Información
Todo lo contenido en este documento está libremente disponible en GitHub. Los paquetes de R que vamos a necesitar son:
- tidyverse: tratamiento y procesamiento de datos.
- skimr: resúmenes numéricos
- purrr: tratamiento de listas
- glue: concatenación de cadenas de texto.
- showtext: añadir fuentes tipográficas
- patchwork: para formar gráficas compuestas
Además para la creación de este tutorial he usado Quarto, el paquete DT para las tablas interactivas y ggtext para añadir html en los textos de las gráficas.
Wordle: el juego de moda
00:01 (hora local). Aterrizaje efectuado sin dificultad. Propulsión convencial (ampliada). Velocidad de aterrizaje: 6:30 de la escala convencional (restringida). Velocidad en el momento del amaraje: 4 de la escala Bajo-U 109 de la escala Molina-Calvo. Cubicaje: AZ-0.3. Denominación local del lugar de aterrizaje: Sardanyola.
Así empieza uno de mis libros favoritos, «Sin noticias de Gurb», en el que Eduardo Mendoza nos contaba la historia de un extraterrestre recién aterrizado en Barcelona. Y es que si tuviéramos que elaborar un método en estas primeras semanas de 2022 para detectar si una persona acaba de llegar del espacio exterior, no habría uno mejor que preguntarle: ¿has jugado a WORDLE?
Este sencillo juego, que imita la dinámica del famoso Master Mind, no tiene muchas reglas pero es «adictivo»: una palabra, 5 letras, y 6 intentos para adivinar el vocablo mientras la aplicación te indica en cada paso que letras están bien colocadas (o mal colocadas o si directamente no aparecen en la palabra). No solo choca su sencillez sino que además es una web distinta a las que hoy nos tiene acostumbrados la red: sin pop-ups, sin anuncios, sin cookies, sin vídeos que se reproducen solos. Nada. Solo un juego, una interfaz sencilla (pero visualmente atractiva) que no reporta ningún beneficio a su creador, el ingenierio de software Josh Wardle. Un juego que, aunque ha alcanzado la categoría de fenómeno de masas a finales de 2021 y principios de 2022, nació además de una historia de amor, como relata el autor al periodista del New York Times Daniel Victor en esta entrevista
Por si alguien llega a esta entrada sin conocer el juego: el objetivo consiste en adivinar una palabra de 5 letras
En cada intento el juego nos indica con amarillo las letras que están pero mal colocadas, en verde las letras que están en la palabra y correctamente colocadas, y en gris las letras que no están en la palabra. Con esas pistas, el usuario tiene 6 intentos y solo podrá jugar una palabra al día (quizás esa sea una de las claves de las ganas de seguir jugando).
Desde unas semanas el juego también cuenta con su versión en castellano, adaptada por Daniel Rodríguez, y su versión en catalán, adaptada por Gerard López, y no han sido pocos los medios que han dedicado sus espacios a hablar de él. Tampoco son pocos los matemáticos y estadísticos que se han lanzado a intentar analizar el juego, las opciones de ganar y la forma en la que juegan sus usuarios. Es el caso de Esteban Moro, a quién entrevistaban hace unos días en El País contando su estrategia para el juego en inglés, el caso del investigador y divulgador Picanúmeros o yo mismo.
El castellano y sus letras: corpus CREA
Dado que se trata de un juego de adivinar palabras en castellano, lo primero que vamos a hacer es analizar (de forma muy de «andar por casa») cómo se comportan las palabras y letras en el castellano, así que necesitamos es un conjunto de palabras (corpus) con las que trabajar.
El problema es que extraer un listado de palabras de la RAE no es sencillo ya que la propia institución no lo pone fácil, hasta el absurdo que su listado de palabras y definiciones no son de uso libre y tiene copyright, como ha comentado en varias ocasiones Jaime Gómez Obregón. Dichos impedimentos hacen incluso difícil saber el número de palabras totales en castellano que la RAE incluye en el diccionario. Según la propia institución:
«Es imposible saber el número de palabras de una lengua. La última edición del diccionario académico (2014), registraba 93 111 artículos y 195 439 acepciones
#RAEconsultas Es imposible saber el número de palabras de una lengua. La última edición del diccionario académico (2014), registraba 93 111 artículos y 195 439 acepciones.
— RAE (@RAEinforma) January 23, 2019
Lo que si pone la RAE a nuestra disposición es el Corpus de Referencia del Español Actual (CREA). El CREA es un «conjunto de textos de diversa procedencia, almacenados en soporte informático, del que es posible extraer información para estudiar las palabras, sus significados y sus contextos». El corpus de referencia de la RAE cuenta con 152 560 documentos analizados, producidos en los países de habla hispana desde 1975 hasta 2004 (sesgo de selección), y seleccionados tanto de libros como de periódicos y revistas (sesgo de selección), y lo tienes en bruto en mi repositorio. Para su lectura podemos usar read_delim() del paquete stringr (cargado en el entorno tidyverse).
# Corpus de Referencia del Español Actual (CREA)
# https://corpus.rae.es/lfrecuencias.html
datos_brutos_CREA <-
read_delim(file = "./datos/CREA_bruto.txt", delim = "\t")Preprocesado corpus
Dicho fichero lo he preprocesado para hacer más fácil su lectura. El archivo preprocesado lo tienes disponible en CREA_procesado.csv y el código que he ejecutado lo tienes debajo. Entre otras cosas, dado que en el juego en castellano no se admiten tildes, pero si la letra ñ, he decidido eliminar todas las tildes, acentos y diéresis del CREA y he eliminado duplicados (por ejemplo, mi y mí tras quitar tildes).
Código
# Eliminamos columna de orden y separamos última columna en dos
datos_CREA <-
datos_brutos_CREA[, -1] %>%
separate(col = 2, sep = "\t",
into = c("frec_abs", "frec_norm"))
# Renombramos columnas
names(datos_CREA) <- c("palabra", "frec_abs", "frec_norm")
# Convertimos a número que vienen como cadenas de texto
datos_CREA <- datos_CREA %>%
mutate(frec_abs = as.numeric(gsub(",", "", frec_abs)),
frec_norm = as.numeric(frec_norm))
# convertimos tildes
datos_CREA <-
datos_CREA %>%
mutate(palabra = gsub(" ", "", iconv(palabra, "latin1")))
# Quitamos tildes pero no queremos eliminar la ñ
datos_CREA <-
datos_CREA %>%
mutate(palabra =
gsub("ö", "o",
gsub("ä", "a",
gsub("ò", "o",
gsub("ï", "i",
gsub("ô", "o",
gsub("â", "a",
gsub("ë", "e",
gsub("ê", "e",
gsub("ã", "a",
gsub("î", "i",
gsub("ù", "u",
gsub("¢", "c",
gsub("ì", "i",
gsub("è", "e",
gsub("à", "a", gsub("ç", "c",
gsub("á", "a",
gsub("é", "e",
gsub("í", "i",
gsub("ó", "o",
gsub("ú", "u",
gsub("ü", "u",
as.character(palabra)))))))))))))))))))))))) %>%
# eliminamos duplicados
distinct(palabra, .keep_all = TRUE) %>%
# Eliminamos palabras con '
filter(!grepl("'", palabra) & !grepl("ø", palabra))
# Exporte
write_csv(datos_CREA, file = "./datos/CREA_procesado.csv")Tras este preprocesamiento nuestro corpus se compone aproximadamente de 700 000 palabras/vocablos, de las que tenemos
-
frecuencia absoluta:
frec_abs(nº de documentos analizados en los que aparece) -
frecuencia normalizada:
frec_norm(veces que aparece por cada 1000 documentos).
La carga desde el archivo ya preprocesado puede hacerse con read_csv().
# Archivo ya preprocesado
datos_CREA <- read_csv(file = "./datos/CREA_procesado.csv")
datos_CREA
## # A tibble: 693,402 × 3
## palabra frec_abs frec_norm
## <chr> <dbl> <dbl>
## 1 de 9999518 65546.
## 2 la 6277560 41149.
## 3 que 4681839 30689.
## 4 el 4569652 29953.
## 5 en 4234281 27755.
## 6 y 4180279 27401.
## 7 a 3260939 21375.
## 8 los 2618657 17165.
## 9 se 2022514 13257.
## 10 del 1857225 12174.
## # ℹ 693,392 more rowsCálculo de frecuencias
He calculado además los siguientes parámetros de cada una de las palabras (tienes el código colapsado debajo) por si nos son de utilidad:
-
frec_rel: la frecuencia relativa (proporción de palabras). -
log_frec_abs: el logaritmo de las frecuencias absolutas. -
log_frec_rel: la frecuencia relativa delog_frec_abs. -
int_frec_norm: una variable intervalo para categorizar las palabras en función de las veces que se repiten. -
nletras: número de letras de cada palabra.
Código
datos_CREA <-
datos_CREA |>
mutate(frec_relativa = frec_abs / sum(frec_abs), # frec. relativa
log_frec_abs = log(frec_abs), # log(frec. absolutas)
log_frec_rel =
log_frec_abs / sum(log_frec_abs), # log(frec. norm)
# distribución de frec_norm
int_frec_norm =
cut(frec_norm,
breaks = c(-Inf, 0.01, 0.05, 0.1, 0.5, 1:5,
10, 20, 40, 60, 80, Inf)),
# número de letras
nletras = nchar(palabra))datos_CREA
## # A tibble: 693,402 × 8
## palabra frec_abs frec_norm frec_relativa log_frec_abs log_frec_rel
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 de 9999518 65546. 0.0664 16.1 0.0000164
## 2 la 6277560 41149. 0.0417 15.7 0.0000160
## 3 que 4681839 30689. 0.0311 15.4 0.0000157
## 4 el 4569652 29953. 0.0303 15.3 0.0000156
## 5 en 4234281 27755. 0.0281 15.3 0.0000156
## 6 y 4180279 27401. 0.0278 15.2 0.0000155
## 7 a 3260939 21375. 0.0217 15.0 0.0000153
## 8 los 2618657 17165. 0.0174 14.8 0.0000151
## 9 se 2022514 13257. 0.0134 14.5 0.0000148
## 10 del 1857225 12174. 0.0123 14.4 0.0000147
## # ℹ 693,392 more rows
## # ℹ 2 more variables: int_frec_norm <fct>, nletras <int>Análisis estadístico
¿Cómo se distribuyen las frecuencias de las palabras?
Si nos fijamos en cómo se reparten las palabras y sus repeticiones a lo largo de los más de 150 000 documentos analizados, obtenemos que el 75% de los vocablos que contiene CREA aparecen, como mucho, en 5 de cada 100 000 documentos.
datos_CREA |>
reframe(quantile(frec_norm))
## # A tibble: 5 × 1
## `quantile(frec_norm)`
## <dbl>
## 1 0
## 2 0
## 3 0.01
## 4 0.05
## 5 65546.
datos_CREA |> skim()| Name | datos_CREA |
| Number of rows | 693402 |
| Number of columns | 8 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 1 |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| palabra | 0 | 1 | 1 | 30 | 0 | 693402 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| int_frec_norm | 0 | 1 | FALSE | 15 | (-I: 423578, (0.: 99155, (0.: 71980, (0.: 38683 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| frec_abs | 0 | 1 | 217.22 | 19637.76 | 1 | 1 | 2.00 | 9.00 | 9999518.00 | ▇▁▁▁▁ |
| frec_norm | 0 | 1 | 1.42 | 128.72 | 0 | 0 | 0.01 | 0.05 | 65545.55 | ▇▁▁▁▁ |
| frec_relativa | 0 | 1 | 0.00 | 0.00 | 0 | 0 | 0.00 | 0.00 | 0.07 | ▇▁▁▁▁ |
| log_frec_abs | 0 | 1 | 1.41 | 1.83 | 0 | 0 | 0.69 | 2.20 | 16.12 | ▇▁▁▁▁ |
| log_frec_rel | 0 | 1 | 0.00 | 0.00 | 0 | 0 | 0.00 | 0.00 | 0.00 | ▇▁▁▁▁ |
| nletras | 0 | 1 | 8.97 | 2.95 | 1 | 7 | 9.00 | 11.00 | 30.00 | ▂▇▂▁▁ |
Es importante advertir que el CREA contiene aproximadamente 8 veces más vocablos que palabras hay registradas en la RAE (según la propia RAE): a diferencia de un diccionario, en CREA no solo hay palabras registradas oficialmente en castellano sino que recopila todo un conjunto de vocablos que aparecen en textos, que no siempre tienen porque estar «validadas» en los diccionarios, incluidos americanismos). Por ello, vamos a hacer un filtro inicial, eliminando aquellas palabras muy poco frecuentes, definiendo como poco frecuente toda aquella palabra que aparezca con una frecuencia inferior a 1 de cada 1000 textos analizados o más (aproximadamente 45 000 vocablos).
datos_CREA_filtrado <-
datos_CREA |>
filter(frec_norm >= 1)Tras dicho filtrado, he hecho una tabla con las 5000 palabras más repetidas en frecuencia absoluta, por si quieres curiosear algunas de ellas escribiendo en el buscador.
Entre todas esas palabras que hemos obtenido quizás sea también relevante analizar la distribución de las letras: ¿de qué número de letras son las palabras más repetidas en castellano (según el corpus de la RAE)?
Código
datatable(datos_CREA_filtrado |>
group_by(nletras) |>
summarise(frec_media_abs = mean(frec_abs),
frec_media_norm = mean(frec_norm)),
colnames = c("nº de letras", "media frec. abs.",
"media frec. norm."),
caption = "Media de las frecuencias en CREA por número de letras",
options = list(pageLength = 10,
headerCallback = JS(
"function(thead) {",
" $(thead).css('font-size', '80%');",
"}"))) |>
formatRound(c("frec_media_abs", "frec_media_norm"), digits = 3)Código
datatable(datos_CREA_filtrado |>
group_by(nletras) |> count() |>
ungroup() |>
mutate(porc = n / sum(n)),
colnames = c("nº de letras", "nº de palabras",
"frec. relativa (%)"),
caption = "Nº de palabras por número de letras",
options = list(pageLength = 10,
headerCallback = JS(
"function(thead) {",
" $(thead).css('font-size', '80%');",
"}"))) |>
formatRound(c("porc"), digits = 3) |>
formatPercentage(c("porc"))Si combinamos la tabla y los gráficos tenemos:
La frecuencia de las palabras se reduce según aumenta el número de letras: las palabras más repetidas tienen menos letras.
Aunque cada una individualmente se repita menos veces, globalmente, son las palabras de 7, 8 y 9 letras las que más aparecen.
Para realizar los primeros gráficos lo primero que haremos será descargarnos fuentes y personalizar el tema de las futuras gráficas
# Para añadir fuentes tipográficas
font_add_google(family = "Quicksand", name = "Quicksand")
font_add_google(family = "KoHo", name = "KoHo")
showtext_auto()
# Fijamos tema base
theme_set(theme_minimal(base_size = 13, base_family = "KoHo"))
# Personalizamos tema
theme_update(
text = element_text(color = "#787c7e"),
axis.title = element_text(family = "Quicksand", color = "#787c7e",
face = "bold", size = 10),
axis.text.x = element_text(family = "KoHo", size = 9),
axis.text.y = element_text(family = "KoHo", size = 9),
panel.grid.major.y = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(family = "Quicksand", size = 27,
face = "bold", color = "black"),
plot.subtitle = element_text(family = "KoHo", size = 11, lineheight = 0.75),
plot.caption =
element_text(family = "KoHo", color = "#6baa64",
face = "bold", size = 9)
)Código
ggplot(datos_CREA_filtrado |> # Marcamos las palabras de 5 letras
mutate(candidata_wordle = nletras == 5),
aes(x = nletras, fill = candidata_wordle)) +
geom_bar(alpha = 0.9) +
scale_fill_manual(values = c("#c9b458", "#6baa64"),
labels = c("5 letras", "Otras")) +
guides(fill = FALSE) +
labs(y = glue("Nº de palabras (totales: {nrow(datos_CREA_filtrado)})"),
x = "Número de letras",
title = "WORDLE",
caption =
paste0("Autor: J. Álvarez Liébana (@dadosdelaplace) | Datos: CREA"))
## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
eje x representa el número de letras y el eje y el número de palabras (de dicho número). Las de 5 letras (las candidatas a aparecer en WORLD) están marcadas en amarillo.Código
ggplot(datos_CREA_filtrado |> # Marcamos las palabras de 5 letras
mutate(candidata_wordle = nletras == 5),
aes(x = nletras, y = frec_norm, color = frec_norm, size = frec_norm)) +
geom_point(alpha = 0.8) + guides(color = FALSE, size = FALSE) +
labs(y = "Frec. normalizada (por 1000 doc)",
x = "Número de letras",
title = "WORDLE",
caption =
paste0("Javier Álvarez Liébana (@dadosdelaplace) | Datos: CREA"))
eje x) frente a la frecuencia normalizada (por cada 1000 documentos) según el corpus de CREA (eje y)Corpus WORDLE
En los datos anteriores se han incluido todas las palabras del CREA que superan cierto número de repeticiones en los documentos (al menos aparecer en 1 de cada 1000 documentos). Sin embargo, hemos observado como de 5 letras tan solo contamos con casi 4000 palabras (daría para jugar 10 años seguidos aproximadamente), que representa aproximadamente el 9% de nuestro corpus. Dado que el juego se reduce a palabras de 5 letras parece lógico preguntarse: ¿cuáles son las palabras más repetidas en CREA de dicho tamaño?
De la tabla anterior deducimos que las 10 palabras de 5 letras más repetidas en castellano son: sobre, entre, había, hasta, desde, puede, todos, parte, tiene y donde/dónde. En su momento el creador del juego disponía de un repositorio abierto en Github conteniendo el listado de las 620 palabras que consideró inicialmente para el juego. Dicho listado está ya descargado en palabras_wordle.csv.
palabras_wordle <- read_csv(file = "./datos/palabras_wordle.csv")
## Rows: 620 Columns: 1
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): palabra
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Nuestro corpus tiene limitaciones, en particular un sesgo de selección ya que analiza textos de un periodo concreto, por lo que palabras más usadas en los últimos años quizás no aparezcan con tanta frecuencia en dichos documentos (como kefir o tesla), amén de que pueden haber sido incluidas por el autor de la aplicación libremente. Las palabras en el WORDLE (dataset original del creador) que no estén incluidas en el filtro de frecuencia vamos a buscarlas en el corpus original, e incluiremos dichas palabras con sus frecuencias en nuestros corpus filtrado.
Código
palabras_ausentes <-
setdiff(palabras_wordle |> pull(palabra),
datos_CREA_filtrado |> filter(nletras == 5) |>
pull(palabra))
datos_CREA_filtrado <-
datos_CREA |>
filter(palabra %in% (palabras_wordle |> pull(palabra)) |
palabra %in% (datos_CREA_filtrado |> pull(palabra)))
datos_CREA_filtrado <-
datos_CREA_filtrado |>
add_row(palabra = "cotar", frec_abs = 10,
log_frec_abs = log(10), nletras = 5) |>
add_row(palabra = "titar", frec_abs = 10,
log_frec_abs = log(10), nletras = 5) |>
add_row(palabra = "kopek", frec_abs = 10,
log_frec_abs = log(10), nletras = 5)
datos_palabras_wordle <-
datos_CREA_filtrado %>%
filter(palabra %in% palabras_wordle$palabra) %>%
arrange(desc(frec_relativa))
datatable(datos_palabras_wordle %>%
select(c(palabra, frec_abs, frec_norm,
frec_relativa, log_frec_rel)),
caption =
"Frecuencia en CREA de las palabras configuradas para el WORLDE",
colnames = c("palabras", "frec. absoluta",
"frec. normalizada", "frec.relativa",
"log-frec relativa"),
options = list(pageLength = 10,
headerCallback = JS(
"function(thead) {",
" $(thead).css('font-size', '80%');",
"}"))) |>
formatRound(c("frec_norm", "frec_relativa",
"log_frec_rel"), digits = 3)Las 5 palabras del WORDLE con mayor frecuencia de repetición en el conjunto de textos que componen el corpus de la RAE son entre, donde, menos, mundo, forma
Posición de letras en palabras WORDLE
No solo será importante el número de veces que se repite una palabra sino cómo se distribuyen las letras dentro de esas palabras: no es lo mismo empezar el juego con una palabra con varias vocales (para obtener información de las mismas) que empezar con una palabra que tiene z, ñ o k (ya que lo más seguro es que te quedes con la misma información que antes de jugar).
¿Cómo se distribuyen las letras en el castellano? ¿Influye el número de palabras? ¿Y su posición?
Para su análisis vamos a definir antes una función que he llamado matriz_letras, que nos devolverá una matriz de palabras tokenizadas (cada palabra en una columna, cada letra en una fila)
Código
# Matriz letras tokenizadas
matriz_letras <- function(corpus, n = 5) {
if (!is.null(n)) {
# Filtramos
corpus_filtrado <- corpus |> filter(nletras == n)
# Creamos matriz de letras
matriz_letras <-
matrix(unlist(strsplit(corpus_filtrado$palabra, "")),
ncol = nrow(corpus_filtrado))
# Frecuencia de letras en las palabras de wordle
frecuencia_letras <-
as_tibble(as.character(matriz_letras)) |>
group_by(value) |>
count() |>
ungroup() |>
mutate(porc = 100 * n / sum(n))
} else {
corpus_filtrado <- corpus
# Creamos matriz de letras
matriz_letras <- unlist(strsplit(corpus_filtrado$palabra, ""))
# Frecuencia de letras en las palabras de wordle
frecuencia_letras <-
as_tibble(as.character(matriz_letras)) |>
group_by(value) |>
count() |>
ungroup() |>
mutate(porc = 100 * n / sum(n))
}
# Output
return(list("corpus_filtrado" = corpus_filtrado,
"matriz_letras" = matriz_letras,
"frecuencia_letras" = frecuencia_letras))
}tokens <- matriz_letras(datos_CREA_filtrado, n = 5)
tokens$matriz_letras
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## [1,] "s" "e" "h" "h" "d" "p" "t" "p" "t" "d" "m" "a" "o" "t"
## [2,] "o" "n" "a" "a" "e" "u" "o" "a" "i" "o" "i" "h" "t" "a"
## [3,] "b" "t" "b" "s" "s" "e" "d" "r" "e" "n" "s" "o" "r" "n"
## [4,] "r" "r" "i" "t" "d" "d" "o" "t" "n" "d" "m" "r" "o" "t"
## [5,] "e" "e" "a" "a" "e" "e" "s" "e" "e" "e" "o" "a" "s" "o"
## [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
## [1,] "s" "m" "m" "a" "f" "h" "e" "m" "h" "e" "h" "m"
## [2,] "e" "e" "u" "n" "o" "a" "s" "a" "a" "l" "e" "u"
## [3,] "g" "n" "n" "t" "r" "c" "t" "y" "c" "l" "c" "c"
## [4,] "u" "o" "d" "e" "m" "e" "o" "o" "i" "o" "h" "h"
## [5,] "n" "s" "o" "s" "a" "r" "s" "r" "a" "s" "o" "o"
## [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
## [1,] "q" "e" "l" "o" "m" "n" "d" "t" "l" "m" "e" "t"
## [2,] "u" "s" "u" "t" "e" "u" "e" "o" "u" "e" "s" "e"
## [3,] "i" "t" "g" "r" "j" "e" "c" "d" "e" "d" "t" "n"
## [4,] "e" "a" "a" "a" "o" "v" "i" "a" "g" "i" "a" "i"
## [5,] "n" "n" "r" "s" "r" "o" "r" "s" "o" "o" "s" "a"
## [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]
## [1,] "n" "p" "v" "g" "m" "n" "m" "c" "t" "p" "n" "h"
## [2,] "u" "o" "e" "r" "i" "u" "u" "o" "e" "u" "o" "a"
## [3,] "n" "d" "c" "u" "s" "e" "j" "s" "n" "n" "c" "b"
## [4,] "c" "e" "e" "p" "m" "v" "e" "a" "e" "t" "h" "e"
## [5,] "a" "r" "s" "o" "a" "a" "r" "s" "r" "o" "e" "r"
## [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62]
## [1,] "f" "u" "n" "h" "t" "e" "p" "g" "f" "m" "c" "s"
## [2,] "u" "s" "a" "o" "a" "s" "a" "e" "i" "a" "i" "i"
## [3,] "e" "t" "d" "r" "r" "t" "d" "n" "n" "d" "n" "g"
## [4,] "r" "e" "i" "a" "d" "a" "r" "t" "a" "r" "c" "l"
## [5,] "a" "d" "e" "s" "e" "r" "e" "e" "l" "e" "o" "o"
## [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74]
## [1,] "m" "m" "s" "j" "a" "d" "c" "m" "n" "p" "l" "f"
## [2,] "e" "a" "e" "u" "q" "i" "a" "a" "i" "o" "a" "a"
## [3,] "s" "r" "r" "n" "u" "c" "s" "n" "v" "d" "r" "l"
## [4,] "e" "i" "i" "t" "e" "h" "o" "o" "e" "i" "g" "t"
## [5,] "s" "a" "a" "o" "l" "o" "s" "s" "l" "a" "o" "a"
## [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86]
## [1,] "h" "t" "a" "s" "c" "o" "b" "l" "i" "e" "t" "t"
## [2,] "e" "r" "l" "e" "l" "r" "u" "i" "g" "l" "o" "e"
## [3,] "m" "a" "g" "ñ" "a" "d" "e" "b" "u" "l" "t" "n"
## [4,] "o" "t" "u" "o" "r" "e" "n" "r" "a" "a" "a" "g"
## [5,] "s" "a" "n" "r" "o" "n" "a" "o" "l" "s" "l" "o"
## [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97] [,98]
## [1,] "u" "p" "c" "v" "c" "s" "o" "r" "n" "e" "f" "p"
## [2,] "n" "e" "a" "i" "a" "a" "b" "a" "i" "s" "o" "a"
## [3,] "i" "s" "l" "s" "m" "b" "r" "z" "ñ" "t" "n" "p"
## [4,] "c" "a" "l" "t" "p" "e" "a" "o" "o" "o" "d" "e"
## [5,] "o" "r" "e" "a" "o" "r" "s" "n" "s" "y" "o" "l"
## [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [,108]
## [1,] "d" "a" "s" "m" "d" "d" "j" "v" "l" "b"
## [2,] "e" "m" "a" "e" "e" "a" "u" "i" "l" "u"
## [3,] "m" "b" "l" "d" "b" "t" "l" "s" "e" "e"
## [4,] "a" "o" "u" "i" "e" "o" "i" "t" "g" "n"
## [5,] "s" "s" "d" "a" "n" "s" "o" "o" "o" "o"
## [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [,118]
## [1,] "j" "s" "c" "v" "s" "h" "j" "e" "b" "m"
## [2,] "o" "i" "e" "a" "e" "i" "u" "p" "a" "e"
## [3,] "v" "g" "r" "l" "r" "j" "e" "o" "n" "n"
## [4,] "e" "u" "c" "o" "i" "o" "g" "c" "c" "o"
## [5,] "n" "e" "a" "r" "e" "s" "o" "a" "o" "r"
## [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127] [,128]
## [1,] "p" "q" "h" "r" "c" "v" "a" "c" "p" "l"
## [2,] "a" "u" "a" "e" "a" "a" "p" "i" "e" "i"
## [3,] "s" "e" "c" "s" "u" "m" "o" "v" "d" "b"
## [4,] "a" "d" "e" "t" "s" "o" "y" "i" "r" "r"
## [5,] "r" "a" "n" "o" "a" "s" "o" "l" "o" "e"
## [,129] [,130] [,131] [,132] [,133] [,134] [,135] [,136] [,137] [,138]
## [1,] "c" "d" "s" "u" "f" "c" "c" "d" "q" "u"
## [2,] "o" "e" "a" "n" "a" "l" "o" "e" "u" "n"
## [3,] "m" "j" "l" "i" "v" "a" "l" "c" "i" "i"
## [4,] "u" "a" "i" "o" "o" "s" "o" "i" "z" "c"
## [5,] "n" "r" "r" "n" "r" "e" "r" "a" "a" "a"
## [,139] [,140] [,141] [,142] [,143] [,144] [,145] [,146] [,147] [,148]
## [1,] "p" "l" "a" "a" "s" "v" "t" "s" "l" "l"
## [2,] "u" "l" "y" "u" "u" "i" "o" "i" "u" "i"
## [3,] "e" "e" "u" "t" "e" "e" "m" "e" "c" "n"
## [4,] "d" "v" "d" "o" "l" "j" "a" "t" "h" "e"
## [5,] "a" "a" "a" "r" "o" "o" "r" "e" "a" "a"
## [,149] [,150] [,151] [,152] [,153] [,154] [,155] [,156] [,157] [,158]
## [1,] "p" "n" "c" "p" "p" "v" "r" "p" "a" "h"
## [2,] "o" "o" "a" "l" "o" "i" "a" "u" "m" "a"
## [3,] "c" "r" "r" "a" "n" "e" "d" "e" "i" "b"
## [4,] "o" "t" "g" "z" "e" "n" "i" "d" "g" "r"
## [5,] "s" "e" "o" "a" "r" "e" "o" "o" "o" "a"
## [,159] [,160] [,161] [,162] [,163] [,164] [,165] [,166] [,167] [,168]
## [1,] "s" "s" "v" "v" "q" "e" "c" "m" "n" "t"
## [2,] "a" "a" "i" "i" "u" "x" "a" "i" "e" "e"
## [3,] "n" "b" "a" "v" "e" "i" "r" "e" "g" "x"
## [4,] "t" "i" "j" "i" "d" "t" "t" "d" "r" "t"
## [5,] "a" "a" "e" "r" "o" "o" "a" "o" "o" "o"
## [,169] [,170] [,171] [,172] [,173] [,174] [,175] [,176] [,177] [,178]
## [1,] "m" "f" "s" "i" "l" "l" "f" "p" "e" "a"
## [2,] "i" "e" "e" "d" "l" "e" "a" "l" "n" "t"
## [3,] "t" "c" "r" "e" "e" "j" "c" "a" "e" "r"
## [4,] "a" "h" "a" "a" "g" "o" "i" "z" "r" "a"
## [5,] "d" "a" "n" "s" "a" "s" "l" "o" "o" "s"
## [,179] [,180] [,181] [,182] [,183] [,184] [,185] [,186] [,187] [,188]
## [1,] "c" "f" "c" "l" "h" "t" "s" "p" "c" "p"
## [2,] "h" "u" "o" "o" "a" "a" "u" "a" "a" "o"
## [3,] "i" "e" "s" "c" "b" "l" "e" "r" "p" "d"
## [4,] "l" "g" "t" "a" "l" "e" "ñ" "i" "a" "r"
## [5,] "e" "o" "a" "l" "a" "s" "o" "s" "z" "a"
## [,189] [,190] [,191] [,192] [,193] [,194] [,195] [,196] [,197] [,198]
## [1,] "d" "z" "t" "j" "m" "m" "d" "b" "a" "l"
## [2,] "o" "o" "e" "u" "a" "u" "i" "u" "b" "o"
## [3,] "l" "n" "m" "n" "r" "c" "c" "s" "r" "p"
## [4,] "o" "a" "a" "i" "c" "h" "e" "c" "i" "e"
## [5,] "r" "s" "s" "o" "o" "a" "n" "a" "l" "z"
## [,199] [,200] [,201] [,202] [,203] [,204] [,205] [,206] [,207] [,208]
## [1,] "a" "d" "g" "c" "l" "j" "c" "e" "t" "d"
## [2,] "r" "e" "r" "a" "l" "o" "o" "t" "i" "e"
## [3,] "m" "b" "a" "r" "a" "r" "r" "a" "p" "s"
## [4,] "a" "i" "d" "n" "m" "g" "t" "p" "o" "e"
## [5,] "s" "a" "o" "e" "a" "e" "e" "a" "s" "o"
## [,209] [,210] [,211] [,212] [,213] [,214] [,215] [,216] [,217] [,218]
## [1,] "m" "j" "c" "p" "l" "l" "t" "s" "c" "a"
## [2,] "a" "a" "u" "a" "a" "i" "o" "o" "i" "m"
## [3,] "r" "m" "r" "b" "r" "d" "r" "m" "e" "b"
## [4,] "z" "a" "s" "l" "g" "e" "n" "o" "l" "a"
## [5,] "o" "s" "o" "o" "a" "r" "o" "s" "o" "s"
## [,219] [,220] [,221] [,222] [,223] [,224] [,225] [,226] [,227] [,228]
## [1,] "p" "d" "c" "c" "l" "l" "j" "g" "t" "l"
## [2,] "e" "o" "r" "a" "i" "e" "e" "r" "e" "u"
## [3,] "r" "b" "e" "s" "s" "y" "s" "a" "n" "n"
## [4,] "e" "l" "a" "a" "t" "e" "u" "v" "g" "e"
## [5,] "z" "e" "r" "s" "a" "s" "s" "e" "a" "s"
## [,229] [,230] [,231] [,232] [,233] [,234] [,235] [,236] [,237] [,238]
## [1,] "j" "s" "g" "c" "m" "g" "h" "s" "n" "a"
## [2,] "u" "i" "u" "l" "o" "u" "o" "a" "u" "b"
## [3,] "n" "t" "s" "a" "r" "s" "t" "l" "e" "a"
## [4,] "t" "i" "t" "r" "a" "t" "e" "i" "v" "j"
## [5,] "a" "o" "a" "a" "l" "o" "l" "o" "e" "o"
## [,239] [,240] [,241] [,242] [,243] [,244] [,245] [,246] [,247] [,248]
## [1,] "v" "r" "a" "a" "d" "g" "p" "l" "c" "l"
## [2,] "e" "a" "i" "g" "i" "o" "o" "l" "o" "e"
## [3,] "n" "m" "r" "u" "c" "l" "b" "e" "c" "c"
## [4,] "t" "o" "e" "a" "h" "p" "r" "v" "h" "h"
## [5,] "a" "n" "s" "s" "a" "e" "e" "o" "e" "e"
## [,249] [,250] [,251] [,252] [,253] [,254] [,255] [,256] [,257] [,258]
## [1,] "t" "p" "d" "g" "c" "s" "m" "r" "p" "p"
## [2,] "a" "l" "a" "a" "a" "u" "i" "i" "a" "e"
## [3,] "r" "a" "n" "n" "l" "e" "l" "t" "s" "s"
## [4,] "e" "t" "d" "a" "o" "l" "e" "m" "o" "o"
## [5,] "a" "a" "o" "r" "r" "e" "s" "o" "s" "s"
## [,259] [,260] [,261] [,262] [,263] [,264] [,265] [,266] [,267] [,268]
## [1,] "p" "j" "g" "v" "g" "p" "v" "p" "c" "f"
## [2,] "l" "u" "e" "a" "o" "o" "e" "i" "o" "i"
## [3,] "a" "g" "s" "s" "m" "c" "r" "d" "m" "n"
## [4,] "n" "a" "t" "c" "e" "a" "d" "i" "e" "e"
## [5,] "o" "r" "o" "o" "z" "s" "e" "o" "r" "s"
## [,269] [,270] [,271] [,272] [,273] [,274] [,275] [,276] [,277] [,278]
## [1,] "l" "j" "a" "m" "p" "s" "a" "s" "v" "m"
## [2,] "a" "u" "c" "u" "a" "a" "r" "a" "i" "a"
## [3,] "b" "s" "t" "s" "g" "b" "e" "n" "e" "r"
## [4,] "o" "t" "o" "e" "a" "e" "a" "t" "j" "i"
## [5,] "r" "o" "s" "o" "r" "s" "s" "o" "a" "o"
## [,279] [,280] [,281] [,282] [,283] [,284] [,285] [,286] [,287] [,288]
## [1,] "r" "s" "q" "a" "m" "p" "b" "a" "e" "s"
## [2,] "e" "a" "u" "c" "a" "l" "r" "c" "r" "e"
## [3,] "i" "l" "i" "a" "r" "e" "a" "a" "r" "r"
## [4,] "n" "v" "s" "b" "c" "n" "z" "s" "o" "e"
## [5,] "a" "o" "o" "a" "a" "o" "o" "o" "r" "s"
## [,289] [,290] [,291] [,292] [,293] [,294] [,295] [,296] [,297] [,298]
## [1,] "p" "a" "h" "d" "c" "v" "l" "s" "d" "f"
## [2,] "o" "l" "o" "a" "l" "o" "o" "i" "e" "e"
## [3,] "e" "t" "j" "r" "a" "t" "g" "r" "u" "l"
## [4,] "t" "o" "a" "l" "v" "o" "r" "v" "d" "i"
## [5,] "a" "s" "s" "e" "e" "s" "o" "e" "a" "z"
## [,299] [,300] [,301] [,302] [,303] [,304] [,305] [,306] [,307] [,308]
## [1,] "t" "m" "b" "f" "j" "c" "c" "c" "r" "a"
## [2,] "a" "e" "r" "i" "a" "a" "o" "a" "e" "b"
## [3,] "n" "n" "e" "r" "i" "n" "n" "r" "y" "r"
## [4,] "t" "t" "v" "m" "m" "a" "d" "g" "e" "i"
## [5,] "a" "e" "e" "a" "e" "l" "e" "a" "s" "r"
## [,309] [,310] [,311] [,312] [,313] [,314] [,315] [,316] [,317] [,318]
## [1,] "c" "n" "m" "c" "b" "f" "b" "c" "e" "h"
## [2,] "u" "e" "o" "a" "a" "r" "a" "u" "n" "a"
## [3,] "y" "g" "r" "i" "n" "a" "s" "l" "t" "y"
## [4,] "o" "r" "i" "d" "d" "s" "e" "p" "r" "a"
## [5,] "s" "a" "r" "a" "a" "e" "s" "a" "a" "n"
## [,319] [,320] [,321] [,322] [,323] [,324] [,325] [,326] [,327] [,328]
## [1,] "d" "a" "s" "m" "s" "c" "d" "s" "c" "b"
## [2,] "i" "c" "a" "u" "a" "o" "a" "a" "i" "o"
## [3,] "e" "t" "c" "r" "b" "r" "v" "l" "f" "l"
## [4,] "g" "o" "a" "i" "e" "t" "i" "o" "r" "s"
## [5,] "o" "r" "r" "o" "n" "o" "d" "n" "a" "a"
## [,329] [,330] [,331] [,332] [,333] [,334] [,335] [,336] [,337] [,338]
## [1,] "f" "s" "r" "p" "v" "a" "l" "a" "c" "d"
## [2,] "u" "e" "e" "l" "e" "z" "e" "b" "h" "e"
## [3,] "e" "r" "i" "e" "n" "n" "g" "r" "i" "d"
## [4,] "s" "i" "n" "n" "i" "a" "a" "i" "n" "o"
## [5,] "e" "o" "o" "a" "a" "r" "l" "o" "a" "s"
## [,339] [,340] [,341] [,342] [,343] [,344] [,345] [,346] [,347] [,348]
## [1,] "c" "v" "a" "t" "p" "d" "l" "v" "c" "v"
## [2,] "r" "o" "n" "e" "e" "u" "l" "a" "i" "a"
## [3,] "e" "c" "g" "m" "n" "d" "e" "c" "c" "l"
## [4,] "e" "e" "e" "o" "s" "a" "n" "i" "l" "l"
## [5,] "r" "s" "l" "r" "o" "s" "o" "o" "o" "e"
## [,349] [,350] [,351] [,352] [,353] [,354] [,355] [,356] [,357] [,358]
## [1,] "l" "p" "h" "p" "m" "c" "p" "p" "e" "p"
## [2,] "l" "e" "o" "e" "i" "l" "u" "o" "n" "a"
## [3,] "a" "c" "n" "d" "r" "i" "n" "s" "t" "c"
## [4,] "m" "h" "o" "i" "a" "m" "t" "e" "r" "t"
## [5,] "o" "o" "r" "r" "r" "a" "a" "e" "o" "o"
## [,359] [,360] [,361] [,362] [,363] [,364] [,365] [,366] [,367] [,368]
## [1,] "p" "l" "d" "i" "a" "v" "v" "m" "r" "b"
## [2,] "e" "l" "i" "d" "r" "i" "e" "i" "u" "a"
## [3,] "n" "e" "s" "e" "t" "l" "n" "a" "i" "s"
## [4,] "a" "n" "c" "a" "e" "l" "i" "m" "d" "t"
## [5,] "l" "a" "o" "l" "s" "a" "r" "i" "o" "a"
## [,369] [,370] [,371] [,372] [,373] [,374] [,375] [,376] [,377] [,378]
## [1,] "t" "a" "c" "h" "h" "d" "g" "d" "a" "p"
## [2,] "a" "v" "u" "a" "u" "a" "a" "o" "l" "a"
## [3,] "b" "i" "y" "b" "m" "r" "n" "s" "t" "r"
## [4,] "l" "o" "a" "l" "o" "s" "a" "i" "a" "e"
## [5,] "a" "n" "s" "o" "r" "e" "s" "s" "s" "d"
## [,379] [,380] [,381] [,382] [,383] [,384] [,385] [,386] [,387] [,388]
## [1,] "p" "a" "v" "d" "h" "p" "f" "e" "p" "l"
## [2,] "e" "ñ" "i" "e" "o" "i" "i" "x" "o" "u"
## [3,] "r" "a" "v" "b" "g" "e" "r" "i" "l" "c"
## [4,] "r" "d" "e" "i" "a" "z" "m" "g" "v" "e"
## [5,] "o" "e" "n" "o" "r" "a" "e" "e" "o" "s"
## [,389] [,390] [,391] [,392] [,393] [,394] [,395] [,396] [,397] [,398]
## [1,] "v" "n" "a" "c" "g" "p" "e" "p" "h" "j"
## [2,] "i" "a" "n" "e" "a" "a" "s" "l" "o" "a"
## [3,] "r" "c" "i" "s" "s" "u" "t" "a" "r" "p"
## [4,] "u" "i" "m" "a" "t" "s" "e" "y" "n" "o"
## [5,] "s" "o" "o" "r" "o" "a" "n" "a" "o" "n"
## [,399] [,400] [,401] [,402] [,403] [,404] [,405] [,406] [,407] [,408]
## [1,] "a" "n" "t" "d" "m" "c" "u" "a" "s" "c"
## [2,] "n" "o" "o" "u" "a" "h" "n" "c" "o" "o"
## [3,] "u" "r" "m" "l" "n" "i" "i" "a" "l" "s"
## [4,] "a" "m" "a" "c" "d" "c" "d" "b" "a" "t"
## [5,] "l" "a" "s" "e" "o" "a" "o" "o" "r" "o"
## [,409] [,410] [,411] [,412] [,413] [,414] [,415] [,416] [,417] [,418]
## [1,] "t" "t" "o" "p" "c" "s" "p" "a" "d" "b"
## [2,] "e" "o" "c" "a" "o" "e" "a" "r" "e" "a"
## [3,] "s" "r" "u" "t" "r" "ñ" "s" "e" "j" "r"
## [4,] "i" "o" "p" "i" "t" "a" "e" "n" "a" "c"
## [5,] "s" "s" "a" "o" "a" "l" "o" "a" "n" "o"
## [,419] [,420] [,421] [,422] [,423] [,424] [,425] [,426] [,427] [,428]
## [1,] "s" "a" "v" "o" "p" "m" "m" "d" "p" "v"
## [2,] "i" "r" "e" "s" "i" "a" "o" "e" "a" "u"
## [3,] "g" "b" "m" "c" "s" "r" "d" "s" "s" "e"
## [4,] "n" "o" "o" "a" "t" "t" "o" "e" "a" "l"
## [5,] "o" "l" "s" "r" "a" "a" "s" "a" "n" "o"
## [,429] [,430] [,431] [,432] [,433] [,434] [,435] [,436] [,437] [,438]
## [1,] "s" "c" "c" "f" "r" "v" "h" "p" "r" "m"
## [2,] "i" "h" "o" "e" "u" "e" "e" "o" "o" "a"
## [3,] "l" "i" "n" "r" "e" "r" "c" "n" "j" "t"
## [4,] "l" "c" "t" "i" "d" "s" "h" "e" "a" "a"
## [5,] "a" "o" "o" "a" "a" "e" "a" "n" "s" "r"
## [,439] [,440] [,441] [,442] [,443] [,444] [,445] [,446] [,447] [,448]
## [1,] "m" "r" "t" "p" "c" "b" "m" "c" "d" "b"
## [2,] "o" "u" "r" "e" "r" "o" "e" "r" "u" "a"
## [3,] "t" "m" "a" "n" "e" "r" "t" "e" "e" "j"
## [4,] "o" "b" "t" "s" "i" "d" "r" "e" "ñ" "a"
## [5,] "r" "o" "o" "e" "a" "e" "o" "n" "o" "r"
## [,449] [,450] [,451] [,452] [,453] [,454] [,455] [,456] [,457] [,458]
## [1,] "r" "v" "s" "d" "b" "j" "v" "r" "e" "d"
## [2,] "u" "i" "u" "r" "a" "e" "i" "e" "l" "a"
## [3,] "s" "d" "b" "o" "j" "f" "v" "l" "e" "n"
## [4,] "i" "a" "i" "g" "a" "e" "i" "o" "n" "z"
## [5,] "a" "s" "r" "a" "s" "s" "a" "j" "a" "a"
## [,459] [,460] [,461] [,462] [,463] [,464] [,465] [,466] [,467] [,468]
## [1,] "n" "s" "f" "m" "a" "i" "g" "f" "t" "s"
## [2,] "o" "u" "o" "a" "r" "s" "o" "r" "o" "a"
## [3,] "t" "a" "t" "s" "r" "l" "l" "u" "r" "l"
## [4,] "a" "v" "o" "a" "o" "a" "e" "t" "r" "a"
## [5,] "s" "e" "s" "s" "z" "s" "s" "o" "e" "s"
## [,469] [,470] [,471] [,472] [,473] [,474] [,475] [,476] [,477] [,478]
## [1,] "v" "s" "t" "d" "a" "p" "r" "t" "t" "d"
## [2,] "i" "a" "a" "i" "n" "i" "i" "r" "e" "i"
## [3,] "t" "b" "s" "e" "d" "l" "v" "a" "c" "r"
## [4,] "a" "o" "a" "t" "a" "a" "a" "j" "h" "i"
## [5,] "l" "r" "s" "a" "r" "r" "l" "e" "o" "a"
## [,479] [,480] [,481] [,482] [,483] [,484] [,485] [,486] [,487] [,488]
## [1,] "r" "s" "a" "h" "v" "f" "i" "t" "b" "m"
## [2,] "i" "a" "m" "a" "i" "i" "n" "o" "a" "a"
## [3,] "c" "l" "i" "r" "v" "d" "d" "c" "j" "l"
## [4,] "o" "s" "g" "i" "o" "e" "i" "a" "o" "o"
## [5,] "s" "a" "a" "a" "s" "l" "a" "r" "s" "s"
## [,489] [,490] [,491] [,492] [,493] [,494] [,495] [,496] [,497] [,498]
## [1,] "o" "r" "n" "l" "l" "o" "a" "b" "c" "d"
## [2,] "e" "u" "a" "e" "o" "p" "c" "a" "a" "e"
## [3,] "s" "r" "r" "t" "g" "e" "i" "n" "n" "b"
## [4,] "t" "a" "i" "r" "r" "r" "d" "c" "t" "i"
## [5,] "e" "l" "z" "a" "a" "a" "o" "a" "o" "l"
## [,499] [,500] [,501] [,502] [,503] [,504] [,505] [,506] [,507] [,508]
## [1,] "p" "m" "e" "s" "p" "d" "s" "m" "b" "s"
## [2,] "l" "o" "t" "a" "u" "a" "a" "o" "o" "u"
## [3,] "a" "n" "i" "l" "j" "ñ" "l" "s" "m" "r"
## [4,] "t" "t" "c" "e" "o" "o" "t" "c" "b" "g"
## [5,] "o" "e" "a" "n" "l" "s" "o" "u" "a" "e"
## [,509] [,510] [,511] [,512] [,513] [,514] [,515] [,516] [,517] [,518]
## [1,] "o" "m" "x" "c" "b" "q" "m" "e" "c" "r"
## [2,] "r" "u" "v" "a" "a" "u" "u" "u" "o" "o"
## [3,] "e" "ñ" "i" "l" "i" "e" "e" "r" "s" "n"
## [4,] "j" "o" "i" "m" "l" "s" "v" "o" "t" "d"
## [5,] "a" "z" "i" "a" "e" "o" "e" "s" "e" "a"
## [,519] [,520] [,521] [,522] [,523] [,524] [,525] [,526] [,527] [,528]
## [1,] "k" "r" "p" "c" "p" "t" "g" "d" "r" "l"
## [2,] "i" "i" "o" "e" "a" "u" "r" "e" "a" "e"
## [3,] "l" "g" "n" "r" "l" "r" "i" "b" "m" "n"
## [4,] "o" "o" "i" "r" "m" "n" "t" "e" "a" "t"
## [5,] "s" "r" "a" "o" "a" "o" "o" "r" "s" "o"
## [,529] [,530] [,531] [,532] [,533] [,534] [,535] [,536] [,537] [,538]
## [1,] "b" "a" "s" "s" "c" "h" "c" "j" "v" "r"
## [2,] "e" "c" "e" "a" "a" "u" "o" "u" "i" "e"
## [3,] "b" "t" "n" "l" "i" "e" "r" "e" "g" "d"
## [4,] "e" "u" "t" "i" "d" "v" "r" "g" "o" "e"
## [5,] "r" "a" "i" "a" "o" "o" "e" "a" "r" "s"
## [,539] [,540] [,541] [,542] [,543] [,544] [,545] [,546] [,547] [,548]
## [1,] "v" "h" "b" "d" "s" "l" "r" "p" "l" "d"
## [2,] "e" "a" "e" "a" "u" "u" "e" "o" "i" "o"
## [3,] "n" "g" "l" "b" "f" "i" "g" "e" "m" "l"
## [4,] "g" "a" "l" "a" "r" "s" "l" "m" "o" "a"
## [5,] "a" "n" "a" "n" "e" "a" "a" "a" "n" "r"
## [,549] [,550] [,551] [,552] [,553] [,554] [,555] [,556] [,557] [,558]
## [1,] "c" "r" "p" "p" "c" "n" "c" "v" "n" "l"
## [2,] "r" "e" "r" "r" "a" "o" "a" "e" "i" "a"
## [3,] "e" "n" "i" "i" "j" "v" "r" "r" "e" "d"
## [4,] "e" "t" "m" "s" "a" "i" "a" "l" "v" "o"
## [5,] "s" "a" "a" "a" "s" "a" "s" "o" "e" "s"
## [,559] [,560] [,561] [,562] [,563] [,564] [,565] [,566] [,567] [,568]
## [1,] "r" "e" "q" "s" "s" "p" "o" "l" "p" "h"
## [2,] "u" "c" "u" "u" "o" "i" "t" "e" "r" "a"
## [3,] "b" "h" "e" "i" "c" "a" "o" "i" "a" "l"
## [4,] "i" "a" "d" "z" "i" "n" "ñ" "d" "d" "l"
## [5,] "o" "r" "e" "a" "o" "o" "o" "o" "o" "a"
## [,569] [,570] [,571] [,572] [,573] [,574] [,575] [,576] [,577] [,578]
## [1,] "j" "g" "m" "p" "u" "i" "n" "d" "l" "p"
## [2,] "o" "r" "e" "a" "n" "r" "u" "i" "a" "e"
## [3,] "r" "a" "n" "r" "i" "e" "b" "c" "n" "s"
## [4,] "d" "s" "e" "a" "d" "n" "e" "e" "z" "c"
## [5,] "i" "a" "m" "r" "a" "e" "s" "s" "o" "a"
## [,579] [,580] [,581] [,582] [,583] [,584] [,585] [,586] [,587] [,588]
## [1,] "s" "s" "f" "c" "a" "s" "c" "g" "n" "e"
## [2,] "o" "e" "a" "h" "d" "u" "u" "u" "i" "n"
## [3,] "l" "l" "l" "i" "i" "y" "l" "i" "e" "v"
## [4,] "o" "v" "s" "n" "o" "o" "t" "o" "g" "i"
## [5,] "s" "a" "o" "o" "s" "s" "o" "n" "a" "o"
## [,589] [,590] [,591] [,592] [,593] [,594] [,595] [,596] [,597] [,598]
## [1,] "c" "s" "f" "n" "b" "m" "h" "l" "r" "f"
## [2,] "r" "i" "i" "u" "a" "u" "i" "u" "a" "e"
## [3,] "e" "t" "l" "ñ" "l" "e" "j" "c" "m" "l"
## [4,] "m" "u" "a" "e" "o" "r" "a" "i" "o" "i"
## [5,] "a" "a" "s" "z" "n" "e" "s" "a" "s" "x"
## [,599] [,600] [,601] [,602] [,603] [,604] [,605] [,606] [,607] [,608]
## [1,] "l" "n" "m" "v" "a" "p" "c" "s" "q" "r"
## [2,] "a" "i" "a" "i" "r" "a" "a" "e" "u" "a"
## [3,] "u" "ñ" "l" "v" "i" "g" "l" "r" "i" "y"
## [4,] "r" "a" "a" "i" "a" "o" "d" "l" "t" "o"
## [5,] "a" "s" "s" "o" "s" "s" "o" "o" "o" "s"
## [,609] [,610] [,611] [,612] [,613] [,614] [,615] [,616] [,617] [,618]
## [1,] "j" "a" "a" "d" "g" "p" "s" "t" "p" "v"
## [2,] "o" "n" "e" "u" "e" "i" "o" "r" "e" "i"
## [3,] "s" "c" "r" "q" "n" "d" "f" "e" "n" "u"
## [4,] "e" "h" "e" "u" "e" "e" "i" "c" "a" "d"
## [5,] "p" "o" "a" "e" "s" "n" "a" "e" "s" "a"
## [,619] [,620] [,621] [,622] [,623] [,624] [,625] [,626] [,627] [,628]
## [1,] "m" "f" "b" "p" "s" "t" "p" "c" "c" "h"
## [2,] "e" "a" "a" "r" "u" "o" "r" "o" "r" "e"
## [3,] "s" "l" "r" "i" "e" "m" "e" "l" "e" "r"
## [4,] "a" "l" "r" "m" "n" "a" "v" "o" "c" "o"
## [5,] "s" "o" "a" "o" "a" "n" "e" "n" "e" "e"
## [,629] [,630] [,631] [,632] [,633] [,634] [,635] [,636] [,637] [,638]
## [1,] "r" "l" "l" "h" "a" "h" "d" "r" "t" "s"
## [2,] "o" "e" "l" "a" "j" "i" "r" "a" "o" "o"
## [3,] "c" "n" "a" "c" "e" "e" "a" "n" "q" "l"
## [4,] "a" "t" "v" "e" "n" "l" "m" "g" "u" "a"
## [5,] "s" "a" "e" "s" "o" "o" "a" "o" "e" "s"
## [,639] [,640] [,641] [,642] [,643] [,644] [,645] [,646] [,647] [,648]
## [1,] "s" "j" "s" "m" "l" "r" "f" "a" "f" "j"
## [2,] "u" "u" "o" "i" "a" "o" "a" "r" "a" "a"
## [3,] "b" "a" "l" "n" "n" "j" "s" "a" "l" "m"
## [4,] "i" "n" "i" "a" "z" "o" "e" "b" "s" "e"
## [5,] "o" "a" "a" "s" "a" "s" "s" "e" "a" "s"
## [,649] [,650] [,651] [,652] [,653] [,654] [,655] [,656] [,657] [,658]
## [1,] "v" "m" "r" "o" "s" "e" "r" "l" "f" "n"
## [2,] "e" "e" "e" "r" "i" "v" "u" "i" "r" "a"
## [3,] "r" "t" "v" "t" "l" "i" "b" "s" "a" "c"
## [4,] "l" "a" "e" "i" "v" "t" "e" "t" "g" "e"
## [5,] "a" "l" "s" "z" "a" "a" "n" "o" "a" "r"
## [,659] [,660] [,661] [,662] [,663] [,664] [,665] [,666] [,667] [,668]
## [1,] "i" "p" "p" "a" "f" "p" "d" "n" "b" "v"
## [2,] "n" "a" "a" "v" "i" "o" "u" "o" "e" "i"
## [3,] "d" "s" "r" "i" "l" "l" "r" "b" "l" "d"
## [4,] "i" "t" "t" "s" "m" "l" "a" "l" "l" "a"
## [5,] "o" "a" "o" "o" "e" "o" "s" "e" "o" "l"
## [,669] [,670] [,671] [,672] [,673] [,674] [,675] [,676] [,677] [,678]
## [1,] "p" "r" "c" "m" "c" "c" "t" "b" "c" "h"
## [2,] "e" "a" "i" "u" "o" "u" "r" "a" "a" "a"
## [3,] "l" "b" "n" "r" "p" "o" "a" "r" "d" "r"
## [4,] "e" "i" "t" "o" "i" "t" "m" "r" "i" "a"
## [5,] "a" "a" "a" "s" "a" "a" "o" "o" "z" "n"
## [,679] [,680] [,681] [,682] [,683] [,684] [,685] [,686] [,687] [,688]
## [1,] "p" "c" "f" "h" "d" "t" "d" "m" "p" "a"
## [2,] "o" "a" "l" "u" "u" "u" "i" "e" "r" "p"
## [3,] "n" "r" "u" "e" "r" "m" "a" "d" "e" "o"
## [4,] "g" "r" "j" "s" "o" "b" "n" "i" "s" "y"
## [5,] "a" "o" "o" "o" "s" "a" "a" "r" "a" "a"
## [,689] [,690] [,691] [,692] [,693] [,694] [,695] [,696] [,697] [,698]
## [1,] "v" "v" "m" "t" "t" "v" "c" "s" "b" "v"
## [2,] "i" "o" "o" "r" "e" "a" "r" "e" "a" "i"
## [3,] "d" "l" "v" "a" "n" "y" "e" "x" "h" "n"
## [4,] "e" "v" "i" "m" "i" "a" "y" "t" "i" "o"
## [5,] "o" "i" "l" "a" "s" "n" "o" "o" "a" "s"
## [,699] [,700] [,701] [,702] [,703] [,704] [,705] [,706] [,707] [,708]
## [1,] "r" "t" "c" "r" "o" "p" "n" "j" "b" "a"
## [2,] "o" "r" "o" "e" "l" "a" "o" "u" "a" "c"
## [3,] "s" "a" "b" "c" "i" "t" "v" "s" "r" "e"
## [4,] "a" "j" "r" "t" "v" "a" "i" "t" "b" "r"
## [5,] "s" "o" "e" "a" "a" "s" "o" "a" "a" "o"
## [,709] [,710] [,711] [,712] [,713] [,714] [,715] [,716] [,717] [,718]
## [1,] "g" "v" "c" "t" "d" "v" "c" "c" "a" "c"
## [2,] "e" "a" "u" "r" "i" "i" "a" "i" "b" "u"
## [3,] "n" "p" "r" "a" "e" "e" "b" "e" "u" "e"
## [4,] "i" "o" "v" "t" "r" "n" "l" "g" "s" "r"
## [5,] "o" "r" "a" "e" "a" "a" "e" "o" "o" "o"
## [,719] [,720] [,721] [,722] [,723] [,724] [,725] [,726] [,727] [,728]
## [1,] "f" "b" "l" "t" "b" "n" "n" "v" "o" "j"
## [2,] "r" "r" "u" "r" "o" "e" "o" "i" "i" "u"
## [3,] "u" "a" "c" "a" "r" "g" "t" "m" "d" "l"
## [4,] "t" "v" "a" "e" "d" "a" "a" "o" "o" "i"
## [5,] "a" "o" "s" "r" "o" "r" "r" "s" "s" "a"
## [,729] [,730] [,731] [,732] [,733] [,734] [,735] [,736] [,737] [,738]
## [1,] "o" "q" "s" "f" "g" "v" "t" "p" "p" "a"
## [2,] "j" "u" "e" "i" "o" "a" "r" "r" "e" "c"
## [3,] "a" "i" "r" "n" "r" "s" "i" "e" "d" "u"
## [4,] "l" "t" "r" "c" "d" "o" "g" "s" "i" "s"
## [5,] "a" "a" "a" "a" "o" "s" "o" "o" "a" "a"
## [,739] [,740] [,741] [,742] [,743] [,744] [,745] [,746] [,747] [,748]
## [1,] "p" "s" "p" "r" "s" "s" "h" "c" "m" "a"
## [2,] "e" "u" "e" "i" "e" "i" "u" "i" "o" "c"
## [3,] "t" "d" "c" "e" "n" "m" "e" "t" "n" "u"
## [4,] "e" "o" "e" "g" "t" "o" "c" "a" "t" "s"
## [5,] "r" "r" "s" "o" "o" "n" "o" "r" "o" "o"
## [,749] [,750] [,751] [,752] [,753] [,754] [,755] [,756] [,757] [,758]
## [1,] "a" "n" "f" "m" "f" "b" "c" "a" "m" "v"
## [2,] "s" "i" "a" "a" "l" "r" "o" "j" "e" "a"
## [3,] "i" "e" "l" "g" "o" "o" "p" "e" "t" "s"
## [4,] "l" "t" "l" "i" "t" "m" "a" "n" "e" "c"
## [5,] "o" "o" "a" "a" "a" "a" "s" "a" "r" "a"
## [,759] [,760] [,761] [,762] [,763] [,764] [,765] [,766] [,767] [,768]
## [1,] "v" "c" "p" "c" "c" "c" "p" "r" "q" "m"
## [2,] "o" "u" "i" "e" "a" "r" "r" "o" "u" "i"
## [3,] "t" "b" "s" "r" "p" "u" "e" "d" "i" "r"
## [4,] "a" "r" "o" "d" "a" "d" "s" "e" "s" "a"
## [5,] "r" "e" "s" "o" "s" "o" "s" "a" "e" "n"
## [,769] [,770] [,771] [,772] [,773] [,774] [,775] [,776] [,777] [,778]
## [1,] "m" "m" "m" "b" "c" "d" "c" "v" "g" "m"
## [2,] "i" "a" "e" "o" "e" "a" "a" "e" "o" "a"
## [3,] "l" "t" "t" "t" "n" "r" "l" "i" "l" "l"
## [4,] "a" "e" "i" "o" "s" "i" "v" "a" "f" "e"
## [5,] "n" "o" "o" "n" "o" "a" "o" "n" "o" "s"
## [,779] [,780] [,781] [,782] [,783] [,784] [,785] [,786] [,787] [,788]
## [1,] "t" "o" "p" "m" "d" "f" "b" "r" "d" "l"
## [2,] "i" "b" "e" "o" "u" "i" "u" "e" "a" "e"
## [3,] "r" "v" "r" "v" "e" "j" "s" "u" "m" "c"
## [4,] "o" "i" "o" "e" "l" "a" "c" "n" "a" "h"
## [5,] "s" "o" "n" "r" "o" "r" "o" "e" "s" "o"
## [,789] [,790] [,791] [,792] [,793] [,794] [,795] [,796] [,797] [,798]
## [1,] "g" "c" "m" "v" "r" "c" "c" "f" "v" "l"
## [2,] "o" "r" "e" "e" "u" "a" "e" "u" "a" "i"
## [3,] "t" "u" "t" "a" "m" "s" "l" "m" "c" "t"
## [4,] "a" "e" "a" "s" "o" "c" "d" "a" "i" "r"
## [5,] "s" "l" "s" "e" "r" "o" "a" "r" "a" "o"
## [,799] [,800] [,801] [,802] [,803] [,804] [,805] [,806] [,807] [,808]
## [1,] "o" "n" "m" "a" "l" "g" "s" "s" "u" "d"
## [2,] "n" "o" "a" "l" "o" "a" "a" "m" "s" "i"
## [3,] "d" "b" "n" "d" "c" "s" "l" "i" "a" "g"
## [4,] "a" "e" "d" "e" "o" "e" "g" "t" "d" "n"
## [5,] "s" "l" "a" "a" "s" "s" "a" "h" "o" "o"
## [,809] [,810] [,811] [,812] [,813] [,814] [,815] [,816] [,817] [,818]
## [1,] "p" "a" "c" "t" "b" "l" "t" "p" "s" "v"
## [2,] "l" "e" "e" "r" "r" "l" "r" "a" "a" "o"
## [3,] "a" "r" "n" "a" "u" "e" "a" "p" "b" "l"
## [4,] "c" "e" "a" "i" "t" "v" "g" "a" "i" "a"
## [5,] "a" "o" "r" "a" "o" "e" "o" "s" "o" "r"
## [,819] [,820] [,821] [,822] [,823] [,824] [,825] [,826] [,827] [,828]
## [1,] "r" "p" "r" "c" "o" "d" "a" "g" "p" "f"
## [2,] "u" "l" "i" "r" "p" "e" "s" "r" "u" "a"
## [3,] "s" "u" "s" "e" "i" "b" "u" "a" "l" "t"
## [4,] "o" "m" "a" "a" "n" "e" "m" "n" "s" "a"
## [5,] "s" "a" "s" "n" "a" "s" "e" "o" "o" "l"
## [,829] [,830] [,831] [,832] [,833] [,834] [,835] [,836] [,837] [,838]
## [1,] "g" "v" "l" "t" "a" "f" "n" "d" "a" "a"
## [2,] "a" "e" "a" "i" "b" "i" "a" "i" "m" "g"
## [3,] "f" "n" "g" "r" "r" "r" "v" "g" "a" "u"
## [4,] "a" "d" "o" "a" "i" "m" "a" "n" "d" "d"
## [5,] "s" "e" "s" "r" "a" "o" "l" "a" "o" "a"
## [,839] [,840] [,841] [,842] [,843] [,844] [,845] [,846] [,847] [,848]
## [1,] "v" "r" "t" "c" "n" "q" "f" "s" "c" "v"
## [2,] "a" "o" "u" "i" "a" "u" "i" "e" "a" "a"
## [3,] "r" "p" "n" "r" "t" "e" "b" "l" "u" "c"
## [4,] "o" "a" "e" "c" "a" "j" "r" "l" "s" "a"
## [5,] "n" "s" "l" "o" "l" "a" "a" "o" "o" "s"
## [,849] [,850] [,851] [,852] [,853] [,854] [,855] [,856] [,857] [,858]
## [1,] "r" "d" "c" "v" "f" "a" "o" "p" "c" "c"
## [2,] "o" "a" "o" "e" "a" "u" "c" "a" "e" "a"
## [3,] "m" "r" "g" "r" "l" "t" "u" "r" "d" "n"
## [4,] "p" "m" "e" "m" "d" "o" "p" "d" "e" "t"
## [5,] "e" "e" "r" "e" "a" "s" "o" "o" "r" "a"
## [,859] [,860] [,861] [,862] [,863] [,864] [,865] [,866] [,867] [,868]
## [1,] "c" "c" "c" "s" "v" "r" "f" "l" "t" "c"
## [2,] "e" "o" "o" "a" "a" "u" "u" "i" "r" "o"
## [3,] "l" "b" "r" "i" "r" "b" "r" "d" "o" "r"
## [4,] "o" "r" "e" "n" "i" "i" "i" "i" "z" "a"
## [5,] "s" "a" "a" "t" "a" "a" "a" "a" "o" "l"
## [,869] [,870] [,871] [,872] [,873] [,874] [,875] [,876] [,877] [,878]
## [1,] "t" "v" "b" "d" "p" "h" "j" "v" "f" "a"
## [2,] "a" "i" "o" "u" "a" "o" "u" "i" "r" "b"
## [3,] "l" "s" "n" "r" "g" "n" "d" "v" "e" "r"
## [4,] "l" "t" "o" "a" "a" "d" "i" "a" "u" "e"
## [5,] "a" "e" "s" "n" "n" "o" "o" "s" "d" "n"
## [,879] [,880] [,881] [,882] [,883] [,884] [,885] [,886] [,887] [,888]
## [1,] "r" "a" "d" "g" "s" "s" "f" "d" "g" "j"
## [2,] "i" "r" "a" "a" "o" "a" "a" "u" "r" "o"
## [3,] "v" "i" "d" "l" "b" "l" "u" "e" "i" "y"
## [4,] "a" "e" "a" "l" "r" "t" "n" "l" "t" "a"
## [5,] "s" "l" "s" "o" "a" "a" "a" "e" "a" "s"
## [,889] [,890] [,891] [,892] [,893] [,894] [,895] [,896] [,897] [,898]
## [1,] "b" "d" "s" "t" "c" "s" "a" "v" "h" "f"
## [2,] "a" "a" "u" "a" "o" "u" "l" "e" "e" "l"
## [3,] "r" "d" "y" "r" "g" "c" "t" "n" "n" "o"
## [4,] "c" "o" "a" "d" "i" "i" "a" "u" "r" "r"
## [5,] "a" "s" "s" "o" "o" "a" "r" "s" "y" "a"
## [,899] [,900] [,901] [,902] [,903] [,904] [,905] [,906] [,907] [,908]
## [1,] "p" "u" "m" "r" "r" "t" "m" "r" "f" "m"
## [2,] "o" "r" "a" "o" "u" "i" "a" "a" "r" "a"
## [3,] "n" "n" "r" "q" "t" "m" "c" "s" "a" "r"
## [4,] "c" "a" "i" "u" "a" "e" "h" "g" "n" "t"
## [5,] "e" "s" "n" "e" "s" "s" "o" "o" "k" "i"
## [,909] [,910] [,911] [,912] [,913] [,914] [,915] [,916] [,917] [,918]
## [1,] "l" "s" "v" "a" "p" "t" "b" "g" "f" "s"
## [2,] "a" "a" "e" "r" "l" "o" "o" "l" "o" "u"
## [3,] "z" "l" "n" "o" "o" "n" "t" "o" "r" "t"
## [4,] "o" "d" "g" "m" "m" "t" "a" "b" "m" "i"
## [5,] "s" "o" "o" "a" "o" "o" "s" "o" "o" "l"
## [,919] [,920] [,921] [,922] [,923] [,924] [,925] [,926] [,927] [,928]
## [1,] "v" "v" "a" "s" "t" "d" "a" "a" "d" "c"
## [2,] "i" "e" "n" "o" "r" "i" "l" "g" "u" "r"
## [3,] "e" "r" "t" "n" "o" "g" "m" "u" "e" "u"
## [4,] "r" "a" "o" "a" "n" "a" "a" "d" "ñ" "c"
## [5,] "a" "s" "n" "r" "o" "s" "s" "o" "a" "e"
## [,929] [,930] [,931] [,932] [,933] [,934] [,935] [,936] [,937] [,938]
## [1,] "m" "o" "r" "t" "p" "p" "p" "m" "n" "p"
## [2,] "o" "r" "i" "e" "a" "e" "a" "a" "a" "o"
## [3,] "v" "i" "v" "n" "l" "l" "r" "r" "v" "n"
## [4,] "i" "n" "e" "o" "o" "o" "e" "t" "e" "g"
## [5,] "a" "a" "r" "r" "s" "s" "s" "e" "s" "o"
## [,939] [,940] [,941] [,942] [,943] [,944] [,945] [,946] [,947] [,948]
## [1,] "v" "b" "s" "e" "s" "r" "l" "a" "g" "c"
## [2,] "i" "u" "u" "l" "a" "o" "o" "y" "o" "e"
## [3,] "a" "q" "m" "e" "l" "m" "r" "u" "r" "s"
## [4,] "j" "u" "a" "v" "e" "a" "c" "d" "d" "i"
## [5,] "o" "e" "r" "a" "s" "n" "a" "o" "a" "d"
## [,949] [,950] [,951] [,952] [,953] [,954] [,955] [,956] [,957] [,958]
## [1,] "r" "h" "s" "j" "s" "n" "s" "h" "j" "c"
## [2,] "a" "a" "e" "o" "e" "i" "i" "u" "e" "u"
## [3,] "t" "r" "c" "n" "c" "ñ" "r" "i" "r" "e"
## [4,] "o" "t" "a" "e" "o" "e" "v" "d" "e" "v"
## [5,] "n" "o" "s" "s" "s" "z" "a" "a" "z" "a"
## [,959] [,960] [,961] [,962] [,963] [,964] [,965] [,966] [,967] [,968]
## [1,] "s" "s" "v" "d" "s" "v" "h" "h" "s" "v"
## [2,] "e" "u" "e" "a" "e" "e" "a" "a" "u" "e"
## [3,] "x" "m" "l" "m" "ñ" "r" "g" "b" "c" "r"
## [4,] "t" "a" "e" "o" "a" "s" "a" "l" "i" "l"
## [5,] "a" "n" "z" "s" "s" "o" "s" "e" "o" "e"
## [,969] [,970] [,971] [,972] [,973] [,974] [,975] [,976] [,977] [,978]
## [1,] "d" "f" "l" "v" "c" "m" "c" "j" "g" "l"
## [2,] "a" "i" "a" "i" "i" "i" "a" "a" "a" "i"
## [3,] "r" "j" "v" "a" "t" "t" "j" "m" "t" "n"
## [4,] "i" "o" "a" "j" "a" "o" "o" "o" "o" "d"
## [5,] "o" "s" "r" "a" "s" "s" "n" "n" "s" "a"
## [,979] [,980] [,981] [,982] [,983] [,984] [,985] [,986] [,987] [,988]
## [1,] "v" "d" "l" "p" "a" "s" "t" "v" "e" "c"
## [2,] "e" "e" "a" "a" "n" "o" "o" "e" "m" "i"
## [3,] "j" "j" "p" "u" "c" "ñ" "n" "l" "i" "e"
## [4,] "e" "e" "s" "l" "h" "a" "o" "a" "t" "g"
## [5,] "z" "n" "o" "a" "a" "r" "s" "s" "e" "a"
## [,989] [,990] [,991] [,992] [,993] [,994] [,995] [,996] [,997] [,998]
## [1,] "r" "r" "a" "f" "f" "b" "m" "a" "g" "s"
## [2,] "i" "a" "c" "a" "e" "r" "o" "c" "i" "a"
## [3,] "o" "t" "t" "e" "r" "u" "v" "u" "r" "i"
## [4,] "j" "o" "u" "n" "o" "n" "i" "d" "a" "n"
## [5,] "a" "s" "o" "a" "z" "o" "o" "e" "r" "z"
## [,999] [,1000] [,1001] [,1002] [,1003] [,1004] [,1005] [,1006] [,1007]
## [1,] "d" "f" "p" "b" "c" "m" "c" "c" "b"
## [2,] "a" "i" "i" "e" "r" "u" "a" "o" "a"
## [3,] "r" "c" "n" "l" "u" "l" "m" "l" "r"
## [4,] "a" "h" "t" "g" "z" "t" "a" "m" "e"
## [5,] "n" "a" "a" "a" "o" "a" "s" "o" "s"
## [,1008] [,1009] [,1010] [,1011] [,1012] [,1013] [,1014] [,1015] [,1016]
## [1,] "c" "a" "b" "p" "p" "h" "r" "b" "s"
## [2,] "o" "c" "a" "l" "r" "a" "u" "e" "u"
## [3,] "b" "o" "ñ" "a" "o" "i" "i" "s" "s"
## [4,] "r" "s" "o" "n" "s" "t" "n" "o" "t"
## [5,] "o" "o" "s" "a" "a" "i" "a" "s" "o"
## [,1017] [,1018] [,1019] [,1020] [,1021] [,1022] [,1023] [,1024] [,1025]
## [1,] "m" "d" "a" "t" "y" "l" "c" "f" "v"
## [2,] "a" "i" "n" "r" "e" "a" "r" "r" "a"
## [3,] "n" "o" "o" "o" "n" "t" "u" "i" "l"
## [4,] "t" "s" "t" "p" "d" "i" "z" "a" "i"
## [5,] "a" "a" "o" "a" "o" "n" "a" "s" "a"
## [,1026] [,1027] [,1028] [,1029] [,1030] [,1031] [,1032] [,1033] [,1034]
## [1,] "l" "a" "d" "t" "m" "c" "m" "a" "r"
## [2,] "i" "c" "i" "i" "a" "e" "i" "l" "o"
## [3,] "b" "e" "g" "n" "r" "l" "r" "b" "c"
## [4,] "r" "r" "a" "t" "e" "t" "a" "u" "i"
## [5,] "a" "a" "n" "a" "s" "a" "s" "m" "o"
## [,1035] [,1036] [,1037] [,1038] [,1039] [,1040] [,1041] [,1042] [,1043]
## [1,] "t" "e" "o" "p" "a" "t" "g" "a" "f"
## [2,] "a" "x" "p" "o" "r" "e" "o" "l" "r"
## [3,] "z" "t" "o" "r" "c" "m" "z" "e" "i"
## [4,] "a" "r" "n" "t" "o" "i" "a" "j" "o"
## [5,] "s" "a" "e" "a" "s" "a" "r" "a" "s"
## [,1044] [,1045] [,1046] [,1047] [,1048] [,1049] [,1050] [,1051] [,1052]
## [1,] "a" "r" "t" "t" "m" "t" "b" "h" "l"
## [2,] "n" "i" "e" "o" "a" "o" "o" "o" "l"
## [3,] "d" "t" "l" "r" "p" "k" "l" "n" "o"
## [4,] "a" "o" "o" "e" "a" "i" "s" "d" "r"
## [5,] "n" "s" "n" "o" "s" "o" "o" "a" "a"
## [,1053] [,1054] [,1055] [,1056] [,1057] [,1058] [,1059] [,1060] [,1061]
## [1,] "v" "c" "n" "t" "v" "e" "v" "h" "m"
## [2,] "e" "a" "o" "i" "e" "t" "e" "i" "a"
## [3,] "r" "l" "m" "g" "r" "i" "n" "l" "n"
## [4,] "a" "l" "a" "r" "t" "c" "a" "o" "g"
## [5,] "n" "a" "s" "e" "e" "o" "s" "s" "a"
## [,1062] [,1063] [,1064] [,1065] [,1066] [,1067] [,1068] [,1069] [,1070]
## [1,] "f" "p" "y" "e" "l" "t" "e" "p" "m"
## [2,] "a" "a" "e" "n" "l" "r" "l" "i" "a"
## [3,] "b" "u" "m" "v" "a" "a" "i" "n" "n"
## [4,] "i" "l" "a" "i" "n" "e" "a" "o" "t"
## [5,] "o" "o" "s" "a" "o" "n" "s" "s" "o"
## [,1071] [,1072] [,1073] [,1074] [,1075] [,1076] [,1077] [,1078] [,1079]
## [1,] "m" "b" "m" "o" "b" "s" "c" "f" "s"
## [2,] "u" "u" "i" "p" "e" "a" "u" "u" "o"
## [3,] "t" "r" "x" "t" "c" "e" "r" "n" "r"
## [4,] "u" "l" "t" "a" "a" "n" "a" "d" "i"
## [5,] "a" "a" "a" "r" "s" "z" "r" "o" "a"
## [,1080] [,1081] [,1082] [,1083] [,1084] [,1085] [,1086] [,1087] [,1088]
## [1,] "t" "n" "f" "s" "t" "r" "b" "t" "m"
## [2,] "a" "a" "r" "i" "a" "a" "r" "e" "o"
## [3,] "c" "c" "e" "g" "n" "t" "o" "x" "l"
## [4,] "t" "e" "n" "a" "g" "a" "w" "a" "d"
## [5,] "o" "n" "o" "n" "o" "s" "n" "s" "e"
## [,1089] [,1090] [,1091] [,1092] [,1093] [,1094] [,1095] [,1096] [,1097]
## [1,] "b" "h" "s" "r" "l" "m" "s" "b" "a"
## [2,] "a" "i" "o" "a" "i" "e" "o" "o" "n"
## [3,] "l" "m" "d" "z" "g" "j" "l" "d" "d"
## [4,] "a" "n" "i" "a" "a" "i" "t" "a" "e"
## [5,] "s" "o" "o" "s" "s" "a" "o" "s" "s"
## [,1098] [,1099] [,1100] [,1101] [,1102] [,1103] [,1104] [,1105] [,1106]
## [1,] "r" "c" "g" "a" "s" "t" "a" "a" "p"
## [2,] "i" "a" "i" "y" "e" "u" "l" "u" "e"
## [3,] "c" "u" "j" "a" "x" "r" "u" "l" "k"
## [4,] "a" "c" "o" "l" "o" "c" "d" "a" "i"
## [5,] "s" "e" "n" "a" "s" "o" "e" "s" "n"
## [,1107] [,1108] [,1109] [,1110] [,1111] [,1112] [,1113] [,1114] [,1115]
## [1,] "f" "f" "p" "a" "d" "g" "g" "o" "b"
## [2,] "a" "o" "u" "g" "u" "a" "u" "t" "r"
## [3,] "l" "c" "r" "u" "d" "l" "a" "e" "i"
## [4,] "t" "o" "o" "j" "a" "a" "p" "r" "s"
## [5,] "o" "s" "s" "a" "r" "n" "a" "o" "a"
## [,1116] [,1117] [,1118] [,1119] [,1120] [,1121] [,1122] [,1123] [,1124]
## [1,] "l" "s" "l" "f" "t" "v" "u" "c" "s"
## [2,] "e" "e" "i" "i" "r" "i" "s" "e" "u"
## [3,] "v" "n" "n" "n" "i" "c" "a" "r" "i"
## [4,] "e" "o" "d" "a" "b" "i" "b" "c" "z"
## [5,] "s" "s" "o" "s" "u" "o" "a" "o" "o"
## [,1125] [,1126] [,1127] [,1128] [,1129] [,1130] [,1131] [,1132] [,1133]
## [1,] "b" "h" "r" "l" "j" "l" "n" "c" "t"
## [2,] "o" "u" "e" "i" "a" "o" "a" "e" "e"
## [3,] "x" "e" "n" "a" "u" "u" "c" "l" "m"
## [4,] "e" "l" "f" "ñ" "l" "i" "h" "i" "e"
## [5,] "o" "e" "e" "o" "a" "s" "o" "a" "n"
## [,1134] [,1135] [,1136] [,1137] [,1138] [,1139] [,1140] [,1141] [,1142]
## [1,] "v" "t" "b" "m" "r" "a" "c" "r" "c"
## [2,] "e" "i" "a" "u" "e" "n" "e" "o" "a"
## [3,] "r" "b" "n" "t" "c" "e" "j" "d" "b"
## [4,] "b" "i" "d" "u" "t" "x" "a" "a" "i"
## [5,] "o" "a" "o" "o" "o" "o" "s" "r" "a"
## [,1143] [,1144] [,1145] [,1146] [,1147] [,1148] [,1149] [,1150] [,1151]
## [1,] "t" "f" "n" "c" "t" "v" "b" "d" "t"
## [2,] "u" "l" "a" "u" "e" "o" "o" "e" "e"
## [3,] "m" "a" "r" "r" "l" "c" "t" "b" "m"
## [4,] "o" "c" "r" "a" "a" "a" "i" "u" "e"
## [5,] "r" "o" "a" "s" "s" "l" "n" "t" "r"
## [,1152] [,1153] [,1154] [,1155] [,1156] [,1157] [,1158] [,1159] [,1160]
## [1,] "c" "d" "p" "s" "g" "c" "m" "f" "b"
## [2,] "a" "u" "a" "u" "a" "o" "i" "u" "e"
## [3,] "ñ" "r" "r" "b" "n" "c" "t" "n" "r"
## [4,] "o" "a" "r" "i" "a" "e" "i" "d" "t"
## [5,] "n" "r" "a" "a" "n" "r" "n" "a" "a"
## [,1161] [,1162] [,1163] [,1164] [,1165] [,1166] [,1167] [,1168] [,1169]
## [1,] "r" "t" "m" "s" "g" "a" "h" "t" "r"
## [2,] "a" "r" "a" "a" "u" "v" "e" "o" "e"
## [3,] "r" "a" "r" "b" "a" "i" "l" "r" "s"
## [4,] "a" "p" "e" "r" "p" "l" "m" "p" "t"
## [5,] "s" "o" "a" "a" "o" "a" "s" "e" "a"
## [,1170] [,1171] [,1172] [,1173] [,1174] [,1175] [,1176] [,1177] [,1178]
## [1,] "d" "o" "v" "a" "p" "q" "f" "g" "s"
## [2,] "a" "p" "e" "s" "o" "u" "u" "u" "e"
## [3,] "v" "i" "r" "o" "d" "e" "g" "i" "n"
## [4,] "i" "n" "i" "m" "r" "m" "a" "a" "d"
## [5,] "s" "o" "a" "a" "e" "a" "z" "s" "a"
## [,1179] [,1180] [,1181] [,1182] [,1183] [,1184] [,1185] [,1186] [,1187]
## [1,] "c" "e" "v" "b" "r" "l" "x" "p" "t"
## [2,] "o" "l" "a" "e" "o" "u" "u" "e" "o"
## [3,] "m" "i" "y" "t" "b" "n" "n" "s" "n"
## [4,] "e" "g" "a" "i" "a" "a" "t" "t" "t"
## [5,] "n" "e" "s" "s" "r" "r" "a" "e" "a"
## [,1188] [,1189] [,1190] [,1191] [,1192] [,1193] [,1194] [,1195] [,1196]
## [1,] "l" "l" "m" "s" "s" "s" "l" "b" "c"
## [2,] "l" "a" "a" "e" "a" "i" "u" "a" "e"
## [3,] "a" "p" "f" "g" "l" "t" "c" "t" "d"
## [4,] "m" "i" "i" "u" "v" "u" "i" "i" "i"
## [5,] "e" "z" "a" "i" "a" "o" "o" "r" "o"
## [,1197] [,1198] [,1199] [,1200] [,1201] [,1202] [,1203] [,1204] [,1205]
## [1,] "b" "f" "j" "r" "t" "r" "b" "p" "b"
## [2,] "e" "i" "a" "u" "u" "u" "e" "a" "o"
## [3,] "a" "l" "b" "e" "b" "e" "l" "s" "l"
## [4,] "c" "m" "o" "d" "o" "g" "e" "t" "a"
## [5,] "h" "s" "n" "o" "s" "o" "n" "o" "s"
## [,1206] [,1207] [,1208] [,1209] [,1210] [,1211] [,1212] [,1213] [,1214]
## [1,] "g" "p" "r" "a" "t" "b" "s" "a" "b"
## [2,] "r" "u" "o" "m" "o" "a" "o" "m" "u"
## [3,] "a" "g" "g" "a" "m" "c" "r" "a" "l"
## [4,] "n" "n" "e" "d" "e" "o" "d" "b" "t"
## [5,] "d" "a" "r" "a" "n" "n" "o" "a" "o"
## [,1215] [,1216] [,1217] [,1218] [,1219] [,1220] [,1221] [,1222] [,1223]
## [1,] "p" "a" "m" "o" "i" "e" "r" "t" "t"
## [2,] "a" "c" "o" "c" "s" "l" "e" "a" "e"
## [3,] "s" "a" "n" "h" "a" "i" "t" "p" "n"
## [4,] "e" "b" "j" "o" "a" "s" "o" "i" "e"
## [5,] "s" "e" "a" "a" "c" "a" "s" "a" "s"
## [,1224] [,1225] [,1226] [,1227] [,1228] [,1229] [,1230] [,1231] [,1232]
## [1,] "m" "d" "n" "u" "l" "s" "s" "s" "b"
## [2,] "a" "u" "a" "t" "o" "u" "o" "u" "o"
## [3,] "u" "c" "z" "e" "b" "c" "l" "e" "r"
## [4,] "r" "h" "i" "r" "o" "r" "e" "c" "i"
## [5,] "o" "a" "s" "o" "s" "e" "r" "o" "s"
## [,1233] [,1234] [,1235] [,1236] [,1237] [,1238] [,1239] [,1240] [,1241]
## [1,] "s" "m" "m" "t" "s" "f" "b" "h" "s"
## [2,] "a" "i" "o" "i" "o" "u" "u" "a" "o"
## [3,] "q" "x" "n" "r" "n" "e" "s" "b" "l"
## [4,] "u" "t" "o" "o" "d" "r" "e" "i" "i"
## [5,] "e" "o" "s" "n" "a" "e" "s" "l" "s"
## [,1242] [,1243] [,1244] [,1245] [,1246] [,1247] [,1248] [,1249] [,1250]
## [1,] "u" "d" "s" "m" "s" "e" "r" "a" "p"
## [2,] "b" "e" "u" "o" "e" "d" "o" "r" "i"
## [3,] "i" "s" "b" "s" "c" "g" "l" "m" "z"
## [4,] "c" "e" "e" "c" "t" "a" "l" "a" "c"
## [5,] "a" "e" "n" "a" "a" "r" "o" "r" "a"
## [,1251] [,1252] [,1253] [,1254] [,1255] [,1256] [,1257] [,1258] [,1259]
## [1,] "i" "l" "c" "s" "p" "p" "v" "m" "j"
## [2,] "ñ" "i" "a" "a" "a" "o" "e" "o" "u"
## [3,] "a" "c" "i" "c" "s" "z" "l" "r" "d"
## [4,] "k" "o" "g" "a" "e" "o" "a" "o" "i"
## [5,] "i" "r" "a" "n" "n" "s" "r" "s" "a"
## [,1260] [,1261] [,1262] [,1263] [,1264] [,1265] [,1266] [,1267] [,1268]
## [1,] "j" "c" "r" "w" "a" "e" "a" "a" "l"
## [2,] "o" "o" "o" "o" "t" "l" "d" "t" "i"
## [3,] "d" "l" "b" "r" "r" "e" "e" "a" "c"
## [4,] "e" "a" "o" "l" "a" "v" "l" "d" "e"
## [5,] "r" "s" "s" "d" "e" "o" "a" "o" "o"
## [,1269] [,1270] [,1271] [,1272] [,1273] [,1274] [,1275] [,1276] [,1277]
## [1,] "c" "i" "a" "p" "p" "i" "m" "u" "g"
## [2,] "o" "n" "y" "a" "a" "s" "a" "s" "u"
## [3,] "m" "t" "u" "n" "s" "l" "n" "u" "s"
## [4,] "i" "e" "d" "e" "a" "a" "i" "a" "t"
## [5,] "a" "r" "e" "l" "s" "m" "a" "l" "e"
## [,1278] [,1279] [,1280] [,1281] [,1282] [,1283] [,1284] [,1285] [,1286]
## [1,] "c" "t" "p" "l" "r" "p" "s" "t" "v"
## [2,] "a" "e" "e" "a" "o" "i" "e" "a" "i"
## [3,] "i" "n" "g" "r" "l" "n" "r" "p" "o"
## [4,] "x" "s" "a" "r" "e" "t" "n" "a" "l"
## [5,] "a" "a" "r" "a" "s" "o" "a" "r" "a"
## [,1287] [,1288] [,1289] [,1290] [,1291] [,1292] [,1293] [,1294] [,1295]
## [1,] "p" "s" "d" "m" "c" "m" "t" "t" "f"
## [2,] "u" "u" "o" "a" "o" "u" "a" "i" "i"
## [3,] "d" "e" "t" "r" "s" "e" "r" "r" "n"
## [4,] "o" "ñ" "a" "i" "m" "r" "d" "a" "o"
## [5,] "r" "a" "r" "e" "e" "a" "a" "s" "s"
## [,1296] [,1297] [,1298] [,1299] [,1300] [,1301] [,1302] [,1303] [,1304]
## [1,] "m" "f" "s" "u" "g" "d" "t" "b" "h"
## [2,] "a" "u" "i" "s" "o" "e" "o" "l" "e"
## [3,] "t" "s" "r" "a" "z" "n" "c" "a" "r"
## [4,] "a" "i" "i" "d" "a" "s" "a" "i" "r"
## [5,] "n" "l" "a" "a" "n" "o" "n" "r" "i"
## [,1305] [,1306] [,1307] [,1308] [,1309] [,1310] [,1311] [,1312] [,1313]
## [1,] "r" "s" "s" "s" "b" "r" "b" "c" "h"
## [2,] "u" "a" "e" "a" "a" "o" "a" "a" "o"
## [3,] "b" "n" "p" "c" "s" "t" "j" "s" "n"
## [4,] "r" "o" "a" "o" "a" "o" "a" "t" "r"
## [5,] "o" "s" "n" "s" "n" "s" "n" "a" "a"
## [,1314] [,1315] [,1316] [,1317] [,1318] [,1319] [,1320] [,1321] [,1322]
## [1,] "a" "c" "p" "c" "t" "v" "a" "d" "p"
## [2,] "t" "l" "r" "a" "e" "a" "r" "i" "a"
## [3,] "a" "a" "a" "b" "n" "l" "a" "c" "u"
## [4,] "c" "v" "g" "a" "u" "e" "ñ" "t" "t"
## [5,] "a" "o" "a" "l" "e" "r" "a" "o" "a"
## [,1323] [,1324] [,1325] [,1326] [,1327] [,1328] [,1329] [,1330] [,1331]
## [1,] "r" "m" "a" "b" "c" "a" "t" "b" "l"
## [2,] "i" "u" "l" "o" "l" "c" "o" "y" "e"
## [3,] "g" "s" "g" "c" "e" "t" "r" "r" "n"
## [4,] "e" "l" "a" "a" "r" "a" "n" "o" "i"
## [5,] "n" "o" "s" "s" "o" "s" "a" "n" "n"
## [,1332] [,1333] [,1334] [,1335] [,1336] [,1337] [,1338] [,1339] [,1340]
## [1,] "p" "r" "a" "b" "c" "p" "s" "v" "b"
## [2,] "u" "e" "l" "e" "a" "o" "u" "e" "l"
## [3,] "ñ" "n" "a" "b" "i" "r" "p" "g" "u"
## [4,] "o" "a" "i" "e" "r" "t" "e" "a" "s"
## [5,] "s" "l" "n" "s" "o" "e" "r" "s" "a"
## [,1341] [,1342] [,1343] [,1344] [,1345] [,1346] [,1347] [,1348] [,1349]
## [1,] "s" "m" "m" "c" "v" "v" "r" "c" "c"
## [2,] "a" "u" "o" "o" "e" "i" "o" "a" "e"
## [3,] "l" "e" "r" "n" "l" "v" "y" "b" "u"
## [4,] "g" "c" "a" "t" "o" "e" "a" "r" "t"
## [5,] "o" "a" "n" "e" "z" "s" "l" "a" "a"
## [,1350] [,1351] [,1352] [,1353] [,1354] [,1355] [,1356] [,1357] [,1358]
## [1,] "c" "d" "f" "d" "a" "m" "u" "a" "r"
## [2,] "a" "i" "i" "e" "m" "o" "r" "t" "e"
## [3,] "f" "m" "j" "n" "e" "n" "i" "r" "j"
## [4,] "e" "o" "a" "s" "d" "t" "b" "o" "a"
## [5,] "s" "s" "s" "a" "o" "a" "e" "z" "s"
## [,1359] [,1360] [,1361] [,1362] [,1363] [,1364] [,1365] [,1366] [,1367]
## [1,] "c" "e" "p" "r" "b" "l" "t" "v" "c"
## [2,] "a" "n" "e" "a" "u" "o" "a" "e" "r"
## [3,] "c" "a" "t" "y" "r" "g" "x" "n" "u"
## [4,] "a" "n" "r" "a" "r" "r" "i" "c" "d"
## [5,] "o" "o" "a" "s" "o" "e" "s" "e" "a"
## [,1368] [,1369] [,1370] [,1371] [,1372] [,1373] [,1374] [,1375] [,1376]
## [1,] "a" "o" "p" "v" "l" "p" "e" "h" "a"
## [2,] "s" "x" "u" "a" "u" "i" "c" "e" "n"
## [3,] "a" "i" "t" "s" "c" "s" "h" "g" "i"
## [4,] "d" "d" "a" "t" "r" "a" "a" "e" "m"
## [5,] "o" "o" "s" "o" "o" "r" "n" "l" "a"
## [,1377] [,1378] [,1379] [,1380] [,1381] [,1382] [,1383] [,1384] [,1385]
## [1,] "v" "t" "o" "h" "r" "p" "c" "a" "m"
## [2,] "a" "a" "j" "a" "a" "o" "a" "c" "a"
## [3,] "l" "l" "e" "l" "c" "l" "i" "o" "n"
## [4,] "e" "l" "d" "l" "h" "o" "a" "g" "g"
## [5,] "n" "o" "a" "o" "a" "s" "n" "e" "o"
## [,1386] [,1387] [,1388] [,1389] [,1390] [,1391] [,1392] [,1393] [,1394]
## [1,] "a" "r" "n" "l" "s" "a" "o" "g" "b"
## [2,] "l" "i" "a" "a" "e" "t" "z" "r" "a"
## [3,] "l" "ñ" "d" "b" "m" "o" "o" "i" "r"
## [4,] "e" "o" "a" "i" "e" "m" "n" "p" "o"
## [5,] "n" "n" "l" "o" "n" "o" "o" "e" "n"
## [,1395] [,1396] [,1397] [,1398] [,1399] [,1400] [,1401] [,1402] [,1403]
## [1,] "r" "e" "i" "c" "f" "a" "f" "l" "o"
## [2,] "a" "s" "d" "h" "e" "p" "u" "e" "p"
## [3,] "r" "p" "o" "a" "t" "a" "e" "w" "u"
## [4,] "o" "i" "l" "p" "a" "g" "r" "i" "s"
## [5,] "s" "a" "o" "a" "l" "a" "o" "s" "o"
## [,1404] [,1405] [,1406] [,1407] [,1408] [,1409] [,1410] [,1411] [,1412]
## [1,] "t" "s" "t" "b" "h" "s" "e" "m" "p"
## [2,] "e" "c" "e" "e" "o" "o" "n" "o" "e"
## [3,] "n" "o" "c" "b" "y" "l" "t" "d" "r"
## [4,] "s" "t" "l" "i" "o" "e" "e" "a" "d"
## [5,] "o" "t" "a" "o" "s" "s" "s" "s" "i"
## [,1413] [,1414] [,1415] [,1416] [,1417] [,1418] [,1419] [,1420] [,1421]
## [1,] "a" "l" "r" "m" "c" "c" "p" "p" "t"
## [2,] "b" "a" "e" "e" "a" "a" "e" "i" "r"
## [3,] "o" "t" "z" "t" "b" "n" "r" "c" "a"
## [4,] "n" "a" "a" "i" "e" "o" "l" "o" "z"
## [5,] "o" "s" "r" "a" "n" "n" "a" "s" "a"
## [,1422] [,1423] [,1424] [,1425] [,1426] [,1427] [,1428] [,1429] [,1430]
## [1,] "g" "h" "m" "c" "l" "a" "w" "a" "y"
## [2,] "o" "a" "o" "i" "o" "r" "e" "t" "o"
## [3,] "d" "y" "z" "n" "c" "g" "b" "a" "u"
## [4,] "o" "a" "o" "e" "a" "e" "e" "u" "n"
## [5,] "y" "s" "s" "s" "s" "l" "r" "d" "g"
## [,1431] [,1432] [,1433] [,1434] [,1435] [,1436] [,1437] [,1438] [,1439]
## [1,] "s" "r" "j" "c" "m" "i" "m" "r" "p"
## [2,] "h" "e" "a" "o" "o" "r" "a" "o" "e"
## [3,] "o" "s" "u" "ñ" "n" "a" "g" "c" "r"
## [4,] "c" "e" "m" "a" "j" "n" "n" "h" "r"
## [5,] "k" "s" "e" "c" "e" "i" "a" "a" "a"
## [,1440] [,1441] [,1442] [,1443] [,1444] [,1445] [,1446] [,1447] [,1448]
## [1,] "s" "a" "f" "w" "t" "e" "j" "t" "g"
## [2,] "u" "u" "a" "h" "r" "v" "u" "i" "a"
## [3,] "m" "d" "r" "i" "u" "i" "e" "n" "l"
## [4,] "a" "a" "s" "t" "c" "t" "z" "t" "e"
## [5,] "s" "z" "a" "e" "o" "o" "a" "o" "s"
## [,1449] [,1450] [,1451] [,1452] [,1453] [,1454] [,1455] [,1456] [,1457]
## [1,] "p" "m" "v" "c" "g" "n" "a" "a" "c"
## [2,] "i" "a" "a" "r" "o" "a" "l" "n" "a"
## [3,] "l" "i" "s" "e" "r" "d" "i" "d" "z"
## [4,] "a" "t" "t" "a" "r" "a" "a" "r" "a"
## [5,] "s" "e" "a" "s" "a" "r" "s" "e" "r"
## [,1458] [,1459] [,1460] [,1461] [,1462] [,1463] [,1464] [,1465] [,1466]
## [1,] "d" "m" "e" "t" "b" "b" "c" "m" "m"
## [2,] "u" "a" "l" "e" "o" "a" "a" "a" "a"
## [3,] "e" "j" "i" "n" "r" "i" "r" "t" "y"
## [4,] "r" "o" "t" "a" "j" "l" "o" "i" "a"
## [5,] "o" "r" "e" "z" "a" "a" "s" "z" "s"
## [,1467] [,1468] [,1469] [,1470] [,1471] [,1472] [,1473] [,1474] [,1475]
## [1,] "p" "d" "c" "d" "p" "t" "v" "c" "n"
## [2,] "a" "a" "a" "o" "a" "o" "a" "o" "u"
## [3,] "l" "r" "r" "t" "o" "r" "l" "t" "r"
## [4,] "a" "t" "l" "e" "l" "a" "g" "a" "i"
## [5,] "u" "e" "a" "s" "o" "x" "a" "s" "a"
## [,1476] [,1477] [,1478] [,1479] [,1480] [,1481] [,1482] [,1483] [,1484]
## [1,] "c" "s" "m" "n" "c" "l" "v" "a" "a"
## [2,] "h" "e" "i" "a" "u" "l" "i" "n" "t"
## [3,] "e" "r" "k" "v" "i" "u" "r" "s" "l"
## [4,] "c" "a" "e" "i" "d" "i" "a" "i" "a"
## [5,] "a" "s" "l" "o" "a" "s" "l" "a" "s"
## [,1485] [,1486] [,1487] [,1488] [,1489] [,1490] [,1491] [,1492] [,1493]
## [1,] "l" "p" "p" "m" "p" "a" "f" "p" "h"
## [2,] "i" "i" "u" "u" "a" "l" "o" "a" "a"
## [3,] "b" "n" "r" "t" "s" "e" "r" "l" "r"
## [4,] "i" "a" "a" "i" "e" "j" "o" "c" "r"
## [5,] "a" "r" "s" "s" "a" "o" "s" "o" "y"
## [,1494] [,1495] [,1496] [,1497] [,1498] [,1499] [,1500] [,1501] [,1502]
## [1,] "g" "a" "a" "a" "c" "b" "a" "d" "l"
## [2,] "i" "t" "z" "p" "a" "r" "r" "i" "l"
## [3,] "r" "a" "a" "e" "c" "u" "i" "r" "o"
## [4,] "o" "c" "ñ" "g" "h" "j" "z" "a" "s"
## [5,] "n" "o" "a" "o" "o" "a" "a" "n" "a"
## [,1503] [,1504] [,1505] [,1506] [,1507] [,1508] [,1509] [,1510] [,1511]
## [1,] "b" "j" "m" "c" "f" "s" "m" "s" "a"
## [2,] "r" "i" "i" "a" "o" "e" "o" "e" "d"
## [3,] "a" "m" "r" "s" "r" "c" "r" "t" "a"
## [4,] "v" "m" "e" "a" "a" "a" "i" "a" "m"
## [5,] "a" "y" "n" "r" "l" "r" "a" "s" "s"
## [,1512] [,1513] [,1514] [,1515] [,1516] [,1517] [,1518] [,1519] [,1520]
## [1,] "w" "b" "a" "h" "d" "b" "a" "o" "b"
## [2,] "i" "r" "r" "e" "a" "o" "s" "v" "l"
## [3,] "l" "o" "c" "n" "n" "s" "o" "e" "u"
## [4,] "l" "t" "a" "r" "t" "c" "m" "j" "e"
## [5,] "y" "e" "s" "i" "e" "h" "o" "a" "s"
## [,1521] [,1522] [,1523] [,1524] [,1525] [,1526] [,1527] [,1528] [,1529]
## [1,] "p" "o" "s" "b" "b" "p" "l" "p" "s"
## [2,] "a" "b" "u" "e" "i" "u" "a" "o" "a"
## [3,] "n" "v" "e" "b" "c" "l" "u" "l" "z"
## [4,] "z" "i" "r" "i" "h" "p" "r" "a" "o"
## [5,] "a" "a" "o" "a" "o" "a" "o" "r" "n"
## [,1530] [,1531] [,1532] [,1533] [,1534] [,1535] [,1536] [,1537] [,1538]
## [1,] "a" "m" "s" "r" "a" "d" "e" "d" "r"
## [2,] "p" "a" "u" "e" "r" "a" "v" "e" "i"
## [3,] "o" "t" "i" "o" "d" "ñ" "i" "j" "n"
## [4,] "l" "a" "t" "j" "o" "a" "t" "a" "d"
## [5,] "o" "s" "e" "o" "r" "r" "e" "s" "e"
## [,1539] [,1540] [,1541] [,1542] [,1543] [,1544] [,1545] [,1546] [,1547]
## [1,] "z" "m" "n" "b" "m" "d" "j" "m" "m"
## [2,] "o" "u" "a" "u" "i" "e" "u" "a" "o"
## [3,] "r" "r" "d" "s" "c" "j" "g" "g" "o"
## [4,] "r" "a" "i" "t" "r" "e" "o" "o" "r"
## [5,] "o" "l" "a" "o" "o" "s" "s" "s" "e"
## [,1548] [,1549] [,1550] [,1551] [,1552] [,1553] [,1554] [,1555] [,1556]
## [1,] "b" "t" "p" "v" "f" "g" "v" "o" "s"
## [2,] "e" "e" "a" "a" "i" "a" "a" "a" "e"
## [3,] "s" "t" "m" "l" "e" "s" "r" "s" "d"
## [4,] "a" "a" "p" "i" "r" "t" "a" "i" "e"
## [5,] "r" "s" "a" "o" "a" "a" "s" "s" "s"
## [,1557] [,1558] [,1559] [,1560] [,1561] [,1562] [,1563] [,1564] [,1565]
## [1,] "b" "p" "e" "p" "f" "h" "p" "b" "f"
## [2,] "r" "l" "v" "a" "r" "a" "o" "u" "o"
## [3,] "u" "a" "o" "t" "a" "r" "s" "f" "s"
## [4,] "j" "g" "c" "t" "n" "t" "t" "o" "i"
## [5,] "o" "a" "a" "y" "z" "a" "e" "n" "l"
## [,1566] [,1567] [,1568] [,1569] [,1570] [,1571] [,1572] [,1573] [,1574]
## [1,] "f" "s" "f" "f" "n" "c" "r" "r" "s"
## [2,] "i" "u" "l" "u" "u" "o" "o" "o" "a"
## [3,] "s" "s" "a" "r" "d" "d" "b" "m" "d"
## [4,] "c" "a" "s" "o" "o" "o" "l" "e" "a"
## [5,] "o" "n" "h" "r" "s" "s" "e" "o" "m"
## [,1575] [,1576] [,1577] [,1578] [,1579] [,1580] [,1581] [,1582] [,1583]
## [1,] "d" "g" "p" "h" "b" "i" "m" "t" "d"
## [2,] "i" "i" "o" "u" "a" "s" "a" "i" "e"
## [3,] "m" "r" "n" "n" "s" "t" "y" "b" "b"
## [4,] "a" "o" "e" "d" "t" "m" "r" "i" "a"
## [5,] "s" "s" "s" "e" "o" "o" "a" "o" "n"
## [,1584] [,1585] [,1586] [,1587] [,1588] [,1589] [,1590] [,1591] [,1592]
## [1,] "l" "c" "e" "c" "b" "f" "p" "t" "h"
## [2,] "e" "a" "s" "e" "o" "r" "a" "a" "o"
## [3,] "r" "ñ" "q" "r" "y" "i" "g" "r" "n"
## [4,] "m" "a" "u" "d" "e" "t" "u" "t" "g"
## [5,] "a" "s" "i" "a" "r" "o" "e" "a" "o"
## [,1593] [,1594] [,1595] [,1596] [,1597] [,1598] [,1599] [,1600] [,1601]
## [1,] "q" "p" "v" "l" "m" "b" "h" "b" "s"
## [2,] "u" "i" "a" "l" "e" "o" "u" "o" "t"
## [3,] "e" "c" "g" "o" "t" "b" "i" "r" "e"
## [4,] "j" "a" "o" "r" "e" "b" "d" "d" "v"
## [5,] "o" "r" "n" "o" "n" "y" "o" "a" "e"
## [,1602] [,1603] [,1604] [,1605] [,1606] [,1607] [,1608] [,1609] [,1610]
## [1,] "l" "c" "s" "s" "c" "m" "v" "s" "l"
## [2,] "a" "u" "e" "a" "h" "u" "u" "a" "a"
## [3,] "s" "r" "r" "n" "a" "s" "e" "r" "n"
## [4,] "e" "r" "g" "t" "c" "i" "l" "a" "c"
## [5,] "r" "o" "i" "i" "o" "c" "a" "h" "e"
## [,1611] [,1612] [,1613] [,1614] [,1615] [,1616] [,1617] [,1618] [,1619]
## [1,] "c" "i" "s" "y" "a" "t" "v" "s" "v"
## [2,] "o" "c" "a" "e" "y" "r" "a" "o" "e"
## [3,] "r" "o" "u" "r" "u" "a" "l" "p" "n"
## [4,] "r" "n" "r" "n" "n" "z" "l" "l" "d"
## [5,] "o" "o" "a" "o" "o" "o" "a" "o" "a"
## [,1620] [,1621] [,1622] [,1623] [,1624] [,1625] [,1626] [,1627] [,1628]
## [1,] "b" "q" "s" "c" "g" "i" "c" "d" "g"
## [2,] "r" "u" "a" "o" "a" "o" "l" "o" "r"
## [3,] "u" "i" "u" "l" "r" "n" "o" "l" "a"
## [4,] "c" "t" "d" "g" "a" "e" "r" "i" "t"
## [5,] "e" "e" "i" "o" "y" "s" "o" "a" "a"
## [,1629] [,1630] [,1631] [,1632] [,1633] [,1634] [,1635] [,1636] [,1637]
## [1,] "l" "j" "s" "a" "j" "d" "m" "g" "r"
## [2,] "o" "u" "u" "s" "u" "e" "a" "r" "a"
## [3,] "m" "z" "e" "t" "d" "l" "l" "e" "j"
## [4,] "a" "g" "c" "r" "a" "t" "l" "c" "o"
## [5,] "s" "a" "a" "o" "s" "a" "a" "o" "y"
## [,1638] [,1639] [,1640] [,1641] [,1642] [,1643] [,1644] [,1645] [,1646]
## [1,] "s" "f" "a" "a" "c" "t" "k" "t" "a"
## [2,] "p" "i" "c" "p" "o" "u" "e" "a" "s"
## [3,] "o" "j" "t" "a" "r" "n" "l" "l" "u"
## [4,] "r" "a" "u" "g" "o" "e" "l" "o" "m"
## [5,] "t" "n" "e" "o" "s" "z" "y" "n" "a"
## [,1647] [,1648] [,1649] [,1650] [,1651] [,1652] [,1653] [,1654] [,1655]
## [1,] "d" "s" "b" "r" "d" "g" "i" "c" "p"
## [2,] "i" "o" "a" "a" "e" "u" "r" "a" "e"
## [3,] "c" "n" "l" "b" "l" "i" "i" "r" "s"
## [4,] "t" "i" "d" "a" "i" "d" "a" "p" "a"
## [5,] "a" "a" "e" "t" "a" "o" "n" "a" "n"
## [,1656] [,1657] [,1658] [,1659] [,1660] [,1661] [,1662] [,1663] [,1664]
## [1,] "e" "m" "c" "t" "a" "e" "k" "o" "d"
## [2,] "x" "u" "a" "i" "r" "s" "a" "b" "o"
## [3,] "o" "e" "n" "m" "a" "t" "r" "r" "r"
## [4,] "d" "r" "t" "o" "n" "e" "e" "a" "s"
## [5,] "o" "o" "e" "n" "a" "s" "n" "r" "o"
## [,1665] [,1666] [,1667] [,1668] [,1669] [,1670] [,1671] [,1672] [,1673]
## [1,] "o" "t" "s" "z" "c" "a" "p" "b" "g"
## [2,] "l" "a" "e" "a" "l" "l" "a" "a" "r"
## [3,] "i" "p" "n" "i" "a" "z" "r" "r" "a"
## [4,] "v" "a" "t" "r" "r" "a" "o" "d" "t"
## [5,] "o" "s" "e" "e" "k" "s" "s" "a" "o"
## [,1674] [,1675] [,1676] [,1677] [,1678] [,1679] [,1680] [,1681] [,1682]
## [1,] "b" "l" "n" "n" "k" "b" "r" "r" "g"
## [2,] "l" "e" "i" "a" "a" "i" "i" "o" "i"
## [3,] "a" "r" "x" "s" "f" "l" "e" "t" "r"
## [4,] "c" "o" "o" "a" "k" "l" "r" "a" "a"
## [5,] "k" "y" "n" "l" "a" "y" "a" "s" "n"
## [,1683] [,1684] [,1685] [,1686] [,1687] [,1688] [,1689] [,1690] [,1691]
## [1,] "i" "l" "t" "a" "c" "m" "s" "c" "p"
## [2,] "b" "e" "o" "p" "h" "a" "o" "o" "o"
## [3,] "i" "n" "r" "u" "a" "n" "p" "s" "d"
## [4,] "z" "t" "s" "r" "r" "s" "a" "e" "i"
## [5,] "a" "e" "o" "o" "o" "o" "s" "r" "o"
## [,1692] [,1693] [,1694] [,1695] [,1696] [,1697] [,1698] [,1699] [,1700]
## [1,] "n" "b" "c" "g" "c" "h" "c" "a" "i"
## [2,] "i" "o" "u" "r" "a" "a" "a" "u" "n"
## [3,] "e" "t" "l" "e" "r" "c" "b" "d" "s"
## [4,] "t" "e" "t" "e" "l" "h" "o" "i" "t"
## [5,] "a" "s" "a" "n" "o" "a" "s" "o" "o"
## [,1701] [,1702] [,1703] [,1704] [,1705] [,1706] [,1707] [,1708] [,1709]
## [1,] "s" "c" "c" "d" "f" "b" "c" "t" "c"
## [2,] "e" "h" "l" "a" "l" "a" "e" "r" "o"
## [3,] "r" "o" "u" "n" "u" "s" "s" "o" "m"
## [4,] "p" "c" "b" "e" "i" "t" "t" "y" "i"
## [5,] "a" "o" "s" "s" "r" "e" "a" "a" "o"
## [,1710] [,1711] [,1712] [,1713] [,1714] [,1715] [,1716] [,1717] [,1718]
## [1,] "o" "r" "a" "z" "m" "a" "p" "r" "c"
## [2,] "c" "o" "v" "u" "e" "g" "e" "u" "o"
## [3,] "u" "n" "e" "r" "r" "o" "ñ" "s" "i"
## [4,] "p" "c" "n" "d" "o" "t" "a" "a" "t"
## [5,] "e" "a" "a" "o" "s" "a" "s" "s" "o"
## [,1719] [,1720] [,1721] [,1722] [,1723] [,1724] [,1725] [,1726] [,1727]
## [1,] "m" "y" "p" "p" "p" "c" "m" "d" "f"
## [2,] "i" "o" "r" "o" "r" "a" "o" "o" "a"
## [3,] "s" "g" "o" "n" "a" "n" "t" "g" "c"
## [4,] "a" "u" "b" "t" "t" "j" "o" "m" "t"
## [5,] "s" "r" "o" "e" "s" "e" "s" "a" "o"
## [,1728] [,1729] [,1730] [,1731] [,1732] [,1733] [,1734] [,1735] [,1736]
## [1,] "m" "o" "a" "e" "v" "a" "t" "l" "m"
## [2,] "a" "i" "t" "n" "e" "p" "u" "a" "e"
## [3,] "i" "m" "a" "o" "r" "o" "r" "r" "n"
## [4,] "c" "o" "ñ" "j" "j" "d" "c" "r" "t"
## [5,] "a" "s" "e" "o" "a" "o" "a" "y" "a"
## [,1737] [,1738] [,1739] [,1740] [,1741] [,1742] [,1743] [,1744] [,1745]
## [1,] "b" "m" "r" "t" "p" "m" "p" "a" "n"
## [2,] "e" "a" "o" "o" "o" "e" "r" "n" "i"
## [3,] "b" "l" "b" "r" "l" "l" "a" "o" "e"
## [4,] "e" "t" "o" "t" "e" "l" "d" "t" "g"
## [5,] "n" "a" "t" "a" "n" "a" "a" "a" "o"
## [,1746] [,1747] [,1748] [,1749] [,1750] [,1751] [,1752] [,1753] [,1754]
## [1,] "p" "h" "j" "p" "h" "h" "p" "v" "f"
## [2,] "o" "a" "a" "o" "e" "i" "e" "i" "r"
## [3,] "r" "d" "r" "r" "r" "n" "l" "t" "e"
## [4,] "t" "a" "r" "o" "a" "d" "a" "r" "i"
## [5,] "o" "s" "a" "s" "s" "u" "r" "o" "r"
## [,1755] [,1756] [,1757] [,1758] [,1759] [,1760] [,1761] [,1762] [,1763]
## [1,] "g" "p" "s" "v" "b" "c" "d" "h" "d"
## [2,] "u" "e" "o" "e" "a" "h" "e" "o" "i"
## [3,] "i" "a" "r" "l" "k" "r" "n" "u" "v"
## [4,] "a" "j" "d" "l" "e" "i" "i" "s" "a"
## [5,] "r" "e" "a" "o" "r" "s" "s" "e" "n"
## [,1764] [,1765] [,1766] [,1767] [,1768] [,1769] [,1770] [,1771] [,1772]
## [1,] "n" "m" "r" "s" "m" "p" "c" "g" "e"
## [2,] "a" "a" "a" "o" "o" "a" "h" "o" "d"
## [3,] "n" "t" "m" "r" "r" "n" "o" "u" "e"
## [4,] "c" "o" "p" "b" "r" "e" "z" "l" "m"
## [5,] "y" "s" "a" "o" "o" "s" "a" "d" "a"
## [,1773] [,1774] [,1775] [,1776] [,1777] [,1778] [,1779] [,1780] [,1781]
## [1,] "o" "p" "m" "f" "t" "a" "c" "l" "t"
## [2,] "c" "e" "a" "o" "e" "c" "o" "u" "i"
## [3,] "a" "m" "r" "r" "d" "u" "n" "c" "n"
## [4,] "s" "e" "c" "m" "i" "ñ" "g" "h" "t"
## [5,] "o" "x" "h" "e" "o" "a" "o" "o" "e"
## [,1782] [,1783] [,1784] [,1785] [,1786] [,1787] [,1788] [,1789] [,1790]
## [1,] "t" "m" "d" "g" "y" "a" "c" "g" "m"
## [2,] "r" "i" "e" "e" "a" "p" "a" "o" "u"
## [3,] "o" "d" "c" "s" "ñ" "i" "j" "r" "l"
## [4,] "t" "e" "i" "t" "e" "c" "a" "r" "a"
## [5,] "e" "n" "s" "a" "z" "e" "l" "o" "s"
## [,1791] [,1792] [,1793] [,1794] [,1795] [,1796] [,1797] [,1798] [,1799]
## [1,] "e" "j" "m" "b" "f" "m" "p" "a" "t"
## [2,] "p" "a" "o" "r" "o" "a" "u" "c" "i"
## [3,] "i" "c" "n" "i" "s" "r" "ñ" "o" "r"
## [4,] "c" "o" "d" "a" "a" "e" "a" "t" "a"
## [5,] "a" "b" "e" "n" "s" "o" "l" "o" "n"
## [,1800] [,1801] [,1802] [,1803] [,1804] [,1805] [,1806] [,1807] [,1808]
## [1,] "b" "r" "l" "l" "r" "p" "p" "t" "a"
## [2,] "a" "e" "u" "u" "o" "a" "e" "y" "r"
## [3,] "l" "c" "n" "c" "d" "v" "r" "s" "d"
## [4,] "s" "i" "a" "i" "e" "o" "a" "o" "u"
## [5,] "a" "o" "s" "r" "o" "r" "s" "n" "a"
## [,1809] [,1810] [,1811] [,1812] [,1813] [,1814] [,1815] [,1816] [,1817]
## [1,] "h" "j" "n" "b" "c" "t" "b" "g" "j"
## [2,] "a" "e" "i" "i" "o" "e" "o" "r" "o"
## [3,] "b" "r" "c" "l" "h" "j" "n" "a" "h"
## [4,] "r" "g" "h" "i" "e" "a" "e" "b" "a"
## [5,] "e" "a" "o" "s" "n" "s" "t" "o" "n"
## [,1818] [,1819] [,1820] [,1821] [,1822] [,1823] [,1824] [,1825] [,1826]
## [1,] "l" "a" "r" "r" "s" "y" "e" "h" "l"
## [2,] "o" "l" "a" "e" "e" "e" "s" "e" "i"
## [3,] "t" "z" "d" "g" "s" "g" "t" "r" "g"
## [4,] "e" "a" "a" "i" "g" "u" "e" "i" "h"
## [5,] "s" "r" "r" "r" "o" "a" "r" "r" "t"
## [,1827] [,1828] [,1829] [,1830] [,1831] [,1832] [,1833] [,1834] [,1835]
## [1,] "h" "s" "v" "f" "o" "r" "v" "l" "p"
## [2,] "e" "a" "a" "l" "l" "e" "e" "e" "e"
## [3,] "l" "n" "i" "o" "l" "i" "r" "t" "t"
## [4,] "i" "a" "n" "t" "a" "c" "d" "a" "i"
## [5,] "o" "s" "a" "e" "s" "h" "i" "l" "t"
## [,1836] [,1837] [,1838] [,1839] [,1840] [,1841] [,1842] [,1843] [,1844]
## [1,] "r" "r" "b" "s" "a" "g" "c" "m" "n"
## [2,] "e" "e" "r" "e" "n" "r" "o" "a" "e"
## [3,] "c" "i" "u" "p" "i" "a" "l" "u" "l"
## [4,] "a" "a" "t" "a" "t" "m" "a" "r" "l"
## [5,] "e" "n" "a" "s" "a" "o" "r" "a" "y"
## [,1845] [,1846] [,1847] [,1848] [,1849] [,1850] [,1851] [,1852] [,1853]
## [1,] "c" "d" "a" "h" "p" "b" "e" "m" "m"
## [2,] "e" "u" "g" "u" "e" "r" "l" "e" "o"
## [3,] "p" "n" "i" "m" "r" "o" "i" "l" "t"
## [4,] "a" "a" "t" "o" "e" "t" "j" "o" "i"
## [5,] "s" "s" "a" "s" "s" "a" "a" "n" "n"
## [,1854] [,1855] [,1856] [,1857] [,1858] [,1859] [,1860] [,1861] [,1862]
## [1,] "n" "h" "a" "r" "a" "d" "y" "f" "c"
## [2,] "i" "e" "l" "o" "n" "i" "a" "l" "h"
## [3,] "d" "c" "p" "m" "d" "r" "c" "o" "i"
## [4,] "o" "e" "e" "p" "e" "a" "i" "j" "v"
## [5,] "s" "s" "s" "a" "n" "s" "a" "o" "o"
## [,1863] [,1864] [,1865] [,1866] [,1867] [,1868] [,1869] [,1870] [,1871]
## [1,] "f" "a" "b" "c" "c" "l" "m" "l" "a"
## [2,] "o" "d" "a" "a" "o" "e" "u" "o" "n"
## [3,] "n" "o" "n" "l" "m" "o" "e" "m" "u"
## [4,] "d" "b" "a" "l" "a" "n" "v" "o" "l"
## [5,] "a" "e" "l" "o" "s" "a" "a" "s" "a"
## [,1872] [,1873] [,1874] [,1875] [,1876] [,1877] [,1878] [,1879] [,1880]
## [1,] "b" "d" "e" "l" "l" "p" "t" "c" "e"
## [2,] "r" "e" "d" "o" "u" "o" "r" "u" "l"
## [3,] "u" "l" "i" "c" "q" "t" "u" "z" "c"
## [4,] "m" "l" "t" "k" "u" "r" "d" "c" "h"
## [5,] "a" "a" "a" "e" "e" "o" "i" "o" "e"
## [,1881] [,1882] [,1883] [,1884] [,1885] [,1886] [,1887] [,1888] [,1889]
## [1,] "o" "v" "c" "t" "f" "f" "m" "z" "p"
## [2,] "v" "i" "u" "e" "e" "l" "e" "u" "u"
## [3,] "u" "g" "b" "l" "r" "u" "n" "l" "b"
## [4,] "l" "a" "o" "m" "r" "y" "u" "i" "i"
## [5,] "o" "s" "s" "o" "o" "e" "s" "a" "s"
## [,1890] [,1891] [,1892] [,1893] [,1894] [,1895] [,1896] [,1897] [,1898]
## [1,] "c" "p" "z" "c" "m" "a" "c" "p" "p"
## [2,] "i" "o" "a" "r" "o" "l" "a" "a" "a"
## [3,] "t" "d" "f" "e" "n" "i" "m" "ñ" "t"
## [4,] "a" "e" "r" "d" "t" "c" "u" "o" "o"
## [5,] "n" "s" "a" "o" "t" "e" "s" "s" "s"
## [,1899] [,1900] [,1901] [,1902] [,1903] [,1904] [,1905] [,1906] [,1907]
## [1,] "v" "p" "r" "v" "c" "f" "l" "s" "u"
## [2,] "i" "a" "a" "i" "o" "l" "i" "o" "b"
## [3,] "u" "l" "b" "v" "n" "o" "m" "u" "i"
## [4,] "d" "m" "i" "a" "t" "r" "b" "t" "c"
## [5,] "o" "o" "n" "n" "i" "o" "o" "h" "o"
## [,1908] [,1909] [,1910] [,1911] [,1912] [,1913] [,1914] [,1915] [,1916]
## [1,] "a" "c" "f" "m" "n" "n" "m" "m" "r"
## [2,] "n" "o" "e" "e" "e" "u" "i" "a" "o"
## [3,] "s" "p" "n" "r" "t" "t" "l" "c" "s"
## [4,] "o" "e" "i" "m" "o" "r" "l" "r" "s"
## [5,] "n" "i" "x" "a" "s" "e" "a" "o" "i"
## [,1917] [,1918] [,1919] [,1920] [,1921] [,1922] [,1923] [,1924] [,1925]
## [1,] "p" "c" "h" "k" "t" "v" "b" "c" "c"
## [2,] "o" "e" "i" "e" "a" "a" "o" "r" "u"
## [3,] "s" "n" "t" "v" "c" "l" "r" "i" "r"
## [4,] "e" "a" "o" "i" "o" "l" "r" "a" "s"
## [5,] "a" "s" "s" "n" "s" "s" "a" "s" "i"
## [,1926] [,1927] [,1928] [,1929] [,1930] [,1931] [,1932] [,1933] [,1934]
## [1,] "g" "n" "y" "s" "g" "o" "c" "d" "f"
## [2,] "u" "a" "e" "o" "r" "l" "h" "o" "u"
## [3,] "i" "c" "r" "p" "o" "m" "o" "c" "g"
## [4,] "ñ" "i" "b" "l" "u" "o" "c" "i" "a"
## [5,] "o" "a" "a" "a" "p" "s" "a" "l" "s"
## [,1935] [,1936] [,1937] [,1938] [,1939] [,1940] [,1941] [,1942] [,1943]
## [1,] "o" "v" "d" "j" "n" "b" "e" "t" "d"
## [2,] "m" "a" "e" "e" "e" "a" "n" "e" "i"
## [3,] "i" "g" "l" "n" "x" "r" "r" "r" "e"
## [4,] "s" "o" "h" "n" "o" "r" "i" "r" "s"
## [5,] "o" "s" "i" "y" "s" "y" "c" "a" "e"
## [,1944] [,1945] [,1946] [,1947] [,1948] [,1949] [,1950] [,1951] [,1952]
## [1,] "a" "p" "f" "s" "h" "r" "r" "t" "d"
## [2,] "l" "i" "o" "e" "a" "a" "e" "u" "o"
## [3,] "e" "q" "c" "r" "b" "n" "g" "y" "n"
## [4,] "r" "u" "a" "l" "a" "a" "a" "o" "e"
## [5,] "o" "e" "l" "e" "s" "s" "r" "s" "s"
## [,1953] [,1954] [,1955] [,1956] [,1957] [,1958] [,1959] [,1960] [,1961]
## [1,] "s" "c" "c" "g" "p" "d" "c" "s" "c"
## [2,] "a" "a" "h" "a" "o" "e" "h" "o" "a"
## [3,] "c" "o" "e" "r" "m" "m" "a" "r" "p"
## [4,] "r" "b" "c" "z" "b" "o" "l" "n" "t"
## [5,] "o" "a" "o" "a" "o" "s" "e" "a" "a"
## [,1962] [,1963] [,1964] [,1965] [,1966] [,1967] [,1968] [,1969] [,1970]
## [1,] "r" "t" "p" "t" "m" "o" "s" "t" "b"
## [2,] "a" "o" "e" "o" "o" "i" "t" "e" "a"
## [3,] "l" "v" "s" "m" "r" "r" "o" "s" "m"
## [4,] "l" "a" "a" "o" "b" "l" "n" "t" "b"
## [5,] "y" "r" "s" "s" "o" "o" "e" "s" "u"
## [,1971] [,1972] [,1973] [,1974] [,1975] [,1976] [,1977] [,1978] [,1979]
## [1,] "d" "f" "f" "j" "a" "h" "o" "o" "a"
## [2,] "i" "a" "l" "o" "b" "u" "r" "i" "p"
## [3,] "s" "n" "o" "y" "r" "y" "i" "r" "t"
## [4,] "t" "g" "j" "c" "a" "e" "o" "s" "o"
## [5,] "e" "o" "a" "e" "n" "n" "l" "e" "s"
## [,1980] [,1981] [,1982] [,1983] [,1984] [,1985] [,1986] [,1987] [,1988]
## [1,] "s" "a" "e" "g" "p" "r" "a" "d" "f"
## [2,] "a" "l" "r" "i" "u" "u" "l" "u" "l"
## [3,] "l" "e" "n" "r" "l" "m" "e" "d" "a"
## [4,] "v" "g" "s" "a" "p" "b" "g" "a" "c"
## [5,] "e" "a" "t" "s" "o" "a" "o" "n" "a"
## [,1989] [,1990] [,1991] [,1992] [,1993] [,1994] [,1995] [,1996] [,1997]
## [1,] "a" "b" "m" "r" "t" "f" "m" "v" "d"
## [2,] "v" "o" "u" "u" "u" "r" "e" "e" "o"
## [3,] "i" "l" "d" "g" "r" "e" "r" "r" "b"
## [4,] "s" "o" "o" "b" "i" "s" "a" "a" "l"
## [5,] "a" "s" "s" "y" "n" "a" "s" "z" "o"
## [,1998] [,1999] [,2000] [,2001] [,2002] [,2003] [,2004] [,2005] [,2006]
## [1,] "g" "t" "m" "r" "r" "t" "b" "g" "l"
## [2,] "r" "u" "a" "o" "o" "e" "o" "u" "y"
## [3,] "a" "l" "z" "b" "c" "l" "i" "i" "n"
## [4,] "n" "i" "a" "a" "e" "l" "n" "s" "c"
## [5,] "t" "o" "s" "n" "s" "o" "a" "o" "h"
## [,2007] [,2008] [,2009] [,2010] [,2011] [,2012] [,2013] [,2014] [,2015]
## [1,] "s" "e" "u" "b" "f" "f" "m" "c" "g"
## [2,] "t" "x" "l" "u" "e" "r" "a" "i" "a"
## [3,] "e" "i" "l" "c" "d" "i" "g" "s" "l"
## [4,] "i" "j" "o" "e" "r" "d" "n" "n" "a"
## [5,] "n" "a" "a" "o" "a" "a" "o" "e" "s"
## [,2016] [,2017] [,2018] [,2019] [,2020] [,2021] [,2022] [,2023] [,2024]
## [1,] "m" "o" "a" "c" "c" "c" "g" "g" "s"
## [2,] "a" "l" "n" "a" "a" "u" "a" "a" "u"
## [3,] "n" "e" "t" "s" "u" "e" "m" "r" "r"
## [4,] "c" "o" "i" "a" "c" "c" "m" "r" "c"
## [5,] "o" "s" "c" "l" "a" "e" "a" "a" "o"
## [,2025] [,2026] [,2027] [,2028] [,2029] [,2030] [,2031] [,2032] [,2033]
## [1,] "t" "i" "p" "r" "v" "a" "e" "l" "p"
## [2,] "a" "n" "a" "e" "i" "n" "t" "i" "y"
## [3,] "c" "t" "r" "h" "r" "d" "n" "c" "m"
## [4,] "o" "e" "m" "e" "i" "a" "i" "e" "e"
## [5,] "n" "l" "a" "n" "l" "s" "a" "u" "s"
## [,2034] [,2035] [,2036] [,2037] [,2038] [,2039] [,2040] [,2041] [,2042]
## [1,] "i" "o" "t" "e" "m" "o" "p" "a" "t"
## [2,] "r" "ñ" "a" "d" "a" "p" "o" "t" "u"
## [3,] "n" "a" "p" "d" "r" "a" "w" "a" "r"
## [4,] "o" "t" "o" "i" "g" "c" "e" "d" "b"
## [5,] "s" "e" "n" "e" "a" "o" "r" "a" "a"
## [,2043] [,2044] [,2045] [,2046] [,2047] [,2048] [,2049] [,2050] [,2051]
## [1,] "b" "c" "j" "r" "c" "p" "a" "o" "r"
## [2,] "a" "a" "a" "u" "a" "i" "r" "d" "o"
## [3,] "b" "e" "i" "b" "n" "z" "d" "i" "b"
## [4,] "e" "r" "r" "o" "s" "z" "u" "o" "i"
## [5,] "l" "a" "o" "r" "a" "a" "o" "s" "n"
## [,2052] [,2053] [,2054] [,2055] [,2056] [,2057] [,2058] [,2059] [,2060]
## [1,] "b" "e" "a" "j" "s" "k" "p" "a" "b"
## [2,] "u" "d" "t" "u" "e" "a" "a" "p" "a"
## [3,] "z" "w" "r" "j" "s" "b" "r" "o" "s"
## [4,] "o" "i" "i" "u" "o" "u" "i" "y" "a"
## [5,] "n" "n" "o" "y" "s" "l" "o" "e" "l"
## [,2061] [,2062] [,2063] [,2064] [,2065] [,2066] [,2067] [,2068] [,2069]
## [1,] "b" "f" "i" "p" "a" "s" "d" "i" "i"
## [2,] "o" "a" "m" "i" "c" "a" "o" "n" "n"
## [3,] "s" "n" "i" "p" "i" "v" "b" "c" "s"
## [4,] "c" "n" "t" "a" "d" "i" "l" "a" "t"
## [5,] "o" "y" "a" "s" "a" "a" "a" "s" "a"
## [,2070] [,2071] [,2072] [,2073] [,2074] [,2075] [,2076] [,2077] [,2078]
## [1,] "r" "u" "s" "m" "t" "x" "p" "s" "a"
## [2,] "o" "r" "u" "i" "a" "x" "a" "u" "h"
## [3,] "z" "a" "d" "t" "p" "i" "r" "f" "o"
## [4,] "a" "n" "a" "r" "i" "i" "a" "r" "g"
## [5,] "s" "o" "r" "e" "z" "i" "n" "a" "a"
## [,2079] [,2080] [,2081] [,2082] [,2083] [,2084] [,2085] [,2086] [,2087]
## [1,] "f" "p" "l" "o" "g" "g" "c" "l" "o"
## [2,] "a" "a" "i" "r" "a" "r" "u" "l" "l"
## [3,] "r" "p" "g" "d" "j" "i" "a" "o" "a"
## [4,] "o" "a" "a" "a" "o" "f" "s" "y" "n"
## [5,] "s" "l" "r" "z" "s" "o" "i" "d" "o"
## [,2088] [,2089] [,2090] [,2091] [,2092] [,2093] [,2094] [,2095] [,2096]
## [1,] "p" "c" "l" "s" "a" "c" "h" "v" "c"
## [2,] "o" "a" "o" "o" "c" "o" "u" "i" "o"
## [3,] "i" "n" "g" "p" "u" "r" "b" "ñ" "r"
## [4,] "n" "a" "s" "o" "d" "r" "e" "a" "a"
## [5,] "t" "s" "e" "r" "a" "i" "r" "s" "n"
## [,2097] [,2098] [,2099] [,2100] [,2101] [,2102] [,2103] [,2104] [,2105]
## [1,] "d" "f" "k" "m" "p" "t" "c" "n" "q"
## [2,] "i" "a" "l" "a" "a" "a" "a" "a" "u"
## [3,] "s" "l" "e" "b" "o" "d" "l" "v" "e"
## [4,] "t" "t" "i" "e" "l" "e" "v" "a" "m"
## [5,] "a" "e" "n" "l" "a" "o" "a" "s" "o"
## [,2106] [,2107] [,2108] [,2109] [,2110] [,2111] [,2112] [,2113] [,2114]
## [1,] "c" "g" "p" "h" "a" "b" "m" "f" "u"
## [2,] "e" "r" "e" "i" "m" "a" "o" "r" "l"
## [3,] "r" "i" "n" "r" "b" "t" "s" "e" "t"
## [4,] "r" "t" "i" "i" "a" "e" "e" "n" "r"
## [5,] "e" "e" "a" "o" "r" "o" "n" "a" "a"
## [,2115] [,2116] [,2117] [,2118] [,2119] [,2120] [,2121] [,2122] [,2123]
## [1,] "d" "a" "a" "a" "c" "a" "c" "j" "n"
## [2,] "u" "g" "g" "m" "o" "l" "u" "u" "e"
## [3,] "v" "n" "r" "a" "g" "v" "p" "r" "t"
## [4,] "e" "e" "i" "y" "i" "a" "o" "a" "a"
## [5,] "t" "s" "o" "a" "a" "r" "s" "r" "s"
## [,2124] [,2125] [,2126] [,2127] [,2128] [,2129] [,2130] [,2131] [,2132]
## [1,] "t" "b" "c" "v" "a" "c" "f" "j" "i"
## [2,] "e" "a" "e" "i" "b" "a" "o" "a" "ñ"
## [3,] "r" "t" "p" "s" "o" "t" "g" "q" "i"
## [4,] "a" "i" "a" "o" "g" "r" "o" "u" "g"
## [5,] "n" "o" "l" "r" "a" "e" "n" "e" "o"
## [,2133] [,2134] [,2135] [,2136] [,2137] [,2138] [,2139] [,2140] [,2141]
## [1,] "m" "m" "p" "l" "p" "p" "s" "b" "f"
## [2,] "a" "e" "i" "a" "a" "r" "u" "r" "u"
## [3,] "n" "c" "s" "n" "v" "a" "e" "i" "n"
## [4,] "d" "h" "c" "d" "o" "t" "n" "t" "d"
## [5,] "e" "a" "o" "a" "n" "o" "e" "o" "e"
## [,2142] [,2143] [,2144] [,2145] [,2146] [,2147] [,2148] [,2149] [,2150]
## [1,] "c" "v" "v" "a" "f" "h" "j" "s" "c"
## [2,] "h" "i" "o" "p" "o" "a" "e" "i" "u"
## [3,] "a" "s" "l" "e" "r" "l" "r" "r" "s"
## [4,] "n" "o" "c" "l" "z" "l" "r" "i" "c"
## [5,] "g" "s" "o" "o" "o" "e" "y" "o" "o"
## [,2151] [,2152] [,2153] [,2154] [,2155] [,2156] [,2157] [,2158] [,2159]
## [1,] "p" "r" "t" "d" "h" "c" "l" "m" "a"
## [2,] "l" "o" "e" "o" "i" "e" "u" "a" "r"
## [3,] "a" "d" "j" "r" "g" "l" "i" "r" "d"
## [4,] "c" "a" "e" "m" "o" "s" "g" "e" "i"
## [5,] "e" "s" "r" "i" "s" "o" "i" "y" "a"
## [,2160] [,2161] [,2162] [,2163] [,2164] [,2165] [,2166] [,2167] [,2168]
## [1,] "p" "c" "e" "f" "s" "c" "g" "h" "p"
## [2,] "e" "e" "m" "o" "a" "o" "r" "a" "a"
## [3,] "z" "r" "a" "l" "b" "f" "a" "m" "r"
## [4,] "o" "p" "n" "i" "r" "r" "s" "a" "i"
## [5,] "n" "a" "a" "o" "e" "e" "o" "s" "r"
## [,2169] [,2170] [,2171] [,2172] [,2173] [,2174] [,2175] [,2176] [,2177]
## [1,] "v" "c" "t" "t" "w" "o" "e" "g" "a"
## [2,] "a" "o" "a" "e" "o" "m" "n" "o" "p"
## [3,] "l" "r" "n" "r" "o" "e" "a" "r" "p"
## [4,] "e" "r" "d" "n" "d" "g" "n" "g" "l"
## [5,] "s" "a" "a" "a" "y" "a" "a" "o" "e"
## [,2178] [,2179] [,2180] [,2181] [,2182] [,2183] [,2184] [,2185] [,2186]
## [1,] "f" "t" "a" "a" "a" "a" "s" "s" "t"
## [2,] "a" "r" "n" "d" "p" "r" "i" "i" "e"
## [3,] "r" "u" "a" "o" "e" "d" "g" "l" "r"
## [4,] "o" "s" "y" "l" "l" "e" "a" "e" "r"
## [5,] "l" "t" "a" "f" "a" "r" "s" "s" "y"
## [,2187] [,2188] [,2189] [,2190] [,2191] [,2192] [,2193] [,2194] [,2195]
## [1,] "c" "c" "c" "l" "r" "v" "f" "p" "w"
## [2,] "h" "r" "r" "o" "e" "o" "e" "r" "a"
## [3,] "e" "a" "i" "b" "y" "r" "u" "i" "y"
## [4,] "m" "x" "a" "b" "n" "a" "d" "v" "n"
## [5,] "a" "i" "r" "y" "a" "z" "o" "a" "e"
## [,2196] [,2197] [,2198] [,2199] [,2200] [,2201] [,2202] [,2203] [,2204]
## [1,] "a" "d" "r" "a" "g" "i" "m" "z" "j"
## [2,] "ñ" "a" "a" "l" "o" "t" "a" "o" "e"
## [3,] "a" "n" "p" "c" "t" "e" "t" "r" "s"
## [4,] "d" "c" "t" "e" "e" "m" "e" "r" "s"
## [5,] "a" "e" "o" "s" "o" "s" "n" "a" "e"
## [,2205] [,2206] [,2207] [,2208] [,2209] [,2210] [,2211] [,2212] [,2213]
## [1,] "s" "c" "a" "c" "j" "k" "g" "m" "s"
## [2,] "a" "o" "r" "u" "a" "e" "r" "o" "t"
## [3,] "b" "l" "i" "b" "r" "r" "u" "m" "a"
## [4,] "l" "i" "c" "r" "r" "r" "t" "i" "n"
## [5,] "e" "n" "a" "a" "o" "y" "a" "a" "d"
## [,2214] [,2215] [,2216] [,2217] [,2218] [,2219] [,2220] [,2221] [,2222]
## [1,] "s" "c" "h" "m" "m" "r" "b" "t" "m"
## [2,] "u" "r" "e" "i" "u" "o" "u" "a" "e"
## [3,] "r" "a" "d" "g" "s" "z" "c" "n" "y"
## [4,] "j" "c" "o" "a" "g" "a" "a" "i" "e"
## [5,] "a" "k" "r" "s" "o" "r" "l" "a" "r"
## [,2223] [,2224] [,2225] [,2226] [,2227] [,2228] [,2229] [,2230] [,2231]
## [1,] "r" "t" "e" "a" "h" "t" "b" "l" "l"
## [2,] "u" "r" "u" "b" "a" "r" "a" "e" "o"
## [3,] "a" "i" "s" "o" "r" "i" "r" "i" "g"
## [4,] "n" "n" "k" "g" "a" "n" "r" "d" "o"
## [5,] "o" "i" "o" "o" "s" "o" "e" "a" "s"
## [,2232] [,2233] [,2234] [,2235] [,2236] [,2237] [,2238] [,2239] [,2240]
## [1,] "s" "h" "o" "t" "a" "c" "o" "t" "r"
## [2,] "t" "e" "s" "o" "h" "o" "i" "e" "e"
## [3,] "i" "l" "a" "s" "m" "l" "r" "s" "z"
## [4,] "j" "e" "d" "c" "e" "o" "l" "o" "o"
## [5,] "l" "n" "o" "a" "d" "m" "a" "n" "s"
## [,2241] [,2242] [,2243] [,2244] [,2245] [,2246] [,2247] [,2248] [,2249]
## [1,] "t" "t" "a" "j" "c" "d" "j" "t" "m"
## [2,] "r" "u" "h" "e" "u" "i" "o" "o" "i"
## [3,] "a" "t" "o" "q" "e" "q" "s" "p" "s"
## [4,] "g" "o" "g" "u" "l" "u" "u" "e" "i"
## [5,] "a" "r" "o" "e" "a" "e" "e" "s" "l"
## [,2250] [,2251] [,2252] [,2253] [,2254] [,2255] [,2256] [,2257] [,2258]
## [1,] "t" "a" "c" "c" "e" "e" "g" "p" "e"
## [2,] "e" "l" "e" "e" "l" "x" "u" "e" "n"
## [3,] "l" "a" "s" "t" "v" "c" "i" "r" "v"
## [4,] "a" "v" "t" "r" "i" "e" "s" "s" "i"
## [5,] "m" "a" "o" "o" "s" "l" "a" "a" "e"
## [,2259] [,2260] [,2261] [,2262] [,2263] [,2264] [,2265] [,2266] [,2267]
## [1,] "g" "o" "b" "c" "r" "s" "v" "z" "b"
## [2,] "r" "d" "r" "o" "i" "h" "o" "a" "e"
## [3,] "a" "i" "a" "p" "f" "o" "d" "n" "l"
## [4,] "b" "a" "s" "l" "l" "w" "k" "j" "l"
## [5,] "a" "r" "a" "a" "e" "s" "a" "a" "e"
## [,2268] [,2269] [,2270] [,2271] [,2272] [,2273] [,2274] [,2275] [,2276]
## [1,] "c" "m" "s" "c" "g" "k" "o" "s" "t"
## [2,] "a" "o" "t" "h" "r" "e" "p" "u" "e"
## [3,] "r" "n" "a" "i" "o" "n" "a" "e" "m"
## [4,] "i" "g" "t" "t" "v" "i" "c" "l" "i"
## [5,] "z" "e" "e" "y" "e" "a" "a" "a" "o"
## [,2277] [,2278] [,2279] [,2280] [,2281] [,2282] [,2283] [,2284] [,2285]
## [1,] "l" "l" "b" "g" "m" "s" "t" "c" "n"
## [2,] "a" "a" "a" "r" "i" "t" "e" "o" "a"
## [3,] "d" "i" "z" "a" "r" "o" "b" "b" "z"
## [4,] "e" "c" "a" "c" "e" "c" "a" "o" "c"
## [5,] "n" "o" "n" "e" "s" "k" "s" "s" "a"
## [,2286] [,2287] [,2288] [,2289] [,2290] [,2291] [,2292] [,2293] [,2294]
## [1,] "r" "a" "p" "b" "e" "g" "m" "r" "d"
## [2,] "a" "l" "i" "e" "b" "a" "o" "e" "u"
## [3,] "m" "z" "t" "n" "r" "r" "r" "u" "a"
## [4,] "a" "a" "o" "e" "i" "b" "o" "n" "t"
## [5,] "l" "n" "s" "t" "o" "o" "n" "a" "o"
## [,2295] [,2296] [,2297] [,2298] [,2299] [,2300] [,2301] [,2302] [,2303]
## [1,] "l" "s" "l" "l" "m" "r" "l" "p" "s"
## [2,] "l" "a" "e" "o" "e" "e" "e" "e" "o"
## [3,] "a" "u" "g" "t" "s" "m" "m" "r" "u"
## [4,] "g" "n" "u" "u" "o" "o" "o" "r" "s"
## [5,] "a" "a" "a" "s" "n" "s" "s" "y" "a"
## [,2304] [,2305] [,2306] [,2307] [,2308] [,2309] [,2310] [,2311] [,2312]
## [1,] "a" "a" "l" "n" "p" "b" "e" "n" "r"
## [2,] "b" "f" "u" "a" "u" "a" "p" "o" "u"
## [3,] "r" "o" "j" "f" "r" "e" "i" "r" "e"
## [4,] "e" "r" "o" "t" "g" "z" "c" "t" "g"
## [5,] "u" "o" "s" "a" "a" "a" "o" "h" "a"
## [,2313] [,2314] [,2315] [,2316] [,2317] [,2318] [,2319] [,2320] [,2321]
## [1,] "c" "t" "a" "p" "v" "a" "b" "e" "s"
## [2,] "a" "u" "n" "o" "a" "g" "r" "j" "o"
## [3,] "t" "y" "u" "m" "n" "r" "a" "i" "n"
## [4,] "o" "a" "l" "p" "o" "i" "d" "d" "e"
## [5,] "n" "s" "o" "a" "s" "a" "y" "o" "s"
## [,2322] [,2323] [,2324] [,2325] [,2326] [,2327] [,2328] [,2329] [,2330]
## [1,] "t" "w" "d" "l" "o" "p" "p" "a" "a"
## [2,] "o" "i" "a" "e" "r" "a" "d" "b" "m"
## [3,] "r" "l" "t" "p" "g" "l" "v" "a" "a"
## [4,] "e" "d" "a" "r" "i" "a" "s" "t" "r"
## [5,] "a" "e" "n" "a" "a" "s" "a" "e" "u"
## [,2331] [,2332] [,2333] [,2334] [,2335] [,2336] [,2337] [,2338] [,2339]
## [1,] "a" "b" "d" "m" "p" "c" "c" "o" "c"
## [2,] "m" "o" "o" "u" "r" "a" "h" "i" "i"
## [3,] "e" "m" "r" "g" "i" "u" "u" "r" "m"
## [4,] "n" "b" "i" "r" "c" "t" "l" "l" "a"
## [5,] "a" "o" "s" "e" "e" "o" "o" "e" "s"
## [,2340] [,2341] [,2342] [,2343] [,2344] [,2345] [,2346] [,2347] [,2348]
## [1,] "l" "p" "p" "s" "l" "p" "e" "f" "p"
## [2,] "a" "e" "i" "i" "u" "r" "v" "o" "i"
## [3,] "v" "t" "e" "m" "c" "i" "a" "r" "n"
## [4,] "a" "e" "r" "i" "e" "o" "n" "j" "u"
## [5,] "n" "n" "o" "l" "n" "r" "s" "a" "s"
## [,2349] [,2350] [,2351] [,2352] [,2353] [,2354] [,2355] [,2356] [,2357]
## [1,] "j" "m" "c" "d" "m" "p" "t" "t" "t"
## [2,] "a" "a" "r" "o" "a" "l" "a" "r" "r"
## [3,] "n" "t" "o" "n" "g" "a" "r" "a" "i"
## [4,] "e" "c" "s" "n" "d" "t" "r" "e" "p"
## [5,] "t" "h" "s" "a" "a" "e" "o" "s" "a"
## [,2358] [,2359] [,2360] [,2361] [,2362] [,2363] [,2364] [,2365] [,2366]
## [1,] "a" "h" "k" "l" "e" "m" "p" "p" "p"
## [2,] "g" "a" "l" "a" "d" "a" "e" "e" "i"
## [3,] "i" "z" "a" "m" "i" "g" "i" "ñ" "d"
## [4,] "t" "l" "u" "a" "p" "r" "n" "o" "a"
## [5,] "o" "o" "s" "s" "o" "o" "e" "n" "n"
## [,2367] [,2368] [,2369] [,2370] [,2371] [,2372] [,2373] [,2374] [,2375]
## [1,] "a" "d" "r" "u" "o" "s" "s" "t" "v"
## [2,] "n" "o" "e" "n" "b" "a" "i" "o" "i"
## [3,] "g" "l" "c" "i" "e" "p" "d" "u" "n"
## [4,] "l" "l" "i" "a" "s" "o" "r" "r" "c"
## [5,] "o" "y" "a" "n" "o" "s" "a" "s" "i"
## [,2376] [,2377] [,2378] [,2379] [,2380] [,2381] [,2382] [,2383] [,2384]
## [1,] "g" "g" "l" "c" "d" "p" "p" "a" "c"
## [2,] "e" "r" "a" "l" "o" "o" "u" "s" "a"
## [3,] "m" "a" "n" "a" "r" "s" "m" "a" "l"
## [4,] "m" "d" "u" "v" "a" "a" "a" "d" "i"
## [5,] "a" "a" "s" "a" "r" "r" "s" "a" "z"
## [,2385] [,2386] [,2387] [,2388] [,2389] [,2390] [,2391] [,2392] [,2393]
## [1,] "g" "g" "r" "c" "f" "m" "n" "p" "u"
## [2,] "r" "u" "o" "u" "i" "a" "u" "i" "s"
## [3,] "e" "e" "s" "i" "r" "g" "e" "n" "a"
## [4,] "t" "r" "a" "d" "s" "m" "r" "z" "r"
## [5,] "a" "o" "l" "e" "t" "a" "a" "a" "a"
## [,2394] [,2395] [,2396] [,2397] [,2398] [,2399] [,2400] [,2401] [,2402]
## [1,] "a" "a" "s" "b" "h" "m" "r" "t" "a"
## [2,] "u" "b" "h" "a" "a" "a" "o" "e" "v"
## [3,] "r" "e" "e" "z" "z" "y" "u" "l" "a"
## [4,] "e" "j" "l" "a" "m" "e" "n" "a" "l"
## [5,] "a" "a" "l" "r" "e" "r" "d" "r" "a"
## [,2403] [,2404] [,2405] [,2406] [,2407] [,2408] [,2409] [,2410] [,2411]
## [1,] "c" "j" "v" "c" "j" "l" "l" "q" "c"
## [2,] "i" "u" "o" "h" "u" "e" "o" "u" "a"
## [3,] "n" "z" "l" "u" "n" "a" "g" "e" "m"
## [4,] "t" "g" "v" "p" "i" "s" "i" "e" "p"
## [5,] "o" "o" "o" "a" "n" "e" "a" "n" "s"
## [,2412] [,2413] [,2414] [,2415] [,2416] [,2417] [,2418] [,2419] [,2420]
## [1,] "l" "s" "a" "c" "h" "l" "a" "b" "n"
## [2,] "a" "u" "a" "o" "u" "l" "n" "e" "a"
## [3,] "i" "d" "r" "r" "i" "a" "c" "s" "t"
## [4,] "c" "a" "o" "s" "a" "n" "l" "a" "a"
## [5,] "a" "n" "n" "o" "n" "a" "a" "n" "n"
## [,2421] [,2422] [,2423] [,2424] [,2425] [,2426] [,2427] [,2428] [,2429]
## [1,] "t" "c" "d" "n" "p" "v" "c" "f" "m"
## [2,] "i" "a" "o" "e" "e" "i" "r" "i" "a"
## [3,] "e" "n" "l" "c" "p" "o" "o" "n" "m"
## [4,] "s" "o" "i" "i" "s" "l" "m" "g" "a"
## [5,] "o" "a" "o" "o" "i" "o" "o" "e" "s"
## [,2430] [,2431] [,2432] [,2433] [,2434] [,2435] [,2436] [,2437] [,2438]
## [1,] "n" "r" "r" "f" "h" "o" "p" "r" "s"
## [2,] "o" "e" "i" "r" "u" "r" "e" "e" "e"
## [3,] "t" "g" "z" "i" "r" "f" "m" "g" "n"
## [4,] "a" "i" "o" "t" "t" "e" "a" "i" "i"
## [5,] "n" "a" "s" "a" "o" "o" "n" "o" "l"
## [,2439] [,2440] [,2441] [,2442] [,2443] [,2444] [,2445] [,2446] [,2447]
## [1,] "t" "v" "w" "n" "r" "e" "l" "o" "r"
## [2,] "u" "e" "e" "a" "o" "d" "o" "p" "e"
## [3,] "m" "r" "l" "c" "s" "i" "r" "t" "n"
## [4,] "b" "n" "l" "a" "c" "t" "e" "a" "z"
## [5,] "o" "e" "s" "r" "a" "o" "n" "n" "i"
## [,2448] [,2449] [,2450] [,2451] [,2452] [,2453] [,2454] [,2455] [,2456]
## [1,] "a" "a" "g" "j" "j" "l" "l" "m" "a"
## [2,] "l" "n" "r" "u" "u" "a" "i" "o" "z"
## [3,] "l" "n" "a" "g" "p" "t" "n" "d" "o"
## [4,] "a" "a" "v" "u" "p" "o" "u" "e" "t"
## [5,] "n" "n" "a" "e" "e" "n" "x" "m" "e"
## [,2457] [,2458] [,2459] [,2460] [,2461] [,2462] [,2463] [,2464] [,2465]
## [1,] "h" "k" "n" "p" "b" "d" "o" "c" "o"
## [2,] "u" "r" "o" "a" "a" "a" "c" "a" "s"
## [3,] "m" "a" "g" "s" "r" "i" "a" "i" "u"
## [4,] "u" "u" "a" "m" "a" "l" "ñ" "g" "n"
## [5,] "s" "s" "l" "o" "k" "y" "a" "o" "a"
## [,2466] [,2467] [,2468] [,2469] [,2470] [,2471] [,2472] [,2473] [,2474]
## [1,] "s" "a" "b" "e" "p" "s" "b" "d" "h"
## [2,] "i" "d" "a" "m" "o" "e" "u" "i" "o"
## [3,] "r" "o" "r" "i" "s" "p" "r" "c" "r"
## [4,] "v" "r" "n" "l" "t" "i" "d" "t" "c"
## [5,] "o" "o" "a" "y" "a" "a" "a" "e" "a"
## [,2475] [,2476] [,2477] [,2478] [,2479] [,2480] [,2481] [,2482] [,2483]
## [1,] "j" "v" "v" "f" "h" "r" "c" "c" "c"
## [2,] "a" "a" "o" "u" "u" "a" "a" "e" "o"
## [3,] "s" "g" "t" "m" "e" "v" "r" "s" "m"
## [4,] "o" "a" "a" "a" "c" "e" "a" "a" "i"
## [5,] "n" "s" "n" "n" "a" "l" "x" "n" "c"
## [,2484] [,2485] [,2486] [,2487] [,2488] [,2489] [,2490] [,2491] [,2492]
## [1,] "f" "q" "g" "r" "v" "g" "h" "p" "t"
## [2,] "e" "u" "a" "o" "i" "r" "i" "i" "e"
## [3,] "l" "e" "u" "v" "r" "u" "e" "c" "s"
## [4,] "i" "p" "d" "e" "g" "a" "r" "a" "e"
## [5,] "u" "a" "i" "r" "o" "s" "e" "s" "o"
## [,2493] [,2494] [,2495] [,2496] [,2497] [,2498] [,2499] [,2500] [,2501]
## [1,] "f" "j" "l" "m" "n" "r" "b" "c" "l"
## [2,] "e" "a" "u" "a" "u" "o" "e" "e" "a"
## [3,] "m" "l" "j" "g" "l" "c" "a" "r" "v"
## [4,] "u" "e" "a" "r" "o" "h" "t" "o" "i"
## [5,] "r" "a" "n" "a" "s" "e" "a" "s" "n"
## [,2502] [,2503] [,2504] [,2505] [,2506] [,2507] [,2508] [,2509] [,2510]
## [1,] "m" "c" "d" "s" "v" "v" "a" "e" "t"
## [2,] "a" "h" "a" "i" "e" "i" "m" "r" "o"
## [3,] "ñ" "i" "r" "s" "n" "e" "a" "w" "c"
## [4,] "a" "p" "l" "m" "d" "s" "g" "i" "h"
## [5,] "s" "s" "o" "o" "o" "e" "o" "n" "o"
## [,2511] [,2512] [,2513] [,2514] [,2515] [,2516] [,2517] [,2518] [,2519]
## [1,] "a" "b" "d" "m" "s" "a" "b" "b" "c"
## [2,] "v" "l" "a" "i" "u" "u" "a" "e" "a"
## [3,] "i" "a" "k" "r" "k" "n" "i" "t" "r"
## [4,] "d" "n" "a" "a" "e" "a" "l" "t" "o"
## [5,] "o" "c" "r" "d" "r" "r" "o" "y" "l"
## [,2520] [,2521] [,2522] [,2523] [,2524] [,2525] [,2526] [,2527] [,2528]
## [1,] "c" "h" "j" "r" "w" "a" "b" "b" "c"
## [2,] "o" "i" "e" "a" "o" "l" "a" "a" "a"
## [3,] "g" "l" "a" "l" "o" "t" "c" "ñ" "r"
## [4,] "e" "d" "n" "p" "d" "e" "h" "a" "a"
## [5,] "n" "a" "s" "h" "s" "a" "e" "r" "y"
## [,2529] [,2530] [,2531] [,2532] [,2533] [,2534] [,2535] [,2536] [,2537]
## [1,] "n" "s" "m" "p" "s" "m" "o" "a" "c"
## [2,] "o" "a" "a" "a" "i" "o" "i" "b" "h"
## [3,] "v" "u" "l" "l" "x" "d" "d" "d" "u"
## [4,] "o" "c" "v" "i" "t" "u" "a" "u" "s"
## [5,] "a" "e" "a" "o" "o" "s" "s" "l" "a"
## [,2538] [,2539] [,2540] [,2541] [,2542] [,2543] [,2544] [,2545] [,2546]
## [1,] "e" "f" "f" "m" "u" "a" "g" "j" "l"
## [2,] "c" "a" "r" "i" "n" "p" "o" "a" "o"
## [3,] "h" "r" "i" "o" "i" "t" "m" "n" "s"
## [4,] "e" "d" "t" "p" "r" "a" "a" "e" "a"
## [5,] "n" "o" "z" "e" "a" "s" "s" "s" "s"
## [,2547] [,2548] [,2549] [,2550] [,2551] [,2552] [,2553] [,2554] [,2555]
## [1,] "t" "z" "c" "c" "d" "n" "s" "t" "w"
## [2,] "u" "u" "a" "e" "y" "e" "a" "o" "a"
## [3,] "p" "m" "p" "d" "l" "i" "c" "s" "n"
## [4,] "a" "o" "t" "r" "a" "r" "r" "c" "d"
## [5,] "c" "s" "o" "o" "n" "a" "a" "o" "a"
## [,2556] [,2557] [,2558] [,2559] [,2560] [,2561] [,2562] [,2563] [,2564]
## [1,] "b" "l" "l" "n" "n" "c" "f" "j" "b"
## [2,] "e" "a" "o" "e" "i" "o" "o" "u" "u"
## [3,] "a" "r" "n" "g" "e" "m" "b" "l" "r"
## [4,] "t" "e" "j" "u" "v" "t" "i" "i" "g"
## [5,] "o" "s" "a" "e" "a" "e" "a" "e" "o"
## [,2565] [,2566] [,2567] [,2568] [,2569] [,2570] [,2571] [,2572] [,2573]
## [1,] "c" "e" "l" "m" "n" "t" "b" "f" "o"
## [2,] "h" "l" "a" "a" "i" "e" "o" "e" "n"
## [3,] "i" "i" "n" "g" "p" "l" "r" "t" "z"
## [4,] "t" "o" "k" "i" "o" "l" "r" "o" "a"
## [5,] "a" "t" "a" "c" "n" "a" "o" "s" "s"
## [,2574] [,2575] [,2576] [,2577] [,2578] [,2579] [,2580] [,2581] [,2582]
## [1,] "t" "t" "v" "c" "k" "u" "e" "m" "o"
## [2,] "a" "r" "e" "u" "e" "r" "r" "a" "y"
## [3,] "l" "a" "l" "t" "i" "b" "i" "n" "e"
## [4,] "l" "d" "o" "i" "t" "e" "g" "s" "r"
## [5,] "e" "e" "s" "s" "h" "s" "e" "a" "a"
## [,2583] [,2584] [,2585] [,2586] [,2587] [,2588] [,2589] [,2590] [,2591]
## [1,] "s" "a" "g" "j" "o" "p" "s" "s" "a"
## [2,] "e" "m" "r" "i" "p" "u" "c" "u" "t"
## [3,] "d" "a" "a" "a" "e" "t" "a" "f" "i"
## [4,] "a" "r" "p" "n" "r" "i" "l" "r" "c"
## [5,] "s" "y" "o" "g" "o" "n" "a" "i" "o"
## [,2592] [,2593] [,2594] [,2595] [,2596] [,2597] [,2598] [,2599] [,2600]
## [1,] "c" "c" "g" "n" "p" "v" "e" "g" "s"
## [2,] "h" "u" "i" "o" "e" "i" "q" "i" "u"
## [3,] "o" "r" "n" "e" "g" "a" "u" "n" "f"
## [4,] "l" "s" "e" "m" "a" "n" "i" "e" "r"
## [5,] "o" "a" "r" "i" "n" "a" "s" "s" "o"
## [,2601] [,2602] [,2603] [,2604] [,2605] [,2606] [,2607] [,2608] [,2609]
## [1,] "c" "c" "c" "m" "n" "o" "s" "a" "g"
## [2,] "r" "e" "u" "o" "y" "i" "e" "c" "a"
## [3,] "i" "i" "p" "i" "l" "s" "l" "n" "r"
## [4,] "o" "b" "o" "r" "o" "t" "e" "u" "r"
## [5,] "s" "a" "n" "a" "n" "e" "s" "r" "o"
## [,2610] [,2611] [,2612] [,2613] [,2614] [,2615] [,2616] [,2617] [,2618]
## [1,] "k" "m" "t" "t" "u" "f" "t" "a" "m"
## [2,] "l" "e" "a" "e" "r" "o" "e" "m" "o"
## [3,] "e" "l" "u" "m" "i" "r" "r" "e" "l"
## [4,] "r" "i" "r" "p" "e" "u" "s" "n" "e"
## [5,] "k" "a" "o" "o" "l" "m" "a" "o" "r"
## [,2619] [,2620] [,2621] [,2622] [,2623] [,2624] [,2625] [,2626] [,2627]
## [1,] "n" "p" "s" "s" "b" "l" "p" "u" "h"
## [2,] "i" "u" "a" "a" "a" "e" "i" "r" "a"
## [3,] "g" "c" "c" "t" "y" "i" "d" "i" "w"
## [4,] "h" "h" "a" "a" "e" "a" "e" "c" "a"
## [5,] "t" "a" "s" "n" "r" "n" "s" "o" "i"
## [,2628] [,2629] [,2630] [,2631] [,2632] [,2633] [,2634] [,2635] [,2636]
## [1,] "o" "o" "r" "v" "c" "s" "s" "t" "a"
## [2,] "r" "s" "o" "a" "a" "a" "p" "e" "d"
## [3,] "u" "e" "m" "r" "s" "g" "o" "r" "o"
## [4,] "r" "a" "a" "i" "a" "a" "t" "c" "r"
## [5,] "o" "s" "y" "o" "n" "z" "s" "o" "a"
## [,2637] [,2638] [,2639] [,2640] [,2641] [,2642] [,2643] [,2644] [,2645]
## [1,] "c" "c" "d" "u" "w" "a" "c" "e" "e"
## [2,] "h" "r" "a" "n" "a" "l" "o" "l" "m"
## [3,] "a" "o" "n" "t" "l" "o" "r" "u" "i"
## [4,] "t" "w" "n" "a" "d" "j" "i" "d" "t"
## [5,] "o" "n" "y" "r" "o" "a" "a" "e" "a"
## [,2646] [,2647] [,2648] [,2649] [,2650] [,2651] [,2652] [,2653] [,2654]
## [1,] "f" "l" "l" "p" "d" "g" "l" "m" "e"
## [2,] "r" "e" "i" "s" "a" "a" "l" "i" "d"
## [3,] "o" "j" "r" "i" "n" "t" "u" "m" "i"
## [4,] "t" "i" "a" "c" "d" "e" "c" "o" "t"
## [5,] "a" "a" "s" "o" "y" "s" "h" "s" "h"
## [,2655] [,2656] [,2657] [,2658] [,2659] [,2660] [,2661] [,2662] [,2663]
## [1,] "g" "a" "b" "d" "j" "l" "p" "p" "s"
## [2,] "o" "c" "u" "r" "a" "a" "a" "u" "u"
## [3,] "d" "e" "l" "i" "n" "t" "c" "l" "b"
## [4,] "o" "b" "b" "v" "t" "i" "h" "s" "a"
## [5,] "s" "o" "o" "e" "i" "r" "o" "e" "n"
## [,2664] [,2665] [,2666] [,2667] [,2668] [,2669] [,2670] [,2671] [,2672]
## [1,] "b" "c" "m" "p" "p" "c" "g" "h" "p"
## [2,] "l" "h" "u" "e" "u" "e" "o" "a" "a"
## [3,] "a" "a" "e" "c" "l" "p" "d" "y" "e"
## [4,] "k" "t" "v" "a" "s" "s" "e" "d" "s"
## [5,] "e" "a" "o" "r" "a" "a" "l" "n" "a"
## [,2673] [,2674] [,2675] [,2676] [,2677] [,2678] [,2679] [,2680] [,2681]
## [1,] "t" "b" "i" "m" "m" "s" "t" "s" "v"
## [2,] "i" "r" "t" "a" "u" "a" "r" "u" "i"
## [3,] "b" "a" "a" "c" "d" "n" "a" "b" "v"
## [4,] "e" "g" "l" "r" "a" "d" "b" "s" "a"
## [5,] "t" "a" "o" "i" "s" "y" "a" "p" "z"
## [,2682] [,2683] [,2684] [,2685] [,2686] [,2687] [,2688] [,2689] [,2690]
## [1,] "b" "c" "l" "o" "p" "v" "f" "p" "r"
## [2,] "r" "u" "i" "d" "o" "a" "e" "i" "o"
## [3,] "y" "r" "t" "i" "r" "g" "c" "d" "n"
## [4,] "c" "a" "i" "a" "n" "a" "a" "a" "c"
## [5,] "e" "n" "o" "n" "o" "r" "l" "l" "o"
## [,2691] [,2692] [,2693] [,2694] [,2695] [,2696] [,2697] [,2698] [,2699]
## [1,] "a" "e" "f" "i" "j" "r" "v" "c" "m"
## [2,] "r" "l" "i" "l" "i" "o" "u" "u" "i"
## [3,] "a" "e" "e" "e" "r" "g" "l" "r" "u"
## [4,] "d" "g" "l" "s" "o" "a" "v" "i" "r"
## [5,] "o" "i" "d" "o" "n" "r" "a" "a" "a"
## [,2700] [,2701] [,2702] [,2703] [,2704] [,2705] [,2706] [,2707] [,2708]
## [1,] "n" "a" "l" "m" "r" "s" "t" "v" "b"
## [2,] "i" "i" "i" "i" "e" "t" "i" "i" "o"
## [3,] "t" "s" "n" "l" "t" "e" "r" "s" "s"
## [4,] "y" "l" "c" "o" "e" "r" "s" "a" "s"
## [5,] "a" "a" "e" "s" "n" "n" "o" "s" "i"
## [,2709] [,2710] [,2711] [,2712] [,2713] [,2714] [,2715] [,2716] [,2717]
## [1,] "c" "c" "c" "y" "b" "c" "c" "n" "r"
## [2,] "a" "l" "o" "e" "i" "e" "u" "a" "e"
## [3,] "r" "a" "r" "p" "n" "d" "b" "r" "n"
## [4,] "e" "m" "s" "e" "g" "i" "a" "c" "a"
## [5,] "o" "a" "e" "s" "o" "a" "s" "o" "n"
## [,2718] [,2719] [,2720] [,2721] [,2722] [,2723] [,2724] [,2725] [,2726]
## [1,] "t" "b" "b" "l" "r" "z" "c" "c" "h"
## [2,] "a" "e" "o" "a" "a" "u" "o" "u" "e"
## [3,] "z" "r" "n" "z" "t" "l" "n" "a" "i"
## [4,] "o" "n" "u" "i" "i" "l" "o" "j" "n"
## [5,] "n" "a" "s" "o" "o" "e" "s" "o" "z"
## [,2727] [,2728] [,2729] [,2730] [,2731] [,2732] [,2733] [,2734] [,2735]
## [1,] "j" "l" "l" "s" "b" "b" "c" "l" "m"
## [2,] "o" "e" "i" "a" "r" "u" "u" "i" "o"
## [3,] "n" "b" "s" "d" "i" "e" "r" "b" "j"
## [4,] "a" "e" "a" "a" "o" "r" "r" "i" "a"
## [5,] "s" "d" "s" "t" "s" "o" "y" "o" "r"
## [,2736] [,2737] [,2738] [,2739] [,2740] [,2741] [,2742] [,2743] [,2744]
## [1,] "o" "p" "b" "m" "p" "p" "a" "a" "b"
## [2,] "r" "a" "r" "u" "a" "i" "g" "ñ" "u"
## [3,] "s" "r" "o" "e" "n" "v" "o" "a" "l"
## [4,] "o" "c" "t" "l" "d" "o" "t" "d" "l"
## [5,] "n" "o" "o" "a" "a" "t" "o" "i" "a"
## [,2745] [,2746] [,2747] [,2748] [,2749] [,2750] [,2751] [,2752] [,2753]
## [1,] "c" "d" "d" "l" "n" "p" "p" "s" "c"
## [2,] "h" "a" "u" "i" "e" "e" "o" "a" "u"
## [3,] "a" "ñ" "m" "s" "p" "p" "l" "n" "e"
## [4,] "n" "a" "a" "o" "a" "i" "l" "a" "t"
## [5,] "o" "n" "s" "s" "l" "n" "a" "r" "o"
## [,2754] [,2755] [,2756] [,2757] [,2758] [,2759] [,2760] [,2761] [,2762]
## [1,] "f" "l" "m" "n" "p" "a" "e" "f" "f"
## [2,] "u" "a" "a" "u" "a" "z" "r" "i" "i"
## [3,] "s" "t" "t" "l" "r" "o" "i" "l" "l"
## [4,] "t" "e" "e" "a" "d" "t" "c" "m" "o"
## [5,] "e" "x" "r" "s" "a" "a" "h" "o" "n"
## [,2763] [,2764] [,2765] [,2766] [,2767] [,2768] [,2769] [,2770] [,2771]
## [1,] "p" "t" "a" "b" "b" "d" "f" "i" "p"
## [2,] "o" "e" "d" "a" "u" "u" "e" "n" "a"
## [3,] "n" "r" "u" "j" "z" "p" "r" "t" "g"
## [4,] "t" "c" "c" "e" "o" "l" "r" "r" "a"
## [5,] "y" "a" "e" "n" "s" "a" "e" "a" "s"
## [,2772] [,2773] [,2774] [,2775] [,2776] [,2777] [,2778] [,2779] [,2780]
## [1,] "a" "a" "b" "l" "s" "b" "b" "e" "m"
## [2,] "j" "ñ" "u" "l" "i" "e" "u" "i" "e"
## [3,] "u" "e" "r" "o" "g" "r" "l" "b" "t"
## [4,] "a" "j" "n" "r" "m" "t" "l" "a" "e"
## [5,] "r" "o" "s" "e" "a" "i" "s" "r" "s"
## [,2781] [,2782] [,2783] [,2784] [,2785] [,2786] [,2787] [,2788] [,2789]
## [1,] "n" "g" "j" "m" "p" "a" "b" "g" "l"
## [2,] "o" "a" "o" "u" "u" "d" "a" "a" "a"
## [3,] "r" "c" "n" "ñ" "l" "u" "j" "u" "t"
## [4,] "i" "e" "e" "i" "i" "j" "o" "s" "i"
## [5,] "a" "l" "t" "z" "r" "o" "n" "s" "a"
## [,2790] [,2791] [,2792] [,2793] [,2794] [,2795] [,2796] [,2797] [,2798]
## [1,] "l" "o" "c" "m" "m" "p" "p" "p" "s"
## [2,] "o" "p" "a" "i" "o" "e" "e" "a" "o"
## [3,] "c" "i" "v" "r" "t" "d" "g" "r" "r"
## [4,] "u" "u" "a" "o" "e" "a" "u" "l" "g"
## [5,] "s" "m" "r" "n" "l" "l" "e" "a" "o"
## [,2799] [,2800] [,2801] [,2802] [,2803] [,2804] [,2805] [,2806] [,2807]
## [1,] "b" "b" "c" "c" "c" "f" "f" "j" "m"
## [2,] "l" "r" "a" "a" "o" "o" "r" "u" "e"
## [3,] "o" "e" "r" "t" "m" "r" "i" "a" "c"
## [4,] "o" "a" "e" "i" "a" "c" "e" "n" "h"
## [5,] "m" "k" "y" "a" "n" "e" "n" "i" "e"
## [,2808] [,2809] [,2810] [,2811] [,2812] [,2813] [,2814] [,2815] [,2816]
## [1,] "r" "v" "a" "l" "u" "v" "v" "e" "g"
## [2,] "i" "e" "r" "e" "r" "e" "i" "f" "e"
## [3,] "a" "r" "i" "i" "e" "r" "c" "r" "o"
## [4,] "d" "g" "e" "v" "ñ" "o" "k" "e" "r"
## [5,] "a" "a" "s" "a" "a" "n" "y" "n" "g"
## [,2817] [,2818] [,2819] [,2820] [,2821] [,2822] [,2823] [,2824] [,2825]
## [1,] "g" "s" "c" "c" "l" "m" "q" "a" "b"
## [2,] "l" "i" "a" "h" "e" "a" "u" "s" "r"
## [3,] "e" "l" "n" "o" "i" "t" "o" "t" "a"
## [4,] "n" "o" "s" "l" "g" "e" "m" "a" "u"
## [5,] "n" "s" "o" "a" "h" "s" "o" "s" "n"
## [,2826] [,2827] [,2828] [,2829] [,2830] [,2831] [,2832] [,2833] [,2834]
## [1,] "k" "m" "t" "v" "w" "c" "g" "l" "m"
## [2,] "e" "i" "r" "i" "a" "h" "i" "l" "a"
## [3,] "l" "n" "e" "l" "t" "a" "n" "e" "n"
## [4,] "m" "g" "j" "a" "e" "s" "t" "n" "e"
## [5,] "e" "o" "o" "r" "r" "e" "y" "e" "l"
## [,2835] [,2836] [,2837] [,2838] [,2839] [,2840] [,2841] [,2842] [,2843]
## [1,] "p" "p" "a" "b" "c" "c" "d" "j" "m"
## [2,] "e" "r" "r" "u" "a" "o" "o" "o" "a"
## [3,] "r" "o" "i" "r" "l" "t" "n" "s" "s"
## [4,] "c" "l" "d" "i" "p" "o" "a" "e" "o"
## [5,] "y" "e" "o" "l" "e" "s" "r" "f" "n"
## [,2844] [,2845] [,2846] [,2847] [,2848] [,2849] [,2850] [,2851] [,2852]
## [1,] "r" "v" "g" "h" "l" "m" "v" "v" "f"
## [2,] "i" "e" "a" "a" "i" "o" "e" "i" "a"
## [3,] "b" "n" "t" "w" "r" "c" "t" "g" "c"
## [4,] "a" "t" "a" "k" "i" "o" "a" "i" "h"
## [5,] "s" "e" "s" "s" "o" "s" "s" "a" "a"
## [,2853] [,2854] [,2855] [,2856] [,2857] [,2858] [,2859] [,2860] [,2861]
## [1,] "f" "h" "i" "l" "p" "t" "o" "p" "r"
## [2,] "o" "o" "m" "i" "o" "r" "i" "r" "o"
## [3,] "r" "s" "p" "k" "r" "i" "g" "i" "l"
## [4,] "r" "c" "a" "u" "r" "a" "a" "m" "l"
## [5,] "o" "o" "r" "d" "o" "s" "n" "e" "s"
## [,2862] [,2863] [,2864] [,2865] [,2866] [,2867] [,2868] [,2869] [,2870]
## [1,] "t" "z" "d" "e" "f" "j" "m" "n" "o"
## [2,] "e" "o" "u" "m" "e" "a" "e" "a" "c"
## [3,] "s" "n" "e" "a" "r" "s" "t" "r" "r"
## [4,] "t" "a" "l" "i" "r" "p" "a" "r" "e"
## [5,] "a" "l" "a" "l" "y" "e" "n" "o" "s"
## [,2871] [,2872] [,2873] [,2874] [,2875] [,2876] [,2877] [,2878] [,2879]
## [1,] "c" "p" "r" "c" "e" "h" "l" "m" "p"
## [2,] "o" "r" "u" "a" "s" "u" "i" "u" "e"
## [3,] "m" "i" "l" "r" "t" "m" "t" "s" "n"
## [4,] "e" "v" "f" "d" "i" "a" "r" "a" "d"
## [5,] "s" "o" "o" "o" "o" "n" "i" "s" "e"
## [,2880] [,2881] [,2882] [,2883] [,2884] [,2885] [,2886] [,2887] [,2888]
## [1,] "r" "s" "c" "l" "p" "t" "b" "b" "c"
## [2,] "a" "e" "a" "e" "a" "a" "a" "e" "a"
## [3,] "o" "n" "m" "m" "r" "l" "z" "i" "p"
## [4,] "u" "t" "e" "a" "i" "c" "a" "g" "o"
## [5,] "l" "a" "t" "s" "a" "o" "s" "e" "s"
## [,2889] [,2890] [,2891] [,2892] [,2893] [,2894] [,2895] [,2896] [,2897]
## [1,] "c" "e" "g" "n" "r" "r" "b" "c" "c"
## [2,] "i" "c" "a" "i" "e" "e" "a" "a" "a"
## [3,] "r" "e" "n" "n" "i" "j" "g" "m" "ñ"
## [4,] "i" "m" "s" "f" "d" "o" "r" "p" "o"
## [5,] "o" "c" "o" "a" "o" "n" "e" "a" "s"
## [,2898] [,2899] [,2900] [,2901] [,2902] [,2903] [,2904] [,2905] [,2906]
## [1,] "h" "m" "m" "p" "p" "s" "t" "t" "b"
## [2,] "a" "i" "y" "a" "a" "t" "o" "o" "r"
## [3,] "p" "d" "e" "s" "v" "a" "m" "s" "o"
## [4,] "p" "i" "r" "c" "o" "t" "e" "e" "n"
## [5,] "y" "o" "s" "o" "s" "u" "s" "r" "x"
## [,2907] [,2908] [,2909] [,2910] [,2911] [,2912] [,2913] [,2914] [,2915]
## [1,] "c" "c" "g" "g" "m" "m" "r" "v" "g"
## [2,] "e" "h" "o" "u" "o" "u" "i" "i" "a"
## [3,] "d" "e" "l" "i" "z" "s" "s" "l" "n"
## [4,] "e" "l" "e" "t" "a" "e" "c" "a" "e"
## [5,] "n" "o" "o" "a" "s" "u" "o" "s" "n"
## [,2916] [,2917] [,2918] [,2919] [,2920] [,2921] [,2922] [,2923] [,2924]
## [1,] "l" "m" "t" "a" "d" "f" "g" "j" "l"
## [2,] "a" "a" "e" "c" "a" "r" "l" "a" "a"
## [3,] "u" "m" "j" "u" "r" "i" "o" "u" "c"
## [4,] "d" "e" "e" "s" "l" "s" "s" "m" "a"
## [5,] "o" "s" "n" "e" "a" "o" "a" "a" "n"
## [,2925] [,2926] [,2927] [,2928] [,2929] [,2930] [,2931] [,2932] [,2933]
## [1,] "m" "p" "r" "v" "c" "c" "n" "s" "v"
## [2,] "o" "a" "i" "e" "r" "u" "a" "i" "i"
## [3,] "u" "t" "c" "r" "e" "i" "l" "t" "b"
## [4,] "s" "x" "k" "d" "t" "d" "g" "u" "r"
## [5,] "e" "i" "y" "u" "a" "o" "a" "e" "a"
## [,2934] [,2935] [,2936] [,2937] [,2938] [,2939] [,2940] [,2941] [,2942]
## [1,] "y" "y" "a" "c" "c" "e" "n" "p" "r"
## [2,] "a" "a" "t" "e" "u" "m" "o" "l" "e"
## [3,] "h" "t" "a" "n" "g" "a" "d" "e" "z"
## [4,] "o" "e" "j" "i" "a" "n" "o" "x" "a"
## [5,] "o" "s" "o" "t" "t" "u" "s" "o" "n"
## [,2943] [,2944] [,2945] [,2946] [,2947] [,2948] [,2949] [,2950] [,2951]
## [1,] "t" "a" "h" "h" "n" "s" "t" "t" "c"
## [2,] "u" "r" "e" "o" "o" "i" "a" "e" "a"
## [3,] "r" "g" "b" "r" "v" "n" "m" "r" "b"
## [4,] "b" "o" "r" "d" "e" "a" "p" "m" "e"
## [5,] "o" "t" "a" "a" "l" "i" "a" "o" "r"
## [,2952] [,2953] [,2954] [,2955] [,2956] [,2957] [,2958] [,2959] [,2960]
## [1,] "c" "p" "r" "s" "v" "b" "f" "i" "l"
## [2,] "h" "i" "a" "h" "o" "a" "r" "n" "a"
## [3,] "e" "b" "s" "i" "t" "s" "o" "g" "c"
## [4,] "l" "e" "c" "v" "e" "a" "m" "l" "i"
## [5,] "a" "s" "a" "a" "n" "r" "m" "e" "o"
## [,2961] [,2962] [,2963] [,2964] [,2965] [,2966] [,2967] [,2968] [,2969]
## [1,] "m" "p" "a" "h" "o" "r" "s" "t" "a"
## [2,] "o" "e" "r" "u" "c" "o" "u" "i" "l"
## [3,] "l" "l" "d" "t" "e" "d" "ñ" "t" "a"
## [4,] "e" "e" "e" "u" "a" "r" "e" "a" "b"
## [5,] "s" "o" "n" "s" "n" "i" "r" "n" "o"
## [,2970] [,2971] [,2972] [,2973] [,2974] [,2975] [,2976] [,2977] [,2978]
## [1,] "f" "f" "j" "m" "r" "t" "w" "y" "b"
## [2,] "i" "l" "a" "a" "a" "r" "a" "a" "u"
## [3,] "e" "o" "l" "m" "p" "e" "l" "s" "r"
## [4,] "r" "y" "o" "b" "a" "p" "s" "i" "d"
## [5,] "o" "d" "n" "o" "z" "o" "h" "r" "o"
## [,2979] [,2980] [,2981] [,2982] [,2983] [,2984] [,2985] [,2986] [,2987]
## [1,] "e" "f" "g" "j" "j" "l" "l" "l" "v"
## [2,] "r" "a" "a" "a" "u" "i" "i" "o" "a"
## [3,] "m" "l" "l" "s" "n" "d" "r" "r" "n"
## [4,] "u" "a" "o" "h" "c" "e" "i" "o" "a"
## [5,] "a" "z" "s" "a" "o" "s" "a" "s" "s"
## [,2988] [,2989] [,2990] [,2991] [,2992] [,2993] [,2994] [,2995] [,2996]
## [1,] "a" "a" "c" "d" "d" "f" "g" "g" "m"
## [2,] "e" "s" "o" "a" "o" "r" "r" "u" "i"
## [3,] "n" "t" "j" "l" "r" "o" "u" "i" "n"
## [4,] "o" "i" "i" "i" "i" "t" "p" "a" "s"
## [5,] "r" "z" "n" "a" "a" "o" "a" "n" "a"
## [,2997] [,2998] [,2999] [,3000] [,3001] [,3002] [,3003] [,3004] [,3005]
## [1,] "p" "t" "t" "u" "b" "d" "e" "j" "p"
## [2,] "o" "a" "o" "n" "o" "e" "x" "a" "i"
## [3,] "l" "c" "l" "e" "d" "r" "p" "d" "t"
## [4,] "i" "h" "d" "s" "e" "b" "o" "e" "o"
## [5,] "s" "a" "o" "a" "s" "y" "s" "o" "n"
## [,3006] [,3007] [,3008] [,3009] [,3010] [,3011] [,3012] [,3013] [,3014]
## [1,] "p" "a" "h" "h" "i" "p" "y" "c" "c"
## [2,] "i" "r" "a" "i" "s" "a" "e" "a" "l"
## [3,] "u" "n" "n" "l" "c" "l" "n" "c" "i"
## [4,] "r" "a" "n" "l" "a" "p" "e" "h" "c"
## [5,] "a" "u" "a" "s" "m" "a" "s" "e" "k"
## [,3015] [,3016] [,3017] [,3018] [,3019] [,3020] [,3021] [,3022] [,3023]
## [1,] "c" "m" "p" "p" "t" "y" "d" "p" "v"
## [2,] "o" "o" "a" "e" "a" "u" "e" "o" "o"
## [3,] "c" "r" "l" "g" "c" "s" "r" "s" "l"
## [4,] "o" "a" "t" "a" "n" "t" "b" "e" "t"
## [5,] "s" "s" "a" "s" "a" "e" "i" "s" "a"
## [,3024] [,3025] [,3026] [,3027] [,3028] [,3029] [,3030] [,3031] [,3032]
## [1,] "a" "a" "p" "p" "z" "a" "a" "h" "l"
## [2,] "p" "t" "e" "i" "u" "i" "v" "a" "u"
## [3,] "n" "e" "r" "ñ" "r" "t" "i" "m" "c"
## [4,] "e" "o" "a" "o" "d" "o" "d" "p" "k"
## [5,] "a" "s" "l" "n" "a" "r" "a" "a" "y"
## [,3033] [,3034] [,3035] [,3036] [,3037] [,3038] [,3039] [,3040] [,3041]
## [1,] "m" "p" "p" "t" "b" "b" "c" "g" "s"
## [2,] "a" "e" "o" "r" "a" "a" "u" "e" "a"
## [3,] "t" "c" "r" "e" "e" "l" "l" "m" "r"
## [4,] "e" "a" "r" "p" "n" "z" "p" "i" "t"
## [5,] "u" "s" "a" "a" "a" "a" "o" "r" "a"
## [,3042] [,3043] [,3044] [,3045] [,3046] [,3047] [,3048] [,3049] [,3050]
## [1,] "y" "a" "a" "a" "c" "c" "e" "e" "f"
## [2,] "e" "l" "p" "s" "a" "o" "l" "r" "u"
## [3,] "m" "b" "u" "p" "z" "l" "e" "i" "n"
## [4,] "e" "a" "r" "a" "a" "e" "v" "c" "e"
## [5,] "n" "r" "a" "s" "s" "s" "e" "k" "s"
## [,3051] [,3052] [,3053] [,3054] [,3055] [,3056] [,3057] [,3058] [,3059]
## [1,] "p" "p" "s" "a" "p" "s" "a" "b" "c"
## [2,] "o" "r" "a" "t" "e" "i" "n" "u" "r"
## [3,] "s" "o" "m" "r" "g" "g" "t" "r" "i"
## [4,] "e" "d" "b" "i" "g" "l" "e" "r" "a"
## [5,] "o" "i" "a" "l" "y" "a" "l" "a" "n"
## [,3060] [,3061] [,3062] [,3063] [,3064] [,3065] [,3066] [,3067] [,3068]
## [1,] "d" "e" "g" "j" "k" "o" "s" "a" "g"
## [2,] "o" "u" "h" "a" "a" "l" "u" "b" "a"
## [3,] "l" "l" "a" "r" "r" "i" "m" "u" "n"
## [4,] "e" "e" "n" "p" "m" "a" "e" "s" "t"
## [5,] "r" "r" "a" "a" "a" "n" "n" "a" "e"
## [,3069] [,3070] [,3071] [,3072] [,3073] [,3074] [,3075] [,3076] [,3077]
## [1,] "g" "n" "a" "h" "m" "o" "s" "t" "u"
## [2,] "a" "o" "l" "e" "a" "v" "t" "e" "r"
## [3,] "r" "b" "a" "a" "x" "i" "a" "ñ" "a"
## [4,] "c" "o" "m" "v" "i" "n" "f" "i" "b"
## [5,] "i" "a" "o" "y" "m" "o" "f" "r" "a"
## [,3078] [,3079] [,3080] [,3081] [,3082] [,3083] [,3084] [,3085] [,3086]
## [1,] "y" "b" "e" "g" "j" "o" "p" "s" "t"
## [2,] "a" "u" "n" "u" "a" "v" "a" "h" "o"
## [3,] "c" "c" "t" "i" "l" "n" "r" "o" "m"
## [4,] "e" "h" "e" "l" "e" "i" "t" "r" "m"
## [5,] "n" "e" "l" "i" "o" "s" "a" "t" "y"
## [,3087] [,3088] [,3089] [,3090] [,3091] [,3092] [,3093] [,3094] [,3095]
## [1,] "a" "a" "a" "b" "b" "f" "m" "m" "p"
## [2,] "b" "p" "s" "o" "r" "a" "o" "o" "a"
## [3,] "d" "u" "u" "x" "y" "j" "n" "r" "d"
## [4,] "e" "r" "m" "e" "a" "a" "e" "s" "u"
## [5,] "l" "e" "o" "s" "n" "s" "o" "e" "a"
## [,3096] [,3097] [,3098] [,3099] [,3100] [,3101] [,3102] [,3103] [,3104]
## [1,] "t" "a" "b" "o" "s" "s" "t" "e" "s"
## [2,] "i" "x" "r" "v" "e" "o" "a" "x" "o"
## [3,] "e" "i" "e" "a" "c" "u" "r" "i" "u"
## [4,] "s" "l" "g" "l" "a" "z" "o" "m" "n"
## [5,] "a" "a" "a" "o" "n" "a" "t" "e" "d"
## [,3105] [,3106] [,3107] [,3108] [,3109] [,3110] [,3111] [,3112] [,3113]
## [1,] "v" "f" "g" "j" "l" "n" "p" "b" "c"
## [2,] "e" "e" "a" "u" "a" "o" "i" "o" "a"
## [3,] "t" "c" "c" "l" "u" "t" "r" "t" "u"
## [4,] "a" "s" "h" "e" "r" "r" "r" "a" "s"
## [5,] "r" "a" "a" "n" "i" "e" "i" "r" "e"
## [,3114] [,3115] [,3116] [,3117] [,3118] [,3119] [,3120] [,3121] [,3122]
## [1,] "e" "f" "k" "t" "a" "a" "c" "g" "m"
## [2,] "r" "a" "o" "r" "c" "l" "a" "a" "a"
## [3,] "r" "b" "d" "a" "u" "i" "n" "l" "m"
## [4,] "a" "r" "a" "n" "ñ" "n" "t" "e" "a"
## [5,] "r" "a" "k" "s" "o" "a" "v" "b" "r"
## [,3123] [,3124] [,3125] [,3126] [,3127] [,3128] [,3129] [,3130] [,3131]
## [1,] "p" "p" "q" "r" "s" "s" "a" "b" "c"
## [2,] "a" "u" "u" "i" "e" "o" "n" "u" "a"
## [3,] "b" "g" "i" "e" "q" "y" "t" "r" "l"
## [4,] "o" "i" "n" "g" "u" "u" "r" "l" "m"
## [5,] "n" "l" "a" "a" "e" "z" "o" "o" "o"
## [,3132] [,3133] [,3134] [,3135] [,3136] [,3137] [,3138] [,3139] [,3140]
## [1,] "c" "m" "p" "s" "s" "a" "b" "g" "l"
## [2,] "o" "a" "a" "p" "u" "o" "a" "h" "o"
## [3,] "r" "c" "r" "a" "m" "r" "d" "a" "r"
## [4,] "t" "h" "o" "c" "i" "t" "e" "l" "e"
## [5,] "s" "u" "n" "e" "o" "a" "n" "i" "s"
## [,3141] [,3142] [,3143] [,3144] [,3145] [,3146] [,3147] [,3148] [,3149]
## [1,] "n" "r" "y" "a" "a" "m" "o" "r" "s"
## [2,] "a" "i" "i" "b" "x" "a" "b" "o" "a"
## [3,] "c" "m" "h" "b" "i" "t" "r" "s" "l"
## [4,] "h" "a" "a" "e" "a" "t" "a" "o" "l"
## [5,] "a" "s" "d" "s" "l" "a" "n" "n" "e"
## [,3150] [,3151] [,3152] [,3153] [,3154] [,3155] [,3156] [,3157] [,3158]
## [1,] "v" "c" "d" "e" "g" "p" "p" "r" "r"
## [2,] "i" "o" "r" "r" "a" "a" "i" "a" "i"
## [3,] "a" "p" "e" "i" "l" "z" "o" "b" "l"
## [4,] "s" "o" "a" "c" "o" "o" "j" "a" "e"
## [5,] "a" "s" "m" "a" "n" "s" "o" "l" "y"
## [,3159] [,3160] [,3161] [,3162] [,3163] [,3164] [,3165] [,3166] [,3167]
## [1,] "r" "s" "t" "a" "a" "b" "e" "p" "p"
## [2,] "y" "u" "e" "i" "r" "o" "b" "e" "e"
## [3,] "d" "a" "m" "m" "a" "w" "a" "d" "t"
## [4,] "e" "z" "e" "a" "y" "i" "n" "o" "r"
## [5,] "r" "o" "s" "r" "a" "e" "o" "s" "i"
## [,3168] [,3169] [,3170] [,3171] [,3172] [,3173] [,3174] [,3175] [,3176]
## [1,] "s" "s" "s" "s" "a" "b" "d" "g" "p"
## [2,] "a" "a" "e" "t" "s" "o" "e" "a" "a"
## [3,] "l" "r" "b" "a" "o" "a" "r" "i" "r"
## [4,] "a" "n" "a" "r" "m" "d" "e" "t" "g"
## [5,] "r" "a" "s" "k" "e" "a" "k" "e" "a"
## [,3177] [,3178] [,3179] [,3180] [,3181] [,3182] [,3183] [,3184] [,3185]
## [1,] "v" "a" "b" "e" "f" "m" "r" "b" "c"
## [2,] "a" "z" "a" "l" "o" "e" "o" "e" "a"
## [3,] "n" "o" "t" "l" "r" "r" "u" "r" "s"
## [4,] "c" "t" "e" "i" "z" "l" "g" "r" "t"
## [5,] "e" "o" "s" "s" "a" "o" "e" "y" "o"
## [,3186] [,3187] [,3188] [,3189] [,3190] [,3191] [,3192] [,3193] [,3194]
## [1,] "c" "d" "j" "l" "r" "s" "v" "j" "l"
## [2,] "u" "a" "o" "a" "o" "o" "e" "o" "i"
## [3,] "e" "r" "d" "r" "m" "l" "i" "i" "m"
## [4,] "z" "d" "a" "v" "p" "t" "g" "n" "a"
## [5,] "a" "o" "s" "a" "i" "e" "a" "t" "r"
## [,3195] [,3196] [,3197] [,3198] [,3199] [,3200] [,3201] [,3202] [,3203]
## [1,] "m" "m" "c" "c" "c" "f" "j" "b" "g"
## [2,] "i" "o" "e" "i" "u" "e" "u" "a" "a"
## [3,] "n" "n" "l" "s" "r" "s" "d" "t" "i"
## [4,] "a" "e" "i" "c" "v" "t" "e" "a" "t"
## [5,] "r" "t" "s" "o" "o" "a" "a" "s" "a"
## [,3204] [,3205] [,3206] [,3207] [,3208] [,3209] [,3210] [,3211] [,3212]
## [1,] "i" "l" "l" "m" "o" "o" "p" "p" "a"
## [2,] "d" "a" "i" "a" "r" "s" "e" "i" "r"
## [3,] "e" "c" "v" "u" "i" "e" "i" "c" "c"
## [4,] "a" "r" "i" "r" "o" "o" "n" "o" "o"
## [5,] "r" "a" "o" "i" "n" "s" "a" "r" "n"
## [,3213] [,3214] [,3215] [,3216] [,3217] [,3218] [,3219] [,3220] [,3221]
## [1,] "a" "f" "f" "o" "p" "p" "t" "a" "a"
## [2,] "u" "l" "u" "i" "a" "a" "r" "n" "n"
## [3,] "g" "u" "s" "r" "n" "v" "a" "c" "i"
## [4,] "e" "o" "t" "t" "d" "e" "c" "a" "d"
## [5,] "r" "r" "a" "e" "o" "l" "y" "s" "a"
## [,3222] [,3223] [,3224] [,3225] [,3226] [,3227] [,3228] [,3229] [,3230]
## [1,] "a" "c" "e" "g" "g" "h" "m" "m" "s"
## [2,] "r" "l" "d" "a" "a" "a" "a" "o" "o"
## [3,] "t" "a" "u" "m" "s" "r" "z" "s" "l"
## [4,] "u" "u" "c" "a" "a" "d" "d" "t" "a"
## [5,] "r" "s" "a" "s" "s" "y" "a" "o" "z"
## [,3231] [,3232] [,3233] [,3234] [,3235] [,3236] [,3237] [,3238] [,3239]
## [1,] "s" "s" "t" "u" "a" "b" "c" "d" "d"
## [2,] "t" "w" "o" "m" "v" "u" "a" "a" "a"
## [3,] "o" "i" "c" "a" "a" "r" "r" "l" "r"
## [4,] "r" "n" "a" "ñ" "r" "k" "m" "a" "a"
## [5,] "y" "g" "s" "a" "o" "e" "e" "i" "s"
## [,3240] [,3241] [,3242] [,3243] [,3244] [,3245] [,3246] [,3247] [,3248]
## [1,] "e" "e" "f" "m" "m" "t" "a" "a" "b"
## [2,] "g" "s" "o" "a" "e" "e" "c" "t" "a"
## [3,] "a" "p" "n" "y" "n" "r" "u" "i" "t"
## [4,] "ñ" "o" "s" "t" "t" "n" "d" "n" "e"
## [5,] "a" "t" "o" "e" "i" "o" "i" "o" "n"
## [,3249] [,3250] [,3251] [,3252] [,3253] [,3254] [,3255] [,3256] [,3257]
## [1,] "e" "h" "m" "m" "s" "t" "w" "a" "c"
## [2,] "v" "e" "i" "o" "e" "e" "o" "n" "r"
## [3,] "o" "s" "t" "h" "n" "d" "o" "n" "a"
## [4,] "c" "s" "c" "i" "n" "d" "l" "i" "i"
## [5,] "o" "e" "h" "n" "a" "y" "f" "e" "g"
## [,3258] [,3259] [,3260] [,3261] [,3262] [,3263] [,3264] [,3265] [,3266]
## [1,] "k" "o" "p" "s" "v" "c" "e" "g" "l"
## [2,] "e" "b" "a" "e" "e" "u" "x" "r" "a"
## [3,] "n" "e" "r" "p" "n" "i" "i" "o" "s"
## [4,] "n" "s" "c" "l" "i" "ñ" "j" "s" "c"
## [5,] "y" "a" "a" "a" "d" "a" "o" "s" "a"
## [,3267] [,3268] [,3269] [,3270] [,3271] [,3272] [,3273] [,3274] [,3275]
## [1,] "m" "o" "r" "r" "r" "u" "a" "g" "i"
## [2,] "a" "i" "i" "i" "u" "s" "c" "e" "r"
## [3,] "i" "r" "a" "ñ" "b" "s" "o" "m" "i"
## [4,] "p" "m" "ñ" "a" "i" "i" "t" "a" "n"
## [5,] "u" "e" "o" "s" "n" "a" "a" "s" "a"
## [,3276] [,3277] [,3278] [,3279] [,3280] [,3281] [,3282] [,3283] [,3284]
## [1,] "j" "l" "p" "b" "l" "l" "p" "p" "s"
## [2,] "u" "e" "a" "a" "i" "o" "e" "i" "h"
## [3,] "l" "y" "j" "t" "l" "g" "l" "l" "a"
## [4,] "e" "v" "a" "e" "i" "a" "a" "o" "r"
## [5,] "s" "a" "r" "a" "a" "n" "s" "n" "e"
## [,3285] [,3286] [,3287] [,3288] [,3289] [,3290] [,3291] [,3292] [,3293]
## [1,] "t" "t" "v" "a" "a" "l" "m" "n" "p"
## [2,] "a" "i" "u" "d" "r" "i" "i" "a" "a"
## [3,] "n" "z" "l" "o" "m" "n" "t" "s" "c"
## [4,] "i" "o" "g" "b" "a" "k" "r" "a" "e"
## [5,] "t" "n" "o" "o" "n" "s" "a" "r" "s"
## [,3294] [,3295] [,3296] [,3297] [,3298] [,3299] [,3300] [,3301] [,3302]
## [1,] "p" "r" "s" "s" "s" "s" "t" "y" "z"
## [2,] "a" "i" "a" "a" "i" "w" "e" "a" "a"
## [3,] "l" "l" "l" "l" "m" "a" "l" "s" "r"
## [4,] "p" "k" "l" "t" "i" "n" "e" "e" "z"
## [5,] "o" "e" "y" "e" "o" "n" "x" "r" "a"
## [,3303] [,3304] [,3305] [,3306] [,3307] [,3308] [,3309] [,3310] [,3311]
## [1,] "a" "e" "g" "g" "l" "l" "o" "p" "s"
## [2,] "l" "r" "a" "i" "i" "y" "m" "i" "o"
## [3,] "e" "i" "b" "m" "a" "d" "i" "ñ" "t"
## [4,] "j" "k" "a" "i" "d" "i" "t" "a" "o"
## [5,] "e" "a" "n" "o" "o" "a" "e" "s" "s"
## [,3312] [,3313] [,3314] [,3315] [,3316] [,3317] [,3318] [,3319] [,3320]
## [1,] "t" "z" "a" "a" "c" "e" "l" "m" "p"
## [2,] "i" "o" "l" "l" "o" "c" "e" "a" "a"
## [3,] "g" "i" "e" "i" "n" "h" "e" "r" "j"
## [4,] "e" "l" "l" "b" "a" "a" "r" "a" "a"
## [5,] "r" "a" "o" "a" "c" "s" "a" "t" "s"
## [,3321] [,3322] [,3323] [,3324] [,3325] [,3326] [,3327] [,3328] [,3329]
## [1,] "p" "s" "t" "v" "a" "b" "c" "e" "g"
## [2,] "e" "i" "a" "e" "l" "a" "e" "x" "e"
## [3,] "r" "e" "m" "n" "f" "s" "s" "c" "m"
## [4,] "e" "n" "i" "i" "i" "i" "e" "m" "i"
## [5,] "a" "a" "z" "s" "l" "c" "n" "o" "a"
## [,3330] [,3331] [,3332] [,3333] [,3334] [,3335] [,3336] [,3337] [,3338]
## [1,] "h" "h" "l" "s" "u" "f" "m" "m" "m"
## [2,] "a" "e" "i" "i" "s" "o" "a" "u" "u"
## [3,] "c" "v" "n" "l" "l" "l" "t" "d" "n"
## [4,] "h" "i" "d" "b" "a" "c" "o" "a" "d"
## [5,] "e" "a" "e" "a" "r" "h" "n" "r" "i"
## [,3339] [,3340] [,3341] [,3342] [,3343] [,3344] [,3345] [,3346] [,3347]
## [1,] "p" "r" "a" "d" "m" "s" "t" "u" "v"
## [2,] "i" "e" "n" "o" "a" "p" "a" "s" "i"
## [3,] "l" "t" "e" "d" "r" "r" "l" "u" "e"
## [4,] "l" "r" "x" "g" "c" "a" "c" "r" "r"
## [5,] "o" "o" "a" "e" "e" "y" "a" "a" "i"
## [,3348] [,3349] [,3350] [,3351] [,3352] [,3353] [,3354] [,3355] [,3356]
## [1,] "a" "a" "c" "e" "g" "g" "h" "j" "m"
## [2,] "l" "r" "o" "m" "a" "u" "o" "o" "u"
## [3,] "o" "p" "r" "i" "n" "a" "b" "h" "e"
## [4,] "j" "i" "z" "l" "g" "s" "b" "n" "l"
## [5,] "o" "a" "o" "e" "a" "a" "y" "s" "e"
## [,3357] [,3358] [,3359] [,3360] [,3361] [,3362] [,3363] [,3364] [,3365]
## [1,] "p" "p" "p" "p" "p" "r" "t" "a" "c"
## [2,] "a" "e" "i" "l" "o" "o" "x" "l" "h"
## [3,] "s" "a" "d" "e" "p" "l" "i" "b" "a"
## [4,] "o" "r" "a" "b" "o" "o" "k" "o" "o"
## [5,] "k" "l" "s" "e" "l" "n" "i" "r" "s"
## [,3366] [,3367] [,3368] [,3369] [,3370] [,3371] [,3372] [,3373] [,3374]
## [1,] "c" "c" "c" "m" "q" "s" "u" "v" "b"
## [2,] "o" "r" "u" "i" "a" "a" "f" "i" "r"
## [3,] "l" "i" "a" "l" "e" "b" "a" "d" "u"
## [4,] "z" "c" "t" "p" "d" "i" "n" "e" "n"
## [5,] "a" "k" "e" "a" "a" "n" "o" "s" "a"
## [,3375] [,3376] [,3377] [,3378] [,3379] [,3380] [,3381] [,3382] [,3383]
## [1,] "c" "c" "d" "f" "f" "h" "l" "l" "s"
## [2,] "a" "i" "o" "a" "e" "o" "e" "i" "e"
## [3,] "c" "s" "s" "i" "l" "r" "r" "s" "t"
## [4,] "h" "m" "e" "r" "p" "t" "d" "z" "o"
## [5,] "i" "a" "l" "e" "a" "a" "o" "t" "s"
## [,3384] [,3385] [,3386] [,3387] [,3388] [,3389] [,3390] [,3391] [,3392]
## [1,] "t" "y" "a" "d" "g" "h" "l" "s" "u"
## [2,] "e" "a" "l" "a" "r" "a" "i" "o" "b"
## [3,] "c" "g" "c" "m" "a" "y" "l" "f" "e"
## [4,] "n" "u" "o" "o" "s" "e" "a" "a" "d"
## [5,] "o" "e" "y" "n" "s" "s" "s" "s" "a"
## [,3393] [,3394] [,3395] [,3396] [,3397] [,3398] [,3399] [,3400] [,3401]
## [1,] "v" "a" "b" "b" "c" "c" "c" "d" "f"
## [2,] "e" "l" "a" "e" "a" "e" "r" "u" "o"
## [3,] "l" "t" "y" "r" "l" "r" "o" "v" "d"
## [4,] "l" "e" "o" "r" "z" "o" "i" "a" "o"
## [5,] "a" "r" "n" "o" "a" "n" "x" "l" "r"
## [,3402] [,3403] [,3404] [,3405] [,3406] [,3407] [,3408] [,3409] [,3410]
## [1,] "g" "g" "h" "l" "p" "a" "b" "b" "b"
## [2,] "a" "r" "i" "u" "a" "ñ" "a" "i" "l"
## [3,] "m" "e" "e" "z" "n" "a" "ñ" "h" "o"
## [4,] "e" "a" "n" "o" "a" "d" "a" "a" "c"
## [5,] "z" "t" "a" "n" "l" "o" "n" "c" "h"
## [,3411] [,3412] [,3413] [,3414] [,3415] [,3416] [,3417] [,3418] [,3419]
## [1,] "f" "i" "n" "r" "s" "t" "v" "a" "a"
## [2,] "o" "l" "e" "a" "t" "i" "e" "f" "l"
## [3,] "s" "u" "v" "j" "i" "c" "r" "t" "i"
## [4,] "s" "s" "e" "a" "c" "o" "o" "e" "j"
## [5,] "a" "o" "s" "b" "h" "s" "s" "r" "o"
## [,3420] [,3421] [,3422] [,3423] [,3424] [,3425] [,3426] [,3427] [,3428]
## [1,] "b" "b" "b" "c" "c" "d" "f" "f" "n"
## [2,] "a" "a" "o" "a" "o" "a" "i" "l" "e"
## [3,] "n" "u" "c" "r" "n" "v" "c" "e" "c"
## [4,] "j" "e" "i" "o" "g" "o" "h" "m" "i"
## [5,] "a" "r" "o" "d" "a" "s" "o" "a" "a"
## [,3429] [,3430] [,3431] [,3432] [,3433] [,3434] [,3435] [,3436] [,3437]
## [1,] "p" "p" "t" "t" "v" "g" "h" "m" "m"
## [2,] "i" "r" "a" "e" "e" "o" "a" "e" "o"
## [3,] "c" "o" "p" "m" "r" "r" "r" "i" "u"
## [4,] "a" "f" "a" "p" "d" "k" "e" "e" "r"
## [5,] "n" "e" "n" "s" "a" "a" "n" "r" "a"
## [,3438] [,3439] [,3440] [,3441] [,3442] [,3443] [,3444] [,3445] [,3446]
## [1,] "r" "r" "t" "a" "a" "b" "d" "l" "m"
## [2,] "e" "o" "a" "l" "ñ" "a" "r" "a" "a"
## [3,] "l" "y" "g" "e" "o" "u" "a" "n" "m"
## [4,] "a" "c" "l" "t" "r" "z" "k" "a" "o"
## [5,] "x" "e" "e" "a" "a" "a" "e" "s" "n"
## [,3447] [,3448] [,3449] [,3450] [,3451] [,3452] [,3453] [,3454] [,3455]
## [1,] "m" "m" "n" "r" "a" "b" "c" "d" "h"
## [2,] "a" "i" "e" "a" "s" "a" "u" "e" "o"
## [3,] "p" "r" "b" "m" "t" "y" "ñ" "c" "c"
## [4,] "e" "t" "o" "b" "r" "a" "a" "a" "e"
## [5,] "i" "a" "t" "o" "a" "s" "s" "e" "s"
## [,3456] [,3457] [,3458] [,3459] [,3460] [,3461] [,3462] [,3463] [,3464]
## [1,] "l" "n" "p" "p" "q" "u" "a" "a" "i"
## [2,] "i" "e" "i" "u" "u" "n" "b" "r" "n"
## [3,] "l" "r" "l" "e" "e" "i" "o" "i" "a"
## [4,] "l" "o" "l" "y" "m" "t" "n" "d" "e"
## [5,] "o" "n" "a" "o" "e" "a" "a" "a" "m"
## [,3465] [,3466] [,3467] [,3468] [,3469] [,3470] [,3471] [,3472] [,3473]
## [1,] "m" "n" "t" "b" "p" "b" "c" "a" "e"
## [2,] "a" "i" "u" "o" "o" "u" "a" "n" "r"
## [3,] "s" "c" "n" "l" "d" "c" "g" "i" "i"
## [4,] "i" "a" "a" "l" "a" "l" "a" "m" "z"
## [5,] "a" "s" "s" "o" "r" "e" "r" "e" "o"
## [,3474] [,3475] [,3476] [,3477] [,3478] [,3479] [,3480] [,3481] [,3482]
## [1,] "r" "s" "g" "l" "p" "a" "f" "l" "a"
## [2,] "o" "a" "a" "a" "u" "l" "a" "a" "r"
## [3,] "t" "l" "m" "d" "l" "b" "u" "b" "p"
## [4,] "a" "m" "b" "r" "g" "u" "n" "i" "o"
## [5,] "r" "o" "a" "a" "a" "r" "o" "a" "n"
## [,3483] [,3484] [,3485] [,3486] [,3487] [,3488] [,3489] [,3490] [,3491]
## [1,] "c" "s" "f" "d" "h" "l" "b" "d" "o"
## [2,] "u" "a" "o" "o" "i" "a" "r" "u" "r"
## [3,] "t" "u" "t" "m" "d" "m" "a" "e" "u"
## [4,] "r" "c" "o" "a" "r" "e" "z" "t" "g"
## [5,] "e" "o" "n" "r" "a" "r" "a" "o" "a"
## [,3492] [,3493] [,3494] [,3495] [,3496] [,3497] [,3498] [,3499] [,3500]
## [1,] "k" "s" "r" "z" "r" "f" "f" "l" "c"
## [2,] "u" "u" "a" "o" "a" "l" "i" "i" "a"
## [3,] "r" "s" "y" "m" "s" "a" "n" "a" "t"
## [4,] "d" "h" "a" "b" "p" "m" "t" "n" "a"
## [5,] "o" "i" "r" "i" "a" "a" "a" "a" "r"
## [,3501] [,3502] [,3503] [,3504] [,3505] [,3506] [,3507] [,3508] [,3509]
## [1,] "m" "a" "s" "a" "b" "r" "o" "c" "c"
## [2,] "i" "r" "a" "r" "o" "u" "s" "a" "a"
## [3,] "m" "e" "r" "e" "x" "b" "t" "g" "g"
## [4,] "a" "p" "r" "t" "e" "l" "i" "o" "u"
## [5,] "r" "a" "o" "e" "r" "o" "a" "n" "e"
## [,3510] [,3511] [,3512] [,3513] [,3514] [,3515] [,3516] [,3517] [,3518]
## [1,] "k" "r" "r" "l" "t" "k" "k" "l" "z"
## [2,] "o" "a" "i" "i" "e" "e" "o" "e" "e"
## [3,] "a" "s" "m" "c" "s" "f" "i" "m" "b"
## [4,] "l" "t" "a" "r" "l" "i" "n" "u" "r"
## [5,] "a" "a" "r" "a" "a" "r" "e" "r" "a"
## [,3519] [,3520] [,3521] [,3522] [,3523] [,3524] [,3525] [,3526] [,3527]
## [1,] "a" "k" "l" "j" "l" "m" "m" "s" "c"
## [2,] "b" "i" "i" "u" "e" "i" "i" "a" "o"
## [3,] "a" "l" "t" "r" "ñ" "n" "s" "r" "t"
## [4,] "n" "i" "u" "c" "a" "a" "i" "z" "a"
## [5,] "o" "m" "o" "o" "r" "l" "o" "a" "r"
## [,3528] [,3529]
## [1,] "t" "k"
## [2,] "i" "o"
## [3,] "t" "p"
## [4,] "a" "e"
## [5,] "r" "k"Con ello podemos contestar a las siguientes preguntas
- ¿Cuál es la frecuencia de las letras en los vocablos de CREA?
Las letras más comunes en CREA son la a, e, o, i, r, y las que menos aparecen son la k, ñ, w.
Código
datatable(tokens$frecuencia_letras %>% arrange(desc(n)) |>
mutate(porc = porc / 100),
caption = "Frecuencia de las letras en los vocablos de CREA",
colnames = c("letra", "nº veces", "porcentaje (%)"),
options = list(pageLength = 10,
headerCallback = JS(
"function(thead) {",
" $(thead).css('font-size', '80%');",
"}"))) |>
formatPercentage(columns = c("porc"), digits = 3)- ¿Se mantiene esa distribución cuando reducimos el corpus a palabras de 5 letras?
Se mantienen las 5 primeras a, e, o, r, i, aunque las letras i,r se intercambian posiciones.
Código
tokens <-
matriz_letras(datos_CREA_filtrado |>
filter(nletras == 5), n = NULL)
datatable(tokens$frecuencia_letras |>
arrange(desc(n)) |>
mutate(porc = porc / 100),
caption = "Frecuencia de las letras en los vocablos de CREA de 5 letras",
colnames = c("letra", "nº veces", "porcentaje (%)"),
options = list(pageLength = 10,
headerCallback = JS(
"function(thead) {",
" $(thead).css('font-size', '80%');",
"}"))) |>
formatPercentage(columns = c("porc"), digits = 3)- ¿Se mantiene esa distribución en el conjunto de candidatas de WORDLE?
En el caso de las palabras candidatas del WORDLE el top5 queda como a, o, r, e, l.
Código
tokens <- matriz_letras(palabras_wordle, n = NULL)
datatable(tokens$frecuencia_letras %>% arrange(desc(n)) %>%
mutate(porc = porc / 100),
caption =
"Frecuencia de las letras en las palabras de WORDLE",
colnames = c("letra", "nº veces", "porcentaje (%)"),
options = list(pageLength = 10,
headerCallback = JS(
"function(thead) {",
" $(thead).css('font-size', '80%');",
"}"))) |>
formatPercentage(columns = c("porc"), digits = 3)Otra pregunta razonable a hacerse sería si influye el número de letras en los caracteres que aparecen.
- ¿La distribución de letras es similar en palabras de 3, 5 u 8 letras?
Código
ggplot(tokens$frecuencia_letras |>
arrange(desc(porc)) |>
select(c(value, porc)) |>
mutate(value = factor(value, levels = value),
vocal = value %in% c("a", "e", "i", "o", "u")),
aes(x = value, y = porc, fill = vocal)) +
geom_col(alpha = 0.9) +
scale_fill_manual(values = c("#c9b458", "#6baa64"),
labels = c("Consonante", "Vocal")) +
labs(y = "Frec. relativa (%)", x = "Letras",
title = "WORDLE", fill = "Tipo",
caption =
paste0("Javier Álvarez Liébana (@dadosdelaplace) | Datos: CREA"))
eje x representa letras del alfabeto. El eje y representa frecuencia relativa (en %). El color en función de vocal o consonanteCódigo
tokens <- matriz_letras(datos_CREA_filtrado, n = 3)
ggplot(tokens$frecuencia_letras %>%
arrange(desc(porc)) %>% select(c(value, porc)) %>%
mutate(value = factor(value, levels = value),
vocal = value %in% c("a", "e", "i", "o", "u")),
aes(x = value, y = porc, fill = vocal)) +
geom_col(stat = "identity", alpha = 0.9) +
scale_fill_manual(values = c("#c9b458", "#6baa64"),
labels = c("Consonante", "Vocal")) +
labs(y = "Frec. relativa (%)", x = "Letras",
title = "WORDLE", fill = "Tipo",
caption =
paste0("Javier Álvarez Liébana (@dadosdelaplace) | Datos: CREA"))
## Warning in geom_col(stat = "identity", alpha = 0.9): Ignoring unknown
## parameters: `stat`
eje x representa letras del alfabeto. El eje y representa frecuencia relativa (en %). El color en función de vocal o consonanteCódigo
tokens <- matriz_letras(datos_CREA_filtrado, n = 5)
ggplot(tokens$frecuencia_letras %>%
arrange(desc(porc)) %>% select(c(value, porc)) %>%
mutate(value = factor(value, levels = value),
vocal = value %in% c("a", "e", "i", "o", "u")),
aes(x = value, y = porc, fill = vocal)) +
geom_col(stat = "identity", alpha = 0.9) +
scale_fill_manual(values = c("#c9b458", "#6baa64"),
labels = c("Consonante", "Vocal")) +
labs(y = "Frec. relativa (%)", x = "Letras",
title = "WORDLE", fill = "Tipo",
subtitle =
paste0("Distribución de las letras en palabras de 5 letras"),
caption =
paste0("Javier Álvarez Liébana (@dadosdelaplace) | Datos: CREA"))
## Warning in geom_col(stat = "identity", alpha = 0.9): Ignoring unknown
## parameters: `stat`
eje x representa letras del alfabeto. El eje y representa frecuencia relativa (en %). Eñ color en función de vocal o consonanteCódigo
tokens <- matriz_letras(datos_CREA_filtrado, n = 8)
ggplot(tokens$frecuencia_letras %>%
arrange(desc(porc)) %>% select(c(value, porc)) %>%
mutate(value = factor(value, levels = value),
vocal = value %in% c("a", "e", "i", "o", "u")),
aes(x = value, y = porc, fill = vocal)) +
geom_col(alpha = 0.9) +
scale_fill_manual(values = c("#c9b458", "#6baa64"),
labels = c("Consonante", "Vocal")) +
labs(y = "Frec. relativa (%)", x = "Letras",
title = "WORDLE", fill = "Tipo",
caption =
paste0("Autor: J. Álvarez Liébana (@dadosdelaplace) | Datos: CREA"))
eje x representa letras del alfabeto. El eje y representa frecuencia relativa (en %). Eñ color en función de vocal o consonanteCódigo
tokens <- matriz_letras(palabras_wordle, n = NULL)
ggplot(tokens$frecuencia_letras |>
arrange(desc(porc)) |>
select(c(value, porc)) |>
mutate(value = factor(value, levels = value),
vocal = value %in% c("a", "e", "i", "o", "u")),
aes(x = value, y = porc, fill = vocal)) +
geom_col(alpha = 0.9) +
scale_fill_manual(values = c("#c9b458", "#6baa64"),
labels = c("Consonante", "Vocal")) +
labs(y = "Frec. relativa (%)", x = "Letras",
title = "WORDLE", fill = "Tipo",
caption =
paste0("Autor: J. Álvarez Liébana (@dadosdelaplace).\nDatos: CREA y Github danielfrg/wordle.es"))
eje x representa letras del alfabeto. El eje y representa frecuencia relativa (en %). Eñ color en función de vocal o consonanteLetras iniciales/finales
Elena Álvarez Mellado, experta en lingüística computacional, apuntaba que quizás una pista o ayuda para adivinar las palabras sea analizar qué letras suelen encabezar y terminas las palabras en castellano.
Añado otro lingutruqui pa quienes estáis con el wordle: la inmensísima mayoría de palabras en castellano terminan en vocal o en R, S, L, N, D o Z (las consonantes de RoSaLiNa DíaZ) https://t.co/i0arWdJLm9
— e'lena 'alβ̞aɾeð me'ʝ̞að̞o (@lirondos) January 11, 2022
Entre las palabras de CREA, analizaremos todas las letras iniciales y finales de las palabras de las que disponemos, y calcularemos la proporción de veces en las que sucede.
Código
letras_iniciales <-
tibble("letras_iniciales" =
map_chr(strsplit(datos_CREA_filtrado$palabra, ""),
function(x) { x[1] })) |>
group_by(letras_iniciales) |>
count() |>
ungroup() |>
mutate(porc = 100 * n / sum(n))
letras_finales <-
tibble("letras_finales" =
map_chr(strsplit(datos_CREA_filtrado$palabra, ""),
function(x) { rev(x)[1] })) |>
group_by(letras_finales) |> count() |> ungroup() |>
mutate(porc = 100 * n / sum(n))
fig1 <- ggplot(letras_iniciales |>
arrange(desc(porc)) |>
mutate(letras_iniciales =
factor(letras_iniciales, levels = letras_iniciales),
vocal =
letras_iniciales %in% c("a", "e", "i", "o", "u")),
aes(x = letras_iniciales, y = porc, fill = vocal)) +
geom_col(alpha = 0.9) +
scale_fill_manual(values = c("#c9b458", "#6baa64"),
labels = c("Consonante", "Vocal")) +
labs(x = "Letras iniciales", y = "Frec. relativa (%)",
fill = "Tipo")
fig2 <- ggplot(letras_finales |>
arrange(desc(porc)) |>
mutate(letras_finales =
factor(letras_finales, levels = letras_finales),
vocal =
letras_finales %in% c("a", "e", "i", "o", "u")),
aes(x = letras_finales, y = porc, fill = vocal)) +
geom_col(alpha = 0.9) +
scale_fill_manual(values = c("#c9b458", "#6baa64"),
labels = c("Consonante", "Vocal")) +
labs(x = "Letras finales", y = "Frec. relativa (%)",
fill = "Tipo")
# composición
(fig1 / fig2) +
plot_annotation(
title = "WORDLE",
caption =
paste0("Autor: J. Álvarez Liébana (@dadosdelaplace). Datos: CREA")) +
plot_layout(guides = "collect") & theme(legend.position = 'bottom')
Del conjunto total de CREA, las letras más frecuentes iniciando palabras son c,a,p,e,d, y las letras más frecuentes terminando palabras son s,a,o,e,n.
Código
letras_iniciales <-
tibble("letras_iniciales" =
map_chr(strsplit(datos_CREA_filtrado %>%
filter(nletras == 5) %>%
pull(palabra), ""),
function(x) { x[1] })) %>%
group_by(letras_iniciales) %>% count() %>% ungroup() %>%
mutate(porc = 100 * n / sum(n))
letras_finales <-
tibble("letras_finales" =
map_chr(strsplit(datos_CREA_filtrado %>%
filter(nletras == 5) %>%
pull(palabra), ""),
function(x) { rev(x)[1] })) %>%
group_by(letras_finales) %>% count() %>% ungroup() %>%
mutate(porc = 100 * n / sum(n))
fig1 <-
ggplot(letras_iniciales %>%
arrange(desc(porc)) %>%
mutate(letras_iniciales =
factor(letras_iniciales, levels = letras_iniciales),
vocal =
letras_iniciales %in% c("a", "e", "i", "o", "u")),
aes(x = letras_iniciales, y = porc, fill = vocal)) +
geom_col(alpha = 0.9) +
scale_fill_manual(values = c("#c9b458", "#6baa64"),
labels = c("Consonante", "Vocal")) +
labs(y = "Frec. relativa (%)", x = "Letras iniciales",
fill = "Tipo")
fig2 <-
ggplot(letras_finales %>%
arrange(desc(porc)) %>%
mutate(letras_finales =
factor(letras_finales, levels = letras_finales),
vocal =
letras_finales %in% c("a", "e", "i", "o", "u")),
aes(x = letras_finales, y = porc, fill = vocal)) +
geom_col(alpha = 0.9) +
scale_fill_manual(values = c("#c9b458", "#6baa64"),
labels = c("Consonante", "Vocal")) +
labs(y = "Frec. relativa (%)", x = "Letras finales",
fill = "Tipo")
# Composición
(fig1 / fig2) +
plot_annotation(
title = "WORDLE",
caption =
paste0("Autor: J. Álvarez Liébana (@dadosdelaplace). Datos: CREA")) +
plot_layout(guides = "collect") & theme(legend.position = 'bottom')
Del conjunto total de CREA con solo 5 letras, las letras más frecuentes iniciando palabras son c,a,p,m,s, y las letras más frecuentes terminando palabras son a,s,o,e,n.
Código
letras_iniciales <-
tibble("letras_iniciales" =
map_chr(strsplit(palabras_wordle$palabra, ""),
function(x) { x[1] })) %>%
group_by(letras_iniciales) %>% count() %>%
ungroup() %>%
mutate(porc = 100 * n / sum(n))
letras_finales <-
tibble("letras_finales" =
map_chr(strsplit(palabras_wordle$palabra, ""),
function(x) { rev(x)[1] })) %>%
group_by(letras_finales) %>% count() %>%
ungroup() %>%
mutate(porc = 100 * n / sum(n))
fig1 <-
ggplot(letras_iniciales %>%
arrange(desc(porc)) %>%
mutate(letras_iniciales =
factor(letras_iniciales, levels = letras_iniciales),
vocal =
letras_iniciales %in% c("a", "e", "i", "o", "u")),
aes(x = letras_iniciales, y = porc, fill = vocal)) +
geom_col(alpha = 0.9) +
scale_fill_manual(values = c("#c9b458", "#6baa64"),
labels = c("Consonante", "Vocal")) +
labs(y = "Frec. relativa (%)", x = "Letras iniciales",
fill = "Tipo")
fig2 <-
ggplot(letras_finales %>%
arrange(desc(porc)) %>%
mutate(letras_finales =
factor(letras_finales, levels = letras_finales),
vocal =
letras_finales %in% c("a", "e", "i", "o", "u")),
aes(x = letras_finales, y = porc, fill = vocal)) +
geom_col(alpha = 0.9) +
scale_fill_manual(values = c("#c9b458", "#6baa64"),
labels = c("Consonante", "Vocal")) +
labs(y = "Frec. relativa (%)", x = "Letras finales",
fill = "Tipo")
# Composición
(fig1 / fig2) +
plot_annotation(
title = "WORDLE",
caption =
paste0("Javier Álvarez Liébana (@dadosdelaplace). Datos: CREA y github.com/danielfrg")) +
plot_layout(guides = "collect") & theme(legend.position = 'bottom')
Del conjunto de palabras de WORDLE las letras más frecuentes iniciando palabras son c,a,m,p,l, y las letras más frecuentes terminando palabras son o,a,r,e,l.
Scoring de palabras
Puntuando letras
Hemos visto cuáles son las letras más frecuentes en las palabras, en general, y al inicio y final de las mismas, y su probabilidad (empírica) de aparecer. Sin embargo, como bien apunta Gabriel Rodríguez Alberich, hemos visto que no todas las palabras aparecerán con la misma frecuencia, así que tendremos una bolsa de palabras donde hay letras más repetidas que otras, por lo que una opción es ponderar cada letra por las opciones que tiene cada palabra que la contiene de aparecer: la letra e en kefir no debería puntuar lo mismo que en sobre.
Para ello extraeremos cada letra pero, a la hora de contarla, la ponderaremos por las opciones que tiene la palabra de aparecer. Para ello crearemos una función propia que definiremos como puntuar_letras.
Código
puntuar_letras <- function(corpus, n = 5) {
if (!is.null(n)) {
# Filtramos
corpus_filtrado <- corpus %>% filter(nletras == n)
# Creamos matriz de letras
matriz_letras <-
matrix(unlist(strsplit(corpus_filtrado$palabra, "")),
ncol = nrow(corpus_filtrado))
pesos <- rep(corpus_filtrado$frec_relativa, each = n)
matriz_letras_pesos <-
tibble("matriz_letras" =
unlist(strsplit(corpus_filtrado$palabra, "")),
pesos)
# Ponderación de letras
frecuencia_letras <-
matriz_letras_pesos %>%
group_by(matriz_letras) %>%
summarise(peso_promediado = sum(pesos, na.rm = TRUE)) %>%
ungroup() %>%
mutate(peso_promediado_rel =
peso_promediado / sum(peso_promediado, na.rm = TRUE))
} else {
corpus_filtrado <- corpus
# Creamos matriz de letras
matriz_letras <- unlist(strsplit(corpus_filtrado$palabra, ""))
pesos <-
unlist(mapply(corpus_filtrado$frec_relativa,
corpus_filtrado$nletras,
FUN = function(x, y) { rep(x, y)}))
matriz_letras_pesos <- tibble(matriz_letras, pesos)
# Ponderación de letras
frecuencia_letras <-
matriz_letras_pesos %>%
group_by(matriz_letras) %>%
summarise(peso_promediado = sum(pesos, na.rm = TRUE)) %>%
ungroup() %>%
mutate(peso_promediado_rel =
peso_promediado /
sum(peso_promediado, na.rm = TRUE))
}
# Output
return(frecuencia_letras)
}puntuacion_letras_global <-
puntuar_letras(datos_CREA_filtrado, n = NULL)
puntuacion_letras_5 <-
puntuar_letras(datos_CREA_filtrado, n = 5)Puntuando palabras
Una vez que tenemos puntuadas las letras que van a formar nuestas palabras vamos a tomar los dos conjuntos de palabras de 5 letras, el conjunto extraído de CREA tras eliminar palabras poco repetidas (casi 10 000 vocablos) y el conjunto de candidatas a WORDLE (620 palabras), y puntuaremos cada palabra en función de cuatro criterios:
Peso de letras: puntuaremos cada palabra sumando las ponderaciones de cada letra que la forma (al tener todas 5 letras, es irrelevante usar la suma o la media en el orden final).
Letras iniciales y finales: además de las letras en general, la puntuación obtenida en el paso anterior será ponderada en función de las probabilidades de que su letra inicial/final sea, efectivamente, letra inicial/final de una palabra, usando las frecuencias relativas que hemos obtenido antes.
Heterogeneidad: para medir no solo la «calidad» de las letras sino su diversidad (a mayor variedad de letras podemos obtener más información de nuestra palabra a adivinar), la puntuación salida de los pasos anteriores será ponderada por un índice de homogeneidad de variables cualitativas conocido como Índice de Blau (B).
\[B = 1 - \sum_{i=1}^{k} f_{i}^{2}\]
donde \(k\) es el número de letras distintas y \(f_i\) es la proporción de veces que se repite cada letra distinta en la palabra. Por ejemplo, la palabra aerea tendrá un índice de \(B = 0.64\) ya que tanto la a como la e tienen una frecuencia relativa de \(2/5\) y la r \(1/5\), tal que \(B = 1 - \left[\left( \frac{2}{5} \right)^2 + \left( \frac{2}{5} \right)^2 + \left( \frac{1}{5} \right)^2 \right] = 0.64\). La máxima puntuación para 5 letras, sería que todas fueran distintas (\(k = 5\)), con un índice de \(B = \frac{k-1}{k} = \frac{4}{5} = 0.8\); la mínima puntuación sería que todas fueran iguales (\(k=1\)) con \(B = 0\). Este índice nos permite medir la probabilidad de que dos letras de la palabra tomadas al azar sean distintas. El índice será normalizado para que aquellas palabras con todas las letras repetidas tengan \(B_{norm} = 0\) y todas las palabras con las letras distintas tengan \(B_{norm} = 1\).
- Ponderación por la palabra: la puntuación obtenida en los pasos anteriores es ponderada finalmente por la «probabilidad» que tiene dicha palabra de ser usada en castellano, basándonos en las log-frecuencias del CREA.
Para realizar dicha ponderación definiremos la función puntuar_palabras()
Código
# Letras iniciales/finales
letras_iniciales <-
tibble("letras_iniciales" =
map_chr(strsplit(datos_CREA_filtrado %>%
filter(nletras == 5) %>%
pull(palabra), ""),
function(x) { x[1] })) %>%
group_by(letras_iniciales) %>% count() %>% ungroup() %>%
mutate(porc = 100 * n / sum(n))
letras_finales <-
tibble("letras_finales" =
map_chr(strsplit(datos_CREA_filtrado %>%
filter(nletras == 5) %>%
pull(palabra), ""),
function(x) { rev(x)[1] })) %>%
group_by(letras_finales) %>% count() %>% ungroup() %>%
mutate(porc = 100 * n / sum(n))
# Puntuamos palabras
puntuar_palabras <-
function(palabras, letras_puntuadas, letras_iniciales,
letras_finales, nletras = 5) {
# Matriz letras
matriz_letras_corpus <- matriz_letras(palabras, n = nletras)
matriz_letras_corpus <- matriz_letras_corpus$matriz_letras
# palabras peso promediado peso relativo
# Puntuar palabras
palabras_puntuadas <-
palabras %>%
mutate(punt_letras =
apply(matriz_letras_corpus, MARGIN = 2,
FUN = function(x) { sum(letras_puntuadas$peso_promediado_rel[
letras_puntuadas$matriz_letras %in% x] *
c((letras_iniciales %>%
filter(letras_iniciales == x[1]) %>%
pull(porc)) / 100,
1/5, 1/5, 1/5, (letras_finales %>%
filter(letras_finales ==
rev(x)[1]) %>%
pull(porc)) / 100))}),
ind_blau =
apply(matriz_letras_corpus, MARGIN = 2,
FUN = function(x) { 1 - sum((table(x) / sum(table(x)))^2)}),
ind_blau_norm = ind_blau / max(ind_blau),
punt_letras_total = punt_letras * ind_blau_norm,
punt_total_w = punt_letras_total * log_frec_abs)
# Iniciales y finales
# Output
return(list("palabras_puntuadas" = palabras_puntuadas,
"matriz_letras" = matriz_letras_corpus))
}CREA_puntuado <-
puntuar_palabras(datos_CREA_filtrado %>%
filter(nletras == 5),
puntuacion_letras_5,
letras_iniciales, letras_finales)
## Warning: There were 1428 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `punt_letras = apply(...)`.
## Caused by warning in `letras_puntuadas$peso_promediado_rel[letras_puntuadas$matriz_letras %in% x] * c(
## (letras_iniciales %>% filter(letras_iniciales == x[1]) %>% pull(porc)) / 100,
## 1 / 5, 1 / 5, 1 / 5, (letras_finales %>% filter(letras_finales == rev(x)[1]) %>%
## pull(porc)) / 100)`:
## ! longer object length is not a multiple of shorter object length
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1427 remaining warnings.
WORDLE_puntuado <-
puntuar_palabras(datos_palabras_wordle,
puntuacion_letras_5,
letras_iniciales, letras_finales)
## Warning: There were 234 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `punt_letras = apply(...)`.
## Caused by warning in `letras_puntuadas$peso_promediado_rel[letras_puntuadas$matriz_letras %in% x] * c(
## (letras_iniciales %>% filter(letras_iniciales == x[1]) %>% pull(porc)) / 100,
## 1 / 5, 1 / 5, 1 / 5, (letras_finales %>% filter(letras_finales == rev(x)[1]) %>%
## pull(porc)) / 100)`:
## ! longer object length is not a multiple of shorter object length
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 233 remaining warnings.
CREA_puntuado$palabras_puntuadas
## # A tibble: 3,529 × 13
## palabra frec_abs frec_norm frec_relativa log_frec_abs log_frec_rel
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 sobre 289704 1899. 0.00192 12.6 0.0000128
## 2 entre 267493 1753. 0.00178 12.5 0.0000127
## 3 habia 223430 1465. 0.00148 12.3 0.0000126
## 4 hasta 202935 1330. 0.00135 12.2 0.0000125
## 5 desde 198647 1302. 0.00132 12.2 0.0000124
## 6 puede 161219 1057. 0.00107 12.0 0.0000122
## 7 todos 158168 1037. 0.00105 12.0 0.0000122
## 8 parte 148750 975. 0.000988 11.9 0.0000121
## 9 tiene 147274 965. 0.000978 11.9 0.0000121
## 10 donde 132077 866. 0.000877 11.8 0.0000120
## # ℹ 3,519 more rows
## # ℹ 7 more variables: int_frec_norm <fct>, nletras <dbl>, punt_letras <dbl>,
## # ind_blau <dbl>, ind_blau_norm <dbl>, punt_letras_total <dbl>,
## # punt_total_w <dbl>
WORDLE_puntuado$palabras_puntuadas
## # A tibble: 620 × 13
## palabra frec_abs frec_norm frec_relativa log_frec_abs log_frec_rel
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 entre 267493 1753. 0.00178 12.5 0.0000127
## 2 donde 132077 866. 0.000877 11.8 0.0000120
## 3 menos 103498 678. 0.000687 11.5 0.0000118
## 4 mundo 101745 667. 0.000676 11.5 0.0000118
## 5 forma 97165 637. 0.000645 11.5 0.0000117
## 6 hacer 96063 630. 0.000638 11.5 0.0000117
## 7 mayor 90166 591. 0.000599 11.4 0.0000116
## 8 ellos 84636 555. 0.000562 11.3 0.0000116
## 9 hecho 83898 550. 0.000557 11.3 0.0000116
## 10 lugar 78250 513. 0.000520 11.3 0.0000115
## # ℹ 610 more rows
## # ℹ 7 more variables: int_frec_norm <fct>, nletras <dbl>, punt_letras <dbl>,
## # ind_blau <dbl>, ind_blau_norm <dbl>, punt_letras_total <dbl>,
## # punt_total_w <dbl>Simulando el WORDLE
Una vez que tenemos un sistema para puntuar palabras, vamos a simular un número de partidas de WORDLE, considerando tres escenarios:
El peor de los casos. El conjunto de palabras que el usuario podría pensar y el conjunto de palabras a adivinar es el mismo, y es el conjunto extenso de vocablos de CREA de 5 letras, con 3529 vocablos.
El mejos de los casos. El conjunto de palabras que el usuario podría pensar y el conjunto de palabras a adivinar es el mismo, y es el conjunto reducido de palabras que tenía originalmente el juego oficial de WORDLE en castellano tiene programadas, con 620 vocablos.
El caso realista. Aunque el conjunto de palabras a adivinar sea uno concreto y reducido, el usuario podría tener en su cabeza muchas palabras en mente que decidiese probar. En el caso realista, el conjunto de palabras que el usuario podría proponer es el conjunto de vocablos de CREA de 5 letras y con una frecuencia normalizada superior a 3 por cada 1000 documentos analizados (un total de 1993 palabras). Sin embargo, el conjunto de palabras a adivinar será el conjunto reducido de palabras consideradas en la versión original, con 620 vocablos.
Una vez tenemos puntuadas las palabras la mecánica será sencilla.
Generaremos un conjunto de simulaciones, adoptando una palabra inicial en cada una de ellas (palabra inicial que se obtendrá aleatoriamente tomando las puntuaciones de las palabras como pesos).
En cada iteración comprobaremos qué letras están bien colocadas, qué letras están pero mal colocadas y qué letras son errores.
Tras dicha comprobación, calcularemos el conjunto de palabras de entre las candidatas que cumplen dichas condiciones
De ese conjunto «superviviente» elegiremos la palabra con mayor puntuación posible. Además, para comprobar que nuestro método mejora la metodología de hacerlo totalmente aleatorio, se compara en cada caso que pasaría si simplemente eligiéramos las palabras al azar del conjunto de candidatas que cumplen las condiciones.
Aunque el juego en inglés si parece elegir las palabras a jugar en base a su frecuencia de uso en inglés, priorizando las palabras más usadas (aquí una metodología propuesta por Esteban Moro para el juego en inglés), no tengo constancia que sea así en castellano, así que la elección de palabras a adivinar será equiprobable y, de momento, la palabra inicial del usuario también.
Código
# Iteración del juego
iteracion <- function(inicial, clave) {
# Jugada
bien_colocadas <-
unlist(map2(strsplit(inicial, ""), strsplit(clave, ""),
function(x, y) { x == y }))
mal_colocadas <-
unlist(map2(strsplit(inicial, ""), strsplit(clave, ""),
function(x, y) { x %in% y })) &
!bien_colocadas
errores <- !(bien_colocadas | mal_colocadas)
# Output
return(list("bien_colocadas" = bien_colocadas,
"mal_colocadas" = mal_colocadas,
"errores" = errores))
}
# Simulación
simular_wordle <-
function(corpus, matriz_corpus, palabras_candidatas = corpus,
intentos = 1, generar_equi = TRUE, iniciar_equi = TRUE,
dummy_random = FALSE, inicial_fija = NULL,
clave_fija = NULL,
extremely_dummmy = FALSE) {
if (is.null(clave_fija)) {
# probabilidades de salir la palabra
# * si generar_equi = TRUE --> equiprobables
# * si generar_equi = FALSE --> en función de pesos
if (generar_equi) {
probs_gen <- rep(1 / nrow(palabras_candidatas),
nrow(palabras_candidatas))
} else {
probs_gen <- palabras_candidatas$punt_total_w /
sum(palabras_candidatas$punt_total_w)
}
# Palabra a adivinar
clave <- sample(palabras_candidatas$palabra,
size = 1, prob = probs_gen)
} else {
clave <- clave_fija
}
propiedades_clave <-
palabras_candidatas %>% filter(palabra == clave)
# Palabra inicial
if (is.null(inicial_fija)) {
if (iniciar_equi) {
inicial <- sample(corpus$palabra, size = 1)
} else {
# Las 50 mejor puntuadas
inicial <- corpus %>%
arrange(desc(punt_total_w)) %>%
slice(30) %>% pull(palabra)
inicial <- sample(inicial, size = 1)
}
} else {
inicial <- inicial_fija
}
propiedades_inicial <- corpus %>% filter(palabra == inicial)
# Inicialización
palabra_0 <- inicial
candidatas <- corpus
matriz_candidatas <- matriz_corpus
salida <- list()
for (i in 1:intentos) {
salida[[i]] <- iteracion(palabra_0, clave)
idx_palabras <-
apply(matriz_candidatas, MARGIN = 2,
FUN = function(x) {
all(x[salida[[i]]$bien_colocadas] ==
unlist(strsplit(palabra_0, ""))[salida[[i]]$bien_colocadas]) }) &
apply(matriz_candidatas, MARGIN = 2,
FUN = function(x) {
all(!(x %in% unlist(strsplit(palabra_0, ""))[salida[[i]]$errores])) })
if (any(salida[[i]]$mal_colocadas)) {
idx_palabras <- idx_palabras &
apply(matriz_candidatas, MARGIN = 2,
FUN = function(x) {
all(unlist(strsplit(palabra_0, ""))[salida[[i]]$mal_colocadas] %in% x) &
all(!mapply(x[which(salida[[i]]$mal_colocadas)],
unlist(strsplit(palabra_0, ""))[salida[[i]]$mal_colocadas],
FUN = function(x, y) { x == y})) } )
}
# Seleccionamos
if (extremely_dummmy) {
matriz_candidatas <- matriz_candidatas
candidatas <- candidatas
} else {
if (any(idx_palabras)) {
matriz_candidatas <- matriz_candidatas[, idx_palabras]
candidatas <- candidatas[idx_palabras, ]
if (!dummy_random) {
palabra_0 <-
candidatas %>% arrange(desc(punt_total_w)) %>%
slice(1) %>% pull(palabra)
} else {
palabra_0 <-
candidatas %>%
slice_sample(n = 1) %>% pull(palabra)
}
}
}
if (nrow(candidatas) <= 1) {
break
}
}
intentos <- ifelse(nrow(candidatas) == 1,
ifelse(palabra_0 == clave, i + 1,
intentos + 1), intentos + 1)
# Output
return(list("palabra_clave" = clave, "inicial" = inicial,
"salida" = salida, "candidatas" = candidatas,
"palabra_0" = palabra_0,
"matriz_candidatas" = matriz_candidatas,
"intentos" = intentos,
"propiedades_clave" = propiedades_clave,
"propiedades_inicial" = propiedades_inicial))
}
simulacion_wordle <-
function(corpus_puntuado,
palabras_candidatas = corpus_puntuado,
simulaciones = 1e3, nintentos = 6,
generar_equi = TRUE, iniciar_equi = TRUE,
dummy_random = FALSE, inicial_fija = NULL,
clave_fija = NULL,
extremely_dummmy = FALSE) {
# Puntuamos palabras
corpus_wordle_puntuado <- corpus_puntuado$palabras_puntuadas
matriz_letras_wordle <- corpus_puntuado$matriz_letras
palabras_candidatas <- palabras_candidatas$palabras_puntuadas
# Simulación
resultados <-
replicate(simulaciones,
simular_wordle(corpus_wordle_puntuado,
matriz_letras_wordle,
palabras_candidatas,
intentos = nintentos,
generar_equi = generar_equi,
iniciar_equi = iniciar_equi,
dummy_random = dummy_random,
inicial_fija = inicial_fija,
clave_fija = clave_fija,
extremely_dummmy = extremely_dummmy))
# Output
return(list("corpus_wordle" = corpus_wordle_puntuado,
"matriz_letras_wordle" = matriz_letras_wordle,
"corpus_wordle_puntuado" = corpus_wordle_puntuado,
"resultados" = resultados))
}Peor escenario
Empecemos por el peor de los casos: la palabra a adivinar puede ser cualquiera de los 3529 vocablos de CREA de 5 letras.
# * 6 intentos y 5 letras
# * con palabra inicial y clave equiprobables
simulaciones <- 1000
generar_equi <- TRUE
iniciar_equi <- FALSE
set.seed(1234567)
simulacion_CREA <-
simulacion_wordle(CREA_puntuado,
simulaciones = simulaciones,
generar_equi = generar_equi,
iniciar_equi = iniciar_equi,
dummy_random = FALSE)
save(simulacion_CREA, file = "./datos/simulacion_CREA.rda")# Dummy (palabra aleatoria entre candidatas)
generar_equi <- TRUE
iniciar_equi <- TRUE
simulacion_dummy <-
simulacion_wordle(CREA_puntuado,
simulaciones = simulaciones,
generar_equi = generar_equi,
iniciar_equi = iniciar_equi,
dummy_random = TRUE)
save(simulacion_dummy, file = "./datos/simulacion_dummy.rda")intentos_dummy <- unlist(simulacion_dummy$resultados["intentos", ])
distrib_intentos_dummy <- 100 * table(intentos_dummy) / simulaciones
media_intentos_dummy <- mean(intentos_dummy)
distrib_intentos_dummy
## intentos_dummy
## 2 3 4 5 6 7
## 1.1 12.1 34.4 29.3 14.4 8.7
media_intentos_dummy
## [1] 4.699En este caso extremo en el que nuestras palabras candidatas podrían ser los 3529 vocablos de CREA de 5 letras, conseguimos ganar en 6 intentos o menos el 94.6% de las veces, con una media de 4.49 intentos para resolverlo y una mediana de 4 (el 50% de las veces lo resuelve en dichos intentos o menos). En el caso de decidir las palabras aleatoriamente (entre las candidatas en cada paso), obtendríamos una media de 4.7 y una mediana de 5, consiguiendo resolverlo el 91.3% de las veces.
Código
# gráfica
ggplot(df %>% mutate(fallo = (intentos == "FALLO"))) +
geom_col(aes(x = intentos, y = frecuencia, fill = fallo),
alpha = 0.9) +
scale_fill_manual(values = c("#6baa64", "#E34D4D"),
labels = c("Acertado", "Fallo")) +
geom_vline(xintercept = median(intentos_CREA), size = 3) +
labs(y = "Frec. relativa (%)", x = "Intentos",
title = "WORDLE", fill = "Tipo",
caption =
paste0("Autor: J. Álvarez Liébana (@dadosdelaplace) | Datos: CREA"))
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
Mejor escenario
El mejor de los casos será aquel en el que el conjunto de palabras que el usuario podría pensar y el conjunto de palabras a adivinar es el mismo, y es el conjunto reducido de palabras que el juego oficial de WORDLE en castellano tiene programadas, con 620 vocablos.
# solo las candidatas a wordle
simulaciones <- 1000
generar_equi <- TRUE
iniciar_equi <- FALSE
set.seed(1234567)
simulacion_WORDLE <-
simulacion_wordle(WORDLE_puntuado,
simulaciones = simulaciones,
generar_equi = generar_equi,
iniciar_equi = iniciar_equi)
save(simulacion_WORDLE, file = "./datos/simulacion_WORDLE.rda")intentos_WORDLE <- unlist(simulacion_WORDLE$resultados["intentos", ])
distrib_intentos_WORDLE <- 100 * table(intentos_WORDLE) / simulaciones
media_intentos_WORDLE <- mean(intentos_WORDLE)
distrib_intentos_WORDLE
## intentos_WORDLE
## 2 3 4 5 6 7
## 3.4 41.5 37.5 12.6 3.3 1.7
media_intentos_WORDLE
## [1] 3.76# Dummy (palabra aleatoria entre candidatas)
generar_equi <- TRUE
iniciar_equi <- TRUE
simulacion_dummy_WORDLE <-
simulacion_wordle(WORDLE_puntuado,
simulaciones = simulaciones,
generar_equi = generar_equi,
iniciar_equi = iniciar_equi,
dummy_random = TRUE)
save(simulacion_dummy_WORDLE, file = "./datos/simulacion_dummy_WORDLE.rda")intentos_dummy_WORDLE <-
unlist(simulacion_dummy_WORDLE$resultados["intentos", ])
distrib_intentos_dummy_WORDLE <-
100 * table(intentos_dummy_WORDLE) / simulaciones
media_intentos_dummy_WORDLE <- mean(intentos_dummy_WORDLE)
distrib_intentos_dummy_WORDLE
## intentos_dummy_WORDLE
## 2 3 4 5 6 7
## 2.6 35.2 39.7 18.3 3.4 0.8
media_intentos_dummy_WORDLE
## [1] 3.871En este caso conseguimos ganar en 6 intentos o menos el 98.3% de las veces, con una media de 3.76 intentos para resolverlo y una mediana de 4 (el 50% de las veces lo resuelve en dichos intentos o menos). En el caso de decidir las palabras aleatoriamente (entre las candidatas en cada paso), obtendríamos una media de 3.87 y una mediana de 4 (el 50% de las veces lo resuelve en dichos intentos o menos)., consiguiendo resolverlo el 99.2% de las veces.
Código
# gráfica
ggplot(df_WORDLE %>% mutate(fallo = (intentos == "FALLO"))) +
geom_col(aes(x = intentos, y = frecuencia, fill = fallo),
alpha = 0.9) +
scale_fill_manual(values = c("#6baa64", "#E34D4D"),
labels = c("Acertado", "Fallo")) +
geom_vline(xintercept = median(intentos_WORDLE), size = 3) +
labs(y = "Frec. relativa (%)", x = "Intentos",
title = "WORDLE", fill = "Tipo",
caption =
paste0("Autor: J. Álvarez Liébana (@dadosdelaplace).\nDatos: CREA y Github danielfrg/wordle.es"))
Escenario realista
Por último el caso más realista: el conjunto de palabras que el usuario podría pensar será el conjunto de vocablos de CREA de 5 letras y con una frecuencia normalizada superior a 3 por cada 1000 documentos analizados (un total de 19818 vocablos, bastante más extenso de las palabras que una persona seguramente pueda considerar, de 1993 palabras si lo reducimos a las palabras de 5 letras). Sin embargo, el conjunto de palabras a adivinar será el conjunto reducido de palabras que el juego oficial de WORDLE en castellano tiene programadas, con 620 vocablos.
# adivinando wordle pero con corpus
simulaciones <- 1000
generar_equi <- TRUE
iniciar_equi <- FALSE
set.seed(1234567)
simulacion_mixta <-
simulacion_wordle(CREA_puntuado,
palabras_candidatas = WORDLE_puntuado,
simulaciones = simulaciones,
generar_equi = generar_equi,
iniciar_equi = iniciar_equi)
save(simulacion_mixta, file = "./datos/simulacion_mixta.rda")intentos_mixta <- unlist(simulacion_mixta$resultados["intentos", ])
distrib_intentos_mixta <-
100 * table(intentos_mixta) / simulaciones
media_intentos_mixta <- mean(intentos_mixta)
distrib_intentos_mixta
## intentos_mixta
## 2 3 4 5 6 7
## 0.5 19.5 42.5 27.3 7.3 2.9
media_intentos_mixta
## [1] 4.301# Dummy (palabra aleatoria entre candidatas)
generar_equi <- TRUE
iniciar_equi <- TRUE
simulacion_dummy_mixta <-
simulacion_wordle(CREA_puntuado,
palabras_candidatas = WORDLE_puntuado,
simulaciones = simulaciones,
generar_equi = generar_equi,
iniciar_equi = iniciar_equi,
dummy_random = TRUE)
save(simulacion_dummy_mixta, file = "./datos/simulacion_dummy_mixta.rda")intentos_dummy_mixta <-
unlist(simulacion_dummy_mixta$resultados["intentos", ])
distrib_intentos_dummy_mixta <-
100 * table(intentos_dummy_mixta) / simulaciones
media_intentos_dummy_mixta <- mean(intentos_dummy_mixta)
distrib_intentos_dummy_mixta
## intentos_dummy_mixta
## 2 3 4 5 6 7
## 0.5 13.2 32.5 32.7 14.0 7.1
media_intentos_dummy_mixta
## [1] 4.678En este caso conseguimos ganar en 6 intentos o menos el 97.1% de las veces, con una media de 4.3 intentos para resolverlo y una mediana de 4 (el 50% de las veces lo resuelve en dichos intentos o menos). En el caso de decidir las palabras aleatoriamente (entre las candidatas en cada paso), obtendríamos una media de 4.68 y una mediana de 5 (el 50% de las veces lo resuelve en dichos intentos o menos), consiguiendo resolverlo el 92.9% de las veces.
Código
palabras_iniciales <-
unlist(simulacion_mixta$resultados["inicial", ])
palabras_clave <-
unlist(simulacion_mixta$resultados["palabra_clave", ])
palabras_iniciales_fallo <- palabras_iniciales[intentos_mixta == 7]
palabras_clave_fallo <- palabras_clave[intentos_mixta == 7]
# gráfica
ggplot(df_mixta %>% mutate(fallo = (intentos == "FALLO"))) +
geom_col(aes(x = intentos, y = frecuencia, fill = fallo),
alpha = 0.9) +
scale_fill_manual(values = c("#6baa64", "#E34D4D"),
labels = c("Acertado", "Fallo")) +
geom_vline(xintercept = median(intentos_mixta), size = 3) +
labs(y = "Frec. relativa (%)", x = "Intentos",
title = "WORDLE", fill = "Tipo",
caption =
paste0("Autor: J. Álvarez Liébana (@dadosdelaplace).\nDatos: CREA y Github danielfrg/wordle.es"))
Las palabras a adivinar en los casos en los que no se puedo completar en 6 menos eran:
botar, limar, juego, forro, kefir, rotar, jarro, rayar, ruego, calva, jamas, gatas, gallo, rasta
Las palabras iniciales fueron:
costa
Importante
Los resultados de elegir una palabra aleatoria tienen truco, ya que no es totalmente aleatorio, sino que estamos cribando palabras en función de los resultados de la iteración anterior. Si la palabra fuese totalmente aleatorio, sin atender a los resultados de los cuadradicos, el resultado sería bastante desastrosos, pero asumimos que en el peor de los casos, la estrategia mínima de un usuario será, al menos, cuadrar una palabra en función de sus cuadradicos anteriores.
Elección de la palabra inicial
Por último vamos a realizar una busqueda de las palabras que mejor funcionan como palabra inicial. Para ello vamos a considerar las palabras del CREA más repetidas (que aparezcan en más de 220 de cada 1000 documentos) amén de las palabras de WORDLE (filtrando las que se repitan en menos de 20 de cada 1000 documentos). Para cada una vamos a generar un número de simulaciones y contabilizar el número de éxitos o fracasos.
Código
idx_WORDLE <-
which(WORDLE_puntuado$palabras_puntuadas$frec_norm > 5)
idx_frec <- which(CREA_puntuado$palabras_puntuadas$frec_norm > 200 &
CREA_puntuado$palabras_puntuadas$nletras == 5 &
!(CREA_puntuado$palabras_puntuadas$palabra %in%
WORDLE_puntuado$palabras_puntuadas$palabra))
datos_CREA_frecuentes <- WORDLE_puntuado
datos_CREA_frecuentes$palabras_puntuadas <-
rbind(WORDLE_puntuado$palabras_puntuadas[idx_WORDLE, ],
CREA_puntuado$palabras_puntuadas[idx_frec, ])
datos_CREA_frecuentes$matriz_letras <-
cbind(WORDLE_puntuado$matriz_letras[, idx_WORDLE],
CREA_puntuado$matriz_letras[, idx_frec])Código
simulaciones <- 500
generar_equi <- TRUE
iniciar_equi <- FALSE
simulacion <- intentos <- distrib_intentos <- list()
media_intentos <- mediana_intentos <- n_fallos <-
rep(0, nrow(datos_CREA_frecuentes$palabras_puntuadas))
for (i in 1:nrow(datos_CREA_frecuentes$palabras_puntuadas)) {
simulacion[[i]] <-
simulacion_wordle(datos_CREA_frecuentes,
palabras_candidatas = WORDLE_puntuado,
simulaciones = simulaciones,
generar_equi = generar_equi,
iniciar_equi = iniciar_equi,
inicial_fija = datos_CREA_frecuentes$palabras_puntuadas$palabra[i],
dummy_random = FALSE)
intentos[[i]] <-
unlist(simulacion[[i]]$resultados["intentos", ])
distrib_intentos[[i]] <- 100 * table(intentos[[i]]) / simulaciones
media_intentos[i] <- mean(intentos[[i]])
mediana_intentos[i] <- median(intentos[[i]])
n_fallos[i] <- sum(intentos[[i]] == 7)
}
save(simulacion, file = "./datos/simulacion_palabra_inicial.rda")
save(intentos, file = "./datos/intentos_palabra_inicial.rda")
save(distrib_intentos, file = "./datos/distrib_intentos_palabra_inicial.rda")
save(media_intentos, file = "./datos/media_intentos_palabra_inicial.rda")
save(mediana_intentos, file = "./datos/mediana_intentos_palabra_inicial.rda")
save(n_fallos, file = "./datos/n_fallos_palabra_inicial.rda")Las palabras iniciales con mejor «rendimiento» han sido:
calor, salon, marco, caldo, carne, clavo, labor.
Puedes simular el juego con el código en https://github.com/dadosdelaplace/blog-R-repo/blob/main/wordle/codigoR.R, con el que podrás introducir los aciertos que te devuelva la web, y la función te propondrá palabras candidatas a introducir.
🛑 Limitaciones
Mi ignorancia
La rama de la ciencia de datos que se dedica al análisis de texto se suele conocer como minería de textos, y es una de las ramas más complejas y difíciles (al menos en mi opinión) ya que perdemos las bondades de los números y pasamos a trabajar no solo con variables cualitativas sino las reglas del lenguaje. Al contrario que el famoso Master Mind, donde cada combinación de colores es posible, al trabajar con letras y palabras no todas las combinaciones son válidas.
La principal limitación de este pequeño análisis es mi propia ignorancia: no soy experto en minería de datos ni en procesamiento natural del lenguaje (NLP). Así que, obviamente, la metodología tiene un mero objetivo pedagógico y lúdico, siendo ampliamente mejorable.
Para aprender de este tipo de herramientas os dejo una lista de expertas y expertos que han tratado estos temas:
Julia Silge, experta en text mining y autora de muchos de los paquetes más útiles de
Rpara el tratamiento de textos.Elena Álvarez Mellado, experta en lingüística computacional, y autora de uno de los repositorios más útiles para aprender a tratar textos, donde recopila los discursos de los jefes de Estado en España desde 1937 hasta 2021 https://github.com/lirondos/discursos-de-navidad
Barri y Mari Luz Congosto, expertos en análisis de mensajes en Twitter.
Dot CSV (Carlos Santana), divulgador en Inteligencia Artificial, y uno de los mayores (y mejores) divulgadores de tecnologías como GPT-3.
Sesgo de selección en el corpus
En los datos analizados del CREA hay un sesgo de selección que depende de la tipología de los textos analizados (de hecho términos relacionados con biología o ciencia aparecen en mucha menor frecuencia) y con la franja temporal. Es importante recordar que el conjunto de vocablos en CREA no tiene porque coincidir con las palabras registradas en el diccionario oficial de la RAE.
Hipótesis de léxico extenso
Todo lo simulado se ha realizado bajo la hipótesis de que los usuarios conocen todas las palabras posibles del conjunto de palabras candidatas, algo que seguramente no suceda, por lo que el éxito en el juego dependará fuertemente del número de palabras conocidas. Algo interesante a analizar sería cómo evolucionan los aciertos en función del número de palabras que uno conoce.