EntregaTarea1CDD2
Alejandra Mtz e Isaac Tienda
2023-08-31
Sys.setlocale("LC_TIME","es_ES.UTF-8")## [1] "es_ES.UTF-8"fechas <- seq.Date(as.Date("2023-07-20"), as.Date("2023-08-29"), by="days") #Fechas de los archivos seleccionados
library(rvest)
library(magrittr)
# Función para hacer el webscraping y guardar en archivo .txt
scrape_y_guarda <- function(fecha) {
# Formatea la fecha para la URL
# Formatea la fecha para la URL
dia <- as.numeric(format(fecha, "%d"))
mes <- format(fecha, "%B")
ano <- format(fecha, "%Y")
fecha_url <- paste(dia, "de", mes, "de", ano, sep="-")
url <- paste0("https://www.gob.mx/presidencia/articulos/version-estenografica-conferencia-de-prensa-del-presidente-andres-manuel-lopez-obrador-del-", fecha_url)
# Intenta hacer el webscraping
pagina <- read_html(url)
# Suponiendo que el contenido de la conferencia está en un selector específico, como <div class="contenido-conferencia">
# (es necesario verificar y ajustar este selector de acuerdo al código fuente real de la página)
texto <- pagina %>%
#html_nodes(".contenido-conferencia") %>% # <-- reemplaza ".contenido-conferencia" con el selector CSS correcto
html_text()
# Crea el nombre del archivo y lo guarda
nombre_archivo <- paste0("conferencia_", gsub("-de-", "_", fecha_url), ".txt")
writeLines(texto, nombre_archivo)
}
lapply(fechas, scrape_y_guarda)## [[1]]
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## NULL#Eliminar archivos son leyenda "Sitio en mantenimiento"
#Este código busca archivos .txt en el directorio de trabajo actual, verifica si contienen la leyenda "Sitio en mantenimiento" y, en caso afirmativo, elimina esos archivos y notifica al usuario sobre los archivos eliminados.
# Lista de archivos .txt en el directorio actual
#list.files(): Esta función devuelve una lista de nombres de archivos en el directorio de trabajo actual.
#pattern = "*.txt": Esta es una expresión regular que indica que sólo quieres listar archivos que terminen en .txt, es decir, archivos de texto.
archivos <- list.files(pattern = "*.txt")
# Función para verificar y eliminar archivos no deseados
verificar_y_eliminar <- function(archivo) {
#readLines(archivo, warn = FALSE): Esta función lee el archivo línea por línea y devuelve un vector donde cada elemento es una línea del archivo.
#warn = FALSE: Evita que se muestren advertencias en caso de problemas al leer el archivo.
texto <- readLines(archivo, warn = FALSE)
#paste(): Esta función concatena el vector de líneas (anteriormente leído) en un solo string.
#collapse = " ": Indica que las líneas se deben separar con un espacio.
texto <- paste(texto, collapse = " ")
# Si el archivo contiene "Sitio en mantenimiento", eliminarlo
#grepl(): Esta función busca una expresión regular (en este caso, la cadena "Sitio en mantenimiento") dentro del texto. Si la encuentra, devuelve TRUE; de lo contrario, devuelve FALSE.
#ignore.case = TRUE: Ignora la diferencia entre mayúsculas y minúsculas al buscar la cadena.
if (grepl("Sitio en mantenimiento", texto, ignore.case = TRUE)) {
file.remove(archivo)
cat(paste("Archivo eliminado:", archivo, "\n"))
}
}
#Si el condicional anterior es verdadero (es decir, si el archivo contiene "Sitio en mantenimiento"), esta línea elimina el archivo.
# Aplicar la función a cada archivo
lapply(archivos, verificar_y_eliminar)## Archivo eliminado: conferencia_12_agosto_2023.txt
## Archivo eliminado: conferencia_13_agosto_2023.txt
## Archivo eliminado: conferencia_18_agosto_2023.txt
## Archivo eliminado: conferencia_19_agosto_2023.txt
## Archivo eliminado: conferencia_20_agosto_2023.txt
## Archivo eliminado: conferencia_22_julio_2023.txt
## Archivo eliminado: conferencia_23_julio_2023.txt
## Archivo eliminado: conferencia_26_agosto_2023.txt
## Archivo eliminado: conferencia_26_julio_2023.txt
## Archivo eliminado: conferencia_27_agosto_2023.txt
## Archivo eliminado: conferencia_28_julio_2023.txt
## Archivo eliminado: conferencia_29_julio_2023.txt
## Archivo eliminado: conferencia_3_agosto_2023.txt
## Archivo eliminado: conferencia_30_julio_2023.txt
## Archivo eliminado: conferencia_4_agosto_2023.txt
## Archivo eliminado: conferencia_5_agosto_2023.txt
## Archivo eliminado: conferencia_6_agosto_2023.txt
## Archivo eliminado: conferencia_7_agosto_2023.txt## [[1]]
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## NULL# Lista de archivos .txt en el directorio actual
archivos <- list.files(pattern = "*.txt")
#list.files(pattern = "*.txt"): Lista todos los archivos en el directorio de trabajo actual que tengan la extensión .txt. Estos nombres de archivos se guardan en el vector archivos.
# Función para limpiar el texto
limpiar_texto <- function(archivo) {
texto <- readLines(archivo, warn = FALSE)
texto <- paste(texto, collapse = " ")
# tolower(texto): Convierte todo el texto a minúsculas para que el análisis y limpieza sean uniformes.
texto <- tolower(texto)
# Eliminar URLs: gsub("http\S+|www\.\S+", "", texto): Usa una expresión regular para encontrar URLs que comiencen con "http" o "www" y las reemplaza con una cadena vacía, esencialmente eliminándolas.
texto <- gsub("http\\S+|www\\.\\S+", "", texto)
# Eliminar etiquetas HTML (si las hubiera): gsub("<.*?>", "", texto): Si hubiera alguna etiqueta HTML en el texto, esta expresión regular las identificaría y las eliminaría.
texto <- gsub("<.*?>", "", texto)
# Eliminar puntuación: gsub("[[:punct:]]", "", texto): Elimina cualquier carácter de puntuación del texto.
texto <- gsub("[[:punct:]]", "", texto)
# Eliminar números: gsub("\d+", "", texto): Elimina cualquier secuencia de números.
texto <- gsub("\\d+", "", texto)
# Eliminar espacios extra: gsub("\s+", " ", texto): Reemplaza cualquier secuencia de espacios (uno o más) con un único espacio.
texto <- gsub("\\s+", " ", texto)
return(texto)
}
# Aplicar la función de limpieza a cada archivo
textos_limpios <- lapply(archivos, limpiar_texto)library(tm)## Loading required package: NLPcorpus <- Corpus(VectorSource(textos_limpios))
#Continuar con la creacion del dtm"
dtm<- DocumentTermMatrix(corpus)
#Ver la matriz"
inspect(dtm)## <<DocumentTermMatrix (documents: 23, terms: 19138)>>
## Non-/sparse entries: 70094/370080
## Sparsity : 84%
## Maximal term length: 596
## Weighting : term frequency (tf)
## Sample :
## Terms
## Docs con del las los para pero por presidente que una
## 1 122 147 118 255 121 121 161 170 642 131
## 12 166 164 103 297 131 65 136 129 571 94
## 13 155 143 90 296 142 110 129 67 623 97
## 15 159 192 92 211 135 61 148 95 532 90
## 17 119 168 99 204 93 66 153 91 442 84
## 19 153 113 92 270 128 67 134 114 547 72
## 20 164 219 120 286 137 79 169 118 636 106
## 21 182 198 145 244 171 67 160 120 656 145
## 23 148 135 103 356 140 109 164 112 721 136
## 5 140 157 117 235 112 59 138 105 482 113#Conteo de las palabras del primer documento.
conteo_primer_doc<- as.matrix(dtm[1, ])
sort(conteo_primer_doc, decreasing = TRUE)## [1] 642 255 170 161 147 131 122 121 121 118 115 112 111 111 104 103 97 73
## [19] 73 69 65 63 63 61 57 53 52 52 51 48 48 47 45 44 43 43
## [37] 41 40 39 38 36 36 36 36 36 35 34 34 34 34 33 32 32 31
## [55] 31 31 30 30 29 28 27 27 27 27 27 26 26 26 26 25 25 25
## [73] 25 24 24 24 23 23 23 23 23 23 23 22 22 22 21 21 20 20
## [91] 19 19 19 19 19 19 19 18 18 18 18 18 17 17 17 17 17 17
## [109] 16 16 16 16 16 16 16 16 16 16 15 15 15 15 15 15 15 15
## [127] 15 14 14 14 14 14 14 14 14 14 14 13 13 13 13 13 13 13
## [145] 13 13 13 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
## [163] 12 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [181] 11 11 11 11 11 11 10 10 10 10 10 10 10 10 10 10 10 10
## [199] 10 10 10 10 10 10 10 10 10 10 10 9 9 9 9 9 9 9
## [217] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [235] 9 9 9 9 9 9 9 9 9 8 8 8 8 8 8 8 8 8
## [253] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [271] 8 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7 7 7
## [289] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [307] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 6
## [325] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [343] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [361] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [379] 6 6 6 6 6 5 5 5 5 5 5 5 5 5 5 5 5 5
## [397] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [415] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [433] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [451] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [469] 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4
## [487] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [505] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [523] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [541] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [559] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [577] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [595] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3
## [613] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [631] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [649] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [667] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [685] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [703] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [721] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [739] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [757] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [775] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [793] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [811] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [829] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [847] 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2
## [865] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [883] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [901] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [919] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [937] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [955] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [973] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [991] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1009] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1027] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1045] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1063] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1081] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1099] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1117] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1135] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1153] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1171] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1189] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1207] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1225] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1243] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1261] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1279] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1315] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1333] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1351] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1
## [1369] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1387] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1405] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1423] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1441] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1459] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1477] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1495] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1513] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1531] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1549] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1567] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1585] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1603] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1621] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1639] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1657] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1675] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1693] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1711] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1729] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1747] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1765] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1783] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1801] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1819] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1837] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1855] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1873] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1891] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1909] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1927] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1945] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1963] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1981] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1999] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2017] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2035] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2053] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2071] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2089] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2107] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2125] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2143] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2161] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2179] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2197] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2215] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2233] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2251] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2269] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2287] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2305] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2323] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2341] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2359] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2377] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2395] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2413] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2431] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2449] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2467] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2485] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2503] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2521] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2539] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2557] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2575] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2611] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2629] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2647] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2665] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2683] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2701] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2719] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2737] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2755] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2773] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2791] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2809] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2827] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2845] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2863] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2881] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2899] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2917] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2935] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2953] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2971] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2989] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3007] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3025] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3043] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3061] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3079] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3097] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3115] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3133] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3151] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3169] 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0
## [3187] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3205] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3241] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3259] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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## [3295] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3313] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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## [19135] 0 0 0 0#Sumar las columnas para obterner cada término.
conteo_total <- colSums(as.matrix(dtm))
#Ordenar y mostrar los términos más comunes.
terminos_comunes <- sort(conteo_total, decreasing = TRUE)
head(terminos_comunes, 50) #Mostrar los 50 términos más comunes. ## que los del con por para presidente
## 11997 5126 3485 2997 2903 2650 2398
## las una pero más porque lópez manuel
## 2180 2127 1734 1662 1636 1465 1443
## andrés como está también hay eso obrador
## 1434 1410 1289 1268 1262 1191 1165
## muy sí este mil var todo qué
## 1150 1143 1102 1035 1035 910 900
## entonces son vamos esto méxico todos tiene
## 863 859 848 839 782 774 773
## nos están aquí esta pues gobierno ver
## 760 729 726 710 692 674 630
## fue bueno ahí millones estamos dimension estado
## 610 605 598 588 587 575 566
## sobre
## 563#Instalar el paquete wordcloud
library(wordcloud)## Loading required package: RColorBrewer#Crear la nube de palabras.
wordcloud(names(terminos_comunes), terminos_comunes, min.freq = 50, max.words =100, random.order =FALSE)
#Nuestra nube tiene muchas palabras que son demasiado comunes y generan ruido en nuestro analisis, Estas palabras incluyen articulos, preposiciones y pronombres, entre otros, que aparecen frecuentemente y que no agregan significado.
library(tm)
##eliminar stopwords de nuestro corpus.Stop words son el, los, las, etc etc.
corpus<-tm_map(corpus, removeWords, stopwords("es"))## Warning in tm_map.SimpleCorpus(corpus, removeWords, stopwords("es")):
## transformation drops documents#Creamos el nuevo dtm
dtm<- DocumentTermMatrix(corpus)
inspect(dtm)## <<DocumentTermMatrix (documents: 23, terms: 18673)>>
## Non-/sparse entries: 66833/362646
## Sparsity : 84%
## Maximal term length: 596
## Weighting : term frequency (tf)
## Sample :
## Terms
## Docs andrés entonces lópez manuel méxico mil obrador presidente vamos var
## 1 112 47 115 111 35 43 111 170 23 45
## 12 71 32 73 71 33 91 71 129 34 45
## 13 41 64 45 41 20 80 44 67 45 45
## 15 54 34 53 53 32 28 11 95 38 45
## 17 46 37 50 46 33 116 46 91 35 45
## 19 63 29 63 64 35 35 10 114 30 45
## 20 58 51 58 58 30 59 58 118 31 45
## 21 75 30 78 78 24 55 12 120 33 45
## 23 84 64 88 84 55 33 86 112 50 45
## 5 46 58 47 47 26 79 46 105 38 45conteo_total <- colSums(as.matrix(dtm))
terminos_comunes <- sort(conteo_total, decreasing = TRUE)
head(terminos_comunes, 100) #Mostrar los 100 términos más comunes. ## presidente lópez manuel andrés obrador
## 2398 1465 1443 1434 1165
## mil var entonces vamos méxico
## 1035 1035 863 848 782
## aquí pues gobierno ver bueno
## 726 692 674 630 605
## ahí millones dimension ahora pesos
## 598 588 575 548 545
## dos cómo pueblo gente así
## 533 482 439 432 429
## caso ciento van function hacer
## 419 416 400 391 388
## ser bien null usted días
## 375 370 368 362 357
## país pagetype poder señor años
## 352 345 342 342 337
## año libros gagobtrackerset tren hace
## 330 323 322 317 313
## puede nacional información tres gracias
## 310 308 304 300 297
## hoy interlocutor tema tener decir
## 295 291 287 283 282
## agosto prensa todas voy manera
## 280 274 274 274 269
## tiempo parte pregunta seguridad importante
## 265 264 263 262 261
## además luego mismo personas gtagset
## 259 259 258 256 253
## general conferencia haciendo nuevo sólo
## 252 247 247 246 236
## presidencia buenos salud ciudad unidos
## 234 231 229 223 223
## día versión siempre trabajo estenográfica
## 222 220 219 209 208
## toda mejor cada creo gran
## 206 202 200 195 195
## medios muchas ramírez social menos
## 195 195 188 187 186
## pública agua secretaría incluso jesús
## 183 183 180 179 178#Instalar el paquete wordcloud
library(wordcloud)
#Crear la nube de palabras.
wordcloud(names(terminos_comunes), terminos_comunes, min.freq = 50, max.words =100, random.order =FALSE)
#Eliminar palabras específicas
palabras_eliminar <- c("null", "gtagset", "function", "var", "pagetipe", "presidente","interlocutor", "andrés", "manuel", "lópez", "obrador", "gagobtrackerset", "pagetype", "sino", "entonces", "pues", "ahí", "sólo", "hoy", "voy", "agosto", "estenográfica", "conferencia", "pagesgroup", "interlocutora")
corpus<- tm_map(corpus, removeWords, palabras_eliminar)## Warning in tm_map.SimpleCorpus(corpus, removeWords, palabras_eliminar):
## transformation drops documents#"Crear la nueva dtm}
dtm<- DocumentTermMatrix(corpus)
#Ver la matriz
inspect(dtm)## <<DocumentTermMatrix (documents: 23, terms: 18617)>>
## Non-/sparse entries: 66237/361954
## Sparsity : 85%
## Maximal term length: 596
## Weighting : term frequency (tf)
## Sample :
## Terms
## Docs ahora aquí bueno dimension gobierno méxico mil millones vamos ver
## 1 33 27 29 25 32 35 43 31 23 48
## 12 25 57 30 25 25 33 91 38 34 35
## 13 31 29 29 25 19 20 80 53 45 35
## 15 24 33 24 25 49 32 28 18 38 27
## 17 23 29 25 25 38 33 116 36 35 15
## 19 22 30 33 25 28 35 35 19 30 27
## 20 28 20 22 25 18 30 59 63 31 28
## 21 17 39 34 25 35 24 55 23 33 36
## 23 31 42 46 25 28 55 33 19 50 28
## 5 23 40 17 25 36 26 79 84 38 38#Sumar las columnas para obtener el conteo total de cada término.
conteo_total<- colSums(as.matrix(dtm))
#Ordenar y mostrar los términos más comunes
terminos_comunes<- sort(conteo_total,decreasing=TRUE)
head(terminos_comunes,100) #Mostrar los 100 términos más comunes. ## mil vamos méxico aquí gobierno ver
## 1035 848 782 726 674 630
## bueno millones dimension ahora pesos dos
## 605 588 575 548 545 533
## cómo pueblo gente así caso ciento
## 482 439 432 429 419 416
## van hacer ser bien usted días
## 400 388 375 370 362 357
## país poder señor años año libros
## 352 342 342 337 330 323
## tren hace puede nacional información tres
## 317 313 310 308 304 300
## gracias tema tener decir prensa todas
## 297 287 283 282 274 274
## manera tiempo parte pregunta seguridad importante
## 269 265 264 263 262 261
## además luego mismo personas general haciendo
## 259 259 258 256 252 247
## nuevo presidencia buenos salud ciudad unidos
## 246 234 231 229 223 223
## día versión siempre trabajo toda mejor
## 222 220 219 209 206 202
## cada creo gran medios muchas ramírez
## 200 195 195 195 195 188
## social menos pública agua secretaría incluso
## 187 186 183 183 180 179
## jesús maya política dinero intervención siguiente
## 178 178 177 175 175 173
## acuerdo trabajadores empresa quién ejemplo ayer
## 171 170 169 169 167 166
## empresas república proceso seguir centavos allá
## 166 166 164 163 161 156
## cuatro inversión conocer jóvenes
## 155 155 154 154#Crear la nube de palabras.
wordcloud(names(terminos_comunes), terminos_comunes, min.freq =50, max.words = 100, random.order = FALSE)## Warning in wordcloud(names(terminos_comunes), terminos_comunes, min.freq = 50,
## : intervención could not be fit on page. It will not be plotted.## Warning in wordcloud(names(terminos_comunes), terminos_comunes, min.freq = 50,
## : siguiente could not be fit on page. It will not be plotted.## Warning in wordcloud(names(terminos_comunes), terminos_comunes, min.freq = 50,
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#Si tienes un DocumentTermMatrix (DTM) en R, puedes buscar la frecuencia de una palabra específica a través de todos los documentos de la siguiente manera:
#Es la palabra que buscarás.
palabra <- "vamos"
#Obtener el índice de la columna que corresponde a la palabra que buscas
indice_palabra <- which(colnames(dtm) == palabra)
#Si la palabra no se encuentra en la DTM, indice_palabra será integer(0)
if (length(indice_palabra) == 0) {
print(paste("La palabra", palabra, "no se encuentra en la DTM"))
}else {
#Obtener la columna que corresponde a esa palabra y sumarla para obtener el total de ocurrencias.
frecuencia_total <- sum(as.matrix(dtm[, indice_palabra]))
print(paste("La palabra", palabra, "aparece", frecuencia_total, "veces en todos los documentos."))
}## [1] "La palabra vamos aparece 848 veces en todos los documentos."#Creamos un df para ver los 100 terminos más comunes junto con su frecuencia.
df_terminos_comunes <- data.frame(termino = terminos_comunes)
#show(df_terminos_comunes)
head(df_terminos_comunes, 100)## termino
## mil 1035
## vamos 848
## méxico 782
## aquí 726
## gobierno 674
## ver 630
## bueno 605
## millones 588
## dimension 575
## ahora 548
## pesos 545
## dos 533
## cómo 482
## pueblo 439
## gente 432
## así 429
## caso 419
## ciento 416
## van 400
## hacer 388
## ser 375
## bien 370
## usted 362
## días 357
## país 352
## poder 342
## señor 342
## años 337
## año 330
## libros 323
## tren 317
## hace 313
## puede 310
## nacional 308
## información 304
## tres 300
## gracias 297
## tema 287
## tener 283
## decir 282
## prensa 274
## todas 274
## manera 269
## tiempo 265
## parte 264
## pregunta 263
## seguridad 262
## importante 261
## además 259
## luego 259
## mismo 258
## personas 256
## general 252
## haciendo 247
## nuevo 246
## presidencia 234
## buenos 231
## salud 229
## ciudad 223
## unidos 223
## día 222
## versión 220
## siempre 219
## trabajo 209
## toda 206
## mejor 202
## cada 200
## creo 195
## gran 195
## medios 195
## muchas 195
## ramírez 188
## social 187
## menos 186
## pública 183
## agua 183
## secretaría 180
## incluso 179
## jesús 178
## maya 178
## política 177
## dinero 175
## intervención 175
## siguiente 173
## acuerdo 171
## trabajadores 170
## empresa 169
## quién 169
## ejemplo 167
## ayer 166
## empresas 166
## república 166
## proceso 164
## seguir 163
## centavos 161
## allá 156
## cuatro 155
## inversión 155
## conocer 154
## jóvenes 154