---
title: "Bar Charts"
date: "`r Sys.Date()`"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    theme: united
    social: menu
    source: embed
---

```{r setup, include=FALSE}
library(flexdashboard)

library(ggplot2)

library(lattice)

```


# Diagramas de Barras {data-icon="fa-chart-bar"}

Column {data-width=333}
-----------------------------------------------------------------------

### barplot()

```{r barplot,fig.align='center'}
x <- table(mtcars$cyl)

colores <- c("orange","blue","purple")

barplot(x,xlab="Cilindros",ylab="Frecuencias",main="Número de Cilindros",col=colores)
```

Column {data-width=333}
-----------------------------------------------------------------------

### ggplot2

```{r ggplot2,fig.align='center'}
ggplot(mtcars,aes(cyl)) + geom_bar(fill=colores) + labs(x="Cillindros",y="Frecuencias",title="Número de Cilindros") + theme_dark()

```



Column {data-width=334}
------------------------------------------------------------------------


### lattice

```{r lattice,fig.align='center'}
barchart(x,xlab="Cilindros",ylab="Frecuencias",main="Número de Cilindros",col=colores,horizontal=FALSE)
```



# Pie Chart {data-icon="fa-chart-pie"}


Column {data-width=1000}
-----------------------------------------------------------------------

El diagrama de sectores (**Pie Chart**) es un gráfico estadístico que nos permite representar la distribución de frecuencias de variables cualitativas discretas.

Para efecto de la creación del presente gráfico estadístico estamos haciendo uso de la función `pie()` para generar el gráfico de torta (**Pie Chart**).

```{r,fig.align='center'}
count_3 <- c(20, 50, 30)
# sum(count_3) # 100

pie(count_3, labels = paste0(count_3, "%"))

legend("topleft", legend = c("Theatre", "Series", "Movies"),
       fill =  c("white", "lightblue", "mistyrose"))
```



# Histograma Frecuencias {data-icon="fa-chart-bar"}

Column {data-width=1000}
-----------------------------------------------------------------------

```{r,fig.align='center'}
distance <- c(241.1, 284.4, 220.2, 272.4, 271.1, 268.3,
              291.6, 241.6, 286.1, 285.9, 259.6, 299.6,
              253.1, 239.6, 277.8, 263.8, 267.2, 272.6,
              283.4, 234.5, 260.4, 264.2, 295.1, 276.4,
              263.1, 251.4, 264.0, 269.2, 281.0, 283.2)

hist(distance, prob = TRUE, main = "Density histogram") 
```



# All together {data-icon="fa-ruler"}


## {.tabset}

### barplot

```{r}
x <- table(mtcars$cyl)

colores <- c("orange","blue","purple")

barplot(x,xlab="Cilindros",ylab="Frecuencias",main="Número de Cilindros",col=colores)
```


### Pie Chart

```{r}
count_3 <- c(20, 50, 30)
# sum(count_3) # 100

pie(count_3, labels = paste0(count_3, "%"))

legend("topleft", legend = c("Theatre", "Series", "Movies"),
       fill =  c("white", "lightblue", "mistyrose"))
```


### Histograma


Un ***Histograma de frecuencias*** es un gráfico estadístico que nos permite la presentación de variables cualitativas continuas.

```{r}
distance <- c(241.1, 284.4, 220.2, 272.4, 271.1, 268.3,
              291.6, 241.6, 286.1, 285.9, 259.6, 299.6,
              253.1, 239.6, 277.8, 263.8, 267.2, 272.6,
              283.4, 234.5, 260.4, 264.2, 295.1, 276.4,
              263.1, 251.4, 264.0, 269.2, 281.0, 283.2)

hist(distance, prob = TRUE, main = "Density histogram") 
```