---
title: "Coronavirus en España"
author: "Víctor López"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    # social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: fill
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
#install.packages (c ("devtools", "flexdashboard", "leaflet", "leafpop")) 
#devtools :: install_github ("RamiKrispin / coronavirus")
library(flexdashboard)
# install.packages("devtools")
#devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
data(coronavirus)
update_datasets()
# View(coronavirus)
# max(coronavirus$date)

`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(Country.Region == "Spain") %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))

df_daily <- coronavirus %>%
  dplyr::filter(Country.Region == "Spain") %>%
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  #dplyr::mutate(active = confirmed - death - recovered) %>%
  dplyr::mutate(active = confirmed - death) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(confirmed),
    death_cum = cumsum(death),
    # recovered_cum = cumsum(recovered),
    active_cum = cumsum(active)
  )


df1 <- coronavirus %>% dplyr::filter(date == max(date))
```

Resumen
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
  caption = "Total casos confirmados",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```
















### death {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Muertes (Ratio de muertes)",
  icon = "fas fa-heart-broken",
  color = death_color
)
```


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

### **Casos acumulados diarios por tipo** (Solo España)
    
```{r}
plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(
    x = ~date,
    # y = ~active_cum,
    y = ~confirmed_cum,
    type = "scatter",
    mode = "lines+markers",
    # name = "Active",
    name = "Confirmados",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~death_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Muertes",
    line = list(color = death_color),
    marker = list(color = death_color)
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-02-26"),
    y = 1,
    text = paste("Primer caso"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-02-13"),
    y = 3,
    text = paste("1a Muerte"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -90,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-14"),
    y = 14,
    text = paste(
      "Confinamiento"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Número de casos acumulados"),
    xaxis = list(title = "Fecha"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

Comparación
=======================================================================


Column {data-width=400}
-------------------------------------


### **Nuevos casos confirmados diarios**
    
```{r}
daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::filter(date >= "2020-02-24") %>%
  dplyr::mutate(country = Country.Region) %>%
  dplyr::group_by(date, country) %>%
  dplyr::summarise(total = sum(cases)) %>%
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = country, values_from = total)

#----------------------------------------
# Plotting the data

daily_confirmed %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~Netherlands,
    type = "scatter",
    mode = "lines+markers",
    name = "Netherlands"
  ) %>%
   plotly::add_trace(
     x = ~date,
     y = ~Germany,
     type = "scatter",
     mode = "lines+markers",
     name = "Germany"
   ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Spain,
    type = "scatter",
    mode = "lines+markers",
    name = "Spain"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Italy,
    type = "scatter",
    mode = "lines+markers",
    name = "Italy"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Nuevos casos confirmados"),
    xaxis = list(title = "Fecha"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```
 
### **Distribución de casos por tipo**

```{r daily_summary}
df_EU <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(Country.Region == "Spain" |
    Country.Region == "Netherlands" |
    Country.Region == "Italy" |
    Country.Region == "France" |
    Country.Region == "Germany") %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))

plotly::plot_ly(
  data = df_EU,
  x = ~country,
  # y = ~unrecovered,
  y = ~ confirmed,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Confirmados",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~death,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Muertes",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Casos Totales"),
    xaxis = list(title = "Países"),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```


Información
=======================================================================

**El dashboard del Coronavirus: el caso de España**

Este cuadro de Coronavirus: el caso de España ofrece una visión general de la epidemia de coronavirus novedoso COVID-19 (2019-nCoV) para España. Este cuadro está construido con R usando el marco R Makrdown.

**Código**

El código detrás de este panel está disponible en la esquina derecha del dashboard como "Source code".

**Datos**

Los datos de entrada para este cuadro son el conjunto de datos disponible del paquete {coronavirus} R. Asegúrese de descargar la versión de desarrollo del paquete para tener los últimos datos:

install.packages ("devtools")
devtools :: install_github ("RamiKrispin / coronavirus")
Los datos y el cuadro se actualizan diariamente.

Los datos sin procesar se extraen del depósito de coronavirus del Centro de la Universidad de Johns Hopkins para Ciencia e Ingeniería de Sistemas (JHU CCSE).

**Información y contacto**

Puedes replicar mas o menos el cuadro de mando copiando el código y adaptándolo a tu país.

Para cualquier pregunta o comentario, puede contactarme.

**Actualizar**

Los datos son del sábado 25 de abril de 2020 y el tablero se actualizó el domingo 26 de abril de 2020.