Ley D'hont

Nescanos <- 57
PorMin <- 0.05
df <- data.frame( Carmena = .284, PP = .219, Ciudantans = .218, PSOE = .137, Franco = .082, IU = 0.023, Total = 1642898)
df <- df[ , df >= PorMin ]
df <- df[ , order(df, decreasing = TRUE)]
df <- df$Total * df
df$PP <- df$PP + 3105
# EscañoPara
# Carmena Ciudantans     Franco         PP       PSOE
#      18         13          5         13          8

df$Total <- rm()
EscanosDF <- df
for( i in 2:Nescanos ){
  EscanosDF <- rbind( EscanosDF , df/i)
}


EscañoPara <-  c()

i <- 1 +1
for( i in 1:Nescanos){
  cat("repartiendo escaño ", i, "\n")
  print( sapply(X = EscanosDF, FUN = max, na.rm = TRUE) )
  print( sapply(X = EscanosDF, FUN = function(x){  max(  length( x [ is.na(x) == TRUE]  )  ) + 1  }) )
  Maximo <- max( EscanosDF , na.rm = TRUE)[1]
  Fila <- which( apply(X = EscanosDF, MARGIN = 1, FUN = function(x){  any(x == Maximo)}) )
  Fila <- Fila[ length(Fila)]
  Columna <- which.max( EscanosDF[Fila,  ] )[1]
  nombrePartido <- names(Columna)
  EscanosDF[ Fila, Columna] <- NA
  EscañoPara <- c(EscañoPara, nombrePartido )
  cat("Escaño Para", nombrePartido, "\n")

}

table( EscañoPara)
EscañoPara

Paseando por shiny dygraph y R Markdown

Dy grpah es una de las librearias más diveridas vamos a ver como generar grupo de gráficos, tablas y grafico interactivos con markdown y shiny


---
title: "Pruebas Shiny"
author: "jvigo6n"
date: "21 de enero de 2020"
output: html_document
  # ioslides_presentation:
  #   keep_md: yes
  #   widescreen: yes
runtime: shiny
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```

# La bolse y R (Paquete quantMode) {.tabset }
Vamos a hacer un mardown para tradear, para ver los nombres de las compañias aquí [YahooFinanciero](https://finance.yahoo.com/quote/TEF?p=TEF&.tsrc=fin-srch  ).

## Genera Grupos de dygraph {.tabset }
```{r dygraphs sincro , echo=FALSE, error=FALSE, warning= FALSE, message=FALSE}
# https://finance.yahoo.com/quote/TEF?p=TEF&.tsrc=fin-srch
library(dplyr)
library(dygraphs)
library(DT)
library(quantmod)

tickers <- c("AAPL", "MSFT", "IBM", "TEF")
getSymbols(tickers)
closePrices <- do.call(merge, lapply(tickers, function(x) Cl(get(x))))
dateWindow <- c("2008-01-01", "2009-01-01")

dygraph(closePrices, main = "Value", group = "stock") %>%
  dyRebase(value = 100) %>%
  dyRangeSelector(dateWindow = dateWindow)

dygraph(closePrices, main = "Percent", group = "stock") %>%
  dyRebase(percent = TRUE) %>%
  dyRangeSelector(dateWindow = dateWindow)

dygraph(closePrices, main = "None", group = "stock2") %>%
  dyRangeSelector(dateWindow = dateWindow)


```

## Interactive Table {.tabset }
```{r dygraphs}
# https://shiny.rstudio.com/gallery/basic-datatable.html
closePrices2 <- closePrices %>% data.frame()
closePrices2$Fecha <- index(closePrices)
closePrices2 <-
  closePrices2[ , c("Fecha", names(closePrices2) [ names(closePrices2) != "Fecha"] )]
row.names(closePrices2) <- rm()
# closePrices2

shinyApp(
  ui = fluidPage(
    selectInput("compania", "Companyia:",
                c("APPLE" = "AAPL.Close",
                  "Microsft" = "MSFT.Close",
                  "IBM" = "IBM.Close",
                  "Telefonica" = "TEF.Close"), state.name, multiple= T, selectize=T) 
    , tableOutput("data")
    , dataTableOutput("DT")
  ),
  server = function(input, output) {
    output$data <-  renderTable({
      if( length(input$compania)> 0 ){
        x <- closePrices2[ ,   c("Fecha" ,names(closePrices2)[ names(closePrices2) %in% 
                                                         input$compania
                                                          # "TEF.Close"
                                                         ] ) ]
        x <- x[order(x[,2], decreasing = TRUE),][1:10,]
        x$Fecha  <- x$Fecha %>%  as.Date %>% as.character()
        x
      }else{
        closePrices2[ !1:nrow(closePrices2),]
      }
      }, rownames = FALSE)
    output$DT <-  renderDataTable({
      if( length(input$compania)> 0 ){
        x <- closePrices2[ ,  names(closePrices2) %in% c("Fecha", input$compania)]
        x <- x[order(x[,1], decreasing = TRUE),]
        x
      }else{
        x <- closePrices2
        x <- x[ !1:nrow(closePrices2),]
        x
      }})
        
  })
```

## Shiny {.tabset }
```{r Shiny}
closePrices3 <- closePrices
# closePrices2

plotdyg <- function(dataPlot){
  dygraph(dataPlot, main = "None", group = "stock") %>%
  dyRangeSelector(dateWindow = dateWindow)
}

shinyApp(
  ui =  fluidPage(
    selectInput("compania", "Companyia:",
                c("APPLE" = "AAPL.Close",
                  "Microsft" = "MSFT.Close",
                  "IBM" = "IBM.Close",
                  "Telefonica" = "TEF.Close"), state.name, multiple= T, selectize=T)
    , dygraphOutput("dyg")
    , dygraphOutput("dyg2")
    # , dataTableOutput("DT")
  ),
  server = function(input, output) {
    output$dyg <-  renderDygraph({
      if( length(input$compania)> 0 ){
        x <- closePrices3[ , names(closePrices3) %in% input$compania ]
        plotdyg( x )
      }else{
        plotdyg( closePrices3[ !1:nrow(closePrices2),] )
      }
      })
    output$dyg2 <-  renderDygraph({plotdyg( closePrices3 )})
  })
```


## Interactive Plot {.tabset }
```{r dygraphs2}
# https://shiny.rstudio.com/gallery/basic-datatable.html
closePrices4 <- closePrices
# closePrices2

plotdyg <- function(dataPlot){
  dygraph(dataPlot, main = "None", group = "stock") %>%
  dyRangeSelector(dateWindow = dateWindow)
}

selectInput(inputId = "compania", label = "Companyia:",
            choices = c("APPLE" = "AAPL.Close",
                        "Microsft" = "MSFT.Close",
                        "IBM" = "IBM.Close",
                        "Telefonica" = "TEF.Close"), state.name, multiple= T, selectize=T) 

renderDygraph({
  if( length(input$compania)> 0 ){
    x <- closePrices3[ , names(closePrices3) %in% input$compania ] 
    plotdyg( x )
    }else{
      plotdyg( closePrices3[ !1:nrow(closePrices2),] )
      }
  })
renderDygraph({plotdyg( closePrices3 )})  


sliderInput("bins", "Number of bins:", min = 1, max = 50, value = 30)

renderPlot({
  x <- faithful[, 2]  # Old Faithful Geyser data
  bins <- seq(min(x), max(x), length.out = input$bins + 1)

  # draw the histogram with the specified number of bins
  hist(x, breaks = bins, col = 'darkgray', border = 'white')
})
```

Gini para continuas volumen 2

GiniFunc <-
  function(a, p){
    df <- data.frame(a = a, p = p)
    df <- df[ order(a, decreasing = FALSE),]
    summary(df)
    df2 <- data.frame(a = df$a  , p = cumsum( df$p  ))
   
    df3 <- df2
    df3$real <- df2$a/max(df2$a)
    df3$prediccion <- df2$p/max(df2$p)
    df3$a <- rm()
    df3$p <- rm()
    df3 <- rbind( data.frame(real =0, prediccion =0), df3)
    summary(df3)
    plot(x = df3$real, y = df3$prediccion , type = "l", col = "red")
    abline(a = 0, b = 1 )
   
    df3 <- df3[ !duplicated(df3) ,]
    # print(df3)
    # se resta la x y se suma la y. Es un trapezoide con base x[i+1] - x[i] y altura
    giniDF  <- data.frame( realEjeX = df3$real - Hmisc::Lag(df3$real, shift = 1)
                           , prediccionlEjeY_i = df3$prediccion
                           ,  prediccionlEjeY_iMenos1 =Hmisc::Lag(df3$prediccion, shift = 1)
                         
    )
    giniDF$prediccionlEjeY_min <- apply(X = giniDF[, c("prediccionlEjeY_i", "prediccionlEjeY_iMenos1")], MARGIN = 1, FUN = min, na.rm = TRUE)
    giniDF$prediccionlEjeY_max <- apply(X = giniDF[, c("prediccionlEjeY_i", "prediccionlEjeY_iMenos1")], MARGIN = 1, FUN = max, na.rm = TRUE)
   
    giniDF$prediccionlEjeY_alturaRectangulo <- giniDF$prediccionlEjeY_min
    giniDF$prediccionlEjeY_alturaTriangulo <-  giniDF$prediccionlEjeY_max - giniDF$prediccionlEjeY_min
    x <- giniDF$realEjeX * giniDF$prediccionlEjeY_alturaRectangulo + # Area rectangulo
      (giniDF$realEjeX * giniDF$prediccionlEjeY_alturaTriangulo)/2 # Area triangulo
    AUC <-  sum(x, na.rm = TRUE) # Sumo todas las areas
    return( list( AUC = AUC, Gini = abs(2 *AUC - 1), BaseDatosOrigianl = df, BasesDatosProporciones = df3) )
  }

# 10 personas con riqueza parecida
z <- GiniFunc(a = 1:10, p = 100 +  1:10*10 )
z$Gini
z$AUC
# Gini 0

# 10 personas con la misma riqueza y una con mucha
z <- GiniFunc(a = rep(1,10), p = rep(10, 1e4) )
z$Gini
z$AUC
# Gini 0.9

# Relación entre uniformes
z <- GiniFunc(a = runif(1e3), p = runif(1e3) )
z$Gini
z$AUC
# Gini  0

# 10 personas con riqueza creciente o dos variables con el mismo valor
z <- GiniFunc(a = 1:10, p = 1:10)
z$Gini
z$AUC


# Una persona con toda la riqueza
N <- 100000
GiniFunc(a = rep(1, N), p = c(rep(0, N-1), 10))$AUC
GiniFunc(a = c(rep(0, N-1), 10), p = rep(1, N))$Gini
# Gini 0.99

# El caso de las dos uniformes
set.seed(123456)
x <-runif(1e3)
set.seed(123457)
y <-runif(1e3)
z <- GiniFunc(a =x , p = y)
z$Gini
z$AUC
# Gini 0

# Para modelos que es lo que importa.Aquí funciona como debería. Se compara la variable a predecir con la predicción
set.seed(123456)
x <-runif(1e3)
y <-  2* x + 3 + rnorm(1e3, mean = 0, sd = 0.01)
modelo <- lm( formula = y ~ x)
summary(modelo)
y_predict <- predict(object = modelo, newdata = data.frame(x))
z <- GiniFunc(a =y , p = y_predict)
z$Gini
z$AUC
cor(y, y_predict)
plot(y, y_predict)
abline(a = 0, b = 1, col = "red", lwd = 4)
# Gini 0.63

set.seed(123456)
x <- mtcars$disp
y <-  mtcars$mpg
modelo <- lm( formula = y ~ x)
summary(modelo)
y_predict <- predict(object = modelo, newdata = data.frame(x))
z <- GiniFunc(a =y , p = y_predict)
abline( v = 0.65, col = "blue", lwd = 3 )
z$Gini
z$AUC
cor(y, y_predict)
plot(y, y_predict)
abline(a = 0, b = 1, col = "red", lwd = 4)
x <- y[ order( y )]
x <- cumsum(x)
x <- x/ max(x)
y[order(y)][ which( x > 0.65 )[1] ]
abline( v = y[order(y)][ which( x > 0.65 )[1] ],  col = "blue", lwd = 3)
title("Los dos dejan de predecir en el mismo sitio")
# Gini 0.23 porque falla al final

CV para xgboost

ROC <- function( ModelProb , test, SAVE = F, LABEL = "", DIR= "") {
  require ('ROCR')
  require ('rpart')

  if(SAVE){
    if(DIR != "" ){
      if(substr(x = DIR, start = nchar(DIR), stop = nchar(DIR))  != "/"  ){
        DIR <- paste0(DIR,"/")
      }     
    }
    jpeg(filename = paste0(DIR ,LABEL,"ROC.jpeg") , width = 20, height = 20, units = "cm", res = 250, bg = "white" )
    par(las = 1)
  }
  pred <- prediction (ModelProb,  test [,ncol(test)])
  perf <- performance (pred, 'tpr', 'fpr')
  gini <- (unlist (performance (pred, measure = 'auc')@y.values) - .5) / .5
  ks <- max (unlist (perf@y.values) - unlist (perf@x.values))
  plot (x= 100 * unlist (perf@x.values), y = 100 * unlist (perf@y.values), type= 'l', lwd = 3
        , col =2, xaxs = 'i', yaxs = 'i', yaxt = 'n', xlab = '% falsos positivos', ylab = '% verdaderos positivos')
  abline (a=0, b=1, lty = 5)
  legend ('bottomright', 'hi', paste0 ('Gini = ', round (100*gini, 1), '%\n', 'Ks = ', round (100*ks,1), '%\n\n'))
  if(SAVE){
    dev.off()
  }
  return( list(gini = gini , ks = ks, aucDf = data.frame( real = 100 * unlist (perf@x.values), y = 100 * unlist (perf@y.values))))
}

FuncionCV <-
  function( dtrain_dt, labelVector
          , PosEta   = seq( from = 0.15, to = 0.35, by = 0.1)
          , PosDep   = seq( from = 1, to = 5, by = 2)
          , PosLoops = seq( from = 200, to = 300, by = 50)
          , objective = "binary:logistic"
          , nfold = 10, missing = "NAN", eval_metric = "auc"){

  ConfiDep <- c()
  ConfiLoops <- c()
  ConfiEta <- c()
  AUCres <- c()
  j <- 1


  for(loops in PosLoops){
    cat( "\n=====================\n", loops, "\n=====================\n" )
    for(maxD in PosDep){
      for(Ceta in PosEta){
     
        model_check <- xgb.cv (data = dtrain,
                               label = label,
                               nrounds = loops,
                               eta = Ceta,
                               max_depth = maxD,
                               nfold = nfold, # Con 10 es que coge uno a 10.
                               objective = objective,
                               eval_metric = eval_metric,
                               pred = TRUE,
                               missing = missing)
     
        val <- glmnet::auc( y = label
                            , prob = model_check$pred)
     
        ROC(ModelProb = model_check$pred, test = data.frame(label), SAVE = FALSE )
        title( paste0( "eta = ", Ceta, ", max_depth =", maxD,", nrounds = ",loops ) )
     
        AUCres[j] <- val
        ConfiDep[j] <- maxD
        ConfiEta[j] <- Ceta
        ConfiLoops[j] <- loops
        j <- j + 1
      }
    }
  }

  Resumen <-
    data.frame( AUCres = AUCres
              , GiniRes = AUCres *2 -1
              , ConfiDep = ConfiDep
              , ConfiEta = ConfiEta
              , ConfiLoops = ConfiLoops)

  return( Resumen)
}

Gini para continuas

El gini para continuas es un poco puñetero. Se puede generar un Gini de riqueza para simular el Gini, el problema es que las variables tienen que estar en la misma proporción.



GiniFunc <-
  function(a, p){
    df <- data.frame(a = a, p = p)
    df <- df[ order(a),]
    summary(df)
    df2 <- data.frame(a = cumsum( df$a  ), p = cumsum( df$p  ))
    cumsum( df$a  )[1:10]
    cumsum( df$p  )[1:10]
    plot(    cumsum( df$a  )[1:100],
             cumsum( df$p  )[1:100], type = "l")
    abline(a = 0, b = 1)
   
   
    df3 <- df2
    df3$real <- df2$a/max(df2$a)
    df3$prediccion <- df2$p/max(df2$p)
    df3$a <- rm()
    df3$p <- rm()
    df3 <- rbind(data.frame(real =0, prediccion =0), df3)
    df3 <- rbind(df3, data.frame(real =1, prediccion =1))
    summary(df3)
    plot(x = df3$real, y = df3$prediccion , type = "l", col = "red")
    abline(a = 0, b = 1 )
   
    df3 <- df3[ !duplicated(df3) ,]
    # print(df3)
    # se resta la x y se suma la y. Es un trapezoide con base x[i+1] - x[i] y altura
    giniDF  <- data.frame( realEjeX = df3$real - Hmisc::Lag(df3$real, shift = 1)
                           , prediccionlEjeY_i = df3$prediccion
                           ,  prediccionlEjeY_iMenos1 =Hmisc::Lag(df3$prediccion, shift = 1)
                         
    )
    giniDF$prediccionlEjeY_min <- apply(X = giniDF[, c("prediccionlEjeY_i", "prediccionlEjeY_iMenos1")], MARGIN = 1, FUN = min, na.rm = TRUE)
    giniDF$prediccionlEjeY_max <- apply(X = giniDF[, c("prediccionlEjeY_i", "prediccionlEjeY_iMenos1")], MARGIN = 1, FUN = max, na.rm = TRUE)
   
    giniDF$prediccionlEjeY_alturaRectangulo <- giniDF$prediccionlEjeY_min
    giniDF$prediccionlEjeY_alturaTriangulo <-  giniDF$prediccionlEjeY_max - giniDF$prediccionlEjeY_min
    x <- giniDF$realEjeX * giniDF$prediccionlEjeY_alturaRectangulo + # Area rectangulo
      (giniDF$realEjeX * giniDF$prediccionlEjeY_alturaTriangulo)/2 # Area triangulo
    AUC <-  sum(x, na.rm = TRUE) # Sumo todas las areas
    return( list( GiniCorrelacion  = 1-(2*AUC-1), AUC = AUC, Gini = 2 *AUC - 1, BaseDatosOrigianl = df, BasesDatosProporciones = df3) )
  }

# 10 personas con riqueza parecida
GiniFunc(a = 1:10, p = 100 +  1:10*10 )$GiniCorrelacion
# 10 personas con la misma riqueza
GiniFunc(a = rep(1,10), p = rep(10, 1e4) )$GiniCorrelacion
# 0

GiniFunc(a = runif(1e3), p = runif(1e3) )$GiniCorrelacion

# 10 personas con riqueza creciente o dos variables con el mismo valor
GiniFunc(a = 1:10, p = 1:10)
# 0
# Una persona con toda la riqueza
N <- 100000
GiniFunc(a = rep(1, N), p = c(rep(0, N-1), 10))$AUC
GiniFunc(a = c(rep(0, N-1), 10), p = rep(1, N))$Gini
# 1

# Este es el caso más raro. Con  dos unifermos
set.seed(123456)
x <-runif(1e3)
set.seed(123457)
y <-runif(1e3)
z <- GiniFunc(a =x , p = y)
z$GiniCorrelacion
z$Gini
z$AUC


# Para modelos que es lo que importa.Aquí funciona como debería. Se compara la variable a predecir con la predicción
set.seed(123456)
x <-runif(1e3)
y <-  2* x + 3 + rnorm(1e3, mean = 0, sd = 0.01)
modelo <- lm( formula = y ~ x)
summary(modelo)
y_predict <- predict(object = modelo, newdata = data.frame(x))
z <- GiniFunc(a =y , p = y_predict)
z$GiniCorrelacion
z$Gini
z$AUC
cor(y, y_predict)
plot(y, y_predict)
abline(a = 0, b = 1, col = "red", lwd = 4)


set.seed(123456)
x <- mtcars$disp
y <-  mtcars$mpg
modelo <- lm( formula = y ~ x)
summary(modelo)
y_predict <- predict(object = modelo, newdata = data.frame(x))
z <- GiniFunc(a =y , p = y_predict)
z$GiniCorrelacion
z$Gini
z$AUC
cor(y, y_predict)
plot(y, y_predict)
abline(a = 0, b = 1, col = "red", lwd = 4)

Scrapenado Ips por pais en R

library(rvest)
library(curl)
library(dplyr)
library(data.table)

t0 <-Sys.time()
listaIP <- c(
    "5.188.10.8"
  , "5.62.56.55"
  , "5.62.58.55"
  , "5.9.158.75"
) %>%  unique

tablon <- data.table(ip = as.character(NA), inetnum = as.character(NA), Pais = as.character(NA),descr = as.character(NA)
                     , Tor = as.character(NA), amazon = as.character(NA), microsoft = as.character(NA) )



for( j in listaIP){
  url <- paste0('http://whois.chromefans.org/', j) 
  Pagina <- html(curl(url, handle = curl::new_handle("useragent" = "Mozilla/5.0")))
    # [contains(concat( " ", @class, " " ), concat( " ", "whois_info", " " ))]
  x <- html_nodes(Pagina,'.whois_info')
 
  funcionLimpia <- function(txt){
    txt %>%  gsub(x =  ., pattern = "(?<=[\\s])\\s*|^\\s+|\\s+$", replacement =  "", perl=TRUE) %>%
      gsub(pattern = "[^(a-z)]", replacement = " ", x = . ) %>% trimws
  }
 
 
  PalabrasClave <- html_nodes(x,'strong') %>% html_text() %>% tolower %>% gsub(x =  ., pattern = "(?<=[\\s])\\s*|^\\s+|\\s+$", replacement =  "", perl=TRUE) %>%
    gsub(pattern = "[^(a-z)]", replacement = " ", x = . ) %>% trimws
 
  PalabrasClave2 <- paste0( "(", PalabrasClave, ")")
  PalabrasClave2 <- PalabrasClave
  PalabraClave <- paste(PalabrasClave2, collapse = "|")
 
  a <-
    html_text(x, trim = TRUE) %>% tolower %>%
    gsub(pattern = "\n", replacement = " ", x = . , fixed = TRUE) %>%
   
    gsub(x =  ., pattern = "(?<=[\\s])\\s*|^\\s+|\\s+$", replacement =  "", perl=TRUE) %>%
    strsplit(x = ., split = ":") %>%  unlist()
  a <- a[ a != ""]
 
 
  i <- grep(pattern = "inetnum", x = (a) )[1]
  inetnum <- a[i+1]  %>% gsub(x = .,, pattern = PalabraClave, replacement = "") %>%  toupper %>% trimws

  i <- grep(pattern = "descr", x = (a), fixed = TRUE )[1]
  descr <- a[i+1]  %>% gsub(x = .,, pattern = PalabraClave, replacement = "") %>%  toupper %>% trimws
 
  i <- grep(pattern = "country", x = (a) )[1]
  Pais <- a[i+1] %>%  funcionLimpia %>% gsub(x = .,, pattern = PalabraClave, replacement = "") %>% toupper

  i <- grep(pattern = "tor", x = (a) )
  Tor <- paste0( a[i], collapse = " @CH@ " )

  i <- grep(pattern = "amazon", x = (a) )
  amazon <- paste0( a[i], collapse = " @CH@ " )

  i <- grep(pattern = "microsoft", x = (a) )
  microsoft <- paste0( a[i], collapse = " @CH@ " )
 
  tablon <- rbind( tablon, data.table(ip = j, inetnum = inetnum, Pais = Pais, descr = descr, Tor = Tor, amazon = amazon, microsoft = microsoft))
 
  espera <- ifelse( test = is.na(Pais) , yes = 1, no = sample(x = 5:15, size = 1) )
  cat( "\nEsperamos ", espera,"\n")
  Sys.sleep( time = espera) # segundos de delay para que la web no cante. Total no son muchos
  cat( "\n", j,"terminado\n")
}

cat( "\n==============================================\n")
difftime( time1 = Sys.time(), time2 = t0, units = "m") %>%  as.numeric %>% round(2) %>%
  cat( "El proceso termino en ", ., " minutos\n")
cat( "\n==============================================\n")
tablon <- tablon[ !is.na(ip)]

Descriptivas PySpark

En R es superfacil, Python, para variar, es un infierno. Pero siempre habrá quien defienda ese lenguaje  no solo por su rapidez que la tiene, pero...

def descripcionDF (DFSpark, StringBool = True, timeStampBool = True, floatBool = True):
    tipoVar  = pd.DataFrame( DFSpark.dtypes, columns= ('variable', 'tipo') )
    print( pd.crosstab(index=tipoVar.tipo,  # Make a crosstab
                              columns="count") )
 
    print( "========================================================================")
    print( "Datos String")
    print( "========================================================================")
 
    x = tipoVar.variable [ tipoVar.tipo =='string' ].values.tolist()
    if len(x) > 0 & StringBool:
        for i in x: print( DFSpark.cube(i).count().orderBy(desc('count')).show() )
 
 
    print( "========================================================================")
    print( "Datos timeStamp")
    print( "========================================================================")
    x = tipoVar.variable [ tipoVar.tipo =='timestamp' ].values.tolist()

    if len(x) > 0 & timeStampBool:
        DFSparkFechas = DFSpark.select(x)
        for i in x:
            DFSparkFechas = DFSparkFechas.withColumn(i + '_Fecha',  (year(i)* 1e10 +  month(i)* 1e8 + dayofmonth(i)* 1e6 +\
                                                                     hour(i)* 1e4 + minute(i) * 1e2 + second(i) ).cast(LongType()) )
        print( DFSparkFechas.describe().show() )
 
    print( "========================================================================")
    print( "Datos float")
    print( "========================================================================")
    if len(tipoVar.variable [ tipoVar.tipo =='float' ]) > 0 & floatBool:
        DFSpark.select(tipoVar.variable [ tipoVar.tipo =='float' ]).describe( ).show()