ACTIVIDAD 2
MEZTLI CRUZ
12/10/2021
INSTRUCCIONES
La segunda actividad corresponde a la elaboración de un archivo Markdown que reporte TODO lo programado hasta el momento. En otras palabras, todo lo programado hasta el
08 DE OCTUBRE 2021
En scripts se debe de reportar en un archivo Markdown compilado como HTML y publicado en Rpubs. Los comentarios insertados mediante el caracter “#” deben ser parte del escrito entre código del archivo HTML y las instrucciones, incluyendo su resultado de la consola, deben ser mostrados en los chunks realizados.
La fecha límite de entrega es el 15 de octubre del 2021 a las 23:59 hrs por este medio. El entregable debe de ser la liga de Rpubs.
CLASE DEL 16 DE AGOSTO DE 2021
Esta fue la primera clase. Exploramos algunas funciones de R para que nos arroje información sobre nuestros conjuntos de datos. Además aprendimos a hacerle preguntas a R sobre sus comandos.
#?rnorm
#Lo que está entre corchetees son posiciones
#lo que está entre paréntesis son argumentos de función u operaciones
apropos("sequ")#buscar entre los valores que tenemos en la base## [1] "sequence" "sequence.default"vect1<-15
vect2<-18
plot(vect1)
vect3<-c(vect1,vect2) #concatenar=unir o juntar
vect4<- seq(from=10,to=40,by=10)
vect4## [1] 10 20 30 40dim_vec<-length(vect4)
dim_vec## [1] 4vect5<-rep(5,6) #rep es repetir
vect6<-rep(vect4,4)
vect7<-rep(vect4,each=4)
length(vect7)## [1] 16dim_vec<-length(vect4)
#operaciones vectoriales y matriciales
knitr::opts_chunk$set(echo = TRUE)CLASE DEL 23 DE AGOSTO DE 2021
En esta clase aprendimos a multiplicar vectores y matrices por escalares, por vectores y por matrices.
##23-08-2021
## Operaciones punto por vector
5*vect3## [1] 75 90vect1*vect3## [1] 225 270## [fila, columna] Matriz 5x4
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) #byrow=TRUE para que lo acomode por fila
dim(practi_mat) #dimensiones de la matriz 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=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 24##Indicadores de posición (matriz y vector) []
dim(practi_mat3)## [1] 4 3practi_mat3<-matrix(c(11,13,2,5,8,4,7,12,21,21,6,8), ncol=4,byrow=TRUE) #Matrix
practi_mat3[1,4] #Escalar]## [1] 5practi_mat3[,3] #Todos los renglones## [1] 2 7 6practi_mat3[2,] #Todas las columnas## [1] 8 4 7 12practi_mat3[1:2,] #Fila 1 y 2, todas las columnas## [,1] [,2] [,3] [,4]
## [1,] 11 13 2 5
## [2,] 8 4 7 12practi_mat3[,1:2] #Fila 1 y 2, todas las columnas## [,1] [,2]
## [1,] 11 13
## [2,] 8 4
## [3,] 21 21matri2<-practi_mat3[-1,-1] #fila 1 y 2, todas las columnas
matri2## [,1] [,2] [,3]
## [1,] 4 7 12
## [2,] 21 6 8#practi_mat3[c(1,3),]
#practi_mat3[-c(2,3),]
#practi_mat3[-c(2,3),c(1,3)]
#Algebra de matrices
##
#t(practi_mat3) #Matriz transpuesta
#solve(practi_mat3) #resuelve sistemas de ecuaciones, tenemos que tener matrices cuadradas nxn
###DESACTIVÉ CON # LA MATRIZ practi_mat3 porque no era una matriz cuadrada, por lo tanto no se podía resolver con el comando "solve"
practi_mate<-practi_mat3[-4,] #hacemos una matriz a partir de practi_mat3, pero le quitamos el cuarto renglon para que sea cuadrada
practi_mate## [,1] [,2] [,3] [,4]
## [1,] 11 13 2 5
## [2,] 8 4 7 12
## [3,] 21 21 6 8practimatet<-t(practi_mate)
practimatet## [,1] [,2] [,3]
## [1,] 11 8 21
## [2,] 13 4 21
## [3,] 2 7 6
## [4,] 5 12 8practi_mat4<-practi_mate[-4,]
practi_mat4## [,1] [,2] [,3] [,4]
## [1,] 11 13 2 5
## [2,] 8 4 7 12
## [3,] 21 21 6 8t(practi_mat4)## [,1] [,2] [,3]
## [1,] 11 8 21
## [2,] 13 4 21
## [3,] 2 7 6
## [4,] 5 12 8#inv_practi_mat_4<-solve(practi_mat4)#matriz cuadrada de nxn, inversa
t1<-t(practi_mat4)
t1## [,1] [,2] [,3]
## [1,] 11 8 21
## [2,] 13 4 21
## [3,] 2 7 6
## [4,] 5 12 8practimat4<-t1[-4,]
practimat4 #ya le quté el renglón que no quería## [,1] [,2] [,3]
## [1,] 11 8 21
## [2,] 13 4 21
## [3,] 2 7 6inv_practimat4<-solve(practimat4) #Matriz cuadrada de nxn, inversa
practi_mat3 #4x2## [,1] [,2] [,3] [,4]
## [1,] 11 13 2 5
## [2,] 8 4 7 12
## [3,] 21 21 6 8matri2## [,1] [,2] [,3]
## [1,] 4 7 12
## [2,] 21 6 8#practi_mat3%*%matri2 #Cuando hablamos de matrices, debemos usar %
matri2%*%practimat4## [,1] [,2] [,3]
## [1,] 159 144 303
## [2,] 325 248 615#practimat4%*%matri2 #no se puede porque las matrices no se pueden multiplicar de esta forma
#matriz por su inversa=identidad
practimat4%*%inv_practimat4## [,1] [,2] [,3]
## [1,] 1.000000e+00 -1.776357e-15 0
## [2,] 0.000000e+00 1.000000e+00 0
## [3,] 8.881784e-16 -4.440892e-16 1knitr::opts_chunk$set(echo = TRUE)CLASE DEL 24 DE AGOSTO DE 2021
En esta clase aprendimos a trabajar con vectores, escalares y matrices. Aplicamos las nociones de matrices para resolver un sistema de ecuaciones en R.
## Sistemas de ecuaciones
## 24 de agosto de 2021
#solve() vamos a resolver el siguiente sistema de ecuaciones
#5x-3y+2z=1
#-2x+2y-z=5
#4x+2y-4z=-3
5*2.38 -3*8.63 +2*7.44## [1] 0.89-2*2.38 +2*8.63 -1*7.44## [1] 5.064*2.38 +2*8.63 -4*7.44## [1] -2.98coeficientes<-matrix(c(5,-3,2,-2,2,-1,4,2,-4), byrow=TRUE,ncol=3)
respuestas<-c(1,5,-3)
#?solve #el signo de interrogción es para preguntarle a R sobre un comando
respuestas## [1] 1 5 -3solve(coeficientes,respuestas)## [1] 2.388889 8.611111 7.444444ainv<-solve(coeficientes) #nos da la inversa de la matriz de coeficientes
solucion<-solve(coeficientes,respuestas) #asignamos el nombre solucion
length(solucion)## [1] 3###
##Intentamos sacar la matriz de respuestas del sistema de ecuaciones
#**es, decir, x=b*a^(-1)##
#1x3 3x3 3x1
respuestas%*%ainv## [,1] [,2] [,3]
## [1,] 1.666667 4.555556 0.4444444ainv%*%respuestas #esta es la buena## [,1]
## [1,] 2.388889
## [2,] 8.611111
## [3,] 7.444444solucion## [1] 2.388889 8.611111 7.444444#Suma hay que cuidar que las dimensiones de las matrices sean compatibles
coeficientes +practimat4## [,1] [,2] [,3]
## [1,] 16 5 23
## [2,] 11 6 20
## [3,] 6 9 2(coeficientes +practimat4)%*%solve(coeficientes+practimat4) #nos tiene que dar la inversa## [,1] [,2] [,3]
## [1,] 1.000000e+00 0.000000e+00 0
## [2,] 2.220446e-16 1.000000e+00 0
## [3,] -1.665335e-16 1.110223e-16 1#¿ctrl+l en la consola?
#Rm? ls? <-son funciones relativamente hermanas
#Rm <-remueve objetos desde un ambiente especificado
#rm(list=c("xyy","xyz"))
#rm(list=ls()) <-remueve todos los elementos del ambiente
#ls <-muestra todos los elementos que tengo en mi ambiente
#rm(list=c("objeto que queremos borrar entre comillas"))
#Alt+a <-selecciona todo
ls()## [1] "ainv" "coeficientes" "dim_vec" "inv_practimat4"
## [5] "matri2" "practi" "practi_mat" "practi_mat2"
## [9] "practi_mat3" "practi_mat4" "practi_mate" "practimat4"
## [13] "practimatet" "respuestas" "solucion" "t1"
## [17] "vect1" "vect2" "vect3" "vect4"
## [21] "vect5" "vect6" "vect7"?rm## starting httpd help server ... done##
t(coeficientes)## [,1] [,2] [,3]
## [1,] 5 -2 4
## [2,] -3 2 2
## [3,] 2 -1 -4diag(coeficientes)## [1] 5 2 -4det(coeficientes) #determinante## [1] -18#?det
#vamos a calcular el determinante de forma un poco más rudimentaria
cbind(coeficientes,coeficientes[,1:2]) #aumentamos columnas hacemos la matriz extendida pegando por columnas## [,1] [,2] [,3] [,4] [,5]
## [1,] 5 -3 2 5 -3
## [2,] -2 2 -1 -2 2
## [3,] 4 2 -4 4 2rbind(coeficientes,coeficientes[1:2,]) #r=row, es decir, que le aumentamos un renglón## [,1] [,2] [,3]
## [1,] 5 -3 2
## [2,] -2 2 -1
## [3,] 4 2 -4
## [4,] 5 -3 2
## [5,] -2 2 -1#factors(categóricos, de categorías), numerics(numéricos)
vari1<-c("M", "H")
vari1## [1] "M" "H"vari2<-c("M","H","M","H","H","M","M","M","M","H","H","H","M")
length(vari2)## [1] 13#rm=list=c("vari2")
table(vari2)#tabla de frecuencias## vari2
## H M
## 6 7vari3<-c("1","0","1","0","0","1","1","1","1","0","0","0","1")
#lo hacemos entre comillas para que ssea una variable nominal
length(vari3)## [1] 13#sum(vari3)
table(vari3) #tabla de frecuencias## vari3
## 0 1
## 6 7table(vari2,vari3)## vari3
## vari2 0 1
## H 6 0
## M 0 7#sum(as.numeric(vari3))#hace el summary statistics de la variable vari3
#forzandolo a ser tomado en cuenta como numérico
factor(vari3)## [1] 1 0 1 0 0 1 1 1 1 0 0 0 1
## Levels: 0 1vari3n<-as.numeric(vari3)
vari3n## [1] 1 0 1 0 0 1 1 1 1 0 0 0 1table(vari3n)## vari3n
## 0 1
## 6 7sum(vari3n)## [1] 7vari3nom<-vari3
vari3nom[5]<-"cero" #cambia el elemento número 5 de la lista a "cero"
vari3nom[7]<-"uno" #cambia el elemento número 7 de la lista por "uno"
vari3n## [1] 1 0 1 0 0 1 1 1 1 0 0 0 1vari3nom## [1] "1" "0" "1" "0" "cero" "1" "uno" "1" "1" "0"
## [11] "0" "0" "1"is.na(as.numeric(vari3nom)) #NA porque no puede tomar los valores nominales le estamos preguntando si es un valor NA## Warning: NAs introducidos por coerción## [1] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE#data.frame
#list(listas)
#listas<-list(matri2,practimat4,practi_mat3)
#listas2<-list(listas,practi,5)
knitr::opts_chunk$set(echo = TRUE)CLASE DEL 30 DE AGOSTO DE 2021
En esta clase trabajamos con vectores, matrices y marcos de datos. Además, aprendimos a hacerle preguntas informativas sobre nuestros datos a R.
###CLASE 30/08/2021###
coeficientes## [,1] [,2] [,3]
## [1,] 5 -3 2
## [2,] -2 2 -1
## [3,] 4 2 -4vect6## [1] 10 20 30 40 10 20 30 40 10 20 30 40 10 20 30 40vect7## [1] 10 10 10 10 20 20 20 20 30 30 30 30 40 40 40 40matriz7<-cbind(vect6,vect7)
dim(matriz7)## [1] 16 2vari2## [1] "M" "H" "M" "H" "H" "M" "M" "M" "M" "H" "H" "H" "M"vari3## [1] "1" "0" "1" "0" "0" "1" "1" "1" "1" "0" "0" "0" "1"matriz8<-cbind(vari2,vari3)
dim(matriz8)## [1] 13 2###cómo localizar elementos de un vector###
vect6[1:6]## [1] 10 20 30 40 10 20matriz9<-cbind(vect6[1:13],vari2) #Todo a no numérico#
matriz9 #es una matriz con valores no numéricos### vari2
## [1,] "10" "M"
## [2,] "20" "H"
## [3,] "30" "M"
## [4,] "40" "H"
## [5,] "10" "H"
## [6,] "20" "M"
## [7,] "30" "M"
## [8,] "40" "M"
## [9,] "10" "M"
## [10,] "20" "H"
## [11,] "30" "H"
## [12,] "40" "H"
## [13,] "10" "M"#######################################
###MARCOS DE DATOS conviven en la misma tabla, distintos tipos de elementos######
#######################################
data.frame(vector=vect6[1:13],vector2=vari2) #Esta me va a dar lo mismo de la matriz anterior a algo que sí es numérico## vector vector2
## 1 10 M
## 2 20 H
## 3 30 M
## 4 40 H
## 5 10 H
## 6 20 M
## 7 30 M
## 8 40 M
## 9 10 M
## 10 20 H
## 11 30 H
## 12 40 H
## 13 10 Mdata.frame(vector=vect6[1:13],vector2=vari3) #Esta me va a dar lo mismo de la matriz anterior a algo que sí es numérico## vector vector2
## 1 10 1
## 2 20 0
## 3 30 1
## 4 40 0
## 5 10 0
## 6 20 1
## 7 30 1
## 8 40 1
## 9 10 1
## 10 20 0
## 11 30 0
## 12 40 0
## 13 10 1matriz11<-data.frame(vector=as.numeric(vect6[1:13]),vector2=as.numeric(vari3)) #Esta me va a dar lo mismo de la matriz anterior a algo que sí es numérico
matriz12<-data.frame(vector=as.numeric(vect6[1:13]),vector2=as.factor(vari3),vector3=as.numeric(vect7[1:13])) #
matriz13<-data.frame(vector=as.numeric(vect6[1:13]),vector2=as.factor(vari3),vector3=as.numeric(vect7[1:13]),vector4=as.factor(vari2)) #
matriz11## vector vector2
## 1 10 1
## 2 20 0
## 3 30 1
## 4 40 0
## 5 10 0
## 6 20 1
## 7 30 1
## 8 40 1
## 9 10 1
## 10 20 0
## 11 30 0
## 12 40 0
## 13 10 1matriz12## vector vector2 vector3
## 1 10 1 10
## 2 20 0 10
## 3 30 1 10
## 4 40 0 10
## 5 10 0 20
## 6 20 1 20
## 7 30 1 20
## 8 40 1 20
## 9 10 1 30
## 10 20 0 30
## 11 30 0 30
## 12 40 0 30
## 13 10 1 40matriz13## vector vector2 vector3 vector4
## 1 10 1 10 M
## 2 20 0 10 H
## 3 30 1 10 M
## 4 40 0 10 H
## 5 10 0 20 H
## 6 20 1 20 M
## 7 30 1 20 M
## 8 40 1 20 M
## 9 10 1 30 M
## 10 20 0 30 H
## 11 30 0 30 H
## 12 40 0 30 H
## 13 10 1 40 M####PREGUNTAS INFORMATIVAS####
is.data.frame(matriz12)## [1] TRUEis.data.frame(matriz9)## [1] FALSEis.numeric(matriz12$vector)## [1] TRUEis.numeric(matriz12$vector2)## [1] FALSEis.factor(matriz12$vector)## [1] FALSEis.factor(matriz12$vector2[1])## [1] TRUEis.numeric(matriz12$vector2[1])## [1] FALSE###Elementos del vector tienen que ser de la misma naturaleza, no pueden ser numéricos o no numéricos###
View(matriz12) ##ver todos los elementos de la matriz
#####RECAPITULACIÓN###
#* 1 escalares..... números sueltos (RECÁMARA 1)
#* 2 vectores (RECÁMARA 2)
#* 3 matrices (RECÁMARA 3)
#* 4 listas #agregar todos los anteriores (CASA)
#*
#?list ###no importa la naturaleza de los elementos
casa<-list("Hola mundo en la lista", vari1, matriz13)
###el primero es un elemento, el segundo es un vector de dos elementos tipo factor y el teercerro es una matriz del tipo data frame
is.list(casa)## [1] TRUE#matriz12[i,j]
#vector[i]
#lista[i,j,k] #esto es un cubo#
casa<-list(Mensaje="Hola mundo en la lista",Vector= vari1, Matriz=matriz13, Generado=as.factor(seq(1,50)))
casa## $Mensaje
## [1] "Hola mundo en la lista"
##
## $Vector
## [1] "M" "H"
##
## $Matriz
## vector vector2 vector3 vector4
## 1 10 1 10 M
## 2 20 0 10 H
## 3 30 1 10 M
## 4 40 0 10 H
## 5 10 0 20 H
## 6 20 1 20 M
## 7 30 1 20 M
## 8 40 1 20 M
## 9 10 1 30 M
## 10 20 0 30 H
## 11 30 0 30 H
## 12 40 0 30 H
## 13 10 1 40 M
##
## $Generado
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## 50 Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ... 50is.list(casa) #check si el elemento casa es una lista## [1] TRUEcasa$Vector## [1] "M" "H"casa$Mensaje## [1] "Hola mundo en la lista"casa$Matriz## vector vector2 vector3 vector4
## 1 10 1 10 M
## 2 20 0 10 H
## 3 30 1 10 M
## 4 40 0 10 H
## 5 10 0 20 H
## 6 20 1 20 M
## 7 30 1 20 M
## 8 40 1 20 M
## 9 10 1 30 M
## 10 20 0 30 H
## 11 30 0 30 H
## 12 40 0 30 H
## 13 10 1 40 M####El signo de $ significa que extraigan dentro de esa lista, tal elemento
casa$Vector[2]## [1] "H"casa$Matriz$Vector## NULLcasa$Generado## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## 50 Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ... 50Generar_secuencia<-as.factor(seq(1,50))
####RESUMENES####
#Estadística de resumen/genéricas
#solve
#mean
#sd
#var
#max
#min
matriz_13<-cbind(matriz13$Vector,matriz13$Vector3)
mean(matriz13[,1])### la media de la PRIMERA columna## [1] 23.84615sd(matriz13[,1])## [1] 11.92928var(matriz13[,1])## [1] 142.3077max(matriz13[,1])## [1] 40min(matriz13[,1])## [1] 10summary(matriz13)## vector vector2 vector3 vector4
## Min. :10.00 0:6 Min. :10.00 H:6
## 1st Qu.:10.00 1:7 1st Qu.:10.00 M:7
## Median :20.00 Median :20.00
## Mean :23.85 Mean :21.54
## 3rd Qu.:30.00 3rd Qu.:30.00
## Max. :40.00 Max. :40.00matriz13## vector vector2 vector3 vector4
## 1 10 1 10 M
## 2 20 0 10 H
## 3 30 1 10 M
## 4 40 0 10 H
## 5 10 0 20 H
## 6 20 1 20 M
## 7 30 1 20 M
## 8 40 1 20 M
## 9 10 1 30 M
## 10 20 0 30 H
## 11 30 0 30 H
## 12 40 0 30 H
## 13 10 1 40 Mmean(matriz13[,3])### la media de la TERCERA columna## [1] 21.53846sd(matriz13[,3])## [1] 9.870962var(matriz13[,3])## [1] 97.4359max(matriz13[,3])## [1] 40min(matriz13[,3])## [1] 10summary(matriz13[,1])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.00 10.00 20.00 23.85 30.00 40.00summary(matriz13[,2])## 0 1
## 6 7summary(matriz13[,3])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.00 10.00 20.00 21.54 30.00 40.00summary(matriz13[,4])## H M
## 6 7knitr::opts_chunk$set(echo = TRUE)CLASE DEL 31 DE AGOSTO 2021
En esta case exploramos la base de datos que ya viene precargada en R iris y aplicamos alguno comandos que ya vienen en R con el comando apply
#31/agosto/2021
#nuevo proyecto
#cargar datos pre-instalados en R
#data(cars)
#data(USAarrests)
#
data(iris)
#?iris
data(iris3)
iris## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicais.matrix(iris)## [1] FALSEis.data.frame(iris)## [1] TRUEis.list(iris)## [1] TRUEis.data.frame(iris3) #es un marco de datos## [1] FALSEis.list(iris3)## [1] FALSEis.matrix(iris3)## [1] FALSEView(iris3)
iris3## , , Setosa
##
## Sepal L. Sepal W. Petal L. Petal W.
## [1,] 5.1 3.5 1.4 0.2
## [2,] 4.9 3.0 1.4 0.2
## [3,] 4.7 3.2 1.3 0.2
## [4,] 4.6 3.1 1.5 0.2
## [5,] 5.0 3.6 1.4 0.2
## [6,] 5.4 3.9 1.7 0.4
## [7,] 4.6 3.4 1.4 0.3
## [8,] 5.0 3.4 1.5 0.2
## [9,] 4.4 2.9 1.4 0.2
## [10,] 4.9 3.1 1.5 0.1
## [11,] 5.4 3.7 1.5 0.2
## [12,] 4.8 3.4 1.6 0.2
## [13,] 4.8 3.0 1.4 0.1
## [14,] 4.3 3.0 1.1 0.1
## [15,] 5.8 4.0 1.2 0.2
## [16,] 5.7 4.4 1.5 0.4
## [17,] 5.4 3.9 1.3 0.4
## [18,] 5.1 3.5 1.4 0.3
## [19,] 5.7 3.8 1.7 0.3
## [20,] 5.1 3.8 1.5 0.3
## [21,] 5.4 3.4 1.7 0.2
## [22,] 5.1 3.7 1.5 0.4
## [23,] 4.6 3.6 1.0 0.2
## [24,] 5.1 3.3 1.7 0.5
## [25,] 4.8 3.4 1.9 0.2
## [26,] 5.0 3.0 1.6 0.2
## [27,] 5.0 3.4 1.6 0.4
## [28,] 5.2 3.5 1.5 0.2
## [29,] 5.2 3.4 1.4 0.2
## [30,] 4.7 3.2 1.6 0.2
## [31,] 4.8 3.1 1.6 0.2
## [32,] 5.4 3.4 1.5 0.4
## [33,] 5.2 4.1 1.5 0.1
## [34,] 5.5 4.2 1.4 0.2
## [35,] 4.9 3.1 1.5 0.2
## [36,] 5.0 3.2 1.2 0.2
## [37,] 5.5 3.5 1.3 0.2
## [38,] 4.9 3.6 1.4 0.1
## [39,] 4.4 3.0 1.3 0.2
## [40,] 5.1 3.4 1.5 0.2
## [41,] 5.0 3.5 1.3 0.3
## [42,] 4.5 2.3 1.3 0.3
## [43,] 4.4 3.2 1.3 0.2
## [44,] 5.0 3.5 1.6 0.6
## [45,] 5.1 3.8 1.9 0.4
## [46,] 4.8 3.0 1.4 0.3
## [47,] 5.1 3.8 1.6 0.2
## [48,] 4.6 3.2 1.4 0.2
## [49,] 5.3 3.7 1.5 0.2
## [50,] 5.0 3.3 1.4 0.2
##
## , , Versicolor
##
## Sepal L. Sepal W. Petal L. Petal W.
## [1,] 7.0 3.2 4.7 1.4
## [2,] 6.4 3.2 4.5 1.5
## [3,] 6.9 3.1 4.9 1.5
## [4,] 5.5 2.3 4.0 1.3
## [5,] 6.5 2.8 4.6 1.5
## [6,] 5.7 2.8 4.5 1.3
## [7,] 6.3 3.3 4.7 1.6
## [8,] 4.9 2.4 3.3 1.0
## [9,] 6.6 2.9 4.6 1.3
## [10,] 5.2 2.7 3.9 1.4
## [11,] 5.0 2.0 3.5 1.0
## [12,] 5.9 3.0 4.2 1.5
## [13,] 6.0 2.2 4.0 1.0
## [14,] 6.1 2.9 4.7 1.4
## [15,] 5.6 2.9 3.6 1.3
## [16,] 6.7 3.1 4.4 1.4
## [17,] 5.6 3.0 4.5 1.5
## [18,] 5.8 2.7 4.1 1.0
## [19,] 6.2 2.2 4.5 1.5
## [20,] 5.6 2.5 3.9 1.1
## [21,] 5.9 3.2 4.8 1.8
## [22,] 6.1 2.8 4.0 1.3
## [23,] 6.3 2.5 4.9 1.5
## [24,] 6.1 2.8 4.7 1.2
## [25,] 6.4 2.9 4.3 1.3
## [26,] 6.6 3.0 4.4 1.4
## [27,] 6.8 2.8 4.8 1.4
## [28,] 6.7 3.0 5.0 1.7
## [29,] 6.0 2.9 4.5 1.5
## [30,] 5.7 2.6 3.5 1.0
## [31,] 5.5 2.4 3.8 1.1
## [32,] 5.5 2.4 3.7 1.0
## [33,] 5.8 2.7 3.9 1.2
## [34,] 6.0 2.7 5.1 1.6
## [35,] 5.4 3.0 4.5 1.5
## [36,] 6.0 3.4 4.5 1.6
## [37,] 6.7 3.1 4.7 1.5
## [38,] 6.3 2.3 4.4 1.3
## [39,] 5.6 3.0 4.1 1.3
## [40,] 5.5 2.5 4.0 1.3
## [41,] 5.5 2.6 4.4 1.2
## [42,] 6.1 3.0 4.6 1.4
## [43,] 5.8 2.6 4.0 1.2
## [44,] 5.0 2.3 3.3 1.0
## [45,] 5.6 2.7 4.2 1.3
## [46,] 5.7 3.0 4.2 1.2
## [47,] 5.7 2.9 4.2 1.3
## [48,] 6.2 2.9 4.3 1.3
## [49,] 5.1 2.5 3.0 1.1
## [50,] 5.7 2.8 4.1 1.3
##
## , , Virginica
##
## Sepal L. Sepal W. Petal L. Petal W.
## [1,] 6.3 3.3 6.0 2.5
## [2,] 5.8 2.7 5.1 1.9
## [3,] 7.1 3.0 5.9 2.1
## [4,] 6.3 2.9 5.6 1.8
## [5,] 6.5 3.0 5.8 2.2
## [6,] 7.6 3.0 6.6 2.1
## [7,] 4.9 2.5 4.5 1.7
## [8,] 7.3 2.9 6.3 1.8
## [9,] 6.7 2.5 5.8 1.8
## [10,] 7.2 3.6 6.1 2.5
## [11,] 6.5 3.2 5.1 2.0
## [12,] 6.4 2.7 5.3 1.9
## [13,] 6.8 3.0 5.5 2.1
## [14,] 5.7 2.5 5.0 2.0
## [15,] 5.8 2.8 5.1 2.4
## [16,] 6.4 3.2 5.3 2.3
## [17,] 6.5 3.0 5.5 1.8
## [18,] 7.7 3.8 6.7 2.2
## [19,] 7.7 2.6 6.9 2.3
## [20,] 6.0 2.2 5.0 1.5
## [21,] 6.9 3.2 5.7 2.3
## [22,] 5.6 2.8 4.9 2.0
## [23,] 7.7 2.8 6.7 2.0
## [24,] 6.3 2.7 4.9 1.8
## [25,] 6.7 3.3 5.7 2.1
## [26,] 7.2 3.2 6.0 1.8
## [27,] 6.2 2.8 4.8 1.8
## [28,] 6.1 3.0 4.9 1.8
## [29,] 6.4 2.8 5.6 2.1
## [30,] 7.2 3.0 5.8 1.6
## [31,] 7.4 2.8 6.1 1.9
## [32,] 7.9 3.8 6.4 2.0
## [33,] 6.4 2.8 5.6 2.2
## [34,] 6.3 2.8 5.1 1.5
## [35,] 6.1 2.6 5.6 1.4
## [36,] 7.7 3.0 6.1 2.3
## [37,] 6.3 3.4 5.6 2.4
## [38,] 6.4 3.1 5.5 1.8
## [39,] 6.0 3.0 4.8 1.8
## [40,] 6.9 3.1 5.4 2.1
## [41,] 6.7 3.1 5.6 2.4
## [42,] 6.9 3.1 5.1 2.3
## [43,] 5.8 2.7 5.1 1.9
## [44,] 6.8 3.2 5.9 2.3
## [45,] 6.7 3.3 5.7 2.5
## [46,] 6.7 3.0 5.2 2.3
## [47,] 6.3 2.5 5.0 1.9
## [48,] 6.5 3.0 5.2 2.0
## [49,] 6.2 3.4 5.4 2.3
## [50,] 5.9 3.0 5.1 1.8dim(iris3)## [1] 50 4 3head(iris3)## , , Setosa
##
## Sepal L. Sepal W. Petal L. Petal W.
## [1,] 5.1 3.5 1.4 0.2
## [2,] 4.9 3.0 1.4 0.2
## [3,] 4.7 3.2 1.3 0.2
## [4,] 4.6 3.1 1.5 0.2
## [5,] 5.0 3.6 1.4 0.2
## [6,] 5.4 3.9 1.7 0.4
##
## , , Versicolor
##
## Sepal L. Sepal W. Petal L. Petal W.
## [1,] 7.0 3.2 4.7 1.4
## [2,] 6.4 3.2 4.5 1.5
## [3,] 6.9 3.1 4.9 1.5
## [4,] 5.5 2.3 4.0 1.3
## [5,] 6.5 2.8 4.6 1.5
## [6,] 5.7 2.8 4.5 1.3
##
## , , Virginica
##
## Sepal L. Sepal W. Petal L. Petal W.
## [1,] 6.3 3.3 6.0 2.5
## [2,] 5.8 2.7 5.1 1.9
## [3,] 7.1 3.0 5.9 2.1
## [4,] 6.3 2.9 5.6 1.8
## [5,] 6.5 3.0 5.8 2.2
## [6,] 7.6 3.0 6.6 2.1#?head
#?iris3
tail(iris3)## , , Setosa
##
## Sepal L. Sepal W. Petal L. Petal W.
## [45,] 5.1 3.8 1.9 0.4
## [46,] 4.8 3.0 1.4 0.3
## [47,] 5.1 3.8 1.6 0.2
## [48,] 4.6 3.2 1.4 0.2
## [49,] 5.3 3.7 1.5 0.2
## [50,] 5.0 3.3 1.4 0.2
##
## , , Versicolor
##
## Sepal L. Sepal W. Petal L. Petal W.
## [45,] 5.6 2.7 4.2 1.3
## [46,] 5.7 3.0 4.2 1.2
## [47,] 5.7 2.9 4.2 1.3
## [48,] 6.2 2.9 4.3 1.3
## [49,] 5.1 2.5 3.0 1.1
## [50,] 5.7 2.8 4.1 1.3
##
## , , Virginica
##
## Sepal L. Sepal W. Petal L. Petal W.
## [45,] 6.7 3.3 5.7 2.5
## [46,] 6.7 3.0 5.2 2.3
## [47,] 6.3 2.5 5.0 1.9
## [48,] 6.5 3.0 5.2 2.0
## [49,] 6.2 3.4 5.4 2.3
## [50,] 5.9 3.0 5.1 1.8iris[,1] #primer columna de la base iris## [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1
## [19] 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.0
## [37] 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0 6.4 6.9 5.5
## [55] 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1
## [73] 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4 6.0 6.7 6.3 5.6 5.5
## [91] 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3
## [109] 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.0 6.9 5.6 7.7 6.3 6.7 7.2
## [127] 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8
## [145] 6.7 6.7 6.3 6.5 6.2 5.9is.factor(iris[,5])## [1] TRUEis.numeric(iris[,1])## [1] TRUEiris3[1,,] #todos los primeros renglones de la base iris3## Setosa Versicolor Virginica
## Sepal L. 5.1 7.0 6.3
## Sepal W. 3.5 3.2 3.3
## Petal L. 1.4 4.7 6.0
## Petal W. 0.2 1.4 2.5iris3[,1,] #fila, columna, profundidad## Setosa Versicolor Virginica
## [1,] 5.1 7.0 6.3
## [2,] 4.9 6.4 5.8
## [3,] 4.7 6.9 7.1
## [4,] 4.6 5.5 6.3
## [5,] 5.0 6.5 6.5
## [6,] 5.4 5.7 7.6
## [7,] 4.6 6.3 4.9
## [8,] 5.0 4.9 7.3
## [9,] 4.4 6.6 6.7
## [10,] 4.9 5.2 7.2
## [11,] 5.4 5.0 6.5
## [12,] 4.8 5.9 6.4
## [13,] 4.8 6.0 6.8
## [14,] 4.3 6.1 5.7
## [15,] 5.8 5.6 5.8
## [16,] 5.7 6.7 6.4
## [17,] 5.4 5.6 6.5
## [18,] 5.1 5.8 7.7
## [19,] 5.7 6.2 7.7
## [20,] 5.1 5.6 6.0
## [21,] 5.4 5.9 6.9
## [22,] 5.1 6.1 5.6
## [23,] 4.6 6.3 7.7
## [24,] 5.1 6.1 6.3
## [25,] 4.8 6.4 6.7
## [26,] 5.0 6.6 7.2
## [27,] 5.0 6.8 6.2
## [28,] 5.2 6.7 6.1
## [29,] 5.2 6.0 6.4
## [30,] 4.7 5.7 7.2
## [31,] 4.8 5.5 7.4
## [32,] 5.4 5.5 7.9
## [33,] 5.2 5.8 6.4
## [34,] 5.5 6.0 6.3
## [35,] 4.9 5.4 6.1
## [36,] 5.0 6.0 7.7
## [37,] 5.5 6.7 6.3
## [38,] 4.9 6.3 6.4
## [39,] 4.4 5.6 6.0
## [40,] 5.1 5.5 6.9
## [41,] 5.0 5.5 6.7
## [42,] 4.5 6.1 6.9
## [43,] 4.4 5.8 5.8
## [44,] 5.0 5.0 6.8
## [45,] 5.1 5.6 6.7
## [46,] 4.8 5.7 6.7
## [47,] 5.1 5.7 6.3
## [48,] 4.6 6.2 6.5
## [49,] 5.3 5.1 6.2
## [50,] 5.0 5.7 5.9iris3[,,1] #muestrame los elementos de la primera dimensión de profundidad de la base iris 3## Sepal L. Sepal W. Petal L. Petal W.
## [1,] 5.1 3.5 1.4 0.2
## [2,] 4.9 3.0 1.4 0.2
## [3,] 4.7 3.2 1.3 0.2
## [4,] 4.6 3.1 1.5 0.2
## [5,] 5.0 3.6 1.4 0.2
## [6,] 5.4 3.9 1.7 0.4
## [7,] 4.6 3.4 1.4 0.3
## [8,] 5.0 3.4 1.5 0.2
## [9,] 4.4 2.9 1.4 0.2
## [10,] 4.9 3.1 1.5 0.1
## [11,] 5.4 3.7 1.5 0.2
## [12,] 4.8 3.4 1.6 0.2
## [13,] 4.8 3.0 1.4 0.1
## [14,] 4.3 3.0 1.1 0.1
## [15,] 5.8 4.0 1.2 0.2
## [16,] 5.7 4.4 1.5 0.4
## [17,] 5.4 3.9 1.3 0.4
## [18,] 5.1 3.5 1.4 0.3
## [19,] 5.7 3.8 1.7 0.3
## [20,] 5.1 3.8 1.5 0.3
## [21,] 5.4 3.4 1.7 0.2
## [22,] 5.1 3.7 1.5 0.4
## [23,] 4.6 3.6 1.0 0.2
## [24,] 5.1 3.3 1.7 0.5
## [25,] 4.8 3.4 1.9 0.2
## [26,] 5.0 3.0 1.6 0.2
## [27,] 5.0 3.4 1.6 0.4
## [28,] 5.2 3.5 1.5 0.2
## [29,] 5.2 3.4 1.4 0.2
## [30,] 4.7 3.2 1.6 0.2
## [31,] 4.8 3.1 1.6 0.2
## [32,] 5.4 3.4 1.5 0.4
## [33,] 5.2 4.1 1.5 0.1
## [34,] 5.5 4.2 1.4 0.2
## [35,] 4.9 3.1 1.5 0.2
## [36,] 5.0 3.2 1.2 0.2
## [37,] 5.5 3.5 1.3 0.2
## [38,] 4.9 3.6 1.4 0.1
## [39,] 4.4 3.0 1.3 0.2
## [40,] 5.1 3.4 1.5 0.2
## [41,] 5.0 3.5 1.3 0.3
## [42,] 4.5 2.3 1.3 0.3
## [43,] 4.4 3.2 1.3 0.2
## [44,] 5.0 3.5 1.6 0.6
## [45,] 5.1 3.8 1.9 0.4
## [46,] 4.8 3.0 1.4 0.3
## [47,] 5.1 3.8 1.6 0.2
## [48,] 4.6 3.2 1.4 0.2
## [49,] 5.3 3.7 1.5 0.2
## [50,] 5.0 3.3 1.4 0.2#iris
#promedio de la longitud del sépalo de la base que estamos trabajando
iris3[,1,]## Setosa Versicolor Virginica
## [1,] 5.1 7.0 6.3
## [2,] 4.9 6.4 5.8
## [3,] 4.7 6.9 7.1
## [4,] 4.6 5.5 6.3
## [5,] 5.0 6.5 6.5
## [6,] 5.4 5.7 7.6
## [7,] 4.6 6.3 4.9
## [8,] 5.0 4.9 7.3
## [9,] 4.4 6.6 6.7
## [10,] 4.9 5.2 7.2
## [11,] 5.4 5.0 6.5
## [12,] 4.8 5.9 6.4
## [13,] 4.8 6.0 6.8
## [14,] 4.3 6.1 5.7
## [15,] 5.8 5.6 5.8
## [16,] 5.7 6.7 6.4
## [17,] 5.4 5.6 6.5
## [18,] 5.1 5.8 7.7
## [19,] 5.7 6.2 7.7
## [20,] 5.1 5.6 6.0
## [21,] 5.4 5.9 6.9
## [22,] 5.1 6.1 5.6
## [23,] 4.6 6.3 7.7
## [24,] 5.1 6.1 6.3
## [25,] 4.8 6.4 6.7
## [26,] 5.0 6.6 7.2
## [27,] 5.0 6.8 6.2
## [28,] 5.2 6.7 6.1
## [29,] 5.2 6.0 6.4
## [30,] 4.7 5.7 7.2
## [31,] 4.8 5.5 7.4
## [32,] 5.4 5.5 7.9
## [33,] 5.2 5.8 6.4
## [34,] 5.5 6.0 6.3
## [35,] 4.9 5.4 6.1
## [36,] 5.0 6.0 7.7
## [37,] 5.5 6.7 6.3
## [38,] 4.9 6.3 6.4
## [39,] 4.4 5.6 6.0
## [40,] 5.1 5.5 6.9
## [41,] 5.0 5.5 6.7
## [42,] 4.5 6.1 6.9
## [43,] 4.4 5.8 5.8
## [44,] 5.0 5.0 6.8
## [45,] 5.1 5.6 6.7
## [46,] 4.8 5.7 6.7
## [47,] 5.1 5.7 6.3
## [48,] 4.6 6.2 6.5
## [49,] 5.3 5.1 6.2
## [50,] 5.0 5.7 5.9iris3[,1,1] #Sepal length de setosa## [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1 5.7
## [20] 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.0 5.5 4.9
## [39] 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0iris3[,1,2] #Sepal l de versicolor## [1] 7.0 6.4 6.9 5.5 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8 6.2
## [20] 5.6 5.9 6.1 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4 6.0 6.7 6.3
## [39] 5.6 5.5 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7iris3[,1,3] #Sepal l de virginica## [1] 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7
## [20] 6.0 6.9 5.6 7.7 6.3 6.7 7.2 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4
## [39] 6.0 6.9 6.7 6.9 5.8 6.8 6.7 6.7 6.3 6.5 6.2 5.9mean(iris3[,1,1]) #media de Sepal length de setosa## [1] 5.006mean(iris3[,1,2]) #Sepal l de versicolor## [1] 5.936mean(iris3[,1,3]) #Sepal l de virginica## [1] 6.588mean(iris3[,2,1]) #media de Sepal width de setosa## [1] 3.428mean(iris3[,2,2]) #Sepal w de versicolor## [1] 2.77mean(iris3[,2,3]) #Sepal w de virginica## [1] 2.974mean(iris[1:50,1]) #sepal l setosa## [1] 5.006mean(iris[51:100,1]) #sepal l versicolor## [1] 5.936mean(iris[101:150,1]) #sepal l virginica## [1] 6.588###Promedios por columnas o por filas de manera automática
#apply() primero va el conjunto de datos, luego va el vector(1 para filas y 2 para columnas) y luego va la función
#aggregate()
#ambas se complementan y sirven para lo mismo, pero tienen argumentos distintos
#?apply
apply(iris[,-5],2,mean)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 5.843333 3.057333 3.758000 1.199333apply(iris[,-5],2,summary)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. 4.300000 2.000000 1.000 0.100000
## 1st Qu. 5.100000 2.800000 1.600 0.300000
## Median 5.800000 3.000000 4.350 1.300000
## Mean 5.843333 3.057333 3.758 1.199333
## 3rd Qu. 6.400000 3.300000 5.100 1.800000
## Max. 7.900000 4.400000 6.900 2.500000apply(iris[1:50,-5],2,mean) #promedio de setosa## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 5.006 3.428 1.462 0.246apply(iris[51:100,-5],2,mean) #promedio de versicolor## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 5.936 2.770 4.260 1.326apply(iris[101:150,-5],2,mean) #promedio de virginica## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 6.588 2.974 5.552 2.026###conjuntar información de las medias en una sola tabla
a<-apply(iris[1:50,-5],2,mean) #promedio de setosa
b<-apply(iris[51:100,-5],2,mean) #promedio de versicolor
c<-apply(iris[101:150,-5],2,mean) #promedio de virginica
promedio_flores<-data.frame(Setosa=a,Versicolor=b,Virginica=c) #vamos a hacer una tabla con las medias de las plantas
promedio_flores## Setosa Versicolor Virginica
## Sepal.Length 5.006 5.936 6.588
## Sepal.Width 3.428 2.770 2.974
## Petal.Length 1.462 4.260 5.552
## Petal.Width 0.246 1.326 2.026###OBTENER LA DESVIACIÓN ESTÁNDAR DE CADA FLOR DE SUS 4 VARIABLES DE LA BASE IRIS3
#HACER UNA TABLA PARECIDA A DATAFRAME promedio_flores
d<-apply(iris3[,,1],2,sd) #desviación estándar de setosa
#dado que queremos todos los renglones y todas las columnas, no especificamos ,, y sólo ponemos el 1 que es de la profundidad 1
#2 porque 1 es para filas y 2 para columnas
e<-apply(iris3[,,2],2,sd) #desviación estándar de versicolor
f<-apply(iris3[,,3],2,sd) #desviación estándar de virginica
dsflores3<-data.frame(Setosa=d,Versicolor=e,Virginica=f)
dsflores3## Setosa Versicolor Virginica
## Sepal L. 0.3524897 0.5161711 0.6358796
## Sepal W. 0.3790644 0.3137983 0.3224966
## Petal L. 0.1736640 0.4699110 0.5518947
## Petal W. 0.1053856 0.1977527 0.2746501iris3## , , Setosa
##
## Sepal L. Sepal W. Petal L. Petal W.
## [1,] 5.1 3.5 1.4 0.2
## [2,] 4.9 3.0 1.4 0.2
## [3,] 4.7 3.2 1.3 0.2
## [4,] 4.6 3.1 1.5 0.2
## [5,] 5.0 3.6 1.4 0.2
## [6,] 5.4 3.9 1.7 0.4
## [7,] 4.6 3.4 1.4 0.3
## [8,] 5.0 3.4 1.5 0.2
## [9,] 4.4 2.9 1.4 0.2
## [10,] 4.9 3.1 1.5 0.1
## [11,] 5.4 3.7 1.5 0.2
## [12,] 4.8 3.4 1.6 0.2
## [13,] 4.8 3.0 1.4 0.1
## [14,] 4.3 3.0 1.1 0.1
## [15,] 5.8 4.0 1.2 0.2
## [16,] 5.7 4.4 1.5 0.4
## [17,] 5.4 3.9 1.3 0.4
## [18,] 5.1 3.5 1.4 0.3
## [19,] 5.7 3.8 1.7 0.3
## [20,] 5.1 3.8 1.5 0.3
## [21,] 5.4 3.4 1.7 0.2
## [22,] 5.1 3.7 1.5 0.4
## [23,] 4.6 3.6 1.0 0.2
## [24,] 5.1 3.3 1.7 0.5
## [25,] 4.8 3.4 1.9 0.2
## [26,] 5.0 3.0 1.6 0.2
## [27,] 5.0 3.4 1.6 0.4
## [28,] 5.2 3.5 1.5 0.2
## [29,] 5.2 3.4 1.4 0.2
## [30,] 4.7 3.2 1.6 0.2
## [31,] 4.8 3.1 1.6 0.2
## [32,] 5.4 3.4 1.5 0.4
## [33,] 5.2 4.1 1.5 0.1
## [34,] 5.5 4.2 1.4 0.2
## [35,] 4.9 3.1 1.5 0.2
## [36,] 5.0 3.2 1.2 0.2
## [37,] 5.5 3.5 1.3 0.2
## [38,] 4.9 3.6 1.4 0.1
## [39,] 4.4 3.0 1.3 0.2
## [40,] 5.1 3.4 1.5 0.2
## [41,] 5.0 3.5 1.3 0.3
## [42,] 4.5 2.3 1.3 0.3
## [43,] 4.4 3.2 1.3 0.2
## [44,] 5.0 3.5 1.6 0.6
## [45,] 5.1 3.8 1.9 0.4
## [46,] 4.8 3.0 1.4 0.3
## [47,] 5.1 3.8 1.6 0.2
## [48,] 4.6 3.2 1.4 0.2
## [49,] 5.3 3.7 1.5 0.2
## [50,] 5.0 3.3 1.4 0.2
##
## , , Versicolor
##
## Sepal L. Sepal W. Petal L. Petal W.
## [1,] 7.0 3.2 4.7 1.4
## [2,] 6.4 3.2 4.5 1.5
## [3,] 6.9 3.1 4.9 1.5
## [4,] 5.5 2.3 4.0 1.3
## [5,] 6.5 2.8 4.6 1.5
## [6,] 5.7 2.8 4.5 1.3
## [7,] 6.3 3.3 4.7 1.6
## [8,] 4.9 2.4 3.3 1.0
## [9,] 6.6 2.9 4.6 1.3
## [10,] 5.2 2.7 3.9 1.4
## [11,] 5.0 2.0 3.5 1.0
## [12,] 5.9 3.0 4.2 1.5
## [13,] 6.0 2.2 4.0 1.0
## [14,] 6.1 2.9 4.7 1.4
## [15,] 5.6 2.9 3.6 1.3
## [16,] 6.7 3.1 4.4 1.4
## [17,] 5.6 3.0 4.5 1.5
## [18,] 5.8 2.7 4.1 1.0
## [19,] 6.2 2.2 4.5 1.5
## [20,] 5.6 2.5 3.9 1.1
## [21,] 5.9 3.2 4.8 1.8
## [22,] 6.1 2.8 4.0 1.3
## [23,] 6.3 2.5 4.9 1.5
## [24,] 6.1 2.8 4.7 1.2
## [25,] 6.4 2.9 4.3 1.3
## [26,] 6.6 3.0 4.4 1.4
## [27,] 6.8 2.8 4.8 1.4
## [28,] 6.7 3.0 5.0 1.7
## [29,] 6.0 2.9 4.5 1.5
## [30,] 5.7 2.6 3.5 1.0
## [31,] 5.5 2.4 3.8 1.1
## [32,] 5.5 2.4 3.7 1.0
## [33,] 5.8 2.7 3.9 1.2
## [34,] 6.0 2.7 5.1 1.6
## [35,] 5.4 3.0 4.5 1.5
## [36,] 6.0 3.4 4.5 1.6
## [37,] 6.7 3.1 4.7 1.5
## [38,] 6.3 2.3 4.4 1.3
## [39,] 5.6 3.0 4.1 1.3
## [40,] 5.5 2.5 4.0 1.3
## [41,] 5.5 2.6 4.4 1.2
## [42,] 6.1 3.0 4.6 1.4
## [43,] 5.8 2.6 4.0 1.2
## [44,] 5.0 2.3 3.3 1.0
## [45,] 5.6 2.7 4.2 1.3
## [46,] 5.7 3.0 4.2 1.2
## [47,] 5.7 2.9 4.2 1.3
## [48,] 6.2 2.9 4.3 1.3
## [49,] 5.1 2.5 3.0 1.1
## [50,] 5.7 2.8 4.1 1.3
##
## , , Virginica
##
## Sepal L. Sepal W. Petal L. Petal W.
## [1,] 6.3 3.3 6.0 2.5
## [2,] 5.8 2.7 5.1 1.9
## [3,] 7.1 3.0 5.9 2.1
## [4,] 6.3 2.9 5.6 1.8
## [5,] 6.5 3.0 5.8 2.2
## [6,] 7.6 3.0 6.6 2.1
## [7,] 4.9 2.5 4.5 1.7
## [8,] 7.3 2.9 6.3 1.8
## [9,] 6.7 2.5 5.8 1.8
## [10,] 7.2 3.6 6.1 2.5
## [11,] 6.5 3.2 5.1 2.0
## [12,] 6.4 2.7 5.3 1.9
## [13,] 6.8 3.0 5.5 2.1
## [14,] 5.7 2.5 5.0 2.0
## [15,] 5.8 2.8 5.1 2.4
## [16,] 6.4 3.2 5.3 2.3
## [17,] 6.5 3.0 5.5 1.8
## [18,] 7.7 3.8 6.7 2.2
## [19,] 7.7 2.6 6.9 2.3
## [20,] 6.0 2.2 5.0 1.5
## [21,] 6.9 3.2 5.7 2.3
## [22,] 5.6 2.8 4.9 2.0
## [23,] 7.7 2.8 6.7 2.0
## [24,] 6.3 2.7 4.9 1.8
## [25,] 6.7 3.3 5.7 2.1
## [26,] 7.2 3.2 6.0 1.8
## [27,] 6.2 2.8 4.8 1.8
## [28,] 6.1 3.0 4.9 1.8
## [29,] 6.4 2.8 5.6 2.1
## [30,] 7.2 3.0 5.8 1.6
## [31,] 7.4 2.8 6.1 1.9
## [32,] 7.9 3.8 6.4 2.0
## [33,] 6.4 2.8 5.6 2.2
## [34,] 6.3 2.8 5.1 1.5
## [35,] 6.1 2.6 5.6 1.4
## [36,] 7.7 3.0 6.1 2.3
## [37,] 6.3 3.4 5.6 2.4
## [38,] 6.4 3.1 5.5 1.8
## [39,] 6.0 3.0 4.8 1.8
## [40,] 6.9 3.1 5.4 2.1
## [41,] 6.7 3.1 5.6 2.4
## [42,] 6.9 3.1 5.1 2.3
## [43,] 5.8 2.7 5.1 1.9
## [44,] 6.8 3.2 5.9 2.3
## [45,] 6.7 3.3 5.7 2.5
## [46,] 6.7 3.0 5.2 2.3
## [47,] 6.3 2.5 5.0 1.9
## [48,] 6.5 3.0 5.2 2.0
## [49,] 6.2 3.4 5.4 2.3
## [50,] 5.9 3.0 5.1 1.8iris## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicarownames(dsflores3)<-c("Largo Sépalo", "Ancho Sépalo", "Largo Pétalo", "Ancho Pétalo")
colnames(dsflores3)<-c("Setosa","Versicolor","Virgínica")
dsflores3## Setosa Versicolor Virgínica
## Largo Sépalo 0.3524897 0.5161711 0.6358796
## Ancho Sépalo 0.3790644 0.3137983 0.3224966
## Largo Pétalo 0.1736640 0.4699110 0.5518947
## Ancho Pétalo 0.1053856 0.1977527 0.2746501knitr::opts_chunk$set(echo = TRUE)CLASE DEL 06 DE SEPTIEMBRE 2021
En esta clase trabajamos con las bases de datos que ya vienen precargadas en R iris e iris3. Además comenzamos a explorar las funciones que ya vienen precargadas en R, así como el comando aggregate.
########CLASE 06 DE SEPTIEMBRE 2021############
apply(iris3[,,3],2,summary) #Estadísticos de resumen## Sepal L. Sepal W. Petal L. Petal W.
## Min. 4.900 2.200 4.500 1.400
## 1st Qu. 6.225 2.800 5.100 1.800
## Median 6.500 3.000 5.550 2.000
## Mean 6.588 2.974 5.552 2.026
## 3rd Qu. 6.900 3.175 5.875 2.300
## Max. 7.900 3.800 6.900 2.500colMeans(iris[,-5])## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 5.843333 3.057333 3.758000 1.199333rbind(colMeans(iris[1:50,-5]) ,
colMeans(iris[51:100,-5]) ,
colMeans(iris[101:150,-5]) )## Sepal.Length Sepal.Width Petal.Length Petal.Width
## [1,] 5.006 3.428 1.462 0.246
## [2,] 5.936 2.770 4.260 1.326
## [3,] 6.588 2.974 5.552 2.026#Aggregate + ~
#x~Y #Izquierd, numérica... derecha, puede ser no numérica
aggregate(Sepal.Length ~ Species,data=iris,mean)## Species Sepal.Length
## 1 setosa 5.006
## 2 versicolor 5.936
## 3 virginica 6.588Sepal.Length ~ Species (max)## Sepal.Length ~ Species(max)iris## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicaaggregate(Sepal.Width ~ Species,data=iris,mean)## Species Sepal.Width
## 1 setosa 3.428
## 2 versicolor 2.770
## 3 virginica 2.974aggregate( cbind(Sepal.Length,Sepal.Width,Petal.Length,Petal.Width)~Species,data=iris,mean)## Species Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 setosa 5.006 3.428 1.462 0.246
## 2 versicolor 5.936 2.770 4.260 1.326
## 3 virginica 6.588 2.974 5.552 2.026aggregate( cbind(Sepal.Length,Sepal.Width,Petal.Length,Petal.Width)~Species,data=iris,sd)## Species Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 setosa 0.3524897 0.3790644 0.1736640 0.1053856
## 2 versicolor 0.5161711 0.3137983 0.4699110 0.1977527
## 3 virginica 0.6358796 0.3224966 0.5518947 0.2746501Sepal.Length~Species(max)## Sepal.Length ~ Species(max)#######VAMOS A TRABAJAR CON OTRA BASE DE DATOS PRECARGADA EN R############
data("ToothGrowth")
ToothGrowth## len supp dose
## 1 4.2 VC 0.5
## 2 11.5 VC 0.5
## 3 7.3 VC 0.5
## 4 5.8 VC 0.5
## 5 6.4 VC 0.5
## 6 10.0 VC 0.5
## 7 11.2 VC 0.5
## 8 11.2 VC 0.5
## 9 5.2 VC 0.5
## 10 7.0 VC 0.5
## 11 16.5 VC 1.0
## 12 16.5 VC 1.0
## 13 15.2 VC 1.0
## 14 17.3 VC 1.0
## 15 22.5 VC 1.0
## 16 17.3 VC 1.0
## 17 13.6 VC 1.0
## 18 14.5 VC 1.0
## 19 18.8 VC 1.0
## 20 15.5 VC 1.0
## 21 23.6 VC 2.0
## 22 18.5 VC 2.0
## 23 33.9 VC 2.0
## 24 25.5 VC 2.0
## 25 26.4 VC 2.0
## 26 32.5 VC 2.0
## 27 26.7 VC 2.0
## 28 21.5 VC 2.0
## 29 23.3 VC 2.0
## 30 29.5 VC 2.0
## 31 15.2 OJ 0.5
## 32 21.5 OJ 0.5
## 33 17.6 OJ 0.5
## 34 9.7 OJ 0.5
## 35 14.5 OJ 0.5
## 36 10.0 OJ 0.5
## 37 8.2 OJ 0.5
## 38 9.4 OJ 0.5
## 39 16.5 OJ 0.5
## 40 9.7 OJ 0.5
## 41 19.7 OJ 1.0
## 42 23.3 OJ 1.0
## 43 23.6 OJ 1.0
## 44 26.4 OJ 1.0
## 45 20.0 OJ 1.0
## 46 25.2 OJ 1.0
## 47 25.8 OJ 1.0
## 48 21.2 OJ 1.0
## 49 14.5 OJ 1.0
## 50 27.3 OJ 1.0
## 51 25.5 OJ 2.0
## 52 26.4 OJ 2.0
## 53 22.4 OJ 2.0
## 54 24.5 OJ 2.0
## 55 24.8 OJ 2.0
## 56 30.9 OJ 2.0
## 57 26.4 OJ 2.0
## 58 27.3 OJ 2.0
## 59 29.4 OJ 2.0
## 60 23.0 OJ 2.0head(ToothGrowth)## len supp dose
## 1 4.2 VC 0.5
## 2 11.5 VC 0.5
## 3 7.3 VC 0.5
## 4 5.8 VC 0.5
## 5 6.4 VC 0.5
## 6 10.0 VC 0.5dim(ToothGrowth)## [1] 60 3# ?ToothGrowth
mean(ToothGrowth$len)## [1] 18.81333mean(ToothGrowth$dose)## [1] 1.166667sd(ToothGrowth$dose)## [1] 0.6288722max(ToothGrowth$len)## [1] 33.9min(ToothGrowth$len)## [1] 4.2###Cuál es el promedio de crecimiento por método de aplicación (supp)?###
aggregate(len~supp,data=ToothGrowth,mean)## supp len
## 1 OJ 20.66333
## 2 VC 16.96333Sepal.Length~Species(max)## Sepal.Length ~ Species(max)head(ToothGrowth)## len supp dose
## 1 4.2 VC 0.5
## 2 11.5 VC 0.5
## 3 7.3 VC 0.5
## 4 5.8 VC 0.5
## 5 6.4 VC 0.5
## 6 10.0 VC 0.5aggregate(len~dose,data=ToothGrowth,mean)## dose len
## 1 0.5 10.605
## 2 1.0 19.735
## 3 2.0 26.100aggregate(len~supp+dose,data=ToothGrowth,mean)## supp dose len
## 1 OJ 0.5 13.23
## 2 VC 0.5 7.98
## 3 OJ 1.0 22.70
## 4 VC 1.0 16.77
## 5 OJ 2.0 26.06
## 6 VC 2.0 26.14aggregate(len~dose+supp,data=ToothGrowth,mean)## dose supp len
## 1 0.5 OJ 13.23
## 2 1.0 OJ 22.70
## 3 2.0 OJ 26.06
## 4 0.5 VC 7.98
## 5 1.0 VC 16.77
## 6 2.0 VC 26.14####VAMOS A AGREGARLE UNA COLUMNA A NUESTRA BASE DE DATOS, GENERANDO UN VECTOR ALEATORIO
set.seed(15148)
pesos<-rnorm(60,10,2)
peso_dientes<-data.frame(ToothGrowth,pesos)
peso_dientes## len supp dose pesos
## 1 4.2 VC 0.5 10.280418
## 2 11.5 VC 0.5 8.110956
## 3 7.3 VC 0.5 12.450044
## 4 5.8 VC 0.5 9.648081
## 5 6.4 VC 0.5 10.382548
## 6 10.0 VC 0.5 8.637649
## 7 11.2 VC 0.5 8.320436
## 8 11.2 VC 0.5 9.866794
## 9 5.2 VC 0.5 9.063384
## 10 7.0 VC 0.5 10.679695
## 11 16.5 VC 1.0 8.781847
## 12 16.5 VC 1.0 11.540903
## 13 15.2 VC 1.0 11.184907
## 14 17.3 VC 1.0 9.652038
## 15 22.5 VC 1.0 9.319646
## 16 17.3 VC 1.0 12.283228
## 17 13.6 VC 1.0 9.666190
## 18 14.5 VC 1.0 9.822652
## 19 18.8 VC 1.0 8.834662
## 20 15.5 VC 1.0 11.847592
## 21 23.6 VC 2.0 7.859877
## 22 18.5 VC 2.0 11.709384
## 23 33.9 VC 2.0 10.325133
## 24 25.5 VC 2.0 8.811817
## 25 26.4 VC 2.0 9.850443
## 26 32.5 VC 2.0 7.835931
## 27 26.7 VC 2.0 12.702399
## 28 21.5 VC 2.0 8.360866
## 29 23.3 VC 2.0 9.530358
## 30 29.5 VC 2.0 7.831811
## 31 15.2 OJ 0.5 11.143076
## 32 21.5 OJ 0.5 8.543388
## 33 17.6 OJ 0.5 5.731709
## 34 9.7 OJ 0.5 13.895892
## 35 14.5 OJ 0.5 8.338295
## 36 10.0 OJ 0.5 11.676682
## 37 8.2 OJ 0.5 8.623471
## 38 9.4 OJ 0.5 11.092726
## 39 16.5 OJ 0.5 10.913397
## 40 9.7 OJ 0.5 10.282987
## 41 19.7 OJ 1.0 9.485296
## 42 23.3 OJ 1.0 8.471348
## 43 23.6 OJ 1.0 7.686649
## 44 26.4 OJ 1.0 8.507136
## 45 20.0 OJ 1.0 6.140012
## 46 25.2 OJ 1.0 8.039177
## 47 25.8 OJ 1.0 11.511190
## 48 21.2 OJ 1.0 9.451776
## 49 14.5 OJ 1.0 6.942627
## 50 27.3 OJ 1.0 6.824122
## 51 25.5 OJ 2.0 10.135482
## 52 26.4 OJ 2.0 9.587137
## 53 22.4 OJ 2.0 8.938045
## 54 24.5 OJ 2.0 6.171893
## 55 24.8 OJ 2.0 9.583907
## 56 30.9 OJ 2.0 8.308800
## 57 26.4 OJ 2.0 11.405421
## 58 27.3 OJ 2.0 8.396101
## 59 29.4 OJ 2.0 8.690716
## 60 23.0 OJ 2.0 10.741280aggregate(cbind(pesos,len)~dose+supp,data=peso_dientes,mean)## dose supp pesos len
## 1 0.5 OJ 10.024162 13.23
## 2 1.0 OJ 8.305933 22.70
## 3 2.0 OJ 9.195878 26.06
## 4 0.5 VC 9.744000 7.98
## 5 1.0 VC 10.293366 16.77
## 6 2.0 VC 9.481802 26.14aggregate(cbind(len,pesos)~dose+supp,data=peso_dientes,mean)## dose supp len pesos
## 1 0.5 OJ 13.23 10.024162
## 2 1.0 OJ 22.70 8.305933
## 3 2.0 OJ 26.06 9.195878
## 4 0.5 VC 7.98 9.744000
## 5 1.0 VC 16.77 10.293366
## 6 2.0 VC 26.14 9.481802pesos2<-runif(60)
aggregate(cbind(len,pesos,pesos2)~dose+supp,data=peso_dientes,mean)## dose supp len pesos pesos2
## 1 0.5 OJ 13.23 10.024162 0.4034515
## 2 1.0 OJ 22.70 8.305933 0.5679498
## 3 2.0 OJ 26.06 9.195878 0.4765870
## 4 0.5 VC 7.98 9.744000 0.4664007
## 5 1.0 VC 16.77 10.293366 0.5118205
## 6 2.0 VC 26.14 9.481802 0.3408846knitr::opts_chunk$set(echo = TRUE)CLASE DEL 07 DE SEPTIEMBRE 2021 Y DEL 14 DE SEPTIEMBRE 2021
En estas dos clases trabajamos con los datos de Aguascalientes. Aprendimos a aplicar funciones que ya vienen precargadas en R a un conjunto de datos. Para esto es de especial utilidad el comando aggregate.
### CLASE 07 DE SEPTIEMBRE 2021
#install.packages("readxl")
library(readxl)
#install.packages("kernlab")
library("kernlab")
datos10<-read_excel("C:/Users/mez1/Documents/CIDE/METPOL/1 SEMESTRE/BASES DE DATOS/datos_ags_estado_2020.xlsx",sheet="Hoja1",range="A1:HV481",na=c("*","N/D"))
attach(datos10)
datos10## # A tibble: 480 x 230
## ENTIDAD NOM_ENT MUN NOM_MUN LOC NOM_LOC AGEB MZA POBTOT POBFEM POBMAS
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 01 Aguasca~ 001 Aguasc~ 0001 Total ~ 0017 000 2237 1137 1100
## 2 01 Aguasca~ 001 Aguasc~ 0001 Total ~ 006A 000 1411 712 699
## 3 01 Aguasca~ 001 Aguasc~ 0001 Total ~ 0106 000 2962 1497 1465
## 4 01 Aguasca~ 001 Aguasc~ 0001 Total ~ 0163 000 2698 1305 1393
## 5 01 Aguasca~ 001 Aguasc~ 0001 Total ~ 0182 000 2218 1110 1108
## 6 01 Aguasca~ 001 Aguasc~ 0001 Total ~ 0229 000 300 147 153
## 7 01 Aguasca~ 001 Aguasc~ 0001 Total ~ 0233 000 1400 731 669
## 8 01 Aguasca~ 001 Aguasc~ 0001 Total ~ 0286 000 3262 1679 1583
## 9 01 Aguasca~ 001 Aguasc~ 0001 Total ~ 0290 000 1668 906 762
## 10 01 Aguasca~ 001 Aguasc~ 0001 Total ~ 0303 000 2202 1217 985
## # ... with 470 more rows, and 219 more variables: P_0A2 <chr>, P_0A2_F <chr>,
## # P_0A2_M <chr>, P_3YMAS <chr>, P_3YMAS_F <chr>, P_3YMAS_M <chr>,
## # P_5YMAS <chr>, P_5YMAS_F <chr>, P_5YMAS_M <chr>, P_12YMAS <chr>,
## # P_12YMAS_F <chr>, P_12YMAS_M <chr>, P_15YMAS <chr>, P_15YMAS_F <chr>,
## # P_15YMAS_M <chr>, P_18YMAS <chr>, P_18YMAS_F <chr>, P_18YMAS_M <chr>,
## # P_3A5 <chr>, P_3A5_F <chr>, P_3A5_M <chr>, P_6A11 <chr>, P_6A11_F <chr>,
## # P_6A11_M <chr>, P_8A14 <chr>, P_8A14_F <chr>, P_8A14_M <chr>, ...View(datos10)
mean(as.numeric(datos10$POBTOT))## [1] 2497.315#mean(datos10$POBTOT)
is.data.frame(datos10)## [1] TRUE#¿cUÁL ES LA POBLACIÓN TOTAL DE LOS 11 MUNICIPIOS?
#TOTAL(sum)
aggregate(as.numeric(POBTOT)~NOM_MUN,data=datos10,mean)## NOM_MUN as.numeric(POBTOT)
## 1 Aguascalientes 2707.045
## 2 Asientos 1110.000
## 3 Calvillo 1668.647
## 4 Cosío 1211.000
## 5 El Llano 2102.333
## 6 Jesús María 2378.634
## 7 Pabellón de Arteaga 2356.733
## 8 Rincón de Romos 2660.125
## 9 San Francisco de los Romo 2431.368
## 10 San José de Gracia 934.500
## 11 Tepezalá 1302.000aggregate(as.numeric(POBTOT)~NOM_MUN,data=datos10,sum)## NOM_MUN as.numeric(POBTOT)
## 1 Aguascalientes 901446
## 2 Asientos 17760
## 3 Calvillo 28367
## 4 Cosío 8477
## 5 El Llano 6307
## 6 Jesús María 97524
## 7 Pabellón de Arteaga 35351
## 8 Rincón de Romos 42562
## 9 San Francisco de los Romo 46196
## 10 San José de Gracia 5607
## 11 Tepezalá 9114#¿cUÁL ES EL TOTAL DE HOMBRES DE LOS 11 MUNICIPIOS?
aggregate(as.numeric(POBMAS)~NOM_MUN,data=datos10,sum)## NOM_MUN as.numeric(POBMAS)
## 1 Aguascalientes 437749
## 2 Asientos 8673
## 3 Calvillo 13806
## 4 Cosío 4067
## 5 El Llano 3088
## 6 Jesús María 48011
## 7 Pabellón de Arteaga 17305
## 8 Rincón de Romos 20808
## 9 San Francisco de los Romo 22865
## 10 San José de Gracia 2635
## 11 Tepezalá 4524#¿CUÁL ES EL TOTAL DE MUJERES DE LOS 11 MUNICIPIOS (PROPORCIÓN)?
aggregate(as.numeric(POBFEM)~NOM_MUN,data=datos10,sum)## NOM_MUN as.numeric(POBFEM)
## 1 Aguascalientes 463697
## 2 Asientos 9087
## 3 Calvillo 14561
## 4 Cosío 4410
## 5 El Llano 3219
## 6 Jesús María 49513
## 7 Pabellón de Arteaga 18046
## 8 Rincón de Romos 21748
## 9 San Francisco de los Romo 23331
## 10 San José de Gracia 2972
## 11 Tepezalá 4590###TABLA POBLACIÓN TOTAL HOMBRES Y MUJERES
tabla1<-aggregate(cbind(as.numeric(POBTOT),as.numeric(POBFEM),as.numeric(POBMAS))~NOM_MUN,data=datos10,sum)
View(tabla1)
attach(tabla1)## The following object is masked from datos10:
##
## NOM_MUNprop_fem<-tabla1$V2/tabla1$V1
tabla2<-data.frame(tabla1,Prop_fem=prop_fem)
View(tabla2)
tabla15<-aggregate(cbind(as.numeric(VIVTOT))~NOM_MUN,data=datos10, FUN=sum)
#rm(TABLA15)
#tabla15$Pro_viv<-tabla15$V2/tabla15$V1
#View(tabla15$Pro_viv)
san_jose<-which(datos10$NOM_MUN=="San José de Gracia")
datos_san_jose<-datos10[san_jose,]
datos_san_jose$POBTOT<-sum(as.numeric(datos_san_jose$POBTOT))
tabla18<-aggregate(cbind(as.numeric(POBTOT),as.numeric(POBFEM))~NOM_MUN+AGEB,data=datos10,FUN=sum)
tabla18$Prop_muj<-tabla18$V2/tabla18$V1
tabla18$V2/tabla18$V1## [1] 0.51292802 0.50827000 0.51157728 0.51379691 0.52498458 0.49601100
## [7] 0.51008493 0.51936555 0.52561608 0.51428571 0.50460666 0.51669545
## [13] 0.50812547 0.51571520 0.51531654 0.51763367 0.51771872 0.50113379
## [19] 0.51734694 0.50205950 0.50818331 0.50540176 0.53290083 0.53894081
## [25] 0.52068345 0.51226693 0.50962672 0.52140351 0.51143854 0.52059004
## [31] 0.51796994 0.52838428 0.50661925 0.50947314 0.49973291 0.47257384
## [37] 0.52464789 0.49200710 0.51657356 0.50402576 0.50255220 0.51721612
## [43] 0.51612903 0.49252839 0.50419287 0.50642674 0.50210970 0.50646950
## [49] 0.49324932 0.48760331 0.48369162 0.54703196 0.48809524 0.50874738
## [55] 0.51339764 0.50365497 0.53590193 0.49734357 0.51113173 0.52342641
## [61] 0.54148472 0.51586844 0.50045086 0.54868270 0.53222342 0.50633293
## [67] 0.49836334 0.50863836 0.51502030 0.49926145 0.51529412 0.48843188
## [73] 0.52459016 0.51201923 0.48798799 0.49480249 0.49509804 0.52655889
## [79] 0.51129944 0.51023465 0.52240896 0.51342129 0.48263349 0.49000000
## [85] 0.52214286 0.51095690 0.46472019 0.51287910 0.49880178 0.51020408
## [91] 0.49202128 0.50492754 0.51205185 0.51001214 0.46952835 0.51224784
## [97] 0.50999512 0.46511628 0.50835866 0.48866499 0.51612903 0.50746269
## [103] 0.55555556 0.51539708 0.50060606 0.51173322 0.52534562 0.51471490
## [109] 0.51918559 0.54316547 0.51898734 0.51396226 0.50059312 0.55267938
## [115] 0.51446541 0.46416382 0.52750225 0.49921198 0.52457695 0.51786727
## [121] 0.54007782 0.51094891 0.50326797 0.50929669 0.54450583 0.52549020
## [127] 0.48958333 0.50420463 0.55180442 0.45121951 0.51648352 0.53341861
## [133] 0.51167445 0.49934938 0.49133449 0.50801688 0.56974460 0.53638814
## [139] 0.50814011 0.53248899 0.50952986 0.51981506 0.51722282 0.50161812
## [145] 0.53098071 0.50160624 0.53289474 0.50000000 0.49589858 0.52793103
## [151] 0.50038329 0.52586558 0.51662887 0.49551387 0.51036637 0.53268859
## [157] 0.52520803 0.50548847 0.52484342 0.50669643 0.51859256 0.49285714
## [163] 0.53990285 0.55109232 0.50568182 0.53288288 0.50219780 0.51966741
## [169] 0.51495378 0.52935323 0.50683413 0.53333333 0.50754098 0.53794731
## [175] 0.54084507 0.53753754 0.52194656 0.54025424 0.49264706 0.55382331
## [181] 0.50061050 0.52290679 0.52259763 0.54618227 0.55524862 0.54326123
## [187] 0.52141467 0.51154734 0.56028369 0.48888889 0.52449799 0.50813871
## [193] 0.52915159 0.54800591 0.51254480 0.54615385 0.55075758 0.55078684
## [199] 0.53624762 0.56538763 0.52917093 0.51030928 0.55446927 0.53321033
## [205] 0.50208160 0.52035887 0.56030702 0.51774113 0.49950249 0.53460246
## [211] 0.52579468 0.50882825 0.52005871 0.52037803 0.49433962 0.49836468
## [217] 0.49543677 0.49770759 0.50223297 0.48270181 0.49449204 0.48189046
## [223] 0.48954373 0.52368648 0.51186270 0.50610717 0.48611111 0.51825935
## [229] 0.51360634 0.50840673 0.50522840 0.50000000 0.52877634 0.52586207
## [235] 0.54191617 0.48802083 0.47058824 0.50936330 0.53164557 0.53392857
## [241] 0.50847458 0.51850401 0.51826923 0.50854449 0.50963763 0.52455048
## [247] 0.50323375 0.51938700 NaN 0.49355625 0.52380952 0.54131054
## [253] 0.53697749 0.51545530 0.54064772 0.52420917 0.52882985 0.54115226
## [259] 0.50841889 0.53441683 0.51382170 0.52796774 0.50792812 0.51900437
## [265] 0.55603080 0.51770658 0.51783518 0.52411041 0.51239669 0.51684263
## [271] 0.51614114 0.54347008 0.51189373 0.52313297 0.50538014 0.50581528
## [277] 0.50631912 0.51206492 0.54510309 0.53114887 0.51802704 0.52247191
## [283] 0.53896104 0.53881098 0.52987066 0.51794728 0.53286853 0.52828380
## [289] 0.49565454 0.50490050 0.49378882 0.50256598 0.53269398 0.51471215
## [295] 0.51616231 0.51172708 0.52283105 0.52586005 0.51136364 0.52548821
## [301] 0.51390801 0.52744186 0.52104080 0.50521610 0.52570574 0.52168525
## [307] 0.53281639 0.51137850 0.50827572 0.52081362 0.51338346 0.51906841
## [313] 0.50976845 0.52470696 0.51417973 0.52245720 0.51888044 0.51875545
## [319] 0.51684509 0.51482480 0.51721715 0.51968504 0.52712846 0.50580396
## [325] 0.50630782 0.51660587 0.50746840 0.51418951 0.50050454 0.51317006
## [331] 0.51710489 0.53259141 0.46453089 0.50127226 0.50914634 0.50366610
## [337] 0.50742983 0.50483255 0.50478522 0.52460457 0.51592357 0.51047248
## [343] 0.51065292 0.49834188 0.51409052 0.51869159 0.51038422 0.51725293
## [349] 0.49699571 0.49097018 0.52252693 0.50571037 0.51631557 0.51934409
## [355] 0.50332712 0.51721377 0.50676111 0.50759878 0.49025579 0.48943270
## [361] 0.51621339 0.51007299 0.51482386 0.52941176 0.53061224 0.50374724
## [367] 0.51694487 0.50389144 0.51643979 0.51018699 0.48687172 0.49491049
## [373] 0.51913379 0.49634026 0.50323276 0.52631579 0.39473684 0.52094972
## [379] 0.05555556 0.51010309 0.50909091 0.51469194 0.47163121 0.49723375
## [385] 0.48750000 0.49505840 0.52150697 0.51218248 0.51865828 0.51236641
## [391] 0.50070588 0.53067729 0.50276799 0.50976997 0.51333333 0.51043155
## [397] 0.50729858 0.49936176 0.52369942 0.51254753 0.51728272 0.54551008
## [403] 0.50880000 0.52427184 0.50788022 0.50629874 0.51083130 0.50877909
## [409] 0.50522534 0.52108037 0.50088928 0.51380928 0.51085142 0.50958096
## [415] 0.50746269 0.50944444 0.48830285 0.50698324 0.49745006 0.51609384
## [421] 0.54155496 0.50264296 0.51005143 0.52080891 0.50699001 0.49787152
## [427] 0.48875256 0.52493438 0.51684088 0.49451888 0.49376947 0.52896552
## [433] 0.50427350 0.49084465 0.49634108 0.50687146 0.50512821 0.49945652
## [439] 0.51355422 0.51947195 0.50701754 0.49249094 0.47274633 0.56276151
## [445] 0.49148418 0.51369356 0.50490490 0.53803596 0.36111111 0.57142857
## [451] 0.49489796 0.49318182 0.49871465 0.50494857 0.49420608 0.51515152
## [457] 0.45454545 0.54612546 0.50838635 0.47849462 0.51309789 0.50271641
## [463] 0.51595457 0.49581173 0.51237113 0.51330989 0.54078014 0.49816850
## [469] 0.50311365 0.50350989 0.50789474 0.47846890 0.49271137 0.49362477
## [475] 0.49218550 0.51851852 0.49814126 0.50966023 0.49686099#?sort
mean(as.numeric(datos10$P_0A2_F))## [1] NAmean(as.numeric(datos10$P_0A2_F),na.rm=TRUE) ## [1] 62.48817####REMOVIÓ LOS NAs PARA QUE ME DIERA UN VALOR NUMÉRICO
sum(is.na(as.numeric(datos10$P_0A2_F)))## [1] 15# summary(as.numeric(datos10$P_0A2_F)
#summary(as.numeric(datos10$P_0A2_F)[7]
#aggregate(as.numeric(P_0A2_F)~NOM_MUN,FUN=mean,data=datos10,na.rm=TRUE)
#LÓGICA DE R: 0=FALSO 1=VERDADERO
#PROMEDIO POR MUNICIPIO
aggregate(as.numeric(P_0A2_F)~NOM_MUN,FUN=mean,data=datos10,na.rm=TRUE)## NOM_MUN as.numeric(P_0A2_F)
## 1 Aguascalientes 63.59077
## 2 Asientos 40.57143
## 3 Calvillo 51.06667
## 4 Cosío 36.42857
## 5 El Llano 63.66667
## 6 Jesús María 64.17949
## 7 Pabellón de Arteaga 63.66667
## 8 Rincón de Romos 83.26667
## 9 San Francisco de los Romo 74.47368
## 10 San José de Gracia 36.50000
## 11 Tepezalá 38.42857#*COEFICIENTE DE VARIACIÓN...
#*FUN= función realizada por nosotros ¿Y cómo la realizamos?
#*
#*FUNCIONES PERSONALIZADAS ##FUNCIONES DEL USUARIO##
#*¿ENTRADA DE LA FUNCIÓN?¿CUÁL ES LA SALIDA?
#*
#*PROGRAMAN COMO SE REQUIERA
#*
#*FUNCIONES PERSONALIZADAS
#* *1) RANGO(max-min)
#* *2) COEFICIENTE DE VARIABILIDAD SIGMA/ABS(media)
# *3) MEDIA CORTADA (descartar los valores mayores o menores)
rango<-function(x){max(x)-min(x)}
### CLASE 14 DE SEPTIEMBRE 2021
#CLASE 2###
NOM_MUN=="San José de Gracia"## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE#*MUN=="011"
#*sanjose<-which(ags$NOM_MUN=="San José de Gracia")
#*dsanjose<-ags[sanjose,]
#*pobsanjose<-dsanjose$POBTOT
#sum(as.numeric(pobsanjose))
#tabla18<-aggregate(cbind(as.numeric(NPOBTOT),as.numeric(NPOBFEM))~NOM_MUN+AGEB,DATA=ags,FUN=sum)
#na.omit(ags)
#NPOBTOT<-gsub(POBTOT)
#NPOBFEM<-na.exclude(POBFEM)
#?gsub
knitr::opts_chunk$set(echo = TRUE)CLASE 21 DE SEPTIEMBRE 2021
En esta clase aprendimos a diseñar funciones y a aplicarlos a un conjunto de datos. Para esto es de especial utilidad el comando apply.
###CLASE 21 DE SEPTIEMBRE 2021
eigen(cor(iris[,-5]))## eigen() decomposition
## $values
## [1] 2.91849782 0.91403047 0.14675688 0.02071484
##
## $vectors
## [,1] [,2] [,3] [,4]
## [1,] 0.5210659 -0.37741762 0.7195664 0.2612863
## [2,] -0.2693474 -0.92329566 -0.2443818 -0.1235096
## [3,] 0.5804131 -0.02449161 -0.1421264 -0.8014492
## [4,] 0.5648565 -0.06694199 -0.6342727 0.5235971descomposi<-function(w){
return(eigen(cor(w))$vector)
}
descomposi(iris3[,,1])## [,1] [,2] [,3] [,4]
## [1,] 0.6044164 0.3349908 -0.0673598261 0.71966982
## [2,] 0.5756194 0.4408461 -0.0007138239 -0.68870645
## [3,] 0.3754348 -0.6269717 -0.6770628102 -0.08683986
## [4,] 0.4029788 -0.5480350 0.7328356536 -0.01475204descomposi(iris3[,,1])## [,1] [,2] [,3] [,4]
## [1,] 0.6044164 0.3349908 -0.0673598261 0.71966982
## [2,] 0.5756194 0.4408461 -0.0007138239 -0.68870645
## [3,] 0.3754348 -0.6269717 -0.6770628102 -0.08683986
## [4,] 0.4029788 -0.5480350 0.7328356536 -0.01475204descomposi(iris3[,1:3,2])## [,1] [,2] [,3]
## [1,] -0.5954707 0.4261450 0.68103976
## [2,] -0.5289341 -0.8460217 0.06690233
## [3,] -0.6046845 0.3203868 -0.72918374descomposi(iris3[1:10,,2])## [,1] [,2] [,3] [,4]
## [1,] -0.5023311 0.5888137 0.1850325 0.60557812
## [2,] -0.4971351 -0.0577743 -0.8607149 -0.09321322
## [3,] -0.5247149 0.1978622 0.3700007 -0.74069177
## [4,] -0.4745466 -0.7815440 0.2967024 0.27561229descomposi2<-function(w){
lista1<-eigen(cor(w))
return(list(lista1$vectors,lista1$values))
}
descomposi2(iris3[,,1])## [[1]]
## [,1] [,2] [,3] [,4]
## [1,] 0.6044164 0.3349908 -0.0673598261 0.71966982
## [2,] 0.5756194 0.4408461 -0.0007138239 -0.68870645
## [3,] 0.3754348 -0.6269717 -0.6770628102 -0.08683986
## [4,] 0.4029788 -0.5480350 0.7328356536 -0.01475204
##
## [[2]]
## [1] 2.0585402 1.0221782 0.6678202 0.2514613descomposi2(iris3[,,1])[[1]][,1]## [1] 0.6044164 0.5756194 0.3754348 0.4029788descomposi3<-function(w){
lista1<-eigen(cor(w))
return(list(Vector=lista1$vectors,Valores=lista1$values))
}
descomposi3(iris3[,,1])$Vector## [,1] [,2] [,3] [,4]
## [1,] 0.6044164 0.3349908 -0.0673598261 0.71966982
## [2,] 0.5756194 0.4408461 -0.0007138239 -0.68870645
## [3,] 0.3754348 -0.6269717 -0.6770628102 -0.08683986
## [4,] 0.4029788 -0.5480350 0.7328356536 -0.01475204descomposi3(iris3[,,1])$Valores## [1] 2.0585402 1.0221782 0.6678202 0.2514613coeficiente<-matrix(c(3,2,1,5,3,4,1,1,-1),ncol=3,byrow=TRUE)
respuesta<-c(1,2,1)
solve(coeficiente,respuesta)## [1] -4 6 1res_ecua<-function(matriz,respu){
solve(matriz,respu) }
res_ecua(coeficiente,respuesta)## [1] -4 6 1res_ecua2<-function(x,y){
solucion<-solve(x,y)
return(paste0("La solución al sistema es: x=",solucion[1]))
}
res_ecua2(y=respuesta,x=coeficiente)## [1] "La solución al sistema es: x=-3.99999999999999"coef2<-matrix(c(5,-3,-1,1,4,-6,2,3,4),ncol=3,byrow=TRUE)
resp2<-c(1,-1,9)
res_ecua2(y=resp2,x=coef2)## [1] "La solución al sistema es: x=1"#res_ecua3<-function(x,y){
# solucion<-solve(x,y)
#return(paste0("La solución al sistema es: x=",solucion[1]))
#return(paste0("La solución al sistema es: y=",solucion[2]))
# return(paste0("La solución al sistema es: z=",solucion[3]))
#}
#res_ecua3(y=resp2,x=coef2)
#res_ecua3<-function(x,y){
# solucion<-solve(x,y)
#return(paste0("La solución al sistema es: x=",solucion[1]),
# "La solución al sistema es: y=",solucion[2],
# "La solución al sistema es: z=",solucion[3])
#}
#res_ecua3(y=resp2,x=coef2)
res_ecua4<-function(x,y){
solucion<-solve(x,y)
return(cat("La solución al sistema es:","\n",
"x=",solucion[1],",","\n",
"y=",solucion[2],", ","\n",
"z=",solucion[3]) )
}
res_ecua4(y=respuesta,x=coeficiente)## La solución al sistema es:
## x= -4 ,
## y= 6 ,
## z= 1#Investigar: substr
#https://rpubs.com/coronamexico/662718
#https://rpubs.com/coronamexico/intentoclase
library(readxl)
covid_oaxaca<-read.csv("C:/Users/mez1/Documents/CIDE/METPOL/1 SEMESTRE/BASES DE DATOS/covid_oaxaca.csv")
funcionas2<-as.Date(covid_oaxaca$FECHA_INGRESO,format="%Y-%m-%d")
as.Date #Transform dates## function (x, ...)
## UseMethod("as.Date")
## <bytecode: 0x0000000022363348>
## <environment: namespace:base>strptime #Handling time and dates at the same time## function (x, format, tz = "")
## {
## y <- .Internal(strptime(as.character(x), format, tz))
## names(y$year) <- names(x)
## y
## }
## <bytecode: 0x00000000238e21a8>
## <environment: namespace:base>#install.packages("lubridate")
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionfuncionas3<-as.Date(covid_oaxaca$FECHA_SINTOMAS,format="%Y-%m-%d")
table(month(funcionas2))##
## 1 2 3 4 5 6 7 8 9 10
## 162 149 384 912 3358 6812 7155 4721 5233 4481hist(as.numeric(funcionas2-funcionas3))
funcionas3<-as.Date(covid_oaxaca$FECHA_SINTOMAS,format="%Y-%m-%d")
#library(lubridate)
day(funcionas2)## [1] 6 31 4 27 26 30 22 31 28 25 30 26 29 13 13 25 20 31 31 1 26 8 31 23
## [25] 25 23 6 3 6 29 21 31 24 19 5 24 23 14 7 30 29 11 14 16 8 1 12 1
## [49] 18 18 3 29 15 2 6 31 24 5 21 27 17 3 24 2 27 2 27 12 26 31 11 24
## [73] 18 27 25 27 31 16 5 31 27 7 6 17 25 9 27 4 13 24 4 10 6 25 10 24
## [97] 16 20 28 25 17 5 13 17 24 14 12 27 14 13 13 21 29 9 26 18 23 2 27 26
## [121] 14 29 23 17 31 13 1 26 1 15 21 24 17 23 29 2 28 20 1 28 2 17 30 19
## [145] 2 22 19 25 17 4 16 27 21 23 4 1 16 23 8 10 18 19 24 10 29 30 11 6
## [169] 9 31 17 27 12 27 5 26 22 2 6 31 12 31 12 20 3 31 27 3 21 18 26 12
## [193] 14 6 26 30 22 3 26 22 2 16 27 27 13 22 25 2 1 31 11 14 27 17 25 18
## [217] 24 22 7 20 15 19 17 15 27 9 18 8 29 16 13 22 25 21 27 18 27 30 5 10
## [241] 12 20 19 27 20 28 19 31 13 1 27 26 5 19 19 25 20 28 27 27 9 10 27 31
## [265] 2 15 29 29 22 7 25 5 21 6 31 23 19 28 9 3 26 27 27 18 2 25 2 1
## [289] 1 17 14 27 19 26 30 13 2 17 27 2 31 11 27 25 1 17 13 6 10 28 27 11
## [313] 9 2 24 6 8 19 26 31 17 12 16 23 3 2 3 10 22 14 3 26 19 27 2 31
## [337] 26 6 6 8 25 18 31 17 13 21 19 31 6 25 25 2 27 26 9 29 22 25 25 31
## [361] 28 10 12 20 19 22 15 18 22 14 14 25 10 2 27 18 13 11 3 15 16 21 26 3
## [385] 11 3 1 3 3 3 11 1 28 19 2 1 16 16 23 1 30 24 25 14 26 10 26 8
## [409] 28 28 25 6 24 1 19 30 27 18 13 5 29 29 16 24 27 2 4 11 10 19 25 4
## [433] 25 8 28 30 30 23 26 29 1 2 18 26 2 5 2 28 26 26 14 23 16 3 26 21
## [457] 1 27 15 22 2 25 1 2 23 16 27 8 25 10 18 21 5 27 8 17 4 29 3 26
## [481] 1 2 25 15 6 14 13 10 1 4 12 3 1 20 31 28 30 25 9 12 29 29 27 27
## [505] 3 3 2 28 7 24 6 12 15 18 5 29 3 25 11 24 14 2 9 22 17 31 4 2
## [529] 18 15 31 22 6 30 15 16 28 28 27 29 13 31 2 31 25 7 6 22 27 5 20 13
## [553] 10 27 1 28 8 1 23 16 19 27 3 24 3 2 4 12 21 26 24 13 26 26 13 31
## [577] 1 8 21 2 9 2 9 8 2 3 2 24 25 28 21 4 19 1 23 24 27 27 3 11
## [601] 27 10 4 30 15 24 31 22 1 30 25 4 27 27 8 13 22 4 9 17 15 17 18 26
## [625] 16 19 30 19 19 18 9 26 26 26 8 24 19 2 3 17 30 1 15 2 19 26 26 12
## [649] 10 27 25 7 30 25 20 20 2 19 25 1 25 30 26 13 27 2 18 25 4 15 1 30
## [673] 28 2 26 12 18 1 11 3 30 21 28 26 21 21 1 10 9 21 24 27 27 26 30 13
## [697] 11 26 14 15 13 21 6 27 24 20 25 4 10 14 19 26 19 20 17 22 10 14 2 30
## [721] 18 30 26 22 23 3 26 20 26 10 19 27 11 16 5 19 31 2 3 2 27 2 19 20
## [745] 30 14 27 18 10 26 25 31 26 11 30 23 29 5 30 1 6 31 20 16 19 20 30 18
## [769] 15 1 3 27 17 2 16 1 30 22 3 3 18 27 29 31 1 26 23 18 14 1 25 26
## [793] 19 18 2 8 10 22 5 27 15 11 13 13 27 11 21 21 3 23 2 2 30 5 30 22
## [817] 8 23 17 2 4 6 9 12 14 4 24 5 12 14 3 24 30 15 16 4 29 7 28 29
## [841] 2 23 11 31 2 8 11 25 12 11 16 11 1 4 19 22 27 13 10 1 6 26 6 12
## [865] 24 25 1 7 4 12 8 30 21 15 8 13 7 12 30 26 21 12 13 23 12 10 11 4
## [889] 27 26 13 20 18 4 1 26 19 14 29 15 14 24 2 3 14 14 2 13 8 5 8 14
## [913] 22 2 17 30 17 29 14 23 22 5 18 27 2 31 29 26 12 1 16 14 22 29 22 2
## [937] 2 30 30 30 18 10 24 18 2 9 16 9 11 5 19 4 2 5 2 25 25 24 25 3
## [961] 18 30 27 28 21 30 30 30 19 12 20 15 25 14 17 21 8 18 17 26 1 21 24 18
## [985] 8 19 13 30 14 4 18 22 29 3 29 3 11 25 23 27 30 30 25 15 13 16 4 10
## [1009] 31 4 28 4 24 21 20 17 1 21 1 10 8 31 10 2 12 18 6 20 20 30 29 11
## [1033] 5 22 3 1 18 10 2 2 15 18 5 1 3 15 10 5 22 4 19 22 19 8 29 26
## [1057] 18 30 24 2 6 26 16 12 4 23 8 28 28 23 1 30 22 10 15 19 20 8 26 12
## [1081] 8 1 30 27 23 15 29 11 16 13 13 23 28 28 21 4 4 4 13 15 20 20 28 7
## [1105] 22 28 23 20 15 15 13 3 28 18 20 6 15 18 11 12 18 4 28 12 23 18 25 9
## [1129] 26 23 14 4 26 3 1 29 4 29 22 29 29 4 30 27 11 23 19 6 22 4 15 4
## [1153] 7 5 3 14 7 6 11 15 11 8 5 8 29 24 3 9 2 16 17 9 14 18 4 14
## [1177] 5 8 6 11 10 25 21 26 21 23 8 17 12 20 29 23 28 31 22 15 25 26 11 1
## [1201] 30 17 11 19 18 16 11 11 20 24 1 22 5 18 2 15 21 30 2 10 11 19 20 9
## [1225] 25 1 1 7 8 4 20 21 30 12 24 2 14 12 15 29 19 27 28 27 13 11 11 11
## [1249] 13 31 18 18 13 16 6 10 1 22 16 2 23 29 4 16 15 5 17 27 10 21 24 15
## [1273] 26 16 17 17 22 2 1 1 6 4 1 1 7 27 10 6 3 2 13 3 10 23 3 8
## [1297] 30 22 16 24 8 23 11 11 26 26 23 2 27 12 24 2 3 16 28 1 17 24 22 2
## [1321] 24 14 9 28 3 28 23 11 8 14 30 24 9 8 3 27 27 23 3 19 19 13 8 3
## [1345] 8 30 18 25 29 9 3 27 18 25 29 24 15 28 17 24 12 22 29 13 23 29 29 4
## [1369] 29 4 1 3 10 11 6 21 1 26 27 7 25 13 7 8 10 2 18 1 18 27 2 26
## [1393] 2 10 8 8 24 29 11 11 27 20 12 15 15 27 30 22 27 9 29 25 20 4 16 1
## [1417] 18 19 23 1 12 6 7 19 22 5 30 11 22 12 26 20 18 4 4 11 3 23 18 21
## [1441] 22 15 16 12 12 21 6 22 1 13 19 22 18 10 12 22 1 6 11 1 30 17 28 3
## [1465] 16 5 14 14 12 18 2 10 26 18 16 15 27 18 10 2 29 3 24 2 20 4 4 13
## [1489] 2 28 30 16 4 15 15 23 17 22 4 1 2 13 29 16 4 25 26 8 20 18 1 25
## [1513] 18 27 18 24 9 3 16 11 3 14 15 27 22 25 6 18 11 11 19 27 13 28 5 19
## [1537] 28 27 5 22 18 22 5 5 4 2 10 29 3 25 25 5 1 16 29 19 13 22 20 21
## [1561] 29 19 29 12 20 3 2 18 11 18 27 29 29 7 4 2 2 24 12 30 5 31 27 22
## [1585] 30 29 12 16 4 12 23 17 29 16 30 3 28 21 23 19 29 23 8 30 23 9 26 15
## [1609] 5 27 22 4 12 18 27 28 27 2 27 25 18 25 2 28 11 21 15 10 3 20 8 5
## [1633] 9 16 19 5 16 27 29 21 5 20 1 22 3 5 12 14 26 4 16 20 4 28 15 1
## [1657] 21 23 26 29 2 8 22 23 17 25 1 29 30 17 14 15 15 21 29 15 20 15 10 15
## [1681] 21 16 24 1 25 14 13 9 30 24 1 17 12 17 13 20 23 29 28 13 15 19 8 13
## [1705] 15 26 21 6 15 18 14 5 25 25 8 22 1 18 18 3 15 5 11 9 19 26 26 11
## [1729] 4 18 8 4 29 6 18 30 24 27 6 27 29 6 17 24 1 30 22 25 4 21 10 12
## [1753] 12 19 27 17 21 5 11 11 4 1 28 17 20 11 20 3 22 19 25 1 3 28 16 16
## [1777] 10 22 29 30 3 3 29 26 28 8 4 12 5 2 13 28 11 2 23 22 9 18 10 9
## [1801] 6 27 9 1 25 3 13 30 6 19 26 18 30 3 22 19 7 13 26 29 28 18 4 1
## [1825] 11 30 11 10 12 13 4 1 22 2 24 23 29 20 2 24 8 30 18 23 5 27 17 7
## [1849] 21 29 22 1 30 5 16 1 28 2 12 4 24 8 26 24 17 2 3 12 11 18 13 23
## [1873] 23 23 23 1 5 1 22 6 21 21 6 30 3 4 27 27 15 24 19 28 16 22 6 10
## [1897] 22 25 3 26 25 26 28 8 1 18 2 24 10 25 2 20 17 19 2 10 10 2 23 25
## [1921] 22 13 29 27 30 26 1 1 2 29 27 23 29 12 10 11 19 2 23 20 20 25 2 4
## [1945] 30 31 22 19 11 5 3 8 27 12 26 20 7 7 5 23 15 16 25 17 7 8 16 8
## [1969] 25 26 8 13 2 13 11 3 1 16 30 26 9 2 25 3 13 13 4 5 31 20 22 20
## [1993] 23 17 21 8 30 9 23 17 8 3 5 5 3 4 2 2 1 14 9 15 21 15 17 28
## [2017] 2 22 3 9 15 15 19 19 29 4 24 14 27 27 12 18 30 16 25 30 8 28 27 13
## [2041] 23 4 3 20 15 25 8 3 15 30 2 18 30 19 8 26 7 29 2 18 5 20 25 25
## [2065] 9 25 13 22 18 8 1 28 14 3 24 14 6 9 22 10 1 29 5 12 8 19 20 25
## [2089] 10 1 22 12 2 25 25 14 17 17 18 9 15 15 14 12 9 28 14 15 18 17 14 24
## [2113] 25 12 27 20 13 20 19 6 27 20 13 3 12 29 29 6 13 28 7 6 1 3 20 22
## [2137] 28 23 17 28 5 1 11 12 30 16 16 23 8 20 22 16 29 16 18 27 16 30 28 29
## [2161] 4 16 29 21 19 22 14 24 23 18 17 18 12 12 18 16 22 18 30 5 3 2 1 29
## [2185] 26 28 30 4 5 5 15 19 8 19 23 11 6 29 1 30 20 1 4 4 20 26 8 12
## [2209] 22 23 22 23 4 19 22 19 6 15 12 6 17 10 24 14 10 29 4 22 18 11 6 25
## [2233] 30 8 10 4 5 24 3 18 12 13 12 24 11 22 28 30 1 20 16 31 6 7 4 24
## [2257] 24 14 5 6 12 28 27 1 30 16 17 3 1 15 4 1 13 7 2 30 2 14 1 29
## [2281] 1 13 24 23 23 15 17 28 28 19 29 2 1 25 7 17 15 2 28 29 2 12 18 2
## [2305] 11 3 3 3 24 17 2 2 19 8 16 2 19 4 29 20 6 25 29 31 2 29 7 12
## [2329] 29 9 4 9 22 16 30 30 4 3 8 23 5 15 1 22 29 30 15 21 8 10 1 19
## [2353] 3 14 9 6 4 4 4 16 22 29 20 3 20 10 9 9 1 13 18 15 28 30 28 15
## [2377] 13 9 21 13 15 20 27 25 6 6 6 4 16 15 2 10 4 8 11 13 24 23 28 28
## [2401] 5 8 20 2 7 22 27 25 9 12 17 22 1 5 7 1 15 24 8 27 7 4 30 23
## [2425] 4 28 29 28 1 1 15 25 14 21 29 28 29 29 8 28 19 26 29 1 6 25 23 23
## [2449] 29 8 2 4 19 31 4 8 24 24 11 9 19 2 13 19 27 27 30 17 15 13 1 4
## [2473] 22 9 4 18 24 28 28 13 18 25 11 25 7 6 27 2 27 18 28 8 15 30 28 5
## [2497] 26 28 22 2 7 5 25 25 15 26 8 29 2 2 26 1 8 25 24 26 13 1 6 26
## [2521] 28 8 20 27 27 27 27 13 2 8 4 2 19 22 26 10 21 15 18 4 17 20 26 25
## [2545] 16 25 30 19 10 27 28 3 14 3 5 26 14 27 8 23 23 6 14 27 15 19 29 17
## [2569] 17 1 2 20 24 25 23 26 23 20 22 3 12 1 26 1 16 25 24 18 13 20 5 4
## [2593] 8 2 29 21 29 6 18 29 28 1 13 24 23 12 18 4 22 7 25 9 13 6 9 18
## [2617] 30 5 14 15 1 18 18 29 19 29 30 9 9 30 28 30 3 15 18 4 6 4 19 10
## [2641] 28 10 5 19 29 25 23 18 4 29 25 21 20 17 1 6 4 12 26 19 1 5 5 5
## [2665] 25 16 5 21 5 1 1 1 30 3 1 29 27 6 1 11 26 2 1 24 17 17 24 28
## [2689] 26 28 30 6 10 27 1 3 10 30 5 16 22 20 18 22 30 25 16 15 12 13 13 17
## [2713] 2 17 5 26 23 17 28 3 1 4 10 10 12 2 1 9 14 25 27 29 3 2 3 11
## [2737] 20 26 27 4 1 8 22 25 30 29 1 12 1 30 12 11 18 18 22 4 4 20 2 28
## [2761] 16 6 19 28 4 12 10 3 9 4 4 21 19 10 30 19 29 7 22 10 8 5 5 1
## [2785] 30 26 26 11 15 23 22 4 30 26 13 20 22 29 7 21 3 17 30 29 26 25 19 26
## [2809] 17 18 27 27 10 19 22 22 3 29 26 19 24 9 26 16 15 17 15 22 11 8 30 3
## [2833] 6 1 15 18 29 9 18 3 24 9 5 22 23 4 18 2 13 13 14 30 23 2 8 12
## [2857] 2 3 21 11 23 1 6 4 2 4 25 22 9 30 8 27 18 15 26 17 22 9 3 8
## [2881] 22 28 2 23 5 26 27 27 26 8 23 14 12 22 8 1 2 23 12 27 1 29 28 24
## [2905] 2 29 10 2 8 21 11 8 12 20 22 5 2 25 8 9 22 22 13 22 23 22 10 8
## [2929] 25 3 26 28 29 28 16 19 12 26 13 9 27 10 22 4 30 18 16 5 10 5 2 4
## [2953] 3 22 1 4 9 25 19 21 17 25 31 2 17 17 3 13 16 1 26 29 11 9 12 7
## [2977] 1 17 2 26 30 10 25 23 3 22 2 2 5 22 27 19 27 20 27 29 19 15 4 16
## [3001] 16 13 2 3 13 20 24 30 13 8 10 13 25 7 23 13 16 3 26 9 1 18 22 27
## [3025] 17 14 14 9 6 18 10 15 22 4 9 15 12 5 27 3 24 13 16 26 3 16 15 28
## [3049] 30 15 12 31 2 27 20 29 1 25 8 29 25 28 15 19 10 26 18 25 2 19 30 13
## [3073] 18 19 18 3 4 25 22 22 5 30 18 14 3 19 18 12 9 27 13 18 26 26 1 9
## [3097] 1 4 22 2 3 3 2 13 23 26 28 4 4 6 15 22 11 16 17 5 12 27 2 10
## [3121] 12 12 13 26 22 23 11 19 3 14 22 23 6 24 8 7 9 2 12 24 25 26 20 29
## [3145] 27 30 16 3 8 6 1 25 7 3 4 14 9 27 13 16 17 28 17 17 18 23 21 22
## [3169] 15 14 8 12 22 17 16 24 19 4 11 20 16 7 3 17 29 25 13 29 3 11 18 3
## [3193] 30 22 24 24 8 20 22 3 22 16 28 10 24 23 23 2 1 9 2 29 30 13 2 3
## [3217] 22 23 13 17 18 2 17 1 30 23 10 12 25 28 4 16 29 22 14 21 2 6 29 29
## [3241] 30 23 17 26 1 27 16 15 3 15 9 31 30 11 19 30 8 15 3 3 25 22 9 26
## [3265] 17 18 12 15 10 29 13 21 3 15 2 15 30 10 8 29 17 17 24 3 10 20 4 13
## [3289] 12 20 1 2 14 1 16 6 10 17 5 1 19 4 21 9 27 22 18 27 12 21 6 25
## [3313] 16 22 21 20 30 15 22 26 24 27 24 5 13 25 28 18 18 25 15 6 13 29 13 13
## [3337] 23 29 13 11 15 14 18 26 26 3 13 14 14 2 26 25 22 9 15 15 29 25 14 9
## [3361] 3 21 21 18 24 11 1 26 2 8 1 5 15 21 13 12 13 4 18 2 18 18 4 19
## [3385] 24 16 26 29 6 28 13 20 19 2 4 15 26 9 4 5 16 29 25 7 23 23 16 16
## [3409] 31 11 16 22 5 2 18 11 13 17 6 5 22 17 28 15 2 2 13 15 15 30 18 10
## [3433] 18 13 8 13 15 23 12 11 5 29 9 20 8 16 28 26 6 29 3 26 28 29 3 23
## [3457] 23 20 21 19 29 9 29 13 3 15 29 18 22 26 4 22 27 4 4 6 18 20 27 3
## [3481] 6 5 27 16 11 6 25 6 19 22 5 5 30 23 12 4 27 23 23 3 1 30 29 19
## [3505] 10 2 25 29 6 23 5 11 3 4 23 18 24 23 19 9 2 8 23 28 25 29 15 29
## [3529] 28 22 8 18 22 4 12 25 3 10 11 10 30 20 29 8 30 18 10 15 5 6 27 30
## [3553] 11 12 15 9 20 9 12 16 30 24 9 25 25 29 13 14 29 5 9 28 1 25 26 31
## [3577] 9 2 15 15 9 6 15 15 12 13 4 19 30 30 1 5 10 14 1 27 6 25 23 23
## [3601] 4 17 3 25 19 13 15 13 28 18 25 28 12 18 13 1 12 10 6 1 1 3 28 25
## [3625] 22 6 26 18 9 16 23 1 19 2 19 30 7 12 7 28 16 23 1 24 21 25 26 8
## [3649] 12 18 3 28 20 26 27 22 6 19 26 12 19 13 12 23 29 3 22 12 23 9 19 22
## [3673] 22 18 18 19 10 2 8 4 1 31 23 5 26 29 11 25 15 8 6 24 18 17 14 13
## [3697] 6 26 3 23 4 14 3 26 4 25 2 4 29 25 8 15 4 8 10 29 27 3 25 22
## [3721] 30 18 30 22 19 29 30 5 6 5 1 9 26 28 29 7 30 28 10 24 9 14 24 22
## [3745] 24 3 24 10 14 5 5 30 11 25 27 29 29 11 11 23 1 27 12 3 21 18 18 15
## [3769] 23 25 4 5 29 26 19 12 23 3 7 27 3 15 20 15 3 24 23 23 3 17 28 12
## [3793] 14 2 5 2 2 25 12 17 13 18 1 23 27 11 22 14 2 30 3 15 10 28 1 1
## [3817] 27 11 26 26 20 30 16 12 14 14 29 1 23 27 25 26 8 18 2 3 19 13 6 8
## [3841] 14 29 9 9 5 26 6 3 9 12 4 4 4 17 23 19 18 1 20 19 6 1 30 27
## [3865] 15 11 29 5 5 6 3 30 30 11 30 30 20 26 27 29 24 17 30 16 10 17 16 12
## [3889] 31 29 10 17 5 30 24 19 14 12 1 1 17 10 5 24 13 10 1 2 11 1 23 29
## [3913] 7 16 22 2 19 29 2 30 28 20 4 12 23 7 5 7 25 23 2 10 2 24 19 10
## [3937] 26 28 9 18 30 20 22 9 6 26 24 21 9 17 24 25 19 18 30 5 28 5 22 6
## [3961] 18 19 26 26 11 11 30 6 6 12 6 7 3 13 25 20 1 23 1 18 16 23 4 2
## [3985] 27 19 13 2 8 19 10 23 1 24 29 23 12 30 7 3 18 24 6 22 25 12 29 16
## [4009] 14 14 18 26 19 23 28 16 11 18 30 9 23 30 15 9 3 30 8 7 3 5 22 13
## [4033] 4 18 15 1 1 13 4 17 2 1 6 17 24 3 1 30 21 2 25 17 15 30 1 3
## [4057] 1 8 8 10 14 30 17 5 17 4 31 12 15 24 8 25 17 27 29 10 17 6 2 6
## [4081] 12 11 9 2 22 25 19 23 10 2 26 15 5 3 15 25 27 26 23 12 2 6 26 2
## [4105] 3 23 18 15 30 6 23 9 13 24 11 22 29 25 25 20 26 8 27 8 7 5 5 30
## [4129] 18 4 9 17 19 13 29 25 28 13 5 3 5 20 24 3 6 17 27 11 10 31 29 20
## [4153] 30 25 12 12 26 4 19 16 28 3 12 4 14 13 1 27 4 5 11 23 1 29 28 22
## [4177] 10 9 19 30 4 15 6 29 16 23 27 23 8 1 24 17 4 6 28 21 15 16 23 9
## [4201] 26 2 10 2 8 1 4 1 21 21 28 11 8 26 12 4 10 16 17 27 26 30 7 11
## [4225] 6 6 27 29 26 17 29 29 1 14 18 21 20 30 1 30 19 19 28 16 13 5 2 26
## [4249] 17 19 12 24 5 29 23 6 16 30 6 4 22 5 15 27 28 25 30 6 28 5 23 18
## [4273] 1 3 8 16 3 24 28 25 13 18 25 19 23 30 18 21 21 27 20 22 27 27 26 16
## [4297] 2 20 22 19 11 22 10 19 9 5 10 26 25 26 23 12 1 1 13 14 2 22 22 4
## [4321] 14 8 24 1 25 5 14 25 6 16 26 19 7 18 11 28 1 1 9 15 18 19 24 8
## [4345] 22 2 25 11 29 29 13 19 22 3 4 9 22 30 24 13 9 3 15 2 26 6 1 30
## [4369] 17 1 9 18 2 24 25 3 23 15 25 26 19 20 23 3 22 3 18 10 1 26 15 24
## [4393] 18 19 12 3 16 8 27 27 3 22 10 1 27 5 26 3 12 5 9 25 4 25 27 3
## [4417] 4 1 29 17 29 18 26 17 19 20 2 18 19 30 7 28 15 12 15 25 6 30 14 10
## [4441] 2 23 14 15 27 10 30 19 20 22 19 29 15 16 22 19 11 6 5 12 22 26 17 11
## [4465] 12 28 1 24 24 7 6 7 23 2 20 2 30 22 1 10 29 19 1 13 14 1 13 30
## [4489] 15 2 17 26 20 20 3 26 8 13 4 4 15 6 2 19 13 31 19 26 1 6 10 8
## [4513] 26 18 18 26 26 27 17 27 2 19 13 19 3 4 19 13 16 18 17 18 3 11 24 3
## [4537] 20 20 27 8 19 3 22 8 18 19 19 20 26 11 15 15 23 15 6 19 30 22 18 10
## [4561] 25 18 19 23 17 7 18 3 29 5 26 10 1 15 25 4 16 9 28 8 6 30 30 25
## [4585] 10 3 20 22 5 5 14 12 30 24 19 2 16 12 12 1 23 29 16 29 8 29 4 8
## [4609] 18 4 29 17 22 4 27 23 22 29 28 19 13 8 27 16 9 12 6 1 3 24 4 20
## [4633] 29 30 23 4 8 28 13 22 2 21 13 5 18 3 22 1 6 17 3 9 2 4 23 3
## [4657] 10 10 17 1 4 9 10 6 13 10 2 24 9 19 2 18 3 25 18 8 16 9 3 2
## [4681] 24 8 3 20 19 30 28 28 3 15 25 1 3 29 26 21 28 23 25 25 5 16 21 8
## [4705] 19 3 16 30 22 27 25 26 15 22 1 13 19 9 19 3 23 5 4 4 5 24 25 4
## [4729] 7 30 9 2 20 15 1 29 24 27 4 10 2 6 28 16 13 16 10 27 27 15 3 25
## [4753] 25 18 4 17 14 1 17 16 30 20 6 23 6 30 25 14 10 17 18 29 20 26 4 30
## [4777] 8 22 22 30 3 17 26 26 28 28 22 11 28 9 11 22 3 10 4 1 13 21 6 27
## [4801] 4 17 28 2 18 15 2 25 6 2 6 23 27 29 5 20 15 22 29 12 29 3 29 29
## [4825] 5 22 2 2 6 3 5 29 30 16 3 16 28 14 14 23 2 17 17 29 11 14 10 1
## [4849] 10 4 18 4 4 5 17 24 24 27 12 3 22 2 21 8 1 20 19 4 18 21 30 10
## [4873] 18 29 6 19 29 25 26 30 5 19 6 30 31 22 2 23 23 31 16 1 28 9 7 16
## [4897] 30 23 7 1 1 18 2 18 11 23 19 28 14 1 22 17 19 17 16 20 1 18 16 19
## [4921] 19 10 30 15 24 30 30 23 2 3 1 3 17 17 9 23 30 17 26 29 16 23 12 3
## [4945] 2 13 9 2 26 8 11 1 12 29 3 14 18 5 21 22 5 20 6 3 18 18 2 24
## [4969] 24 14 1 29 28 27 27 18 5 18 3 21 5 23 17 5 3 1 6 16 1 12 22 30
## [4993] 10 24 15 26 11 26 29 3 10 14 19 25 25 26 24 10 27 29 5 11 18 22 4 23
## [5017] 28 3 28 30 12 15 20 10 12 22 23 25 13 18 12 25 3 5 27 1 22 1 11 7
## [5041] 12 9 28 11 3 1 23 30 26 3 29 9 10 9 21 10 23 16 6 8 9 3 18 17
## [5065] 12 18 28 25 19 2 18 17 1 14 17 19 30 2 18 25 17 2 2 2 2 12 2 29
## [5089] 21 2 4 15 25 3 22 13 28 19 24 27 2 2 6 11 4 27 29 5 19 19 5 17
## [5113] 18 13 20 21 18 27 27 13 26 12 11 17 20 19 18 7 15 14 5 15 24 2 5 18
## [5137] 30 22 4 19 4 15 27 11 19 30 19 2 2 1 4 23 12 24 22 25 12 17 20 12
## [5161] 15 15 6 7 4 8 30 5 23 24 15 4 4 30 21 25 6 20 1 7 18 10 28 17
## [5185] 6 23 30 16 30 4 4 1 20 21 4 16 9 4 13 8 26 2 22 18 3 10 21 23
## [5209] 28 1 1 3 3 3 15 30 29 16 15 18 23 21 1 17 25 22 1 18 5 23 19 13
## [5233] 29 25 25 11 26 25 14 8 30 30 27 30 1 3 20 25 11 19 16 2 2 10 30 20
## [5257] 12 22 18 20 2 3 15 21 26 10 22 14 1 23 15 25 20 20 25 4 25 22 19 22
## [5281] 12 18 27 2 28 16 16 4 2 9 17 20 27 3 26 6 2 25 30 17 10 14 25 1
## [5305] 17 4 10 2 25 8 28 6 13 17 10 22 27 25 15 29 29 30 23 26 27 27 10 6
## [5329] 24 11 8 18 10 19 5 29 11 31 1 3 5 15 13 27 21 23 5 10 19 24 3 20
## [5353] 18 6 27 2 8 19 19 13 18 26 14 10 25 28 13 9 11 19 13 18 16 26 1 9
## [5377] 29 25 26 3 8 25 26 5 8 22 24 12 18 29 20 1 25 3 3 12 13 16 20 2
## [5401] 22 27 16 5 26 15 6 25 27 11 5 15 18 21 5 30 24 17 25 25 22 16 26 17
## [5425] 9 22 30 17 4 13 15 29 2 20 3 22 26 22 29 1 2 24 5 17 1 16 7 1
## [5449] 15 3 13 5 6 6 15 8 15 29 8 5 1 18 23 10 20 16 14 5 13 29 10 20
## [5473] 25 20 25 1 30 16 15 31 16 3 15 20 2 6 28 23 29 24 25 29 28 2 30 26
## [5497] 26 30 3 22 4 13 11 14 1 4 19 11 14 10 16 29 1 20 3 17 25 24 8 2
## [5521] 21 11 1 16 5 22 10 20 4 4 30 18 8 15 26 31 1 4 1 24 20 10 24 9
## [5545] 2 25 24 30 27 27 19 28 25 14 16 28 2 19 26 2 26 28 29 12 29 23 19 26
## [5569] 10 14 6 15 13 1 1 3 27 12 4 24 12 25 27 1 3 30 1 25 1 24 21 6
## [5593] 25 22 23 18 17 4 30 4 17 5 23 31 22 26 4 1 23 29 2 13 30 29 3 24
## [5617] 8 12 23 26 25 30 1 17 15 25 11 9 3 8 29 9 13 26 3 19 30 28 9 27
## [5641] 15 6 5 1 10 21 11 13 20 29 29 29 11 22 2 3 4 26 4 26 26 26 5 5
## [5665] 10 20 1 22 5 30 28 13 13 1 1 3 23 13 1 4 6 22 4 5 13 15 11 25
## [5689] 8 15 5 2 11 18 27 10 2 4 26 29 12 20 23 23 15 31 17 4 30 28 5 1
## [5713] 24 26 5 11 18 12 18 3 16 6 15 11 10 27 26 29 27 20 27 20 30 2 1 25
## [5737] 10 21 16 2 5 28 3 8 29 29 5 5 1 18 14 24 4 29 23 22 15 23 8 3
## [5761] 15 10 14 28 29 29 8 19 21 20 21 20 29 29 25 27 19 19 31 22 6 20 25 5
## [5785] 23 1 5 12 4 1 23 12 9 29 9 8 12 15 1 23 8 23 27 27 4 2 3 29
## [5809] 30 23 23 29 7 10 22 2 2 2 18 25 18 19 10 1 11 8 2 22 1 20 24 24
## [5833] 13 29 16 3 2 17 27 16 18 15 15 3 2 25 19 19 14 24 17 9 2 19 8 21
## [5857] 1 28 15 3 15 3 20 11 2 26 2 3 19 13 6 30 16 21 30 20 1 19 12 19
## [5881] 18 20 20 2 27 15 12 20 21 1 12 2 8 30 28 30 2 14 30 22 24 18 2 20
## [5905] 21 30 4 4 20 24 17 1 13 17 29 11 1 26 17 13 10 24 7 24 1 13 24 2
## [5929] 4 16 18 13 3 29 15 3 2 16 3 18 30 26 6 18 25 22 28 15 13 18 8 4
## [5953] 2 27 10 11 14 15 1 13 18 10 3 27 27 19 25 25 27 26 18 5 23 4 13 26
## [5977] 5 5 17 19 23 29 8 6 2 7 6 21 30 29 20 13 16 30 23 16 12 28 12 15
## [6001] 3 12 19 19 3 24 15 3 9 3 28 23 3 6 15 17 1 30 6 1 3 19 17 17
## [6025] 5 28 27 28 10 2 6 19 1 19 20 24 17 21 4 25 11 21 22 25 28 8 13 8
## [6049] 28 14 3 2 6 11 25 24 12 14 9 1 18 10 3 15 2 10 13 18 22 1 30 3
## [6073] 22 27 21 4 6 24 30 7 25 3 24 15 15 30 22 21 21 29 16 15 21 11 8 23
## [6097] 1 29 7 30 23 12 17 5 1 30 15 29 18 6 15 3 17 17 22 6 15 19 21 29
## [6121] 23 20 17 29 3 2 2 12 10 2 26 21 2 8 29 1 19 4 14 21 9 4 30 22
## [6145] 31 11 4 4 27 16 23 1 1 21 5 24 5 12 4 22 4 23 5 19 5 26 5 2
## [6169] 22 25 24 11 29 13 8 12 11 23 17 26 25 29 21 17 17 29 20 11 29 6 27 28
## [6193] 2 27 25 11 23 22 19 5 13 2 7 12 3 31 28 19 18 8 9 1 26 16 16 1
## [6217] 28 1 4 16 9 9 1 18 27 15 15 15 29 11 8 18 11 22 23 23 18 29 25 7
## [6241] 15 25 10 24 8 19 17 27 3 26 8 24 30 12 27 13 26 15 10 11 27 25 17 11
## [6265] 3 26 13 20 13 29 10 10 10 6 4 9 1 22 4 26 29 24 23 11 22 30 9 28
## [6289] 14 4 16 12 30 8 3 3 11 27 6 1 30 24 25 4 31 31 10 23 1 1 26 26
## [6313] 25 15 22 20 15 10 18 2 12 24 2 3 26 1 2 11 22 3 24 21 23 20 30 16
## [6337] 26 15 1 23 14 30 1 23 18 8 25 1 9 14 21 29 12 18 25 24 26 13 29 17
## [6361] 21 29 4 15 2 8 29 23 4 28 11 13 9 24 16 16 26 2 16 18 18 22 3 8
## [6385] 9 11 6 29 26 28 30 27 25 25 3 3 15 17 13 14 19 12 26 4 2 3 29 22
## [6409] 3 30 13 20 2 17 29 12 29 23 26 28 27 22 29 2 9 5 8 28 11 27 27 2
## [6433] 13 6 6 12 31 9 17 20 23 1 20 26 5 11 12 18 20 22 1 9 5 2 20 10
## [6457] 27 2 5 12 25 25 29 28 11 30 18 15 13 12 2 5 26 28 6 29 15 5 26 23
## [6481] 1 3 8 21 27 30 21 30 10 30 29 5 22 4 23 22 21 5 6 1 18 27 23 22
## [6505] 15 11 4 18 22 17 2 23 16 13 29 10 17 1 17 11 26 8 11 2 4 22 2 2
## [6529] 27 12 12 2 26 3 3 6 24 24 7 25 8 22 18 14 19 8 2 1 1 6 6 7
## [6553] 25 4 14 11 29 2 5 3 19 29 28 1 8 21 10 22 10 27 27 29 9 30 1 25
## [6577] 3 2 16 19 15 15 29 2 1 10 17 15 16 9 1 12 10 14 2 2 1 27 8 25
## [6601] 1 8 2 18 16 2 28 7 6 21 9 30 17 1 13 24 6 18 24 19 14 2 14 6
## [6625] 14 23 29 23 13 16 3 25 15 3 11 20 28 9 14 13 15 29 22 18 18 22 9 27
## [6649] 4 14 21 2 11 8 25 4 11 3 3 3 26 24 2 29 29 29 12 12 8 13 18 20
## [6673] 20 30 13 18 1 3 22 30 15 28 5 2 11 25 1 6 1 29 18 26 29 3 3 12
## [6697] 29 1 26 29 4 22 2 29 13 12 27 2 3 4 27 4 13 15 9 17 15 23 13 5
## [6721] 5 8 17 1 13 16 1 20 22 1 12 24 24 6 15 23 21 22 22 8 8 24 6 1
## [6745] 9 27 2 10 1 29 29 4 29 2 3 3 25 25 4 15 11 8 8 22 11 19 18 19
## [6769] 1 18 2 16 23 20 10 23 8 15 1 26 3 5 28 30 24 11 1 16 25 19 24 30
## [6793] 12 12 25 1 1 11 2 9 15 1 29 2 14 2 4 23 22 20 1 26 30 10 12 17
## [6817] 25 7 5 24 6 28 3 25 11 22 2 25 11 10 25 25 11 28 26 27 25 26 10 13
## [6841] 27 19 16 11 18 29 4 19 20 13 16 14 23 11 13 26 26 27 15 6 3 8 24 15
## [6865] 21 25 30 11 21 29 23 18 30 17 1 6 22 22 19 30 11 1 25 17 1 13 20 23
## [6889] 20 19 10 10 19 11 28 3 28 26 4 2 24 2 2 14 25 24 28 20 16 21 1 23
## [6913] 24 27 3 9 26 28 17 27 6 23 27 23 4 22 1 29 18 14 16 4 20 13 3 9
## [6937] 27 4 5 17 28 23 1 23 23 23 15 22 13 21 15 7 29 4 19 11 25 29 15 28
## [6961] 4 19 4 16 5 16 13 24 17 14 16 13 25 3 3 4 19 20 18 30 25 11 6 5
## [6985] 4 25 30 18 22 29 1 30 23 28 23 16 6 6 23 2 7 11 7 5 26 26 18 15
## [7009] 29 6 26 9 29 30 28 28 18 5 24 29 1 19 19 1 4 22 9 3 6 9 10 23
## [7033] 26 8 6 27 23 2 13 5 13 15 2 19 9 20 20 20 15 22 13 3 24 27 15 25
## [7057] 17 6 1 10 25 30 1 29 22 14 17 28 3 28 22 24 19 19 1 24 1 16 3 26
## [7081] 23 15 15 16 30 31 12 25 12 21 18 18 30 16 22 2 15 1 6 22 14 1 12 15
## [7105] 12 4 10 10 30 13 19 6 8 9 29 11 22 27 26 27 4 29 9 6 22 27 4 16
## [7129] 16 10 15 15 1 19 7 18 7 22 4 20 25 3 9 16 24 7 27 16 14 28 6 19
## [7153] 26 15 29 27 28 27 13 4 20 26 2 27 1 23 30 30 25 12 12 18 24 22 29 10
## [7177] 10 18 18 6 17 18 26 28 22 3 4 18 22 1 1 1 8 25 26 24 20 13 15 15
## [7201] 24 21 24 3 16 30 9 20 3 26 8 18 18 25 15 3 15 25 18 28 26 22 3 30
## [7225] 5 20 30 17 1 4 29 1 22 19 22 16 15 11 29 8 1 12 2 26 11 15 16 3
## [7249] 2 24 27 29 2 23 31 25 13 24 1 2 10 9 27 30 13 7 30 21 5 23 18 8
## [7273] 30 30 18 11 20 18 13 21 4 5 15 1 30 4 25 2 8 25 21 30 25 26 12 1
## [7297] 15 2 8 7 1 18 31 19 12 3 11 11 21 5 21 23 30 16 17 24 23 16 17 3
## [7321] 16 2 3 3 28 20 21 15 30 2 13 5 3 21 4 30 16 17 1 28 27 20 21 21
## [7345] 30 27 5 8 26 16 11 13 7 7 17 5 7 4 6 23 1 22 24 24 21 2 14 1
## [7369] 9 9 28 9 12 5 11 26 15 6 1 26 11 6 13 25 26 5 28 22 30 21 30 30
## [7393] 1 6 7 30 2 13 25 10 14 4 16 11 5 22 1 27 4 17 17 16 5 16 25 29
## [7417] 29 16 20 20 23 23 30 1 9 9 14 24 24 2 1 15 15 26 26 30 30 15 5 7
## [7441] 20 26 26 28 22 22 17 6 2 31 25 10 16 26 12 29 6 1 1 6 4 30 2 8
## [7465] 9 24 13 14 15 15 24 30 18 10 1 23 30 6 16 17 26 13 26 3 9 6 8 30
## [7489] 21 19 1 1 22 7 25 25 20 21 18 1 24 23 3 24 5 18 25 22 15 23 15 28
## [7513] 29 16 16 25 27 28 14 30 2 9 27 20 12 4 5 10 9 19 26 1 23 31 16 21
## [7537] 5 1 11 2 2 2 11 10 9 28 29 19 1 10 1 29 23 10 18 12 6 16 3 2
## [7561] 28 17 25 6 25 27 14 26 5 6 20 29 1 26 7 2 18 28 20 3 17 21 25 12
## [7585] 1 18 13 12 24 2 15 26 6 26 15 15 29 11 27 19 4 20 31 24 23 21 11 16
## [7609] 25 11 27 21 18 20 4 22 22 7 18 6 16 16 13 11 2 18 6 17 23 1 10 4
## [7633] 2 24 23 1 17 24 3 29 29 23 31 15 16 4 19 17 6 7 3 8 19 12 11 4
## [7657] 23 24 16 2 8 13 4 25 20 17 29 28 21 23 28 8 30 25 24 9 13 1 23 1
## [7681] 2 6 12 26 3 11 27 20 20 28 22 20 18 2 25 11 13 13 7 24 24 4 15 30
## [7705] 22 8 4 24 9 24 22 21 1 2 15 25 19 16 9 3 27 26 16 21 15 13 2 15
## [7729] 28 25 26 29 16 18 1 3 19 8 4 6 21 5 1 8 2 16 16 29 13 8 3 30
## [7753] 31 31 19 5 22 11 23 22 24 7 15 29 22 10 22 11 3 25 27 25 25 17 27 2
## [7777] 25 19 19 26 29 2 11 18 22 19 20 19 4 19 20 20 1 4 18 20 22 9 24 1
## [7801] 1 6 4 10 8 26 9 25 27 4 2 26 6 20 10 23 11 12 12 8 24 18 15 27
## [7825] 14 18 11 7 6 1 1 5 13 11 25 22 16 17 1 17 19 3 3 3 4 4 25 30
## [7849] 30 10 10 13 6 27 1 7 23 26 2 2 13 27 10 24 15 23 9 3 22 24 29 25
## [7873] 7 14 16 11 15 2 19 25 3 4 28 8 26 22 5 5 17 17 1 10 16 28 23 12
## [7897] 18 2 13 18 29 3 20 5 30 30 11 22 22 18 30 31 2 13 13 9 24 24 8 1
## [7921] 1 3 5 18 26 8 28 12 26 16 24 25 9 24 13 3 27 1 26 18 20 11 4 28
## [7945] 5 29 22 23 10 27 3 11 22 9 21 6 20 25 18 22 22 6 2 1 24 13 13 30
## [7969] 29 16 17 29 30 7 1 8 18 11 13 9 1 24 9 13 2 14 23 30 15 3 16 23
## [7993] 4 30 4 29 22 30 30 11 1 8 10 6 10 25 3 8 27 28 15 1 6 15 10 20
## [8017] 21 14 24 1 21 20 29 10 3 1 18 15 2 29 24 26 8 23 12 13 2 19 24 12
## [8041] 19 25 25 27 15 15 27 29 11 30 6 22 17 3 22 22 23 8 8 4 5 20 10 2
## [8065] 4 2 10 25 13 17 15 29 28 17 30 22 2 26 16 20 5 20 21 18 25 27 6 1
## [8089] 4 25 23 12 29 25 26 5 2 9 9 28 8 11 28 8 1 22 10 28 24 26 28 11
## [8113] 29 18 25 15 20 19 4 21 22 28 1 22 23 4 21 21 17 11 4 9 10 6 2 30
## [8137] 29 30 16 30 31 23 24 12 1 1 25 14 22 31 10 1 2 28 17 15 16 17 12 4
## [8161] 16 23 4 5 9 19 11 5 20 21 27 2 16 2 2 17 23 8 19 15 20 27 18 30
## [8185] 26 25 28 4 2 3 26 4 20 20 4 11 20 22 28 1 15 17 5 23 23 7 21 22
## [8209] 12 24 26 18 25 29 29 20 16 6 19 28 4 29 4 3 30 12 7 25 30 17 9 18
## [8233] 15 26 26 16 16 14 13 4 17 1 25 8 8 27 12 3 3 28 10 28 4 21 30 29
## [8257] 30 13 1 8 3 20 18 25 26 27 31 17 13 13 1 18 19 8 27 20 5 30 29 1
## [8281] 2 23 2 26 18 18 15 22 26 30 15 26 29 6 6 1 7 17 14 9 9 26 27 29
## [8305] 3 3 2 20 9 3 25 9 17 24 24 11 22 30 28 15 29 2 6 16 10 26 27 30
## [8329] 18 30 14 2 30 15 11 1 30 22 16 30 30 29 5 7 25 11 23 13 3 13 17 30
## [8353] 9 23 24 9 18 9 7 7 11 26 2 26 1 10 17 25 18 10 29 12 12 9 25 3
## [8377] 11 28 20 12 12 12 11 25 3 4 29 18 20 20 7 5 19 19 20 3 22 5 3 2
## [8401] 21 27 18 22 3 3 22 23 20 23 13 14 5 1 4 16 15 6 25 25 12 8 25 10
## [8425] 27 18 20 12 29 6 19 19 6 28 8 29 12 11 21 8 3 25 23 21 17 11 29 25
## [8449] 20 15 29 18 5 17 13 26 21 25 1 26 13 7 26 18 18 10 3 13 27 9 30 14
## [8473] 15 17 12 27 5 19 12 13 8 23 9 20 28 14 15 19 24 19 19 24 16 16 28 14
## [8497] 11 11 19 29 2 9 28 20 2 13 2 16 25 29 19 2 2 29 4 30 26 25 16 28
## [8521] 20 16 17 17 30 20 10 2 4 5 21 18 6 6 17 23 22 13 13 16 29 20 7 22
## [8545] 17 17 20 21 23 7 26 19 22 8 21 21 1 26 1 8 10 13 19 4 12 25 5 10
## [8569] 25 2 2 4 13 12 12 2 28 26 13 19 13 27 11 29 17 4 18 12 10 24 30 4
## [8593] 17 10 20 4 18 2 2 1 19 2 27 18 6 6 11 3 1 19 27 31 7 18 15 21
## [8617] 19 4 11 7 10 29 3 20 12 25 25 25 12 18 30 26 29 19 18 28 13 13 23 18
## [8641] 8 26 22 17 12 15 17 17 29 29 4 29 30 22 20 27 26 30 10 22 15 2 5 29
## [8665] 22 28 5 27 25 25 25 22 5 25 29 27 19 29 1 1 19 29 7 17 5 19 6 6
## [8689] 18 16 16 4 27 3 3 21 25 10 30 30 22 19 8 2 11 9 18 28 30 19 30 21
## [8713] 24 19 1 7 7 29 6 12 14 10 10 24 6 27 8 29 15 25 15 2 12 17 7 30
## [8737] 18 18 19 13 16 4 2 27 25 17 16 31 23 17 26 11 12 10 25 25 9 10 2 12
## [8761] 25 10 3 19 19 11 11 2 18 5 18 18 19 4 18 18 17 10 20 3 19 16 6 2
## [8785] 30 20 30 18 18 30 27 28 23 24 6 7 1 2 12 14 19 21 6 8 10 22 18 2
## [8809] 5 22 5 15 20 2 7 15 24 21 25 22 14 18 23 9 24 29 8 26 15 10 9 9
## [8833] 6 26 18 9 28 2 18 29 24 10 25 27 12 5 12 29 29 5 27 19 18 8 16 16
## [8857] 26 4 1 4 2 30 22 12 7 11 16 15 12 23 30 8 24 26 25 8 23 25 10 25
## [8881] 23 3 16 25 9 29 3 12 8 2 7 2 19 23 6 15 19 30 10 28 18 25 16 18
## [8905] 10 2 26 22 9 7 16 24 23 3 8 27 28 18 1 8 4 1 13 14 5 2 16 22
## [8929] 5 24 1 19 9 30 3 27 15 15 13 24 11 31 5 18 29 19 20 13 28 28 22 29
## [8953] 2 21 25 29 25 20 30 11 13 28 18 24 11 4 17 16 15 18 24 16 2 3 19 27
## [8977] 18 1 17 8 9 14 5 30 18 27 6 28 6 1 19 25 29 19 19 5 28 15 10 24
## [9001] 5 21 5 25 8 2 31 20 2 9 3 22 6 6 6 28 8 12 15 7 17 21 10 6
## [9025] 18 26 17 23 25 12 1 7 12 23 18 28 26 29 26 5 18 11 26 9 1 27 15 11
## [9049] 6 22 2 20 15 16 29 17 29 25 2 5 16 1 2 23 7 29 14 16 2 4 2 30
## [9073] 4 13 22 24 15 10 8 4 30 18 26 26 1 18 11 26 1 24 1 12 20 28 18 29
## [9097] 19 6 1 9 3 13 30 8 26 25 13 31 2 19 20 29 22 4 10 6 27 10 29 4
## [9121] 12 5 13 28 3 13 15 14 26 8 9 14 12 29 17 12 24 14 24 17 24 30 30 16
## [9145] 26 18 20 29 13 28 13 15 1 25 10 20 21 25 18 25 29 2 12 18 1 19 2 5
## [9169] 2 19 6 3 5 28 25 19 1 12 17 5 22 22 3 3 3 12 30 9 5 5 29 18
## [9193] 26 6 8 21 2 7 8 8 24 5 1 18 9 1 1 16 30 20 7 14 24 25 25 29
## [9217] 16 31 28 28 12 6 17 15 16 25 18 19 15 28 27 15 17 9 30 5 18 11 8 2
## [9241] 11 28 8 16 25 18 15 17 1 2 25 20 22 19 29 11 18 24 8 1 18 25 2 23
## [9265] 3 31 8 16 18 18 21 12 13 16 16 6 22 25 9 17 10 30 12 27 19 20 3 10
## [9289] 15 12 28 30 22 22 22 11 12 2 19 3 1 13 3 3 25 29 4 19 10 1 2 22
## [9313] 22 27 18 21 16 7 22 17 20 18 1 24 24 25 11 16 19 5 6 20 11 19 17 13
## [9337] 20 8 21 16 4 29 18 5 30 13 25 1 4 19 19 2 17 29 24 17 6 7 19 22
## [9361] 23 3 25 24 25 13 23 25 2 12 27 26 5 18 4 17 13 18 11 12 8 28 11 30
## [9385] 2 12 12 20 20 4 2 20 6 1 7 19 10 3 5 2 29 21 7 11 15 7 4 18
## [9409] 21 2 25 25 24 20 8 19 1 7 12 15 19 21 25 3 7 10 12 20 11 5 2 5
## [9433] 18 8 16 27 9 10 15 13 6 25 6 24 8 17 22 18 27 18 15 2 6 16 3 19
## [9457] 5 2 29 13 19 4 29 16 9 17 2 10 26 21 18 22 30 24 25 22 22 1 18 4
## [9481] 10 5 26 17 2 20 18 27 10 30 30 27 15 25 13 11 11 7 17 22 7 5 26 25
## [9505] 21 25 12 13 18 11 7 1 26 7 2 12 2 21 3 7 4 27 10 2 25 3 2 19
## [9529] 13 22 28 15 1 3 17 27 18 8 5 17 27 9 25 15 26 6 22 27 24 23 25 11
## [9553] 16 29 29 29 17 19 19 6 29 25 20 25 25 24 25 10 9 4 29 17 11 16 10 18
## [9577] 3 17 15 19 26 21 29 19 19 13 10 18 16 17 11 18 18 13 9 14 28 10 18 26
## [9601] 18 18 14 30 18 3 30 29 21 29 3 14 20 27 29 15 9 8 21 19 25 29 3 13
## [9625] 18 12 18 8 22 18 23 19 13 19 21 19 16 19 18 20 19 12 3 14 30 30 19 17
## [9649] 17 20 9 27 17 17 21 14 26 5 27 6 16 1 16 22 22 29 29 10 5 24 22 6
## [9673] 3 5 6 12 19 1 24 15 19 25 8 8 2 25 26 13 11 13 13 5 4 8 10 12
## [9697] 11 7 30 9 10 29 22 18 28 15 24 28 9 14 5 19 29 20 25 5 25 21 5 29
## [9721] 13 11 11 25 12 8 11 24 16 30 9 24 25 8 9 6 24 26 16 7 15 10 27 24
## [9745] 18 1 23 17 2 7 6 27 1 5 17 25 19 18 4 19 10 26 23 31 3 20 21 5
## [9769] 10 19 2 15 10 4 12 7 17 20 30 9 3 3 9 25 27 26 1 18 26 10 10 10
## [9793] 5 17 24 1 19 18 26 3 8 18 19 19 19 18 19 19 29 2 20 1 4 18 6 21
## [9817] 30 29 18 16 16 8 28 27 20 28 15 13 3 5 30 24 27 6 29 21 5 7 22 24
## [9841] 30 9 19 19 17 7 19 17 29 18 17 2 17 7 3 9 19 24 19 30 9 8 2 11
## [9865] 7 2 28 4 28 6 3 6 8 7 9 25 12 18 21 17 13 30 12 28 9 8 29 1
## [9889] 15 11 3 9 25 8 1 13 7 7 13 8 30 1 14 13 19 19 18 2 3 20 19 19
## [9913] 2 19 29 30 12 29 16 2 19 20 1 9 16 18 16 11 4 10 6 5 18 14 14 1
## [9937] 18 30 17 7 11 22 8 29 29 28 29 25 19 18 20 19 21 29 30 4 16 31 25 15
## [9961] 29 17 7 2 11 19 18 5 16 24 1 6 15 5 12 1 18 26 5 16 15 16 17 31
## [9985] 25 17 22 19 17 11 21 30 23 23 14 4 4 3 20 12 2 27 25 12 1 16 9 30
## [10009] 30 3 3 2 5 9 8 20 8 24 7 4 25 11 30 27 28 19 15 18 20 14 18 17
## [10033] 29 27 2 10 30 26 13 16 8 11 14 5 10 28 3 8 9 24 14 12 27 30 14 30
## [10057] 20 28 28 28 20 21 29 5 17 15 12 25 29 16 2 22 1 11 29 16 24 24 22 9
## [10081] 25 8 8 17 30 12 17 26 23 1 16 4 23 28 23 15 10 4 4 13 3 18 29 9
## [10105] 17 4 11 22 30 28 18 28 28 29 8 29 6 20 2 30 6 29 15 25 20 14 6 29
## [10129] 29 28 25 29 16 29 19 25 20 16 17 25 15 19 5 23 1 3 9 30 30 7 18 3
## [10153] 14 9 18 4 19 26 19 19 11 30 29 3 22 4 8 6 8 8 29 11 6 7 9 31
## [10177] 16 1 9 19 10 7 2 26 12 13 13 16 25 13 11 9 2 16 4 4 13 7 26 27
## [10201] 9 16 9 14 24 28 12 3 25 1 18 11 9 15 12 24 29 12 27 7 30 14 5 22
## [10225] 1 30 28 13 23 7 3 25 28 28 28 19 18 13 3 29 20 18 4 16 8 10 1 19
## [10249] 4 12 2 9 1 16 1 10 11 29 11 16 28 4 30 15 8 19 28 17 29 2 18 29
## [10273] 1 3 26 20 16 9 3 30 25 22 17 17 11 11 26 18 21 10 29 20 23 18 19 19
## [10297] 20 19 17 19 29 3 15 21 12 20 12 2 13 15 10 20 24 18 19 14 10 18 28 2
## [10321] 5 5 16 18 28 27 6 5 30 30 26 19 5 30 30 19 3 22 24 6 21 7 12 9
## [10345] 15 21 28 2 9 15 24 2 13 5 25 20 24 16 25 27 13 30 8 24 23 28 20 26
## [10369] 29 18 27 23 12 25 13 18 5 4 25 18 13 19 18 2 10 10 2 16 19 11 17 11
## [10393] 29 18 9 18 4 4 2 10 1 18 29 30 16 19 5 30 27 11 3 16 23 22 1 19
## [10417] 18 22 16 8 25 12 3 3 11 24 12 19 26 20 4 7 26 23 3 13 16 25 23 24
## [10441] 24 9 18 27 10 2 9 2 26 2 11 9 20 9 20 25 24 12 24 21 4 3 15 28
## [10465] 4 20 17 7 22 4 30 25 11 30 10 22 17 18 26 26 4 5 6 1 12 21 22 4
## [10489] 1 1 19 9 25 1 9 26 16 23 13 21 15 15 29 1 22 14 8 15 18 25 4 5
## [10513] 30 13 5 15 5 4 16 2 18 28 27 15 25 23 6 30 7 19 14 27 1 9 10 22
## [10537] 30 27 16 13 12 29 6 7 25 2 9 19 30 1 10 19 8 5 30 17 2 17 12 25
## [10561] 29 28 28 25 27 16 20 22 16 13 10 11 2 23 26 13 27 28 19 5 11 5 16 19
## [10585] 10 14 17 27 29 18 21 22 1 18 29 19 1 23 1 3 19 14 25 4 15 19 10 24
## [10609] 13 28 28 11 19 13 3 20 13 5 17 20 20 9 29 30 9 12 24 26 3 31 2 6
## [10633] 12 14 1 4 30 18 22 30 30 30 11 12 2 18 18 10 30 21 21 16 4 5 28 6
## [10657] 18 18 26 18 4 29 21 30 19 2 5 23 19 23 1 6 12 20 2 20 26 26 17 7
## [10681] 1 23 9 1 28 1 8 12 2 12 11 25 25 2 22 28 14 14 6 9 11 11 16 16
## [10705] 26 3 25 23 29 9 29 12 25 26 26 13 15 17 8 25 28 25 12 17 5 29 11 12
## [10729] 29 4 16 28 2 6 18 19 3 3 9 9 21 1 10 12 21 31 16 12 27 1 2 19
## [10753] 27 1 13 20 22 11 18 13 25 18 10 29 3 21 21 20 4 11 28 2 4 18 1 1
## [10777] 3 2 28 8 9 3 30 20 17 2 22 1 7 19 19 3 12 4 25 23 10 18 22 4
## [10801] 30 6 21 21 4 16 25 25 25 25 2 22 18 15 8 16 1 10 30 3 27 13 9 2
## [10825] 3 4 13 4 20 11 15 15 30 25 4 20 3 18 13 6 22 9 17 17 28 2 1 12
## [10849] 21 6 8 17 22 25 2 4 6 6 8 30 6 21 6 2 12 8 25 28 1 10 11 9
## [10873] 4 14 24 2 14 7 1 2 29 26 26 9 11 13 2 1 13 13 14 3 19 19 1 9
## [10897] 28 11 20 16 29 19 19 19 18 17 17 29 8 15 12 11 17 29 21 27 1 30 29 29
## [10921] 16 1 25 18 21 21 20 10 19 18 19 24 19 4 15 28 7 7 7 21 18 3 9 15
## [10945] 2 5 15 18 28 4 9 9 2 16 11 11 4 3 24 24 15 7 11 6 30 22 1 6
## [10969] 3 23 18 19 7 7 2 10 22 27 29 13 11 2 2 7 24 8 13 1 25 29 3 4
## [10993] 17 29 29 17 6 10 26 23 16 2 7 2 27 6 27 26 20 19 28 25 20 16 30 19
## [11017] 7 6 13 14 24 2 24 5 18 12 13 3 19 9 24 23 15 27 16 4 29 20 27 13
## [11041] 2 2 22 16 18 18 5 12 1 15 4 15 28 2 18 14 29 13 8 2 21 14 11 12
## [11065] 18 18 18 16 16 2 27 9 2 11 5 17 27 23 10 12 3 28 17 29 18 19 27 11
## [11089] 27 3 10 29 19 18 28 2 21 4 19 11 2 12 12 25 7 15 28 18 29 20 29 23
## [11113] 16 16 2 2 20 28 27 17 4 4 17 1 30 6 21 30 16 2 1 8 25 30 2 11
## [11137] 26 23 2 2 27 9 10 24 19 18 29 10 30 29 29 29 17 9 5 9 30 17 25 17
## [11161] 2 17 1 11 29 7 3 9 21 6 19 29 30 26 3 1 8 5 1 30 22 23 30 4
## [11185] 27 7 15 6 6 2 2 8 26 2 26 8 2 9 9 14 29 18 14 29 3 27 3 3
## [11209] 14 30 15 16 16 19 17 28 18 1 25 2 18 19 21 29 15 30 4 3 14 7 30 18
## [11233] 25 4 10 8 3 6 11 3 2 15 29 7 24 22 29 17 25 15 9 26 18 4 16 29
## [11257] 19 5 25 6 29 18 4 4 29 19 29 21 21 3 29 25 29 30 1 21 5 29 16 14
## [11281] 19 16 5 21 8 30 9 25 11 22 22 22 23 15 25 29 23 27 25 11 27 12 15 6
## [11305] 6 12 1 20 13 18 27 23 12 25 18 29 18 25 27 27 3 18 18 27 19 19 6 28
## [11329] 4 29 19 16 29 6 3 8 18 5 18 4 1 11 22 22 21 17 15 4 3 23 1 21
## [11353] 22 2 2 22 19 1 19 8 21 21 21 16 20 4 19 2 6 18 8 25 25 12 6 9
## [11377] 14 12 16 13 18 4 11 19 20 6 10 6 16 17 17 16 2 9 25 25 29 11 16 21
## [11401] 11 26 10 29 28 30 13 14 26 19 20 20 2 29 19 16 29 2 1 6 11 1 20 9
## [11425] 15 24 26 2 19 1 4 7 21 1 24 18 2 12 11 17 18 30 1 3 6 20 17 24
## [11449] 14 17 20 30 3 2 9 1 18 28 8 16 30 30 27 21 2 6 22 7 26 18 10 25
## [11473] 8 18 28 17 1 24 24 28 12 12 2 27 20 15 17 21 3 8 12 18 3 19 16 16
## [11497] 2 12 22 7 8 24 28 29 19 17 26 3 20 2 16 15 16 16 24 23 2 13 15 5
## [11521] 25 6 16 17 24 2 18 29 17 22 6 13 23 25 25 29 29 1 26 18 28 19 27 2
## [11545] 28 29 29 3 5 5 8 10 31 30 25 2 1 18 11 2 15 7 4 16 23 30 7 6
## [11569] 7 26 7 28 18 21 17 29 23 24 25 4 22 13 25 8 9 9 29 26 17 17 14 3
## [11593] 30 5 30 11 13 23 25 29 25 25 19 14 6 21 26 13 9 30 24 2 29 13 17 17
## [11617] 25 6 26 3 20 4 30 7 25 8 25 16 18 29 4 5 7 7 27 16 26 3 20 23
## [11641] 5 28 22 5 12 13 28 18 2 19 16 13 10 2 18 2 2 29 29 30 6 2 4 4
## [11665] 2 30 25 12 25 3 1 19 2 19 4 26 4 3 23 25 25 8 30 24 10 25 4 27
## [11689] 29 1 17 17 19 5 22 11 1 26 9 9 29 23 1 18 27 21 2 12 9 20 16 29
## [11713] 6 21 21 25 12 28 28 10 3 17 22 11 7 3 9 16 29 16 1 11 2 2 15 22
## [11737] 1 7 21 12 26 18 13 19 13 6 4 29 23 3 16 29 30 18 13 22 15 20 24 22
## [11761] 29 26 21 29 22 2 20 21 1 6 10 24 20 13 10 9 20 9 28 13 18 1 11 22
## [11785] 22 10 15 10 25 28 19 7 8 14 26 22 13 19 21 28 2 31 30 25 6 30 14 22
## [11809] 14 4 25 30 6 21 9 2 21 27 17 15 26 22 5 15 11 8 30 17 17 28 13 25
## [11833] 8 22 22 14 23 10 24 13 15 30 15 25 19 7 27 25 30 2 1 9 8 25 27 29
## [11857] 3 9 12 23 28 16 4 22 24 24 25 1 4 6 10 20 3 6 7 20 30 15 7 26
## [11881] 20 12 16 5 11 17 31 22 19 27 19 10 1 26 27 29 5 22 14 19 11 19 6 22
## [11905] 29 30 13 11 31 1 15 25 6 30 22 9 29 30 10 9 28 6 1 21 9 29 15 14
## [11929] 24 22 30 22 24 22 19 23 21 18 27 14 12 3 7 15 17 10 21 30 3 15 6 23
## [11953] 8 30 23 21 23 4 26 22 17 1 28 26 8 30 24 19 4 29 14 19 17 4 8 14
## [11977] 14 8 16 17 1 5 3 5 24 25 13 29 17 18 14 15 24 7 27 7 25 30 3 22
## [12001] 17 18 8 19 20 27 27 13 26 2 13 1 5 18 27 22 19 26 7 28 22 22 20 13
## [12025] 30 22 3 19 23 29 21 22 6 24 22 30 13 25 25 16 8 5 25 19 28 27 10 20
## [12049] 12 19 17 4 10 3 30 30 10 21 30 23 26 12 26 21 18 26 17 28 28 23 15 9
## [12073] 29 28 31 21 28 2 6 10 29 12 4 6 30 2 30 4 26 6 12 9 16 20 1 11
## [12097] 8 19 22 16 27 3 4 25 11 8 12 10 29 28 27 16 17 27 6 14 22 13 24 12
## [12121] 28 27 29 20 11 20 24 26 23 27 29 24 2 17 25 5 17 31 16 18 21 30 4 27
## [12145] 21 26 11 27 17 23 22 17 21 26 15 28 23 27 30 28 6 20 5 4 22 15 9 23
## [12169] 16 22 27 6 22 20 19 8 16 15 18 12 16 24 16 28 2 14 8 29 15 30 25 16
## [12193] 11 14 19 17 5 15 25 23 13 1 18 26 13 5 22 10 16 15 24 8 27 10 29 18
## [12217] 20 30 2 21 2 20 28 20 16 10 1 14 4 22 30 16 24 23 5 27 30 30 18 19
## [12241] 2 28 12 18 1 13 8 11 27 16 21 10 30 1 23 24 14 29 8 14 5 25 8 30
## [12265] 21 2 1 29 30 22 5 23 4 28 23 17 12 2 24 27 25 13 5 22 13 7 26 22
## [12289] 22 12 29 2 23 6 16 15 14 20 27 21 20 28 4 6 18 27 29 23 24 21 5 23
## [12313] 8 26 30 22 30 19 2 22 23 15 20 3 2 25 4 25 23 6 21 12 14 23 24 22
## [12337] 21 7 7 14 9 19 22 27 19 1 3 4 3 16 30 29 25 28 20 14 14 29 24 19
## [12361] 23 12 26 18 16 8 19 20 4 6 25 10 10 2 17 19 27 27 26 19 24 20 28 29
## [12385] 18 14 23 12 4 5 14 12 12 10 7 2 22 4 26 31 19 10 27 26 4 23 8 4
## [12409] 21 13 23 18 28 1 28 22 15 5 29 22 23 25 13 22 19 29 15 27 19 22 3 22
## [12433] 12 22 13 1 21 25 27 10 21 30 29 21 23 23 16 23 29 6 22 8 8 14 12 20
## [12457] 14 14 30 26 30 17 7 4 22 11 9 11 2 29 4 16 18 8 16 16 27 10 24 22
## [12481] 23 28 27 21 28 13 24 14 23 25 12 23 29 20 20 4 22 14 20 22 2 3 20 22
## [12505] 22 5 22 27 1 21 22 23 8 23 31 23 19 1 23 8 18 12 10 6 17 8 24 9
## [12529] 4 28 29 11 13 15 4 30 30 16 29 21 10 5 4 11 9 27 30 30 17 17 20 30
## [12553] 21 30 15 8 21 17 23 6 21 5 22 22 10 21 16 26 29 25 12 14 27 17 20 1
## [12577] 27 5 20 3 23 7 13 12 14 9 11 7 8 29 1 21 1 18 21 2 25 3 22 28
## [12601] 30 13 6 17 22 30 7 4 4 16 27 26 16 30 8 27 6 17 19 7 14 14 23 30
## [12625] 17 19 24 16 2 25 7 24 5 19 24 27 17 16 27 25 8 12 4 22 28 29 12 1
## [12649] 15 3 29 16 15 30 30 15 30 14 29 29 2 22 6 4 13 4 30 4 22 17 10 20
## [12673] 3 15 27 14 23 28 28 7 8 22 11 7 20 30 28 9 17 1 12 27 26 5 26 4
## [12697] 25 8 13 22 17 30 5 3 22 27 20 14 2 22 30 22 21 14 30 23 26 23 30 17
## [12721] 21 3 24 6 2 10 30 8 1 22 30 27 13 27 29 9 20 24 19 23 23 27 15 18
## [12745] 4 21 20 15 29 29 2 8 28 28 8 7 7 10 4 30 26 6 29 14 29 29 28 24
## [12769] 18 27 20 29 5 18 19 20 17 9 3 27 6 24 25 28 22 22 22 27 14 24 17 1
## [12793] 22 3 30 23 30 10 11 22 26 22 27 5 13 16 2 5 17 15 17 29 22 11 22 15
## [12817] 6 28 13 23 3 4 16 17 19 15 30 16 30 30 28 22 11 25 30 27 24 12 22 25
## [12841] 22 20 21 28 12 21 19 14 18 29 15 15 21 16 28 30 29 29 16 18 18 31 27 27
## [12865] 6 18 30 12 12 17 17 5 18 28 12 19 18 10 1 8 22 14 14 25 24 25 2 11
## [12889] 1 19 8 26 24 21 12 18 15 17 16 15 24 23 16 10 5 4 20 29 21 6 28 21
## [12913] 11 26 5 28 27 25 6 18 20 20 16 30 11 9 15 18 23 12 16 6 29 2 31 29
## [12937] 25 29 8 29 20 18 7 25 15 17 16 24 14 14 24 10 11 28 8 18 3 28 18 6
## [12961] 9 12 6 11 2 5 23 15 4 16 1 12 23 23 22 2 29 22 9 8 28 30 30 30
## [12985] 21 7 7 4 27 30 29 15 1 30 2 6 7 6 7 25 22 3 1 3 12 7 17 14
## [13009] 1 22 21 10 5 4 28 17 27 18 24 6 19 25 17 21 10 28 17 20 7 28 31 25
## [13033] 15 1 1 13 26 13 7 23 8 21 21 29 21 9 4 6 7 6 18 14 22 16 16 1
## [13057] 15 29 26 20 8 20 8 30 15 22 14 27 12 5 2 5 5 17 21 14 13 10 8 28
## [13081] 3 21 17 10 16 22 10 5 11 6 15 25 25 23 25 8 21 1 1 18 19 21 17 19
## [13105] 12 10 26 29 1 14 12 12 2 12 17 26 29 6 3 4 12 27 28 5 4 25 10 25
## [13129] 25 12 19 28 28 27 2 4 31 19 18 3 19 30 3 6 25 21 24 13 24 12 25 26
## [13153] 23 18 10 29 6 22 6 5 21 3 3 3 17 10 30 8 4 17 4 18 25 19 20 31
## [13177] 2 5 21 23 18 13 9 10 25 7 20 18 23 14 20 8 8 10 16 21 10 17 10 10
## [13201] 7 7 13 10 30 4 5 24 14 15 7 7 30 22 12 11 20 10 28 7 22 22 22 14
## [13225] 24 7 15 15 23 14 15 9 21 8 14 24 16 10 16 7 24 18 29 13 12 1 25 16
## [13249] 22 7 10 19 9 20 23 13 7 15 18 15 27 8 2 6 15 7 12 21 3 1 7 20
## [13273] 26 16 8 19 16 28 3 13 17 21 14 5 1 13 26 18 31 21 6 20 29 24 22 29
## [13297] 18 27 29 3 12 28 13 4 7 30 26 31 24 16 6 12 17 8 30 2 15 5 6 21
## [13321] 2 28 18 13 5 3 31 15 22 15 12 14 15 18 15 2 30 23 24 12 21 16 13 13
## [13345] 1 15 14 9 20 12 21 26 26 30 15 10 24 21 8 2 24 21 8 18 16 14 22 11
## [13369] 14 14 29 27 14 28 28 30 24 3 4 23 15 1 15 6 27 18 17 30 24 23 30 4
## [13393] 20 31 16 17 24 25 14 9 29 10 10 14 9 25 17 30 25 26 25 24 28 8 4 10
## [13417] 3 14 8 11 15 9 9 15 31 10 28 22 5 12 25 1 4 11 5 7 24 6 5 4
## [13441] 24 18 25 9 5 24 3 21 29 2 6 5 24 30 29 25 15 3 3 28 18 23 28 5
## [13465] 6 31 30 16 24 16 17 17 22 3 22 22 5 8 13 3 7 4 16 18 10 11 17 4
## [13489] 15 8 16 3 19 6 20 28 4 29 27 8 19 22 4 22 5 28 6 15 16 9 7 21
## [13513] 15 13 10 27 17 11 1 21 8 17 21 8 25 7 23 11 8 27 17 5 4 7 20 9
## [13537] 1 17 1 26 10 28 30 22 7 5 19 6 12 6 4 8 3 23 30 11 21 14 15 29
## [13561] 29 25 23 16 9 13 4 9 7 30 7 26 4 25 29 12 16 3 30 22 22 7 29 27
## [13585] 22 17 23 26 23 10 31 1 29 4 9 20 2 3 28 6 14 11 2 4 26 31 3 26
## [13609] 20 1 16 21 16 24 21 20 21 9 5 9 14 6 25 29 18 18 10 10 26 28 8 17
## [13633] 27 29 1 17 23 17 3 1 28 7 15 15 25 24 19 14 31 30 15 15 26 18 30 12
## [13657] 5 21 10 31 28 23 10 26 19 19 31 18 30 18 16 12 14 1 23 11 7 25 15 3
## [13681] 21 15 19 7 7 13 10 14 29 11 17 19 15 17 12 7 28 24 13 15 3 10 6 24
## [13705] 26 13 17 22 1 7 2 9 12 29 3 14 2 21 1 12 30 22 27 18 15 23 23 9
## [13729] 12 17 12 23 29 18 13 3 9 16 13 20 16 29 12 1 9 13 4 12 14 3 17 11
## [13753] 25 21 7 9 29 22 27 18 4 20 1 15 3 6 18 21 21 8 15 16 20 13 29 27
## [13777] 19 10 5 22 21 22 27 10 21 31 30 18 13 2 22 12 20 29 31 16 25 25 19 15
## [13801] 1 31 31 4 17 30 12 24 7 24 18 21 28 7 21 17 22 1 10 22 10 19 22 31
## [13825] 10 28 10 16 8 12 30 17 10 14 6 27 30 23 17 22 16 8 23 23 20 2 9 3
## [13849] 19 9 11 28 11 3 20 25 17 31 19 18 16 17 28 10 19 15 22 24 23 15 18 27
## [13873] 12 1 11 8 13 16 17 15 13 20 7 8 4 6 8 19 27 7 6 21 6 10 17 18
## [13897] 3 10 4 6 6 6 16 11 13 17 28 29 10 17 7 15 8 15 28 29 30 19 20 25
## [13921] 21 10 6 7 7 26 2 15 21 27 8 30 18 30 31 20 13 8 12 13 3 18 13 2
## [13945] 28 22 31 24 17 2 13 9 9 9 10 18 14 31 12 22 19 3 3 25 8 21 6 2
## [13969] 25 25 1 18 19 28 21 6 30 13 25 26 16 1 12 13 10 28 25 28 24 26 31 5
## [13993] 10 28 28 15 15 25 21 10 7 27 1 21 26 14 9 11 15 16 15 14 30 18 29 11
## [14017] 11 9 28 29 10 13 31 8 8 15 5 3 7 4 15 8 9 7 29 11 3 19 17 11
## [14041] 22 15 3 28 9 6 31 22 1 23 17 29 29 10 25 7 5 4 22 19 9 31 5 7
## [14065] 17 30 18 27 22 18 3 2 16 7 17 19 24 4 29 15 4 24 26 14 3 9 12 23
## [14089] 16 9 24 20 30 23 20 23 25 17 12 12 2 7 17 12 5 23 18 13 15 9 21 10
## [14113] 20 28 30 10 24 6 10 10 3 17 19 22 5 7 10 3 17 24 7 7 1 21 11 27
## [14137] 23 22 5 28 3 6 24 31 25 26 29 4 22 31 10 5 13 22 9 10 13 24 6 7
## [14161] 30 9 10 29 10 14 22 22 5 15 28 28 28 13 25 1 7 21 30 29 12 8 28 4
## [14185] 17 24 31 9 21 21 26 27 26 28 24 26 3 25 11 20 7 24 1 24 3 2 18 7
## [14209] 9 17 11 10 12 17 20 11 8 23 13 12 7 14 24 3 19 17 24 4 16 4 20 13
## [14233] 14 2 29 27 22 21 4 16 8 26 29 23 31 8 21 15 27 21 6 22 5 29 18 6
## [14257] 21 11 12 5 18 18 8 4 6 7 3 17 13 26 2 24 14 19 18 12 29 13 13 28
## [14281] 14 7 11 25 18 4 4 13 10 10 18 8 30 22 24 11 15 1 3 9 4 3 22 23
## [14305] 3 21 21 17 15 28 1 2 14 24 22 23 12 4 11 29 14 15 29 15 1 22 10 2
## [14329] 15 9 21 7 18 24 20 26 22 29 26 26 23 28 29 28 24 7 27 14 28 24 21 19
## [14353] 12 7 12 23 18 18 21 30 15 14 8 8 27 23 29 19 12 29 11 17 5 24 24 7
## [14377] 3 22 30 10 10 1 27 28 30 29 23 8 29 28 9 2 19 20 2 11 1 19 3 20
## [14401] 9 27 20 23 18 24 29 10 11 20 7 17 12 31 15 8 21 25 28 2 10 28 25 9
## [14425] 11 21 30 14 10 24 16 16 17 17 5 8 9 10 1 4 21 21 7 24 14 12 1 12
## [14449] 31 31 24 28 2 24 16 1 17 6 28 10 10 10 21 21 15 20 11 24 23 17 8 8
## [14473] 15 18 17 4 25 11 8 26 22 27 27 4 12 6 10 27 7 7 5 28 11 25 8 13
## [14497] 19 20 25 2 25 25 13 31 5 7 13 25 27 3 16 27 10 15 7 6 10 18 12 22
## [14521] 27 14 27 22 22 28 4 14 28 14 16 31 4 26 21 14 25 6 1 11 10 7 27 31
## [14545] 7 9 29 26 7 1 23 18 14 23 1 3 28 21 24 14 24 15 18 18 26 17 6 16
## [14569] 12 26 14 14 10 7 18 18 19 17 17 15 9 30 3 23 19 26 21 3 14 9 24 17
## [14593] 17 8 14 28 8 8 30 12 10 10 27 4 10 21 2 17 1 13 18 6 11 11 17 4
## [14617] 29 21 24 25 20 26 30 16 23 4 13 5 7 23 15 15 29 16 15 14 29 27 12 3
## [14641] 5 7 25 29 18 20 21 28 15 29 5 5 28 29 10 15 1 14 6 2 15 1 27 17
## [14665] 15 21 30 17 29 24 6 6 22 30 26 3 9 25 21 16 17 17 17 26 22 17 21 17
## [14689] 14 18 17 1 18 28 8 7 25 23 14 15 14 30 8 30 14 10 26 29 17 24 8 22
## [14713] 16 29 3 17 29 22 13 19 4 22 24 12 7 10 19 9 17 13 8 18 22 6 4 18
## [14737] 1 2 7 5 22 31 15 16 9 21 3 4 30 18 22 24 29 21 6 28 8 17 3 18
## [14761] 3 6 10 4 4 1 10 14 25 30 25 3 12 24 25 14 27 10 19 13 11 13 16 16
## [14785] 27 23 20 17 22 19 31 7 13 11 13 8 14 17 22 22 23 23 29 6 31 2 17 23
## [14809] 17 18 6 3 11 9 31 14 30 3 19 13 23 22 14 25 28 9 22 4 4 19 25 17
## [14833] 19 17 23 31 10 14 29 30 4 25 12 27 7 28 28 3 11 27 12 8 23 24 5 29
## [14857] 15 15 28 21 6 4 12 30 26 10 13 14 28 29 3 15 17 25 29 14 15 15 29 8
## [14881] 1 5 23 25 8 19 19 1 2 16 14 3 23 17 8 10 19 6 6 11 10 29 4 8
## [14905] 6 18 4 22 11 24 9 12 21 11 6 17 15 23 18 3 30 9 14 12 24 13 12 21
## [14929] 21 19 9 23 8 11 22 17 22 1 28 17 28 28 1 13 23 7 5 31 26 18 17 16
## [14953] 22 30 1 14 18 6 26 17 21 27 9 3 14 1 1 16 2 25 28 16 3 21 19 16
## [14977] 22 21 6 4 18 7 9 24 24 1 10 22 26 4 28 21 6 8 16 20 17 30 10 23
## [15001] 3 14 30 27 21 15 13 16 21 21 15 2 3 15 7 21 27 14 23 8 31 22 11 27
## [15025] 27 16 13 5 7 25 18 4 20 6 4 17 12 10 3 3 21 7 20 10 14 27 12 28
## [15049] 21 9 11 27 15 29 20 7 20 26 3 11 10 14 24 3 29 25 15 31 31 13 18 14
## [15073] 17 6 21 26 31 1 5 17 28 20 13 5 8 21 29 30 27 29 9 31 22 4 3 8
## [15097] 2 12 30 17 29 29 1 25 12 21 1 19 8 4 10 2 27 9 27 15 17 14 23 4
## [15121] 9 12 16 31 31 30 28 9 17 8 13 14 25 30 11 10 25 21 31 11 27 11 28 25
## [15145] 14 30 14 22 24 22 27 21 15 21 27 8 21 1 31 13 17 2 19 24 30 2 10 26
## [15169] 23 28 18 22 30 16 9 18 25 9 14 16 12 6 1 4 7 13 6 4 16 6 2 3
## [15193] 9 27 22 22 16 14 14 21 25 31 13 20 11 8 18 10 19 14 11 18 19 14 15 3
## [15217] 17 25 21 13 30 30 30 11 14 25 7 24 21 16 13 13 22 25 10 6 10 6 8 30
## [15241] 16 20 18 30 28 4 5 18 10 7 21 6 4 21 1 15 19 14 27 5 24 24 4 25
## [15265] 14 27 29 5 8 8 27 17 18 26 16 8 8 8 20 27 15 11 29 28 5 27 21 9
## [15289] 3 7 6 16 28 15 30 25 27 18 28 29 28 24 9 15 9 4 25 21 14 5 22 29
## [15313] 9 16 15 22 27 18 3 9 9 13 14 6 12 17 17 23 4 1 23 11 2 24 24 15
## [15337] 13 26 17 4 2 12 9 3 4 4 10 10 1 24 17 22 20 19 9 28 26 11 13 24
## [15361] 9 6 24 22 22 26 18 7 19 6 26 7 24 16 24 25 30 23 23 13 4 21 20 27
## [15385] 30 21 26 20 24 15 21 6 4 29 3 19 29 27 26 1 15 17 8 20 20 22 27 21
## [15409] 19 23 8 31 31 30 21 31 3 28 6 14 30 26 19 5 29 29 7 19 17 20 18 25
## [15433] 5 11 13 29 1 28 7 16 6 4 3 11 19 7 2 2 3 31 3 21 28 7 4 11
## [15457] 1 27 24 27 31 16 28 31 21 27 29 5 1 2 22 11 16 24 21 21 31 17 3 31
## [15481] 1 20 26 28 31 5 14 28 1 6 21 5 29 21 21 17 20 24 18 24 21 27 9 4
## [15505] 21 16 10 9 3 7 7 3 8 25 31 8 3 7 30 14 23 23 26 6 8 5 20 7
## [15529] 13 28 11 10 20 22 14 4 23 3 4 24 8 21 9 20 21 27 31 9 11 1 21 16
## [15553] 1 9 13 7 2 23 24 24 25 9 4 18 24 6 15 23 4 7 30 4 27 26 27 22
## [15577] 17 31 4 29 2 6 8 20 23 4 3 29 18 30 25 18 1 3 3 28 11 22 16 12
## [15601] 7 9 28 20 20 14 15 27 31 6 17 14 17 22 8 23 4 4 26 29 17 24 27 29
## [15625] 15 28 4 14 3 30 18 6 17 6 1 2 10 25 1 9 14 20 22 20 7 11 4 20
## [15649] 27 18 26 10 22 27 19 31 10 18 2 5 16 10 11 17 4 2 28 28 7 3 10 16
## [15673] 25 21 15 8 3 26 21 18 4 4 28 23 21 21 13 5 1 29 16 4 3 5 22 31
## [15697] 21 5 5 7 22 16 2 16 1 23 12 23 1 7 21 12 21 22 12 30 5 22 9 12
## [15721] 22 8 7 30 4 15 11 22 27 31 31 12 17 24 3 23 8 27 4 13 12 29 9 17
## [15745] 18 23 1 22 20 5 8 1 1 3 27 24 22 5 2 2 5 8 7 14 19 21 17 13
## [15769] 16 5 10 24 28 11 20 23 27 4 4 1 9 13 8 5 28 20 9 31 22 27 16 8
## [15793] 21 20 11 8 25 7 26 29 22 5 20 14 29 4 2 27 8 8 5 10 14 7 6 4
## [15817] 19 5 25 29 24 19 10 4 6 15 24 6 1 20 26 8 30 4 21 25 5 29 26 29
## [15841] 30 28 1 30 27 27 28 20 7 12 9 21 22 9 12 10 31 12 24 20 8 2 2 6
## [15865] 19 11 15 8 9 26 8 17 27 10 11 6 10 22 5 6 8 12 7 16 6 7 9 6
## [15889] 18 24 15 14 2 12 23 15 10 6 13 29 7 13 21 3 12 28 11 14 11 12 25 23
## [15913] 11 16 16 17 5 4 29 19 9 9 23 27 5 29 31 30 15 28 11 20 6 27 21 6
## [15937] 8 22 6 29 12 27 22 9 24 28 25 5 3 5 1 14 9 4 7 16 21 8 20 8
## [15961] 27 12 24 17 29 17 30 16 2 12 19 13 30 24 29 19 15 13 6 23 19 10 1 26
## [15985] 26 14 23 16 2 3 27 31 10 10 9 31 24 29 31 5 26 10 1 18 24 15 6 2
## [16009] 7 31 22 14 4 24 24 8 28 13 22 1 28 29 1 23 30 16 10 10 13 12 6 5
## [16033] 5 5 6 6 21 26 23 18 15 19 7 24 13 1 7 4 11 21 2 18 6 4 5 29
## [16057] 21 23 16 5 25 14 4 31 3 3 17 29 16 17 19 17 11 29 8 10 1 13 21 3
## [16081] 12 17 5 26 29 1 13 15 7 13 8 10 14 21 17 8 22 25 29 28 5 7 27 28
## [16105] 16 26 8 12 31 29 9 12 14 10 3 14 12 8 21 31 31 28 16 12 7 6 8 28
## [16129] 6 8 14 19 13 16 25 22 17 9 13 13 28 4 29 6 9 30 25 20 5 14 17 10
## [16153] 7 23 4 8 20 13 21 9 10 10 19 12 4 5 22 1 14 18 9 27 23 15 8 31
## [16177] 30 29 21 15 13 9 26 29 8 15 30 28 25 19 26 22 23 28 24 4 1 3 8 30
## [16201] 4 28 28 28 21 10 6 9 30 23 19 12 12 28 16 10 17 11 20 10 28 5 12 16
## [16225] 21 13 29 26 16 14 4 14 11 12 14 23 9 8 4 27 19 21 10 1 24 11 17 10
## [16249] 25 11 29 30 2 31 3 24 9 7 31 2 15 30 25 9 14 21 24 19 15 10 24 17
## [16273] 14 17 3 8 28 4 4 1 4 21 4 17 29 29 29 1 21 4 29 17 29 4 30 28
## [16297] 19 7 3 15 8 17 7 16 2 25 19 17 24 8 19 3 31 17 30 27 16 29 4 29
## [16321] 8 23 12 29 9 22 22 29 13 10 18 13 17 24 22 22 21 26 11 22 19 15 3 17
## [16345] 6 10 9 8 12 31 18 10 21 13 18 18 23 3 8 27 23 29 23 15 15 10 24 14
## [16369] 14 15 15 30 14 28 15 28 8 21 5 24 10 11 29 13 11 23 21 15 8 28 10 27
## [16393] 17 14 29 16 9 2 2 27 14 24 25 29 9 17 19 2 3 10 6 31 16 10 17 20
## [16417] 25 30 30 30 4 6 25 12 14 30 14 25 29 6 11 27 27 26 9 24 13 22 22 10
## [16441] 16 21 4 28 31 17 16 7 4 26 27 17 23 29 26 26 15 14 7 21 20 21 22 1
## [16465] 21 29 12 7 8 26 22 21 17 7 17 27 7 22 14 10 3 7 3 17 27 27 3 8
## [16489] 29 29 12 12 17 9 22 3 1 17 29 18 9 14 12 18 22 7 7 1 9 8 9 18
## [16513] 12 23 12 7 14 18 28 21 13 9 14 14 17 13 22 2 24 24 22 10 8 18 21 13
## [16537] 15 28 18 31 3 15 8 15 23 26 28 29 25 27 31 30 14 31 24 12 15 1 10 3
## [16561] 5 7 16 13 14 20 25 25 26 23 10 17 7 29 13 18 22 28 30 14 21 3 6 21
## [16585] 28 16 10 15 24 14 16 30 8 15 14 13 25 18 25 7 8 1 20 3 15 11 10 30
## [16609] 30 24 17 17 25 30 5 16 24 4 6 6 7 31 30 30 22 17 19 23 22 8 29 27
## [16633] 12 22 11 17 17 28 27 6 22 22 8 7 10 11 6 22 11 28 7 24 28 24 27 4
## [16657] 22 6 14 17 26 11 12 21 19 14 13 28 5 7 5 7 1 19 25 6 29 23 29 31
## [16681] 9 22 21 1 23 22 14 19 14 21 16 28 18 2 3 21 28 23 29 29 31 6 5 7
## [16705] 7 26 25 4 26 8 6 20 28 26 31 8 3 23 27 23 22 3 13 15 18 17 3 15
## [16729] 29 1 17 30 19 14 24 8 9 16 1 1 27 29 11 30 10 23 26 17 7 12 31 15
## [16753] 4 7 17 12 5 12 24 6 6 28 24 18 6 29 12 1 5 4 6 19 21 20 21 29
## [16777] 14 3 12 12 11 6 17 5 22 20 1 21 13 10 19 8 6 13 1 15 18 6 9 12
## [16801] 9 25 10 25 20 4 14 16 18 13 20 20 8 4 1 5 19 7 18 30 4 4 10 4
## [16825] 4 4 17 1 1 22 16 1 12 3 3 7 7 28 20 26 29 21 14 15 8 24 30 25
## [16849] 10 7 21 21 22 3 15 10 28 14 21 8 24 11 17 30 25 3 10 31 26 30 9 30
## [16873] 14 9 17 17 27 17 7 5 14 24 23 31 27 24 4 16 19 12 7 15 2 25 14 7
## [16897] 14 14 1 28 16 15 4 10 12 7 21 16 24 5 23 9 1 4 29 16 17 30 6 22
## [16921] 27 13 24 8 30 7 16 17 17 30 28 21 10 24 21 3 10 21 28 21 7 28 15 29
## [16945] 24 16 24 29 19 3 16 23 4 20 6 20 20 17 23 1 10 10 22 17 21 31 30 24
## [16969] 31 26 18 2 15 31 31 28 29 18 13 29 30 29 25 16 18 4 3 28 30 23 13 22
## [16993] 31 7 22 21 20 4 22 17 3 3 7 5 28 9 27 8 29 23 7 11 9 9 29 29
## [17017] 22 17 9 17 25 11 25 8 9 16 6 21 21 18 7 13 17 27 24 27 21 8 11 20
## [17041] 20 17 20 29 19 1 8 13 6 8 6 10 13 20 20 21 14 22 10 6 5 4 5 14
## [17065] 3 26 21 17 31 26 8 21 8 14 8 5 6 4 15 30 7 24 5 4 14 10 1 22
## [17089] 28 27 8 28 14 14 14 6 11 13 21 3 14 14 2 28 28 22 18 10 14 7 24 17
## [17113] 20 17 24 24 10 14 13 27 29 5 23 31 6 4 8 22 17 1 21 29 11 23 15 11
## [17137] 21 2 1 8 8 2 2 21 18 14 4 4 10 4 31 29 23 27 15 18 29 1 25 28
## [17161] 16 28 8 23 7 16 5 30 15 30 14 14 16 12 21 5 27 28 8 17 17 17 18 8
## [17185] 27 10 26 17 25 28 25 19 3 23 21 9 21 19 26 30 6 7 22 25 17 16 13 12
## [17209] 1 6 13 6 19 10 29 1 6 13 15 5 13 24 5 5 31 31 6 9 22 4 5 22
## [17233] 28 19 20 11 22 22 4 4 9 25 20 23 7 24 4 11 27 12 17 25 31 2 24 3
## [17257] 29 18 17 6 30 11 14 8 24 21 27 24 25 11 8 23 1 21 30 16 23 23 28 21
## [17281] 26 24 21 28 1 15 25 2 28 17 25 23 13 3 29 12 13 25 10 22 30 20 10 6
## [17305] 29 29 19 2 14 6 14 26 17 3 15 29 1 13 14 17 4 8 18 13 14 2 21 23
## [17329] 2 21 28 8 22 28 22 13 4 1 8 29 24 16 9 21 12 2 1 29 17 14 22 24
## [17353] 7 7 26 1 1 31 21 3 29 25 14 10 21 7 22 19 28 6 13 17 8 5 7 25
## [17377] 25 21 24 21 11 28 2 28 17 15 13 17 27 12 16 16 7 23 22 23 14 8 23 8
## [17401] 7 8 21 1 29 28 28 16 16 3 31 9 6 30 17 2 15 25 26 2 31 21 20 11
## [17425] 4 20 13 25 24 29 13 3 14 7 15 3 13 10 8 20 26 7 1 6 24 20 7 11
## [17449] 14 17 17 9 23 21 17 1 27 7 8 26 14 13 24 10 10 21 22 29 4 21 4 8
## [17473] 12 14 30 25 24 9 29 22 2 11 26 1 21 21 9 9 20 8 7 29 10 20 11 1
## [17497] 20 27 27 4 21 23 25 31 7 5 13 29 10 19 14 28 14 28 24 8 1 19 7 31
## [17521] 6 14 15 7 5 19 12 31 26 28 15 21 6 28 9 9 16 16 16 25 20 17 16 28
## [17545] 15 3 13 11 6 25 4 16 22 15 6 27 6 8 17 15 22 12 21 23 7 16 10 17
## [17569] 30 3 5 7 24 16 21 28 7 25 1 22 4 10 25 28 24 18 9 5 10 12 3 23
## [17593] 24 17 18 2 12 30 20 31 15 27 18 27 29 23 3 29 24 6 4 23 29 18 1 8
## [17617] 10 7 6 22 28 12 25 29 30 29 2 29 15 24 3 31 17 24 25 10 26 27 23 10
## [17641] 29 17 26 26 17 26 6 21 16 24 9 20 22 10 11 1 17 29 1 23 12 13 5 29
## [17665] 3 7 6 6 28 29 16 8 16 28 21 19 31 27 1 14 11 22 22 27 12 9 23 11
## [17689] 16 28 10 1 19 22 15 18 19 11 17 15 9 28 10 9 1 13 27 28 15 9 7 31
## [17713] 12 24 23 10 12 13 28 3 8 25 24 29 30 7 11 20 26 30 7 10 25 29 26 6
## [17737] 8 4 4 16 5 20 5 15 23 30 17 3 13 21 24 30 20 24 29 18 24 21 23 13
## [17761] 29 18 25 17 15 25 22 24 31 14 8 11 21 15 7 15 24 25 10 27 22 7 13 13
## [17785] 8 27 27 8 27 22 31 4 19 13 15 11 4 8 4 27 3 17 6 10 12 7 8 2
## [17809] 10 1 17 11 24 18 22 20 13 13 20 11 4 19 23 10 23 20 5 12 12 30 24 25
## [17833] 19 20 27 6 7 1 10 23 31 27 5 8 12 13 8 30 12 24 28 20 18 10 24 5
## [17857] 1 14 10 14 7 7 5 22 16 3 29 12 26 29 11 17 24 4 17 16 29 27 29 9
## [17881] 18 11 21 29 8 3 20 22 4 29 20 14 24 25 20 6 13 8 31 18 18 18 23 19
## [17905] 16 22 3 6 18 6 8 14 5 18 20 7 22 18 12 25 31 17 2 5 26 23 8 15
## [17929] 17 15 22 22 16 16 6 14 29 1 6 8 28 17 10 10 14 28 13 16 8 29 23 7
## [17953] 9 15 16 22 1 3 29 18 2 15 15 16 1 9 24 7 14 4 16 9 17 3 12 13
## [17977] 9 28 28 2 1 5 6 12 17 16 8 28 26 5 11 12 17 25 24 8 24 17 14 26
## [18001] 29 30 3 4 25 2 3 7 21 20 9 20 10 14 17 29 20 10 8 19 18 6 21 19
## [18025] 11 19 15 8 16 22 16 15 14 8 25 1 25 24 12 17 19 23 29 14 15 23 22 21
## [18049] 6 20 19 19 3 6 20 9 12 5 29 13 24 9 28 6 4 1 24 26 19 10 8 8
## [18073] 16 14 1 22 15 1 25 14 22 10 14 8 14 18 17 15 15 25 29 7 29 20 8 16
## [18097] 10 2 13 31 22 19 2 21 16 11 11 15 2 13 31 1 1 1 29 19 18 4 9 19
## [18121] 10 21 13 1 2 14 3 23 25 29 16 18 9 4 31 14 17 14 21 14 26 14 28 22
## [18145] 8 9 12 12 11 20 8 3 28 14 15 20 22 24 25 17 2 11 9 25 2 3 14 13
## [18169] 20 7 5 17 2 15 11 9 9 23 31 18 16 21 23 10 22 27 6 19 5 8 16 2
## [18193] 28 24 9 2 14 15 16 15 9 16 15 22 23 27 13 18 3 4 29 7 31 15 7 26
## [18217] 26 25 9 9 16 17 9 9 29 15 10 28 17 22 3 27 18 17 28 18 22 9 8 14
## [18241] 10 10 12 23 27 6 27 8 29 5 17 4 9 18 2 10 7 21 28 22 10 15 12 13
## [18265] 6 13 14 14 22 14 24 18 13 20 24 20 2 18 10 4 25 18 23 23 9 7 7 13
## [18289] 7 8 11 4 8 22 7 10 21 18 16 27 8 8 16 7 9 3 21 7 11 27 3 23
## [18313] 7 21 5 6 24 7 21 8 7 20 19 31 1 30 19 15 29 29 27 10 4 7 24 15
## [18337] 2 8 8 4 4 11 14 11 9 13 12 3 29 10 26 23 17 12 26 1 15 28 19 2
## [18361] 24 27 10 8 4 5 4 10 9 22 16 15 24 8 12 3 24 19 5 21 22 28 19 26
## [18385] 18 29 21 5 5 26 25 22 24 14 10 9 7 13 1 26 17 28 5 1 19 22 24 10
## [18409] 10 19 19 10 20 6 21 21 19 22 10 9 19 4 31 22 27 24 28 17 24 10 21 28
## [18433] 25 24 19 11 10 17 10 23 11 6 4 28 7 29 2 9 22 4 7 17 21 22 25 6
## [18457] 6 7 23 15 29 21 18 28 24 27 23 23 9 13 5 22 25 27 3 1 1 8 21 1
## [18481] 21 18 3 24 3 21 22 18 10 2 9 17 28 28 20 3 3 20 21 21 15 11 30 9
## [18505] 30 17 18 3 3 20 30 13 20 1 11 7 5 7 29 25 20 20 30 12 6 7 27 6
## [18529] 10 8 26 27 27 4 19 10 27 17 29 24 16 5 30 28 1 9 3 30 28 24 31 8
## [18553] 25 17 10 10 8 9 30 6 9 22 26 17 3 12 13 29 23 17 30 17 16 8 17 13
## [18577] 6 15 16 17 18 7 7 16 25 20 11 29 21 31 14 14 14 10 6 6 25 28 21 28
## [18601] 16 3 4 19 29 1 26 29 22 29 19 16 22 29 24 30 8 5 25 25 6 19 19 21
## [18625] 27 29 21 3 29 19 6 31 7 7 30 3 18 11 25 16 10 11 4 28 8 26 14 26
## [18649] 17 3 24 11 22 5 31 2 30 6 26 17 24 8 23 12 25 10 2 18 15 8 27 8
## [18673] 15 1 1 17 21 9 27 28 8 26 12 18 22 7 24 18 1 5 15 3 4 10 14 8
## [18697] 1 17 11 27 25 6 18 15 22 9 15 9 14 18 17 21 24 19 11 6 1 24 4 23
## [18721] 2 28 10 14 14 11 2 7 3 24 28 27 12 22 6 13 9 17 7 24 31 28 12 29
## [18745] 23 14 7 23 2 11 24 21 14 1 2 16 15 7 20 24 3 22 5 3 29 22 24 1
## [18769] 14 16 16 10 1 16 31 8 1 11 29 21 28 8 3 29 14 15 22 29 7 20 20 6
## [18793] 11 30 20 17 28 28 29 14 20 29 15 3 23 25 19 18 17 12 15 15 8 17 29 14
## [18817] 6 3 3 2 3 16 24 6 14 15 3 13 8 21 20 5 27 16 22 29 1 19 29 15
## [18841] 24 14 13 29 31 17 8 7 19 11 1 8 23 28 23 5 20 14 25 26 1 18 27 21
## [18865] 18 23 29 24 17 19 31 21 8 15 7 22 22 17 10 4 26 1 27 20 30 30 22 13
## [18889] 18 8 7 21 7 10 16 8 28 26 26 13 9 17 20 18 9 9 7 14 14 18 27 28
## [18913] 27 23 20 21 23 20 21 10 21 8 11 11 1 20 3 11 23 17 2 12 8 8 30 24
## [18937] 7 31 2 11 23 25 7 18 14 27 28 11 30 9 15 5 28 29 31 24 11 7 1 6
## [18961] 31 7 31 25 2 21 18 15 16 9 18 10 18 2 27 3 14 7 13 4 27 19 19 17
## [18985] 17 14 29 3 29 6 28 24 14 4 24 16 25 22 26 20 6 21 10 16 1 3 18 18
## [19009] 13 5 28 17 23 9 15 12 14 13 25 1 2 19 17 22 3 28 8 8 16 10 10 31
## [19033] 19 14 28 4 16 9 15 27 13 5 13 9 18 12 15 3 13 7 10 9 7 28 17 9
## [19057] 9 15 14 22 3 7 25 3 9 23 16 26 9 6 29 6 8 15 24 8 16 29 7 9
## [19081] 29 18 18 22 24 20 1 25 24 3 25 7 19 14 17 30 16 28 22 31 10 10 14 12
## [19105] 16 28 25 19 21 2 29 18 4 24 2 22 17 24 10 19 27 14 19 24 15 12 3 6
## [19129] 27 13 13 22 6 6 9 16 28 14 26 13 27 5 11 9 28 14 11 10 23 8 14 20
## [19153] 8 15 7 22 27 5 7 7 10 20 7 14 6 28 24 20 19 7 20 8 8 24 28 29
## [19177] 24 14 28 24 8 5 21 31 8 11 27 25 14 29 24 10 27 25 6 11 27 17 20 2
## [19201] 1 28 27 6 7 21 22 28 31 30 31 29 1 6 27 6 20 28 21 29 31 2 21 21
## [19225] 14 16 28 14 28 22 28 25 5 21 29 17 15 17 30 12 21 8 5 12 16 25 18 7
## [19249] 23 29 14 5 6 12 15 15 20 24 26 17 4 8 22 20 28 18 11 18 23 7 14 7
## [19273] 21 14 15 6 2 22 6 22 22 16 18 6 27 15 13 24 21 12 13 10 8 3 23 10
## [19297] 18 8 7 3 12 2 10 23 7 25 6 21 19 18 7 4 8 14 19 18 15 15 6 19
## [19321] 8 21 11 20 14 9 6 16 14 5 18 30 13 24 9 2 23 6 25 8 1 22 14 19
## [19345] 16 15 31 21 26 16 15 31 16 26 17 17 1 30 14 7 23 18 24 10 31 29 11 13
## [19369] 2 21 31 27 13 29 13 5 11 25 4 8 11 9 22 5 14 9 20 25 29 9 7 1
## [19393] 9 31 20 3 4 20 5 25 4 30 4 4 4 29 15 10 24 11 2 15 2 17 7 21
## [19417] 12 31 16 12 15 21 17 24 3 1 4 8 8 25 3 16 8 18 9 10 18 11 10 21
## [19441] 24 10 6 9 21 19 9 24 19 23 13 25 8 2 2 24 30 23 4 14 29 28 24 5
## [19465] 10 7 7 8 5 14 8 3 10 20 15 28 1 14 31 20 20 11 6 7 8 14 22 7
## [19489] 6 27 23 25 8 19 7 3 21 17 12 12 1 1 1 11 1 21 21 6 5 3 3 14
## [19513] 31 13 20 12 28 13 26 4 21 26 2 14 4 11 26 4 3 19 25 20 28 29 4 4
## [19537] 16 20 10 26 5 26 17 22 16 22 9 23 30 23 23 13 17 23 19 25 23 23 20 14
## [19561] 28 27 20 4 23 15 15 19 17 25 24 1 24 5 21 28 27 24 5 25 21 9 24 20
## [19585] 21 4 23 30 3 19 22 17 28 17 11 17 1 14 24 24 4 3 6 6 17 28 8 19
## [19609] 18 27 26 21 7 15 21 31 30 28 9 17 7 29 10 24 7 24 29 15 22 22 19 28
## [19633] 7 4 29 21 16 23 10 7 12 1 17 15 28 15 24 5 3 29 12 24 28 17 16 7
## [19657] 11 13 11 29 30 7 3 10 9 2 21 21 21 22 2 2 26 31 18 23 26 9 22 27
## [19681] 6 9 16 18 30 6 11 28 20 24 24 16 21 21 7 14 22 26 16 17 29 18 31 14
## [19705] 18 7 24 8 19 23 15 15 25 28 14 20 27 16 27 17 17 8 7 25 16 21 14 27
## [19729] 8 10 23 17 14 24 10 31 23 14 27 22 8 9 8 17 8 23 29 3 15 10 10 6
## [19753] 25 17 18 8 7 13 8 20 4 6 12 23 18 19 13 24 6 27 27 30 9 30 1 4
## [19777] 6 26 6 25 25 20 7 3 6 16 13 29 18 19 9 4 7 12 30 19 21 24 25 18
## [19801] 1 6 22 25 22 26 21 3 2 8 27 21 20 15 3 20 8 1 11 18 8 7 30 10
## [19825] 4 25 5 1 11 1 27 15 17 24 12 8 26 30 24 6 18 25 8 22 22 6 4 22
## [19849] 9 8 9 22 3 20 9 9 25 5 21 29 16 22 17 19 9 25 30 26 1 18 19 19
## [19873] 19 23 9 18 31 11 13 9 3 12 21 8 11 27 28 16 12 22 22 15 7 3 12 7
## [19897] 8 19 9 24 26 11 21 31 2 23 9 2 17 9 27 7 15 30 10 7 7 10 1 14
## [19921] 27 22 22 16 28 31 22 4 15 15 14 28 13 21 31 28 3 16 30 29 9 20 1 21
## [19945] 30 9 17 29 11 12 10 28 4 31 7 4 5 13 29 12 6 6 29 24 7 4 22 13
## [19969] 21 28 19 28 5 3 15 17 20 20 23 21 2 9 17 20 25 20 10 24 6 13 21 29
## [19993] 24 21 14 15 4 10 14 4 17 14 8 31 10 16 21 11 13 24 20 17 13 4 1 29
## [20017] 15 8 29 5 1 5 4 26 28 20 4 11 11 16 8 14 29 4 29 23 1 27 29 8
## [20041] 9 2 10 16 25 30 26 28 25 8 28 8 18 21 31 28 26 13 25 2 17 31 20 4
## [20065] 4 12 23 3 7 6 20 5 3 9 4 17 25 15 5 12 12 9 21 4 28 28 26 10
## [20089] 12 26 28 12 15 4 11 24 15 19 5 1 3 25 14 27 9 2 5 12 21 12 30 14
## [20113] 29 14 12 30 7 10 24 29 23 2 2 9 29 18 13 13 20 24 25 10 30 5 3 17
## [20137] 3 22 11 1 13 17 12 31 16 6 8 9 26 15 1 12 1 12 25 8 12 22 10 9
## [20161] 28 25 2 6 6 23 28 10 31 6 9 12 20 18 23 3 27 16 22 2 22 21 16 17
## [20185] 19 19 17 11 24 18 14 8 7 31 17 13 10 31 4 14 11 11 5 14 19 11 9 17
## [20209] 17 8 26 3 28 13 15 7 12 17 1 8 12 31 6 21 21 2 27 28 17 2 4 30
## [20233] 5 23 10 6 6 29 30 8 3 13 22 29 26 6 17 26 20 27 28 5 25 24 19 31
## [20257] 21 28 14 19 12 24 4 1 21 6 14 26 22 4 31 11 14 7 5 27 24 10 27 6
## [20281] 14 11 14 12 12 8 26 31 24 3 29 6 22 13 22 22 3 11 28 1 1 21 15 14
## [20305] 11 15 27 4 27 13 21 16 21 4 15 21 21 16 1 16 10 15 3 24 12 22 11 19
## [20329] 23 22 11 20 8 10 15 14 7 30 22 1 18 16 22 15 7 10 24 22 5 6 7 20
## [20353] 8 14 16 2 1 15 21 26 20 13 6 15 21 27 27 21 14 3 26 20 17 15 23 13
## [20377] 3 24 3 25 27 5 6 2 5 22 18 13 17 17 9 7 23 20 20 28 7 15 1 1
## [20401] 13 10 20 3 21 19 16 10 22 31 6 8 27 16 25 29 8 11 21 17 18 11 11 22
## [20425] 13 11 19 4 30 8 10 3 17 25 27 22 23 25 19 6 24 20 12 4 25 8 12 8
## [20449] 8 27 6 11 23 15 5 21 15 15 24 14 26 29 3 24 25 9 23 25 25 14 14 7
## [20473] 7 7 27 20 5 24 23 24 23 12 14 6 4 6 14 29 15 17 20 25 14 30 14 20
## [20497] 21 10 14 4 23 25 26 28 11 13 8 7 29 10 1 21 28 29 16 13 21 3 24 26
## [20521] 9 20 25 28 25 16 3 14 26 24 18 18 19 10 17 27 27 27 5 6 23 23 25 9
## [20545] 17 12 7 4 2 3 21 16 20 18 4 16 6 31 12 29 10 14 14 30 17 2 11 31
## [20569] 27 17 22 25 10 9 1 24 5 10 29 4 9 17 2 6 6 6 6 21 10 12 9 26
## [20593] 29 17 22 30 17 28 7 22 13 14 19 18 15 17 21 5 12 18 18 13 16 15 20 16
## [20617] 7 4 23 17 3 21 22 28 12 29 7 7 26 9 23 1 11 26 23 3 3 14 21 9
## [20641] 30 8 30 17 11 17 4 31 5 1 5 5 5 24 31 12 1 7 13 3 13 6 9 8
## [20665] 1 17 24 23 3 15 9 27 4 3 19 20 30 29 20 3 30 14 22 31 31 23 18 9
## [20689] 9 18 15 28 19 7 4 28 6 8 23 24 10 21 20 8 16 19 26 29 14 4 6 29
## [20713] 18 7 29 8 27 1 19 14 6 15 5 28 28 10 14 12 9 19 31 7 8 18 23 28
## [20737] 29 25 25 26 29 8 11 10 14 10 18 10 15 5 3 21 23 12 12 9 9 15 26 28
## [20761] 24 8 15 9 27 27 30 10 9 2 24 22 18 21 2 16 12 13 28 27 20 18 27 14
## [20785] 25 28 25 15 17 10 2 24 26 16 8 20 3 20 15 10 25 21 20 28 11 26 23 21
## [20809] 28 8 10 14 16 7 21 26 9 6 22 27 21 21 10 15 7 8 9 23 24 21 9 29
## [20833] 16 13 27 14 14 22 7 12 22 16 29 27 24 7 8 13 21 22 1 31 11 3 16 17
## [20857] 19 13 22 10 8 12 18 21 25 5 6 5 6 24 30 13 8 10 14 7 24 20 24 21
## [20881] 16 7 10 14 13 12 16 7 8 16 17 8 16 20 22 14 22 22 30 13 18 7 21 6
## [20905] 8 10 10 14 8 7 10 31 27 7 21 7 14 21 13 1 22 7 11 27 7 14 15 9
## [20929] 12 29 28 26 13 7 15 10 16 10 4 25 20 13 10 10 4 7 2 28 14 24 6 29
## [20953] 5 6 7 18 18 26 6 27 28 25 10 24 17 8 11 14 23 4 18 17 17 26 20 14
## [20977] 21 22 6 11 27 13 22 20 8 14 21 26 25 1 31 14 12 24 12 4 7 27 17 28
## [21001] 6 8 18 6 21 7 1 24 1 27 19 28 17 15 22 27 12 26 24 15 27 14 23 27
## [21025] 29 4 20 10 28 24 4 15 18 10 24 14 13 4 14 27 24 5 25 27 10 17 10 28
## [21049] 19 7 12 12 24 26 8 25 20 6 26 1 8 20 7 22 6 10 3 12 10 21 4 14
## [21073] 19 9 19 25 1 14 31 24 26 3 10 3 7 11 3 20 26 27 30 25 24 10 3 18
## [21097] 25 3 26 26 22 17 9 24 29 6 5 29 9 8 26 12 29 16 31 16 18 30 6 22
## [21121] 10 1 19 19 12 2 21 18 29 4 4 9 12 24 24 28 3 4 9 4 14 30 20 1
## [21145] 7 27 18 24 18 1 31 25 26 19 12 2 9 16 25 7 24 5 18 28 8 4 4 24
## [21169] 23 30 1 1 16 15 1 3 19 3 28 10 11 8 24 17 12 23 22 30 13 21 31 23
## [21193] 17 28 4 28 26 5 14 1 3 15 9 30 24 24 20 7 19 27 3 3 18 28 18 13
## [21217] 2 21 28 29 28 24 20 10 18 21 26 7 5 16 1 7 18 22 9 9 23 9 13 23
## [21241] 24 16 12 9 17 1 1 11 15 16 29 21 5 12 24 19 24 31 18 8 19 28 8 27
## [21265] 2 2 28 3 3 16 16 10 16 21 31 12 27 24 5 27 27 11 26 23 23 15 9 31
## [21289] 13 28 28 1 14 28 3 18 17 19 28 11 18 14 12 12 20 7 24 19 26 21 10 10
## [21313] 14 24 12 31 14 5 12 11 14 28 3 21 21 9 7 27 10 26 7 13 22 15 28 27
## [21337] 14 14 4 20 14 21 10 11 22 7 12 7 10 22 13 28 9 24 8 14 18 17 8 21
## [21361] 7 27 17 17 10 21 13 15 4 3 25 17 16 25 15 26 23 4 9 30 17 4 8 12
## [21385] 11 24 23 24 15 15 29 31 7 6 10 28 12 15 18 8 9 12 24 15 22 6 11 25
## [21409] 30 13 11 8 22 24 29 26 12 22 8 4 18 17 14 16 10 5 22 7 14 9 27 17
## [21433] 20 30 1 7 30 22 11 7 29 8 13 18 20 7 10 11 6 17 1 11 8 23 10 25
## [21457] 25 11 10 2 6 4 7 27 8 31 11 19 20 3 1 29 20 21 8 13 28 20 30 6
## [21481] 21 4 15 18 25 18 27 21 10 20 26 8 12 5 12 4 26 9 20 19 27 19 28 14
## [21505] 4 4 18 18 17 8 7 29 4 7 15 20 25 19 10 6 1 22 5 27 9 3 24 9
## [21529] 9 8 7 12 14 19 6 24 6 21 20 1 2 2 21 2 14 13 29 14 24 16 29 30
## [21553] 28 30 14 29 18 10 23 18 12 4 13 17 10 21 7 15 11 14 24 15 24 20 19 19
## [21577] 13 8 29 2 22 1 7 30 1 24 14 2 9 10 27 22 31 30 22 7 10 7 27 25
## [21601] 20 16 3 3 23 10 7 24 11 14 31 12 23 23 15 17 8 8 23 12 14 10 12 12
## [21625] 28 11 10 12 26 26 12 22 22 10 11 10 4 1 17 17 21 16 22 11 11 13 15 1
## [21649] 10 17 13 13 15 26 3 20 6 7 10 6 20 6 26 9 26 12 7 26 20 5 7 7
## [21673] 3 9 5 30 15 11 7 17 15 14 28 2 31 5 22 13 11 4 16 4 15 25 25 18
## [21697] 25 8 26 10 20 23 28 23 9 7 21 13 11 12 24 15 14 15 4 25 21 22 8 9
## [21721] 11 14 16 10 28 30 14 19 3 21 15 4 29 22 6 12 16 9 30 13 2 14 25 13
## [21745] 13 8 12 18 31 5 25 18 23 14 5 3 13 26 5 6 16 30 1 15 2 1 2 23
## [21769] 14 15 17 6 16 1 29 19 29 19 30 31 17 25 25 23 26 18 22 22 19 24 12 19
## [21793] 12 10 14 24 13 12 9 7 28 13 26 9 21 16 19 16 13 28 7 13 7 23 13 25
## [21817] 25 21 21 2 21 6 14 8 23 29 21 25 21 14 30 16 23 28 22 2 12 29 30 17
## [21841] 6 16 7 25 26 10 19 27 3 6 30 2 22 24 20 28 24 29 17 4 15 16 21 24
## [21865] 10 22 16 22 8 5 6 17 29 2 17 27 5 3 22 31 4 6 22 5 3 15 19 3
## [21889] 8 22 25 9 29 24 24 28 28 24 14 15 20 24 13 22 19 30 21 9 22 13 28 16
## [21913] 17 16 26 8 27 15 17 22 10 29 29 18 16 2 7 20 19 24 13 6 13 4 11 23
## [21937] 9 22 5 26 7 27 15 3 25 9 28 23 18 21 28 1 15 26 15 26 24 19 4 14
## [21961] 23 25 26 20 28 20 2 10 18 11 20 18 30 8 8 12 6 8 23 1 8 2 9 12
## [21985] 13 22 15 29 9 4 15 29 6 29 21 26 7 24 3 4 21 18 12 15 10 27 18 24
## [22009] 8 28 11 12 28 14 15 14 2 27 4 6 7 13 8 7 7 26 15 24 19 24 24 14
## [22033] 2 29 8 25 14 14 8 7 29 20 28 24 11 10 17 20 10 14 4 4 5 29 6 26
## [22057] 8 11 14 18 10 3 31 28 3 3 21 27 15 27 3 7 23 17 13 23 20 21 7 6
## [22081] 21 13 3 27 9 8 27 3 30 11 22 10 11 22 11 18 21 6 7 23 10 22 5 4
## [22105] 12 2 15 6 22 12 10 8 17 24 8 28 25 24 13 3 29 5 27 8 31 5 11 8
## [22129] 1 22 23 3 3 31 15 11 7 8 11 9 11 14 15 28 10 14 27 14 9 11 12 19
## [22153] 7 27 10 13 22 31 21 12 15 3 12 31 9 14 30 18 7 15 29 21 7 23 23 14
## [22177] 17 22 5 17 21 10 24 27 24 31 7 30 13 7 21 1 6 15 15 7 23 23 23 16
## [22201] 11 16 23 29 11 11 4 30 6 14 9 4 12 9 5 11 31 7 6 4 1 2 28 6
## [22225] 25 10 7 29 20 25 11 18 22 23 9 9 24 24 3 2 5 26 13 30 28 27 28 1
## [22249] 21 26 13 29 14 10 10 8 14 28 29 31 15 3 24 24 25 15 23 2 17 17 23 23
## [22273] 2 12 23 23 30 17 9 2 23 27 14 14 16 6 20 2 22 6 16 26 23 28 13 18
## [22297] 10 27 23 8 9 4 11 10 10 20 11 27 10 22 14 15 8 14 3 19 22 17 23 9
## [22321] 12 21 12 28 17 1 13 14 21 28 26 26 28 28 21 18 16 26 1 1 1 1 16 19
## [22345] 4 15 26 10 15 20 9 25 31 3 3 15 17 10 18 17 31 17 17 14 20 23 29 4
## [22369] 10 30 12 5 4 4 22 28 15 15 7 24 23 30 21 30 8 1 21 20 14 20 22 13
## [22393] 8 30 8 1 8 8 15 10 9 4 24 9 11 26 6 10 12 6 11 22 15 17 29 12
## [22417] 13 20 21 14 19 4 13 18 6 6 12 2 10 6 6 4 5 8 3 3 3 8 31 12
## [22441] 12 26 16 20 15 5 14 23 17 21 17 12 29 1 12 17 12 16 27 28 24 28 16 27
## [22465] 22 6 23 14 15 7 27 2 14 7 2 24 10 16 20 28 7 13 21 16 27 29 12 4
## [22489] 22 21 8 11 7 13 12 6 10 25 10 6 3 21 19 16 18 31 6 9 17 31 6 7
## [22513] 28 29 27 13 6 23 18 23 11 2 14 14 14 28 1 26 14 19 5 29 4 29 16 7
## [22537] 7 15 16 9 12 11 8 1 10 23 23 30 26 1 30 13 18 21 11 11 14 15 8 27
## [22561] 5 5 11 13 10 10 23 19 22 1 22 14 1 9 1 24 20 30 10 19 2 18 4 29
## [22585] 30 13 5 14 21 30 20 2 15 4 4 12 16 2 23 28 15 14 22 15 8 8 10 30
## [22609] 20 13 18 25 10 14 11 14 9 9 25 19 19 6 31 17 21 3 18 2 13 3 20 28
## [22633] 15 2 28 1 15 16 16 1 28 16 3 5 4 4 17 10 9 22 7 16 23 12 23 28
## [22657] 30 14 27 29 18 8 20 6 14 7 10 12 24 20 23 30 5 1 1 14 30 25 7 8
## [22681] 25 8 30 24 4 10 6 4 19 16 15 1 29 13 23 5 5 11 28 30 27 26 15 22
## [22705] 15 21 22 24 14 27 24 10 4 3 13 19 6 19 3 2 24 19 23 1 31 16 8 24
## [22729] 22 28 11 17 15 4 31 3 29 8 19 17 22 22 21 30 29 4 2 18 28 29 31 3
## [22753] 7 27 22 12 27 13 25 21 3 21 27 8 7 1 7 12 19 19 19 8 18 27 20 25
## [22777] 2 9 14 29 28 16 29 15 27 26 31 22 18 11 27 13 25 13 7 5 5 7 5 4
## [22801] 27 20 25 4 31 14 31 4 14 19 19 14 24 25 3 2 29 27 7 8 3 15 18 30
## [22825] 30 29 30 8 8 23 18 11 12 24 1 8 16 15 20 22 16 23 10 3 21 14 27 16
## [22849] 15 18 27 25 12 4 2 13 27 11 22 29 10 8 20 17 20 1 21 18 13 10 18 11
## [22873] 13 31 22 25 17 25 13 18 3 12 7 16 16 12 20 4 14 27 4 9 9 6 19 29
## [22897] 29 26 4 4 24 1 21 6 29 4 10 1 22 29 21 7 8 5 26 3 28 3 14 14
## [22921] 24 29 8 4 28 20 20 13 13 14 22 10 24 19 26 20 4 1 15 2 25 7 29 15
## [22945] 17 1 1 17 22 23 11 19 8 19 28 15 20 6 24 24 28 25 8 21 15 15 9 24
## [22969] 8 29 29 11 29 29 4 13 29 20 22 19 19 22 5 27 28 9 23 24 25 7 15 15
## [22993] 22 20 6 27 27 10 27 4 4 25 20 27 3 6 3 5 1 30 20 2 28 7 27 15
## [23017] 10 18 11 19 17 15 1 12 10 12 21 28 12 15 9 6 28 12 29 17 13 7 20 6
## [23041] 11 3 5 9 24 2 1 1 3 2 2 21 5 16 30 12 17 7 2 12 12 28 2 27
## [23065] 15 20 17 19 25 30 14 8 5 6 5 26 4 22 1 19 30 16 28 30 2 29 29 22
## [23089] 19 28 15 26 17 29 25 28 14 21 7 10 14 14 21 22 17 30 12 17 13 6 31 7
## [23113] 7 1 31 20 30 9 17 21 22 26 15 15 2 21 11 12 14 25 9 10 28 17 18 22
## [23137] 7 7 6 6 21 4 30 19 6 13 28 15 16 1 10 30 29 28 28 1 17 30 4 5
## [23161] 22 17 3 14 15 1 10 4 12 11 12 24 19 20 7 19 20 16 6 24 27 14 13 10
## [23185] 19 10 6 26 31 11 22 19 9 25 19 17 2 24 24 2 2 1 14 22 22 9 26 2
## [23209] 20 15 24 18 27 29 24 15 27 28 11 10 8 31 17 1 21 13 10 8 4 14 14 15
## [23233] 16 10 11 29 5 29 25 25 29 30 30 28 29 12 25 21 17 17 10 23 23 23 23 12
## [23257] 24 22 7 13 13 23 7 17 17 28 13 10 10 10 4 14 24 10 20 13 31 26 21 24
## [23281] 19 1 12 3 31 15 10 1 4 18 18 9 12 31 16 7 24 11 10 20 10 10 12 17
## [23305] 13 27 10 10 13 21 8 21 15 9 24 22 23 28 9 3 9 11 5 28 24 31 18 8
## [23329] 29 27 23 14 14 17 2 18 21 18 15 28 24 19 7 10 5 6 10 23 9 31 8 10
## [23353] 15 6 13 7 22 7 16 29 1 6 4 17 7 28 18 7 25 23 13 23 7 21 24 29
## [23377] 11 14 31 18 3 22 2 9 22 18 3 21 24 14 15 23 11 19 22 2 20 8 20 30
## [23401] 11 13 6 24 29 24 30 13 20 22 14 14 30 19 18 12 17 17 15 23 19 6 28 17
## [23425] 25 19 18 30 17 24 3 22 18 19 18 8 17 27 10 15 4 19 5 25 7 20 15 21
## [23449] 28 11 20 21 7 3 2 11 15 11 4 2 24 14 10 3 14 14 12 11 15 3 14 8
## [23473] 19 17 17 26 7 23 3 26 15 14 23 5 17 29 28 28 9 24 19 9 13 14 22 9
## [23497] 17 21 21 23 18 6 4 18 7 13 23 11 29 19 1 17 26 21 8 13 27 16 24 13
## [23521] 14 17 22 4 14 4 17 30 21 17 2 25 20 25 6 2 23 1 29 30 29 2 16 28
## [23545] 16 19 2 19 9 21 31 27 18 30 11 11 19 27 21 29 5 16 11 5 23 17 3 7
## [23569] 6 17 29 22 9 10 24 21 22 8 19 20 30 21 8 3 29 4 1 11 23 18 30 15
## [23593] 9 26 31 28 4 30 30 17 10 14 27 22 4 21 16 22 29 5 17 25 13 11 15 30
## [23617] 30 14 29 13 20 23 22 28 1 31 24 16 1 21 7 9 9 3 9 22 21 9 27 22
## [23641] 30 3 11 5 4 18 11 9 15 31 2 3 8 6 17 10 18 27 1 19 6 21 17 8
## [23665] 24 17 14 23 27 15 20 8 22 9 3 29 24 27 25 2 10 4 14 27 20 13 13 15
## [23689] 22 20 17 15 22 9 24 1 14 4 28 19 11 27 22 8 17 30 22 20 25 10 11 29
## [23713] 2 18 19 13 8 24 19 2 5 7 7 19 15 17 18 24 24 12 22 18 23 13 23 8
## [23737] 4 18 16 21 25 16 1 11 8 1 15 20 3 4 31 8 2 20 9 21 10 7 8 18
## [23761] 4 7 28 7 7 7 9 7 22 30 10 24 5 8 26 11 30 31 12 28 7 7 30 6
## [23785] 17 10 2 11 9 30 18 3 31 7 6 17 28 14 14 19 22 4 24 19 17 1 11 10
## [23809] 23 19 29 13 22 23 29 29 24 24 6 17 28 3 21 30 5 28 25 30 12 25 31 20
## [23833] 20 16 9 17 3 27 13 12 18 5 26 28 10 25 6 20 21 18 22 22 3 7 18 6
## [23857] 24 12 3 27 19 24 2 27 27 29 23 18 24 26 8 18 24 17 12 27 14 24 18 4
## [23881] 7 23 30 25 25 10 18 6 6 20 4 12 6 29 7 19 14 24 21 10 8 17 20 14
## [23905] 13 13 8 21 14 31 20 7 24 4 27 8 16 14 24 17 14 4 15 7 7 23 21 4
## [23929] 24 29 31 20 22 8 29 17 4 2 3 19 22 27 29 15 2 12 8 22 28 6 15 17
## [23953] 12 19 10 20 7 29 2 11 17 26 17 30 16 23 13 21 16 26 4 23 11 2 7 3
## [23977] 18 16 17 7 10 13 15 21 28 10 12 3 31 15 2 17 11 21 7 9 7 3 3 11
## [24001] 18 11 17 18 12 24 29 6 7 1 19 11 1 7 4 5 16 25 24 17 20 14 31 9
## [24025] 24 7 29 3 8 13 27 20 19 4 23 18 3 17 25 3 5 7 29 16 26 17 14 28
## [24049] 31 5 15 25 15 30 6 29 20 30 27 19 30 3 30 22 28 21 7 28 1 22 27 17
## [24073] 7 24 28 26 19 19 28 24 29 11 23 21 10 7 6 2 3 30 11 8 6 7 2 24
## [24097] 14 22 4 3 28 2 11 9 6 31 31 4 25 6 17 4 26 18 30 7 13 31 6 27
## [24121] 14 13 21 11 8 3 24 2 15 10 11 11 2 28 22 17 5 21 13 28 15 8 31 26
## [24145] 6 12 26 1 10 1 23 7 28 4 31 17 23 1 6 20 7 5 13 18 10 27 3 12
## [24169] 18 19 3 29 14 10 27 4 1 7 6 21 21 15 26 2 8 18 7 27 13 3 28 28
## [24193] 17 7 3 25 22 13 2 27 29 23 20 21 10 14 3 17 20 28 9 4 24 20 10 30
## [24217] 22 16 23 11 4 13 7 15 4 25 3 13 24 3 21 5 30 13 24 13 19 19 5 4
## [24241] 3 23 10 10 28 19 8 7 17 27 17 3 26 24 14 14 25 3 11 6 21 21 23 6
## [24265] 6 8 29 13 13 22 23 30 18 15 9 19 20 30 15 24 5 28 20 25 29 19 31 25
## [24289] 21 26 27 17 24 19 11 12 9 31 23 18 1 28 13 28 17 25 31 9 1 18 5 29
## [24313] 12 13 9 21 29 18 20 20 10 1 24 13 31 6 8 29 18 11 6 16 12 2 14 2
## [24337] 7 4 14 10 9 3 23 26 7 8 25 3 15 27 4 10 17 15 23 28 24 22 23 31
## [24361] 3 18 20 10 11 11 12 4 4 21 24 10 14 26 3 15 6 14 14 18 12 4 23 5
## [24385] 7 7 15 22 22 18 23 29 27 3 13 12 24 31 19 13 23 11 12 25 11 7 14 2
## [24409] 16 15 3 22 14 31 10 19 5 7 5 5 13 12 11 28 22 3 31 13 15 11 14 12
## [24433] 14 21 22 15 20 28 28 26 16 9 26 27 1 7 18 4 22 29 5 8 17 17 15 21
## [24457] 25 5 16 4 9 31 21 7 15 4 6 12 21 21 26 1 20 9 27 11 17 21 14 3
## [24481] 18 1 29 22 28 11 7 13 14 13 12 24 26 25 1 10 15 16 20 25 21 9 26 23
## [24505] 15 21 12 11 5 28 4 21 30 10 27 12 24 11 29 29 13 23 28 10 18 5 16 20
## [24529] 9 20 10 7 31 24 13 8 3 19 31 29 6 14 25 25 24 16 3 6 15 17 19 27
## [24553] 25 29 29 11 10 6 6 29 7 10 28 28 11 15 30 26 22 26 3 12 3 3 12 25
## [24577] 1 9 23 10 26 11 31 30 23 28 28 7 9 13 15 18 7 12 30 10 19 7 22 9
## [24601] 9 8 14 21 24 14 29 14 3 21 22 14 17 16 10 12 25 8 18 27 30 11 14 21
## [24625] 24 16 10 11 28 31 8 24 12 26 7 18 3 28 13 7 7 7 15 25 14 26 13 26
## [24649] 9 27 20 21 20 21 18 1 13 10 10 18 22 4 3 3 14 7 3 23 17 30 10 7
## [24673] 21 6 9 27 10 6 22 10 27 2 30 15 29 10 31 15 6 2 6 8 15 31 30 8
## [24697] 2 7 24 21 8 24 9 30 10 19 12 15 27 4 21 18 17 29 9 14 18 20 22 23
## [24721] 13 18 30 16 20 21 24 23 30 23 1 29 26 13 3 4 12 1 7 24 5 16 10 2
## [24745] 28 16 14 13 8 28 10 9 6 3 1 1 20 25 15 26 6 19 28 28 24 21 15 26
## [24769] 27 10 2 10 22 23 28 7 26 16 9 7 4 8 11 22 29 19 24 8 26 27 12 8
## [24793] 30 31 10 16 21 21 8 25 14 4 25 1 14 18 1 9 7 6 19 7 3 11 13 22
## [24817] 2 22 27 27 7 20 24 24 18 31 20 23 24 4 27 22 11 15 15 31 14 31 12 14
## [24841] 21 19 30 16 10 31 28 25 15 16 19 3 24 13 6 12 9 7 7 31 17 30 18 30
## [24865] 14 3 7 2 20 21 2 27 2 31 11 10 20 10 27 20 5 17 28 20 19 3 28 26
## [24889] 22 19 7 20 7 24 27 2 29 7 16 24 29 26 24 23 1 27 14 3 13 27 25 26
## [24913] 14 1 14 8 18 1 3 23 14 20 21 18 18 1 10 30 14 1 1 21 1 28 17 18
## [24937] 28 28 27 7 10 2 9 7 27 11 14 28 25 25 11 11 11 6 21 20 20 27 1 17
## [24961] 18 11 14 1 7 10 3 28 2 11 14 18 16 2 24 11 4 4 21 24 4 23 31 23
## [24985] 15 15 3 29 24 7 28 24 12 22 24 5 9 13 21 25 28 16 8 3 17 14 25 17
## [25009] 21 1 26 22 10 14 18 28 31 21 28 24 3 17 27 11 16 24 10 28 17 29 31 2
## [25033] 29 15 7 17 18 14 15 21 14 24 24 10 19 8 30 8 15 17 19 18 18 11 18 21
## [25057] 14 16 8 11 23 18 5 9 4 22 4 1 31 16 17 6 19 10 7 7 4 21 24 7
## [25081] 8 4 29 17 18 31 26 7 7 27 3 1 1 22 16 18 30 16 10 4 28 23 19 25
## [25105] 21 27 12 4 6 21 5 29 23 21 23 29 11 17 28 18 20 2 12 10 30 25 21 25
## [25129] 15 31 3 30 24 15 2 21 9 11 19 18 21 17 3 18 3 30 11 14 28 19 28 6
## [25153] 17 28 21 8 25 12 7 18 27 3 16 6 1 17 25 22 13 27 13 8 13 7 12 17
## [25177] 17 31 2 4 4 25 25 15 10 31 10 15 7 24 23 23 8 21 9 3 21 20 13 1
## [25201] 27 20 27 16 22 28 7 11 20 14 20 15 11 7 21 9 24 20 21 11 11 4 6 18
## [25225] 17 28 27 26 7 2 27 21 18 31 25 18 25 18 30 14 15 29 24 20 28 18 20 24
## [25249] 21 28 1 13 1 11 5 22 4 20 13 3 7 20 25 9 14 21 14 19 12 6 29 17
## [25273] 24 21 28 29 5 29 20 12 28 17 18 2 14 22 4 19 12 29 28 31 4 1 27 3
## [25297] 10 28 13 28 4 2 21 28 8 25 23 13 23 22 24 28 6 19 4 1 2 22 11 21
## [25321] 12 12 21 2 16 17 7 27 6 30 7 20 6 24 13 7 14 11 29 26 15 30 28 29
## [25345] 14 16 13 21 16 12 17 1 9 28 19 25 26 15 12 28 26 16 17 27 11 25 28 6
## [25369] 29 22 3 21 2 28 18 10 22 2 24 29 2 15 16 16 17 22 29 24 12 9 28 17
## [25393] 31 14 3 13 22 3 7 28 18 2 13 12 15 10 11 30 4 10 11 5 17 25 31 21
## [25417] 13 4 31 16 15 10 21 2 19 10 15 2 3 20 5 21 30 14 16 22 7 28 9 8
## [25441] 17 20 14 29 12 15 20 4 4 24 15 6 25 23 7 28 15 22 10 17 17 28 11 31
## [25465] 13 9 31 13 24 25 25 24 9 16 7 28 6 19 27 11 25 24 23 1 28 12 30 24
## [25489] 29 12 11 26 22 11 31 26 28 3 28 19 12 17 1 25 21 1 7 10 26 24 13 13
## [25513] 21 3 22 22 23 13 21 8 21 1 10 3 29 9 17 7 5 7 7 18 21 7 15 22
## [25537] 29 22 11 8 4 14 21 1 14 12 4 15 26 25 18 28 21 14 27 11 19 23 6 17
## [25561] 22 15 27 7 22 11 10 29 18 8 28 28 27 24 24 26 21 25 4 7 13 8 6 21
## [25585] 6 31 29 28 15 4 4 28 9 16 27 22 24 15 25 22 1 1 8 11 16 5 9 27
## [25609] 20 11 24 2 14 27 19 23 21 24 18 17 12 10 8 10 24 9 15 22 1 24 1 12
## [25633] 29 3 10 20 12 18 7 14 31 11 13 17 20 1 13 12 31 29 25 11 11 14 14 15
## [25657] 26 15 20 30 17 12 17 14 17 21 27 7 27 28 10 9 30 11 8 27 11 4 7 11
## [25681] 11 25 24 17 4 21 12 13 15 3 15 2 1 1 5 11 16 29 23 25 23 13 23 12
## [25705] 6 12 18 12 18 6 7 15 4 30 31 29 14 9 25 13 10 31 7 25 7 25 5 3
## [25729] 10 3 10 25 23 1 4 5 9 3 19 28 16 3 21 9 29 6 6 7 15 31 11 5
## [25753] 3 29 3 3 3 17 20 17 15 3 22 28 16 13 13 2 21 29 14 14 13 14 13 2
## [25777] 29 15 14 26 3 22 5 5 11 21 27 30 30 13 7 29 16 29 14 11 17 14 7 22
## [25801] 8 22 9 25 19 14 19 26 11 25 23 13 18 26 2 19 4 3 27 15 17 9 26 28
## [25825] 9 1 7 18 27 15 31 1 31 6 2 30 20 5 20 6 6 27 14 24 28 20 11 9
## [25849] 11 12 13 28 30 17 28 7 1 26 11 28 24 9 6 19 31 27 1 21 24 4 8 31
## [25873] 8 3 22 30 28 6 7 3 8 17 22 17 7 4 1 22 20 17 11 16 11 8 24 13
## [25897] 8 28 14 30 14 24 31 2 25 26 20 30 13 25 14 7 21 8 11 6 16 1 2 30
## [25921] 24 4 25 9 20 18 14 6 24 15 11 28 6 28 26 7 8 11 15 8 1 21 10 11
## [25945] 17 19 9 21 13 27 4 4 21 13 20 7 31 6 7 6 7 7 1 27 10 9 24 15
## [25969] 29 30 28 30 28 6 24 5 7 15 17 10 10 18 14 21 18 26 19 20 22 9 2 17
## [25993] 20 23 23 9 14 3 10 8 28 18 13 31 19 30 28 28 23 12 17 16 21 3 19 14
## [26017] 10 5 22 3 19 19 24 20 9 24 20 2 13 20 21 28 21 27 6 25 23 8 14 12
## [26041] 24 11 8 22 21 17 12 1 12 29 3 4 22 24 18 12 22 27 7 26 26 27 11 14
## [26065] 7 5 12 2 10 18 8 27 11 22 8 14 3 17 15 13 5 29 17 28 23 20 1 18
## [26089] 18 6 6 15 7 7 25 4 18 1 3 28 3 29 6 20 27 29 3 5 4 21 14 13
## [26113] 13 21 23 18 11 28 30 8 21 10 20 23 20 12 13 6 11 8 31 14 23 6 10 25
## [26137] 12 12 30 9 1 15 7 6 18 14 25 25 6 7 22 31 15 5 17 29 18 23 2 17
## [26161] 29 24 18 7 9 25 26 28 24 23 21 3 3 25 29 2 10 10 16 22 4 20 30 3
## [26185] 3 18 18 27 20 30 5 11 23 12 2 9 28 29 8 18 29 29 5 19 13 10 2 27
## [26209] 18 1 21 20 11 24 9 22 13 14 11 3 21 4 27 22 9 11 10 1 1 15 18 28
## [26233] 28 8 4 10 6 6 21 1 20 26 17 7 17 12 10 19 7 9 3 1 20 23 27 7
## [26257] 11 28 3 27 20 15 8 8 7 15 19 23 19 25 4 16 16 21 10 21 12 3 8 4
## [26281] 23 25 5 20 24 19 26 7 19 22 21 11 24 21 1 24 10 7 24 5 29 18 13 18
## [26305] 30 24 10 21 5 1 15 15 18 4 20 28 24 22 18 4 2 13 26 8 11 21 8 23
## [26329] 29 23 11 1 30 21 14 6 2 7 6 16 3 1 5 24 22 9 24 29 17 19 16 12
## [26353] 11 21 25 30 30 19 9 31 24 19 6 15 4 4 8 4 1 11 5 11 3 20 8 22
## [26377] 1 27 13 6 25 25 21 4 7 17 13 31 20 13 9 8 4 29 31 16 23 17 16 1
## [26401] 3 5 30 25 30 10 6 21 22 2 9 29 23 30 28 13 18 10 1 25 3 10 25 19
## [26425] 2 11 24 24 21 3 18 14 26 11 22 26 22 5 13 20 6 25 6 7 5 7 12 10
## [26449] 19 8 10 11 29 30 8 23 26 7 25 18 5 26 28 9 25 15 17 22 18 18 8 28
## [26473] 9 1 22 22 1 26 1 19 17 21 14 15 15 29 3 17 22 18 25 29 14 8 23 28
## [26497] 29 6 15 31 21 22 10 30 29 10 15 29 26 14 31 26 31 29 14 29 18 28 24 21
## [26521] 27 16 4 7 10 16 4 2 28 10 12 10 6 10 28 9 31 12 5 21 20 29 17 17
## [26545] 4 15 31 13 21 19 11 9 17 1 24 17 21 27 15 10 24 4 23 3 13 31 31 17
## [26569] 29 5 3 24 3 28 28 12 17 11 14 2 14 29 7 17 27 1 1 23 3 7 6 27
## [26593] 1 14 14 22 29 16 12 8 12 30 21 18 24 17 3 17 14 18 31 6 1 7 17 21
## [26617] 31 15 6 14 7 22 14 31 18 1 27 8 11 22 31 27 2 12 16 21 15 8 23 13
## [26641] 8 18 15 7 7 7 23 11 20 10 19 16 30 20 1 26 10 21 1 22 12 8 8 30
## [26665] 5 22 2 29 13 25 7 5 31 8 4 10 29 2 10 24 22 18 14 13 15 8 1 18
## [26689] 9 27 12 18 21 28 26 6 29 15 13 13 19 24 3 26 9 10 17 7 1 5 22 7
## [26713] 3 28 21 2 4 14 23 24 28 18 26 4 28 12 9 17 24 21 22 28 24 2 11 9
## [26737] 8 27 7 16 16 27 15 19 9 23 16 8 21 9 24 13 23 3 14 3 7 29 24 17
## [26761] 8 23 29 9 2 22 13 13 6 15 22 15 28 17 15 11 31 1 10 10 25 18 16 5
## [26785] 8 3 18 4 17 17 10 20 6 21 2 19 28 28 23 15 1 1 26 31 3 20 20 18
## [26809] 25 24 1 12 21 15 10 13 21 7 11 6 6 5 14 4 23 6 20 21 11 4 27 20
## [26833] 21 1 15 30 27 15 17 3 18 23 20 30 3 1 11 1 2 3 4 30 15 29 14 17
## [26857] 31 16 22 25 7 31 15 27 28 28 18 14 4 7 28 10 25 7 7 30 30 25 26 14
## [26881] 31 15 19 27 21 21 1 29 23 28 23 24 4 24 22 18 9 27 21 1 3 10 3 11
## [26905] 22 18 18 25 8 9 14 11 13 12 23 28 1 28 20 12 28 21 27 7 29 12 31 22
## [26929] 16 23 28 26 17 24 28 18 7 19 2 13 8 14 18 2 31 1 23 1 10 2 25 11
## [26953] 21 21 21 17 17 16 18 21 9 3 19 10 16 31 17 4 22 23 20 3 12 10 24 3
## [26977] 4 11 21 13 11 20 29 9 8 30 25 25 31 14 21 6 15 24 10 24 14 8 27 9
## [27001] 25 17 24 30 19 14 2 31 7 12 11 16 8 1 21 7 27 30 5 17 5 16 23 17
## [27025] 16 19 19 8 19 6 17 27 11 27 30 17 31 24 1 3 15 11 19 7 10 6 27 16
## [27049] 7 10 12 15 10 19 21 2 17 2 2 16 23 19 13 18 9 3 17 21 21 8 1 29
## [27073] 20 29 6 1 29 1 20 20 20 21 31 19 20 28 29 12 13 14 18 20 24 11 8 1
## [27097] 29 6 24 24 17 17 24 11 6 30 20 19 23 25 17 25 15 28 28 10 23 22 24 12
## [27121] 2 18 8 28 3 29 30 28 5 15 19 25 10 3 28 24 9 6 21 22 3 12 6 6
## [27145] 3 27 7 12 13 26 30 7 15 10 29 7 22 7 31 18 3 16 18 25 19 21 9 31
## [27169] 31 19 13 18 14 20 10 28 17 21 21 23 3 26 15 23 26 8 8 18 2 21 21 9
## [27193] 23 15 29 2 25 14 8 17 28 1 7 17 24 29 25 25 10 23 9 22 11 8 4 3
## [27217] 6 29 28 4 13 27 3 21 12 3 1 24 7 6 31 19 31 16 31 18 13 2 4 23
## [27241] 18 27 31 31 5 13 10 11 20 13 31 8 29 15 15 15 4 15 16 18 19 9 26 28
## [27265] 12 15 28 5 28 21 19 6 3 15 21 20 6 6 13 19 1 1 7 8 9 21 20 7
## [27289] 10 29 10 3 25 25 13 31 7 13 12 9 3 17 2 15 14 13 12 6 10 13 30 4
## [27313] 22 30 10 11 11 8 2 27 25 4 30 8 18 25 3 19 31 17 25 25 13 30 4 9
## [27337] 4 1 13 5 5 19 12 6 19 1 7 29 17 11 27 12 19 11 14 13 7 17 8 20
## [27361] 26 2 27 7 25 7 23 5 21 20 4 26 29 20 12 15 17 23 17 27 15 17 18 3
## [27385] 5 23 29 18 23 3 29 14 21 22 9 12 31 9 16 14 21 17 14 2 18 3 4 9
## [27409] 18 11 28 9 25 10 3 28 17 12 21 14 21 10 9 22 9 3 6 15 10 24 8 16
## [27433] 15 28 23 4 9 22 11 15 15 12 20 16 19 20 3 20 9 4 10 14 27 4 15 2
## [27457] 3 4 18 17 3 15 23 20 21 29 21 7 27 7 14 20 29 10 4 19 1 27 26 18
## [27481] 25 20 3 17 21 30 3 21 28 28 10 18 21 31 8 7 20 11 30 3 14 22 12 30
## [27505] 15 11 1 17 1 29 3 4 9 13 13 14 17 16 24 17 8 18 11 1 9 13 28 14
## [27529] 21 14 18 21 30 9 12 2 6 31 20 27 21 3 11 7 7 21 15 8 11 19 9 7
## [27553] 10 24 3 20 31 4 16 29 31 8 13 31 30 24 26 14 29 22 18 28 19 2 9 23
## [27577] 27 6 18 30 29 23 8 19 1 14 26 24 22 22 18 22 14 4 22 14 27 18 19 22
## [27601] 19 19 21 28 3 15 7 21 21 11 19 13 31 21 2 12 12 19 19 17 23 20 4 11
## [27625] 24 31 19 17 3 20 11 16 20 3 18 4 4 5 21 2 29 10 14 6 9 5 8 29
## [27649] 10 12 4 3 31 12 17 26 29 31 12 16 18 17 22 24 22 21 22 10 20 30 21 14
## [27673] 3 17 22 26 9 31 9 8 8 24 13 24 31 10 23 12 11 6 3 7 21 1 6 4
## [27697] 23 29 2 7 26 21 25 16 15 24 24 9 17 26 30 12 14 14 8 17 13 21 21 19
## [27721] 21 25 5 14 13 28 11 28 10 14 9 11 10 15 18 25 27 11 13 27 16 2 14 11
## [27745] 26 1 17 27 4 6 14 20 7 13 23 12 30 12 19 31 15 10 6 29 5 18 2 23
## [27769] 22 24 24 2 8 8 3 3 7 8 10 4 12 3 10 7 17 28 7 3 26 2 27 23
## [27793] 2 19 3 12 31 27 16 4 9 18 17 2 23 10 17 17 10 12 16 20 28 29 25 9
## [27817] 4 8 30 26 21 27 12 4 14 1 6 11 3 19 7 27 6 28 29 7 23 14 27 19
## [27841] 7 24 19 19 27 12 3 21 27 27 12 2 19 10 27 9 6 7 24 30 15 25 23 11
## [27865] 16 7 6 6 9 19 27 27 27 29 21 8 1 3 19 3 13 8 20 20 12 10 7 8
## [27889] 28 11 5 27 21 9 27 15 4 24 18 25 15 27 4 13 17 4 20 3 3 3 4 25
## [27913] 12 28 1 27 28 2 31 13 13 10 1 7 17 5 25 11 28 28 21 26 28 28 29 4
## [27937] 4 12 24 17 6 2 13 2 20 31 29 8 15 4 3 22 9 17 23 8 23 21 2 4
## [27961] 12 28 28 25 5 3 17 31 8 1 16 4 7 26 5 20 21 16 10 25 4 1 28 19
## [27985] 16 21 1 13 5 26 10 16 9 3 9 17 26 26 11 20 16 12 13 13 6 10 6 10
## [28009] 7 31 25 27 8 4 24 2 13 18 25 26 19 5 7 29 21 11 11 30 4 15 20 28
## [28033] 11 19 7 17 18 21 3 9 27 23 17 29 21 4 25 23 17 23 24 13 10 17 24 7
## [28057] 17 14 28 18 16 21 15 15 2 4 3 2 27 24 6 13 6 6 18 27 26 22 23 15
## [28081] 1 27 21 7 22 14 3 8 30 14 3 17 6 7 23 24 5 5 9 30 21 13 10 22
## [28105] 12 25 14 17 15 3 1 20 4 24 8 15 11 27 24 9 14 26 22 26 26 19 19 26
## [28129] 11 20 17 14 17 11 25 27 2 28 29 14 14 4 1 21 11 21 28 18 27 27 29 28
## [28153] 8 20 22 8 26 30 5 17 28 18 21 10 26 27 24 17 14 15 11 25 15 24 12 2
## [28177] 14 20 5 25 16 28 17 13 1 17 17 19 2 17 17 22 28 8 25 15 23 3 1 7
## [28201] 10 31 15 28 7 11 28 11 20 3 18 17 31 3 12 17 9 21 22 22 9 26 6 16
## [28225] 29 4 10 6 3 1 12 31 9 5 4 22 4 8 4 23 8 10 24 26 10 23 6 10
## [28249] 28 9 12 31 20 11 24 31 19 23 18 24 24 20 20 5 28 11 7 17 7 18 15 26
## [28273] 11 10 28 19 18 14 24 11 16 16 6 6 14 17 12 30 14 25 24 14 7 28 18 23
## [28297] 15 29 23 25 27 17 21 1 1 7 27 26 16 12 6 27 3 1 8 8 12 22 29 15
## [28321] 16 5 3 3 10 26 7 2 28 10 14 3 4 2 30 25 8 20 23 12 8 14 17 31
## [28345] 2 19 23 23 17 14 17 27 13 28 9 23 6 31 20 18 28 30 7 7 1 24 26 2
## [28369] 24 3 31 12 15 27 22 21 17 4 31 22 20 2 9 18 28 29 10 28 29 6 3 26
## [28393] 20 24 30 14 22 7 8 12 7 4 29 21 10 11 10 3 21 12 13 3 29 14 10 19
## [28417] 24 7 22 17 1 30 28 24 18 7 30 20 15 31 16 17 25 7 31 25 17 23 30 24
## [28441] 29 10 19 25 21 16 17 22 18 17 10 22 28 29 27 16 11 6 4 14 8 26 7 14
## [28465] 7 22 18 20 23 24 15 13 27 5 15 13 6 20 30 5 8 14 27 24 18 17 23 23
## [28489] 26 20 20 11 20 1 5 20 15 30 12 13 2 6 22 1 23 16 4 6 22 22 17 9
## [28513] 31 7 18 24 16 22 30 21 20 29 3 27 3 12 15 13 29 29 28 7 16 30 22 3
## [28537] 4 17 25 20 12 1 30 15 26 25 26 3 3 7 7 6 16 5 1 14 16 25 21 4
## [28561] 16 4 14 25 12 22 11 4 8 8 24 29 14 31 14 28 17 13 28 25 28 17 27 16
## [28585] 25 18 13 6 18 17 11 12 12 5 24 3 20 24 20 12 23 27 10 7 7 3 9 13
## [28609] 24 3 4 17 14 13 15 20 7 28 21 1 20 18 20 11 12 5 3 22 26 27 20 2
## [28633] 30 22 31 28 2 21 6 23 26 6 4 1 27 26 27 25 1 27 22 8 12 26 20 23
## [28657] 10 26 23 19 10 21 9 22 5 6 6 7 26 19 20 19 7 26 20 9 21 2 9 12
## [28681] 7 23 23 24 10 11 21 20 21 17 7 29 25 14 21 28 24 10 26 8 30 27 27 8
## [28705] 12 7 10 25 26 1 5 8 8 3 1 31 11 7 10 15 30 9 2 4 3 17 13 25
## [28729] 30 25 9 14 30 1 11 6 13 14 12 13 24 30 12 30 17 5 5 25 4 11 5 28
## [28753] 8 6 12 8 1 15 3 22 30 12 4 16 3 22 10 10 9 2 27 21 14 27 24 1
## [28777] 12 27 27 13 20 10 23 23 24 7 11 15 8 30 29 13 21 24 24 26 17 18 30 17
## [28801] 5 13 12 8 10 10 7 15 15 11 26 19 9 1 13 2 4 2 10 17 31 25 26 19
## [28825] 20 10 29 20 15 25 25 21 14 11 4 10 7 21 15 9 1 28 6 31 10 28 31 24
## [28849] 14 3 17 3 11 15 1 8 10 20 19 10 1 6 31 16 28 12 9 31 21 12 4 7
## [28873] 2 10 11 10 15 27 9 18 12 16 17 7 10 18 18 28 1 7 11 17 25 17 29 27
## [28897] 14 28 3 18 30 18 9 18 17 26 28 14 8 28 28 18 19 7 24 5 28 2 7 7
## [28921] 7 13 19 5 11 15 8 16 18 16 3 29 13 24 27 23 17 9 20 31 17 17 10 26
## [28945] 21 18 6 23 31 12 1 29 22 9 25 7 6 14 13 6 10 28 28 15 18 25 1 19
## [28969] 28 15 4 21 5 27 11 24 20 24 3 11 25 17 18 29 10 10 29 26 16 6 6 15
## [28993] 7 21 23 3 7 27 5 23 10 19 3 9 18 24 7 6 12 21 28 7 6 20 23 6
## [29017] 9 2 2 4 13 7 29 10 10 19 2 7 7 18 24 21 17 4 14 13 22 17 30 26
## [29041] 26 26 26 31 12 28 9 19 3 10 12 12 20 4 16 18 8 26 6 18 25 19 28 12
## [29065] 15 11 29 3 10 14 6 9 16 13 17 18 17 4 15 12 19 15 27 15 3 22 7 4
## [29089] 13 6 12 11 6 17 19 6 14 13 19 20 5 30 22 31 3 9 31 13 7 6 26 28
## [29113] 7 22 24 10 20 21 10 5 4 7 31 16 7 15 5 10 2 14 4 14 5 29 15 23
## [29137] 3 27 7 3 29 4 29 20 21 20 15 9 16 17 2 9 15 1 18 12 15 13 26 2
## [29161] 16 25 16 25 11 6 3 24 8 5 21 24 4 8 19 2 17 8 4 24 26 24 3 14
## [29185] 17 14 1 9 20 16 28 14 15 15 11 20 29 27 25 28 30 21 18 11 27 21 17 12
## [29209] 4 21 28 9 22 18 5 21 19 18 5 31 12 14 18 7 9 28 29 12 16 16 20 16
## [29233] 30 2 21 14 30 15 21 5 14 23 3 10 19 12 24 8 9 7 14 19 19 6 15 13
## [29257] 2 21 18 6 14 13 19 21 7 22 7 3 24 9 11 14 13 8 6 12 6 16 16 16
## [29281] 19 9 22 5 16 14 15 19 17 4 19 22 22 9 12 10 6 20 23 12 8 7 6 11
## [29305] 15 14 19 17 7 13 13 5 1 5 5 17 16 19 13 13 11 11 5 13 9 9 12 9
## [29329] 8 20 14 3 3 12 7 10 9 16 6 6 22 22 5 12 9 9 8 9 10 21 21 13
## [29353] 14 14 9 14 12 12 10 16 4 22 11 7 15 18 16 5 15 22 25 19 8 20 20 12
## [29377] 3 21 20 14 20 23 5 20 10 7 3 12 21 23 4 8 20 16 19 13 10 16 14 19
## [29401] 12 7 8 16 10 8 13 12 22 5 13 20 8 12 9 3 16 16 16 19 3 10 5 4
## [29425] 10 10 18 27 6 6 16 23 19 21 24 20 4 17 20 10 3 21 7 9 12 4 2 5
## [29449] 27 18 13 13 21 19 23 5 7 7 5 4 21 10 15 20 9 13 14 7 2 20 7 13
## [29473] 9 19 19 23 20 3 13 13 14 12 13 7 6 3 13 6 8 8 18 25 5 7 10 15
## [29497] 2 10 10 8 21 6 15 10 10 12 15 10 4 6 3 20 3 8 6 21 3 4 21 21
## [29521] 9 19 26 6 4 6 15 3 21 22 21 22 7 5 11 17 14 5 6 5 3 5 5 15
## [29545] 12 10 12 10 16 14 17 3 2 10 6 7 16 8 10 20 17 6 6 2 14 5 7 7
## [29569] 9 7 7 8 5 3 17 3 20 4 15 12 17 22 12 24 17 9 11 10 13 9 5 7
## [29593] 3 15 12 21 14 10 19 5 5 14 21 12 10 16 20 13 19 16 17 10 6 19 5 20
## [29617] 5 7 25 15 22 23 14 12 13 7 6 7 3 10 12 30 8 7 17 4 5 10 8 9
## [29641] 9 7 15 20 7 15 9 26 14 16 12 15 19 15 17 19 23 6 7 5 20 9 5 2
## [29665] 10 18 10 23 26 10 14 18 11 10 17 14 11 11 14 15 15 6 11 11 20 4 5 6
## [29689] 18 12 8 9 20 7 10 7 20 7 6 6 7 7 19 9 8 23 22 7 3 6 13 13
## [29713] 19 21 3 6 16 19 19 19 4 20 16 18 16 22 26 21 17 8 21 10 21 23 2 19
## [29737] 7 20 9 12 12 3 7 25 10 13 6 3 21 6 13 14 7 7 12 12 26 17 21 13
## [29761] 6 21 7 7 6 9 14 11 10 7 15 2 5 16 16 8 13 18 21 12 13 15 9 14
## [29785] 6 13 13 21 26 7 6 6 5 9 13 16 15 10 26 17 19 13 15 18 5 7 12 8
## [29809] 19 6 8 15 10 10 13 9 9 22 19 13 9 7 20 12 16 6 24 23 7 3 21 20
## [29833] 20 2 22 13 13 19 12 7 19 19 15 20 21 9 8 16 8 6 14 18 13 14 15 21
## [29857] 4 21 7 13 4 14 8 17 17 19 8 7 1 5 3 23 2 21 10 8 15 15 20 12
## [29881] 20 6 9 6 6 22 6 15 20 17 16 1 13 20 8 11 3 12 15 19 14 13 13 10
## [29905] 10 20 4 17 7 14 17 23 19 12 4 21 20 22 20 19 16 10 3 18 20 16 21 11
## [29929] 20 16 7 16 22 5 18 16 19 22 20 3 27 6 9 9 10 4 21 10 14 5 20 5
## [29953] 17 15 12 13 19 18 14 21 23 3 6 7 7 5 9 4 6 14 13 14 15 22 6 19
## [29977] 5 13 4 26 12 5 4 10 21 16 23 6 9 20 9 19 3 9 18 19 20 19 12 21
## [30001] 9 14 3 6 5 23 11 4 23 15 7 15 10 10 21 15 7 12 23 7 24 3 16 24
## [30025] 4 7 8 8 15 14 20 7 16 15 17 5 3 23 13 19 15 5 12 25 26 11 19 6
## [30049] 6 4 8 9 9 21 21 19 10 1 21 12 19 14 16 4 7 14 5 9 20 13 8 21
## [30073] 7 17 24 19 15 9 14 10 16 11 25 6 6 10 10 16 9 8 10 21 5 8 3 14
## [30097] 14 9 21 3 20 21 21 24 4 3 16 13 8 16 22 6 7 7 13 7 18 16 6 17
## [30121] 10 13 20 10 16 16 12 19 5 13 15 7 12 5 12 12 5 9 23 21 11 5 5 21
## [30145] 6 24 5 16 7 12 13 5 17 15 16 10 13 26 13 14 14 7 7 9 9 2 3 3
## [30169] 6 22 11 10 3 22 20 7 23 15 17 21 14 19 14 8 5 5 9 16 20 17 14 21
## [30193] 23 14 22 11 14 15 3 13 15 3 15 10 22 9 5 21 6 14 19 5 5 12 12 1
## [30217] 6 13 15 9 14 3 6 8 2 19 22 13 14 15 12 13 23 12 19 10 10 15 16 19
## [30241] 19 10 2 12 12 14 19 16 22 21 6 10 10 3 3 12 21 5 23 4 18 15 16 4
## [30265] 6 11 21 17 20 12 9 4 21 9 19 7 4 21 15 20 16 4 22 19 8 6 13 13
## [30289] 4 8 6 21 17 8 9 17 18 10 14 11 12 6 13 6 4 17 16 22 12 5 6 12
## [30313] 6 9 13 6 12 9 22 12 13 16 21 15 7 8 17 13 9 8 6 9 11 5 14 10
## [30337] 8 5 22 20 5 24 12 16 13 10 11 21 13 14 3 15 16 6 10 23 10 7 6 7
## [30361] 10 12 5 17 13 20 19 19 20 6 6 14 19 3 21 16 20 12 9 20 22 15 14 15
## [30385] 5 6 21 5 10 4 6 7 15 11 11 6 18 15 16 16 16 16 7 9 7 23 6 10
## [30409] 9 20 19 13 6 6 16 18 22 16 11 16 2 20 13 3 21 16 14 19 23 24 9 20
## [30433] 7 17 8 9 5 6 20 7 3 5 22 19 22 22 22 20 15 23 10 19 9 4 7 7
## [30457] 19 5 14 12 16 15 5 9 15 8 19 21 6 21 10 12 10 20 18 14 21 8 3 5
## [30481] 13 12 20 16 8 5 12 13 5 8 6 22 22 8 8 15 22 22 6 9 22 20 19 5
## [30505] 5 7 20 13 5 21 7 10 17 12 3 5 22 15 19 19 12 16 9 12 9 1 10 6
## [30529] 14 15 21 21 2 14 16 19 12 14 15 5 10 13 13 15 5 21 14 14 17 8 6 13
## [30553] 21 15 13 15 10 11 19 5 11 5 15 7 10 16 10 21 6 6 6 10 16 11 9 26
## [30577] 13 13 6 14 4 18 20 19 13 3 23 5 16 3 14 21 10 14 19 3 23 7 4 8
## [30601] 11 20 8 10 5 12 6 14 1 12 3 6 22 7 15 21 14 22 19 17 21 20 21 20
## [30625] 10 26 13 10 2 3 1 10 21 13 7 14 14 10 2 17 15 10 11 10 12 10 10 11
## [30649] 13 12 13 12 15 5 18 18 7 8 6 15 11 19 24 12 24 15 21 25 12 14 14 7
## [30673] 13 15 7 16 12 25 10 8 7 9 5 21 21 7 13 5 12 8 9 8 13 9 7 7
## [30697] 16 13 17 8 10 15 15 9 3 25 15 7 10 12 3 6 8 10 8 10 9 22 20 12
## [30721] 19 19 19 25 12 16 6 9 16 5 13 2 14 4 10 19 14 21 21 17 17 26 17 5
## [30745] 8 16 16 19 4 16 17 13 4 4 3 17 19 19 3 22 20 21 14 7 8 13 20 8
## [30769] 24 9 6 16 11 23 17 5 9 3 26 15 7 11 16 6 19 20 13 14 11 3 5 16
## [30793] 15 19 23 18 2 8 17 5 8 23 5 21 4 15 23 4 9 19 21 7 6 9 9 20
## [30817] 1 16 15 6 8 3 16 11 16 14 8 13 13 8 19 9 25 7 6 8 7 16 5 12
## [30841] 16 8 8 10 23 20 13 20 8 8 15 11 6 16 12 6 2 8 7 24 14 16 14 6
## [30865] 8 18 21 19 5 13 15 14 12 11 21 16 9 11 6 7 13 19 21 13 14 12 6 26
## [30889] 7 17 9 10 17 13 4 21 21 20 14 15 12 9 6 21 6 14 14 5 14 20 15 11
## [30913] 11 22 9 17 12 8 22 24 5 4 16 9 7 8 9 16 20 19 19 6 12 14 6 8
## [30937] 13 16 7 8 13 20 10 13 8 24 24 8 10 3 7 5 8 12 5 9 6 9 8 5
## [30961] 6 14 15 16 9 12 23 9 5 2 6 21 20 10 13 12 9 12 20 19 16 21 6 16
## [30985] 14 6 23 12 23 15 24 15 9 13 22 19 14 19 13 19 16 10 3 13 5 9 19 19
## [31009] 14 17 21 7 14 11 15 12 10 5 23 18 19 13 9 24 20 15 19 14 13 23 20 20
## [31033] 19 21 21 8 15 14 12 22 15 7 5 16 7 13 23 22 26 8 10 10 13 12 11 3
## [31057] 8 14 5 12 18 11 10 13 21 10 10 5 20 6 2 7 7 5 20 22 12 8 6 13
## [31081] 9 8 9 14 14 20 4 3 20 7 6 9 9 24 9 16 6 6 12 16 16 14 8 7
## [31105] 8 13 7 14 7 7 2 22 5 19 20 7 14 8 20 21 7 7 21 17 30 15 3 22
## [31129] 21 22 14 26 17 8 9 12 15 21 15 23 16 8 16 20 3 17 12 17 10 17 6 19
## [31153] 17 16 7 19 7 13 13 8 3 10 9 12 12 25 14 12 7 17 11 22 11 9 5 7
## [31177] 6 6 12 6 7 6 20 7 19 10 10 18 6 20 24 9 12 5 13 21 8 25 14 7
## [31201] 13 14 26 15 5 10 13 6 14 16 13 3 9 18 7 1 14 3 12 14 2 20 9 15
## [31225] 23 3 7 23 5 13 15 7 6 13 9 6 15 2 9 16 11 7 19 11 16 20 20 17
## [31249] 19 19 5 13 20 24 19 20 14 7 8 20 13 7 7 10 15 9 8 13 8 6 11 6
## [31273] 21 12 13 14 5 21 14 14 19 19 24 6 15 12 22 6 10 6 18 3 15 10 2 13
## [31297] 19 15 5 7 20 21 16 6 2 17 4 5 21 5 9 9 10 10 17 24 8 10 14 22
## [31321] 4 27 8 9 3 15 5 7 17 8 13 17 10 10 3 4 13 14 7 9 14 15 6 14
## [31345] 6 20 7 22 15 6 10 27 12 8 13 13 16 16 9 7 2 8 19 9 5 4 10 8
## [31369] 22 10 16 23 14 6 11 15 7 14 24 25 15 8 21 19 13 5 13 22 17 7 6 20
## [31393] 17 9 7 10 21 17 17 19 19 9 6 7 8 16 24 7 9 17 16 10 4 3 14 14
## [31417] 21 22 17 25 8 14 5 21 9 16 13 6 6 8 3 8 9 10 13 10 20 22 3 17
## [31441] 20 15 27 6 9 9 17 22 6 10 18 10 12 24 9 14 14 13 12 1 3 8 2 21
## [31465] 21 3 8 7 23 14 23 16 24 16 9 14 16 21 21 11 25 9 13 13 16 7 7 5
## [31489] 16 14 14 19 7 13 14 14 6 2 13 17 14 5 15 23 27 16 8 5 18 10 12 10
## [31513] 5 6 10 21 17 5 12 15 4 19 9 14 5 7 5 12 13 21 10 5 7 20 20 22
## [31537] 5 16 12 7 13 19 9 9 5 16 6 8 19 16 6 7 21 15 7 19 3 16 12 7
## [31561] 20 2 8 2 2 19 21 12 15 16 13 7 8 21 16 19 2 9 9 5 13 16 19 14
## [31585] 18 7 18 14 15 2 14 21 22 16 20 9 21 6 12 8 9 9 18 14 7 21 24 22
## [31609] 7 3 8 13 21 8 8 19 22 12 8 10 19 6 18 12 15 7 11 22 17 8 2 6
## [31633] 21 25 22 7 10 20 21 26 13 20 4 23 8 20 19 6 8 12 12 12 8 14 9 9
## [31657] 12 8 24 16 14 18 13 16 12 8 12 7 9 17 21 13 20 22 3 13 21 13 13 21
## [31681] 22 6 13 13 22 16 12 5 6 15 16 5 7 9 14 12 19 16 19 25 19 22 14 16
## [31705] 12 5 20 1 2 7 21 21 19 21 19 11 24 3 8 8 24 12 8 8 19 18 13 8
## [31729] 9 4 19 19 10 13 23 9 9 18 12 14 8 7 9 6 14 8 19 9 13 19 12 15
## [31753] 1 8 16 8 5 7 24 16 2 13 16 23 22 14 14 21 19 16 4 21 8 19 12 12
## [31777] 13 14 7 2 16 20 15 20 2 10 15 7 17 13 15 2 2 13 5 8 15 19 19 7
## [31801] 19 10 22 21 22 21 19 22 9 5 5 13 3 10 6 5 8 20 12 7 13 2 5 4
## [31825] 15 12 1 5 8 14 12 15 19 5 12 24 7 6 14 9 9 8 15 26 12 16 12 9
## [31849] 12 19 21 12 6 5 11 9 2 5 7 14 15 5 12 12 2 9 14 15 20 8 15 12
## [31873] 15 12 9 12 6 5 1 1 7 21 14 8 24 1 13 13 21 14 22 20 3 9 12 12
## [31897] 16 4 22 17 13 6 2 3 14 6 7 8 22 19 5 19 7 5 9 21 4 20 25 30
## [31921] 6 7 21 6 14 17 20 8 12 8 2 13 21 15 14 11 19 12 19 14 13 8 21 7
## [31945] 5 16 21 7 5 6 11 22 19 18 8 7 14 9 12 16 1 5 16 21 6 14 12 15
## [31969] 19 9 6 16 16 2 22 18 23 6 12 7 22 22 19 19 2 12 12 26 20 13 4 22
## [31993] 12 20 9 25 3 24 2 13 4 13 8 14 1 6 8 12 19 14 15 22 18 20 7 9
## [32017] 9 11 8 7 6 13 17 20 7 17 19 14 12 9 20 7 11 12 7 14 12 8 10 5
## [32041] 11 16 7 11 20 9 5 19 19 18 7 13 8 21 9 18 8 7 8 20 20 1 8 21
## [32065] 17 21 5 12 26 13 8 12 6 7 9 8 3 3 20 13 9 12 19 20 9 14 11 8
## [32089] 7 13 9 8 13 12 9 17 18 9 1 7 10 5 5 5 6 23 18 19 9 9 5 19
## [32113] 14 12 12 12 16 20 23 16 20 19 25 11 9 26 17 20 20 5 12 14 13 13 8 10
## [32137] 5 19 17 7 17 20 22 24 14 10 20 11 13 6 19 12 15 9 7 6 24 22 20 22
## [32161] 8 22 5 20 25 13 12 6 20 14 16 19 6 10 30 17 13 1 19 2 12 21 3 20
## [32185] 20 15 5 8 5 22 16 10 11 5 24 18 13 15 19 20 15 7 6 11 12 9 6 16
## [32209] 14 12 7 24 19 22 6 2 19 8 5 11 12 21 5 8 6 16 1 20 13 14 8 12
## [32233] 8 14 5 10 22 15 14 12 20 8 19 8 22 12 13 22 2 3 3 22 15 4 15 11
## [32257] 16 22 23 14 19 17 15 18 15 6 16 7 6 18 18 14 19 18 17 5 16 18 12 7
## [32281] 21 21 21 6 18 19 21 19 13 8 8 21 24 2 26 10 9 24 14 2 22 21 20 15
## [32305] 10 5 16 20 12 12 5 14 20 20 21 2 25 15 18 24 21 22 14 12 1 8 12 16
## [32329] 7 10 21 16 25 9 14 13 20 19 2 26 19 5 16 16 5 1 9 2 10 8 14 21
## [32353] 19 9 12 22 13 9 13 16 17 9 19 3 15 21 20 21 14 20 12 19 24 8 13 9
## [32377] 2 21 8 17 21 7 9 12 23 16 12 14 10 20 15 23 4 19 9 23 16 6 9 6
## [32401] 19 23 20 5 8 16 23 7 24 7 20 6 13 20 9 20 16 5 16 5 18 7 5 25
## [32425] 1 5 7 20 8 5 7 22 9 6 6 20 21 6 10 18 7 12 6 18 7 2 8 19
## [32449] 19 22 24 12 8 15 7 13 20 5 22 13 2 13 13 8 20 8 13 5 20 2 11 17
## [32473] 15 15 22 20 11 19 9 5 10 16 19 14 9 5 23 7 6 21 24 5 7 12 22 15
## [32497] 18 30 1 12 8 13 5 23 21 17 1 20 12 5 22 20 1 24 18 18 18 14 5 14
## [32521] 22 20 7 13 9 25 12 7 12 7 8 5 14 21 20 22 13 13 12 26 12 16 6 6
## [32545] 20 9 5 19 20 20 13 7 12 17 18 22 7 16 8 13 26 11 8 24 14 5 23 22
## [32569] 2 4 5 9 8 20 9 13 30 19 19 15 8 12 16 6 22 18 1 4 16 23 6 6
## [32593] 11 15 20 10 6 17 19 11 13 7 14 14 17 20 11 7 9 14 9 5 17 22 12 9
## [32617] 8 13 25 4 2 15 21 6 13 20 21 19 19 15 22 6 8 9 4 6 16 15 10 19
## [32641] 5 9 14 8 22 18 18 19 6 21 21 14 22 11 12 12 7 21 12 12 7 7 12 19
## [32665] 7 8 21 14 14 13 2 9 19 21 9 22 21 26 26 21 21 22 26 26 26 24 17 24
## [32689] 27 21 22 23 27 20 8 19 28 23 7 19 21 27 26 22 22 25 11 25 26 5 6 22
## [32713] 22 15 23 15 20 24 27 5 27 3 9 8 27 25 21 25 26 25 27 24 26 7 27 21
## [32737] 27 27 7 19 11 27 22 27 21 3 21 21 17 27 22 17 10 27 24 13 5 3 25 26
## [32761] 16 26 8 2 21 27 27 23 23 7 26 10 22 21 26 14 15 5 20 27 15 27 16 26
## [32785] 24 23 15 8 5 22 26 9 27 10 23 27 25 23 27 24 24 19 27 27 23 2 7 27
## [32809] 23 8 24 23 20 12 9 13 23 27 22 23 27 22 27 19 14 12 11 24 27 23 27 25
## [32833] 27 27 21 27 17 6 26 26 10 22 26 27 19 7 14 27 19 1 13 13 21 27 6 9
## [32857] 22 23 19 22 21 26 27 13 26 7 9 21 21 27 27 25 6 27 27 8 23 7 26 22
## [32881] 27 22 22 23 11 21 12 19 25 23 26 26 26 5 26 26 12 24 23 13 27 27 25 27
## [32905] 27 27 27 27 27 8 9 27 20 19 21 12 20 25 21 26 26 22 22 27 27 26 7 18
## [32929] 16 26 26 25 8 13 6 16 8 10 15 27 22 25 23 27 10 13 3 26 23 27 24 14
## [32953] 16 6 25 10 27 24 12 9 26 27 3 27 12 19 27 26 10 19 14 22 21 24 15 8
## [32977] 16 13 26 25 27 16 23 3 26 26 26 23 26 27 27 11 27 26 13 27 27 21 26 2
## [33001] 23 11 13 26 26 8 1 27 27 26 5 15 8 27 3 21 8 25 27 27 27 15 16 14
## [33025] 25 8 26 27 6 26 26 26 27 15 27 25 23 7 21 13 27 13 23 26 27 21 25 27
## [33049] 11 9 21 26 27 27 27 21 25 20 16 11 25 26 23 21 26 26 25 26 6 21 27 9
## [33073] 26 26 9 21 6 23 8 5 26 23 26 9 23 21 9 23 14 21 26 1 27 24 27 26
## [33097] 12 23 21 22 14 27 22 27 26 25 26 21 15 27 27 9 13 16 26 26 26 26 22 22
## [33121] 6 13 26 2 25 20 23 22 21 26 22 25 27 19 4 27 26 26 15 9 26 26 27 27
## [33145] 23 23 27 27 25 27 25 21 15 7 7 23 8 27 26 26 21 27 27 26 27 26 18 25
## [33169] 27 16 27 20 26 4 27 26 18 23 27 13 26 18 23 27 13 13 9 27 27 20 13 27
## [33193] 27 15 26 18 27 28 2 8 13 23 27 11 16 23 27 21 21 8 11 27 25 27 27 26
## [33217] 22 26 27 27 26 26 24 26 27 27 5 27 21 26 13 12 10 10 18 3 22 26 27 15
## [33241] 27 27 20 27 22 27 23 22 23 28 27 21 25 26 26 19 14 23 13 26 1 27 27 13
## [33265] 17 17 21 23 27 21 5 20 8 26 7 26 27 11 27 26 27 27 21 27 7 9 19 1
## [33289] 23 24 22 26 12 25 16 26 21 26 27 17 4 26 27 27 27 22 12 16 26 25 19 27
## [33313] 20 26 17 24 13 27 22 15 21 26 6 26 27 7 26 20 15 7 18 13 26 22 27 26
## [33337] 25 27 7 21 21 23 26 21 12 16 10 23 22 27 11 25 27 25 26 17 16 22 26 23
## [33361] 27 27 23 26 26 7 12table(month(funcionas2))##
## 1 2 3 4 5 6 7 8 9 10
## 162 149 384 912 3358 6812 7155 4721 5233 4481hist(as.numeric(funcionas2-funcionas3))
knitr::opts_chunk$set(echo = TRUE)CLASE 28 DE SEPTIEMBRE 2021
En esta clase trabajamos con el paquete lubridate que es muy útil para el manejo de fechas y horas.
###CLASE 28 DE SEPTIEMBRE 2021
bosque<-read.csv("C:/Users/mez1/Documents/CIDE/METPOL/1 SEMESTRE/BASES DE DATOS/CLASE/Harvard.csv")
bosque$datetime[1]## [1] "2005-01-01T00:15"library(lubridate)
fechax<-substr(bosque$datetime[1],1,10)
horax<-substr(bosque$datetime[1],12,16)
hora_fechx<-paste0(fechax," ",horax)
##VAMOS A GENERAR UNA FUNCIÓN QUE CONVIERTA TODA LA BASE A FORMATO FECHA
trans_fecha<-function(dia_fecha){
fechax<-substr(dia_fecha,1,10)
horax<-substr(dia_fecha,12,16)
hora_fechx<-paste0(fechax," ",horax)
return(hora_fechx)
}
#lapply(bosque$datetime,FUN = trans_fecha)
#sapply(bosque$datetime,FUN = trans_fecha)
#La desactivé por ineficiente
#trans_fecha(bosque$datetime)
#unlist(lapply(bosque$datetime,FUN = trans_fecha)) #A elementos, no de tablas
#sapply(bosque$datetime,FUN = trans_fecha) #A elementos, no de tablas
#trans_fecha<-function(dia_fecha){
# fechax<-substr(dia_fecha,1,10)
# horax<-substr(dia_fecha,12,16)
# hora_fechx<-paste0(fechax," ",horax)
# return(hora_fechx)
#}
#trans_fecha(bosque$datetime[2])
#apply por filas? Por columnas? Pero aquí queremos
#por cada elemento de un vector!!
#trans_fecha2<-function(trans_fecha){
#vecto_lar<-unlist(strsplit(as.character(trans_fecha),"T"))
# return(paste0(vecto_lar[1]," ",vecto_lar[2])) }
#datetime2<-trans_fecha(bosque$datetime)
#bosque$datetime22<-datetime2
#datetime2<-trans_fecha(bosque$datetime)
#bosque$datetime22<-datetime2
#bosque$datetime22
#¿cuándo es menor la temperatura promedio, entre 3am y 6am, o 10pm y 11:59pm?
#¿cuándo es menor la temperatura promedio, entre 3am y 5:59am, o 10pm y 11:59pm?
grupo1<-which(hour(bosque$datetime22)>=3 & hour(bosque$datetime22)<6)## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.
## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.grupo2<-which(hour(bosque$datetime22)>=22 & hour(bosque$datetime22)<=23)## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.
## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.mean(bosque[grupo1,3],na.rm = TRUE)## [1] NaNmean(bosque[grupo2,3],na.rm = TRUE)## [1] NaN#La temperatura entre 3 y 5:59 es más baja
#Otra manera
grupo11<-c(which(hour(bosque$datetime22)==3),
which(hour(bosque$datetime22)==4),
which(hour(bosque$datetime22)==5) )## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.
## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.
## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.grupo22<-c(which(hour(bosque$datetime22)==22),
which(hour(bosque$datetime22)==23) )## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.
## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.mean(bosque[grupo11,3],na.rm=TRUE)## [1] NaNmean(bosque[grupo22,3],na.rm=TRUE)## [1] NaNgrupo1<-which(hour(bosque$datetime22)>=3 & hour(bosque$datetime22)<6)## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.
## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.grupo2<-which(hour(bosque$datetime22)>=22 & hour(bosque$datetime22)<=23)## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.
## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.mean(bosque[grupo1,3],na.rm = TRUE)## [1] NaNmean(bosque[grupo2,3],na.rm = TRUE)## [1] NaN#La temperatura entre 3 y 5:59 es más baja
#Otra manera
grupo11<-c(which(hour(bosque$datetime22)==3),
which(hour(bosque$datetime22)==4),
which(hour(bosque$datetime22)==5) )## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.
## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.
## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.grupo22<-c(which(hour(bosque$datetime22)==22),
which(hour(bosque$datetime22)==23) )## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.
## Warning: tz(): Don't know how to compute timezone for object of class NULL;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.mean(bosque[grupo11,3],na.rm=TRUE)## [1] NaNmean(bosque[grupo22,3],na.rm=TRUE)## [1] NaNknitr::opts_chunk$set(echo = TRUE)CLASE 05 DE OCTUBRE 2021
Esta clase estuvo dedicada a los emparejamientos anidados.
### CLASE 05 OCTUBRE 2021
###COVID
library(readxl)
datos_covid20 <- read.csv("C:/Users/mez1/Documents/CIDE/METPOL/1 SEMESTRE/BASES DE DATOS/CLASE/COVID2020.csv")
#DIMENSIÓN DE LAS TABLAS
dim(datos_covid20) ## [1] 149564 39catalo_sexo <- read_excel("C:/Users/mez1/Documents/CIDE/METPOL/1 SEMESTRE/BASES DE DATOS/CLASE/catalogo.xlsx", sheet = "Catálogo SEXO")
empate_estadoUM2<-merge(x=datos_covid20,y=catalo_sexo,by.x="SEXO",by.y="CLAVE",sort=FALSE)
dim(empate_estadoUM2)## [1] 149564 40POBTOT <- (149564)
POB_FEM <- (76777)
prop_fem<-((POB_FEM/POBTOT)*100 )
prop_fem<- round((prop_fem), 0)
#all.x=TRUE agrega valores con NA si no existe con que emparejar
#all.x= FALSE no agrega valores con NA cuando no existe con que emparejar
#sort acomoda
knitr::opts_chunk$set(echo = TRUE)