Comparación del número de Likes:
Comparación del número de Dislikes:
Comparación del número de Comentarios:
Polaridad en los comentarios de los videos:
Youtube es uno de los principales canales que los artistas musicales usan para promoverse, esto lo convierte en un escenario viable para analizar cómo es la relación entre los diferentes artistas. Analizar sus canales usando la API pública de Youtube y el paquete tuber de R es relativamente “sencillo”, basta con tener una cuenta de google y registrar una app, para obtener un “ID de cliente” y un “Secret Id”. Para ilustrar vamos a descargar datos de las agrupaciones Iron Maiden, Ac/Dc, GunsNRoses, Queen y Metallica, y realizar una comparación de sus estadísticas principales (Views, Likes, Unlikes y Comments).
Comparación del número de Visualizaciones:
La agrupación GunsNRoses presentó una mayor variabilidad en sus estadísticas, esto no permitía distinguir la variabilidad en el boxplot de las demás agrupaciones (ya que las hacia muy pequeñas), por esta razón no fue incluido en la comparación de los bloxplot (al igual que el bloxplot de GunsNRoses, por motivos de claridad visual, tampoco se incluyeron los Outliers en los gráficos). Vale la pena resaltar que el objetivo del análisis es mostrar como extraer datos de YouTube y visualizarlos en AnalyStats-App ya que, en un análisis estadístico formal deberia incluirse un análisis de residuales.
El código de R es:
#devtools::install_github("soodoku/tuber", build_vignettes)
library("tuber")
library("data.table")
library("httr")
library("tm")
library("sentiment")
library("dplyr")
library('RCurl')
library("stringr")
###### Autenticación ######
google <- oauth_endpoint(NULL, "auth", "token", base_url = "https://accounts.google.com/o/oauth2")
myapp <- oauth_app("google", "ID de cliente", secret = "SecretId")
cred <- oauth2.0_token(google, myapp, scope = "https://www.googleapis.com/auth/yt-analytics.readonly")
###### Descarga Datos ######
### Iron Maiden ###
# Información del Canal:
a <- list_channel_resources(filter = c(channel_id = "UCaisXKBdNOYqGr2qOXCLchQ"), part="contentDetails")
# Id de la lista de videos:
playlist_id <- a$items[[1]]$contentDetails$relatedPlaylists$uploads
# Descarga de los videos en la lista:
vids <- get_playlist_items(filter= c(playlist_id=playlist_id))
# Ids de los Videos:
mytest <- sapply(vids$items, "[", "contentDetails")
# Numero de videos en la lista
n <- length(mytest)
#Estadisticas de los videos
statistics <- data.frame(rbindlist(sapply(1:n,function(x) data.frame(get_stats(mytest[x]$contentDetails$videoId))),fill=TRUE))
#Informacion adicional de los videos
details <- sapply(1:n,function(x) get_video_details(mytest[x]$contentDetails$videoId))
#Pasando a data.frame y pasando a numeric las columnas que son factor
nombre <- colnames(statistics)
statistics[,2:6] <- sapply(statistics[,2:6],function(x)as.numeric(as.character(x)))
colnames(statistics) <- nombre
statistics[is.na(statistics)]<-0
#Adicionando información
for( i in 1:n){
statistics$title[i] <-details[[i]]$title
statistics$publishedAt[i] <-details[[i]]$publishedAt
statistics$channelId[i] <-details[[i]]$channelId
statistics$channelTitle[i] <-details[[i]]$channelTitle
statistics$defaultAudioLanguage[i] <-ifelse(is.null(details[[i]]$defaultAudioLanguage),"NA",details[[i]]$defaultAudioLanguage)
}
### Análisis de sentimientos ###
#Traer los comentarios de los videos:
comments <- lapply(1:n,function(x) get_comment_threads(c(video_id=mytest[x]$contentDetails$videoId),text_format = "plainText"))
comments <- as.data.frame(rbindlist(comments, fill=TRUE))
comments <-comments[,c(5,6,11)]
comments$textDisplay<-sapply(1:length(comments[,1]),function(x)iconv(comments$textDisplay[x], to="UTF-8", sub="byte"))
sentiments<-lapply(comments$textDisplay,function(x)tryCatch(sentiment(x),error=function(e)NULL))
sentiments <- as.data.frame(rbindlist(sentiments, fill=TRUE))
#Cálculo la polaridad:
sentiments <- sentiment(comments$textDisplay)
sentimentsPolarity<-as.data.frame(round(prop.table(table(sentiments$polarity))*100,1))
### Linea de tiempo ###
sentiments$score <- 0
sentiments$score[sentiments$polarity == "positive"] <- 1
sentiments$score[sentiments$polarity == "negative"] <- -1
sentiments$score[sentiments$polarity == "neutral"] <- 0
sentiments$date <- as.IDate(comments$publishedAt)
result <- aggregate(score ~ date, data = sentiments, sum)
### AC/DC ###
# Información del Canal:
b <- list_channel_resources(filter = c(channel_id = "UCmPuJ2BltKsGE2966jLgCnw"), part="contentDetails")
# Id de la lista de videos:
b_playlist_id <- b$items[[1]]$contentDetails$relatedPlaylists$uploads
# Descarga de los videos en la lista
b_vids <- get_playlist_items(filter= c(playlist_id=b_playlist_id))
# Ids de los Videos
b_mytest <- sapply(b_vids$items, "[", "contentDetails")
# Numero de videos en la lista
b_n <- length(mytest)
#Estadisticas de los videos
b_statistics <- data.frame(rbindlist(lapply(1:b_n,function(x) data.frame(get_stats(b_mytest[x]$contentDetails$videoId))),fill=TRUE))
#Informacion adicional de los videos
b_details <- lapply(1:n,function(x) get_video_details(b_mytest[x]$contentDetails$videoId))
#Pasando a data.frame y pasando a numeric las columnas que son factor
b_nombre <- colnames(b_statistics)
b_statistics[,2:6] <- sapply(b_statistics[,2:6],function(x)as.numeric(as.character(x)))
colnames(b_statistics) <- b_nombre
b_statistics[is.na(b_statistics)]<-0
#Adicionando información
for( i in 1:n){
b_statistics$title[i] <-b_details[[i]]$title
b_statistics$publishedAt[i] <-b_details[[i]]$publishedAt
b_statistics$channelId[i] <-b_details[[i]]$channelId
b_statistics$channelTitle[i] <-b_details[[i]]$channelTitle
b_statistics$defaultAudioLanguage[i] <-ifelse(is.null(b_details[[i]]$defaultAudioLanguage),"NA",b_details[[i]]$defaultAudioLanguage)
}
### Análisis de sentimientos ###
#Traer los comentarios de los videos:
b_comments <- lapply(1:b_n,function(x) get_comment_threads(c(video_id=b_mytest[x]$contentDetails$videoId),text_format = "plainText"))
b_comments <- as.data.frame(rbindlist(b_comments, fill=TRUE))
b_comments <-b_comments[,c(5,6,11)]
b_comments$textDisplay<-sapply(1:length(b_comments[,1]),function(x)iconv(b_comments$textDisplay[x], to="UTF-8", sub="byte"))
b_sentiments<-lapply(b_comments$textDisplay,function(x)tryCatch(sentiment(x),error=function(e)NULL))
b_sentiments <- as.data.frame(rbindlist(b_sentiments, fill=TRUE))
#Cálculo de la polaridad:
b_sentiments <- b_sentiments[b_sentiments$language=="en",]
b_sentimentsPolarity<-as.data.frame(round(prop.table(table(b_sentiments$polarity))*100,1))
### Linea de tiempo ###
b_sentiments$score <- 0
b_sentiments$score[b_sentiments$polarity == "positive"] <- 1
b_sentiments$score[b_sentiments$polarity == "negative"] <- -1
b_sentiments$score[b_sentiments$polarity == "neutral"] <- 0
b_sentiments$date <- as.IDate(b_comments$publishedAt)
b_result <- aggregate(score ~ date, data = b_sentiments, sum)
### GunsNRosesVEVO ###
# Información del Canal:
d <- list_channel_resources(filter = c(channel_id = "UCJN4c_lZorb_0eyIP_tSS3A"), part="contentDetails")
# Id de la lista de videos:
d_playlist_id <- d$items[[1]]$contentDetails$relatedPlaylists$uploads
# Descarga de los videos en la lista
d_vids <- get_playlist_items(filter= c(playlist_id=d_playlist_id))
# Ids de los Videos
d_mytest <- sapply(d_vids$items, "[", "contentDetails")
#Estadisticas de los videos
d_statistics <- data.frame(rbindlist(lapply(1:d_n,function(x) data.frame(tryCatch(get_stats(d_mytest[x]$contentDetails$videoId),error=function(e) NULL))),fill=TRUE))
#Informacion adicional de los videos
d_details <- lapply(1:n,function(x) tryCatch(get_video_details(d_mytest[x]$contentDetails$videoId),error=function(e) NULL))
#Pasando a data.frame y pasando a numeric las columnas que son factor
d_nombre <- colnames(d_statistics)
d_statistics[,2:6] <- sapply(d_statistics[,2:6],function(x)as.numeric(as.character(x)))
colnames(d_statistics) <- d_nombre
d_statistics[is.na(d_statistics)]<-0
# Numero de videos en la lista
d_n<-length(d_statistics[,1])
#Adicionando información
for( i in 1:d_n){
d_statistics$title[i] <-d_details[[i]]$title
d_statistics$publishedAt[i] <-d_details[[i]]$publishedAt
d_statistics$channelId[i] <-d_details[[i]]$channelId
d_statistics$channelTitle[i] <-d_details[[i]]$channelTitle
d_statistics$defaultAudioLanguage[i] <-ifelse(is.null(d_details[[i]]$defaultAudioLanguage),"NA",d_details[[i]]$defaultAudioLanguage)
}
### Análisis de sentimientos ###
#Traer los comentarios de los videos:
d_comments <- lapply(1:d_n,function(x) get_comment_threads(c(video_id=d_mytest[x]$contentDetails$videoId)))
d_comments <- as.data.frame(rbindlist(d_comments, fill=TRUE))
d_comments <-d_comments[,c(5,6,11)]
d_comments$textDisplay<-sapply(1:length(d_comments[,1]),function(x)iconv(d_comments$textDisplay[x], to="UTF-8", sub="byte"))
d_sentiments<-lapply(d_comments$textDisplay,function(x)tryCatch(sentiment(x),error=function(e) NULL))
d_sentiments <- as.data.frame(rbindlist(d_sentiments, fill=TRUE))
#Cálculo de la polarilidad:
d_sentiments <- d_sentiments[d_sentiments$language=="en",]
d_sentimentsPolarity<-as.data.frame(round(prop.table(table(d_sentiments$polarity))*100,1))
### Linea de tiempo ###
d_sentiments$score <- 0
d_sentiments$score[d_sentiments$polarity == "positive"] <- 1
d_sentiments$score[d_sentiments$polarity == "negative"] <- -1
d_sentiments$score[d_sentiments$polarity == "neutral"] <- 0
d_sentiments$date <- as.IDate(d_comments$publishedAt)
d_result <- aggregate(score ~ date, data = d_sentiments, sum)
### Queen ###
# Información del Canal:
e <- list_channel_resources(filter = c(channel_id = "UCiMhD4jzUqG-IgPzUmmytRQ"), part="contentDetails")
# Id de la lista de videos:
e_playlist_id <- e$items[[1]]$contentDetails$relatedPlaylists$uploads
# Descarga de los videos en la lista:
e_vids <- get_playlist_items(filter= c(playlist_id=e_playlist_id))
# Ids de los Videos:
e_mytest <- sapply(e_vids$items, "[", "contentDetails")
e_n<-length(e_mytest)
#Estadisticas de los videos:
e_statistics <- data.frame(rbindlist(lapply(1:e_n,function(x) data.frame(tryCatch(get_stats(e_mytest[x]$contentDetails$videoId),error=function(e) NULL))),fill=TRUE))
#Informacion adicional de los videos:
e_details <- lapply(1:e_n,function(x) tryCatch(get_video_details(e_mytest[x]$contentDetails$videoId),error=function(e) NULL))
#Pasando a data.frame y pasando a numeric las columnas que son factor:
e_nombre <- colnames(e_statistics)
e_statistics[,2:6] <- sapply(e_statistics[,2:6],function(x)as.numeric(as.character(x)))
colnames(e_statistics) <- e_nombre
e_statistics[is.na(e_statistics)]<-0
# Numero de videos en la lista:
e_n<-length(e_statistics[,1])
#Adicionando informacón:
for( i in 1:e_n){
e_statistics$title[i] <-e_details[[i]]$title
e_statistics$publishedAt[i] <-e_details[[i]]$publishedAt
e_statistics$channelId[i] <-e_details[[i]]$channelId
e_statistics$channelTitle[i] <-e_details[[i]]$channelTitle
e_statistics$defaultAudioLanguage[i] <-ifelse(is.null(e_details[[i]]$defaultAudioLanguage),"NA",e_details[[i]]$defaultAudioLanguage)
}
### Análisis de sentimientos ###
#Traer los comentarios de los videos:
e_comments <- lapply(1:e_n,function(x) get_comment_threads(c(video_id=e_mytest[x]$contentDetails$videoId)))
e_comments <- as.data.frame(rbindlist(e_comments, fill=TRUE))
e_comments <-e_comments[,c(5,6,11)]
e_comments$textDisplay<-sapply(1:length(e_comments[,1]),function(x)iconv(e_comments$textDisplay[x], to="UTF-8", sub="byte"))
e_sentiments<-sapply(e_comments$textDisplay,function(x)tryCatch(sentiment(x),error=function(e)NULL))
e_sentiments <- as.data.frame(rbindlist(e_sentiments, fill=TRUE))
#Cálculo de la polaridad:
e_sentiments <- e_sentiments[e_sentiments$language=="en",]
e_sentimentsPolarity<-as.data.frame(round(prop.table(table(e_sentiments$polarity))*100,1))
### Linea de tiempo ###
e_sentiments$score <- 0
e_sentiments$score[e_sentiments$polarity == "positive"] <- 1
e_sentiments$score[e_sentiments$polarity == "negative"] <- -1
e_sentiments$score[e_sentiments$polarity == "neutral"] <- 0
e_sentiments$date <- as.IDate(e_comments$publishedAt)
e_result <- aggregate(score ~ date, data = e_sentiments, sum)
### Metallica ###
# Información del Canal:
f <- list_channel_resources(filter = c(channel_id = "UCbulh9WdLtEXiooRcYK7SWw"), part="contentDetails")
# Id de la lista de videos:
f_playlist_id <- f$items[[1]]$contentDetails$relatedPlaylists$uploads
# Descarga de los videos en la lista:
f_vids <- get_playlist_items(filter= c(playlist_id=f_playlist_id))
# Ids de los Videos:
f_mytest <- sapply(f_vids$items, "[", "contentDetails")
f_n<-length(f_mytest)
#Estadisticas de los videos
f_statistics <- data.frame(rbindlist(lapply(1:f_n,function(x) data.frame(tryCatch(get_stats(f_mytest[x]$contentDetails$videoId),error=function(e) NULL))),fill=TRUE))
#Informacion adicional de los videos
f_details <- lapply(1:f_n,function(x) tryCatch(get_video_details(f_mytest[x]$contentDetails$videoId),error=function(e) NULL))
#Pasando a data.frame y pasando a numeric las columnas que son factor
f_nombre <- colnames(f_statistics)
f_statistics[,2:6] <- sapply(f_statistics[,2:6],function(x)as.numeric(as.character(x)))
colnames(f_statistics) <- f_nombre
f_statistics[is.na(f_statistics)]<-0
# Numero de videos en la lista
f_n<-length(f_statistics[,1])
#Adicionando información
for( i in 1:f_n){
f_statistics$title[i] <-f_details[[i]]$title
f_statistics$publishedAt[i] <-f_details[[i]]$publishedAt
f_statistics$channelId[i] <-f_details[[i]]$channelId
f_statistics$channelTitle[i] <-f_details[[i]]$channelTitle
f_statistics$defaultAudioLanguage[i] <-ifelse(is.null(f_details[[i]]$defaultAudioLanguage),"NA",f_details[[i]]$defaultAudioLanguage)
}
### Análisis de sentimientos ###
#Traer los comentarios de los videos:
f_comments <- lapply(1:f_n,function(x) get_comment_threads(c(video_id=f_mytest[x]$contentDetails$videoId)))
f_comments <- as.data.frame(rbindlist(f_comments, fill=TRUE))
f_comments <-f_comments[,c(5,6,11)]
f_comments$textDisplay<-sapply(1:length(f_comments[,1]),function(x)iconv(f_comments$textDisplay[x], to="UTF-8", sub="byte"))
f_sentiments<-lapply(f_comments$textDisplay,function(x)tryCatch(sentiment(x),error=function(e)NULL))
f_sentiments <- as.data.frame(rbindlist(f_sentiments, fill=TRUE))
#Cálculo de la polaridad:
f_sentiments <- f_sentiments[f_sentiments$language=="en",]
f_sentimentsPolarity<-as.data.frame(round(prop.table(table(f_sentiments$polarity))*100,1))
### Linea de tiempo ###
f_sentiments$score <- 0
f_sentiments$score[f_sentiments$polarity == "positive"] <- 1
f_sentiments$score[f_sentiments$polarity == "negative"] <- -1
f_sentiments$score[f_sentiments$polarity == "neutral"] <- 0
f_sentiments$date <- as.IDate(f_comments$publishedAt)
f_result <- aggregate(score ~ date, data = f_sentiments, sum)
#statistics <- data.frame(sapply(1:6,function(x) unlist(statistics[,x])))
### Outliers ###
### Iron_Maiden viewCount outliers ###
statistics_viewCount_out<- boxplot.stats(statistics$viewCount)$out
statistics_viewCount_box<- setdiff(statistics$viewCount, statistics_viewCount_out )
### AcDc viewCount outliers ###
b_statistics_viewCount_out<- boxplot.stats(b_statistics$viewCount)$out
b_statistics_viewCount_box<- setdiff(b_statistics$viewCount, b_statistics_viewCount_out )
### GunsNRoses viewCount outliers ###
d_statistics_viewCount_out<- boxplot.stats(d_statistics$viewCount)$out
d_statistics_viewCount_box<- setdiff(d_statistics$viewCount, d_statistics_viewCount_out )
### Queen viewCount outliers ###
e_statistics_viewCount_out<- boxplot.stats(e_statistics$viewCount)$out
e_statistics_viewCount_box<- setdiff(e_statistics$viewCount, e_statistics_viewCount_out )
### Metallica viewCount outliers ###
f_statistics_viewCount_out<- boxplot.stats(f_statistics$viewCount)$out
f_statistics_viewCount_box<- setdiff(f_statistics$viewCount, f_statistics_viewCount_out )
### Iron_Maiden likeCount outliers ###
statistics_likeCount_out<- boxplot.stats(statistics$likeCount)$out
statistics_likeCount_box<- setdiff(statistics$likeCount, statistics_likeCount_out )
### AcDc likeCount outliers ###
b_statistics_likeCount_out<- boxplot.stats(b_statistics$likeCount)$out
b_statistics_likeCount_box<- setdiff(b_statistics$likeCount, b_statistics_likeCount_out )
### GunsNRoses likeCount outliers ###
d_statistics_likeCount_out<- boxplot.stats(d_statistics$likeCount)$out
d_statistics_likeCount_box<- setdiff(d_statistics$likeCount, d_statistics_likeCount_out )
### Queen likeCount outliers ###
e_statistics_likeCount_out<- boxplot.stats(e_statistics$likeCount)$out
e_statistics_likeCount_box<- setdiff(e_statistics$likeCount, e_statistics_likeCount_out )
### Metallica likeCount outliers ###
f_statistics_likeCount_out<- boxplot.stats(f_statistics$likeCount)$out
f_statistics_likeCount_box<- setdiff(f_statistics$likeCount, f_statistics_likeCount_out )
### Iron_Maiden dislikeCount outliers ###
statistics_dislikeCount_out<- boxplot.stats(statistics$dislikeCount)$out
statistics_dislikeCount_box<- setdiff(statistics$dislikeCount, statistics_dislikeCount_out )
### AcDc dislikeCount outliers ###
b_statistics_dislikeCount_out<- boxplot.stats(b_statistics$dislikeCount)$out
b_statistics_dislikeCount_box<- setdiff(b_statistics$dislikeCount, b_statistics_dislikeCount_out )
### GunsNRoses dislikeCount outliers ###
d_statistics_dislikeCount_out<- boxplot.stats(d_statistics$dislikeCount)$out
d_statistics_dislikeCount_box<- setdiff(d_statistics$dislikeCount, d_statistics_dislikeCount_out )
### Queen dislikeCount outliers ###
e_statistics_dislikeCount_out<- boxplot.stats(e_statistics$dislikeCount)$out
e_statistics_dislikeCount_box<- setdiff(e_statistics$dislikeCount, e_statistics_dislikeCount_out )
### Metallica dislikeCount outliers ###
f_statistics_dislikeCount_out<- boxplot.stats(f_statistics$dislikeCount)$out
f_statistics_dislikeCount_box<- setdiff(f_statistics$dislikeCount, f_statistics_dislikeCount_out )
### Iron_Maiden commentCount outliers ###
statistics_commentCount_out<- boxplot.stats(statistics$commentCount)$out
statistics_commentCount_box<- setdiff(statistics$commentCount, statistics_commentCount_out )
### AcDc commentCount outliers ###
b_statistics_commentCount_out<- boxplot.stats(b_statistics$commentCount)$out
b_statistics_commentCount_box<- setdiff(b_statistics$commentCount, b_statistics_commentCount_out )
### GunsNRoses commentCount outliers ###
d_statistics_commentCount_out<- boxplot.stats(d_statistics$commentCount)$out
d_statistics_commentCount_box<- setdiff(d_statistics$commentCount, d_statistics_commentCount_out )
### Queen commentCount outliers ###
e_statistics_commentCount_out<- boxplot.stats(e_statistics$commentCount)$out
e_statistics_commentCount_box<- setdiff(e_statistics$commentCount, e_statistics_commentCount_out )
### Metallica commentCount outliers ###
f_statistics_commentCount_out<- boxplot.stats(f_statistics$commentCount)$out
f_statistics_commentCount_box<- setdiff(f_statistics$commentCount, f_statistics_commentCount_out )
DataToDash <- list(
# Información para los Bloxplot#
viewCount = list(Iron_Maiden = c(min(statistics_viewCount_box), quantile(statistics_viewCount_box,c(0.25,0.50,0.75)), max(statistics_viewCount_box)),
AcDc = c(min(b_statistics_viewCount_box),quantile(b_statistics_viewCount_box,c(0.25,0.50,0.75)),max(b_statistics_viewCount_box)),
GunsNRoses = c(min(d_statistics_viewCount_box),quantile(d_statistics_viewCount_box,c(0.25,0.50,0.75)),max(d_statistics_viewCount_box)),
Queen = c(min(e_statistics_viewCount_box),quantile(e_statistics_viewCount_box,c(0.25,0.50,0.75)),max(e_statistics_viewCount_box)),
Metallica = c(min(f_statistics_viewCount_box),quantile(f_statistics_viewCount_box,c(0.25,0.50,0.75)),max(f_statistics_viewCount_box))
),
likeCount = list(Iron_Maiden = c(min(statistics_likeCount_box), quantile(statistics_likeCount_box,c(0.25,0.50,0.75)), max(statistics_likeCount_box)),
AcDc = c(min(b_statistics_likeCount_box),quantile(b_statistics_likeCount_box,c(0.25,0.50,0.75)),max(b_statistics_likeCount_box)),
GunsNRoses = c(min(d_statistics_likeCount_box),quantile(d_statistics_likeCount_box,c(0.25,0.50,0.75)),max(d_statistics_likeCount_box)),
Queen = c(min(e_statistics_likeCount_box),quantile(e_statistics_likeCount_box,c(0.25,0.50,0.75)),max(e_statistics_likeCount_box)),
Metallica = c(min(f_statistics_likeCount_box),quantile(f_statistics_likeCount_box,c(0.25,0.50,0.75)),max(f_statistics_likeCount_box))
),
dislikeCount = list(Iron_Maiden = c(min(statistics_dislikeCount_box), quantile(statistics_dislikeCount_box,c(0.25,0.50,0.75)), max(statistics_dislikeCount_box)),
AcDc = c(min(b_statistics_dislikeCount_box),quantile(b_statistics_dislikeCount_box,c(0.25,0.50,0.75)),max(b_statistics_dislikeCount_box)),
GunsNRoses = c(min(d_statistics_dislikeCount_box),quantile(d_statistics_dislikeCount_box,c(0.25,0.50,0.75)),max(d_statistics_dislikeCount_box)),
Queen = c(min(e_statistics_dislikeCount_box),quantile(e_statistics_dislikeCount_box,c(0.25,0.50,0.75)),max(e_statistics_dislikeCount_box)),
Metallica = c(min(f_statistics$dislikeCount),quantile(f_statistics$dislikeCount,c(0.25,0.50,0.75)),max(f_statistics$dislikeCount))
),
commentCount = list(Iron_Maiden = c(min(statistics_commentCount_box), quantile(statistics_commentCount_box,c(0.25,0.50,0.75)), max(statistics_commentCount_box)),
AcDc = c(min(b_statistics_commentCount_box),quantile(b_statistics_commentCount_box,c(0.25,0.50,0.75)),max(b_statistics_commentCount_box)),
GunsNRoses = c(min(d_statistics$commentCount),quantile(d_statistics$commentCount,c(0.25,0.50,0.75)),max(d_statistics$commentCount)),
Queen = c(min(e_statistics$commentCount),quantile(e_statistics$commentCount,c(0.25,0.50,0.75)),max(e_statistics$commentCount)),
Metallica = c(min(f_statistics$commentCount),quantile(f_statistics$commentCount,c(0.25,0.50,0.75)),max(f_statistics$commentCount))
),
viewCount_out = c(lapply(1:length(statistics_viewCount_out),function(x) c(0, statistics_viewCount_out[x])),
lapply(1:length(b_statistics_viewCount_out),function(x) c(1, b_statistics_viewCount_out[x])),
lapply(1:length(d_statistics_viewCount_out),function(x) c(2, d_statistics_viewCount_out[x])),
lapply(1:length(e_statistics_viewCount_out),function(x) c(3, e_statistics_viewCount_out[x])),
lapply(1:length(f_statistics_viewCount_out),function(x) c(4, f_statistics_viewCount_out[x]))
),
# Polaridad en lo Comentarios #
AC_DC = b_sentimentsPolarity,
GunsNRoses = d_sentimentsPolarity,
Queen = e_sentimentsPolarity,
Metallica = f_sentimentsPolarity
)
### Enviando los datos para app.analystats ###
### Url de la aplicación ###
url<-"https://app.analystats.com/api/24421902328004610/topics/26/content/upload"
### Enviando los datos para app.analystats:
r <- POST(url, body = DataToDash, encode = "json",authenticate("User","pass"))Puedes ver Dash-Board on-line AnalyStats => Go to Demo => YouTube DashBoard/Ver. Si estas interesado en probar nuestra app escribenos a [email protected].