Sesión 1
Curso: R Aplicado a los Proyectos de Investigación
Percy Soto-Becerra, M.D., M.Sc(c)
InkaStats Data Science Solutions | Medical Branch
2022-10-03
Introducción a la Estadística y Ciencia de Datos
Agenda
Introducción a la Estadística y Ciencia de Datos
Análisis de datos reproducible
R y RStudio
Aspectos elementales de R y Rstudio
Primeros pasos en R
Datos en R
Datos y mundo actual
La crisis de replicabilidad de la Ciencia
(incluyendo Ciencias Médicas)
Ciencia de Datos
En general
La Ciencia de Datos es una disciplina que se dedica al estudio y aplicación de técnicas diversas (estadística, informática, etc.) para convertir datos crudos en información y conocimiento.
En este curso aprenderemos a hacer esto de manera ordenada.
Este es un curso introductorio con énfasis en pensamiento estadístico.
En salud
Ciencia de Datos en Salud (Health Data Science en inglés) es la ciencia y el arte de generar soluciones basadas en datos a través de la comprensión de problemas de salud complejos del mundo real, empleando el pensamiento crítico y el análisis para derivar conocimiento de los (grandes) datos.1
Análisis de datos reproducible
Agenda
Introducción a la Estadística y Ciencia de Datos
Análisis de datos reproducible
R y RStudio
Aspectos elementales de R y Rstudio
Primeros pasos en R
Datos en R
Checklist de reproducibilidad
¿Qué significa que un análisis de datos es “reproducible”?
A corto plazo
¿Las tablas y figuras se pueden reproducir del código y datos?
¿El código realmenet hace lo que quieres que haga?
¿Es claro por qué el código hace lo que hace?
A largo plazo
¿Puede el código ser usado con otros datos?
¿Puedes extender el código a otros usos?
Flujograma de ciencia de datos
Kit de herramientas para reproducibilidad
Flujo de trabajo reproducible
Scriptabilidad
Programación literaria (código, narrativa, salida en un solo lugar) Markdown
Versión de control Git / Github
- En este curso nos enfocaremos en
ScriptabilidadyProgramación literaria.
R y RStudio
Agenda
Introducción a la Estadística y Ciencia de Datos
Análisis de datos reproducible
R y RStudio
Aspectos elementales de R y Rstudio
Primeros pasos en R
Datos en R
R y RStudio
R es un
lenguaje de programaciónestadística de código abierto.R también es un entorno para computación estadística y gráfica.
Está potenciado con
paquetes.
El problema de R: su aspecto
RStudio: el chasis del carro
En resumen, R y RStudio
En resumen, R y RStudio
Jerga básica de R
Objeto:Cualquier abstracción en R. Los datos son objetos en R.
Función:Código capaz de realizar una acción específica con los datos. También es un objeto: un objeto que actúa sobre otros objetos.
Paquetes:Library en inglés. Conjunto de funciones que realizan acciones a menudo relacionadas en R.
Paquetes de R base:Conjunto de paquetes instalados por defecto en R.
Paquetes de R tidy:Conjunto de paquetes que siguen el estilo tidy en R.
Paquetes de R
Los
paquetesson lasunidades fundamentalesdel código reproducible en R.Al 13 de junio de 2022, hay
18 560 paquetesdisponibles.¡Trabajaremos con un pequeño (pero importante) conjunto de estos!
Cada paquete contiene una o más
funcionesque ejecutan tareas.¡Hay paquetes de todo tipo y para todos los gustos!
R y sus paquetes
En resumen: R, RStudio y sus paquetes
tidyverse
tidyverse.org
Tidyversees unacolección grande de paquetes de Rdiseñados para hacer ciencia de datos: Es unmetapaquete.- Todos los paquetes comparten una filosofía subyacente y una gramática común.
R, como todo lenguaje, tiene varios dialectos.
Dos dialectos predominan:
R baseyR tidy.R tidyse basa en la filosofía de tidyverse.
Usaremos este enfoque predominantemente y
R basecuando no tenga remplazo o sea más sencillo usarlo.
Aspectos elementales de R y Rstudio
Agenda
Introducción a la Estadística y Ciencia de Datos
Análisis de datos reproducible
R y RStudio
Aspectos elementales de R y Rstudio
Primeros pasos en R
Datos en R
Primero instale R, R Studio y R tools
Si aún no ha instalado R y RStudio, revise el tutorial de instalación de R que hemos preparado para el curso:
Pause el video, instale y vuelva cuando todo esté listo.
Nuestro turno
Ingrese al siguiente enlace https://cloud.r-project.org/ y descargue R.
Ingrese al siguiente enlace https://www.rstudio.com/products/rstudio/download/#download y descargue RStudio.
¿Es usuario de MS Windows?
- Ingrese al siguiente enlace https://cloud.r-project.org/ y descargue RTools.
¿Es usuario de Mac o Linux?
- No haga nada más.
10:00
Ventanas de RStudio
Código en scripts de R
El
códigose escribe en elScript, pero no se imprime.Los
resultadosse imprimen en laconsola.Aunque es útil usar la consola, es preferible usar herramientas de programación literaria:
- RMarkdown o Quarto.
R Markdown y Quarto
rmarkdown.rstudio.com
rmarkdownyquartopermiten a los usuarios de R escribir su código y prosa en documentos computacionales reproducibles.Por lo general, nos referimos a documentos R Markdown con la extensión
.Rmdy a los documentos Quarto con la extensión.qmd.
Archivo R Markdown (.Rmd) a PDF
R Markdown evolucionó a Quarto
- Recientemente, RStudio presentó Quarto: https://quarto.org/.
- Quarto es la
versión mejoradade RMarkdwon. - Si usaste RMarkdown, puedes ejecutar tus códigos en Quarto sin ningún problema.
- Quarto permite ejecutar código Python, Julia y Observable.
R Markdown y Quarto son muy flexibles
Herramientas de
programación literaria:- Legibles para la computadora y para el ser humano.
Útiles para alcanzar
reproducibilidad.También muy útiles para diversas actividades académicas:
Presentaciones académicas
Artículos científicos
Diseño de blogs, web u otra herramienta de difusión de conocimiento.
Quarto permite crear artículos
Quarto permite crear presentaciones
De hecho, los slides de este curso están hechas en Quarto
Quarto permite crear Website
¡Sí!, la web de este curso también está hecha en Quarto
Quarto permite crear Libros
Quizás para una futura edición del curso
Programación Literaria
¿Cómo usaremos Quarto en el curso?
Cada ejercicio / reporte / proyecto / tarea / etc. será hecho en un documento R Markdown aumentado: Quarto.
Siempre te proporcionaremos una
plantillade documento Quarto para iniciar.Las plantillas irán modificándose conforme avancemos el curso.
Las plantillas son simplemente eso, plantillas, para usarlas en su día a día no necesita pensar mucho en ellas ni entenderlas a detalle.
Yo entiendo menos del 20% de estas y aún así me sirven mucho para mi trabajo diario. ¡Sugiero comenzar haciendo lo mismo!
Partes de un documento Quarto / R Markdown
Hagamos una pausa
Tomemos un descanso de 5 minutos…
Estire las piernas …
Deje de ver las pantallas …
… cualquier , las del celular también.
05:00
Primeros pasos en R
Agenda
Introducción a la Estadística y Ciencia de Datos
Análisis de datos reproducible
R y RStudio
Aspectos elementales de R y Rstudio
Primeros pasos en R
Datos en R
Antes de usar R, configure su ambiente de trabajo
Paso 1: Cree una carpeta de trabajo .
- En donde quiera. Sea ordenado.
Paso 2: Abra RStudio .
Paso 3: Cree un proyecto y vincule el proyecto con la carpeta nueva.
A partir de este momento, la carpeta que contiene el archivo
.Rprojes denominadacarpeta raíz.Todo lo que ocurra en R partirá de la carpeta raíz en adelante.
Paso 3: Comience a trabajar. Puede usar
R scriptsoR Markdown / Quarto.- Nosotros usaremos
Quarto.
- Nosotros usaremos
Nuestro turno
- Cree una carpeta de trabajo en Documentos, llámela
mi_primer_proyecto. - Cree un proyecto de Rstudio y ubíquelo en la carpeta nueva
mi_primer_proyecto. - Cree el primer script de R, anote lo siguiente:
"# Este es mi primer script de R"y guárdelo comomi_primer_script.r. - Cree el primer archivo .qmd de Quarto, anote lo siguiente:
"# Este es mi primer archivo quarto"y guárdelo comomi_primer_quarto.qmd. - Renderice el primer archivo quarto creado:
mi_primer_quarto.qmd. - Verifique que los archivos han sido creados y se encuentran en la carpeta raíz
mi_primer_proyecto.
10:00
R es una calculadora
- Si tu teclado puede usar ASCII, puedes usar los siguientes atajos:
| Atajo | Símbolo | Significado | Operador |
|---|---|---|---|
| Alt + 94 | ^ | … elevado a la potencia … | Aritmético |
| Alt + 126 | ~ | … en relación a … | Aritmético |
| Alt + 38 | & | … Y … | Booleano |
| Alt + 124 | | | … O … | Booleano |
- Si tu teclado no tiene ASCII, sugiero configurar teclado para que coincida con el idioma: Tutorial 1 y Tutorial 2.
Nuestro turno
En el script previamente creado
mi_primer_script.r, escriba las siguientes operaciones matemáticas: cinco más ocho; tres por 23; veinte elevado al cubo; setenta y tres entre cinco. Ejecute el código y mire el resultado en la consola.Copie las operaciones matemáticas escritas en
mi_primer_script.rsobremi_primer_quarto.qmd.El código debe colocarse como
chunkde código par que pueda ejecutarse. Ejecute el código de cada chunk.Compare las salidas del
mi_primer_script.ry demi_primer_quarto.qmd.¿El símbolo
#se ejecuta igual en un script de R y en un quarto markdown? ¿Cuál es la diferencia?
10:00
R es más que una simple calculadora
Pero…, R es
más que solo una calculadora…R es un
lenguajecompuesto por “palabras”.Las palabras son
objetosen R:- Todo (casi) es un objeto.
Los
verbosde R son lasfunciones:Ejecutan acciones sobre otros objetos.
Los objetos sobre los que recae la acción se incluyen entre paréntesis.
Funciones en R
Ayuda en R
- La documentación acerca de una función puede ser consultada mediante
?
Cargar conjunto de datos de R
Se pueden importar de diversas fuentes: MS Excel, .csv, .txt, Stata, SAS, SPSS, etc.
Algunos paquetes cuentan también con datos en formato de R:
*.RDatao.rds.
- Importar datos de paquetes de R:
- Consultar por descripción de los datos:
- Ver un resumen de los datos
# A tibble: 6 × 17
subject_id fake_…¹ fake_…² gender pan_day test_id clini…³ result demo_…⁴ age
<dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 1412 jhezane wester… female 4 covid inpati… negat… patient 0
2 533 penny targar… female 7 covid clinic… negat… patient 0
3 9134 grunt rivers male 7 covid clinic… negat… patient 0.8
4 8518 melisa… swyft female 8 covid clinic… negat… patient 0.8
5 8967 rolley karsta… male 8 covid emerge… negat… patient 0.8
6 11048 megga karsta… female 8 covid oncolo… negat… patient 0.8
# … with 7 more variables: drive_thru_ind <dbl>, ct_result <dbl>,
# orderset <dbl>, payor_group <chr>, patient_class <chr>, col_rec_tat <dbl>,
# rec_ver_tat <dbl>, and abbreviated variable names ¹fake_first_name,
# ²fake_last_name, ³clinic_name, ⁴demo_groupOperador esenciales de R base
- En un conjunto de datos, permite seleccionar la columna de interés.
- Por ejemplo, género de los participantes:
[1] "female" "female" "male" "female" "male" "female" "male" "female"
[9] "male" "male" "male" "female" "female" "male" "female" "female"
[17] "male" "male" "female" "female" "female" "male" "female" "male"
[25] "female" "male" "female" "male" "female" "female" "male" "female"
[33] "female" "female" "female" "female" "male" "female" "male" "female"
[41] "female" "male" "male" "female" "female" "male" "male" "male"
[49] "male" "female" "female" "female" "male" "male" "male" "male"
[57] "female" "female" "female" "female" "male" "male" "male" "female"
[65] "female" "female" "male" "female" "male" "female" "male" "male"
[73] "female" "female" "female" "male" "male" "male" "female" "male"
[81] "female" "female" "female" "female" "female" "male" "female" "male"
[89] "male" "male" "male" "female" "male" "male" "female" "female"
[97] "female" "female" "male" "male" "male" "female" "male" "female"
[105] "female" "female" "male" "male" "female" "male" "male" "male"
[113] "female" "female" "male" "male" "male" "female" "female" "male"
[121] "female" "female" "male" "male" "female" "female" "female" "male"
[129] "female" "female" "male" "female" "female" "female" "female" "male"
[137] "male" "male" "male" "male" "male" "female" "female" "male"
[145] "male" "female" "male" "female" "female" "male" "male" "male"
[153] "female" "female" "female" "male" "female" "male" "male" "female"
[161] "male" "female" "male" "male" "male" "female" "female" "male"
[169] "female" "male" "female" "female" "female" "male" "female" "male"
[177] "female" "female" "male" "male" "female" "female" "female" "female"
[185] "female" "male" "male" "male" "female" "male" "male" "female"
[193] "female" "male" "male" "male" "male" "male" "male" "female"
[201] "female" "female" "female" "female" "male" "male" "male" "male"
[209] "male" "female" "female" "female" "male" "male" "male" "male"
[217] "male" "female" "male" "male" "male" "female" "male" "male"
[225] "male" "male" "female" "female" "female" "female" "female" "male"
[233] "female" "male" "male" "female" "female" "male" "female" "female"
[241] "female" "male" "female" "female" "male" "male" "female" "male"
[249] "female" "female" "female" "male" "female" "female" "female" "male"
[257] "male" "female" "male" "female" "male" "female" "male" "male"
[265] "female" "female" "male" "male" "female" "female" "male" "male"
[273] "female" "male" "male" "male" "male" "male" "male" "male"
[281] "female" "female" "male" "female" "female" "female" "female" "female"
[289] "female" "female" "male" "female" "female" "male" "male" "female"
[297] "female" "female" "female" "female" "male" "female" "male" "female"
[305] "female" "male" "male" "male" "male" "female" "male" "female"
[313] "male" "female" "male" "male" "female" "female" "male" "female"
[321] "female" "female" "female" "female" "male" "male" "male" "female"
[329] "female" "male" "male" "female" "female" "female" "male" "male"
[337] "female" "male" "female" "female" "male" "male" "male" "male"
[345] "female" "male" "male" "male" "female" "male" "female" "female"
[353] "female" "female" "female" "female" "female" "male" "female" "male"
[361] "male" "male" "male" "female" "male" "male" "female" "male"
[369] "female" "female" "female" "male" "female" "female" "male" "male"
[377] "female" "female" "female" "male" "male" "male" "male" "female"
[385] "male" "female" "male" "female" "female" "male" "female" "male"
[393] "female" "female" "male" "female" "female" "male" "female" "female"
[401] "female" "female" "male" "male" "male" "female" "female" "male"
[409] "male" "female" "male" "female" "male" "male" "male" "female"
[417] "female" "male" "male" "male" "female" "male" "female" "male"
[425] "female" "male" "female" "female" "male" "female" "female" "female"
[433] "male" "male" "male" "male" "male" "male" "male" "male"
[441] "male" "male" "male" "female" "female" "male" "male" "male"
[449] "male" "male" "male" "female" "male" "female" "female" "male"
[457] "female" "female" "male" "female" "male" "male" "male" "female"
[465] "female" "female" "male" "male" "male" "female" "male" "female"
[473] "female" "male" "male" "female" "female" "female" "female" "female"
[481] "female" "female" "female" "female" "male" "female" "female" "female"
[489] "female" "female" "female" "female" "female" "male" "male" "female"
[497] "female" "male" "male" "male" "female" "female" "male" "male"
[505] "female" "male" "male" "male" "male" "female" "male" "female"
[513] "female" "female" "female" "female" "female" "male" "female" "male"
[521] "female" "female" "female" "female" "female" "male" "female" "female"
[529] "female" "female" "female" "female" "female" "male" "male" "male"
[537] "female" "male" "female" "male" "female" "female" "male" "female"
[545] "male" "male" "female" "female" "female" "male" "male" "female"
[553] "male" "male" "female" "female" "male" "male" "male" "male"
[561] "male" "female" "female" "female" "male" "female" "female" "female"
[569] "male" "female" "female" "male" "male" "female" "female" "female"
[577] "female" "female" "female" "female" "male" "male" "female" "male"
[585] "female" "female" "female" "male" "female" "female" "female" "female"
[593] "female" "female" "male" "female" "female" "female" "male" "male"
[601] "female" "female" "male" "female" "male" "female" "male" "female"
[609] "female" "female" "female" "female" "female" "male" "male" "male"
[617] "female" "female" "female" "female" "male" "male" "female" "female"
[625] "male" "male" "male" "female" "female" "female" "female" "male"
[633] "female" "female" "female" "male" "male" "male" "male" "female"
[641] "male" "male" "male" "female" "male" "male" "male" "female"
[649] "male" "male" "male" "male" "female" "male" "male" "female"
[657] "male" "male" "male" "female" "male" "male" "female" "male"
[665] "female" "male" "male" "female" "male" "female" "female" "male"
[673] "female" "female" "male" "female" "male" "female" "female" "male"
[681] "male" "female" "male" "male" "female" "female" "female" "female"
[689] "male" "male" "male" "female" "male" "female" "male" "female"
[697] "male" "female" "female" "female" "female" "female" "female" "male"
[705] "male" "male" "female" "female" "female" "female" "female" "male"
[713] "female" "male" "male" "female" "female" "female" "male" "female"
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[729] "male" "female" "female" "female" "female" "male" "female" "female"
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[745] "female" "female" "male" "female" "male" "female" "female" "female"
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[761] "female" "male" "male" "male" "male" "male" "male" "female"
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[777] "female" "female" "female" "female" "female" "male" "female" "female"
[785] "male" "female" "female" "female" "male" "male" "male" "male"
[793] "female" "female" "female" "male" "male" "female" "female" "female"
[801] "female" "male" "female" "male" "female" "female" "female" "male"
[809] "female" "female" "female" "female" "female" "male" "male" "male"
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[825] "male" "female" "male" "female" "male" "female" "male" "male"
[833] "female" "female" "female" "male" "female" "female" "female" "male"
[841] "female" "male" "female" "female" "male" "female" "female" "female"
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[993] "female" "male" "male" "female" "male" "male" "male" "female"
[1001] "male" "female" "male" "male" "male" "male" "female" "female"
[1009] "female" "female" "male" "male" "male" "female" "male" "female"
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[14177] "male" "female" "male" "male" "female" "male" "male" "female"
[14185] "male" "female" "male" "male" "male" "female" "female" "female"
[14193] "female" "female" "male" "male" "male" "male" "male" "female"
[14201] "female" "male" "female" "female" "male" "male" "female" "male"
[14209] "male" "female" "female" "male" "male" "male" "male" "male"
[14217] "male" "female" "female" "male" "male" "male" "male" "male"
[14225] "female" "female" "female" "female" "female" "male" "male" "female"
[14233] "male" "male" "female" "female" "female" "female" "female" "male"
[14241] "female" "male" "female" "male" "male" "female" "female" "male"
[14249] "female" "female" "male" "female" "male" "male" "female" "male"
[14257] "male" "male" "male" "female" "female" "male" "male" "male"
[14265] "female" "female" "female" "female" "female" "male" "male" "female"
[14273] "female" "female" "female" "male" "male" "male" "female" "male"
[14281] "male" "male" "female" "male" "female" "female" "female" "female"
[14289] "female" "female" "female" "female" "male" "female" "female" "female"
[14297] "female" "female" "female" "male" "female" "male" "male" "female"
[14305] "female" "female" "male" "male" "female" "female" "male" "female"
[14313] "female" "female" "female" "male" "female" "male" "male" "female"
[14321] "female" "male" "female" "male" "male" "male" "male" "female"
[14329] "female" "male" "male" "female" "female" "female" "male" "female"
[14337] "female" "female" "female" "male" "female" "female" "male" "female"
[14345] "male" "female" "female" "female" "female" "female" "female" "female"
[14353] "male" "female" "female" "female" "male" "female" "female" "male"
[14361] "female" "female" "female" "male" "male" "male" "male" "male"
[14369] "female" "male" "male" "female" "male" "female" "male" "female"
[14377] "female" "female" "female" "male" "male" "female" "male" "male"
[14385] "male" "male" "male" "female" "female" "female" "male" "male"
[14393] "female" "male" "female" "male" "female" "male" "female" "male"
[14401] "female" "female" "female" "female" "male" "male" "male" "male"
[14409] "male" "female" "male" "female" "female" "male" "male" "male"
[14417] "female" "female" "male" "female" "male" "female" "female" "female"
[14425] "female" "female" "male" "male" "male" "male" "male" "male"
[14433] "male" "female" "male" "male" "male" "female" "female" "male"
[14441] "female" "female" "male" "male" "female" "male" "female" "female"
[14449] "female" "female" "male" "male" "male" "female" "male" "female"
[14457] "female" "female" "female" "male" "male" "male" "female" "male"
[14465] "female" "female" "male" "male" "male" "female" "male" "female"
[14473] "female" "female" "male" "female" "male" "male" "male" "female"
[14481] "male" "female" "female" "male" "male" "male" "female" "female"
[14489] "male" "male" "female" "male" "female" "male" "male" "female"
[14497] "male" "male" "female" "female" "female" "male" "male" "female"
[14505] "female" "female" "male" "male" "male" "female" "female" "female"
[14513] "male" "male" "male" "female" "male" "male" "male" "female"
[14521] "female" "female" "female" "female" "male" "male" "male" "male"
[14529] "female" "male" "male" "female" "female" "male" "male" "male"
[14537] "male" "female" "female" "male" "male" "female" "female" "male"
[14545] "female" "male" "female" "female" "male" "male" "male" "male"
[14553] "female" "female" "female" "male" "male" "female" "male" "male"
[14561] "female" "female" "female" "male" "male" "male" "male" "male"
[14569] "male" "female" "male" "male" "female" "male" "male" "female"
[14577] "male" "female" "female" "male" "female" "male" "female" "female"
[14585] "male" "male" "female" "female" "female" "female" "female" "male"
[14593] "male" "male" "female" "female" "female" "male" "female" "female"
[14601] "female" "female" "female" "female" "female" "male" "male" "female"
[14609] "female" "male" "female" "female" "female" "female" "female" "male"
[14617] "male" "female" "female" "female" "male" "male" "male" "female"
[14625] "female" "female" "male" "female" "male" "female" "male" "female"
[14633] "male" "female" "male" "female" "male" "male" "female" "female"
[14641] "female" "female" "male" "female" "male" "male" "female" "female"
[14649] "female" "female" "male" "female" "male" "male" "female" "male"
[14657] "female" "male" "male" "male" "female" "female" "female" "male"
[14665] "male" "male" "male" "male" "male" "female" "male" "female"
[14673] "female" "female" "male" "female" "male" "female" "female" "female"
[14681] "female" "female" "male" "female" "male" "female" "male" "male"
[14689] "female" "female" "female" "male" "female" "female" "female" "female"
[14697] "female" "male" "female" "female" "male" "female" "male" "female"
[14705] "female" "female" "male" "female" "female" "male" "female" "female"
[14713] "male" "female" "female" "female" "male" "female" "female" "female"
[14721] "female" "female" "male" "female" "female" "female" "male" "male"
[14729] "male" "male" "male" "male" "female" "female" "male" "male"
[14737] "female" "female" "female" "male" "male" "female" "female" "female"
[14745] "male" "male" "female" "male" "male" "female" "male" "male"
[14753] "female" "female" "female" "female" "female" "female" "female" "male"
[14761] "female" "female" "female" "female" "male" "male" "male" "male"
[14769] "male" "male" "female" "female" "male" "female" "female" "male"
[14777] "female" "male" "female" "male" "male" "female" "female" "male"
[14785] "female" "male" "male" "female" "male" "female" "male" "female"
[14793] "female" "female" "female" "female" "female" "female" "male" "female"
[14801] "male" "male" "female" "female" "female" "female" "female" "female"
[14809] "female" "male" "male" "female" "male" "male" "female" "female"
[14817] "male" "female" "male" "male" "male" "female" "female" "male"
[14825] "female" "male" "male" "male" "male" "male" "male" "male"
[14833] "male" "female" "female" "female" "male" "male" "male" "female"
[14841] "male" "female" "male" "male" "female" "female" "female" "male"
[14849] "male" "male" "male" "male" "male" "female" "male" "female"
[14857] "male" "male" "female" "male" "male" "female" "female" "male"
[14865] "male" "male" "female" "female" "male" "male" "female" "female"
[14873] "female" "female" "female" "female" "male" "female" "female" "female"
[14881] "female" "female" "female" "female" "female" "male" "female" "female"
[14889] "male" "male" "female" "female" "male" "female" "female" "female"
[14897] "female" "male" "male" "female" "male" "female" "male" "female"
[14905] "male" "male" "female" "male" "female" "female" "male" "female"
[14913] "female" "male" "female" "female" "male" "female" "male" "male"
[14921] "male" "female" "male" "female" "male" "female" "male" "female"
[14929] "female" "female" "male" "male" "female" "male" "male" "female"
[14937] "male" "female" "female" "female" "male" "male" "male" "male"
[14945] "female" "male" "male" "female" "male" "male" "female" "female"
[14953] "male" "female" "male" "female" "male" "male" "female" "male"
[14961] "male" "female" "female" "male" "female" "male" "female" "male"
[14969] "male" "male" "female" "male" "female" "female" "male" "female"
[14977] "female" "female" "female" "female" "male" "male" "male" "female"
[14985] "female" "male" "male" "female" "male" "female" "male" "female"
[14993] "male" "female" "male" "female" "female" "female" "female" "female"
[15001] "male" "male" "female" "female" "female" "female" "female" "female"
[15009] "female" "male" "female" "male" "female" "male" "female" "female"
[15017] "male" "male" "female" "male" "male" "female" "male" "male"
[15025] "female" "female" "male" "female" "male" "male" "female" "female"
[15033] "female" "male" "female" "female" "female" "female" "male" "male"
[15041] "male" "female" "female" "female" "female" "male" "female" "male"
[15049] "male" "male" "male" "male" "female" "male" "male" "male"
[15057] "male" "female" "female" "female" "female" "female" "male" "female"
[15065] "male" "female" "male" "male" "male" "female" "male" "male"
[15073] "male" "male" "female" "male" "male" "male" "male" "male"
[15081] "female" "male" "male" "female" "male" "female" "male" "female"
[15089] "female" "female" "female" "female" "female" "female" "male" "female"
[15097] "female" "female" "male" "male" "male" "male" "female" "male"
[15105] "female" "female" "male" "male" "male" "female" "male" "female"
[15113] "female" "female" "female" "female" "male" "female" "male" "male"
[15121] "male" "male" "female" "male" "female" "female" "male" "male"
[15129] "male" "male" "female" "female" "female" "female" "male" "female"
[15137] "male" "female" "male" "female" "male" "male" "male" "male"
[15145] "female" "female" "male" "female" "female" "female" "female" "male"
[15153] "male" "female" "female" "male" "female" "male" "male" "male"
[15161] "male" "male" "female" "male" "male" "female" "female" "female"
[15169] "male" "male" "male" "male" "male" "female" "male" "female"
[15177] "male" "female" "male" "female" "male" "male" "female" "female"
[15185] "female" "male" "male" "female" "female" "female" "male" "female"
[15193] "male" "male" "female" "female" "female" "male" "male" "male"
[15201] "male" "female" "male" "female" "female" "female" "male" "female"
[15209] "male" "male" "male" "male" "female" "male" "male" "female"
[15217] "male" "female" "female" "female" "male" "female" "female" "female"
[15225] "male" "female" "male" "male" "male" "female" "female" "male"
[15233] "male" "female" "male" "male" "male" "female" "male" "male"
[15241] "male" "male" "female" "female" "male" "female" "female" "female"
[15249] "male" "female" "female" "male" "male" "male" "female" "female"
[15257] "male" "female" "female" "male" "female" "female" "female" "male"
[15265] "male" "male" "male" "female" "male" "male" "male" "female"
[15273] "male" "female" "male" "female" "female" "male" "female" "female"
[15281] "female" "female" "female" "male" "female" "female" "male" "male"
[15289] "male" "female" "female" "male" "female" "female" "male" "male"
[15297] "female" "female" "male" "male" "male" "female" "male" "male"
[15305] "male" "female" "male" "male" "female" "female" "male" "female"
[15313] "male" "male" "female" "male" "female" "female" "female" "female"
[15321] "male" "female" "male" "male" "female" "female" "female" "male"
[15329] "female" "male" "female" "female" "female" "female" "female" "female"
[15337] "male" "male" "female" "female" "female" "male" "male" "male"
[15345] "male" "female" "female" "male" "male" "female" "female" "female"
[15353] "male" "male" "male" "male" "female" "male" "female" "male"
[15361] "female" "female" "male" "female" "female" "male" "male" "male"
[15369] "female" "female" "female" "female" "male" "male" "male" "female"
[15377] "male" "male" "male" "female" "male" "female" "male" "male"
[15385] "male" "male" "female" "female" "female" "female" "male" "male"
[15393] "male" "male" "male" "male" "male" "female" "male" "female"
[15401] "female" "male" "female" "male" "male" "female" "male" "female"
[15409] "female" "male" "male" "female" "male" "male" "male" "female"
[15417] "male" "female" "female" "male" "male" "female" "female" "female"
[15425] "female" "female" "female" "male" "female" "male" "male" "female"
[15433] "male" "male" "female" "female" "male" "male" "male" "male"
[15441] "female" "female" "male" "female" "female" "male" "female" "female"
[15449] "male" "female" "male" "female" "female" "male" "male" "male"
[15457] "male" "male" "male" "female" "male" "female" "male" "male"
[15465] "male" "male" "male" "female" "female" "female" "female" "female"
[15473] "female" "female" "female" "male" "female" "male" "male" "male"
[15481] "female" "female" "male" "female" "male" "female" "male" "female"
[15489] "female" "female" "male" "female" "female" "female" "male" "male"
[15497] "male" "female" "male" "male" "female" "female" "male" "female"
[15505] "male" "male" "female" "female" "male" "male" "female" "female"
[15513] "male" "female" "male" "male" "male" "female" "female" "female"
[15521] "female" "female" "male" "female"- O la edad:
[1] 0.0 0.0 0.8 0.8 0.8 0.8 0.8 0.0 0.0 0.9 0.9 18.0
[13] 0.9 0.0 21.0 19.0 0.9 18.0 0.9 0.9 18.0 0.9 20.0 18.0
[25] 0.0 19.0 18.0 0.9 0.9 18.0 0.0 1.0 1.0 21.0 18.0 0.9
[37] 21.0 1.0 18.0 0.0 22.0 0.9 0.9 1.0 22.0 21.0 1.0 1.0
[49] 1.0 0.0 18.0 1.0 21.0 1.0 22.0 18.0 1.0 1.0 1.0 1.0
[61] 1.0 21.0 0.0 18.0 1.0 18.0 18.0 1.0 4.0 1.0 1.0 1.0
[73] 5.0 1.0 22.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 5.0 1.0
[85] 1.0 1.0 0.8 22.0 18.0 18.0 18.0 0.1 1.0 13.0 18.0 1.0
[97] 1.0 19.0 1.0 1.0 0.1 22.0 18.0 0.0 1.0 18.0 18.0 1.0
[109] 22.0 1.0 1.0 22.0 18.0 1.0 18.0 1.0 22.0 1.0 28.0 1.0
[121] 1.0 1.0 1.0 1.0 1.0 19.0 23.0 1.0 20.0 1.0 22.0 1.0
[133] 1.0 0.0 1.0 1.0 18.0 1.0 1.0 1.0 1.0 1.0 23.0 23.0
[145] 19.0 1.0 1.0 0.8 1.0 23.0 0.0 1.0 23.0 0.0 0.9 1.0
[157] 1.0 1.0 20.0 1.0 0.0 0.7 1.0 23.0 23.0 1.0 1.0 1.0
[169] 1.0 18.0 21.0 1.0 23.0 1.0 18.0 23.0 1.0 23.0 23.0 18.0
[181] 1.0 0.0 1.0 18.0 1.0 23.0 0.0 1.0 1.0 1.0 1.0 4.0
[193] 18.0 1.0 0.3 1.0 23.0 1.0 0.0 23.0 18.0 1.0 1.0 0.0
[205] 1.0 1.0 1.0 1.0 1.0 23.0 23.0 1.0 1.0 18.0 1.0 1.0
[217] 5.0 1.0 23.0 1.0 1.0 1.0 1.0 1.0 7.0 1.0 1.0 1.0
[229] 1.0 23.0 1.0 2.0 1.0 23.0 1.0 1.0 1.0 21.0 1.0 18.0
[241] 1.0 7.0 0.0 18.0 1.0 1.0 1.0 1.0 23.0 0.2 1.0 1.0
[253] 1.0 1.0 0.0 23.0 0.4 0.0 1.0 1.0 1.0 1.0 18.0 1.0
[265] 1.0 0.5 1.0 1.0 0.1 1.0 1.0 1.0 1.0 1.0 1.0 23.0
[277] 1.0 0.0 23.0 0.0 23.0 23.0 1.0 21.0 21.0 23.0 1.0 1.0
[289] 1.0 0.9 9.0 23.0 1.0 1.0 4.0 1.0 1.0 1.0 1.0 1.0
[301] 1.0 0.2 23.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 23.0 23.0
[313] 1.0 1.0 24.0 0.1 1.0 1.0 1.0 23.0 1.0 1.0 1.0 0.0
[325] 0.0 23.0 1.0 1.0 0.4 1.0 24.0 1.0 1.0 22.0 1.0 1.0
[337] 1.0 24.0 23.0 1.0 1.0 1.0 1.0 1.0 24.0 1.0 1.0 1.0
[349] 1.0 1.0 21.0 1.0 1.0 1.0 1.0 1.0 24.0 1.0 1.0 1.0
[361] 1.0 24.0 24.0 1.0 0.0 18.0 18.0 1.0 1.0 0.0 18.0 1.0
[373] 24.0 1.0 1.0 1.0 1.0 24.0 24.0 1.0 1.0 24.0 18.0 0.1
[385] 0.2 1.0 1.0 1.0 1.0 1.0 1.0 18.0 1.0 0.0 1.0 1.0
[397] 1.0 1.0 1.0 24.0 0.0 21.0 1.0 1.0 1.0 1.0 1.0 24.0
[409] 1.0 1.0 18.0 24.0 0.0 24.0 1.0 0.7 1.0 0.0 1.0 1.0
[421] 1.0 1.0 0.0 18.0 1.0 1.0 18.0 1.0 1.0 1.0 1.0 1.0
[433] 0.0 1.0 1.0 24.0 0.0 1.0 4.0 22.0 0.0 1.0 1.0 1.0
[445] 1.0 1.0 0.0 24.0 18.0 24.0 24.0 24.0 1.0 1.0 1.0 1.0
[457] 1.0 0.5 0.0 1.0 1.0 1.0 1.0 0.1 24.0 1.0 1.0 1.0
[469] 1.0 1.0 1.0 18.0 0.1 1.0 1.0 1.0 1.0 1.0 19.0 0.2
[481] 1.0 1.0 1.0 10.0 1.0 1.0 0.0 1.0 0.7 0.0 0.4 0.0
[493] 18.0 1.0 24.0 0.0 24.0 1.0 1.0 24.0 1.0 0.0 1.0 1.0
[505] 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 22.0
[517] 1.0 24.0 18.0 0.2 12.0 0.0 24.0 0.3 1.0 0.0 0.0 1.0
[529] 24.0 1.0 1.0 22.0 0.1 1.0 1.0 40.0 0.0 1.0 1.0 1.0
[541] 1.0 1.0 18.0 24.0 1.0 1.0 1.0 1.0 31.0 1.0 1.0 18.0
[553] 1.0 24.0 1.0 1.0 0.0 25.0 25.0 24.0 1.0 1.0 0.0 18.0
[565] 1.0 22.0 1.0 24.0 1.0 24.0 0.0 1.0 1.0 1.0 1.0 18.0
[577] 3.0 0.0 18.0 0.0 24.0 24.0 1.0 0.3 1.0 1.0 1.0 24.0
[589] 0.1 1.0 1.0 24.0 24.0 24.0 24.0 1.0 0.8 1.0 1.0 1.0
[601] 1.0 18.0 1.0 24.0 18.0 1.0 7.0 1.0 1.0 1.0 1.0 1.0
[613] 0.0 25.0 25.0 1.0 1.0 0.0 0.0 1.0 1.0 1.0 1.0 18.0
[625] 1.0 24.0 0.4 1.0 1.0 18.0 0.0 1.0 1.0 1.0 1.0 1.0
[637] 24.0 1.0 1.0 1.0 0.1 25.0 24.0 24.0 1.0 1.0 18.0 0.0
[649] 1.0 0.0 0.4 22.0 24.0 22.0 1.0 1.0 1.0 0.0 1.0 24.0
[661] 18.0 18.0 1.0 1.0 1.0 1.0 1.0 24.0 1.0 1.0 1.0 1.0
[673] 1.0 0.0 0.0 0.0 24.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
[685] 1.0 1.0 1.0 1.0 25.0 1.0 1.0 1.0 25.0 25.0 25.0 0.0
[697] 0.1 1.0 25.0 1.0 5.0 1.0 1.0 0.0 1.0 22.0 0.0 1.0
[709] 1.0 1.0 1.0 5.0 1.0 18.0 1.0 1.0 1.0 0.7 1.0 0.0
[721] 22.0 1.0 1.0 25.0 25.0 23.0 25.0 1.0 1.0 1.0 1.0 0.0
[733] 18.0 1.0 1.0 1.0 0.9 1.0 0.3 1.0 0.0 1.0 26.0 25.0
[745] 25.0 25.0 18.0 5.0 17.0 1.0 5.0 1.0 1.0 1.0 18.0 1.0
[757] 1.0 1.0 0.0 0.6 1.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0
[769] 1.0 1.0 0.0 14.0 1.0 1.0 1.0 0.0 1.0 25.0 1.0 24.0
[781] 1.0 1.0 1.0 18.0 1.0 25.0 18.0 25.0 1.0 1.0 0.0 1.0
[793] 1.0 0.0 1.0 1.0 25.0 1.0 25.0 18.0 1.0 18.0 1.0 25.0
[805] 0.0 0.2 1.0 1.0 0.0 23.0 2.0 1.0 25.0 25.0 1.0 25.0
[817] 1.0 18.0 1.0 1.0 1.0 1.0 25.0 1.0 1.0 1.0 1.0 1.0
[829] 1.0 25.0 25.0 1.0 18.0 1.0 1.0 1.0 0.0 1.0 1.0 18.0
[841] 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 23.0 1.0 1.0 1.0
[853] 1.0 0.0 1.0 1.0 2.0 25.0 1.0 1.0 1.0 1.0 0.0 0.0
[865] 1.0 1.0 25.0 1.0 1.0 1.0 0.0 1.0 1.0 18.0 1.0 53.0
[877] 1.0 1.0 18.0 1.0 1.0 1.0 25.0 18.0 26.0 23.0 1.0 26.0
[889] 1.0 1.0 1.0 1.0 1.0 25.0 1.0 1.0 3.0 1.0 0.0 1.0
[901] 18.0 1.0 1.0 1.0 1.0 25.0 0.5 1.0 1.0 1.0 1.0 0.0
[913] 0.0 1.0 18.0 25.0 1.0 1.0 1.0 1.0 0.0 25.0 25.0 1.0
[925] 1.0 1.0 23.0 18.0 1.0 1.0 18.0 1.0 0.7 25.0 1.0 1.0
[937] 1.0 1.0 1.0 18.0 18.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
[949] 0.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 25.0 0.0 1.0 0.6
[961] 1.0 1.0 0.0 1.0 0.0 1.0 18.0 18.0 1.0 1.0 25.0 25.0
[973] 1.0 1.0 25.0 0.0 0.0 25.0 1.0 1.0 15.0 1.0 1.0 2.0
[985] 1.0 1.0 23.0 23.0 24.0 25.0 1.0 0.0 27.0 1.0 1.0 28.0
[997] 1.0 18.0 1.0 5.0 25.0 1.0 25.0 25.0 25.0 18.0 0.0 1.0
[1009] 1.0 1.0 1.0 1.0 0.2 1.0 0.1 1.0 1.0 18.0 23.0 1.0
[1021] 25.0 18.0 0.0 24.0 1.0 1.0 1.0 1.0 1.0 18.0 1.0 0.0
[1033] 1.0 29.0 1.0 25.0 25.0 1.0 1.0 1.0 1.0 1.0 1.0 0.5
[1045] 1.0 25.0 1.0 1.0 1.0 0.0 0.7 2.0 1.0 25.0 1.0 1.0
[1057] 0.5 1.0 0.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0 2.0
[1069] 0.0 2.0 25.0 1.0 0.0 0.0 2.0 0.0 0.0 18.0 1.0 1.0
[1081] 25.0 0.8 1.0 25.0 25.0 26.0 0.0 1.0 9.0 2.0 18.0 2.0
[1093] 2.0 1.0 18.0 2.0 18.0 2.0 2.0 2.0 2.0 2.0 25.0 18.0
[1105] 25.0 2.0 10.0 1.0 2.0 1.0 2.0 18.0 2.0 26.0 1.0 1.0
[1117] 0.0 2.0 1.0 1.0 2.0 2.0 2.0 18.0 25.0 18.0 25.0 26.0
[1129] 2.0 0.0 2.0 24.0 1.0 1.0 26.0 25.0 0.0 2.0 26.0 1.0
[1141] 18.0 2.0 2.0 2.0 2.0 2.0 25.0 1.0 26.0 18.0 1.0 24.0
[1153] 0.0 0.0 1.0 25.0 25.0 18.0 18.0 29.0 1.0 1.0 25.0 25.0
[1165] 0.8 1.0 26.0 24.0 0.0 1.0 1.0 1.0 1.0 20.0 2.0 1.0
[1177] 1.0 25.0 1.0 22.0 1.0 2.0 2.0 2.0 25.0 2.0 1.0 2.0
[1189] 0.0 26.0 2.0 2.0 2.0 18.0 1.0 26.0 26.0 2.0 24.0 1.0
[1201] 0.1 0.0 0.0 18.0 2.0 1.0 1.0 1.0 1.0 2.0 1.0 0.0
[1213] 2.0 26.0 0.0 1.0 0.9 2.0 1.0 1.0 1.0 1.0 0.0 0.0
[1225] 1.0 2.0 18.0 2.0 26.0 2.0 0.6 1.0 26.0 0.1 1.0 2.0
[1237] 2.0 1.0 0.0 1.0 26.0 0.0 1.0 1.0 1.0 0.0 26.0 1.0
[1249] 2.0 2.0 1.0 1.0 2.0 27.0 2.0 1.0 2.0 1.0 2.0 18.0
[1261] 0.0 0.0 2.0 2.0 2.0 2.0 2.0 18.0 2.0 2.0 2.0 2.0
[1273] 2.0 0.0 2.0 2.0 2.0 2.0 2.0 0.0 2.0 26.0 2.0 2.0
[1285] 0.0 2.0 2.0 2.0 2.0 4.0 2.0 2.0 18.0 2.0 2.0 2.0
[1297] 26.0 26.0 0.0 2.0 2.0 26.0 2.0 1.0 18.0 0.1 18.0 2.0
[1309] 28.0 12.0 2.0 29.0 2.0 0.0 2.0 2.0 18.0 0.0 2.0 18.0
[1321] 26.0 2.0 2.0 2.0 18.0 2.0 2.0 2.0 9.0 2.0 2.0 2.0
[1333] 2.0 2.0 0.5 2.0 0.0 2.0 2.0 2.0 0.0 2.0 2.0 0.3
[1345] 2.0 2.0 2.0 26.0 26.0 0.0 1.0 2.0 18.0 2.0 2.0 0.8
[1357] 2.0 29.0 18.0 18.0 0.8 26.0 0.0 1.0 0.0 1.0 2.0 0.0
[1369] 0.0 2.0 0.0 2.0 0.0 2.0 24.0 26.0 26.0 1.0 2.0 2.0
[1381] 26.0 1.0 26.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 0.2 2.0
[1393] 18.0 26.0 0.0 26.0 2.0 0.0 0.0 0.0 2.0 2.0 29.0 2.0
[1405] 2.0 2.0 2.0 2.0 0.0 0.0 2.0 0.0 2.0 2.0 0.7 0.0
[1417] 26.0 30.0 2.0 0.0 2.0 0.0 18.0 2.0 0.0 0.0 2.0 26.0
[1429] 2.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 0.3 0.1
[1441] 2.0 2.0 2.0 0.0 1.0 2.0 26.0 2.0 2.0 26.0 26.0 2.0
[1453] 2.0 2.0 2.0 18.0 2.0 2.0 0.6 24.0 30.0 26.0 0.0 26.0
[1465] 18.0 2.0 0.0 2.0 18.0 2.0 31.0 1.0 2.0 1.0 18.0 2.0
[1477] 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 18.0 2.0
[1489] 26.0 0.0 2.0 18.0 2.0 2.0 0.1 2.0 2.0 18.0 1.0 1.0
[1501] 2.0 2.0 2.0 2.0 2.0 31.0 1.0 0.0 2.0 18.0 2.0 2.0
[1513] 1.0 2.0 2.0 16.0 2.0 31.0 0.0 2.0 2.0 2.0 2.0 24.0
[1525] 18.0 18.0 0.0 0.0 2.0 2.0 2.0 18.0 2.0 0.0 2.0 2.0
[1537] 2.0 2.0 0.0 18.0 1.0 0.2 18.0 1.0 2.0 18.0 26.0 2.0
[1549] 0.7 2.0 2.0 2.0 18.0 26.0 26.0 2.0 0.0 18.0 0.0 18.0
[1561] 26.0 2.0 2.0 2.0 26.0 0.0 26.0 2.0 1.0 2.0 2.0 2.0
[1573] 2.0 26.0 0.0 0.0 2.0 0.0 2.0 2.0 2.0 2.0 2.0 18.0
[1585] 2.0 0.0 27.0 27.0 27.0 26.0 24.0 26.0 2.0 2.0 2.0 2.0
[1597] 2.0 28.0 0.7 26.0 2.0 2.0 2.0 2.0 0.2 2.0 2.0 2.0
[1609] 2.0 2.0 2.0 26.0 0.9 2.0 0.1 18.0 2.0 2.0 1.0 0.0
[1621] 2.0 0.7 2.0 2.0 26.0 2.0 26.0 2.0 26.0 2.0 2.0 0.2
[1633] 2.0 2.0 2.0 2.0 2.0 2.0 2.0 0.0 26.0 18.0 18.0 2.0
[1645] 2.0 35.0 2.0 2.0 2.0 2.0 2.0 2.0 26.0 0.0 0.7 26.0
[1657] 1.0 2.0 2.0 91.0 0.1 2.0 2.0 0.1 2.0 2.0 2.0 2.0
[1669] 18.0 18.0 2.0 15.0 2.0 26.0 26.0 18.0 2.0 2.0 2.0 25.0
[1681] 2.0 0.3 18.0 0.1 26.0 0.5 2.0 2.0 26.0 2.0 1.0 2.0
[1693] 2.0 26.0 2.0 0.1 2.0 2.0 2.0 26.0 2.0 26.0 2.0 1.0
[1705] 18.0 0.6 2.0 2.0 2.0 2.0 2.0 26.0 18.0 2.0 26.0 1.0
[1717] 1.0 2.0 2.0 2.0 18.0 2.0 26.0 18.0 26.0 2.0 26.0 18.0
[1729] 18.0 26.0 2.0 2.0 2.0 18.0 0.3 2.0 18.0 26.0 2.0 0.0
[1741] 2.0 0.6 2.0 2.0 2.0 0.0 2.0 2.0 91.0 27.0 1.0 1.0
[1753] 2.0 2.0 0.9 2.0 2.0 2.0 12.0 2.0 31.0 2.0 1.0 0.1
[1765] 28.0 26.0 27.0 26.0 27.0 2.0 18.0 2.0 2.0 18.0 2.0 26.0
[1777] 2.0 2.0 2.0 2.0 27.0 2.0 2.0 2.0 0.1 2.0 18.0 1.0
[1789] 2.0 27.0 2.0 2.0 2.0 0.1 2.0 27.0 0.0 2.0 2.0 2.0
[1801] 2.0 26.0 18.0 2.0 2.0 2.0 0.3 2.0 2.0 2.0 2.0 2.0
[1813] 18.0 10.0 0.0 27.0 2.0 2.0 18.0 2.0 18.0 1.0 0.1 0.1
[1825] 18.0 2.0 2.0 2.0 0.9 25.0 18.0 2.0 24.0 18.0 2.0 18.0
[1837] 18.0 27.0 2.0 18.0 2.0 27.0 2.0 26.0 26.0 18.0 2.0 2.0
[1849] 2.0 0.0 2.0 2.0 2.0 2.0 26.0 26.0 18.0 2.0 2.0 2.0
[1861] 18.0 2.0 2.0 3.0 0.6 2.0 26.0 18.0 2.0 0.0 18.0 2.0
[1873] 2.0 27.0 26.0 2.0 2.0 1.0 2.0 2.0 2.0 2.0 0.0 2.0
[1885] 2.0 27.0 2.0 0.7 2.0 0.0 2.0 18.0 2.0 3.0 26.0 50.0
[1897] 2.0 2.0 2.0 2.0 2.0 0.3 27.0 0.1 3.0 18.0 2.0 2.0
[1909] 0.5 2.0 2.0 1.0 2.0 18.0 2.0 27.0 27.0 0.1 2.0 2.0
[1921] 2.0 27.0 0.0 1.0 2.0 27.0 2.0 2.0 31.0 0.0 2.0 27.0
[1933] 2.0 2.0 2.0 27.0 14.0 1.0 0.2 18.0 2.0 18.0 2.0 2.0
[1945] 0.7 1.0 2.0 2.0 2.0 2.0 2.0 18.0 2.0 2.0 2.0 27.0
[1957] 27.0 26.0 18.0 0.1 2.0 0.8 2.0 2.0 2.0 2.0 11.0 3.0
[1969] 2.0 2.0 2.0 2.0 2.0 2.0 2.0 18.0 2.0 27.0 2.0 2.0
[1981] 2.0 2.0 2.0 18.0 3.0 18.0 2.0 18.0 2.0 27.0 0.1 2.0
[1993] 1.0 27.0 2.0 2.0 26.0 2.0 2.0 2.0 2.0 2.0 18.0 2.0
[2005] 27.0 2.0 27.0 2.0 27.0 26.0 0.1 27.0 1.0 2.0 4.0 27.0
[2017] 16.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 18.0 2.0
[2029] 2.0 2.0 2.0 2.0 2.0 2.0 0.0 2.0 0.1 2.0 2.0 2.0
[2041] 2.0 27.0 2.0 0.0 2.0 2.0 0.6 27.0 2.0 18.0 18.0 3.0
[2053] 0.1 2.0 2.0 2.0 27.0 2.0 2.0 18.0 2.0 32.0 0.1 2.0
[2065] 2.0 2.0 0.2 3.0 3.0 27.0 2.0 27.0 2.0 27.0 2.0 2.0
[2077] 2.0 12.0 2.0 2.0 18.0 27.0 2.0 0.2 0.5 27.0 1.0 2.0
[2089] 2.0 12.0 2.0 2.0 2.0 2.0 2.0 18.0 2.0 0.1 2.0 2.0
[2101] 2.0 5.0 2.0 2.0 2.0 7.0 1.0 2.0 2.0 2.0 27.0 2.0
[2113] 27.0 27.0 2.0 18.0 2.0 2.0 2.0 28.0 27.0 2.0 3.0 0.1
[2125] 0.1 27.0 27.0 27.0 27.0 2.0 2.0 18.0 2.0 2.0 2.0 18.0
[2137] 0.7 2.0 2.0 0.1 2.0 2.0 2.0 27.0 27.0 27.0 2.0 60.0
[2149] 2.0 2.0 27.0 2.0 2.0 2.0 2.0 18.0 0.1 18.0 2.0 27.0
[2161] 25.0 2.0 2.0 2.0 2.0 2.0 0.9 2.0 2.0 2.0 2.0 2.0
[2173] 25.0 2.0 3.0 27.0 18.0 2.0 0.5 2.0 18.0 13.0 27.0 28.0
[2185] 1.0 0.1 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 0.6 2.0
[2197] 27.0 18.0 12.0 2.0 2.0 2.0 2.0 2.0 2.0 27.0 2.0 2.0
[2209] 18.0 18.0 42.0 27.0 2.0 2.0 2.0 4.0 28.0 0.1 27.0 27.0
[2221] 2.0 2.0 32.0 0.7 2.0 2.0 0.1 2.0 2.0 2.0 2.0 12.0
[2233] 25.0 2.0 2.0 0.3 2.0 18.0 0.3 32.0 2.0 2.0 2.0 2.0
[2245] 2.0 0.0 3.0 1.0 2.0 27.0 27.0 1.0 2.0 2.0 1.0 2.0
[2257] 18.0 2.0 1.0 2.0 29.0 2.0 2.0 0.6 0.6 0.1 1.0 2.0
[2269] 2.0 2.0 2.0 2.0 0.2 0.1 18.0 2.0 2.0 18.0 0.1 2.0
[2281] 2.0 2.0 2.0 13.0 2.0 0.2 28.0 27.0 18.0 0.1 2.0 27.0
[2293] 2.0 2.0 2.0 32.0 2.0 18.0 2.0 2.0 2.0 2.0 2.0 2.0
[2305] 4.0 2.0 27.0 2.0 2.0 2.0 2.0 2.0 2.0 0.1 18.0 29.0
[2317] 0.1 2.0 2.0 2.0 27.0 0.1 18.0 0.7 2.0 32.0 2.0 0.1
[2329] 18.0 4.0 0.1 2.0 2.0 2.0 27.0 1.0 2.0 0.1 0.1 2.0
[2341] 0.1 2.0 0.2 18.0 2.0 2.0 2.0 2.0 2.0 27.0 2.0 2.0
[2353] 2.0 2.0 2.0 0.1 18.0 0.0 27.0 0.0 27.0 0.1 1.0 27.0
[2365] 2.0 18.0 2.0 2.0 1.0 18.0 27.0 0.2 0.1 6.0 18.0 1.0
[2377] 2.0 2.0 27.0 2.0 0.1 2.0 29.0 2.0 2.0 18.0 2.0 3.0
[2389] 2.0 27.0 1.0 27.0 2.0 2.0 1.0 18.0 2.0 18.0 27.0 2.0
[2401] 2.0 5.0 18.0 20.0 27.0 18.0 2.0 2.0 1.0 2.0 5.0 2.0
[2413] 18.0 33.0 2.0 2.0 0.1 4.0 27.0 15.0 35.0 28.0 2.0 0.1
[2425] 2.0 18.0 0.1 2.0 2.0 18.0 2.0 2.0 0.0 2.0 1.0 0.2
[2437] 1.0 0.9 28.0 27.0 2.0 28.0 2.0 0.0 2.0 2.0 0.1 2.0
[2449] 2.0 1.0 18.0 2.0 0.1 2.0 2.0 2.0 33.0 2.0 0.1 28.0
[2461] 18.0 0.1 0.1 2.0 2.0 2.0 28.0 28.0 0.1 18.0 2.0 27.0
[2473] 2.0 2.0 2.0 18.0 0.1 2.0 2.0 0.1 2.0 2.0 2.0 2.0
[2485] 18.0 0.2 2.0 2.0 28.0 28.0 18.0 2.0 1.0 28.0 18.0 28.0
[2497] 2.0 2.0 2.0 0.1 2.0 2.0 0.1 27.0 2.0 18.0 28.0 0.5
[2509] 0.1 2.0 0.1 2.0 2.0 2.0 2.0 2.0 27.0 28.0 0.1 0.1
[2521] 27.0 0.4 2.0 18.0 2.0 2.0 18.0 2.0 28.0 28.0 28.0 2.0
[2533] 0.1 18.0 2.0 18.0 2.0 33.0 18.0 2.0 2.0 18.0 28.0 2.0
[2545] 18.0 2.0 2.0 2.0 2.0 30.0 2.0 2.0 2.0 2.0 2.0 4.0
[2557] 12.0 2.0 28.0 2.0 15.0 2.0 2.0 2.0 30.0 2.0 91.0 0.1
[2569] 2.0 0.1 2.0 2.0 1.0 2.0 2.0 18.0 18.0 2.0 31.0 27.0
[2581] 2.0 2.0 27.0 0.1 2.0 2.0 2.0 18.0 2.0 2.0 0.1 25.0
[2593] 27.0 28.0 28.0 2.0 2.0 2.0 2.0 0.1 28.0 2.0 0.1 2.0
[2605] 0.0 2.0 2.0 18.0 2.0 2.0 2.0 2.0 1.0 2.0 28.0 3.0
[2617] 28.0 28.0 29.0 28.0 28.0 30.0 2.0 2.0 28.0 18.0 2.0 2.0
[2629] 2.0 2.0 2.0 28.0 2.0 2.0 0.0 0.1 2.0 0.1 0.1 28.0
[2641] 2.0 2.0 2.0 91.0 2.0 0.7 0.0 2.0 28.0 2.0 1.0 2.0
[2653] 0.1 28.0 2.0 28.0 2.0 3.0 3.0 2.0 13.0 1.0 2.0 0.1
[2665] 0.2 18.0 3.0 0.3 13.0 18.0 0.1 2.0 3.0 3.0 1.0 3.0
[2677] 0.1 0.8 0.1 2.0 3.0 0.1 3.0 3.0 0.1 1.0 1.0 28.0
[2689] 0.1 28.0 3.0 28.0 2.0 28.0 3.0 18.0 2.0 3.0 1.0 18.0
[2701] 3.0 28.0 3.0 0.1 3.0 3.0 3.0 0.0 33.0 0.1 3.0 3.0
[2713] 3.0 3.0 18.0 2.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 0.1
[2725] 3.0 3.0 3.0 28.0 0.7 3.0 3.0 3.0 28.0 3.0 0.1 4.0
[2737] 0.1 18.0 18.0 2.0 3.0 18.0 3.0 2.0 3.0 3.0 3.0 3.0
[2749] 3.0 3.0 3.0 3.0 0.3 35.0 28.0 18.0 18.0 0.6 3.0 18.0
[2761] 3.0 18.0 3.0 3.0 3.0 28.0 2.0 3.0 3.0 0.1 3.0 3.0
[2773] 3.0 3.0 3.0 3.0 33.0 3.0 91.0 3.0 3.0 3.0 28.0 25.0
[2785] 3.0 28.0 3.0 28.0 28.0 28.0 1.0 3.0 0.9 0.1 18.0 3.0
[2797] 0.3 3.0 3.0 3.0 28.0 3.0 7.0 1.0 3.0 28.0 18.0 28.0
[2809] 0.0 28.0 3.0 18.0 0.1 3.0 3.0 3.0 3.0 3.0 3.0 3.0
[2821] 2.0 3.0 3.0 29.0 28.0 3.0 3.0 0.1 2.0 0.1 3.0 3.0
[2833] 18.0 0.1 28.0 3.0 18.0 18.0 3.0 0.6 3.0 3.0 3.0 0.5
[2845] 25.0 28.0 28.0 3.0 0.1 3.0 0.1 0.1 33.0 2.0 3.0 1.0
[2857] 3.0 18.0 3.0 3.0 0.1 3.0 26.0 18.0 0.1 1.0 28.0 0.1
[2869] 15.0 3.0 28.0 3.0 3.0 0.1 28.0 2.0 3.0 3.0 3.0 14.0
[2881] 3.0 18.0 3.0 4.0 18.0 29.0 29.0 30.0 3.0 29.0 29.0 3.0
[2893] 3.0 0.1 3.0 3.0 0.1 18.0 3.0 3.0 0.3 3.0 3.0 29.0
[2905] 3.0 3.0 0.1 3.0 92.0 0.3 25.0 3.0 0.1 3.0 0.1 3.0
[2917] 3.0 29.0 0.3 3.0 0.1 3.0 3.0 3.0 0.1 3.0 25.0 34.0
[2929] 28.0 28.0 3.0 3.0 28.0 2.0 18.0 3.0 3.0 3.0 29.0 0.4
[2941] 3.0 3.0 3.0 3.0 3.0 3.0 1.0 3.0 3.0 4.0 3.0 3.0
[2953] 3.0 18.0 3.0 3.0 3.0 29.0 3.0 28.0 3.0 3.0 3.0 18.0
[2965] 3.0 3.0 3.0 3.0 3.0 3.0 0.1 0.7 3.0 2.0 3.0 0.1
[2977] 4.0 0.2 3.0 3.0 0.1 29.0 3.0 18.0 3.0 2.0 1.0 3.0
[2989] 3.0 28.0 3.0 2.0 3.0 18.0 28.0 3.0 3.0 3.0 29.0 18.0
[3001] 18.0 29.0 29.0 28.0 28.0 3.0 18.0 3.0 1.0 3.0 34.0 3.0
[3013] 18.0 28.0 29.0 0.3 3.0 3.0 3.0 3.0 32.0 3.0 3.0 3.0
[3025] 3.0 3.0 18.0 3.0 0.1 3.0 3.0 3.0 28.0 29.0 0.1 3.0
[3037] 1.0 3.0 21.0 2.0 3.0 3.0 3.0 2.0 3.0 0.0 29.0 3.0
[3049] 0.1 3.0 29.0 3.0 34.0 2.0 3.0 3.0 29.0 3.0 3.0 3.0
[3061] 3.0 34.0 3.0 3.0 18.0 0.1 18.0 3.0 3.0 3.0 3.0 29.0
[3073] 1.0 3.0 18.0 0.4 3.0 0.1 3.0 0.1 29.0 18.0 3.0 3.0
[3085] 3.0 29.0 29.0 29.0 29.0 0.1 18.0 0.1 29.0 3.0 0.1 3.0
[3097] 91.0 3.0 3.0 1.0 29.0 18.0 29.0 3.0 3.0 3.0 3.0 2.0
[3109] 1.0 29.0 9.0 0.1 3.0 3.0 3.0 3.0 0.1 3.0 3.0 3.0
[3121] 3.0 3.0 2.0 22.0 0.1 3.0 0.1 3.0 29.0 3.0 3.0 3.0
[3133] 3.0 29.0 3.0 0.8 18.0 3.0 3.0 3.0 14.0 3.0 18.0 3.0
[3145] 3.0 3.0 0.8 29.0 18.0 3.0 3.0 2.0 3.0 3.0 3.0 2.0
[3157] 0.1 0.1 2.0 3.0 0.1 3.0 3.0 18.0 91.0 12.0 29.0 3.0
[3169] 29.0 3.0 3.0 3.0 29.0 35.0 33.0 29.0 3.0 1.0 3.0 29.0
[3181] 0.5 3.0 29.0 29.0 3.0 3.0 3.0 3.0 3.0 29.0 18.0 3.0
[3193] 3.0 18.0 3.0 18.0 3.0 18.0 1.0 18.0 2.0 3.0 3.0 30.0
[3205] 30.0 29.0 2.0 3.0 0.1 3.0 3.0 3.0 3.0 18.0 0.1 3.0
[3217] 1.0 3.0 2.0 3.0 0.1 29.0 1.0 3.0 18.0 0.1 3.0 3.0
[3229] 3.0 3.0 29.0 3.0 0.1 34.0 0.4 29.0 3.0 18.0 3.0 3.0
[3241] 3.0 34.0 1.0 18.0 3.0 1.0 18.0 0.2 3.0 3.0 18.0 29.0
[3253] 18.0 3.0 3.0 0.1 3.0 29.0 14.0 29.0 29.0 0.1 3.0 0.7
[3265] 29.0 3.0 18.0 3.0 3.0 3.0 0.6 3.0 0.1 0.0 3.0 0.7
[3277] 3.0 3.0 3.0 3.0 0.1 3.0 29.0 91.0 3.0 3.0 4.0 29.0
[3289] 29.0 34.0 3.0 0.1 35.0 3.0 3.0 18.0 0.1 29.0 3.0 0.1
[3301] 29.0 3.0 0.7 0.1 3.0 3.0 3.0 0.0 0.2 29.0 0.1 18.0
[3313] 3.0 3.0 4.0 18.0 3.0 0.0 0.1 18.0 29.0 3.0 3.0 3.0
[3325] 3.0 3.0 3.0 3.0 28.0 18.0 25.0 29.0 3.0 3.0 3.0 3.0
[3337] 3.0 3.0 18.0 3.0 18.0 44.0 3.0 3.0 3.0 0.1 18.0 24.0
[3349] 3.0 3.0 18.0 3.0 29.0 3.0 3.0 3.0 30.0 30.0 29.0 18.0
[3361] 0.1 3.0 0.2 2.0 0.8 1.0 3.0 29.0 3.0 29.0 0.3 3.0
[3373] 18.0 3.0 3.0 3.0 13.0 3.0 3.0 3.0 4.0 0.1 3.0 3.0
[3385] 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 4.0 29.0 1.0 29.0
[3397] 29.0 1.0 0.2 3.0 3.0 3.0 29.0 0.2 0.1 29.0 3.0 18.0
[3409] 3.0 1.0 0.3 2.0 0.1 1.0 30.0 29.0 3.0 36.0 18.0 3.0
[3421] 3.0 13.0 29.0 30.0 3.0 0.1 3.0 18.0 3.0 0.6 3.0 3.0
[3433] 12.0 4.0 19.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 29.0 3.0
[3445] 1.0 3.0 0.2 18.0 3.0 0.7 18.0 3.0 3.0 1.0 4.0 2.0
[3457] 35.0 3.0 3.0 3.0 4.0 3.0 3.0 36.0 3.0 29.0 29.0 29.0
[3469] 0.2 3.0 3.0 4.0 30.0 18.0 30.0 30.0 18.0 0.1 3.0 3.0
[3481] 3.0 3.0 38.0 30.0 29.0 3.0 30.0 29.0 29.0 3.0 18.0 3.0
[3493] 1.0 3.0 30.0 0.2 3.0 3.0 0.1 3.0 0.1 4.0 30.0 3.0
[3505] 3.0 29.0 0.2 3.0 3.0 0.1 0.2 29.0 3.0 30.0 3.0 3.0
[3517] 3.0 29.0 0.9 3.0 38.0 30.0 3.0 3.0 0.4 18.0 3.0 30.0
[3529] 0.1 3.0 0.1 3.0 0.2 2.0 3.0 3.0 39.0 3.0 29.0 3.0
[3541] 0.2 18.0 0.7 18.0 2.0 36.0 2.0 3.0 3.0 0.1 18.0 3.0
[3553] 3.0 18.0 3.0 18.0 3.0 36.0 0.2 39.0 5.0 0.2 3.0 0.2
[3565] 3.0 18.0 3.0 3.0 1.0 0.1 0.2 0.2 3.0 3.0 30.0 3.0
[3577] 3.0 29.0 3.0 17.0 0.7 3.0 3.0 0.2 3.0 18.0 3.0 39.0
[3589] 3.0 3.0 3.0 4.0 2.0 29.0 18.0 3.0 3.0 3.0 3.0 3.0
[3601] 3.0 2.0 0.2 3.0 3.0 18.0 30.0 30.0 0.2 18.0 39.0 3.0
[3613] 3.0 0.2 3.0 30.0 3.0 3.0 3.0 4.0 3.0 29.0 29.0 3.0
[3625] 0.4 3.0 3.0 3.0 92.0 3.0 3.0 37.0 3.0 18.0 39.0 29.0
[3637] 0.2 3.0 3.0 1.0 3.0 3.0 0.2 3.0 3.0 14.0 2.0 40.0
[3649] 29.0 18.0 0.2 3.0 0.2 0.9 3.0 3.0 3.0 30.0 2.0 30.0
[3661] 3.0 3.0 1.0 3.0 38.0 18.0 3.0 0.7 3.0 3.0 3.0 4.0
[3673] 29.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 5.0 3.0 3.0
[3685] 3.0 3.0 0.5 30.0 3.0 0.2 3.0 3.0 3.0 0.2 3.0 3.0
[3697] 3.0 3.0 3.0 3.0 5.0 3.0 30.0 30.0 2.0 3.0 3.0 35.0
[3709] 10.0 3.0 3.0 3.0 0.2 18.0 0.2 3.0 3.0 3.0 30.0 18.0
[3721] 30.0 30.0 3.0 1.0 3.0 18.0 18.0 3.0 3.0 3.0 3.0 3.0
[3733] 3.0 18.0 5.0 5.0 30.0 31.0 31.0 30.0 30.0 3.0 4.0 0.4
[3745] 2.0 3.0 0.2 2.0 30.0 30.0 2.0 3.0 3.0 4.0 18.0 4.0
[3757] 0.2 2.0 18.0 4.0 4.0 18.0 0.9 1.0 4.0 4.0 4.0 4.0
[3769] 4.0 4.0 4.0 18.0 4.0 30.0 30.0 4.0 18.0 0.2 4.0 4.0
[3781] 18.0 4.0 1.0 4.0 30.0 4.0 4.0 4.0 4.0 4.0 18.0 0.7
[3793] 4.0 0.6 4.0 4.0 4.0 4.0 3.0 30.0 4.0 30.0 18.0 18.0
[3805] 14.0 4.0 5.0 15.0 30.0 4.0 30.0 0.0 4.0 0.2 4.0 4.0
[3817] 30.0 4.0 40.0 30.0 0.2 0.2 4.0 30.0 4.0 4.0 4.0 25.0
[3829] 4.0 4.0 0.2 30.0 18.0 18.0 4.0 2.0 4.0 4.0 4.0 16.0
[3841] 30.0 4.0 31.0 4.0 1.0 30.0 30.0 0.2 4.0 4.0 4.0 4.0
[3853] 0.2 4.0 0.2 4.0 18.0 4.0 41.0 30.0 4.0 0.2 38.0 18.0
[3865] 31.0 4.0 4.0 30.0 15.0 4.0 4.0 0.2 18.0 0.6 1.0 0.2
[3877] 30.0 30.0 30.0 4.0 4.0 3.0 3.0 0.2 4.0 4.0 4.0 5.0
[3889] 4.0 30.0 4.0 18.0 18.0 25.0 5.0 4.0 30.0 4.0 0.9 4.0
[3901] 25.0 4.0 30.0 30.0 31.0 30.0 4.0 30.0 4.0 30.0 4.0 4.0
[3913] 2.0 30.0 4.0 4.0 4.0 4.0 4.0 4.0 30.0 4.0 4.0 4.0
[3925] 4.0 30.0 4.0 4.0 4.0 5.0 5.0 4.0 30.0 30.0 4.0 30.0
[3937] 1.0 0.0 4.0 0.2 4.0 4.0 4.0 4.0 1.0 4.0 4.0 41.0
[3949] 0.2 37.0 4.0 0.2 4.0 18.0 4.0 4.0 4.0 0.2 30.0 18.0
[3961] 18.0 30.0 4.0 4.0 4.0 4.0 4.0 30.0 3.0 4.0 4.0 4.0
[3973] 1.0 0.5 26.0 4.0 30.0 39.0 4.0 0.2 4.0 0.2 30.0 30.0
[3985] 3.0 18.0 41.0 4.0 30.0 30.0 0.2 4.0 4.0 4.0 4.0 4.0
[3997] 0.2 4.0 4.0 30.0 4.0 18.0 4.0 0.2 30.0 4.0 0.2 1.0
[4009] 0.2 4.0 4.0 2.0 18.0 4.0 4.0 0.5 4.0 4.0 4.0 30.0
[4021] 2.0 4.0 4.0 4.0 30.0 4.0 4.0 4.0 4.0 4.0 2.0 30.0
[4033] 4.0 4.0 1.0 4.0 0.2 0.2 4.0 4.0 2.0 18.0 4.0 4.0
[4045] 0.2 4.0 3.0 30.0 4.0 0.2 4.0 4.0 18.0 4.0 5.0 15.0
[4057] 3.0 31.0 31.0 4.0 4.0 4.0 31.0 0.2 18.0 4.0 30.0 31.0
[4069] 31.0 4.0 2.0 4.0 4.0 39.0 4.0 1.0 4.0 4.0 1.0 91.0
[4081] 31.0 24.0 31.0 25.0 32.0 4.0 0.2 12.0 0.2 4.0 0.2 18.0
[4093] 4.0 4.0 4.0 4.0 4.0 4.0 4.0 18.0 31.0 4.0 31.0 18.0
[4105] 4.0 18.0 0.2 18.0 31.0 31.0 18.0 42.0 0.0 4.0 4.0 18.0
[4117] 25.0 4.0 30.0 0.2 4.0 4.0 4.0 31.0 30.0 30.0 0.2 1.0
[4129] 4.0 4.0 31.0 30.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0
[4141] 18.0 21.0 0.2 4.0 18.0 42.0 13.0 7.0 24.0 31.0 4.0 30.0
[4153] 4.0 39.0 4.0 4.0 0.2 4.0 4.0 4.0 0.2 0.0 4.0 4.0
[4165] 4.0 4.0 4.0 4.0 4.0 2.0 31.0 4.0 4.0 32.0 1.0 4.0
[4177] 4.0 4.0 0.5 4.0 4.0 0.2 32.0 18.0 31.0 0.2 31.0 0.7
[4189] 4.0 18.0 18.0 92.0 4.0 30.0 0.2 4.0 4.0 4.0 4.0 4.0
[4201] 4.0 0.2 4.0 0.2 0.0 4.0 4.0 18.0 0.2 13.0 4.0 4.0
[4213] 4.0 31.0 31.0 4.0 5.0 5.0 0.2 31.0 31.0 4.0 4.0 4.0
[4225] 39.0 4.0 4.0 18.0 4.0 31.0 31.0 31.0 32.0 32.0 0.2 4.0
[4237] 0.2 0.2 4.0 30.0 1.0 0.2 4.0 4.0 31.0 39.0 1.0 0.2
[4249] 4.0 18.0 30.0 31.0 3.0 4.0 4.0 4.0 18.0 18.0 4.0 18.0
[4261] 4.0 4.0 4.0 0.2 4.0 0.5 1.0 1.0 3.0 42.0 0.2 4.0
[4273] 4.0 18.0 0.2 3.0 1.0 2.0 31.0 30.0 0.2 4.0 30.0 26.0
[4285] 18.0 31.0 30.0 18.0 4.0 4.0 4.0 31.0 0.2 4.0 4.0 0.4
[4297] 4.0 32.0 32.0 33.0 31.0 4.0 31.0 4.0 4.0 0.2 4.0 4.0
[4309] 4.0 4.0 3.0 4.0 31.0 31.0 4.0 4.0 39.0 0.2 5.0 4.0
[4321] 4.0 4.0 4.0 4.0 31.0 4.0 4.0 5.0 31.0 31.0 2.0 4.0
[4333] 4.0 4.0 0.2 4.0 4.0 4.0 4.0 4.0 5.0 31.0 31.0 4.0
[4345] 18.0 4.0 4.0 4.0 4.0 4.0 18.0 4.0 0.2 4.0 4.0 4.0
[4357] 4.0 27.0 4.0 31.0 4.0 4.0 0.2 1.0 4.0 1.0 4.0 0.2
[4369] 4.0 0.3 3.0 4.0 0.2 5.0 15.0 4.0 4.0 0.2 3.0 18.0
[4381] 4.0 31.0 18.0 18.0 18.0 4.0 4.0 31.0 1.0 4.0 4.0 4.0
[4393] 39.0 4.0 4.0 0.2 6.0 18.0 31.0 43.0 4.0 4.0 4.0 31.0
[4405] 2.0 4.0 4.0 4.0 4.0 4.0 4.0 2.0 4.0 4.0 31.0 1.0
[4417] 4.0 3.0 18.0 1.0 31.0 0.2 4.0 2.0 2.0 4.0 6.0 5.0
[4429] 31.0 18.0 43.0 4.0 19.0 31.0 4.0 4.0 3.0 31.0 31.0 0.2
[4441] 31.0 2.0 4.0 31.0 4.0 1.0 4.0 18.0 27.0 0.2 31.0 25.0
[4453] 1.0 31.0 1.0 4.0 1.0 4.0 18.0 30.0 23.0 32.0 4.0 31.0
[4465] 0.2 0.2 0.1 1.0 31.0 0.2 2.0 18.0 4.0 19.0 4.0 2.0
[4477] 4.0 4.0 31.0 18.0 4.0 31.0 0.2 0.1 4.0 0.2 4.0 4.0
[4489] 4.0 4.0 4.0 4.0 31.0 4.0 0.2 1.0 18.0 23.0 31.0 0.2
[4501] 18.0 31.0 31.0 31.0 31.0 4.0 4.0 18.0 4.0 4.0 4.0 4.0
[4513] 4.0 4.0 4.0 0.2 3.0 4.0 18.0 4.0 4.0 2.0 31.0 31.0
[4525] 0.2 18.0 4.0 18.0 4.0 4.0 4.0 0.2 6.0 4.0 0.2 26.0
[4537] 1.0 0.2 18.0 4.0 19.0 4.0 4.0 4.0 4.0 0.2 0.8 4.0
[4549] 4.0 4.0 4.0 4.0 2.0 4.0 32.0 1.0 4.0 0.2 18.0 4.0
[4561] 0.2 4.0 4.0 4.0 4.0 6.0 32.0 4.0 31.0 31.0 18.0 4.0
[4573] 4.0 1.0 18.0 4.0 4.0 4.0 4.0 0.6 32.0 19.0 0.2 32.0
[4585] 19.0 4.0 4.0 0.2 4.0 4.0 31.0 4.0 4.0 4.0 4.0 32.0
[4597] 1.0 18.0 4.0 4.0 4.0 0.2 4.0 4.0 4.0 0.2 4.0 0.2
[4609] 19.0 4.0 4.0 1.0 4.0 4.0 4.0 4.0 4.0 4.0 18.0 27.0
[4621] 31.0 4.0 32.0 4.0 0.2 4.0 4.0 2.0 19.0 4.0 1.0 7.0
[4633] 4.0 32.0 4.0 4.0 32.0 31.0 1.0 31.0 0.2 19.0 0.6 4.0
[4645] 0.2 4.0 0.2 19.0 6.0 4.0 0.2 31.0 4.0 4.0 18.0 0.2
[4657] 4.0 31.0 4.0 44.0 4.0 4.0 4.0 4.0 18.0 19.0 33.0 32.0
[4669] 4.0 31.0 4.0 32.0 0.2 23.0 0.2 4.0 4.0 4.0 7.0 27.0
[4681] 1.0 31.0 31.0 0.2 18.0 4.0 4.0 1.0 4.0 4.0 4.0 4.0
[4693] 4.0 4.0 4.0 4.0 19.0 4.0 4.0 32.0 4.0 31.0 4.0 4.0
[4705] 3.0 0.2 1.0 4.0 32.0 4.0 4.0 0.2 0.2 4.0 0.2 32.0
[4717] 91.0 4.0 4.0 32.0 1.0 4.0 18.0 4.0 4.0 4.0 32.0 18.0
[4729] 2.0 32.0 4.0 2.0 4.0 40.0 0.2 0.2 4.0 4.0 1.0 0.9
[4741] 0.2 4.0 2.0 4.0 31.0 1.0 0.2 45.0 4.0 31.0 29.0 4.0
[4753] 4.0 4.0 4.0 4.0 18.0 4.0 4.0 4.0 0.2 4.0 4.0 4.0
[4765] 4.0 6.0 0.0 4.0 4.0 4.0 0.2 4.0 4.0 0.2 0.2 4.0
[4777] 4.0 32.0 4.0 4.0 31.0 4.0 4.0 4.0 4.0 2.0 0.2 2.0
[4789] 4.0 7.0 4.0 4.0 4.0 4.0 4.0 2.0 31.0 4.0 1.0 4.0
[4801] 29.0 4.0 0.2 4.0 31.0 2.0 4.0 4.0 6.0 4.0 29.0 32.0
[4813] 2.0 0.2 32.0 4.0 4.0 0.7 32.0 32.0 4.0 18.0 31.0 4.0
[4825] 4.0 4.0 2.0 0.2 0.1 4.0 5.0 18.0 0.2 31.0 5.0 4.0
[4837] 40.0 26.0 5.0 4.0 5.0 4.0 5.0 5.0 19.0 5.0 21.0 32.0
[4849] 32.0 19.0 0.2 5.0 5.0 5.0 5.0 0.2 46.0 5.0 5.0 5.0
[4861] 5.0 2.0 7.0 18.0 32.0 33.0 32.0 5.0 13.0 5.0 5.0 18.0
[4873] 5.0 5.0 5.0 33.0 5.0 5.0 0.0 0.2 0.7 5.0 1.0 5.0
[4885] 31.0 14.0 5.0 31.0 5.0 2.0 0.2 5.0 1.0 1.0 5.0 5.0
[4897] 5.0 5.0 31.0 2.0 5.0 0.2 0.2 5.0 0.2 5.0 33.0 18.0
[4909] 5.0 0.3 31.0 0.2 5.0 5.0 32.0 5.0 0.3 5.0 3.0 0.2
[4921] 0.3 5.0 0.2 5.0 32.0 5.0 2.0 18.0 5.0 5.0 32.0 46.0
[4933] 5.0 0.3 5.0 20.0 28.0 27.0 5.0 0.3 10.0 5.0 5.0 5.0
[4945] 19.0 5.0 5.0 0.4 0.2 32.0 32.0 5.0 0.3 5.0 5.0 32.0
[4957] 5.0 32.0 0.2 3.0 5.0 0.6 5.0 19.0 32.0 0.3 5.0 5.0
[4969] 4.0 5.0 5.0 5.0 1.0 0.2 41.0 5.0 18.0 19.0 46.0 18.0
[4981] 5.0 5.0 5.0 0.3 5.0 5.0 32.0 5.0 5.0 5.0 5.0 18.0
[4993] 5.0 5.0 2.0 29.0 32.0 32.0 5.0 5.0 5.0 5.0 5.0 0.2
[5005] 33.0 39.0 34.0 32.0 18.0 5.0 5.0 4.0 0.3 5.0 5.0 5.0
[5017] 5.0 41.0 5.0 2.0 0.5 7.0 0.2 19.0 31.0 32.0 15.0 41.0
[5029] 5.0 5.0 32.0 5.0 29.0 5.0 2.0 5.0 18.0 32.0 3.0 33.0
[5041] 32.0 5.0 5.0 19.0 5.0 5.0 2.0 5.0 14.0 19.0 32.0 19.0
[5053] 5.0 1.0 2.0 5.0 47.0 5.0 5.0 12.0 32.0 32.0 11.0 32.0
[5065] 0.2 5.0 1.0 8.0 5.0 5.0 0.6 41.0 0.3 2.0 2.0 19.0
[5077] 5.0 5.0 48.0 19.0 5.0 5.0 5.0 0.4 0.2 32.0 5.0 0.2
[5089] 32.0 0.2 48.0 0.2 32.0 5.0 5.0 5.0 19.0 0.5 0.2 0.3
[5101] 5.0 32.0 18.0 1.0 4.0 5.0 0.3 0.3 5.0 5.0 5.0 5.0
[5113] 2.0 32.0 5.0 5.0 5.0 0.3 32.0 0.3 5.0 5.0 5.0 5.0
[5125] 5.0 5.0 5.0 5.0 18.0 48.0 0.3 0.3 5.0 5.0 5.0 48.0
[5137] 38.0 0.4 8.0 1.0 32.0 0.3 32.0 5.0 5.0 18.0 5.0 5.0
[5149] 41.0 32.0 5.0 26.0 32.0 1.0 5.0 0.3 32.0 0.3 5.0 5.0
[5161] 5.0 3.0 9.0 14.0 18.0 5.0 5.0 5.0 5.0 1.0 1.0 5.0
[5173] 43.0 15.0 0.3 6.0 33.0 26.0 5.0 0.3 32.0 5.0 5.0 9.0
[5185] 5.0 5.0 1.0 0.0 5.0 1.0 5.0 1.0 5.0 5.0 43.0 0.5
[5197] 32.0 32.0 0.3 0.3 0.3 1.0 0.8 32.0 32.0 5.0 5.0 5.0
[5209] 5.0 5.0 5.0 0.3 32.0 5.0 32.0 0.3 32.0 1.0 5.0 5.0
[5221] 5.0 0.3 18.0 2.0 3.0 5.0 5.0 2.0 5.0 32.0 32.0 5.0
[5233] 5.0 32.0 32.0 5.0 5.0 5.0 5.0 5.0 5.0 32.0 44.0 5.0
[5245] 3.0 32.0 6.0 1.0 5.0 0.3 5.0 4.0 5.0 32.0 18.0 0.3
[5257] 45.0 5.0 5.0 5.0 1.0 5.0 1.0 18.0 5.0 6.0 18.0 5.0
[5269] 5.0 32.0 5.0 19.0 0.3 7.0 0.3 4.0 33.0 1.0 4.0 5.0
[5281] 16.0 18.0 0.3 18.0 5.0 5.0 2.0 49.0 5.0 34.0 34.0 35.0
[5293] 35.0 43.0 5.0 47.0 5.0 0.7 9.0 5.0 9.0 1.0 0.3 32.0
[5305] 32.0 32.0 5.0 0.3 1.0 5.0 1.0 5.0 0.3 5.0 47.0 119.0
[5317] 5.0 9.0 5.0 5.0 5.0 5.0 5.0 18.0 5.0 5.0 32.0 0.2
[5329] 5.0 0.3 5.0 5.0 12.0 5.0 33.0 5.0 33.0 0.3 5.0 32.0
[5341] 5.0 16.0 5.0 5.0 1.0 18.0 19.0 19.0 29.0 5.0 5.0 32.0
[5353] 5.0 16.0 5.0 5.0 10.0 0.3 5.0 5.0 32.0 33.0 5.0 47.0
[5365] 51.0 5.0 0.3 0.3 5.0 48.0 1.0 5.0 0.3 5.0 0.3 5.0
[5377] 3.0 0.3 2.0 1.0 5.0 35.0 35.0 36.0 33.0 32.0 33.0 5.0
[5389] 5.0 5.0 51.0 1.0 5.0 2.0 3.0 5.0 5.0 3.0 5.0 5.0
[5401] 5.0 51.0 2.0 5.0 5.0 5.0 32.0 1.0 33.0 5.0 4.0 5.0
[5413] 2.0 33.0 5.0 9.0 18.0 29.0 1.0 2.0 5.0 18.0 5.0 5.0
[5425] 0.3 5.0 5.0 5.0 5.0 5.0 5.0 5.0 33.0 5.0 5.0 4.0
[5437] 5.0 0.3 5.0 14.0 5.0 5.0 33.0 2.0 5.0 2.0 33.0 5.0
[5449] 5.0 33.0 0.0 33.0 0.6 5.0 18.0 5.0 5.0 5.0 5.0 2.0
[5461] 5.0 5.0 5.0 5.0 33.0 7.0 5.0 33.0 5.0 19.0 51.0 5.0
[5473] 18.0 19.0 0.3 5.0 18.0 5.0 33.0 19.0 5.0 5.0 5.0 18.0
[5485] 49.0 1.0 0.3 5.0 5.0 9.0 29.0 0.3 33.0 5.0 5.0 0.3
[5497] 0.3 5.0 0.3 9.0 5.0 33.0 0.3 5.0 3.0 19.0 33.0 0.3
[5509] 5.0 0.3 0.0 5.0 0.3 3.0 5.0 0.3 0.5 0.4 5.0 5.0
[5521] 9.0 5.0 10.0 24.0 36.0 37.0 4.0 33.0 5.0 5.0 15.0 5.0
[5533] 2.0 0.3 3.0 4.0 5.0 33.0 5.0 5.0 33.0 5.0 33.0 19.0
[5545] 5.0 5.0 5.0 5.0 1.0 5.0 5.0 5.0 14.0 4.0 33.0 5.0
[5557] 5.0 5.0 19.0 1.0 0.3 5.0 5.0 15.0 33.0 5.0 5.0 5.0
[5569] 0.3 5.0 5.0 49.0 0.0 16.0 5.0 0.0 33.0 5.0 5.0 5.0
[5581] 19.0 50.0 5.0 33.0 2.0 33.0 33.0 5.0 19.0 4.0 5.0 5.0
[5593] 5.0 7.0 1.0 5.0 5.0 33.0 5.0 5.0 0.3 5.0 0.3 5.0
[5605] 1.0 2.0 5.0 18.0 33.0 33.0 30.0 33.0 0.3 5.0 5.0 5.0
[5617] 0.2 41.0 5.0 19.0 18.0 4.0 10.0 33.0 5.0 19.0 1.0 5.0
[5629] 33.0 0.9 5.0 33.0 5.0 10.0 5.0 5.0 4.0 5.0 5.0 51.0
[5641] 5.0 5.0 5.0 0.5 5.0 4.0 33.0 5.0 5.0 19.0 5.0 5.0
[5653] 5.0 33.0 5.0 33.0 33.0 2.0 5.0 5.0 5.0 5.0 0.2 5.0
[5665] 5.0 33.0 33.0 51.0 5.0 5.0 19.0 4.0 5.0 33.0 0.3 5.0
[5677] 5.0 19.0 0.3 7.0 37.0 37.0 5.0 28.0 19.0 5.0 5.0 0.1
[5689] 0.3 1.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 51.0 0.3
[5701] 52.0 2.0 14.0 10.0 5.0 5.0 5.0 5.0 0.6 5.0 7.0 5.0
[5713] 0.3 2.0 4.0 5.0 0.6 15.0 5.0 5.0 51.0 5.0 5.0 0.3
[5725] 33.0 19.0 33.0 5.0 5.0 5.0 33.0 5.0 5.0 5.0 5.0 5.0
[5737] 2.0 19.0 19.0 5.0 0.6 33.0 0.3 5.0 3.0 5.0 0.1 18.0
[5749] 0.3 0.0 5.0 0.3 4.0 5.0 51.0 33.0 0.3 3.0 5.0 5.0
[5761] 0.3 33.0 33.0 5.0 0.3 5.0 5.0 19.0 4.0 0.6 5.0 19.0
[5773] 19.0 0.1 0.3 5.0 7.0 14.0 29.0 33.0 5.0 33.0 0.3 6.0
[5785] 2.0 33.0 6.0 0.3 6.0 6.0 5.0 5.0 6.0 1.0 33.0 2.0
[5797] 1.0 5.0 0.3 10.0 0.7 6.0 1.0 6.0 5.0 52.0 51.0 6.0
[5809] 6.0 33.0 18.0 6.0 6.0 6.0 0.3 6.0 6.0 6.0 0.0 6.0
[5821] 14.0 8.0 2.0 6.0 6.0 6.0 6.0 33.0 0.3 6.0 6.0 6.0
[5833] 6.0 6.0 6.0 6.0 30.0 18.0 33.0 33.0 6.0 0.3 15.0 6.0
[5845] 0.2 6.0 6.0 2.0 0.3 19.0 6.0 6.0 6.0 6.0 6.0 6.0
[5857] 6.0 6.0 6.0 6.0 52.0 0.3 6.0 6.0 33.0 6.0 0.3 33.0
[5869] 6.0 0.4 33.0 18.0 0.3 0.3 0.3 19.0 92.0 6.0 4.0 0.6
[5881] 19.0 18.0 6.0 1.0 6.0 6.0 0.3 6.0 6.0 0.5 6.0 6.0
[5893] 0.2 0.3 2.0 33.0 0.0 0.3 0.4 6.0 6.0 33.0 53.0 6.0
[5905] 0.3 6.0 6.0 6.0 6.0 18.0 33.0 0.2 4.0 6.0 0.3 0.4
[5917] 6.0 6.0 6.0 19.0 6.0 6.0 6.0 0.2 33.0 6.0 2.0 6.0
[5929] 4.0 6.0 6.0 0.4 19.0 6.0 3.0 19.0 6.0 6.0 2.0 0.5
[5941] 33.0 6.0 2.0 6.0 6.0 6.0 6.0 0.3 2.0 33.0 33.0 33.0
[5953] 33.0 33.0 5.0 52.0 52.0 19.0 6.0 6.0 6.0 52.0 0.3 30.0
[5965] 0.2 6.0 6.0 1.0 33.0 19.0 6.0 1.0 19.0 6.0 6.0 6.0
[5977] 1.0 6.0 53.0 33.0 6.0 19.0 2.0 6.0 6.0 6.0 6.0 4.0
[5989] 0.8 27.0 33.0 53.0 6.0 0.2 4.0 0.5 19.0 6.0 0.3 0.3
[6001] 25.0 6.0 34.0 0.2 92.0 6.0 30.0 6.0 6.0 6.0 33.0 30.0
[6013] 6.0 0.3 7.0 8.0 10.0 27.0 34.0 34.0 6.0 0.3 6.0 6.0
[6025] 4.0 6.0 19.0 0.1 19.0 19.0 4.0 19.0 6.0 6.0 34.0 10.0
[6037] 0.1 6.0 34.0 6.0 2.0 0.8 6.0 4.0 6.0 18.0 13.0 6.0
[6049] 2.0 3.0 19.0 6.0 6.0 93.0 0.3 6.0 34.0 34.0 34.0 19.0
[6061] 6.0 2.0 34.0 1.0 6.0 6.0 19.0 6.0 34.0 6.0 6.0 0.3
[6073] 6.0 34.0 53.0 6.0 34.0 6.0 1.0 2.0 2.0 6.0 1.0 6.0
[6085] 6.0 6.0 6.0 19.0 6.0 6.0 34.0 6.0 6.0 6.0 6.0 34.0
[6097] 6.0 4.0 1.0 0.3 6.0 0.3 0.9 34.0 34.0 6.0 54.0 16.0
[6109] 19.0 6.0 0.3 0.4 6.0 20.0 6.0 6.0 34.0 6.0 0.6 1.0
[6121] 0.4 6.0 6.0 6.0 6.0 0.3 8.0 34.0 0.2 2.0 1.0 19.0
[6133] 34.0 6.0 6.0 6.0 6.0 6.0 6.0 0.3 34.0 1.0 6.0 6.0
[6145] 1.0 0.3 6.0 10.0 6.0 6.0 6.0 6.0 34.0 0.0 0.3 6.0
[6157] 1.0 6.0 0.3 19.0 34.0 0.4 6.0 34.0 6.0 0.3 6.0 0.5
[6169] 0.3 6.0 0.4 6.0 6.0 5.0 6.0 6.0 6.0 1.0 0.4 6.0
[6181] 6.0 6.0 10.0 2.0 1.0 34.0 6.0 18.0 53.0 6.0 6.0 6.0
[6193] 30.0 19.0 6.0 10.0 0.4 34.0 2.0 6.0 6.0 34.0 2.0 55.0
[6205] 19.0 6.0 34.0 6.0 6.0 6.0 6.0 0.3 18.0 6.0 4.0 18.0
[6217] 34.0 6.0 34.0 0.3 18.0 1.0 34.0 6.0 6.0 6.0 0.4 6.0
[6229] 34.0 6.0 6.0 0.9 6.0 0.3 1.0 6.0 1.0 55.0 6.0 0.9
[6241] 6.0 6.0 6.0 14.0 33.0 37.0 28.0 6.0 6.0 2.0 34.0 6.0
[6253] 34.0 1.0 6.0 28.0 0.3 6.0 6.0 6.0 0.3 6.0 18.0 6.0
[6265] 19.0 8.0 30.0 34.0 6.0 27.0 56.0 10.0 19.0 34.0 30.0 6.0
[6277] 0.3 6.0 6.0 6.0 0.2 34.0 1.0 6.0 0.7 6.0 0.4 6.0
[6289] 6.0 0.4 2.0 6.0 34.0 6.0 6.0 19.0 33.0 34.0 19.0 19.0
[6301] 4.0 92.0 6.0 6.0 57.0 0.4 34.0 2.0 6.0 34.0 19.0 34.0
[6313] 34.0 1.0 6.0 6.0 6.0 34.0 6.0 6.0 19.0 6.0 6.0 6.0
[6325] 6.0 6.0 2.0 34.0 0.2 6.0 2.0 6.0 19.0 6.0 4.0 0.3
[6337] 6.0 0.6 0.3 6.0 6.0 6.0 6.0 34.0 4.0 4.0 6.0 6.0
[6349] 19.0 19.0 1.0 34.0 5.0 0.3 0.4 6.0 6.0 6.0 6.0 6.0
[6361] 6.0 19.0 6.0 10.0 10.0 34.0 6.0 0.1 32.0 6.0 6.0 19.0
[6373] 6.0 2.0 34.0 30.0 2.0 34.0 34.0 6.0 6.0 34.0 6.0 0.4
[6385] 0.1 6.0 1.0 34.0 18.0 0.4 6.0 1.0 6.0 6.0 0.3 19.0
[6397] 6.0 6.0 6.0 0.4 0.4 3.0 6.0 19.0 6.0 6.0 6.0 4.0
[6409] 0.4 0.4 34.0 2.0 6.0 0.4 0.4 34.0 54.0 1.0 0.4 6.0
[6421] 19.0 6.0 1.0 8.0 34.0 34.0 4.0 6.0 34.0 0.4 6.0 5.0
[6433] 6.0 0.4 1.0 19.0 6.0 6.0 6.0 6.0 6.0 8.0 33.0 34.0
[6445] 1.0 0.4 6.0 6.0 19.0 6.0 6.0 0.4 0.4 0.4 34.0 34.0
[6457] 6.0 0.5 35.0 3.0 6.0 6.0 1.0 4.0 6.0 35.0 0.4 6.0
[6469] 0.3 6.0 6.0 19.0 0.4 57.0 0.8 6.0 6.0 6.0 1.0 46.0
[6481] 6.0 6.0 19.0 34.0 18.0 5.0 0.4 57.0 6.0 6.0 6.0 6.0
[6493] 2.0 16.0 8.0 6.0 19.0 0.8 0.4 5.0 6.0 58.0 6.0 6.0
[6505] 6.0 35.0 6.0 35.0 6.0 6.0 6.0 6.0 0.3 54.0 0.4 0.4
[6517] 35.0 19.0 6.0 0.4 0.4 35.0 6.0 6.0 6.0 0.1 6.0 0.4
[6529] 6.0 15.0 28.0 7.0 5.0 6.0 34.0 19.0 0.4 0.4 0.4 6.0
[6541] 2.0 2.0 6.0 6.0 0.1 1.0 35.0 19.0 6.0 0.4 6.0 6.0
[6553] 58.0 31.0 6.0 6.0 6.0 18.0 2.0 6.0 35.0 6.0 1.0 6.0
[6565] 6.0 6.0 0.4 35.0 3.0 35.0 0.4 6.0 35.0 93.0 6.0 6.0
[6577] 0.4 6.0 6.0 6.0 18.0 6.0 32.0 19.0 0.4 6.0 6.0 19.0
[6589] 34.0 38.0 38.0 34.0 6.0 6.0 19.0 1.0 0.4 6.0 54.0 18.0
[6601] 6.0 23.0 19.0 6.0 6.0 6.0 6.0 31.0 18.0 34.0 34.0 18.0
[6613] 35.0 6.0 19.0 6.0 2.0 34.0 0.4 6.0 1.0 6.0 1.0 6.0
[6625] 6.0 35.0 35.0 4.0 6.0 0.2 6.0 0.4 6.0 0.4 6.0 2.0
[6637] 6.0 2.0 6.0 2.0 6.0 0.4 0.3 0.4 6.0 55.0 19.0 6.0
[6649] 34.0 5.0 6.0 6.0 0.4 0.4 6.0 19.0 34.0 10.0 35.0 6.0
[6661] 6.0 6.0 2.0 34.0 0.4 34.0 6.0 2.0 6.0 6.0 2.0 6.0
[6673] 35.0 34.0 0.2 3.0 18.0 6.0 6.0 5.0 0.4 6.0 35.0 6.0
[6685] 0.4 6.0 6.0 34.0 6.0 6.0 35.0 58.0 35.0 6.0 0.7 6.0
[6697] 6.0 4.0 4.0 6.0 0.7 6.0 35.0 4.0 19.0 6.0 31.0 4.0
[6709] 6.0 6.0 5.0 17.0 2.0 6.0 0.8 2.0 5.0 2.0 6.0 1.0
[6721] 91.0 6.0 6.0 6.0 4.0 59.0 6.0 7.0 2.0 93.0 59.0 18.0
[6733] 0.0 6.0 6.0 7.0 35.0 35.0 35.0 7.0 35.0 55.0 0.8 6.0
[6745] 6.0 0.4 7.0 6.0 6.0 6.0 34.0 38.0 35.0 7.0 7.0 2.0
[6757] 7.0 19.0 7.0 7.0 0.4 7.0 35.0 35.0 35.0 35.0 35.0 7.0
[6769] 7.0 7.0 5.0 7.0 35.0 6.0 0.4 31.0 3.0 8.0 7.0 0.0
[6781] 6.0 0.4 19.0 35.0 0.4 7.0 7.0 0.4 7.0 18.0 7.0 7.0
[6793] 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 2.0
[6805] 7.0 0.8 7.0 2.0 10.0 0.4 35.0 7.0 0.2 0.4 7.0 7.0
[6817] 7.0 0.7 55.0 7.0 49.0 0.4 7.0 2.0 7.0 7.0 2.0 6.0
[6829] 33.0 35.0 0.4 1.0 7.0 7.0 7.0 7.0 7.0 7.0 30.0 7.0
[6841] 7.0 4.0 0.1 35.0 7.0 0.1 7.0 7.0 7.0 4.0 0.4 35.0
[6853] 7.0 7.0 35.0 0.4 0.4 7.0 7.0 59.0 0.0 35.0 0.4 7.0
[6865] 7.0 0.4 0.4 19.0 4.0 7.0 7.0 7.0 7.0 7.0 35.0 35.0
[6877] 7.0 7.0 6.0 7.0 0.4 7.0 7.0 1.0 7.0 7.0 7.0 59.0
[6889] 34.0 2.0 31.0 35.0 18.0 7.0 0.7 4.0 7.0 0.4 7.0 7.0
[6901] 7.0 7.0 7.0 0.4 7.0 7.0 7.0 19.0 5.0 19.0 7.0 8.0
[6913] 34.0 35.0 0.4 0.4 35.0 2.0 31.0 19.0 19.0 7.0 7.0 19.0
[6925] 0.1 0.5 35.0 0.4 0.4 7.0 1.0 7.0 7.0 35.0 19.0 7.0
[6937] 7.0 0.5 7.0 0.9 9.0 19.0 0.9 7.0 0.9 7.0 18.0 32.0
[6949] 0.4 0.8 19.0 3.0 7.0 7.0 7.0 7.0 7.0 7.0 35.0 19.0
[6961] 7.0 7.0 7.0 7.0 7.0 2.0 7.0 0.4 7.0 35.0 35.0 35.0
[6973] 0.4 7.0 7.0 7.0 7.0 19.0 7.0 6.0 1.0 0.4 10.0 11.0
[6985] 19.0 35.0 1.0 19.0 1.0 7.0 7.0 0.4 7.0 7.0 7.0 7.0
[6997] 0.4 7.0 19.0 7.0 7.0 7.0 19.0 7.0 19.0 1.0 7.0 7.0
[7009] 7.0 7.0 1.0 0.8 0.3 0.4 7.0 4.0 7.0 7.0 0.2 7.0
[7021] 11.0 35.0 19.0 7.0 19.0 7.0 7.0 7.0 19.0 9.0 2.0 32.0
[7033] 7.0 7.0 7.0 56.0 7.0 0.4 7.0 7.0 1.0 7.0 7.0 7.0
[7045] 0.4 7.0 19.0 11.0 35.0 7.0 0.4 1.0 7.0 7.0 19.0 7.0
[7057] 1.0 0.9 7.0 7.0 10.0 36.0 7.0 35.0 56.0 7.0 7.0 7.0
[7069] 7.0 19.0 0.4 5.0 7.0 1.0 9.0 34.0 38.0 7.0 4.0 0.4
[7081] 7.0 17.0 0.4 11.0 9.0 7.0 35.0 12.0 1.0 7.0 94.0 0.4
[7093] 18.0 0.4 2.0 5.0 0.4 18.0 3.0 7.0 7.0 19.0 0.4 7.0
[7105] 1.0 7.0 7.0 7.0 7.0 2.0 19.0 7.0 19.0 8.0 35.0 36.0
[7117] 19.0 0.6 0.4 7.0 17.0 0.1 0.4 7.0 36.0 7.0 1.0 0.1
[7129] 7.0 0.4 7.0 6.0 2.0 36.0 35.0 35.0 31.0 7.0 7.0 7.0
[7141] 7.0 7.0 35.0 0.4 7.0 0.4 7.0 7.0 7.0 7.0 0.5 7.0
[7153] 7.0 7.0 7.0 36.0 7.0 0.4 7.0 9.0 9.0 7.0 7.0 59.0
[7165] 35.0 7.0 0.0 36.0 7.0 7.0 7.0 0.4 7.0 19.0 18.0 34.0
[7177] 91.0 0.4 35.0 19.0 7.0 0.4 7.0 6.0 7.0 7.0 0.5 4.0
[7189] 1.0 7.0 7.0 0.4 7.0 0.8 16.0 11.0 7.0 35.0 36.0 7.0
[7201] 7.0 0.8 6.0 39.0 39.0 35.0 19.0 19.0 36.0 7.0 7.0 7.0
[7213] 4.0 1.0 0.4 7.0 4.0 0.5 0.4 36.0 7.0 7.0 1.0 7.0
[7225] 7.0 35.0 17.0 35.0 28.0 0.4 7.0 7.0 0.0 7.0 7.0 17.0
[7237] 36.0 5.0 7.0 7.0 35.0 18.0 0.1 6.0 7.0 1.0 34.0 32.0
[7249] 18.0 7.0 7.0 20.0 7.0 35.0 0.4 7.0 7.0 0.4 7.0 7.0
[7261] 36.0 7.0 7.0 7.0 35.0 7.0 36.0 7.0 93.0 1.0 7.0 3.0
[7273] 36.0 19.0 7.0 18.0 27.0 6.0 7.0 7.0 31.0 32.0 34.0 19.0
[7285] 9.0 7.0 7.0 7.0 7.0 2.0 7.0 7.0 0.4 0.4 0.9 6.0
[7297] 30.0 0.4 7.0 7.0 0.5 7.0 19.0 5.0 19.0 36.0 7.0 7.0
[7309] 7.0 57.0 11.0 0.5 7.0 18.0 7.0 35.0 7.0 7.0 5.0 7.0
[7321] 7.0 7.0 35.0 36.0 7.0 35.0 1.0 0.5 0.4 0.4 0.0 7.0
[7333] 7.0 7.0 35.0 59.0 7.0 35.0 11.0 7.0 7.0 32.0 11.0 64.0
[7345] 7.0 36.0 7.0 0.5 36.0 7.0 7.0 7.0 7.0 32.0 7.0 7.0
[7357] 7.0 35.0 18.0 0.4 7.0 7.0 7.0 0.4 36.0 35.0 2.0 36.0
[7369] 7.0 19.0 19.0 57.0 5.0 7.0 0.4 0.1 7.0 59.0 0.5 7.0
[7381] 36.0 35.0 4.0 7.0 0.5 19.0 0.4 0.5 0.0 35.0 35.0 19.0
[7393] 7.0 3.0 36.0 7.0 19.0 7.0 0.5 36.0 35.0 6.0 7.0 7.0
[7405] 6.0 9.0 11.0 8.0 7.0 8.0 7.0 8.0 0.4 58.0 7.0 8.0
[7417] 35.0 36.0 58.0 0.9 8.0 7.0 8.0 7.0 18.0 8.0 36.0 31.0
[7429] 7.0 36.0 5.0 8.0 7.0 7.0 8.0 35.0 7.0 29.0 36.0 35.0
[7441] 7.0 8.0 8.0 18.0 36.0 0.5 7.0 7.0 7.0 0.5 36.0 7.0
[7453] 60.0 8.0 4.0 0.6 0.5 7.0 9.0 8.0 0.3 36.0 16.0 8.0
[7465] 19.0 0.5 8.0 7.0 8.0 7.0 14.0 8.0 36.0 36.0 7.0 1.0
[7477] 8.0 59.0 59.0 8.0 36.0 8.0 36.0 59.0 8.0 0.3 7.0 7.0
[7489] 0.5 33.0 19.0 7.0 8.0 0.6 7.0 36.0 0.5 19.0 0.5 24.0
[7501] 7.0 8.0 9.0 0.5 7.0 18.0 7.0 8.0 7.0 19.0 8.0 8.0
[7513] 7.0 8.0 1.0 7.0 7.0 8.0 4.0 7.0 7.0 7.0 7.0 37.0
[7525] 0.2 2.0 7.0 8.0 8.0 19.0 8.0 36.0 1.0 8.0 8.0 8.0
[7537] 8.0 8.0 37.0 36.0 36.0 19.0 2.0 0.5 19.0 8.0 8.0 8.0
[7549] 8.0 2.0 37.0 8.0 8.0 19.0 0.5 8.0 3.0 2.0 0.0 17.0
[7561] 11.0 8.0 8.0 0.5 36.0 1.0 8.0 8.0 0.8 8.0 0.5 0.5
[7573] 8.0 8.0 1.0 2.0 8.0 1.0 8.0 8.0 25.0 0.5 8.0 8.0
[7585] 0.5 0.5 0.5 8.0 8.0 19.0 8.0 8.0 8.0 37.0 0.5 19.0
[7597] 0.5 8.0 19.0 8.0 8.0 8.0 10.0 33.0 36.0 36.0 36.0 37.0
[7609] 59.0 0.5 1.0 29.0 36.0 1.0 0.5 8.0 37.0 0.5 60.0 19.0
[7621] 8.0 11.0 0.5 2.0 5.0 9.0 0.5 8.0 8.0 2.0 19.0 8.0
[7633] 8.0 19.0 36.0 36.0 37.0 2.0 0.5 6.0 1.0 36.0 0.0 8.0
[7645] 8.0 36.0 8.0 8.0 8.0 8.0 27.0 13.0 8.0 1.0 8.0 60.0
[7657] 59.0 0.6 26.0 3.0 37.0 37.0 60.0 6.0 8.0 8.0 8.0 8.0
[7669] 37.0 1.0 5.0 8.0 19.0 8.0 0.5 18.0 8.0 37.0 4.0 36.0
[7681] 7.0 0.5 0.5 36.0 8.0 36.0 8.0 36.0 8.0 36.0 16.0 8.0
[7693] 30.0 8.0 8.0 1.0 8.0 8.0 35.0 36.0 8.0 4.0 0.5 36.0
[7705] 8.0 8.0 8.0 8.0 0.8 8.0 8.0 8.0 8.0 33.0 8.0 19.0
[7717] 0.8 8.0 8.0 37.0 8.0 8.0 0.5 8.0 8.0 5.0 8.0 8.0
[7729] 8.0 8.0 37.0 1.0 30.0 1.0 18.0 0.7 8.0 0.5 8.0 8.0
[7741] 8.0 19.0 36.0 37.0 36.0 8.0 8.0 8.0 19.0 36.0 8.0 2.0
[7753] 0.5 8.0 19.0 7.0 8.0 19.0 40.0 8.0 19.0 11.0 0.5 8.0
[7765] 8.0 8.0 37.0 0.5 8.0 8.0 8.0 0.5 8.0 19.0 19.0 19.0
[7777] 0.5 39.0 8.0 19.0 36.0 19.0 19.0 36.0 7.0 8.0 19.0 8.0
[7789] 8.0 19.0 6.0 60.0 60.0 8.0 39.0 36.0 40.0 8.0 24.0 8.0
[7801] 37.0 36.0 19.0 8.0 8.0 0.7 8.0 8.0 7.0 4.0 0.5 8.0
[7813] 2.0 3.0 37.0 8.0 1.0 19.0 8.0 19.0 34.0 11.0 37.0 37.0
[7825] 1.0 8.0 8.0 0.5 0.5 8.0 8.0 7.0 8.0 36.0 33.0 36.0
[7837] 8.0 19.0 8.0 8.0 8.0 14.0 0.5 37.0 2.0 8.0 8.0 37.0
[7849] 37.0 7.0 8.0 8.0 7.0 8.0 37.0 37.0 36.0 0.3 0.0 8.0
[7861] 8.0 8.0 36.0 2.0 37.0 0.5 0.5 8.0 8.0 0.5 8.0 37.0
[7873] 0.5 8.0 20.0 20.0 32.0 8.0 8.0 20.0 19.0 19.0 0.5 8.0
[7885] 8.0 37.0 0.2 8.0 38.0 8.0 8.0 38.0 8.0 1.0 8.0 11.0
[7897] 38.0 38.0 8.0 1.0 0.5 1.0 4.0 8.0 18.0 8.0 8.0 8.0
[7909] 20.0 16.0 36.0 8.0 8.0 8.0 1.0 3.0 8.0 20.0 19.0 8.0
[7921] 8.0 94.0 8.0 8.0 11.0 19.0 12.0 8.0 8.0 36.0 8.0 8.0
[7933] 0.5 8.0 8.0 34.0 61.0 8.0 38.0 1.0 8.0 4.0 8.0 8.0
[7945] 3.0 8.0 8.0 19.0 8.0 60.0 8.0 0.3 2.0 8.0 8.0 8.0
[7957] 8.0 0.5 60.0 8.0 38.0 37.0 1.0 8.0 8.0 8.0 8.0 8.0
[7969] 8.0 1.0 38.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0
[7981] 8.0 0.5 0.5 19.0 38.0 8.0 8.0 0.6 8.0 8.0 8.0 0.5
[7993] 62.0 1.0 2.0 8.0 20.0 37.0 8.0 15.0 19.0 37.0 3.0 0.5
[8005] 0.5 8.0 8.0 8.0 8.0 8.0 8.0 4.0 8.0 8.0 8.0 0.3
[8017] 8.0 8.0 36.0 8.0 8.0 8.0 8.0 8.0 0.5 19.0 8.0 8.0
[8029] 8.0 15.0 8.0 37.0 27.0 0.2 20.0 38.0 37.0 7.0 8.0 18.0
[8041] 27.0 37.0 8.0 8.0 8.0 2.0 8.0 8.0 1.0 0.0 1.0 0.5
[8053] 8.0 38.0 0.0 31.0 8.0 19.0 6.0 0.2 8.0 8.0 0.5 8.0
[8065] 30.0 19.0 8.0 18.0 8.0 8.0 18.0 12.0 37.0 8.0 18.0 8.0
[8077] 8.0 37.0 8.0 19.0 37.0 8.0 8.0 8.0 8.0 19.0 37.0 34.0
[8089] 9.0 8.0 8.0 8.0 8.0 0.7 8.0 9.0 38.0 0.5 2.0 36.0
[8101] 1.0 38.0 37.0 0.5 0.5 0.8 8.0 0.5 9.0 1.0 9.0 0.8
[8113] 33.0 8.0 3.0 9.0 9.0 0.5 9.0 8.0 0.5 20.0 9.0 0.1
[8125] 9.0 3.0 20.0 8.0 6.0 1.0 8.0 60.0 9.0 8.0 8.0 6.0
[8137] 38.0 30.0 0.1 8.0 8.0 8.0 38.0 19.0 8.0 14.0 0.4 37.0
[8149] 18.0 9.0 0.1 0.5 1.0 9.0 9.0 0.5 9.0 9.0 10.0 15.0
[8161] 9.0 37.0 9.0 9.0 9.0 0.8 9.0 0.5 10.0 42.0 9.0 9.0
[8173] 2.0 38.0 9.0 9.0 38.0 44.0 9.0 9.0 3.0 0.5 9.0 0.5
[8185] 9.0 8.0 9.0 9.0 9.0 9.0 9.0 4.0 11.0 1.0 9.0 8.0
[8197] 9.0 9.0 9.0 37.0 9.0 9.0 2.0 1.0 9.0 62.0 20.0 20.0
[8209] 9.0 0.5 9.0 1.0 9.0 0.5 3.0 2.0 0.5 2.0 0.5 9.0
[8221] 7.0 0.5 0.5 0.1 0.5 9.0 9.0 0.6 38.0 38.0 37.0 9.0
[8233] 0.5 20.0 9.0 19.0 36.0 40.0 38.0 2.0 9.0 9.0 35.0 38.0
[8245] 0.5 37.0 9.0 5.0 1.0 4.0 38.0 9.0 0.5 2.0 4.0 9.0
[8257] 34.0 9.0 0.5 9.0 20.0 9.0 0.5 34.0 37.0 37.0 9.0 0.8
[8269] 60.0 24.0 9.0 10.0 9.0 9.0 37.0 38.0 9.0 9.0 35.0 9.0
[8281] 4.0 38.0 9.0 9.0 5.0 9.0 0.1 9.0 38.0 37.0 9.0 37.0
[8293] 7.0 9.0 9.0 9.0 8.0 37.0 9.0 9.0 2.0 38.0 39.0 0.2
[8305] 3.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 12.0 1.0 3.0 9.0
[8317] 9.0 2.0 9.0 20.0 9.0 10.0 9.0 39.0 27.0 9.0 1.0 19.0
[8329] 9.0 9.0 0.5 9.0 18.0 9.0 0.5 9.0 9.0 9.0 39.0 37.0
[8341] 19.0 9.0 0.8 0.5 9.0 9.0 9.0 61.0 9.0 1.0 3.0 25.0
[8353] 0.5 9.0 19.0 9.0 0.5 19.0 37.0 9.0 37.0 0.5 9.0 4.0
[8365] 20.0 9.0 37.0 9.0 12.0 0.6 39.0 1.0 9.0 39.0 0.8 9.0
[8377] 61.0 9.0 0.4 9.0 3.0 63.0 20.0 37.0 9.0 0.5 0.5 9.0
[8389] 38.0 0.5 9.0 9.0 38.0 2.0 9.0 9.0 9.0 39.0 38.0 0.5
[8401] 2.0 9.0 62.0 1.0 39.0 9.0 0.5 0.4 9.0 9.0 1.0 1.0
[8413] 9.0 20.0 25.0 9.0 10.0 2.0 39.0 18.0 9.0 20.0 2.0 4.0
[8425] 9.0 0.5 9.0 9.0 20.0 9.0 2.0 9.0 9.0 9.0 0.5 2.0
[8437] 20.0 9.0 9.0 9.0 1.0 0.5 0.5 9.0 9.0 9.0 9.0 0.5
[8449] 9.0 0.5 19.0 38.0 9.0 9.0 9.0 3.0 39.0 7.0 6.0 1.0
[8461] 38.0 9.0 9.0 9.0 9.0 2.0 20.0 38.0 9.0 9.0 9.0 9.0
[8473] 9.0 9.0 9.0 35.0 9.0 6.0 0.5 9.0 9.0 9.0 9.0 9.0
[8485] 20.0 9.0 10.0 9.0 9.0 38.0 1.0 64.0 9.0 9.0 9.0 3.0
[8497] 9.0 11.0 35.0 40.0 36.0 36.0 37.0 38.0 0.5 4.0 9.0 1.0
[8509] 19.0 9.0 9.0 9.0 10.0 5.0 15.0 39.0 62.0 9.0 1.0 7.0
[8521] 9.0 9.0 9.0 9.0 15.0 38.0 32.0 39.0 9.0 9.0 9.0 0.5
[8533] 9.0 19.0 9.0 9.0 9.0 1.0 9.0 6.0 62.0 5.0 0.5 0.5
[8545] 3.0 0.5 9.0 1.0 0.5 38.0 9.0 9.0 0.5 0.8 9.0 9.0
[8557] 19.0 9.0 9.0 9.0 9.0 38.0 39.0 9.0 2.0 8.0 64.0 9.0
[8569] 9.0 6.0 9.0 9.0 9.0 38.0 6.0 20.0 38.0 39.0 9.0 38.0
[8581] 2.0 20.0 9.0 1.0 9.0 0.5 4.0 9.0 9.0 9.0 38.0 9.0
[8593] 9.0 0.5 5.0 9.0 20.0 39.0 9.0 39.0 19.0 18.0 9.0 9.0
[8605] 9.0 64.0 9.0 62.0 0.5 9.0 15.0 9.0 9.0 0.1 9.0 9.0
[8617] 19.0 38.0 9.0 19.0 1.0 9.0 63.0 0.5 9.0 4.0 20.0 9.0
[8629] 10.0 9.0 9.0 9.0 9.0 38.0 38.0 0.5 2.0 9.0 9.0 7.0
[8641] 0.5 9.0 9.0 9.0 39.0 9.0 0.1 9.0 0.5 2.0 64.0 0.5
[8653] 0.2 65.0 9.0 9.0 9.0 4.0 9.0 9.0 38.0 4.0 9.0 39.0
[8665] 9.0 9.0 9.0 10.0 0.5 0.5 9.0 1.0 9.0 13.0 9.0 0.5
[8677] 19.0 0.5 9.0 9.0 9.0 10.0 9.0 11.0 9.0 9.0 9.0 9.0
[8689] 9.0 11.0 27.0 0.4 9.0 38.0 38.0 38.0 39.0 1.0 0.2 62.0
[8701] 2.0 9.0 63.0 9.0 63.0 0.5 9.0 4.0 0.5 1.0 1.0 9.0
[8713] 39.0 9.0 39.0 9.0 9.0 9.0 9.0 0.5 5.0 11.0 11.0 2.0
[8725] 9.0 9.0 39.0 39.0 63.0 9.0 3.0 9.0 9.0 9.0 9.0 9.0
[8737] 8.0 9.0 9.0 2.0 1.0 9.0 20.0 39.0 9.0 63.0 35.0 12.0
[8749] 0.5 18.0 1.0 9.0 39.0 0.5 19.0 9.0 9.0 39.0 0.6 39.0
[8761] 0.5 0.5 6.0 20.0 9.0 1.0 9.0 9.0 19.0 39.0 40.0 19.0
[8773] 3.0 9.0 1.0 40.0 9.0 19.0 9.0 2.0 9.0 9.0 40.0 9.0
[8785] 9.0 40.0 19.0 9.0 20.0 9.0 9.0 9.0 9.0 20.0 2.0 9.0
[8797] 3.0 9.0 8.0 6.0 9.0 1.0 0.5 13.0 0.5 40.0 0.5 20.0
[8809] 40.0 2.0 2.0 31.0 0.0 9.0 40.0 10.0 0.6 10.0 4.0 9.0
[8821] 10.0 40.0 10.0 9.0 7.0 20.0 9.0 9.0 13.0 18.0 3.0 9.0
[8833] 9.0 9.0 10.0 9.0 10.0 0.7 10.0 18.0 10.0 40.0 20.0 4.0
[8845] 10.0 5.0 9.0 65.0 9.0 0.5 9.0 9.0 0.5 10.0 12.0 13.0
[8857] 12.0 10.0 40.0 10.0 0.5 0.5 10.0 9.0 9.0 3.0 9.0 10.0
[8869] 39.0 9.0 40.0 24.0 15.0 24.0 19.0 9.0 10.0 9.0 9.0 66.0
[8881] 10.0 20.0 19.0 37.0 10.0 10.0 1.0 2.0 10.0 2.0 10.0 10.0
[8893] 1.0 5.0 10.0 20.0 1.0 35.0 13.0 0.5 2.0 10.0 39.0 10.0
[8905] 1.0 0.2 10.0 0.6 40.0 6.0 20.0 39.0 10.0 10.0 10.0 6.0
[8917] 20.0 10.0 10.0 0.2 18.0 39.0 10.0 0.6 10.0 10.0 0.6 6.0
[8929] 2.0 10.0 6.0 10.0 10.0 10.0 3.0 10.0 10.0 40.0 18.0 4.0
[8941] 39.0 10.0 10.0 1.0 10.0 10.0 39.0 10.0 10.0 10.0 1.0 1.0
[8953] 9.0 10.0 96.0 10.0 0.5 18.0 0.5 10.0 0.6 6.0 0.2 10.0
[8965] 39.0 10.0 39.0 3.0 2.0 10.0 12.0 19.0 7.0 10.0 10.0 0.3
[8977] 0.6 10.0 10.0 10.0 39.0 20.0 40.0 0.6 10.0 10.0 10.0 39.0
[8989] 8.0 10.0 10.0 10.0 10.0 10.0 4.0 10.0 10.0 10.0 18.0 44.0
[9001] 10.0 0.6 10.0 10.0 3.0 10.0 10.0 20.0 5.0 10.0 40.0 10.0
[9013] 10.0 40.0 10.0 10.0 21.0 10.0 10.0 10.0 10.0 2.0 10.0 26.0
[9025] 48.0 0.6 2.0 40.0 20.0 0.6 0.7 10.0 10.0 0.6 10.0 20.0
[9037] 37.0 36.0 40.0 7.0 0.6 6.0 0.6 10.0 10.0 39.0 10.0 10.0
[9049] 10.0 0.3 10.0 0.6 0.6 10.0 19.0 4.0 4.0 7.0 0.6 2.0
[9061] 10.0 40.0 4.0 18.0 1.0 10.0 10.0 0.6 10.0 10.0 40.0 40.0
[9073] 2.0 10.0 10.0 32.0 10.0 10.0 40.0 4.0 40.0 40.0 5.0 10.0
[9085] 20.0 6.0 10.0 20.0 20.0 10.0 40.0 10.0 35.0 44.0 10.0 20.0
[9097] 0.9 10.0 10.0 9.0 10.0 10.0 0.6 40.0 41.0 22.0 10.0 1.0
[9109] 0.6 2.0 10.0 2.0 10.0 10.0 10.0 10.0 2.0 1.0 20.0 0.6
[9121] 0.6 10.0 66.0 34.0 10.0 40.0 10.0 1.0 2.0 41.0 10.0 10.0
[9133] 20.0 0.6 10.0 41.0 1.0 40.0 0.5 0.6 10.0 20.0 10.0 10.0
[9145] 40.0 10.0 10.0 10.0 41.0 10.0 3.0 1.0 2.0 41.0 10.0 66.0
[9157] 12.0 35.0 41.0 40.0 10.0 2.0 0.5 0.3 10.0 10.0 10.0 0.6
[9169] 12.0 12.0 10.0 10.0 10.0 18.0 3.0 41.0 37.0 41.0 40.0 10.0
[9181] 10.0 40.0 10.0 0.6 20.0 10.0 40.0 10.0 10.0 10.0 0.6 41.0
[9193] 41.0 25.0 1.0 40.0 1.0 4.0 10.0 10.0 7.0 41.0 10.0 10.0
[9205] 10.0 20.0 19.0 6.0 10.0 0.9 18.0 0.6 40.0 10.0 10.0 20.0
[9217] 1.0 10.0 10.0 10.0 5.0 10.0 20.0 10.0 20.0 10.0 10.0 10.0
[9229] 10.0 40.0 0.0 10.0 2.0 10.0 20.0 0.6 10.0 4.0 0.5 10.0
[9241] 3.0 67.0 10.0 10.0 10.0 10.0 12.0 0.6 10.0 10.0 40.0 18.0
[9253] 20.0 10.0 20.0 20.0 10.0 20.0 41.0 10.0 10.0 1.0 10.0 19.0
[9265] 10.0 10.0 1.0 41.0 18.0 10.0 10.0 1.0 10.0 5.0 0.5 10.0
[9277] 10.0 10.0 10.0 0.6 30.0 67.0 40.0 10.0 10.0 10.0 10.0 64.0
[9289] 20.0 10.0 0.6 0.6 10.0 0.6 10.0 41.0 10.0 13.0 36.0 10.0
[9301] 15.0 2.0 3.0 10.0 20.0 10.0 0.6 10.0 10.0 2.0 7.0 10.0
[9313] 10.0 64.0 41.0 40.0 18.0 0.6 41.0 10.0 1.0 67.0 65.0 10.0
[9325] 10.0 8.0 10.0 2.0 40.0 10.0 2.0 39.0 10.0 10.0 20.0 40.0
[9337] 10.0 10.0 10.0 13.0 10.0 10.0 40.0 10.0 1.0 10.0 40.0 10.0
[9349] 20.0 10.0 8.0 0.6 20.0 0.2 10.0 10.0 65.0 5.0 13.0 68.0
[9361] 42.0 40.0 3.0 0.6 10.0 10.0 0.2 20.0 10.0 16.0 10.0 2.0
[9373] 2.0 10.0 2.0 9.0 0.6 0.6 52.0 40.0 10.0 0.0 10.0 10.0
[9385] 11.0 3.0 10.0 10.0 28.0 41.0 6.0 40.0 10.0 42.0 62.0 10.0
[9397] 10.0 10.0 10.0 10.0 0.4 10.0 0.6 1.0 10.0 40.0 40.0 20.0
[9409] 10.0 10.0 0.0 10.0 10.0 20.0 4.0 0.6 18.0 4.0 10.0 6.0
[9421] 10.0 40.0 36.0 0.1 42.0 10.0 10.0 14.0 12.0 10.0 20.0 10.0
[9433] 20.0 10.0 36.0 42.0 10.0 42.0 10.0 10.0 10.0 20.0 10.0 36.0
[9445] 42.0 68.0 20.0 10.0 20.0 4.0 20.0 4.0 10.0 0.1 11.0 0.6
[9457] 39.0 10.0 0.6 20.0 0.6 10.0 25.0 39.0 42.0 2.0 20.0 10.0
[9469] 19.0 19.0 2.0 2.0 10.0 10.0 11.0 12.0 11.0 10.0 10.0 11.0
[9481] 10.0 11.0 0.6 10.0 0.6 66.0 11.0 67.0 11.0 9.0 40.0 11.0
[9493] 11.0 8.0 11.0 42.0 10.0 10.0 11.0 0.6 2.0 20.0 20.0 33.0
[9505] 11.0 0.7 0.6 0.6 36.0 11.0 40.0 42.0 42.0 0.5 11.0 26.0
[9517] 11.0 0.6 11.0 0.7 11.0 42.0 11.0 20.0 41.0 11.0 11.0 11.0
[9529] 10.0 4.0 10.0 10.0 30.0 11.0 35.0 13.0 11.0 29.0 11.0 10.0
[9541] 67.0 20.0 11.0 41.0 11.0 10.0 36.0 42.0 42.0 11.0 10.0 11.0
[9553] 3.0 10.0 11.0 10.0 20.0 0.6 10.0 11.0 10.0 10.0 29.0 10.0
[9565] 14.0 25.0 10.0 10.0 11.0 11.0 0.4 11.0 0.6 10.0 3.0 0.6
[9577] 20.0 20.0 10.0 20.0 10.0 0.6 10.0 0.6 2.0 10.0 11.0 11.0
[9589] 10.0 10.0 11.0 0.6 10.0 2.0 7.0 11.0 28.0 11.0 1.0 10.0
[9601] 11.0 11.0 11.0 20.0 20.0 4.0 18.0 11.0 11.0 42.0 68.0 0.2
[9613] 42.0 0.6 10.0 11.0 10.0 11.0 11.0 10.0 3.0 4.0 0.6 36.0
[9625] 91.0 10.0 11.0 43.0 2.0 11.0 11.0 11.0 11.0 43.0 20.0 11.0
[9637] 1.0 11.0 11.0 0.6 2.0 68.0 11.0 9.0 11.0 68.0 0.7 35.0
[9649] 11.0 20.0 1.0 11.0 0.6 11.0 43.0 11.0 25.0 7.0 1.0 11.0
[9661] 68.0 20.0 11.0 11.0 13.0 36.0 7.0 11.0 11.0 41.0 20.0 41.0
[9673] 11.0 20.0 20.0 5.0 11.0 4.0 43.0 31.0 20.0 11.0 11.0 1.0
[9685] 3.0 2.0 20.0 20.0 41.0 18.0 0.6 11.0 11.0 11.0 11.0 11.0
[9697] 11.0 11.0 20.0 11.0 11.0 18.0 41.0 11.0 68.0 11.0 43.0 1.0
[9709] 0.6 0.1 0.6 18.0 0.6 11.0 20.0 0.6 11.0 0.5 11.0 11.0
[9721] 41.0 38.0 38.0 42.0 38.0 11.0 0.5 28.0 11.0 11.0 11.0 14.0
[9733] 11.0 11.0 43.0 7.0 0.6 11.0 20.0 20.0 11.0 0.3 41.0 31.0
[9745] 41.0 11.0 2.0 11.0 41.0 20.0 2.0 3.0 11.0 1.0 36.0 2.0
[9757] 43.0 11.0 24.0 9.0 11.0 11.0 9.0 18.0 0.6 95.0 11.0 11.0
[9769] 11.0 41.0 41.0 0.8 41.0 0.6 26.0 11.0 0.6 0.6 41.0 11.0
[9781] 11.0 11.0 20.0 0.7 18.0 1.0 0.6 11.0 41.0 0.1 0.6 11.0
[9793] 11.0 11.0 11.0 2.0 20.0 68.0 11.0 2.0 20.0 11.0 0.6 11.0
[9805] 11.0 43.0 43.0 41.0 11.0 11.0 36.0 11.0 35.0 43.0 11.0 43.0
[9817] 20.0 42.0 20.0 11.0 11.0 11.0 11.0 11.0 30.0 37.0 32.0 7.0
[9829] 11.0 19.0 11.0 2.0 11.0 11.0 11.0 11.0 37.0 0.6 43.0 42.0
[9841] 11.0 43.0 2.0 0.6 11.0 11.0 11.0 11.0 11.0 11.0 0.6 2.0
[9853] 11.0 36.0 43.0 42.0 11.0 24.0 42.0 4.0 11.0 11.0 42.0 11.0
[9865] 11.0 11.0 11.0 11.0 8.0 11.0 11.0 0.6 11.0 42.0 44.0 1.0
[9877] 11.0 11.0 42.0 11.0 11.0 0.6 11.0 20.0 20.0 42.0 11.0 14.0
[9889] 11.0 11.0 38.0 44.0 44.0 18.0 11.0 11.0 11.0 20.0 11.0 19.0
[9901] 11.0 11.0 13.0 0.6 11.0 44.0 3.0 42.0 42.0 11.0 11.0 11.0
[9913] 11.0 2.0 11.0 22.0 42.0 42.0 1.0 18.0 44.0 11.0 0.6 11.0
[9925] 44.0 11.0 2.0 0.9 40.0 40.0 43.0 40.0 11.0 42.0 11.0 0.6
[9937] 11.0 0.1 13.0 0.5 42.0 0.6 1.0 0.9 11.0 11.0 1.0 11.0
[9949] 11.0 2.0 11.0 11.0 0.6 11.0 11.0 11.0 44.0 11.0 11.0 11.0
[9961] 4.0 11.0 11.0 20.0 0.6 11.0 11.0 11.0 21.0 65.0 5.0 13.0
[9973] 11.0 11.0 6.0 11.0 44.0 20.0 21.0 38.0 32.0 3.0 38.0 11.0
[9985] 28.0 11.0 6.0 0.6 44.0 41.0 18.0 11.0 11.0 0.6 11.0 21.0
[9997] 0.1 0.6 20.0 11.0 42.0 20.0 11.0 42.0 33.0 0.3 69.0 11.0
[10009] 11.0 21.0 11.0 11.0 0.5 11.0 20.0 11.0 11.0 0.6 11.0 11.0
[10021] 42.0 4.0 42.0 11.0 11.0 1.0 1.0 2.0 0.7 11.0 11.0 11.0
[10033] 9.0 21.0 2.0 11.0 4.0 0.3 1.0 11.0 11.0 11.0 2.0 0.6
[10045] 0.6 44.0 42.0 11.0 11.0 3.0 0.1 11.0 1.0 9.0 11.0 11.0
[10057] 11.0 18.0 11.0 11.0 21.0 20.0 11.0 11.0 11.0 11.0 11.0 0.6
[10069] 11.0 11.0 11.0 18.0 2.0 42.0 0.6 11.0 70.0 43.0 0.6 11.0
[10081] 0.6 11.0 11.0 7.0 11.0 11.0 2.0 11.0 11.0 11.0 0.6 11.0
[10093] 21.0 70.0 37.0 11.0 43.0 11.0 0.6 4.0 43.0 11.0 17.0 44.0
[10105] 2.0 11.0 7.0 21.0 11.0 11.0 11.0 0.6 11.0 11.0 0.6 12.0
[10117] 14.0 91.0 9.0 0.7 11.0 12.0 26.0 44.0 12.0 11.0 12.0 43.0
[10129] 2.0 12.0 19.0 21.0 12.0 11.0 12.0 12.0 0.6 0.6 38.0 13.0
[10141] 13.0 0.6 1.0 49.0 12.0 0.6 9.0 4.0 11.0 12.0 0.6 12.0
[10153] 0.2 43.0 11.0 0.6 12.0 20.0 12.0 11.0 43.0 18.0 11.0 12.0
[10165] 12.0 0.6 0.3 11.0 0.6 43.0 0.6 12.0 12.0 4.0 12.0 11.0
[10177] 38.0 10.0 11.0 12.0 0.6 44.0 12.0 43.0 11.0 0.9 2.0 0.6
[10189] 18.0 11.0 12.0 12.0 5.0 11.0 70.0 0.6 21.0 37.0 7.0 13.0
[10201] 15.0 43.0 6.0 0.7 12.0 11.0 44.0 70.0 12.0 12.0 18.0 13.0
[10213] 12.0 34.0 12.0 24.0 30.0 32.0 43.0 20.0 2.0 44.0 4.0 11.0
[10225] 11.0 95.0 7.0 0.6 35.0 1.0 11.0 11.0 10.0 12.0 11.0 0.6
[10237] 0.6 11.0 43.0 44.0 1.0 70.0 0.9 21.0 7.0 4.0 5.0 0.6
[10249] 0.6 12.0 12.0 0.6 11.0 12.0 14.0 31.0 23.0 20.0 10.0 0.9
[10261] 43.0 44.0 44.0 22.0 45.0 20.0 21.0 11.0 11.0 12.0 11.0 19.0
[10273] 41.0 41.0 43.0 41.0 41.0 42.0 42.0 18.0 11.0 45.0 12.0 45.0
[10285] 71.0 0.7 11.0 12.0 2.0 8.0 0.6 11.0 44.0 2.0 45.0 11.0
[10297] 0.1 2.0 12.0 96.0 12.0 12.0 12.0 12.0 12.0 12.0 11.0 0.2
[10309] 45.0 18.0 12.0 1.0 2.0 11.0 11.0 12.0 9.0 12.0 28.0 11.0
[10321] 11.0 1.0 11.0 1.0 5.0 70.0 11.0 11.0 12.0 1.0 12.0 12.0
[10333] 41.0 12.0 12.0 11.0 2.0 43.0 11.0 13.0 97.0 12.0 29.0 12.0
[10345] 11.0 15.0 43.0 45.0 45.0 12.0 11.0 12.0 2.0 20.0 12.0 12.0
[10357] 11.0 4.0 97.0 12.0 19.0 14.0 11.0 12.0 1.0 12.0 2.0 16.0
[10369] 12.0 0.6 11.0 12.0 8.0 12.0 12.0 5.0 18.0 12.0 12.0 12.0
[10381] 12.0 12.0 12.0 12.0 9.0 12.0 1.0 12.0 12.0 12.0 2.0 0.6
[10393] 1.0 2.0 12.0 39.0 2.0 43.0 43.0 5.0 12.0 12.0 12.0 10.0
[10405] 45.0 45.0 12.0 71.0 12.0 12.0 12.0 45.0 45.0 13.0 14.0 2.0
[10417] 43.0 45.0 18.0 45.0 12.0 5.0 5.0 20.0 25.0 45.0 70.0 0.5
[10429] 12.0 0.6 0.1 14.0 12.0 43.0 21.0 12.0 43.0 12.0 0.6 14.0
[10441] 12.0 8.0 12.0 12.0 12.0 12.0 12.0 12.0 14.0 12.0 43.0 46.0
[10453] 16.0 21.0 20.0 12.0 18.0 0.9 12.0 0.6 0.6 20.0 46.0 12.0
[10465] 12.0 12.0 12.0 12.0 12.0 2.0 12.0 12.0 8.0 71.0 12.0 12.0
[10477] 12.0 12.0 43.0 46.0 43.0 43.0 30.0 21.0 6.0 12.0 98.0 4.0
[10489] 20.0 12.0 72.0 20.0 38.0 14.0 5.0 12.0 0.6 12.0 12.0 71.0
[10501] 12.0 0.0 12.0 18.0 12.0 21.0 4.0 43.0 20.0 12.0 12.0 12.0
[10513] 19.0 19.0 43.0 0.6 44.0 46.0 12.0 12.0 21.0 71.0 72.0 46.0
[10525] 44.0 20.0 0.6 12.0 72.0 14.0 46.0 46.0 46.0 12.0 2.0 12.0
[10537] 12.0 12.0 12.0 12.0 0.6 19.0 0.6 46.0 1.0 12.0 19.0 12.0
[10549] 12.0 12.0 12.0 14.0 12.0 12.0 46.0 44.0 0.2 12.0 21.0 20.0
[10561] 3.0 44.0 46.0 44.0 44.0 0.6 12.0 44.0 26.0 12.0 12.0 12.0
[10573] 3.0 12.0 20.0 46.0 0.5 18.0 5.0 0.6 12.0 12.0 72.0 12.0
[10585] 46.0 12.0 46.0 44.0 12.0 46.0 0.6 12.0 12.0 12.0 2.0 0.3
[10597] 12.0 44.0 62.0 12.0 28.0 71.0 0.7 10.0 45.0 46.0 12.0 19.0
[10609] 12.0 12.0 7.0 12.0 3.0 16.0 44.0 44.0 10.0 12.0 12.0 44.0
[10621] 12.0 12.0 71.0 12.0 12.0 12.0 43.0 12.0 0.0 12.0 12.0 44.0
[10633] 44.0 44.0 2.0 2.0 12.0 12.0 12.0 0.6 30.0 44.0 6.0 12.0
[10645] 4.0 71.0 12.0 20.0 12.0 72.0 12.0 2.0 12.0 12.0 1.0 12.0
[10657] 12.0 18.0 12.0 46.0 12.0 12.0 12.0 0.6 12.0 0.7 72.0 1.0
[10669] 0.6 39.0 14.0 12.0 12.0 12.0 12.0 15.0 0.7 44.0 0.7 12.0
[10681] 2.0 14.0 12.0 12.0 12.0 44.0 46.0 19.0 3.0 0.7 14.0 12.0
[10693] 46.0 0.7 12.0 46.0 46.0 12.0 0.6 11.0 12.0 12.0 44.0 12.0
[10705] 7.0 2.0 1.0 14.0 12.0 0.1 12.0 12.0 12.0 1.0 0.6 0.7
[10717] 21.0 12.0 12.0 12.0 0.8 8.0 12.0 0.6 12.0 5.0 2.0 4.0
[10729] 12.0 12.0 44.0 20.0 12.0 0.5 20.0 2.0 12.0 44.0 43.0 44.0
[10741] 0.6 0.3 12.0 2.0 44.0 3.0 9.0 73.0 12.0 38.0 12.0 12.0
[10753] 46.0 12.0 12.0 9.0 12.0 21.0 12.0 39.0 0.6 13.0 0.7 0.7
[10765] 12.0 12.0 20.0 47.0 13.0 8.0 47.0 20.0 2.0 13.0 6.0 13.0
[10777] 21.0 0.7 24.0 13.0 2.0 0.7 13.0 0.7 13.0 12.0 12.0 18.0
[10789] 0.7 4.0 1.0 44.0 12.0 13.0 0.7 12.0 72.0 13.0 1.0 39.0
[10801] 5.0 21.0 72.0 21.0 4.0 12.0 36.0 13.0 13.0 13.0 45.0 12.0
[10813] 12.0 13.0 2.0 9.0 12.0 12.0 15.0 13.0 12.0 45.0 12.0 13.0
[10825] 13.0 21.0 46.0 12.0 2.0 12.0 14.0 12.0 14.0 1.0 21.0 13.0
[10837] 39.0 4.0 12.0 13.0 44.0 44.0 44.0 2.0 19.0 2.0 65.0 42.0
[10849] 47.0 13.0 0.7 72.0 45.0 0.3 1.0 12.0 0.7 45.0 47.0 42.0
[10861] 1.0 73.0 0.7 0.9 18.0 0.7 0.7 13.0 12.0 12.0 10.0 39.0
[10873] 1.0 45.0 0.0 0.1 0.7 9.0 12.0 12.0 12.0 0.7 12.0 39.0
[10885] 12.0 31.0 47.0 12.0 21.0 12.0 13.0 12.0 73.0 0.5 13.0 0.7
[10897] 2.0 13.0 13.0 47.0 45.0 21.0 5.0 13.0 18.0 47.0 21.0 12.0
[10909] 12.0 0.1 1.0 1.0 45.0 27.0 13.0 45.0 0.7 18.0 12.0 23.0
[10921] 12.0 13.0 21.0 13.0 45.0 13.0 47.0 13.0 13.0 12.0 5.0 13.0
[10933] 12.0 0.7 47.0 45.0 20.0 13.0 73.0 0.7 21.0 12.0 0.7 0.7
[10945] 4.0 12.0 13.0 45.0 18.0 0.2 13.0 12.0 5.0 13.0 13.0 12.0
[10957] 12.0 12.0 13.0 22.0 12.0 4.0 21.0 14.0 15.0 1.0 45.0 47.0
[10969] 13.0 5.0 47.0 30.0 4.0 0.7 0.9 21.0 12.0 13.0 5.0 27.0
[10981] 15.0 12.0 35.0 45.0 45.0 47.0 47.0 14.0 0.2 45.0 12.0 12.0
[10993] 53.0 12.0 12.0 2.0 45.0 12.0 12.0 12.0 13.0 12.0 39.0 24.0
[11005] 10.0 45.0 45.0 13.0 13.0 13.0 74.0 12.0 19.0 2.0 45.0 22.0
[11017] 13.0 2.0 10.0 12.0 13.0 74.0 12.0 18.0 14.0 19.0 2.0 7.0
[11029] 12.0 1.0 47.0 1.0 18.0 47.0 12.0 74.0 12.0 13.0 0.3 0.7
[11041] 13.0 12.0 0.7 18.0 13.0 13.0 0.9 0.7 0.7 54.0 13.0 12.0
[11053] 12.0 2.0 45.0 0.7 12.0 46.0 22.0 2.0 75.0 12.0 8.0 39.0
[11065] 13.0 0.7 46.0 13.0 22.0 12.0 12.0 13.0 2.0 12.0 12.0 46.0
[11077] 3.0 21.0 21.0 13.0 21.0 13.0 72.0 46.0 47.0 46.0 12.0 0.7
[11089] 0.7 2.0 0.7 0.7 13.0 46.0 12.0 13.0 13.0 13.0 13.0 0.7
[11101] 75.0 13.0 12.0 47.0 13.0 48.0 48.0 48.0 38.0 13.0 5.0 13.0
[11113] 13.0 13.0 13.0 13.0 1.0 13.0 39.0 14.0 48.0 46.0 46.0 0.3
[11125] 21.0 13.0 13.0 0.7 13.0 0.7 0.9 0.7 9.0 13.0 22.0 40.0
[11137] 66.0 32.0 3.0 37.0 13.0 13.0 1.0 20.0 13.0 0.0 13.0 22.0
[11149] 13.0 13.0 13.0 47.0 47.0 0.7 0.1 33.0 13.0 48.0 13.0 0.7
[11161] 21.0 13.0 39.0 13.0 13.0 47.0 73.0 9.0 13.0 13.0 13.0 0.7
[11173] 13.0 26.0 13.0 13.0 13.0 1.0 0.2 13.0 0.8 0.7 0.7 47.0
[11185] 47.0 13.0 2.0 0.7 13.0 13.0 48.0 47.0 13.0 13.0 2.0 18.0
[11197] 13.0 1.0 4.0 21.0 13.0 13.0 48.0 0.7 4.0 0.7 18.0 13.0
[11209] 2.0 13.0 0.7 13.0 48.0 48.0 13.0 22.0 11.0 0.1 47.0 0.7
[11221] 76.0 13.0 3.0 13.0 21.0 20.0 15.0 35.0 48.0 13.0 14.0 48.0
[11233] 13.0 13.0 22.0 0.8 6.0 11.0 13.0 13.0 47.0 13.0 22.0 6.0
[11245] 27.0 39.0 13.0 77.0 48.0 13.0 14.0 3.0 13.0 13.0 21.0 13.0
[11257] 13.0 13.0 20.0 13.0 0.7 13.0 35.0 0.7 48.0 13.0 0.7 6.0
[11269] 4.0 2.0 5.0 13.0 13.0 13.0 13.0 13.0 13.0 39.0 11.0 13.0
[11281] 48.0 48.0 0.4 18.0 0.7 22.0 13.0 15.0 16.0 47.0 3.0 2.0
[11293] 7.0 9.0 0.7 40.0 14.0 45.0 24.0 44.0 44.0 44.0 49.0 13.0
[11305] 0.7 3.0 13.0 49.0 46.0 49.0 18.0 47.0 13.0 13.0 13.0 13.0
[11317] 0.3 3.0 13.0 19.0 49.0 13.0 13.0 3.0 13.0 18.0 13.0 21.0
[11329] 48.0 13.0 21.0 13.0 13.0 1.0 13.0 21.0 13.0 13.0 2.0 13.0
[11341] 0.7 13.0 13.0 13.0 77.0 21.0 21.0 49.0 21.0 1.0 48.0 48.0
[11353] 13.0 0.6 0.7 77.0 0.7 40.0 13.0 2.0 19.0 48.0 2.0 13.0
[11365] 13.0 13.0 13.0 13.0 73.0 1.0 0.7 22.0 13.0 1.0 13.0 48.0
[11377] 13.0 13.0 13.0 2.0 1.0 29.0 18.0 13.0 22.0 13.0 13.0 13.0
[11389] 22.0 14.0 15.0 49.0 49.0 49.0 3.0 13.0 10.0 13.0 13.0 13.0
[11401] 13.0 0.7 4.0 13.0 13.0 0.5 18.0 13.0 48.0 5.0 13.0 0.6
[11413] 13.0 13.0 13.0 13.0 0.2 6.0 13.0 9.0 42.0 15.0 49.0 18.0
[11425] 3.0 49.0 4.0 4.0 49.0 0.5 7.0 19.0 13.0 0.7 13.0 0.2
[11437] 12.0 22.0 13.0 13.0 0.7 49.0 78.0 0.8 21.0 13.0 22.0 20.0
[11449] 1.0 21.0 48.0 13.0 13.0 21.0 0.3 13.0 73.0 21.0 0.7 34.0
[11461] 48.0 4.0 13.0 49.0 5.0 48.0 49.0 3.0 13.0 13.0 0.7 13.0
[11473] 7.0 2.0 2.0 21.0 13.0 13.0 49.0 34.0 33.0 49.0 48.0 1.0
[11485] 0.6 9.0 2.0 0.7 13.0 48.0 0.5 13.0 13.0 18.0 13.0 49.0
[11497] 0.7 2.0 13.0 2.0 8.0 12.0 13.0 13.0 48.0 4.0 13.0 48.0
[11509] 30.0 4.0 3.0 48.0 0.7 0.0 2.0 49.0 0.7 13.0 0.7 0.7
[11521] 13.0 12.0 0.7 15.0 18.0 48.0 13.0 18.0 49.0 13.0 13.0 13.0
[11533] 13.0 49.0 0.7 3.0 2.0 13.0 21.0 13.0 14.0 0.7 13.0 13.0
[11545] 13.0 4.0 49.0 31.0 0.5 0.7 13.0 0.7 13.0 13.0 21.0 14.0
[11557] 14.0 14.0 0.3 27.0 32.0 19.0 48.0 49.0 14.0 0.7 14.0 49.0
[11569] 13.0 0.0 4.0 1.0 5.0 0.7 14.0 0.7 18.0 0.7 14.0 13.0
[11581] 49.0 13.0 9.0 13.0 20.0 73.0 14.0 14.0 0.8 2.0 14.0 13.0
[11593] 0.7 0.7 14.0 20.0 15.0 44.0 22.0 44.0 49.0 14.0 9.0 13.0
[11605] 14.0 13.0 15.0 29.0 14.0 0.7 14.0 19.0 14.0 21.0 13.0 50.0
[11617] 0.7 1.0 0.7 13.0 0.7 13.0 55.0 0.5 0.7 0.7 67.0 13.0
[11629] 50.0 14.0 22.0 21.0 14.0 13.0 49.0 50.0 5.0 49.0 13.0 22.0
[11641] 98.0 0.7 15.0 2.0 14.0 34.0 22.0 13.0 50.0 13.0 10.0 1.0
[11653] 14.0 28.0 13.0 14.0 2.0 14.0 14.0 13.0 13.0 49.0 50.0 13.0
[11665] 0.0 14.0 49.0 13.0 14.0 73.0 15.0 18.0 14.0 13.0 14.0 13.0
[11677] 50.0 49.0 50.0 1.0 12.0 49.0 14.0 13.0 15.0 15.0 18.0 0.7
[11689] 49.0 1.0 13.0 19.0 0.7 1.0 13.0 32.0 0.7 13.0 13.0 13.0
[11701] 2.0 13.0 38.0 14.0 22.0 13.0 14.0 13.0 13.0 13.0 13.0 7.0
[11713] 1.0 18.0 4.0 1.0 0.7 13.0 14.0 0.7 13.0 0.7 0.7 0.7
[11725] 13.0 1.0 41.0 50.0 0.0 6.0 0.7 13.0 2.0 22.0 13.0 22.0
[11737] 19.0 49.0 4.0 14.0 14.0 12.0 11.0 14.0 14.0 13.0 13.0 14.0
[11749] 50.0 13.0 1.0 18.0 50.0 14.0 50.0 12.0 14.0 91.0 44.0 45.0
[11761] 119.0 52.0 18.0 13.0 50.0 13.0 1.0 0.7 14.0 0.7 14.0 16.0
[11773] 50.0 17.0 50.0 14.0 20.0 13.0 0.8 14.0 13.0 13.0 0.7 3.0
[11785] 13.0 13.0 13.0 13.0 14.0 14.0 14.0 14.0 13.0 14.0 45.0 29.0
[11797] 50.0 0.7 13.0 0.4 22.0 6.0 13.0 14.0 1.0 22.0 13.0 71.0
[11809] 0.7 13.0 1.0 0.9 0.5 13.0 13.0 13.0 13.0 15.0 28.0 18.0
[11821] 50.0 14.0 14.0 2.0 74.0 14.0 0.3 14.0 50.0 14.0 50.0 13.0
[11833] 13.0 14.0 15.0 50.0 51.0 14.0 0.3 13.0 50.0 74.0 0.7 13.0
[11845] 50.0 50.0 2.0 13.0 14.0 50.0 14.0 1.0 14.0 0.7 14.0 51.0
[11857] 34.0 4.0 50.0 9.0 3.0 6.0 0.7 51.0 14.0 24.0 2.0 13.0
[11869] 13.0 14.0 13.0 1.0 51.0 51.0 1.0 14.0 13.0 75.0 13.0 14.0
[11881] 13.0 14.0 15.0 62.0 0.5 75.0 0.7 13.0 3.0 22.0 13.0 14.0
[11893] 12.0 50.0 3.0 50.0 14.0 14.0 14.0 14.0 98.0 14.0 0.9 67.0
[11905] 15.0 14.0 14.0 20.0 14.0 14.0 16.0 38.0 58.0 41.0 44.0 22.0
[11917] 14.0 50.0 4.0 0.3 14.0 51.0 12.0 51.0 50.0 51.0 0.7 14.0
[11929] 14.0 1.0 14.0 2.0 5.0 17.0 14.0 14.0 51.0 14.0 74.0 10.0
[11941] 4.0 22.0 14.0 14.0 50.0 51.0 0.7 50.0 14.0 14.0 14.0 14.0
[11953] 14.0 0.7 14.0 11.0 1.0 14.0 14.0 43.0 16.0 9.0 51.0 2.0
[11965] 14.0 14.0 75.0 4.0 14.0 14.0 14.0 42.0 14.0 51.0 0.7 3.0
[11977] 0.7 14.0 14.0 2.0 68.0 10.0 12.0 0.7 14.0 50.0 18.0 14.0
[11989] 14.0 14.0 22.0 14.0 14.0 14.0 0.7 79.0 14.0 14.0 2.0 14.0
[12001] 0.3 51.0 13.0 14.0 0.7 14.0 0.7 0.7 51.0 75.0 0.7 5.0
[12013] 77.0 9.0 14.0 51.0 22.0 12.0 18.0 1.0 14.0 3.0 1.0 19.0
[12025] 14.0 14.0 14.0 2.0 51.0 32.0 14.0 14.0 14.0 22.0 14.0 20.0
[12037] 43.0 14.0 2.0 16.0 14.0 14.0 1.0 14.0 52.0 14.0 1.0 0.7
[12049] 18.0 2.0 4.0 0.7 14.0 14.0 18.0 14.0 2.0 0.7 16.0 0.7
[12061] 14.0 45.0 14.0 4.0 14.0 14.0 79.0 14.0 6.0 14.0 50.0 14.0
[12073] 50.0 19.0 2.0 14.0 23.0 13.0 0.7 14.0 42.0 16.0 14.0 14.0
[12085] 14.0 16.0 14.0 14.0 14.0 6.0 1.0 50.0 0.7 14.0 46.0 0.7
[12097] 10.0 1.0 1.0 14.0 6.0 14.0 51.0 0.7 52.0 14.0 14.0 14.0
[12109] 52.0 0.8 51.0 14.0 22.0 14.0 14.0 14.0 10.0 77.0 14.0 25.0
[12121] 0.7 31.0 51.0 0.7 6.0 0.7 6.0 14.0 14.0 0.7 14.0 4.0
[12133] 14.0 52.0 22.0 52.0 14.0 14.0 0.2 22.0 14.0 14.0 14.0 14.0
[12145] 14.0 0.7 9.0 0.3 0.7 22.0 37.0 0.0 14.0 0.9 23.0 80.0
[12157] 14.0 23.0 1.0 15.0 14.0 52.0 14.0 51.0 14.0 14.0 81.0 14.0
[12169] 2.0 14.0 14.0 14.0 14.0 4.0 14.0 0.7 14.0 0.7 4.0 14.0
[12181] 1.0 0.7 23.0 72.0 1.0 14.0 22.0 14.0 22.0 11.0 29.0 2.0
[12193] 51.0 14.0 14.0 14.0 14.0 23.0 0.7 14.0 51.0 14.0 23.0 14.0
[12205] 51.0 14.0 14.0 14.0 0.4 14.0 22.0 23.0 42.0 20.0 0.0 43.0
[12217] 14.0 51.0 0.7 14.0 14.0 14.0 0.7 14.0 1.0 0.7 7.0 81.0
[12229] 2.0 14.0 14.0 14.0 2.0 22.0 77.0 14.0 14.0 52.0 38.0 16.0
[12241] 2.0 52.0 14.0 0.7 14.0 10.0 9.0 7.0 14.0 14.0 14.0 12.0
[12253] 1.0 14.0 47.0 47.0 51.0 2.0 14.0 14.0 14.0 14.0 14.0 1.0
[12265] 14.0 22.0 46.0 45.0 46.0 47.0 77.0 14.0 14.0 51.0 52.0 14.0
[12277] 23.0 22.0 14.0 14.0 14.0 14.0 14.0 0.9 14.0 13.0 14.0 22.0
[12289] 1.0 0.7 14.0 14.0 14.0 14.0 10.0 14.0 14.0 14.0 52.0 11.0
[12301] 0.7 52.0 14.0 39.0 14.0 13.0 0.5 14.0 14.0 53.0 0.7 51.0
[12313] 14.0 51.0 3.0 0.8 14.0 14.0 22.0 0.1 14.0 0.7 14.0 14.0
[12325] 15.0 14.0 14.0 2.0 0.6 14.0 28.0 14.0 15.0 14.0 14.0 28.0
[12337] 0.0 53.0 14.0 10.0 14.0 23.0 0.8 18.0 59.0 14.0 14.0 14.0
[12349] 51.0 28.0 2.0 53.0 0.3 14.0 0.7 14.0 14.0 3.0 20.0 64.0
[12361] 2.0 23.0 53.0 14.0 14.0 0.8 0.7 14.0 14.0 14.0 44.0 15.0
[12373] 53.0 14.0 14.0 1.0 0.1 14.0 14.0 18.0 14.0 14.0 14.0 14.0
[12385] 14.0 14.0 82.0 6.0 1.0 0.8 0.8 53.0 14.0 14.0 14.0 14.0
[12397] 14.0 14.0 21.0 12.0 14.0 53.0 22.0 37.0 23.0 14.0 53.0 4.0
[12409] 14.0 14.0 14.0 23.0 23.0 14.0 14.0 14.0 46.0 35.0 1.0 1.0
[12421] 0.8 0.8 14.0 0.7 14.0 14.0 10.0 0.7 18.0 14.0 14.0 14.0
[12433] 53.0 14.0 23.0 14.0 1.0 77.0 1.0 14.0 0.8 13.0 44.0 14.0
[12445] 51.0 23.0 14.0 0.7 7.0 53.0 23.0 14.0 14.0 0.7 14.0 14.0
[12457] 14.0 51.0 14.0 14.0 0.8 14.0 6.0 10.0 14.0 6.0 11.0 2.0
[12469] 14.0 14.0 0.7 0.7 0.8 0.8 43.0 7.0 20.0 5.0 14.0 53.0
[12481] 53.0 53.0 0.7 11.0 2.0 0.0 14.0 53.0 13.0 6.0 14.0 7.0
[12493] 0.8 16.0 47.0 47.0 47.0 45.0 45.0 15.0 53.0 0.8 54.0 52.0
[12505] 25.0 17.0 54.0 14.0 12.0 18.0 0.8 2.0 14.0 21.0 15.0 52.0
[12517] 14.0 15.0 0.7 0.8 15.0 14.0 0.8 14.0 2.0 15.0 15.0 4.0
[12529] 15.0 11.0 52.0 52.0 14.0 14.0 2.0 14.0 15.0 14.0 52.0 0.0
[12541] 14.0 14.0 14.0 4.0 82.0 14.0 14.0 6.0 0.8 2.0 15.0 15.0
[12553] 14.0 0.3 15.0 15.0 15.0 0.8 78.0 18.0 0.3 0.8 14.0 1.0
[12565] 14.0 14.0 24.0 15.0 2.0 0.7 52.0 77.0 7.0 52.0 52.0 18.0
[12577] 18.0 23.0 54.0 0.8 15.0 17.0 15.0 0.3 15.0 14.0 16.0 15.0
[12589] 52.0 15.0 2.0 15.0 15.0 0.8 54.0 4.0 15.0 0.5 15.0 15.0
[12601] 15.0 0.4 52.0 54.0 28.0 1.0 54.0 15.0 15.0 0.8 26.0 15.0
[12613] 2.0 52.0 37.0 1.0 15.0 15.0 15.0 15.0 46.0 38.0 15.0 2.0
[12625] 15.0 15.0 0.7 15.0 27.0 15.0 15.0 24.0 15.0 2.0 15.0 29.0
[12637] 54.0 52.0 54.0 0.7 15.0 1.0 23.0 15.0 15.0 15.0 0.8 15.0
[12649] 4.0 15.0 15.0 0.0 20.0 1.0 9.0 2.0 0.8 2.0 0.8 0.8
[12661] 47.0 15.0 52.0 15.0 15.0 0.8 15.0 44.0 31.0 16.0 48.0 33.0
[12673] 82.0 0.8 15.0 52.0 54.0 18.0 15.0 5.0 15.0 15.0 15.0 14.0
[12685] 0.1 0.8 0.6 16.0 1.0 0.3 0.0 54.0 13.0 1.0 6.0 1.0
[12697] 1.0 15.0 15.0 31.0 15.0 0.8 0.8 91.0 81.0 18.0 0.8 1.0
[12709] 5.0 34.0 54.0 52.0 0.8 15.0 1.0 15.0 2.0 15.0 15.0 12.0
[12721] 15.0 15.0 23.0 0.8 15.0 1.0 24.0 15.0 15.0 15.0 15.0 18.0
[12733] 15.0 48.0 16.0 0.8 15.0 15.0 23.0 0.8 0.8 53.0 15.0 2.0
[12745] 50.0 15.0 53.0 15.0 53.0 0.8 0.8 54.0 15.0 24.0 1.0 53.0
[12757] 2.0 15.0 15.0 24.0 0.8 25.0 53.0 15.0 0.8 53.0 15.0 15.0
[12769] 15.0 15.0 15.0 15.0 38.0 0.8 78.0 15.0 15.0 15.0 24.0 8.0
[12781] 15.0 0.8 15.0 15.0 4.0 15.0 14.0 15.0 0.8 54.0 53.0 53.0
[12793] 20.0 15.0 0.0 0.3 37.0 15.0 15.0 0.8 24.0 0.8 24.0 7.0
[12805] 15.0 15.0 15.0 0.0 24.0 0.8 15.0 15.0 53.0 15.0 30.0 15.0
[12817] 15.0 24.0 0.8 15.0 15.0 15.0 11.0 15.0 9.0 15.0 11.0 15.0
[12829] 30.0 11.0 32.0 2.0 15.0 0.3 15.0 15.0 53.0 0.8 13.0 5.0
[12841] 0.8 15.0 15.0 16.0 4.0 24.0 53.0 27.0 4.0 3.0 26.0 14.0
[12853] 15.0 82.0 15.0 15.0 15.0 2.0 20.0 15.0 0.8 78.0 24.0 10.0
[12865] 15.0 0.8 53.0 24.0 0.5 24.0 11.0 24.0 15.0 24.0 18.0 15.0
[12877] 10.0 15.0 15.0 15.0 2.0 0.8 16.0 24.0 15.0 54.0 1.0 0.8
[12889] 0.8 2.0 15.0 4.0 15.0 14.0 24.0 0.9 15.0 15.0 15.0 15.0
[12901] 15.0 15.0 16.0 15.0 15.0 15.0 2.0 23.0 0.5 16.0 53.0 15.0
[12913] 15.0 24.0 0.8 19.0 0.8 15.0 53.0 0.8 15.0 1.0 6.0 15.0
[12925] 55.0 14.0 55.0 15.0 18.0 15.0 35.0 15.0 0.4 0.1 5.0 0.2
[12937] 0.4 15.0 15.0 53.0 1.0 0.8 25.0 78.0 18.0 4.0 15.0 15.0
[12949] 15.0 15.0 15.0 0.8 47.0 48.0 49.0 49.0 4.0 25.0 0.3 12.0
[12961] 2.0 52.0 3.0 0.4 15.0 2.0 83.0 18.0 14.0 48.0 31.0 55.0
[12973] 11.0 55.0 0.8 15.0 0.8 0.1 54.0 0.6 55.0 55.0 15.0 0.8
[12985] 15.0 0.8 0.4 15.0 0.6 15.0 15.0 52.0 55.0 15.0 55.0 55.0
[12997] 24.0 0.8 15.0 16.0 15.0 15.0 0.1 15.0 15.0 12.0 13.0 15.0
[13009] 2.0 16.0 2.0 15.0 6.0 78.0 78.0 25.0 6.0 15.0 55.0 54.0
[13021] 55.0 0.0 4.0 54.0 55.0 54.0 15.0 15.0 15.0 13.0 15.0 98.0
[13033] 15.0 15.0 15.0 0.8 79.0 15.0 15.0 1.0 25.0 18.0 15.0 99.0
[13045] 80.0 0.8 2.0 54.0 38.0 0.8 25.0 15.0 15.0 41.0 15.0 54.0
[13057] 15.0 55.0 15.0 15.0 2.0 25.0 1.0 6.0 15.0 15.0 54.0 55.0
[13069] 0.4 55.0 15.0 14.0 12.0 4.0 7.0 56.0 15.0 18.0 15.0 15.0
[13081] 84.0 56.0 13.0 20.0 27.0 15.0 54.0 1.0 15.0 15.0 15.0 15.0
[13093] 49.0 49.0 49.0 15.0 1.0 0.8 15.0 15.0 0.6 15.0 26.0 56.0
[13105] 2.0 54.0 1.0 15.0 15.0 25.0 1.0 80.0 1.0 1.0 54.0 26.0
[13117] 2.0 25.0 15.0 15.0 0.6 15.0 15.0 15.0 15.0 26.0 0.8 54.0
[13129] 54.0 15.0 15.0 15.0 15.0 15.0 0.8 2.0 56.0 54.0 15.0 56.0
[13141] 15.0 15.0 15.0 15.0 0.8 12.0 52.0 26.0 15.0 56.0 15.0 15.0
[13153] 54.0 1.0 14.0 18.0 15.0 56.0 2.0 15.0 6.0 55.0 15.0 3.0
[13165] 53.0 15.0 0.0 15.0 15.0 55.0 1.0 26.0 15.0 15.0 84.0 15.0
[13177] 11.0 26.0 15.0 27.0 15.0 6.0 2.0 13.0 56.0 56.0 0.8 10.0
[13189] 18.0 12.0 0.8 1.0 15.0 15.0 15.0 19.0 0.8 4.0 15.0 15.0
[13201] 15.0 54.0 54.0 80.0 55.0 15.0 13.0 0.8 16.0 20.0 15.0 6.0
[13213] 4.0 1.0 2.0 15.0 15.0 16.0 15.0 27.0 15.0 15.0 15.0 1.0
[13225] 15.0 15.0 15.0 72.0 29.0 6.0 15.0 15.0 55.0 3.0 16.0 15.0
[13237] 44.0 50.0 0.8 15.0 15.0 0.8 56.0 15.0 15.0 15.0 15.0 5.0
[13249] 0.7 15.0 54.0 16.0 6.0 31.0 55.0 11.0 1.0 0.8 80.0 27.0
[13261] 3.0 56.0 0.2 15.0 6.0 1.0 56.0 2.0 15.0 0.8 1.0 15.0
[13273] 12.0 15.0 10.0 56.0 15.0 19.0 12.0 81.0 0.8 20.0 16.0 0.8
[13285] 0.8 10.0 15.0 0.8 15.0 15.0 55.0 27.0 14.0 27.0 15.0 55.0
[13297] 15.0 2.0 19.0 15.0 1.0 15.0 0.8 15.0 15.0 15.0 0.8 0.6
[13309] 27.0 15.0 0.8 15.0 55.0 28.0 1.0 50.0 0.8 57.0 5.0 2.0
[13321] 81.0 15.0 0.8 15.0 15.0 15.0 30.0 55.0 15.0 2.0 7.0 3.0
[13333] 15.0 28.0 57.0 2.0 15.0 15.0 15.0 4.0 55.0 15.0 15.0 15.0
[13345] 15.0 15.0 15.0 15.0 0.8 15.0 57.0 1.0 57.0 0.8 15.0 15.0
[13357] 15.0 6.0 15.0 20.0 1.0 1.0 0.6 2.0 7.0 15.0 15.0 55.0
[13369] 15.0 57.0 15.0 15.0 28.0 0.8 41.0 35.0 50.0 15.0 55.0 6.0
[13381] 15.0 15.0 15.0 15.0 2.0 55.0 15.0 15.0 34.0 15.0 2.0 0.1
[13393] 4.0 56.0 5.0 15.0 22.0 15.0 15.0 15.0 15.0 17.0 15.0 15.0
[13405] 15.0 0.8 0.8 15.0 15.0 15.0 15.0 0.0 15.0 15.0 15.0 15.0
[13417] 15.0 32.0 15.0 15.0 56.0 19.0 19.0 15.0 15.0 57.0 15.0 15.0
[13429] 0.8 16.0 28.0 29.0 15.0 4.0 18.0 56.0 15.0 14.0 14.0 28.0
[13441] 1.0 15.0 28.0 15.0 15.0 56.0 15.0 15.0 28.0 16.0 17.0 15.0
[13453] 56.0 36.0 15.0 2.0 14.0 4.0 15.0 28.0 15.0 16.0 15.0 30.0
[13465] 11.0 15.0 15.0 57.0 0.7 28.0 29.0 15.0 15.0 2.0 0.8 30.0
[13477] 15.0 15.0 1.0 43.0 1.0 0.2 0.8 3.0 15.0 15.0 15.0 15.0
[13489] 0.8 15.0 15.0 2.0 29.0 57.0 16.0 29.0 15.0 6.0 15.0 18.0
[13501] 2.0 15.0 2.0 20.0 16.0 5.0 57.0 15.0 44.0 13.0 0.8 15.0
[13513] 15.0 56.0 34.0 15.0 2.0 57.0 15.0 0.8 0.8 15.0 15.0 6.0
[13525] 2.0 15.0 57.0 0.8 0.8 27.0 15.0 15.0 0.7 53.0 15.0 2.0
[13537] 9.0 0.9 0.8 15.0 15.0 18.0 15.0 20.0 18.0 15.0 15.0 0.8
[13549] 16.0 0.8 16.0 15.0 28.0 0.8 9.0 85.0 15.0 28.0 1.0 29.0
[13561] 15.0 23.0 29.0 3.0 15.0 18.0 8.0 15.0 28.0 16.0 0.6 0.8
[13573] 0.8 15.0 16.0 50.0 50.0 51.0 51.0 60.0 16.0 16.0 6.0 0.8
[13585] 57.0 17.0 4.0 16.0 0.8 16.0 1.0 20.0 16.0 16.0 29.0 15.0
[13597] 15.0 0.8 0.8 5.0 0.6 15.0 15.0 30.0 4.0 15.0 0.1 0.8
[13609] 13.0 15.0 1.0 1.0 2.0 16.0 16.0 8.0 16.0 16.0 57.0 57.0
[13621] 57.0 0.6 16.0 57.0 29.0 2.0 16.0 1.0 16.0 16.0 29.0 16.0
[13633] 16.0 16.0 5.0 57.0 0.9 0.8 16.0 16.0 16.0 50.0 16.0 16.0
[13645] 0.8 16.0 57.0 57.0 16.0 16.0 16.0 16.0 81.0 72.0 0.8 16.0
[13657] 16.0 0.8 16.0 2.0 0.8 25.0 16.0 16.0 57.0 1.0 16.0 63.0
[13669] 16.0 29.0 1.0 16.0 0.0 16.0 16.0 2.0 16.0 16.0 0.8 0.7
[13681] 57.0 16.0 57.0 2.0 85.0 57.0 16.0 57.0 0.8 30.0 16.0 16.0
[13693] 85.0 16.0 16.0 16.0 16.0 22.0 19.0 16.0 16.0 16.0 0.8 1.0
[13705] 12.0 14.0 16.0 5.0 16.0 16.0 6.0 16.0 0.8 57.0 58.0 57.0
[13717] 16.0 5.0 16.0 16.0 18.0 29.0 16.0 16.0 16.0 16.0 20.0 54.0
[13729] 0.8 27.0 57.0 0.8 2.0 57.0 16.0 16.0 16.0 1.0 2.0 16.0
[13741] 16.0 0.5 55.0 16.0 40.0 0.8 57.0 58.0 16.0 16.0 58.0 16.0
[13753] 29.0 1.0 18.0 30.0 0.8 0.8 55.0 55.0 16.0 14.0 16.0 58.0
[13765] 0.9 30.0 16.0 16.0 14.0 0.8 16.0 55.0 6.0 17.0 26.0 58.0
[13777] 2.0 0.8 16.0 30.0 16.0 2.0 16.0 16.0 16.0 16.0 0.5 29.0
[13789] 16.0 16.0 16.0 1.0 57.0 0.8 16.0 16.0 85.0 16.0 16.0 58.0
[13801] 58.0 1.0 16.0 0.8 16.0 58.0 0.8 16.0 16.0 16.0 16.0 16.0
[13813] 1.0 51.0 53.0 33.0 58.0 16.0 81.0 15.0 16.0 2.0 6.0 58.0
[13825] 0.8 16.0 16.0 16.0 16.0 30.0 4.0 0.9 16.0 58.0 16.0 40.0
[13837] 58.0 16.0 0.8 16.0 16.0 0.8 58.0 17.0 21.0 0.8 24.0 16.0
[13849] 12.0 1.0 8.0 16.0 16.0 0.8 30.0 5.0 45.0 16.0 16.0 16.0
[13861] 0.8 16.0 0.3 58.0 30.0 0.2 0.8 16.0 58.0 1.0 0.8 85.0
[13873] 2.0 16.0 16.0 16.0 7.0 11.0 2.0 16.0 16.0 16.0 0.8 58.0
[13885] 16.0 82.0 30.0 58.0 0.4 16.0 58.0 30.0 2.0 16.0 18.0 16.0
[13897] 16.0 16.0 16.0 16.0 58.0 2.0 0.8 16.0 16.0 16.0 84.0 16.0
[13909] 5.0 20.0 16.0 16.0 0.6 1.0 0.8 16.0 18.0 9.0 14.0 1.0
[13921] 29.0 1.0 0.9 58.0 1.0 36.0 16.0 31.0 0.8 5.0 16.0 16.0
[13933] 16.0 0.8 16.0 0.8 0.9 0.5 10.0 58.0 68.0 2.0 16.0 16.0
[13945] 16.0 30.0 16.0 16.0 16.0 56.0 5.0 47.0 1.0 58.0 2.0 16.0
[13957] 6.0 16.0 31.0 91.0 58.0 59.0 58.0 2.0 16.0 7.0 16.0 16.0
[13969] 16.0 16.0 0.9 58.0 35.0 0.2 0.8 86.0 16.0 31.0 16.0 0.5
[13981] 16.0 81.0 32.0 59.0 45.0 7.0 59.0 16.0 15.0 2.0 2.0 16.0
[13993] 16.0 3.0 0.8 16.0 57.0 0.9 0.8 4.0 82.0 2.0 16.0 16.0
[14005] 16.0 17.0 57.0 58.0 16.0 16.0 58.0 16.0 16.0 16.0 0.9 16.0
[14017] 16.0 82.0 3.0 21.0 53.0 51.0 53.0 54.0 1.0 59.0 1.0 58.0
[14029] 8.0 10.0 0.8 16.0 35.0 16.0 16.0 0.9 52.0 16.0 16.0 16.0
[14041] 16.0 16.0 16.0 5.0 16.0 8.0 16.0 6.0 16.0 16.0 59.0 17.0
[14053] 0.3 58.0 16.0 16.0 58.0 58.0 16.0 16.0 14.0 1.0 84.0 16.0
[14065] 16.0 16.0 16.0 0.7 0.8 16.0 31.0 59.0 19.0 0.9 59.0 16.0
[14077] 16.0 1.0 6.0 31.0 59.0 0.8 59.0 59.0 59.0 59.0 59.0 32.0
[14089] 59.0 16.0 16.0 30.0 16.0 2.0 0.8 16.0 16.0 16.0 16.0 16.0
[14101] 14.0 16.0 16.0 59.0 16.0 0.8 59.0 19.0 10.0 30.0 11.0 1.0
[14113] 16.0 30.0 16.0 14.0 16.0 59.0 16.0 16.0 2.0 1.0 0.9 10.0
[14125] 4.0 0.9 6.0 16.0 16.0 30.0 50.0 15.0 9.0 9.0 2.0 1.0
[14137] 16.0 59.0 2.0 16.0 16.0 6.0 0.9 16.0 0.2 37.0 16.0 59.0
[14149] 60.0 0.9 13.0 9.0 16.0 0.9 0.4 16.0 16.0 6.0 6.0 16.0
[14161] 0.9 13.0 16.0 17.0 0.3 86.0 60.0 16.0 16.0 16.0 82.0 0.9
[14173] 16.0 16.0 32.0 0.9 32.0 82.0 60.0 54.0 16.0 16.0 9.0 16.0
[14185] 4.0 60.0 16.0 2.0 60.0 16.0 60.0 60.0 60.0 60.0 30.0 31.0
[14197] 16.0 0.8 16.0 16.0 14.0 16.0 16.0 16.0 60.0 16.0 59.0 16.0
[14209] 16.0 0.8 60.0 16.0 16.0 0.9 0.9 16.0 16.0 17.0 68.0 16.0
[14221] 16.0 16.0 16.0 1.0 16.0 16.0 16.0 16.0 82.0 60.0 30.0 16.0
[14233] 16.0 31.0 16.0 21.0 16.0 20.0 12.0 16.0 2.0 16.0 0.9 16.0
[14245] 9.0 4.0 0.3 16.0 119.0 16.0 16.0 60.0 16.0 16.0 40.0 16.0
[14257] 1.0 16.0 31.0 60.0 16.0 31.0 0.9 0.7 61.0 17.0 11.0 16.0
[14269] 16.0 16.0 0.9 16.0 16.0 1.0 6.0 20.0 16.0 15.0 54.0 53.0
[14281] 16.0 31.0 2.0 60.0 1.0 16.0 2.0 16.0 0.9 16.0 0.8 44.0
[14293] 16.0 16.0 16.0 17.0 83.0 2.0 14.0 87.0 49.0 0.9 16.0 61.0
[14305] 0.2 16.0 16.0 16.0 18.0 16.0 47.0 0.9 16.0 1.0 18.0 16.0
[14317] 16.0 16.0 60.0 16.0 16.0 87.0 16.0 31.0 15.0 17.0 16.0 16.0
[14329] 16.0 60.0 16.0 61.0 61.0 16.0 32.0 6.0 16.0 16.0 10.0 2.0
[14341] 16.0 13.0 4.0 0.9 0.9 16.0 16.0 0.9 16.0 61.0 61.0 16.0
[14353] 60.0 83.0 84.0 16.0 16.0 27.0 16.0 32.0 61.0 0.9 84.0 0.0
[14365] 16.0 16.0 16.0 16.0 16.0 16.0 60.0 1.0 16.0 61.0 3.0 16.0
[14377] 16.0 16.0 0.9 91.0 60.0 16.0 2.0 16.0 13.0 16.0 0.9 16.0
[14389] 16.0 16.0 16.0 0.2 16.0 2.0 4.0 61.0 16.0 11.0 16.0 16.0
[14401] 16.0 2.0 16.0 16.0 16.0 16.0 58.0 14.0 16.0 2.0 16.0 16.0
[14413] 16.0 17.0 32.0 4.0 16.0 2.0 16.0 16.0 4.0 17.0 43.0 16.0
[14425] 61.0 0.9 16.0 16.0 16.0 16.0 91.0 16.0 16.0 16.0 5.0 16.0
[14437] 18.0 16.0 16.0 25.0 54.0 16.0 16.0 16.0 4.0 61.0 16.0 32.0
[14449] 32.0 16.0 87.0 61.0 2.0 0.9 4.0 2.0 16.0 16.0 16.0 16.0
[14461] 16.0 61.0 16.0 21.0 32.0 16.0 0.9 16.0 16.0 16.0 0.6 16.0
[14473] 0.8 16.0 16.0 16.0 16.0 15.0 16.0 16.0 17.0 61.0 61.0 16.0
[14485] 1.0 16.0 16.0 17.0 16.0 16.0 17.0 61.0 61.0 2.0 61.0 61.0
[14497] 16.0 0.9 33.0 33.0 17.0 16.0 16.0 17.0 17.0 61.0 16.0 0.9
[14509] 0.9 51.0 0.9 62.0 0.9 4.0 1.0 0.9 20.0 16.0 7.0 4.0
[14521] 0.5 7.0 0.9 20.0 16.0 4.0 16.0 47.0 57.0 0.9 10.0 57.0
[14533] 17.0 16.0 4.0 17.0 18.0 18.0 0.9 16.0 2.0 6.0 62.0 18.0
[14545] 17.0 33.0 85.0 17.0 16.0 33.0 1.0 6.0 17.0 51.0 17.0 17.0
[14557] 88.0 0.9 17.0 0.9 16.0 21.0 17.0 3.0 61.0 17.0 17.0 17.0
[14569] 17.0 17.0 17.0 56.0 55.0 58.0 82.0 17.0 17.0 0.2 36.0 17.0
[14581] 51.0 1.0 18.0 85.0 61.0 26.0 0.3 17.0 0.9 86.0 17.0 17.0
[14593] 0.9 2.0 54.0 62.0 62.0 5.0 18.0 32.0 17.0 9.0 17.0 17.0
[14605] 1.0 17.0 17.0 0.9 17.0 17.0 17.0 17.0 17.0 16.0 17.0 62.0
[14617] 62.0 17.0 17.0 2.0 0.9 17.0 17.0 17.0 0.9 1.0 17.0 62.0
[14629] 35.0 37.0 17.0 26.0 17.0 17.0 0.9 17.0 17.0 44.0 34.0 0.9
[14641] 62.0 17.0 62.0 17.0 17.0 17.0 17.0 17.0 17.0 34.0 17.0 17.0
[14653] 62.0 17.0 2.0 2.0 17.0 2.0 0.9 17.0 22.0 31.0 0.9 0.9
[14665] 59.0 62.0 17.0 0.9 81.0 35.0 17.0 0.9 7.0 56.0 12.0 17.0
[14677] 0.9 13.0 0.9 28.0 0.9 10.0 17.0 17.0 17.0 2.0 17.0 17.0
[14689] 3.0 35.0 62.0 0.9 1.0 17.0 15.0 17.0 17.0 17.0 17.0 17.0
[14701] 17.0 3.0 17.0 62.0 17.0 0.9 3.0 17.0 17.0 17.0 17.0 17.0
[14713] 31.0 17.0 17.0 17.0 17.0 17.0 0.6 17.0 18.0 14.0 62.0 62.0
[14725] 17.0 17.0 33.0 84.0 62.0 17.0 15.0 17.0 17.0 17.0 17.0 62.0
[14737] 58.0 60.0 17.0 0.9 9.0 2.0 62.0 0.9 17.0 0.9 17.0 5.0
[14749] 62.0 23.0 62.0 62.0 62.0 17.0 0.9 16.0 17.0 17.0 63.0 17.0
[14761] 63.0 62.0 0.9 17.0 0.9 17.0 17.0 17.0 17.0 18.0 19.0 24.0
[14773] 17.0 17.0 1.0 17.0 85.0 17.0 17.0 33.0 17.0 17.0 6.0 87.0
[14785] 17.0 17.0 17.0 63.0 17.0 0.2 18.0 17.0 0.9 17.0 18.0 17.0
[14797] 6.0 2.0 2.0 19.0 17.0 17.0 0.8 17.0 35.0 17.0 63.0 17.0
[14809] 63.0 2.0 17.0 2.0 17.0 17.0 63.0 17.0 17.0 0.9 0.9 0.9
[14821] 33.0 1.0 1.0 0.1 17.0 17.0 14.0 17.0 17.0 0.9 0.2 17.0
[14833] 17.0 17.0 3.0 37.0 34.0 62.0 2.0 35.0 88.0 0.5 34.0 34.0
[14845] 7.0 0.9 63.0 17.0 17.0 0.9 2.0 17.0 18.0 2.0 34.0 17.0
[14857] 0.9 0.9 17.0 35.0 63.0 57.0 17.0 22.0 60.0 0.9 36.0 1.0
[14869] 0.9 17.0 17.0 17.0 17.0 17.0 4.0 31.0 0.9 17.0 17.0 17.0
[14881] 17.0 17.0 17.0 19.0 17.0 23.0 9.0 17.0 63.0 4.0 0.9 1.0
[14893] 1.0 17.0 86.0 0.9 25.0 12.0 17.0 17.0 1.0 0.2 5.0 0.9
[14905] 8.0 17.0 18.0 17.0 0.0 58.0 17.0 0.9 17.0 0.9 18.0 0.9
[14917] 63.0 17.0 17.0 11.0 17.0 0.9 0.7 24.0 24.0 17.0 63.0 35.0
[14929] 17.0 36.0 17.0 17.0 17.0 0.5 17.0 14.0 0.9 17.0 17.0 1.0
[14941] 0.3 17.0 17.0 17.0 17.0 17.0 58.0 63.0 63.0 6.0 17.0 2.0
[14953] 17.0 119.0 17.0 17.0 17.0 17.0 2.0 17.0 17.0 17.0 14.0 17.0
[14965] 17.0 17.0 31.0 63.0 17.0 29.0 64.0 6.0 3.0 17.0 0.1 0.9
[14977] 17.0 17.0 17.0 17.0 17.0 17.0 17.0 17.0 0.9 17.0 17.0 64.0
[14989] 17.0 1.0 18.0 17.0 0.9 0.9 17.0 0.2 86.0 17.0 0.7 23.0
[15001] 1.0 17.0 64.0 17.0 64.0 0.9 0.0 17.0 22.0 17.0 138.0 16.0
[15013] 17.0 35.0 60.0 1.0 64.0 64.0 17.0 64.0 17.0 36.0 0.9 0.9
[15025] 32.0 14.0 17.0 17.0 17.0 6.0 16.0 0.9 1.0 1.0 3.0 17.0
[15037] 0.9 14.0 64.0 0.9 6.0 24.0 17.0 17.0 19.0 17.0 17.0 0.4
[15049] 17.0 17.0 17.0 0.9 17.0 17.0 65.0 17.0 17.0 4.0 7.0 15.0
[15061] 24.0 1.0 10.0 9.0 17.0 0.9 17.0 89.0 9.0 10.0 17.0 17.0
[15073] 22.0 17.0 17.0 35.0 65.0 65.0 65.0 17.0 17.0 0.9 17.0 87.0
[15085] 17.0 17.0 66.0 64.0 58.0 64.0 65.0 66.0 66.0 17.0 66.0 17.0
[15097] 17.0 7.0 3.0 17.0 17.0 66.0 17.0 17.0 66.0 17.0 17.0 17.0
[15109] 0.9 2.0 17.0 17.0 17.0 0.9 60.0 17.0 0.8 17.0 17.0 90.0
[15121] 35.0 66.0 0.9 5.0 17.0 17.0 4.0 17.0 89.0 17.0 17.0 17.0
[15133] 17.0 17.0 11.0 66.0 0.9 0.9 2.0 17.0 2.0 0.9 0.9 17.0
[15145] 17.0 0.2 17.0 17.0 17.0 36.0 38.0 17.0 17.0 2.0 0.9 17.0
[15157] 0.9 36.0 17.0 17.0 17.0 36.0 17.0 17.0 17.0 17.0 17.0 67.0
[15169] 0.2 3.0 18.0 17.0 17.0 0.0 16.0 17.0 1.0 38.0 17.0 58.0
[15181] 1.0 31.0 38.0 0.5 17.0 17.0 17.0 17.0 34.0 15.0 17.0 63.0
[15193] 58.0 59.0 17.0 6.0 17.0 17.0 2.0 0.9 17.0 17.0 36.0 17.0
[15205] 17.0 87.0 66.0 17.0 17.0 18.0 17.0 87.0 0.9 17.0 17.0 17.0
[15217] 17.0 17.0 17.0 17.0 66.0 67.0 19.0 18.0 17.0 0.3 36.0 21.0
[15229] 17.0 17.0 87.0 17.0 17.0 39.0 4.0 1.0 17.0 67.0 3.0 17.0
[15241] 36.0 90.0 17.0 17.0 0.9 88.0 88.0 0.9 9.0 17.0 0.0 4.0
[15253] 15.0 17.0 67.0 17.0 67.0 17.0 17.0 37.0 17.0 39.0 1.0 37.0
[15265] 17.0 67.0 37.0 17.0 0.3 0.9 17.0 17.0 17.0 67.0 17.0 68.0
[15277] 0.7 0.5 89.0 17.0 3.0 17.0 18.0 17.0 0.9 17.0 4.0 17.0
[15289] 18.0 17.0 17.0 17.0 17.0 4.0 17.0 0.9 17.0 14.0 17.0 0.9
[15301] 64.0 17.0 67.0 68.0 0.2 89.0 17.0 17.0 5.0 17.0 17.0 3.0
[15313] 17.0 0.9 17.0 17.0 17.0 17.0 60.0 17.0 36.0 4.0 0.0 17.0
[15325] 37.0 17.0 0.9 37.0 9.0 17.0 40.0 24.0 90.0 90.0 17.0 88.0
[15337] 39.0 17.0 18.0 17.0 0.9 17.0 0.8 0.9 0.9 40.0 0.6 17.0
[15349] 17.0 6.0 1.0 0.4 39.0 17.0 17.0 0.8 17.0 17.0 18.0 17.0
[15361] 17.0 17.0 69.0 62.0 69.0 17.0 4.0 6.0 3.0 40.0 17.0 17.0
[15373] 17.0 0.8 0.7 17.0 7.0 17.0 71.0 42.0 9.0 36.0 14.0 90.0
[15385] 11.0 39.0 17.0 0.9 17.0 0.9 17.0 17.0 0.3 23.0 0.9 39.0
[15397] 15.0 17.0 4.0 17.0 19.0 17.0 40.0 17.0 17.0 41.0 17.0 66.0
[15409] 17.0 17.0 45.0 34.0 72.0 0.9 0.9 0.9 13.0 0.9 17.0 17.0
[15421] 0.4 31.0 17.0 17.0 0.8 0.9 0.9 17.0 16.0 17.0 1.0 2.0
[15433] 0.9 40.0 17.0 41.0 65.0 0.9 59.0 61.0 68.0 68.0 0.8 17.0
[15445] 70.0 17.0 17.0 4.0 18.0 17.0 17.0 67.0 17.0 17.0 25.0 1.0
[15457] 17.0 17.0 17.0 0.9 0.9 0.9 3.0 17.0 17.0 0.9 0.9 2.0
[15469] 17.0 4.0 41.0 17.0 17.0 17.0 17.0 11.0 0.9 3.0 0.9 17.0
[15481] 30.0 17.0 29.0 13.0 17.0 48.0 17.0 17.0 17.0 17.0 17.0 71.0
[15493] 75.0 49.0 74.0 10.0 43.0 0.6 0.1 17.0 17.0 53.0 71.0 17.0
[15505] 17.0 44.0 17.0 0.6 0.9 0.4 77.0 17.0 5.0 45.0 18.0 19.0
[15517] 0.9 0.6 17.0 17.0 18.0 69.0 69.0 72.0<-representa una flecha que se lee:asignar a.Permite guardar un objeto con un nombre dado
- El objeto se guarda en el Environment.
- Luego, lo puedes llamar utilizando el nombre con el que lo guardaste.
[1] "female" "female" "male" "female" "male" "female" "male" "female"
[9] "male" "male" "male" "female" "female" "male" "female" "female"
[17] "male" "male" "female" "female" "female" "male" "female" "male"
[25] "female" "male" "female" "male" "female" "female" "male" "female"
[33] "female" "female" "female" "female" "male" "female" "male" "female"
[41] "female" "male" "male" "female" "female" "male" "male" "male"
[49] "male" "female" "female" "female" "male" "male" "male" "male"
[57] "female" "female" "female" "female" "male" "male" "male" "female"
[65] "female" "female" "male" "female" "male" "female" "male" "male"
[73] "female" "female" "female" "male" "male" "male" "female" "male"
[81] "female" "female" "female" "female" "female" "male" "female" "male"
[89] "male" "male" "male" "female" "male" "male" "female" "female"
[97] "female" "female" "male" "male" "male" "female" "male" "female"
[105] "female" "female" "male" "male" "female" "male" "male" "male"
[113] "female" "female" "male" "male" "male" "female" "female" "male"
[121] "female" "female" "male" "male" "female" "female" "female" "male"
[129] "female" "female" "male" "female" "female" "female" "female" "male"
[137] "male" "male" "male" "male" "male" "female" "female" "male"
[145] "male" "female" "male" "female" "female" "male" "male" "male"
[153] "female" "female" "female" "male" "female" "male" "male" "female"
[161] "male" "female" "male" "male" "male" "female" "female" "male"
[169] "female" "male" "female" "female" "female" "male" "female" "male"
[177] "female" "female" "male" "male" "female" "female" "female" "female"
[185] "female" "male" "male" "male" "female" "male" "male" "female"
[193] "female" "male" "male" "male" "male" "male" "male" "female"
[201] "female" "female" "female" "female" "male" "male" "male" "male"
[209] "male" "female" "female" "female" "male" "male" "male" "male"
[217] "male" "female" "male" "male" "male" "female" "male" "male"
[225] "male" "male" "female" "female" "female" "female" "female" "male"
[233] "female" "male" "male" "female" "female" "male" "female" "female"
[241] "female" "male" "female" "female" "male" "male" "female" "male"
[249] "female" "female" "female" "male" "female" "female" "female" "male"
[257] "male" "female" "male" "female" "male" "female" "male" "male"
[265] "female" "female" "male" "male" "female" "female" "male" "male"
[273] "female" "male" "male" "male" "male" "male" "male" "male"
[281] "female" "female" "male" "female" "female" "female" "female" "female"
[289] "female" "female" "male" "female" "female" "male" "male" "female"
[297] "female" "female" "female" "female" "male" "female" "male" "female"
[305] "female" "male" "male" "male" "male" "female" "male" "female"
[313] "male" "female" "male" "male" "female" "female" "male" "female"
[321] "female" "female" "female" "female" "male" "male" "male" "female"
[329] "female" "male" "male" "female" "female" "female" "male" "male"
[337] "female" "male" "female" "female" "male" "male" "male" "male"
[345] "female" "male" "male" "male" "female" "male" "female" "female"
[353] "female" "female" "female" "female" "female" "male" "female" "male"
[361] "male" "male" "male" "female" "male" "male" "female" "male"
[369] "female" "female" "female" "male" "female" "female" "male" "male"
[377] "female" "female" "female" "male" "male" "male" "male" "female"
[385] "male" "female" "male" "female" "female" "male" "female" "male"
[393] "female" "female" "male" "female" "female" "male" "female" "female"
[401] "female" "female" "male" "male" "male" "female" "female" "male"
[409] "male" "female" "male" "female" "male" "male" "male" "female"
[417] "female" "male" "male" "male" "female" "male" "female" "male"
[425] "female" "male" "female" "female" "male" "female" "female" "female"
[433] "male" "male" "male" "male" "male" "male" "male" "male"
[441] "male" "male" "male" "female" "female" "male" "male" "male"
[449] "male" "male" "male" "female" "male" "female" "female" "male"
[457] "female" "female" "male" "female" "male" "male" "male" "female"
[465] "female" "female" "male" "male" "male" "female" "male" "female"
[473] "female" "male" "male" "female" "female" "female" "female" "female"
[481] "female" "female" "female" "female" "male" "female" "female" "female"
[489] "female" "female" "female" "female" "female" "male" "male" "female"
[497] "female" "male" "male" "male" "female" "female" "male" "male"
[505] "female" "male" "male" "male" "male" "female" "male" "female"
[513] "female" "female" "female" "female" "female" "male" "female" "male"
[521] "female" "female" "female" "female" "female" "male" "female" "female"
[529] "female" "female" "female" "female" "female" "male" "male" "male"
[537] "female" "male" "female" "male" "female" "female" "male" "female"
[545] "male" "male" "female" "female" "female" "male" "male" "female"
[553] "male" "male" "female" "female" "male" "male" "male" "male"
[561] "male" "female" "female" "female" "male" "female" "female" "female"
[569] "male" "female" "female" "male" "male" "female" "female" "female"
[577] "female" "female" "female" "female" "male" "male" "female" "male"
[585] "female" "female" "female" "male" "female" "female" "female" "female"
[593] "female" "female" "male" "female" "female" "female" "male" "male"
[601] "female" "female" "male" "female" "male" "female" "male" "female"
[609] "female" "female" "female" "female" "female" "male" "male" "male"
[617] "female" "female" "female" "female" "male" "male" "female" "female"
[625] "male" "male" "male" "female" "female" "female" "female" "male"
[633] "female" "female" "female" "male" "male" "male" "male" "female"
[641] "male" "male" "male" "female" "male" "male" "male" "female"
[649] "male" "male" "male" "male" "female" "male" "male" "female"
[657] "male" "male" "male" "female" "male" "male" "female" "male"
[665] "female" "male" "male" "female" "male" "female" "female" "male"
[673] "female" "female" "male" "female" "male" "female" "female" "male"
[681] "male" "female" "male" "male" "female" "female" "female" "female"
[689] "male" "male" "male" "female" "male" "female" "male" "female"
[697] "male" "female" "female" "female" "female" "female" "female" "male"
[705] "male" "male" "female" "female" "female" "female" "female" "male"
[713] "female" "male" "male" "female" "female" "female" "male" "female"
[721] "male" "male" "female" "female" "female" "male" "female" "female"
[729] "male" "female" "female" "female" "female" "male" "female" "female"
[737] "male" "male" "female" "female" "male" "male" "male" "male"
[745] "female" "female" "male" "female" "male" "female" "female" "female"
[753] "female" "female" "male" "male" "male" "female" "male" "female"
[761] "female" "male" "male" "male" "male" "male" "male" "female"
[769] "male" "female" "male" "male" "female" "male" "male" "male"
[777] "female" "female" "female" "female" "female" "male" "female" "female"
[785] "male" "female" "female" "female" "male" "male" "male" "male"
[793] "female" "female" "female" "male" "male" "female" "female" "female"
[801] "female" "male" "female" "male" "female" "female" "female" "male"
[809] "female" "female" "female" "female" "female" "male" "male" "male"
[817] "female" "female" "male" "male" "male" "female" "female" "female"
[825] "male" "female" "male" "female" "male" "female" "male" "male"
[833] "female" "female" "female" "male" "female" "female" "female" "male"
[841] "female" "male" "female" "female" "male" "female" "female" "female"
[849] "male" "male" "male" "female" "male" "female" "male" "male"
[857] "female" "male" "male" "female" "female" "male" "female" "female"
[865] "male" "female" "male" "male" "male" "female" "female" "female"
[873] "male" "male" "female" "female" "female" "male" "male" "female"
[881] "male" "female" "female" "female" "female" "male" "female" "female"
[889] "male" "male" "female" "female" "female" "female" "male" "female"
[897] "male" "female" "male" "female" "female" "female" "female" "female"
[905] "female" "female" "male" "female" "female" "male" "female" "female"
[913] "female" "male" "female" "female" "male" "male" "female" "male"
[921] "female" "female" "male" "male" "female" "female" "male" "female"
[929] "female" "male" "male" "male" "female" "male" "male" "male"
[937] "male" "male" "female" "male" "female" "male" "male" "male"
[945] "male" "male" "male" "male" "male" "male" "male" "female"
[953] "male" "male" "male" "male" "male" "male" "male" "female"
[961] "male" "female" "male" "male" "female" "female" "female" "male"
[969] "female" "female" "female" "male" "male" "male" "male" "female"
[977] "female" "male" "female" "male" "female" "male" "female" "male"
[985] "female" "female" "male" "male" "male" "female" "male" "male"
[993] "female" "male" "male" "female" "male" "male" "male" "female"
[1001] "male" "female" "male" "male" "male" "male" "female" "female"
[1009] "female" "female" "male" "male" "male" "female" "male" "female"
[1017] "female" "male" "male" "female" "male" "female" "male" "female"
[1025] "female" "female" "female" "male" "female" "female" "female" "female"
[1033] "male" "female" "male" "female" "male" "male" "male" "female"
[1041] "male" "male" "male" "male" "male" "female" "female" "female"
[1049] "female" "male" "male" "female" "female" "male" "male" "male"
[1057] "male" "male" "female" "male" "male" "male" "male" "male"
[1065] "female" "female" "male" "female" "male" "female" "female" "male"
[1073] "female" "male" "female" "male" "female" "female" "male" "male"
[1081] "female" "male" "male" "male" "female" "female" "female" "male"
[1089] "male" "female" "female" "female" "male" "male" "female" "female"
[1097] "male" "female" "female" "female" "female" "female" "male" "female"
[1105] "male" "female" "female" "male" "female" "male" "female" "female"
[1113] "female" "female" "male" "male" "male" "female" "male" "male"
[1121] "female" "female" "female" "male" "male" "female" "male" "female"
[1129] "female" "male" "female" "female" "male" "male" "female" "male"
[1137] "male" "female" "female" "male" "female" "female" "female" "female"
[1145] "female" "female" "male" "male" "female" "male" "male" "male"
[1153] "female" "female" "male" "male" "male" "female" "male" "female"
[1161] "male" "male" "female" "female" "male" "male" "female" "male"
[1169] "female" "male" "female" "male" "male" "female" "female" "male"
[1177] "male" "male" "male" "male" "female" "female" "female" "female"
[1185] "male" "female" "male" "female" "male" "female" "female" "female"
[1193] "female" "female" "male" "male" "female" "female" "male" "male"
[1201] "female" "female" "male" "female" "female" "male" "male" "male"
[1209] "male" "female" "male" "male" "female" "female" "female" "male"
[1217] "male" "female" "male" "male" "male" "male" "male" "female"
[1225] "male" "female" "male" "female" "male" "female" "female" "male"
[1233] "male" "female" "male" "female" "female" "male" "female" "male"
[1241] "female" "female" "male" "male" "male" "female" "male" "male"
[1249] "female" "female" "male" "male" "female" "male" "female" "male"
[1257] "female" "male" "male" "female" "female" "male" "male" "female"
[1265] "male" "female" "male" "male" "female" "female" "female" "female"
[1273] "male" "male" "female" "female" "male" "female" "male" "male"
[1281] "male" "male" "female" "male" "male" "female" "female" "male"
[1289] "male" "female" "male" "female" "male" "female" "male" "female"
[1297] "male" "male" "female" "female" "male" "male" "male" "male"
[1305] "male" "male" "female" "male" "male" "male" "male" "female"
[1313] "female" "male" "male" "female" "female" "male" "female" "male"
[1321] "male" "male" "male" "male" "female" "male" "male" "male"
[1329] "male" "male" "male" "female" "female" "female" "female" "male"
[1337] "male" "female" "male" "female" "male" "female" "female" "female"
[1345] "female" "female" "female" "female" "female" "female" "male" "female"
[1353] "female" "female" "female" "male" "male" "female" "male" "female"
[1361] "female" "male" "male" "female" "female" "male" "male" "male"
[1369] "female" "male" "male" "female" "male" "male" "female" "female"
[1377] "male" "female" "female" "male" "male" "male" "female" "male"
[1385] "male" "male" "female" "male" "female" "female" "female" "female"
[1393] "male" "female" "female" "female" "male" "male" "female" "female"
[1401] "female" "male" "female" "female" "female" "male" "female" "male"
[1409] "male" "male" "male" "male" "female" "male" "female" "male"
[1417] "male" "female" "female" "female" "male" "male" "female" "male"
[1425] "female" "female" "male" "female" "male" "female" "male" "female"
[1433] "male" "female" "male" "male" "female" "male" "male" "female"
[1441] "female" "male" "female" "female" "female" "female" "male" "male"
[1449] "male" "male" "female" "male" "male" "female" "male" "female"
[1457] "male" "male" "male" "female" "female" "female" "female" "female"
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[15521] "female" "female" "male" "female"El panel Environment
Es un panel que muestra los objetos (data frames, matrices, vectores, funciones, etc.) que se tienen en el entorno de trabajo.
Imprimir y guardar resultado
Datos en R
Agenda
Introducción a la Estadística y Ciencia de Datos
Análisis de datos reproducible
R y RStudio
Aspectos elementales de R y Rstudio
Primeros pasos en R
Datos en R
Conjunto de datos en R
En investigación, las bases de datos se organizan en
tablas rectangularesestructuradas.- Cada fila es una
observación - Cada columna es una
variable
- Cada fila es una
Hay dos clases de objetos que comúnmente usamos para almacenar y manipular datos en R:
data.framedata.tibble
Adaptado de Smit Aj, Abrahams A & Schlegel RW. Intro R Workshop: Data Manipulation, Analysius, and Visualization.
data.tibble versus data.frame
Los
data.tibbletambién sondata.frame, pero con algunospoderes adicionales.
Así se ve un data tibble
# A tibble: 15,524 × 5
subject_id fake_first_name fake_last_name gender pan_day
<dbl> <chr> <chr> <chr> <dbl>
1 1412 jhezane westerling female 4
2 533 penny targaryen female 7
3 9134 grunt rivers male 7
4 8518 melisandre swyft female 8
5 8967 rolley karstark male 8
6 11048 megga karstark female 8
7 663 ithoke targaryen male 9
8 2158 ravella frey female 9
9 3794 styr tyrell male 9
10 4706 wynafryd seaworth male 9
# … with 15,514 more rows- Notar que los
data.tibblesondata.frame aumentados:
Y así un data frame
subject_id fake_first_name fake_last_name gender pan_day
1 1412 jhezane westerling female 4
2 533 penny targaryen female 7
3 9134 grunt rivers male 7
4 8518 melisandre swyft female 8
5 8967 rolley karstark male 8
6 11048 megga karstark female 8
7 663 ithoke targaryen male 9
8 2158 ravella frey female 9
9 3794 styr tyrell male 9
10 4706 wynafryd seaworth male 9
11 8115 patrek frey male 9
12 9309 maege sand female 9
13 8943 myria rivers female 9
14 6965 arthor lannister male 9
15 12236 waif harlaw female 9
16 998 harra sand female 10
17 2103 ollo snow male 10
18 2349 yezzan royce male 10
19 4930 sarra frey female 10
20 5408 alia ryswell female 10
21 2083 weasel tarly female 10
22 8031 gueren sand male 10
23 8138 frenya swyft female 10
24 9502 lorcas mormont male 10
25 10468 chella mormont female 10
26 10919 woth snow male 10
27 9217 ragwyle martell female 10
28 2114 azzak tully male 10
29 227 maege sand female 11
30 252 nymeria karstark female 11
31 392 moon mormont male 11
32 587 morra tully female 11
33 725 lysa umber female 11
34 1818 ravella royce female 11
35 2427 daenerys umber female 11
36 1299 alys manderly female 11
37 2793 syrio stark male 11
38 2912 osha harlaw female 11
39 2983 ronnel snow male 11
40 3247 frenya westerling female 11
41 3618 sylva frey female 11
42 918 lenwood clegane male 11
43 3854 husband snow male 11
44 281 jacelyn swyft female 11
45 4032 frenya swyft female 11
46 4270 rolley tyrell male 11
47 4454 eddison frey male 11
48 4525 eddard karstark male 11
49 4536 torrhen tully male 11
50 2378 jeyne baelish female 11
51 5160 brienne tyrell female 11
52 5401 tion swyft female 11
53 5439 tobho tully male 11
54 3690 janos lannister male 11
55 6055 mathos seaworth male 11
56 6103 torrhen targaryen male 11
57 6340 gysella tarly female 11
58 6384 gysella rivers female 11
59 6569 lanna baelish female 11
60 7077 jeyne snow female 11
61 7081 lew martell male 11
62 7240 arryk mormont male 11
63 7334 renly bolton male 11
64 7545 catelyn royce female 11
65 7693 randa tully female 11
66 7705 donella westerling female 11
67 1175 ben ryswell male 11
68 7867 roslin royce female 11
69 4088 lennocks greyjoy male 11
70 8165 arianne clegane female 11
71 6292 eddard tarly male 11
72 8392 mad mormont male 11
73 1383 margaery bolton female 11
74 8395 nan frey female 11
75 1280 nymella westerling female 11
76 8878 hullen stark male 11
77 2084 rafford stark male 11
78 9022 gorold tully male 11
79 6211 sybelle greyjoy female 11
80 9218 balon rivers male 11
81 9482 penny ryswell female 11
82 9597 beth tully female 11
83 1383 margaery bolton female 11
84 9726 mariya westerling female 11
85 9775 grisel swyft female 11
86 9915 wun baratheon male 11
87 3934 arwyn stark female 11
88 921 patrek karstark male 11
89 4652 woth martell male 11
90 10459 alyx baelish male 11
91 10986 brenett karstark male 11
92 11391 zei umber female 11
93 11560 ulwyck stark male 11
94 140 walton kettleblack male 11
95 11746 gwin snow female 11
96 11826 maerie mormont female 11
97 10476 jeyne tyrell female 11
98 11870 walda targaryen female 11
99 12043 brandon westerling male 11
100 722 joseran greyjoy male 12
101 902 owen seaworth male 12
102 1840 jocelyn tarly female 12
103 2025 urswyck baelish male 12
104 2026 bellonara snow female 12
105 2116 penny kettleblack female 12
106 2341 penny kettleblack female 12
107 2573 glendon lannister male 12
108 2859 boros lannister male 12
109 3781 randa tyrell female 12
110 1252 morton baratheon male 12
111 2943 shagwell rivers male 12
112 4376 viserys ryswell male 12
113 4555 lady mormont female 12
114 4640 genna harlaw female 12
115 4699 ottyn sand male 12
116 4815 greydon seaworth male 12
117 5086 jhogo seaworth male 12
118 5236 sarella westerling female 12
119 5303 marissa greyjoy female 12
120 5358 butterbumps manderly male 12
121 4790 val tully female 12
122 5771 janna lannister female 12
123 5845 marq harlaw male 12
124 885 dick rivers male 12
125 6422 catelyn baelish female 12
126 7226 nymeria baelish female 12
127 7402 jhiqui snow female 12
128 7650 cellador sand male 12
129 7811 ghost baratheon female 12
130 8075 wylla bolton female 12
131 8152 alyx mormont male 12
132 8351 alysane seaworth female 12
133 8430 falyse royce female 12
134 8916 frynne baratheon female 12
135 7363 gilly royce female 12
136 9878 cuger stark male 12
137 10734 jyck sand male 12
138 10835 kraznys royce male 12
139 10994 luwin umber male 12
140 11120 jon manderly male 12
141 1991 stygg lannister male 12
142 11316 larra baelish female 12
143 11513 waif tyrell female 12
144 11568 dhazzar clegane male 12
145 11707 orphan snow male 12
146 3424 brienne manderly female 12
147 11824 mago tarly male 12
148 396 tanda martell female 13
149 506 cersei rivers female 13
150 515 robett royce male 13
151 1472 colemon harlaw male 13
152 1738 meldred kettleblack male 13
153 1861 bella mormont female 13
154 2782 asha martell female 13
155 3110 bellonara royce female 13
156 3203 jarl stark male 13
157 3861 asha tarly female 13
158 3873 ben greyjoy male 13
159 1492 endehar tully male 13
160 5668 sharna karstark female 13
161 6611 gilwood stark male 13
162 3443 donyse targaryen female 13
163 3075 jonos sand male 13
164 7368 alesander martell male 13
165 7481 varys baratheon male 13
166 8635 falyse umber female 13
167 8736 dalla harlaw female 13
168 9039 arthor kettleblack male 13
169 9172 megga harlaw female 13
170 9554 raymar seaworth male 13
171 9901 bessa tarly female 13
172 10667 olenna bolton female 13
173 10926 shae baelish female 13
174 10976 donnel greyjoy male 13
175 11381 sansa martell female 13
176 1098 groleo harlaw male 13
177 11786 jeyne targaryen female 13
178 12035 roslin baelish female 13
179 191 goghor bolton male 14
180 261 blane ryswell male 14
181 557 walda tarly female 14
182 626 asha kettleblack female 14
183 630 matrice clegane female 14
184 864 meera westerling female 14
185 1131 ermesande clegane female 14
186 1290 wallen westerling male 14
187 1933 marwyn lannister male 14
188 2208 donnel manderly male 14
189 2294 megga tarly female 14
190 2463 cuger martell male 14
191 3098 tim frey male 14
192 3235 marissa rivers female 14
193 3302 ghost umber female 14
194 3693 kyle mormont male 14
195 2081 terrance tyrell male 14
196 1229 mord tyrell male 14
197 4157 davos ryswell male 14
198 4195 gilwood sand male 14
199 4295 tallad mormont male 14
200 4378 jeyne martell female 14
201 3784 irri bolton female 14
202 4998 penny harlaw female 14
203 5489 larra tarly female 14
204 6174 lanna seaworth female 14
205 6204 adrack sand male 14
206 6217 pylos baratheon male 14
207 2541 timeon sand male 14
208 6332 reek baratheon male 14
209 6445 urswyck kettleblack male 14
210 6580 mya baelish female 14
211 4711 shyra manderly female 14
212 7159 selyse baratheon female 14
213 7586 torwynd baratheon male 14
214 7743 balon frey male 14
215 8954 qyburn sand male 14
216 9508 illyrio targaryen male 14
217 3242 frenken kettleblack male 14
218 281 jacelyn swyft female 14
219 11573 pypar frey male 14
220 10758 gorold clegane male 14
221 7753 renly clegane male 14
222 11763 meliana tully female 14
223 11929 grazdan westerling male 14
224 11941 gariss harlaw male 14
225 12127 clement martell male 14
226 60 sandor swyft male 15
227 388 marei kettleblack female 15
228 400 nolla snow female 15
229 1200 jhezane sand female 15
230 385 tanda snow female 15
231 1667 ezzara westerling female 15
232 1772 forley greyjoy male 15
233 1993 bella umber female 15
234 2839 varamyr westerling male 15
235 885 dick rivers male 15
236 3015 bella mormont female 15
237 2169 pia tully female 15
238 4115 greydon baelish male 15
239 4963 tanda baelish female 15
240 5023 chataya mormont female 15
241 5144 falyse bolton female 15
242 2990 edric stark male 15
243 5918 nymella westerling female 15
244 6252 galazza swyft female 15
245 6336 skinner mormont male 15
246 6348 ulmer seaworth male 15
247 6469 myrtle harlaw female 15
248 6975 lenwood seaworth male 15
249 1127 beth westerling female 15
250 2851 myria kettleblack female 15
251 7428 myria seaworth female 15
252 7470 tregar umber male 15
253 7606 hali tully female 15
254 7839 dalla sand female 15
255 8048 maris swyft female 15
256 8102 lenwood tyrell male 15
257 8328 parmen baelish male 15
258 8381 myranda karstark female 15
259 8644 rhogoro tully male 15
260 9198 cersei bolton female 15
261 9397 elwood royce male 15
262 9629 maerie rivers female 15
263 10096 chiswyck umber male 15
264 459 kurleket tyrell male 15
265 10553 maris karstark female 15
266 3418 melly clegane female 15
267 1499 viserys ryswell male 15
268 11581 tumco baelish male 15
269 11621 mariya tyrell female 15
270 7638 tyene mormont female 15
271 8231 lyle tarly male 15
272 11754 murenmure seaworth male 15
273 11889 myranda martell female 15
274 11961 eddard umber male 15
275 11977 pate mormont male 15
276 113 jhaqo umber male 16
277 724 barth umber male 16
278 775 lucas martell male 16
279 1063 arryk targaryen male 16
280 1476 edric karstark male 16
281 1896 hazzea tarly female 16
282 2008 arwyn tarly female 16
283 2675 gormond sand male 16
284 3187 melisandre swyft female 16
285 3234 kojja seaworth female 16
286 3332 barsena tarly female 16
287 3479 quaithe tarly female 16
288 4273 nan greyjoy female 16
289 4824 squirrel kettleblack female 16
290 4647 falyse tyrell female 16
291 5040 hallyne rivers male 16
292 5071 roslin bolton female 16
293 5137 myrtle westerling female 16
294 5435 mace seaworth male 16
295 3886 aegon martell male 16
296 5741 arya baelish female 16
297 2060 margaery westerling female 16
298 6493 sybelle karstark female 16
299 7038 ezzara stark female 16
300 7363 gilly royce female 16
301 7471 dunstan karstark male 16
302 428 gysella sand female 16
303 8014 gylbert karstark male 16
304 8026 jeyne targaryen female 16
305 8205 beth ryswell female 16
306 8223 groleo tyrell male 16
307 8471 dobber harlaw male 16
308 8662 ronald manderly male 16
309 8685 black lannister male 16
310 8801 becca ryswell female 16
311 8827 gormond tyrell male 16
312 2619 megga rivers female 16
313 8968 grazdar kettleblack male 16
314 3562 obara kettleblack female 16
315 9593 ilyn karstark male 16
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15310 6144 shae tyrell female 106
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15312 4140 zei manderly female 106
15313 6339 dermot martell male 106
15314 6346 hodor baratheon male 106
15315 6354 senelle karstark female 106
15316 6389 jared mormont male 106
15317 6453 mariya tully female 106
15318 6456 tanda targaryen female 106
15319 6497 chataya kettleblack female 106
15320 6512 bella tyrell female 106
15321 6519 hyle kettleblack male 106
15322 6521 meredyth tully female 106
15323 1960 marillion tarly male 106
15324 6605 old baratheon male 106
15325 6689 meha bolton female 106
15326 6730 mordane mormont female 106
15327 6772 maerie bolton female 106
15328 6786 dagon karstark male 106
15329 1266 sarella tyrell female 106
15330 6801 tytos swyft male 106
15331 6911 holly clegane female 106
15332 6913 matrice manderly female 106
15333 6916 mordane royce female 106
15334 6964 pia kettleblack female 106
15335 6968 maege swyft female 106
15336 7227 randa westerling female 106
15337 7341 oberyn seaworth male 106
15338 7374 ragnor targaryen male 106
15339 5204 larra greyjoy female 106
15340 7550 palla karstark female 106
15341 2228 sybelle baelish female 106
15342 7595 lharys greyjoy male 106
15343 6020 qyburn westerling male 106
15344 7642 gawen martell male 106
15345 7704 mors tarly male 106
15346 7780 mezzara tully female 106
15347 1426 falia swyft female 106
15348 7800 hobb swyft male 106
15349 7914 triston snow male 106
15350 4027 jynessa baelish female 106
15351 4260 jynessa martell female 106
15352 3591 walda kettleblack female 106
15353 8210 ygon kettleblack male 106
15354 8284 erryk umber male 106
15355 8368 varamyr lannister male 106
15356 8454 bonifer swyft male 106
15357 8468 megga frey female 106
15358 8482 kennos stark male 106
15359 2427 daenerys umber female 106
15360 8571 jonos sand male 106
15361 8620 maege westerling female 106
15362 8624 leona seaworth female 106
15363 8629 dick frey male 106
15364 8666 arya kettleblack female 106
15365 8741 janna bolton female 106
15366 8797 kurz seaworth male 106
15367 1775 softfoot tarly male 106
15368 2944 benedar tarly male 106
15369 514 sharna karstark female 106
15370 8984 alysane clegane female 106
15371 9004 bella tyrell female 106
15372 9076 alys harlaw female 106
15373 9133 vayon baelish male 106
15374 5530 mycah kettleblack male 106
15375 682 emmon clegane male 106
15376 9229 jeyne westerling female 106
15377 7962 byron harlaw male 106
15378 9412 gerold targaryen male 106
15379 9512 munciter lannister male 106
15380 2693 sarra swyft female 106
15381 1050 colemon sand male 106
15382 9579 mya clegane female 106
15383 9711 roger manderly male 106
15384 9753 janos kettleblack male 106
15385 765 qyle baratheon male 106
15386 9785 cotter manderly male 106
15387 9833 qezza westerling female 106
15388 469 val karstark female 106
15389 9859 lysa swyft female 106
15390 10006 morra tully female 106
15391 10067 maric harlaw male 106
15392 10079 robett martell male 106
15393 7992 ryon mormont male 106
15394 191 goghor bolton male 106
15395 10150 bryce frey male 106
15396 2070 forley clegane male 106
15397 4176 gendry ryswell male 106
15398 10262 elinor targaryen female 106
15399 1338 humfrey sand male 106
15400 10305 val snow female 106
15401 1877 jhiqui lannister female 106
15402 10438 flement baratheon male 106
15403 10515 kojja rivers female 106
15404 10517 amory greyjoy male 106
15405 10582 martyn rivers male 106
15406 10643 arianne greyjoy female 106
15407 10723 deziel martell male 106
15408 10727 doreah swyft female 106
15409 10748 bellonara royce female 106
15410 10759 jeren greyjoy male 106
15411 10774 dunsen seaworth male 106
15412 1343 frenya mormont female 106
15413 10895 hop-robin targaryen male 106
15414 10942 kyle targaryen male 106
15415 11005 william royce male 106
15416 11026 myria tarly female 106
15417 320 vyman tarly male 106
15418 11106 sarra stark female 106
15419 11123 leona sand female 106
15420 11148 lem bolton male 106
15421 3157 jeor tully male 106
15422 5827 jonelle harlaw female 106
15423 11231 willow royce female 106
15424 11263 fat baratheon female 106
15425 9355 wylla snow female 106
15426 11407 galazza stark female 106
15427 11706 morna bolton female 106
15428 11713 brusco ryswell male 106
15429 4075 sybell royce female 106
15430 11770 grazdan snow male 106
15431 2943 shagwell rivers male 106
15432 5588 eleyna ryswell female 106
15433 11936 donnel tarly male 106
15434 11937 gelmarr ryswell male 106
15435 11995 sybelle tully female 106
15436 12002 joyeuse manderly female 106
15437 12096 kevan frey male 106
15438 12133 lyn sand male 106
15439 12152 moon bolton male 106
15440 12207 jon manderly male 106
15441 12223 ghost baratheon female 106
15442 12342 quaithe tully female 106
15443 6 preston tarly male 107
15444 584 mordane snow female 107
15445 994 dalla tarly female 107
15446 1028 kurleket clegane male 107
15447 1100 walda harlaw female 107
15448 133 galazza snow female 107
15449 1292 kerwin westerling male 107
15450 1334 galazza sand female 107
15451 1542 cayn tyrell male 107
15452 1705 pia rivers female 107
15453 1737 nymeria tarly female 107
15454 1780 tom greyjoy male 107
15455 1973 weese seaworth male 107
15456 1151 mors baratheon male 107
15457 2300 reek snow male 107
15458 2395 dagmer royce male 107
15459 2607 ygon tyrell male 107
15460 2612 serra baelish female 107
15461 2669 umfred manderly male 107
15462 2973 donyse westerling female 107
15463 3280 symond greyjoy male 107
15464 3439 fornio greyjoy male 107
15465 3694 lamprey tyrell male 107
15466 4170 osmund umber male 107
15467 4423 styr westerling male 107
15468 1558 weasel targaryen female 107
15469 4588 asha targaryen female 107
15470 3639 joyeuse sand female 107
15471 4786 lanna mormont female 107
15472 4795 mariya manderly female 107
15473 4834 chataya kettleblack female 107
15474 5026 osha rivers female 107
15475 5080 obara baelish female 107
15476 5407 ulmer kettleblack male 107
15477 5763 alla mormont female 107
15478 3319 pyg baratheon male 107
15479 5891 deziel sand male 107
15480 5933 mikken baratheon male 107
15481 6018 brienne martell female 107
15482 6100 ermesande baratheon female 107
15483 3700 ralf targaryen male 107
15484 6250 zei stark female 107
15485 6455 hallis westerling male 107
15486 6591 ygritte lannister female 107
15487 6779 rolph royce male 107
15488 6874 masha mormont female 107
15489 6905 sybell seaworth female 107
15490 7196 missandei clegane female 107
15491 7246 porridge karstark male 107
15492 7268 walda kettleblack female 107
15493 7380 alys harlaw female 107
15494 7506 val baratheon female 107
15495 7530 benedar karstark male 107
15496 6667 little baratheon male 107
15497 7663 brynden stark male 107
15498 2457 melisandre umber female 107
15499 657 wat mormont male 107
15500 8347 beric harlaw male 107
15501 8484 ygritte bolton female 107
15502 8625 melly martell female 107
15503 8679 nute frey male 107
15504 8681 myrcella manderly female 107
15505 8683 mossador mormont male 107
15506 9084 willam baelish male 107
15507 9600 janna sand female 107
15508 619 palla bolton female 107
15509 9695 jaime targaryen male 107
15510 8328 parmen baelish male 107
15511 10192 sylva karstark female 107
15512 10286 nymella royce female 107
15513 17 desmond tully male 107
15514 5981 marissa tully female 107
15515 10914 aegon harlaw male 107
15516 6300 fletcher frey male 107
15517 11181 kevan lannister male 107
15518 1919 elinor tully female 107
15519 11608 leonette baelish female 107
15520 11798 leona royce female 107
15521 11986 gilly tarly female 107
15522 12174 barbrey manderly female 107
15523 12191 walder tully male 107
15524 12328 jeyne baratheon female 107- Los
data.frameson solo eso,data.frame:
¿De qué están hechos los data.frame y data.tibble?
- Sus unidades básicas son los
vectores.
Los
vectoressoncolecciones de datosde unsolo tipo:
| Tipo de dato | (En inglés) | Descripción | Ejemplo |
|---|---|---|---|
| Entero | Integer | Solo números enteros | c(1, 2, 3, 4) |
| Doble | Double | Números | c(1, 2.4, 3.14, 4) |
| Caracter | Character | Texto o cadenas (‘strings’) | c(‘1’, ‘azul’, ‘divertido’, ‘monstruo’) |
| Lógicos | Logical | Verdadero o Falso | c(TRUE, FALSE, TRUE, FALSE) |
| Factor | Factor | Categorías | c(‘Muy intenso’, ‘Leve’, ‘Moderado’, ‘Leve’) |
Vectores como átomos de R
Vectores se limitan a
un solo tipo de dato.Conforman
diversas estructuras: matrices, arreglos, listas, etc.Usaremos principalmente data frames y data tibbles.
Los data frames y tibbles:
Son columnas de vectores.
Cada vector puede ser de distinto tipo.
Los data frames y tibbles:
- Almacenan más de un tipo de dato: integer, double, character, logic, factor, etc.
Vectores y data frame / data tibble en R
¿Cómo podemos ver qué hay en los conjuntos de datos?
Podemos hechar un vistazo (glimpse en inglés) a los datos:
Rows: 15,524
Columns: 17
$ subject_id <dbl> 1412, 533, 9134, 8518, 8967, 11048, 663, 2158, 3794, 4…
$ fake_first_name <chr> "jhezane", "penny", "grunt", "melisandre", "rolley", "…
$ fake_last_name <chr> "westerling", "targaryen", "rivers", "swyft", "karstar…
$ gender <chr> "female", "female", "male", "female", "male", "female"…
$ pan_day <dbl> 4, 7, 7, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 1…
$ test_id <chr> "covid", "covid", "covid", "covid", "covid", "covid", …
$ clinic_name <chr> "inpatient ward a", "clinical lab", "clinical lab", "c…
$ result <chr> "negative", "negative", "negative", "negative", "negat…
$ demo_group <chr> "patient", "patient", "patient", "patient", "patient",…
$ age <dbl> 0.0, 0.0, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.9, 0.9,…
$ drive_thru_ind <dbl> 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, …
$ ct_result <dbl> 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45…
$ orderset <dbl> 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, …
$ payor_group <chr> "government", "commercial", NA, NA, "government", "com…
$ patient_class <chr> "inpatient", "not applicable", NA, NA, "emergency", "r…
$ col_rec_tat <dbl> 1.4, 2.3, 7.3, 5.8, 1.2, 1.4, 2.6, 0.7, 1.0, 7.1, 2.5,…
$ rec_ver_tat <dbl> 5.2, 5.8, 4.7, 5.0, 6.4, 7.0, 4.2, 6.3, 5.6, 7.0, 3.8,…¿Cómo obtener más información?
Hagamos una última pausa
Tomemos un descanso de 5 minutos…
Estire las piernas …
Deje de ver las pantallas …
… cualquier , las del celular también.
05:00
Nuestro turno
Descargue la carpeta denominada
taller01.Descomprímala y abra el projecto denominado
taller01.Rproj.Con el proyecto abierto, abra el archivo quarto denomnado
taller01.qmd.Resuelva el taller según las indicaciones.
Al finalizar, renderice el documento. Observe el documento
.htmlgenerado.
30:00
@psotob91
https://github.com/psotob91
percys1991@gmail.com
R Aplicado a los Proyectos de Investigación - Sesión 1
Sesión 1 Curso: R Aplicado a los Proyectos de Investigación Percy Soto-Becerra, M.D., M.Sc(c) InkaStats Data Science Solutions | Medical Branch 2022-10-03 https://github.com/psotob91