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Tema 6 completado
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‎data/EnergyDrink‎

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"estudio" "bebe" "sexo"
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"1" "Informatica" "No" "Mujer"
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"2" "Mates" "No" "Hombre"
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"3" "Industriales" "Si" "Mujer"
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"4" "Informatica" "Si" "Hombre"
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"5" "Industriales" "No" "Mujer"
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"6" "Mates" "No" "Mujer"
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"7" "Informatica" "Si" "Hombre"
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"8" "Industriales" "No" "Hombre"
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"9" "Informatica" "No" "Hombre"
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"10" "Informatica" "No" "Hombre"
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"11" "Informatica" "No" "Hombre"
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"12" "Industriales" "No" "Hombre"
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"13" "Informatica" "Si" "Hombre"
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"14" "Informatica" "No" "Hombre"
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"15" "Telematica" "No" "Hombre"
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"16" "Industriales" "No" "Mujer"
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"17" "Informatica" "No" "Hombre"
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"18" "Industriales" "No" "Hombre"
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"19" "Informatica" "No" "Hombre"
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"20" "Informatica" "No" "Hombre"
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"21" "Industriales" "No" "Hombre"
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"22" "Industriales" "No" "Mujer"
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"23" "Mates" "No" "Hombre"
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"24" "Industriales" "No" "Hombre"
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"25" "Informatica" "No" "Mujer"
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"26" "Industriales" "No" "Hombre"
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"27" "Telematica" "No" "Hombre"
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"28" "Telematica" "Si" "Hombre"
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"29" "Informatica" "No" "Mujer"
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"30" "Industriales" "Si" "Hombre"
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"31" "Industriales" "Si" "Hombre"
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"32" "Industriales" "No" "Hombre"
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"33" "Mates" "No" "Mujer"
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"34" "Mates" "No" "Hombre"
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"35" "Informatica" "No" "Hombre"
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"36" "Industriales" "No" "Hombre"
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"37" "Industriales" "No" "Mujer"
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"38" "Telematica" "No" "Hombre"
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"39" "Informatica" "No" "Mujer"
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"40" "Mates" "No" "Mujer"
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"41" "Industriales" "Si" "Mujer"
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"42" "Mates" "No" "Hombre"
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"43" "Industriales" "No" "Hombre"
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"44" "Industriales" "No" "Hombre"
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"45" "Informatica" "No" "Mujer"
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"46" "Informatica" "Si" "Mujer"
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"47" "Informatica" "No" "Hombre"
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"48" "Informatica" "Si" "Hombre"
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"49" "Mates" "Si" "Hombre"
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"50" "Industriales" "No" "Hombre"
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"51" "Industriales" "No" "Hombre"
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"52" "Informatica" "Si" "Hombre"
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"53" "Industriales" "Si" "Hombre"
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"54" "Industriales" "No" "Hombre"
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"55" "Informatica" "No" "Mujer"
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"56" "Informatica" "Si" "Mujer"
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"57" "Industriales" "No" "Mujer"
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"58" "Industriales" "No" "Hombre"
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"59" "Telematica" "No" "Hombre"
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"60" "Industriales" "Si" "Hombre"
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"61" "Informatica" "No" "Hombre"
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"62" "Industriales" "No" "Hombre"
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"63" "Informatica" "No" "Hombre"
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"64" "Informatica" "No" "Hombre"
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"65" "Telematica" "No" "Hombre"
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"66" "Informatica" "Si" "Mujer"
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"67" "Informatica" "No" "Hombre"
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"68" "Informatica" "No" "Hombre"
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"69" "Telematica" "No" "Hombre"
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"70" "Industriales" "No" "Mujer"
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"71" "Informatica" "No" "Hombre"
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"72" "Informatica" "No" "Mujer"
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"73" "Informatica" "No" "Hombre"
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"74" "Industriales" "No" "Hombre"
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"75" "Mates" "No" "Hombre"
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"76" "Informatica" "No" "Hombre"
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"77" "Telematica" "No" "Mujer"
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"78" "Informatica" "No" "Hombre"
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"79" "Mates" "No" "Mujer"
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"80" "Telematica" "No" "Hombre"
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"81" "Informatica" "No" "Hombre"
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"82" "Industriales" "Si" "Hombre"
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"83" "Informatica" "No" "Hombre"
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"84" "Informatica" "No" "Hombre"
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"85" "Informatica" "No" "Hombre"
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"86" "Industriales" "No" "Mujer"
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"87" "Telematica" "No" "Mujer"
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"88" "Informatica" "No" "Mujer"
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"89" "Informatica" "No" "Mujer"
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"90" "Industriales" "No" "Mujer"
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"91" "Industriales" "No" "Hombre"
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"92" "Mates" "No" "Hombre"
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"93" "Informatica" "No" "Hombre"
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"94" "Telematica" "No" "Hombre"
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"95" "Industriales" "No" "Hombre"
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"96" "Informatica" "Si" "Mujer"
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"97" "Mates" "No" "Hombre"
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"98" "Industriales" "No" "Mujer"
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"99" "Telematica" "Si" "Hombre"
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"100" "Informatica" "Si" "Mujer"
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"101" "Mates" "No" "Mujer"
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"102" "Telematica" "No" "Hombre"
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"103" "Informatica" "No" "Hombre"
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"104" "Industriales" "No" "Mujer"
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"105" "Informatica" "No" "Hombre"
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"106" "Informatica" "Si" "Hombre"
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"107" "Industriales" "No" "Hombre"
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"108" "Informatica" "No" "Hombre"
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"109" "Informatica" "No" "Hombre"
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"110" "Informatica" "No" "Hombre"
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"111" "Mates" "Si" "Mujer"
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"112" "Mates" "No" "Mujer"
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"113" "Industriales" "Si" "Hombre"
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"114" "Telematica" "Si" "Mujer"
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"115" "Informatica" "No" "Mujer"
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"116" "Telematica" "No" "Hombre"
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"117" "Informatica" "No" "Mujer"
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"118" "Informatica" "No" "Hombre"
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"119" "Informatica" "No" "Hombre"
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"120" "Telematica" "No" "Mujer"
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"121" "Informatica" "Si" "Hombre"
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"122" "Mates" "No" "Hombre"

‎scripts/tema6/01-table.Rmd‎

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---
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title: "Tablas de Contingencia"
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author: "Curso de Estadística Descriptiva"
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date: "24/12/2018"
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output: html_document
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---
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# Tablas de contingencia
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```{r}
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datos = factor(c("H", "M", "M", "M", "H", "H", "M", "M"))
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table(datos)
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table(datos)["M"]
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sum(table(datos))
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```
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# Frecuencias relativas
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$$f_i = \frac{n_i}{n}$$
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```{r}
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prop.table(table(datos))
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100*prop.table(table(datos))
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table(datos)/length(datos)
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names(which(table(datos)==3))
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moda <- function(d){
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names(which(table(d)==max(table(d))))
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}
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m_t = moda(datos)
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```
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La moda del data frame es: `r m_t`.
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# Paquete `gmodels`
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```{r}
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library(gmodels)
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sex = factor(c("H", "M", "M", "M", "H", "H", "M", "M"))
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ans = factor(c("S", "N", "S", "S", "S", "N", "N", "S"))
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CrossTable(sex, ans, prop.chisq = FALSE)
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```
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# Sumas por filas y columnas
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```{r}
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tt <- table(sex, ans)
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tt# Frec. absolutas
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prop.table(tt)#Frec. Rel. Global
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prop.table(tt, margin = 1)#Frec. Rel. Por sexo
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prop.table(tt, margin = 2)#Frec. Rel. Por respuesta
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colSums(tt)
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rowSums(tt)
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colSums(prop.table(tt))
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rowSums(prop.table(tt))
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apply(tt, FUN = sum, MARGIN = 1)
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apply(tt, FUN = sqrt, MARGIN = c(1,2))
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```
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‎scripts/tema6/01-table.html‎

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‎scripts/tema6/02-multivariant.Rmd‎

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---
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title: "Multivariante"
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author: "Curso de Estadística Descriptiva"
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date: "24/12/2018"
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output: html_document
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---
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# Datos multidimensionales
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## Ejemplo con tres dimensiones
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```{r}
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ans = sample(c("Si", "No"), size = 100, replace = TRUE)
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sex = sample(c("H", "M"), size = 100, replace = TRUE)
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place = sample(c("San Francisco", "Barcelona", "Valencia", "Cobija", "Asturias"), size = 100, replace = TRUE)
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table(sex, ans, place)
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ftable(sex, ans, place)
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ftable(sex, ans, place, col.vars = c("sex", "ans"))
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```
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### Filtrar las tablas
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```{r}
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table(sex, ans, place)["M", "Si", "San Francisco"]
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table(sex, ans, place)[ , "Si", "Valencia"]
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table(sex, ans, place)[ , "No", ]
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table(sex, ans, place)["M", , "Cobija"]
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```
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### Frecuencias relativas
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```{r}
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prop.table(table(sex, ans, place))#Frec. Rel. Globales
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prop.table(table(sex, ans, place), margin = 3) # Frec. Rel. Marginal por Lugar
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prop.table(table(sex, ans, place), margin = c(1, 3)) # Frec. Rel. Marg. por Sexo y País
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ftable(prop.table(table(sex, ans, place)))
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```
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‎scripts/tema6/02-multivariant.html‎

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‎scripts/tema6/03-people.Rmd‎

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---
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title: "03-people"
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author: "Curso de Estadística Descriptiva"
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date: "24/12/2018"
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output:
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html_document: default
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pdf_document: default
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---
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# Ejemplo de color de ojos y de pelo
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```{r}
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HairEyeColor
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sum(HairEyeColor) -> total
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```
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El total de individuos de la tabla de datos es `r total`.
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```{r}
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prop.table(HairEyeColor, margin = 3)
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prop.table(HairEyeColor, margin = c(1,2))
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```
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```{r}
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aperm(HairEyeColor, perm = c("Sex", "Hair", "Eye"))
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```
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```{r}
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library(kableExtra)
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kable(HairEyeColor)
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```
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```{r, results='asis'}
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library(xtable)
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sex = factor(c("H", "M", "M", "M", "H", "H", "M", "M"))
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ans = factor(c("S", "N", "S", "S", "S", "N", "N", "S"))
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xtable(table(sex, ans))
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```
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‎scripts/tema6/03-people.html‎

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‎scripts/tema6/03-people.pdf‎

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‎scripts/tema6/04-final-example.Rmd‎

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---
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title: "Ejemplo Final"
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author: "Curso de Estadística Descriptiva"
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date: "24/12/2018"
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output: html_document
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---
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# Ejemplo final
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## Juntar color de ojos y pelo sin distinguir por sexo
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```{r}
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ftable(HairEyeColor)
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male <- HairEyeColor[, ,"Male"]
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female <- HairEyeColor[, ,"Female"]
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data <- as.table(male+female)
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data
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```
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## Manipulación de datos
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```{r}
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dimnames(data) = list(
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Pelo = c("Negro", "Marron", "Pelirrojo", "Rubio"),
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Ojos = c("Marrones", "Azules", "Pardos", "Verdes")
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)
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data
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```
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## Diagrama de Mosaico
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```{r}
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plot(data, col = c("lightblue"), main = "Diagrama de Mosaico")
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```
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## Datos numéricos
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```{r}
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sum(data)
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colSums(data)
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rowSums(data)
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round(prop.table(colSums(data)), 3)
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round(prop.table(rowSums(data)), 3)
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```
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## Diagramas de barras
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```{r}
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barplot(prop.table(colSums(data)), ylim = c(0, 0.4),
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main = "Frecuencias relativas del color de ojos",
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col = c("burlywood4", "lightblue", "orange3", "lightgreen")
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)
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```
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## Frecuencias relativas globales y marginales
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```{r}
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round(prop.table(data), 3)
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round(prop.table(data, margin = 1), 3)
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round(prop.table(data, margin = 2), 3)
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barplot(prop.table(data, margin = 1), beside = TRUE,
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legend.text = TRUE, ylim = c(0, 0.8),
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col = c("black", "burlywood4", "red", "gold"),
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main = "Frecuencias relativas del color de pelo\n para cada color de ojo.")
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barplot(t(prop.table(data, margin = 2)), beside = TRUE,
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legend.text = TRUE, ylim = c(0, 0.6),
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col = c("burlywood4", "lightblue", "orange3", "lightgreen"),
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main = "Frecuencias relativas del color de ojos\n para cada color de pelo")
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```
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‎scripts/tema6/04-final-example.html‎

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