1 Generalidades de R
Resolver problemas de loibrerias en R * se puede usar este comando cuando alguna libreria tiene problemas ( da la direccion para borrar la carpeta he intentar reinstalarlo) + Subpunto
.libPaths()
actualizar la version de R
install.packages(“installr”) library(installr) updateR()
1.1 Cargar librerias
library("XML")
library("tidyverse")## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Consultar donde esta ubicado un archivo
getwd()## [1] "C:/Users/Antonio Constantino/Desktop/Rstudio/Cursos/curso youtube"Como cambiar esa direccion
setwd("C:/Users/Antonio Constantino/Desktop/Rstudio/Cursos/curso youtube/")Como se lee un Csv
Dentro de la funcion read , sep= indica por que simbolo separa los datos * Nota de oro : cuando se tiene los datos separados por un “;” entoces se usa la funcion read.ccv2 y en el caso que este separado por tabuladores , entonces se debe usar dento de la funcion como “sep=”t”
auto1 <-read.csv("../../r-course-master/data/tema1/auto-mpg.csv",header = TRUE,sep = ",")Cuando no tiene encabezado el archivo
sin_encabezado <- read.csv("../../r-course-master/data/tema1/auto-mpg-noheader.csv")Lo que veras es que pone una x de un primer dato ( la coma y el cuato lo que te dice es que solo te muestre las primeras 4 filas )
head(sin_encabezado,4)## X1 X28 X4 X140 X90 X2264 X15.5 X71 chevrolet.vega.2300
## 1 2 19 3 70 97 2330 13.5 72 mazda rx2 coupe
## 2 3 36 4 107 75 2205 14.5 82 honda accord
## 3 4 28 4 97 92 2288 17.0 72 datsun 510 (sw)
## 4 5 21 6 199 90 2648 15.0 70 amc gremlinComo introduzco el encabezado
#Se va a reescribir
sin_encabezado <- read.csv("../../r-course-master/data/tema1/auto-mpg-noheader.csv",header = F,col.names = c("numero","MPG","Cilindros","desplazamiento","Caballos_de_potencia","peso","aceleracion","ano","modelo"))
# aqui lo que haces es :header( carnal no tienes encabezados ),colnames = ( te voy a poner nombre c( estos son los nombres )) Ahora veremos como se observan este dato
head(sin_encabezado,4)## numero MPG Cilindros desplazamiento Caballos_de_potencia peso aceleracion ano
## 1 1 28 4 140 90 2264 15.5 71
## 2 2 19 3 70 97 2330 13.5 72
## 3 3 36 4 107 75 2205 14.5 82
## 4 4 28 4 97 92 2288 17.0 72
## modelo
## 1 chevrolet vega 2300
## 2 mazda rx2 coupe
## 3 honda accord
## 4 datsun 510 (sw)#ya quedo Ahora : que chingao hago cuando falta un dato Pasan 2 cosas * Cuando tengo datos de tipo numero :asi aparecen como N/A ( Not Available “No disponible”) * Y categoricas ( stirngs o letras ):se tiene que poner (na.sting) * Tambien puede pasar que el string lo este tomando como factor ( letras como numero (por ejemplo el numero de cilindros de un carro))
Hagamoslo
sin_encabezado <- read.csv("../../r-course-master/data/tema1/auto-mpg-noheader.csv",stringsAsFactors = TRUE,na.strings = "null",header = F,col.names = c("numero","MPG","Cilindros","desplazamiento","Caballos_de_potencia","peso","aceleracion","ano","modelo"))as.character: es para cambiar variables categoricas a caracteres
Esto escribe el nombre de la cabecera
names(auto1)## [1] "No" "mpg" "cylinders" "displacement" "horsepower"
## [6] "weight" "acceleration" "model_year" "car_name"Como ver el numero de filas que tiene el DF
nrow(auto1)## [1] 398Como extraer datos de una pagina web o “scraping”
library(XML)
#esto es un valor
#Nota una coleccion de valores estructurado es lo que se llama un data frame ( como solo un valor de una celda de excel)
url <- "../../r-course-master/data/tema1/cd_catalog.xml"
#esto es una varibale que casualmete se llama igual que la funcion
#esta variable es un "apuntador" que localiza el docmuento y debe pasar por todod los nodos
xmldoc<- xmlParse(url)
#Se debe obtener la raiz
rootnode<- xmlRoot(xmldoc)
#Aqui puedes ver una parte de la estuctura
rootnode[2]## $CD
## <CD>
## <TITLE>Hide your heart</TITLE>
## <ARTIST>Bonnie Tyler</ARTIST>
## <COUNTRY>UK</COUNTRY>
## <COMPANY>CBS Records</COMPANY>
## <PRICE>9.90</PRICE>
## <YEAR>1988</YEAR>
## </CD>
##
## attr(,"class")
## [1] "XMLInternalNodeList" "XMLNodeList"Creemos un data frame desde un xml
cds_data <- xmlSApply(rootnode,function(x)xmlSApply(x,xmlValue))
#xmlValue Estrae informacion del nodo raiz
# la funcion xmlApply hace una matriz por este motivo en las variables de la derecha se tiene un DF pero sin el boton de play
cds_data[1:5,]## CD CD CD
## TITLE "Empire Burlesque" "Hide your heart" "Greatest Hits"
## ARTIST "Bob Dylan" "Bonnie Tyler" "Dolly Parton"
## COUNTRY "USA" "UK" "USA"
## COMPANY "Columbia" "CBS Records" "RCA"
## PRICE "10.90" "9.90" "9.90"
## CD CD CD
## TITLE "Still got the blues" "Eros" "One night only"
## ARTIST "Gary Moore" "Eros Ramazzotti" "Bee Gees"
## COUNTRY "UK" "EU" "UK"
## COMPANY "Virgin records" "BMG" "Polydor"
## PRICE "10.20" "9.90" "10.90"
## CD CD CD
## TITLE "Sylvias Mother" "Maggie May" "Romanza"
## ARTIST "Dr.Hook" "Rod Stewart" "Andrea Bocelli"
## COUNTRY "UK" "UK" "EU"
## COMPANY "CBS" "Pickwick" "Polydor"
## PRICE "8.10" "8.50" "10.80"
## CD CD CD
## TITLE "When a man loves a woman" "Black angel" "1999 Grammy Nominees"
## ARTIST "Percy Sledge" "Savage Rose" "Many"
## COUNTRY "USA" "EU" "USA"
## COMPANY "Atlantic" "Mega" "Grammy"
## PRICE "8.70" "10.90" "10.20"
## CD CD CD CD
## TITLE "For the good times" "Big Willie style" "Tupelo Honey" "Soulsville"
## ARTIST "Kenny Rogers" "Will Smith" "Van Morrison" "Jorn Hoel"
## COUNTRY "UK" "USA" "UK" "Norway"
## COMPANY "Mucik Master" "Columbia" "Polydor" "WEA"
## PRICE "8.70" "9.90" "8.20" "7.90"
## CD CD CD CD
## TITLE "The very best of" "Stop" "Bridge of Spies" "Private Dancer"
## ARTIST "Cat Stevens" "Sam Brown" "T'Pau" "Tina Turner"
## COUNTRY "UK" "UK" "UK" "UK"
## COMPANY "Island" "A and M" "Siren" "Capitol"
## PRICE "8.90" "8.90" "7.90" "8.90"
## CD CD CD
## TITLE "Midt om natten" "Pavarotti Gala Concert" "The dock of the bay"
## ARTIST "Kim Larsen" "Luciano Pavarotti" "Otis Redding"
## COUNTRY "EU" "UK" "USA"
## COMPANY "Medley" "DECCA" "Stax Records"
## PRICE "7.80" "9.90" "7.90"
## CD CD CD
## TITLE "Picture book" "Red" "Unchain my heart"
## ARTIST "Simply Red" "The Communards" "Joe Cocker"
## COUNTRY "EU" "UK" "USA"
## COMPANY "Elektra" "London" "EMI"
## PRICE "7.20" "7.80" "8.20"Ahora tenemos que trasponerlo
cds_catalog <- data.frame(t(cds_data),row.names = NULL)
cds_catalog[1:5,]## TITLE ARTIST COUNTRY COMPANY PRICE YEAR
## 1 Empire Burlesque Bob Dylan USA Columbia 10.90 1985
## 2 Hide your heart Bonnie Tyler UK CBS Records 9.90 1988
## 3 Greatest Hits Dolly Parton USA RCA 9.90 1982
## 4 Still got the blues Gary Moore UK Virgin records 10.20 1990
## 5 Eros Eros Ramazzotti EU BMG 9.90 1997Otras funciones que no se explican en este curso pero que
Nota : Los corchetes se leen asi [Filas,Columnas], fin del comunicado jejeje son utiles para XMLson: xpathSApply(): getNodeSet():
Ahora agamos un ejercicio para obtener valores de wikipedia
#population_url<-readHTMLTable("../../r-course-master/data/tema1/WorldPopulation-wiki.htm")
#tables<- (population_url)
#View(tables)Esto te crea a una lista de tablas que estan dentro de la paguina ahora seleccionaremos una tabla dentro de ese listado
#most_populated<-tables[[6]]
#head(most_populated)Ahora : si no quieres la lista y despues extarer la tabla , osea , ya sabes dentro del xml cual es el numero de tabla que quieres
#custom_table <- readHTMLTable(population_url,which = 6)
#head(custom_table)Esto te puede ayudar a identificar si la paguina tiene demaciado peso y no se vuelva lenta tu computadora entonces puede usarla
Ficheros tipo json
library("jsonlite")##
## Attaching package: 'jsonlite'## The following object is masked from 'package:purrr':
##
## flattendat.1<- fromJSON("../../r-course-master/data/tema1/students.json")
dat.2<- fromJSON("../../r-course-master/data/tema1/student-courses.json")Para obtener valores de Internet en formato jason
urljason <- "http://www.floatrates.com/daily/usd.json"Aqui solo lo extrae : ahora lo que hace falta es meterlo a una lista
library("curl")## Using libcurl 7.64.1 with Schannel##
## Attaching package: 'curl'## The following object is masked from 'package:readr':
##
## parse_datedfdeurl <- fromJSON(urljason)Ahora debes tener cuidado con este tema ya que depende mucho de la estuctura del JSON por que dentro de cada llave puede tener catergorias
Lo leeremos y lo sobre escibiremos a la mismo DF ( Esta madre aun no me sale )
#Aqui tienes que hacer una conversion
dfdeurl <- dfdeurl$eur$code
#Esto me crea un valor , pero quiero un DFPara convertir un un dataframe a JSON
#conversion.df.JSON<- toJSON(mos_populated)
#head(mos_populated)Leer archivo con Formato con ancho fijo (.TXT) fwf
los widths=ancho ( los numeros dentro de la c son los numeros de carateres por columna )
student<- read.fwf("C:/Users/Antonio Constantino/Desktop/Rstudio/curso de youtube/curso youtube/../../r-course-master/data/tema1/student-fwf.txt",
width=c(4,15,20,15,4),
col.names = c("id","name","email","carrera","año"))ahora si tuviera encabezados Sep=separador
student1 <- read.fwf("C:/Users/Antonio Constantino/Desktop/Rstudio/curso de youtube/curso youtube/../../r-course-master/data/tema1/student-fwf.txt",width=c(4,15,20,15,4),header = F)
head(student1)## V1 V2 V3 V4 V5
## 1 1044 Fran Morton fran.m@quama.edu Statistics 2015
## 2 1035 Kato Mullins tempor@giat.com Marketing 2014
## 3 1046 Jordan Keller Cum@gmail.com Chemistry 2014
## 4 1084 Macey Simpson eu@famesacturpis.com Finance 2016
## 5 1010 Deanna Ortega Dea.Ortega@velit.ca Chemistry 2016
## 6 1096 Joe Eaton je@euismod.org MIS 2014Notaras que pone una V en el lufar de la fila que no tiene nombre
Te quieres saltar algunas lineas agregas skip Si quieres excluir una columna agregas los que quieras eliminar Nota Importante :Si usas agregar las columnas aparte de agregar el menos debes borrar el nombre de la columna
student2<- read.fwf("C:/Users/Antonio Constantino/Desktop/Rstudio/curso de youtube/curso youtube/../../r-course-master/data/tema1/student-fwf.txt",width=c(4,15,-20,15,4),header = F,sep = "\t",skip = 2)
head(student2)## V1 V2 V3 V4
## 1 1046 Jordan Keller Chemistry 2014
## 2 1084 Macey Simpson Finance 2016
## 3 1010 Deanna Ortega Chemistry 2016
## 4 1096 Joe Eaton MIS 2014
## 5 1051 Yoko Washington Marketing 2017
## 6 1074 Ebony Farrell Chemistry 2017Aqui se salto 2 y elimino la fila del correo
Como se crean las variables
Clientes <- c("juan gabriel","ricardo","pedro ")
fechas <- as.Date(c("2017-12-27","2017-11-1","2018-1-1"))
pago<- c(315,192.55,40.15)crear data frame
pedidos <- data.frame(Clientes,fechas,pago)Como se guarda el data frame
este es formato .Rdata
save(pedidos,file = "../../r-course-master/data/tema1/pedidos.Rdata ")#m Como se guarda el data frame este es formato .rds
saveRDS(pedidos,file = "../../r-course-master/data/tema1/pedidos.rds")Asi de borra una variable o un DF
# remove (pedidos )Como se Carga una varibale ya trabajada
No es lo mismo guardarlo que “mandarlo llamar” cargarlo para trabajarlo en R
Para formatos .Rdata
load("../../r-course-master/data/tema1/pedidos.Rdata ")Para formatos RDS, necesitas crear una variable para que tenga un nombre ( por decirlo asi ), te lo guarda para datos en bruto como si tubieras un excel sin nombre y necesitas cargarlo
orders <-readRDS("../../r-course-master/data/tema1/pedidos.rds")Como se guarada toda la sesión
#No es tan conveniente
save.image("../curso youtube/Trabajo.Rdata")Cargar datos que ya viene con R( precargados )
data("iris")
data("cars")
cars[1:5,]## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16Nota : el que guarda todo en archivo es el formato Rdata
Valiar que estoy sobre escribiendo variables
#Atach Cuando faltan datos dentro de un DF
library(readr)
datosfaltantes <-read.csv("../../r-course-master/data/tema1/missing-data.csv",na.strings = "")
#na,string : conviene los vacios datos los cambia a N/A
datosfaltantes[1:5,]## Income Phone_type Car_type
## 1 89800 Android Luxury
## 2 47500 Android Non-Luxury
## 3 45000 iPhone Luxury
## 4 44700 <NA> Luxury
## 5 59500 iPhone LuxuryPara Limpiar los datos , donde no hay N/A: Te elimina estas filas donde hay N/A
datoslimpios<- na.omit(datosfaltantes)Eliminar N/A de filas de solo de una columna seleccionada
la columna Ingresos = Income
df.elim.igresos<- datosfaltantes[!is.na(datosfaltantes$Income),]
df.elim.igresos[1:5,]## Income Phone_type Car_type
## 1 89800 Android Luxury
## 2 47500 Android Non-Luxury
## 3 45000 iPhone Luxury
## 4 44700 <NA> Luxury
## 5 59500 iPhone LuxuryValidar si tiene valores faltantes
complete.cases(datosfaltantes)## [1] TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [13] FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [25] TRUE TRUE TRUETodos los faltantes te los muestra como FALSE
Reemplazar datos que faltan a un N/A
Paso 1 cuando tiene ceros ,convertirlos en N/A
datosfaltantes1 <-read.csv("../../r-course-master/data/tema1/missing-data.csv",na.strings = "")
datosfaltantes1$Income[datosfaltantes1$Income==0]<- NA
datosfaltantes1$Income[datosfaltantes1$Phone_type==0]<- NAAjustar N/A o cero al promedio
Pero tiene N/A asi que nos va a amandar error , veamoslo
mean(datosfaltantes$Income)## [1] NAEntonces lo que le pediremos es : Borralos y te saldra
mean(datosfaltantes$Income,na.rm = T)## [1] 57872Desviacion estandar : eliminando los N/A
sd(datosfaltantes$Income,na.rm = T)## [1] 33170.09Para no perder esos datos , se reemplaza por el promedio (pero existe un algoritmo mejor )
#ingresaremos una columna donde los que faltan se vana ingresar los datos pendientes (N/A)
datosfaltantes$columna_Incertada <-
ifelse(is.na(datosfaltantes$Income),
mean(datosfaltantes$Income,na.rm= TRUE),datosfaltantes$Income)Algoritmo para agregar datos faltantes (Funcion parte 1)
Ahora si el N/A es una variable categorica ( en vez de numeros son letras ) Abusado que esta bien perro
pero es basada en distribuciones gussianas , es muy impotante que se limpie o se asigne valores
# X es un vector que contiene N/A
rand.impute <- function(x){ #x viene a ser la columna del data frame con la que trabajaremos
missing <- is.na(x) #missing va a ser un vector del mismo tamano que x pero que contiene TRUE or false dependiendo si es X es igual NA o no
n.missing <- sum(missing)# esta variable indica cuantos valores son NA dentro de x
# Nota : cuando se pide sumar valores booleanos solo suma los FALSOS
x.obs <- x[!missing]# x.obs son los valores x que NO tienen un valor N/A dentro de x
imputed <- x #imputed es el valor que vamos a devolver, le damos el valor x por defecto
imputed[missing] <- sample(x.obs, n.missing, replace = TRUE) # imputed en la posicion que falten los missing values, va a ser un muestreo aleatorio de las observaviones de x que no esten missing, de tama?o n.missing, para que sea representativa respecto los missing values, y con la possibilidad de remplazo para que cambie los NA por valores aleatorios
return(imputed) # devuelveme el valor imputed
}Funcion parte 2
rand.impute.data.frame <- function(df,cols){
names <- names(df) #damos al vector name, los nombres de las columnas
for (col in cols) {
name <- paste(names[col], ".imputed", sep = "") #Anadir el nombre imputed despues del column name para identificar las nuevas columnas
df[name] = rand.impute(df[,col])#llamaremos la funcion de sustitucion de valores con valores aeatorios
}
df #recordar devolver el data frame con el que estamos trabajando
}ahora aplicamos la funcion al Df para que agregue las columnas
Leyendo esta sentencia es: al objeto “datos faltantes” le vas a aplicar la funcion de agregar columnas en los dtaos faltantes de forma aleatorias al DF y entre parentesis le dices cuales columnas va a sumar (de la 1 a la 2)
Otra forma de explicarlo :ahora se manda llamar la funcion Ahora metemos en una variable llamada data ,las columnas del df “datos flatantes” + las variables categoricas y numericas faltantes distribucion gaussiana ( solo aplica para los que no tenia valores) los demas los deja igual
datosfaltantes1<-rand.impute.data.frame(datosfaltantes1,c(1,2))
head(datosfaltantes1)## Income Phone_type Car_type Income.imputed Phone_type.imputed
## 1 89800 Android Luxury 89800 Android
## 2 47500 Android Non-Luxury 47500 Android
## 3 45000 iPhone Luxury 45000 iPhone
## 4 44700 <NA> Luxury 44700 iPhone
## 5 59500 iPhone Luxury 59500 iPhone
## 6 NA Android Non-Luxury 47300 AndroidComo Borrar una Funcion, Variable , puntero y DF
# rm("Nombre de la funcion")Evitar duplicaciones de datos
Llenado de datos para crear un DF
family.salary = c(40000, 60000, 50000, 80000, 60000, 70000, 60000)
family.size = c(4, 3, 2, 2, 3, 4, 3)
family.car = c("Lujo", "Compacto", "Utilitario", "Lujo",
"Compacto", "Compacto", "Compacto")Creamos un DF
family <- data.frame(family.salary, family.size, family.car)La funcion unique borra los registros que estan duplicados y esto lo guardamos en otra variable
family.unique <- unique(family)Esta funcion nos manda una variable de tipo booleanos ( verdaero o falso ) solo manda los que estan duplicados el “original , no lo detecta”
duplicated(family)## [1] FALSE FALSE FALSE FALSE TRUE FALSE TRUESi quiero saber cuales son las filas duplicadas
family[duplicated(family),]## family.salary family.size family.car
## 5 60000 3 Compacto
## 7 60000 3 Compacto#Ojo: los corchetes le estan diciendo [dame esto,"todo"( es dejarlo en blanco)]Reescalar datos
Primero se instala un paquete ( Aqui esta comentado por que solo se tiene que instalar una ves recoordemos)
#install.packages("scales")Acivar el paquete y cargamos los datos
library("scales")##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
##
## discard## The following object is masked from 'package:readr':
##
## col_factorstudents <-read.csv( "../../r-course-master/data/tema1/data-conversion.csv")
head(students)## Age State Gender Height Income
## 1 23 NJ F 61 5000
## 2 13 NY M 55 1000
## 3 36 NJ M 66 3000
## 4 31 VA F 64 4000
## 5 58 NY F 70 30000
## 6 29 TX F 63 10000Ceramos una nueva variable reescalar es una transformacion lineal
students$Income.rescaled<- rescale(students$Income)
head(students)## Age State Gender Height Income Income.rescaled
## 1 23 NJ F 61 5000 0.07407407
## 2 13 NY M 55 1000 0.00000000
## 3 36 NJ M 66 3000 0.03703704
## 4 31 VA F 64 4000 0.05555556
## 5 58 NY F 70 30000 0.53703704
## 6 29 TX F 63 10000 0.16666667La funcion rescale es un resumen de la formula los resultados son exactamente lo mismo
(students$Income - min(students$Income))/(max(students$Income)-min(students$Income))## [1] 0.07407407 0.00000000 0.03703704 0.05555556 0.53703704 0.16666667
## [7] 0.90740741 1.00000000 0.01851852 0.35185185En el caso que esten en valores porcentuales aqui lo que se hace es agregar “to=c(min, max)”
rescale(students$Income, to= c(0,100))## [1] 7.407407 0.000000 3.703704 5.555556 53.703704 16.666667
## [7] 90.740741 100.000000 1.851852 35.185185# El resultado es el mismo pero multiplicado por 100 en este caso Re-esclado para varias columnas/Variables
rescaled.many<-function(dataframe ,cols){
names<- names(dataframe)
for (col in cols){
name<- paste(names[col],"rescaled",sep = ".")
dataframe[name]<- rescale(dataframe[,col])
}
cat(paste("Hemos reescalado", length(cols),"variable(s)"))
dataframe
}Se obtiene para cada variable un reescalamiento
students<- rescaled.many(students,c(1,4))## Hemos reescalado 2 variable(s)head(students)## Age State Gender Height Income Income.rescaled Age.rescaled Height.rescaled
## 1 23 NJ F 61 5000 0.07407407 0.2222222 0.4000000
## 2 13 NY M 55 1000 0.00000000 0.0000000 0.0000000
## 3 36 NJ M 66 3000 0.03703704 0.5111111 0.7333333
## 4 31 VA F 64 4000 0.05555556 0.4000000 0.6000000
## 5 58 NY F 70 30000 0.53703704 1.0000000 1.0000000
## 6 29 TX F 63 10000 0.16666667 0.3555556 0.5333333Normalizando y Estandarizando un DF
Normalizar (campana de Gauss):Hacer que algo se ajuste a una norma, una regla o un modelo común Nota Importante : esta tecnica solo sirve cuando el DF contiene solo Numeros
housing<-read.csv("../../r-course-master/data/tema1/BostonHousing.csv")
housing.z<- scale(housing)Ente mas cerca del cero :son los mas cencanos a la media Ente mas lejos del cero :son mas alejados de la media
Si quisiera ajustar la media ( que no se calculara con respecto a esta)
housing.mean<- scale(housing,center = T,scale = T)
#scale es la desviacion estandar Ahora hagamos una funcion que agrege esa normalizacion y no sobre escriba el DF ( para conservar los datos )
scale.many <- function(dataframe,cols){
names <- names(dataframe)
for (col in cols){
name<-paste(names[col],"z",sep = ".")
dataframe[name]<- scale(dataframe[,cols])
}
cat(paste("hemos normalizado",length(cols),"variables"))
dataframe
}Ahora ejecutaremos la funcion
housing <- scale.many(housing,c(1,3,5:8))## hemos normalizado 6 variablesCategorizando Informacion numerica
Ejemplo:Meter numeros en categorias ( 10 = pobre y 100 = rico )
students3 <-read.csv("../../r-course-master/data/tema1/data-conversion.csv")
bp<-c(-Inf,10000,31000,Inf)
names<-c("low","averange", "High")
#siempre es una categoria menos que los parametros y hace intervalos de tipo (abierto y cerrado]
# Inf a 10000, 10000 a 31000,31000 a Inf
students3$Income.cat<- cut(students3$Income,breaks=bp,labels=names)
head(students3)## Age State Gender Height Income Income.cat
## 1 23 NJ F 61 5000 low
## 2 13 NY M 55 1000 low
## 3 36 NJ M 66 3000 low
## 4 31 VA F 64 4000 low
## 5 58 NY F 70 30000 averange
## 6 29 TX F 63 10000 lowTambien puedo decirle que no ponga etiquetas (no las defino yo) observa como sumo una columna con los valores en intervalo y no en etiquetas
students3$Income.cat2<-cut(students3$Income,breaks=bp)
head(students3)## Age State Gender Height Income Income.cat Income.cat2
## 1 23 NJ F 61 5000 low (-Inf,1e+04]
## 2 13 NY M 55 1000 low (-Inf,1e+04]
## 3 36 NJ M 66 3000 low (-Inf,1e+04]
## 4 31 VA F 64 4000 low (-Inf,1e+04]
## 5 58 NY F 70 30000 averange (1e+04,3.1e+04]
## 6 29 TX F 63 10000 low (-Inf,1e+04]Tambien puedo pedirle a R corte como el quiera solo diciendo en cuantas categorias cortar
students3$Income.cat3<-cut(students3$Income,breaks=4,labels = c("1","2","3","4"))
head(students3)## Age State Gender Height Income Income.cat Income.cat2 Income.cat3
## 1 23 NJ F 61 5000 low (-Inf,1e+04] 1
## 2 13 NY M 55 1000 low (-Inf,1e+04] 1
## 3 36 NJ M 66 3000 low (-Inf,1e+04] 1
## 4 31 VA F 64 4000 low (-Inf,1e+04] 1
## 5 58 NY F 70 30000 averange (1e+04,3.1e+04] 3
## 6 29 TX F 63 10000 low (-Inf,1e+04] 1Variables ficticias
Instalamos la libreria
#install.packages("dummies")
library("dummies")## dummies-1.5.6 provided by Decision PatternsComo una conversion de variables a todo el Df
students4 <-read.csv("../../r-course-master/data/tema1/data-conversion.csv")
students.dummy<- dummy.data.frame(students4,sep=".", all = T)## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignorednames(students.dummy)## [1] "Age" "State.NJ" "State.NY" "State.TX" "State.VA" "Gender.F" "Gender.M"
## [8] "Height" "Income"#all detecta los valores numericos y los respeta , no los hace como los estados Como una conversion de variables a una columna ( femenino y Masculino )
students4$nuevo <- dummy(students4$Gender)## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignoredSelecionamos los datos que queremos aplicarle la funcion dummy a algunas columas en especial
students4<- dummy.data.frame(students4,names = c("State"))## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored#Este ejemplo lo que hace es convierte una dummy en varias columans
students4$Gender.dummy<-dummy(students4$Gender)## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored#cuando solo tienes 2 ( hombre y mujer ) te crea una columna y no hace 2 columnas : asigna para F=1 y M=0Formas de Eliminar información que falta
bostonprincipal<- read.csv("../../r-course-master/data/tema1/housing-with-missing-value.csv",header = T,stringsAsFactors = F)Si queremos una vista rapida de los datos ( estadiasticos basicos ) Enterga :Minimo,Primer cuartil ,mediana,media,3er quartil, maximo y numero de N/A por columna
summary(bostonprincipal)## X crim zn indus
## Min. : 1.0 Min. : 0.00632 Min. : 0.00 Min. : 0.46
## 1st Qu.:127.2 1st Qu.: 0.08205 1st Qu.: 0.00 1st Qu.: 5.19
## Median :253.5 Median : 0.25651 Median : 0.00 Median : 9.69
## Mean :253.5 Mean : 3.61352 Mean : 11.36 Mean :11.14
## 3rd Qu.:379.8 3rd Qu.: 3.67708 3rd Qu.: 12.50 3rd Qu.:18.10
## Max. :506.0 Max. :88.97620 Max. :100.00 Max. :27.74
##
## chas nox rm age
## Min. :0.00000 Min. :0.3850 Min. :3.561 Min. : 2.90
## 1st Qu.:0.00000 1st Qu.:0.4490 1st Qu.:5.886 1st Qu.: 45.02
## Median :0.00000 Median :0.5380 Median :6.208 Median : 77.50
## Mean :0.06917 Mean :0.5547 Mean :6.285 Mean : 68.57
## 3rd Qu.:0.00000 3rd Qu.:0.6240 3rd Qu.:6.623 3rd Qu.: 94.08
## Max. :1.00000 Max. :0.8710 Max. :8.780 Max. :100.00
##
## dis rad tax ptratio
## Min. : 1.130 Min. : 1.000 Min. :187.0 Min. :12.60
## 1st Qu.: 2.100 1st Qu.: 4.000 1st Qu.:279.0 1st Qu.:17.40
## Median : 3.207 Median : 5.000 Median :330.0 Median :19.10
## Mean : 3.795 Mean : 9.515 Mean :408.2 Mean :18.47
## 3rd Qu.: 5.188 3rd Qu.:24.000 3rd Qu.:666.0 3rd Qu.:20.20
## Max. :12.127 Max. :24.000 Max. :711.0 Max. :22.00
## NA's :40 NA's :40
## b lstat medv
## Min. : 0.32 Min. : 1.73 Min. : 5.00
## 1st Qu.:375.38 1st Qu.: 6.95 1st Qu.:17.02
## Median :391.44 Median :11.36 Median :21.20
## Mean :356.67 Mean :12.65 Mean :22.53
## 3rd Qu.:396.23 3rd Qu.:16.95 3rd Qu.:25.00
## Max. :396.90 Max. :37.97 Max. :50.00
## Primera solucion n.omit ( eliminar las filas ) Pero esto borra datos que pueden ser muy importantes para el analsis y se mueven algunos datos
boston1<- bostonprincipal
#na.omit(boston1)Puedes compararlos
summary(boston1)## X crim zn indus
## Min. : 1.0 Min. : 0.00632 Min. : 0.00 Min. : 0.46
## 1st Qu.:127.2 1st Qu.: 0.08205 1st Qu.: 0.00 1st Qu.: 5.19
## Median :253.5 Median : 0.25651 Median : 0.00 Median : 9.69
## Mean :253.5 Mean : 3.61352 Mean : 11.36 Mean :11.14
## 3rd Qu.:379.8 3rd Qu.: 3.67708 3rd Qu.: 12.50 3rd Qu.:18.10
## Max. :506.0 Max. :88.97620 Max. :100.00 Max. :27.74
##
## chas nox rm age
## Min. :0.00000 Min. :0.3850 Min. :3.561 Min. : 2.90
## 1st Qu.:0.00000 1st Qu.:0.4490 1st Qu.:5.886 1st Qu.: 45.02
## Median :0.00000 Median :0.5380 Median :6.208 Median : 77.50
## Mean :0.06917 Mean :0.5547 Mean :6.285 Mean : 68.57
## 3rd Qu.:0.00000 3rd Qu.:0.6240 3rd Qu.:6.623 3rd Qu.: 94.08
## Max. :1.00000 Max. :0.8710 Max. :8.780 Max. :100.00
##
## dis rad tax ptratio
## Min. : 1.130 Min. : 1.000 Min. :187.0 Min. :12.60
## 1st Qu.: 2.100 1st Qu.: 4.000 1st Qu.:279.0 1st Qu.:17.40
## Median : 3.207 Median : 5.000 Median :330.0 Median :19.10
## Mean : 3.795 Mean : 9.515 Mean :408.2 Mean :18.47
## 3rd Qu.: 5.188 3rd Qu.:24.000 3rd Qu.:666.0 3rd Qu.:20.20
## Max. :12.127 Max. :24.000 Max. :711.0 Max. :22.00
## NA's :40 NA's :40
## b lstat medv
## Min. : 0.32 Min. : 1.73 Min. : 5.00
## 1st Qu.:375.38 1st Qu.: 6.95 1st Qu.:17.02
## Median :391.44 Median :11.36 Median :21.20
## Mean :356.67 Mean :12.65 Mean :22.53
## 3rd Qu.:396.23 3rd Qu.:16.95 3rd Qu.:25.00
## Max. :396.90 Max. :37.97 Max. :50.00
## Segunda solucion: para eliminar solo de alguna columna
drop_na<-c("rad")
boston2<- bostonprincipal[complete.cases(bostonprincipal[,!names(bostonprincipal)%in% drop_na]),]Elimianr columna de un DF
Esto modifica el DF, elimina la columna
bostonsin1col<- bostonprincipal
bostonsin1col$rad=NULL Eliminar diferentes columnas a la vez
eliminarcol<-c("rad","ptratio")
bostonsin2col<- bostonprincipal[,!names(bostonprincipal)%in% eliminarcol]Formas de completar información que falta
#install.packages("Hmisc")Activamos la libreria y cargamos los datos
library("Hmisc")## Loading required package: lattice## Loading required package: survival## Loading required package: Formula##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
##
## src, summarize## The following objects are masked from 'package:base':
##
## format.pval, unitsbostoncopy<- bostonprincipalImpute :Es para rellenar valores NA con la media =mean
bostoncopy$rad<-impute(bostoncopy$rad,mean)
bostoncopy$ptratio<-impute(bostoncopy$ptratio,mean)
summary(bostoncopy)##
## 40 values imputed to 9.515021
##
##
## 40 values imputed to 18.4676## X crim zn indus
## Min. : 1.0 Min. : 0.00632 Min. : 0.00 Min. : 0.46
## 1st Qu.:127.2 1st Qu.: 0.08205 1st Qu.: 0.00 1st Qu.: 5.19
## Median :253.5 Median : 0.25651 Median : 0.00 Median : 9.69
## Mean :253.5 Mean : 3.61352 Mean : 11.36 Mean :11.14
## 3rd Qu.:379.8 3rd Qu.: 3.67708 3rd Qu.: 12.50 3rd Qu.:18.10
## Max. :506.0 Max. :88.97620 Max. :100.00 Max. :27.74
## chas nox rm age
## Min. :0.00000 Min. :0.3850 Min. :3.561 Min. : 2.90
## 1st Qu.:0.00000 1st Qu.:0.4490 1st Qu.:5.886 1st Qu.: 45.02
## Median :0.00000 Median :0.5380 Median :6.208 Median : 77.50
## Mean :0.06917 Mean :0.5547 Mean :6.285 Mean : 68.57
## 3rd Qu.:0.00000 3rd Qu.:0.6240 3rd Qu.:6.623 3rd Qu.: 94.08
## Max. :1.00000 Max. :0.8710 Max. :8.780 Max. :100.00
## dis rad tax ptratio
## Min. : 1.130 Min. : 1.000 Min. :187.0 Min. :12.60
## 1st Qu.: 2.100 1st Qu.: 4.000 1st Qu.:279.0 1st Qu.:17.40
## Median : 3.207 Median : 5.000 Median :330.0 Median :18.60
## Mean : 3.795 Mean : 9.515 Mean :408.2 Mean :18.47
## 3rd Qu.: 5.188 3rd Qu.: 9.515 3rd Qu.:666.0 3rd Qu.:20.20
## Max. :12.127 Max. :24.000 Max. :711.0 Max. :22.00
## b lstat medv
## Min. : 0.32 Min. : 1.73 Min. : 5.00
## 1st Qu.:375.38 1st Qu.: 6.95 1st Qu.:17.02
## Median :391.44 Median :11.36 Median :21.20
## Mean :356.67 Mean :12.65 Mean :22.53
## 3rd Qu.:396.23 3rd Qu.:16.95 3rd Qu.:25.00
## Max. :396.90 Max. :37.97 Max. :50.00Nota : ya no tiene N/A
Para este caso lo que se va a reemplazar con la mediana
bostonmedian<-bostonprincipal
bostonmedian$rad<-impute(bostonmedian$rad,median)
bostonmedian$ptratio<-impute(bostonmedian$ptratio,median)Nota Importante : se puede usar con la media , mediana , un numero etc,etc ,etc
Matriz donde vemos los campos vacios
#install.packages("mice")
#install.packages("VIM")
library(mice)##
## Attaching package: 'mice'## The following object is masked from 'package:stats':
##
## filter## The following objects are masked from 'package:base':
##
## cbind, rbindlibrary(VIM)## Loading required package: colorspace## Loading required package: grid## VIM is ready to use.## Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues##
## Attaching package: 'VIM'## The following object is masked from 'package:datasets':
##
## sleepEntrega una matriz donde te muestra los datos completos y los casos donde faltan valores
md.pattern(bostonprincipal)
## X crim zn indus chas nox rm age dis tax b lstat medv rad ptratio
## 431 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
## 35 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
## 35 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
## 5 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2
## 0 0 0 0 0 0 0 0 0 0 0 0 0 40 40 80Grafiquemoslo Col= color labels= pone el nombre de la columna cex.axis=Tamaño de letra Gap= distancia entre graficos ylab=Nombre de la grafica en el eje y
aggr(bostonprincipal,col=c('blue','red',numbers=T,shortVar=T),labels=names(bostonprincipal),cey.axis=0.5,cex.axis=0.6, gap=3,ylab=c("histograma de NA","Matriz"))
# Combinando y Separando datos Lo interesante de este tema es lo que tiene valor Cargamos libreria tidyr
#install.packages("tidyr")
library(tidyr)Carga de datos
crime.data <- read.csv("../../r-course-master/data/tema1/USArrests.csv", stringsAsFactors = F)
head(crime.data)## X Murder Assault UrbanPop Rape
## 1 Alabama 13.2 236 58 21.2
## 2 Alaska 10.0 263 48 44.5
## 3 Arizona 8.1 294 80 31.0
## 4 Arkansas 8.8 190 50 19.5
## 5 California 9.0 276 91 40.6
## 6 Colorado 7.9 204 78 38.7Como Ingresar una columna
crime.data<-cbind(state=rownames(crime.data),crime.data)Rownames ( enumera las filas) # Como se pueden resumir datos Array asociativo /diccionario gater = Función que unifica columnas Tablas de doble entrada
crime.data1<- gather(crime.data,key="crime_type", value = "arrest_estimate",Murder:UrbanPop)Resume en 2 variables
crime.data2<- gather(crime.data,key="crime_type", value = "arrest_estimate",-state)lo contrario a gether
crime.data3<-spread(crime.data2,key="crime_type", value="arrest_estimate")Es como Juntar valores
crime.data4<- unite(crime.data,col="Mordet_Assailt",Murder,Assault,sep="_")Ahora si te pasan datos que estan juntos con caracteres se separan con la funcion separate
crime.data5<-separate(crime.data4,col="Mordet_Assailt",
into = c("Murder","Assault"),
sep = "_")Modelos predictivos para imputar datos
Paso 1:pedrice valores que se parezcan con un metodo Paso 2:acompletar los datos paso 3:Sustituir en el Df original los datos completos
Borrarlos no es una opción Tecnicas de imputación multivariable
library(mice)
housingdata <- read.csv("../../r-course-master/data/tema1/housing-with-missing-value.csv",header = T,stringsAsFactors = F)columns<-c("ptratio", "rad")
imputed_data<-mice(housingdata[,names(housingdata)%in% columns],
m=5,# numero de imputaciones
maxit = 3,#Numero de iteraciones
method="pmm",#Predice el promedio de los datos
seed=2018#Semilla
)##
## iter imp variable
## 1 1 rad ptratio
## 1 2 rad ptratio
## 1 3 rad ptratio
## 1 4 rad ptratio
## 1 5 rad ptratio
## 2 1 rad ptratio
## 2 2 rad ptratio
## 2 3 rad ptratio
## 2 4 rad ptratio
## 2 5 rad ptratio
## 3 1 rad ptratio
## 3 2 rad ptratio
## 3 3 rad ptratio
## 3 4 rad ptratio
## 3 5 rad ptratioMetodos =method pmm= comparación predictiva de medios logreg =regresión logistica “solo se pude con 2” polyreg = regresión logistica politomica polr= modelo de probabilidades proporcionales
summary(imputed_data)## Class: mids
## Number of multiple imputations: 5
## Imputation methods:
## rad ptratio
## "pmm" "pmm"
## PredictorMatrix:
## rad ptratio
## rad 0 1
## ptratio 1 0Ahora con el modelo hecho acompletaremos los datos
complete.cases<-mice::complete(imputed_data)asi se indica cuando los metodos se confunden o R o entiende de que libreria (mice::complete)la libreria + el metodo Ahora se sustituye los valores na del Df original por los que estimaste con el metodo mice en otras palabras: mete en esta columna lo que esta en esta columna
housingdata$ptratio<-complete.cases$ptratio
housingdata$rad<-complete.cases$radValidamos cuantos NA existen
anyNA(housingdata)## [1] FALSEOtra forma de inferir los NA
library(Hmisc)
housingdata1 <- read.csv("../../r-course-master/data/tema1/housing-with-missing-value.csv",header = T,stringsAsFactors = F)
impute_arg<- aregImpute(~ptratio+rad,data = housingdata1,n.impute = 5)## Iteration 1
Iteration 2
Iteration 3
Iteration 4
Iteration 5
Iteration 6
Iteration 7
Iteration 8 Veamos que nos arroja
impute_arg##
## Multiple Imputation using Bootstrap and PMM
##
## aregImpute(formula = ~ptratio + rad, data = housingdata1, n.impute = 5)
##
## n: 506 p: 2 Imputations: 5 nk: 3
##
## Number of NAs:
## ptratio rad
## 40 40
##
## type d.f.
## ptratio s 2
## rad s 1
##
## Transformation of Target Variables Forced to be Linear
##
## R-squares for Predicting Non-Missing Values for Each Variable
## Using Last Imputations of Predictors
## ptratio rad
## 0.237 0.251con este metodo de donde obtengo los valores que quiero imputar
impute_arg$imputed$ptratio## [,1] [,2] [,3] [,4] [,5]
## 43 18.7 21.2 18.0 17.8 19.1
## 61 18.0 19.2 17.8 19.1 18.7
## 99 19.1 19.1 12.6 21.0 15.5
## 103 18.9 16.6 16.6 19.2 21.0
## 104 16.0 17.4 17.6 14.7 20.2
## 113 13.0 15.3 17.8 19.1 14.7
## 117 16.6 15.3 18.8 17.4 16.4
## 118 19.6 15.3 20.1 17.4 13.0
## 125 19.1 12.6 17.8 17.8 17.8
## 136 18.4 14.8 17.8 14.7 21.2
## 153 14.9 17.4 17.9 20.2 14.9
## 156 18.8 17.8 17.3 17.8 19.1
## 164 13.0 17.4 18.9 19.7 17.6
## 168 18.9 19.1 18.6 19.2 20.9
## 173 20.2 17.4 17.9 18.7 21.0
## 197 21.0 17.4 21.1 20.2 20.2
## 214 18.4 15.2 18.3 14.7 16.6
## 215 21.0 13.0 18.6 14.7 18.4
## 219 14.7 18.4 14.7 18.4 19.6
## 226 19.7 15.3 17.9 17.4 14.9
## 247 17.4 15.3 17.9 19.1 17.8
## 272 18.7 21.0 13.0 18.7 18.0
## 291 17.9 14.9 18.5 16.8 17.0
## 292 21.0 14.7 18.5 14.7 21.0
## 295 17.9 18.4 15.9 21.2 18.2
## 303 17.9 15.3 18.9 17.8 14.7
## 313 17.6 20.9 21.2 19.6 21.0
## 314 19.0 15.2 18.4 18.9 18.8
## 327 20.9 17.4 21.0 13.0 21.0
## 332 15.9 15.3 15.3 21.0 19.7
## 364 20.2 20.2 20.2 20.2 20.2
## 391 20.2 20.2 20.2 20.2 20.2
## 392 20.2 20.2 20.2 20.2 20.2
## 403 20.2 20.2 20.1 20.2 20.2
## 417 20.2 20.2 20.2 20.2 20.2
## 437 17.6 19.2 17.9 19.1 16.1
## 442 20.2 20.2 20.2 19.7 19.2
## 458 20.2 20.2 20.2 20.2 20.2
## 465 20.2 20.2 20.2 20.2 20.2
## 500 16.6 15.3 21.2 19.1 19.2impute_arg$imputed$rad## [,1] [,2] [,3] [,4] [,5]
## 29 4 1 5 4 4
## 61 4 4 6 7 4
## 83 2 5 3 5 4
## 85 5 4 4 4 4
## 101 4 5 1 5 5
## 131 4 4 4 4 4
## 133 4 4 5 4 4
## 139 4 4 4 4 4
## 156 2 3 3 2 7
## 166 5 5 5 1 5
## 175 8 4 4 4 3
## 176 8 4 6 6 1
## 185 4 2 3 2 2
## 197 5 8 5 24 24
## 203 5 4 1 5 5
## 233 3 8 6 3 4
## 236 6 2 3 3 6
## 243 6 4 4 4 3
## 261 5 2 5 5 2
## 263 5 5 5 5 5
## 273 4 5 3 5 7
## 279 3 3 2 3 8
## 296 5 5 1 6 5
## 310 3 3 4 5 2
## 329 4 8 6 8 4
## 337 5 24 4 24 24
## 345 7 6 6 6 4
## 362 24 24 4 24 24
## 370 24 5 4 24 24
## 376 24 24 4 24 24
## 407 24 24 6 5 24
## 419 24 24 4 24 24
## 423 24 24 4 24 24
## 437 4 4 6 6 5
## 442 24 24 6 5 4
## 452 24 24 6 24 24
## 464 24 24 4 24 24
## 486 24 24 24 24 24
## 491 24 24 1 24 24
## 497 6 5 2 4 4Solo falatria sustituirlo con las columnas que se tienen en el DF Original
Detección de outliners atravez de box pot
Los valores que se salen de lo comun es decir (1 o 2 y aparece un 73 de la nada ) Cambiar ruta del archivo
ozone.data<- read.csv("../../r-course-master/data/tema1/ozone.csv",header = T,stringsAsFactors = F)Nota importante : antes de esimar los NA seria importente detectar esto para que los metodos no muevan el resultado
outliner_values<-boxplot(ozone.data$pressure_height,main="presure Heigh",boxwex=0.5)
summary(ozone.data$pressure_height)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5320 5700 5770 5753 5830 5950 12Comparando los datos
boxplot(pressure_height~Month,
data = ozone.data,
main="presure Height per Month"
)
boxplot(ozone_reading~Month,
data = ozone.data,
main="ozone reading per onth"
)$out # el out muestra un mensaje en consola ## [1] 11.06 9.93 22.89 24.29 29.79mtext("hola")
# Enmascarando Outlinerscon trasnformaciones y cappings Lo que hacemos es los que esten arriba del percentil 95 se sustitute con la media , asi los datos no se ven tan alejados
impute_outliners<- function(x,removeNA=T){
quantiles<- quantile(x,c(0.05,0.95),na.rm=removeNA)
x[x<quantiles[1]]<- mean(x,na.rm=removeNA)
x[x>quantiles[2]]<- mean(x,na.rm=removeNA)
x
}Validemos si acomodo los datos que estaban separados Aplicamos la función
imputed.data.ozone<-impute_outliners(ozone.data$pressure_height)Ordenar 2 graficos
par(mfrow=c(1,2))
boxplot(ozone.data$pressure_height,main="presión con Outliners")
boxplot(imputed.data.ozone,main="presión sin Outliners")
# Outliners , pero reemplazando datos Nota: es diferente imputar los datos a reemplazarlos
replace.outliners<- function(x,removeNA=T){
qrts<-quantile(x,probs=c(0.25,0.75),na.rm=removeNA)
caps<-quantile(x,probs=c(0.05,0.95),na.rm=removeNA)
iqr<-qrts[2]-qrts[1]#Rango Intercuartilico
h<- 1.5*iqr
x[x<qrts[2]-h]<-caps[1]
x[x<qrts[1]+h]<-caps[2]
x
}Ahora esa selección de valores fuera de esos rangos , se van a sutituir con la función de arriba ya cargada
capped_presure_heigh<-replace.outliners(ozone.data$pressure_height)Grafiquemoslo 5
par(mfrow=c(1,2))
boxplot(ozone.data$pressure_height,main="presión con Outliners")
boxplot(capped_presure_heigh,main="Presión sin Outliners")
# Resumen de datos y estructura del DF
estructura<-read.csv("../../r-course-master/data/tema2/auto-mpg.csv",header = T,stringsAsFactors = F)
anyNA(estructura)#validamos si tiene NA's el DF## [1] FALSELos cilindros de los carros no nos sirven como valores , por eso los vamos a comvertir en factores
estructura$cylinders<-factor(estructura$cylinders,
levels= c(3,4,6,8),
labels= c("3cil","4cil","6cil","8cil"))summary da valores estadisticos
summary(estructura)## No mpg cylinders displacement horsepower
## Min. : 1.0 Min. : 9.00 3cil: 4 Min. : 68.0 Min. : 46.0
## 1st Qu.:100.2 1st Qu.:17.50 4cil:204 1st Qu.:104.2 1st Qu.: 76.0
## Median :199.5 Median :23.00 6cil: 84 Median :148.5 Median : 92.0
## Mean :199.5 Mean :23.51 8cil:103 Mean :193.4 Mean :104.1
## 3rd Qu.:298.8 3rd Qu.:29.00 NA's: 3 3rd Qu.:262.0 3rd Qu.:125.0
## Max. :398.0 Max. :46.60 Max. :455.0 Max. :230.0
## weight acceleration model_year car_name
## Min. :1613 Min. : 8.00 Min. :70.00 Length:398
## 1st Qu.:2224 1st Qu.:13.82 1st Qu.:73.00 Class :character
## Median :2804 Median :15.50 Median :76.00 Mode :character
## Mean :2970 Mean :15.57 Mean :76.01
## 3rd Qu.:3608 3rd Qu.:17.18 3rd Qu.:79.00
## Max. :5140 Max. :24.80 Max. :82.00Aqui lo que vas a ver es como R esta leyendo el archivo previamnte cargado
str(estructura)## 'data.frame': 398 obs. of 9 variables:
## $ No : int 1 2 3 4 5 6 7 8 9 10 ...
## $ mpg : num 28 19 36 28 21 23 15.5 32.9 16 13 ...
## $ cylinders : Factor w/ 4 levels "3cil","4cil",..: 2 1 2 2 3 2 4 2 3 4 ...
## $ displacement: num 140 70 107 97 199 115 304 119 250 318 ...
## $ horsepower : int 90 97 75 92 90 95 120 100 105 150 ...
## $ weight : int 2264 2330 2205 2288 2648 2694 3962 2615 3897 3755 ...
## $ acceleration: num 15.5 13.5 14.5 17 15 15 13.9 14.8 18.5 14 ...
## $ model_year : int 71 72 82 72 70 75 76 81 75 76 ...
## $ car_name : chr "chevrolet vega 2300" "mazda rx2 coupe" "honda accord" "datsun 510 (sw)" ...Tambien puedes verlo en un zoom de cada columna lo que vas a ver es: cuantos niveles ( etiquetas y que posicion con respecto a esa etiqueta tiene ) 2=4cil ,1=4cil y asi sucesivamente
str(estructura$cylinders)## Factor w/ 4 levels "3cil","4cil",..: 2 1 2 2 3 2 4 2 3 4 ...Estadisticos y Medidas(solo valores numericos)
#install.packages(c("modeest","raster","moments"))
library(modeest)## Registered S3 method overwritten by 'rmutil':
## method from
## print.response httrlibrary(raster)## Loading required package: sp##
## Attaching package: 'raster'## The following object is masked from 'package:dplyr':
##
## selectlibrary(moments)##
## Attaching package: 'moments'## The following object is masked from 'package:modeest':
##
## skewnessCreamos un vector desde una DF
x<-estructura$mpgmean(x)#promedio (medida aritmetica)## [1] 23.51457median(x)#mediana ## [1] 23mfv(x)#moda es el valor que mas se repite ## [1] 13quantile(x)#percentiles## 0% 25% 50% 75% 100%
## 9.0 17.5 23.0 29.0 46.6Medidas de Dispercion
como se distribuyen los datos (que tan lejos estan de la media)
var(x)#Varianza## [1] 61.08961sd(x)#desviacion tipica(se usa intervalos de confianza )## [1] 7.815984cv(x)#Coheficiente de variacion ## [1] 33.2389Momento de # Asimetria de Fisher Asimetria de fisher(que tan chueca esta la campana de gauss), si sale negativo es asimetrica negativa , y si es positiva , pues es asimetrica positiva
skewness(x)## [1] 0.4553419Curtosos
Que tan chaparra esta la campana de gauss Leptocurtica = sin colas ,“una curva flaquita” Mesocurtica=Esta es perfecta platicurtica=La curva es casi toda curvas
moments::kurtosis(x)## [1] 2.480575#Histograma
hist(x)
# Obtener un subconnjunto de los datos Cargamos los datos
dat<- read.csv("../../r-course-master/data/tema2/auto-mpg.csv",header = T,stringsAsFactors = F)Indice por posicion
dat[1:5,8:9]## model_year car_name
## 1 71 chevrolet vega 2300
## 2 72 mazda rx2 coupe
## 3 82 honda accord
## 4 72 datsun 510 (sw)
## 5 70 amc gremlinind.posic<-dat[c(1:5,c(8,9))]
ind.posic[1:3,]## No mpg cylinders displacement horsepower model_year car_name
## 1 1 28 4 140 90 71 chevrolet vega 2300
## 2 2 19 3 70 97 72 mazda rx2 coupe
## 3 3 36 4 107 75 82 honda accordO tienes la opcion de seleccionar por nombre de la columna: Se lle mas o menos asi :De la fila 1 a la 5 de las columnas que se llaman “etc”
dat[1:5,c("model_year","car_name")]## model_year car_name
## 1 71 chevrolet vega 2300
## 2 72 mazda rx2 coupe
## 3 82 honda accord
## 4 72 datsun 510 (sw)
## 5 70 amc gremlinObtener el Minimo y maximo de una fila
dat[dat$mpg==max(dat$mpg)|dat$mpg==min(dat$mpg),]## No mpg cylinders displacement horsepower weight acceleration model_year
## 190 190 9.0 8 304 193 4732 18.5 70
## 269 269 46.6 4 86 65 2110 17.9 80
## car_name
## 190 hi 1200d
## 269 mazda glcObtener valores con rangos
dat[dat$mpg<30 & dat$cylinders==6,c("car_name","mpg")]## car_name mpg
## 5 amc gremlin 21.0
## 9 chevroelt chevelle malibu 16.0
## 21 plymouth volare 20.5
## 29 amc hornet 18.0
## 37 amc concord 19.4
## 41 dodge aspen 6 20.6
## 43 ford mustang 18.0
## 48 dodge aspen 19.1
## 50 chevrolet citation 23.5
## 52 ford torino 500 19.0
## 69 chevrolet chevelle malibu classic 16.0
## 72 ford maverick 15.0
## 73 pontiac firebird 19.0
## 75 chevrolet nova 18.0
## 77 chevrolet malibu 20.5
## 85 ford maverick 21.0
## 110 plymouth valiant 18.0
## 115 buick century 22.4
## 117 pontiac lemans v6 21.5
## 124 ford pinto 18.0
## 137 ford fairmont (auto) 20.2
## 147 mercury capri v6 21.0
## 149 amc gremlin 18.0
## 157 plymouth satellite custom 16.0
## 161 datsun 810 maxima 24.2
## 174 buick skyhawk 21.0
## 177 amc pacer d/l 17.5
## 178 ford granada l 22.0
## 182 plymouth duster 23.0
## 183 chevrolet nova 15.0
## 184 chevrolet nova 22.0
## 201 amc hornet 19.0
## 202 toyota cressida 25.4
## 203 ford maverick 24.0
## 207 plymouth satellite sebring 18.0
## 209 amc hornet sportabout (sw) 18.0
## 215 buick century special 20.6
## 219 amc matador 18.0
## 223 amc gremlin 20.0
## 225 oldsmobile omega brougham 26.8
## 238 dodge aspen 18.6
## 239 mercury zephyr 6 19.8
## 247 plymouth duster 20.0
## 258 peugeot 604sl 16.2
## 270 buick skylark 20.5
## 273 ford granada 18.5
## 274 buick regal sport coupe (turbo) 17.7
## 276 ford granada gl 20.2
## 277 buick century 17.0
## 282 chevrolet concours 17.5
## 284 amc matador 15.0
## 287 amc concord dl 6 20.2
## 292 mercury monarch 15.0
## 294 toyota mark ii 20.0
## 305 chrysler lebaron salon 17.6
## 308 plymouth valiant 22.0
## 310 pontiac ventura sj 18.5
## 312 toyota mark ii 19.0
## 315 ford granada ghia 18.0
## 324 ford maverick 18.0
## 325 amc hornet 18.0
## 333 ford maverick 21.0
## 334 plymouth volare custom 19.0
## 336 datsun 810 22.0
## 337 amc pacer 19.0
## 341 pontiac phoenix lj 19.2
## 343 chevrolet chevelle malibu 17.0
## 353 chevrolet citation 28.8
## 356 dodge aspen se 20.0
## 363 amc concord d/l 18.1
## 366 plymouth duster 22.0
## 370 buick century limited 25.0
## 373 chevrolet nova custom 16.0
## 374 plymouth valiant custom 19.0
## 377 volvo 264gl 17.0
## 380 amc hornet 22.5
## 381 mercury zephyr 20.8
## 382 amc matador 16.0
## 385 plymouth fury 18.0
## 394 mercedes-benz 280s 16.5
## 398 amc gremlin 19.0Subset ( subconjunto )
subset(dat,mpg>30 & cylinders==6,select = c("car_name","mpg"))#si no se incluye el select lo que se hace es : Te entrega las columnas que selecciones , de lo contrario , te entrega todas las columnas ## car_name mpg
## 12 volvo diesel 30.7
## 300 oldsmobile cutlass ciera (diesel) 38.0
## 364 datsun 280-zx 32.7Nota importante : Una recoemndacion es , cuando se trabaja con solo unas columnas es mejor trabajar con nombre de la columna
Ejemplo c1:2,2:3 # Nomenclatura & = y ( este y este ) | = o ( este o este ) ! = Lo contrario “==” = igual
#Excuir columnas
dat[1:5,c(-1,-9)]## mpg cylinders displacement horsepower weight acceleration model_year
## 1 28 4 140 90 2264 15.5 71
## 2 19 3 70 97 2330 13.5 72
## 3 36 4 107 75 2205 14.5 82
## 4 28 4 97 92 2288 17.0 72
## 5 21 6 199 90 2648 15.0 70#si se quiere usar con los nombres de las columnas no se puede
#dat[1:5,-c("car_name",mpg)]
#se tiene que hacer de esta forma
excluir.col<-dat[dat$mpg ,!names(dat)%in% c("No","car_name")]# evito el dato escrito en las columnas
excluir.col[1:3,]## mpg cylinders displacement horsepower weight acceleration model_year
## 28 15 8 383 170 3563 10.0 70
## 19 14 8 302 140 4638 16.0 74
## 36 14 8 351 153 4154 13.5 71Como estrarer valores exactos
dat[dat$mpg %in% c(15,20),c("car_name","mpg")]## car_name mpg
## 23 chevrolet vega 20
## 24 audi 100ls 20
## 28 dodge challenger se 15
## 55 chevrolet monza 2+2 20
## 72 ford maverick 15
## 91 mercury cougar brougham 15
## 98 ford galaxie 500 15
## 108 buick skylark 320 15
## 134 chevrolet monte carlo s 15
## 141 amc ambassador dpl 15
## 170 amc matador (sw) 15
## 172 dodge coronet custom 15
## 183 chevrolet nova 15
## 187 plymouth fury iii 15
## 194 toyota carina 20
## 223 amc gremlin 20
## 226 dodge dart custom 15
## 235 chevrolet bel air 15
## 247 plymouth duster 20
## 250 volvo 245 20
## 284 amc matador 15
## 285 chevrolet monte carlo 15
## 292 mercury monarch 15
## 294 toyota mark ii 20
## 356 dodge aspen se 20Diviciones con split /unsplit
Hace subconjuntos y crea listas de DF
car.list<-split(dat,dat$cylinders)como accedemos a este Df
car.list[[1]]## No mpg cylinders displacement horsepower weight acceleration model_year
## 2 2 19.0 3 70 97 2330 13.5 72
## 199 199 18.0 3 70 90 2124 13.5 73
## 251 251 23.7 3 70 100 2420 12.5 80
## 365 365 21.5 3 80 110 2720 13.5 77
## car_name
## 2 mazda rx2 coupe
## 199 maxda rx3
## 251 mazda rx-7 gs
## 365 mazda rx-4compara como se diferencia de usar un corchete doble de uno sencillo
str(dat[1])## 'data.frame': 398 obs. of 1 variable:
## $ No: int 1 2 3 4 5 6 7 8 9 10 ...str(dat[[1]])## int [1:398] 1 2 3 4 5 6 7 8 9 10 ...Particion de data frame con variable numerica
#install.packages("caret")
library(caret)##
## Attaching package: 'caret'## The following object is masked from 'package:survival':
##
## cluster## The following object is masked from 'package:purrr':
##
## liftdat.casa<- read.csv("../../r-course-master/data/tema2/BostonHousing.csv")metV: Valor mediano de casas ocupadas con mil dolares # Training practicion con el 80% de los datos
training.ids<- createDataPartition(dat.casa$MEDV,p=.80, list=F)
data.training<-dat.casa[training.ids,]#
data.validation <- dat.casa[-training.ids,]Se interpreta:crea una particion( array) con el 80% de los datos y mo ,me lo des como lista
Crearemos una particion con el 70%
training.ids2<- createDataPartition(dat.casa$MEDV,p=.70, list=F)
data.training2<-dat.casa[training.ids2,]
temp<- dat.casa[-training.ids2,]
data.validation2 <-createDataPartition(temp$MEDV,p=.5, list=F)
data.testing<- temp[-data.validation2,]Particion de data frame con variable categorica
data2<-read.csv("../../r-course-master/data/tema2/boston-housing-classification.csv", head =T)Paricion 70-30
training.ids.3<- createDataPartition(data2$MEDV_CAT,p=0.7,list = F)
data.training.3<-data2[training.ids.3,]
data.validation3<-data2[-training.ids.3,]Funciones de 2 con pariciones definidas
rda.cb.partition2<- function(dataframe,target.index,prob){
library(caret)
training.ids<- createDataPartition(dataframe[,target.index],p=prob, list = F)
list(train=dataframe[training.ids,],val=dataframe[-training.ids,])
}Funcion de 3 particiones
rda.cb.partition3<- function(dataframe,target.index,
prob.training,prob.val){
library(caret)
training.ids<- createDataPartition(dataframe[,target.index],p=prob.training, list = F)
train.data<-dataframe[training.ids,]
temp<- dataframe[-training.ids,]
validation.ids<-createDataPartition(temp[,target.index],p=prob.val,list=F)
list(train=train.data,val=validation.ids,test=temp[-validation.ids,])
}veamos como hace las particiones de la funcion de 2 particiones
data.particion2<-rda.cb.partition2(dat.casa,14,0.8)Para partir con la funcion de 3
data.particion3<- rda.cb.partition3(dat.casa,14,0.7,0.5)Como se accede a las propiedades de una lista
data.particion2.1<-data.particion2[[1]]
data.particion2.1[1:3,]## CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B LSTAT
## 1 0.00632 18 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98
## 3 0.02729 0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03
## 4 0.03237 0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94
## MEDV
## 1 24.0
## 3 34.7
## 4 33.4data.particion3.1<-data.particion3[[1]]
data.particion3.1[1:3,]## CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B LSTAT
## 1 0.00632 18 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98
## 2 0.02731 0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14
## 4 0.03237 0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94
## MEDV
## 1 24.0
## 2 21.6
## 4 33.4obtener una muestra del DF Original ( muestreo aleatorio )
muestra<-sample(dat.casa$CRIM,40,replace = F)# replase = False significa que esos 40 que tome valores quer no estan repoetidos ( Nota :no puedes pedir mas de los que tiene tu DF)
# puede validar el numero de filas con nrowHistogramas ,boxplots y scatterplots
auto.data<- read.csv("../../r-course-master/data/tema2/auto-mpg.csv", stringsAsFactors = F)
#sobre escribiremos a cartor la columna de cilindros
auto.data$cylinders<-factor(auto.data$cylinders,
levels= c(3,4,5,6,8),labels=c("3cil","4cil","5cil","6cil","8cil"))
attach(auto.data)#carga el paquete para trabajar y con esto te evitas referenciar el DF , solo indicas la columna ## The following object is masked from package:ggplot2:
##
## mpgHistograma
hist(acceleration,
col =rainbow(16),#Color del Histograma o rainbow para arcohiris o solo col="Blue"
xlab="Aceleracion",# Nombre del eje X
ylab = "Frecuencia",# Nombre del eje y
main ="Histograma de la aceleraciones",# Titulo que quieres poner
breaks = 16)#numero de diviciones que deseas
Boxplot ( Caja de gatos con bigotes )
Se puede hacer que te mida todo el Df de un jalon
#boxplot(auto.data)boxplot(mpg,
xlab="Millas por Galeon")
Hacer un boxplot en funcion de otra variable
boxplot(mpg~model_year, #muestra las millas por galeon por ano
xlab="millas por galeon" )
Hacer otro boxplot en fincion de otra varioable distinta a la mencionada arriba
boxplot(mpg~cylinders,
xlab="Millas por Galeon en funcion de los cilindros" )
# Scatter Plot (solo variables numericas)
plot(mpg~horsepower)
linearmodel1<-lm(mpg~horsepower)#Crea una linea de regresion lineal
abline(linearmodel1)# sobrepone la linea sobre la grafica anterior 
Es mas funcional tener una matriz de scaterplot
pairs(~mpg+displacement+horsepower+weight)
Como pintar una grafica
plot(mpg~horsepower,#Grafica de nube de puntos
type="n") # No pintes nada ( no pongas los puntos )
linearmodel<-lm(mpg~horsepower)#Crea una linea de regresion lineal
abline(linearmodel)# sobrepone la linea sobre la grafica anterior
# Agregar colores para una variable ( en este caso los cilindros )
with(subset(auto.data,# toma es te subconjunto
cylinders=="8cil"),# que se llama asi
points(horsepower,mpg,# de estas variables que mandate llamar en el grafico
col="red"))# y los coloreas de este color
with(subset(auto.data,cylinders=="6cil"), points(horsepower,mpg,col="blue"))
with(subset(auto.data,cylinders=="5cil"), points(horsepower,mpg,col="green"))
with(subset(auto.data,cylinders=="4cil"), points(horsepower,mpg))# si no se manda llamar algun color , lo pone negro por defoult
with(subset(auto.data,cylinders=="3cil"),points(horsepower,mpg,,col="yellow"))
Histograma con linea de tendencia
Nota el Histograma tiene que estar en frecuencias acumuladas
hist(mpg,prob=T)
lines(density(mpg))
Combinar 2 graficas en una sola imagen con par()
old.par<- par()#guardas que esta era la configuracion inicial de los parametros :ojo esto es importante
par(mfrow=c(1,2))# Cmabias la configuracion ( por columnas, una al lado de otra )
# para mfcol ( los tienes un gafico uno sobre otro )
with(auto.data,{
plot(mpg~weight, main="Peso vs Consumo")
plot(mpg~acceleration,main="Aceleracion Vs el Consumo ")
}
)
par(old.par)# aqui regresas a decirle que solo es para este grafico ( esto que te mandas en rojo, solo es ten cuidado , pero no esta mal )## Warning in par(old.par): el parámetro del gráfico "cin" no puede ser
## especificado## Warning in par(old.par): el parámetro del gráfico "cra" no puede ser
## especificado## Warning in par(old.par): el parámetro del gráfico "csi" no puede ser
## especificado## Warning in par(old.par): el parámetro del gráfico "cxy" no puede ser
## especificado## Warning in par(old.par): el parámetro del gráfico "din" no puede ser
## especificado## Warning in par(old.par): el parámetro del gráfico "page" no puede ser
## especificadoLattise
bwplot xyplot densityplot splom
library(lattice)
bwplot(~auto.data$mpg|auto.data$cylinders,#asi se representan valores(x) con factores(y) de manera rapida
main="MPG segun los cilindros ",
xlab = "Millas por Galeon")
# Cajas de gatos con vigotes de menara rapida con valores numeros y vategorias
xyplot(mpg~weight|cylinders,data = auto.data,
main="Peso Vs Consumo Vs Cilindros",
xlab = "Peso en KG",
ylab="Consumo (MPG)")
#Se puede agregar el valor aspect=1 a la funcion para que guarde la proporcion Comparar a travez de representaciones
alquiler<- read.csv("../../r-course-master/data/tema2/daily-bike-rentals.csv", header = T)
alquiler$season<-factor(alquiler$season, levels = c(1,2,3,4),labels=c("Invierno","Primavera","Verano","Otono"))
alquiler$workingday<-factor(alquiler$workingday, levels = c(0,1),labels=c("festivo ","De trabajo"))
alquiler$weathersit<-factor(alquiler$weathersit, levels = c(1,2,3),labels=c("Despejado","Nublado", "LLuvia/Nieve "))
alquiler$dteday<- as.Date(alquiler$dteday, format = "%Y-%m-%d")# cambiar la columna a dia en el formatro especializado
attach(alquiler)## The following object is masked _by_ .GlobalEnv:
##
## temp## The following object is masked from package:mice:
##
## windspeed4 graficas en una sola imagen
winter <- subset(alquiler,season=="Invierno")$cnt
spring<-subset(alquiler,season=="Primavera")$cnt
summer<-subset(alquiler,season=="Verano")$cnt
fall<-subset(alquiler,season=="Otono")$cntpar(mfrow=c(2,2))#2 filas y 2 columnas hist(winter,prob=T, xlab = “alquiler de bicicletas en Invierno”, main = ““)
par(mfrow=c(2,2))#2 filas y 2 columnas
hist(winter,prob=T, xlab = "alquiler de bicicletas en Invierno", main = "")
lines(density(winter))
abline(v=mean(winter), col="blue")
abline(v=median(winter), col="red")
hist(spring,prob=T, xlab = "alquiler de bicicletas en Primavera", main = "")
lines(density(spring))
abline(v=mean(spring), col="blue")
abline(v=median(spring), col="red")
hist(summer,prob=T, xlab = "alquiler de bicicletas en Verano", main = "")
lines(density(summer))
abline(v=mean(summer), col="blue")
abline(v=median(summer), col="red")
hist(fall,prob=T, xlab = "alquiler de bicicletas en otono", main = "")
lines(density(fall))
abline(v=mean(fall), col="blue")
abline(v=median(fall), col="red")
# Visualizacion de datos
#install.packages("beanplot")
library(beanplot)
beanplot(alquiler$cnt~alquiler$season, col = c("blue","red","green"))
#ctm es la cantidad de bicicletas que se alqularon Posible Causalidad ( Esto se presenta a direccion )
Esto puede ayudar validar hipotesis Analisis de causalidad
bwplot(cnt~weathersit, data = alquiler,
layout=c(1,1),
xlab = "Pronostico del tiempo",
ylab = "Frecuencias"
)
Ahora si queriamos comparar cuantos datos se tienen por distribucion
bwplot(cnt~weathersit, data = alquiler,
layout=c(1,1),
xlab = "Pronostico del tiempo",
ylab = "Frecuencias",
panel = function(x,y,...){
panel.bwplot(x,y,...)
panel.stripplot(x,y,jitter.data = T,...,col="blue")#anade /sobrepone los datos de los puntos
}
)
# Tecnica de Validacion cruzada Se explicara mas a detalle mas adelante # Q-Q Plot Graficos Cuartil- Cuartil ( validar si es normal una muestra o datos )
s<-seq(0.01,0.99,0.01)# crea una secuencia desde el 0.001 hasta el 0.99 en contadores de 0.001
qn<-qnorm(s)#cuartiles normales
df<-data.frame(p=s,q=qn)
sample<-rnorm(200)# se hizo una distribucion normal con 200 datos
quantile(sample,probs = s)## 1% 2% 3% 4% 5% 6%
## -2.055577679 -1.992587557 -1.923669383 -1.819028832 -1.781607474 -1.687751134
## 7% 8% 9% 10% 11% 12%
## -1.508056688 -1.455795697 -1.298594111 -1.275686301 -1.196551456 -1.170797954
## 13% 14% 15% 16% 17% 18%
## -1.137196582 -1.077353836 -1.009672373 -0.992896091 -0.965983635 -0.899407571
## 19% 20% 21% 22% 23% 24%
## -0.874598243 -0.858187236 -0.767458782 -0.747680443 -0.727995697 -0.716790398
## 25% 26% 27% 28% 29% 30%
## -0.701607965 -0.662561116 -0.620113773 -0.544906561 -0.530396062 -0.508232899
## 31% 32% 33% 34% 35% 36%
## -0.475261809 -0.465008877 -0.454095238 -0.438904163 -0.410543511 -0.394888059
## 37% 38% 39% 40% 41% 42%
## -0.388175220 -0.357129631 -0.315099855 -0.275215674 -0.266799437 -0.247391137
## 43% 44% 45% 46% 47% 48%
## -0.231328523 -0.206997353 -0.178793544 -0.168080581 -0.131701120 -0.104470825
## 49% 50% 51% 52% 53% 54%
## -0.099925444 -0.088655855 -0.057255416 -0.046189812 -0.023463017 0.008122843
## 55% 56% 57% 58% 59% 60%
## 0.061224700 0.104525861 0.130282941 0.142039331 0.169750936 0.188530312
## 61% 62% 63% 64% 65% 66%
## 0.208795272 0.220380875 0.230618120 0.244648746 0.256915945 0.268875238
## 67% 68% 69% 70% 71% 72%
## 0.289933316 0.362914355 0.372145602 0.410711232 0.446006964 0.472163111
## 73% 74% 75% 76% 77% 78%
## 0.501493524 0.527359701 0.557780203 0.602611089 0.663977468 0.695990469
## 79% 80% 81% 82% 83% 84%
## 0.722022081 0.778221977 0.846321370 0.912534405 0.952579796 1.021396271
## 85% 86% 87% 88% 89% 90%
## 1.073874417 1.136068446 1.143140328 1.154871615 1.175476688 1.257221019
## 91% 92% 93% 94% 95% 96%
## 1.281232264 1.311270023 1.360024297 1.399522634 1.432030721 1.655224747
## 97% 98% 99%
## 1.818300390 1.946532206 2.126562356qqnorm (tiene que seguir aproximadamente una linea recta )
qqnorm(trees$Height)
# como diferenciar cuando es una distribucion normal de una uniforme Se ve como una ojiva y recordemos que tiene que ser una linea recta
qqnorm(randu$x)
qqplot otra forma de hacer grafica quantil-cuantil para distribuciones uniformes
#randu es una distribucion uniforme( todos tienen la misma probabilidad de salir )
n<-length(randu$x)# obtiene el largo de los datos ( son 400)
y<- qunif(ppoints(n))#qunif distribucion uniforme
#ppoints genera un nuemro de probabilidades , en este caso , ya sacamos el largo por eso es n
qqplot(y,randu$x)
representacion de ji-cuadrado
Asi se ve una distribucion de ji cuadrado
chi3<- qchisq(ppoints(30)#qchiq= cuantil chi square
,df=3)# df son los grados de libertad
n30<- qnorm(ppoints(30))
qqplot(n30,chi3)
distribucion de cuauchy
se ve como una onda y tiene que ser una linea recta
cauchy<-qcauchy(ppoints(30))
qqplot(n30,cauchy)
Ahora , si comparas la misma
x<- seq(-3,3,0.01)# aqui solo generas numeros
par(mfrow=c(3,2))# configuracion par que sean 2 graficas
plot(x,dnorm(x),
main="Distibucion Normal")#funcion de distribucion
plot(x,pnorm(x),
main="Probabilidad de una normal")# funcion de probabilidad acumulativa
plot(x,dchisq(x,df=3),
#Grados de libertad
main="Distibucion chi")#funcion de distribucion de chi
plot(x,pchisq(x,df=3),
main="probabilidad chi")#funcion de probabilidad de Chi
plot(x,dcauchy(x),
#Grados de libertad
main="Distibucion chi")#funcion de distribucion de cauchi
plot(x,pcauchy(x),
main="probabilidad chi")#funcion de probabilidad de cauchi
Clasificacion en data science
Algortimos de ML que no se ven en este curso
XGBoost Long Short-Term Memory (LSTM)
Matices de confucion
calificaciones<-read.csv("../../r-course-master/data/tema3/college-perf.csv",header = T)
calificaciones$Perf<-ordered(calificaciones$Perf,levels=c("Low","Medium","High"))# odenamos los valores ne este orden , en el DF no se ve pero ya lo hizo
calificaciones$Pred<-ordered(calificaciones$Pred,levels=c("Low","Medium","High"))# mismo comentario de arriba pero a otr columna
table<-table(calificaciones$Perf,calificaciones$Pred,dnn = c("Actual"," Prediccion"))
table# matriz de confucion ## Prediccion
## Actual Low Medium High
## Low 1150 84 98
## Medium 166 1801 170
## High 35 38 458si se quiere saber en porcentaje
prop.table(table)*100#en porcentaje ( pero esto es del total) ## Prediccion
## Actual Low Medium High
## Low 28.750 2.100 2.450
## Medium 4.150 45.025 4.250
## High 0.875 0.950 11.450round(prop.table(table,1)*100,2)#esto es por filas## Prediccion
## Actual Low Medium High
## Low 86.34 6.31 7.36
## Medium 7.77 84.28 7.96
## High 6.59 7.16 86.25round(prop.table(table,2)*100,2)#por columnas ## Prediccion
## Actual Low Medium High
## Low 85.12 4.37 13.50
## Medium 12.29 93.66 23.42
## High 2.59 1.98 63.09Matiz de confusión en una grafica
barplot(table,leyend=T,
Xlab="Nota predecida por el modelo")## Warning in plot.window(xlim, ylim, log = log, ...): "leyend" is not a graphical
## parameter## Warning in plot.window(xlim, ylim, log = log, ...): "Xlab" is not a graphical
## parameter## Warning in axis(if (horiz) 2 else 1, at = at.l, labels = names.arg, lty =
## axis.lty, : "leyend" is not a graphical parameter## Warning in axis(if (horiz) 2 else 1, at = at.l, labels = names.arg, lty =
## axis.lty, : "Xlab" is not a graphical parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "leyend" is not a graphical parameter## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...): "Xlab"
## is not a graphical parameter## Warning in axis(if (horiz) 1 else 2, cex.axis = cex.axis, ...): "leyend" is not
## a graphical parameter## Warning in axis(if (horiz) 1 else 2, cex.axis = cex.axis, ...): "Xlab" is not a
## graphical parameter
mosaicplot(table,main="Eficiencia del modelo")
summary(table)# el p-valor indica dependencia entre el valor actual y el predicho , es decir el modelo es eficiente## Number of cases in table: 4000
## Number of factors: 2
## Test for independence of all factors:
## Chisq = 4449, df = 4, p-value = 0Analisis de Componentes Principales
Tranforma los datos de muchas dimenciones y busca los basicos
crimenes<-read.csv("../../r-course-master/data/tema3/USArrests.csv")
rownames(crimenes)<-crimenes$X #cambia como el identificador a una columna en particular
crimenes$X<- NULL# Eliminamos la fila que ya no necesitamos
apply(crimenes,
2,#por columnas
var)# la funcion varianza## Murder Assault UrbanPop Rape
## 18.97047 6945.16571 209.51878 87.72916Podemos observar que la que tiene mayor varianza es la de assault
Normaliza y estandarizar los datos
acp<-prcomp(crimenes,# analisis de componentes principales
center = T,#Resta la media de cada variable
scale. = T)#y divide por la desviación estandar
acp## Standard deviations (1, .., p=4):
## [1] 1.5748783 0.9948694 0.5971291 0.4164494
##
## Rotation (n x k) = (4 x 4):
## PC1 PC2 PC3 PC4
## Murder -0.5358995 0.4181809 -0.3412327 0.64922780
## Assault -0.5831836 0.1879856 -0.2681484 -0.74340748
## UrbanPop -0.2781909 -0.8728062 -0.3780158 0.13387773
## Rape -0.5434321 -0.1673186 0.8177779 0.08902432Graficar la lista Nota:usar la tecnica del codo la cual consiste en donde la tecnica del “codo del brazo”, por ejemplo, en este caso lo que se mostraria, serian las 2 variables leyendolas de izquierda a derecha
plot(acp,type="l")
summary(acp)## Importance of components:
## PC1 PC2 PC3 PC4
## Standard deviation 1.5749 0.9949 0.59713 0.41645
## Proportion of Variance 0.6201 0.2474 0.08914 0.04336
## Cumulative Proportion 0.6201 0.8675 0.95664 1.00000cuando se observa el summary de acp en la caracteristica cumulative proportion significa la cantidad de datos que se representan #representacion de acp
biplot(acp,scale=0,col=c("grey","red")) 
si se observa hacia donde se mueve mas es rape en el espacio PC2 #Analizando la diferentes componentes principales
pc1<- apply(acp$rotation[,1]*crimenes,1,sum)
pc2<- apply(acp$rotation[,1]*crimenes,1,sum)#de la lista sumamos el vector de rotacion , recuerda que son matrices
crimenes$pc1<-pc1#sumamos una nueva fila a dataset
crimenes$pc2<-pc2curva ROC ( caracteristicas operativas del receptor )
Es una tecnica de clasificacion: Sensibilidad vs Especificidad Entre mas sensibilidad es mejor y entre menos especificidad es mejor Cargamos los paquetes
#install.packages("ROCR")
library(ROCR)
curvaroc1<- read.csv("../../r-course-master/data/tema3/roc-example-1.csv")# variables numericas
curvaroc2<- read.csv("../../r-course-master/data/tema3/roc-example-2.csv")# variables categoricasPara el DF de variables numericas tpr=true, positive rate, fpr=False Positive Rate
#nota : las clases del DF significa: 0 fallo y 1 exito
pred1<-prediction(curvaroc1$prob,curvaroc1$class)#Con respecto a este objeto
perf1<-performance(pred1,"tpr","fpr")
#Objeto de probabilidad con respecto ala predicción
plot(perf1)
lines(par()$usr[1:2],par()$usr[3:4])
Cuales son las variables que son los que se tienen que perfecionar
probcortes<-data.frame(cut=perf1@alpha.values[[1]],#valor de corte
pfr=perf1@x.values[[1]],#toma los valores de perfilamiento del eje x
tpr=perf1@y.values)#toma los valores de perfilamiento del eje x
colnames(probcortes)<-c("cut","pfr","tpr")# renombraremos las columnas
head(probcortes)#La cabeza de los datos ## cut pfr tpr
## 1 Inf 0 0.00000000
## 2 0.9917340 0 0.01851852
## 3 0.9768288 0 0.03703704
## 4 0.9763148 0 0.05555556
## 5 0.9601505 0 0.07407407
## 6 0.9351574 0 0.09259259tail(probcortes)#Cola de los datos ## cut pfr tpr
## 96 0.10426897 0.8913043 1
## 97 0.07292866 0.9130435 1
## 98 0.07154785 0.9347826 1
## 99 0.04703280 0.9565217 1
## 100 0.04652589 0.9782609 1
## 101 0.00112760 1.0000000 1Se debe encontar un equilibrio donde la empresa se sienta comoda con los verdederos positivos
probcortes[probcortes$tpr>.80,]#dame todos los valores verdaderos positivos por arriba del 80%## cut pfr tpr
## 55 0.49815058 0.2173913 0.8148148
## 56 0.49616956 0.2173913 0.8333333
## 57 0.47840739 0.2391304 0.8333333
## 58 0.47754679 0.2608696 0.8333333
## 59 0.46323419 0.2826087 0.8333333
## 60 0.45227354 0.2826087 0.8518519
## 61 0.44950615 0.3043478 0.8518519
## 62 0.43443516 0.3260870 0.8518519
## 63 0.43274841 0.3478261 0.8518519
## 64 0.42845777 0.3695652 0.8518519
## 65 0.40701592 0.3695652 0.8703704
## 66 0.40272660 0.3695652 0.8888889
## 67 0.40248242 0.3913043 0.8888889
## 68 0.40140505 0.3913043 0.9074074
## 69 0.37646732 0.4130435 0.9074074
## 70 0.36254324 0.4347826 0.9074074
## 71 0.35547851 0.4565217 0.9074074
## 72 0.34872470 0.4782609 0.9074074
## 73 0.33814262 0.5000000 0.9074074
## 74 0.33528819 0.5217391 0.9074074
## 75 0.33041954 0.5434783 0.9074074
## 76 0.31878806 0.5652174 0.9074074
## 77 0.31438300 0.5869565 0.9074074
## 78 0.31164060 0.6086957 0.9074074
## 79 0.30761685 0.6086957 0.9259259
## 80 0.23584000 0.6304348 0.9259259
## 81 0.23310990 0.6304348 0.9444444
## 82 0.22243908 0.6521739 0.9444444
## 83 0.20977357 0.6739130 0.9444444
## 84 0.19511299 0.6739130 0.9629630
## 85 0.18730064 0.6739130 0.9814815
## 86 0.18210618 0.6956522 0.9814815
## 87 0.17060700 0.7173913 0.9814815
## 88 0.15536249 0.7391304 0.9814815
## 89 0.14907332 0.7391304 1.0000000
## 90 0.14288827 0.7608696 1.0000000
## 91 0.13868194 0.7826087 1.0000000
## 92 0.13090790 0.8043478 1.0000000
## 93 0.12741177 0.8260870 1.0000000
## 94 0.12676979 0.8478261 1.0000000
## 95 0.11107864 0.8695652 1.0000000
## 96 0.10426897 0.8913043 1.0000000
## 97 0.07292866 0.9130435 1.0000000
## 98 0.07154785 0.9347826 1.0000000
## 99 0.04703280 0.9565217 1.0000000
## 100 0.04652589 0.9782609 1.0000000
## 101 0.00112760 1.0000000 1.0000000Para el DF de variables numericas
predict2<-prediction(curvaroc2$prob,curvaroc2$class,label.ordering = c("non-buyer","buyer"))
# nota importante :deben cohincidri exactamente los nombres con las etiquetas del DF
#Probabilidad mas baja de compra a la mas alta ( en ese orden )
perf2<-performance(predict2,"tpr","fpr")
plot(perf2)
lines(par()$usr[1:2],par()$usr[3:4])# pinta la linea recta # Arboles de clasificacion
#install.packages(c("rpart","rpart.plot","caret"))
library(rpart)#construye los arboles
library(rpart.plot)#grafico de esos arboles
library(caret)
library(ggplot2)
library(lattice)Cargamos los datos
banknote<-read.csv("../../r-course-master/data/tema3/banknote-authentication.csv")#caret:: set.seed(2018)# semilla
training.ids<-caret::createDataPartition(banknote$class,p=0.7,list=F)#revisar Particion de data frame con variable numerica
mod<-rpart(class~.,# especifica las variables dependientes e independientes ( clase depende de todas las anteriores(.))
data = banknote[training.ids,],#De donde va a tomar los datos ( de solo los datos de entrenamiento)
method = "class",#Clasifique casos
control = rpart.control(minsplit = 20,#Solo debe tener mas elementos en cada clase
cp=0.01))#ajustar la complejidad
mod # Asi se ve el modelo de forma (analitica )## n= 961
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 961 427 0 (0.555671176 0.444328824)
## 2) variance>=0.320165 498 57 0 (0.885542169 0.114457831)
## 4) variance>=1.5922 353 6 0 (0.983002833 0.016997167) *
## 5) variance< 1.5922 145 51 0 (0.648275862 0.351724138)
## 10) curtosis>=-2.2722 107 16 0 (0.850467290 0.149532710)
## 20) entropy< 0.0353305 81 1 0 (0.987654321 0.012345679) *
## 21) entropy>=0.0353305 26 11 1 (0.423076923 0.576923077)
## 42) curtosis>=1.99915 9 0 0 (1.000000000 0.000000000) *
## 43) curtosis< 1.99915 17 2 1 (0.117647059 0.882352941) *
## 11) curtosis< -2.2722 38 3 1 (0.078947368 0.921052632) *
## 3) variance< 0.320165 463 93 1 (0.200863931 0.799136069)
## 6) skew>=5.86535 106 31 0 (0.707547170 0.292452830)
## 12) variance>=-3.4449 74 0 0 (1.000000000 0.000000000) *
## 13) variance< -3.4449 32 1 1 (0.031250000 0.968750000) *
## 7) skew< 5.86535 357 18 1 (0.050420168 0.949579832)
## 14) curtosis>=6.21565 115 16 1 (0.139130435 0.860869565)
## 28) skew>=-4.6745 16 1 0 (0.937500000 0.062500000) *
## 29) skew< -4.6745 99 1 1 (0.010101010 0.989898990) *
## 15) curtosis< 6.21565 242 2 1 (0.008264463 0.991735537) *Como verlo de manera grafica
prp(mod,type=2,#cada nodo quede etiquetado y sea exactamente a la mitad( investigar/ variar)
extra=104,#mostrar la probabilidad de cada uno de los casos por en base acada rama
nn=T,# numero del nodo
fallen.leaves=T,#Nodos finales los muestra en la parte final
faclen=4,# abreviar los nombres de las clases
varlen=8,# abreviar la longitud de los nombres de las variables
shadow.col="blue")#sombras 
# Podar arboles de Clasificacion Seleccionar solo algunas ramas muestra las componentes principales
mod$cptable## CP nsplit rel error xerror xstd
## 1 0.64871194 0 1.00000000 1.00000000 0.03607406
## 2 0.10304450 1 0.35128806 0.38875878 0.02744404
## 3 0.07025761 2 0.24824356 0.31850117 0.02530503
## 4 0.03747073 3 0.17798595 0.20140515 0.02072353
## 5 0.01639344 5 0.10304450 0.17096019 0.01923439
## 6 0.01522248 7 0.07025761 0.13817330 0.01742769
## 7 0.01000000 9 0.03981265 0.08665105 0.01396842se debe seleccionar minimo xerror y sumada alguna la fila de xstd( desviacion estandar) debe dar un valor minimo de xerror para este ejemplo tomamos la numero 6( desviacion estandar wey) # Creamos el modelo de clasificacion recortado
mod.pruned<-prune(mod,mod$cptable[6,"CP"])Ver el modelo de clasificacion de arboles “Podado”
prp(mod.pruned,type=2,#cada nodo quede etiquetado y sea exactamente a la mitad( investigar/ variar)
extra=104,#mostrar la probabilidad de cada uno de los casos por en base acada rama
nn=T,# numero del nodo
fallen.leaves=T,#Nodos finales los muestra en la parte final
faclen=4,# abreviar los nombres de las clases
varlen=8,# abreviar la longitud de los nombres de las variables
shadow.col="blue")#sombras 
Esto es una Predicion hecha con el 70% de los datos
Con los datos totales coteja que tan bien se apega a los valores de el DF original
pred.pruned<-predict(mod,banknote[-training.ids,],type = "class")# generea la prediccion
table(banknote[-training.ids,]$class,pred.pruned,dnn=c("Actual","Predicho"))## Predicho
## Actual 0 1
## 0 228 0
## 1 4 179Se ve excelente
#comparando con el arbol “podado” Se han movido los datos y subieron los errores
pred.pruned.recort<-predict(mod.pruned,banknote[-training.ids,],type = "class")# generea la prediccion
table(banknote[-training.ids,]$class,pred.pruned.recort,dnn=c("Actual","Predicho"))## Predicho
## Actual 0 1
## 0 228 0
## 1 6 177Diagramas ROC para arboles
pred.pruned.predict<-predict(mod.pruned,banknote[-training.ids,],type = "prob")# genera modelo probabilistico
pred.prob<-ROCR::prediction(pred.pruned.predict[,2],banknote[-training.ids,"class"])
perf.pruned.pred<-ROCR::performance(pred.prob,"tpr","fpr")
plot(perf.pruned.pred)
Esto si es un pinche buen modelo
Random Forest
#install.packages("randomForest")
library(randomForest)## randomForest 4.7-1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:dplyr':
##
## combine## The following object is masked from 'package:ggplot2':
##
## marginlibrary(ROCR)
library(caret)
library(ggplot2)Cargamos los datos
random.forest<-read.csv("../../r-course-master/data/tema3/banknote-authentication.csv")Cambiemos a factores la columna de class
random.forest$class<-factor(random.forest$class)Hagamos una semilla y particion de datos
set.seed(2018)
trainining.random.fores<-createDataPartition(random.forest$class,p=.70,list=F)Apliquemos la tecnica de Random Forest
mod<-randomForest(x=random.forest[trainining.random.fores,1:4],# todas las variables independientes ( todo el Df)
y=random.forest[trainining.random.fores,5],# esta es la variable que queremos predecir
ntree = 500,#numero de arboles
keep.forest =TRUE)#se queda con todos los arboles intermedios ( para predicciones futuras)Predecimos los valores del Df con los valores predichos por el modelo
pred.random.forest<-predict(mod,random.forest[-trainining.random.fores,])
table(random.forest[-trainining.random.fores,"class"],pred.random.forest,dnn=c("Actual","Predicho"))## Predicho
## Actual 0 1
## 0 225 3
## 1 0 183Diagrama ROC para el Random Forest
prob.random.forest<-predict(mod,random.forest[-trainining.random.fores,],type = "prob")# genero probabilidades
head(prob.random.forest)## 0 1
## 4 1.000 0.000
## 6 1.000 0.000
## 7 1.000 0.000
## 9 1.000 0.000
## 10 0.958 0.042
## 12 1.000 0.000library(ROCR)
pred.random.forest.roc<-ROCR::prediction(prob.random.forest[,2],random.forest[-trainining.random.fores,"class"])#Predecir
perf.random.forest<-performance(pred.random.forest.roc,"tpr","fpr")# desempeno de las prediciiones
plot(perf.random.forest)
Este modelo es la leche # maquinas de soporte vectorial ( Suport Verctor Machine ) Hiper plano de separacion se trata de encontrar la linea que separa ambos conjuntos de datos
#install.packages("e1071")
library(e1071)##
## Attaching package: 'e1071'## The following objects are masked from 'package:moments':
##
## kurtosis, moment, skewness## The following object is masked from 'package:raster':
##
## interpolate## The following object is masked from 'package:modeest':
##
## skewness## The following object is masked from 'package:Hmisc':
##
## imputelibrary(caret)
banknote.svm<-read.csv("../../r-course-master/data/tema3/banknote-authentication.csv")
banknote.svm$class =factor(banknote.svm$class)Plantamos la semilla
set.seed(2018)
t.ids<-createDataPartition(banknote.svm$class,p=.70,list = F)
#La fuccion svm toma una media y desviacion estandar ( unificada )
# el parametro puede darle diferentes pesos a las clases en las cuales se quieres orrer la funcion svm ( class.weights =c("0"=0.03),"1"=0.7))
# cost =1 (por default)es el parametro donde se le dice al modelo qe margen de error podemos darle
mod.svm<-svm(class~.,#tipo de modelo ( calsificacion o de regresion )cuando es clasificacion solo separa
data=banknote.svm[t.ids,],
cost=1)Como calculo el margen de error mas eficiente Investigar un poco mas, ya que aqui nos sale un error
tuned<-tune.svm(class~.,data=banknote.svm[t.ids,],gama=10^(-6:-1),10^(1:2))
summary(tuned)##
## Error estimation of 'svm' using 10-fold cross validation: 0Grafiquemos los datos del svm
table(banknote.svm[t.ids,"class"],fitted(mod.svm)#Trae los valores dentro de si , vienen intrinsecos
,dnn=c("Actual","Predicho"))## Predicho
## Actual 0 1
## 0 534 0
## 1 0 427NO existe error alguno
Se fue catalogado de manera correcta
pred.smv<-predict(mod.svm,banknote.svm[-t.ids,])# predice
table(banknote.svm[-t.ids,"class"],pred.smv,dnn=c("Actual","Predicho"))## Predicho
## Actual 0 1
## 0 228 0
## 1 0 183Grafica de 2 variables ( en este caso con los valores de entrenamiento)
Grafiquemoslo para ver como se observa como tenemos 4 variables seleccionamos 2 , pero esto es para los valores de entrenamiento
plot(mod.svm,data=banknote.svm[t.ids,],skew~variance)
# Grafica del SVM sin los valores de entrenamiento
plot(mod.svm,data=banknote.svm[-t.ids,],skew~variance)
Generemos la interpolacion de bayes
pred.bayes<-predict(mod.bayes,ep[-t.id.bayes,])# prediccion
# tabla de confucion
tab.bayes<-table(ep[-t.id.bayes,]$Purchase,pred.bayes,dnn=c("Actual", "Predicho"))
confusionMatrix(tab.bayes)## Confusion Matrix and Statistics
##
## Predicho
## Actual No Yes
## No 1 1
## Yes 2 0
##
## Accuracy : 0.25
## 95% CI : (0.0063, 0.8059)
## No Information Rate : 0.75
## P-Value [Acc > NIR] : 0.9961
##
## Kappa : -0.5
##
## Mcnemar's Test P-Value : 1.0000
##
## Sensitivity : 0.3333
## Specificity : 0.0000
## Pos Pred Value : 0.5000
## Neg Pred Value : 0.0000
## Prevalence : 0.7500
## Detection Rate : 0.2500
## Detection Prevalence : 0.5000
## Balanced Accuracy : 0.1667
##
## 'Positive' Class : No
## K-Nearest Neighbors
Es una metodologia no parametrica de clasificacion y regresion
#install.packages("class")# regularmente instalado pero se puede actualizar
library(class)
library(caret)cargamos el archivo
vacaciones<-read.csv("../../r-course-master/data/tema3/vacation-trip-classification.csv")
anyNA(vacaciones)# existen NA## [1] FALSENormalizaremos el DF con 2 nuevas variables ya que estan super diferentes los valores de entrada
vacaciones$income.z<-scale(vacaciones$Income)#crea una columna normalizada
vacaciones$Family_size.z<-scale(vacaciones$Family_size)#crea una columna normalizadaSe hara de la siguiente forma 50% del Df para entrenar 25% para validar 25% para testear
set.seed(2018)
#50% de los datos
t.ids.kneigh<-createDataPartition(vacaciones$Result,p=0.5, list = F)#Data de entrenamiento
train.kneigh<-vacaciones[t.ids.kneigh,]# Data de entrenamiento
temp.kneigh<-vacaciones[-t.ids.kneigh,]# es el restante es decir todos los que no son el conjunto de entrenamiento
# Aqui se encuentra la partición del 25% que esta ( 50% del 50%)
validation.id.kneigh<-createDataPartition(temp.kneigh$Result, p=0.5, list=F)#Datos de validacion
validation.kneigh<-temp.kneigh[validation.id.kneigh,]
test.kneigh<- temp.kneigh[-t.ids.kneigh,]Empecemos a predecir , para este momento es iterativo , es decir manualmente se varia k=1,2,3,4..n
pred1.kneigh<-knn(train.kneigh[,4:5],validation.kneigh[,4:5],train.kneigh[,3],k=1)
errormatrix1<-table(validation.kneigh$Result,pred1.kneigh,dnn=c("Actual","Predict"))
errormatrix1## Predict
## Actual Buyer Non-buyer
## Buyer 3 2
## Non-buyer 5 0Generareremos una predidcion 2 y sale diferente y observamos que con K=5 nos manda un error de tan solo 3 datos que no predice
pred2.kneigh<-knn(train.kneigh[,4:5],test.kneigh[,4:5],train.kneigh[,3],k=5)
errormatrix2<- table(test.kneigh$Result,pred2.kneigh,dnn =c("Actual","Predict"))
errormatrix2## Predict
## Actual Buyer Non-buyer
## Buyer 4 2
## Non-buyer 1 4Funcion que elija el mejor numero de vecinos en la metodologia K-Nearest Neighbors
knn.automate<-function(tr.prediction, val.prediction,tr.target,val.target,start.k,end.k){
for(k in start.k:end.k){
pred.k<-knn(tr.prediction,val.prediction,tr.target,k)
tab<- table(val.target,pred.k,dnn = c("Actual","Predicho"))
cat(paste("Matriz de confusion para k=",k,"\n"))
cat("===============================\n")
print(tab)
cat("-------------------------------\n")
}
}Como chingaos se utiliza esta funcion recuerda que el nombre de las variables puede variar dependeindo de como tu llamaste tus variables
knn.automate(train.kneigh[,4:5],# el conjunto de entrenamiento en este caso normalizado
validation.kneigh[,4:5],#las columnas de validacion
train.kneigh[,3],# le decimos que de la columna 3 que es por la categoria que queremos clasificar
validation.kneigh[,3],#tambien le decimos que lo queremos por esta variable de este conjunto de datos
1,8#Aqui le decimos de que numero a que numero va a iterar
)## Matriz de confusion para k= 1
## ===============================
## Predicho
## Actual Buyer Non-buyer
## Buyer 3 2
## Non-buyer 4 1
## -------------------------------
## Matriz de confusion para k= 2
## ===============================
## Predicho
## Actual Buyer Non-buyer
## Buyer 3 2
## Non-buyer 4 1
## -------------------------------
## Matriz de confusion para k= 3
## ===============================
## Predicho
## Actual Buyer Non-buyer
## Buyer 3 2
## Non-buyer 5 0
## -------------------------------
## Matriz de confusion para k= 4
## ===============================
## Predicho
## Actual Buyer Non-buyer
## Buyer 3 2
## Non-buyer 4 1
## -------------------------------
## Matriz de confusion para k= 5
## ===============================
## Predicho
## Actual Buyer Non-buyer
## Buyer 3 2
## Non-buyer 1 4
## -------------------------------
## Matriz de confusion para k= 6
## ===============================
## Predicho
## Actual Buyer Non-buyer
## Buyer 3 2
## Non-buyer 1 4
## -------------------------------
## Matriz de confusion para k= 7
## ===============================
## Predicho
## Actual Buyer Non-buyer
## Buyer 3 2
## Non-buyer 1 4
## -------------------------------
## Matriz de confusion para k= 8
## ===============================
## Predicho
## Actual Buyer Non-buyer
## Buyer 3 2
## Non-buyer 0 5
## -------------------------------ya existe un algoritmo que no es a “mano” se puede usar la funcion de carte::trainControl
training.control<-trainControl(method = "repeatedcv",#Valodacion cruzada repetida
number = 10,#numero de remuestreos
repeats = 3)#repeticiones de la validacion cruzada
cater.knn.fit<-train(Result~Family_size.z+Income,data = train.kneigh,
method="knn",#metodo por el cual se va a hacer
trControl=training.control,#el de arriba
preProcess=c("center","scale"),#Centrar y restar la media
tuneLength=10)#longitud del ajuste## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.28892483532644, -0.937399880237408, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.28892483532644, -0.937399880237408, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.44871854585396, -0.84508581841481, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.44871854585396, -0.84508581841481, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.35320178424367, -0.789367707475475, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.35320178424367, -0.789367707475475, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.79845760605179, -0.83005735663929, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.79845760605179, -0.83005735663929, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.48638756616766, -0.772921534407183, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.48638756616766, -0.772921534407183, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(-0.772921534407183,
## -0.772921534407183, : k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(-0.772921534407183,
## -0.772921534407183, : k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.23421938529559, -0.987375508236473, :
## k = 19 exceeds number 18 of patterns## Warning in knn3Train(train = structure(c(1.23421938529559, -0.987375508236473, :
## k = 21 exceeds number 18 of patterns## Warning in knn3Train(train = structure(c(1.23421938529559, -0.987375508236473, :
## k = 23 exceeds number 18 of patterns## Warning in knn3Train(train = structure(c(1.28892483532644, -0.937399880237408, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.28892483532644, -0.937399880237408, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.44871854585396, -0.84508581841481, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.44871854585396, -0.84508581841481, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.28892483532644, -0.937399880237408, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.28892483532644, -0.937399880237408, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.28892483532644, -0.937399880237408, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.28892483532644, -0.937399880237408, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.35320178424367, -0.789367707475475, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.35320178424367, -0.789367707475475, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(-0.84508581841481, -0.84508581841481, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(-0.84508581841481, -0.84508581841481, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.75609839671568, -0.913171166292152, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.75609839671568, -0.913171166292152, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.46926177338491, -0.734630886692453, :
## k = 19 exceeds number 18 of patterns## Warning in knn3Train(train = structure(c(1.46926177338491, -0.734630886692453, :
## k = 21 exceeds number 18 of patterns## Warning in knn3Train(train = structure(c(1.46926177338491, -0.734630886692453, :
## k = 23 exceeds number 18 of patterns## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.26324401467532, -0.902317153339516, :
## k = 19 exceeds number 18 of patterns## Warning in knn3Train(train = structure(c(1.26324401467532, -0.902317153339516, :
## k = 21 exceeds number 18 of patterns## Warning in knn3Train(train = structure(c(1.26324401467532, -0.902317153339516, :
## k = 23 exceeds number 18 of patterns## Warning in knn3Train(train = structure(c(1.28892483532644, -0.937399880237408, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.28892483532644, -0.937399880237408, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(2.05211309734947, -0.836046076697931, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(2.05211309734947, -0.836046076697931, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.31914046975573, -0.860309002014607, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.28892483532644, -0.937399880237408, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.28892483532644, -0.937399880237408, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(-0.772921534407183,
## -0.772921534407183, : k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(-0.772921534407183,
## -0.772921534407183, : k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.35320178424367, -0.789367707475475, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.35320178424367, -0.789367707475475, :
## k = 23 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.44871854585396, -0.84508581841481, :
## k = 21 exceeds number 19 of patterns## Warning in knn3Train(train = structure(c(1.44871854585396, -0.84508581841481, :
## k = 23 exceeds number 19 of patternscater.knn.fit## k-Nearest Neighbors
##
## 21 samples
## 2 predictor
## 2 classes: 'Buyer', 'Non-buyer'
##
## Pre-processing: centered (2), scaled (2)
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 19, 19, 19, 19, 19, 19, ...
## Resampling results across tuning parameters:
##
## k Accuracy Kappa
## 5 0.7944444 0.58000000
## 7 0.7722222 0.53000000
## 9 0.6833333 0.35000000
## 11 0.7333333 0.45000000
## 13 0.6500000 0.28333333
## 15 0.7444444 0.48333333
## 17 0.7111111 0.43000000
## 19 0.5277778 0.04666667
## 21 0.5055556 0.01000000
## 23 0.4722222 -0.04333333
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was k = 5.Como lo menciona la salida , este ejercicio el algoritmo encuentra que el mas optimo es de K=5
ahora que ya conocemos el valor mas optimo de K , correremos el valor mas eficiente
pred5<- knn(train.kneigh[,4:5],validation.kneigh[,4:5],train.kneigh[,3],k=5,prob = T)
pred5## [1] Buyer Buyer Buyer Non-buyer Non-buyer Non-buyer Non-buyer
## [8] Buyer Non-buyer Non-buyer
## attr(,"prob")
## [1] 1.0000000 0.6000000 0.8333333 0.6000000 0.6000000 0.6666667 0.6000000
## [8] 0.8333333 0.6000000 0.6000000
## Levels: Buyer Non-buyerRedes Neuronales
Las cajas negras
#install.packages("nnet")
library(nnet)
library(caret)cargamos los datos
bn<-read.csv("../../r-course-master/data/tema3/banknote-authentication.csv")
bn$class<-factor(bn$class)# solo para corroborar que lo toma como factor Para seleccionar el rango que se va a utilizar dentro de la red neuronal sigue a grosomodo la fomrmula rango*maximo (|variables|)~1
apply(bn,2,max)## variance skew curtosis entropy class
## " 6.8248000" " 12.95160000" "17.92740000" " 2.4495000" "1"el maximo de las varibles que vemos es 17, por lo tanto se debe buscar un numero que se acerque a uno
podemos hacer una ecuacion rango= 1/ maximo de los numeros que nos mostro la funcion apply
Creamos particion
t.id.redn<-createDataPartition(bn$class,p=0.70,list=F)
mod.redn<-nnet::nnet(class~.,data=bn[t.id.redn,],
size=3,# especifica el numero de capas , puede ser el numero de columnas
maxit=10000,#Numero de iteraciones maximas
decay=.001,#Se utiliza para el overfiting ( no se especializa en los datos que entrenamos )se debe dejar rango para las validacioens
rang=0.05,#ver nota superior
na.action=na.omit)#Para que omita los NA## # weights: 19
## initial value 665.779419
## iter 10 value 445.463295
## iter 20 value 61.023803
## iter 30 value 32.802513
## iter 40 value 17.213270
## iter 50 value 2.682251
## iter 60 value 2.006200
## iter 70 value 1.788214
## iter 80 value 1.581753
## iter 90 value 1.522893
## iter 100 value 1.477252
## iter 110 value 1.454335
## iter 120 value 1.354654
## iter 130 value 1.258758
## iter 140 value 1.191089
## iter 150 value 1.012912
## iter 160 value 0.922466
## iter 170 value 0.855131
## iter 180 value 0.825344
## iter 190 value 0.752956
## iter 200 value 0.662098
## iter 210 value 0.632855
## iter 220 value 0.626612
## iter 230 value 0.625244
## iter 240 value 0.624736
## iter 250 value 0.624530
## iter 260 value 0.624512
## iter 270 value 0.624510
## iter 280 value 0.624509
## iter 290 value 0.624509
## final value 0.624509
## convergedHagamos la prediccion
pred.redn<-predict(mod.redn,newdata = bn[-t.id.redn,],type = "class")
table(bn[-t.id.redn,]$class,pred.redn,dnn = c("Actual","Predicho"))## Predicho
## Actual 0 1
## 0 228 0
## 1 0 183#curva Roc de una red neuronal
library(ROCR)
pred2.redn<-predict(mod.redn,newdata = bn[-t.id.redn,],type = "raw")
perf.redn<-performance(prediction(pred2.redn,bn[-t.id.redn,"class"]),"tpr","fpr")
plot(perf.redn)
Esta madre es perfecta
Analisis del discriminante lineal(lda)
Se trata de ver si las medias o varianzas son parecidas entre ellas Se parece mucho a la prueba de ANoVA la varible dependiente :categorica la varible independiente: continua
#install.packages("MASS")
library(MASS)##
## Attaching package: 'MASS'## The following object is masked _by_ '.GlobalEnv':
##
## housing## The following objects are masked from 'package:raster':
##
## area, select## The following object is masked from 'package:dplyr':
##
## selectlibrary(caret)cargamos datos
#seet.seed(2018)
bn.disc.lin<- read.csv("../../r-course-master/data/tema3/banknote-authentication.csv")
bn.disc.lin$class<-factor(bn.disc.lin$class)
t.id.disc.lin<-createDataPartition(bn.disc.lin$class,p=0.7,list=F)modelo lineal de dicriminacion
mod.disc.lin<-lda(bn.disc.lin[t.id.disc.lin,1:4],#parametro de entrada
bn.disc.lin[t.id.disc.lin,5] )#parametro de salida , con la indicación de la columna
# otra forma de escribirlo
# mod.disc.lin.2<-lda(class~.,data = bn.disc.lin[t.id.disc.lin,])agregamos una columana al DF y creamos la tabla para los datos de entrenamiento
bn.disc.lin[t.id.disc.lin,"Pred"]<-predict(mod.disc.lin,bn.disc.lin[t.id.disc.lin,1:4])$class# valor predicho y se mete desde el una columna nueva
table(bn.disc.lin[t.id.disc.lin,"class"],
bn.disc.lin[t.id.disc.lin,"Pred"],dnn = c("Actual","Predicho"))## Predicho
## Actual 0 1
## 0 515 19
## 1 0 427Excluimos los valores de etrenamiento y predecimos la matriz de confusion
bn.disc.lin[-t.id.disc.lin,"Pred"]<-predict(mod.disc.lin,bn.disc.lin[-t.id.disc.lin,1:4])$class
table(bn.disc.lin[-t.id.disc.lin,"class"],
bn.disc.lin[-t.id.disc.lin,"Pred"],dnn = c("Actual","Predicho"))## Predicho
## Actual 0 1
## 0 215 13
## 1 0 183Regresión logistica (gml)
Resultado de una variable categorica en fucnión de variables predictoras calculo integral (supongo resuelve las integrales a travez de metodos numericos )
library(caret)
library(lattice)
#cargamos datos
boston_Reg_log<-read.csv("../../r-course-master/data/tema3/boston-housing-logistic.csv")
boston_Reg_log$CLASS<-factor(boston_Reg_log$CLASS,levels=c(0,1))
set.seed(2018)
# creamos la particion aleatoria
t_id_reg_log<-createDataPartition(boston_Reg_log$CLASS,p=0.7,list = F)
#creamos el modelo
mod_reg_log<- glm(CLASS~.,data=boston_Reg_log[t_id_reg_log,],family=binomial)#si es vaerdadero o falso
summary(mod_reg_log)##
## Call:
## glm(formula = CLASS ~ ., family = binomial, data = boston_Reg_log[t_id_reg_log,
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.17116 -0.47682 0.09069 0.44375 2.83196
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 23.873324 3.936114 6.065 1.32e-09 ***
## NOX -21.564510 4.658177 -4.629 3.67e-06 ***
## DIS -0.368276 0.143892 -2.559 0.0105 *
## RAD 0.174650 0.071923 2.428 0.0152 *
## TAX -0.005127 0.003755 -1.365 0.1722
## PTRATIO -0.717774 0.133575 -5.374 7.72e-08 ***
## B 0.009092 0.003918 2.320 0.0203 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 353.03 on 254 degrees of freedom
## Residual deviance: 167.07 on 248 degrees of freedom
## AIC: 181.07
##
## Number of Fisher Scoring iterations: 6Investigar fisher scoring
Creamos una columana de datos de las probabilidades
boston_Reg_log[-t_id_reg_log,"Prob_success"]<-predict(mod_reg_log,newdata = boston_Reg_log[-t_id_reg_log,],
type="response")# mide las probabilidades de los objetos clasificados
# creamos otra columna donde si esta por encima de 50% de prob, entonces es un exito , de lo contrario es un fracaso
boston_Reg_log[-t_id_reg_log,"pred_50"]<-ifelse(boston_Reg_log[-t_id_reg_log,"Prob_success"]>=0.5,1,0)Creamos la matriz de confusion para la regresion logistica
table(boston_Reg_log[-t_id_reg_log,"CLASS"],boston_Reg_log[-t_id_reg_log,"pred_50"],dnn=c("Actual","Predicho"))## Predicho
## Actual 0 1
## 0 45 6
## 1 6 51Analizar en tiempo real de TWiter
Se deja pendiente por validacion de perfil de desarrollados por parte de Twiter
#La raiz del error cuadratico medio Se trata de restar Para este ejemplo se toma el precio=price y lo predicho por alguno de los algotirmos antes vistos
dat_error_medio<-read.csv("../../r-course-master/data/tema4/rmse.csv")
# Paso 1 se resta la columna del precio menos la columana predicha y este resultado al cuadrado
sqrt(mean((dat_error_medio$price-dat_error_medio$pred)^2))## [1] 2.934995#graficamos los datos de prediccion vs precio real
plot(dat_error_medio$price,dat_error_medio$pred,xlab = "Actual",ylab = "Predicho")
abline(0,1)
Hagamos una funcion para error cuadratico medio
rmse.fuction<-function(actual,predicted){
return(sqrt(mean((actual-predicted)^2)))
}Como se usa esta vaina
rmse.fuction(dat_error_medio$price,dat_error_medio$pred)## [1] 2.934995regresion K nearest neiighbors
#install.packages("FNN")
library(FNN)##
## Attaching package: 'FNN'## The following objects are masked from 'package:class':
##
## knn, knn.cvlibrary(dummies)
library(scales)
library(caret)
edu<-read.csv("../../r-course-master/data/tema4/education.csv")Se va a hacer dummy la variable región
dms<-dummy(edu$region,sep="_")## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignoredLe agregamos esta varibel al DF edu
edu<-cbind(edu,dms)Cambiemos a factores los datos que son necesarios estandarizar el resto de
edu$urban.s<-rescale(edu$urban)
edu$income.z<-rescale(edu$income)
edu$under18.z<-rescale(edu$under18)Creamos la perticion de los datos
set.seed(2018)
library(caret)
t_id_kneigh.regresion<-caret::createDataPartition(edu$expense,p=0.6, list=F)conjunto de entrenamiento
tr_knn_regresion<-edu[t_id_kneigh.regresion,]# meten todos lo que fueron seleccionados
temp<-edu[-t_id_kneigh.regresion,] #Aqui entra el resto de los datosDatos de validacion
v_ids_kneigh.regresion<-createDataPartition(temp$expense,p=0.5,list=F)#Se Creo otra partición de los datos de temp
val.kneigh.regresion<-temp[v_ids_kneigh.regresion,]
test_kneight_regresion<-temp[-v_ids_kneigh.regresion,]Determinemos la K que es mas eficiente
training_control<-trainControl(method = "repeatedcv",#Valodacion cruzada repetida
number = 10,#numero de remuestreos
repeats = 3)#repeticiones de la validacion cruzada
cater.knn.fit<-train(expense~.,data =edu,
method="knn",#metodo por el cual se va a hacer
trControl=training_control,#el de arriba
tuneLength=10)#longitud del ajuste
cater.knn.fit## k-Nearest Neighbors
##
## 50 samples
## 12 predictors
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 45, 46, 43, 45, 44, 46, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 47.66062 0.4239637 37.55211
## 7 46.00489 0.4819194 36.71542
## 9 46.63363 0.4587353 36.98325
## 11 45.77538 0.4655686 36.77972
## 13 46.30492 0.4597074 36.87004
## 15 45.32533 0.4888495 36.30289
## 17 45.44537 0.4919484 36.72798
## 19 46.36769 0.4783885 37.50904
## 21 46.42500 0.4896844 37.61464
## 23 46.60530 0.4987181 37.80011
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 15.Regresion 1
reg1<-knn.reg(tr_knn_regresion[,7:12],# conjunto de entrenamiento
val.kneigh.regresion[,7:12],#Conjunto de validacion
tr_knn_regresion$expense, # variable ( columna) que se desea predecir
k=25, # iterar para el valor mas cercano
algorithm = "brute")#este es por defaultregrecion predicha
reg.test<-knn.reg(tr_knn_regresion[,7:12],test_kneight_regresion[,7:12],tr_knn_regresion$expense,k=25,algorithm = "brute")Cargamos la funcion que en la sesión pasada generamos
rmse_reg_kneight<-rmse.fuction(reg1$pred,edu$expense)
rmse_reg_kneight## [1] 61.36413Regresion K nearest neiighbors sin particiones de test
creamos la regresion de donde se van a tomar los valores sin test
reg_knn_sin.test<-knn.reg(tr_knn_regresion[,7:12],#valores a de entrenamiento
test = NULL,# sin valores de prueba
y=tr_knn_regresion$expense,#respuesta de cada observacion de entrenamiento
k=3,# numero de vecinos
algorithm = "brute")Error medio
rmse_reg_knn<- sqrt(mean((reg_knn_sin.test$residuals)^2))
rmse_reg_knn## [1] 40.9394funcion que calcula la regresion lineal
regresion_knn<-function(tr_predictor,val_predictors,
tr_target,val_target,k) {
library(FNN)
result<-knn.reg(tr_predictor,val_predictors,tr_target,k,algorithm = "brute")
rmserror<-sqrt(mean((tr_target-result$pred)^2))
cat(paste("RMSE para k=",toString(k),":",rmserror,"\n",sep=" "))
rmserror
}Como se usa esta funcion de regresion knn con particiones de validacion (test)
regresion_knn(tr_knn_regresion[,7:12],val.kneigh.regresion[,7:12]
,tr_knn_regresion$expense,val.kneigh.regresion$expense,25)## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 25 : 48.4137955545731## [1] 48.4138Funcion que grafica los valores de error medio con base a lo calculado anteriormente RMSE
error_knn_cuadratico_regresion <-function(tr_predictors, val_predictors,
tr_target, val_target, start_k, end_k){
rms_errors <- vector()
for(k in start_k:end_k){
rms_error <- regresion_knn(tr_predictors, val_predictors,
tr_target, val_target, k)
rms_errors <- c(rms_errors, rms_error)
}
plot(rms_errors, type = 'o', xlab = "k", ylab = "RMSE")
}como se usa esta pinche funcion de RMSE
error_knn_cuadratico_regresion(tr_knn_regresion[,7:12],val.kneigh.regresion[,7:12],tr_knn_regresion$expense,val.kneigh.regresion$expense,1,25)## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 1 : 69.0455013016779## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 2 : 64.9357855885335## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 3 : 66.3299569911246## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 4 : 64.8152572942899## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 5 : 63.6186096987352## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 6 : 62.3610608450676## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 7 : 60.011111853709## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 8 : 59.4158371077842## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 9 : 57.8842635174964## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 10 : 57.3507029163549## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 11 : 57.1439027354162## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 12 : 56.6673138749805## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 13 : 55.613860163701## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 14 : 54.0501866064668## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 15 : 53.570560945355## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 16 : 52.7946669226045## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 17 : 52.3436748243534## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 18 : 51.0961651607614## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 19 : 51.0052869862921## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 20 : 51.0679594694658## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 21 : 49.8425056400732## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 22 : 49.1411825647657## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 23 : 48.5951736558941## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 24 : 48.0292263769835## Warning in tr_target - result$pred: longitud de objeto mayor no es múltiplo de
## la longitud de uno menor## RMSE para k= 25 : 48.4137955545731
#Regresion lineal Es
library(caret)
auto <- read.csv("../../r-course-master/data/tema4/auto-mpg.csv")
auto$cylinders <- factor(auto$cylinders,
levels = c(3,4,5,6,8),
labels = c("3c", "4c", "5c", "6c", "8c"))Realizamos la particion
set.seed(2018)#muestreo
t_id_reg_lin<- createDataPartition(auto$mpg, p = 0.7, list = F)# creamos particion
names(auto)#imprimimos el nombre de las columnas## [1] "No" "mpg" "cylinders" "displacement" "horsepower"
## [6] "weight" "acceleration" "model_year" "car_name"Creamos el modelo Ya hace las varibles dummy de por default la funcion lm
mod <- lm(mpg ~ .#mpg en funcion de todos
, data = auto[t_id_reg_lin,# con los datos de entrenamieto
-c(1,8,9)])#quitamos las columnas que no necesitamos(solo la 1,8 y 9)
mod##
## Call:
## lm(formula = mpg ~ ., data = auto[t_id_reg_lin, -c(1, 8, 9)])
##
## Coefficients:
## (Intercept) cylinders4c cylinders5c cylinders6c cylinders8c
## 37.284202 6.231475 8.248195 2.131026 4.568171
## displacement horsepower weight acceleration
## 0.002245 -0.057543 -0.004665 0.050745Este es el modelo que predice el Millas Por galeon #mpg = 37.284202 + # + 6.2314754c + 8.2481955c + 2.1310266c + 4.5681718c + # + 0.002245 * displacement -0.057543 * horsepower + # - 0.004665 * weight + 0.050745 * acceleration
Residual es el error que estoy cometiendo
Recuerda que los * que pinta summary (son mas importantes para el modelo lineal) residual standar error =Rmse Si el p-value es chiquito es mejor la estimacion
summary(mod)##
## Call:
## lm(formula = mpg ~ ., data = auto[t_id_reg_lin, -c(1, 8, 9)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.0606 -2.4686 -0.4435 1.9821 16.0907
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.2842024 3.6497412 10.216 < 2e-16 ***
## cylinders4c 6.2314753 2.4926855 2.500 0.01301 *
## cylinders5c 8.2481946 3.8091396 2.165 0.03123 *
## cylinders6c 2.1310256 2.7759570 0.768 0.44335
## cylinders8c 4.5681710 3.2054454 1.425 0.15527
## displacement 0.0022449 0.0108924 0.206 0.83687
## horsepower -0.0575428 0.0202773 -2.838 0.00489 **
## weight -0.0046652 0.0009999 -4.665 4.84e-06 ***
## acceleration 0.0507454 0.1443575 0.352 0.72547
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.092 on 271 degrees of freedom
## Multiple R-squared: 0.7304, Adjusted R-squared: 0.7224
## F-statistic: 91.75 on 8 and 271 DF, p-value: < 2.2e-16Haremos una caja de gato con bigotes de los residuos (de los errores)
boxplot(mod$residuals)
RMSE, aunque le summary lo arroja lo podemos comparar
sqrt(mean((mod$fitted.values - auto[t_id_reg_lin,]$mpg)^2))## [1] 4.026021Predecimos los valores de consumo de Millas por Galeon
pred <- predict(mod, auto[-t_id_reg_lin, -c(1,8,9)])
sqrt(mean((pred - auto[-t_id_reg_lin,]$mpg)^2))## [1] 3.894627Interpretemos los parametros
par(mfrow=c(2,2))#recuerda que esto es para graficar 2 por 2
plot(mod)
Analicemos los modelos
los numeros de los plots son el numero de dato de la izquierda del DF original
1._se contesta la pregunta ,Existe una relacion que es lineal? 2._El de la parte superior derecha ( sirve para ver si los errores siguen una distribucion normal) 3._ El de la parte inferior izquierda(Representa los residuos estandarizados sobre los valores ajustados)spred location ( si todos los valores tienen la misma varianza “homosedasticos”)si estan ordenados , es bueno
4._la de la derecha abajo Posibles sujetos influyentes (los outliners pueden influenciar los valores )(distancia de cook’s distance) arriba derecha y abajo derecha son los puntos criticos
Si se quiere indicar al modelo que utilice un dato especifico , se le pude que sea otro el de referencia
Nota importante : el que tengas mas valores categoricos se recomienda ponerlo como principal
auto <- within(auto,
cylinders <- relevel(cylinders, ref="4c"))
# se vuelve a correr el modelo
mod <- lm(mpg ~. , data = auto[t_id_reg_lin, -c(1,8,9)])
summary(mod)##
## Call:
## lm(formula = mpg ~ ., data = auto[t_id_reg_lin, -c(1, 8, 9)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.0606 -2.4686 -0.4435 1.9821 16.0907
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.5156778 2.8644210 15.192 < 2e-16 ***
## cylinders3c -6.2314753 2.4926855 -2.500 0.013014 *
## cylinders5c 2.0167192 2.9414057 0.686 0.493532
## cylinders6c -4.1004497 1.0580642 -3.875 0.000134 ***
## cylinders8c -1.6633044 1.8447503 -0.902 0.368048
## displacement 0.0022449 0.0108924 0.206 0.836872
## horsepower -0.0575428 0.0202773 -2.838 0.004886 **
## weight -0.0046652 0.0009999 -4.665 4.84e-06 ***
## acceleration 0.0507454 0.1443575 0.352 0.725467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.092 on 271 degrees of freedom
## Multiple R-squared: 0.7304, Adjusted R-squared: 0.7224
## F-statistic: 91.75 on 8 and 271 DF, p-value: < 2.2e-16pred <- predict(mod, auto[-t_id_reg_lin, -c(1,8,9)])
sqrt(mean((pred-auto[-t_id_reg_lin,]$mpg)^2))## [1] 3.894627par(mfrow=c(2,2))
plot(mod)
Se puede mejorar el analisis , esto sirve para determinar que columnas (variables )son importates y cuales no (stepAIC)
library(MASS)
mod##
## Call:
## lm(formula = mpg ~ ., data = auto[t_id_reg_lin, -c(1, 8, 9)])
##
## Coefficients:
## (Intercept) cylinders3c cylinders5c cylinders6c cylinders8c
## 43.515678 -6.231475 2.016719 -4.100450 -1.663304
## displacement horsepower weight acceleration
## 0.002245 -0.057543 -0.004665 0.050745summary(mod)##
## Call:
## lm(formula = mpg ~ ., data = auto[t_id_reg_lin, -c(1, 8, 9)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.0606 -2.4686 -0.4435 1.9821 16.0907
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.5156778 2.8644210 15.192 < 2e-16 ***
## cylinders3c -6.2314753 2.4926855 -2.500 0.013014 *
## cylinders5c 2.0167192 2.9414057 0.686 0.493532
## cylinders6c -4.1004497 1.0580642 -3.875 0.000134 ***
## cylinders8c -1.6633044 1.8447503 -0.902 0.368048
## displacement 0.0022449 0.0108924 0.206 0.836872
## horsepower -0.0575428 0.0202773 -2.838 0.004886 **
## weight -0.0046652 0.0009999 -4.665 4.84e-06 ***
## acceleration 0.0507454 0.1443575 0.352 0.725467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.092 on 271 degrees of freedom
## Multiple R-squared: 0.7304, Adjusted R-squared: 0.7224
## F-statistic: 91.75 on 8 and 271 DF, p-value: < 2.2e-16step.model <- MASS::stepAIC(mod, direction="backward")## Start: AIC=797.96
## mpg ~ cylinders + displacement + horsepower + weight + acceleration
##
## Df Sum of Sq RSS AIC
## - displacement 1 0.71 4539.2 796.00
## - acceleration 1 2.07 4540.5 796.08
## <none> 4538.5 797.96
## - horsepower 1 134.87 4673.3 804.16
## - weight 1 364.53 4903.0 817.59
## - cylinders 4 619.82 5158.3 825.80
##
## Step: AIC=796
## mpg ~ cylinders + horsepower + weight + acceleration
##
## Df Sum of Sq RSS AIC
## - acceleration 1 1.76 4541.0 794.11
## <none> 4539.2 796.00
## - horsepower 1 136.06 4675.2 802.27
## - weight 1 475.09 5014.3 821.87
## - cylinders 4 629.80 5169.0 824.38
##
## Step: AIC=794.11
## mpg ~ cylinders + horsepower + weight
##
## Df Sum of Sq RSS AIC
## <none> 4541.0 794.11
## - horsepower 1 289.06 4830.0 809.39
## - cylinders 4 642.38 5183.3 823.16
## - weight 1 605.26 5146.2 827.14summary(step.model)##
## Call:
## lm(formula = mpg ~ cylinders + horsepower + weight, data = auto[t_id_reg_lin,
## -c(1, 8, 9)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.9108 -2.4854 -0.4535 1.9444 16.2137
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.3060838 1.5539812 28.511 < 2e-16 ***
## cylinders3c -6.4358865 2.3970441 -2.685 0.0077 **
## cylinders5c 2.0004828 2.9289747 0.683 0.4952
## cylinders6c -4.0112530 0.8426183 -4.760 3.14e-06 ***
## cylinders8c -1.4973133 1.3818662 -1.084 0.2795
## horsepower -0.0611080 0.0146588 -4.169 4.12e-05 ***
## weight -0.0044161 0.0007321 -6.032 5.24e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.078 on 273 degrees of freedom
## Multiple R-squared: 0.7302, Adjusted R-squared: 0.7243
## F-statistic: 123.2 on 6 and 273 DF, p-value: < 2.2e-16Lo que te arroja los variables de la ultima iteracion por lo que se puede concluir que (cylinders, hoursepower y wheight) son las varibles para la obtener un modelo completo
Regrecion con Arboles de clasificacion
Instalamos y cargamos las librerias
#install.packages("rpart.plot")
library(rpart)
library(rpart.plot)
library(caret)Cargamos el documento ( cambiar dirección)
bh <- read.csv("../../r-course-master/data/tema4/BostonHousing.csv")Creamos una paticion
t.id <-caret::createDataPartition(bh$MEDV, p = .7, list = F)bfit <- rpart(MEDV ~., data = bh[t.id,])
bfit## n= 356
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 356 28822.1900 22.40618
## 2) RM< 6.805 293 11281.7400 19.51433
## 4) LSTAT>=14.91 112 1896.9530 14.34196
## 8) CRIM>=5.76921 58 591.3710 11.96552 *
## 9) CRIM< 5.76921 54 626.2083 16.89444 *
## 5) LSTAT< 14.91 181 4534.2900 22.71492
## 10) LSTAT>=5.245 172 2639.5900 22.13140
## 20) LSTAT>=9.715 89 566.7760 20.60674 *
## 21) LSTAT< 9.715 83 1644.0860 23.76627 *
## 11) LSTAT< 5.245 9 716.8800 33.86667 *
## 3) RM>=6.805 63 3694.3560 35.85556
## 6) RM< 7.437 44 1094.5590 32.39545 *
## 7) RM>=7.437 19 853.1011 43.86842 *Grafiquemoslo
par(mfrow=c(1,1))
prp(bfit, type = 2, nn=T,
fallen.leaves = T, faclen = 4,
varlen = 8, shadow.col = "gray")
Detalle de tabla
bfit$cptable## CP nsplit rel error xerror xstd
## 1 0.48039697 0 1.0000000 1.0033907 0.09825971
## 2 0.16829040 1 0.5196030 0.5827505 0.06549191
## 3 0.06060246 2 0.3513126 0.3935690 0.05315030
## 4 0.04086502 3 0.2907102 0.3487069 0.05377561
## 5 0.02357120 4 0.2498451 0.3007673 0.04694361
## 6 0.01487496 5 0.2262740 0.2785457 0.04780752
## 7 0.01000000 6 0.2113990 0.2642192 0.04714776Grafiquemos lo que dice la tabla
cuando se tiene muchas clasificaciones se puede elejir alguno de los factores que sea representativo
plotcp(bfit)
Subes a la tablay tomas el valor que sea mas factible (y recortas el arbol)
bfitpruned <- prune(bfit, cp = 0.02343779)Volvemos a representar el arbol
par(mfrow=c(1,1))
prp(bfitpruned, type = 2, nn=T,
fallen.leaves = T, faclen = 4,
varlen = 8, shadow.col = "gray")
Calculamos las predicciones
Pedeciremos los datos de entrenamiento para el arbol no recortado, todos los datos de entrenamiento
preds <- predict(bfit, bh[t.id,])
#Calculamos el error medio para los datos de validacion
sqrt(mean((preds - bh[t.id,]$MEDV)^2))## [1] 4.137042Pedeciremos los datos deentrenamiento para el arbol no recortado, todos los datos que no son de entrenamiento
preds <- predict(bfit, bh[-t.id, ])
sqrt(mean((preds - bh[-t.id,]$MEDV)^2))## [1] 5.689694Pedeciremos los datos de entrenamiento para el arbol recortado, todos los datos entrenamiento
preds <- predict(bfitpruned, bh[t.id,])
sqrt(mean((preds - bh[t.id,]$MEDV)^2))## [1] 4.280118Pedeciremos los datos de validacion para el arbol recortado, todos los datos que no son de entrenamiento
preds <- predict(bfitpruned, bh[-t.id, ])
sqrt(mean((preds - bh[-t.id,]$MEDV)^2))## [1] 5.898846#predictores categoricos
#Cargamos los datos
ed <- read.csv("../../r-course-master/data/tema4/education.csv")
#convertimos a factores
ed$region <- factor(ed$region)
#partimos el data frame
t.id <- caret::createDataPartition(ed$expense, p = 0.7, list = F)
#Predeciren función de todo la variable
fit <- rpart::rpart(expense ~ region+urban+income+under18, data = ed[t.id,])
#pintemos el arbol que diseñamos
rpart.plot::prp(fit, type = 2, nn=T, fallen.leaves = T, faclen=4, varlen=8, shadow.col = "gray")
#Bagging Combina de forma conjunta lo de diferentes arboles
#install.packages("ipred")
library(ipred)##
## Attaching package: 'ipred'## The following object is masked from 'package:raster':
##
## cv# Construimos el bagguing a partir de los datos de entrenamiento
bagging.fit <- bagging(MEDV~.,#predice esta variable en función de las demas
data=bh[t.id, ])# vas a tomar del DF inicial y las filas de entrenamiento
# Predeciremos apartir de el conjunto de entrenamiento
prediction.t <- predict(bagging.fit, bh[t.id,])
#RMSE de bagging
sqrt(mean((prediction.t-bh[t.id,]$MEDV)^2))## [1] 2.561796#Predecirmos con los valores a partir de los de validación ( los que nos on de entrenamiento)
prediction.v <- predict(bagging.fit, bh[-t.id,])
sqrt(mean((prediction.v-bh[-t.id,]$MEDV)^2))## [1] 6.936346#Boosting
#install.packages("gbm")
library(gbm)#gradient busted machine ## Loaded gbm 2.1.8gbmfit <- gbm(MEDV~., #predice en funciónde todas las variables
data = bh, #datos tomalos de aqui
distribution = "gaussian")# distribucion gaussina
#Predice en función de
prediction.t <- predict(gbmfit, bh)## Using 100 trees...Random Forest para regresiones
library(randomForest)
library(caret)
bh <- read.csv("../../r-course-master/data/tema4/BostonHousing.csv")
#elejimos semilla
set.seed(2018)
#Creamos datos de entrenamiento
t.id <- createDataPartition(bh$MEDV, p = 0.7, list = F)Creamos el modelo
mod <- randomForest(x = bh[t.id, 1:13],#predictores
y = bh[t.id, 14],#las variables a predecir
ntree = 1000, #numero de arboles
xtest = bh[-t.id, 1:13],#son los predictors que se usan en la particion
ytest = bh[-t.id, 14],#las variables de salida en la misma particio de validacion
importance = T,#Si tiene que computar o no las puntuaciones de varibales predictoras
keep.forest = T)#mantener los arboles del modelo
mod##
## Call:
## randomForest(x = bh[t.id, 1:13], y = bh[t.id, 14], xtest = bh[-t.id, 1:13], ytest = bh[-t.id, 14], ntree = 1000, importance = T, keep.forest = T)
## Type of random forest: regression
## Number of trees: 1000
## No. of variables tried at each split: 4
##
## Mean of squared residuals: 11.77997
## % Var explained: 86.72
## Test set MSE: 10.05
## % Var explained: 86.51Parametros que se pueden introducir en la tecnica de Random Forest
mtry = m/3, donde m = # de predictores nodesize = 5 minimos tamaño que debe tener un nodo termina maxnodes ( maximo de nodos terminales )
Se ven la importancia para cada variable %purity el dato mayor es la que tine mayor importancia
mod$importance## %IncMSE IncNodePurity
## CRIM 8.1353944 1795.0927
## ZN 0.9797628 240.7091
## INDUS 4.7049920 1536.7625
## CHAS 0.5831449 194.5591
## NOX 8.1279708 1623.9652
## RM 41.3092319 10179.2089
## AGE 3.8170358 802.6095
## DIS 6.4509516 1953.6404
## RAD 1.0707028 263.1413
## TAX 4.4276314 1188.9411
## PTRATIO 8.9288034 2591.7216
## B 1.8193482 667.7755
## LSTAT 55.9722090 8001.2091Grafiquemoslo soble los datos predichos ( con los datos de entrenamiento)
plot(bh[t.id,]$MEDV, predict(mod, newdata = bh[t.id,]),
xlab = "Actual", ylab = "Predichos")
abline(0,1)
Grafiquemos los datos en caso de los datos de predicccion
plot(bh[-t.id,]$MEDV, predict(mod, newdata = bh[-t.id,]),
xlab = "Actual", ylab = "Predichos")
abline(0,1)
Regresion con Redes neuronales
library(nnet)
library(caret)
library(devtools)## Loading required package: usethisbh <- read.csv("../../r-course-master/data/tema4/BostonHousing.csv")
set.seed(2018)
t.id <- createDataPartition(bh$MEDV, p= 0.7, list = F)
summary(bh$MEDV)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.00 17.02 21.20 22.53 25.00 50.00creamos la red neuronal ( parara en 290 iteraciones)
fit <- nnet(MEDV/50 ~., data=bh[t.id, ],
size = 6, decay = 0.1,
maxit = 1000, linout=T)## # weights: 91
## initial value 1711.376818
## iter 10 value 14.279679
## iter 20 value 9.849751
## iter 30 value 7.511037
## iter 40 value 6.285160
## iter 50 value 3.989395
## iter 60 value 3.214674
## iter 70 value 2.455499
## iter 80 value 1.950545
## iter 90 value 1.782552
## iter 100 value 1.735083
## iter 110 value 1.686014
## iter 120 value 1.632630
## iter 130 value 1.516829
## iter 140 value 1.490113
## iter 150 value 1.438664
## iter 160 value 1.385513
## iter 170 value 1.362224
## iter 180 value 1.349518
## iter 190 value 1.322281
## iter 200 value 1.307838
## iter 210 value 1.304445
## iter 220 value 1.303188
## iter 230 value 1.302788
## iter 240 value 1.302763
## iter 250 value 1.302370
## iter 260 value 1.301663
## iter 270 value 1.300696
## iter 280 value 1.298021
## iter 290 value 1.293863
## iter 300 value 1.290511
## iter 310 value 1.279228
## iter 320 value 1.264173
## iter 330 value 1.241595
## iter 340 value 1.211158
## iter 350 value 1.193082
## iter 360 value 1.187331
## iter 370 value 1.184634
## iter 380 value 1.181169
## iter 390 value 1.179331
## iter 400 value 1.177550
## iter 410 value 1.170499
## iter 420 value 1.169408
## iter 430 value 1.159805
## iter 440 value 1.149498
## iter 450 value 1.147599
## iter 460 value 1.147367
## final value 1.147343
## convergedDescargueos el recurso
source_url("https://gist.githubusercontent.com/fawda123/7471137/raw/466c1474d0a505ff044412703516c34f1a4684a5/nnet_plot_update.r")## i SHA-1 hash of file is 74c80bd5ddbc17ab3ae5ece9c0ed9beb612e87efGrafiquemos la red neuronal
plot(fit, max.sp = T)
Calculemos el valor medio de la red neuronal de los datos de entrenamiento
sqrt(mean((fit$fitted.values*50# esto se hace por que estaban escalados (x/50)
-bh[t.id,"MEDV"])^2))## [1] 2.354379Predigamos los valores de validación con base al modelo
# calculamos la predicción
pred <- predict(fit, bh[-t.id,])
# calculemos el error medio
sqrt(mean((pred*50 - bh[-t.id,"MEDV"])^2))## [1] 4.082578#k-fold cross validation( para evitar el over-fiting ) La técnica que hace un muestreo de los datos dejando siempre un conjunto no vacío y de más de un elemento como testing de los mismos usando el resto como conjunto de entrenamiento, de modo que en algún momento todo elemento del data set habrá sido parte del conjunto de testing
bh <- read.csv("../../r-course-master/data/tema4/BostonHousing.csv")Función que nos ayuda de validacion cruzada (Kross Validaion )
kfold.crossval.reg <- function(df, nfolds){
fold <- sample(1:nfolds,#diviciones
nrow(df),#valor maximo es el numero de columnas
replace = T)# con remplazo
cat(fold)
mean.sqr.errs <- sapply(1:nfolds,
kfold.cval.reg.iter,
df, fold)
list("MSE "= mean.sqr.errs,
"Overall_Mean_Sqr_Error" = mean(mean.sqr.errs),
"Std_Mean_Sqr_Error" = sd(mean.sqr.errs))
}Esta fucnión es tal vez ala mas importante como lo hemos visto
kfold.cval.reg.iter <- function(k, df, fold){
tr.ids <- !fold %in% c(k)# los que no estan el el fold
test.ids <- fold %in% c(k)
mod <- lm(MEDV ~., data = df[tr.ids,])#este es Lineal , pero puede ser cualquiera de los que se han visto
pred <- predict(mod, df[test.ids,])#predecimos los valores con los de entrenamiento
sqr.errs <- (pred - df[test.ids,"MEDV"])^2# calculamos los valores con los de entreanamiento
mean(sqr.errs)
}como se ocupa la funcion
res <- kfold.crossval.reg(bh, 5)## 2 5 5 4 3 1 5 1 4 4 1 4 4 4 1 4 1 5 3 4 5 3 3 1 4 2 4 5 3 2 1 2 5 5 2 3 1 5 4 5 1 2 5 4 4 3 4 5 2 3 3 2 4 3 3 5 4 2 1 2 4 2 1 4 2 4 4 4 2 4 2 1 5 3 1 1 5 3 2 3 2 3 1 4 2 5 1 1 4 3 4 5 1 1 1 1 2 5 2 1 4 4 5 5 3 5 2 3 4 5 2 3 1 2 2 5 3 5 4 5 1 5 1 5 3 4 4 5 1 1 5 5 1 3 2 5 3 3 5 3 5 3 2 3 1 3 2 4 2 5 2 3 3 3 1 4 3 3 1 2 1 3 2 2 3 1 5 4 3 5 3 4 4 2 4 5 1 3 4 5 2 4 3 2 4 1 1 5 3 1 1 2 5 3 5 1 2 1 2 1 2 4 1 1 1 5 5 5 1 1 3 3 3 1 2 5 4 2 2 2 4 2 1 2 3 4 4 5 1 3 5 4 3 5 4 5 3 5 1 4 4 4 1 5 2 3 3 3 5 4 5 2 2 3 2 5 4 5 3 4 1 2 3 4 4 3 4 2 2 5 4 5 5 1 4 4 1 2 2 5 5 1 4 2 4 3 5 4 1 5 3 3 4 1 3 1 3 5 4 5 2 5 5 2 2 5 3 3 1 5 2 4 4 5 5 3 3 4 4 2 4 5 3 3 5 4 2 3 5 5 5 5 2 5 1 3 1 1 2 2 5 5 1 3 1 5 4 4 2 1 3 4 5 1 1 3 5 5 2 2 5 2 1 3 2 3 5 4 5 1 1 2 5 2 2 5 4 2 2 3 4 3 2 4 3 1 4 4 2 4 3 1 1 4 3 4 4 2 3 3 4 1 1 3 3 3 4 5 4 2 3 2 5 2 5 2 5 5 4 1 5 4 2 1 2 3 5 4 2 2 4 1 1 1 4 4 1 4 5 4 3 5 2 4 4 1 4 3 4 1 1 3 5 5 1 2 1 1 3 1 2 1 4 2 2 5 5 3 4 4 3 5 2 1 4 4 1 5 2 2 4 4 5 2 3 4 2 5 4 2 2 3 4 4 2 1 4 5 4 3 5 5 5 3 2 2res## $`MSE `
## [1] 26.53045 29.62104 19.72100 15.59297 29.42232
##
## $Overall_Mean_Sqr_Error
## [1] 24.17756
##
## $Std_Mean_Sqr_Error
## [1] 6.249624Función que
loocv.reg <- function(df){
mean.sqr.errors <- sapply(1:nrow(df),
loocv.reg.iter, df)
list("MSE"=mean.sqr.errors,
"overall_mean_sqr_errors" = mean(mean.sqr.errors),
"sd_mean_sqr_errors" = sd(mean.sqr.errors))
}#Leave one out cross validatión La técnica que hace un muestreo de los datos dejando siempre un elemento como testing de los mismos usando el resto como conjunto de entrenamiento, de modo que en algún momento todo elemento del data set habrá sido probado en el mismo es
loocv.reg.iter <- function(k, df){
mod <- lm(MEDV~., data = df[-k,])
pred <- predict(mod, df[k,])
sqr.error <- (pred - df[k,"MEDV"])^2
sqr.error
}Vemos el resultado de la funcion locv.reg(bh)
res <- loocv.reg(bh)
res## $MSE
## 1 2 3 4 5 6
## 3.729797e+01 1.199961e+01 1.748783e+01 2.371101e+01 7.041998e+01 1.221852e+01
## 7 8 9 10 11 12
## 1.066078e-02 6.083233e+01 2.760620e+01 4.354925e-04 1.718911e+01 7.572428e+00
## 13 14 15 16 17 18
## 6.522578e-01 7.377726e-01 1.208910e+00 3.733962e-01 6.923949e+00 3.547527e-01
## 19 20 21 22 23 24
## 1.697014e+01 4.355471e-02 1.201640e+00 3.821703e+00 4.135365e-01 4.993998e-01
## 25 26 27 28 29 30
## 6.331468e-03 2.721472e-01 1.331116e+00 8.652999e-03 1.362318e+00 1.574726e-02
## 31 32 33 34 35 36
## 1.611519e+00 1.313453e+01 2.067570e+01 1.443976e+00 4.475714e-02 2.463602e+01
## 37 38 39 40 41 42
## 5.592485e+00 4.552686e+00 3.286605e+00 3.312408e-01 5.014142e-01 2.130980e+00
## 43 44 45 46 47 48
## 9.679261e-03 8.527549e-03 3.100071e+00 8.032073e+00 1.844215e-01 2.157248e+00
## 49 50 51 52 53 54
## 3.108574e+01 4.973245e+00 2.566659e+00 1.243208e+01 7.237428e+00 4.330235e-01
## 55 56 57 58 59 60
## 1.389389e+01 1.940582e+01 2.618686e-02 2.472834e+00 2.411174e+00 2.266668e+00
## 61 62 63 64 65 66
## 7.126901e-01 6.707352e+00 3.332020e+00 6.237148e+00 1.001863e+02 4.957845e+01
## 67 68 69 70 71 72
## 3.986293e+01 8.085272e-01 4.762654e-04 1.357698e-02 1.056106e+00 1.892730e-03
## 73 74 75 76 77 78
## 3.244263e+00 4.352050e-01 2.105917e+00 6.779739e+00 8.939215e+00 6.729250e+00
## 79 80 81 82 83 84
## 3.953892e-03 4.682032e+00 1.670276e-01 9.770162e+00 1.549403e+00 4.721297e+00
## 85 86 87 88 89 90
## 7.947959e-01 1.439888e+00 1.121866e-01 1.391472e+01 5.172308e+01 4.657649e+00
## 91 92 93 94 95 96
## 2.092745e+01 3.007600e+01 3.806832e+01 1.761120e+01 4.348412e+01 5.188964e-02
## 97 98 99 100 101 102
## 1.140565e+01 8.993158e+00 7.964098e+01 9.323476e-01 8.796581e+00 8.471550e-01
## 103 104 105 106 107 108
## 1.561794e+00 1.054589e+00 1.843013e+00 9.606184e-01 5.567635e+00 1.272343e-01
## 109 110 111 112 113 114
## 8.422781e+00 1.431191e-01 1.141503e+00 1.421192e+01 4.012310e+00 4.185680e+00
## 115 116 117 118 119 120
## 4.570305e+01 4.663181e+00 4.841275e+00 2.068423e+01 4.232158e-03 2.282826e+00
## 121 122 123 124 125 126
## 7.941337e-03 5.336048e+00 3.731817e-03 1.002866e+00 3.516524e+00 1.325469e+00
## 127 128 129 130 131 132
## 1.355923e+00 1.089893e+00 9.276111e-01 6.229524e-02 7.355950e-01 3.796536e-02
## 133 134 135 136 137 138
## 9.110577e+00 7.297429e+00 5.771485e+00 7.366954e-01 2.418407e+00 5.385606e+00
## 139 140 141 142 143 144
## 2.782415e-01 1.916950e+00 1.958247e-01 1.187180e+02 1.648491e+00 1.312498e+01
## 145 146 147 148 149 150
## 1.051908e+01 3.492333e+00 5.403145e-02 4.135578e+01 7.243438e+01 3.865230e-01
## 151 152 153 154 155 156
## 4.774235e-01 1.861299e+00 2.725144e+01 4.887157e+00 3.267753e+01 2.424213e+01
## 157 158 159 160 161 162
## 3.113948e-01 6.897296e+01 2.403866e+01 5.712380e+00 3.625518e+01 1.883310e+02
## 163 164 165 166 167 168
## 1.003511e+02 7.590807e+01 4.601966e+00 1.531457e-01 1.776428e+02 5.385503e-01
## 169 170 171 172 173 174
## 7.141359e+00 1.992025e+01 2.800766e+01 2.847223e+01 1.597189e-02 3.089412e+01
## 175 176 177 178 179 180
## 1.593424e+01 1.814710e+00 6.004608e+00 2.117228e+01 2.417781e+00 1.889265e+01
## 181 182 183 184 185 186
## 2.700943e+01 7.380785e+01 1.683769e+01 2.388951e+00 1.426146e+01 2.407323e+01
## 187 188 189 190 191 192
## 2.082895e+02 2.136408e+00 7.143840e+00 1.544500e-01 4.038493e+01 4.588249e-02
## 193 194 195 196 197 198
## 1.256855e+01 1.061280e+00 6.230196e+00 8.896089e+01 8.516689e+00 6.006171e+00
## 199 200 201 202 203 204
## 1.170881e-02 2.496221e+01 5.508817e+00 2.875440e+01 2.919602e+01 4.615213e+01
## 205 206 207 208 209 210
## 5.129553e+01 8.475094e-03 5.215106e-01 2.201520e+01 8.825574e-01 9.969343e+00
## 211 212 213 214 215 216
## 5.206310e-01 5.555872e+00 1.244453e-01 8.517094e+00 1.843480e+02 2.440186e-01
## 217 218 219 220 221 222
## 1.180092e+01 1.224423e-01 1.264901e+01 4.821454e+01 4.536309e+01 4.762079e+00
## 223 224 225 226 227 228
## 2.311447e+01 1.282296e-01 4.341674e+01 1.114443e+02 2.034105e-04 6.545355e-01
## 229 230 231 232 233 234
## 1.348988e+02 7.382200e-02 3.471605e-02 2.588687e+00 1.403737e+01 1.299645e+02
## 235 236 237 238 239 240
## 7.888862e+00 1.634536e+00 2.682745e+01 1.525101e+00 2.281558e+01 2.668342e+01
## 241 242 243 244 245 246
## 2.865031e+01 1.363589e+01 3.776324e+00 1.412930e+01 1.715674e+00 2.743474e+01
## 247 248 249 250 251 252
## 1.891622e+01 4.323744e-01 1.062594e+01 4.655989e+00 3.867855e-02 6.070924e-02
## 253 254 255 256 257 258
## 2.333755e+01 1.857514e+02 4.552659e+00 6.702129e-01 4.504348e+01 4.929602e+01
## 259 260 261 262 263 264
## 2.498378e-01 2.562539e+01 1.085240e+00 3.742820e+01 6.618498e+01 1.265035e+01
## 265 266 267 268 269 270
## 4.714732e-01 3.236633e+01 2.956476e-01 9.015552e+01 1.837542e+01 2.719936e+01
## 271 272 273 274 275 276
## 1.479861e+00 4.164324e+00 1.721232e+01 8.352803e-02 1.497046e+01 3.314929e+00
## 277 278 279 280 281 282
## 6.268429e+00 3.281041e+00 1.615043e+00 4.566495e-02 4.540504e+01 1.176022e+00
## 283 284 285 286 287 288
## 3.475458e+01 3.231028e+01 3.856496e-01 2.956488e+01 3.221712e-06 1.517697e+01
## 289 290 291 292 293 294
## 2.489079e+01 4.593853e+00 2.655275e+01 9.144115e+00 1.689299e+01 3.928269e+00
## 295 296 297 298 299 300
## 7.862971e+00 2.158638e-02 7.318452e-02 6.199754e-01 4.571617e+01 8.848605e+00
## 301 302 303 304 305 306
## 3.740617e+01 4.908793e+01 6.331184e+00 9.313059e-02 8.707894e+00 5.865127e+00
## 307 308 309 310 311 312
## 4.925755e+00 2.134519e+01 3.490444e+01 1.097246e+01 6.295753e+00 2.339483e+01
## 313 314 315 316 317 318
## 1.532674e+01 1.582541e+01 2.870317e+00 1.912256e+01 3.450631e-02 2.052398e+00
## 319 320 321 322 323 324
## 1.430690e+00 1.069377e-01 1.198376e+00 3.176837e+00 6.198795e+00 9.227381e-01
## 325 326 327 328 329 330
## 1.416033e-02 4.804102e-03 4.765176e-01 8.336834e+00 3.781088e+00 2.954469e+00
## 331 332 333 334 335 336
## 3.441631e+00 8.528804e+00 1.587045e+01 3.698186e-03 7.654037e-01 2.415336e-01
## 337 338 339 340 341 342
## 4.512952e-01 6.365567e-01 2.542731e+00 5.219794e+00 7.668080e+00 5.806470e+00
## 343 344 345 346 347 348
## 3.408684e+01 1.489448e+01 7.243904e+00 9.687327e-01 6.213789e+00 4.995692e+00
## 349 350 351 352 353 354
## 9.689524e+00 2.109834e+01 6.324526e+00 1.385283e+01 3.218312e+00 2.582157e+01
## 355 356 357 358 359 360
## 1.685384e+01 1.801083e+01 3.683016e+00 1.141446e+00 2.744186e-01 1.208874e+01
## 361 362 363 364 365 366
## 5.772775e+00 9.752284e-01 7.091829e+00 1.291493e+01 2.852837e+02 2.149911e+02
## 367 368 369 370 371 372
## 4.275165e+01 1.779938e+02 7.873947e+02 3.351657e+02 2.640801e+02 6.543936e+02
## 373 374 375 376 377 378
## 6.339490e+02 6.339202e+01 1.869516e+02 1.116278e+02 1.532786e+01 5.002861e+01
## 379 380 381 382 383 384
## 7.834563e+00 4.540748e+01 3.271900e+01 5.936606e+01 4.666056e+00 5.998654e-01
## 385 386 387 388 389 390
## 3.213960e+01 7.774770e-01 2.024656e+01 3.378258e+00 1.470464e+01 7.556259e+00
## 391 392 393 394 395 396
## 4.588322e+00 3.579287e+01 3.790106e-02 4.228283e+01 2.818196e+01 5.328189e+01
## 397 398 399 400 401 402
## 4.761296e+01 6.304668e+01 2.668367e+00 2.232802e+01 4.152712e+01 1.158000e+02
## 403 404 405 406 407 408
## 3.896707e+01 2.289148e+01 1.396653e+00 1.449052e+01 1.583904e+01 6.525032e+01
## 409 410 411 412 413 414
## 1.275883e+01 6.108433e+01 6.495700e-02 6.170145e-02 2.925406e+02 2.156831e+01
## 415 416 417 418 419 420
## 1.483805e+02 6.146177e+00 3.742888e+01 1.289712e+01 1.072386e+01 4.155393e+01
## 421 422 423 424 425 426
## 8.599456e+00 1.575544e+01 5.345426e+00 5.494603e-02 9.396361e+00 2.815285e+00
## 427 428 429 430 431 432
## 4.101458e+01 1.140076e+01 1.113683e+01 1.329283e+01 1.418733e+01 2.253565e+01
## 433 434 435 436 437 438
## 3.126912e+01 7.832180e+00 1.898620e+01 1.566939e-03 2.581520e+01 1.540751e-02
## 439 440 441 442 443 444
## 1.363672e+01 7.302687e-02 5.062740e+00 3.949323e-02 1.197830e-01 7.300796e+00
## 445 446 447 448 449 450
## 5.229042e-01 3.231554e-02 7.953288e+00 3.147774e+01 1.200647e+01 1.829087e+01
## 451 452 453 454 455 456
## 1.057631e+01 1.831199e+01 6.343967e+00 2.318438e+01 1.557890e-01 3.159516e+00
## 457 458 459 460 461 462
## 1.753985e-04 4.202941e-01 5.355962e+00 2.282126e+00 7.235331e+00 6.317140e+00
## 463 464 465 466 467 468
## 7.758864e-02 5.171410e+00 1.204679e+00 4.230099e+00 2.308054e+01 4.803341e+00
## 469 470 471 472 473 474
## 4.610735e+00 2.372174e+00 7.392626e-02 1.200459e+01 5.737959e-01 1.851058e+01
## 475 476 477 478 479 480
## 6.970657e+00 8.239081e+00 1.522962e+01 2.160582e-01 2.176554e+01 2.202696e-01
## 481 482 483 484 485 486
## 2.296869e-01 1.214821e+01 1.347174e+01 5.440092e-01 1.376131e+00 1.091587e+00
## 487 488 489 490 491 492
## 3.195204e-01 5.479531e-01 1.304814e+01 1.753278e+00 2.335405e+01 2.967204e-02
## 493 494 495 496 497 498
## 2.040458e+01 1.419016e+00 1.572809e+01 4.111324e+01 3.327803e+01 6.650704e-01
## 499 500 501 502 503 504
## 9.817067e-03 9.329858e-01 1.369270e+01 1.336045e+00 3.277817e+00 1.463799e+01
## 505 506
## 1.785779e+01 1.136972e+02
##
## $overall_mean_sqr_errors
## [1] 23.72575
##
## $sd_mean_sqr_errors
## [1] 65.33334este conjunto de funciones hace la cantidad de daros que se tiene , pues ese numero , hace regresiones lineales para cada uno de los valores
Reduciendo datos con Clustering o analisis componantes principales
Merodos partitivos Aglomeramos o separamos ( divisitibo)( dendograma ) Metodos gerargicos #clustering gerargico
protein <- read.csv("../../r-course-master/data/tema5/protein.csv")Escalamos los valores numericos
data <- as.data.frame(scale(protein[,-1]))pero se elimino la columna , no pasa nada, lo incluimos
data$Country = protein$CountryLe ponemos numero a las filas
rownames(data) = data$Countrycluster gerargico ( este es “el mas eficiente”)
hc <- hclust(dist# medir distancias
(data,#Data frame
method = "euclidean"),#metodo para distancia
method = "ward.D2")#es de minima varianza ## Warning in dist(data, method = "euclidean"): NAs introducidos por coerciónhc##
## Call:
## hclust(d = dist(data, method = "euclidean"), method = "ward.D2")
##
## Cluster method : ward.D2
## Distance : euclidean
## Number of objects: 25veamoslo graficamente
plot(hc, hang = -0.01, cex = 0.7)
hc2 <- hclust(dist(data, method = "euclidean"),
method = "single")## Warning in dist(data, method = "euclidean"): NAs introducidos por coerciónplot(hc2, hang=-0.01, cex = 0.7)
Las diferencias de metodos de aglomeracion
hc3 <- hclust(dist(data, method = "euclidean"),
method = "complete")## Warning in dist(data, method = "euclidean"): NAs introducidos por coerciónplot(hc3, hang=-0.01, cex = 0.7)
hc3$merge## [,1] [,2]
## [1,] -18 -25
## [2,] -2 -14
## [3,] -6 -20
## [4,] -3 -24
## [5,] -12 4
## [6,] -5 -7
## [7,] -4 1
## [8,] -15 3
## [9,] -10 -13
## [10,] -21 2
## [11,] -9 -22
## [12,] -8 8
## [13,] -16 6
## [14,] 5 10
## [15,] -17 -19
## [16,] -1 7
## [17,] -23 13
## [18,] -11 17
## [19,] 11 14
## [20,] 9 16
## [21,] 12 19
## [22,] 18 21
## [23,] 15 20
## [24,] 22 23hc4 <- hclust(dist(data, method = "euclidean"),
method = "average")## Warning in dist(data, method = "euclidean"): NAs introducidos por coerciónplot(hc4, hang=-0.01, cex = 0.7)
Esta matriz lo que hace es te da las posiciones de como aglomera cada uno de los valores
hc4$merge## [,1] [,2]
## [1,] -18 -25
## [2,] -2 -14
## [3,] -6 -20
## [4,] -3 -24
## [5,] -12 4
## [6,] -15 3
## [7,] -5 -7
## [8,] -4 1
## [9,] -21 2
## [10,] -10 -13
## [11,] 5 9
## [12,] -8 6
## [13,] -16 7
## [14,] -9 -22
## [15,] 11 13
## [16,] -1 8
## [17,] -17 -19
## [18,] 14 15
## [19,] 12 18
## [20,] -11 16
## [21,] -23 20
## [22,] 10 21
## [23,] 17 22
## [24,] 19 23En esta tabla podemos ver como se ven las relaciones numericas entre los nuemros de aglomeracion
d <- dist(data, method = "euclidean")## Warning in dist(data, method = "euclidean"): NAs introducidos por coerciónd ## Albania Austria Belgium Bulgaria Czechoslovakia Denmark
## Austria 6.467966
## Belgium 6.270545 2.583921
## Bulgaria 2.914078 5.165346 5.520376
## Czechoslovakia 5.419246 2.246156 2.340354 4.164048
## Denmark 6.993021 3.182556 2.676462 6.363126 3.551469
## E Germany 6.738023 2.721647 2.228452 5.701727 1.981974 2.914117
## Finland 6.197500 4.291913 3.693850 6.132320 4.199483 2.775939
## France 6.648173 3.783072 2.313117 5.861471 3.551333 3.860951
## Greece 4.486144 5.443265 4.949126 3.966231 5.133578 5.899629
## Hungary 4.926246 3.463473 4.210760 3.526341 2.900155 5.311659
## Ireland 7.122631 2.889077 1.746375 6.548645 3.328471 2.982614
## Italy 4.243320 3.918642 3.919850 3.020464 3.527166 5.037933
## Netherlands 6.333064 1.184075 2.370619 5.448173 2.312485 2.673810
## Norway 5.760859 4.085139 3.120846 5.562267 3.717820 2.101509
## Poland 6.201022 2.947240 3.094715 4.674394 2.218277 4.052664
## Portugal 6.969670 6.882349 5.956965 6.329434 5.817515 6.187499
## Romania 2.835084 4.902056 5.017853 1.991640 3.754924 5.833256
## Spain 5.869554 5.152500 4.213951 5.103852 4.373624 5.388490
## Sweden 5.959496 3.095772 2.725222 5.688855 3.433509 1.456405
## Switzerland 5.400868 2.321822 2.471070 4.725284 2.765509 3.360708
## UK 6.261715 3.950516 2.051293 6.109584 4.038158 3.662983
## USSR 4.580364 4.387765 3.331584 4.038101 2.863559 4.387004
## W Germany 6.698441 1.733344 1.494659 5.914431 2.302033 2.521632
## Yugoslavia 3.101508 5.740029 5.906917 2.100768 4.575402 6.706354
## E Germany Finland France Greece Hungary Ireland Italy
## Austria
## Belgium
## Bulgaria
## Czechoslovakia
## Denmark
## E Germany
## Finland 4.292653
## France 3.998525 4.843663
## Greece 5.923580 5.801368 4.790900
## Hungary 3.875142 5.686633 5.243799 4.332346
## Ireland 3.197670 3.411083 3.322193 6.013141 5.078293
## Italy 4.554132 5.180912 4.007816 2.266429 3.323988 5.105872
## Netherlands 2.668781 3.567546 3.592490 5.434954 3.679952 2.470828 4.132090
## Norway 3.449373 2.174253 4.132575 4.877926 5.173575 3.804993 4.216275
## Poland 2.855449 4.352077 3.793482 4.652913 3.207114 3.939571 3.286878
## Portugal 5.536763 6.859720 5.961973 5.042438 6.006109 7.445740 4.914201
## Romania 5.042962 5.340870 5.825068 3.815704 2.604885 5.907937 3.277615
## Spain 4.308394 5.777546 4.690822 3.266241 4.090129 5.568623 3.029450
## Sweden 3.225851 2.177177 4.023670 5.255041 4.917562 3.009951 4.359957
## Switzerland 3.764376 3.728661 2.555926 4.329069 4.062262 2.968257 3.095835
## UK 4.140596 4.092941 2.710334 4.871947 5.396215 2.375613 4.411907
## USSR 3.601734 3.657170 4.466362 4.336835 3.615445 4.109018 3.752108
## W Germany 1.994169 3.851187 3.094365 5.653969 4.113548 1.905283 4.361052
## Yugoslavia 5.823788 6.108572 6.647119 4.143284 3.194559 6.811222 3.774715
## Netherlands Norway Poland Portugal Romania Spain
## Austria
## Belgium
## Bulgaria
## Czechoslovakia
## Denmark
## E Germany
## Finland
## France
## Greece
## Hungary
## Ireland
## Italy
## Netherlands
## Norway 3.545298
## Poland 2.922854 3.907456
## Portugal 6.714237 5.055744 5.075801
## Romania 4.893325 4.936560 4.168287 5.934467
## Spain 5.129482 4.386674 3.582102 3.091414 4.472014
## Sweden 2.532559 1.585203 4.049537 6.186614 5.122213 5.069184
## Switzerland 2.003929 3.518374 3.239766 6.453545 4.594953 4.827802
## UK 3.707378 3.741913 4.742940 6.891621 5.716957 4.969019
## USSR 4.091736 3.436253 3.074917 5.349661 2.905607 3.823926
## W Germany 1.341817 3.477531 3.159113 6.474562 5.365969 4.852100
## Yugoslavia 5.800653 5.700872 4.733978 6.141142 1.039579 4.814148
## Sweden Switzerland UK USSR W Germany
## Austria
## Belgium
## Bulgaria
## Czechoslovakia
## Denmark
## E Germany
## Finland
## France
## Greece
## Hungary
## Ireland
## Italy
## Netherlands
## Norway
## Poland
## Portugal
## Romania
## Spain
## Sweden
## Switzerland 2.834669
## UK 3.302204 2.995056
## USSR 3.974365 4.000245 4.222185
## W Germany 2.602745 2.408679 3.051419 4.105817
## Yugoslavia 6.010580 5.491393 6.605472 3.536161 6.286440Calculemos las distanciasentre los paises
alb<-data["Albania",-10]
aus<-data["Austria",-10]
sqrt(sum((alb-aus)^2))#Errror medio ## [1] 6.136051sum(abs(alb-aus))#este se calcula para la distancia de manhatan## [1] 15.97134Como hacemos el clustering divicitivo
#install.packages("cluster")
library(cluster)dv <- diana(data, metric = "euclidean")
par(mfrow=c(1,2))
plot(dv)
Ver como se comport la separacionde cada uno de esos valores
par(mfrow=c(1,1))
#Se recorta el arbol en el numero indicado
fit <- cutree(hc, k=4)
#Asi se ve como se van categorizando los datos
fit## Albania Austria Belgium Bulgaria Czechoslovakia
## 1 2 2 1 3
## Denmark E Germany Finland France Greece
## 2 3 2 2 4
## Hungary Ireland Italy Netherlands Norway
## 3 2 4 2 2
## Poland Portugal Romania Spain Sweden
## 3 4 1 4 2
## Switzerland UK USSR W Germany Yugoslavia
## 2 2 3 2 1#Esta tabla se ejecutan los datos se agruparon por grupo (parte superior) y como se fueron acomodando los paises(el nuemro de abajo )
table(fit)## fit
## 1 2 3 4
## 4 12 5 4#graficamos los datos
plot(hc, hang = -0.01, cex = 0.7)
#Le pedimos que nos pinte las segmentaciones
rect.hclust(hc, k=4, border="red")
# Clustering con K-means
Cuidado con los outliners por que se mueve sumamente feo
protein <- read.csv("../../r-course-master/data/tema5/protein.csv")
rownames(protein) = protein$Country # asignamos los valores como identificador para cada fila Eliminamos la columna donde se mostraban estos numeros
protein$Country = NULLAhora que todo los valores son numericos , entoces escalamos los valores
protein.scaled = as.data.frame(scale(protein))Cargamos e instalamos desde paquete desde git_hub Minimiza el uso a traves de analisis de componentes principales
library(devtools)
devtools::install_github("kassambara/factoextra")#esta libreria nos ayuda cuando tenemos muchas variables ## Skipping install of 'factoextra' from a github remote, the SHA1 (1689fc74) has not changed since last install.
## Use `force = TRUE` to force installation#Se puede validar en el github kassambra (https://github.com/kassambara/factoextra)Creamos los valores que nos ayudan a hacer los K-means devuelve los grupos a los cuales pertenece cada uno de esos valores Que porcentaje esta informacion esta resumida dentro de la informacion (between_SS( suma de cada cluster ) / total_SS(suma de los cuadrados totales ) = 59.8 % )
km <- kmeans(protein.scaled,4)
km## K-means clustering with 4 clusters of sizes 4, 11, 4, 6
##
## Cluster means:
## RedMeat WhiteMeat Eggs Milk Fish Cereals
## 1 -0.508801956 -1.1088009 -0.4124850 -0.8320414 0.9819154 0.1300253
## 2 0.613615213 0.7738178 0.7161344 0.3066547 -0.2598064 -0.5146342
## 3 0.006572897 -0.2290150 0.1914789 1.3458748 1.1582546 -0.8722721
## 4 -0.790141851 -0.5267887 -1.1655757 -0.9047559 -0.9504683 1.4383272
## Starch Nuts Fr.Veg
## 1 -0.1842010 1.3108846 1.6292449
## 2 0.4208082 -0.6131165 0.1060327
## 3 0.1676780 -0.9553392 -1.1148048
## 4 -0.7604664 0.8870168 -0.5373533
##
## Clustering vector:
## Albania Austria Belgium Bulgaria Czechoslovakia
## 4 2 2 4 2
## Denmark E Germany Finland France Greece
## 3 2 3 2 1
## Hungary Ireland Italy Netherlands Norway
## 4 2 1 2 3
## Poland Portugal Romania Spain Sweden
## 2 1 4 1 3
## Switzerland UK USSR W Germany Yugoslavia
## 2 2 4 2 4
##
## Within cluster sum of squares by cluster:
## [1] 18.925874 37.885439 5.900318 24.091128
## (between_SS / total_SS = 59.8 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"Hora lo que hacemos es
aggregate(protein.scaled,# de este DF
by = list(cluster = km$cluster),# de la lista de cluster que esta contenida en el metodo de calculo anterior
mean)#dame la media para cada cluster ( conjunto de datos )## cluster RedMeat WhiteMeat Eggs Milk Fish Cereals
## 1 1 -0.508801956 -1.1088009 -0.4124850 -0.8320414 0.9819154 0.1300253
## 2 2 0.613615213 0.7738178 0.7161344 0.3066547 -0.2598064 -0.5146342
## 3 3 0.006572897 -0.2290150 0.1914789 1.3458748 1.1582546 -0.8722721
## 4 4 -0.790141851 -0.5267887 -1.1655757 -0.9047559 -0.9504683 1.4383272
## Starch Nuts Fr.Veg
## 1 -0.1842010 1.3108846 1.6292449
## 2 0.4208082 -0.6131165 0.1060327
## 3 0.1676780 -0.9553392 -1.1148048
## 4 -0.7604664 0.8870168 -0.5373533Visualicemos como se ve de manera grafica este
library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBafviz_cluster(km, data = protein.scaled)
#mini Bach K-means para segmentar imagenes intalamos paquetes y cargamos
#install.packages(c("OpenImageR", "ClusterR"))
library(OpenImageR)#
library(ClusterR)### Loading required package: gtools##
## Attaching package: 'gtools'## The following object is masked from 'package:e1071':
##
## permutationsCargamos la imagen pequena
imagename <- "../../r-course-master/data/tema5/bodegon.jpg"Hacemos que leea la imagen
img <- readImage(imagename)Usamos un algoritmo que redimenciona el tamano y cantidad de pixeles dependiendo del algoritmo que se desee usar
img.resize <- resizeImage(img, 350, 350,
method = "bilinear")#lo que hacemos es redimencionar los pixeles que se desea
imageShow(img.resize)
Vectorizamos la imagen
img.vector <- apply(img.resize, 3,# se compacta de 3 en 3 los pixeles
as.vector)Veamos la dimencion del vector
dim(img.vector)#Elementos y pixeles ## [1] 122500 3Predice el centroide de cadauno de los trosos el los que se dividira la imagen
kmmb<-MiniBatchKmeans(img.vector,
clusters = 10,#aprox numero de colores
batch_size = 20, #
num_init = 5,#Numero inicial
max_iters = 100,#Maximo de iteraciones
init_fraction = 0.2,#
initializer = "kmeans++",#metodo
early_stop_iter = 10, # que no se pare nunca antes de la iteracion 10
verbose = F)
kmmb## KMeans Cluster
## Call: NULL
## Data cols: 3
## Centroids: 10
## BSS/SS:
## SS: = 15136.32 (WSS) + (BSS)Predice los centroides Mira cuales de los 10 colores tiene mas cerca
prmb <- predict_MBatchKMeans(img.vector,
kmmb$centroids)#de esta variable toma los centroides por alguna extrana rason esto no me lo compila
library(OpenImageR)
get.cent.mb <- kmmb$centroids#jala los centroides
new.img <- get.cent.mb[prmb,]#crea la nueva imagen
#dim(new.img) <- c(nrow(new.img), ncol(img),3)
#imageShow(new.img)#caracteristicas de clustering estandar 1. Tiende a crear clusters convexos 2.Tiende a crear clusters redondos 3. clasifica todos los puntos aunque sean outliners
#Tecnicas de Particionar alrededor de los k medoides medoide: es el que mas se parece a los datos de ese grupo de datos (geometricamente el de en medio )
protein<-read.csv("../../r-course-master/data/tema5/protein.csv")Trasformaciones al DF Nota importante: el escalado es fundamental esn estas tecnicas con la finalidad de evitar inconvenientes con los outliners
rownames(protein) = protein$Country#ponemos nombre a las filas
protein$Country = NULL#eliminamos esas filas
protein.scaled <- as.data.frame(scale(protein))#escalamos el DFCargamos las librerias
library(cluster)
library(factoextra)Medoides (algoritmo pam ) Caracteristica y ventaja de este algoritmo ( lo que hace es toma un valor existente dentro de Df y lo toma para cada uno)
km <- pam(protein.scaled,#DF
4)# numero de medoides
km## Medoids:
## ID RedMeat WhiteMeat Eggs Milk Fish Cereals
## Romania 18 -1.0839304 -0.43204252 -1.2848772 -0.8461152 -0.9651632 1.5810786
## W Germany 24 0.4696633 1.24631815 1.0415022 0.2375653 -0.2598064 -1.2435778
## Sweden 20 0.0215113 -0.02598752 0.5046454 1.0679178 0.9451781 -1.1615716
## Spain 19 -0.8150392 -1.21708219 0.1467409 -1.1979595 0.7982288 -0.2777275
## Starch Nuts Fr.Veg
## Romania -0.7196689 1.1220326 -0.7406162
## W Germany 0.5654541 -0.7916675 -0.1862628
## Sweden -0.3524909 -0.8420280 -1.1840990
## Spain 0.8714358 1.4241958 1.6985391
## Clustering vector:
## Albania Austria Belgium Bulgaria Czechoslovakia
## 1 2 2 1 2
## Denmark E Germany Finland France Greece
## 3 2 3 2 4
## Hungary Ireland Italy Netherlands Norway
## 1 2 4 2 3
## Poland Portugal Romania Spain Sweden
## 2 4 1 4 3
## Switzerland UK USSR W Germany Yugoslavia
## 2 2 1 2 1
## Objective function:
## build swap
## 1.847172 1.839215
##
## Available components:
## [1] "medoids" "id.med" "clustering" "objective" "isolation"
## [6] "clusinfo" "silinfo" "diss" "call" "data"Veamoslo graficamente
fviz_cluster(km)
Medoide ( algoritmo clara) Extrae muestras y utiliza el partition arround mediods(pAm) Tecnica de particion para datos enormes ( ayuda para problemas de memoria)
1._Hace muetreo del data set original y elabora un cluster a la muestra 2._Devuelve el mejor clustering de todos los que elabora
clarafit <- clara(protein.scaled, 4,
samples = 6)#tamano de la muestra
clarafit## Call: clara(x = protein.scaled, k = 4, samples = 6)
## Medoids:
## RedMeat WhiteMeat Eggs Milk Fish Cereals
## Romania -1.0839304 -0.43204252 -1.2848772 -0.8461152 -0.9651632 1.5810786
## W Germany 0.4696633 1.24631815 1.0415022 0.2375653 -0.2598064 -1.2435778
## Sweden 0.0215113 -0.02598752 0.5046454 1.0679178 0.9451781 -1.1615716
## Spain -0.8150392 -1.21708219 0.1467409 -1.1979595 0.7982288 -0.2777275
## Starch Nuts Fr.Veg
## Romania -0.7196689 1.1220326 -0.7406162
## W Germany 0.5654541 -0.7916675 -0.1862628
## Sweden -0.3524909 -0.8420280 -1.1840990
## Spain 0.8714358 1.4241958 1.6985391
## Objective function: 1.839215
## Clustering vector: Named int [1:25] 1 2 2 1 2 3 2 3 2 4 1 2 4 2 3 2 4 1 ...
## - attr(*, "names")= chr [1:25] "Albania" "Austria" "Belgium" "Bulgaria" "Czechoslovakia" "Denmark" "E Germany" ...
## Cluster sizes: 6 11 4 4
## Best sample:
## [1] Albania Austria Belgium Bulgaria Czechoslovakia
## [6] Denmark E Germany Finland France Greece
## [11] Hungary Ireland Italy Netherlands Norway
## [16] Poland Portugal Romania Spain Sweden
## [21] Switzerland UK USSR W Germany Yugoslavia
##
## Available components:
## [1] "sample" "medoids" "i.med" "clustering" "objective"
## [6] "clusinfo" "diss" "call" "silinfo" "data"Veamos este algoritmo
fviz_cluster(clarafit)
#Validando los resultados de un cluster
Como puedo saber que porcenteje estaq bien catalogado y cuando no esto sirve para elejir o variar el metodo de clustering Validacion de estabilidad (consistencia del cluster )= va quitando columnas (variables )y ve si se meueve mucho la casificacion Cargamos los paquetes
#install.packages(c("fpc", "NbClust"))
library(factoextra)
library(cluster)
library(fpc)
library(NbClust)volvemos a trabajar con el dataset de proteinas explicado en las lineas de codigo 2502 aprox
protein <- read.csv("../../r-course-master/data/tema5/protein.csv")
rownames(protein) = protein$Country
protein$Country = NULL
protein.scaled <- as.data.frame(scale(protein))Determinar numero de clusters que se desean nota ver “Conclusion”, muestra k=3 , pero el numero de diviciones es 7 que correponde a esa linea
nb <- NbClust(protein.scaled,#DF escalado
distance = "euclidean",#Tipo de distancia
min.nc = 2,# minimo de cluster
max.nc = 12, #Maximo de cluster
method = "ward.D2",# metodo aglomerativo
index = "all")#paramerto por defecto ## Warning in pf(beale, pp, df2): NaNs produced
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
## 
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 6 proposed 2 as the best number of clusters
## * 7 proposed 3 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
## * 1 proposed 7 as the best number of clusters
## * 1 proposed 10 as the best number of clusters
## * 5 proposed 12 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 3
##
##
## *******************************************************************generemos una visualizacion mas sencilla El analisis
fviz_nbclust(nb) + theme_minimal()## Warning in if (class(best_nc) == "numeric") print(best_nc) else if
## (class(best_nc) == : la condición tiene longitud > 1 y sólo el primer elemento
## será usado## Warning in if (class(best_nc) == "matrix") .viz_NbClust(x, print.summary, : la
## condición tiene longitud > 1 y sólo el primer elemento será usado## Warning in if (class(best_nc) == "numeric") print(best_nc) else if
## (class(best_nc) == : la condición tiene longitud > 1 y sólo el primer elemento
## será usado## Warning in if (class(best_nc) == "matrix") {: la condición tiene longitud > 1 y
## sólo el primer elemento será usado## Among all indices:
## ===================
## * 2 proposed 0 as the best number of clusters
## * 1 proposed 1 as the best number of clusters
## * 6 proposed 2 as the best number of clusters
## * 7 proposed 3 as the best number of clusters
## * 3 proposed 6 as the best number of clusters
## * 1 proposed 7 as the best number of clusters
## * 1 proposed 10 as the best number of clusters
## * 5 proposed 12 as the best number of clusters
##
## Conclusion
## =========================
## * According to the majority rule, the best number of clusters is 3 .
Ahora que tenemos el numero de clusters mas eficiente generamos kmeans
km.res <- kmeans(protein.scaled, 3)Creamos una sirueta : que es otro tipo de analisis
sil.km <- silhouette(km.res$cluster, #del objeto creado
dist(protein.scaled))#calcule la distancia del Df inicial Del objeto que creamos le la varibale de la sirueta le hacemos un summary
sil.sum <- summary(sil.km)
sil.sum## Silhouette of 25 units in 3 clusters from silhouette.default(x = km.res$cluster, dist = dist(protein.scaled)) :
## Cluster sizes and average silhouette widths:
## 4 10 11
## 0.4187964 0.1936819 0.2068237
## Individual silhouette widths:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.03801 0.14773 0.23808 0.23548 0.32609 0.46940visualicemos este objeto (la sirueta) Para medir la eficiencia del objeto anchura (analisis de DAnn),conectividad , separacion ,
fviz_silhouette(sil.km)## cluster size ave.sil.width
## 1 1 4 0.42
## 2 2 10 0.19
## 3 3 11 0.21
25 paises , pues 25 columnas mirar la grafica de arriba entre mas cerca de -1 esta peor clusterizado y entre mas cercano a 1 mejor esta clasificado
la linea intermedia es la linea roja punteada es el promedio de que tan bien se clasifico entre la k elejida
“ver teoria de clusters” nota : es importante notar que si todos salen positivos es muy bueno
fviz_cluster(km.res, data = protein.scaled)
dd <- dist(protein.scaled, method = "euclidean")#calculo de distancias
km_stats <- fpc::cluster.stats(dd, km.res$cluster)#calculo de los estados con base a las distancias recien hechas
km_stats## $n
## [1] 25
##
## $cluster.number
## [1] 3
##
## $cluster.size
## [1] 4 10 11
##
## $min.cluster.size
## [1] 4
##
## $noisen
## [1] 0
##
## $diameter
## [1] 2.633487 6.612010 4.499548
##
## $average.distance
## [1] 1.940134 3.792570 2.654427
##
## $median.distance
## [1] 2.028172 3.627695 2.581981
##
## $separation
## [1] 2.39223 2.71661 2.39223
##
## $average.toother
## [1] 4.196834 4.904011 4.398396
##
## $separation.matrix
## [,1] [,2] [,3]
## [1,] 0.000000 3.259916 2.39223
## [2,] 3.259916 0.000000 2.71661
## [3,] 2.392230 2.716610 0.00000
##
## $ave.between.matrix
## [,1] [,2] [,3]
## [1,] 0.000000 5.134783 3.344152
## [2,] 5.134783 0.000000 4.820093
## [3,] 3.344152 4.820093 0.000000
##
## $average.between
## [1] 4.550228
##
## $average.within
## [1] 2.995397
##
## $n.between
## [1] 194
##
## $n.within
## [1] 106
##
## $max.diameter
## [1] 6.61201
##
## $min.separation
## [1] 2.39223
##
## $within.cluster.ss
## [1] 114.7435
##
## $clus.avg.silwidths
## 1 2 3
## 0.4187964 0.1936819 0.2068237
##
## $avg.silwidth
## [1] 0.2354826
##
## $g2
## NULL
##
## $g3
## NULL
##
## $pearsongamma
## [1] 0.5320644
##
## $dunn
## [1] 0.3618008
##
## $dunn2
## [1] 0.8817642
##
## $entropy
## [1] 1.020961
##
## $wb.ratio
## [1] 0.658296
##
## $ch
## [1] 9.707054
##
## $cwidegap
## [1] 2.062677 2.932773 2.194416
##
## $widestgap
## [1] 2.932773
##
## $sindex
## [1] 2.39223
##
## $corrected.rand
## NULL
##
## $vi
## NULLSuma de los cuadrados internos del cluster
km_stats$within.cluster.ss# este es el total de todos los centros ## [1] 114.7435km_stats$clus.avg.silwidths#promedio de cada K## 1 2 3
## 0.4187964 0.1936819 0.2068237#indice de Dunn entre mas alejado este un cluster ( ente mas grande es este numero es mejor , significa que el equivocarse al clasificar se hace mas raro , minimiza el reisgo de equivocarse)
km_stats$dunn# indice de dun (distancia mas pequena de un dato entre dos clusters /distancia maxima entre elementos dentro de un cluster)## [1] 0.3618008Vamos a comparar la diferencia de los indices de Dunn entre metodos
kmed <- pam(protein.scaled, 3)
sil.kmed <- silhouette(kmed$clustering,
dist(protein.scaled))
fviz_cluster(kmed, data = protein.scaled)#graficamos las categorias del calculo de pam 
fviz_silhouette(sil.kmed)# calculamos la cirueta del paquete PAM ## cluster size ave.sil.width
## 1 1 6 0.31
## 2 2 15 0.38
## 3 3 4 0.21
kmed_stats <- cluster.stats(dd, kmed$clustering)
kmed_stats$within.cluster.ss## [1] 105.9863kmed_stats$clus.avg.silwidths## 1 2 3
## 0.3062056 0.3811136 0.2063242kmed_stats$dunn## [1] 0.5678917Comparamos los 2 clusters
res.com <- cluster.stats(dd, km.res$cluster,
kmed$clustering)
res.com$corrected.rand#Indice de correcion aleatoria ( simiitudes )entre -1 y 1( conhincidencia casi exacta )( este lo queremos mas hacia el 1)## [1] 0.5243425res.com$vi#Variacion entre custers (distancia entre custers )( lo mas pequeno posible )## [1] 0.6171538#tecnicas avanzadas de clustering Un [unto de la frontera en un cluster es quel que :tiene unvecindario inferior al minimo estipulado a la distancia menor o igual a la indicada #clustering basado en densidades ( cuando tenemos ruido y outliners )
library(fpc)
library(factoextra)
data("multishapes", package = "factoextra")# esta precargado como se ve el DF
par(mfrow=c(1,1))
head(multishapes)## x y shape
## 1 -0.8037393 -0.8530526 1
## 2 0.8528507 0.3676184 1
## 3 0.9271795 -0.2749024 1
## 4 -0.7526261 -0.5115652 1
## 5 0.7068462 0.8106792 1
## 6 1.0346985 0.3946550 1Obtenemos la columna 1(X) y 2(y)
dataPoints <- multishapes[,1:2]
plot(multishapes)
plot(dataPoints)# esta es la que vamos a trabajar 
intentemos meter estos valores dentro de un k-means Esta agrupando horrible (jajajaja)
km <- kmeans(dataPoints, 5)
fviz_cluster(km, data= dataPoints)
eliminamos los ruidos
dsFit <- dbscan(dataPoints,
eps = 0.15,#epsilon :Radio que debe de existir entre valores
MinPts = 5)#minimo de puntos:no se cree un cluster abajo del numero indicado
dsFit## dbscan Pts=1100 MinPts=5 eps=0.15
## 0 1 2 3 4 5
## border 31 24 1 5 7 1
## seed 0 386 404 99 92 50
## total 31 410 405 104 99 51veamos como lo categoriza
fviz_cluster(dsFit, dataPoints, geom = "point")
Clustering basado en modelos
BIC:Criterio de informacion bayesiano clasification : uncertanty :regiones de incertidumbre ( el algoritmo no sabe donde poner estos datos ) density:
#install.packages("mclust")
library(mclust)## Package 'mclust' version 5.4.9
## Type 'citation("mclust")' for citing this R package in publications.##
## Attaching package: 'mclust'## The following object is masked _by_ '.GlobalEnv':
##
## banknote## The following object is masked from 'package:VIM':
##
## diabetes## The following object is masked from 'package:purrr':
##
## mapmclust <- Mclust(dataPoints)
plot(mclust)
que hizo Mclust Modelo Gaussiano mixto finito
summary(mclust)## ----------------------------------------------------
## Gaussian finite mixture model fitted by EM algorithm
## ----------------------------------------------------
##
## Mclust VEV (ellipsoidal, equal shape) model with 9 components:
##
## log-likelihood n df BIC ICL
## -2062.757 1100 45 -4440.653 -4577.743
##
## Clustering table:
## 1 2 3 4 5 6 7 8 9
## 134 187 112 132 128 142 108 104 53Reducir dimenciones con ACP(Analisis de componentes principales )
#install.packages("corrplot")
library(corrplot)#correlaciones ## corrplot 0.92 loadedbh <- read.csv("../../r-course-master/data/tema5/BostonHousing.csv")Hacemos el objeto para las correlaciones
corr <- cor(bh[,-14])
corr## CRIM ZN INDUS CHAS NOX
## CRIM 1.00000000 -0.20046922 0.40658341 -0.055891582 0.42097171
## ZN -0.20046922 1.00000000 -0.53382819 -0.042696719 -0.51660371
## INDUS 0.40658341 -0.53382819 1.00000000 0.062938027 0.76365145
## CHAS -0.05589158 -0.04269672 0.06293803 1.000000000 0.09120281
## NOX 0.42097171 -0.51660371 0.76365145 0.091202807 1.00000000
## RM -0.21924670 0.31199059 -0.39167585 0.091251225 -0.30218819
## AGE 0.35273425 -0.56953734 0.64477851 0.086517774 0.73147010
## DIS -0.37967009 0.66440822 -0.70802699 -0.099175780 -0.76923011
## RAD 0.62550515 -0.31194783 0.59512927 -0.007368241 0.61144056
## TAX 0.58276431 -0.31456332 0.72076018 -0.035586518 0.66802320
## PTRATIO 0.28994558 -0.39167855 0.38324756 -0.121515174 0.18893268
## B -0.38506394 0.17552032 -0.35697654 0.048788485 -0.38005064
## LSTAT 0.45562148 -0.41299457 0.60379972 -0.053929298 0.59087892
## RM AGE DIS RAD TAX PTRATIO
## CRIM -0.21924670 0.35273425 -0.37967009 0.625505145 0.58276431 0.2899456
## ZN 0.31199059 -0.56953734 0.66440822 -0.311947826 -0.31456332 -0.3916785
## INDUS -0.39167585 0.64477851 -0.70802699 0.595129275 0.72076018 0.3832476
## CHAS 0.09125123 0.08651777 -0.09917578 -0.007368241 -0.03558652 -0.1215152
## NOX -0.30218819 0.73147010 -0.76923011 0.611440563 0.66802320 0.1889327
## RM 1.00000000 -0.24026493 0.20524621 -0.209846668 -0.29204783 -0.3555015
## AGE -0.24026493 1.00000000 -0.74788054 0.456022452 0.50645559 0.2615150
## DIS 0.20524621 -0.74788054 1.00000000 -0.494587930 -0.53443158 -0.2324705
## RAD -0.20984667 0.45602245 -0.49458793 1.000000000 0.91022819 0.4647412
## TAX -0.29204783 0.50645559 -0.53443158 0.910228189 1.00000000 0.4608530
## PTRATIO -0.35550149 0.26151501 -0.23247054 0.464741179 0.46085304 1.0000000
## B 0.12806864 -0.27353398 0.29151167 -0.444412816 -0.44180801 -0.1773833
## LSTAT -0.61380827 0.60233853 -0.49699583 0.488676335 0.54399341 0.3740443
## B LSTAT
## CRIM -0.38506394 0.4556215
## ZN 0.17552032 -0.4129946
## INDUS -0.35697654 0.6037997
## CHAS 0.04878848 -0.0539293
## NOX -0.38005064 0.5908789
## RM 0.12806864 -0.6138083
## AGE -0.27353398 0.6023385
## DIS 0.29151167 -0.4969958
## RAD -0.44441282 0.4886763
## TAX -0.44180801 0.5439934
## PTRATIO -0.17738330 0.3740443
## B 1.00000000 -0.3660869
## LSTAT -0.36608690 1.0000000Grafiquemos las correlaciones Rojo =poco correlacionado azul =muy correlacionado
corrplot(corr, method = "circle")
#scale = T, matriz de correlaciones #scale = F, matriz de covarianzas
aqui vamos a correr el ejercicio donde podemos observar cuales son
bh.acp <- prcomp(bh[,-14], scale = T)#calculo de analisis de con cuantas variables que % del DF puedo explicar (cumulative Proportion )
summary(bh.acp)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.4752 1.1972 1.11473 0.92605 0.91368 0.81081 0.73168
## Proportion of Variance 0.4713 0.1103 0.09559 0.06597 0.06422 0.05057 0.04118
## Cumulative Proportion 0.4713 0.5816 0.67713 0.74310 0.80732 0.85789 0.89907
## PC8 PC9 PC10 PC11 PC12 PC13
## Standard deviation 0.62936 0.5263 0.46930 0.43129 0.41146 0.25201
## Proportion of Variance 0.03047 0.0213 0.01694 0.01431 0.01302 0.00489
## Cumulative Proportion 0.92954 0.9508 0.96778 0.98209 0.99511 1.00000Grafiquemoslo lo que vamos a observar son las componentes que explican el DF
plot(bh.acp)
plot(bh.acp, type = "lines")
Grafiquemos con color estas son las columnas que tenemos que utilizar
biplot(bh.acp, col = c("gray", "red"))
head(bh.acp$x,5)
La matriz de rotacion Cual es la que explica mejor los datos
#bh.acp$rotationDesviacion estandar conclusion ( las primeras 7 son suficientes para entender los datos ) alijerar ( disminuir el tamano del df)
bh.acp$sdev## [1] 2.4752472 1.1971947 1.1147272 0.9260535 0.9136826 0.8108065 0.7316803
## [8] 0.6293626 0.5262541 0.4692950 0.4312938 0.4114644 0.2520104#Series temporales y su utilidad Cargamos los datos
AAPL = read.csv("../../r-course-master/data/tema6/AAPL.csv", stringsAsFactors = F)
AMZN = read.csv("../../r-course-master/data/tema6/AMZN.csv", stringsAsFactors = F)
FB = read.csv("../../r-course-master/data/tema6/FB.csv", stringsAsFactors = F)
GOOG = read.csv("../../r-course-master/data/tema6/GOOG.csv", stringsAsFactors = F)
str(AAPL)## 'data.frame': 2519 obs. of 7 variables:
## $ Date : chr "2007-12-31" "2008-01-02" "2008-01-03" "2008-01-04" ...
## $ Open : num 28.5 28.5 27.9 27.4 25.9 ...
## $ High : num 28.6 28.6 28.2 27.6 26.2 ...
## $ Low : num 28.2 27.5 27.5 25.6 24.3 ...
## $ Close : num 28.3 27.8 27.8 25.7 25.4 ...
## $ Adj.Close: num 25.4 25 25 23.1 22.7 ...
## $ Volume : int 134833300 269794700 210516600 363958000 518048300 380954000 453470500 370743800 308071400 275112600 ...Filtramos el dia que no nos sirve
AAPL=AAPL[AAPL$Date>="2008-01-01",]
AMZN=AMZN[AMZN$Date>="2008-01-01",]
GOOG=GOOG[GOOG$Date>="2008-01-01",]Hacemos el casting a fecha de las mismas…
AAPL$Date = as.Date(AAPL$Date)
AMZN$Date = as.Date(AMZN$Date)
FB$Date = as.Date(FB$Date)
GOOG$Date = as.Date(GOOG$Date)Grafiquemos
library(ggplot2)
ggplot(AAPL, aes(Date#eje X
, Close#eje Y
)) +
geom_line(aes(color="Apple")) +
geom_line(data=AMZN, aes(color = "Amazon"))+
geom_line(data=FB, aes(color="Facebook"))+
geom_line(data=GOOG, aes(color="Google"))+
labs(color="Legend")+
scale_color_manual("",
breaks = c("Apple", "Amazon", "Facebook", "Google" ),
values = c("green", "yellow", "blue", "red"))+
ggtitle("Comparaciones de cierre de stocks")+
theme(plot.title = element_text(lineheight = 0.7, face = "bold"))
# cargar datos en tiempo real
#install.packages("quantmod")
library(quantmod)## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## Attaching package: 'xts'## The following objects are masked from 'package:dplyr':
##
## first, last## Loading required package: TTR## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo##
## Attaching package: 'quantmod'## The following object is masked from 'package:Hmisc':
##
## Lagse conecta y es un objeto de tipo xts
getSymbols("AAPL")## 'getSymbols' currently uses auto.assign=TRUE by default, but will
## use auto.assign=FALSE in 0.5-0. You will still be able to use
## 'loadSymbols' to automatically load data. getOption("getSymbols.env")
## and getOption("getSymbols.auto.assign") will still be checked for
## alternate defaults.
##
## This message is shown once per session and may be disabled by setting
## options("getSymbols.warning4.0"=FALSE). See ?getSymbols for details.## [1] "AAPL"Grafica volumen en millones
barChart(AAPL)
chartSeries(AAPL, TA = "NULL")#tecnival indicatior 
candleChart(AAPL, up.col = "black",
dn.col="red", theme = "white")
Aqui muestra los porcentajes de cada uno de los valores ( variaciones en la media)
chartSeries(AAPL[,4],TA = "addMACD()")
Obtengamos la de Netflix
getSymbols("BTC")## [1] "BTC"barChart(BTC)
chartSeries(BTC, TA = "NULL")
chartSeries(BTC[,4],TA = "addMACD()")
Fechas
EPOCH : 1 de Enero de 1970
HOY
Sys.Date()## [1] "2022-04-03"ano a dos digitos
as.Date("1/1/80", format="%m/%d/%y")## [1] "1980-01-01"ano cuatro digitos
as.Date("1/1/1980", format="%m/%d/%Y")## [1] "1980-01-01"Tipo de formato de fecha yyyy-mm-dd o yyyy/mm/dd
as.Date("2018/01/06")## [1] "2018-01-06"nac <- as.Date("88/05/19")fecha a -> numero
as.numeric(as.Date("1988/05/19"))#cuantos dias han pasado ## [1] 6713as.numeric(Sys.Date())## [1] 19085#Nombre de los meses
as.Date("Ene 6, 2018", format="%b %d, %Y")#debes tener cuidado con ls nombres## [1] NAas.Date("Enero 6, 18", format="%B %d, %y")## [1] "2018-01-06"Fechas desde dias de EPOCH
dt <- 2018 #2018 dias
class(dt) <- "Date"#cambias de numero a fecha
dt#imprimes dt## [1] "1975-07-12"ahora agamoslo alreves
dt <- -2018
class(dt) <- "Date"
dt## [1] "1964-06-23"Fechas desde dias de un punto dado
dt <- as.Date(2018, origin =
as.Date("1988-05-19"))#sumas dias desde la fecha dada
as.Date(-2018, origin = as.Date("1988-05-19"))#restas desde un numero ## [1] "1982-11-09"Componentes de las fechas
dt## [1] "1993-11-27"anoo en 4 digitos
format(dt, "%Y")#dame solo el ano## [1] "1993"ano como numero en lugar de String
as.numeric(format(dt, "%Y"))## [1] 1993ano en 2 digitos
format(dt, "%y")## [1] "93"ano como numero en lugar de String
as.numeric(format(dt, "%y"))## [1] 93Mes como String
format(dt, "%b")#mes cortado## [1] "nov."format(dt, "%B")# mes completo ## [1] "noviembre"months(dt)# mes completo## [1] "noviembre"weekdays(dt)#dia de la semana ## [1] "sábado"quarters(dt)#trimestre ## [1] "Q4"julian(dt)#Calendario juliano (dias que han pasado )## [1] 8731
## attr(,"origin")
## [1] "1970-01-01"julian(dt, origin = as.Date("1980/06/25"))##calendario juliano## [1] 4903
## attr(,"origin")
## [1] "1980-06-25"dt <- as.Date("1/1/2001", format = "%d/%m/%Y")dt2 <- as.Date("2001/01/02")
dt2-dt#( lo arroja como string) muestra la diferencia de fechas en dias ## Time difference of 1 daysdt-dt2# muestra la diferencia de fechas en dias( lo arroja como string)## Time difference of -1 daysas.numeric(dt-dt2)## [1] -1Comparar fechas
dt<dt2## [1] TRUEdt==dt2## [1] FALSEdt2<dt## [1] FALSEsecuencia de fechas
seq(dt, dt+365, "month")# va contando por mes ## [1] "2001-01-01" "2001-02-01" "2001-03-01" "2001-04-01" "2001-05-01"
## [6] "2001-06-01" "2001-07-01" "2001-08-01" "2001-09-01" "2001-10-01"
## [11] "2001-11-01" "2001-12-01" "2002-01-01"seq(dt, as.Date("2001/01/10"), "day")# cuenta por dias ## [1] "2001-01-01" "2001-01-02" "2001-01-03" "2001-01-04" "2001-01-05"
## [6] "2001-01-06" "2001-01-07" "2001-01-08" "2001-01-09" "2001-01-10"Biestral
seq(dt, dt+365, "2 months")## [1] "2001-01-01" "2001-03-01" "2001-05-01" "2001-07-01" "2001-09-01"
## [6] "2001-11-01" "2002-01-01"Cuatrimestral
seq(from = dt, by = "4 months", length.out = 6)[3]#logitud total de 6 meses# el trecer semestre ## [1] "2001-09-01"secuencias por semana
seq(from = dt, by = "3 weeks", length.out = 6)## [1] "2001-01-01" "2001-01-22" "2001-02-12" "2001-03-05" "2001-03-26"
## [6] "2001-04-16"Analisis preliminar de una serie temporal
wmt <- read.csv("../../r-course-master/data/tema6/WMT.csv", stringsAsFactors = F)Grafiquemos
plot(wmt$Adj.Close, type = "l")
calcula las diferencias del uno al dos diff(difreencia)y lo graficamos
d <- diff(wmt$Adj.Close, lag = 2)
plot(d, type="l")
Creemos un histograma
hist(d, prob=T,
ylim = c(0,0.6),#Altura maxima en el histograma
breaks = 30,
main = "Walmart Stocks", col = "green")
lines(density(d), lwd=3)
carguemos una variación de los datos
wmt.m <- read.csv("../../r-course-master/data/tema6/WMT-monthly.csv", stringsAsFactors = F)hacemos la fila 2 la nueva fila 1
wmt.m <- wmt.m[2:nrow(wmt.m),]
wmt.m$Date <- as.Date(wmt.m$Date)Creemos una serie temporal
wmt.m.ts <- ts(wmt.m$Adj.Close)Veamos las difreencias
d <- diff(as.numeric(wmt.m.ts))
plot(d, type="l")
wmt.m.return <- d / lag(as.numeric(wmt.m.ts),#calcula en intrevalos de 2 en 2 etc
k = -1)# con respecto al periodo anterior## Warning in d/lag(as.numeric(wmt.m.ts), k = -1): longitud de objeto mayor no es
## múltiplo de la longitud de uno menorPintemos el hitograma de este analisis
#hist(wmt.m.return, prob = T, col = "green",ylim=c(0,8))
#lines(density(wmt.m.return), lwd=3)Series Temporales
s <- read.csv("../../r-course-master/data/tema6/ts-example.csv")
head(s)[1:3,]## [1] 51 56 37s.ts <- ts(s)#hacemos una serie temporal
class(s.ts)#vemos la case el objeto que acabamos de definir## [1] "ts"plot(s.ts)#graficamos este objeto que es una serie temporal
Pedimos que inicie de los diferentes puntos de partida
s.ts.a <- ts(s, start = 2000)# esto es anualizado
plot(s.ts.a)
Pedimos que la serie temporal se ejecute de manera mensual
s.ts.m <- ts(s, start = c(2001,1),#En este caso
frequency = 12)#12 muestras anuales (12 meses en el ano)
plot(s.ts.m)
Pedimos que la serie temporal se ejecute de manera trimestestral
s.ts.q <- ts(s, start = 2001, frequency = 4)
plot(s.ts.q)
Asi se analiza el objeto de tipo mensual
start(s.ts.m)# cuando empieza## [1] 2001 1end(s.ts.m)#cuando termina## [1] 2009 4frequency(s.ts.m)#frecuencia ## [1] 12Asi se analiza el objeto de tipo trimestral
start(s.ts.q)## [1] 2001 1end(s.ts.q)## [1] 2025 4frequency(s.ts.q)## [1] 4#cuando se tienene diferentes columnas
prices <- read.csv("../../r-course-master/data/tema6/prices.csv")
head(prices)[1:5,]## white_flour unleaded_gas
## 1 0.203 1.131
## 2 0.205 1.207
## 3 0.211 1.252
## 4 0.206 1.264
## 5 0.207 1.266Signamos una serie temporal
prices.ts <- ts(prices, start = c(1980,1), frequency = 12)
plot(prices.ts)
Hacer un unico grafico con las 2 lineas
plot(prices.ts, plot.type = "single", col = 1:2)
legend("topleft"#donde lo vas a poner
, colnames(prices.ts), col = 1:2, lty = 1)
Donde buscar datos en EUA
https://www.bls.gov/ # Donde buscar datos en Mexico https://en.www.inegi.org.mx/
Descomposición de una serie temporal
Nota: cuando se observa una grafica como la antrior , se podria inferir que es de tipo multiplicativa
El numero anterior multiplicado por otro numero
Recordemos la propiedad de los logaritmos log(a*b) = log (a) + log(b) #SLT(Seasonal Decomposition of Time Series by Loess) metodo de descomposicion por metodo slt Estacionalidad
flour.l <- log(prices.ts[,1])#se aplica el logaritmo a la columna 1 ( la harina )
flour.stl<- stl(flour.l,#datos con el logaritmo
s.window = "period")#como se tomo el periodo original Gafriquemoslo 1ra gafica :la grafica original 2da: periodicidad 3ra;La tendencia general con los datos aplicados 4ta:el ruido que presentan las fluctuaciones
plot(flour.stl)#la de arriba es la mismo 
flour.stl## Call:
## stl(x = flour.l, s.window = "period")
##
## Components
## seasonal trend remainder
## Jan 1980 0.0019097886 -1.5955813 -8.777615e-04
## Feb 1980 0.0012341216 -1.5896830 3.703570e-03
## Mar 1980 0.0006245546 -1.5837847 2.726295e-02
## Apr 1980 -0.0125038908 -1.5775729 1.019768e-02
## May 1980 0.0136392482 -1.5713611 -1.731459e-02
## Jun 1980 0.0209864093 -1.5649811 -1.665309e-02
## Jul 1980 0.0283284639 -1.5586010 -1.150674e-02
## Aug 1980 0.0318618948 -1.5523594 -1.661979e-02
## Sep 1980 0.0151345828 -1.5461177 -1.079612e-02
## Oct 1980 -0.0107891741 -1.5393430 -1.036836e-03
## Nov 1980 -0.0424312547 -1.5325683 1.910237e-02
## Dec 1980 -0.0479947475 -1.5255146 1.761217e-02
## Jan 1981 0.0019097886 -1.5184609 -1.130684e-02
## Feb 1981 0.0012341216 -1.5132263 6.914299e-03
## Mar 1981 0.0006245546 -1.5079918 1.125798e-02
## Apr 1981 -0.0125038908 -1.5040587 2.490767e-02
## May 1981 0.0136392482 -1.5001256 3.681048e-03
## Jun 1981 0.0209864093 -1.4973157 -2.080388e-03
## Jul 1981 0.0283284639 -1.4945058 -1.223233e-02
## Aug 1981 0.0318618948 -1.4931905 -8.347395e-03
## Sep 1981 0.0151345828 -1.4918752 -1.491430e-02
## Oct 1981 -0.0107891741 -1.4918316 1.540053e-02
## Nov 1981 -0.0424312547 -1.4917881 2.462679e-02
## Dec 1981 -0.0479947475 -1.4918078 1.654234e-02
## Jan 1982 0.0019097886 -1.4918275 -6.191515e-03
## Feb 1982 0.0012341216 -1.4926852 4.230768e-03
## Mar 1982 0.0006245546 -1.4935428 -7.665224e-03
## Apr 1982 -0.0125038908 -1.4951881 -6.435745e-03
## May 1982 0.0136392482 -1.4968334 4.784457e-03
## Jun 1982 0.0209864093 -1.4987342 -5.057449e-03
## Jul 1982 0.0283284639 -1.5006351 1.128872e-02
## Aug 1982 0.0318618948 -1.5034516 -1.121551e-02
## Sep 1982 0.0151345828 -1.5062682 -9.449887e-03
## Oct 1982 -0.0107891741 -1.5099866 1.118324e-02
## Nov 1982 -0.0424312547 -1.5137051 2.365946e-02
## Dec 1982 -0.0479947475 -1.5168532 3.237112e-02
## Jan 1983 0.0019097886 -1.5200014 -9.766308e-03
## Feb 1983 0.0012341216 -1.5218917 -2.112168e-02
## Mar 1983 0.0006245546 -1.5237820 -3.273970e-02
## Apr 1983 -0.0125038908 -1.5243686 -9.590591e-03
## May 1983 0.0136392482 -1.5249553 -2.116086e-02
## Jun 1983 0.0209864093 -1.5246495 3.079598e-03
## Jul 1983 0.0283284639 -1.5243438 -4.568200e-03
## Aug 1983 0.0318618948 -1.5233090 8.641852e-03
## Sep 1983 0.0151345828 -1.5222742 6.556156e-03
## Oct 1983 -0.0107891741 -1.5221062 1.421184e-02
## Nov 1983 -0.0424312547 -1.5219382 3.651151e-02
## Dec 1983 -0.0479947475 -1.5237496 1.584724e-02
## Jan 1984 0.0019097886 -1.5255611 -2.281181e-02
## Feb 1984 0.0012341216 -1.5293462 -4.364775e-03
## Mar 1984 0.0006245546 -1.5331313 2.991762e-05
## Apr 1984 -0.0125038908 -1.5369253 1.231198e-02
## May 1984 0.0136392482 -1.5407193 -1.003717e-02
## Jun 1984 0.0209864093 -1.5430927 -5.751634e-03
## Jul 1984 0.0283284639 -1.5454661 -1.545945e-03
## Aug 1984 0.0318618948 -1.5465776 -2.240158e-02
## Sep 1984 0.0151345828 -1.5476891 -1.861453e-02
## Oct 1984 -0.0107891741 -1.5483655 3.257522e-03
## Nov 1984 -0.0424312547 -1.5490419 2.125598e-02
## Dec 1984 -0.0479947475 -1.5491985 7.557915e-03
## Jan 1985 0.0019097886 -1.5493550 -3.723813e-03
## Feb 1985 0.0012341216 -1.5497681 2.070907e-03
## Mar 1985 0.0006245546 -1.5501813 -1.612255e-03
## Apr 1985 -0.0125038908 -1.5507902 7.396947e-03
## May 1985 0.0136392482 -1.5513991 6.426010e-04
## Jun 1985 0.0209864093 -1.5523379 -1.125388e-03
## Jul 1985 0.0283284639 -1.5532767 -7.528650e-03
## Aug 1985 0.0318618948 -1.5545778 -9.760983e-03
## Sep 1985 0.0151345828 -1.5558789 -1.042470e-02
## Oct 1985 -0.0107891741 -1.5574127 1.230469e-02
## Nov 1985 -0.0424312547 -1.5589464 2.634122e-02
## Dec 1985 -0.0479947475 -1.5600807 8.587829e-03
## Jan 1986 0.0019097886 -1.5612149 -1.573140e-02
## Feb 1986 0.0012341216 -1.5623179 -4.337218e-03
## Mar 1986 0.0006245546 -1.5634210 -7.420770e-03
## Apr 1986 -0.0125038908 -1.5647632 -2.612059e-03
## May 1986 0.0136392482 -1.5661053 -3.431058e-03
## Jun 1986 0.0209864093 -1.5674035 4.637811e-03
## Jul 1986 0.0283284639 -1.5687016 -1.406095e-03
## Aug 1986 0.0318618948 -1.5698116 8.324287e-04
## Sep 1986 0.0151345828 -1.5709215 -1.102126e-04
## Oct 1986 -0.0107891741 -1.5725248 1.309679e-02
## Nov 1986 -0.0424312547 -1.5741281 7.121460e-03
## Dec 1986 -0.0479947475 -1.5758375 4.344016e-03
## Jan 1987 0.0019097886 -1.5775469 -2.385045e-02
## Feb 1987 0.0012341216 -1.5791943 2.206304e-02
## Mar 1987 0.0006245546 -1.5808417 -1.927045e-02
## Apr 1987 -0.0125038908 -1.5819146 -5.069120e-03
## May 1987 0.0136392482 -1.5829875 -8.689958e-04
## Jun 1987 0.0209864093 -1.5835838 -7.619848e-03
## Jul 1987 0.0283284639 -1.5841801 9.388492e-03
## Aug 1987 0.0318618948 -1.5851830 -2.576015e-03
## Sep 1987 0.0151345828 -1.5861860 -3.985087e-03
## Oct 1987 -0.0107891741 -1.5872167 3.456532e-03
## Nov 1987 -0.0424312547 -1.5882473 2.124068e-02
## Dec 1987 -0.0479947475 -1.5878022 1.630872e-02
## Jan 1988 0.0019097886 -1.5873571 -1.404026e-02
## Feb 1988 0.0012341216 -1.5829710 -3.271353e-02
## Mar 1988 0.0006245546 -1.5785850 -2.152716e-02
## Apr 1988 -0.0125038908 -1.5697492 -3.219736e-02
## May 1988 0.0136392482 -1.5609134 -2.294301e-02
## Jun 1988 0.0209864093 -1.5492956 -3.233852e-02
## Jul 1988 0.0283284639 -1.5376778 -2.776789e-02
## Aug 1988 0.0318618948 -1.5243953 9.728149e-03
## Sep 1988 0.0151345828 -1.5111128 3.926137e-02
## Oct 1988 -0.0107891741 -1.4976842 4.313583e-02
## Nov 1988 -0.0424312547 -1.4842557 1.255918e-02
## Dec 1988 -0.0479947475 -1.4722600 1.967127e-02
## Jan 1989 0.0019097886 -1.4602644 -2.005505e-02
## Feb 1989 0.0012341216 -1.4504088 -7.542167e-03
## Mar 1989 0.0006245546 -1.4405532 -1.250555e-02
## Apr 1989 -0.0125038908 -1.4314891 8.508421e-03
## May 1989 0.0136392482 -1.4224251 -1.417249e-02
## Jun 1989 0.0209864093 -1.4134516 -9.958593e-03
## Jul 1989 0.0283284639 -1.4044780 5.728539e-03
## Aug 1989 0.0318618948 -1.3972011 1.054352e-02
## Sep 1989 0.0151345828 -1.3899242 1.221179e-02
## Oct 1989 -0.0107891741 -1.3850216 2.931901e-02
## Nov 1989 -0.0424312547 -1.3801189 2.822364e-02
## Dec 1989 -0.0479947475 -1.3775733 1.498102e-02
## Jan 1990 0.0019097886 -1.3750277 -1.247843e-03
## Feb 1990 0.0012341216 -1.3757281 -1.580838e-02
## Mar 1990 0.0006245546 -1.3764285 -2.522240e-03
## Apr 1990 -0.0125038908 -1.3793494 9.550985e-03
## May 1990 0.0136392482 -1.3822704 -1.789899e-03
## Jun 1990 0.0209864093 -1.3857398 1.382621e-02
## Jul 1990 0.0283284639 -1.3892093 1.380721e-02
## Aug 1990 0.0318618948 -1.3935675 -4.786113e-03
## Sep 1990 0.0151345828 -1.3979257 -7.511255e-03
## Oct 1990 -0.0107891741 -1.4038051 4.007198e-03
## Nov 1990 -0.0424312547 -1.4096844 3.945934e-03
## Dec 1990 -0.0479947475 -1.4163039 2.037515e-02
## Jan 1991 0.0019097886 -1.4229233 1.042647e-02
## Feb 1991 0.0012341216 -1.4294036 -2.000028e-02
## Mar 1991 0.0006245546 -1.4358839 -1.291042e-02
## Apr 1991 -0.0125038908 -1.4417382 1.454699e-02
## May 1991 0.0136392482 -1.4475926 -1.421643e-02
## Jun 1991 0.0209864093 -1.4511142 -9.567368e-03
## Jul 1991 0.0283284639 -1.4546358 -4.984412e-03
## Aug 1991 0.0318618948 -1.4545196 -1.282687e-02
## Sep 1991 0.0151345828 -1.4544035 -2.174901e-02
## Oct 1991 -0.0107891741 -1.4519779 -6.908881e-03
## Nov 1991 -0.0424312547 -1.4495524 -3.127661e-02
## Dec 1991 -0.0479947475 -1.4455924 -6.996366e-03
## Jan 1992 0.0019097886 -1.4416324 1.676430e-02
## Feb 1992 0.0012341216 -1.4363746 2.455342e-02
## Mar 1992 0.0006245546 -1.4311168 -7.995205e-04
## Apr 1992 -0.0125038908 -1.4261899 3.209227e-03
## May 1992 0.0136392482 -1.4212631 1.126805e-03
## Jun 1992 0.0209864093 -1.4184065 -1.316697e-02
## Jul 1992 0.0283284639 -1.4155499 8.895210e-03
## Aug 1992 0.0318618948 -1.4152353 2.079560e-02
## Sep 1992 0.0151345828 -1.4149208 9.483833e-03
## Oct 1992 -0.0107891741 -1.4165389 4.369692e-03
## Nov 1992 -0.0424312547 -1.4181569 8.154019e-03
## Dec 1992 -0.0479947475 -1.4218655 1.314337e-02
## Jan 1993 0.0019097886 -1.4255740 4.846635e-03
## Feb 1993 0.0012341216 -1.4309384 1.088676e-02
## Mar 1993 0.0006245546 -1.4363029 1.937400e-04
## Apr 1993 -0.0125038908 -1.4413576 -1.581447e-02
## May 1993 0.0136392482 -1.4464123 1.481339e-03
## Jun 1993 0.0209864093 -1.4502776 -6.193381e-03
## Jul 1993 0.0283284639 -1.4541430 -1.388065e-02
## Aug 1993 0.0318618948 -1.4572404 -1.010605e-02
## Sep 1993 0.0151345828 -1.4603379 -1.581455e-02
## Oct 1993 -0.0107891741 -1.4627443 -4.876224e-03
## Nov 1993 -0.0424312547 -1.4651506 2.477655e-02
## Dec 1993 -0.0479947475 -1.4665033 9.420168e-03
## Jan 1994 0.0019097886 -1.4678561 9.229461e-03
## Feb 1994 0.0012341216 -1.4680999 -2.810206e-03
## Mar 1994 0.0006245546 -1.4683437 -1.950114e-02
## Apr 1994 -0.0125038908 -1.4679198 -6.796566e-03
## May 1994 0.0136392482 -1.4674960 -2.017657e-02
## Jun 1994 0.0209864093 -1.4658702 9.602820e-04
## Jul 1994 0.0283284639 -1.4642444 -8.007558e-03
## Aug 1994 0.0318618948 -1.4604466 -6.899895e-03
## Sep 1994 0.0151345828 -1.4566488 1.819110e-03
## Oct 1994 -0.0107891741 -1.4519479 1.719120e-03
## Nov 1994 -0.0424312547 -1.4472469 -1.976747e-03
## Dec 1994 -0.0479947475 -1.4428576 -8.024839e-04
## Jan 1995 0.0019097886 -1.4384684 1.074022e-03
## Feb 1995 0.0012341216 -1.4334547 1.340303e-02
## Mar 1995 0.0006245546 -1.4284410 8.998887e-03
## Apr 1995 -0.0125038908 -1.4222552 -9.164375e-03
## May 1995 0.0136392482 -1.4160694 -8.156881e-03
## Jun 1995 0.0209864093 -1.4090573 -2.251617e-02
## Jul 1995 0.0283284639 -1.4020452 -2.060983e-02
## Aug 1995 0.0318618948 -1.3936015 -4.752136e-03
## Sep 1995 0.0151345828 -1.3851578 7.445404e-03
## Oct 1995 -0.0107891741 -1.3740860 1.445414e-02
## Nov 1995 -0.0424312547 -1.3630141 -9.248440e-03
## Dec 1995 -0.0479947475 -1.3486000 -1.809907e-02
## Jan 1996 0.0019097886 -1.3341859 -7.134665e-03
## Feb 1996 0.0012341216 -1.3180288 -2.261612e-02
## Mar 1996 0.0006245546 -1.3018717 6.619934e-03
## Apr 1996 -0.0125038908 -1.2858805 -2.587460e-02
## May 1996 0.0136392482 -1.2698893 -1.315055e-02
## Jun 1996 0.0209864093 -1.2547384 1.972881e-02
## Jul 1996 0.0283284639 -1.2395874 3.360346e-02
## Aug 1996 0.0318618948 -1.2268670 3.664278e-02
## Sep 1996 0.0151345828 -1.2141465 2.135643e-02
## Oct 1996 -0.0107891741 -1.2054103 -1.138319e-02
## Nov 1996 -0.0424312547 -1.1966741 1.832542e-02
## Dec 1996 -0.0479947475 -1.1929035 9.896800e-03
## Jan 1997 0.0019097886 -1.1891330 1.280918e-02
## Feb 1997 0.0012341216 -1.1891736 1.028394e-02
## Mar 1997 0.0006245546 -1.1892141 1.146085e-03
## Apr 1997 -0.0125038908 -1.1911072 1.288347e-02
## May 1997 0.0136392482 -1.1930002 8.177941e-03
## Jun 1997 0.0209864093 -1.1961315 3.962059e-03
## Jul 1997 0.0283284639 -1.1992627 -3.479736e-03
## Aug 1997 0.0318618948 -1.2021109 -1.065852e-02
## Sep 1997 0.0151345828 -1.2049591 2.381005e-03
## Oct 1997 -0.0107891741 -1.2064514 -6.934898e-03
## Nov 1997 -0.0424312547 -1.2079438 -1.193334e-02
## Dec 1997 -0.0479947475 -1.2083379 -2.021081e-02
## Jan 1998 0.0019097886 -1.2087321 -4.894096e-04
## Feb 1998 0.0012341216 -1.2081438 -7.113442e-03
## Mar 1998 0.0006245546 -1.2075556 9.602739e-03
## Apr 1998 -0.0125038908 -1.2064784 8.320523e-03
## May 1998 0.0136392482 -1.2054013 1.410655e-02
## Jun 1998 0.0209864093 -1.2051366 -3.293285e-03
## Jul 1998 0.0283284639 -1.2048720 -4.364036e-03
## Aug 1998 0.0318618948 -1.2066460 3.601136e-03
## Sep 1998 0.0151345828 -1.2084201 5.841979e-03
## Oct 1998 -0.0107891741 -1.2109681 2.111231e-02
## Nov 1998 -0.0424312547 -1.2135162 7.674418e-03
## Dec 1998 -0.0479947475 -1.2149780 -1.357075e-02
## Jan 1999 0.0019097886 -1.2164398 5.068362e-04
## Feb 1999 0.0012341216 -1.2168130 -1.200376e-02
## Mar 1999 0.0006245546 -1.2171863 -2.476684e-02
## Apr 1999 -0.0125038908 -1.2184404 -3.136412e-02
## May 1999 0.0136392482 -1.2196944 2.188501e-02
## Jun 1999 0.0209864093 -1.2224778 7.468965e-03
## Jul 1999 0.0283284639 -1.2252613 1.927730e-02
## Aug 1999 0.0318618948 -1.2291771 2.935283e-02
## Sep 1999 0.0151345828 -1.2330929 4.677537e-02
## Oct 1999 -0.0107891741 -1.2362251 2.219462e-03
## Nov 1999 -0.0424312547 -1.2393572 -2.016472e-02
## Dec 1999 -0.0479947475 -1.2412633 -3.124858e-02
## Jan 2000 0.0019097886 -1.2431694 -1.050390e-02
## Feb 2000 0.0012341216 -1.2430948 -7.864597e-02
## Mar 2000 0.0006245546 -1.2430202 -1.287046e-02
## Apr 2000 -0.0125038908 -1.2410197 -8.784795e-03
## May 2000 0.0136392482 -1.2390192 2.805169e-02
## Jun 2000 0.0209864093 -1.2349432 2.651326e-02
## Jul 2000 0.0283284639 -1.2308671 1.509518e-02
## Aug 2000 0.0318618948 -1.2259309 3.341431e-03
## Sep 2000 0.0151345828 -1.2209947 1.183760e-02
## Oct 2000 -0.0107891741 -1.2180190 4.463800e-02
## Nov 2000 -0.0424312547 -1.2150433 -8.193618e-02
## Dec 2000 -0.0479947475 -1.2131974 -2.979199e-02
## Jan 2001 0.0019097886 -1.2113515 1.871418e-02
## Feb 2001 0.0012341216 -1.2090107 2.686902e-02
## Mar 2001 0.0006245546 -1.2066698 4.129315e-02
## Apr 2001 -0.0125038908 -1.2040040 -7.084655e-02
## May 2001 0.0136392482 -1.2013382 1.651593e-02
## Jun 2001 0.0209864093 -1.1984668 1.592829e-02
## Jul 2001 0.0283284639 -1.1955954 1.208431e-02
## Aug 2001 0.0318618948 -1.1927666 1.520078e-02
## Sep 2001 0.0151345828 -1.1899377 -4.259269e-02
## Oct 2001 -0.0107891741 -1.1861201 2.249531e-02
## Nov 2001 -0.0424312547 -1.1823026 -2.702965e-02
## Dec 2001 -0.0479947475 -1.1787578 -3.909567e-02
## Jan 2002 0.0019097886 -1.1752130 3.073906e-02
## Feb 2002 0.0012341216 -1.1723424 2.540441e-02
## Mar 2002 0.0006245546 -1.1694718 -2.335707e-03
## Apr 2002 -0.0125038908 -1.1683518 -5.181128e-05
## May 2002 0.0136392482 -1.1672318 2.348963e-02
## Jun 2002 0.0209864093 -1.1679032 2.298669e-02
## Jul 2002 0.0283284639 -1.1685746 8.118102e-04
## Aug 2002 0.0318618948 -1.1698461 4.780512e-03
## Sep 2002 0.0151345828 -1.1711177 2.277941e-02
## Oct 2002 -0.0107891741 -1.1707210 -5.981837e-02
## Nov 2002 -0.0424312547 -1.1703244 -2.167639e-02
## Dec 2002 -0.0479947475 -1.1685714 -1.443531e-02
## Jan 2003 0.0019097886 -1.1668185 1.289562e-02
## Feb 2003 0.0012341216 -1.1646478 -4.389803e-02
## Mar 2003 0.0006245546 -1.1624771 1.928838e-02
## Apr 2003 -0.0125038908 -1.1621766 2.897655e-02
## May 2003 0.0136392482 -1.1618760 3.044164e-02
## Jun 2003 0.0209864093 -1.1637608 2.497932e-02
## Jul 2003 0.0283284639 -1.1656457 2.257554e-02
## Aug 2003 0.0318618948 -1.1698918 2.633236e-02
## Sep 2003 0.0151345828 -1.1741379 1.014981e-02
## Oct 2003 -0.0107891741 -1.1807632 -5.324243e-02
## Nov 2003 -0.0424312547 -1.1873885 -2.544635e-02
## Dec 2003 -0.0479947475 -1.1924608 -7.817485e-03
## Jan 2004 0.0019097886 -1.1975332 1.471585e-02
## Feb 2004 0.0012341216 -1.1986549 9.255880e-05
## Mar 2004 0.0006245546 -1.1997767 -4.820645e-03
## Apr 2004 -0.0125038908 -1.1986082 -5.119633e-02
## May 2004 0.0136392482 -1.1974396 -3.022279e-02
## Jun 2004 0.0209864093 -1.1948853 9.146821e-03
## Jul 2004 0.0283284639 -1.1923310 3.699082e-02
## Aug 2004 0.0318618948 -1.1867073 2.783366e-02
## Sep 2004 0.0151345828 -1.1810836 -1.495852e-02
## Oct 2004 -0.0107891741 -1.1733704 -2.315215e-02
## Nov 2004 -0.0424312547 -1.1656572 -2.634359e-02
## Dec 2004 -0.0479947475 -1.1587244 -4.854690e-02
## Jan 2005 0.0019097886 -1.1517917 2.595184e-02
## Feb 2005 0.0012341216 -1.1458186 2.372662e-02
## Mar 2005 0.0006245546 -1.1398456 1.836310e-02
## Apr 2005 -0.0125038908 -1.1353997 1.469985e-02
## May 2005 0.0136392482 -1.1309538 5.617049e-03
## Jun 2005 0.0209864093 -1.1292706 2.061185e-02
## Jul 2005 0.0283284639 -1.1275874 2.044927e-02
## Aug 2005 0.0318618948 -1.1278540 2.011926e-02
## Sep 2005 0.0151345828 -1.1281205 1.288392e-03
## Oct 2005 -0.0107891741 -1.1285998 -2.857337e-02
## Nov 2005 -0.0424312547 -1.1290791 -3.915140e-02
## Dec 2005 -0.0479947475 -1.1290028 -1.702496e-02
## Jan 2006 0.0019097886 -1.1289264 1.227494e-02
## Feb 2006 0.0012341216 -1.1271577 5.065679e-03
## Mar 2006 0.0006245546 -1.1253890 3.906549e-03
## Apr 2006 -0.0125038908 -1.1220686 -4.861744e-03
## May 2006 0.0136392482 -1.1187483 -5.278563e-04
## Jun 2006 0.0209864093 -1.1145881 2.957535e-03
## Jul 2006 0.0283284639 -1.1104278 3.441958e-04
## Aug 2006 0.0318618948 -1.1046366 -3.098127e-03
## Sep 2006 0.0151345828 -1.0988453 7.837923e-03
## Oct 2006 -0.0107891741 -1.0919928 -3.353223e-02
## Nov 2006 -0.0424312547 -1.0851402 -2.128205e-02
## Dec 2006 -0.0479947475 -1.0796463 -1.806288e-02
## Jan 2007 0.0019097886 -1.0741523 1.092605e-02
## Feb 2007 0.0012341216 -1.0691792 3.512057e-02
## Mar 2007 0.0006245546 -1.0642061 3.356207e-02
## Apr 2007 -0.0125038908 -1.0557588 2.980432e-02
## May 2007 0.0136392482 -1.0473115 -1.329684e-02
## Jun 2007 0.0209864093 -1.0332113 -3.759724e-02
## Jul 2007 0.0283284639 -1.0191111 -5.050456e-02
## Aug 2007 0.0318618948 -0.9975344 -6.996499e-02
## Sep 2007 0.0151345828 -0.9759577 -6.082818e-02
## Oct 2007 -0.0107891741 -0.9446527 -8.019561e-02
## Nov 2007 -0.0424312547 -0.9133478 9.029079e-03
## Dec 2007 -0.0479947475 -0.8758432 2.534649e-03
## Jan 2008 0.0019097886 -0.8383386 -2.869365e-02
## Feb 2008 0.0012341216 -0.8022114 1.351945e-02
## Mar 2008 0.0006245546 -0.7660843 5.006693e-02
## Apr 2008 -0.0125038908 -0.7373481 9.207193e-02
## May 2008 0.0136392482 -0.7086119 6.761319e-02
## Jun 2008 0.0209864093 -0.6901676 3.241430e-02
## Jul 2008 0.0283284639 -0.6717232 3.458873e-02
## Aug 2008 0.0318618948 -0.6641972 1.429562e-02
## Sep 2008 0.0151345828 -0.6566712 1.042486e-02
## Oct 2008 -0.0107891741 -0.6578350 1.661897e-02
## Nov 2008 -0.0424312547 -0.6589988 -3.789664e-03
## Dec 2008 -0.0479947475 -0.6627949 2.561066e-02
## Jan 2009 0.0019097886 -0.6665910 4.968801e-03
## Feb 2009 0.0012341216 -0.6704676 -6.073739e-03
## Mar 2009 0.0006245546 -0.6743443 -1.344537e-02
## Apr 2009 -0.0125038908 -0.6787876 -3.231492e-02
## May 2009 0.0136392482 -0.6832309 6.003240e-03
## Jun 2009 0.0209864093 -0.6877563 5.121374e-03
## Jul 2009 0.0283284639 -0.6922817 8.101867e-03
## Aug 2009 0.0318618948 -0.6960198 4.445452e-03
## Sep 2009 0.0151345828 -0.6997578 1.127864e-02
## Oct 2009 -0.0107891741 -0.7036330 2.127500e-02
## Nov 2009 -0.0424312547 -0.7075082 -3.094661e-02
## Dec 2009 -0.0479947475 -0.7125964 -7.279599e-03
## Jan 2010 0.0019097886 -0.7176845 4.463593e-03
## Feb 2010 0.0012341216 -0.7226329 2.021941e-02
## Mar 2010 0.0006245546 -0.7275812 -9.098006e-03
## Apr 2010 -0.0125038908 -0.7317055 -4.450452e-03
## May 2010 0.0136392482 -0.7358299 -1.415772e-03
## Jun 2010 0.0209864093 -0.7388198 -7.837029e-03
## Jul 2010 0.0283284639 -0.7418096 -1.218920e-02
## Aug 2010 0.0318618948 -0.7418462 -5.408487e-03
## Sep 2010 0.0151345828 -0.7418828 -9.904531e-04
## Oct 2010 -0.0107891741 -0.7375253 -2.461833e-03
## Nov 2010 -0.0424312547 -0.7331678 -3.183726e-02
## Dec 2010 -0.0479947475 -0.7258052 -4.491046e-02
## Jan 2011 0.0019097886 -0.7184426 -2.370600e-02
## Feb 2011 0.0012341216 -0.7087158 2.261903e-03
## Mar 2011 0.0006245546 -0.6989890 2.109061e-02
## Apr 2011 -0.0125038908 -0.6873280 6.684740e-03
## May 2011 0.0136392482 -0.6756671 1.002258e-02
## Jun 2011 0.0209864093 -0.6650994 5.453957e-03
## Jul 2011 0.0283284639 -0.6545317 -3.030659e-03
## Aug 2011 0.0318618948 -0.6484687 -5.150421e-03
## Sep 2011 0.0151345828 -0.6424057 2.943407e-02
## Oct 2011 -0.0107891741 -0.6407908 3.909065e-02
## Nov 2011 -0.0424312547 -0.6391759 2.189471e-02
## Dec 2011 -0.0479947475 -0.6408412 1.549143e-02
## Jan 2012 0.0019097886 -0.6425066 1.937825e-03
## Feb 2012 0.0012341216 -0.6461765 6.283416e-03
## Mar 2012 0.0006245546 -0.6498465 -6.629494e-03
## Apr 2012 -0.0125038908 -0.6537685 2.683978e-03
## May 2012 0.0136392482 -0.6576905 -2.342821e-02
## Jun 2012 0.0209864093 -0.6597560 -1.708182e-02
## Jul 2012 0.0283284639 -0.6618215 -2.815548e-02
## Aug 2012 0.0318618948 -0.6623380 -2.517174e-03
## Sep 2012 0.0151345828 -0.6628545 9.060886e-03
## Oct 2012 -0.0107891741 -0.6618825 -2.635565e-03
## Nov 2012 -0.0424312547 -0.6609106 4.176585e-03
## Dec 2012 -0.0479947475 -0.6593134 1.416092e-02
## Jan 2013 0.0019097886 -0.6577161 9.542740e-03
## Feb 2013 0.0012341216 -0.6570613 9.563618e-03
## Mar 2013 0.0006245546 -0.6564065 -3.936719e-02
## Apr 2013 -0.0125038908 -0.6557676 1.818382e-02
## May 2013 0.0136392482 -0.6551287 6.611173e-03
## Jun 2013 0.0209864093 -0.6526385 2.418186e-03
## Jul 2013 0.0283284639 -0.6501482 -1.305853e-02
## Aug 2013 0.0318618948 -0.6455608 -2.306791e-02
## Sep 2013 0.0151345828 -0.6409735 -2.424881e-02
## Oct 2013 -0.0107891741 -0.6365156 -1.434377e-02
## Nov 2013 -0.0424312547 -0.6320577 1.477652e-02
## Dec 2013 -0.0479947475 -0.6300190 1.053435e-02
## Jan 2014 0.0019097886 -0.6279804 1.542466e-02
## Feb 2014 0.0012341216 -0.6285842 3.856286e-02
## Mar 2014 0.0006245546 -0.6291879 3.254287e-02
## Apr 2014 -0.0125038908 -0.6325910 4.178844e-02
## May 2014 0.0136392482 -0.6359942 -8.756870e-03
## Jun 2014 0.0209864093 -0.6386767 -1.342149e-02
## Jul 2014 0.0283284639 -0.6413593 -2.752394e-02
## Aug 2014 0.0318618948 -0.6445851 -2.027003e-02
## Sep 2014 0.0151345828 -0.6478110 -3.480303e-02
## Oct 2014 -0.0107891741 -0.6513302 -2.902978e-02
## Nov 2014 -0.0424312547 -0.6548494 3.369232e-02
## Dec 2014 -0.0479947475 -0.6582799 3.488891e-02hagamos ele ejmplo para el gas
gas.l <- log(prices.ts[,2])
gas.stl <- stl(gas.l, s.window = "period")
plot(gas.stl)
#decompose #Classical Seasonal Decomposition by Moving Averages( moviendo medias ) metodo de descomposicion por metodo decompsre
flour.dec <- decompose(flour.l)# de la harina
plot(flour.dec)#grafica 
Grafiquemos el gas
gas.dec <- decompose(gas.l)
plot(gas.dec)
Ahora si ya sabemos que existe una estacionalidad , cosas que se repiten de manera continua , podemos eliminarlos , ya que se analizo por factores
Del precio Original resta el valor estacional (valor que se repite periodicamente )“Elimina el ruido”
gas.season.adjusted <- prices.ts[,2] - (gas.dec$seasonal)
plot(gas.season.adjusted)
Filtrar datos de series temporales para localizar tendencias
Esto es lo que estaremos haciendo ( con el ejemplo de la gas)
n <- 12
gas.f.1 <- stats::filter(prices.ts[,2], filter = rep(1/n, n), sides = 2)
gas.f.2 <- stats::filter(prices.ts[,2], filter = rep(1/n,n), sides = 1)
plot(prices.ts[,2])
lines(gas.f.1, col = "blue", lwd = 3)
lines(gas.f.2, col = "red", lwd = 3)
Visualizacion temprana de los datos
s <- read.csv("../../r-course-master/data/tema6/ts-example.csv")
plot(s$sales, type = "l")
generamos la muestea semanal
n <- 7# 7 dias de la semana
weights <- rep(1/n, n)
weights## [1] 0.1428571 0.1428571 0.1428571 0.1428571 0.1428571 0.1428571 0.1428571creemos lo objetos que van a a promediar
s.fil.1 <- stats::filter(s$sales, filter = weights, sides = 2)
s.fil.2 <- stats::filter(s$sales, filter = weights, sides = 1)grafiquemoslo
plot(s$sales, type = "l")
lines(s.fil.1, col = "blue", lwd = 3)
lines(s.fil.2, col = "red", lwd = 3)
#suavisando y prediciendo con el metodo Hot-Winters Caunto voy a vender
inf <- read.csv("../../r-course-master/data/tema6/INFY-monthly.csv")inf.ts <- ts(inf$Adj.Close,#la columana que queremos analizar
start=c(1999,3),#En que fecha iniciamos
frequency = 12)#mensual Grafiquemos
plot(inf.ts)
Suavicemos los valores (holtwinters) metodo exponencial y combolucional
inf.hw <- HoltWinters(inf.ts)Grafiquemos
plot(inf.hw, col = "blue", col.predicted = "red")
Suma de los cuadrados de los errores
inf.hw$SSE## [1] 347.1345Patrametro alfa y beta
inf.hw$alpha## alpha
## 0.5519187inf.hw$beta## beta
## 0.01042698inf.hw$gamma## gamma
## 1head(inf.hw$fitted)## xhat level trend season
## [1,] 6.371099 5.038675 0.2702327 1.06219146
## [2,] 8.459056 6.876835 0.2865814 1.29563975
## [3,] 6.911948 7.842393 0.2936611 -1.22410629
## [4,] 8.684473 8.364917 0.2960474 0.02350837
## [5,] 6.968464 8.538907 0.2947748 -1.86521779
## [6,] 8.455786 8.490957 0.2912012 -0.32637179Predigamos en base al modelo que genero forecast
#install.packages("forecast")
library(forecast)#prediccion ## Registered S3 method overwritten by 'forecast':
## method from
## predict.default statip##
## Attaching package: 'forecast'## The following object is masked from 'package:modeest':
##
## naiveNota importante : para que arroje un resultado mas acetado es importante obtener el objeto sin ruido (con la funcion holtwinters)
infy.fore <- forecast(inf.hw,#datos ( modelo)
h=24)#calcula los siguientes 2 mesesGrafica como se veran los valores La forma azul es la predicha la zona gris es :los valores dentro del intervalo de confianza al 80% la zona gris claro : es el intervalo de confianza al 95%
plot(infy.fore)
Aqui se puede ver si esta en el intervalo de confianza de la parte superior de la linea
infy.fore$lower## 80% 95%
## Jan 2017 13.820929 12.936811
## Feb 2017 13.442656 12.430351
## Mar 2017 13.872663 12.744433
## Apr 2017 12.028455 10.793086
## May 2017 11.468344 10.132484
## Jun 2017 10.255859 8.824740
## Jul 2017 10.437364 8.915217
## Aug 2017 10.819623 9.209944
## Sep 2017 11.239823 9.545552
## Oct 2017 11.626875 9.850518
## Nov 2017 11.442917 9.586633
## Dec 2017 11.645096 9.710761
## Jan 2018 12.310981 10.158075
## Feb 2018 12.042318 9.819248
## Mar 2018 12.560493 10.268172
## Apr 2018 10.789409 8.428657
## May 2018 10.291257 7.862816
## Jun 2018 9.132117 6.636656
## Jul 2018 9.360123 6.798248
## Aug 2018 9.783316 7.155581
## Sep 2018 10.239846 7.546750
## Oct 2018 10.659357 7.901357
## Nov 2018 10.504566 7.682078
## Dec 2018 10.733080 7.846482infy.fore$upper## 80% 95%
## Jan 2017 17.16121 18.04533
## Feb 2017 17.26724 18.27954
## Mar 2017 18.13522 19.26345
## Apr 2017 16.69579 17.93116
## May 2017 16.51534 17.85120
## Jun 2017 15.66275 17.09387
## Jul 2017 16.18817 17.71032
## Aug 2017 16.90113 18.51081
## Sep 2017 17.64093 19.33520
## Oct 2017 18.33811 20.11447
## Nov 2017 18.45613 20.31241
## Dec 2017 18.95319 20.88752
## Jan 2018 20.44485 22.59776
## Feb 2018 20.44127 22.66434
## Mar 2018 21.22109 23.51341
## Apr 2018 19.70854 22.06929
## May 2018 19.46613 21.89457
## Jun 2018 18.56019 21.05566
## Jul 2018 19.03911 21.60099
## Aug 2018 19.71114 22.33887
## Sep 2018 20.41460 23.10770
## Oct 2018 21.07933 23.83733
## Nov 2018 21.16818 23.99067
## Dec 2018 21.63890 24.52550#modelo autorregresivo integrado de media móvil (auto.arima)
inf <- read.csv("../../r-course-master/data/tema6/INFY-monthly.csv")creamos la serie temporal
inf.ts <- ts(inf$Adj.Close, start = c(1999,3), frequency = 12)Aplicamos el algoritmo
inf.arima <- auto.arima(inf.ts)Veamos sus caracteristicas
summary(inf.arima)## Series: inf.ts
## ARIMA(0,1,1)(2,0,0)[12]
##
## Coefficients:
## ma1 sar1 sar2
## -0.1598 0.0505 -0.0533
## s.e. 0.0607 0.0799 0.0844
##
## sigma^2 = 0.982: log likelihood = -298.84
## AIC=605.69 AICc=605.88 BIC=619.13
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.07413207 0.9816299 0.7329892 0.4829683 9.84262 0.3027089
## ACF1
## Training set -0.02620284Predigamos el futuro con Arima
inf.fore <- forecast(inf.arima, h = 12)
plot(inf.fore, col = "red",#cambia el color de la grafica
fcol = "blue")#cambia el color de la prediccion 
recordemos lo aprendido las series temporales : -divide la informacion estacional de las tendencias y el ruido adicional de la muestra -nos permite obtener una tendencia del mercado a corto y medio plazo -puede descomponerse con el metodo de promedios movilers siempre y cuando sea aditiva
si se sospecha que una serie temporal es multiplicativa( logaritmo) : -debemos aplicar la funcion logaritmo para convertirla en aditiva -Cada valor procede del anterior por factor multiplicativo temporal no puede descomponerse directamente usando la descomposicion clasica o de loess
Al crear una serie temporal : -Se tiene que subministrar en algun momento el inicio y la frecuencia del muestreo en el periodo
Filtrar una serie temporal da una nocion de :Tendencia , estacionalidad ,ruido
Descomponer una serie temporal aditiva nos da una idea de:Tendencia , estacionalidad ,ruido
El metodo de Hot-Winters -Suavisa la curva usando un nucleo de convolucion exponencial -Usa los valores suvisadod para extraer informacion de estacionalidad y tendencia -Puede usarse como modelo para predecir valores a posteriori
#visiualizacion ggplot2
#Scatter Plots(graficos de dispercion )
#install.packages("ggplot2")
library(ggplot2)auto <- read.csv("../../r-course-master/data/tema7/auto-mpg.csv", stringsAsFactors = F)
library(ggplot2)se cambian de nuemro a factores
auto$cylinders <- factor(auto$cylinders,
labels = c("3C", "4C", "5C", "6C", "8C"))
auto[1:5,]## No mpg cylinders displacement horsepower weight acceleration model_year
## 1 1 28 4C 140 90 2264 15.5 71
## 2 2 19 3C 70 97 2330 13.5 72
## 3 3 36 4C 107 75 2205 14.5 82
## 4 4 28 4C 97 92 2288 17.0 72
## 5 5 21 6C 199 90 2648 15.0 70
## car_name
## 1 chevrolet vega 2300
## 2 mazda rx2 coupe
## 3 honda accord
## 4 datsun 510 (sw)
## 5 amc gremlinpriemro graficamos
plot <- ggplot2::ggplot(auto,#DF
aes(weight, mpg))#X y Y
plot + geom_point()# y le decimos que lom represente con puntos 
plot + ggplot2::geom_point(alpha = 1/2, #trasparencia
size = 3, #Tamano
aes(color = factor(cylinders)))#+ #que ponga etiquetas y los pinte 
#geom_smooth(method = "lm", #geometria suave , con el metodo de lineas
# se = F, #Se muetres el intervalo de confianza
# col ="blue")+#color
#ggplot2::facet_grid(cylinders~.) +
# theme_bw(base_family = "Arial", #Tipo de letra
# base_size = 10) #tamano de la letra
#+ labs(x = "Peso") + labs (y = "Millas por Galeón")
#+ labs(title = "Consumo vs Peso") # titulo de la grafica Hagamos otro grafico con otro tipo de instrucciones
qplot(x=weight,
y = mpg,
data = auto,
geom=c("point", "smooth"), method = "lm", se=F,
formula = y~x, color = cylinders,
main = "Regresión de MPG sobre el Peso")## Warning: Ignoring unknown parameters: method, se, formula
# Graficos de lineas #Line Charts
library(ggplot2)
mtcars <- read.csv("../../r-course-master/data/tema7/mtcars.csv", stringsAsFactors = F)plot <- ggplot(mtcars, aes(wt, mpg))
plot + geom_line()#linea geometrica 
plot + geom_line(linetype = "dashed", color = "blue")# que este punteada
plot + geom_line(aes(color = as.factor(carb)))#una etiqueta agrupada por la categoria carb (numero de cambios )
#Grafica de Barras
#Bar Graphs
library(ggplot2)
bike <- read.csv("../../r-course-master/data/tema7/daily-bike-rentals.csv")Convertimos a factores las estaciones del ano
#estaciones del ano
bike$season <- factor(bike$season,
levels = c(1,2,3,4),
labels = c("Invierno", "Primavera", "Verano", "Otoño"))
#Dia trabajado o dia libre
bike$workingday <- factor(bike$workingday,
levels = c(0,1),
labels = c("Día libre", "Día de trabajo"))
#dia nublado o dia libre
bike$weathersit <- factor(bike$weathersit,
levels = c(1,2,3),
labels = c("Buen tiempo", "Día nublado", "Mal tiempo"))Creamos un Df donde se grupe por algunos valores
es como hacer una tabla dinamica en excel
library(dplyr)
bike.sum = bike %>%
dplyr::group_by(season, workingday) %>%
dplyr::summarize(reantal = sum(cnt))## `summarise()` has grouped output by 'season'. You can override using the
## `.groups` argument.bike.sum## # A tibble: 8 x 3
## # Groups: season [4]
## season workingday reantal
## <fct> <fct> <int>
## 1 Invierno Día libre 137683
## 2 Invierno Día de trabajo 333665
## 3 Primavera Día libre 287976
## 4 Primavera Día de trabajo 630613
## 5 Verano Día libre 312056
## 6 Verano Día de trabajo 749073
## 7 Otoño Día libre 262554
## 8 Otoño Día de trabajo 579059Grafiquemos esa tabla dimanica
ggplot(bike.sum, #del Df resien creado
aes(x = season, y = reantal, #cuales son los ejes de X y Y
fill = workingday, # etiquetas
label = scales::comma(reantal))) +#
geom_bar(show.legend = T,
stat = "identity",
fill="lightblue", #color de las barras
colour = "black") + #color de las lineas de las barras
labs(title = "Alquileres de bicicletas por estación y día") +
scale_y_continuous(labels = scales::comma) +
geom_text(size = 3,#tamano de letra de la etiqueta de datos
position = position_stack(vjust = 0.5))#posicion de las etiquetas de datos 
# Grafico de dsitribuciones
library(ggplot2)geiser <- read.csv("../../r-course-master/data/tema7/geiser.csv")
# alrura y erupciones de este geiser Empecemos a graficar ( el tiempo de espera entre erupciones )
plot <- ggplot(geiser, aes(x=waiting))
plot + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
sumemos cosas
plot + geom_histogram(binwidth = 5, #anchura de la divicion
fill="white", #relleno
colour = "black")#linea de negro 
Otra grafica Final
ggplot(geiser, aes(x=waiting,
y = ..density..))+#asi es la densidad
geom_histogram(fill="cornsilk",
color = "grey60",#color
size=.2)+# tamano
geom_density() + xlim(35, 105) # linea de distibucion ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing missing values (geom_bar).
Grafiquemos la erupciones
ggplot(geiser, aes(x=eruptions, y = ..density..))+
geom_histogram(fill="cornsilk", color = "grey60", size=.2)+
geom_density() + xlim(0,7)#asi se redimenciona el eje x ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 2 rows containing missing values (geom_bar).
#install.packages("ggplot2")
library(ggplot2)#Mosaic Plot
library(stats)
mtcars <- read.csv("../../r-course-master/data/tema7/mtcars.csv")
mosaicplot(~ gear + carb, # se muestra que variables queremos comparara
data = mtcars, #datos de donde se toman
color=2:7,#rango de colore
las = 1)# orientacion de los textos superiores 
#atambien se puede usar color=T( lo hara en tonalidad de grises )#Tree Maps
#install.packages("htmltools")
#install.packages("treemap")
library(treemap)
library(htmltools)##
## Attaching package: 'htmltools'## The following object is masked from 'package:mclust':
##
## embranch <- c(rep("branch-1",4), rep("branch-2",2), rep("branch-3",3))#ramas
subbranch <- paste("subbranch",c(1,2,3,4,1,2,1,2,3), sep = "-")#subramas
values = c(15,4,22,13,11,8,6,1,25)#valores de las ramas
data <- data.frame(branch, subbranch, values)#hacemos el DF
treemap::treemap(data,# datos
index = c("branch","subbranch"),#columnas X y Y
vSize = "values",#Tamano de los cubos
type = "index")#Indicar que son valores numericos 
Analisis de informacion de Redes Sociales
posts <- read.csv("../../r-course-master/data/tema7/post-data.csv")Veamos como se distribuye
treemap::treemap(posts,
index = c("category", "comments"),
vSize = "views", type = "index")
#Correlation Matrix
library(ggplot2)
library(corrplot)
mtcars <- read.csv("../../r-course-master/data/tema7/mtcars.csv")Eliminamos la columna X
mtcars$X = NULL#eliminmos la columna XHacemos la coorrelacion
mtcars.cor <- cor(mtcars, method = "pearson")Datos de correlacion de los datos en tabla
round(mtcars.cor, #datos
digits = 2)#digitos ## mpg cyl disp hp drat wt qsec vs am gear carb
## mpg 1.00 -0.85 -0.85 -0.78 0.68 -0.87 0.42 0.66 0.60 0.48 -0.55
## cyl -0.85 1.00 0.90 0.83 -0.70 0.78 -0.59 -0.81 -0.52 -0.49 0.53
## disp -0.85 0.90 1.00 0.79 -0.71 0.89 -0.43 -0.71 -0.59 -0.56 0.39
## hp -0.78 0.83 0.79 1.00 -0.45 0.66 -0.71 -0.72 -0.24 -0.13 0.75
## drat 0.68 -0.70 -0.71 -0.45 1.00 -0.71 0.09 0.44 0.71 0.70 -0.09
## wt -0.87 0.78 0.89 0.66 -0.71 1.00 -0.17 -0.55 -0.69 -0.58 0.43
## qsec 0.42 -0.59 -0.43 -0.71 0.09 -0.17 1.00 0.74 -0.23 -0.21 -0.66
## vs 0.66 -0.81 -0.71 -0.72 0.44 -0.55 0.74 1.00 0.17 0.21 -0.57
## am 0.60 -0.52 -0.59 -0.24 0.71 -0.69 -0.23 0.17 1.00 0.79 0.06
## gear 0.48 -0.49 -0.56 -0.13 0.70 -0.58 -0.21 0.21 0.79 1.00 0.27
## carb -0.55 0.53 0.39 0.75 -0.09 0.43 -0.66 -0.57 0.06 0.27 1.00Grafiqueoslo
corrplot(mtcars.cor)#mapa de correlacion 
corrplot(mtcars.cor,
method = "shade",#Sombras cuadradas
shade.col = NA,#si aplica alguna sombra dentro
tl.col = "black",# Color negro
tl.srt = 45)#etiqueta rotada a 45 grados 
Paleta de colores
col <- colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF",
"#77AADD", "#4477AA"))Grafica
corrplot(mtcars.cor,
method = "pie", # elipse, pie, circle , square, se pueden usar estas formas
tl.col = "black",#color de las etiquetas
tl.srt = 45, #rotete
col = col(200), #la funcion anterior( escalado a 200 valores)
addCoef.col = "black",#anade dentro de los cuadrados las etiquetas de los valores
order = "FPC", #AOE:odenarlos segun el valor de correlaciones tambien se puede usar fpc
type = "upper",#lower es la parte inferior , upper muestra la de arriba
diag = F,#quita la diagonal de en medio
addshade = "all")
library(reshape2)##
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##
## smithsmtcars.melted <- melt(mtcars.cor)#Fundir los datos( combiente de matriz a un DF)
head(mtcars.cor)## mpg cyl disp hp drat wt
## mpg 1.0000000 -0.8521620 -0.8475514 -0.7761684 0.6811719 -0.8676594
## cyl -0.8521620 1.0000000 0.9020329 0.8324475 -0.6999381 0.7824958
## disp -0.8475514 0.9020329 1.0000000 0.7909486 -0.7102139 0.8879799
## hp -0.7761684 0.8324475 0.7909486 1.0000000 -0.4487591 0.6587479
## drat 0.6811719 -0.6999381 -0.7102139 -0.4487591 1.0000000 -0.7124406
## wt -0.8676594 0.7824958 0.8879799 0.6587479 -0.7124406 1.0000000
## qsec vs am gear carb
## mpg 0.41868403 0.6640389 0.5998324 0.4802848 -0.5509251
## cyl -0.59124207 -0.8108118 -0.5226070 -0.4926866 0.5269883
## disp -0.43369788 -0.7104159 -0.5912270 -0.5555692 0.3949769
## hp -0.70822339 -0.7230967 -0.2432043 -0.1257043 0.7498125
## drat 0.09120476 0.4402785 0.7127111 0.6996101 -0.0907898
## wt -0.17471588 -0.5549157 -0.6924953 -0.5832870 0.4276059head(mtcars.melted)## Var1 Var2 value
## 1 mpg mpg 1.0000000
## 2 cyl mpg -0.8521620
## 3 disp mpg -0.8475514
## 4 hp mpg -0.7761684
## 5 drat mpg 0.6811719
## 6 wt mpg -0.8676594Grafiquemoslo
ggplot(data = mtcars.melted,
aes(x=Var1, y=Var2,fill=value))+
geom_tile()+
coord_fixed()
Funciones Para crear matriz de correlacion
Obtener triangulo inferiro
get_upper_triangle <- function(cormat){
cormat[upper.tri(cormat)] <- NA
return(cormat)
}Tomamos el tringulo inferior
get_lower_triangle <- function(cormat){
cormat[lower.tri(cormat)] <- NA
return(cormat)
}reordenar
reorder_cormat <- function(cormat){
dd <- as.dist((1-cormat)/2)#siempre sean positivas
hc <- hclust(dd)#
cormat <- cormat[hc$order, hc$order]#
}Veamos como funcionan las funciones
cormat <- reorder_cormat(mtcars.cor)#permite reoerdenar
cormat.ut <- get_upper_triangle(cormat)#quedarte con la parte superiro
cormat.ut.melted <- melt(cormat.ut,
na.rm = T)# elimina NA'sggplot(cormat.ut.melted, aes(Var2, Var1, fill = value)) +
geom_tile(color = "white")+
# escala de valores del mas bajo al mas alto
scale_fill_gradient2(low = "blue", high = "red",
mid = "white",
midpoint = 0,#punto medio
limit = c(-1,1),#limites
space = "Lab",
name="Pearson\nCorrelation")+
#Limpia y pone mas presentable el dato
theme_minimal()+
#como se ve las etiquetas
theme(axis.text.x = element_text(angle = 45, vjust=1,
size = 12, hjust=1))+
coord_fixed()# correlaciones arregladas (se justa exactamente )
#Redes( grafos )
library(igraph)##
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## uniong.dir <- graph(edges = c(1,2, 2,3, 2,4, 1,4, 5,5, 3,6, 5,6), n=6)
g.n_dir <- graph(edges = c(1,2, 2,3, 2,4, 1,4,
5,5, 3,6, 5,6), n=6, directed = F)Grafiquemoslo
plot(g.dir)
plot(g.n_dir)
Gaficos con aristas
g_isolated <- graph(edges = c("Juan", "María",
"María", "Ana",
"Ana", "Juan",
"José", "María",
"Pedro", "José",
"Joel", "Pedro"),
isolates = c( "Carmen", "Antonio",
"Mario", "Vicente"))#Agentes aislados Grafiquemoslo
par(mfrow = c(1,1))
plot(g_isolated, edge.arrow.size = 1,#tamano de flecha
vertex.color="gold",#color del vertice
vertex.size = 15,#
vertex.frame.color = "gray", #color del la flecha
vertex.label.color = "black",#color de los nombres
vertex.label.cex = 0.8,#Factor de escala
vertex.label.dist = 3,#distrancia entre vertices
edge.curved=0.2)# curvee las flechas 
#labels and legends ( Leyendas y titulos )
ggtitle(titulo) xlab(eje x) ylab(eje y) labs(….)
library(ggplot2)
tooth <- read.csv("../../r-course-master/data/tema7/ToothGrowth.csv")
tooth[1:5,]## X len supp dose
## 1 1 4.2 VC 0.5
## 2 2 11.5 VC 0.5
## 3 3 7.3 VC 0.5
## 4 4 5.8 VC 0.5
## 5 5 6.4 VC 0.5#box plot
ggplot(tooth, aes(x=dose, y=len, fill = as.factor(dose)))+
geom_boxplot() +
ggtitle("Crecimiento dental en función de una dosis de vitamina C
(mg/día) ")+
xlab("Dosis de vitamina C (mg/día)") +
ylab("Crecimiento dental (en mm)") +
labs(fill = "Dosis en mg/día") +#se alinea
theme(legend.position = "bottom") +#determian la posicion
guides(fill = F)#gias ( donde eliminamos las leyendas )
Cambiamos la apariencia
ggplot(tooth, aes(x=dose, y = len))+
geom_boxplot()+
theme_bw()+
#theme_dark()+#Tema obsucro
#theme_classic()+# tema clasico( defoult )
#theme_grey()+#tema gris
#theme(plot.background = element_rect(fill="darkblue"))# el fondo #apariencia de los ejes
theme(axis.text.x = element_text(face = "bold",
family = "Times",
size = 14,# tamano
angle = 45,# angulo
color = "#995566"),# color
axis.text.y = element_text(face = "italic",#tipo
family = "Courier",# tipo de letra
size = 16,#tamano
angle = 30, #angulo
color = "#449955")#color de las letras
)+
theme(panel.border = element_blank())+ # borde en el panel
theme(panel.grid.major = element_blank(),
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#Graficas Miltivaribles
library(ggplot2)
#install.packages("GGally")
library(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2bike <- read.csv("../../r-course-master/data/tema7/daily-bike-rentals.csv")Cabiamos los valores
bike$season = factor(bike$season,
levels = c(1,2,3,4),
labels = c("Invierno", "Primavera", "Verano", "Otonoo"))Combertimo en factores
bike$weathersit <- factor(bike$weathersit,
levels = c(1,2,3),
labels = c("Despejado", "Nublado", "Lluvia"))
bike$weekday <- factor(bike$weekday,
levels = 0:6,
labels = c("D", "L", "M", "X", "J", "V", "S"))Hacemos el histograma
hist(bike$windspeed)
La funcion cut Divide el df en 3 segmentos
bike$windspeed.fac <- cut(bike$windspeed,
breaks = 3,#3 difereentes diviciones
labels = c("Poco", "Medio", "Elevado"))Grafiquemoo con GGplot
ggplot(bike, aes(x=temp, y = cnt))+
geom_point(size=3,# tamano del punto
aes(color=windspeed.fac))+
theme(legend.position = "bottom")+# en donde quieres la leyenda
geom_smooth(method="lm", se=F# intervalo de confianza
, col="red") +# regrsion lineal
facet_grid(weekday ~ season)# formato parrila de dia en funcion se estaciones ## `geom_smooth()` using formula 'y ~ x'
otro ejemplo
auto <- read.csv("../../r-course-master/data/tema7/auto-mpg.csv", stringsAsFactors = F)
#Convertimos en factores
auto$cylinders <- factor(auto$cylinders,
labels = c("3C", "4C", "5C", "6C", "8C"))#Multi grafico donde hace todas la combinaciones posibles
ggpairs(auto[,2:5])## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggpairs(auto[,2:5],
aes(colour=cylinders, alpha=0.4),
title = "Analisis multivariante de carros",
upper=list(continuos="density"),
lower=list(combo="denstrip")+
theme(plot.title=element_text(hjust=0.5)))
Las correlaciones como densidades (se recomienda analisis exploratorio inicial )
ggpairs(auto[,2:7],
aes(colour = cylinders,
alpha = 0.4),
title = "Analisis multivariante de coches",
upper = list(continuous = "density"),
lower = list(combo = "denstrip"))+
theme(plot.title = element_text(hjust = 0.5)) 
#Graficos en 3D
#install.packages("plot3D")
library(plot3D)
mtcars <- read.csv("../../r-course-master/data/tema7/mtcars.csv")comberimos las columna 1 a que sea su identificador
rownames(mtcars) = mtcars$X
mtcars$X = NULL# hecho esto lo eliminamos Grafiquemos en 3D
scatter3D(x=mtcars$disp,
y=mtcars$wt,
z=mtcars$mpg,
clab = c("Millas/Galeon"),
pch=19,#forma de os puntos
cex = 0.5,#
theta = 18, #Rotacion del eje vertical azimutal
phi=20, #rotacione sobre el angulo Horizontal colatitud
main="Coches de los 70'",
xlab = "Desplazamiento (cu.in.)",
ylab = "Peso (x1000lb)",
zlab = "Millas por galeón",
bty = "g")# tipo de caja
text3D(x=mtcars$disp,
y=mtcars$wt,
z=mtcars$mpg,
labels = rownames(mtcars),
add = T, #Superponer a los puntos
colkey = F,#
cex = 0.5)# tamano de texto 
#Histograma tridiencional
data(VADeaths)#muertes por cada mil habitantes de virginia del ano 1940
hist3D(z = VADeaths,
scale = F,#que no escale los valores
expand = 0.01,#se ajuste mejor los valores
bty = "g",#Para las paredes
theta = -40,
phi = 30,#bajar la visualizacion
col = "#1188CC", #color
border = "black",#color del borde
shade = 0.15, #sombra
ltheta =80,#iliminacion
space = 0.3,
ticktype = "detailed")
#Grafica de alturas
scatter3D(x = mtcars$disp,
y = mtcars$wt,
z = mtcars$mpg,
type = "h")
#Como hacemos una grafica una imagen como vector caulqueira de estas 3 opciones se puede usar ( pdf,postscripst,png)
#pdf(file="multivariant.pdf")
#postscript(file="multivariant.ps")
png(file="multivariant.png",
width = 3000,# ancho
height = 3000, # Largo
res = 72)#resolución
GGally::ggpairs(auto[,2:5])## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.dev.off()# y debemos cerar el comando decidido## png
## 2#sistema de recomedaciones Regularmemte funciona por usuarios o Items
#install.packages("recommenderlab")
library(recommenderlab)## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:reshape':
##
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##
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##
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## method from
## print.registry_field proxy
## print.registry_entry proxy##
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##
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##
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##
## getDatadata(MovieLense)#cargamos los datos
head(MovieLense)#mostramos los necesarios ## 6 x 1664 rating matrix of class 'realRatingMatrix' with 789 ratings.peliculas mas valoradas por los usuarios
rating_movies <- MovieLense[rowCounts(MovieLense)>50,#filas ( usuario que valida la pelicula mas de 50 veces )
colCounts(MovieLense)>100]#columnas (mas de 100 visualizaciones de peliculas )Matrices Sparsed
#rating_movies@data[1,1]
# rating_movies@data[1,]#primer usuario cuales peliculas valoro y con que calificacion por nombre
# rating_movies@data[,1]#todas las peliculas con todas las valoraciones
head(rownames(rating_movies))# identificadores, los numeros ## [1] "1" "2" "3" "5" "6" "7"head(colnames(rating_movies))#Peliculas evaluadas mas de 50 veces y que se vio mas de 100 veces## [1] "Toy Story (1995)" "GoldenEye (1995)"
## [3] "Get Shorty (1995)" "Twelve Monkeys (1995)"
## [5] "Babe (1995)" "Dead Man Walking (1995)"Particion de entrenamiento y test del data set
t.id <- sample(x = c(T,F),# verdaderos y falsos
size = nrow(rating_movies),# tamano de de filas = tamano de rating movies
replace = T,
prob = c(0.8,#80% de valores verdaderos
0.2))#20% valores falsos
data_train <- rating_movies[t.id,]# datos de entrenamiento
data_test <- rating_movies[!t.id,]# datos de test que es el 20%Filtrado colaborativo basado en los items (IBCF)
Indice que mide que tanto se parecen esos items Medidas de similitud
ibcf <- Recommender(data = data_train,#entrenamos el modelo
method = "IBCF",
parameter = list(k = 30))
ibcf## Recommender of type 'IBCF' for 'realRatingMatrix'
## learned using 438 users.obtenemos el modelo
ibcf.mod <- getModel(ibcf)
#ibcf.mod#vemos el modelo( matris sparced)Ahora vemos
View(ibcf.mod$sim)
n_rec <- 10# numero de recomendaciones que queremos
ibcf.pred <- predict(object = ibcf,#modelo del recomender
newdata = data_test,
n = n_rec)
ibcf.pred #creo 10 peliculas de recomendacion para 118 usuarios ## Recommendations as 'topNList' with n = 10 for 122 users.ibcf.rec.matrix <- sapply(ibcf.pred@items,
function(x){
colnames(rating_movies)[x]#solo el nombre de las peliculas
}
)
ibcf.rec.matrix[,1:2]#solo muestrame para la persona numero 2## 2 3
## [1,] "Taxi Driver (1976)" "Unforgiven (1992)"
## [2,] "Hoop Dreams (1994)" "Full Monty, The (1997)"
## [3,] "Pulp Fiction (1994)" "Emma (1996)"
## [4,] "Fugitive, The (1993)" "Rainmaker, The (1997)"
## [5,] "Blade Runner (1982)" "Bonnie and Clyde (1967)"
## [6,] "Snow White and the Seven Dwarfs (1937)" "My Left Foot (1989)"
## [7,] "Citizen Kane (1941)" "Arsenic and Old Lace (1944)"
## [8,] "Monty Python and the Holy Grail (1974)" "Ed Wood (1994)"
## [9,] "Princess Bride, The (1987)" "Deer Hunter, The (1978)"
## [10,] "Aliens (1986)" "Clockwork Orange, A (1971)"Filtrado colaborativo basado en usuarios (UBCF)
Calcula las similitudes entre usuarios, como un usuario valorara basado en otros usuarios teniendo gustos similares
ubcf <- Recommender(data = data_train, method = "UBCF")
ubcf.mod <- getModel(ubcf)
ubcf.mod## $description
## [1] "UBCF-Real data: contains full or sample of data set"
##
## $data
## 438 x 332 rating matrix of class 'realRatingMatrix' with 43118 ratings.
## Normalized using center on rows.
##
## $method
## [1] "cosine"
##
## $nn
## [1] 25
##
## $sample
## [1] FALSE
##
## $weighted
## [1] TRUE
##
## $normalize
## [1] "center"
##
## $min_matching_items
## [1] 0
##
## $min_predictive_items
## [1] 0
##
## $verbose
## [1] FALSEubcf.mod$data[,2]## 438 x 1 rating matrix of class 'realRatingMatrix' with 109 ratings.
## Normalized using center on rows.Predigamos
ubcf.pred <- predict(object = ubcf,
newdata = data_test,
n = n_rec)
ubcf.pred## Recommendations as 'topNList' with n = 10 for 122 users.hagamos la matriz de recomendacion
ubcf.rec.matrix <- sapply(ubcf.pred@items,
function(x){
colnames(rating_movies)[x]
}
)
ubcf.rec.matrix[,1:3]## 2
## [1,] "Trainspotting (1996)"
## [2,] "Amistad (1997)"
## [3,] "Big Night (1996)"
## [4,] "Close Shave, A (1995)"
## [5,] "Jackie Brown (1997)"
## [6,] "Strictly Ballroom (1992)"
## [7,] "Cold Comfort Farm (1995)"
## [8,] "Blade Runner (1982)"
## [9,] "Crying Game, The (1992)"
## [10,] "Welcome to the Dollhouse (1995)"
## 3
## [1,] "Crying Game, The (1992)"
## [2,] "Close Shave, A (1995)"
## [3,] "Wrong Trousers, The (1993)"
## [4,] "Killing Fields, The (1984)"
## [5,] "Austin Powers: International Man of Mystery (1997)"
## [6,] "Kolya (1996)"
## [7,] "Ben-Hur (1959)"
## [8,] "Shine (1996)"
## [9,] "Harold and Maude (1971)"
## [10,] "North by Northwest (1959)"
## 6
## [1,] "Hoop Dreams (1994)"
## [2,] "Amistad (1997)"
## [3,] "Rear Window (1954)"
## [4,] "Singin' in the Rain (1952)"
## [5,] "Empire Strikes Back, The (1980)"
## [6,] "Clockwork Orange, A (1971)"
## [7,] "Chinatown (1974)"
## [8,] "Welcome to the Dollhouse (1995)"
## [9,] "Clerks (1994)"
## [10,] "Arsenic and Old Lace (1944)"head(ubcf.pred@items)## $`2`
## [1] 254 318 76 230 216 310 61 48 293 51
##
## $`3`
## [1] 293 230 92 278 160 155 276 279 238 258
##
## $`6`
## [1] 22 318 291 308 94 101 295 51 20 298
##
## $`7`
## [1] 127 81 44 188 318 173 216 217 230 76
##
## $`10`
## [1] 200 114 201 127 271 272 81 318 10 273
##
## $`11`
## [1] 136 244 248 121 89 256 272 127 173 253colnames(rating_movies)[94]## [1] "Empire Strikes Back, The (1980)"#Representacion de la matriz de recomendaciones Existen muchos metodos , que se exportan para este tipo de recomendaciones aqui se observan algunos
recommender_models <- recommenderRegistry$get_entries(dataType = "realRatingMatrix")
names(recommender_models)## [1] "HYBRID_realRatingMatrix" "ALS_realRatingMatrix"
## [3] "ALS_implicit_realRatingMatrix" "IBCF_realRatingMatrix"
## [5] "LIBMF_realRatingMatrix" "POPULAR_realRatingMatrix"
## [7] "RANDOM_realRatingMatrix" "RERECOMMEND_realRatingMatrix"
## [9] "SVD_realRatingMatrix" "SVDF_realRatingMatrix"
## [11] "UBCF_realRatingMatrix"Como se distribuye esta informacion (5500 valoraciones) item =peliculas
image(MovieLense, main = "Mapa de calor de la matriz de valoraciones de peliculas")
Hagamos quintiles
min_n_movies <- quantile(rowCounts(MovieLense), 0.99)#cuantil que mas valoran las peliculas
min_n_movies#de este numero en adelante( mas de ese numero en valoraciones ) ## 99%
## 440.96min_n_users <- quantile(colCounts(MovieLense), 0.99)#cuantil con peliculas mas visto
min_n_users#de este numero en adelante ( ha valorado mas estas peliculas )## 99%
## 371.07grafiquemoslo
image(MovieLense[rowCounts(MovieLense)> min_n_movies,#usuarios que valoraron mas de 371
colCounts(MovieLense)>min_n_users])#valoradas 4040.96
Movamos los valores de los cuartiles Basado en las particiones para entrenar el modelo y sacamos los cuantiles
min_r_movies <- quantile(rowCounts(rating_movies), 0.98)
min_r_users <- quantile(colCounts(rating_movies), 0.98)los graficamos
image(rating_movies[rowCounts(rating_movies)> min_r_movies,
colCounts(rating_movies)>min_r_users],
main = "Mapa de calor del top de peliculas y usuarios")
#Recomendaciones basadas en datos binarios
rating_movies_viewed <- binarize(rating_movies,#Datos iniciales
minRating = 1)#Valores positivos
image(rating_movies_viewed)#ver la imagen del objeto vinario 
Creemos datos de entrenamiento para el sistema binario
t.id <- sample(x=c(T,F),
size = nrow(rating_movies_viewed),# tamano de todas las filas del dataframe
replace = T, #reemplazo
prob = c(0.8, 0.2))#proporciones 80% verdaderas , 20% falsas
b_data_train <- rating_movies_viewed[t.id,]#datos de ntrenamiento igual al 80%
b_data_test <- rating_movies_viewed[!t.id,]# datos de validacion igual al 20% retante #Jaccard index: d(i,j) = (i y j) / (i o j) cohincidencias entre usuarios y vistas (i,j) No es igual que la media y valores tomados , no son continuos , son discretos los valores creamos el modelo que es un jacard
b_model <- Recommender(data = b_data_train,#datos de entrenamiento
method = "IBCF",#metodo
parameter = list(method = "Jaccard")#como metodo el indice de jacard
)
b_details <- getModel(b_model)#el objeto lo convertimos en el modelo predeciremos en base a los t.id’s
b_pred <- predict(object = b_model,
newdata = b_data_test,
n = n_rec)creamos la matriz de recoemendacion
b_rec_matrix <- sapply(b_pred@items, function(x){
colnames(rating_movies)[x]
})veamos las recoemendaciones
b_rec_matrix[,1:3]## 26
## [1,] "Empire Strikes Back, The (1980)"
## [2,] "Raiders of the Lost Ark (1981)"
## [3,] "Back to the Future (1985)"
## [4,] "Indiana Jones and the Last Crusade (1989)"
## [5,] "Terminator, The (1984)"
## [6,] "Silence of the Lambs, The (1991)"
## [7,] "Alien (1979)"
## [8,] "Pulp Fiction (1994)"
## [9,] "Forrest Gump (1994)"
## [10,] "Monty Python and the Holy Grail (1974)"
## 41
## [1,] "Empire Strikes Back, The (1980)"
## [2,] "Back to the Future (1985)"
## [3,] "Indiana Jones and the Last Crusade (1989)"
## [4,] "Fugitive, The (1993)"
## [5,] "Alien (1979)"
## [6,] "Independence Day (ID4) (1996)"
## [7,] "Jurassic Park (1993)"
## [8,] "Aliens (1986)"
## [9,] "Twelve Monkeys (1995)"
## [10,] "Blade Runner (1982)"
## 44
## [1,] "Indiana Jones and the Last Crusade (1989)"
## [2,] "Fugitive, The (1993)"
## [3,] "Batman (1989)"
## [4,] "Speed (1994)"
## [5,] "Jaws (1975)"
## [6,] "Rock, The (1996)"
## [7,] "Apollo 13 (1995)"
## [8,] "Die Hard: With a Vengeance (1995)"
## [9,] "Blues Brothers, The (1980)"
## [10,] "Die Hard 2 (1990)"#Filtros basados en el contenido Sistema de recomendacion basado en contenido (clustering) iniciamos haciendo clustering leemos las URL’s
movie_url <- "http://files.grouplens.org/datasets/movielens/ml-100k/u.item"#las peliculas
users_url <- "http://files.grouplens.org/datasets/movielens/ml-100k/u.data"#los usuarios leemos los datos de scraping
#leemos el objeto
movie_title_df <- read.table(movie_url,
header=F,#no tiene titulos
sep = "|",#el separador lo identifica
quote = "\"")#detectar las secuencia de caracteres
users_df <- read.table(users_url,
header = F,
sep = "\t",
quote = "\"")
names(movie_title_df) <- c("MovieID", "Title","ReleaseDate",
"video_release_date",
"IMDb_URL", "unknown", "Action", "Adventure",
"Animation", "Childrens", "Comedy", "Crime",
"Documentary", "Drama", "Fantasy",
"FilmNoir", "Horror", "Musical",
"Mystery", "Romance", "SciFi",
"Thriller", "War", "Western")veamos is esta bien aplicado
movie_title_df[,1:5]## MovieID
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## Title
## 1 Toy Story (1995)
## 2 GoldenEye (1995)
## 3 Four Rooms (1995)
## 4 Get Shorty (1995)
## 5 Copycat (1995)
## 6 Shanghai Triad (Yao a yao yao dao waipo qiao) (1995)
## 7 Twelve Monkeys (1995)
## 8 Babe (1995)
## 9 Dead Man Walking (1995)
## 10 Richard III (1995)
## 11 Seven (Se7en) (1995)
## 12 Usual Suspects, The (1995)
## 13 Mighty Aphrodite (1995)
## 14 Postino, Il (1994)
## 15 Mr. Holland's Opus (1995)
## 16 French Twist (Gazon maudit) (1995)
## 17 From Dusk Till Dawn (1996)
## 18 White Balloon, The (1995)
## 19 Antonia's Line (1995)
## 20 Angels and Insects (1995)
## 21 Muppet Treasure Island (1996)
## 22 Braveheart (1995)
## 23 Taxi Driver (1976)
## 24 Rumble in the Bronx (1995)
## 25 Birdcage, The (1996)
## 26 Brothers McMullen, The (1995)
## 27 Bad Boys (1995)
## 28 Apollo 13 (1995)
## 29 Batman Forever (1995)
## 30 Belle de jour (1967)
## 31 Crimson Tide (1995)
## 32 Crumb (1994)
## 33 Desperado (1995)
## 34 Doom Generation, The (1995)
## 35 Free Willy 2: The Adventure Home (1995)
## 36 Mad Love (1995)
## 37 Nadja (1994)
## 38 Net, The (1995)
## 39 Strange Days (1995)
## 40 To Wong Foo, Thanks for Everything! Julie Newmar (1995)
## 41 Billy Madison (1995)
## 42 Clerks (1994)
## 43 Disclosure (1994)
## 44 Dolores Claiborne (1994)
## 45 Eat Drink Man Woman (1994)
## 46 Exotica (1994)
## 47 Ed Wood (1994)
## 48 Hoop Dreams (1994)
## 49 I.Q. (1994)
## 50 Star Wars (1977)
## 51 Legends of the Fall (1994)
## 52 Madness of King George, The (1994)
## 53 Natural Born Killers (1994)
## 54 Outbreak (1995)
## 55 Professional, The (1994)
## 56 Pulp Fiction (1994)
## 57 Priest (1994)
## 58 Quiz Show (1994)
## 59 Three Colors: Red (1994)
## 60 Three Colors: Blue (1993)
## 61 Three Colors: White (1994)
## 62 Stargate (1994)
## 63 Santa Clause, The (1994)
## 64 Shawshank Redemption, The (1994)
## 65 What's Eating Gilbert Grape (1993)
## 66 While You Were Sleeping (1995)
## 67 Ace Ventura: Pet Detective (1994)
## 68 Crow, The (1994)
## 69 Forrest Gump (1994)
## 70 Four Weddings and a Funeral (1994)
## 71 Lion King, The (1994)
## 72 Mask, The (1994)
## 73 Maverick (1994)
## 74 Faster Pussycat! Kill! Kill! (1965)
## 75 Brother Minister: The Assassination of Malcolm X (1994)
## 76 Carlito's Way (1993)
## 77 Firm, The (1993)
## 78 Free Willy (1993)
## 79 Fugitive, The (1993)
## 80 Hot Shots! Part Deux (1993)
## 81 Hudsucker Proxy, The (1994)
## 82 Jurassic Park (1993)
## 83 Much Ado About Nothing (1993)
## 84 Robert A. Heinlein's The Puppet Masters (1994)
## 85 Ref, The (1994)
## 86 Remains of the Day, The (1993)
## 87 Searching for Bobby Fischer (1993)
## 88 Sleepless in Seattle (1993)
## 89 Blade Runner (1982)
## 90 So I Married an Axe Murderer (1993)
## 91 Nightmare Before Christmas, The (1993)
## 92 True Romance (1993)
## 93 Welcome to the Dollhouse (1995)
## 94 Home Alone (1990)
## 95 Aladdin (1992)
## 96 Terminator 2: Judgment Day (1991)
## 97 Dances with Wolves (1990)
## 98 Silence of the Lambs, The (1991)
## 99 Snow White and the Seven Dwarfs (1937)
## 100 Fargo (1996)
## 101 Heavy Metal (1981)
## 102 Aristocats, The (1970)
## 103 All Dogs Go to Heaven 2 (1996)
## 104 Theodore Rex (1995)
## 105 Sgt. Bilko (1996)
## 106 Diabolique (1996)
## 107 Moll Flanders (1996)
## 108 Kids in the Hall: Brain Candy (1996)
## 109 Mystery Science Theater 3000: The Movie (1996)
## 110 Operation Dumbo Drop (1995)
## 111 Truth About Cats & Dogs, The (1996)
## 112 Flipper (1996)
## 113 Horseman on the Roof, The (Hussard sur le toit, Le) (1995)
## 114 Wallace & Gromit: The Best of Aardman Animation (1996)
## 115 Haunted World of Edward D. Wood Jr., The (1995)
## 116 Cold Comfort Farm (1995)
## 117 Rock, The (1996)
## 118 Twister (1996)
## 119 Maya Lin: A Strong Clear Vision (1994)
## 120 Striptease (1996)
## 121 Independence Day (ID4) (1996)
## 122 Cable Guy, The (1996)
## 123 Frighteners, The (1996)
## 124 Lone Star (1996)
## 125 Phenomenon (1996)
## 126 Spitfire Grill, The (1996)
## 127 Godfather, The (1972)
## 128 Supercop (1992)
## 129 Bound (1996)
## 130 Kansas City (1996)
## 131 Breakfast at Tiffany's (1961)
## 132 Wizard of Oz, The (1939)
## 133 Gone with the Wind (1939)
## 134 Citizen Kane (1941)
## 135 2001: A Space Odyssey (1968)
## 136 Mr. Smith Goes to Washington (1939)
## 137 Big Night (1996)
## 138 D3: The Mighty Ducks (1996)
## 139 Love Bug, The (1969)
## 140 Homeward Bound: The Incredible Journey (1993)
## 141 20,000 Leagues Under the Sea (1954)
## 142 Bedknobs and Broomsticks (1971)
## 143 Sound of Music, The (1965)
## 144 Die Hard (1988)
## 145 Lawnmower Man, The (1992)
## 146 Unhook the Stars (1996)
## 147 Long Kiss Goodnight, The (1996)
## 148 Ghost and the Darkness, The (1996)
## 149 Jude (1996)
## 150 Swingers (1996)
## 151 Willy Wonka and the Chocolate Factory (1971)
## 152 Sleeper (1973)
## 153 Fish Called Wanda, A (1988)
## 154 Monty Python's Life of Brian (1979)
## 155 Dirty Dancing (1987)
## 156 Reservoir Dogs (1992)
## 157 Platoon (1986)
## 158 Weekend at Bernie's (1989)
## 159 Basic Instinct (1992)
## 160 Glengarry Glen Ross (1992)
## 161 Top Gun (1986)
## 162 On Golden Pond (1981)
## 163 Return of the Pink Panther, The (1974)
## 164 Abyss, The (1989)
## 165 Jean de Florette (1986)
## 166 Manon of the Spring (Manon des sources) (1986)
## 167 Private Benjamin (1980)
## 168 Monty Python and the Holy Grail (1974)
## 169 Wrong Trousers, The (1993)
## 170 Cinema Paradiso (1988)
## 171 Delicatessen (1991)
## 172 Empire Strikes Back, The (1980)
## 173 Princess Bride, The (1987)
## 174 Raiders of the Lost Ark (1981)
## 175 Brazil (1985)
## 176 Aliens (1986)
## 177 Good, The Bad and The Ugly, The (1966)
## 178 12 Angry Men (1957)
## 179 Clockwork Orange, A (1971)
## 180 Apocalypse Now (1979)
## 181 Return of the Jedi (1983)
## 182 GoodFellas (1990)
## 183 Alien (1979)
## 184 Army of Darkness (1993)
## 185 Psycho (1960)
## 186 Blues Brothers, The (1980)
## 187 Godfather: Part II, The (1974)
## 188 Full Metal Jacket (1987)
## 189 Grand Day Out, A (1992)
## 190 Henry V (1989)
## 191 Amadeus (1984)
## 192 Raging Bull (1980)
## 193 Right Stuff, The (1983)
## 194 Sting, The (1973)
## 195 Terminator, The (1984)
## 196 Dead Poets Society (1989)
## 197 Graduate, The (1967)
## 198 Nikita (La Femme Nikita) (1990)
## 199 Bridge on the River Kwai, The (1957)
## 200 Shining, The (1980)
## 201 Evil Dead II (1987)
## 202 Groundhog Day (1993)
## 203 Unforgiven (1992)
## 204 Back to the Future (1985)
## 205 Patton (1970)
## 206 Akira (1988)
## 207 Cyrano de Bergerac (1990)
## 208 Young Frankenstein (1974)
## 209 This Is Spinal Tap (1984)
## 210 Indiana Jones and the Last Crusade (1989)
## 211 M*A*S*H (1970)
## 212 Unbearable Lightness of Being, The (1988)
## 213 Room with a View, A (1986)
## 214 Pink Floyd - The Wall (1982)
## 215 Field of Dreams (1989)
## 216 When Harry Met Sally... (1989)
## 217 Bram Stoker's Dracula (1992)
## 218 Cape Fear (1991)
## 219 Nightmare on Elm Street, A (1984)
## 220 Mirror Has Two Faces, The (1996)
## 221 Breaking the Waves (1996)
## 222 Star Trek: First Contact (1996)
## 223 Sling Blade (1996)
## 224 Ridicule (1996)
## 225 101 Dalmatians (1996)
## 226 Die Hard 2 (1990)
## 227 Star Trek VI: The Undiscovered Country (1991)
## 228 Star Trek: The Wrath of Khan (1982)
## 229 Star Trek III: The Search for Spock (1984)
## 230 Star Trek IV: The Voyage Home (1986)
## 231 Batman Returns (1992)
## 232 Young Guns (1988)
## 233 Under Siege (1992)
## 234 Jaws (1975)
## 235 Mars Attacks! (1996)
## 236 Citizen Ruth (1996)
## 237 Jerry Maguire (1996)
## 238 Raising Arizona (1987)
## 239 Sneakers (1992)
## 240 Beavis and Butt-head Do America (1996)
## 241 Last of the Mohicans, The (1992)
## 242 Kolya (1996)
## 243 Jungle2Jungle (1997)
## 244 Smilla's Sense of Snow (1997)
## 245 Devil's Own, The (1997)
## 246 Chasing Amy (1997)
## 247 Turbo: A Power Rangers Movie (1997)
## 248 Grosse Pointe Blank (1997)
## 249 Austin Powers: International Man of Mystery (1997)
## 250 Fifth Element, The (1997)
## 251 Shall We Dance? (1996)
## 252 Lost World: Jurassic Park, The (1997)
## 253 Pillow Book, The (1995)
## 254 Batman & Robin (1997)
## 255 My Best Friend's Wedding (1997)
## 256 When the Cats Away (Chacun cherche son chat) (1996)
## 257 Men in Black (1997)
## 258 Contact (1997)
## 259 George of the Jungle (1997)
## 260 Event Horizon (1997)
## 261 Air Bud (1997)
## 262 In the Company of Men (1997)
## 263 Steel (1997)
## 264 Mimic (1997)
## 265 Hunt for Red October, The (1990)
## 266 Kull the Conqueror (1997)
## 267 unknown
## 268 Chasing Amy (1997)
## 269 Full Monty, The (1997)
## 270 Gattaca (1997)
## 271 Starship Troopers (1997)
## 272 Good Will Hunting (1997)
## 273 Heat (1995)
## 274 Sabrina (1995)
## 275 Sense and Sensibility (1995)
## 276 Leaving Las Vegas (1995)
## 277 Restoration (1995)
## 278 Bed of Roses (1996)
## 279 Once Upon a Time... When We Were Colored (1995)
## 280 Up Close and Personal (1996)
## 281 River Wild, The (1994)
## 282 Time to Kill, A (1996)
## 283 Emma (1996)
## 284 Tin Cup (1996)
## 285 Secrets & Lies (1996)
## 286 English Patient, The (1996)
## 287 Marvin's Room (1996)
## 288 Scream (1996)
## 289 Evita (1996)
## 290 Fierce Creatures (1997)
## 291 Absolute Power (1997)
## 292 Rosewood (1997)
## 293 Donnie Brasco (1997)
## 294 Liar Liar (1997)
## 295 Breakdown (1997)
## 296 Promesse, La (1996)
## 297 Ulee's Gold (1997)
## 298 Face/Off (1997)
## 299 Hoodlum (1997)
## 300 Air Force One (1997)
## 301 In & Out (1997)
## 302 L.A. Confidential (1997)
## 303 Ulee's Gold (1997)
## 304 Fly Away Home (1996)
## 305 Ice Storm, The (1997)
## 306 Mrs. Brown (Her Majesty, Mrs. Brown) (1997)
## 307 Devil's Advocate, The (1997)
## 308 FairyTale: A True Story (1997)
## 309 Deceiver (1997)
## 310 Rainmaker, The (1997)
## 311 Wings of the Dove, The (1997)
## 312 Midnight in the Garden of Good and Evil (1997)
## 313 Titanic (1997)
## 314 3 Ninjas: High Noon At Mega Mountain (1998)
## 315 Apt Pupil (1998)
## 316 As Good As It Gets (1997)
## 317 In the Name of the Father (1993)
## 318 Schindler's List (1993)
## 319 Everyone Says I Love You (1996)
## 320 Paradise Lost: The Child Murders at Robin Hood Hills (1996)
## 321 Mother (1996)
## 322 Murder at 1600 (1997)
## 323 Dante's Peak (1997)
## 324 Lost Highway (1997)
## 325 Crash (1996)
## 326 G.I. Jane (1997)
## 327 Cop Land (1997)
## 328 Conspiracy Theory (1997)
## 329 Desperate Measures (1998)
## 330 187 (1997)
## 331 Edge, The (1997)
## 332 Kiss the Girls (1997)
## 333 Game, The (1997)
## 334 U Turn (1997)
## 335 How to Be a Player (1997)
## 336 Playing God (1997)
## 337 House of Yes, The (1997)
## 338 Bean (1997)
## 339 Mad City (1997)
## 340 Boogie Nights (1997)
## 341 Critical Care (1997)
## 342 Man Who Knew Too Little, The (1997)
## 343 Alien: Resurrection (1997)
## 344 Apostle, The (1997)
## 345 Deconstructing Harry (1997)
## 346 Jackie Brown (1997)
## 347 Wag the Dog (1997)
## 348 Desperate Measures (1998)
## 349 Hard Rain (1998)
## 350 Fallen (1998)
## 351 Prophecy II, The (1998)
## 352 Spice World (1997)
## 353 Deep Rising (1998)
## 354 Wedding Singer, The (1998)
## 355 Sphere (1998)
## 356 Client, The (1994)
## 357 One Flew Over the Cuckoo's Nest (1975)
## 358 Spawn (1997)
## 359 Assignment, The (1997)
## 360 Wonderland (1997)
## 361 Incognito (1997)
## 362 Blues Brothers 2000 (1998)
## 363 Sudden Death (1995)
## 364 Ace Ventura: When Nature Calls (1995)
## 365 Powder (1995)
## 366 Dangerous Minds (1995)
## 367 Clueless (1995)
## 368 Bio-Dome (1996)
## 369 Black Sheep (1996)
## 370 Mary Reilly (1996)
## 371 Bridges of Madison County, The (1995)
## 372 Jeffrey (1995)
## 373 Judge Dredd (1995)
## 374 Mighty Morphin Power Rangers: The Movie (1995)
## 375 Showgirls (1995)
## 376 Houseguest (1994)
## 377 Heavyweights (1994)
## 378 Miracle on 34th Street (1994)
## 379 Tales From the Crypt Presents: Demon Knight (1995)
## 380 Star Trek: Generations (1994)
## 381 Muriel's Wedding (1994)
## 382 Adventures of Priscilla, Queen of the Desert, The (1994)
## 383 Flintstones, The (1994)
## 384 Naked Gun 33 1/3: The Final Insult (1994)
## 385 True Lies (1994)
## 386 Addams Family Values (1993)
## 387 Age of Innocence, The (1993)
## 388 Beverly Hills Cop III (1994)
## 389 Black Beauty (1994)
## 390 Fear of a Black Hat (1993)
## 391 Last Action Hero (1993)
## 392 Man Without a Face, The (1993)
## 393 Mrs. Doubtfire (1993)
## 394 Radioland Murders (1994)
## 395 Robin Hood: Men in Tights (1993)
## 396 Serial Mom (1994)
## 397 Striking Distance (1993)
## 398 Super Mario Bros. (1993)
## 399 Three Musketeers, The (1993)
## 400 Little Rascals, The (1994)
## 401 Brady Bunch Movie, The (1995)
## 402 Ghost (1990)
## 403 Batman (1989)
## 404 Pinocchio (1940)
## 405 Mission: Impossible (1996)
## 406 Thinner (1996)
## 407 Spy Hard (1996)
## 408 Close Shave, A (1995)
## 409 Jack (1996)
## 410 Kingpin (1996)
## 411 Nutty Professor, The (1996)
## 412 Very Brady Sequel, A (1996)
## 413 Tales from the Crypt Presents: Bordello of Blood (1996)
## 414 My Favorite Year (1982)
## 415 Apple Dumpling Gang, The (1975)
## 416 Old Yeller (1957)
## 417 Parent Trap, The (1961)
## 418 Cinderella (1950)
## 419 Mary Poppins (1964)
## 420 Alice in Wonderland (1951)
## 421 William Shakespeare's Romeo and Juliet (1996)
## 422 Aladdin and the King of Thieves (1996)
## 423 E.T. the Extra-Terrestrial (1982)
## 424 Children of the Corn: The Gathering (1996)
## 425 Bob Roberts (1992)
## 426 Transformers: The Movie, The (1986)
## 427 To Kill a Mockingbird (1962)
## 428 Harold and Maude (1971)
## 429 Day the Earth Stood Still, The (1951)
## 430 Duck Soup (1933)
## 431 Highlander (1986)
## 432 Fantasia (1940)
## 433 Heathers (1989)
## 434 Forbidden Planet (1956)
## 435 Butch Cassidy and the Sundance Kid (1969)
## 436 American Werewolf in London, An (1981)
## 437 Amityville 1992: It's About Time (1992)
## 438 Amityville 3-D (1983)
## 439 Amityville: A New Generation (1993)
## 440 Amityville II: The Possession (1982)
## 441 Amityville Horror, The (1979)
## 442 Amityville Curse, The (1990)
## 443 Birds, The (1963)
## 444 Blob, The (1958)
## 445 Body Snatcher, The (1945)
## 446 Burnt Offerings (1976)
## 447 Carrie (1976)
## 448 Omen, The (1976)
## 449 Star Trek: The Motion Picture (1979)
## 450 Star Trek V: The Final Frontier (1989)
## 451 Grease (1978)
## 452 Jaws 2 (1978)
## 453 Jaws 3-D (1983)
## 454 Bastard Out of Carolina (1996)
## 455 Jackie Chan's First Strike (1996)
## 456 Beverly Hills Ninja (1997)
## 457 Free Willy 3: The Rescue (1997)
## 458 Nixon (1995)
## 459 Cry, the Beloved Country (1995)
## 460 Crossing Guard, The (1995)
## 461 Smoke (1995)
## 462 Like Water For Chocolate (Como agua para chocolate) (1992)
## 463 Secret of Roan Inish, The (1994)
## 464 Vanya on 42nd Street (1994)
## 465 Jungle Book, The (1994)
## 466 Red Rock West (1992)
## 467 Bronx Tale, A (1993)
## 468 Rudy (1993)
## 469 Short Cuts (1993)
## 470 Tombstone (1993)
## 471 Courage Under Fire (1996)
## 472 Dragonheart (1996)
## 473 James and the Giant Peach (1996)
## 474 Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1963)
## 475 Trainspotting (1996)
## 476 First Wives Club, The (1996)
## 477 Matilda (1996)
## 478 Philadelphia Story, The (1940)
## 479 Vertigo (1958)
## 480 North by Northwest (1959)
## 481 Apartment, The (1960)
## 482 Some Like It Hot (1959)
## 483 Casablanca (1942)
## 484 Maltese Falcon, The (1941)
## 485 My Fair Lady (1964)
## 486 Sabrina (1954)
## 487 Roman Holiday (1953)
## 488 Sunset Blvd. (1950)
## 489 Notorious (1946)
## 490 To Catch a Thief (1955)
## 491 Adventures of Robin Hood, The (1938)
## 492 East of Eden (1955)
## 493 Thin Man, The (1934)
## 494 His Girl Friday (1940)
## 495 Around the World in 80 Days (1956)
## 496 It's a Wonderful Life (1946)
## 497 Bringing Up Baby (1938)
## 498 African Queen, The (1951)
## 499 Cat on a Hot Tin Roof (1958)
## 500 Fly Away Home (1996)
## 501 Dumbo (1941)
## 502 Bananas (1971)
## 503 Candidate, The (1972)
## 504 Bonnie and Clyde (1967)
## 505 Dial M for Murder (1954)
## 506 Rebel Without a Cause (1955)
## 507 Streetcar Named Desire, A (1951)
## 508 People vs. Larry Flynt, The (1996)
## 509 My Left Foot (1989)
## 510 Magnificent Seven, The (1954)
## 511 Lawrence of Arabia (1962)
## 512 Wings of Desire (1987)
## 513 Third Man, The (1949)
## 514 Annie Hall (1977)
## 515 Boot, Das (1981)
## 516 Local Hero (1983)
## 517 Manhattan (1979)
## 518 Miller's Crossing (1990)
## 519 Treasure of the Sierra Madre, The (1948)
## 520 Great Escape, The (1963)
## 521 Deer Hunter, The (1978)
## 522 Down by Law (1986)
## 523 Cool Hand Luke (1967)
## 524 Great Dictator, The (1940)
## 525 Big Sleep, The (1946)
## 526 Ben-Hur (1959)
## 527 Gandhi (1982)
## 528 Killing Fields, The (1984)
## 529 My Life as a Dog (Mitt liv som hund) (1985)
## 530 Man Who Would Be King, The (1975)
## 531 Shine (1996)
## 532 Kama Sutra: A Tale of Love (1996)
## 533 Daytrippers, The (1996)
## 534 Traveller (1997)
## 535 Addicted to Love (1997)
## 536 Ponette (1996)
## 537 My Own Private Idaho (1991)
## 538 Anastasia (1997)
## 539 Mouse Hunt (1997)
## 540 Money Train (1995)
## 541 Mortal Kombat (1995)
## 542 Pocahontas (1995)
## 543 Misérables, Les (1995)
## 544 Things to Do in Denver when You're Dead (1995)
## 545 Vampire in Brooklyn (1995)
## 546 Broken Arrow (1996)
## 547 Young Poisoner's Handbook, The (1995)
## 548 NeverEnding Story III, The (1994)
## 549 Rob Roy (1995)
## 550 Die Hard: With a Vengeance (1995)
## 551 Lord of Illusions (1995)
## 552 Species (1995)
## 553 Walk in the Clouds, A (1995)
## 554 Waterworld (1995)
## 555 White Man's Burden (1995)
## 556 Wild Bill (1995)
## 557 Farinelli: il castrato (1994)
## 558 Heavenly Creatures (1994)
## 559 Interview with the Vampire (1994)
## 560 Kid in King Arthur's Court, A (1995)
## 561 Mary Shelley's Frankenstein (1994)
## 562 Quick and the Dead, The (1995)
## 563 Stephen King's The Langoliers (1995)
## 564 Tales from the Hood (1995)
## 565 Village of the Damned (1995)
## 566 Clear and Present Danger (1994)
## 567 Wes Craven's New Nightmare (1994)
## 568 Speed (1994)
## 569 Wolf (1994)
## 570 Wyatt Earp (1994)
## 571 Another Stakeout (1993)
## 572 Blown Away (1994)
## 573 Body Snatchers (1993)
## 574 Boxing Helena (1993)
## 575 City Slickers II: The Legend of Curly's Gold (1994)
## 576 Cliffhanger (1993)
## 577 Coneheads (1993)
## 578 Demolition Man (1993)
## 579 Fatal Instinct (1993)
## 580 Englishman Who Went Up a Hill, But Came Down a Mountain, The (1995)
## 581 Kalifornia (1993)
## 582 Piano, The (1993)
## 583 Romeo Is Bleeding (1993)
## 584 Secret Garden, The (1993)
## 585 Son in Law (1993)
## 586 Terminal Velocity (1994)
## 587 Hour of the Pig, The (1993)
## 588 Beauty and the Beast (1991)
## 589 Wild Bunch, The (1969)
## 590 Hellraiser: Bloodline (1996)
## 591 Primal Fear (1996)
## 592 True Crime (1995)
## 593 Stalingrad (1993)
## 594 Heavy (1995)
## 595 Fan, The (1996)
## 596 Hunchback of Notre Dame, The (1996)
## 597 Eraser (1996)
## 598 Big Squeeze, The (1996)
## 599 Police Story 4: Project S (Chao ji ji hua) (1993)
## 600 Daniel Defoe's Robinson Crusoe (1996)
## 601 For Whom the Bell Tolls (1943)
## 602 American in Paris, An (1951)
## 603 Rear Window (1954)
## 604 It Happened One Night (1934)
## 605 Meet Me in St. Louis (1944)
## 606 All About Eve (1950)
## 607 Rebecca (1940)
## 608 Spellbound (1945)
## 609 Father of the Bride (1950)
## 610 Gigi (1958)
## 611 Laura (1944)
## 612 Lost Horizon (1937)
## 613 My Man Godfrey (1936)
## 614 Giant (1956)
## 615 39 Steps, The (1935)
## 616 Night of the Living Dead (1968)
## 617 Blue Angel, The (Blaue Engel, Der) (1930)
## 618 Picnic (1955)
## 619 Extreme Measures (1996)
## 620 Chamber, The (1996)
## 621 Davy Crockett, King of the Wild Frontier (1955)
## 622 Swiss Family Robinson (1960)
## 623 Angels in the Outfield (1994)
## 624 Three Caballeros, The (1945)
## 625 Sword in the Stone, The (1963)
## 626 So Dear to My Heart (1949)
## 627 Robin Hood: Prince of Thieves (1991)
## 628 Sleepers (1996)
## 629 Victor/Victoria (1982)
## 630 Great Race, The (1965)
## 631 Crying Game, The (1992)
## 632 Sophie's Choice (1982)
## 633 Christmas Carol, A (1938)
## 634 Microcosmos: Le peuple de l'herbe (1996)
## 635 Fog, The (1980)
## 636 Escape from New York (1981)
## 637 Howling, The (1981)
## 638 Return of Martin Guerre, The (Retour de Martin Guerre, Le) (1982)
## 639 Tin Drum, The (Blechtrommel, Die) (1979)
## 640 Cook the Thief His Wife & Her Lover, The (1989)
## 641 Paths of Glory (1957)
## 642 Grifters, The (1990)
## 643 The Innocent (1994)
## 644 Thin Blue Line, The (1988)
## 645 Paris Is Burning (1990)
## 646 Once Upon a Time in the West (1969)
## 647 Ran (1985)
## 648 Quiet Man, The (1952)
## 649 Once Upon a Time in America (1984)
## 650 Seventh Seal, The (Sjunde inseglet, Det) (1957)
## 651 Glory (1989)
## 652 Rosencrantz and Guildenstern Are Dead (1990)
## 653 Touch of Evil (1958)
## 654 Chinatown (1974)
## 655 Stand by Me (1986)
## 656 M (1931)
## 657 Manchurian Candidate, The (1962)
## 658 Pump Up the Volume (1990)
## 659 Arsenic and Old Lace (1944)
## 660 Fried Green Tomatoes (1991)
## 661 High Noon (1952)
## 662 Somewhere in Time (1980)
## 663 Being There (1979)
## 664 Paris, Texas (1984)
## 665 Alien 3 (1992)
## 666 Blood For Dracula (Andy Warhol's Dracula) (1974)
## 667 Audrey Rose (1977)
## 668 Blood Beach (1981)
## 669 Body Parts (1991)
## 670 Body Snatchers (1993)
## 671 Bride of Frankenstein (1935)
## 672 Candyman (1992)
## 673 Cape Fear (1962)
## 674 Cat People (1982)
## 675 Nosferatu (Nosferatu, eine Symphonie des Grauens) (1922)
## 676 Crucible, The (1996)
## 677 Fire on the Mountain (1996)
## 678 Volcano (1997)
## 679 Conan the Barbarian (1981)
## 680 Kull the Conqueror (1997)
## 681 Wishmaster (1997)
## 682 I Know What You Did Last Summer (1997)
## 683 Rocket Man (1997)
## 684 In the Line of Fire (1993)
## 685 Executive Decision (1996)
## 686 Perfect World, A (1993)
## 687 McHale's Navy (1997)
## 688 Leave It to Beaver (1997)
## 689 Jackal, The (1997)
## 690 Seven Years in Tibet (1997)
## 691 Dark City (1998)
## 692 American President, The (1995)
## 693 Casino (1995)
## 694 Persuasion (1995)
## 695 Kicking and Screaming (1995)
## 696 City Hall (1996)
## 697 Basketball Diaries, The (1995)
## 698 Browning Version, The (1994)
## 699 Little Women (1994)
## 700 Miami Rhapsody (1995)
## 701 Wonderful, Horrible Life of Leni Riefenstahl, The (1993)
## 702 Barcelona (1994)
## 703 Widows' Peak (1994)
## 704 House of the Spirits, The (1993)
## 705 Singin' in the Rain (1952)
## 706 Bad Moon (1996)
## 707 Enchanted April (1991)
## 708 Sex, Lies, and Videotape (1989)
## 709 Strictly Ballroom (1992)
## 710 Better Off Dead... (1985)
## 711 Substance of Fire, The (1996)
## 712 Tin Men (1987)
## 713 Othello (1995)
## 714 Carrington (1995)
## 715 To Die For (1995)
## 716 Home for the Holidays (1995)
## 717 Juror, The (1996)
## 718 In the Bleak Midwinter (1995)
## 719 Canadian Bacon (1994)
## 720 First Knight (1995)
## 721 Mallrats (1995)
## 722 Nine Months (1995)
## 723 Boys on the Side (1995)
## 724 Circle of Friends (1995)
## 725 Exit to Eden (1994)
## 726 Fluke (1995)
## 727 Immortal Beloved (1994)
## 728 Junior (1994)
## 729 Nell (1994)
## 730 Queen Margot (Reine Margot, La) (1994)
## 731 Corrina, Corrina (1994)
## 732 Dave (1993)
## 733 Go Fish (1994)
## 734 Made in America (1993)
## 735 Philadelphia (1993)
## 736 Shadowlands (1993)
## 737 Sirens (1994)
## 738 Threesome (1994)
## 739 Pretty Woman (1990)
## 740 Jane Eyre (1996)
## 741 Last Supper, The (1995)
## 742 Ransom (1996)
## 743 Crow: City of Angels, The (1996)
## 744 Michael Collins (1996)
## 745 Ruling Class, The (1972)
## 746 Real Genius (1985)
## 747 Benny & Joon (1993)
## 748 Saint, The (1997)
## 749 MatchMaker, The (1997)
## 750 Amistad (1997)
## 751 Tomorrow Never Dies (1997)
## 752 Replacement Killers, The (1998)
## 753 Burnt By the Sun (1994)
## 754 Red Corner (1997)
## 755 Jumanji (1995)
## 756 Father of the Bride Part II (1995)
## 757 Across the Sea of Time (1995)
## 758 Lawnmower Man 2: Beyond Cyberspace (1996)
## 759 Fair Game (1995)
## 760 Screamers (1995)
## 761 Nick of Time (1995)
## 762 Beautiful Girls (1996)
## 763 Happy Gilmore (1996)
## 764 If Lucy Fell (1996)
## 765 Boomerang (1992)
## 766 Man of the Year (1995)
## 767 Addiction, The (1995)
## 768 Casper (1995)
## 769 Congo (1995)
## 770 Devil in a Blue Dress (1995)
## 771 Johnny Mnemonic (1995)
## 772 Kids (1995)
## 773 Mute Witness (1994)
## 774 Prophecy, The (1995)
## 775 Something to Talk About (1995)
## 776 Three Wishes (1995)
## 777 Castle Freak (1995)
## 778 Don Juan DeMarco (1995)
## 779 Drop Zone (1994)
## 780 Dumb & Dumber (1994)
## 781 French Kiss (1995)
## 782 Little Odessa (1994)
## 783 Milk Money (1994)
## 784 Beyond Bedlam (1993)
## 785 Only You (1994)
## 786 Perez Family, The (1995)
## 787 Roommates (1995)
## 788 Relative Fear (1994)
## 789 Swimming with Sharks (1995)
## 790 Tommy Boy (1995)
## 791 Baby-Sitters Club, The (1995)
## 792 Bullets Over Broadway (1994)
## 793 Crooklyn (1994)
## 794 It Could Happen to You (1994)
## 795 Richie Rich (1994)
## 796 Speechless (1994)
## 797 Timecop (1994)
## 798 Bad Company (1995)
## 799 Boys Life (1995)
## 800 In the Mouth of Madness (1995)
## 801 Air Up There, The (1994)
## 802 Hard Target (1993)
## 803 Heaven & Earth (1993)
## 804 Jimmy Hollywood (1994)
## 805 Manhattan Murder Mystery (1993)
## 806 Menace II Society (1993)
## 807 Poetic Justice (1993)
## 808 Program, The (1993)
## 809 Rising Sun (1993)
## 810 Shadow, The (1994)
## 811 Thirty-Two Short Films About Glenn Gould (1993)
## 812 Andre (1994)
## 813 Celluloid Closet, The (1995)
## 814 Great Day in Harlem, A (1994)
## 815 One Fine Day (1996)
## 816 Candyman: Farewell to the Flesh (1995)
## 817 Frisk (1995)
## 818 Girl 6 (1996)
## 819 Eddie (1996)
## 820 Space Jam (1996)
## 821 Mrs. Winterbourne (1996)
## 822 Faces (1968)
## 823 Mulholland Falls (1996)
## 824 Great White Hype, The (1996)
## 825 Arrival, The (1996)
## 826 Phantom, The (1996)
## 827 Daylight (1996)
## 828 Alaska (1996)
## 829 Fled (1996)
## 830 Power 98 (1995)
## 831 Escape from L.A. (1996)
## 832 Bogus (1996)
## 833 Bulletproof (1996)
## 834 Halloween: The Curse of Michael Myers (1995)
## 835 Gay Divorcee, The (1934)
## 836 Ninotchka (1939)
## 837 Meet John Doe (1941)
## 838 In the Line of Duty 2 (1987)
## 839 Loch Ness (1995)
## 840 Last Man Standing (1996)
## 841 Glimmer Man, The (1996)
## 842 Pollyanna (1960)
## 843 Shaggy Dog, The (1959)
## 844 Freeway (1996)
## 845 That Thing You Do! (1996)
## 846 To Gillian on Her 37th Birthday (1996)
## 847 Looking for Richard (1996)
## 848 Murder, My Sweet (1944)
## 849 Days of Thunder (1990)
## 850 Perfect Candidate, A (1996)
## 851 Two or Three Things I Know About Her (1966)
## 852 Bloody Child, The (1996)
## 853 Braindead (1992)
## 854 Bad Taste (1987)
## 855 Diva (1981)
## 856 Night on Earth (1991)
## 857 Paris Was a Woman (1995)
## 858 Amityville: Dollhouse (1996)
## 859 April Fool's Day (1986)
## 860 Believers, The (1987)
## 861 Nosferatu a Venezia (1986)
## 862 Jingle All the Way (1996)
## 863 Garden of Finzi-Contini, The (Giardino dei Finzi-Contini, Il) (1970)
## 864 My Fellow Americans (1996)
## 865 Ice Storm, The (1997)
## 866 Michael (1996)
## 867 Whole Wide World, The (1996)
## 868 Hearts and Minds (1996)
## 869 Fools Rush In (1997)
## 870 Touch (1997)
## 871 Vegas Vacation (1997)
## 872 Love Jones (1997)
## 873 Picture Perfect (1997)
## 874 Career Girls (1997)
## 875 She's So Lovely (1997)
## 876 Money Talks (1997)
## 877 Excess Baggage (1997)
## 878 That Darn Cat! (1997)
## 879 Peacemaker, The (1997)
## 880 Soul Food (1997)
## 881 Money Talks (1997)
## 882 Washington Square (1997)
## 883 Telling Lies in America (1997)
## 884 Year of the Horse (1997)
## 885 Phantoms (1998)
## 886 Life Less Ordinary, A (1997)
## 887 Eve's Bayou (1997)
## 888 One Night Stand (1997)
## 889 Tango Lesson, The (1997)
## 890 Mortal Kombat: Annihilation (1997)
## 891 Bent (1997)
## 892 Flubber (1997)
## 893 For Richer or Poorer (1997)
## 894 Home Alone 3 (1997)
## 895 Scream 2 (1997)
## 896 Sweet Hereafter, The (1997)
## 897 Time Tracers (1995)
## 898 Postman, The (1997)
## 899 Winter Guest, The (1997)
## 900 Kundun (1997)
## 901 Mr. Magoo (1997)
## 902 Big Lebowski, The (1998)
## 903 Afterglow (1997)
## 904 Ma vie en rose (My Life in Pink) (1997)
## 905 Great Expectations (1998)
## 906 Oscar & Lucinda (1997)
## 907 Vermin (1998)
## 908 Half Baked (1998)
## 909 Dangerous Beauty (1998)
## 910 Nil By Mouth (1997)
## 911 Twilight (1998)
## 912 U.S. Marshalls (1998)
## 913 Love and Death on Long Island (1997)
## 914 Wild Things (1998)
## 915 Primary Colors (1998)
## 916 Lost in Space (1998)
## 917 Mercury Rising (1998)
## 918 City of Angels (1998)
## 919 City of Lost Children, The (1995)
## 920 Two Bits (1995)
## 921 Farewell My Concubine (1993)
## 922 Dead Man (1995)
## 923 Raise the Red Lantern (1991)
## 924 White Squall (1996)
## 925 Unforgettable (1996)
## 926 Down Periscope (1996)
## 927 Flower of My Secret, The (Flor de mi secreto, La) (1995)
## 928 Craft, The (1996)
## 929 Harriet the Spy (1996)
## 930 Chain Reaction (1996)
## 931 Island of Dr. Moreau, The (1996)
## 932 First Kid (1996)
## 933 Funeral, The (1996)
## 934 Preacher's Wife, The (1996)
## 935 Paradise Road (1997)
## 936 Brassed Off (1996)
## 937 Thousand Acres, A (1997)
## 938 Smile Like Yours, A (1997)
## 939 Murder in the First (1995)
## 940 Airheads (1994)
## 941 With Honors (1994)
## 942 What's Love Got to Do with It (1993)
## 943 Killing Zoe (1994)
## 944 Renaissance Man (1994)
## 945 Charade (1963)
## 946 Fox and the Hound, The (1981)
## 947 Big Blue, The (Grand bleu, Le) (1988)
## 948 Booty Call (1997)
## 949 How to Make an American Quilt (1995)
## 950 Georgia (1995)
## 951 Indian in the Cupboard, The (1995)
## 952 Blue in the Face (1995)
## 953 Unstrung Heroes (1995)
## 954 Unzipped (1995)
## 955 Before Sunrise (1995)
## 956 Nobody's Fool (1994)
## 957 Pushing Hands (1992)
## 958 To Live (Huozhe) (1994)
## 959 Dazed and Confused (1993)
## 960 Naked (1993)
## 961 Orlando (1993)
## 962 Ruby in Paradise (1993)
## 963 Some Folks Call It a Sling Blade (1993)
## 964 Month by the Lake, A (1995)
## 965 Funny Face (1957)
## 966 Affair to Remember, An (1957)
## 967 Little Lord Fauntleroy (1936)
## 968 Inspector General, The (1949)
## 969 Winnie the Pooh and the Blustery Day (1968)
## 970 Hear My Song (1991)
## 971 Mediterraneo (1991)
## 972 Passion Fish (1992)
## 973 Grateful Dead (1995)
## 974 Eye for an Eye (1996)
## 975 Fear (1996)
## 976 Solo (1996)
## 977 Substitute, The (1996)
## 978 Heaven's Prisoners (1996)
## 979 Trigger Effect, The (1996)
## 980 Mother Night (1996)
## 981 Dangerous Ground (1997)
## 982 Maximum Risk (1996)
## 983 Rich Man's Wife, The (1996)
## 984 Shadow Conspiracy (1997)
## 985 Blood & Wine (1997)
## 986 Turbulence (1997)
## 987 Underworld (1997)
## 988 Beautician and the Beast, The (1997)
## 989 Cats Don't Dance (1997)
## 990 Anna Karenina (1997)
## 991 Keys to Tulsa (1997)
## 992 Head Above Water (1996)
## 993 Hercules (1997)
## 994 Last Time I Committed Suicide, The (1997)
## 995 Kiss Me, Guido (1997)
## 996 Big Green, The (1995)
## 997 Stuart Saves His Family (1995)
## 998 Cabin Boy (1994)
## 999 Clean Slate (1994)
## 1000 Lightning Jack (1994)
## 1001 Stupids, The (1996)
## 1002 Pest, The (1997)
## 1003 That Darn Cat! (1997)
## 1004 Geronimo: An American Legend (1993)
## 1005 Double vie de Véronique, La (Double Life of Veronique, The) (1991)
## 1006 Until the End of the World (Bis ans Ende der Welt) (1991)
## 1007 Waiting for Guffman (1996)
## 1008 I Shot Andy Warhol (1996)
## 1009 Stealing Beauty (1996)
## 1010 Basquiat (1996)
## 1011 2 Days in the Valley (1996)
## 1012 Private Parts (1997)
## 1013 Anaconda (1997)
## 1014 Romy and Michele's High School Reunion (1997)
## 1015 Shiloh (1997)
## 1016 Con Air (1997)
## 1017 Trees Lounge (1996)
## 1018 Tie Me Up! Tie Me Down! (1990)
## 1019 Die xue shuang xiong (Killer, The) (1989)
## 1020 Gaslight (1944)
## 1021 8 1/2 (1963)
## 1022 Fast, Cheap & Out of Control (1997)
## 1023 Fathers' Day (1997)
## 1024 Mrs. Dalloway (1997)
## 1025 Fire Down Below (1997)
## 1026 Lay of the Land, The (1997)
## 1027 Shooter, The (1995)
## 1028 Grumpier Old Men (1995)
## 1029 Jury Duty (1995)
## 1030 Beverly Hillbillies, The (1993)
## 1031 Lassie (1994)
## 1032 Little Big League (1994)
## 1033 Homeward Bound II: Lost in San Francisco (1996)
## 1034 Quest, The (1996)
## 1035 Cool Runnings (1993)
## 1036 Drop Dead Fred (1991)
## 1037 Grease 2 (1982)
## 1038 Switchback (1997)
## 1039 Hamlet (1996)
## 1040 Two if by Sea (1996)
## 1041 Forget Paris (1995)
## 1042 Just Cause (1995)
## 1043 Rent-a-Kid (1995)
## 1044 Paper, The (1994)
## 1045 Fearless (1993)
## 1046 Malice (1993)
## 1047 Multiplicity (1996)
## 1048 She's the One (1996)
## 1049 House Arrest (1996)
## 1050 Ghost and Mrs. Muir, The (1947)
## 1051 Associate, The (1996)
## 1052 Dracula: Dead and Loving It (1995)
## 1053 Now and Then (1995)
## 1054 Mr. Wrong (1996)
## 1055 Simple Twist of Fate, A (1994)
## 1056 Cronos (1992)
## 1057 Pallbearer, The (1996)
## 1058 War, The (1994)
## 1059 Don't Be a Menace to South Central While Drinking Your Juice in the Hood (1996)
## 1060 Adventures of Pinocchio, The (1996)
## 1061 Evening Star, The (1996)
## 1062 Four Days in September (1997)
## 1063 Little Princess, A (1995)
## 1064 Crossfire (1947)
## 1065 Koyaanisqatsi (1983)
## 1066 Balto (1995)
## 1067 Bottle Rocket (1996)
## 1068 Star Maker, The (Uomo delle stelle, L') (1995)
## 1069 Amateur (1994)
## 1070 Living in Oblivion (1995)
## 1071 Party Girl (1995)
## 1072 Pyromaniac's Love Story, A (1995)
## 1073 Shallow Grave (1994)
## 1074 Reality Bites (1994)
## 1075 Man of No Importance, A (1994)
## 1076 Pagemaster, The (1994)
## 1077 Love and a .45 (1994)
## 1078 Oliver & Company (1988)
## 1079 Joe's Apartment (1996)
## 1080 Celestial Clockwork (1994)
## 1081 Curdled (1996)
## 1082 Female Perversions (1996)
## 1083 Albino Alligator (1996)
## 1084 Anne Frank Remembered (1995)
## 1085 Carried Away (1996)
## 1086 It's My Party (1995)
## 1087 Bloodsport 2 (1995)
## 1088 Double Team (1997)
## 1089 Speed 2: Cruise Control (1997)
## 1090 Sliver (1993)
## 1091 Pete's Dragon (1977)
## 1092 Dear God (1996)
## 1093 Live Nude Girls (1995)
## 1094 Thin Line Between Love and Hate, A (1996)
## 1095 High School High (1996)
## 1096 Commandments (1997)
## 1097 Hate (Haine, La) (1995)
## 1098 Flirting With Disaster (1996)
## 1099 Red Firecracker, Green Firecracker (1994)
## 1100 What Happened Was... (1994)
## 1101 Six Degrees of Separation (1993)
## 1102 Two Much (1996)
## 1103 Trust (1990)
## 1104 C'est arrivé près de chez vous (1992)
## 1105 Firestorm (1998)
## 1106 Newton Boys, The (1998)
## 1107 Beyond Rangoon (1995)
## 1108 Feast of July (1995)
## 1109 Death and the Maiden (1994)
## 1110 Tank Girl (1995)
## 1111 Double Happiness (1994)
## 1112 Cobb (1994)
## 1113 Mrs. Parker and the Vicious Circle (1994)
## 1114 Faithful (1996)
## 1115 Twelfth Night (1996)
## 1116 Mark of Zorro, The (1940)
## 1117 Surviving Picasso (1996)
## 1118 Up in Smoke (1978)
## 1119 Some Kind of Wonderful (1987)
## 1120 I'm Not Rappaport (1996)
## 1121 Umbrellas of Cherbourg, The (Parapluies de Cherbourg, Les) (1964)
## 1122 They Made Me a Criminal (1939)
## 1123 Last Time I Saw Paris, The (1954)
## 1124 Farewell to Arms, A (1932)
## 1125 Innocents, The (1961)
## 1126 Old Man and the Sea, The (1958)
## 1127 Truman Show, The (1998)
## 1128 Heidi Fleiss: Hollywood Madam (1995)
## 1129 Chungking Express (1994)
## 1130 Jupiter's Wife (1994)
## 1131 Safe (1995)
## 1132 Feeling Minnesota (1996)
## 1133 Escape to Witch Mountain (1975)
## 1134 Get on the Bus (1996)
## 1135 Doors, The (1991)
## 1136 Ghosts of Mississippi (1996)
## 1137 Beautiful Thing (1996)
## 1138 Best Men (1997)
## 1139 Hackers (1995)
## 1140 Road to Wellville, The (1994)
## 1141 War Room, The (1993)
## 1142 When We Were Kings (1996)
## 1143 Hard Eight (1996)
## 1144 Quiet Room, The (1996)
## 1145 Blue Chips (1994)
## 1146 Calendar Girl (1993)
## 1147 My Family (1995)
## 1148 Tom & Viv (1994)
## 1149 Walkabout (1971)
## 1150 Last Dance (1996)
## 1151 Original Gangstas (1996)
## 1152 In Love and War (1996)
## 1153 Backbeat (1993)
## 1154 Alphaville (1965)
## 1155 Rendezvous in Paris (Rendez-vous de Paris, Les) (1995)
## 1156 Cyclo (1995)
## 1157 Relic, The (1997)
## 1158 Fille seule, La (A Single Girl) (1995)
## 1159 Stalker (1979)
## 1160 Love! Valour! Compassion! (1997)
## 1161 Palookaville (1996)
## 1162 Phat Beach (1996)
## 1163 Portrait of a Lady, The (1996)
## 1164 Zeus and Roxanne (1997)
## 1165 Big Bully (1996)
## 1166 Love & Human Remains (1993)
## 1167 Sum of Us, The (1994)
## 1168 Little Buddha (1993)
## 1169 Fresh (1994)
## 1170 Spanking the Monkey (1994)
## 1171 Wild Reeds (1994)
## 1172 Women, The (1939)
## 1173 Bliss (1997)
## 1174 Caught (1996)
## 1175 Hugo Pool (1997)
## 1176 Welcome To Sarajevo (1997)
## 1177 Dunston Checks In (1996)
## 1178 Major Payne (1994)
## 1179 Man of the House (1995)
## 1180 I Love Trouble (1994)
## 1181 Low Down Dirty Shame, A (1994)
## 1182 Cops and Robbersons (1994)
## 1183 Cowboy Way, The (1994)
## 1184 Endless Summer 2, The (1994)
## 1185 In the Army Now (1994)
## 1186 Inkwell, The (1994)
## 1187 Switchblade Sisters (1975)
## 1188 Young Guns II (1990)
## 1189 Prefontaine (1997)
## 1190 That Old Feeling (1997)
## 1191 Letter From Death Row, A (1998)
## 1192 Boys of St. Vincent, The (1993)
## 1193 Before the Rain (Pred dozhdot) (1994)
## 1194 Once Were Warriors (1994)
## 1195 Strawberry and Chocolate (Fresa y chocolate) (1993)
## 1196 Savage Nights (Nuits fauves, Les) (1992)
## 1197 Family Thing, A (1996)
## 1198 Purple Noon (1960)
## 1199 Cemetery Man (Dellamorte Dellamore) (1994)
## 1200 Kim (1950)
## 1201 Marlene Dietrich: Shadow and Light (1996)
## 1202 Maybe, Maybe Not (Bewegte Mann, Der) (1994)
## 1203 Top Hat (1935)
## 1204 To Be or Not to Be (1942)
## 1205 Secret Agent, The (1996)
## 1206 Amos & Andrew (1993)
## 1207 Jade (1995)
## 1208 Kiss of Death (1995)
## 1209 Mixed Nuts (1994)
## 1210 Virtuosity (1995)
## 1211 Blue Sky (1994)
## 1212 Flesh and Bone (1993)
## 1213 Guilty as Sin (1993)
## 1214 In the Realm of the Senses (Ai no corrida) (1976)
## 1215 Barb Wire (1996)
## 1216 Kissed (1996)
## 1217 Assassins (1995)
## 1218 Friday (1995)
## 1219 Goofy Movie, A (1995)
## 1220 Higher Learning (1995)
## 1221 When a Man Loves a Woman (1994)
## 1222 Judgment Night (1993)
## 1223 King of the Hill (1993)
## 1224 Scout, The (1994)
## 1225 Angus (1995)
## 1226 Night Falls on Manhattan (1997)
## 1227 Awfully Big Adventure, An (1995)
## 1228 Under Siege 2: Dark Territory (1995)
## 1229 Poison Ivy II (1995)
## 1230 Ready to Wear (Pret-A-Porter) (1994)
## 1231 Marked for Death (1990)
## 1232 Madonna: Truth or Dare (1991)
## 1233 Nénette et Boni (1996)
## 1234 Chairman of the Board (1998)
## 1235 Big Bang Theory, The (1994)
## 1236 Other Voices, Other Rooms (1997)
## 1237 Twisted (1996)
## 1238 Full Speed (1996)
## 1239 Cutthroat Island (1995)
## 1240 Ghost in the Shell (Kokaku kidotai) (1995)
## 1241 Van, The (1996)
## 1242 Old Lady Who Walked in the Sea, The (Vieille qui marchait dans la mer, La) (1991)
## 1243 Night Flier (1997)
## 1244 Metro (1997)
## 1245 Gridlock'd (1997)
## 1246 Bushwhacked (1995)
## 1247 Bad Girls (1994)
## 1248 Blink (1994)
## 1249 For Love or Money (1993)
## 1250 Best of the Best 3: No Turning Back (1995)
## 1251 A Chef in Love (1996)
## 1252 Contempt (Mépris, Le) (1963)
## 1253 Tie That Binds, The (1995)
## 1254 Gone Fishin' (1997)
## 1255 Broken English (1996)
## 1256 Designated Mourner, The (1997)
## 1257 Designated Mourner, The (1997)
## 1258 Trial and Error (1997)
## 1259 Pie in the Sky (1995)
## 1260 Total Eclipse (1995)
## 1261 Run of the Country, The (1995)
## 1262 Walking and Talking (1996)
## 1263 Foxfire (1996)
## 1264 Nothing to Lose (1994)
## 1265 Star Maps (1997)
## 1266 Bread and Chocolate (Pane e cioccolata) (1973)
## 1267 Clockers (1995)
## 1268 Bitter Moon (1992)
## 1269 Love in the Afternoon (1957)
## 1270 Life with Mikey (1993)
## 1271 North (1994)
## 1272 Talking About Sex (1994)
## 1273 Color of Night (1994)
## 1274 Robocop 3 (1993)
## 1275 Killer (Bulletproof Heart) (1994)
## 1276 Sunset Park (1996)
## 1277 Set It Off (1996)
## 1278 Selena (1997)
## 1279 Wild America (1997)
## 1280 Gang Related (1997)
## 1281 Manny & Lo (1996)
## 1282 Grass Harp, The (1995)
## 1283 Out to Sea (1997)
## 1284 Before and After (1996)
## 1285 Princess Caraboo (1994)
## 1286 Shall We Dance? (1937)
## 1287 Ed (1996)
## 1288 Denise Calls Up (1995)
## 1289 Jack and Sarah (1995)
## 1290 Country Life (1994)
## 1291 Celtic Pride (1996)
## 1292 Simple Wish, A (1997)
## 1293 Star Kid (1997)
## 1294 Ayn Rand: A Sense of Life (1997)
## 1295 Kicked in the Head (1997)
## 1296 Indian Summer (1996)
## 1297 Love Affair (1994)
## 1298 Band Wagon, The (1953)
## 1299 Penny Serenade (1941)
## 1300 'Til There Was You (1997)
## 1301 Stripes (1981)
## 1302 Late Bloomers (1996)
## 1303 Getaway, The (1994)
## 1304 New York Cop (1996)
## 1305 National Lampoon's Senior Trip (1995)
## 1306 Delta of Venus (1994)
## 1307 Carmen Miranda: Bananas Is My Business (1994)
## 1308 Babyfever (1994)
## 1309 Very Natural Thing, A (1974)
## 1310 Walk in the Sun, A (1945)
## 1311 Waiting to Exhale (1995)
## 1312 Pompatus of Love, The (1996)
## 1313 Palmetto (1998)
## 1314 Surviving the Game (1994)
## 1315 Inventing the Abbotts (1997)
## 1316 Horse Whisperer, The (1998)
## 1317 Journey of August King, The (1995)
## 1318 Catwalk (1995)
## 1319 Neon Bible, The (1995)
## 1320 Homage (1995)
## 1321 Open Season (1996)
## 1322 Metisse (Café au Lait) (1993)
## 1323 Wooden Man's Bride, The (Wu Kui) (1994)
## 1324 Loaded (1994)
## 1325 August (1996)
## 1326 Boys (1996)
## 1327 Captives (1994)
## 1328 Of Love and Shadows (1994)
## 1329 Low Life, The (1994)
## 1330 An Unforgettable Summer (1994)
## 1331 Last Klezmer: Leopold Kozlowski, His Life and Music, The (1995)
## 1332 My Life and Times With Antonin Artaud (En compagnie d'Antonin Artaud) (1993)
## 1333 Midnight Dancers (Sibak) (1994)
## 1334 Somebody to Love (1994)
## 1335 American Buffalo (1996)
## 1336 Kazaam (1996)
## 1337 Larger Than Life (1996)
## 1338 Two Deaths (1995)
## 1339 Stefano Quantestorie (1993)
## 1340 Crude Oasis, The (1995)
## 1341 Hedd Wyn (1992)
## 1342 Convent, The (Convento, O) (1995)
## 1343 Lotto Land (1995)
## 1344 Story of Xinghua, The (1993)
## 1345 Day the Sun Turned Cold, The (Tianguo niezi) (1994)
## 1346 Dingo (1992)
## 1347 Ballad of Narayama, The (Narayama Bushiko) (1958)
## 1348 Every Other Weekend (1990)
## 1349 Mille bolle blu (1993)
## 1350 Crows and Sparrows (1949)
## 1351 Lover's Knot (1996)
## 1352 Shadow of Angels (Schatten der Engel) (1976)
## 1353 1-900 (1994)
## 1354 Venice/Venice (1992)
## 1355 Infinity (1996)
## 1356 Ed's Next Move (1996)
## 1357 For the Moment (1994)
## 1358 The Deadly Cure (1996)
## 1359 Boys in Venice (1996)
## 1360 Sexual Life of the Belgians, The (1994)
## 1361 Search for One-eye Jimmy, The (1996)
## 1362 American Strays (1996)
## 1363 Leopard Son, The (1996)
## 1364 Bird of Prey (1996)
## 1365 Johnny 100 Pesos (1993)
## 1366 JLG/JLG - autoportrait de décembre (1994)
## 1367 Faust (1994)
## 1368 Mina Tannenbaum (1994)
## 1369 Forbidden Christ, The (Cristo proibito, Il) (1950)
## 1370 I Can't Sleep (J'ai pas sommeil) (1994)
## 1371 Machine, The (1994)
## 1372 Stranger, The (1994)
## 1373 Good Morning (1971)
## 1374 Falling in Love Again (1980)
## 1375 Cement Garden, The (1993)
## 1376 Meet Wally Sparks (1997)
## 1377 Hotel de Love (1996)
## 1378 Rhyme & Reason (1997)
## 1379 Love and Other Catastrophes (1996)
## 1380 Hollow Reed (1996)
## 1381 Losing Chase (1996)
## 1382 Bonheur, Le (1965)
## 1383 Second Jungle Book: Mowgli & Baloo, The (1997)
## 1384 Squeeze (1996)
## 1385 Roseanna's Grave (For Roseanna) (1997)
## 1386 Tetsuo II: Body Hammer (1992)
## 1387 Fall (1997)
## 1388 Gabbeh (1996)
## 1389 Mondo (1996)
## 1390 Innocent Sleep, The (1995)
## 1391 For Ever Mozart (1996)
## 1392 Locusts, The (1997)
## 1393 Stag (1997)
## 1394 Swept from the Sea (1997)
## 1395 Hurricane Streets (1998)
## 1396 Stonewall (1995)
## 1397 Of Human Bondage (1934)
## 1398 Anna (1996)
## 1399 Stranger in the House (1997)
## 1400 Picture Bride (1995)
## 1401 M. Butterfly (1993)
## 1402 Ciao, Professore! (1993)
## 1403 Caro Diario (Dear Diary) (1994)
## 1404 Withnail and I (1987)
## 1405 Boy's Life 2 (1997)
## 1406 When Night Is Falling (1995)
## 1407 Specialist, The (1994)
## 1408 Gordy (1995)
## 1409 Swan Princess, The (1994)
## 1410 Harlem (1993)
## 1411 Barbarella (1968)
## 1412 Land Before Time III: The Time of the Great Giving (1995) (V)
## 1413 Street Fighter (1994)
## 1414 Coldblooded (1995)
## 1415 Next Karate Kid, The (1994)
## 1416 No Escape (1994)
## 1417 Turning, The (1992)
## 1418 Joy Luck Club, The (1993)
## 1419 Highlander III: The Sorcerer (1994)
## 1420 Gilligan's Island: The Movie (1998)
## 1421 My Crazy Life (Mi vida loca) (1993)
## 1422 Suture (1993)
## 1423 Walking Dead, The (1995)
## 1424 I Like It Like That (1994)
## 1425 I'll Do Anything (1994)
## 1426 Grace of My Heart (1996)
## 1427 Drunks (1995)
## 1428 SubUrbia (1997)
## 1429 Sliding Doors (1998)
## 1430 Ill Gotten Gains (1997)
## 1431 Legal Deceit (1997)
## 1432 Mighty, The (1998)
## 1433 Men of Means (1998)
## 1434 Shooting Fish (1997)
## 1435 Steal Big, Steal Little (1995)
## 1436 Mr. Jones (1993)
## 1437 House Party 3 (1994)
## 1438 Panther (1995)
## 1439 Jason's Lyric (1994)
## 1440 Above the Rim (1994)
## 1441 Moonlight and Valentino (1995)
## 1442 Scarlet Letter, The (1995)
## 1443 8 Seconds (1994)
## 1444 That Darn Cat! (1965)
## 1445 Ladybird Ladybird (1994)
## 1446 Bye Bye, Love (1995)
## 1447 Century (1993)
## 1448 My Favorite Season (1993)
## 1449 Pather Panchali (1955)
## 1450 Golden Earrings (1947)
## 1451 Foreign Correspondent (1940)
## 1452 Lady of Burlesque (1943)
## 1453 Angel on My Shoulder (1946)
## 1454 Angel and the Badman (1947)
## 1455 Outlaw, The (1943)
## 1456 Beat the Devil (1954)
## 1457 Love Is All There Is (1996)
## 1458 Damsel in Distress, A (1937)
## 1459 Madame Butterfly (1995)
## 1460 Sleepover (1995)
## 1461 Here Comes Cookie (1935)
## 1462 Thieves (Voleurs, Les) (1996)
## 1463 Boys, Les (1997)
## 1464 Stars Fell on Henrietta, The (1995)
## 1465 Last Summer in the Hamptons (1995)
## 1466 Margaret's Museum (1995)
## 1467 Saint of Fort Washington, The (1993)
## 1468 Cure, The (1995)
## 1469 Tom and Huck (1995)
## 1470 Gumby: The Movie (1995)
## 1471 Hideaway (1995)
## 1472 Visitors, The (Visiteurs, Les) (1993)
## 1473 Little Princess, The (1939)
## 1474 Nina Takes a Lover (1994)
## 1475 Bhaji on the Beach (1993)
## 1476 Raw Deal (1948)
## 1477 Nightwatch (1997)
## 1478 Dead Presidents (1995)
## 1479 Reckless (1995)
## 1480 Herbie Rides Again (1974)
## 1481 S.F.W. (1994)
## 1482 Gate of Heavenly Peace, The (1995)
## 1483 Man in the Iron Mask, The (1998)
## 1484 Jerky Boys, The (1994)
## 1485 Colonel Chabert, Le (1994)
## 1486 Girl in the Cadillac (1995)
## 1487 Even Cowgirls Get the Blues (1993)
## 1488 Germinal (1993)
## 1489 Chasers (1994)
## 1490 Fausto (1993)
## 1491 Tough and Deadly (1995)
## 1492 Window to Paris (1994)
## 1493 Modern Affair, A (1995)
## 1494 Mostro, Il (1994)
## 1495 Flirt (1995)
## 1496 Carpool (1996)
## 1497 Line King: Al Hirschfeld, The (1996)
## 1498 Farmer & Chase (1995)
## 1499 Grosse Fatigue (1994)
## 1500 Santa with Muscles (1996)
## 1501 Prisoner of the Mountains (Kavkazsky Plennik) (1996)
## 1502 Naked in New York (1994)
## 1503 Gold Diggers: The Secret of Bear Mountain (1995)
## 1504 Bewegte Mann, Der (1994)
## 1505 Killer: A Journal of Murder (1995)
## 1506 Nelly & Monsieur Arnaud (1995)
## 1507 Three Lives and Only One Death (1996)
## 1508 Babysitter, The (1995)
## 1509 Getting Even with Dad (1994)
## 1510 Mad Dog Time (1996)
## 1511 Children of the Revolution (1996)
## 1512 World of Apu, The (Apur Sansar) (1959)
## 1513 Sprung (1997)
## 1514 Dream With the Fishes (1997)
## 1515 Wings of Courage (1995)
## 1516 Wedding Gift, The (1994)
## 1517 Race the Sun (1996)
## 1518 Losing Isaiah (1995)
## 1519 New Jersey Drive (1995)
## 1520 Fear, The (1995)
## 1521 Mr. Wonderful (1993)
## 1522 Trial by Jury (1994)
## 1523 Good Man in Africa, A (1994)
## 1524 Kaspar Hauser (1993)
## 1525 Object of My Affection, The (1998)
## 1526 Witness (1985)
## 1527 Senseless (1998)
## 1528 Nowhere (1997)
## 1529 Underground (1995)
## 1530 Jefferson in Paris (1995)
## 1531 Far From Home: The Adventures of Yellow Dog (1995)
## 1532 Foreign Student (1994)
## 1533 I Don't Want to Talk About It (De eso no se habla) (1993)
## 1534 Twin Town (1997)
## 1535 Enfer, L' (1994)
## 1536 Aiqing wansui (1994)
## 1537 Cosi (1996)
## 1538 All Over Me (1997)
## 1539 Being Human (1993)
## 1540 Amazing Panda Adventure, The (1995)
## 1541 Beans of Egypt, Maine, The (1994)
## 1542 Scarlet Letter, The (1926)
## 1543 Johns (1996)
## 1544 It Takes Two (1995)
## 1545 Frankie Starlight (1995)
## 1546 Shadows (Cienie) (1988)
## 1547 Show, The (1995)
## 1548 The Courtyard (1995)
## 1549 Dream Man (1995)
## 1550 Destiny Turns on the Radio (1995)
## 1551 Glass Shield, The (1994)
## 1552 Hunted, The (1995)
## 1553 Underneath, The (1995)
## 1554 Safe Passage (1994)
## 1555 Secret Adventures of Tom Thumb, The (1993)
## 1556 Condition Red (1995)
## 1557 Yankee Zulu (1994)
## 1558 Aparajito (1956)
## 1559 Hostile Intentions (1994)
## 1560 Clean Slate (Coup de Torchon) (1981)
## 1561 Tigrero: A Film That Was Never Made (1994)
## 1562 Eye of Vichy, The (Oeil de Vichy, L') (1993)
## 1563 Promise, The (Versprechen, Das) (1994)
## 1564 To Cross the Rubicon (1991)
## 1565 Daens (1992)
## 1566 Man from Down Under, The (1943)
## 1567 Careful (1992)
## 1568 Vermont Is For Lovers (1992)
## 1569 Vie est belle, La (Life is Rosey) (1987)
## 1570 Quartier Mozart (1992)
## 1571 Touki Bouki (Journey of the Hyena) (1973)
## 1572 Wend Kuuni (God's Gift) (1982)
## 1573 Spirits of the Dead (Tre passi nel delirio) (1968)
## 1574 Pharaoh's Army (1995)
## 1575 I, Worst of All (Yo, la peor de todas) (1990)
## 1576 Hungarian Fairy Tale, A (1987)
## 1577 Death in the Garden (Mort en ce jardin, La) (1956)
## 1578 Collectionneuse, La (1967)
## 1579 Baton Rouge (1988)
## 1580 Liebelei (1933)
## 1581 Woman in Question, The (1950)
## 1582 T-Men (1947)
## 1583 Invitation, The (Zaproszenie) (1986)
## 1584 Symphonie pastorale, La (1946)
## 1585 American Dream (1990)
## 1586 Lashou shentan (1992)
## 1587 Terror in a Texas Town (1958)
## 1588 Salut cousin! (1996)
## 1589 Schizopolis (1996)
## 1590 To Have, or Not (1995)
## 1591 Duoluo tianshi (1995)
## 1592 Magic Hour, The (1998)
## 1593 Death in Brunswick (1991)
## 1594 Everest (1998)
## 1595 Shopping (1994)
## 1596 Nemesis 2: Nebula (1995)
## 1597 Romper Stomper (1992)
## 1598 City of Industry (1997)
## 1599 Someone Else's America (1995)
## 1600 Guantanamera (1994)
## 1601 Office Killer (1997)
## 1602 Price Above Rubies, A (1998)
## 1603 Angela (1995)
## 1604 He Walked by Night (1948)
## 1605 Love Serenade (1996)
## 1606 Deceiver (1997)
## 1607 Hurricane Streets (1998)
## 1608 Buddy (1997)
## 1609 B*A*P*S (1997)
## 1610 Truth or Consequences, N.M. (1997)
## 1611 Intimate Relations (1996)
## 1612 Leading Man, The (1996)
## 1613 Tokyo Fist (1995)
## 1614 Reluctant Debutante, The (1958)
## 1615 Warriors of Virtue (1997)
## 1616 Desert Winds (1995)
## 1617 Hugo Pool (1997)
## 1618 King of New York (1990)
## 1619 All Things Fair (1996)
## 1620 Sixth Man, The (1997)
## 1621 Butterfly Kiss (1995)
## 1622 Paris, France (1993)
## 1623 Cérémonie, La (1995)
## 1624 Hush (1998)
## 1625 Nightwatch (1997)
## 1626 Nobody Loves Me (Keiner liebt mich) (1994)
## 1627 Wife, The (1995)
## 1628 Lamerica (1994)
## 1629 Nico Icon (1995)
## 1630 Silence of the Palace, The (Saimt el Qusur) (1994)
## 1631 Slingshot, The (1993)
## 1632 Land and Freedom (Tierra y libertad) (1995)
## 1633 Á köldum klaka (Cold Fever) (1994)
## 1634 Etz Hadomim Tafus (Under the Domin Tree) (1994)
## 1635 Two Friends (1986)
## 1636 Brothers in Trouble (1995)
## 1637 Girls Town (1996)
## 1638 Normal Life (1996)
## 1639 Bitter Sugar (Azucar Amargo) (1996)
## 1640 Eighth Day, The (1996)
## 1641 Dadetown (1995)
## 1642 Some Mother's Son (1996)
## 1643 Angel Baby (1995)
## 1644 Sudden Manhattan (1996)
## 1645 Butcher Boy, The (1998)
## 1646 Men With Guns (1997)
## 1647 Hana-bi (1997)
## 1648 Niagara, Niagara (1997)
## 1649 Big One, The (1997)
## 1650 Butcher Boy, The (1998)
## 1651 Spanish Prisoner, The (1997)
## 1652 Temptress Moon (Feng Yue) (1996)
## 1653 Entertaining Angels: The Dorothy Day Story (1996)
## 1654 Chairman of the Board (1998)
## 1655 Favor, The (1994)
## 1656 Little City (1998)
## 1657 Target (1995)
## 1658 Substance of Fire, The (1996)
## 1659 Getting Away With Murder (1996)
## 1660 Small Faces (1995)
## 1661 New Age, The (1994)
## 1662 Rough Magic (1995)
## 1663 Nothing Personal (1995)
## 1664 8 Heads in a Duffel Bag (1997)
## 1665 Brother's Kiss, A (1997)
## 1666 Ripe (1996)
## 1667 Next Step, The (1995)
## 1668 Wedding Bell Blues (1996)
## 1669 MURDER and murder (1996)
## 1670 Tainted (1998)
## 1671 Further Gesture, A (1996)
## 1672 Kika (1993)
## 1673 Mirage (1995)
## 1674 Mamma Roma (1962)
## 1675 Sunchaser, The (1996)
## 1676 War at Home, The (1996)
## 1677 Sweet Nothing (1995)
## 1678 Mat' i syn (1997)
## 1679 B. Monkey (1998)
## 1680 Sliding Doors (1998)
## 1681 You So Crazy (1994)
## 1682 Scream of Stone (Schrei aus Stein) (1991)
## ReleaseDate video_release_date
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## 1449 http://us.imdb.com/M/title-exact?Pather%20Panchali%20(1955)
## 1450 http://us.imdb.com/M/title-exact?Golden%20Earrings%20%281947%29
## 1451 http://us.imdb.com/M/title-exact?Foreign%20Correspondent%20(1940)
## 1452 http://us.imdb.com/M/title-exact?Lady%20of%20Burlesque%20(1943)
## 1453 http://us.imdb.com/M/title-exact?Angel%20on%20My%20Shoulder%20(1946)
## 1454 http://us.imdb.com/M/title-exact?Angel%20and%20the%20Badman%20(1947)
## 1455 http://us.imdb.com/M/title-exact?Outlaw,%20The%20(1943)
## 1456 http://us.imdb.com/M/title-exact?Beat%20the%20Devil%20(1954)
## 1457 http://us.imdb.com/M/title-exact?Love%20Is%20All%20There%20Is%20(1996)
## 1458 http://us.imdb.com/M/title-exact?Damsel%20in%20Distress,%20A%20(1937)
## 1459 http://us.imdb.com/M/title-exact?Madame%20Butterfly%20(1995)
## 1460 http://us.imdb.com/M/title-exact?Sleepover%20(1995)
## 1461 http://us.imdb.com/M/title-exact?Here%20Comes%20Cookie%20(1935)
## 1462 http://us.imdb.com/M/title-exact?Voleurs,%20Les%20(1996)
## 1463 http://us.imdb.com/M/title-exact?imdb-title-118764
## 1464 http://us.imdb.com/Title?Stars+Fell+on+Henrietta,+The+(1995)
## 1465 http://us.imdb.com/M/title-exact?Last%20Summer%20in%20the%20Hamptons%20(1995)
## 1466 http://us.imdb.com/M/title-exact?Margaret's%20Museum%20(1995)
## 1467 http://us.imdb.com/M/title-exact?Saint%20of%20Fort%20Washington,%20The%20(1993)
## 1468 http://us.imdb.com/M/title-exact?Cure,%20The%20(1995)
## 1469 http://us.imdb.com/M/title-exact?Tom%20and%20Huck%20(1995)
## 1470 http://us.imdb.com/M/title-exact?Gumby:%20The%20Movie%20(1995)
## 1471 http://us.imdb.com/M/title-exact?Hideaway%20(1995)
## 1472 http://us.imdb.com/M/title-exact?Visiteurs,%20Les%20(1993)
## 1473 http://us.imdb.com/M/title-exact?Little%20Princess,%20The%20(1939)
## 1474 http://us.imdb.com/M/title-exact?Nina%20Takes%20a%20Lover%20(1994)
## 1475 http://us.imdb.com/M/title-exact?Bhaji%20on%20the%20Beach%20(1993)
## 1476 http://us.imdb.com/M/title-exact?Raw%20Deal%20(1948)
## 1477 http://us.imdb.com/M/title-exact?Nightwatch%20(1997)
## 1478 http://us.imdb.com/M/title-exact?Dead%20Presidents%20(1995)
## 1479 http://us.imdb.com/Title?Reckless+(1995/I)
## 1480 http://us.imdb.com/M/title-exact?Herbie%20Rides%20Again%20(1974)
## 1481 http://us.imdb.com/M/title-exact?S.F.W.%20(1994)
## 1482 http://us.imdb.com/M/title-exact?Gate%20of%20Heavenly%20Peace,%20The%20(1995)
## 1483 http://us.imdb.com/Title?Man+in+the+Iron+Mask,+The+(1998/I)
## 1484 http://us.imdb.com/M/title-exact?Jerky%20Boys,%20The%20(1994)
## 1485 http://us.imdb.com/M/title-exact?Colonel%20Chabert,%20Le%20(1994)
## 1486 http://us.imdb.com/M/title-exact?Girl%20in%20the%20Cadillac%20(1995)
## 1487 http://us.imdb.com/M/title-exact?Even%20Cowgirls%20Get%20the%20Blues%20(1993)
## 1488 http://us.imdb.com/M/title-exact?Germinal%20(1993)
## 1489 http://us.imdb.com/M/title-exact?Chasers%20(1994)
## 1490 http://us.imdb.com/M/title-exact?Fausto%20%281993%29
## 1491 http://us.imdb.com/M/title-exact?Tough%20and%20Deadly%20(1995)
## 1492 http://us.imdb.com/Title?Okno+v+Parizh+(1994)
## 1493 http://us.imdb.com/M/title-exact?Modern%20Affair,%20A%20(1995)
## 1494 http://us.imdb.com/M/title-exact?Mostro,%20Il%20(1994)
## 1495 http://us.imdb.com/Title?Flirt+(1995/I)
## 1496 http://us.imdb.com/M/title-exact?Carpool%20(1996)
## 1497 http://us.imdb.com/M/title-exact?Line%20King,%20The%20(1996)
## 1498 http://us.imdb.com/M/title-exact?Farmer%20&%20Chase%20(1995)
## 1499 http://us.imdb.com/M/title-exact?Grosse%20fatigue%20(1994)
## 1500 http://us.imdb.com/M/title-exact?Santa%20with%20Muscles%20(1996)
## 1501 http://us.imdb.com/M/title-exact?Kavkazsky%20Plennik%20(1996)
## 1502 http://us.imdb.com/Title?Naked+in+New+York+(1994)
## 1503 http://us.imdb.com/M/title-exact?Gold%20Diggers:%20The%20Secret%20of%20Bear%20Mountain%20(1995)
## 1504 http://us.imdb.com/M/title-exact?Bewegte%20Mann%2C%20Der%20%281994%29
## 1505 http://us.imdb.com/M/title-exact?Killer:%20A%20Journal%20of%20Murder%20(1995)
## 1506 http://us.imdb.com/M/title-exact?Nelly%20%26%20Monsieur%20Arnaud%20%281995%29
## 1507 http://us.imdb.com/M/title-exact?Trois%20vies%20et%20une%20seule%20mort%20(1996)
## 1508 http://us.imdb.com/M/title-exact?Babysitter,%20The%20(1995)
## 1509 http://us.imdb.com/M/title-exact?Getting%20Even%20with%20Dad%20(1994)
## 1510 http://us.imdb.com/M/title-exact?Mad%20Dog%20Time%20(1996)
## 1511 http://us.imdb.com/M/title-exact?Children%20of%20the%20Revolution%20%281996%29
## 1512 http://us.imdb.com/M/title-exact?Apur%20Sansar%20(1959)
## 1513 http://us.imdb.com/M/title-exact?Sprung%20%281997%29
## 1514 http://us.imdb.com/M/title-exact?Dream+With+the+Fishes+(1997)
## 1515 http://us.imdb.com/M/title-exact?Wings%20of%20Courage%20(1995)
## 1516 http://us.imdb.com/M/title-exact?Wedding%20Gift,%20The%20(1994)%20(TV)
## 1517 http://us.imdb.com/M/title-exact?Race%20the%20Sun%20(1996)
## 1518 http://us.imdb.com/M/title-exact?Losing%20Isaiah%20(1995)
## 1519 http://us.imdb.com/M/title-exact?New%20Jersey%20Drive%20(1995)
## 1520 http://us.imdb.com/M/title-exact?Fear,%20The%20(1995)
## 1521 http://us.imdb.com/M/title-exact?Mr.%20Wonderful%20(1993)
## 1522 http://us.imdb.com/M/title-exact?Trial%20by%20Jury%20(1994)
## 1523 http://us.imdb.com/M/title-exact?Good%20Man%20in%20Africa,%20A%20(1994)
## 1524 http://us.imdb.com/Title?Kaspar+Hauser+(1993)
## 1525 http://us.imdb.com/Title?Object+of+My+Affection,+The+(1998)
## 1526 http://us.imdb.com/M/title-exact?Witness+(1985)
## 1527 http://us.imdb.com/M/title-exact?imdb-title-120820
## 1528 http://us.imdb.com/M/title-exact?Nowhere%20%281997%29
## 1529 http://us.imdb.com/M/title-exact?Underground%20(1995)
## 1530 http://us.imdb.com/M/title-exact?Jefferson%20in%20Paris%20(1995)
## 1531 http://us.imdb.com/M/title-exact?Far%20From%20Home:%20The%20Adventures%20of%20Yellow%20Dog%20(1995)
## 1532 http://us.imdb.com/M/title-exact?Foreign%20Student%20(1994)
## 1533 http://us.imdb.com/M/title-exact?De%20Eso%20No%20Se%20Habla%20(1993)
## 1534 http://us.imdb.com/M/title-exact?Twin%20Town%20%281997%29
## 1535 http://us.imdb.com/M/title-exact?Enfer,%20L'%20(1994)
## 1536 http://us.imdb.com/M/title-exact?Aiqing%20Wansui%20(1994)
## 1537 http://us.imdb.com/M/title-exact?Cosi%20(1996)
## 1538 http://us.imdb.com/M/title-exact?All%20Over%20Me%20%281997%29
## 1539 http://us.imdb.com/M/title-exact?Being%20Human%20(1993)
## 1540 http://us.imdb.com/M/title-exact?Amazing%20Panda%20Adventure,%20The%20(1995)
## 1541 http://us.imdb.com/M/title-exact?Beans%20of%20Egypt,%20Maine,%20The%20(1994)
## 1542 http://us.imdb.com/M/title-exact?Scarlet%20Letter,%20The%20(1926)
## 1543 http://us.imdb.com/M/title-exact?Johns%20(1996)
## 1544 http://us.imdb.com/M/title-exact?It%20Takes%20Two%20(1995)
## 1545 http://us.imdb.com/M/title-exact?Frankie%20Starlight%20(1995)
## 1546 http://us.imdb.com/M/title-exact?Cienie%20(1988)
## 1547 http://us.imdb.com/M/title-exact?Show,%20The%20(1995)
## 1548 http://us.imdb.com/M/title-exact?Courtyard,%20The%20(1995)%20(TV)
## 1549 http://us.imdb.com/M/title-exact?Dream%20Man%20(1995)
## 1550 http://us.imdb.com/M/title-exact?Destiny%20Turns%20on%20the%20Radio%20(1995)
## 1551 http://us.imdb.com/M/title-exact?Glass%20Shield,%20The%20(1994)
## 1552 http://us.imdb.com/M/title-exact?Hunted,%20The%20(1995)
## 1553 http://us.imdb.com/M/title-exact?Underneath,%20The%20(1995)
## 1554 http://us.imdb.com/M/title-exact?Safe%20Passage%20(1994)
## 1555 http://us.imdb.com/M/title-exact?Secret%20Adventures%20of%20Tom%20Thumb,%20The%20(1993)
## 1556 http://us.imdb.com/M/title-exact?Condition%20Red%20(1995)
## 1557 http://us.imdb.com/M/title-exact?Yankee%20Zulu%20(1994)
## 1558 http://us.imdb.com/M/title-exact?Aparajito%20(1956)
## 1559 http://us.imdb.com/M/title-exact?Hostile%20Intentions%20(1994)
## 1560 http://us.imdb.com/M/title-exact?Coup%20de%20torchon%20(1981)
## 1561 http://us.imdb.com/M/title-exact?Tigrero:%20A%20Film%20That%20Was%20Never%20Made%20(1994)
## 1562 http://us.imdb.com/M/title-exact?Oeil%20de%20Vichy,%20L'%20(1993)
## 1563 http://us.imdb.com/M/title-exact?Versprechen,%20Das%20(1994)
## 1564 http://us.imdb.com/M/title-exact?To%20Cross%20the%20Rubicon%20(1991)
## 1565 http://us.imdb.com/M/title-exact?Daens%20(1992)
## 1566 http://us.imdb.com/Title?Man+from+Down+Under,+The+(1943)
## 1567 http://us.imdb.com/M/title-exact?Careful%20(1992)
## 1568 http://us.imdb.com/M/title-exact?Vermont%20Is%20For%20Lovers%20(1992)
## 1569 http://us.imdb.com/M/title-exact?Vie%20est%20belle,%20La%20(1987)
## 1570 http://us.imdb.com/M/title-exact?Quartier%20Mozart%20(1992)
## 1571 http://us.imdb.com/M/title-exact?Touki%20Bouki%20(1973)
## 1572 http://us.imdb.com/M/title-exact?Wend%20Kuuni%20(1982)
## 1573 http://us.imdb.com/M/title-exact?Tre%20passi%20nel%20delirio%20(1968)
## 1574 http://us.imdb.com/M/title-exact?Pharaoh's%20Army%20(1995)
## 1575 http://us.imdb.com/M/title-exact?Yo,%20la%20Peor%20de%20Todas%20(1990)
## 1576 http://us.imdb.com/M/title-exact?Hol%20volt,%20hol%20nem%20volt%20(1987)
## 1577 http://us.imdb.com/Title?Mort+en+ce+jardin,+La+(1956)
## 1578 http://us.imdb.com/M/title-exact?Collectionneuse,%20La%20(1967)
## 1579 http://us.imdb.com/Title?B%E2ton+rouge+(1988)
## 1580 http://us.imdb.com/M/title-exact?Liebelei%20(1933)
## 1581 http://us.imdb.com/M/title-exact?Woman%20in%20Question,%20The%20(1950)
## 1582 http://us.imdb.com/M/title-exact?T-Men%20(1947)
## 1583 http://us.imdb.com/M/title-exact?Zaproszenie%20(1986)
## 1584 http://us.imdb.com/M/title-exact?Symphonie%20pastorale,%20La%20(1946)
## 1585 http://us.imdb.com/M/title-exact?American%20Dream%20(1990)
## 1586 http://us.imdb.com/M/title-exact?Lashou%20Shentan%20(1992)
## 1587 http://us.imdb.com/M/title-exact?Terror%20in%20a%20Texas%20Town%20(1958)
## 1588 http://us.imdb.com/M/title-exact?Salut%20cousin!%20(1996)
## 1589 http://us.imdb.com/Title?Schizopolis+(1996)
## 1590 http://us.imdb.com/M/title-exact?En%20avoir%20%28ou%20pas%29%20%281995%29
## 1591 http://us.imdb.com/M/title-exact?imdb-title-112913
## 1592 http://us.imdb.com/M/title-exact?imdb-title-119594
## 1593 http://us.imdb.com/M/title-exact?Death%20in%20Brunswick%20(1991)
## 1594 http://us.imdb.com/Title?Everest+(1998)
## 1595 http://us.imdb.com/M/title-exact?Shopping%20(1994)
## 1596 http://us.imdb.com/M/title-exact?Nemesis%202:%20Nebula%20(1995)
## 1597 http://us.imdb.com/M/title-exact?Romper%20Stomper%20(1992)
## 1598 http://us.imdb.com/M/title-exact?City%20of%20Industry%20(1997)
## 1599 http://us.imdb.com/M/title-exact?Someone%20Else's%20America%20(1995)
## 1600 http://us.imdb.com/M/title-exact?Guantanamera%20(1994)
## 1601 http://us.imdb.com/M/title-exact?imdb-title-119819
## 1602 http://us.imdb.com/Title?Price+Above+Rubies,+A+(1998)
## 1603 http://us.imdb.com/M/title-exact?Angela%20(1995)
## 1604 http://us.imdb.com/M/title-exact?He%20Walked%20by%20Night%20(1948)
## 1605 http://us.imdb.com/M/title-exact?Love+Serenade+(1996)
## 1606 http://us.imdb.com/M/title-exact?Liar+(1997)
## 1607 http://us.imdb.com/Title?Hurricane+Streets+(1998)
## 1608 http://us.imdb.com/M/title-exact?Buddy%20%281997%29
## 1609 http://us.imdb.com/M/title-exact?B%2EA%2EP%2ES%2E%20(1997)
## 1610 http://us.imdb.com/Title?Truth+or+Consequences,+N.M.+(1997)
## 1611 http://us.imdb.com/M/title-exact?Intimate%20Relations%20%281996%29
## 1612 http://us.imdb.com/M/title-exact?imdb-title-116845
## 1613 http://us.imdb.com/M/title-exact?Tokyo+Fist+(1995)
## 1614 http://us.imdb.com/M/title-exact?Reluctant%20Debutante,%20The%20(1958)
## 1615 http://us.imdb.com/M/title-exact?Warriors%20of%20Virtue%20%281997%29
## 1616 http://us.imdb.com/M/title-exact?Desert%20Winds%20(1995)
## 1617 http://us.imdb.com/M/title-exact?Hugo+Pool+(1997)
## 1618 http://us.imdb.com/Title?King+of+New+York+(1990)
## 1619 http://us.imdb.com/Title?Lust+och+f%E4gring+stor+(1995)
## 1620 http://us.imdb.com/M/title-exact?Sixth%20Man%2C%20The%20(1997)
## 1621 http://us.imdb.com/M/title-exact?Butterfly%20Kiss%20(1995)
## 1622 http://us.imdb.com/M/title-exact?Paris,%20France%20(1993)
## 1623 http://us.imdb.com/M/title-exact?C%E9r%E9monie%2C%20La%20%281995%29
## 1624 http://us.imdb.com/Title?Hush+(1998)
## 1625 http://us.imdb.com/M/title-exact?Nightwatch%20(1997)
## 1626 http://us.imdb.com/M/title-exact?Keiner%20liebt%20mich%20(1994)
## 1627 http://us.imdb.com/Title?Wife,+The+(1995)
## 1628 http://us.imdb.com/M/title-exact?Lamerica%20(1994)
## 1629 http://us.imdb.com/M/title-exact?Nico%20Icon%20(1995)
## 1630 http://us.imdb.com/M/title-exact?Saimt%20el%20Qusur%20(1994)
## 1631 http://us.imdb.com/Title?K%E5disbellan+(1993)
## 1632 http://us.imdb.com/M/title-exact?Tierra%20y%20libertad%20(1995)
## 1633 http://us.imdb.com/Title?%C1+k%F6ldum+klaka+(1994)
## 1634 http://us.imdb.com/M/title-exact?Etz%20Hadomim%20Tafus%20(1994)
## 1635 http://us.imdb.com/M/title-exact?Two%20Friends%20(1986)%20(TV)
## 1636 http://us.imdb.com/M/title-exact?Brothers%20in%20Trouble%20(1995)
## 1637 http://us.imdb.com/M/title-exact?Girls%20Town%20(1996)
## 1638 http://us.imdb.com/M/title-exact?Normal%20Life%20(1996)
## 1639 http://us.imdb.com/M/title-exact?Bitter%20Sugar%20(1996)
## 1640 http://us.imdb.com/Title?Huiti%E8me+jour,+Le+(1996)
## 1641 http://us.imdb.com/M/title-exact?Dadetown%20(1995)
## 1642 http://us.imdb.com/M/title-exact?Some%20Mother's%20Son%20(1996)
## 1643 http://us.imdb.com/Title?Angel+Baby+(1995/I)
## 1644 http://us.imdb.com/M/title-exact?Sudden%20Manhattan%20%281996%29
## 1645 http://us.imdb.com/M/title-exact?imdb-title-118804
## 1646 http://us.imdb.com/Title?Men+with+Guns+(1997/I)
## 1647 http://us.imdb.com/Title?Hana-bi+(1997)
## 1648 http://us.imdb.com/Title?Niagara,+Niagara+(1997)
## 1649 http://us.imdb.com/Title?Big+One,+The+(1997)
## 1650 http://us.imdb.com/M/title-exact?imdb-title-118804
## 1651 http://us.imdb.com/Title?Spanish+Prisoner,+The+(1997)
## 1652 http://us.imdb.com/M/title-exact?Feng%20Yue%20%281996%29
## 1653 http://us.imdb.com/M/title-exact?Entertaining%20Angels:%20The%20Dorothy%20Day%20Story%20(1996)
## 1654 http://us.imdb.com/Title?Chairman+of+the+Board+(1998)
## 1655 http://us.imdb.com/M/title-exact?Favor,%20The%20(1994)
## 1656 http://us.imdb.com/M/title-exact?Little+City+(1998)
## 1657 http://us.imdb.com/M/title-exact?Target%20(1995)
## 1658 http://us.imdb.com/M/title-exact?Substance%20of%20Fire,%20The%20(1996)
## 1659 http://us.imdb.com/Title?Getting+Away+With+Murder+(1996)
## 1660 http://us.imdb.com/M/title-exact?Small%20Faces%20(1995)
## 1661 http://us.imdb.com/M/title-exact?New%20Age,%20The%20(1994)
## 1662 http://us.imdb.com/M/title-exact?Rough%20Magic%20%281995%29
## 1663 http://us.imdb.com/M/title-exact?Nothing%20Personal%20(1995)
## 1664 http://us.imdb.com/Title?8+Heads+in+a+Duffel+Bag+(1997)
## 1665 http://us.imdb.com/M/title-exact?Brother%27s%20Kiss%2C%20A%20%281997%29
## 1666 http://us.imdb.com/M/title-exact?Ripe%20%281996%29
## 1667 http://us.imdb.com/M/title-exact?Next%20Step%2C%20The%20%281995%29
## 1668 http://us.imdb.com/M/title-exact?Wedding%20Bell%20Blues%20%281996%29
## 1669 http://us.imdb.com/M/title-exact?MURDER+and+murder+(1996)
## 1670 http://us.imdb.com/M/title-exact?Tainted+(1998)
## 1671 http://us.imdb.com/M/title-exact?Further+Gesture%2C+A+(1996)
## 1672 http://us.imdb.com/M/title-exact?Kika%20(1993)
## 1673 http://us.imdb.com/M/title-exact?Mirage%20(1995)
## 1674 http://us.imdb.com/M/title-exact?Mamma%20Roma%20(1962)
## 1675 http://us.imdb.com/M/title-exact?Sunchaser,%20The%20(1996)
## 1676 http://us.imdb.com/M/title-exact?War%20at%20Home%2C%20The%20%281996%29
## 1677 http://us.imdb.com/M/title-exact?Sweet%20Nothing%20(1995)
## 1678 http://us.imdb.com/M/title-exact?Mat%27+i+syn+(1997)
## 1679 http://us.imdb.com/M/title-exact?B%2E+Monkey+(1998)
## 1680 http://us.imdb.com/Title?Sliding+Doors+(1998)
## 1681 http://us.imdb.com/M/title-exact?You%20So%20Crazy%20(1994)
## 1682 http://us.imdb.com/M/title-exact?Schrei%20aus%20Stein%20(1991)Eliminamos la informacion que no aporta informacion
movie_title_df$ReleaseDate <- NULL#Eliminamos la fecha de lanzamiento
movie_title_df$video_release_date <- NULL#Toda la columna es NA
movie_title_df$IMDb_URL <- NULL#Que se quede con datos unicos (no los queremos repetidos )
movie_title_df <- unique(movie_title_df)Ahora con el DF de usuarios Le ponemos encabezados
names(users_df) <- c("UserID", "MovieID", "rating", "timestamp")Elimianas las columnas que no aportan
users_df$timestamp <- NULLveamos la estructura del objeto
str(movie_title_df)## 'data.frame': 1682 obs. of 21 variables:
## $ MovieID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Title : chr "Toy Story (1995)" "GoldenEye (1995)" "Four Rooms (1995)" "Get Shorty (1995)" ...
## $ unknown : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Action : int 0 1 0 1 0 0 0 0 0 0 ...
## $ Adventure : int 0 1 0 0 0 0 0 0 0 0 ...
## $ Animation : int 1 0 0 0 0 0 0 0 0 0 ...
## $ Childrens : int 1 0 0 0 0 0 0 1 0 0 ...
## $ Comedy : int 1 0 0 1 0 0 0 1 0 0 ...
## $ Crime : int 0 0 0 0 1 0 0 0 0 0 ...
## $ Documentary: int 0 0 0 0 0 0 0 0 0 0 ...
## $ Drama : int 0 0 0 1 1 1 1 1 1 1 ...
## $ Fantasy : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FilmNoir : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Horror : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Musical : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Mystery : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Romance : int 0 0 0 0 0 0 0 0 0 0 ...
## $ SciFi : int 0 0 0 0 0 0 1 0 0 0 ...
## $ Thriller : int 0 1 1 0 1 0 0 0 0 0 ...
## $ War : int 0 0 0 0 0 0 0 0 0 1 ...
## $ Western : int 0 0 0 0 0 0 0 0 0 0 ...Creemos una funcion que nos ayude a clusterizar con kmeans
clusterMovies <- function(df, kclust = 10){
df <- df[,c(-1,-2)]#elimino el identificador y el titulo
mclust <- kmeans(df,
centers = kclust,
nstart = 20)#inicie con 20
return(mclust)
}Funcion que detecte las peliculas que un usuario vio y valoro busca en este DF este ID (pelicula), que usuarios vieron esta pelicula
getUserInfo <- function(df, id){
#subconjunto de este dato
myUser <- subset(df, UserID == id, select = c(MovieID, rating))
cluster <- 0
activeUser <- data.frame(myUser[order(myUser$MovieID),],
cluster)#cluster original del usuario
return(activeUser)
}setUserMovieCluster <- function(m_title_df, mclust, activeUser){
#haz un DF y dime a que cluster pertenece esta pelicula
df_aux <- data.frame(cbind(m_title_df$MovieID,
clustNum = mclust$cluster))
#los nombres de las columnas son :
names(df_aux) <- c("MovieID", "Cluster")
#cohincidencias que existe entre el DF original y el Df creado ya con el cluster
activeUser$cluster <- df_aux[match(activeUser$MovieID, df_aux$MovieID),2]
return(activeUser)
}Calcula el promedio para cada cluster
getAverageClusterRating <- function(mclust, activeUser, minRating = 3){
#mete en un DF donde vallas por valoracion por cada cluster que halla encontrado y haciendo la media
like <- aggregate(activeUser$rating,
by=list(cluster=activeUser$cluster),
mean)
if(max(like$x)<minRating){
like <- as.vector(0)
} else {
like <- as.vector(t(max(subset(like, x>=minRating, select=cluster))))
}
return(like)
}Obtener las peliculas recomendadas
getRecommendedMovies <- function(like, mclust, m_title_df){
df_aux <- data.frame(cbind(m_title_df$MovieID,
clusterNum = mclust$cluster)
)
names(df_aux) = c("MovieID", "Cluster")
if(like==0){
recommend <- m_title_df[sample.int(n = nrow(m_title_df),
size = 100),1]
} else {
recommend <- as.vector(
t(subset(df_aux,
Cluster == like,
select = MovieID
)
)
)
}
}Una funcion que use las funciones anteriores Df de el peliculas ,Df de usuarios y toma el usuario
getRecommendations <- function(movie_df, user_df, userID){
#que divida las peliculas
mclust <- clusterMovies(movie_df)
#informacion del usuario actual
activeUser <- getUserInfo(user_df, userID)
#anadir informacion de la pelicula
activeUser <- setUserMovieCluster(movie_df, mclust, activeUser)
# Cluster de peliculas y informacion de usuario
like <- getAverageClusterRating(mclust, activeUser)
#Recomendaciones entre gustos del usuario ,el cluster y el DF inicial
recomendation <- getRecommendedMovies(like, mclust, movie_df)
#eliminamos las películas que el usuario ya ha visto
recomendation <- recomendation[-activeUser$MovieID]
#añadimos el título de la película*hago un cluse )
movieTitle <- movie_df[match(recomendation, movie_df$MovieID),2]
recomendation <- data.frame(recomendation, movieTitle)
return(recomendation)
}sugerir Peliculas
suggestMovies <- function(movie_df, user_df, user_id, num_movies){
suggestions <- getRecommendations(movie_df, user_df, user_id)
suggestions <- suggestions[1:num_movies,]
writeLines("Tal vez te gustaría ver también las siguientes películas:")
#Imprima el nombre de la pelicula
write.table(suggestions[2], row.names = F, col.names = F)
}Ejecutamos las funciones
suggestMovies(movie_title_df, users_df, 308, 10)## Tal vez te gustaría ver también las siguientes películas:
## "Braveheart (1995)"
## "Crimson Tide (1995)"
## "Platoon (1986)"
## "Bridge on the River Kwai, The (1957)"
## "Last of the Mohicans, The (1992)"
## "Devil's Own, The (1997)"
## "Schindler's List (1993)"
## "Courage Under Fire (1996)"
## "Casablanca (1942)"
## "Rob Roy (1995)"le pedimos al sistema de recoemndacion
ab<-na.omit(movie_title_df[match(movie_title_df$MovieID,
users_df[users_df$UserID==196,]$MovieID),2])
#abSistemas hibridos : UBCF + Random Selection
library(recommenderlab)
data("MovieLense")
data_frame <- MovieLense[rowCounts(MovieLense)>50,]#usuarios nota: se puede hacer con validacion cruzada
train <- data_frame[1:100]#selecionamos los primeros 1001 usuarios
test <- data_frame[101:110]# seleccionamos de validacion del 100 al 110hacemos la recomendacion hibrida
hybrid_recom <- HybridRecommender(#ha un sistema hibido
Recommender(train, method = "UBCF"),#Lo que valoran los usuarios
Recommender(train, method = "RANDOM"), # y uno aleatorio
weights = c(0.25, 0.75)#decidimos que peso se le da a cada caso
)Predigamos para los valores de test
recomend<-as(predict(hybrid_recom, test, 3), "list")
head(recomend)[1]## $`177`
## [1] "Fallen (1998)" "Spice World (1997)" "Incognito (1997)"Medias de semejansa
1.- Distancia euclidea 2.-Distancia del coseno 3.-Correlacion de pearson
#install.packages("lsa")
library(lsa)## Loading required package: SnowballC##
## Attaching package: 'lsa'## The following object is masked from 'package:FNN':
##
## entropymtcars <- read.csv("../../r-course-master/data/tema8/mtcars.csv")
head(mtcars)## X mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## 2 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## 3 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## 4 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## 5 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## 6 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1mtcars$X <- NULLDistancia Euclidea
coche1 <- mtcars[1,]# mete la fila 1 en una variable y todas las columnas
coche2 <- mtcars[2,]# mete la fila 2 en una variable y todas las columnasCalculemos la distancia entre las 2 variables
sqrt(sum((coche1 - coche2) * (coche1 - coche2)))#Esta esla explicacion de las disntancias aentre estos 2 datos ## [1] 0.6153251dist(coche1, coche2, method="euclidean")## 2
## 1 0.6153251x1 <- rnorm(100)#Tome dies filas con distribucion normal
x2 <- rnorm(100)
dist(rbind(x1,x2), #por filas( cbine es por columnas )
method = "euclidean")## x1
## x2 16.82865Coseno
v1 <- c(1,0,1,1,0,1,1,0,0,1)
v2 <- c(0,1,1,1,0,0,1,0,1,0)
lsa::cosine(v1, v2)# el angulo entre dos vectores## [,1]
## [1,] 0.5477226cosine(as.vector(t(coche1)), as.vector(t(coche2)))#Esta es la forma en de calcular el coseno para las variables de coche ## [,1]
## [1,] 0.9999951Correlación de Pearson
pear <- cor(mtcars, method = "pearson")
pear## mpg cyl disp hp drat wt
## mpg 1.0000000 -0.8521620 -0.8475514 -0.7761684 0.68117191 -0.8676594
## cyl -0.8521620 1.0000000 0.9020329 0.8324475 -0.69993811 0.7824958
## disp -0.8475514 0.9020329 1.0000000 0.7909486 -0.71021393 0.8879799
## hp -0.7761684 0.8324475 0.7909486 1.0000000 -0.44875912 0.6587479
## drat 0.6811719 -0.6999381 -0.7102139 -0.4487591 1.00000000 -0.7124406
## wt -0.8676594 0.7824958 0.8879799 0.6587479 -0.71244065 1.0000000
## qsec 0.4186840 -0.5912421 -0.4336979 -0.7082234 0.09120476 -0.1747159
## vs 0.6640389 -0.8108118 -0.7104159 -0.7230967 0.44027846 -0.5549157
## am 0.5998324 -0.5226070 -0.5912270 -0.2432043 0.71271113 -0.6924953
## gear 0.4802848 -0.4926866 -0.5555692 -0.1257043 0.69961013 -0.5832870
## carb -0.5509251 0.5269883 0.3949769 0.7498125 -0.09078980 0.4276059
## qsec vs am gear carb
## mpg 0.41868403 0.6640389 0.59983243 0.4802848 -0.55092507
## cyl -0.59124207 -0.8108118 -0.52260705 -0.4926866 0.52698829
## disp -0.43369788 -0.7104159 -0.59122704 -0.5555692 0.39497686
## hp -0.70822339 -0.7230967 -0.24320426 -0.1257043 0.74981247
## drat 0.09120476 0.4402785 0.71271113 0.6996101 -0.09078980
## wt -0.17471588 -0.5549157 -0.69249526 -0.5832870 0.42760594
## qsec 1.00000000 0.7445354 -0.22986086 -0.2126822 -0.65624923
## vs 0.74453544 1.0000000 0.16834512 0.2060233 -0.56960714
## am -0.22986086 0.1683451 1.00000000 0.7940588 0.05753435
## gear -0.21268223 0.2060233 0.79405876 1.0000000 0.27407284
## carb -0.65624923 -0.5696071 0.05753435 0.2740728 1.00000000Machine Learning
#Evaluación de modelos y parámetros que tan bien funciona un modelo u otro
library(recommenderlab)
library(ggplot2)
data(MovieLense)Mismas dinamica se seleccionan los valores que se tienen con mas de 50 valoraciones y 100 vistas
rating_movies <- MovieLense[rowCounts(MovieLense)>50,
colCounts(MovieLense)>100]Validacion cruzada
n_folds <- 4
items_to_keep = 15
rating_threshold <- 3# humbral minimo de calificacion
eval_sets <- evaluationScheme(data = rating_movies,
method = "cross-validation",#validacion cruzada
k = n_folds, #numero de veces que se hace la validacion cruzada
given = items_to_keep,#Elementos que nos queremos quedar
goodRating = rating_threshold)#size_sets <- sapply(eval_sets@runsTrain, length)#tamano de las diferentes segmentaciones conjuntos de testing
size_sets## [1] 420 420 420 420#Evaluación de los Ratings
para este caso basado en items
model_type <- "IBCF"
model_params <- NULL
eval_recommender <- Recommender(data = getData(eval_sets, "train"),
method = model_type,
parameter = model_params)items_to_recommend <- 10#numero de items a recomendar
eval_prediction <- predict(object = eval_recommender,#el modelo
newdata = getData(eval_sets, #de las semilla
"known"),
n = items_to_recommend,
type = "ratings")#las valoraciones de los usuarios
class(eval_prediction)# mariz de valoraciones ## [1] "realRatingMatrix"
## attr(,"package")
## [1] "recommenderlab"Graficamos los sistemas de recomendacion vs los reales
qplot(rowCounts(eval_prediction)#conteo por filas
) + geom_histogram(binwidth = 20)+
ggtitle("Distribución de películas por usuario")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Evaluamos la presicion de la recomendacion
eval_accuracy <- calcPredictionAccuracy(x = eval_prediction,
data = getData(eval_sets, "unknown"),
byUser = T)
head(eval_accuracy)## RMSE MSE MAE
## 2 1.0036682 1.0073498 0.6203865
## 8 0.8921704 0.7959680 0.7055295
## 14 1.1531288 1.3297059 0.7911716
## 24 0.8879267 0.7884138 0.6002726
## 28 1.0132547 1.0266852 0.8078669
## 38 1.8404768 3.3873547 1.3263309Graficamos los errores cuadratricos medios
qplot(eval_accuracy[,1])## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Evaluación de las recomendaciones
results <- evaluate(x = eval_sets,
method = model_type,
n = seq(10,100,10))#secuencia de 10 a 10 ## IBCF run fold/sample [model time/prediction time]
## 1 [0.17sec/0.01sec]
## 2 [0.14sec/0.04sec]
## 3 [0.15sec/0.04sec]
## 4 [0.16sec/0.01sec]getConfusionMatrix(results)[[1]]# lo que importa es la presicion ## TP FP FN TN N precision recall TPR
## [1,] 3.471429 6.528571 67.53571 239.4643 317 0.3471429 0.05132702 0.05132702
## [2,] 6.807143 13.192857 64.20000 232.8000 317 0.3403571 0.09955412 0.09955412
## [3,] 9.735714 20.264286 61.27143 225.7286 317 0.3245238 0.14093090 0.14093090
## [4,] 12.357143 27.642857 58.65000 218.3500 317 0.3089286 0.17676420 0.17676420
## [5,] 14.935714 35.064286 56.07143 210.9286 317 0.2987143 0.21546191 0.21546191
## [6,] 17.685714 42.314286 53.32143 203.6786 317 0.2947619 0.25768330 0.25768330
## [7,] 20.528571 49.471429 50.47857 196.5214 317 0.2932653 0.30214066 0.30214066
## [8,] 23.278571 56.721429 47.72857 189.2714 317 0.2909821 0.34281773 0.34281773
## [9,] 25.871429 64.128571 45.13571 181.8643 317 0.2874603 0.38231415 0.38231415
## [10,] 28.621429 71.378571 42.38571 174.6143 317 0.2862143 0.42329104 0.42329104
## FPR n
## [1,] 0.02584638 10
## [2,] 0.05241861 20
## [3,] 0.08068599 30
## [4,] 0.11035236 40
## [5,] 0.14049982 50
## [6,] 0.17011279 60
## [7,] 0.19910260 70
## [8,] 0.22856543 80
## [9,] 0.25876035 90
## [10,] 0.28792267 100Grafiquemoslo
plot(results, "prec/rec", annotate = T, main = "Precisión vs Eficacia")
results## Evaluation results for 4 folds/samples using method 'IBCF'.#Comparar modelos para sistemas de recomendacion
models <- list(IBCF_cos = list(name = "IBCF", params = list(method = "cosine")),#item coseno
IBCF_cor = list(name = "IBCF", params = list(method = "pearson")),#item correlacion
UBCF_cos = list(name = "UBCF", params = list(method = "cosine")),#usuario coseno
UBCF_cor = list(name = "UBCF", params = list(method = "pearson")),#usuario correlacion
random = list(name = "RANDOM", params = NULL))#random
n_recomends <- c(1:5,#Del 1 al 5 ( la de la lista )
seq(10,100,10))#de 10 al 100 y de 10 en 10veamos los resultados de la validacion cruzada
results <- evaluate(x = eval_sets,#conjunto de validacion
method = models,#la lista previa
n = n_recomends)### IBCF run fold/sample [model time/prediction time]
## 1 [0.15sec/0.03sec]
## 2 [0.16sec/0.03sec]
## 3 [0.16sec/0.05sec]
## 4 [0.16sec/0.05sec]
## IBCF run fold/sample [model time/prediction time]
## 1 [0.2sec/0.01sec]
## 2 [0.17sec/0.03sec]
## 3 [0.17sec/0.05sec]
## 4 [0.21sec/0.01sec]
## UBCF run fold/sample [model time/prediction time]
## 1 [0.01sec/0.22sec]
## 2 [0sec/0.22sec]
## 3 [0sec/0.2sec]
## 4 [0sec/0.21sec]
## UBCF run fold/sample [model time/prediction time]
## 1 [0sec/0.2sec]
## 2 [0sec/0.22sec]
## 3 [0sec/0.22sec]
## 4 [0sec/0.2sec]
## RANDOM run fold/sample [model time/prediction time]
## 1 [0sec/0.05sec]
## 2 [0sec/0.04sec]
## 3 [0sec/0.05sec]
## 4 [0sec/0.03sec]results## List of evaluation results for 5 recommenders:
##
## $IBCF_cos
## Evaluation results for 4 folds/samples using method 'IBCF'.
##
## $IBCF_cor
## Evaluation results for 4 folds/samples using method 'IBCF'.
##
## $UBCF_cos
## Evaluation results for 4 folds/samples using method 'UBCF'.
##
## $UBCF_cor
## Evaluation results for 4 folds/samples using method 'UBCF'.
##
## $random
## Evaluation results for 4 folds/samples using method 'RANDOM'.Grafiquemos esas diferencias (area bajo la curva )
entre mayor sea el area mejor es el modelo
plot(results, annotate = 1,
legend = "topleft") + title("Curva AUC")
## integer(0)#True positive ,y false positiveIBCF en cos es el mejor
plot(results, "prec/rec", annotate = 1,
legend = "bottomright") + title("Precisión vs Eficacia")
## integer(0)#Ajustar parámetros( optimizar el modelo)
vector_k <- c(5,#5 vecinos
seq(10,40,10))#de 10 al 40 y contando de 10 en 10
models <- lapply(vector_k, function(k){
list(name = "IBCF", param = list(method = "pearson", k = k))
})
names(models) <- paste0("IBCF_k_",vector_k)# caracteres
results <- evaluate(x = eval_sets,
method = models,
n=n_recomends)## IBCF run fold/sample [model time/prediction time]
## 1 [0.2sec/0.02sec]
## 2 [0.17sec/0.02sec]
## 3 [0.19sec/0.02sec]
## 4 [0.18sec/0.02sec]
## IBCF run fold/sample [model time/prediction time]
## 1 [0.17sec/0.03sec]
## 2 [0.17sec/0.03sec]
## 3 [0.17sec/0.02sec]
## 4 [0.17sec/0.02sec]
## IBCF run fold/sample [model time/prediction time]
## 1 [0.17sec/0.04sec]
## 2 [0.17sec/0.03sec]
## 3 [0.17sec/0.03sec]
## 4 [0.17sec/0.03sec]
## IBCF run fold/sample [model time/prediction time]
## 1 [0.17sec/0.05sec]
## 2 [0.18sec/0.01sec]
## 3 [0.17sec/0.04sec]
## 4 [0.17sec/0.04sec]
## IBCF run fold/sample [model time/prediction time]
## 1 [0.17sec/0.05sec]
## 2 [0.17sec/0.03sec]
## 3 [0.17sec/0.03sec]
## 4 [0.19sec/0.03sec]results## List of evaluation results for 5 recommenders:
##
## $IBCF_k_5
## Evaluation results for 4 folds/samples using method 'IBCF'.
##
## $IBCF_k_10
## Evaluation results for 4 folds/samples using method 'IBCF'.
##
## $IBCF_k_20
## Evaluation results for 4 folds/samples using method 'IBCF'.
##
## $IBCF_k_30
## Evaluation results for 4 folds/samples using method 'IBCF'.
##
## $IBCF_k_40
## Evaluation results for 4 folds/samples using method 'IBCF'.plot(results, annotate = 1, legend = "topleft") + title("Curva ROC")
## integer(0)plot(results, "prec/rec", annotate = 1, legend = "bottomright")+title("Precisión vs Eficacia")
## integer(0)Deteccion de Fraudes Bancarios
library(caret)#diviciones
#install.packages("pROC")
#remotes::install_github("cran/DMwR")
#install.packages("caTools")
library(pROC)### Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following object is masked from 'package:colorspace':
##
## coords## The following objects are masked from 'package:stats':
##
## cov, smooth, varlibrary(DMwR)##
## Attaching package: 'DMwR'## The following object is masked from 'package:VIM':
##
## kNNlibrary(caTools)#ccdata <- read.csv("../../r-course-master/data/tema8/creditcard.csv/creditcard.csv")head(ccdata)## Time V1 V2 V3 V4 V5 V6
## 1 0 -1.3598071 -0.07278117 2.5363467 1.3781552 -0.33832077 0.46238778
## 2 0 1.1918571 0.26615071 0.1664801 0.4481541 0.06001765 -0.08236081
## 3 1 -1.3583541 -1.34016307 1.7732093 0.3797796 -0.50319813 1.80049938
## 4 1 -0.9662717 -0.18522601 1.7929933 -0.8632913 -0.01030888 1.24720317
## 5 2 -1.1582331 0.87773675 1.5487178 0.4030339 -0.40719338 0.09592146
## 6 2 -0.4259659 0.96052304 1.1411093 -0.1682521 0.42098688 -0.02972755
## V7 V8 V9 V10 V11 V12
## 1 0.23959855 0.09869790 0.3637870 0.09079417 -0.5515995 -0.61780086
## 2 -0.07880298 0.08510165 -0.2554251 -0.16697441 1.6127267 1.06523531
## 3 0.79146096 0.24767579 -1.5146543 0.20764287 0.6245015 0.06608369
## 4 0.23760894 0.37743587 -1.3870241 -0.05495192 -0.2264873 0.17822823
## 5 0.59294075 -0.27053268 0.8177393 0.75307443 -0.8228429 0.53819555
## 6 0.47620095 0.26031433 -0.5686714 -0.37140720 1.3412620 0.35989384
## V13 V14 V15 V16 V17 V18
## 1 -0.9913898 -0.3111694 1.4681770 -0.4704005 0.20797124 0.02579058
## 2 0.4890950 -0.1437723 0.6355581 0.4639170 -0.11480466 -0.18336127
## 3 0.7172927 -0.1659459 2.3458649 -2.8900832 1.10996938 -0.12135931
## 4 0.5077569 -0.2879237 -0.6314181 -1.0596472 -0.68409279 1.96577500
## 5 1.3458516 -1.1196698 0.1751211 -0.4514492 -0.23703324 -0.03819479
## 6 -0.3580907 -0.1371337 0.5176168 0.4017259 -0.05813282 0.06865315
## V19 V20 V21 V22 V23 V24
## 1 0.40399296 0.25141210 -0.018306778 0.277837576 -0.11047391 0.06692807
## 2 -0.14578304 -0.06908314 -0.225775248 -0.638671953 0.10128802 -0.33984648
## 3 -2.26185710 0.52497973 0.247998153 0.771679402 0.90941226 -0.68928096
## 4 -1.23262197 -0.20803778 -0.108300452 0.005273597 -0.19032052 -1.17557533
## 5 0.80348692 0.40854236 -0.009430697 0.798278495 -0.13745808 0.14126698
## 6 -0.03319379 0.08496767 -0.208253515 -0.559824796 -0.02639767 -0.37142658
## V25 V26 V27 V28 Amount Class
## 1 0.1285394 -0.1891148 0.133558377 -0.02105305 149.62 0
## 2 0.1671704 0.1258945 -0.008983099 0.01472417 2.69 0
## 3 -0.3276418 -0.1390966 -0.055352794 -0.05975184 378.66 0
## 4 0.6473760 -0.2219288 0.062722849 0.06145763 123.50 0
## 5 -0.2060096 0.5022922 0.219422230 0.21515315 69.99 0
## 6 -0.2327938 0.1059148 0.253844225 0.08108026 3.67 0ccdata$Class <- factor(ifelse(ccdata$Class==0, "0", "1"))
table(ccdata$Class)#fraudulentas y no fraudulentas ##
## 0 1
## 284315 492Hagamos los valores de training
t.id <- createDataPartition(ccdata$Class, p = 0.7, list = F, times = 1)
training <- ccdata[t.id,]
test <- ccdata[-t.id,]Validemos las proporciones de fraudulentas por clase
table(training$Class)##
## 0 1
## 199021 345table(test$Class)##
## 0 1
## 85294 147algoritmo :Sintetic minoriti over sampling (SMOTE) tarjetas fraudulentas simuladas
training <- SMOTE(Class ~ .,#Clase en funcion de las demas
training,
perc.under = 100,
perc.over = 200)#margen
table(training$Class)##
## 0 1
## 690 1035training$Class <- as.numeric(training$Class)
trControl <- trainControl(method = "cv", # validacion cruzada
number = 10)# 10 vecestreebag lo que hace es que
model <- train(Class ~., data = training, method = "treebag",#basado en arboles
trControl = trControl)## Warning in train.default(x, y, weights = w, ...): You are trying to do
## regression and your outcome only has two possible values Are you trying to do
## classification? If so, use a 2 level factor as your outcome column.model## Bagged CART
##
## 1725 samples
## 30 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1552, 1552, 1552, 1553, 1553, 1553, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 0.2066201 0.8199493 0.09200191Predictores
#aqui le digo al modelo predictor ( estas son todas las variables a considerar , en este caso son todas , menos la clase)
predictors <- names(training)[names(training)!='Class']# de entrenamiento en las posiciones donde sea diferente de la clase
pred <- predict(model$finalModel,#del modelo
test[,predictors])#todas las filas menos la claseArea under curve
auc <- roc(test$Class, pred)## Setting levels: control = 0, case = 1## Setting direction: controls < casesauc##
## Call:
## roc.default(response = test$Class, predictor = pred)
##
## Data: pred in 85294 controls (test$Class 0) < 147 cases (test$Class 1).
## Area under the curve: 0.9674Grafiquemos el modelo
plot(auc, ylim = c(0,1), print.thres= T, #imprime el valor mas optimo
main = paste("AUC con SMOTE: ", round(auc$auc[[1]],2)) )#redoonde el valor de los valores de presicion de los valores
abline(h=1, col = "green", lwd=2)
abline(h=0, col = "red", lwd = 2)
Map Reduce Operaciones vectorizadas arrays
names.first <- c("Juan Gabriel", "Zach", "Jack", "Sansa")
names.last <- c("Gomila", "Effron", "Sparrow", "Stark")La función paste, combina los dos vectores como si hubieramos hecho un bucle for
paste(names.first, names.last)## [1] "Juan Gabriel Gomila" "Zach Effron" "Jack Sparrow"
## [4] "Sansa Stark"con un vector de solo un elemento
single.surname <- c("Zuccherberg")La función paste sirve para combinar inclusovectores de diferente tamaño!
paste(names.first, single.surname)## [1] "Juan Gabriel Zuccherberg" "Zach Zuccherberg"
## [3] "Jack Zuccherberg" "Sansa Zuccherberg"funcion para los arrays
username <- function(first, last){
tolower(#de minuscula
paste0(#
last,
substr(first, 1, 2)))
}
#uso de la funcion
username(names.first, names.last)## [1] "gomilaju" "effronza" "sparrowja" "starksa"Aplicarlo a un ejemplo
auto <- read.csv("../../r-course-master/data/tema11/auto-mpg.csv")head(auto)[1:5,]## No mpg cylinders displacement horsepower weight acceleration model_year
## 1 1 28 4 140 90 2264 15.5 71
## 2 2 19 3 70 97 2330 13.5 72
## 3 3 36 4 107 75 2205 14.5 82
## 4 4 28 4 97 92 2288 17.0 72
## 5 5 21 6 199 90 2648 15.0 70
## car_name
## 1 chevrolet vega 2300
## 2 mazda rx2 coupe
## 3 honda accord
## 4 datsun 510 (sw)
## 5 amc gremlinauto$dmpg <- auto$mpg * 2.0
auto$kmpg <- auto$mpg * 1.609# convertir a kilometros un vector es muy rapido y eficiente
head(auto)[1:5,]## No mpg cylinders displacement horsepower weight acceleration model_year
## 1 1 28 4 140 90 2264 15.5 71
## 2 2 19 3 70 97 2330 13.5 72
## 3 3 36 4 107 75 2205 14.5 82
## 4 4 28 4 97 92 2288 17.0 72
## 5 5 21 6 199 90 2648 15.0 70
## car_name dmpg kmpg
## 1 chevrolet vega 2300 56 45.052
## 2 mazda rx2 coupe 38 30.571
## 3 honda accord 72 57.924
## 4 datsun 510 (sw) 56 45.052
## 5 amc gremlin 42 33.789sum(auto$mpg)#sumar un vector ## [1] 9358.8min(auto$mpg)#encuentre el minimo ## [1] 9max(auto$mpg)# Encuentre el maximo ## [1] 46.6range(auto$mpg)#Encuentre el rango## [1] 9.0 46.6prod(auto$mpg)#Multiplicacion (Producto)## [1] Infmean(auto$mpg)#promedio## [1] 23.51457median(auto$mpg)#Mediana ## [1] 23var(auto$mpg)#varianza## [1] 61.08961sd(auto$mpg)#Desviacion ## [1] 7.815984Normalizar los datos ( ver las lineas 430 de codigo) la formula es :
#(auto$mpg - mean(auto$mpg))/sd(auto$mpg)uso de apply
m <- matrix(seq(1,16), 4, 4)
m## [,1] [,2] [,3] [,4]
## [1,] 1 5 9 13
## [2,] 2 6 10 14
## [3,] 3 7 11 15
## [4,] 4 8 12 16Funcion que busca en los arrays
apply(m, 1, min )# 1 -> función aplicada al minimo de cada filas## [1] 1 2 3 4apply(m, 2, max)# 2 -> función aplicada al maximo por columnas## [1] 4 8 12 16# c(1,2) -> función aplicada a elementosapply(m, 1, prod)# multiplica por fila ## [1] 585 1680 3465 6144apply(m, 2, sum)#suma por columna ## [1] 10 26 42 58Ahora al cuadrado
apply(m, c(1,2), function(x){x^2})## [,1] [,2] [,3] [,4]
## [1,] 1 25 81 169
## [2,] 4 36 100 196
## [3,] 9 49 121 225
## [4,] 16 64 144 256cuantiles
apply(m, 1, quantile,#cuantiles por filas
probs = c(0.4, 0.6, 0.8))#por probalidad## [,1] [,2] [,3] [,4]
## 40% 5.8 6.8 7.8 8.8
## 60% 8.2 9.2 10.2 11.2
## 80% 10.6 11.6 12.6 13.6colSums(m)#sumar por columnas ## [1] 10 26 42 58rowSums(m)#Sumar por filas ## [1] 28 32 36 40colMeans(m)#promedio por columnas ## [1] 2.5 6.5 10.5 14.5rowMeans(m)#promedio por filas ## [1] 7 8 9 10Una matriz tridimencional
array3D <- array(seq(1, 32), dim = c(4,#Filas
4,#Columnas
2))#Nivel o profundidad
array3D## , , 1
##
## [,1] [,2] [,3] [,4]
## [1,] 1 5 9 13
## [2,] 2 6 10 14
## [3,] 3 7 11 15
## [4,] 4 8 12 16
##
## , , 2
##
## [,1] [,2] [,3] [,4]
## [1,] 17 21 25 29
## [2,] 18 22 26 30
## [3,] 19 23 27 31
## [4,] 20 24 28 32sume la tercera dimencion suma de la primera cara y la suma de la segunda cara
apply(array3D, 3, sum)## [1] 136 392sum(1:16)## [1] 136apply(array3D, 3, mean)#media de la primera dimencion ## [1] 8.5 24.5apply(array3D, c(1,2), sum)# sumar entre las caras , como suma de matrices ## [,1] [,2] [,3] [,4]
## [1,] 18 26 34 42
## [2,] 20 28 36 44
## [3,] 22 30 38 46
## [4,] 24 32 40 48Lapply y sapply
lapply -> l = lista, se puede aplicar vectores, list, data frame
sapply -> lista o vector ,si se quiere aplicar a un DF entonces se tiene que ap;icar un ddply
Nota importante ( estas dos funciones trabajan con data frame , si solo trabajas con una columna tienes que convertirlo a DF) se redacta asi sapply o lapply(El DF y la funcion a aplicar)
auto <- read.csv("../../r-course-master/data/tema11/auto-mpg.csv", stringsAsFactors = F)x <- c(1,2,3)
lapply(x, sqrt)## [[1]]
## [1] 1
##
## [[2]]
## [1] 1.414214
##
## [[3]]
## [1] 1.732051x
sapply(x, sqrt) class(lapply(x, sqrt))
x <- list(a = 1:10, b = c(1,10,100,1000), c = seq(5,50, by = 5))
x## $a
## [1] 1 2 3 4 5 6 7 8 9 10
##
## $b
## [1] 1 10 100 1000
##
## $c
## [1] 5 10 15 20 25 30 35 40 45 50Promedio por lista
lapply(x, mean)## $a
## [1] 5.5
##
## $b
## [1] 277.75
##
## $c
## [1] 27.5class(lapply(x,mean))## [1] "list"Convierte la lista a numerico (sapply)
sapply(x, mean)## a b c
## 5.50 277.75 27.50class(sapply(x, mean))## [1] "numeric"Diferencias entre lapply y saply
lapply(auto[,2:8], min)## $mpg
## [1] 9
##
## $cylinders
## [1] 3
##
## $displacement
## [1] 68
##
## $horsepower
## [1] 46
##
## $weight
## [1] 1613
##
## $acceleration
## [1] 8
##
## $model_year
## [1] 70sapply(auto[,2:8], min)## mpg cylinders displacement horsepower weight acceleration
## 9 3 68 46 1613 8
## model_year
## 70lapply(auto[,2:8], summary)## $mpg
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.00 17.50 23.00 23.51 29.00 46.60
##
## $cylinders
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.000 4.000 4.000 5.455 8.000 8.000
##
## $displacement
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 68.0 104.2 148.5 193.4 262.0 455.0
##
## $horsepower
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 46.0 76.0 92.0 104.1 125.0 230.0
##
## $weight
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1613 2224 2804 2970 3608 5140
##
## $acceleration
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 8.00 13.82 15.50 15.57 17.18 24.80
##
## $model_year
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 70.00 73.00 76.00 76.01 79.00 82.00sapply(auto[,2:8], summary)## mpg cylinders displacement horsepower weight acceleration
## Min. 9.00000 3.000000 68.0000 46.0000 1613.000 8.00000
## 1st Qu. 17.50000 4.000000 104.2500 76.0000 2223.750 13.82500
## Median 23.00000 4.000000 148.5000 92.0000 2803.500 15.50000
## Mean 23.51457 5.454774 193.4259 104.1281 2970.425 15.56809
## 3rd Qu. 29.00000 8.000000 262.0000 125.0000 3608.000 17.17500
## Max. 46.60000 8.000000 455.0000 230.0000 5140.000 24.80000
## model_year
## Min. 70.00000
## 1st Qu. 73.00000
## Median 76.00000
## Mean 76.01005
## 3rd Qu. 79.00000
## Max. 82.00000sapply(auto[,2:8], range)## mpg cylinders displacement horsepower weight acceleration model_year
## [1,] 9.0 3 68 46 1613 8.0 70
## [2,] 46.6 8 455 230 5140 24.8 82sapply(auto[,2:8], min)## mpg cylinders displacement horsepower weight acceleration
## 9 3 68 46 1613 8
## model_year
## 70#sapply(auto[,2], min)#esto falla porque R trabaja con vector
sapply(as.data.frame(auto[,2]),min,
simplify = T)#True ( numeric o matriz ) y F devuelve una lista ## auto[, 2]
## 9class(sapply(as.data.frame(auto[,2]),min,
simplify = T))## [1] "numeric"tapply
Funcion a cada subconjunto por cada factor
auto$cylinders <- factor(auto$cylinders,
levels = c(3,4,5,6,8),
labels = c("3C", "4C", "5C", "6C", "8C"))Dame la media por cada variable categotica
tapply(auto$mpg, #variable numerica
auto$cylinders, mean)#Variable categorica## 3C 4C 5C 6C 8C
## 20.55000 29.28676 27.36667 19.98571 14.96311tapply(auto$mpg, list(cyl = auto$cylinders), mean)## cyl
## 3C 4C 5C 6C 8C
## 20.55000 29.28676 27.36667 19.98571 14.96311Funcion que lo que hace es :por
by(auto,#por (data Frame )
auto$cylinders,#filtre por
function(row){# para cada fila
cor(row$mpg, row$acceleration)} )#correlacion entre vsriables ## auto$cylinders: 3C
## [1] -0.8188727
## ------------------------------------------------------------
## auto$cylinders: 4C
## [1] 0.07488465
## ------------------------------------------------------------
## auto$cylinders: 5C
## [1] 0.714897
## ------------------------------------------------------------
## auto$cylinders: 6C
## [1] -0.339605
## ------------------------------------------------------------
## auto$cylinders: 8C
## [1] 0.3212278#split -> apply -> combine #plyr #XYply X,Y = a -> array, d -> data.frame, l -> list, _(nada) -> no output
Cargamos y etiquetamos datos
auto <- read.csv("../../r-course-master/data/tema11/auto-mpg.csv", stringsAsFactors = F)
auto$cylinders <- factor(auto$cylinders, levels = c(3,4,5,6,8),
labels = c("3C", "4C", "5C", "6C", "8C"))#install.packages("plyr")
library(plyr)## ------------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## ------------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following object is masked from 'package:DMwR':
##
## join## The following objects are masked from 'package:reshape':
##
## rename, round_any## The following objects are masked from 'package:Hmisc':
##
## is.discrete, summarize## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following object is masked from 'package:purrr':
##
## compactEstas dos son iguales y nos manda un el mismo DF
ddply(auto, "cylinders", function(df) { mean(df$mpg)})## cylinders V1
## 1 3C 20.55000
## 2 4C 29.28676
## 3 5C 27.36667
## 4 6C 19.98571
## 5 8C 14.96311ddply(auto, ~cylinders, function(df){ mean(df$mpg)})## cylinders V1
## 1 3C 20.55000
## 2 4C 29.28676
## 3 5C 27.36667
## 4 6C 19.98571
## 5 8C 14.96311Filtados multiples :para cada combinacion de
p<-ddply(auto, c("cylinders", "model_year"),
function(df){
c(mean= mean(df$mpg),
min = min(df$mpg),
max = max(df$mpg))
})
p[1:6,]## cylinders model_year mean min max
## 1 3C 72 19.00000 19.0 19.0
## 2 3C 73 18.00000 18.0 18.0
## 3 3C 77 21.50000 21.5 21.5
## 4 3C 80 23.70000 23.7 23.7
## 5 4C 70 25.28571 24.0 27.0
## 6 4C 71 27.46154 22.0 35.0Otra froma de escribir lo anterior
p<-ddply(auto, ~cylinders + model_year,
function(df){
c(mean= mean(df$mpg),
min = min(df$mpg),
max = max(df$mpg))
})
p[1:5,]## cylinders model_year mean min max
## 1 3C 72 19.00000 19.0 19.0
## 2 3C 73 18.00000 18.0 18.0
## 3 3C 77 21.50000 21.5 21.5
## 4 3C 80 23.70000 23.7 23.7
## 5 4C 70 25.28571 24.0 27.0funcion que resume (poner atencion que se escribio .() )
ddply(auto, .(cylinders), summarize, #resumir
freq = length(cylinders),#cuente cuantas veces esta esta variable
meanmpg = mean(mpg))#crea una columna que calcule la media por cada variable ## cylinders freq meanmpg
## 1 3C 4 20.55000
## 2 4C 204 29.28676
## 3 5C 3 27.36667
## 4 6C 84 19.98571
## 5 8C 103 14.96311par(mfrow=c(3,2))
d_ply(auto, "cylinders", summarise,#resumir
hist(mpg, xlab="Millas por galeón",
main = "Histograma de frecuencias",
breaks = 5))#diviciones 
sqL ______ dplyr SELECT -> select() WHERE -> filter() GROUP BY -> group_by() / summarise() ORDER BY -> arrange() JOIN -> join() “Unir” COLUMN ALIAS -> mutate() “agregar”
#install.packages("dplyr")
library(dplyr)Obtener solo estas columnas
subset.auto <- dplyr::select(auto, mpg, horsepower, acceleration)
subset.auto[1:5,]## mpg horsepower acceleration
## 1 28 90 15.5
## 2 19 97 13.5
## 3 36 75 14.5
## 4 28 92 17.0
## 5 21 90 15.0auto.80 <- dplyr::filter(auto, #DF
auto$model_year>80)#este es el condicional
auto.80[1:5,]## No mpg cylinders displacement horsepower weight acceleration model_year
## 1 3 36.0 4C 107 75 2205 14.5 82
## 2 8 32.9 4C 119 100 2615 14.8 81
## 3 12 30.7 6C 145 76 3160 19.6 81
## 4 20 29.0 4C 135 84 2525 16.0 82
## 5 22 26.6 4C 151 84 2635 16.4 81
## car_name
## 1 honda accord
## 2 datsun 200sx
## 3 volvo diesel
## 4 dodge aries se
## 5 buick skylarkmutate (agrega una columna )
auto.norm <- dplyr::mutate(auto,
mpg.norm = round((mpg-mean(mpg))/sd(mpg),2))#redondea la normalizacion de esta variable
auto.norm[1:5,]## No mpg cylinders displacement horsepower weight acceleration model_year
## 1 1 28 4C 140 90 2264 15.5 71
## 2 2 19 3C 70 97 2330 13.5 72
## 3 3 36 4C 107 75 2205 14.5 82
## 4 4 28 4C 97 92 2288 17.0 72
## 5 5 21 6C 199 90 2648 15.0 70
## car_name mpg.norm
## 1 chevrolet vega 2300 0.57
## 2 mazda rx2 coupe -0.58
## 3 honda accord 1.60
## 4 datsun 510 (sw) 0.57
## 5 amc gremlin -0.32summarise
dplyr::summarise(group_by(auto,#df
cylinders),#en funcion de los Cilindros
mean(mpg))#y calcula la media ## # A tibble: 5 x 2
## cylinders `mean(mpg)`
## <fct> <dbl>
## 1 3C 20.6
## 2 4C 29.3
## 3 5C 27.4
## 4 6C 20.0
## 5 8C 15.0dplyr::summarise(group_by(auto, model_year), mean(mpg))## # A tibble: 13 x 2
## model_year `mean(mpg)`
## <int> <dbl>
## 1 70 17.7
## 2 71 21.2
## 3 72 18.7
## 4 73 17.1
## 5 74 22.7
## 6 75 20.3
## 7 76 21.6
## 8 77 23.4
## 9 78 24.1
## 10 79 25.1
## 11 80 33.7
## 12 81 30.3
## 13 82 31.7combinan varias lineas de codigo (paips)
auto %>%#df ( paso 1)
filter(model_year<78) %>%#filtra por el modelo 78 ( paso 3)
group_by(cylinders) %>%# agrupa por cilindros ( paso 4)
summarise(mean(mpg))#aplica la funcion summarise ( paso 5)## mean(mpg)
## 1 20.18443Gran cantidad de datos ( muchos datos )
#install.packages("data.table")
library(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:reshape2':
##
## dcast, melt## The following objects are masked from 'package:xts':
##
## first, last## The following object is masked from 'package:reshape':
##
## melt## The following object is masked from 'package:raster':
##
## shift## The following objects are masked from 'package:dplyr':
##
## between, first, last## The following object is masked from 'package:purrr':
##
## transposeauto <- read.csv("../../r-course-master/data/tema11/auto-mpg.csv", stringsAsFactors = F)
auto$cylinders <- factor(auto$cylinders,
levels = c(3,4,5,6,8),
labels = c("3C", "4C", "5C", "6C", "8C"))Creamos un data table
auto.dt <- data.table(auto)
class(auto.dt)## [1] "data.table" "data.frame"cambia la redacion para la seleccion de
auto.dt[, .(mpg)]## mpg
## 1: 28.0
## 2: 19.0
## 3: 36.0
## 4: 28.0
## 5: 21.0
## ---
## 394: 16.5
## 395: 34.5
## 396: 38.1
## 397: 30.5
## 398: 19.0auto.dt[, .(mpg, horsepower, acceleration)]## mpg horsepower acceleration
## 1: 28.0 90 15.5
## 2: 19.0 97 13.5
## 3: 36.0 75 14.5
## 4: 28.0 92 17.0
## 5: 21.0 90 15.0
## ---
## 394: 16.5 120 16.7
## 395: 34.5 70 14.9
## 396: 38.1 60 18.8
## 397: 30.5 63 17.0
## 398: 19.0 100 13.0dame todos los carros con estos cilindros ( se hace referencia dentro del mismo vector , ademas no se indica que devuelva columnas )
#auto.dt[cylinders %in% c("3C", "4C", "5C")]auto.dt[cylinders == "4C" & horsepower>100]## No mpg cylinders displacement horsepower weight acceleration model_year
## 1: 14 27.9 4C 156 105 2800 14.4 80
## 2: 51 21.5 4C 121 110 2600 12.8 77
## 3: 131 26.0 4C 121 113 2234 12.5 70
## 4: 180 18.0 4C 121 112 2933 14.5 72
## 5: 181 25.0 4C 121 115 2671 13.5 75
## 6: 188 21.6 4C 121 115 2795 15.7 78
## 7: 241 19.0 4C 121 112 2868 15.5 73
## 8: 250 20.0 4C 130 102 3150 15.7 76
## 9: 297 23.2 4C 156 105 2745 16.7 78
## 10: 307 24.0 4C 121 110 2660 14.0 73
## car_name
## 1: dodge colt
## 2: bmw 320i
## 3: bmw 2002
## 4: volvo 145e (sw)
## 5: saab 99le
## 6: saab 99gle
## 7: volvo 144ea
## 8: volvo 245
## 9: plymouth sapporo
## 10: saab 99leValores aproximados Como la funcion buscar en excel
auto.dt[car_name %like% "mazda",]## No mpg cylinders displacement horsepower weight acceleration model_year
## 1: 2 19.0 3C 70 97 2330 13.5 72
## 2: 66 31.0 4C 91 68 1970 17.6 82
## 3: 74 31.3 4C 120 75 2542 17.5 80
## 4: 146 34.1 4C 91 68 1985 16.0 81
## 5: 251 23.7 3C 70 100 2420 12.5 80
## 6: 262 31.6 4C 120 74 2635 18.3 81
## 7: 269 46.6 4C 86 65 2110 17.9 80
## 8: 298 37.0 4C 91 68 2025 18.2 82
## 9: 361 32.8 4C 78 52 1985 19.4 78
## 10: 365 21.5 3C 80 110 2720 13.5 77
## car_name
## 1: mazda rx2 coupe
## 2: mazda glc custom
## 3: mazda 626
## 4: mazda glc 4
## 5: mazda rx-7 gs
## 6: mazda 626
## 7: mazda glc
## 8: mazda glc custom l
## 9: mazda glc deluxe
## 10: mazda rx-4calcula la media de mpg por cilindros
auto.dt[, mean(mpg), by= cylinders]## cylinders V1
## 1: 4C 29.28676
## 2: 3C 20.55000
## 3: 6C 19.98571
## 4: 8C 14.96311
## 5: 5C 27.36667auto.dt[, meanmpg := mean(mpg), by = cylinders]#creando una nueva columna y que calcule la media de mpg
head(auto.dt)## No mpg cylinders displacement horsepower weight acceleration model_year
## 1: 1 28 4C 140 90 2264 15.5 71
## 2: 2 19 3C 70 97 2330 13.5 72
## 3: 3 36 4C 107 75 2205 14.5 82
## 4: 4 28 4C 97 92 2288 17.0 72
## 5: 5 21 6C 199 90 2648 15.0 70
## 6: 6 23 4C 115 95 2694 15.0 75
## car_name meanmpg
## 1: chevrolet vega 2300 29.28676
## 2: mazda rx2 coupe 20.55000
## 3: honda accord 29.28676
## 4: datsun 510 (sw) 29.28676
## 5: amc gremlin 19.98571
## 6: audi 100ls 29.28676auto.dt[, c("sd_mpg", "z_mpg") := list(
sd(mpg), round((mpg - mean(mpg))/sd(mpg),2)
), by = cylinders
]
auto.dt[1:5, c(1:3, 9:12), with = FALSE]#que se vea de una columna a otra y que se convieta un data table a un DF (whith=F) ## No mpg cylinders car_name meanmpg sd_mpg z_mpg
## 1: 1 28 4C chevrolet vega 2300 29.28676 5.710156 -0.23
## 2: 2 19 3C mazda rx2 coupe 20.55000 2.564501 -0.60
## 3: 3 36 4C honda accord 29.28676 5.710156 1.18
## 4: 4 28 4C datsun 510 (sw) 29.28676 5.710156 -0.23
## 5: 5 21 6C amc gremlin 19.98571 3.807322 0.27Calculo de media ( a un conjunto de variables )
auto.dt[, lapply(.SD, mean),
.SDcols = c("mpg", "horsepower")]## mpg horsepower
## 1: 23.51457 104.1281setkey(auto.dt, cylinders)#cargamos la columna de cylinders del Df tables()
#auto.dt["4C", c(1:3, 9:10), with = F]Calcula la media , minimo y maximo y desviacion estandar por cylindros
auto.dt[,list(mean = mean(mpg), min = min(mpg),
max = max(mpg), sd = sd(mpg)),
by = cylinders]## cylinders mean min max sd
## 1: 3C 20.55000 18.0 23.7 2.564501
## 2: 4C 29.28676 18.0 46.6 5.710156
## 3: 5C 27.36667 20.3 36.4 8.228204
## 4: 6C 19.98571 15.0 38.0 3.807322
## 5: 8C 14.96311 9.0 26.6 2.836284DT[i,j,by]
setkeyv(auto.dt, c("cylinders", "model_year"))
auto.dt[.("4C"), c(1:3, 9:10), with = F][1:5,]## No mpg cylinders car_name meanmpg
## 1: 35 25 4C saab 99e 29.28676
## 2: 45 27 4C datsun pl510 29.28676
## 3: 89 26 4C volkswagen 1131 deluxe sedan 29.28676
## 4: 131 26 4C bmw 2002 29.28676
## 5: 197 24 4C audi 100 ls 29.28676#cuenta los elementos de hay (contar) por rubro
auto.dt[,.N, by = cylinders]## cylinders N
## 1: 3C 4
## 2: 4C 204
## 3: 5C 3
## 4: 6C 84
## 5: 8C 103auto.dt["3C", .N]## [1] 4Elimianr columnas en un data table
auto.dt[, meanmpg:=NULL]#DT[i,j,by] Los joins (buscar RV o X)
empl <- read.csv("../../r-course-master/data/tema11/employees.csv", stringsAsFactors = F)
dept <- read.csv("../../r-course-master/data/tema11/departments-1.csv", stringsAsFactors = F) Los combertomos a data.table
empl.dt <- data.table(empl)
dept.dt <- data.table(dept)clave es
setkey(empl.dt,#DF
"DeptId") #definel que sea este id , si no se define toma el numero 1combine <- empl.dt[dept.dt]
combine[1:5,]## Id FirstName LastName Phone sex HireDate JobTitle
## 1: 1001 Laurel Mendoza 1-931-665-3116 F 12-Apr-14 Finance Manager
## 2: 1010 Zenaida Mcdonald 1-297-102-4226 F 16-Jan-14 Accounting Manager
## 3: 1010 Andrew Mckinney 1-820-174-1396 F 19-Jul-14 Accounting Manager
## 4: 1010 Jaquelyn Snider 1-166-427-4633 M 31-Jan-14 Accounting Manager
## 5: 1013 Shea Turner 1-408-913-8681 M 8-Jan-14 Accounting Manager
## Salary Commission MgrId DeptId DeptName
## 1: 67324 0 1 1 Finance
## 2: 89107 0 8 1 Finance
## 3: 74212 0 1 1 Finance
## 4: 20312 0 9 1 Finance
## 5: 58110 0 4 1 FinanceOtro ejemplo de Joins
dept2 <- read.csv("../../r-course-master/data/tema11/departments-2.csv", stringsAsFactors = F)
dept2.dt <- data.table(dept2)#suma 2 filas con N/A
combine <- empl.dt[dept2.dt, allow.cartesian = T, #producto carteciano las que estan bacias las llena con N/A
nomatch = 0]#los que no tienen conjuntos entoces elimina los n/aJuntar 2 tablas
merge(empl.dt, dept2.dt, by = "DeptId")[1:5,] #InnerJoin## DeptId Id FirstName LastName Phone sex HireDate
## 1: 1 1001 Laurel Mendoza 1-931-665-3116 F 12-Apr-14
## 2: 1 1010 Zenaida Mcdonald 1-297-102-4226 F 16-Jan-14
## 3: 1 1010 Andrew Mckinney 1-820-174-1396 F 19-Jul-14
## 4: 1 1010 Jaquelyn Snider 1-166-427-4633 M 31-Jan-14
## 5: 1 1013 Shea Turner 1-408-913-8681 M 8-Jan-14
## JobTitle Salary Commission MgrId DeptName
## 1: Finance Manager 67324 0 1 Finance
## 2: Accounting Manager 89107 0 8 Finance
## 3: Accounting Manager 74212 0 1 Finance
## 4: Accounting Manager 20312 0 9 Finance
## 5: Accounting Manager 58110 0 4 Financemerge(empl.dt, dept2.dt, by = "DeptId", all.x = T)[1:5,] #Left Join## DeptId Id FirstName LastName Phone sex HireDate
## 1: 1 1001 Laurel Mendoza 1-931-665-3116 F 12-Apr-14
## 2: 1 1010 Zenaida Mcdonald 1-297-102-4226 F 16-Jan-14
## 3: 1 1010 Andrew Mckinney 1-820-174-1396 F 19-Jul-14
## 4: 1 1010 Jaquelyn Snider 1-166-427-4633 M 31-Jan-14
## 5: 1 1013 Shea Turner 1-408-913-8681 M 8-Jan-14
## JobTitle Salary Commission MgrId DeptName
## 1: Finance Manager 67324 0 1 Finance
## 2: Accounting Manager 89107 0 8 Finance
## 3: Accounting Manager 74212 0 1 Finance
## 4: Accounting Manager 20312 0 9 Finance
## 5: Accounting Manager 58110 0 4 Financemerge(empl.dt, dept2.dt, by = "DeptId", all.y = T)[1:5,] #Right Join## DeptId Id FirstName LastName Phone sex HireDate
## 1: 1 1001 Laurel Mendoza 1-931-665-3116 F 12-Apr-14
## 2: 1 1010 Zenaida Mcdonald 1-297-102-4226 F 16-Jan-14
## 3: 1 1010 Andrew Mckinney 1-820-174-1396 F 19-Jul-14
## 4: 1 1010 Jaquelyn Snider 1-166-427-4633 M 31-Jan-14
## 5: 1 1013 Shea Turner 1-408-913-8681 M 8-Jan-14
## JobTitle Salary Commission MgrId DeptName
## 1: Finance Manager 67324 0 1 Finance
## 2: Accounting Manager 89107 0 8 Finance
## 3: Accounting Manager 74212 0 1 Finance
## 4: Accounting Manager 20312 0 9 Finance
## 5: Accounting Manager 58110 0 4 Financemerge(empl.dt, dept2.dt, by = "DeptId", all = T)[1:5,] # Full Join## DeptId Id FirstName LastName Phone sex HireDate
## 1: 1 1001 Laurel Mendoza 1-931-665-3116 F 12-Apr-14
## 2: 1 1010 Zenaida Mcdonald 1-297-102-4226 F 16-Jan-14
## 3: 1 1010 Andrew Mckinney 1-820-174-1396 F 19-Jul-14
## 4: 1 1010 Jaquelyn Snider 1-166-427-4633 M 31-Jan-14
## 5: 1 1013 Shea Turner 1-408-913-8681 M 8-Jan-14
## JobTitle Salary Commission MgrId DeptName
## 1: Finance Manager 67324 0 1 Finance
## 2: Accounting Manager 89107 0 8 Finance
## 3: Accounting Manager 74212 0 1 Finance
## 4: Accounting Manager 20312 0 9 Finance
## 5: Accounting Manager 58110 0 4 Finance#DT[i,j,by] #.SD -> guardar referencia a todas las columnas (salvo las del by) #.SDcols -> la referencia guardada a las columnas (son las que se pueden incluir o excluir en la ‘j’) #.EACHI -> para agrupar por claves #.N -> número de filas #.I -> los índices indicados en el DT
#Sueldo máximo de cada departamento….
empl.dt[dept2.dt,
max(.SD), #maximo de las variables a usar
by = .EACHI, #los departamentos
.SDcols = "Salary" ]#de la columna a la que quiero aplicar lo anterior ## DeptId V1
## 1: 1 99211
## 2: 2 98291
## 3: 3 70655
## 4: 4 99397
## 5: 5 92429
## 6: 6 NA
## 7: 7 NA#Sueldo promedio por departamento
empl.dt[, #todas las claves
.(AvgSalary = lapply(.SD,# con el conjunto que te indique
mean)),#dame la media
by = "DeptId", .SDcols = "Salary"]# de estas variables ## DeptId AvgSalary
## 1: 1 63208.02
## 2: 2 59668.06
## 3: 3 47603.64
## 4: 4 59448.24
## 5: 5 51957.44empl.dt[dept2.dt, list(DeptName,
AvgSalary = lapply(.SD, mean)),
by = .EACHI, .SDcols = "Salary"]## DeptId DeptName AvgSalary
## 1: 1 Finance 63208.02
## 2: 2 HR 59668.06
## 3: 3 Marketing 47603.64
## 4: 4 IT 59448.24
## 5: 5 Service 51957.44
## 6: 6 Sales NA
## 7: 7 Quality NA#R Google Maps
#install.packages("RgoogleMaps")
library(RgoogleMaps)Latitud y longitud
lat <- 39.637492
lon <- 2.644557
uib.map <- GetMap(center = c(lat, lon), zoom = 17,
destfile = "../Curso Udemy/r-course-master/data/tema12/uib.pdf",
format = "pdf",# puede ser
maptype = "terrain")#satellite o terrain
PlotOnStaticMap(uib.map)
#satellite, roadmap, terrain
library(RgoogleMaps)
wages <- read.csv("../../r-course-master/data/tema12/nj-wages.csv")wages$wgClass <- cut(wages$Avgwg,#divide
quantile(wages$Avgwg,#y dame los cuantiles de esta columna
probs = seq(0,1, 0.2)),# con probabilidades de 0 a 1 con saltos del 20 %
labels = F,
include.lowest = T)#si alguine cae en el corte lo mande abajo
head(wages)## Zipcode ZipCodeType City State LocationType Lat Long
## 1 7675 STANDARD WESTWOOD NJ PRIMARY 40.98 -74.03
## 2 7677 STANDARD WOODCLIFF LAKE NJ PRIMARY 41.02 -74.05
## 3 7885 STANDARD WHARTON NJ PRIMARY 40.89 -74.58
## 4 7981 STANDARD WHIPPANY NJ PRIMARY 40.82 -74.41
## 5 8889 STANDARD WHITEHOUSE STATION NJ PRIMARY 40.60 -74.76
## 6 7095 STANDARD WOODBRIDGE NJ PRIMARY 40.55 -74.28
## Location Decommisioned TaxReturnsFiled EstimatedPopulation
## 1 NA-US-NJ-WESTWOOD FALSE 13245 24083
## 2 NA-US-NJ-WOODCLIFF LAKE FALSE 2945 5471
## 3 NA-US-NJ-WHARTON FALSE 5273 8999
## 4 NA-US-NJ-WHIPPANY FALSE 4585 8057
## 5 NA-US-NJ-WHITEHOUSE STATION FALSE 4691 8570
## 6 NA-US-NJ-WOODBRIDGE FALSE 10018 17272
## TotalWages Avgwg wgClass
## 1 1089095041 82226.88 5
## 2 325436960 110504.91 5
## 3 240827990 45671.91 3
## 4 292096795 63707.04 4
## 5 401312434 85549.44 5
## 6 528315021 52736.58 3#paleta de colores
pal <- palette(rainbow(5))# 5 colores map <- MapBackground(lat = wages$Lat, lon= wages$Long)## user coords: 73.61933 76.12324 94.55942 97.06333
## center, zoom: 40.115 -74.715 8PlotOnStaticMap(map, wages$Lat, wages$Long,
pch=21, #forma de los indicadores
cex = sqrt(wages$wgClass),#Raiz cuadrada de los quintiles que generamos( tamano de la bola)
bg=pal[wages$wgClass]#color
)
legend("topleft", #donde quieres la cajita
legend = paste("<=", round(tapply(wages$Avgwg,# de las ganancias
wages$wgClass,# por los cuantiles definidos con anterioridad
mean# quiero el media de los ingresos
),0# cero decimales
)
),
pch = 21,#Del mismo tipo
pt.bg = pal,#misma paleta de colores
pt.cex = 1.0, #todas del mismo tamano
bg="gray", title = "Ingresos promedio"# titulo
)
#Crimenes de Chicago se necesita pagar a google
#install.packages(c("ggmap", "maps"))
library(ggmap)## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.## Please cite ggmap if you use it! See citation("ggmap") for details.library(maps)##
## Attaching package: 'maps'## The following object is masked from 'package:plyr':
##
## ozone## The following object is masked from 'package:mclust':
##
## map## The following object is masked from 'package:cluster':
##
## votes.repub## The following object is masked from 'package:purrr':
##
## mapcrimes <- read.csv("../../r-course-master/data/tema12/chicago-crime.csv")Fecha
crimes$Date <- strptime(crimes$Date, format = "%m/%d/%y %H:%M")mapa ( se necesita una appi )
chicago <- get_map(location = “chicago”, #nombre de la ciudad zoom = 11)
ggmap(chicago) + geom_point(data = crimes[1:500,], aes(x=Longitude, y = Latitude))
counts <- as.data.frame(table(round(crimes\(Longitude,2), round(crimes\)Latitude,2) ) ) counts\(Lon <- as.numeric(as.character(counts\)Var1)) counts\(Lat <- as.numeric(as.character(counts\)Var2)) counts\(Var1 <- NULL counts\)Var2 <- NULL
summary(counts)
ggmap(chicago) + geom_tile(data = counts, aes(x = Lon, y = Lat, alpha = Freq), fill = “red”)
#Ficheros ESRI (trabajar con formas )
#install.packages("sp")
#install.packages("rgdal")
library(sp)
library(rgdal)## Please note that rgdal will be retired by the end of 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
##
## rgdal: version: 1.5-29, (SVN revision 1165M)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/Antonio Constantino/Documents/R/win-library/4.1/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/Antonio Constantino/Documents/R/win-library/4.1/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-6
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.
## Overwritten PROJ_LIB was C:/Users/Antonio Constantino/Documents/R/win-library/4.1/rgdal/projcountries0.sp <- readOGR("../../r-course-master/data/tema12/natural_earth", "ne_50m_admin_0_countries" )## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\Antonio Constantino\Desktop\Rstudio\r-course-master\data\tema12\natural_earth", layer: "ne_50m_admin_0_countries"
## with 241 features
## It has 63 fieldsairports.sp <- readOGR("../../r-course-master/data/tema12/natural_earth","ne_50m_airports")## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\Antonio Constantino\Desktop\Rstudio\r-course-master\data\tema12\natural_earth", layer: "ne_50m_airports"
## with 281 features
## It has 10 fieldsVemos que clase es esta ESRI
class(countries0.sp)## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"class(airports.sp)## [1] "SpatialPointsDataFrame"
## attr(,"package")
## [1] "sp"Pinta en el mapa mundi los aeropuertos del mundo
plot(countries0.sp)
# se puede anadir que ponga color en cada pais(,col = countries0.sp@data$admin))
plot(airports.sp, add=TRUE)
Pinta en el mapa mundi como se ven la distribucion de datos
spplot(countries0.sp, c("pop_est"))
#Maps
#install.packages("maps")
library(maps)map("world")#mundo
map("world", interior = F)#el mundo sin fronteras 
map("world", fill = TRUE, #rellanealo
col = palette(rainbow(12)))#de 12 colores 
map("world", "mexico",interior = F)
map("france")
map("italy")
map("world", "spain")
map("usa")
map("state")
map("state", interior=F)
map("county")
Conversión entre data frame y datos espaciales
library(sp)
nj <- read.csv("../../r-course-master/data/tema12/nj-wages.csv")class(nj)## [1] "data.frame"#Coordinates sirve para indicar cuales serán las dos columnas #con la info de las coordenadas (long, lat) Fijarte que esto yas e cambio
coordinates(nj) <- c("Long", "Lat")
class(nj)## [1] "SpatialPointsDataFrame"
## attr(,"package")
## [1] "sp"plot(nj)
Funcion que una los puntos con lineas
spatial_lines <- function(spdf, name = "dummy"){
temp <- SpatialLines(
list(
Lines(
list(Line(coordinates(spdf))), name)
)
)
return(temp)
}sp <- spatial_lines(nj)
plot(sp)
#Conversiones entre DF, Mapas y objetos espaciales
#install.packages("maptools")
library(maptools) # carga sp## Checking rgeos availability: FALSE
## Please note that 'maptools' will be retired by the end of 2023,
## plan transition at your earliest convenience;
## some functionality will be moved to 'sp'.
## Note: when rgeos is not available, polygon geometry computations in maptools depend on gpclib,
## which has a restricted licence. It is disabled by default;
## to enable gpclib, type gpclibPermit()##
## Attaching package: 'maptools'## The following object is masked from 'package:Hmisc':
##
## labella forma global para usar estas transformaciones : SpatialXXXXXXXDataFrame
#maps -> spatial poligons -> spatial poligos data frame
nj.data <- read.csv("../../r-course-master/data/tema12/nj-county-data.csv")
rownames(nj.data) <- nj.data$name
nj.data$name <- NULL
nj.data[1:5,]## per_capita_income median_household_income median_family_income
## hunterdon 48489 100980 121166
## morris 47342 96747 114694
## somerset 47067 97440 115214
## bergen 42006 81708 100310
## monmouth 40976 82265 102074
## population no_households area_sq_mi
## hunterdon 128349 47169 430
## morris 492276 180534 469
## somerset 323444 117759 305
## bergen 905116 335730 234
## monmouth 630380 233983 472pedimos los rangos de latitudes y longitudes
nj.map <- map("county", "new jersey", fill = T,#que me ayude a rellenarlo
plot = F)#Aun no me des el mapa
nj.map$names## [1] "new jersey,atlantic" "new jersey,bergen" "new jersey,burlington"
## [4] "new jersey,camden" "new jersey,cape may" "new jersey,cumberland"
## [7] "new jersey,essex" "new jersey,gloucester" "new jersey,hudson"
## [10] "new jersey,hunterdon" "new jersey,mercer" "new jersey,middlesex"
## [13] "new jersey,monmouth" "new jersey,morris" "new jersey,ocean"
## [16] "new jersey,passaic" "new jersey,salem" "new jersey,somerset"
## [19] "new jersey,sussex" "new jersey,union" "new jersey,warren"Vector de nombres (separar nombres por la coma )
nj.county <- sapply(strsplit(nj.map$names, ","),#separa los 2 strings que tengan coma ( como un texto en columnas separado por caracter especial )
function(x) x[2])#solo devuelve el segundo valor ( el condado )Combirtamos en un spatial poligon
nj.sp <- map2SpatialPolygons(nj.map,#objeto
IDs = nj.county,#Estos son los ID's
proj4string = CRS("+proj=longlat +ellps=WGS84"))# CRS (Como van a venir descritos los poligonos ) #world Geopositon Standar (wgs)## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj
## = prefer_proj): Discarded datum Unknown based on WGS84 ellipsoid in Proj4
## definitionValidemos que ya sea un poligono espacial
class(nj.sp)## [1] "SpatialPolygons"
## attr(,"package")
## [1] "sp"nj.spdf <- SpatialPolygonsDataFrame(nj.sp, nj.data)#dunatamos los daros espaciales con el DF Inicial
class(nj.spdf)## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"Grafiquemoslo
plot(nj.spdf)
spplot(nj.spdf, "per_capita_income", main = "Renta per Cápita")
spplot(nj.spdf, "population", main = "Población")
spplot(nj.spdf, "median_family_income", main = "Ingresos promedio familiares")
spplot(nj.spdf, c("per_capita_income", "median_family_income"),
main = "Ingresos")