---
title: "Figuras"
author: "René Díaz Flores"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
---
```{r setup, include=FALSE}

library(pacman)
p_load("data.table","ggthemes","lubridate","plyr","flexdashboard","readxl","tidyverse","plotly",
       "ggExtra")

BP <- read_excel("SERIES SUBSISTEMA BANCA PRIVADA 2022_06.xlsx", 
                  sheet = "BAL", skip = 5)%>%
      select(CODIGO, CUENTA,`2020-01`:`2022-06`)


# Make some noisily increasing data
set.seed(955)
dat <- data.frame(cond = rep(c("A", "B"), each=10),
                  xvar = 1:20 + rnorm(20,sd=3),
                  yvar = 1:20 + rnorm(20,sd=3))
```

Balance General
=======================================================================

Row
-----------------------------------------------------------------------

### Evolución Cartera de Créditos

```{r}
Cartera <- BP %>%
           filter(CODIGO == 14) %>%
           pivot_longer(cols = `2020-01`:`2022-06`,
                        names_to = c("Meses"),
                        values_to = c("Cartera de Crédito"))%>%
           mutate(Meses     = seq(ymd('2020-01-01'),ymd('2022-06-01'), by = 'month'),
                  Meses_II  = month(Meses)) %>%
           filter(Meses_II %in% c(3,6,9,12))%>% 
           mutate(`Cartera de Crédito` = round(`Cartera de Crédito`,2),
                  Variacion = (`Cartera de Crédito`/lag(`Cartera de Crédito`)-1))%>%
           mutate_if(is.numeric, ~ifelse(is.na(.),0,.))%>%
           mutate_at(vars(`Cartera de Crédito`),list(~round(./1000000,2)))%>%
           mutate(var_iii = (Variacion*340)+30)
           
Composicion <- Cartera %>%
               select(Meses, Variacion)

Max_Min <- Cartera %>%
           summarise_at(vars(`Cartera de Crédito`, Variacion),
                             list(Min=min, Max=max))  


HG <- theme(axis.line        = element_line(size=1, colour = "black"),
            panel.grid.minor = element_blank(),
            panel.border     = element_blank(), 
            panel.background = element_blank(),
            axis.text.x      = element_text(colour="black", size = 12),
            axis.text.y      = element_text(colour="black", size = 14),
            axis.title       = element_text(size = 15))

af <- ggplot() + 
      geom_bar(data=Cartera, aes(x=Meses, y = `Cartera de Crédito`),stat = "identity",colour="gray2", 
               fill="cadetblue3") +     
      #theme_minimal() +ggExtra::removeGrid()
       theme_economist()

af <- af + scale_x_date(date_labels = '%Y%-%b',
                          breaks    = as.Date(c( '2020-03-01',
                                                 '2020-06-01',
                                                 '2020-09-01',
                                                 '2020-12-01',
                                                 '2021-03-01',
                                                 '2021-06-01',
                                                 '2021-09-01',
                                                 '2021-12-01',
                                                 '2022-03-01',
                                                 '2022-06-01'))) 
  

all<- af + geom_line(data=Cartera,mapping = aes(x = Meses, y = Variacion*340+30), 
                                           color="black", size=0.6,linetype = 1)+
           geom_point(data=Cartera,mapping = aes(x = Meses, y = Variacion*340+30), 
                                           color="darkblue", size=1)+
           scale_y_continuous(expand     = c(0,0),
                              sec.axis   = ggplot2::sec_axis(~(.-30)/340, 
                                    name = "Variación Porcentual",
                                labels   = scales::label_percent()))
  
#all

ggplotly(all)
```


geom_density
=======================================================================

Row
-----------------------------------------------------------------------

### stat_density Example

```{r}
dfGamma = data.frame(nu75 = rgamma(100, 0.75),
           nu1 = rgamma(100, 1),
           nu2 = rgamma(100, 2))

dfGamma = stack(dfGamma)

p <- ggplot(dfGamma, aes(x = values)) +
            stat_density(aes(group = ind, color = ind),position="identity",geom="line")
ggplotly(p)
```

### stat_density Example

```{r}
dfGamma = data.frame(nu75 = rgamma(100, 0.75),
           nu1 = rgamma(100, 1),
           nu2 = rgamma(100, 2))

dfGamma = stack(dfGamma)

p <- ggplot(dfGamma, aes(x = values)) +
            stat_density(aes(group = ind, color = ind),position="identity",geom="line")
ggplotly(p)
```

Row
-----------------------------------------------------------------------

### geom_density and facet_wrap Together

```{r}
dd<-data.frame(matrix(rnorm(144, mean=2, sd=2),72,2),c(rep("A",24),rep("B",24),rep("C",24)))
colnames(dd) <- c("x_value", "Predicted_value",  "State_CD")

dd <- data.frame(
  predicted = rnorm(72, mean = 2, sd = 2),
  state = rep(c("A", "B", "C"), each = 24)
)

grid <- with(dd, seq(min(predicted), max(predicted), length = 100))
normaldens <- ddply(dd, "state", function(df) {
  data.frame(
    predicted = grid,
    density = dnorm(grid, mean(df$predicted), sd(df$predicted))
  )
})

p <- ggplot(dd, aes(predicted))  +
            geom_density() +
            geom_line(aes(y = density), data = normaldens, colour = "red") +
            facet_wrap(~ state)
ggplotly(p)
```

### Density and Scatterplot Overlay Using geom_density

```{r}
df <- data.frame(x <- rchisq(1000, 10, 10),
                 y <- rnorm(1000))

p <- ggplot(df, aes(x, y)) + 
     geom_point(alpha = 0.5) + 
     geom_density_2d() + 
     theme(panel.background = element_rect(fill = '#ffffff'))

ggplotly(p)
```