21 1.587874906 POSITIVO 1.26010909
## 22 0.566618950 POSITIVO 0.75274096
## 23 -0.430137615 NEGATIVO NA
## 24 -0.302063809 NEGATIVO NA
## 25 1.187834052 POSITIVO 1.08987800
## 26 1.771063213 POSITIVO 1.33081299
## 27 1.386767214 POSITIVO 1.17761081
## 28 -0.283075685 NEGATIVO NA
## 29 0.010986612 POSITIVO 0.10481704
## 30 1.630178913 POSITIVO 1.27678460
## 31 1.580244403 POSITIVO 1.25707772
## 32 -1.197719691 NEGATIVO NA
## 33 0.506073643 POSITIVO 0.71138853
## 34 0.780081562 POSITIVO 0.88322226
## 35 -2.010425615 NEGATIVO NA
## 36 1.431557012 POSITIVO 1.19647692
## 37 0.651269438 POSITIVO 0.80701266
## 38 0.753337572 POSITIVO 0.86795021
## 39 1.329248466 POSITIVO 1.15293038
## 40 0.646396054 POSITIVO 0.80398760
## 41 1.757325299 POSITIVO 1.32564147
## 42 0.174937464 POSITIVO 0.41825526
## 43 -0.800785350 NEGATIVO NA
## 44 -0.064597993 NEGATIVO NA
## 45 0.034650197 POSITIVO 0.18614563
## 46 0.682741968 POSITIVO 0.82628201
## 47 -0.319486886 NEGATIVO NA
## 48 0.103888425 POSITIVO 0.32231727
## 49 -0.659512092 NEGATIVO NA
## 50 -1.393997644 NEGATIVO NA
## 51 0.417742478 POSITIVO 0.64633001
## 52 -0.357987106 NEGATIVO NA
## 53 0.476007521 POSITIVO 0.68993298
## 54 -0.080791612 NEGATIVO NA
## 55 0.683940591 POSITIVO 0.82700701
## 56 0.655000908 POSITIVO 0.80932126
## 57 0.356801374 POSITIVO 0.59732853
## 58 1.341896488 POSITIVO 1.15840256
## 59 1.256919619 POSITIVO 1.12112427
## 60 0.192451455 POSITIVO 0.43869289
## 61 1.095982690 POSITIVO 1.04689192
## 62 -0.276200568 NEGATIVO NA
## 63 -1.908416315 NEGATIVO NA
## 64 1.899729158 POSITIVO 1.37830663
## 65 0.059438884 POSITIVO 0.24380091
## 66 1.625552977 POSITIVO 1.27497176
## 67 1.170450453 POSITIVO 1.08187358
## 68 -0.510007827 NEGATIVO NA
## 69 -0.367641831 NEGATIVO NA
## 70 -0.739238231 NEGATIVO NA
## 71 -0.600748431 NEGATIVO NA
## 72 -0.566748528 NEGATIVO NA
## 73 0.555035288 POSITIVO 0.74500690
## 74 -2.289636221 NEGATIVO NA
## 75 0.228301316 POSITIVO 0.47780887
## 76 -1.537612886 NEGATIVO NA
## 77 -0.244526835 NEGATIVO NA
## 78 0.366352460 POSITIVO 0.60527057
## 79 2.054523032 POSITIVO 1.43336075
## 80 -2.278994286 NEGATIVO NA
## 81 0.990817820 POSITIVO 0.99539832
## 82 1.744650835 POSITIVO 1.32085231
## 83 -1.574177644 NEGATIVO NA
## 84 0.008950944 POSITIVO 0.09460943
## 85 0.045588465 POSITIVO 0.21351455
## 86 -1.060760295 NEGATIVO NA
## 87 -0.447306199 NEGATIVO NA
## 88 0.185583107 POSITIVO 0.43079358
## 89 -0.085532098 NEGATIVO NA
## 90 0.210148265 POSITIVO 0.45841931
## 91 -0.765701558 NEGATIVO NA
## 92 0.284076252 POSITIVO 0.53298804
## 93 -0.989708844 NEGATIVO NA
## 94 0.849289213 POSITIVO 0.92156889
## 95 -0.209282694 NEGATIVO NA
## 96 -0.846237012 NEGATIVO NA
## 97 1.940352916 POSITIVO 1.39296551
## 98 -0.368603862 NEGATIVO NA
## 99 0.408210928 POSITIVO 0.63891387
## 100 -1.505913817 NEGATIVO NAClase 9 de noviembre
#### CLASE 09 noviembre###
# 5) Aplicar la función de Coeficiente de variabilidad a
# 1000 conjuntos de distribución normal estándar de longitud 50 números
# 6) ¿Qué es más rápido en lo anterior, un apply o un for-loop?
# if ...
# 7) Ejemplo elaborado de codificación de variables (Hombre, mujer) (meh!)
# 8) vector de longitud 1000 con distribución normal estándar, sacarle
# la raiz cuadrada a cada uno (solo a los positivos)
# 9) Generar una matriz de 1000 x 50 y aplicar sqrt a cada valor,
# almacenarlo en otra matriz (hacer un doble loop-for)
# 10) La media de cada fila y cada columna... generar un vector con
# cada una (almacenar uno por uno... generar un vector "vacío")
# 11) Generar un vector donde pruebe si el CV es menor o mayor a cierto
# valor y generarle una etiqueta
# 1) Generar matriz con dim. 1000 x 50 con valores "Nulos" (NA)
# 2) Rellenar dicha matriz con 50 muestras de tamaño 100 de Norm(0,1)
# 3) A cada valor de esa matriz, obtener la raiz cuadrada si es un valor >0, y si no, "Etiqueta"
# 4) Almacenar los resultados anteriores en una nueva matriz (1000 x 50)
# 5) De cada fila, y de cada columna, obtener su CV (CV = una función personalizada)
# ¿Paralelizar (apply's) o repetición(for-loop)
#########
#1) Matriz a rellenar
mat_fic<-matrix(rep(NA,50*1000), ncol=50)
for (j in 1:50) {
set.seed(1235+j)
mat_fic[,j]<-rnorm(1000,0,1)
}
mean(mat_fic[,50]) ##los valores de la columna 50## [1] 0.01789922#para un valor
if(mat_fic[1,1]>0){sqrt(mat_fic[1,1])}else{NA} #si es mayor a 0, es decir## [1] NA#positiva entonces que imprima la matriz si es negativa que
#imprima NA
#para una columna
vect_fic<-rep(NA,1000)
vect_fic[1]<-if(mat_fic[1,1]>0){sqrt(mat_fic[1,1])}else{NA}
vect_fic[2]<-if(mat_fic[2,1]>0){sqrt(mat_fic[2,1])}else{NA}
vect_fic[3]<-if(mat_fic[3,1]>0){sqrt(mat_fic[4,1])}else{NA}
vect_fic[4]<-if(mat_fic[4,1]>0){sqrt(mat_fic[4,1])}else{NA}
vect_fic[5]<-if(mat_fic[5,1]>0){sqrt(mat_fic[5,1])}else{NA}
for(k in 1:1000){
vect_fic[k]<-if(mat_fic[k,1]>0){sqrt(mat_fic[k,1])}else{NA}
}
#for(k in 1:1000){
# if(vect_fic[k,1]>0){vect_fic[k]<-sqrt(mat_fic[k+1])}else{vect_fic[k]<-NA}
#### VARIAS COLUMNAS#####
###LOOPS ANIDADOS####
#j para columnas
#k para renglones
matriz_ficti2<-matrix(rep(NA,50*1000), ncol=50)
for(j in 1:50){
for(k in 1:1000){
matriz_ficti2[k,j]<-if(mat_fic[k,j]>0){sqrt(mat_fic[k,j])}else{NA}
}}
## 1) qué es el coeficiente de variabilidad, como se calcula?
#la desviación estándar entre la media de valor absoluto
##2) obtener el CV de todas las columnas, y todas la filas
# de la tabla mat_fic
mean(mat_fic[1,]) ##la media de mat_fic## [1] -0.1381448med<-mean(mat_fic[1,])
abs(med)## [1] 0.1381448med2<-abs(med)
sd(mat_fic[,]) #la desviación estandar## [1] 1.001632desvest<-sd(mat_fic[1,])
desvest/med2## [1] 8.303273#####OTRA FORMA###
sd(mat_fic[1,])/abs(mean(mat_fic[1,]))## [1] 8.303273vecc<-rep(NA,50)
for (c in 1:50) {
vecc[c]<-sd(mat_fic[,c])/abs(mean(mat_fic[,c]))
}
vecr<-rep(NA,1000)
for(r in 1:1000){
vecr[r]<-sd(mat_fic[r,])/abs(mean(mat_fic[r,]))
}
###cuánto tiempo me toma este ciclo?###
#Para las columnas
start_time <- Sys.time()
#for(i in 1:50){
#vec_col_cv[i]<-coef_var(mat_fic[,i])
#}
end_time <- Sys.time()
end_time - start_time## Time difference of 0.001506805 secsstart_time <- Sys.time()
#vec_col_cv<-apply(mat_fic,2,coef_var)
end_time <- Sys.time()
end_time - start_time## Time difference of 0.001004934 secs#Para los renglones
#¿Cuánto tiempo me toma este ciclo?
start_time <- Sys.time()
#for(j in 1:1000){
#vec_reng_cv[j]<-coef_var(mat_fic[j,])
#}
end_time <- Sys.time()
end_time - start_time## Time difference of 0.001005173 secsstart_time <- Sys.time()
#vec_reng_cv<-apply(mat_fic,1,coef_var)
end_time <- Sys.time()
end_time - start_time## Time difference of 0.001003981 secsclase 16 nov, mapas geoespaciales
library(rgdal)## Warning: package 'rgdal' was built under R version 4.1.2## Loading required package: sp## Please note that rgdal will be retired by the end of 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
##
## rgdal: version: 1.5-27, (SVN revision 1148)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/ARRA/Documents/R/win-library/4.1/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/ARRA/Documents/R/win-library/4.1/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-6
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.
## Overwritten PROJ_LIB was C:/Users/ARRA/Documents/R/win-library/4.1/rgdal/projmapa_jalisco<-readOGR("C:\\Users\\ARRA\\Desktop\\METPOL 1er SEMESTRE\\Programac", layer="mapajalisco")## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\ARRA\Desktop\METPOL 1er SEMESTRE\Programac", layer: "mapajalisco"
## with 125 features
## It has 4 fieldsplot(mapa_jalisco)
plot(mapa_jalisco[74,])
####################
mapa_jalisco@data[74,]## CVEGEO CVE_ENT CVE_MUN NOMGEO
## 73 14064 14 064 Ojuelos de Jalisco#############
#Descargar datos del censo Población y Vivienda 2020
library(readxl)
datos_inegi_jal<-read_xlsx("C:\\Users\\ARRA\\Desktop\\METPOL 1er SEMESTRE\\Programac\\ITER_14XLSX20.xlsx")
datos_inegi_jal2<-read.csv("C:\\Users\\ARRA\\Desktop\\METPOL 1er SEMESTRE\\Programac\\ITER_14CSV20.csv")
datos_inegi_jal$LOC## [1] "0000" "9998" "9999" "0000" "0001" "0002" "0003" "0006" "0007" "0009"
## [11] "0010" "0011" "0012" "0013" "0014" "0016" "0018" "0019" "0020" "0021"
## [21] "0022" "0023" "0024" "0026" "0027" "0028" "0029" "0030" "0031" "0032"
## [31] "0033" "0034" "0035" "0036" "0037" "0038" "0040" "0041" "0042" "0043"
## [41] "0044" "0046" "0047" "0048" "0049" "0052" "0053" "0054" "0055" "0056"
## [51] "0057" "0059" "0060" "0062" "0063" "0066" "0067" "0068" "0069" "0070"
## [61] "0071" "0072" "0073" "0074" "0075" "0078" "0079" "0081" "0084" "0086"
## [71] "0087" "0088" "0094" "0102" "0104" "0106" "0107" "0108" "0109" "0111"
## [81] "0112" "0113" "0115" "0116" "0118" "0119" "0122" "0123" "0128" "0129"
## [91] "0131" "0132" "0133" "0135" "0137" "0139" "0141" "0142" "9998" "9999"
## [101] "0000" "0001" "0002" "0004" "0005" "0007" "0008" "0009" "0010" "0013"
## [111] "0019" "0021" "0024" "0025" "0027" "0030" "0034" "0035" "0036" "0037"
## [121] "0039" "0040" "0041" "0043" "0048" "0053" "0055" "0058" "0061" "0062"
## [131] "0063" "0064" "0065" "9998" "9999" "0000" "0001" "0004" "0007" "0019"
## [141] "0021" "0028" "0030" "0031" "0032" "0038" "0051" "0056" "0057" "0058"
## [151] "0059" "0068" "0074" "0080" "0082" "0083" "0086" "0094" "0096" "0097"
## [161] "0099" "9998" "9999" "0000" "0001" "0003" "0004" "0005" "0007" "0008"
## [171] "0010" "0014" "0017" "0020" "0022" "0023" "0025" "0026" "0030" "0032"
## [181] "0039" "0053" "0061" "0064" "0066" "9998" "9999" "0000" "0001" "0002"
## [191] "0003" "0004" "0005" "0007" "0008" "0009" "0010" "0013" "0015" "0020"
## [201] "0022" "0026" "0029" "0031" "0032" "0035" "0036" "0039" "0040" "0043"
## [211] "0045" "0056" "0065" "0070" "0071" "0074" "0075" "9998" "9999" "0000"
## [221] "0001" "0002" "0003" "0004" "0005" "0007" "0010" "0011" "0012" "0013"
## [231] "0014" "0015" "0017" "0018" "0019" "0020" "0022" "0023" "0024" "0025"
## [241] "0026" "0027" "0028" "0030" "0031" "0032" "0033" "0034" "0037" "0038"
## [251] "0039" "0040" "0041" "0042" "0043" "0044" "0045" "0046" "0047" "0048"
## [261] "0049" "0050" "0051" "0052" "0053" "0054" "0057" "0058" "0059" "0061"
## [271] "0062" "0070" "0073" "0075" "0076" "0077" "0079" "0080" "0082" "0083"
## [281] "0084" "0085" "0087" "0088" "0089" "0091" "0092" "0095" "0097" "0098"
## [291] "0100" "0102" "0107" "0109" "0111" "0112" "0116" "0121" "0122" "0123"
## [301] "0130" "0133" "0137" "0140" "0142" "0147" "0148" "0149" "0153" "0156"
## [311] "0157" "0158" "0160" "0162" "0165" "0167" "0171" "0174" "0182" "0185"
## [321] "0187" "0189" "0190" "0192" "0193" "0194" "9998" "9999" "0000" "0001"
## [331] "0003" "0004" "0008" "0009" "0010" "0012" "0013" "0014" "0015" "0022"
## [341] "0076" "0086" "9998" "0000" "0001" "0002" "0003" "0004" "0005" "0006"
## [351] "0007" "0009" "0011" "0014" "0016" "0021" "0022" "0024" "0026" "0028"
## [361] "0032" "0036" "0045" "0046" "0048" "0050" "0051" "0054" "0056" "0057"
## [371] "0058" "0061" "0063" "0064" "0065" "0066" "0068" "0072" "0073" "0076"
## [381] "0078" "0083" "0089" "0090" "0093" "0095" "0096" "0097" "0101" "0102"
## [391] "0103" "0106" "0108" "0110" "0113" "0115" "0116" "0117" "0118" "0120"
## [401] "0127" "0130" "0131" "0133" "0137" "0139" "0141" "0142" "0143" "0145"
## [411] "0146" "0147" "0150" "0151" "0153" "0154" "0156" "0158" "0160" "0161"
## [421] "0162" "0167" "0171" "0172" "0175" "0177" "0178" "0181" "0184" "0187"
## [431] "0188" "0189" "0191" "0193" "0194" "0196" "0198" "0201" "0202" "0203"
## [441] "0207" "0210" "0212" "0213" "0214" "0216" "0217" "0222" "0225" "0228"
## [451] "0230" "0231" "0234" "0238" "0239" "0241" "0243" "0244" "0245" "0247"
## [461] "0248" "0249" "0250" "0257" "0258" "0261" "0262" "0263" "0266" "0267"
## [471] "0269" "0271" "0272" "0274" "0275" "0277" "0279" "0280" "0281" "0283"
## [481] "0287" "0288" "0289" "0294" "0295" "0301" "0303" "0307" "0311" "0317"
## [491] "0318" "0320" "0321" "0322" "0327" "0328" "0331" "0337" "0339" "0342"
## [501] "0343" "0345" "0346" "0348" "0351" "0352" "0353" "0358" "0360" "0361"
## [511] "0371" "0372" "0375" "0376" "0378" "0379" "0381" "0382" "0390" "0393"
## [521] "0394" "0395" "0396" "0403" "0405" "0406" "0408" "0409" "0411" "0412"
## [531] "0415" "0417" "0418" "0422" "0423" "0424" "0425" "0494" "0497" "0500"
## [541] "0501" "0503" "0509" "0511" "0512" "0513" "0516" "0519" "0520" "0521"
## [551] "0523" "0525" "0532" "0533" "0536" "0537" "0538" "0540" "0541" "0543"
## [561] "0548" "0552" "0553" "0557" "0560" "0567" "0570" "0571" "0574" "0575"
## [571] "0576" "0579" "0582" "0585" "0586" "0587" "0589" "0590" "0592" "0593"
## [581] "0596" "0602" "0606" "0607" "0610" "0611" "0612" "0614" "0617" "0621"
## [591] "0624" "0626" "0627" "0631" "0632" "0633" "0635" "0637" "0638" "0639"
## [601] "0641" "0643" "0645" "0646" "0649" "0650" "0657" "0659" "0661" "0662"
## [611] "0664" "0665" "0666" "0667" "0668" "0669" "0672" "0673" "0675" "0676"
## [621] "0677" "0681" "0682" "0683" "0684" "0686" "0687" "0688" "0689" "0690"
## [631] "0691" "0694" "0695" "0696" "0697" "0698" "0701" "9998" "9999" "0000"
## [641] "0001" "0003" "0004" "0005" "0006" "0007" "0009" "0012" "0014" "0020"
## [651] "0021" "0024" "0030" "0032" "0034" "0041" "0043" "0044" "0048" "0054"
## [661] "0055" "0057" "0060" "0065" "0066" "0067" "0068" "0069" "0070" "0071"
## [671] "0072" "0073" "0074" "0075" "0076" "0077" "0078" "0079" "0080" "0081"
## [681] "9998" "9999" "0000" "0001" "0003" "0016" "0021" "0023" "0024" "0025"
## [691] "0026" "0027" "0028" "0029" "0032" "0033" "0046" "0051" "0052" "0055"
## [701] "0058" "0060" "0061" "0107" "9998" "0000" "0001" "0003" "0008" "0009"
## [711] "0011" "0015" "0016" "0017" "0021" "0022" "0024" "0026" "0029" "0030"
## [721] "0033" "0041" "0047" "0054" "0068" "9998" "9999" "0000" "0001" "0002"
## [731] "0003" "0004" "0005" "0010" "0012" "0013" "0016" "0021" "0032" "0038"
## [741] "0039" "0040" "0041" "0044" "0045" "0046" "0049" "0050" "0053" "0056"
## [751] "0058" "0059" "0060" "0061" "0063" "0064" "0066" "0067" "0073" "0074"
## [761] "0076" "0090" "0095" "0103" "0104" "0107" "0109" "0111" "0114" "0116"
## [771] "0119" "0130" "0139" "0141" "0142" "0148" "0149" "0152" "0155" "0156"
## [781] "9998" "9999" "0000" "0001" "0002" "0003" "0004" "0005" "0006" "0007"
## [791] "0008" "0010" "0011" "0012" "0013" "0014" "0016" "0017" "0018" "0019"
## [801] "0023" "0024" "0025" "0026" "0028" "0029" "0030" "0032" "0035" "0036"
## [811] "0037" "0038" "0039" "0041" "0043" "0044" "0045" "0046" "0047" "0048"
## [821] "0050" "0051" "0052" "0054" "0055" "0057" "0058" "0060" "0061" "0065"
## [831] "0067" "0068" "0069" "0070" "0074" "0075" "0076" "0077" "0078" "0080"
## [841] "0082" "0083" "0084" "0085" "0087" "0089" "0090" "0091" "0092" "0093"
## [851] "0094" "0096" "0098" "0099" "0101" "0105" "0106" "0109" "0114" "0121"
## [861] "0123" "0125" "0127" "0128" "0130" "0131" "0134" "0136" "0137" "0141"
## [871] "0142" "0145" "0146" "0147" "0148" "0151" "0154" "0155" "0160" "0165"
## [881] "0166" "0167" "0169" "0173" "0175" "0177" "0178" "0180" "0185" "0188"
## [891] "0191" "0195" "0203" "0204" "0205" "0207" "0211" "0212" "0213" "0215"
## [901] "0216" "0218" "0219" "0220" "0221" "0224" "0225" "0227" "0230" "0232"
## [911] "0235" "0240" "0241" "0242" "0243" "0245" "0246" "0248" "9998" "9999"
## [921] "0000" "0001" "0003" "0004" "0005" "0006" "0008" "0009" "0010" "0013"
## [931] "0015" "0016" "0019" "0020" "0021" "0023" "0024" "0025" "0026" "0028"
## [941] "0029" "0031" "0045" "0047" "0056" "0057" "0062" "0063" "0068" "0071"
## [951] "0073" "0079" "9998" "9999" "0000" "0001" "0002" "0003" "0005" "0007"
## [961] "0009" "0011" "0012" "0013" "0014" "0015" "0017" "0019" "0020" "0022"
## [971] "0023" "0024" "0025" "0026" "0027" "0028" "0031" "0033" "0036" "0038"
## [981] "0041" "0042" "0046" "0047" "0048" "0051" "0053" "0055" "0058" "0060"
## [991] "0063" "0064" "0068" "0069" "0071" "0075" "0076" "0077" "0079" "0081"
## [1001] "0082" "0084" "0085" "0086" "0088" "0097" "0102" "0104" "0105" "0107"
## [1011] "0108" "0112" "0116" "0119" "0120" "0125" "0130" "0131" "0132" "0133"
## [1021] "0134" "0136" "0138" "0139" "0140" "0142" "0144" "0146" "0152" "0154"
## [1031] "0156" "0158" "0160" "0161" "0163" "0165" "0175" "0176" "0178" "0186"
## [1041] "0190" "0194" "0195" "0203" "0204" "0207" "0208" "0212" "0213" "0216"
## [1051] "0225" "0227" "0237" "0239" "0242" "0244" "0247" "0248" "0251" "0252"
## [1061] "0253" "0255" "0257" "0258" "0259" "0264" "0265" "0266" "0267" "0270"
## [1071] "0273" "0275" "0276" "0283" "0287" "0296" "0298" "0301" "0302" "0304"
## [1081] "0309" "0314" "0333" "0337" "0347" "0348" "0358" "0360" "0369" "0372"
## [1091] "0375" "0378" "0379" "0382" "0383" "9998" "9999" "0000" "0001" "0002"
## [1101] "0003" "0004" "0005" "0008" "0010" "0011" "0012" "0014" "0015" "0017"
## [1111] "0018" "0020" "0024" "0027" "0029" "0030" "0033" "0035" "0037" "0038"
## [1121] "0039" "0043" "0046" "0047" "0048" "0049" "0050" "0051" "0052" "0053"
## [1131] "0054" "0055" "0069" "0070" "0071" "0072" "0073" "0075" "0077" "0079"
## [1141] "0080" "0082" "0083" "0085" "0086" "0087" "0089" "0093" "0095" "0100"
## [1151] "0101" "0103" "0104" "0106" "0108" "0112" "0114" "0115" "0116" "0121"
## [1161] "0122" "0124" "0128" "0133" "0141" "0142" "0145" "0152" "0156" "0160"
## [1171] "0161" "0162" "0164" "0170" "9998" "9999" "0000" "0001" "0005" "0006"
## [1181] "0012" "0013" "0017" "0018" "0020" "0026" "0037" "0041" "0042" "0054"
## [1191] "0056" "0058" "0064" "0066" "0076" "0078" "0083" "0084" "0085" "0086"
## [1201] "0087" "0088" "0093" "0096" "0097" "0100" "0102" "0103" "0104" "0105"
## [1211] "0110" "0111" "0112" "0116" "0119" "0120" "0121" "0123" "0124" "0125"
## [1221] "0130" "0133" "0134" "0135" "0139" "0141" "0145" "0150" "0181" "0182"
## [1231] "0188" "0200" "0203" "0205" "0206" "0211" "0214" "0215" "0227" "0230"
## [1241] "0233" "0242" "0245" "0247" "0250" "0251" "0253" "9998" "9999" "0000"
## [1251] "0001" "0003" "0005" "0007" "0008" "0009" "0010" "0011" "0012" "0013"
## [1261] "0015" "0017" "0018" "0019" "0022" "0023" "0024" "0026" "0027" "0028"
## [1271] "0029" "0031" "0032" "0033" "0034" "0036" "0037" "0038" "0039" "0040"
## [1281] "0041" "0042" "0055" "0060" "0061" "0062" "0071" "0074" "0075" "0086"
## [1291] "0089" "0095" "0109" "0112" "0123" "0129" "0135" "0139" "0144" "0149"
## [1301] "0154" "0174" "0178" "0180" "0191" "0195" "0204" "0210" "0211" "0214"
## [1311] "0220" "0230" "0231" "0232" "0240" "0243" "0244" "0245" "0246" "0251"
## [1321] "0257" "0259" "0262" "0264" "0265" "0266" "9998" "9999" "0000" "0001"
## [1331] "0002" "0007" "0010" "0013" "0017" "0018" "0020" "0023" "0026" "0027"
## [1341] "0032" "0035" "0036" "0040" "0044" "0048" "0050" "0052" "0061" "0063"
## [1351] "0067" "0070" "0072" "0073" "0075" "0076" "0081" "0085" "0092" "0094"
## [1361] "0096" "0098" "0100" "0101" "0102" "0103" "0105" "0109" "0110" "0111"
## [1371] "0114" "0119" "0131" "0133" "0136" "0137" "0138" "0152" "0153" "0158"
## [1381] "0162" "0165" "0167" "0174" "0177" "0187" "0188" "0193" "0197" "0200"
## [1391] "0201" "0217" "0221" "0238" "0239" "0243" "0244" "0249" "0261" "0262"
## [1401] "0263" "0266" "0275" "0276" "0277" "0285" "0286" "0287" "0292" "0293"
## [1411] "0296" "0307" "0315" "0334" "0335" "0336" "0340" "0345" "0350" "0351"
## [1421] "0352" "0358" "0359" "0362" "0366" "0367" "0368" "0375" "0377" "0381"
## [1431] "0384" "0387" "0389" "0390" "0391" "0394" "0396" "0397" "0400" "0405"
## [1441] "0407" "0411" "0412" "0413" "0415" "0417" "0420" "0421" "0423" "0425"
## [1451] "0427" "0428" "0429" "0430" "0431" "0432" "0436" "0438" "0440" "0441"
## [1461] "0443" "0444" "9998" "9999" "0000" "0001" "0007" "0010" "0013" "0020"
## [1471] "0021" "0022" "0032" "0033" "0034" "0035" "0037" "0038" "0041" "0043"
## [1481] "0047" "0053" "0055" "0057" "0060" "0062" "0063" "0064" "0065" "0066"
## [1491] "0069" "0070" "0073" "0074" "0075" "0076" "0078" "0079" "0082" "0086"
## [1501] "0087" "0088" "0090" "0091" "0092" "0094" "0095" "0096" "0097" "0099"
## [1511] "0100" "0101" "0102" "0106" "0107" "0109" "0110" "0111" "0114" "0116"
## [1521] "0117" "0121" "0123" "0125" "0127" "0128" "0129" "0130" "0131" "0134"
## [1531] "0135" "0136" "0138" "0148" "0152" "0153" "0155" "0161" "0164" "0177"
## [1541] "0191" "0193" "0194" "0196" "0203" "0207" "0208" "0211" "0212" "0213"
## [1551] "0216" "0217" "0219" "0220" "0225" "0226" "0228" "0234" "0237" "0238"
## [1561] "0240" "0241" "0242" "0245" "0248" "0249" "0251" "0258" "0260" "0263"
## [1571] "0264" "0267" "0271" "0273" "0279" "0282" "0284" "0285" "0286" "0287"
## [1581] "0289" "0293" "0295" "0296" "0297" "0298" "0299" "0300" "0302" "0303"
## [1591] "0309" "0311" "0312" "0313" "9998" "9999" "0000" "0001" "0002" "0003"
## [1601] "0008" "0011" "0015" "0018" "0020" "0021" "0024" "0029" "0033" "0034"
## [1611] "0035" "0042" "0043" "0044" "0046" "0049" "0050" "0051" "0053" "0057"
## [1621] "0060" "0061" "0062" "0064" "0068" "0075" "0078" "0084" "0089" "0092"
## [1631] "0094" "0103" "0104" "0109" "0131" "0135" "0136" "0140" "0162" "0164"
## [1641] "9998" "9999" "0000" "0001" "0002" "0003" "0004" "0006" "0007" "0009"
## [1651] "0012" "0016" "0017" "0024" "0030" "0033" "0034" "0036" "0047" "0049"
## [1661] "0051" "0058" "0059" "0062" "0068" "0069" "0071" "0077" "0082" "0087"
## [1671] "0094" "0100" "0102" "0103" "0104" "0107" "0108" "0109" "0110" "0112"
## [1681] "0113" "0115" "0118" "0119" "0120" "0123" "0124" "0133" "0134" "0136"
## [1691] "0138" "0139" "0141" "0143" "0144" "0145" "0146" "0147" "0150" "0151"
## [1701] "0154" "0157" "0159" "0162" "0163" "9998" "9999" "0000" "0001" "0003"
## [1711] "0005" "0008" "0011" "0014" "0021" "0024" "0028" "0037" "0038" "0042"
## [1721] "0069" "0071" "0074" "0076" "0093" "0101" "0102" "0125" "0137" "0139"
## [1731] "0149" "0152" "0167" "0171" "0174" "0194" "0206" "0207" "0211" "0222"
## [1741] "0232" "0234" "0235" "0236" "0237" "0238" "9998" "9999" "0000" "0001"
## [1751] "0002" "0003" "0004" "0005" "0007" "0008" "0010" "0011" "0013" "0015"
## [1761] "0016" "0017" "0018" "0019" "0020" "0021" "0022" "0023" "0024" "0032"
## [1771] "0037" "0038" "0040" "0041" "0043" "0045" "0047" "0052" "0053" "0055"
## [1781] "0056" "0058" "0060" "0062" "0066" "0067" "0068" "0069" "9998" "9999"
## [1791] "0000" "0001" "0005" "0006" "0007" "0008" "0012" "0013" "0014" "0016"
## [1801] "0018" "0020" "0023" "0026" "0028" "0031" "0032" "0035" "0037" "0042"
## [1811] "0043" "0045" "0051" "0058" "0061" "0062" "0063" "0065" "0066" "0067"
## [1821] "0068" "0070" "0074" "0075" "0076" "0079" "0080" "0085" "0089" "0090"
## [1831] "0099" "0103" "0107" "0112" "0119" "0120" "0128" "0130" "0133" "0135"
## [1841] "0136" "0140" "0143" "0144" "0145" "0147" "0149" "0153" "0154" "0169"
## [1851] "0178" "0179" "0190" "0193" "0195" "0196" "0200" "0204" "0207" "9998"
## [1861] "9999" "0000" "0001" "0005" "0009" "0014" "0018" "0021" "0024" "0025"
## [1871] "0030" "0031" "0032" "0223" "0228" "0233" "0235" "0238" "0239" "0241"
## [1881] "0242" "0243" "0244" "9998" "0000" "0001" "0005" "0008" "0010" "0012"
## [1891] "0014" "0021" "0025" "0029" "0030" "0031" "0032" "0033" "0034" "0035"
## [1901] "0038" "0040" "0041" "0042" "0043" "0044" "0049" "0051" "0054" "0055"
## [1911] "0056" "0059" "0060" "0061" "0063" "0067" "0068" "0071" "0076" "0078"
## [1921] "0080" "0081" "0084" "0087" "0092" "0093" "0095" "0096" "0099" "0101"
## [1931] "0103" "0104" "0106" "0110" "0111" "0114" "0115" "0116" "0121" "0128"
## [1941] "0129" "0132" "0134" "0138" "0140" "0148" "0150" "0152" "0153" "0162"
## [1951] "0163" "0164" "0165" "0167" "0168" "0174" "0176" "0181" "0188" "0191"
## [1961] "0192" "0195" "0196" "0199" "0204" "0205" "0208" "0209" "0210" "0212"
## [1971] "0213" "0215" "0216" "0218" "0220" "0221" "0227" "0228" "0231" "0232"
## [1981] "0237" "0238" "0239" "0240" "0246" "0247" "0248" "0252" "0254" "0255"
## [1991] "0256" "0259" "0262" "0264" "0273" "0279" "0282" "0283" "0284" "0286"
## [2001] "0287" "0288" "0294" "0295" "0296" "0297" "9998" "9999" "0000" "0001"
## [2011] "0004" "0005" "0007" "0008" "0009" "0010" "0016" "0017" "0019" "0021"
## [2021] "0022" "0023" "0026" "0028" "0029" "0031" "0052" "0060" "0061" "0062"
## [2031] "0064" "0065" "0068" "0069" "0073" "9998" "9999" "0000" "0001" "0002"
## [2041] "0003" "0004" "0005" "0006" "0008" "0010" "0012" "0013" "0015" "0017"
## [2051] "0018" "0019" "0020" "0022" "0024" "0025" "0027" "0028" "0029" "0030"
## [2061] "0031" "0035" "0036" "0040" "0042" "0043" "0044" "0045" "0047" "0048"
## [2071] "0049" "0050" "0051" "0054" "0057" "0058" "0059" "0060" "0062" "0063"
## [2081] "0066" "0069" "0071" "0072" "0074" "0076" "0077" "0079" "0080" "0081"
## [2091] "0083" "0084" "0085" "0086" "0088" "0089" "0091" "0092" "0093" "0094"
## [2101] "0095" "0097" "0099" "0101" "0102" "0103" "0104" "0105" "0106" "0107"
## [2111] "0108" "0109" "0110" "0111" "0112" "0113" "0115" "0116" "0117" "0118"
## [2121] "0120" "0122" "0123" "0124" "0126" "0128" "0134" "0135" "0138" "0144"
## [2131] "0145" "0146" "0152" "0154" "0155" "0156" "0167" "0168" "0169" "0170"
## [2141] "0173" "0174" "0176" "0178" "0180" "0183" "0188" "0189" "0191" "0192"
## [2151] "0194" "0195" "0196" "0197" "0198" "0200" "0203" "0205" "0206" "0212"
## [2161] "0216" "0217" "0219" "0222" "0227" "0228" "0231" "0232" "0234" "0236"
## [2171] "0237" "0239" "0242" "0243" "0245" "0247" "0248" "0249" "0251" "0253"
## [2181] "0259" "0260" "0261" "0262" "9998" "9999" "0000" "0001" "0002" "0003"
## [2191] "0004" "0005" "0006" "0007" "0018" "0024" "0025" "0026" "0027" "0031"
## [2201] "0038" "0039" "0041" "0042" "0044" "0048" "0050" "0051" "0052" "0053"
## [2211] "0054" "0055" "0056" "0057" "0058" "0060" "0064" "0065" "0066" "0067"
## [2221] "0072" "0074" "0077" "0078" "0081" "0082" "0086" "0087" "0092" "0093"
## [2231] "0094" "0096" "0099" "0100" "0101" "0102" "0104" "9998" "9999" "0000"
## [2241] "0001" "0003" "0007" "0012" "0013" "0014" "0016" "0018" "0020" "0021"
## [2251] "0022" "0024" "0029" "0030" "0035" "0037" "0039" "0040" "0045" "0050"
## [2261] "0051" "0055" "0058" "0060" "0064" "0066" "0069" "0070" "0071" "0073"
## [2271] "0074" "0088" "0089" "0090" "0091" "0093" "0094" "0096" "0098" "0109"
## [2281] "0111" "0118" "0119" "0120" "0124" "0126" "0137" "0140" "0141" "0152"
## [2291] "0160" "0161" "0176" "0179" "0189" "0190" "0206" "0211" "0214" "0215"
## [2301] "0223" "0234" "9998" "9999" "0000" "0001" "0002" "0003" "0004" "0005"
## [2311] "0006" "0008" "0009" "0010" "0012" "0014" "0016" "0020" "0024" "0025"
## [2321] "0027" "0034" "0040" "0045" "0047" "0053" "0054" "0055" "9998" "9999"
## [2331] "0000" "0001" "0002" "0006" "0007" "0008" "0010" "0011" "0013" "0014"
## [2341] "0019" "0022" "0023" "0024" "0026" "0027" "0029" "0030" "0033" "0035"
## [2351] "0036" "0037" "0038" "0039" "0044" "0046" "0048" "0049" "0051" "0053"
## [2361] "0054" "0056" "0058" "0059" "0060" "0061" "0062" "0064" "0065" "0067"
## [2371] "0068" "0069" "0070" "0071" "0072" "0073" "0074" "0076" "0077" "0078"
## [2381] "0079" "0080" "0081" "0085" "0091" "0092" "0094" "0095" "0097" "0101"
## [2391] "0103" "0108" "0110" "0119" "0122" "0132" "0133" "0137" "0139" "0141"
## [2401] "0142" "0144" "0146" "0151" "0152" "0153" "0159" "0160" "0163" "0167"
## [2411] "0168" "0169" "0171" "0172" "0174" "0176" "0177" "9998" "9999" "0000"
## [2421] "0001" "0003" "0008" "0013" "0015" "0017" "0020" "0021" "0023" "0034"
## [2431] "0042" "0050" "0057" "0065" "0069" "0073" "0074" "0077" "9998" "0000"
## [2441] "0001" "0002" "0003" "0017" "0021" "0025" "0032" "0033" "0034" "0035"
## [2451] "0036" "0039" "0040" "0044" "0048" "0050" "0051" "0054" "0061" "0062"
## [2461] "0066" "0069" "0073" "0075" "0079" "0081" "0082" "0085" "0086" "0087"
## [2471] "0090" "0100" "0104" "0108" "0110" "0117" "0123" "0125" "0127" "0128"
## [2481] "0132" "0134" "0136" "0138" "0139" "0140" "0148" "0150" "0156" "0158"
## [2491] "0161" "0162" "0169" "0172" "0176" "0178" "0181" "0184" "0190" "0194"
## [2501] "0202" "0203" "0207" "0210" "0213" "0216" "0217" "0218" "0222" "0225"
## [2511] "0226" "0228" "0230" "0235" "0236" "0238" "0241" "0243" "0244" "0245"
## [2521] "0246" "0247" "0249" "0251" "0252" "0259" "0260" "0261" "0264" "0268"
## [2531] "0269" "0270" "0273" "0277" "0278" "0283" "0284" "0286" "0292" "0293"
## [2541] "0295" "0300" "0301" "0302" "0303" "0305" "0313" "0314" "0320" "0329"
## [2551] "0331" "0332" "0350" "0351" "0355" "0357" "0359" "0363" "0375" "0379"
## [2561] "0381" "0382" "0383" "0385" "0386" "0395" "0398" "0399" "0406" "0407"
## [2571] "0409" "0410" "0412" "0413" "0414" "0415" "0416" "0417" "0419" "0421"
## [2581] "0422" "0423" "0425" "0427" "0428" "0429" "0430" "0431" "0432" "0433"
## [2591] "0434" "0435" "0438" "0440" "0442" "0443" "0446" "0449" "0452" "0453"
## [2601] "0454" "0460" "0463" "0466" "0468" "0471" "0472" "0473" "0474" "0475"
## [2611] "0476" "0478" "0481" "0483" "0487" "0491" "0492" "0495" "0497" "0498"
## [2621] "0500" "0501" "0502" "0504" "0507" "0508" "0509" "0510" "0516" "0521"
## [2631] "0523" "0524" "0525" "0526" "0527" "0529" "0534" "0536" "0542" "0546"
## [2641] "0549" "0551" "0552" "0563" "0564" "0566" "0568" "0569" "0571" "0573"
## [2651] "0575" "0576" "0577" "0590" "0591" "0594" "0598" "0599" "0602" "0603"
## [2661] "0604" "0605" "0606" "0608" "0609" "0610" "0611" "0612" "0613" "0617"
## [2671] "0619" "0620" "0622" "0624" "0625" "0627" "0631" "0632" "0633" "0634"
## [2681] "0635" "0636" "0637" "0638" "0639" "0640" "0641" "0642" "0643" "0646"
## [2691] "0647" "0650" "0653" "0655" "0656" "0657" "0659" "0661" "0663" "0666"
## [2701] "0671" "0672" "0673" "0677" "0678" "0679" "0682" "0683" "0684" "0685"
## [2711] "0687" "0691" "0692" "0693" "0694" "0696" "0699" "0705" "0706" "0707"
## [2721] "0710" "0711" "0713" "0714" "0716" "0717" "0719" "0720" "0721" "0722"
## [2731] "0723" "0724" "0727" "0728" "0731" "0732" "0734" "0736" "0737" "0738"
## [2741] "0739" "0740" "0741" "0743" "0744" "0750" "0755" "0757" "0763" "0764"
## [2751] "0765" "0766" "0769" "0774" "0776" "0777" "0778" "0779" "0781" "0782"
## [2761] "0786" "0787" "0789" "0790" "0791" "0792" "0793" "0794" "0795" "0796"
## [2771] "0797" "0800" "0802" "9998" "9999" "0000" "0001" "0003" "0012" "0014"
## [2781] "0019" "0020" "0025" "0027" "0030" "0031" "0032" "0036" "0037" "0039"
## [2791] "0046" "0047" "0052" "0055" "0057" "0058" "0059" "0060" "0066" "0076"
## [2801] "0083" "0086" "0090" "0102" "0127" "0132" "0133" "9998" "9999" "0000"
## [2811] "0001" "0002" "0007" "0009" "0011" "0012" "0018" "0020" "0024" "0025"
## [2821] "0026" "0027" "0031" "0043" "0048" "0054" "0057" "0060" "0076" "0079"
## [2831] "0080" "0081" "0082" "0087" "0088" "0089" "0090" "0093" "0098" "0115"
## [2841] "0118" "0119" "0126" "0133" "0135" "0138" "0161" "0162" "0164" "0167"
## [2851] "0168" "9998" "9999" "0000" "0001" "0015" "0016" "0022" "0027" "0028"
## [2861] "0029" "0030" "0032" "0033" "0034" "0042" "0044" "0045" "0049" "0053"
## [2871] "0061" "0065" "0070" "0072" "0074" "0076" "0077" "0078" "0084" "0090"
## [2881] "0095" "0097" "0098" "0099" "0100" "0113" "0115" "0116" "0117" "0119"
## [2891] "0121" "0126" "0130" "0131" "0132" "0155" "0166" "0170" "0171" "0175"
## [2901] "0183" "0234" "0260" "0261" "0263" "0264" "9998" "9999" "0000" "0001"
## [2911] "0002" "9998" "0000" "0001" "0008" "0010" "0011" "0013" "0015" "0016"
## [2921] "0018" "0019" "0021" "0023" "0025" "0030" "0031" "0035" "0038" "0039"
## [2931] "0043" "0044" "0046" "0047" "0048" "0049" "0057" "0060" "0064" "0065"
## [2941] "0067" "0070" "0071" "0072" "0074" "0077" "0078" "0082" "0085" "0086"
## [2951] "0095" "0100" "0121" "0122" "0123" "0130" "0147" "0149" "0156" "0163"
## [2961] "9998" "9999" "0000" "0001" "0002" "0003" "0004" "0005" "0006" "0007"
## [2971] "0009" "0010" "0012" "0013" "0014" "0015" "0017" "0018" "0019" "0020"
## [2981] "0021" "0022" "0023" "0026" "0044" "0045" "0046" "0052" "0055" "0062"
## [2991] "0063" "0068" "0074" "0077" "0078" "0084" "0093" "0095" "0117" "9998"
## [3001] "9999" "0000" "0001" "0002" "0003" "0006" "0007" "0008" "0009" "0010"
## [3011] "0012" "0013" "0014" "0015" "0016" "0018" "0019" "0020" "0021" "0022"
## [3021] "0023" "0024" "0026" "0027" "0028" "0029" "0030" "0031" "0033" "0034"
## [3031] "0035" "0036" "0037" "0038" "0039" "0041" "0042" "0043" "0044" "0046"
## [3041] "0047" "0048" "0049" "0061" "0063" "0067" "0082" "0105" "0110" "0117"
## [3051] "0130" "0132" "0133" "0134" "0140" "0148" "0151" "0163" "0172" "0173"
## [3061] "0175" "0176" "0183" "0199" "0200" "9998" "9999" "0000" "0001" "0004"
## [3071] "0006" "0007" "0009" "0010" "0012" "0015" "0016" "0022" "0024" "0025"
## [3081] "0026" "0030" "0031" "0033" "0036" "0039" "0040" "0041" "0042" "0048"
## [3091] "0052" "0057" "0060" "0062" "0063" "0066" "0067" "0068" "0074" "0081"
## [3101] "0085" "0086" "0091" "0092" "0094" "0095" "0097" "0099" "0101" "0102"
## [3111] "0105" "0110" "0111" "0114" "0115" "0117" "0123" "0124" "0125" "0126"
## [3121] "0132" "0135" "0136" "0137" "0139" "0140" "0141" "0146" "0147" "0148"
## [3131] "0150" "0152" "0154" "0159" "0161" "0165" "0166" "0173" "0176" "0182"
## [3141] "0183" "0186" "0190" "0191" "0202" "0212" "0213" "0216" "0220" "0226"
## [3151] "0227" "0232" "0236" "0242" "0247" "0249" "0252" "0253" "0257" "0263"
## [3161] "0276" "0281" "0284" "0290" "0291" "0306" "0308" "0325" "0329" "0330"
## [3171] "0331" "0338" "0345" "0350" "0358" "0363" "9998" "9999" "0000" "0001"
## [3181] "0002" "0003" "0004" "0005" "0006" "0007" "0009" "0010" "0011" "0012"
## [3191] "0014" "0018" "0019" "0020" "0027" "0030" "0033" "0034" "0035" "0041"
## [3201] "0042" "0043" "0048" "0049" "0050" "0051" "0054" "0057" "0059" "0060"
## [3211] "0062" "0064" "0067" "0069" "0070" "0071" "0073" "0074" "0075" "0077"
## [3221] "0078" "0079" "0080" "0082" "0083" "0084" "0085" "0089" "0091" "0094"
## [3231] "0096" "0097" "0099" "0101" "0103" "0105" "0106" "0109" "0110" "0113"
## [3241] "0114" "0116" "0120" "0127" "0128" "0129" "0130" "0131" "0134" "0135"
## [3251] "0137" "0139" "0141" "0142" "0145" "0147" "0152" "0153" "0154" "0155"
## [3261] "0156" "0157" "0161" "0162" "0163" "0164" "0165" "0166" "0168" "0169"
## [3271] "0170" "0171" "0172" "0173" "0174" "0175" "0176" "0177" "0178" "0179"
## [3281] "0180" "0181" "9998" "9999" "0000" "0001" "0005" "0007" "0008" "0009"
## [3291] "0010" "0011" "0013" "0016" "0021" "0022" "0024" "0025" "0026" "0027"
## [3301] "0028" "0030" "0032" "0033" "0034" "0035" "0040" "0043" "0045" "0046"
## [3311] "0047" "0048" "0050" "0051" "0054" "0056" "0057" "0060" "0063" "0065"
## [3321] "0071" "0072" "0076" "0077" "0078" "0079" "0080" "0081" "0083" "0084"
## [3331] "0088" "0089" "0093" "0095" "0098" "0099" "0100" "0101" "0104" "0106"
## [3341] "0107" "0108" "0109" "0110" "0114" "0115" "0116" "0117" "0119" "0120"
## [3351] "0121" "0122" "0123" "0124" "0126" "0127" "0130" "0131" "0132" "0135"
## [3361] "0136" "0137" "0139" "0140" "0141" "0142" "0144" "0147" "0148" "0151"
## [3371] "0157" "0158" "0159" "0164" "0165" "0169" "0174" "0179" "0181" "0186"
## [3381] "0199" "0203" "0205" "0206" "0207" "0208" "0210" "0211" "0212" "0214"
## [3391] "0217" "0218" "0221" "0226" "0227" "0231" "0232" "0233" "0234" "0236"
## [3401] "0239" "0240" "0241" "0242" "0250" "0253" "0255" "0258" "0264" "0265"
## [3411] "0266" "0268" "0272" "0273" "0274" "0278" "0279" "0280" "0281" "0289"
## [3421] "0292" "0295" "0297" "0298" "0302" "0306" "0308" "0310" "0317" "0319"
## [3431] "0320" "0327" "0330" "0333" "0337" "0341" "0344" "0345" "0346" "0347"
## [3441] "0351" "0352" "9998" "9999" "0000" "0001" "0002" "0003" "0006" "0008"
## [3451] "0012" "0013" "0014" "0017" "0018" "0020" "0021" "0024" "0025" "0026"
## [3461] "0027" "0028" "0029" "0031" "0032" "0034" "0035" "0037" "0038" "0041"
## [3471] "0042" "0045" "0046" "0047" "0051" "0052" "0055" "0056" "0057" "0059"
## [3481] "0061" "0062" "0064" "0065" "0066" "0067" "0068" "0069" "0070" "0071"
## [3491] "0072" "0074" "0076" "0078" "0080" "0081" "0087" "0088" "0091" "0092"
## [3501] "0093" "0095" "0098" "0100" "0101" "0102" "0103" "0104" "0108" "0110"
## [3511] "0111" "0112" "0113" "0114" "0115" "0116" "0117" "0118" "0120" "0121"
## [3521] "0122" "0123" "0130" "0133" "0136" "0139" "0142" "0143" "0144" "0147"
## [3531] "0149" "0151" "0152" "0153" "0154" "0163" "0177" "0178" "0181" "0182"
## [3541] "0183" "0189" "0192" "0193" "0194" "0195" "0198" "0199" "0203" "0206"
## [3551] "0207" "0210" "0213" "0217" "0223" "0224" "0228" "0230" "0232" "0235"
## [3561] "0238" "0239" "0241" "0242" "0243" "0244" "0245" "0246" "0249" "0251"
## [3571] "0252" "0253" "0254" "0255" "0256" "0257" "0258" "0259" "0260" "0261"
## [3581] "0262" "0265" "0266" "0268" "0271" "0273" "0274" "0275" "0277" "0283"
## [3591] "0284" "0285" "0286" "0291" "0292" "0293" "0295" "0296" "0297" "0298"
## [3601] "0299" "0300" "0301" "0302" "0303" "0304" "0307" "0309" "9998" "9999"
## [3611] "0000" "0001" "0002" "0003" "0004" "0005" "0006" "0007" "0028" "0029"
## [3621] "0033" "0038" "0039" "0044" "0045" "0056" "0057" "0058" "0061" "0063"
## [3631] "9998" "9999" "0000" "0001" "0003" "0004" "0006" "0007" "0009" "0011"
## [3641] "0015" "0017" "0018" "0019" "0021" "0022" "0023" "0024" "0025" "0026"
## [3651] "0027" "0029" "0030" "0032" "0033" "0034" "0037" "0038" "0039" "0040"
## [3661] "0042" "0044" "0046" "0048" "0049" "0050" "0052" "0053" "0055" "0056"
## [3671] "0058" "0060" "0061" "0063" "0065" "0066" "0067" "0069" "0070" "0071"
## [3681] "0074" "0075" "0076" "0077" "0079" "0080" "0082" "0083" "0084" "0086"
## [3691] "0087" "0089" "0090" "0093" "0096" "0097" "0099" "0101" "0102" "0103"
## [3701] "0104" "0105" "0106" "0109" "0110" "0111" "0113" "0114" "0116" "0118"
## [3711] "0120" "0121" "0122" "0124" "0126" "0128" "0129" "0132" "0133" "0134"
## [3721] "0136" "0137" "0138" "0139" "0140" "0142" "0143" "0145" "0146" "0149"
## [3731] "0150" "0151" "0152" "0159" "0164" "0169" "0175" "0176" "0180" "0181"
## [3741] "0183" "0187" "0192" "0202" "0203" "0204" "0205" "0206" "0207" "0214"
## [3751] "0215" "0217" "0218" "0219" "0220" "0221" "0222" "0223" "0224" "0225"
## [3761] "0226" "0227" "0228" "0229" "0232" "0237" "0238" "0239" "0243" "0245"
## [3771] "0249" "0250" "0251" "0254" "0256" "0259" "0260" "0263" "0264" "0265"
## [3781] "0266" "0267" "0271" "0272" "0273" "0274" "0275" "0276" "0278" "0286"
## [3791] "0288" "0293" "0294" "0295" "0296" "0297" "9998" "9999" "0000" "0001"
## [3801] "0005" "0006" "0008" "0009" "0011" "0014" "0015" "0019" "0021" "0023"
## [3811] "0026" "0028" "0030" "0031" "0033" "0037" "0038" "0039" "0040" "0042"
## [3821] "0049" "0053" "0055" "0058" "0062" "0064" "0069" "0073" "0078" "0079"
## [3831] "0086" "0087" "0088" "0089" "0090" "0091" "0096" "0097" "0098" "0099"
## [3841] "0100" "0108" "0109" "0112" "0113" "0116" "0117" "0118" "0126" "0128"
## [3851] "0130" "0131" "0134" "0136" "0137" "0139" "0140" "0148" "0153" "0154"
## [3861] "0157" "0159" "0163" "0165" "0170" "0173" "0174" "0177" "0178" "0179"
## [3871] "0185" "0187" "0188" "0189" "0191" "0195" "0197" "0199" "0200" "0210"
## [3881] "0219" "0220" "0221" "0222" "0226" "0227" "0230" "0235" "0245" "0248"
## [3891] "0250" "0253" "0256" "0258" "0260" "0261" "0266" "0267" "0268" "0269"
## [3901] "0270" "0274" "0276" "0278" "0283" "0286" "0289" "0291" "0294" "0304"
## [3911] "0306" "0307" "0308" "0310" "0312" "0313" "0314" "0317" "0318" "0321"
## [3921] "0345" "0347" "0355" "0358" "0362" "0363" "0368" "0427" "0435" "0437"
## [3931] "0442" "0448" "0452" "0458" "0461" "0464" "0466" "0467" "0474" "0475"
## [3941] "0476" "0479" "0480" "0481" "0483" "0485" "0496" "0497" "0503" "0505"
## [3951] "0510" "0511" "0512" "0513" "9998" "9999" "0000" "0001" "0002" "0003"
## [3961] "0004" "0005" "0007" "0008" "0010" "0011" "0012" "0013" "0014" "0015"
## [3971] "0016" "0017" "0019" "0020" "0021" "0022" "0024" "0025" "0027" "0029"
## [3981] "0031" "0039" "0045" "0046" "0047" "0048" "0050" "0051" "0053" "0054"
## [3991] "0057" "0066" "0070" "0071" "0076" "0077" "0078" "0079" "0080" "0089"
## [4001] "0090" "0091" "0098" "0101" "0103" "0105" "0108" "0109" "9998" "9999"
## [4011] "0000" "0001" "0002" "0004" "0007" "0008" "0009" "0010" "0011" "0013"
## [4021] "0014" "0015" "0016" "0017" "0019" "0021" "0024" "0027" "0028" "0029"
## [4031] "0030" "0031" "0035" "0036" "0039" "0043" "0044" "0048" "9998" "9999"
## [4041] "0000" "0001" "0002" "0005" "0007" "0008" "0009" "0012" "0014" "0018"
## [4051] "0023" "0025" "0028" "0032" "0033" "0035" "0043" "0051" "0056" "0057"
## [4061] "0059" "0064" "0066" "9998" "9999" "0000" "0001" "0002" "0006" "0007"
## [4071] "0010" "0019" "0020" "0021" "0022" "0024" "0025" "0026" "0027" "0028"
## [4081] "0030" "0031" "0032" "0034" "0035" "0036" "0037" "0038" "0042" "0044"
## [4091] "0046" "0048" "0050" "0051" "0053" "0055" "0057" "0059" "0063" "0064"
## [4101] "0065" "0066" "0067" "0069" "0070" "0071" "0072" "0075" "0076" "0077"
## [4111] "0079" "0081" "0082" "0084" "0085" "0086" "0090" "0091" "0093" "0094"
## [4121] "0095" "0096" "0098" "0099" "0101" "0102" "0103" "0104" "0107" "0110"
## [4131] "0113" "0115" "0117" "0118" "0119" "0120" "0121" "0122" "0124" "0125"
## [4141] "0127" "0129" "0131" "0132" "0133" "0135" "0138" "0142" "0144" "0145"
## [4151] "0153" "0154" "0156" "0158" "0159" "0161" "0165" "0166" "0167" "0168"
## [4161] "0171" "0176" "0177" "0178" "0179" "0183" "0184" "0185" "0188" "0189"
## [4171] "0190" "0193" "0194" "0195" "0196" "0198" "0199" "0201" "0211" "0213"
## [4181] "0215" "0216" "0219" "0222" "0224" "0225" "0228" "0230" "0233" "0234"
## [4191] "0237" "0240" "0241" "0242" "0245" "0246" "0248" "0249" "0250" "0255"
## [4201] "0256" "0258" "0260" "0261" "0265" "0268" "0270" "0273" "0275" "0277"
## [4211] "0278" "0279" "0281" "0284" "0287" "0291" "0292" "0293" "0299" "0301"
## [4221] "0307" "0308" "0309" "0310" "0311" "0313" "0317" "0322" "0323" "0324"
## [4231] "0326" "0328" "0332" "0333" "0334" "0336" "0337" "0338" "0342" "0344"
## [4241] "0345" "0347" "0348" "0351" "0360" "0361" "0363" "0365" "0367" "0369"
## [4251] "0375" "0378" "0386" "0387" "0388" "0390" "0391" "0392" "0393" "0400"
## [4261] "0401" "0404" "0408" "0410" "0415" "0419" "0431" "0433" "0434" "0446"
## [4271] "0458" "0461" "0464" "0467" "0468" "0473" "0474" "0475" "0476" "0478"
## [4281] "0479" "0483" "0484" "0488" "0489" "0490" "0492" "0494" "0495" "0496"
## [4291] "0498" "0502" "0503" "0506" "0515" "0517" "0518" "0520" "0521" "0525"
## [4301] "0527" "0530" "0531" "0532" "0537" "0540" "0541" "0545" "0551" "0557"
## [4311] "0559" "0560" "0565" "0566" "0570" "0573" "0579" "0580" "0581" "0582"
## [4321] "0583" "0587" "0589" "0590" "0591" "0592" "0594" "0600" "0604" "0605"
## [4331] "0606" "0613" "0617" "0619" "0622" "0623" "0624" "0625" "0626" "0627"
## [4341] "0628" "0632" "0633" "0634" "0637" "0638" "0641" "0643" "0649" "0650"
## [4351] "0651" "0656" "0657" "0658" "0659" "0663" "0665" "0667" "0668" "0671"
## [4361] "0672" "0675" "0677" "0680" "0683" "0684" "0686" "0687" "0688" "0689"
## [4371] "0690" "0691" "0692" "0694" "0695" "0696" "0697" "0699" "0701" "0702"
## [4381] "0705" "0706" "0707" "0709" "0710" "0711" "0712" "0713" "0715" "0716"
## [4391] "0718" "0721" "0722" "0723" "0724" "0726" "0729" "0730" "0732" "0734"
## [4401] "0735" "0736" "0737" "0738" "0739" "0740" "0742" "0744" "0745" "0749"
## [4411] "0750" "0751" "0752" "0754" "0755" "0757" "0759" "0763" "0765" "0766"
## [4421] "0767" "0768" "0771" "0772" "0773" "0776" "0777" "0779" "0783" "0784"
## [4431] "0785" "0793" "0794" "0795" "0799" "0800" "0801" "0802" "0805" "0806"
## [4441] "0807" "0808" "0811" "0812" "0813" "0814" "0815" "0816" "0817" "0818"
## [4451] "0819" "0820" "0821" "0823" "0824" "0825" "0826" "0828" "0829" "0830"
## [4461] "0831" "0832" "0833" "0834" "0837" "0838" "0840" "0841" "0842" "0843"
## [4471] "0845" "0846" "0847" "0849" "0852" "0857" "0859" "0861" "0862" "0863"
## [4481] "0865" "0866" "0867" "0869" "0870" "0871" "0873" "0874" "0878" "0880"
## [4491] "0883" "0884" "0885" "0887" "0888" "0891" "0893" "0895" "0896" "0898"
## [4501] "0900" "0901" "0903" "0904" "0907" "0908" "0909" "0910" "0912" "0914"
## [4511] "0916" "0917" "0918" "0919" "0920" "0921" "0922" "0923" "0926" "0927"
## [4521] "0928" "0929" "0931" "9998" "9999" "0000" "0001" "0002" "0003" "0004"
## [4531] "0005" "0006" "0007" "0008" "0010" "0043" "0049" "9998" "0000" "0001"
## [4541] "0002" "0003" "0004" "0006" "0007" "0008" "0009" "0010" "0015" "0021"
## [4551] "0037" "0039" "0043" "0048" "0049" "0052" "0062" "0063" "9998" "0000"
## [4561] "0001" "0002" "0003" "0005" "0006" "0017" "0021" "0024" "0027" "0032"
## [4571] "0039" "0041" "0043" "0044" "0045" "0047" "0049" "0052" "0054" "0055"
## [4581] "0059" "0066" "0069" "0070" "0071" "0078" "0082" "0087" "0089" "0092"
## [4591] "0095" "0106" "0108" "0109" "0110" "0112" "0120" "0121" "0125" "0133"
## [4601] "0135" "0136" "0137" "0139" "0141" "0142" "0144" "0145" "0146" "0147"
## [4611] "0163" "0171" "0175" "0177" "0192" "0196" "0197" "0199" "0204" "0208"
## [4621] "0214" "0215" "0219" "0227" "0232" "0240" "0252" "0255" "0257" "9998"
## [4631] "9999" "0000" "0001" "0002" "0003" "0012" "0013" "0014" "0016" "0018"
## [4641] "0024" "0038" "0040" "9998" "9999" "0000" "0001" "0002" "0006" "0008"
## [4651] "0014" "0017" "0018" "0020" "0021" "0023" "0030" "0032" "0035" "0038"
## [4661] "0043" "0044" "0047" "0048" "0049" "0052" "0055" "0056" "0059" "0060"
## [4671] "0064" "0066" "0067" "0068" "0069" "0072" "0075" "0076" "0078" "0079"
## [4681] "0083" "0085" "0086" "0087" "0089" "0094" "0097" "0100" "0101" "0106"
## [4691] "0112" "0113" "0114" "0116" "0117" "0119" "0120" "0122" "0125" "0126"
## [4701] "0127" "0131" "0133" "0134" "0136" "0137" "0138" "0140" "0142" "0146"
## [4711] "0152" "0154" "0156" "0160" "0161" "0162" "0163" "0164" "0165" "0166"
## [4721] "0168" "0169" "0174" "0178" "0182" "0187" "0190" "0192" "0193" "0194"
## [4731] "0195" "0196" "0197" "0198" "0201" "0202" "0205" "0208" "0210" "0211"
## [4741] "0214" "0215" "0216" "0217" "0218" "0221" "0224" "0225" "0226" "0231"
## [4751] "0234" "0241" "0242" "0249" "0251" "0252" "0253" "0257" "0258" "0260"
## [4761] "0263" "0264" "0267" "0268" "0272" "0279" "0290" "0291" "0294" "0295"
## [4771] "0299" "0300" "0302" "0303" "0304" "0305" "0306" "0308" "0309" "0315"
## [4781] "0316" "0319" "0323" "0324" "0328" "0329" "0330" "0333" "0334" "0335"
## [4791] "0340" "0341" "0342" "0362" "0363" "0366" "0367" "0368" "0379" "0380"
## [4801] "0383" "9998" "9999" "0000" "0001" "0006" "0007" "0008" "0009" "0012"
## [4811] "0013" "0015" "0016" "0017" "0022" "0023" "0025" "0026" "0028" "0029"
## [4821] "0030" "0032" "0035" "0038" "0039" "0041" "0042" "0043" "0044" "0045"
## [4831] "0047" "0048" "0053" "0055" "0056" "0058" "0065" "0068" "0069" "0071"
## [4841] "0072" "0074" "0076" "0077" "0084" "0089" "0090" "0095" "0097" "0099"
## [4851] "0100" "0104" "0105" "0107" "0108" "0109" "0110" "0113" "0114" "0115"
## [4861] "0116" "0118" "9998" "9999" "0000" "0001" "0002" "0008" "0009" "0011"
## [4871] "0012" "0013" "0017" "0019" "0020" "0021" "0026" "0027" "0029" "0033"
## [4881] "0034" "0039" "0041" "0050" "0053" "0054" "0056" "0057" "0058" "0060"
## [4891] "0063" "0065" "0071" "0074" "0078" "0079" "0095" "0097" "0100" "0103"
## [4901] "0105" "0108" "0115" "0118" "0133" "0135" "0136" "0137" "0146" "0156"
## [4911] "0157" "0167" "0169" "0170" "9998" "9999" "0000" "0001" "0003" "0005"
## [4921] "0006" "0008" "0011" "0013" "0015" "0024" "0025" "0027" "0031" "0033"
## [4931] "0036" "0037" "0039" "0040" "0042" "0045" "0048" "0051" "0053" "0056"
## [4941] "0058" "0065" "0066" "0067" "0077" "0080" "0082" "0087" "0088" "0091"
## [4951] "0095" "0098" "0100" "0104" "0105" "0109" "0110" "0117" "0120" "0129"
## [4961] "0130" "0131" "0132" "0139" "0140" "0142" "0143" "0146" "0149" "0150"
## [4971] "0151" "0152" "0158" "0163" "0164" "0165" "0166" "0168" "0177" "0178"
## [4981] "0186" "0187" "0190" "0191" "0193" "0195" "0197" "0198" "0201" "0202"
## [4991] "0204" "0205" "0207" "0208" "0213" "0221" "0227" "0228" "0229" "0230"
## [5001] "0235" "0236" "0238" "0240" "0241" "0245" "0248" "0251" "0259" "0268"
## [5011] "0293" "0294" "0298" "0302" "0309" "0325" "0326" "0328" "0330" "0331"
## [5021] "0335" "0336" "0337" "0350" "0351" "0358" "0363" "0365" "0367" "0373"
## [5031] "0374" "0376" "0377" "0380" "0384" "0385" "0388" "0393" "0394" "0402"
## [5041] "0404" "0406" "0412" "0413" "0416" "0419" "0420" "0421" "0423" "0425"
## [5051] "0426" "0427" "0428" "0430" "0432" "0434" "0435" "0441" "0442" "0444"
## [5061] "0446" "0448" "0451" "0454" "0455" "0457" "0458" "0462" "0465" "0467"
## [5071] "0468" "0470" "0472" "0474" "0475" "0477" "0478" "0480" "0482" "0483"
## [5081] "0484" "0486" "0488" "0489" "0490" "0491" "0492" "0493" "0494" "0495"
## [5091] "0499" "0500" "0503" "0506" "0508" "0510" "0514" "0515" "0517" "0520"
## [5101] "0521" "0522" "0524" "0525" "0528" "0532" "0533" "0535" "0538" "0539"
## [5111] "0540" "0541" "0542" "0544" "0545" "0547" "0548" "0552" "0555" "0556"
## [5121] "0557" "0560" "0563" "0564" "0570" "0571" "0573" "0575" "0589" "0590"
## [5131] "0591" "0592" "0593" "0595" "0596" "0598" "0600" "0601" "0603" "0605"
## [5141] "0606" "0608" "0609" "0610" "0611" "0615" "0617" "0618" "0619" "0621"
## [5151] "0622" "0626" "0627" "0629" "0632" "0634" "0637" "0646" "0648" "0649"
## [5161] "0651" "0661" "0662" "0663" "0666" "0668" "0679" "0684" "0703" "0704"
## [5171] "0705" "0731" "0737" "0745" "0749" "0760" "0763" "0765" "0768" "0773"
## [5181] "0775" "0777" "0778" "0780" "0784" "0787" "0793" "0799" "0802" "0807"
## [5191] "0810" "0815" "0818" "0821" "0822" "0824" "0831" "0835" "0839" "0844"
## [5201] "0848" "0850" "0851" "0854" "0855" "0857" "0858" "0859" "0861" "0862"
## [5211] "0863" "0865" "0867" "0870" "0873" "0874" "0877" "0879" "0880" "0883"
## [5221] "0885" "0887" "0891" "0892" "0895" "0902" "0906" "0908" "0913" "0917"
## [5231] "0920" "0922" "0923" "0926" "0927" "0929" "0931" "0932" "0933" "0935"
## [5241] "0937" "0940" "0942" "0945" "0948" "0952" "0953" "0954" "0958" "0959"
## [5251] "0960" "0961" "0963" "0966" "0967" "0968" "0971" "0974" "0976" "0981"
## [5261] "0983" "0984" "0985" "0990" "0992" "0994" "0999" "1000" "1004" "1006"
## [5271] "1008" "1014" "1019" "1024" "1025" "1026" "1028" "1029" "1031" "1032"
## [5281] "1034" "1035" "1036" "1037" "1039" "1040" "1041" "1042" "1044" "1045"
## [5291] "1046" "1052" "1053" "1055" "1058" "1059" "1062" "1063" "1064" "1067"
## [5301] "1068" "1069" "1070" "1073" "1074" "1078" "1079" "1080" "1082" "1084"
## [5311] "1087" "1095" "1096" "1099" "1100" "1101" "1102" "1103" "1104" "1106"
## [5321] "1107" "1108" "1110" "1111" "1112" "1115" "1116" "1124" "1127" "1128"
## [5331] "1130" "1145" "1146" "1147" "1148" "1151" "1153" "1154" "1155" "1157"
## [5341] "1158" "1159" "1160" "1161" "1162" "1163" "1164" "1165" "1166" "1167"
## [5351] "1168" "1170" "1171" "1172" "1173" "1174" "1176" "1178" "1179" "1181"
## [5361] "1182" "1183" "1185" "1186" "1188" "1189" "1193" "1194" "1196" "1197"
## [5371] "1199" "1200" "1203" "1206" "1208" "1209" "1213" "1214" "1216" "1217"
## [5381] "1218" "1220" "1221" "1222" "1224" "1225" "1227" "1229" "1235" "1236"
## [5391] "1238" "1239" "1242" "1243" "1244" "1246" "1248" "1249" "1251" "1252"
## [5401] "1253" "1254" "1256" "1260" "1261" "1262" "1263" "1264" "1265" "1266"
## [5411] "1267" "1268" "1269" "1270" "1271" "1273" "1274" "1275" "9998" "9999"
## [5421] "0000" "0001" "0003" "0004" "0007" "0008" "0010" "0011" "0012" "0015"
## [5431] "0016" "0019" "0021" "0022" "0023" "0024" "0025" "0027" "0030" "0033"
## [5441] "0034" "0036" "0043" "0054" "0066" "0073" "0075" "0079" "0084" "0092"
## [5451] "0095" "0096" "0099" "0103" "0107" "0108" "0109" "9998" "9999" "0000"
## [5461] "0001" "0009" "0010" "0011" "0012" "0014" "0016" "0017" "0018" "0019"
## [5471] "0021" "0022" "0023" "0024" "0026" "0028" "0029" "0031" "0032" "0040"
## [5481] "0041" "0043" "0045" "0047" "0060" "0062" "0063" "0066" "0069" "0072"
## [5491] "0075" "0077" "0080" "0081" "0082" "0090" "0092" "0098" "0100" "0102"
## [5501] "0106" "0109" "0112" "0113" "0117" "0118" "0121" "0122" "0123" "0125"
## [5511] "0126" "9998" "9999" "0000" "0001" "0003" "0007" "0009" "0015" "0017"
## [5521] "0019" "0020" "0021" "0024" "0025" "0026" "0028" "0029" "0032" "0033"
## [5531] "0035" "0038" "0039" "0045" "0046" "0048" "0049" "0053" "0057" "0058"
## [5541] "0063" "0065" "0067" "0071" "0072" "0090" "0092" "0096" "0097" "0105"
## [5551] "0108" "0114" "0117" "0120" "0123" "0127" "0128" "0130" "0140" "0142"
## [5561] "0144" "0149" "0151" "0155" "0156" "0163" "0164" "0168" "0171" "0183"
## [5571] "0184" "9998" "9999" "0000" "0001" "0003" "0005" "0009" "0011" "0018"
## [5581] "0019" "0020" "0021" "0024" "0025" "0031" "0032" "0034" "0035" "0037"
## [5591] "0038" "0040" "0042" "0044" "0045" "0049" "0050" "0051" "0052" "0063"
## [5601] "0065" "0066" "0067" "0069" "0070" "0072" "0073" "0074" "0075" "0081"
## [5611] "0083" "0084" "0086" "0087" "0093" "0096" "0098" "0100" "0104" "0108"
## [5621] "0109" "0121" "0127" "0132" "0134" "0136" "0137" "0141" "0144" "0147"
## [5631] "0148" "0149" "0151" "0153" "0154" "0156" "0161" "0162" "0164" "0175"
## [5641] "0187" "0195" "0196" "0198" "0200" "0202" "0207" "0210" "0230" "0238"
## [5651] "0254" "0255" "0256" "0269" "0335" "0336" "0346" "0349" "0354" "0366"
## [5661] "0373" "0379" "0394" "0396" "0398" "0401" "0406" "0407" "0408" "0416"
## [5671] "0419" "0420" "0427" "0430" "0432" "0449" "0451" "0461" "9998" "9999"
## [5681] "0000" "0001" "0002" "0004" "0006" "0007" "0010" "0011" "0013" "0015"
## [5691] "0016" "0017" "0019" "0020" "0021" "0022" "0025" "0026" "0027" "0028"
## [5701] "0030" "0031" "0032" "0033" "0036" "0037" "0040" "0041" "0042" "0043"
## [5711] "0044" "0045" "0048" "0051" "0052" "0054" "0056" "0057" "0058" "0059"
## [5721] "0060" "0062" "0067" "0075" "0076" "0079" "0080" "0081" "0089" "0092"
## [5731] "0096" "0102" "0106" "0109" "0114" "0122" "0123" "0124" "0133" "0136"
## [5741] "0142" "0145" "0147" "0152" "0153" "9998" "9999" "0000" "0001" "0004"
## [5751] "0007" "0008" "0009" "0011" "0014" "0015" "0022" "0023" "0024" "0027"
## [5761] "0028" "0030" "0031" "0038" "0043" "0044" "0045" "0048" "0049" "0050"
## [5771] "0052" "0057" "0058" "0059" "0061" "0066" "0068" "0073" "0077" "0078"
## [5781] "0085" "0086" "0103" "0104" "0109" "0112" "0115" "0117" "0120" "0121"
## [5791] "0126" "0127" "0128" "0130" "0131" "0133" "0134" "0141" "0142" "0148"
## [5801] "0156" "0157" "0158" "0162" "0164" "0166" "0167" "0168" "0169" "0173"
## [5811] "0178" "0179" "0180" "0182" "0183" "0190" "0191" "0194" "0195" "0198"
## [5821] "0202" "0204" "0205" "0208" "0222" "0227" "0231" "0232" "0235" "0239"
## [5831] "0240" "0242" "0244" "0245" "0246" "0247" "0248" "0249" "0251" "0252"
## [5841] "0255" "0257" "0258" "0260" "0261" "0262" "0267" "0268" "9998" "9999"
## [5851] "0000" "0001" "0002" "0005" "0008" "0009" "0010" "0021" "0023" "0025"
## [5861] "0027" "0030" "0032" "0039" "0040" "0042" "0043" "0044" "0049" "0052"
## [5871] "0053" "0055" "0057" "0063" "0064" "0065" "0066" "0069" "0076" "0082"
## [5881] "0084" "0087" "0091" "0100" "0101" "0105" "0107" "0115" "0116" "0119"
## [5891] "0123" "0130" "0131" "0132" "0136" "0139" "0141" "0145" "0146" "0147"
## [5901] "0148" "0153" "0157" "0160" "0163" "0164" "0165" "0166" "0167" "0168"
## [5911] "0169" "0170" "0172" "0173" "0174" "0175" "0178" "0180" "0182" "0185"
## [5921] "0186" "0190" "0191" "0192" "0193" "0204" "0206" "0208" "0215" "0217"
## [5931] "0219" "0228" "0233" "0240" "0242" "0244" "0245" "0254" "0255" "0256"
## [5941] "0266" "0269" "0270" "0274" "0429" "0437" "0447" "0449" "0451" "0471"
## [5951] "0479" "0483" "0488" "0490" "0494" "0496" "0500" "0506" "0508" "0512"
## [5961] "0513" "0518" "0521" "0526" "0527" "0530" "0534" "0540" "0542" "0544"
## [5971] "0545" "0549" "0551" "0558" "0571" "0573" "0574" "0580" "0593" "0597"
## [5981] "0598" "0602" "0603" "0607" "0608" "0619" "9998" "9999" "0000" "0001"
## [5991] "0002" "0006" "0010" "0013" "0018" "0020" "0023" "0024" "0028" "0030"
## [6001] "0031" "0033" "0040" "0041" "0043" "0044" "0045" "0047" "0049" "0054"
## [6011] "0057" "0059" "0061" "0063" "0064" "0068" "0073" "0077" "0079" "0080"
## [6021] "0082" "0083" "0086" "0087" "0088" "0090" "0091" "0092" "0095" "0097"
## [6031] "0098" "0099" "0101" "0106" "0107" "0108" "0111" "0117" "0118" "0119"
## [6041] "0123" "0129" "0132" "0136" "0137" "0138" "0139" "0140" "0142" "0143"
## [6051] "0145" "0148" "0149" "0151" "0153" "0154" "0155" "0156" "0161" "0163"
## [6061] "0164" "0165" "0166" "0167" "0169" "0170" "0173" "0175" "0189" "0193"
## [6071] "0201" "0202" "0203" "0204" "0212" "0216" "0217" "0220" "0221" "0222"
## [6081] "0224" "0228" "0232" "0237" "0239" "0240" "0243" "0245" "0257" "0263"
## [6091] "0267" "0268" "0270" "0271" "0278" "0279" "9998" "9999" "0000" "0001"
## [6101] "0002" "0004" "0009" "0011" "0012" "0013" "0014" "0020" "0021" "0026"
## [6111] "0027" "0034" "0035" "0043" "0044" "0051" "0061" "0064" "0083" "0086"
## [6121] "0089" "0096" "0105" "0111" "0114" "0115" "0116" "0117" "0118" "0120"
## [6131] "0121" "0124" "0125" "0126" "0127" "0128" "0129" "0130" "0131" "0132"
## [6141] "0133" "0134" "9998" "9999" "0000" "0001" "0003" "0004" "0010" "0012"
## [6151] "0013" "0017" "0018" "0023" "0024" "0028" "0029" "0030" "0035" "0036"
## [6161] "0042" "0044" "0045" "0049" "0050" "0054" "0056" "0060" "0061" "0066"
## [6171] "0067" "0069" "0071" "0073" "0074" "0075" "0076" "0079" "0081" "0083"
## [6181] "0084" "0087" "0089" "0090" "0091" "0095" "0096" "0098" "0099" "0109"
## [6191] "0112" "0115" "0117" "0118" "0121" "0162" "0167" "0168" "0170" "0178"
## [6201] "0179" "0180" "0181" "0201" "0209" "0211" "0212" "0216" "0218" "0219"
## [6211] "0226" "0231" "0235" "0239" "0251" "0263" "0267" "0269" "0272" "9998"
## [6221] "9999" "0000" "0001" "0002" "0005" "0006" "0011" "0012" "0014" "0015"
## [6231] "0016" "0018" "0021" "0022" "0024" "0025" "0032" "0033" "0036" "0043"
## [6241] "0046" "0049" "0053" "0054" "0056" "0057" "0063" "0064" "0069" "0074"
## [6251] "0075" "0076" "0078" "0079" "0081" "0082" "0083" "0086" "0090" "0091"
## [6261] "0092" "0094" "0098" "0100" "0102" "0103" "0105" "0107" "0111" "0120"
## [6271] "0126" "0130" "0131" "0147" "0148" "0151" "0153" "0154" "0155" "0156"
## [6281] "0161" "0162" "0166" "0167" "0168" "0197" "0209" "0213" "0219" "0220"
## [6291] "0223" "0224" "0225" "0226" "0227" "0228" "0229" "0230" "0231" "0232"
## [6301] "0234" "9998" "9999" "0000" "0001" "0002" "0003" "0004" "0006" "0007"
## [6311] "0008" "0010" "0011" "0012" "0013" "0019" "0020" "0025" "0026" "0027"
## [6321] "0028" "0031" "0038" "0040" "0041" "0045" "0046" "0047" "0048" "0049"
## [6331] "0051" "0052" "0053" "0055" "0056" "0057" "0058" "0059" "0064" "0065"
## [6341] "0066" "0067" "0070" "0071" "0072" "0075" "0078" "0081" "0082" "0085"
## [6351] "0087" "0092" "0097" "0101" "0103" "0107" "0108" "0111" "0112" "0113"
## [6361] "0115" "0116" "0117" "0118" "0120" "0121" "0122" "0124" "0127" "0128"
## [6371] "0133" "0134" "0135" "0136" "0138" "0140" "0142" "0148" "0149" "0150"
## [6381] "0152" "0154" "0157" "0158" "0160" "0161" "0162" "0165" "0166" "0167"
## [6391] "0170" "0173" "0174" "0178" "0179" "0180" "0181" "0182" "0183" "0185"
## [6401] "0186" "0187" "0188" "0189" "0190" "0194" "0195" "0196" "0197" "0198"
## [6411] "0200" "0204" "0206" "0208" "0209" "0210" "0212" "0214" "0215" "0216"
## [6421] "0218" "0219" "0220" "0221" "0223" "0224" "0225" "0227" "0228" "0229"
## [6431] "0232" "0233" "0234" "0236" "0237" "0239" "0241" "0242" "0243" "0244"
## [6441] "0246" "0247" "0251" "0252" "0253" "0254" "0256" "0258" "0260" "0264"
## [6451] "0268" "0269" "0270" "0271" "0273" "0275" "0276" "0277" "0281" "0282"
## [6461] "0291" "0292" "0294" "0295" "0296" "0297" "0300" "0302" "0303" "0304"
## [6471] "0305" "0311" "0313" "0314" "0316" "0317" "0319" "0321" "0323" "0329"
## [6481] "0332" "0333" "0335" "0336" "0337" "0338" "0340" "0343" "0345" "0346"
## [6491] "0347" "0349" "0350" "0351" "0352" "0354" "0356" "0357" "0358" "0360"
## [6501] "0362" "0363" "0364" "0365" "0366" "0367" "0368" "0369" "0371" "0372"
## [6511] "0373" "0374" "0375" "0376" "0378" "0379" "0380" "0381" "0382" "0384"
## [6521] "0385" "0387" "0389" "0390" "0391" "0392" "0393" "0394" "0395" "0396"
## [6531] "0398" "0399" "0401" "0402" "0403" "0404" "0405" "0408" "0409" "0414"
## [6541] "0417" "0418" "0419" "0420" "0421" "0423" "0424" "0425" "0426" "0427"
## [6551] "0428" "0429" "0430" "0431" "0432" "0433" "0434" "0435" "0437" "0438"
## [6561] "0439" "0440" "0441" "0445" "9998" "9999" "0000" "0001" "0002" "0003"
## [6571] "0006" "0009" "0011" "0013" "0014" "0015" "0017" "0019" "0020" "0021"
## [6581] "0022" "0025" "0030" "0031" "0032" "0034" "0036" "0038" "0039" "0041"
## [6591] "0042" "0043" "0044" "0045" "0046" "0048" "0052" "0053" "0054" "0056"
## [6601] "0057" "0061" "0062" "0065" "0072" "0073" "0075" "0078" "0090" "0103"
## [6611] "0109" "0114" "0117" "0122" "0123" "0127" "9998" "9999" "0000" "0001"
## [6621] "0003" "0012" "0019" "0020" "0022" "0039" "0044" "0047" "0048" "0049"
## [6631] "0051" "0052" "0060" "0068" "0071" "0076" "0082" "0086" "0088" "0090"
## [6641] "0093" "0112" "0195" "0196" "0197" "0198" "0199" "9998" "9999" "0000"
## [6651] "0001" "0002" "0003" "0007" "0015" "0027" "0028" "0032" "0033" "0034"
## [6661] "0036" "0037" "0040" "0041" "0047" "0057" "0064" "0067" "0073" "0074"
## [6671] "0083" "0085" "0086" "0092" "0093" "0096" "0097" "0098" "0099" "0103"
## [6681] "0105" "0110" "0112" "0128" "0131" "0133" "0139" "0140" "0141" "0153"
## [6691] "0158" "0174" "0185" "0188" "0190" "0191" "0202" "0207" "0210" "0214"
## [6701] "0220" "0227" "0229" "0236" "0237" "0241" "0244" "0250" "0253" "0271"
## [6711] "0274" "0281" "0283" "0285" "0286" "0289" "9998" "9999" "0000" "0001"
## [6721] "0002" "0003" "0004" "0005" "0006" "0007" "0009" "0010" "0011" "0012"
## [6731] "0013" "0014" "0015" "0016" "0017" "0018" "0019" "0020" "0022" "0023"
## [6741] "0024" "0027" "0032" "0037" "0040" "0051" "0053" "0054" "0063" "0064"
## [6751] "0065" "0066" "0074" "0075" "0083" "0084" "0086" "9998" "9999" "0000"
## [6761] "0001" "0003" "0005" "0006" "0007" "0009" "0012" "0013" "0014" "0020"
## [6771] "0022" "0023" "0026" "0027" "0034" "0037" "0039" "0041" "0045" "0046"
## [6781] "0047" "0049" "0051" "0054" "0055" "0056" "0057" "0058" "0059" "0060"
## [6791] "0061" "0062" "0063" "0064" "0065" "0066" "0068" "0070" "0074" "0080"
## [6801] "0081" "0082" "0086" "0087" "0088" "0093" "0095" "0096" "0100" "0103"
## [6811] "0104" "0105" "0108" "0112" "0115" "0117" "0120" "0121" "0122" "0123"
## [6821] "0125" "0126" "0129" "0131" "0133" "0134" "0135" "0137" "0138" "0139"
## [6831] "0141" "0144" "0146" "0148" "0153" "0155" "0157" "0158" "0164" "0165"
## [6841] "0167" "0170" "0173" "0175" "0176" "0178" "0180" "0181" "0182" "0183"
## [6851] "0184" "0185" "0186" "0187" "0189" "0190" "0191" "0195" "0196" "0198"
## [6861] "0202" "0203" "0204" "0216" "0217" "0218" "0220" "0224" "0225" "0226"
## [6871] "0227" "0228" "0233" "0240" "0241" "0245" "0250" "0253" "0254" "0258"
## [6881] "0261" "0267" "0270" "0274" "0307" "0308" "0309" "0310" "0313" "0317"
## [6891] "0320" "0324" "0327" "0328" "0330" "0333" "0336" "0338" "0339" "0346"
## [6901] "0350" "0352" "0354" "0355" "0357" "0358" "0359" "0360" "0361" "0363"
## [6911] "0364" "0368" "0370" "0371" "0378" "9998" "9999" "0000" "0001" "0008"
## [6921] "0011" "0013" "0014" "0026" "0029" "0030" "0032" "0065" "0073" "0087"
## [6931] "9998" "0000" "0001" "0005" "0014" "0015" "0016" "0019" "0020" "0021"
## [6941] "0028" "0031" "0032" "0033" "0034" "0035" "0037" "0040" "0041" "0045"
## [6951] "0061" "0062" "0065" "0067" "0068" "0069" "0070" "0071" "0079" "0082"
## [6961] "0084" "0085" "0086" "0087" "0092" "0095" "0101" "0103" "0104" "0108"
## [6971] "0111" "0112" "0114" "0115" "0120" "0122" "0124" "0127" "0129" "0137"
## [6981] "0141" "0146" "0148" "0150" "0156" "0157" "0161" "0167" "0170" "0171"
## [6991] "0172" "0180" "0181" "0185" "0191" "0198" "0200" "0215" "0227" "0229"
## [7001] "9998" "9999" "0000" "0001" "0002" "0003" "0006" "0007" "0008" "0009"
## [7011] "0011" "0012" "0014" "0016" "0022" "0023" "0026" "0027" "0028" "0030"
## [7021] "0034" "0038" "0046" "0054" "0056" "9999" "0000" "0001" "0003" "0004"
## [7031] "0010" "0020" "0025" "0026" "0031" "0041" "0046" "0049" "0055" "0062"
## [7041] "0065" "0067" "0073" "0078" "0080" "0086" "0102" "0114" "0116" "0117"
## [7051] "0134" "0136" "0146" "0151" "0159" "9998" "9999" "0000" "0001" "0002"
## [7061] "0004" "0005" "0007" "0008" "0009" "0010" "0011" "0012" "0013" "0014"
## [7071] "0015" "0016" "0017" "0018" "0024" "0025" "0026" "0033" "0034" "0035"
## [7081] "0039" "0043" "0049" "0054" "0075" "0082" "0085" "0092" "0104" "0107"
## [7091] "0110" "0112" "0117" "0118" "0119" "0123" "0125" "0128" "0130" "0132"
## [7101] "0134" "0138" "0141" "0142" "0144" "0153" "0154" "0156" "0159" "0163"
## [7111] "0166" "0168" "0176" "0177" "0179" "0180" "0181" "0182" "0188" "0191"
## [7121] "0192" "0198" "0205" "0207" "0209" "0210" "0212" "0213" "0215" "0217"
## [7131] "0219" "0220" "0221" "0222" "0228" "0229" "0236" "0240" "0248" "0249"
## [7141] "0252" "0253" "0255" "9998" "9999" "0000" "0001" "0002" "0003" "0004"
## [7151] "0006" "0007" "0008" "0010" "0015" "0024" "0026" "0028" "0029" "0030"
## [7161] "0033" "0038" "0041" "0044" "0045" "0050" "0053" "0054" "0063" "0066"
## [7171] "0070" "0072" "0077" "0079" "0081" "0082" "0083" "0084" "0086" "0090"
## [7181] "0098" "0100" "0103" "0106" "0111" "0113" "0115" "0119" "0120" "0129"
## [7191] "0131" "0134" "0136" "0144" "0146" "0151" "0155" "0156" "0160" "0163"
## [7201] "0167" "0169" "0172" "0174" "0175" "0181" "0183" "0188" "0191" "0204"
## [7211] "0206" "0209" "0215" "0217" "0218" "0223" "0224" "0227" "0228" "0233"
## [7221] "0234" "0235" "0237" "0241" "0247" "0249" "0251" "0252" "0257" "0264"
## [7231] "0266" "0268" "0269" "0270" "0271" "0272" "0276" "0277" "0278" "0279"
## [7241] "0280" "0284" "0285" "0287" "0288" "0289" "0292" "0299" "0300" "0305"
## [7251] "0311" "0406" "0410" "0411" "0417" "0429" "0430" "0442" "0452" "0453"
## [7261] "0454" "0455" "0460" "0464" "0476" "0488" "0502" "0504" "0505" "0513"
## [7271] "0517" "0519" "0523" "0524" "0527" "0541" "0549" "0551" "0553" "0554"
## [7281] "0555" "0556" "0561" "0566" "0568" "0575" "0589" "0592" "0594" "0596"
## [7291] "0760" "0766" "0767" "9998" "9999" "0000" "0001" "0002" "0005" "0006"
## [7301] "0010" "0011" "0013" "0020" "0024" "0025" "0031" "0032" "0033" "0034"
## [7311] "0035" "0037" "0038" "0040" "0042" "0050" "0051" "0052" "0053" "0058"
## [7321] "0060" "0061" "0063" "0066" "0071" "0072" "0076" "0077" "0080" "0083"
## [7331] "0084" "0087" "0088" "0089" "0090" "0094" "0095" "0096" "0098" "0103"
## [7341] "0104" "0105" "0106" "0111" "0112" "0116" "0118" "0120" "0123" "0124"
## [7351] "0126" "0128" "0129" "0131" "0133" "0135" "0137" "0140" "0141" "0142"
## [7361] "0146" "0148" "0152" "0153" "0154" "0155" "0156" "0157" "0161" "0163"
## [7371] "0164" "0170" "0172" "0173" "0174" "0179" "0183" "0184" "0188" "0189"
## [7381] "0190" "0195" "0205" "0222" "0229" "0230" "0231" "0232" "0235" "0239"
## [7391] "0242" "0248" "0251" "0252" "0259" "0260" "0261" "0264" "0266" "0272"
## [7401] "0275" "0276" "0288" "0302" "0304" "0310" "0315" "0319" "0322" "0323"
## [7411] "0324" "0332" "0335" "0339" "0345" "0346" "0352" "0357" "0361" "0385"
## [7421] "0391" "0398" "0400" "0412" "0418" "0422" "0423" "0432" "0433" "0435"
## [7431] "0442" "0443" "0444" "0447" "0449" "0453" "0455" "0458" "0460" "0461"
## [7441] "0462" "0463" "0464" "0466" "0467" "0468" "0469" "0470" "0473" "0477"
## [7451] "9998" "9999" "0000" "0001" "0003" "0004" "0005" "0006" "0007" "0009"
## [7461] "0010" "0012" "0015" "0016" "0017" "0019" "0020" "0024" "0025" "0026"
## [7471] "0030" "0031" "0032" "0033" "0034" "0035" "0036" "0038" "0040" "0042"
## [7481] "0043" "0044" "0047" "0050" "0051" "0052" "0059" "0062" "0075" "0087"
## [7491] "0089" "0092" "0093" "0094" "0097" "0098" "0102" "0105" "0111" "0113"
## [7501] "0115" "0119" "0125" "0127" "0130" "0132" "0135" "0137" "0140" "0143"
## [7511] "0147" "0148" "0150" "0151" "0152" "0154" "0156" "0157" "0161" "0162"
## [7521] "0164" "0167" "0169" "0172" "0180" "0185" "0194" "0195" "0196" "0197"
## [7531] "0198" "0201" "0202" "0206" "0207" "0209" "0210" "0211" "0215" "0223"
## [7541] "0229" "0230" "0232" "0233" "0234" "0239" "0240" "9998" "9999" "0000"
## [7551] "0001" "0008" "0009" "0015" "0017" "0027" "0031" "0032" "0043" "0047"
## [7561] "0050" "0070" "0072" "0084" "0087" "0089" "0095" "0096" "0100" "0102"
## [7571] "0103" "0107" "0108" "0111" "0116" "0132" "0144" "0145" "0148" "0149"
## [7581] "0155" "0156" "0160" "0161" "0169" "0172" "0177" "0178" "0180" "0182"
## [7591] "0185" "0190" "0198" "0207" "0209" "0210" "0212" "0213" "0220" "0227"
## [7601] "0233" "0238" "0239" "0242" "0244" "0246" "0250" "0251" "0253" "0256"
## [7611] "0261" "0262" "0272" "0273" "0274" "0280" "0287" "0289" "0290" "0291"
## [7621] "0296" "0298" "0301" "0303" "0304" "0308" "0316" "0324" "0357" "0362"
## [7631] "0363" "0379" "0386" "0389" "0398" "0412" "0425" "0447" "0449" "0454"
## [7641] "0474" "0481" "0483" "0490" "0492" "0493" "0494" "0500" "0504" "0505"
## [7651] "0508" "0509" "0516" "0519" "0531" "0532" "0541" "0544" "0545" "0549"
## [7661] "0553" "0556" "0557" "0560" "0561" "0562" "0566" "0568" "0569" "0571"
## [7671] "0575" "0576" "0578" "0580" "0581" "0582" "0584" "0585" "0590" "0593"
## [7681] "0594" "0602" "0603" "0604" "0605" "0607" "0608" "0609" "0611" "0613"
## [7691] "0614" "0615" "0617" "0619" "0620" "0623" "9998" "9999" "0000" "0001"
## [7701] "0003" "0004" "0005" "0006" "0007" "0010" "0012" "0013" "0014" "0016"
## [7711] "0018" "0021" "0022" "0023" "0025" "0026" "0030" "0031" "0032" "0035"
## [7721] "0036" "0041" "0044" "0047" "0048" "0051" "0053" "0058" "0078" "0091"
## [7731] "0096" "0107" "0112" "0121" "0130" "0132" "0138" "9998" "9999" "0000"
## [7741] "0001" "0003" "0004" "0008" "0013" "0017" "0018" "0023" "0027" "0030"
## [7751] "0035" "0038" "0041" "9998" "9999" "0000" "0001" "0007" "0008" "0009"
## [7761] "0011" "0012" "0013" "0014" "0017" "0019" "0027" "0029" "0032" "0038"
## [7771] "0044" "0054" "0055" "0087" "9998" "0000" "0001" "0008" "0009" "0011"
## [7781] "0012" "0014" "0015" "0016" "0019" "0021" "0022" "0024" "0025" "0027"
## [7791] "0029" "0030" "0033" "0037" "0039" "0044" "0046" "0047" "0048" "0050"
## [7801] "0052" "0053" "0057" "0058" "0059" "0060" "0064" "0069" "0070" "0071"
## [7811] "0073" "0081" "0085" "0092" "0094" "0095" "0096" "0101" "0104" "0105"
## [7821] "0107" "0111" "0112" "0113" "0115" "0116" "0120" "0121" "0122" "0125"
## [7831] "0129" "0130" "0131" "0132" "0135" "0136" "0137" "0138" "0140" "0141"
## [7841] "0143" "0144" "0150" "0151" "0153" "0155" "0159" "0160" "0165" "0167"
## [7851] "0173" "0178" "0182" "0185" "0187" "0193" "0198" "0199" "0202" "0204"
## [7861] "0209" "0213" "0215" "0217" "0218" "0221" "0222" "0223" "0224" "0226"
## [7871] "0231" "0232" "0237" "0240" "0244" "0246" "0248" "0250" "0252" "0253"
## [7881] "0255" "0262" "0269" "0272" "0282" "0289" "0290" "0295" "0298" "0299"
## [7891] "0301" "0302" "0303" "0304" "0308" "0310" "0313" "0318" "0320" "0324"
## [7901] "0327" "0331" "0332" "0333" "0336" "0337" "0338" "0344" "0346" "0347"
## [7911] "0348" "0351" "0353" "0355" "0359" "0360" "0361" "0364" "0367" "0373"
## [7921] "0374" "0375" "0376" "0378" "0381" "9998" "9999" "0000" "0001" "0002"
## [7931] "0003" "0004" "0006" "0008" "0009" "0010" "0011" "0012" "0014" "0015"
## [7941] "0016" "0017" "0018" "0019" "0020" "0021" "0022" "0023" "0024" "0025"
## [7951] "0026" "0027" "0034" "0035" "0037" "0041" "0042" "0043" "0044" "0046"
## [7961] "0048" "9998" "9999" "0000" "0001" "0002" "0004" "0005" "0008" "0009"
## [7971] "0012" "0018" "0024" "0029" "0032" "0038" "0039" "0041" "0042" "0044"
## [7981] "0047" "0049" "0051" "0053" "0054" "0058" "0059" "0062" "0063" "0068"
## [7991] "0069" "0070" "0071" "0073" "0074" "0075" "0079" "0082" "0087" "0089"
## [8001] "0095" "0099" "0100" "0103" "0104" "0105" "0109" "0110" "0112" "0113"
## [8011] "0115" "0117" "0119" "0122" "0128" "0129" "0130" "0131" "0134" "0135"
## [8021] "0136" "0137" "0138" "0140" "0149" "0151" "0152" "0153" "0154" "0155"
## [8031] "0157" "0158" "0162" "0167" "0169" "0171" "0174" "0176" "0177" "0178"
## [8041] "0180" "0181" "0182" "0183" "0185" "0186" "0187" "0188" "0189" "0190"
## [8051] "0193" "0196" "0197" "0198" "0199" "0200" "0202" "0203" "0204" "0205"
## [8061] "0208" "0209" "0210" "0211" "0214" "0216" "0218" "0219" "0220" "0223"
## [8071] "0224" "0226" "0227" "0230" "0241" "0242" "0244" "0250" "0255" "0257"
## [8081] "0258" "0264" "0265" "0266" "0267" "0268" "0270" "0271" "0273" "0276"
## [8091] "0277" "0278" "0282" "0285" "0287" "0290" "0291" "0292" "0293" "0295"
## [8101] "0296" "0297" "0298" "0299" "0300" "0302" "0303" "0305" "0307" "0310"
## [8111] "0311" "0312" "0315" "0316" "0318" "0320" "0322" "0324" "0325" "0326"
## [8121] "0327" "0328" "0329" "0330" "0332" "0333" "0334" "0337" "0339" "0340"
## [8131] "0341" "0344" "0345" "0348" "0358" "0360" "0365" "0371" "0374" "0376"
## [8141] "0377" "0378" "0382" "0384" "0385" "0387" "0388" "0389" "0391" "0392"
## [8151] "0394" "0395" "0396" "0399" "0400" "0402" "0403" "0404" "0405" "0406"
## [8161] "0408" "0412" "0413" "0414" "0418" "0419" "0424" "0425" "0427" "0428"
## [8171] "0429" "0430" "0431" "0432" "0433" "0435" "0436" "0447" "0461" "0462"
## [8181] "0463" "0464" "0465" "0468" "0475" "0476" "0481" "0483" "0484" "0485"
## [8191] "0487" "0488" "0489" "0665" "0666" "0667" "0673" "0676" "0678" "0680"
## [8201] "0683" "0685" "0694" "0695" "0700" "0702" "0703" "0706" "0707" "0708"
## [8211] "0710" "0715" "0716" "0717" "0718" "0721" "0722" "0723" "0726" "0730"
## [8221] "0731" "0732" "0733" "0734" "0736" "0739" "0740" "0741" "0744" "0745"
## [8231] "0746" "0748" "0749" "0751" "0752" "0755" "0757" "0758" "0759" "0760"
## [8241] "0761" "0762" "0764" "0765" "0766" "0768" "0769" "0770" "0771" "0774"
## [8251] "0775" "0777" "0779" "0783" "0784" "0785" "0786" "0787" "0788" "0789"
## [8261] "0790" "0791" "0793" "0794" "0798" "0800" "0801" "0803" "0805" "0807"
## [8271] "0808" "0811" "0812" "0813" "0814" "0815" "0816" "0826" "0828" "0831"
## [8281] "0839" "0841" "0844" "0845" "0848" "0853" "0854" "0857" "0858" "0859"
## [8291] "0860" "0861" "0862" "0863" "0864" "0866" "0867" "0868" "0869" "0870"
## [8301] "9998" "9999" "0000" "0001" "0003" "0005" "0012" "0013" "0016" "0017"
## [8311] "0018" "0019" "0022" "0026" "0027" "0033" "0035" "0039" "0040" "0041"
## [8321] "0045" "0048" "0056" "0058" "0062" "0063" "0066" "0068" "0069" "0071"
## [8331] "0077" "0079" "0086" "0088" "0093" "0094" "0095" "0096" "0102" "0109"
## [8341] "0110" "0118" "0119" "0124" "0127" "0128" "0129" "0133" "0134" "0136"
## [8351] "0138" "0140" "0141" "0144" "0145" "0152" "0164" "0166" "0169" "0177"
## [8361] "0180" "0182" "0185" "0188" "0191" "0193" "0197" "0200" "0213" "0219"
## [8371] "0220" "0221" "0223" "0224" "0227" "0231" "0232" "0233" "0234" "0235"
## [8381] "0236" "0237" "0252" "0254" "0256" "0265" "0270" "0272" "0280" "0282"
## [8391] "0284" "0285" "0292" "0295" "0296" "0301" "0303" "0311" "0317" "0417"
## [8401] "0440" "0447" "0453" "0591" "0596" "0597" "0600" "0605" "0607" "0612"
## [8411] "0618" "0621" "0623" "0625" "0627" "0634" "0635" "0641" "0642" "0643"
## [8421] "0646" "0647" "0650" "0651" "0652" "0653" "0656" "0661" "0670" "0673"
## [8431] "0684" "0686" "0690" "0692" "0694" "0695" "0696" "0700" "0703" "0712"
## [8441] "0717" "0720" "0722" "0726" "0728" "0729" "0736" "0738" "0741" "0743"
## [8451] "0746" "0750" "0751" "0754" "0765" "0766" "0767" "0768" "0769" "9998"
## [8461] "9999" "0000" "0001" "0002" "0003" "0004" "0005" "0006" "0007" "0011"
## [8471] "0015" "0026" "0028" "0029" "0038" "0039" "0048" "0054" "0057" "0058"
## [8481] "0061" "9998" "9999" "0000" "0001" "0003" "0004" "0005" "0006" "0008"
## [8491] "0009" "0010" "0011" "0013" "0014" "0015" "0016" "0017" "0018" "0023"
## [8501] "0026" "0027" "0029" "0033" "0035" "0043" "0049" "0050" "9998" "9999"
## [8511] "0000" "0001" "0003" "0004" "0005" "0006" "0008" "0009" "0011" "0013"
## [8521] "0014" "0015" "0019" "0020" "0021" "0023" "0024" "0025" "0026" "0028"
## [8531] "0031" "0032" "0033" "0034" "0035" "0037" "0038" "0039" "0041" "0042"
## [8541] "0043" "0045" "0046" "0048" "0050" "0051" "0055" "0060" "0075" "0087"
## [8551] "0089" "0093" "0103" "0105" "0106" "0107" "0110" "0115" "0117" "0119"
## [8561] "0122" "0125" "0140" "0166" "0169" "0172" "0174" "0177" "0180" "0186"
## [8571] "0200" "0235" "0237" "0242" "0244" "0325" "0333" "0349" "0372" "0383"
## [8581] "0394" "0397" "0402" "0403" "0424" "0435" "0439" "0441" "0459" "0461"
## [8591] "0464" "0466" "0473" "0474" "0487" "0500" "0501" "0502" "0510" "0519"
## [8601] "0529" "0535" "0538" "0540" "0552" "0560" "0561" "0563" "0569" "0573"
## [8611] "0575" "0583" "0591" "0606" "0611" "0613" "0617" "0622" "0624" "0626"
## [8621] "0629" "0632" "0633" "0636" "0637" "0647" "0657" "0658" "0659" "0666"
## [8631] "0672" "0674" "0676" "0682" "0694" "0695" "0698" "0699" "0701" "0710"
## [8641] "0714" "0720" "0725" "0729" "0734" "0737" "0738" "0743" "0745" "0750"
## [8651] "0754" "0756" "0760" "0762" "0763" "0765" "0766" "0767" "0768" "0769"
## [8661] "0771" "0772" "0773" "0774" "0775" "0777" "0783" "0784" "0787" "0789"
## [8671] "0790" "0791" "0792" "0793" "0795" "0798" "0799" "0800" "0801" "0802"
## [8681] "0803" "0806" "0807" "0808" "0809" "0811" "0813" "0814" "0818" "0819"
## [8691] "0822" "0823" "0827" "0831" "0832" "0833" "0835" "0836" "0837" "0838"
## [8701] "0842" "0843" "0844" "0845" "0846" "0848" "0852" "0853" "0854" "0856"
## [8711] "0857" "0858" "0859" "0860" "0861" "0862" "0863" "0864" "0866" "0869"
## [8721] "0870" "0871" "0872" "0874" "0875" "0877" "0878" "0879" "0880" "0881"
## [8731] "0882" "0883" "0884" "0890" "0891" "0894" "0895" "0897" "0901" "0902"
## [8741] "0903" "0904" "0905" "9998" "9999" "0000" "0001" "0003" "0014" "0017"
## [8751] "0029" "0039" "0049" "0070" "0081" "0083" "0091" "0092" "0095" "0105"
## [8761] "0108" "0113" "0124" "0126" "0128" "0130" "0131" "0132" "0133" "0134"
## [8771] "0135" "9998" "9999" "0000" "0001" "0002" "0003" "0006" "0007" "0008"
## [8781] "0010" "0011" "0012" "0014" "0015" "0017" "0018" "0019" "0020" "0021"
## [8791] "0023" "0025" "0027" "0029" "0031" "0032" "0036" "0042" "0043" "0044"
## [8801] "0045" "0058" "0061" "0064" "0071" "0073" "0074" "0077" "0085" "0086"
## [8811] "0087" "0088" "0089" "9998" "9999" "0000" "0001" "0005" "0009" "0012"
## [8821] "0013" "0018" "0020" "0021" "0023" "0025" "0026" "0029" "0030" "0033"
## [8831] "0034" "0038" "0039" "0045" "0046" "0047" "0048" "0050" "0051" "0053"
## [8841] "0054" "0057" "0058" "0059" "0065" "0067" "0069" "0070" "0073" "0075"
## [8851] "0076" "0078" "0079" "0080" "0081" "0082" "0086" "0087" "0091" "0092"
## [8861] "0095" "0096" "0098" "0100" "0101" "0105" "0109" "0110" "0112" "0113"
## [8871] "0115" "0116" "0120" "0122" "0123" "0125" "0132" "0137" "0138" "0143"
## [8881] "0145" "0147" "0149" "0152" "0153" "0156" "0157" "0159" "0161" "0165"
## [8891] "0167" "0168" "0171" "0172" "0180" "0189" "0215" "0217" "0218" "0219"
## [8901] "0220" "0221" "0230" "0235" "0237" "0242" "0244" "0249" "0250" "0252"
## [8911] "0261" "0263" "0266" "0267" "0269" "0270" "0271" "0272" "0273" "0275"
## [8921] "0276" "0280" "0285" "0286" "0287" "0288" "0290" "0292" "0293" "0296"
## [8931] "0302" "0303" "0306" "0308" "0313" "0314" "0320" "0329" "0338" "0341"
## [8941] "0343" "0345" "0348" "0351" "0354" "0357" "0358" "0361" "0362" "0363"
## [8951] "0366" "0367" "0368" "0372" "0374" "0379" "0382" "0383" "0387" "0388"
## [8961] "0391" "0392" "0393" "0402" "0403" "0406" "0408" "0414" "0418" "0424"
## [8971] "0427" "0432" "0438" "0452" "0460" "0463" "0473" "0476" "0477" "0479"
## [8981] "0480" "0482" "0484" "0488" "0490" "0493" "0499" "0509" "0511" "0516"
## [8991] "0520" "0522" "9998" "9999" "0000" "0001" "0002" "0009" "0012" "0022"
## [9001] "0023" "0025" "0026" "0027" "0031" "0034" "0038" "0050" "0051" "0056"
## [9011] "0061" "0073" "0101" "0105" "0109" "0111" "0112" "0114" "0123" "0125"
## [9021] "0126" "0130" "0133" "0135" "0136" "0154" "0156" "0157" "0164" "0165"
## [9031] "0169" "0174" "0184" "0188" "0189" "0190" "0192" "0193" "0194" "0195"
## [9041] "0196" "0197" "0199" "0200" "0204" "0205" "9998" "9999" "0000" "0001"
## [9051] "0002" "0003" "0009" "0010" "0013" "0015" "0016" "0019" "0022" "0023"
## [9061] "0025" "0026" "0027" "0028" "0034" "0035" "0037" "0043" "0044" "0047"
## [9071] "0073" "9998" "9999" "0000" "0001" "0006" "0007" "0009" "0010" "0011"
## [9081] "0015" "0016" "0019" "0033" "0044" "0048" "0054" "0064" "0065" "0067"
## [9091] "0068" "0069" "9998" "0000" "0001" "0002" "0003" "0004" "0005" "0006"
## [9101] "0011" "0013" "0014" "0015" "0016" "0020" "0021" "0023" "0027" "0031"
## [9111] "0034" "0035" "0037" "0047" "0050" "0052" "0055" "0056" "0061" "0062"
## [9121] "0063" "0068" "0070" "0071" "0072" "0074" "0075" "0076" "0077" "0078"
## [9131] "0079" "0082" "0084" "0089" "0104" "0110" "0119" "0135" "0146" "0156"
## [9141] "0157" "0160" "0161" "0181" "0198" "9998" "9999" "0000" "0001" "0002"
## [9151] "0005" "0006" "0007" "0008" "0009" "0010" "0011" "0012" "0013" "0015"
## [9161] "0016" "0017" "0018" "0021" "0027" "0029" "0031" "0032" "0034" "0035"
## [9171] "0037" "0038" "0039" "0040" "0041" "0044" "0045" "0046" "0047" "0049"
## [9181] "0050" "0053" "0054" "0058" "0061" "0062" "0063" "0065" "0067" "0068"
## [9191] "0070" "0074" "0075" "0076" "0105" "0106" "0107" "0113" "0114" "0115"
## [9201] "0116" "0117" "0118" "0119" "0120" "0127" "0131" "0134" "0136" "0138"
## [9211] "0140" "0141" "0144" "0148" "0149" "0154" "0155" "0160" "0162" "0165"
## [9221] "0166" "0170" "0176" "0180" "0181" "0183" "0184" "0185" "0187" "0191"
## [9231] "0194" "0196" "0199" "0201" "0202" "0206" "0207" "0212" "0215" "0216"
## [9241] "0222" "0226" "0230" "0232" "9998" "9999" "0000" "0001" "0003" "0006"
## [9251] "0007" "0008" "0010" "0011" "0013" "0014" "0016" "0019" "0020" "0021"
## [9261] "0022" "0023" "0024" "0025" "0033" "0036" "0039" "0042" "0046" "9998"
## [9271] "9999" "0000" "0001" "0002" "0003" "0004" "0005" "0006" "0007" "0008"
## [9281] "0009" "0010" "0014" "0019" "0020" "0022" "0024" "0040" "0042" "0045"
## [9291] "0047" "0048" "9998" "9999" "0000" "0001" "0003" "0005" "0006" "0007"
## [9301] "0010" "0012" "0014" "0022" "0024" "0026" "0028" "0029" "0031" "0032"
## [9311] "0034" "0037" "0038" "0041" "0042" "0046" "0047" "0048" "0050" "0051"
## [9321] "0053" "0056" "0058" "0059" "0062" "0065" "0066" "0072" "0086" "0093"
## [9331] "0101" "0109" "0111" "0112" "0122" "0125" "0126" "0127" "0131" "0132"
## [9341] "0134" "0136" "0260" "0262" "0270" "0271" "0276" "0280" "0285" "0288"
## [9351] "0290" "0293" "0294" "0295" "0297" "0298" "0300" "0301" "0303" "0306"
## [9361] "0309" "0312" "0315" "0324" "0327" "0331" "0333" "0334" "0338" "0340"
## [9371] "0345" "0346" "0349" "0355" "0359" "0362" "9998" "9999" "0000" "0001"
## [9381] "0002" "0005" "0006" "0008" "0011" "0014" "0015" "0017" "0020" "0022"
## [9391] "0023" "0027" "0030" "0032" "0036" "0037" "0044" "0045" "0046" "0047"
## [9401] "0049" "0050" "0060" "0061" "0066" "0071" "0072" "0074" "0075" "0076"
## [9411] "0081" "0082" "0084" "0086" "0088" "0089" "0091" "0092" "0094" "0098"
## [9421] "0100" "0101" "0102" "0106" "0107" "0108" "0109" "0110" "0111" "0112"
## [9431] "0117" "0118" "0120" "0121" "0125" "0126" "0128" "0129" "0130" "0140"
## [9441] "0144" "0146" "0151" "0152" "0153" "0154" "0157" "0158" "0161" "0167"
## [9451] "0168" "0170" "0176" "0183" "0238" "0244" "0245" "0246" "0247" "0249"
## [9461] "0255" "0256" "0265" "0267" "0270" "0271" "0278" "0344" "0362" "0378"
## [9471] "0380" "0382" "0385" "0392" "0394" "0395" "0399" "0400" "0402" "0403"
## [9481] "0404" "0405" "0407" "0409" "0412" "0417" "0419" "0420" "0423" "0427"
## [9491] "0429" "0431" "0432" "0434" "0435" "0438" "0440" "0441" "0443" "0444"
## [9501] "0446" "0448" "0451" "0452" "0453" "0455" "0458" "0461" "0462" "0463"
## [9511] "0464" "0466" "0467" "0469" "0471" "0472" "9998" "9999" "0000" "0001"
## [9521] "0003" "0006" "0008" "0014" "0021" "0022" "0027" "0032" "0034" "0036"
## [9531] "0038" "0039" "0040" "0041" "0042" "0044" "0045" "0047" "0048" "0049"
## [9541] "0050" "0058" "0060" "0064" "0066" "0068" "0073" "0086" "0089" "0091"
## [9551] "0110" "0125" "0135" "0138" "0143" "0155" "0156" "9998" "9999" "0000"
## [9561] "0001" "0005" "0008" "0009" "0011" "0012" "0014" "0016" "0017" "0018"
## [9571] "0019" "0020" "0022" "0029" "0035" "0036" "0039" "0040" "0047" "0048"
## [9581] "0054" "0055" "0057" "0060" "0062" "0064" "0065" "0067" "0070" "0071"
## [9591] "0072" "0075" "0076" "0078" "0080" "0081" "0085" "0086" "0091" "0092"
## [9601] "0101" "0104" "0105" "0106" "0107" "0125" "0132" "0135" "0147" "0149"
## [9611] "0150" "0152" "0155" "0156" "0157" "0160" "0162" "0163" "0164" "0165"
## [9621] "0167" "0168" "0169" "0170" "0173" "0174" "0176" "0177" "0178" "0182"
## [9631] "0183" "9998" "9999" "0000" "0001" "0002" "0005" "0006" "0009" "0012"
## [9641] "0013" "0014" "0021" "0026" "0028" "0029" "0030" "0031" "0032" "0033"
## [9651] "0035" "0036" "0037" "0041" "0043" "0045" "0046" "0053" "0054" "0055"
## [9661] "0056" "0057" "0063" "0064" "0067" "0071" "0072" "0077" "0078" "0079"
## [9671] "0080" "9998" "9999" "0000" "0001" "0002" "0003" "0004" "0006" "0009"
## [9681] "0010" "0011" "0012" "0013" "0018" "0020" "0021" "0023" "0024" "0026"
## [9691] "0027" "0028" "0029" "0030" "0033" "0034" "0040" "0041" "0042" "0043"
## [9701] "0046" "0047" "0051" "0052" "0053" "0055" "0058" "0059" "0060" "0061"
## [9711] "0062" "0064" "0066" "0068" "0080" "0081" "0084" "0092" "0093" "0096"
## [9721] "0097" "0104" "0115" "0116" "0119" "0124" "0125" "0126" "0137" "0138"
## [9731] "0141" "0143" "0151" "0173" "0178" "0187" "0189" "9998" "9999" "0000"
## [9741] "0001" "0002" "0003" "0004" "0005" "0006" "0008" "0009" "0017" "0018"
## [9751] "0019" "0020" "0022" "0024" "0025" "0026" "0028" "0029" "0030" "0032"
## [9761] "0033" "0036" "0037" "0040" "0041" "0043" "0044" "0047" "0050" "9998"
## [9771] "9999" "0000" "0001" "0003" "0008" "0009" "0011" "0013" "0015" "0018"
## [9781] "0021" "0028" "0033" "0035" "0040" "0041" "0042" "0045" "0046" "0048"
## [9791] "0058" "0060" "0061" "0066" "0069" "0070" "0080" "0083" "0084" "0086"
## [9801] "0088" "0090" "0094" "0097" "0099" "0116" "0130" "0136" "0149" "0150"
## [9811] "0152" "0156" "0163" "0164" "0165" "0170" "0208" "0231" "0234" "0260"
## [9821] "0270" "0275" "0276" "9998" "9999" "0000" "0001" "0002" "0004" "0006"
## [9831] "0012" "0013" "0016" "0018" "0019" "0020" "0023" "0024" "0025" "0027"
## [9841] "0028" "0031" "0032" "0036" "0044" "0045" "0046" "0048" "0052" "0055"
## [9851] "0067" "0074" "0085" "0086" "0091" "0093" "0094" "0097" "0099" "0102"
## [9861] "0103" "0104" "0107" "0109" "0110" "0112" "0113" "0119" "0121" "0123"
## [9871] "0124" "0129" "0130" "0131" "9998" "9999" "0000" "0001" "0002" "0003"
## [9881] "0004" "0007" "0013" "0015" "0020" "0021" "0023" "0026" "0027" "0032"
## [9891] "0041" "0047" "0051" "0052" "0053" "0054" "0057" "0058" "0062" "0063"
## [9901] "0064" "0065" "0066" "0067" "0073" "0074" "0075" "0076" "0080" "0089"
## [9911] "0092" "0101" "0102" "0109" "0116" "0120" "0124" "0130" "0132" "0133"
## [9921] "0135" "0136" "9998" "9999" "0000" "0001" "0002" "0005" "0006" "0009"
## [9931] "0010" "0012" "0013" "0019" "0021" "0024" "0025" "0026" "0027" "0028"
## [9941] "0031" "0032" "0036" "0046" "0047" "0049" "0052" "0056" "0058" "0059"
## [9951] "0061" "0062" "0066" "0068" "0069" "0071" "0074" "0075" "0076" "0077"
## [9961] "0079" "0081" "0083" "0084" "0087" "0088" "0091" "0095" "0097" "0098"
## [9971] "0101" "0103" "0105" "0108" "0110" "0112" "0114" "0118" "0120" "0121"
## [9981] "0122" "0124" "0125" "0128" "0130" "0131" "0133" "0135" "0137" "0140"
## [9991] "0141" "0143" "0147" "0153" "0154" "0164" "0165" "0169" "0172" "0177"
## [10001] "0180" "0183" "0186" "0187" "0189" "0193" "0195" "0199" "0209" "0210"
## [10011] "0213" "0222" "0224" "0225" "0229" "0230" "0232" "0233" "0235" "0236"
## [10021] "0239" "0240" "0244" "0247" "0248" "0249" "0250" "0252" "0253" "0254"
## [10031] "0255" "0257" "0260" "0264" "0267" "0271" "0273" "0278" "0280" "0282"
## [10041] "0283" "0286" "0289" "0291" "0309" "0310" "0314" "0317" "0318" "0321"
## [10051] "0322" "0324" "0331" "0332" "0333" "0334" "0335" "0340" "0345" "0346"
## [10061] "0347" "0348" "0350" "9998" "9999" "0000" "0001" "0002" "0003" "0004"
## [10071] "0005" "0006" "0007" "0008" "0009" "0010" "0011" "0012" "0013" "0014"
## [10081] "0015" "0016" "0017" "0018" "0022" "0024" "0025" "0026" "0027" "0035"
## [10091] "0036" "0042" "0043" "0044" "0057" "0059" "0060" "9998" "9999" "0000"
## [10101] "0001" "0004" "0006" "0018" "0020" "0022" "0027" "0029" "0033" "0038"
## [10111] "0047" "0050" "0056" "0057" "0062" "0064" "0070" "0078" "0089" "0095"
## [10121] "0097" "0099" "0111" "0124" "0129" "0133" "0134" "0139" "0143" "0151"
## [10131] "0153" "0168" "0183" "0190" "0194" "0198" "0201" "0202" "0206" "0210"
## [10141] "0213" "0214" "0218" "0227" "0230" "0231" "0232" "0235" "0242" "0243"
## [10151] "0249" "0250" "0253" "0255" "0258" "0259" "0263" "0264" "0268" "0270"
## [10161] "0271" "0272" "0273" "0274" "0276" "0277" "0279" "0280" "0286" "0288"
## [10171] "0297" "0300" "0302" "0312" "0313" "0314" "0315" "0317" "0318" "0319"
## [10181] "0324" "0329" "0334" "0342" "0343" "0350" "0355" "0356" "0360" "0369"
## [10191] "0376" "0377" "0378" "0380" "0391" "0396" "0405" "0407" "0415" "0417"
## [10201] "0430" "0435" "0440" "0444" "0449" "0450" "0451" "0452" "0453" "0458"
## [10211] "0460" "0463" "0464" "0468" "0470" "0471" "0476" "0486" "0511" "0512"
## [10221] "0513" "0516" "0522" "0526" "0528" "0529" "0535" "0536" "0537" "0539"
## [10231] "0540" "0542" "0544" "0547" "0550" "0560" "0565" "0566" "0568" "0572"
## [10241] "0573" "0574" "0575" "0577" "0583" "0587" "0588" "0591" "0592" "0593"
## [10251] "0594" "0595" "0596" "0597" "0600" "0605" "0608" "0610" "0611" "0613"
## [10261] "0615" "0620" "0621" "0622" "0626" "0632" "0660" "0668" "0683" "0684"
## [10271] "0690" "0697" "0703" "0706" "0708" "0709" "0712" "0714" "0715" "0718"
## [10281] "0721" "0722" "0724" "0726" "0727" "0728" "0735" "0737" "0738" "0739"
## [10291] "0741" "0747" "0748" "0749" "0750" "0752" "0753" "0754" "0755" "0756"
## [10301] "0757" "0758" "0760" "0761" "0763" "0764" "0766" "0767" "0770" "0771"
## [10311] "9998" "9999" "0000" "0001" "0005" "0009" "0010" "0011" "0012" "0013"
## [10321] "0018" "0020" "0021" "0022" "0023" "0024" "0025" "0029" "0031" "0037"
## [10331] "0052" "0053" "0057" "0059" "0067" "0071" "0072" "0074" "0084" "0086"
## [10341] "9998" "9999" "0000" "0001" "0009" "0010" "0011" "0014" "0016" "0017"
## [10351] "0018" "0019" "0020" "0021" "0023" "0025" "0027" "0028" "0029" "0031"
## [10361] "0033" "0034" "0035" "0036" "0037" "0038" "0039" "0040" "0041" "0042"
## [10371] "0046" "0047" "0049" "0053" "0054" "0068" "0069" "0071" "0072" "0081"
## [10381] "0082" "0083" "0084" "9998" "9999" "0000" "0001" "0002" "0003" "0005"
## [10391] "0008" "0009" "0011" "0014" "0015" "0016" "0018" "0019" "0020" "0024"
## [10401] "0026" "0027" "0028" "0029" "0030" "0031" "0034" "0036" "0037" "0038"
## [10411] "0039" "0041" "0042" "0043" "0044" "0045" "0046" "0047" "0051" "0052"
## [10421] "0053" "0069" "0070" "0071" "0077" "0079" "0083" "0084" "0085" "0086"
## [10431] "0087" "0090" "0096" "0112" "0118" "0127" "0132" "0134" "0141" "0144"
## [10441] "0150" "0151" "0152" "0153" "9998" "9999" "0000" "0001" "0003" "0004"
## [10451] "0006" "0007" "0009" "0010" "0015" "0016" "0017" "0018" "0019" "0020"
## [10461] "0025" "0026" "0027" "0030" "0031" "0034" "0036" "0039" "0042" "0043"
## [10471] "0044" "0045" "0046" "0047" "0049" "0050" "0051" "0052" "0054" "0055"
## [10481] "0058" "0059" "0060" "0061" "0062" "0063" "0064" "0067" "0068" "0070"
## [10491] "0071" "0073" "0075" "0081" "0082" "0086" "0088" "0089" "0092" "0093"
## [10501] "0094" "0095" "0096" "0097" "0100" "0102" "0104" "0105" "0107" "0109"
## [10511] "0114" "0115" "0116" "0117" "0120" "0122" "0124" "0125" "0127" "0129"
## [10521] "0131" "0133" "0134" "0135" "0139" "0143" "0147" "0149" "0154" "0155"
## [10531] "0156" "0158" "0161" "0162" "0163" "0167" "0168" "0174" "0175" "0176"
## [10541] "0179" "0180" "0181" "0182" "0184" "0185" "0187" "0189" "0191" "0192"
## [10551] "0195" "0196" "0197" "0198" "0199" "0200" "0201" "0203" "0205" "0209"
## [10561] "0210" "0212" "0214" "0216" "0217" "0218" "0221" "0223" "0226" "0228"
## [10571] "0233" "0235" "0237" "0240" "0244" "0246" "0247" "0248" "0250" "0251"
## [10581] "0253" "0255" "0258" "0260" "0271" "0272" "0273" "0276" "0277" "0284"
## [10591] "0286" "0288" "0289" "0290" "0291" "0293" "0295" "0297" "0302" "0303"
## [10601] "0307" "0310" "0312" "0314" "0317" "0318" "0322" "0325" "0326" "0327"
## [10611] "0331" "0332" "0333" "0337" "0338" "0340" "0342" "0343" "0344" "0346"
## [10621] "0348" "0349" "0350" "0351" "0352" "0355" "0357" "0358" "0359" "0360"
## [10631] "0361" "0362" "0365" "9998" "9999" "0000" "0001" "0003" "0004" "0006"
## [10641] "0007" "0009" "0010" "0011" "0012" "0013" "0014" "0015" "0016" "0018"
## [10651] "0019" "0020" "0022" "0023" "0024" "0025" "0026" "0027" "0028" "0029"
## [10661] "0030" "0031" "0032" "0033" "0034" "0035" "0036" "0037" "0039" "0040"
## [10671] "0041" "0042" "0043" "0044" "0045" "0047" "0050" "0051" "0052" "0053"
## [10681] "0055" "0056" "0057" "0058" "0059" "0060" "0062" "0063" "0064" "0066"
## [10691] "0067" "0068" "0069" "0070" "0072" "0073" "0074" "0075" "0076" "0077"
## [10701] "0078" "0079" "0080" "0081" "0082" "0083" "0085" "0086" "0087" "0088"
## [10711] "0089" "0090" "0091" "9998" "9999"datos_inegi_jal2$LOC## [1] 0 9998 9999 0 1 2 3 6 7 9 10 11 12 13
## [15] 14 16 18 19 20 21 22 23 24 26 27 28 29 30
## [29] 31 32 33 34 35 36 37 38 40 41 42 43 44 46
## [43] 47 48 49 52 53 54 55 56 57 59 60 62 63 66
## [57] 67 68 69 70 71 72 73 74 75 78 79 81 84 86
## [71] 87 88 94 102 104 106 107 108 109 111 112 113 115 116
## [85] 118 119 122 123 128 129 131 132 133 135 137 139 141 142
## [99] 9998 9999 0 1 2 4 5 7 8 9 10 13 19 21
## [113] 24 25 27 30 34 35 36 37 39 40 41 43 48 53
## [127] 55 58 61 62 63 64 65 9998 9999 0 1 4 7 19
## [141] 21 28 30 31 32 38 51 56 57 58 59 68 74 80
## [155] 82 83 86 94 96 97 99 9998 9999 0 1 3 4 5
## [169] 7 8 10 14 17 20 22 23 25 26 30 32 39 53
## [183] 61 64 66 9998 9999 0 1 2 3 4 5 7 8 9
## [197] 10 13 15 20 22 26 29 31 32 35 36 39 40 43
## [211] 45 56 65 70 71 74 75 9998 9999 0 1 2 3 4
## [225] 5 7 10 11 12 13 14 15 17 18 19 20 22 23
## [239] 24 25 26 27 28 30 31 32 33 34 37 38 39 40
## [253] 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [267] 57 58 59 61 62 70 73 75 76 77 79 80 82 83
## [281] 84 85 87 88 89 91 92 95 97 98 100 102 107 109
## [295] 111 112 116 121 122 123 130 133 137 140 142 147 148 149
## [309] 153 156 157 158 160 162 165 167 171 174 182 185 187 189
## [323] 190 192 193 194 9998 9999 0 1 3 4 8 9 10 12
## [337] 13 14 15 22 76 86 9998 0 1 2 3 4 5 6
## [351] 7 9 11 14 16 21 22 24 26 28 32 36 45 46
## [365] 48 50 51 54 56 57 58 61 63 64 65 66 68 72
## [379] 73 76 78 83 89 90 93 95 96 97 101 102 103 106
## [393] 108 110 113 115 116 117 118 120 127 130 131 133 137 139
## [407] 141 142 143 145 146 147 150 151 153 154 156 158 160 161
## [421] 162 167 171 172 175 177 178 181 184 187 188 189 191 193
## [435] 194 196 198 201 202 203 207 210 212 213 214 216 217 222
## [449] 225 228 230 231 234 238 239 241 243 244 245 247 248 249
## [463] 250 257 258 261 262 263 266 267 269 271 272 274 275 277
## [477] 279 280 281 283 287 288 289 294 295 301 303 307 311 317
## [491] 318 320 321 322 327 328 331 337 339 342 343 345 346 348
## [505] 351 352 353 358 360 361 371 372 375 376 378 379 381 382
## [519] 390 393 394 395 396 403 405 406 408 409 411 412 415 417
## [533] 418 422 423 424 425 494 497 500 501 503 509 511 512 513
## [547] 516 519 520 521 523 525 532 533 536 537 538 540 541 543
## [561] 548 552 553 557 560 567 570 571 574 575 576 579 582 585
## [575] 586 587 589 590 592 593 596 602 606 607 610 611 612 614
## [589] 617 621 624 626 627 631 632 633 635 637 638 639 641 643
## [603] 645 646 649 650 657 659 661 662 664 665 666 667 668 669
## [617] 672 673 675 676 677 681 682 683 684 686 687 688 689 690
## [631] 691 694 695 696 697 698 701 9998 9999 0 1 3 4 5
## [645] 6 7 9 12 14 20 21 24 30 32 34 41 43 44
## [659] 48 54 55 57 60 65 66 67 68 69 70 71 72 73
## [673] 74 75 76 77 78 79 80 81 9998 9999 0 1 3 16
## [687] 21 23 24 25 26 27 28 29 32 33 46 51 52 55
## [701] 58 60 61 107 9998 0 1 3 8 9 11 15 16 17
## [715] 21 22 24 26 29 30 33 41 47 54 68 9998 9999 0
## [729] 1 2 3 4 5 10 12 13 16 21 32 38 39 40
## [743] 41 44 45 46 49 50 53 56 58 59 60 61 63 64
## [757] 66 67 73 74 76 90 95 103 104 107 109 111 114 116
## [771] 119 130 139 141 142 148 149 152 155 156 9998 9999 0 1
## [785] 2 3 4 5 6 7 8 10 11 12 13 14 16 17
## [799] 18 19 23 24 25 26 28 29 30 32 35 36 37 38
## [813] 39 41 43 44 45 46 47 48 50 51 52 54 55 57
## [827] 58 60 61 65 67 68 69 70 74 75 76 77 78 80
## [841] 82 83 84 85 87 89 90 91 92 93 94 96 98 99
## [855] 101 105 106 109 114 121 123 125 127 128 130 131 134 136
## [869] 137 141 142 145 146 147 148 151 154 155 160 165 166 167
## [883] 169 173 175 177 178 180 185 188 191 195 203 204 205 207
## [897] 211 212 213 215 216 218 219 220 221 224 225 227 230 232
## [911] 235 240 241 242 243 245 246 248 9998 9999 0 1 3 4
## [925] 5 6 8 9 10 13 15 16 19 20 21 23 24 25
## [939] 26 28 29 31 45 47 56 57 62 63 68 71 73 79
## [953] 9998 9999 0 1 2 3 5 7 9 11 12 13 14 15
## [967] 17 19 20 22 23 24 25 26 27 28 31 33 36 38
## [981] 41 42 46 47 48 51 53 55 58 60 63 64 68 69
## [995] 71 75 76 77 79 81 82 84 85 86 88 97 102 104
## [1009] 105 107 108 112 116 119 120 125 130 131 132 133 134 136
## [1023] 138 139 140 142 144 146 152 154 156 158 160 161 163 165
## [1037] 175 176 178 186 190 194 195 203 204 207 208 212 213 216
## [1051] 225 227 237 239 242 244 247 248 251 252 253 255 257 258
## [1065] 259 264 265 266 267 270 273 275 276 283 287 296 298 301
## [1079] 302 304 309 314 333 337 347 348 358 360 369 372 375 378
## [1093] 379 382 383 9998 9999 0 1 2 3 4 5 8 10 11
## [1107] 12 14 15 17 18 20 24 27 29 30 33 35 37 38
## [1121] 39 43 46 47 48 49 50 51 52 53 54 55 69 70
## [1135] 71 72 73 75 77 79 80 82 83 85 86 87 89 93
## [1149] 95 100 101 103 104 106 108 112 114 115 116 121 122 124
## [1163] 128 133 141 142 145 152 156 160 161 162 164 170 9998 9999
## [1177] 0 1 5 6 12 13 17 18 20 26 37 41 42 54
## [1191] 56 58 64 66 76 78 83 84 85 86 87 88 93 96
## [1205] 97 100 102 103 104 105 110 111 112 116 119 120 121 123
## [1219] 124 125 130 133 134 135 139 141 145 150 181 182 188 200
## [1233] 203 205 206 211 214 215 227 230 233 242 245 247 250 251
## [1247] 253 9998 9999 0 1 3 5 7 8 9 10 11 12 13
## [1261] 15 17 18 19 22 23 24 26 27 28 29 31 32 33
## [1275] 34 36 37 38 39 40 41 42 55 60 61 62 71 74
## [1289] 75 86 89 95 109 112 123 129 135 139 144 149 154 174
## [1303] 178 180 191 195 204 210 211 214 220 230 231 232 240 243
## [1317] 244 245 246 251 257 259 262 264 265 266 9998 9999 0 1
## [1331] 2 7 10 13 17 18 20 23 26 27 32 35 36 40
## [1345] 44 48 50 52 61 63 67 70 72 73 75 76 81 85
## [1359] 92 94 96 98 100 101 102 103 105 109 110 111 114 119
## [1373] 131 133 136 137 138 152 153 158 162 165 167 174 177 187
## [1387] 188 193 197 200 201 217 221 238 239 243 244 249 261 262
## [1401] 263 266 275 276 277 285 286 287 292 293 296 307 315 334
## [1415] 335 336 340 345 350 351 352 358 359 362 366 367 368 375
## [1429] 377 381 384 387 389 390 391 394 396 397 400 405 407 411
## [1443] 412 413 415 417 420 421 423 425 427 428 429 430 431 432
## [1457] 436 438 440 441 443 444 9998 9999 0 1 7 10 13 20
## [1471] 21 22 32 33 34 35 37 38 41 43 47 53 55 57
## [1485] 60 62 63 64 65 66 69 70 73 74 75 76 78 79
## [1499] 82 86 87 88 90 91 92 94 95 96 97 99 100 101
## [1513] 102 106 107 109 110 111 114 116 117 121 123 125 127 128
## [1527] 129 130 131 134 135 136 138 148 152 153 155 161 164 177
## [1541] 191 193 194 196 203 207 208 211 212 213 216 217 219 220
## [1555] 225 226 228 234 237 238 240 241 242 245 248 249 251 258
## [1569] 260 263 264 267 271 273 279 282 284 285 286 287 289 293
## [1583] 295 296 297 298 299 300 302 303 309 311 312 313 9998 9999
## [1597] 0 1 2 3 8 11 15 18 20 21 24 29 33 34
## [1611] 35 42 43 44 46 49 50 51 53 57 60 61 62 64
## [1625] 68 75 78 84 89 92 94 103 104 109 131 135 136 140
## [1639] 162 164 9998 9999 0 1 2 3 4 6 7 9 12 16
## [1653] 17 24 30 33 34 36 47 49 51 58 59 62 68 69
## [1667] 71 77 82 87 94 100 102 103 104 107 108 109 110 112
## [1681] 113 115 118 119 120 123 124 133 134 136 138 139 141 143
## [1695] 144 145 146 147 150 151 154 157 159 162 163 9998 9999 0
## [1709] 1 3 5 8 11 14 21 24 28 37 38 42 69 71
## [1723] 74 76 93 101 102 125 137 139 149 152 167 171 174 194
## [1737] 206 207 211 222 232 234 235 236 237 238 9998 9999 0 1
## [1751] 2 3 4 5 7 8 10 11 13 15 16 17 18 19
## [1765] 20 21 22 23 24 32 37 38 40 41 43 45 47 52
## [1779] 53 55 56 58 60 62 66 67 68 69 9998 9999 0 1
## [1793] 5 6 7 8 12 13 14 16 18 20 23 26 28 31
## [1807] 32 35 37 42 43 45 51 58 61 62 63 65 66 67
## [1821] 68 70 74 75 76 79 80 85 89 90 99 103 107 112
## [1835] 119 120 128 130 133 135 136 140 143 144 145 147 149 153
## [1849] 154 169 178 179 190 193 195 196 200 204 207 9998 9999 0
## [1863] 1 5 9 14 18 21 24 25 30 31 32 223 228 233
## [1877] 235 238 239 241 242 243 244 9998 0 1 5 8 10 12
## [1891] 14 21 25 29 30 31 32 33 34 35 38 40 41 42
## [1905] 43 44 49 51 54 55 56 59 60 61 63 67 68 71
## [1919] 76 78 80 81 84 87 92 93 95 96 99 101 103 104
## [1933] 106 110 111 114 115 116 121 128 129 132 134 138 140 148
## [1947] 150 152 153 162 163 164 165 167 168 174 176 181 188 191
## [1961] 192 195 196 199 204 205 208 209 210 212 213 215 216 218
## [1975] 220 221 227 228 231 232 237 238 239 240 246 247 248 252
## [1989] 254 255 256 259 262 264 273 279 282 283 284 286 287 288
## [2003] 294 295 296 297 9998 9999 0 1 4 5 7 8 9 10
## [2017] 16 17 19 21 22 23 26 28 29 31 52 60 61 62
## [2031] 64 65 68 69 73 9998 9999 0 1 2 3 4 5 6
## [2045] 8 10 12 13 15 17 18 19 20 22 24 25 27 28
## [2059] 29 30 31 35 36 40 42 43 44 45 47 48 49 50
## [2073] 51 54 57 58 59 60 62 63 66 69 71 72 74 76
## [2087] 77 79 80 81 83 84 85 86 88 89 91 92 93 94
## [2101] 95 97 99 101 102 103 104 105 106 107 108 109 110 111
## [2115] 112 113 115 116 117 118 120 122 123 124 126 128 134 135
## [2129] 138 144 145 146 152 154 155 156 167 168 169 170 173 174
## [2143] 176 178 180 183 188 189 191 192 194 195 196 197 198 200
## [2157] 203 205 206 212 216 217 219 222 227 228 231 232 234 236
## [2171] 237 239 242 243 245 247 248 249 251 253 259 260 261 262
## [2185] 9998 9999 0 1 2 3 4 5 6 7 18 24 25 26
## [2199] 27 31 38 39 41 42 44 48 50 51 52 53 54 55
## [2213] 56 57 58 60 64 65 66 67 72 74 77 78 81 82
## [2227] 86 87 92 93 94 96 99 100 101 102 104 9998 9999 0
## [2241] 1 3 7 12 13 14 16 18 20 21 22 24 29 30
## [2255] 35 37 39 40 45 50 51 55 58 60 64 66 69 70
## [2269] 71 73 74 88 89 90 91 93 94 96 98 109 111 118
## [2283] 119 120 124 126 137 140 141 152 160 161 176 179 189 190
## [2297] 206 211 214 215 223 234 9998 9999 0 1 2 3 4 5
## [2311] 6 8 9 10 12 14 16 20 24 25 27 34 40 45
## [2325] 47 53 54 55 9998 9999 0 1 2 6 7 8 10 11
## [2339] 13 14 19 22 23 24 26 27 29 30 33 35 36 37
## [2353] 38 39 44 46 48 49 51 53 54 56 58 59 60 61
## [2367] 62 64 65 67 68 69 70 71 72 73 74 76 77 78
## [2381] 79 80 81 85 91 92 94 95 97 101 103 108 110 119
## [2395] 122 132 133 137 139 141 142 144 146 151 152 153 159 160
## [2409] 163 167 168 169 171 172 174 176 177 9998 9999 0 1 3
## [2423] 8 13 15 17 20 21 23 34 42 50 57 65 69 73
## [2437] 74 77 9998 0 1 2 3 17 21 25 32 33 34 35
## [2451] 36 39 40 44 48 50 51 54 61 62 66 69 73 75
## [2465] 79 81 82 85 86 87 90 100 104 108 110 117 123 125
## [2479] 127 128 132 134 136 138 139 140 148 150 156 158 161 162
## [2493] 169 172 176 178 181 184 190 194 202 203 207 210 213 216
## [2507] 217 218 222 225 226 228 230 235 236 238 241 243 244 245
## [2521] 246 247 249 251 252 259 260 261 264 268 269 270 273 277
## [2535] 278 283 284 286 292 293 295 300 301 302 303 305 313 314
## [2549] 320 329 331 332 350 351 355 357 359 363 375 379 381 382
## [2563] 383 385 386 395 398 399 406 407 409 410 412 413 414 415
## [2577] 416 417 419 421 422 423 425 427 428 429 430 431 432 433
## [2591] 434 435 438 440 442 443 446 449 452 453 454 460 463 466
## [2605] 468 471 472 473 474 475 476 478 481 483 487 491 492 495
## [2619] 497 498 500 501 502 504 507 508 509 510 516 521 523 524
## [2633] 525 526 527 529 534 536 542 546 549 551 552 563 564 566
## [2647] 568 569 571 573 575 576 577 590 591 594 598 599 602 603
## [2661] 604 605 606 608 609 610 611 612 613 617 619 620 622 624
## [2675] 625 627 631 632 633 634 635 636 637 638 639 640 641 642
## [2689] 643 646 647 650 653 655 656 657 659 661 663 666 671 672
## [2703] 673 677 678 679 682 683 684 685 687 691 692 693 694 696
## [2717] 699 705 706 707 710 711 713 714 716 717 719 720 721 722
## [2731] 723 724 727 728 731 732 734 736 737 738 739 740 741 743
## [2745] 744 750 755 757 763 764 765 766 769 774 776 777 778 779
## [2759] 781 782 786 787 789 790 791 792 793 794 795 796 797 800
## [2773] 802 9998 9999 0 1 3 12 14 19 20 25 27 30 31
## [2787] 32 36 37 39 46 47 52 55 57 58 59 60 66 76
## [2801] 83 86 90 102 127 132 133 9998 9999 0 1 2 7 9
## [2815] 11 12 18 20 24 25 26 27 31 43 48 54 57 60
## [2829] 76 79 80 81 82 87 88 89 90 93 98 115 118 119
## [2843] 126 133 135 138 161 162 164 167 168 9998 9999 0 1 15
## [2857] 16 22 27 28 29 30 32 33 34 42 44 45 49 53
## [2871] 61 65 70 72 74 76 77 78 84 90 95 97 98 99
## [2885] 100 113 115 116 117 119 121 126 130 131 132 155 166 170
## [2899] 171 175 183 234 260 261 263 264 9998 9999 0 1 2 9998
## [2913] 0 1 8 10 11 13 15 16 18 19 21 23 25 30
## [2927] 31 35 38 39 43 44 46 47 48 49 57 60 64 65
## [2941] 67 70 71 72 74 77 78 82 85 86 95 100 121 122
## [2955] 123 130 147 149 156 163 9998 9999 0 1 2 3 4 5
## [2969] 6 7 9 10 12 13 14 15 17 18 19 20 21 22
## [2983] 23 26 44 45 46 52 55 62 63 68 74 77 78 84
## [2997] 93 95 117 9998 9999 0 1 2 3 6 7 8 9 10
## [3011] 12 13 14 15 16 18 19 20 21 22 23 24 26 27
## [3025] 28 29 30 31 33 34 35 36 37 38 39 41 42 43
## [3039] 44 46 47 48 49 61 63 67 82 105 110 117 130 132
## [3053] 133 134 140 148 151 163 172 173 175 176 183 199 200 9998
## [3067] 9999 0 1 4 6 7 9 10 12 15 16 22 24 25
## [3081] 26 30 31 33 36 39 40 41 42 48 52 57 60 62
## [3095] 63 66 67 68 74 81 85 86 91 92 94 95 97 99
## [3109] 101 102 105 110 111 114 115 117 123 124 125 126 132 135
## [3123] 136 137 139 140 141 146 147 148 150 152 154 159 161 165
## [3137] 166 173 176 182 183 186 190 191 202 212 213 216 220 226
## [3151] 227 232 236 242 247 249 252 253 257 263 276 281 284 290
## [3165] 291 306 308 325 329 330 331 338 345 350 358 363 9998 9999
## [3179] 0 1 2 3 4 5 6 7 9 10 11 12 14 18
## [3193] 19 20 27 30 33 34 35 41 42 43 48 49 50 51
## [3207] 54 57 59 60 62 64 67 69 70 71 73 74 75 77
## [3221] 78 79 80 82 83 84 85 89 91 94 96 97 99 101
## [3235] 103 105 106 109 110 113 114 116 120 127 128 129 130 131
## [3249] 134 135 137 139 141 142 145 147 152 153 154 155 156 157
## [3263] 161 162 163 164 165 166 168 169 170 171 172 173 174 175
## [3277] 176 177 178 179 180 181 9998 9999 0 1 5 7 8 9
## [3291] 10 11 13 16 21 22 24 25 26 27 28 30 32 33
## [3305] 34 35 40 43 45 46 47 48 50 51 54 56 57 60
## [3319] 63 65 71 72 76 77 78 79 80 81 83 84 88 89
## [3333] 93 95 98 99 100 101 104 106 107 108 109 110 114 115
## [3347] 116 117 119 120 121 122 123 124 126 127 130 131 132 135
## [3361] 136 137 139 140 141 142 144 147 148 151 157 158 159 164
## [3375] 165 169 174 179 181 186 199 203 205 206 207 208 210 211
## [3389] 212 214 217 218 221 226 227 231 232 233 234 236 239 240
## [3403] 241 242 250 253 255 258 264 265 266 268 272 273 274 278
## [3417] 279 280 281 289 292 295 297 298 302 306 308 310 317 319
## [3431] 320 327 330 333 337 341 344 345 346 347 351 352 9998 9999
## [3445] 0 1 2 3 6 8 12 13 14 17 18 20 21 24
## [3459] 25 26 27 28 29 31 32 34 35 37 38 41 42 45
## [3473] 46 47 51 52 55 56 57 59 61 62 64 65 66 67
## [3487] 68 69 70 71 72 74 76 78 80 81 87 88 91 92
## [3501] 93 95 98 100 101 102 103 104 108 110 111 112 113 114
## [3515] 115 116 117 118 120 121 122 123 130 133 136 139 142 143
## [3529] 144 147 149 151 152 153 154 163 177 178 181 182 183 189
## [3543] 192 193 194 195 198 199 203 206 207 210 213 217 223 224
## [3557] 228 230 232 235 238 239 241 242 243 244 245 246 249 251
## [3571] 252 253 254 255 256 257 258 259 260 261 262 265 266 268
## [3585] 271 273 274 275 277 283 284 285 286 291 292 293 295 296
## [3599] 297 298 299 300 301 302 303 304 307 309 9998 9999 0 1
## [3613] 2 3 4 5 6 7 28 29 33 38 39 44 45 56
## [3627] 57 58 61 63 9998 9999 0 1 3 4 6 7 9 11
## [3641] 15 17 18 19 21 22 23 24 25 26 27 29 30 32
## [3655] 33 34 37 38 39 40 42 44 46 48 49 50 52 53
## [3669] 55 56 58 60 61 63 65 66 67 69 70 71 74 75
## [3683] 76 77 79 80 82 83 84 86 87 89 90 93 96 97
## [3697] 99 101 102 103 104 105 106 109 110 111 113 114 116 118
## [3711] 120 121 122 124 126 128 129 132 133 134 136 137 138 139
## [3725] 140 142 143 145 146 149 150 151 152 159 164 169 175 176
## [3739] 180 181 183 187 192 202 203 204 205 206 207 214 215 217
## [3753] 218 219 220 221 222 223 224 225 226 227 228 229 232 237
## [3767] 238 239 243 245 249 250 251 254 256 259 260 263 264 265
## [3781] 266 267 271 272 273 274 275 276 278 286 288 293 294 295
## [3795] 296 297 9998 9999 0 1 5 6 8 9 11 14 15 19
## [3809] 21 23 26 28 30 31 33 37 38 39 40 42 49 53
## [3823] 55 58 62 64 69 73 78 79 86 87 88 89 90 91
## [3837] 96 97 98 99 100 108 109 112 113 116 117 118 126 128
## [3851] 130 131 134 136 137 139 140 148 153 154 157 159 163 165
## [3865] 170 173 174 177 178 179 185 187 188 189 191 195 197 199
## [3879] 200 210 219 220 221 222 226 227 230 235 245 248 250 253
## [3893] 256 258 260 261 266 267 268 269 270 274 276 278 283 286
## [3907] 289 291 294 304 306 307 308 310 312 313 314 317 318 321
## [3921] 345 347 355 358 362 363 368 427 435 437 442 448 452 458
## [3935] 461 464 466 467 474 475 476 479 480 481 483 485 496 497
## [3949] 503 505 510 511 512 513 9998 9999 0 1 2 3 4 5
## [3963] 7 8 10 11 12 13 14 15 16 17 19 20 21 22
## [3977] 24 25 27 29 31 39 45 46 47 48 50 51 53 54
## [3991] 57 66 70 71 76 77 78 79 80 89 90 91 98 101
## [4005] 103 105 108 109 9998 9999 0 1 2 4 7 8 9 10
## [4019] 11 13 14 15 16 17 19 21 24 27 28 29 30 31
## [4033] 35 36 39 43 44 48 9998 9999 0 1 2 5 7 8
## [4047] 9 12 14 18 23 25 28 32 33 35 43 51 56 57
## [4061] 59 64 66 9998 9999 0 1 2 6 7 10 19 20 21
## [4075] 22 24 25 26 27 28 30 31 32 34 35 36 37 38
## [4089] 42 44 46 48 50 51 53 55 57 59 63 64 65 66
## [4103] 67 69 70 71 72 75 76 77 79 81 82 84 85 86
## [4117] 90 91 93 94 95 96 98 99 101 102 103 104 107 110
## [4131] 113 115 117 118 119 120 121 122 124 125 127 129 131 132
## [4145] 133 135 138 142 144 145 153 154 156 158 159 161 165 166
## [4159] 167 168 171 176 177 178 179 183 184 185 188 189 190 193
## [4173] 194 195 196 198 199 201 211 213 215 216 219 222 224 225
## [4187] 228 230 233 234 237 240 241 242 245 246 248 249 250 255
## [4201] 256 258 260 261 265 268 270 273 275 277 278 279 281 284
## [4215] 287 291 292 293 299 301 307 308 309 310 311 313 317 322
## [4229] 323 324 326 328 332 333 334 336 337 338 342 344 345 347
## [4243] 348 351 360 361 363 365 367 369 375 378 386 387 388 390
## [4257] 391 392 393 400 401 404 408 410 415 419 431 433 434 446
## [4271] 458 461 464 467 468 473 474 475 476 478 479 483 484 488
## [4285] 489 490 492 494 495 496 498 502 503 506 515 517 518 520
## [4299] 521 525 527 530 531 532 537 540 541 545 551 557 559 560
## [4313] 565 566 570 573 579 580 581 582 583 587 589 590 591 592
## [4327] 594 600 604 605 606 613 617 619 622 623 624 625 626 627
## [4341] 628 632 633 634 637 638 641 643 649 650 651 656 657 658
## [4355] 659 663 665 667 668 671 672 675 677 680 683 684 686 687
## [4369] 688 689 690 691 692 694 695 696 697 699 701 702 705 706
## [4383] 707 709 710 711 712 713 715 716 718 721 722 723 724 726
## [4397] 729 730 732 734 735 736 737 738 739 740 742 744 745 749
## [4411] 750 751 752 754 755 757 759 763 765 766 767 768 771 772
## [4425] 773 776 777 779 783 784 785 793 794 795 799 800 801 802
## [4439] 805 806 807 808 811 812 813 814 815 816 817 818 819 820
## [4453] 821 823 824 825 826 828 829 830 831 832 833 834 837 838
## [4467] 840 841 842 843 845 846 847 849 852 857 859 861 862 863
## [4481] 865 866 867 869 870 871 873 874 878 880 883 884 885 887
## [4495] 888 891 893 895 896 898 900 901 903 904 907 908 909 910
## [4509] 912 914 916 917 918 919 920 921 922 923 926 927 928 929
## [4523] 931 9998 9999 0 1 2 3 4 5 6 7 8 10 43
## [4537] 49 9998 0 1 2 3 4 6 7 8 9 10 15 21
## [4551] 37 39 43 48 49 52 62 63 9998 0 1 2 3 5
## [4565] 6 17 21 24 27 32 39 41 43 44 45 47 49 52
## [4579] 54 55 59 66 69 70 71 78 82 87 89 92 95 106
## [4593] 108 109 110 112 120 121 125 133 135 136 137 139 141 142
## [4607] 144 145 146 147 163 171 175 177 192 196 197 199 204 208
## [4621] 214 215 219 227 232 240 252 255 257 9998 9999 0 1 2
## [4635] 3 12 13 14 16 18 24 38 40 9998 9999 0 1 2
## [4649] 6 8 14 17 18 20 21 23 30 32 35 38 43 44
## [4663] 47 48 49 52 55 56 59 60 64 66 67 68 69 72
## [4677] 75 76 78 79 83 85 86 87 89 94 97 100 101 106
## [4691] 112 113 114 116 117 119 120 122 125 126 127 131 133 134
## [4705] 136 137 138 140 142 146 152 154 156 160 161 162 163 164
## [4719] 165 166 168 169 174 178 182 187 190 192 193 194 195 196
## [4733] 197 198 201 202 205 208 210 211 214 215 216 217 218 221
## [4747] 224 225 226 231 234 241 242 249 251 252 253 257 258 260
## [4761] 263 264 267 268 272 279 290 291 294 295 299 300 302 303
## [4775] 304 305 306 308 309 315 316 319 323 324 328 329 330 333
## [4789] 334 335 340 341 342 362 363 366 367 368 379 380 383 9998
## [4803] 9999 0 1 6 7 8 9 12 13 15 16 17 22 23
## [4817] 25 26 28 29 30 32 35 38 39 41 42 43 44 45
## [4831] 47 48 53 55 56 58 65 68 69 71 72 74 76 77
## [4845] 84 89 90 95 97 99 100 104 105 107 108 109 110 113
## [4859] 114 115 116 118 9998 9999 0 1 2 8 9 11 12 13
## [4873] 17 19 20 21 26 27 29 33 34 39 41 50 53 54
## [4887] 56 57 58 60 63 65 71 74 78 79 95 97 100 103
## [4901] 105 108 115 118 133 135 136 137 146 156 157 167 169 170
## [4915] 9998 9999 0 1 3 5 6 8 11 13 15 24 25 27
## [4929] 31 33 36 37 39 40 42 45 48 51 53 56 58 65
## [4943] 66 67 77 80 82 87 88 91 95 98 100 104 105 109
## [4957] 110 117 120 129 130 131 132 139 140 142 143 146 149 150
## [4971] 151 152 158 163 164 165 166 168 177 178 186 187 190 191
## [4985] 193 195 197 198 201 202 204 205 207 208 213 221 227 228
## [4999] 229 230 235 236 238 240 241 245 248 251 259 268 293 294
## [5013] 298 302 309 325 326 328 330 331 335 336 337 350 351 358
## [5027] 363 365 367 373 374 376 377 380 384 385 388 393 394 402
## [5041] 404 406 412 413 416 419 420 421 423 425 426 427 428 430
## [5055] 432 434 435 441 442 444 446 448 451 454 455 457 458 462
## [5069] 465 467 468 470 472 474 475 477 478 480 482 483 484 486
## [5083] 488 489 490 491 492 493 494 495 499 500 503 506 508 510
## [5097] 514 515 517 520 521 522 524 525 528 532 533 535 538 539
## [5111] 540 541 542 544 545 547 548 552 555 556 557 560 563 564
## [5125] 570 571 573 575 589 590 591 592 593 595 596 598 600 601
## [5139] 603 605 606 608 609 610 611 615 617 618 619 621 622 626
## [5153] 627 629 632 634 637 646 648 649 651 661 662 663 666 668
## [5167] 679 684 703 704 705 731 737 745 749 760 763 765 768 773
## [5181] 775 777 778 780 784 787 793 799 802 807 810 815 818 821
## [5195] 822 824 831 835 839 844 848 850 851 854 855 857 858 859
## [5209] 861 862 863 865 867 870 873 874 877 879 880 883 885 887
## [5223] 891 892 895 902 906 908 913 917 920 922 923 926 927 929
## [5237] 931 932 933 935 937 940 942 945 948 952 953 954 958 959
## [5251] 960 961 963 966 967 968 971 974 976 981 983 984 985 990
## [5265] 992 994 999 1000 1004 1006 1008 1014 1019 1024 1025 1026 1028 1029
## [5279] 1031 1032 1034 1035 1036 1037 1039 1040 1041 1042 1044 1045 1046 1052
## [5293] 1053 1055 1058 1059 1062 1063 1064 1067 1068 1069 1070 1073 1074 1078
## [5307] 1079 1080 1082 1084 1087 1095 1096 1099 1100 1101 1102 1103 1104 1106
## [5321] 1107 1108 1110 1111 1112 1115 1116 1124 1127 1128 1130 1145 1146 1147
## [5335] 1148 1151 1153 1154 1155 1157 1158 1159 1160 1161 1162 1163 1164 1165
## [5349] 1166 1167 1168 1170 1171 1172 1173 1174 1176 1178 1179 1181 1182 1183
## [5363] 1185 1186 1188 1189 1193 1194 1196 1197 1199 1200 1203 1206 1208 1209
## [5377] 1213 1214 1216 1217 1218 1220 1221 1222 1224 1225 1227 1229 1235 1236
## [5391] 1238 1239 1242 1243 1244 1246 1248 1249 1251 1252 1253 1254 1256 1260
## [5405] 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1273 1274 1275
## [5419] 9998 9999 0 1 3 4 7 8 10 11 12 15 16 19
## [5433] 21 22 23 24 25 27 30 33 34 36 43 54 66 73
## [5447] 75 79 84 92 95 96 99 103 107 108 109 9998 9999 0
## [5461] 1 9 10 11 12 14 16 17 18 19 21 22 23 24
## [5475] 26 28 29 31 32 40 41 43 45 47 60 62 63 66
## [5489] 69 72 75 77 80 81 82 90 92 98 100 102 106 109
## [5503] 112 113 117 118 121 122 123 125 126 9998 9999 0 1 3
## [5517] 7 9 15 17 19 20 21 24 25 26 28 29 32 33
## [5531] 35 38 39 45 46 48 49 53 57 58 63 65 67 71
## [5545] 72 90 92 96 97 105 108 114 117 120 123 127 128 130
## [5559] 140 142 144 149 151 155 156 163 164 168 171 183 184 9998
## [5573] 9999 0 1 3 5 9 11 18 19 20 21 24 25 31
## [5587] 32 34 35 37 38 40 42 44 45 49 50 51 52 63
## [5601] 65 66 67 69 70 72 73 74 75 81 83 84 86 87
## [5615] 93 96 98 100 104 108 109 121 127 132 134 136 137 141
## [5629] 144 147 148 149 151 153 154 156 161 162 164 175 187 195
## [5643] 196 198 200 202 207 210 230 238 254 255 256 269 335 336
## [5657] 346 349 354 366 373 379 394 396 398 401 406 407 408 416
## [5671] 419 420 427 430 432 449 451 461 9998 9999 0 1 2 4
## [5685] 6 7 10 11 13 15 16 17 19 20 21 22 25 26
## [5699] 27 28 30 31 32 33 36 37 40 41 42 43 44 45
## [5713] 48 51 52 54 56 57 58 59 60 62 67 75 76 79
## [5727] 80 81 89 92 96 102 106 109 114 122 123 124 133 136
## [5741] 142 145 147 152 153 9998 9999 0 1 4 7 8 9 11
## [5755] 14 15 22 23 24 27 28 30 31 38 43 44 45 48
## [5769] 49 50 52 57 58 59 61 66 68 73 77 78 85 86
## [5783] 103 104 109 112 115 117 120 121 126 127 128 130 131 133
## [5797] 134 141 142 148 156 157 158 162 164 166 167 168 169 173
## [5811] 178 179 180 182 183 190 191 194 195 198 202 204 205 208
## [5825] 222 227 231 232 235 239 240 242 244 245 246 247 248 249
## [5839] 251 252 255 257 258 260 261 262 267 268 9998 9999 0 1
## [5853] 2 5 8 9 10 21 23 25 27 30 32 39 40 42
## [5867] 43 44 49 52 53 55 57 63 64 65 66 69 76 82
## [5881] 84 87 91 100 101 105 107 115 116 119 123 130 131 132
## [5895] 136 139 141 145 146 147 148 153 157 160 163 164 165 166
## [5909] 167 168 169 170 172 173 174 175 178 180 182 185 186 190
## [5923] 191 192 193 204 206 208 215 217 219 228 233 240 242 244
## [5937] 245 254 255 256 266 269 270 274 429 437 447 449 451 471
## [5951] 479 483 488 490 494 496 500 506 508 512 513 518 521 526
## [5965] 527 530 534 540 542 544 545 549 551 558 571 573 574 580
## [5979] 593 597 598 602 603 607 608 619 9998 9999 0 1 2 6
## [5993] 10 13 18 20 23 24 28 30 31 33 40 41 43 44
## [6007] 45 47 49 54 57 59 61 63 64 68 73 77 79 80
## [6021] 82 83 86 87 88 90 91 92 95 97 98 99 101 106
## [6035] 107 108 111 117 118 119 123 129 132 136 137 138 139 140
## [6049] 142 143 145 148 149 151 153 154 155 156 161 163 164 165
## [6063] 166 167 169 170 173 175 189 193 201 202 203 204 212 216
## [6077] 217 220 221 222 224 228 232 237 239 240 243 245 257 263
## [6091] 267 268 270 271 278 279 9998 9999 0 1 2 4 9 11
## [6105] 12 13 14 20 21 26 27 34 35 43 44 51 61 64
## [6119] 83 86 89 96 105 111 114 115 116 117 118 120 121 124
## [6133] 125 126 127 128 129 130 131 132 133 134 9998 9999 0 1
## [6147] 3 4 10 12 13 17 18 23 24 28 29 30 35 36
## [6161] 42 44 45 49 50 54 56 60 61 66 67 69 71 73
## [6175] 74 75 76 79 81 83 84 87 89 90 91 95 96 98
## [6189] 99 109 112 115 117 118 121 162 167 168 170 178 179 180
## [6203] 181 201 209 211 212 216 218 219 226 231 235 239 251 263
## [6217] 267 269 272 9998 9999 0 1 2 5 6 11 12 14 15
## [6231] 16 18 21 22 24 25 32 33 36 43 46 49 53 54
## [6245] 56 57 63 64 69 74 75 76 78 79 81 82 83 86
## [6259] 90 91 92 94 98 100 102 103 105 107 111 120 126 130
## [6273] 131 147 148 151 153 154 155 156 161 162 166 167 168 197
## [6287] 209 213 219 220 223 224 225 226 227 228 229 230 231 232
## [6301] 234 9998 9999 0 1 2 3 4 6 7 8 10 11 12
## [6315] 13 19 20 25 26 27 28 31 38 40 41 45 46 47
## [6329] 48 49 51 52 53 55 56 57 58 59 64 65 66 67
## [6343] 70 71 72 75 78 81 82 85 87 92 97 101 103 107
## [6357] 108 111 112 113 115 116 117 118 120 121 122 124 127 128
## [6371] 133 134 135 136 138 140 142 148 149 150 152 154 157 158
## [6385] 160 161 162 165 166 167 170 173 174 178 179 180 181 182
## [6399] 183 185 186 187 188 189 190 194 195 196 197 198 200 204
## [6413] 206 208 209 210 212 214 215 216 218 219 220 221 223 224
## [6427] 225 227 228 229 232 233 234 236 237 239 241 242 243 244
## [6441] 246 247 251 252 253 254 256 258 260 264 268 269 270 271
## [6455] 273 275 276 277 281 282 291 292 294 295 296 297 300 302
## [6469] 303 304 305 311 313 314 316 317 319 321 323 329 332 333
## [6483] 335 336 337 338 340 343 345 346 347 349 350 351 352 354
## [6497] 356 357 358 360 362 363 364 365 366 367 368 369 371 372
## [6511] 373 374 375 376 378 379 380 381 382 384 385 387 389 390
## [6525] 391 392 393 394 395 396 398 399 401 402 403 404 405 408
## [6539] 409 414 417 418 419 420 421 423 424 425 426 427 428 429
## [6553] 430 431 432 433 434 435 437 438 439 440 441 445 9998 9999
## [6567] 0 1 2 3 6 9 11 13 14 15 17 19 20 21
## [6581] 22 25 30 31 32 34 36 38 39 41 42 43 44 45
## [6595] 46 48 52 53 54 56 57 61 62 65 72 73 75 78
## [6609] 90 103 109 114 117 122 123 127 9998 9999 0 1 3 12
## [6623] 19 20 22 39 44 47 48 49 51 52 60 68 71 76
## [6637] 82 86 88 90 93 112 195 196 197 198 199 9998 9999 0
## [6651] 1 2 3 7 15 27 28 32 33 34 36 37 40 41
## [6665] 47 57 64 67 73 74 83 85 86 92 93 96 97 98
## [6679] 99 103 105 110 112 128 131 133 139 140 141 153 158 174
## [6693] 185 188 190 191 202 207 210 214 220 227 229 236 237 241
## [6707] 244 250 253 271 274 281 283 285 286 289 9998 9999 0 1
## [6721] 2 3 4 5 6 7 9 10 11 12 13 14 15 16
## [6735] 17 18 19 20 22 23 24 27 32 37 40 51 53 54
## [6749] 63 64 65 66 74 75 83 84 86 9998 9999 0 1 3
## [6763] 5 6 7 9 12 13 14 20 22 23 26 27 34 37
## [6777] 39 41 45 46 47 49 51 54 55 56 57 58 59 60
## [6791] 61 62 63 64 65 66 68 70 74 80 81 82 86 87
## [6805] 88 93 95 96 100 103 104 105 108 112 115 117 120 121
## [6819] 122 123 125 126 129 131 133 134 135 137 138 139 141 144
## [6833] 146 148 153 155 157 158 164 165 167 170 173 175 176 178
## [6847] 180 181 182 183 184 185 186 187 189 190 191 195 196 198
## [6861] 202 203 204 216 217 218 220 224 225 226 227 228 233 240
## [6875] 241 245 250 253 254 258 261 267 270 274 307 308 309 310
## [6889] 313 317 320 324 327 328 330 333 336 338 339 346 350 352
## [6903] 354 355 357 358 359 360 361 363 364 368 370 371 378 9998
## [6917] 9999 0 1 8 11 13 14 26 29 30 32 65 73 87
## [6931] 9998 0 1 5 14 15 16 19 20 21 28 31 32 33
## [6945] 34 35 37 40 41 45 61 62 65 67 68 69 70 71
## [6959] 79 82 84 85 86 87 92 95 101 103 104 108 111 112
## [6973] 114 115 120 122 124 127 129 137 141 146 148 150 156 157
## [6987] 161 167 170 171 172 180 181 185 191 198 200 215 227 229
## [7001] 9998 9999 0 1 2 3 6 7 8 9 11 12 14 16
## [7015] 22 23 26 27 28 30 34 38 46 54 56 9999 0 1
## [7029] 3 4 10 20 25 26 31 41 46 49 55 62 65 67
## [7043] 73 78 80 86 102 114 116 117 134 136 146 151 159 9998
## [7057] 9999 0 1 2 4 5 7 8 9 10 11 12 13 14
## [7071] 15 16 17 18 24 25 26 33 34 35 39 43 49 54
## [7085] 75 82 85 92 104 107 110 112 117 118 119 123 125 128
## [7099] 130 132 134 138 141 142 144 153 154 156 159 163 166 168
## [7113] 176 177 179 180 181 182 188 191 192 198 205 207 209 210
## [7127] 212 213 215 217 219 220 221 222 228 229 236 240 248 249
## [7141] 252 253 255 9998 9999 0 1 2 3 4 6 7 8 10
## [7155] 15 24 26 28 29 30 33 38 41 44 45 50 53 54
## [7169] 63 66 70 72 77 79 81 82 83 84 86 90 98 100
## [7183] 103 106 111 113 115 119 120 129 131 134 136 144 146 151
## [7197] 155 156 160 163 167 169 172 174 175 181 183 188 191 204
## [7211] 206 209 215 217 218 223 224 227 228 233 234 235 237 241
## [7225] 247 249 251 252 257 264 266 268 269 270 271 272 276 277
## [7239] 278 279 280 284 285 287 288 289 292 299 300 305 311 406
## [7253] 410 411 417 429 430 442 452 453 454 455 460 464 476 488
## [7267] 502 504 505 513 517 519 523 524 527 541 549 551 553 554
## [7281] 555 556 561 566 568 575 589 592 594 596 760 766 767 9998
## [7295] 9999 0 1 2 5 6 10 11 13 20 24 25 31 32
## [7309] 33 34 35 37 38 40 42 50 51 52 53 58 60 61
## [7323] 63 66 71 72 76 77 80 83 84 87 88 89 90 94
## [7337] 95 96 98 103 104 105 106 111 112 116 118 120 123 124
## [7351] 126 128 129 131 133 135 137 140 141 142 146 148 152 153
## [7365] 154 155 156 157 161 163 164 170 172 173 174 179 183 184
## [7379] 188 189 190 195 205 222 229 230 231 232 235 239 242 248
## [7393] 251 252 259 260 261 264 266 272 275 276 288 302 304 310
## [7407] 315 319 322 323 324 332 335 339 345 346 352 357 361 385
## [7421] 391 398 400 412 418 422 423 432 433 435 442 443 444 447
## [7435] 449 453 455 458 460 461 462 463 464 466 467 468 469 470
## [7449] 473 477 9998 9999 0 1 3 4 5 6 7 9 10 12
## [7463] 15 16 17 19 20 24 25 26 30 31 32 33 34 35
## [7477] 36 38 40 42 43 44 47 50 51 52 59 62 75 87
## [7491] 89 92 93 94 97 98 102 105 111 113 115 119 125 127
## [7505] 130 132 135 137 140 143 147 148 150 151 152 154 156 157
## [7519] 161 162 164 167 169 172 180 185 194 195 196 197 198 201
## [7533] 202 206 207 209 210 211 215 223 229 230 232 233 234 239
## [7547] 240 9998 9999 0 1 8 9 15 17 27 31 32 43 47
## [7561] 50 70 72 84 87 89 95 96 100 102 103 107 108 111
## [7575] 116 132 144 145 148 149 155 156 160 161 169 172 177 178
## [7589] 180 182 185 190 198 207 209 210 212 213 220 227 233 238
## [7603] 239 242 244 246 250 251 253 256 261 262 272 273 274 280
## [7617] 287 289 290 291 296 298 301 303 304 308 316 324 357 362
## [7631] 363 379 386 389 398 412 425 447 449 454 474 481 483 490
## [7645] 492 493 494 500 504 505 508 509 516 519 531 532 541 544
## [7659] 545 549 553 556 557 560 561 562 566 568 569 571 575 576
## [7673] 578 580 581 582 584 585 590 593 594 602 603 604 605 607
## [7687] 608 609 611 613 614 615 617 619 620 623 9998 9999 0 1
## [7701] 3 4 5 6 7 10 12 13 14 16 18 21 22 23
## [7715] 25 26 30 31 32 35 36 41 44 47 48 51 53 58
## [7729] 78 91 96 107 112 121 130 132 138 9998 9999 0 1 3
## [7743] 4 8 13 17 18 23 27 30 35 38 41 9998 9999 0
## [7757] 1 7 8 9 11 12 13 14 17 19 27 29 32 38
## [7771] 44 54 55 87 9998 0 1 8 9 11 12 14 15 16
## [7785] 19 21 22 24 25 27 29 30 33 37 39 44 46 47
## [7799] 48 50 52 53 57 58 59 60 64 69 70 71 73 81
## [7813] 85 92 94 95 96 101 104 105 107 111 112 113 115 116
## [7827] 120 121 122 125 129 130 131 132 135 136 137 138 140 141
## [7841] 143 144 150 151 153 155 159 160 165 167 173 178 182 185
## [7855] 187 193 198 199 202 204 209 213 215 217 218 221 222 223
## [7869] 224 226 231 232 237 240 244 246 248 250 252 253 255 262
## [7883] 269 272 282 289 290 295 298 299 301 302 303 304 308 310
## [7897] 313 318 320 324 327 331 332 333 336 337 338 344 346 347
## [7911] 348 351 353 355 359 360 361 364 367 373 374 375 376 378
## [7925] 381 9998 9999 0 1 2 3 4 6 8 9 10 11 12
## [7939] 14 15 16 17 18 19 20 21 22 23 24 25 26 27
## [7953] 34 35 37 41 42 43 44 46 48 9998 9999 0 1 2
## [7967] 4 5 8 9 12 18 24 29 32 38 39 41 42 44
## [7981] 47 49 51 53 54 58 59 62 63 68 69 70 71 73
## [7995] 74 75 79 82 87 89 95 99 100 103 104 105 109 110
## [8009] 112 113 115 117 119 122 128 129 130 131 134 135 136 137
## [8023] 138 140 149 151 152 153 154 155 157 158 162 167 169 171
## [8037] 174 176 177 178 180 181 182 183 185 186 187 188 189 190
## [8051] 193 196 197 198 199 200 202 203 204 205 208 209 210 211
## [8065] 214 216 218 219 220 223 224 226 227 230 241 242 244 250
## [8079] 255 257 258 264 265 266 267 268 270 271 273 276 277 278
## [8093] 282 285 287 290 291 292 293 295 296 297 298 299 300 302
## [8107] 303 305 307 310 311 312 315 316 318 320 322 324 325 326
## [8121] 327 328 329 330 332 333 334 337 339 340 341 344 345 348
## [8135] 358 360 365 371 374 376 377 378 382 384 385 387 388 389
## [8149] 391 392 394 395 396 399 400 402 403 404 405 406 408 412
## [8163] 413 414 418 419 424 425 427 428 429 430 431 432 433 435
## [8177] 436 447 461 462 463 464 465 468 475 476 481 483 484 485
## [8191] 487 488 489 665 666 667 673 676 678 680 683 685 694 695
## [8205] 700 702 703 706 707 708 710 715 716 717 718 721 722 723
## [8219] 726 730 731 732 733 734 736 739 740 741 744 745 746 748
## [8233] 749 751 752 755 757 758 759 760 761 762 764 765 766 768
## [8247] 769 770 771 774 775 777 779 783 784 785 786 787 788 789
## [8261] 790 791 793 794 798 800 801 803 805 807 808 811 812 813
## [8275] 814 815 816 826 828 831 839 841 844 845 848 853 854 857
## [8289] 858 859 860 861 862 863 864 866 867 868 869 870 9998 9999
## [8303] 0 1 3 5 12 13 16 17 18 19 22 26 27 33
## [8317] 35 39 40 41 45 48 56 58 62 63 66 68 69 71
## [8331] 77 79 86 88 93 94 95 96 102 109 110 118 119 124
## [8345] 127 128 129 133 134 136 138 140 141 144 145 152 164 166
## [8359] 169 177 180 182 185 188 191 193 197 200 213 219 220 221
## [8373] 223 224 227 231 232 233 234 235 236 237 252 254 256 265
## [8387] 270 272 280 282 284 285 292 295 296 301 303 311 317 417
## [8401] 440 447 453 591 596 597 600 605 607 612 618 621 623 625
## [8415] 627 634 635 641 642 643 646 647 650 651 652 653 656 661
## [8429] 670 673 684 686 690 692 694 695 696 700 703 712 717 720
## [8443] 722 726 728 729 736 738 741 743 746 750 751 754 765 766
## [8457] 767 768 769 9998 9999 0 1 2 3 4 5 6 7 11
## [8471] 15 26 28 29 38 39 48 54 57 58 61 9998 9999 0
## [8485] 1 3 4 5 6 8 9 10 11 13 14 15 16 17
## [8499] 18 23 26 27 29 33 35 43 49 50 9998 9999 0 1
## [8513] 3 4 5 6 8 9 11 13 14 15 19 20 21 23
## [8527] 24 25 26 28 31 32 33 34 35 37 38 39 41 42
## [8541] 43 45 46 48 50 51 55 60 75 87 89 93 103 105
## [8555] 106 107 110 115 117 119 122 125 140 166 169 172 174 177
## [8569] 180 186 200 235 237 242 244 325 333 349 372 383 394 397
## [8583] 402 403 424 435 439 441 459 461 464 466 473 474 487 500
## [8597] 501 502 510 519 529 535 538 540 552 560 561 563 569 573
## [8611] 575 583 591 606 611 613 617 622 624 626 629 632 633 636
## [8625] 637 647 657 658 659 666 672 674 676 682 694 695 698 699
## [8639] 701 710 714 720 725 729 734 737 738 743 745 750 754 756
## [8653] 760 762 763 765 766 767 768 769 771 772 773 774 775 777
## [8667] 783 784 787 789 790 791 792 793 795 798 799 800 801 802
## [8681] 803 806 807 808 809 811 813 814 818 819 822 823 827 831
## [8695] 832 833 835 836 837 838 842 843 844 845 846 848 852 853
## [8709] 854 856 857 858 859 860 861 862 863 864 866 869 870 871
## [8723] 872 874 875 877 878 879 880 881 882 883 884 890 891 894
## [8737] 895 897 901 902 903 904 905 9998 9999 0 1 3 14 17
## [8751] 29 39 49 70 81 83 91 92 95 105 108 113 124 126
## [8765] 128 130 131 132 133 134 135 9998 9999 0 1 2 3 6
## [8779] 7 8 10 11 12 14 15 17 18 19 20 21 23 25
## [8793] 27 29 31 32 36 42 43 44 45 58 61 64 71 73
## [8807] 74 77 85 86 87 88 89 9998 9999 0 1 5 9 12
## [8821] 13 18 20 21 23 25 26 29 30 33 34 38 39 45
## [8835] 46 47 48 50 51 53 54 57 58 59 65 67 69 70
## [8849] 73 75 76 78 79 80 81 82 86 87 91 92 95 96
## [8863] 98 100 101 105 109 110 112 113 115 116 120 122 123 125
## [8877] 132 137 138 143 145 147 149 152 153 156 157 159 161 165
## [8891] 167 168 171 172 180 189 215 217 218 219 220 221 230 235
## [8905] 237 242 244 249 250 252 261 263 266 267 269 270 271 272
## [8919] 273 275 276 280 285 286 287 288 290 292 293 296 302 303
## [8933] 306 308 313 314 320 329 338 341 343 345 348 351 354 357
## [8947] 358 361 362 363 366 367 368 372 374 379 382 383 387 388
## [8961] 391 392 393 402 403 406 408 414 418 424 427 432 438 452
## [8975] 460 463 473 476 477 479 480 482 484 488 490 493 499 509
## [8989] 511 516 520 522 9998 9999 0 1 2 9 12 22 23 25
## [9003] 26 27 31 34 38 50 51 56 61 73 101 105 109 111
## [9017] 112 114 123 125 126 130 133 135 136 154 156 157 164 165
## [9031] 169 174 184 188 189 190 192 193 194 195 196 197 199 200
## [9045] 204 205 9998 9999 0 1 2 3 9 10 13 15 16 19
## [9059] 22 23 25 26 27 28 34 35 37 43 44 47 73 9998
## [9073] 9999 0 1 6 7 9 10 11 15 16 19 33 44 48
## [9087] 54 64 65 67 68 69 9998 0 1 2 3 4 5 6
## [9101] 11 13 14 15 16 20 21 23 27 31 34 35 37 47
## [9115] 50 52 55 56 61 62 63 68 70 71 72 74 75 76
## [9129] 77 78 79 82 84 89 104 110 119 135 146 156 157 160
## [9143] 161 181 198 9998 9999 0 1 2 5 6 7 8 9 10
## [9157] 11 12 13 15 16 17 18 21 27 29 31 32 34 35
## [9171] 37 38 39 40 41 44 45 46 47 49 50 53 54 58
## [9185] 61 62 63 65 67 68 70 74 75 76 105 106 107 113
## [9199] 114 115 116 117 118 119 120 127 131 134 136 138 140 141
## [9213] 144 148 149 154 155 160 162 165 166 170 176 180 181 183
## [9227] 184 185 187 191 194 196 199 201 202 206 207 212 215 216
## [9241] 222 226 230 232 9998 9999 0 1 3 6 7 8 10 11
## [9255] 13 14 16 19 20 21 22 23 24 25 33 36 39 42
## [9269] 46 9998 9999 0 1 2 3 4 5 6 7 8 9 10
## [9283] 14 19 20 22 24 40 42 45 47 48 9998 9999 0 1
## [9297] 3 5 6 7 10 12 14 22 24 26 28 29 31 32
## [9311] 34 37 38 41 42 46 47 48 50 51 53 56 58 59
## [9325] 62 65 66 72 86 93 101 109 111 112 122 125 126 127
## [9339] 131 132 134 136 260 262 270 271 276 280 285 288 290 293
## [9353] 294 295 297 298 300 301 303 306 309 312 315 324 327 331
## [9367] 333 334 338 340 345 346 349 355 359 362 9998 9999 0 1
## [9381] 2 5 6 8 11 14 15 17 20 22 23 27 30 32
## [9395] 36 37 44 45 46 47 49 50 60 61 66 71 72 74
## [9409] 75 76 81 82 84 86 88 89 91 92 94 98 100 101
## [9423] 102 106 107 108 109 110 111 112 117 118 120 121 125 126
## [9437] 128 129 130 140 144 146 151 152 153 154 157 158 161 167
## [9451] 168 170 176 183 238 244 245 246 247 249 255 256 265 267
## [9465] 270 271 278 344 362 378 380 382 385 392 394 395 399 400
## [9479] 402 403 404 405 407 409 412 417 419 420 423 427 429 431
## [9493] 432 434 435 438 440 441 443 444 446 448 451 452 453 455
## [9507] 458 461 462 463 464 466 467 469 471 472 9998 9999 0 1
## [9521] 3 6 8 14 21 22 27 32 34 36 38 39 40 41
## [9535] 42 44 45 47 48 49 50 58 60 64 66 68 73 86
## [9549] 89 91 110 125 135 138 143 155 156 9998 9999 0 1 5
## [9563] 8 9 11 12 14 16 17 18 19 20 22 29 35 36
## [9577] 39 40 47 48 54 55 57 60 62 64 65 67 70 71
## [9591] 72 75 76 78 80 81 85 86 91 92 101 104 105 106
## [9605] 107 125 132 135 147 149 150 152 155 156 157 160 162 163
## [9619] 164 165 167 168 169 170 173 174 176 177 178 182 183 9998
## [9633] 9999 0 1 2 5 6 9 12 13 14 21 26 28 29
## [9647] 30 31 32 33 35 36 37 41 43 45 46 53 54 55
## [9661] 56 57 63 64 67 71 72 77 78 79 80 9998 9999 0
## [9675] 1 2 3 4 6 9 10 11 12 13 18 20 21 23
## [9689] 24 26 27 28 29 30 33 34 40 41 42 43 46 47
## [9703] 51 52 53 55 58 59 60 61 62 64 66 68 80 81
## [9717] 84 92 93 96 97 104 115 116 119 124 125 126 137 138
## [9731] 141 143 151 173 178 187 189 9998 9999 0 1 2 3 4
## [9745] 5 6 8 9 17 18 19 20 22 24 25 26 28 29
## [9759] 30 32 33 36 37 40 41 43 44 47 50 9998 9999 0
## [9773] 1 3 8 9 11 13 15 18 21 28 33 35 40 41
## [9787] 42 45 46 48 58 60 61 66 69 70 80 83 84 86
## [9801] 88 90 94 97 99 116 130 136 149 150 152 156 163 164
## [9815] 165 170 208 231 234 260 270 275 276 9998 9999 0 1 2
## [9829] 4 6 12 13 16 18 19 20 23 24 25 27 28 31
## [9843] 32 36 44 45 46 48 52 55 67 74 85 86 91 93
## [9857] 94 97 99 102 103 104 107 109 110 112 113 119 121 123
## [9871] 124 129 130 131 9998 9999 0 1 2 3 4 7 13 15
## [9885] 20 21 23 26 27 32 41 47 51 52 53 54 57 58
## [9899] 62 63 64 65 66 67 73 74 75 76 80 89 92 101
## [9913] 102 109 116 120 124 130 132 133 135 136 9998 9999 0 1
## [9927] 2 5 6 9 10 12 13 19 21 24 25 26 27 28
## [9941] 31 32 36 46 47 49 52 56 58 59 61 62 66 68
## [9955] 69 71 74 75 76 77 79 81 83 84 87 88 91 95
## [9969] 97 98 101 103 105 108 110 112 114 118 120 121 122 124
## [9983] 125 128 130 131 133 135 137 140 141 143 147 153 154 164
## [9997] 165 169 172 177 180 183 186 187 189 193 195 199 209 210
## [10011] 213 222 224 225 229 230 232 233 235 236 239 240 244 247
## [10025] 248 249 250 252 253 254 255 257 260 264 267 271 273 278
## [10039] 280 282 283 286 289 291 309 310 314 317 318 321 322 324
## [10053] 331 332 333 334 335 340 345 346 347 348 350 9998 9999 0
## [10067] 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## [10081] 15 16 17 18 22 24 25 26 27 35 36 42 43 44
## [10095] 57 59 60 9998 9999 0 1 4 6 18 20 22 27 29
## [10109] 33 38 47 50 56 57 62 64 70 78 89 95 97 99
## [10123] 111 124 129 133 134 139 143 151 153 168 183 190 194 198
## [10137] 201 202 206 210 213 214 218 227 230 231 232 235 242 243
## [10151] 249 250 253 255 258 259 263 264 268 270 271 272 273 274
## [10165] 276 277 279 280 286 288 297 300 302 312 313 314 315 317
## [10179] 318 319 324 329 334 342 343 350 355 356 360 369 376 377
## [10193] 378 380 391 396 405 407 415 417 430 435 440 444 449 450
## [10207] 451 452 453 458 460 463 464 468 470 471 476 486 511 512
## [10221] 513 516 522 526 528 529 535 536 537 539 540 542 544 547
## [10235] 550 560 565 566 568 572 573 574 575 577 583 587 588 591
## [10249] 592 593 594 595 596 597 600 605 608 610 611 613 615 620
## [10263] 621 622 626 632 660 668 683 684 690 697 703 706 708 709
## [10277] 712 714 715 718 721 722 724 726 727 728 735 737 738 739
## [10291] 741 747 748 749 750 752 753 754 755 756 757 758 760 761
## [10305] 763 764 766 767 770 771 9998 9999 0 1 5 9 10 11
## [10319] 12 13 18 20 21 22 23 24 25 29 31 37 52 53
## [10333] 57 59 67 71 72 74 84 86 9998 9999 0 1 9 10
## [10347] 11 14 16 17 18 19 20 21 23 25 27 28 29 31
## [10361] 33 34 35 36 37 38 39 40 41 42 46 47 49 53
## [10375] 54 68 69 71 72 81 82 83 84 9998 9999 0 1 2
## [10389] 3 5 8 9 11 14 15 16 18 19 20 24 26 27
## [10403] 28 29 30 31 34 36 37 38 39 41 42 43 44 45
## [10417] 46 47 51 52 53 69 70 71 77 79 83 84 85 86
## [10431] 87 90 96 112 118 127 132 134 141 144 150 151 152 153
## [10445] 9998 9999 0 1 3 4 6 7 9 10 15 16 17 18
## [10459] 19 20 25 26 27 30 31 34 36 39 42 43 44 45
## [10473] 46 47 49 50 51 52 54 55 58 59 60 61 62 63
## [10487] 64 67 68 70 71 73 75 81 82 86 88 89 92 93
## [10501] 94 95 96 97 100 102 104 105 107 109 114 115 116 117
## [10515] 120 122 124 125 127 129 131 133 134 135 139 143 147 149
## [10529] 154 155 156 158 161 162 163 167 168 174 175 176 179 180
## [10543] 181 182 184 185 187 189 191 192 195 196 197 198 199 200
## [10557] 201 203 205 209 210 212 214 216 217 218 221 223 226 228
## [10571] 233 235 237 240 244 246 247 248 250 251 253 255 258 260
## [10585] 271 272 273 276 277 284 286 288 289 290 291 293 295 297
## [10599] 302 303 307 310 312 314 317 318 322 325 326 327 331 332
## [10613] 333 337 338 340 342 343 344 346 348 349 350 351 352 355
## [10627] 357 358 359 360 361 362 365 9998 9999 0 1 3 4 6
## [10641] 7 9 10 11 12 13 14 15 16 18 19 20 22 23
## [10655] 24 25 26 27 28 29 30 31 32 33 34 35 36 37
## [10669] 39 40 41 42 43 44 45 47 50 51 52 53 55 56
## [10683] 57 58 59 60 62 63 64 66 67 68 69 70 72 73
## [10697] 74 75 76 77 78 79 80 81 82 83 85 86 87 88
## [10711] 89 90 91 9998 9999########## ARREGLAR LA BASE CSV
library(stringr)
datos_inegi_jal2$LOC<-str_pad(datos_inegi_jal$LOC,3,side="left",pad="0")
#Datos en xlsx, cortar el texto
datos_inegi_jal$LOC<-substr(datos_inegi_jal$LOC,2,4)
#which(datos_inegi_jal$LOC=="000")
posis_mun<-which(datos_inegi_jal$NOM_LOC=="Total del Municipio")
datos_inegi_jal_mun<-datos_inegi_jal[posis_mun,]
dim(datos_inegi_jal_mun)## [1] 125 232######################
# ¿% personas con discapacidad en cada municipio?
# ¿% personas con 65 o más años de cada municipio?
datos_inegi_jal_mun$Prop_disc<-as.numeric(datos_inegi_jal_mun$PCON_DISC)/as.numeric(datos_inegi_jal_mun$POBTOT)
datos_inegi_jal_mun$Prop_adulto<-as.numeric(datos_inegi_jal_mun$POB65_MAS)/as.numeric(datos_inegi_jal_mun$POBTOT)
red_jal_mun<-datos_inegi_jal_mun[,c(3,233,234)]
mapa_jalisco@data## CVEGEO CVE_ENT CVE_MUN NOMGEO
## 0 14073 14 073 San Juan de los Lagos
## 1 14074 14 074 San Julián
## 2 14075 14 075 San Marcos
## 3 14076 14 076 San MartÃn de Bolaños
## 4 14077 14 077 San MartÃn Hidalgo
## 5 14078 14 078 San Miguel el Alto
## 6 14079 14 079 Gómez FarÃas
## 7 14080 14 080 San Sebastián del Oeste
## 8 14081 14 081 Santa MarÃa de los Ã\201ngeles
## 9 14082 14 082 Sayula
## 10 14083 14 083 Tala
## 11 14084 14 084 Talpa de Allende
## 12 14085 14 085 Tamazula de Gordiano
## 13 14086 14 086 Tapalpa
## 14 14087 14 087 Tecalitlán
## 15 14006 14 006 Ameca
## 16 14007 14 007 San Juanito de Escobedo
## 17 14008 14 008 Arandas
## 18 14009 14 009 El Arenal
## 19 14010 14 010 Atemajac de Brizuela
## 20 14011 14 011 Atengo
## 21 14012 14 012 Atenguillo
## 22 14013 14 013 Atotonilco el Alto
## 23 14014 14 014 Atoyac
## 24 14015 14 015 Autlán de Navarro
## 25 14016 14 016 Ayotlán
## 26 14017 14 017 Ayutla
## 27 14018 14 018 La Barca
## 28 14019 14 019 Bolaños
## 29 14020 14 020 Cabo Corrientes
## 30 14021 14 021 Casimiro Castillo
## 31 14022 14 022 Cihuatlán
## 32 14023 14 023 Zapotlán el Grande
## 33 14024 14 024 Cocula
## 34 14025 14 025 Colotlán
## 35 14026 14 026 Concepción de Buenos Aires
## 36 14027 14 027 Cuautitlán de GarcÃa Barragán
## 37 14028 14 028 Cuautla
## 38 14029 14 029 CuquÃo
## 39 14030 14 030 Chapala
## 40 14031 14 031 Chimaltitán
## 41 14032 14 032 Chiquilistlán
## 42 14033 14 033 Degollado
## 43 14034 14 034 Ejutla
## 44 14035 14 035 Encarnación de DÃaz
## 45 14036 14 036 Etzatlán
## 46 14037 14 037 El Grullo
## 47 14038 14 038 Guachinango
## 48 14039 14 039 Guadalajara
## 49 14040 14 040 Hostotipaquillo
## 50 14041 14 041 Huejúcar
## 51 14042 14 042 Huejuquilla el Alto
## 52 14043 14 043 La Huerta
## 53 14044 14 044 Ixtlahuacán de los Membrillos
## 54 14045 14 045 Ixtlahuacán del RÃo
## 55 14046 14 046 Jalostotitlán
## 56 14047 14 047 Jamay
## 57 14048 14 048 Jesús MarÃa
## 58 14049 14 049 Jilotlán de los Dolores
## 59 14050 14 050 Jocotepec
## 60 14051 14 051 Juanacatlán
## 61 14052 14 052 Juchitlán
## 62 14053 14 053 Lagos de Moreno
## 63 14054 14 054 El Limón
## 64 14055 14 055 Magdalena
## 65 14056 14 056 Santa MarÃa del Oro
## 66 14057 14 057 La Manzanilla de la Paz
## 67 14058 14 058 Mascota
## 68 14059 14 059 Mazamitla
## 69 14060 14 060 Mexticacán
## 70 14061 14 061 Mezquitic
## 71 14062 14 062 Mixtlán
## 72 14063 14 063 Ocotlán
## 73 14064 14 064 Ojuelos de Jalisco
## 74 14065 14 065 Pihuamo
## 75 14066 14 066 Poncitlán
## 76 14067 14 067 Puerto Vallarta
## 77 14068 14 068 Villa Purificación
## 78 14069 14 069 Quitupan
## 79 14070 14 070 El Salto
## 80 14071 14 071 San Cristóbal de la Barranca
## 81 14072 14 072 San Diego de AlejandrÃa
## 82 14088 14 088 Tecolotlán
## 83 14089 14 089 Techaluta de Montenegro
## 84 14090 14 090 Tenamaxtlán
## 85 14091 14 091 Teocaltiche
## 86 14092 14 092 Teocuitatlán de Corona
## 87 14093 14 093 Tepatitlán de Morelos
## 88 14094 14 094 Tequila
## 89 14095 14 095 Teuchitlán
## 90 14096 14 096 Tizapán el Alto
## 91 14097 14 097 Tlajomulco de Zúñiga
## 92 14098 14 098 San Pedro Tlaquepaque
## 93 14099 14 099 Tolimán
## 94 14100 14 100 Tomatlán
## 95 14101 14 101 Tonalá
## 96 14102 14 102 Tonaya
## 97 14103 14 103 Tonila
## 98 14104 14 104 Totatiche
## 99 14105 14 105 Tototlán
## 100 14106 14 106 Tuxcacuesco
## 101 14107 14 107 Tuxcueca
## 102 14108 14 108 Tuxpan
## 103 14109 14 109 Unión de San Antonio
## 104 14110 14 110 Unión de Tula
## 105 14111 14 111 Valle de Guadalupe
## 106 14112 14 112 Valle de Juárez
## 107 14113 14 113 San Gabriel
## 108 14114 14 114 Villa Corona
## 109 14115 14 115 Villa Guerrero
## 110 14116 14 116 Villa Hidalgo
## 111 14117 14 117 Cañadas de Obregón
## 112 14118 14 118 Yahualica de González Gallo
## 113 14119 14 119 Zacoalco de Torres
## 114 14120 14 120 Zapopan
## 115 14121 14 121 Zapotiltic
## 116 14122 14 122 Zapotitlán de Vadillo
## 117 14123 14 123 Zapotlán del Rey
## 118 14124 14 124 Zapotlanejo
## 119 14001 14 001 Acatic
## 120 14002 14 002 Acatlán de Juárez
## 121 14003 14 003 Ahualulco de Mercado
## 122 14004 14 004 Amacueca
## 123 14005 14 005 Amatitán
## 124 14125 14 125 San Ignacio Cerro Gordotabla11<-merge(x=mapa_jalisco@data,y=red_jal_mun,by.x="CVE_MUN",by.y="MUN",sort=FALSE)
mapa_jalisco@data$Discapa<-tabla11$Prop_disc
mapa_jalisco@data$Adultos<-tabla11$Prop_adulto
#################### Termina clase martes 16.11.21 ######CLASE 23 NOVIEMBRE
Mapas interactivos
#Generar Mapa "estático"
plot(mapa_jalisco)
summary(mapa_jalisco@data$Discapa)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.03390 0.04715 0.05497 0.05867 0.06842 0.11590cut(mapa_jalisco@data$Discapa,4)## [1] (0.0338,0.0544] (0.0749,0.0954] (0.0749,0.0954] (0.0749,0.0954]
## [5] (0.0544,0.0749] (0.0544,0.0749] (0.0338,0.0544] (0.0544,0.0749]
## [9] (0.0749,0.0954] (0.0338,0.0544] (0.0338,0.0544] (0.0544,0.0749]
## [13] (0.0544,0.0749] (0.0338,0.0544] (0.0338,0.0544] (0.0544,0.0749]
## [17] (0.0338,0.0544] (0.0338,0.0544] (0.0338,0.0544] (0.0338,0.0544]
## [21] (0.0544,0.0749] (0.0544,0.0749] (0.0338,0.0544] (0.0544,0.0749]
## [25] (0.0338,0.0544] (0.0544,0.0749] (0.0544,0.0749] (0.0544,0.0749]
## [29] (0.0544,0.0749] (0.0338,0.0544] (0.0544,0.0749] (0.0338,0.0544]
## [33] (0.0338,0.0544] (0.0544,0.0749] (0.0544,0.0749] (0.0338,0.0544]
## [37] (0.0338,0.0544] (0.0749,0.0954] (0.0544,0.0749] (0.0338,0.0544]
## [41] (0.0954,0.116] (0.0338,0.0544] (0.0338,0.0544] (0.0749,0.0954]
## [45] (0.0544,0.0749] (0.0338,0.0544] (0.0544,0.0749] (0.0544,0.0749]
## [49] (0.0338,0.0544] (0.0338,0.0544] (0.0749,0.0954] (0.0544,0.0749]
## [53] (0.0749,0.0954] (0.0338,0.0544] (0.0544,0.0749] (0.0338,0.0544]
## [57] (0.0544,0.0749] (0.0749,0.0954] (0.0544,0.0749] (0.0338,0.0544]
## [61] (0.0338,0.0544] (0.0338,0.0544] (0.0338,0.0544] (0.0954,0.116]
## [65] (0.0338,0.0544] (0.0749,0.0954] (0.0544,0.0749] (0.0749,0.0954]
## [69] (0.0338,0.0544] (0.0749,0.0954] (0.0544,0.0749] (0.0544,0.0749]
## [73] (0.0544,0.0749] (0.0338,0.0544] (0.0544,0.0749] (0.0338,0.0544]
## [77] (0.0338,0.0544] (0.0544,0.0749] (0.0544,0.0749] (0.0338,0.0544]
## [81] (0.0749,0.0954] (0.0338,0.0544] (0.0338,0.0544] (0.0338,0.0544]
## [85] (0.0749,0.0954] (0.0544,0.0749] (0.0749,0.0954] (0.0338,0.0544]
## [89] (0.0338,0.0544] (0.0544,0.0749] (0.0544,0.0749] (0.0338,0.0544]
## [93] (0.0338,0.0544] (0.0338,0.0544] (0.0544,0.0749] (0.0338,0.0544]
## [97] (0.0338,0.0544] (0.0338,0.0544] (0.0338,0.0544] (0.0544,0.0749]
## [101] (0.0544,0.0749] (0.0338,0.0544] (0.0338,0.0544] (0.0544,0.0749]
## [105] (0.0544,0.0749] (0.0338,0.0544] (0.0544,0.0749] (0.0544,0.0749]
## [109] (0.0544,0.0749] (0.0749,0.0954] (0.0338,0.0544] (0.0954,0.116]
## [113] (0.0544,0.0749] (0.0338,0.0544] (0.0338,0.0544] (0.0338,0.0544]
## [117] (0.0338,0.0544] (0.0544,0.0749] (0.0544,0.0749] (0.0338,0.0544]
## [121] (0.0338,0.0544] (0.0544,0.0749] (0.0338,0.0544] (0.0544,0.0749]
## [125] (0.0338,0.0544]
## Levels: (0.0338,0.0544] (0.0544,0.0749] (0.0749,0.0954] (0.0954,0.116]library(RColorBrewer)
my_colors <- brewer.pal(5, "PuBuGn")
my_colors <- colorRampPalette(my_colors)(4)
cuantil <- cut(mapa_jalisco@data$Discapa, 4)
my_colors <- my_colors[as.numeric(cuantil)]
plot(mapa_jalisco , col=my_colors , bg = "white")
intermedio <-rep(NA,125)
intermedio[which(as.numeric(cuantil)==1)]<-"#AE017E"
intermedio[which(as.numeric(cuantil)==2)]<-"#F768A1"
intermedio[which(as.numeric(cuantil)==3)]<-"#FBB4B9"
intermedio[which(as.numeric(cuantil)==4)]<-"#FEEBE2"
#my_colors <- my_colors[as.numeric(cuantil)]
plot(mapa_jalisco , col=intermedio , bg = "white")
#### Variable "Adulto", 5 cortes, Jalisco también... ¿paleta? la que quieran
summary(mapa_jalisco@data$Adultos)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.03587 0.08596 0.10427 0.10725 0.12842 0.19182cut(mapa_jalisco@data$Adultos,5)## [1] (0.0671,0.0983] (0.0983,0.129] (0.0983,0.129] (0.0983,0.129]
## [5] (0.129,0.161] (0.0671,0.0983] (0.0671,0.0983] (0.129,0.161]
## [9] (0.161,0.192] (0.0671,0.0983] (0.0671,0.0983] (0.0983,0.129]
## [13] (0.0983,0.129] (0.0357,0.0671] (0.0983,0.129] (0.0983,0.129]
## [17] (0.0983,0.129] (0.0671,0.0983] (0.0671,0.0983] (0.0671,0.0983]
## [21] (0.129,0.161] (0.129,0.161] (0.0671,0.0983] (0.129,0.161]
## [25] (0.0671,0.0983] (0.0671,0.0983] (0.0983,0.129] (0.0671,0.0983]
## [29] (0.0357,0.0671] (0.0983,0.129] (0.0983,0.129] (0.0671,0.0983]
## [33] (0.0671,0.0983] (0.0983,0.129] (0.0983,0.129] (0.0983,0.129]
## [37] (0.0983,0.129] (0.129,0.161] (0.0983,0.129] (0.129,0.161]
## [41] (0.0983,0.129] (0.0671,0.0983] (0.0983,0.129] (0.161,0.192]
## [45] (0.0671,0.0983] (0.0983,0.129] (0.0983,0.129] (0.129,0.161]
## [49] (0.0983,0.129] (0.0983,0.129] (0.161,0.192] (0.0983,0.129]
## [53] (0.0983,0.129] (0.0357,0.0671] (0.0983,0.129] (0.0671,0.0983]
## [57] (0.0671,0.0983] (0.0983,0.129] (0.0671,0.0983] (0.0671,0.0983]
## [61] (0.0357,0.0671] (0.129,0.161] (0.0671,0.0983] (0.161,0.192]
## [65] (0.0671,0.0983] (0.129,0.161] (0.0983,0.129] (0.129,0.161]
## [69] (0.0671,0.0983] (0.129,0.161] (0.0357,0.0671] (0.129,0.161]
## [73] (0.0671,0.0983] (0.0671,0.0983] (0.129,0.161] (0.0671,0.0983]
## [77] (0.0357,0.0671] (0.129,0.161] (0.161,0.192] (0.0357,0.0671]
## [81] (0.0983,0.129] (0.0671,0.0983] (0.129,0.161] (0.0983,0.129]
## [85] (0.129,0.161] (0.0983,0.129] (0.129,0.161] (0.0671,0.0983]
## [89] (0.0671,0.0983] (0.0983,0.129] (0.0983,0.129] (0.0357,0.0671]
## [93] (0.0357,0.0671] (0.0983,0.129] (0.0983,0.129] (0.0357,0.0671]
## [97] (0.129,0.161] (0.0983,0.129] (0.161,0.192] (0.0671,0.0983]
## [101] (0.0983,0.129] (0.0983,0.129] (0.0983,0.129] (0.0671,0.0983]
## [105] (0.129,0.161] (0.0983,0.129] (0.129,0.161] (0.0983,0.129]
## [109] (0.0983,0.129] (0.129,0.161] (0.0671,0.0983] (0.129,0.161]
## [113] (0.129,0.161] (0.0983,0.129] (0.0671,0.0983] (0.0671,0.0983]
## [117] (0.0983,0.129] (0.0671,0.0983] (0.0671,0.0983] (0.0671,0.0983]
## [121] (0.0671,0.0983] (0.0983,0.129] (0.0983,0.129] (0.0671,0.0983]
## [125] (0.0671,0.0983]
## 5 Levels: (0.0357,0.0671] (0.0671,0.0983] (0.0983,0.129] ... (0.161,0.192]my_colors <- brewer.pal(5, "BuGn")
my_colors <- colorRampPalette(my_colors)(5)
cuantil <- cut(mapa_jalisco@data$Adultos, 5)
my_colors <- my_colors[as.numeric(cuantil)]
plot(mapa_jalisco , col=my_colors , bg = "yellow")
legend("topright",levels(cut(mapa_jalisco@data$Adultos, 5)),
cex = 0.6)
# imagen
#jpeg(filename = "mapa_jalisco1222.jpg",
#width = 480, height = 480, units = "px", pointsize = 12,
#quality = 100,
#bg = "white", res = NA, family = "", restoreConsole = TRUE,
#type = c("windows", "cairo"))
#plot(mapa_jalisco , col=my_colors , bg = "white")
#legend("topleft",texto5,cex = 1.2,bty = "n",
# col = c("#FEE5D9","#FCBBA1","#FC9272","#FB6A4A","#DE2D26"),pch = "@")
#dev.off()
library(leaflet)## Warning: package 'leaflet' was built under R version 4.1.2library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionleaflet(data=mapa_jalisco) %>% addTiles() %>% addPolygons()