Graficos

---
title: "Pizzas: Analisis de nutrientes"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
    theme: cerulean
---

```{r setup, include=FALSE}
library(ggplot2)
library(plotly)
library(plyr)
library(flexdashboard)
library (factoextra)


# Lectura de datos

pizza <- read.csv("Pizza.csv")




# Eliminar identificador de la observación
pizza$id <- NULL

# Cálculo de los componentes principales para las variables
pc <- prcomp(~mois + prot + fat + ash + sodium + carb + cal, 
             data=pizza, center = TRUE, scale = TRUE)

# Resumen de resultados
summary(pc)

pc

# Agrego los scores al dataset original
pizza <<- within(pizza, {
  PC2 <- pc$x[,2]
  PC1 <- pc$x[,1]
})



```

Graficos {data-orientation=columns}
=======================================================================

Column
-----------------------------------------------------------------------

### Grafico los datos para componentes PC1 y PC2 

```{r}


# Grafico los datos para componentes PC1 y PC2
a = ggplot(pizza, aes(x=PC1, y=PC2, color=brand)) +
  geom_point() +
  xlab("PC1") +
  ylab("PC2") 

ggplotly(a)



```


Column
-----------------------------------------------------------------------


### Gráfico de componentes principales con información en clustering 

```{r}
# Gráfico de componentes principales con información en clustering
p = fviz_pca_ind(pc, geom.ind = "point", pointshape = 21, 
             pointsize = 1, 
             fill.ind = pizza$brand, 
             col.ind = "black", 
             palette = "jco", 
             addEllipses = TRUE,
             label = "var",
             col.var = "black",
             repel = TRUE,
             legend.title = "Brand") +
  ggtitle("Grafico de Componentes principales") +
  theme(plot.title = element_text(hjust = 0.5))


ggplotly(p)

```