Capítulo26 Nubes de palabra en R
## [1] "2024-11-07"26.1 Ejemplo #1
Para generar nubes de palabras con R. Se necesita los paquetes wordcloud, RColorBrewer y wordcloud2
Ese ejemplo es una copia de la siguiente pagina de web
if (!require("pacman")) install.packages("pacman")
pacman::p_load(tm, SnowballC, wordcloud, RColorBrewer, wordcloud2)
library(wordcloud) # Un paquete para hacer word cloud
library(wordcloud2) # paquete más sencillo para hacer word cloud2
library(RColorBrewer) # paquete para cambiar los colores
library(tm) # paquete de text mining
library(SnowballC) # paquete para trabajar en otro idioma aparte de ingles26.2 Usando los datos en el paquete wordcloud2 que se llama demoFreq
## word freq
## oil oil 85
## said said 73
## prices prices 48
## opec opec 42
## mln mln 31
## the the 26
## last last 24
## bpd bpd 23
## dlrs dlrs 23
## crude crude 21
## market market 20
## reuter reuter 20
## saudi saudi 18
## will will 18
## one one 17
## barrel barrel 15
## kuwait kuwait 14
## new new 14
## official official 14
## pct pct 14
## price price 13
## barrels barrels 11
## government government 11
## production production 11
## sheikh sheikh 11
## industry industry 10
## meeting meeting 10
## minister minister 10
## world world 10
## also also 9
## billion billion 9
## futures futures 9
## month month 9
## output output 9
## petroleum petroleum 9
## quota quota 9
## sources sources 9
## accord accord 8
## analysts analysts 8
## but but 8
## group group 8
## gulf gulf 8
## january january 8
## markets markets 8
## report report 8
## today today 8
## december december 7
## demand demand 7
## economic economic 7
## economy economy 7
## energy energy 7
## help help 7
## international international 7
## may may 7
## nazer nazer 7
## nymex nymex 7
## posted posted 7
## present present 7
## they they 7
## traders traders 7
## 158 158 6
## ability ability 6
## agreement agreement 6
## ali ali 6
## april april 6
## arabia arabia 6
## budget budget 6
## emergency emergency 6
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## hold hold 6
## imports imports 6
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## recent recent 6
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## ceiling ceiling 5
## company company 5
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## growth growth 5
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## opecs opecs 5
## plans plans 5
## qatar qatar 5
## quoted quoted 5
## research research 5
## reserve reserve 5
## reserves reserves 5
## states states 5
## study study 5
## united united 5
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## 150 150 4
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## crude crude 2126.3 Paso 1
Importar los datos de la web
filePath <- "http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt"
text <- readLines(filePath)
text## [1] ""
## [2] "And so even though we face the difficulties of today and tomorrow, I still have a dream. It is a dream deeply rooted in the American dream."
## [3] " "
## [4] "I have a dream that one day this nation will rise up and live out the true meaning of its creed:"
## [5] " "
## [6] "We hold these truths to be self-evident, that all men are created equal."
## [7] " "
## [8] "I have a dream that one day on the red hills of Georgia, the sons of former slaves and the sons of former slave owners will be able to sit down together at the table of brotherhood."
## [9] " "
## [10] "I have a dream that one day even the state of Mississippi, a state sweltering with the heat of injustice, sweltering with the heat of oppression, will be transformed into an oasis of freedom and justice."
## [11] " "
## [12] "I have a dream that my four little children will one day live in a nation where they will not be judged by the color of their skin but by the content of their character."
## [13] " "
## [14] "I have a dream today!"
## [15] " "
## [16] "I have a dream that one day, down in Alabama, with its vicious racists, with its governor having his lips dripping with the words of interposition and nullification, one day right there in Alabama little black boys and black girls will be able to join hands with little white boys and white girls as sisters and brothers."
## [17] " "
## [18] "I have a dream today!"
## [19] " "
## [20] "I have a dream that one day every valley shall be exalted, and every hill and mountain shall be made low, the rough places will be made plain, and the crooked places will be made straight; and the glory of the Lord shall be revealed and all flesh shall see it together."
## [21] " "
## [22] "This is our hope, and this is the faith that I go back to the South with."
## [23] " "
## [24] "With this faith, we will be able to hew out of the mountain of despair a stone of hope. With this faith, we will be able to transform the jangling discords of our nation into a beautiful symphony of brotherhood. With this faith, we will be able to work together, to pray together, to struggle together, to go to jail together, to stand up for freedom together, knowing that we will be free one day."
## [25] " "
## [26] "And this will be the day, this will be the day when all of God s children will be able to sing with new meaning:"
## [27] " "
## [28] "My country tis of thee, sweet land of liberty, of thee I sing."
## [29] "Land where my fathers died, land of the Pilgrim s pride,"
## [30] "From every mountainside, let freedom ring!"
## [31] "And if America is to be a great nation, this must become true."
## [32] "And so let freedom ring from the prodigious hilltops of New Hampshire."
## [33] "Let freedom ring from the mighty mountains of New York."
## [34] "Let freedom ring from the heightening Alleghenies of Pennsylvania."
## [35] "Let freedom ring from the snow-capped Rockies of Colorado."
## [36] "Let freedom ring from the curvaceous slopes of California."
## [37] " "
## [38] "But not only that:"
## [39] "Let freedom ring from Stone Mountain of Georgia."
## [40] "Let freedom ring from Lookout Mountain of Tennessee."
## [41] "Let freedom ring from every hill and molehill of Mississippi."
## [42] "From every mountainside, let freedom ring."
## [43] "And when this happens, when we allow freedom ring, when we let it ring from every village and every hamlet, from every state and every city, we will be able to speed up that day when all of God s children, black men and white men, Jews and Gentiles, Protestants and Catholics, will be able to join hands and sing in the words of the old Negro spiritual:"
## [44] "Free at last! Free at last!"
## [45] " "
## [46] "Thank God Almighty, we are free at last!"Importar un texto de su computadora en formato .txt No va a funcionar el formato .doc de MSWord.
26.4 Subir el texto en formato Corpus
mi_texto=iconv(text,"WINDOWS-1252","UTF-8") # Use this for removing accents and non - english characters
# Load the data as a corpus
docs <- Corpus(VectorSource(mi_texto))
docs## <<SimpleCorpus>>
## Metadata: corpus specific: 1, document level (indexed): 0
## Content: documents: 4626.6 Transformar el texto para reemplazar algunos caracteres especiales, y remplazarlos por espacio en blanco
26.7 el paquete tm es para text mining
# Convert the text to lower case
docs <- tm_map(docs, content_transformer(tolower))
# Remove numbers
docs <- tm_map(docs, removeNumbers)
# Remove english common stopwords
stopwords("english") # list of common english stopwords that are often removed## [1] "i" "me" "my" "myself" "we"
## [6] "our" "ours" "ourselves" "you" "your"
## [11] "yours" "yourself" "yourselves" "he" "him"
## [16] "his" "himself" "she" "her" "hers"
## [21] "herself" "it" "its" "itself" "they"
## [26] "them" "their" "theirs" "themselves" "what"
## [31] "which" "who" "whom" "this" "that"
## [36] "these" "those" "am" "is" "are"
## [41] "was" "were" "be" "been" "being"
## [46] "have" "has" "had" "having" "do"
## [51] "does" "did" "doing" "would" "should"
## [56] "could" "ought" "i'm" "you're" "he's"
## [61] "she's" "it's" "we're" "they're" "i've"
## [66] "you've" "we've" "they've" "i'd" "you'd"
## [71] "he'd" "she'd" "we'd" "they'd" "i'll"
## [76] "you'll" "he'll" "she'll" "we'll" "they'll"
## [81] "isn't" "aren't" "wasn't" "weren't" "hasn't"
## [86] "haven't" "hadn't" "doesn't" "don't" "didn't"
## [91] "won't" "wouldn't" "shan't" "shouldn't" "can't"
## [96] "cannot" "couldn't" "mustn't" "let's" "that's"
## [101] "who's" "what's" "here's" "there's" "when's"
## [106] "where's" "why's" "how's" "a" "an"
## [111] "the" "and" "but" "if" "or"
## [116] "because" "as" "until" "while" "of"
## [121] "at" "by" "for" "with" "about"
## [126] "against" "between" "into" "through" "during"
## [131] "before" "after" "above" "below" "to"
## [136] "from" "up" "down" "in" "out"
## [141] "on" "off" "over" "under" "again"
## [146] "further" "then" "once" "here" "there"
## [151] "when" "where" "why" "how" "all"
## [156] "any" "both" "each" "few" "more"
## [161] "most" "other" "some" "such" "no"
## [166] "nor" "not" "only" "own" "same"
## [171] "so" "than" "too" "very"docs <- tm_map(docs, removeWords, stopwords("english"))
# Remove your own stop word
# specify your stopwords as a character vector
docs <- tm_map(docs, removeWords, c("blabla1", "blabla2"))
# Remove punctuations
docs <- tm_map(docs, removePunctuation)
# Eliminate extra white spaces
docs <- tm_map(docs, stripWhitespace)
# Text stemming
# docs <- tm_map(docs, stemDocument)
#stopwords() # Here are all the stopwords in the function **stopwords**26.8 Crear una matriz de las palabras de mi documento
dtm <- TermDocumentMatrix(docs) # convirtir el texto en una lista de palabras
m <- as.matrix(dtm) # convertir en una matriz
v <- sort(rowSums(m),decreasing=TRUE) # ordenar las palabras por frecuencia
d <- data.frame(word = names(v),freq=v) # crea un nuevo data frame de las palabras y su frecuencia
head(d, n=10) # las primeras 10 palabras más comunes## word freq
## will will 17
## freedom freedom 13
## ring ring 12
## dream dream 11
## day day 11
## let let 11
## every every 9
## one one 8
## able able 8
## together together 726.8.2 How would you remove from the data frame all words that have less or equal to 3 counts
Del paquete wordcloud
#head(d)
wordcloud(words = d$word, # las palabras
freq = d$freq, # la frecuencia
min.freq = 10, # la frecuencia mínima
max.words=200, # el número máximo de palabras
random.order=TRUE, # orden aleatorio
rot.per=0.35, # rotación de las palabras
colors=brewer.pal(8, "Dark2")) # colores
26.10 Como remover palabras de otra idioma
26.10.1 Vea este enlace para los “stopwords” de muchos idiomas
https://cran.r-project.org/web/packages/stopwords/readme/README.html
26.11 En español
# from CRAN
install.packages("stopwords")
# Or get the development version from GitHub:
# install.packages("devtools")
# devtools::install_github("quanteda/stopwords")26.11.1 Las 30 primeras palabras en la lista de stopword del paquete “stopwords” en español
## [1] "de" "la" "que" "el" "en" "y" "a" "los"
## [9] "del" "se" "las" "por" "un" "para" "con" "no"
## [17] "una" "su" "al" "lo" "como" "más" "pero" "sus"
## [25] "le" "ya" "o" "este" "sí" "porque"Ejemplos #3
Aqui un tercer ejemplo
# install.packages("pacman") # Si no tiene instalada la Biblioteca Pacman ejecutar esta línea de código
library("pacman")
p_load("tm") # Biblioteca para realizar el preprocesado del texto,
p_load("tidyverse") # Biblioteca con funciones para manipular datos.
p_load("wordcloud") # Biblioteca para graficar nuestra nube de palabras.
p_load("RColorBrewer") # Biblioteca para seleccionar una paleta de colores de nuestra nube de palabras.26.13 Convertir su documento en Corpus y identificar que es en español
texto2 <- VCorpus(VectorSource(texto),
readerControl = list(reader = readPlain, language = "es", load=TRUE))
texto2## <<VCorpus>>
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 126.14 Limpieza del documento
- remover los números
- remover las puntuaciones
- cambiar a letras minúsculas
- remover las palabras comunes en español
- usar solamente la base de las palabres (“stem”) por ejemplo remover las conjugaciones espero, esparas, espera, esperamos… se convierte en “esper”
- remover espacios blancos
texto2 <- tm_map(texto2, removeNumbers)
texto2 <- tm_map(texto2, removePunctuation)
texto2 <- tm_map(texto2, tolower)
texto2 <- tm_map(texto2, removeWords, stopwords::stopwords("es", source = "snowball"))
#texto2 <- tm_map(texto2, stemDocument, language="spanish")
texto2 <- tm_map(texto2, stripWhitespace)26.18 Calcular la frecuencia de de cada palabra
tabla_frecuencia <- cbind(palabras = tabla_frecuencia$dimnames$Terms,
frecuencia = tabla_frecuencia$v)# Convertimos los valores enlazados con cbind a un objeto dataframe.
tabla_frecuencia<-as.data.frame(tabla_frecuencia)
# Forzamos a que la columna de frecuencia contenga valores numéricos.
tabla_frecuencia$frecuencia<-as.numeric(tabla_frecuencia$frecuencia)
# Ordenamos muestra tabla de frecuencias de acuerdo a sus valores numéricos.
tabla_frecuencia<-tabla_frecuencia[order(tabla_frecuencia$frecuencia, decreasing=TRUE),]
head(tabla_frecuencia) # aqui vemos las 6 palabras más comunes en el texto## palabras frecuencia
## 809 inteligencia 79
## 126 artificial 67
## 1378 sistemas 32
## 1347 ser 22
## 737 humano 19
## 1172 problemas 18wordcloud(words = tabla_frecuencia$palabras,
freq = tabla_frecuencia$frecuencia,
min.freq = 5,
max.words = 100,
random.order = FALSE,
colors = brewer.pal(8,"Paired"))
Como ver la figuras en la pestaña “Plots”
De esa forma puede bajar los WordClouds como .pdf o otro formato.
<https://stackoverflow.com/questions/40570621/rstudio-how-to-show-plot-output-in-bottom-right-pane>