---
title: "Análisis de datos"
output: 
  flexdashboard::flex_dashboard:
    theme: cosmo 
    orientation: columns
    vertical_layout: fill
    logo: https://cdn-icons-png.flaticon.com/32/25/25231.png
    storyboard: true
    social: menu
    source: embed
---


# Introducción {data-icon="fa fa-battery-1"}

## Objetivos

### Objetivo principal

Desarrollar un modelo de machine learning que calcule la probabilidad de aprobar un score digital.

![](https://docs.microsoft.com/es-ES/azure/architecture/data-science-process/media/overview/tdsp-lifecycle2.png)

## Detalles

### Alcance

El proyecto plantea tener un alcance según los lineamientos:

1. Número de clientes afectados
2. Patrones visitas
3. Información recopilada
4. Otros

### Población

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

![](https://docs.microsoft.com/es-ES/azure/architecture/data-science-process/media/overview/tdsp-dir-structure.png)

# EDA {data-icon="fa fa-bug" .storyboard}

### Data

```{r}
library(DMwR2)
data(sales, package="DMwR2")
library(data.table)
library(knitr)
#df = fread('https://storage.googleapis.com/rmarkdowntaller/01Borrar/BD_COBERTURA%20EXTRAORDINARIA.csv',encoding = 'UTF-8',nrows=50)

knitr::kable(sales[1:10,], caption = 'ventas')
```

### Distribuciones

* El promedio de los ingresos de venta es `r format(mean(sales$Val , na.rm=TRUE), scientific = FALSE)`
* La cantidad de ventas que superan el promedio son `r sum(sales$Quant > mean(sales$Quant, na.rm=TRUE), na.rm=TRUE)`
* Ingreso promedio por ventas fraudulentas es `r library(dplyr); sales %>% filter(Insp =="fraud") %>% summarise(mean(Val, na.rm = TRUE)) %>% as.data.frame() %>%  format(scientific = FALSE)`
```{r}
library(skimr)
skim(sales)
```



### Gráficos

#### G1
```{r echo=FALSE, fig.height=4, fig.width=8, message=FALSE, warning=FALSE}
library(tidyverse)
fig1 <- ggplot(data=sales, aes(x = Insp))+
  geom_bar()
```

#### G2
```{r fig.height=4, fig.width=8}
library(plotly)
ggplotly(fig1)
```


# Resultados

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

Column {data-width=650}
-----------------------------------------------------------------------

### Chart A

```{r}
library(leaflet)

m <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=174.768, lat=-36.852, popup="The birthplace of R")
m
```

Column {data-width=350}
-----------------------------------------------------------------------

### Chart B

```{r}

```

### Chart C

```{r}

```