flexdash.Rmd

---
title: "Índice de Precios"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
---

```{r setup, include=FALSE}
library(flexdashboard)
# import the data
library(readxl)
library(xts)
library(ggplot2)
library(TSstudio)
data <- readxl::read_xlsx("indice_precios_de_viviendas_nuevas.xlsx")

# transform each column in a distinct time series except the first three columns

df_xts <- ts(data[, -1], start=c(2016,1), frequency = 4)
#df_xts <- xts(data[, -1], )

by_apartment <- c()

by_all_cities <- c("Bogotá","Medellín", "Cali", "Bucaramanga", "Cartagena", "Barranquilla", "Manizales", "Pereira", "Villavicencio", "Pasto", "Neiva", "Cucuta", "Armenia", "Popayan", "Ibague")

by_important_cities <- c("Bogotá","Medellín", "Cali", "Bucaramanga", "Cartagena",  "Barranquilla")

by_apartment <- c("barranquilla_apartamentos", "bogotacundi_apartamentos", "bucaramanga_apartamentos", "cali_apartamentos", "cartagena_apartamentos", "medellin_apartamentos")

by_casas <- c("barranquilla_casas", "bogotacundi_casas", "bucaramanga_casas", "cali_casas", "cartagena_casas", "medellin_casas")

by_high <- c("Alto/Barranquilla", "Alto/Bogotá", "Alto/Bucaramanga", "Alto/Cali", "Alto/Cartagena", "Alto/Medellín")

by_medium <- c("Medio/Barranquilla", "Medio/Bogotá", "Medio/Bucaramanga", "Medio/Cali", "Medio/Cartagena", "Medio/Medellín")

by_low <- c("Bajo/Barranquilla", "Bajo/Bogotá", "Bajo/Bucaramanga", "Bajo/Cali", "Bajo/Cartagena", "Bajo/Medellín")



```




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

### Gráfico de Lineas

```{r}
ts_plot(df_xts[, by_important_cities], title= "")

```

### Barranquilla City Heatmap
```{r}
ts_heatmap(df_xts[, "Barranquilla"], title = "Barranquilla", color="viridis")

```

### Distribuciones
```{r}
library(tidyr)
library(plotly)

long_form <- data[ , by_important_cities] |>
  pivot_longer(cols = everything(), names_to = "series", values_to = "value")

graph <- ggplot(long_form, aes(x = series, y = value)) + 
  geom_jitter(aes(color = series), width = 0.2, size = 3, alpha= 0.7) +
  theme_minimal() +
  scale_color_viridis_d()
  
ggplotly(graph)

```

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


### Chart C


```{r}
library(highcharter)
library(viridis)
  # Asegúrate de que tu dataframe tiene una columna 'year'
  # 

# color light blueish
color <- "#7cb5ec"

#Map list of coordinates for every city

coordinates <- list(
  Bogotá = c(4.7110, -74.0721),
  Medellín = c(6.2442, -75.5812),
  Cali = c(3.4516, -76.5320),
  Bucaramanga = c(7.1254, -73.1198),
  Cartagena = c(10.3910, -75.4794),
  Barranquilla = c(10.9639, -74.7964)
)

coordinates_of_all_cities <- list(
  Bogotá = c(4.7110, -74.0721),
  Medellín = c(6.2442, -75.5812),
  Cali = c(3.4516, -76.5320),
  Bucaramanga = c(7.1254, -73.1198),
  Cartagena = c(10.3910, -75.4794),
  Barranquilla = c(10.9639, -74.7964),
  Manizales = c(5.0689, -75.5174),
  Pereira = c(4.8133, -75.6961),
  Villavicencio = c(4.1420, -73.6266),
  Pasto = c(1.2136, -77.2811),
  Neiva = c(2.9273, -75.2819),
  Cucuta = c(7.8939, -72.5078),
  Armenia = c(4.5341, -75.6757),
  Popayan = c(2.4540, -76.6093),
  Ibague = c(4.4377, -75.2421)
)


data_for_second_column = coordinates_of_all_cities
cities_to_plot = by_all_cities

# Crear un dataframe con las coordenadas y los valores
map_data <- data.frame(
  city = names(data_for_second_column),
  latitude = sapply(data_for_second_column, `[`, 1),
  longitude = sapply(data_for_second_column, `[`, 2),
  value = unlist(data[3, cities_to_plot])  # Asegúrate de que los índices se alinean correctamente
)

options(highcharter.download_map_data = TRUE)

hcmap("countries/co/co-all", showInLegend = FALSE) |> 
  hc_add_series(
      data = map_data,
      name = "Precios de vivienda",  
      type = "mapbubble",
      minSize = "1%",
      maxSize = "10%",
      color = color,
      zIndex = 1,
      #dataLabels = list(enabled = TRUE, format = '{point.city}: {point.value}'),
      mapping = hcaes(
        lat = latitude,  # Coordenadas de latitud
        lon = longitude, # Coordenadas de longitud
        z = value,        # Valores para ajustar el tamaño de las burbujas
        name = city
      )
      ) |> 
  hc_mapNavigation(enabled = TRUE) |>
#
  hc_colorAxis(
    stops = color_stops(colors = viridis(max(map_data[, "value"])))  # Define la escala de colores Viridis
  ) 
  
```

### Chart D {data-height=225}


```{r}
library(plotly)
# bar grapht with plotly
barplot <- ggplot(map_data, aes(x = city, y = value, fill=value)) +
  geom_bar(stat = "identity") +
  scale_fill_viridis() +
  labs(x = "Ciudades", y = "Indice de precios") +
  theme_minimal() 

ggplotly(barplot)
```



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

### card E 

```{r}
sliderInput("quarter", "Selecciona el trimestre:",
                  min = as.Date("2016-01-01"),
                  max = as.Date("2024-04-01"),
                  value = as.Date("2020-01-01"),
                  width = "100%",
                  timeFormat = "%D",  # Formato de trimestre
                  step = 90,  # Aproximadamente 3 meses (un trimestre)
                  animate = TRUE  # Esto añade un botón de play
    )
```
```{r}
selectInput("city", "Selecciona la ciudad", choices = by_all_cities)
```

```{r}
selectInput("stratum", "Selecciona el estrato", choices = c("Alto", "Medio", "Bajo"))
```

```{r}
selectInput("type", "Selecciona el tipo de vivienda", choices = c("Apartamentos", "Casas"))
```
```{r}

```
### Pronóstico

```{r}
library(TSstudio)
library(forecast)
## Modelo ARIMA (1,0,1) univariado
fcp <- data |> 
  select("Barranquilla")|>
  ts(start=c(2016,1), frequency = 4) |> 
  arima(order = c(0,2,0)) |> 
  forecast(h=4)
plot <- plot_forecast(fcp)
plot
```

### Prónostico Backtested
```{r}
library(forecast)
library(TSstudio)

fc_span <- 4

quilla <- data |>
  select("Barranquilla") |>
  ts(start=c(2016,1), frequency = fc_span)

quillas <- ts_split(quilla, sample.out = fc_span)

train <- quillas$train
test <- quillas$test

fc <- arima(train, order = c(0,2,0)) |>
  forecast(h = fc_span)
test_forecast(actual = quilla, forecast.obj = fc, test = test)

```