index.Rmd

---
title: "Status Coronavirus di Indonesia"
author: "Aldilas Achmad N."
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    # social: ["facebook", "twitter", "linkedin"]
    vertical_layout: fill
editor_options: 
  chunk_output_type: inline
---

```{r setup, include=FALSE}
library(tidyverse)
library(flexdashboard)
library(plotly)
library(googlesheets4)
library(gargle)
library(curl)
library(networkD3)
library(igraph)
library(htmlwidgets)
library(htmltools)
library(sf)
library(leaflet)
library(leafpop)
#library(readxl)
```

```{r 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"
numtest_color <- "orange"
```

```{r data}
# designate project-specific cache
#options(gargle_oauth_cache = ".kawalcovidauth", gargle_oauth_email = "mail.aldilas@gmail.com")
gs4_auth(email = "mail.aldilas@gmail.com")
df <- read_sheet("1ma1T9hWbec1pXlwZ89WakRk-OfVUQZsOCFl4FwZxzVw", sheet = "Statistik Harian") %>% rename(date = 1, case_daily = 2, case_cum = 5, recovered_daily = 8, recovered_cum = 9, death_daily = 12, death_cum = 13, test_daily = 25)
df$date <- as.Date(df$date, format = "%d %b")

# Data lokal pulau Jawa
provCaseCum <- read_sheet("1ma1T9hWbec1pXlwZ89WakRk-OfVUQZsOCFl4FwZxzVw", range = "Timeline!A1:AI130") %>% rename(date = 1)
provCaseDaily <- read_sheet("1ma1T9hWbec1pXlwZ89WakRk-OfVUQZsOCFl4FwZxzVw", range = "Timeline!A132:AI263", col_names = TRUE) %>% rename(date = 1)
provCaseDaily[is.na(provCaseDaily)] <- 0
provCaseDat <- read_sheet("1ma1T9hWbec1pXlwZ89WakRk-OfVUQZsOCFl4FwZxzVw", range = "Kasus per Provinsi!B2:H38", col_names = TRUE)
provCaseDat[4,1] <- "Kepulauan Bangka Belitung"
provCaseDat[6,1] <- "Daerah Istimewa Yogyakarta"
provCaseDat[7,1] <- "Daerah Khusus Ibukota Jakarta"
#provRecoverCum <- read_sheet("1ma1T9hWbec1pXlwZ89WakRk-OfVUQZsOCFl4FwZxzVw", sheet = "Timeline", range = cell_rows(50:68)) %>% rename(date = 1)
#provDeathCum<- read_sheet("1ma1T9hWbec1pXlwZ89WakRk-OfVUQZsOCFl4FwZxzVw", sheet = "Timeline", range = cell_rows(90:111)) %>% rename(date = 1)
idn <- read_sf("D:/Analisis Data Transaksi Halodoc/idn_map/Indonesia_Polygon.shp")
provCaseDat <- provCaseDat %>% pivot_longer(cols = -`Provinsi Asal`, names_to = "type")
provCaseMap <- merge(idn, provCaseDat, all = TRUE)

#Google Mobility data
mobility <- read_csv("Global_Mobility_Report.csv") %>% filter(sub_region_1 == "Special Region of Yogyakarta")
```

Statistik Nasional
=======================================================================

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

### Confirmed {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df$case_daily, na.rm = T), big.mark = ","), "", sep = " "),
  caption = "Total kasus terkonfirmasi",
  icon = "fas fa-user-md",
  color = active_color
)
```

### Recovered {.value-box}
```{r}
valueBox(
  value = paste(format(sum(df$recovered_daily, na.rm=TRUE), big.mark = ","), " (", round(100 *  sum(df$recovered_daily, na.rm = TRUE) / sum(df$case_daily, na.rm=TRUE), 1), "%)", sep = ""),
  caption = "Jumlah kasus sembuh",
  icon = "fas fa-laugh",
  color = recovered_color)
```

### death {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df$death_daily, na.rm = TRUE), big.mark = ","), " (", round(100 * sum(df$death_daily, na.rm = TRUE) / sum(df$case_daily, na.rm = TRUE), 1), "%)", sep = ""),
  caption = "Jumlah kasus meninggal",
  icon = "fas fa-sad-tear",
  color = death_color
)
```

### Num of Test {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df$test_daily, na.rm = TRUE), big.mark = ","), "", sep = " "),
  caption = "Jumlah tes yang telah dilakukan",
  icon = "fas fa-vial",
  color = numtest_color
)
```

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

### **Laporan kasus kumulatif**
    
```{r message=FALSE, warning=FALSE}
plot_ly(data = df, x = ~date) %>%
  add_trace(
    y = ~case_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Jumlah kasus Terkonfirmasi",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
  add_trace(
    y = ~death_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Jumlah kasus kematian",
    line = list(color = death_color),
    marker = list(color = death_color)
  ) %>%
  add_trace(
    y = ~recovered_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Jumlah kasus sembuh",
    line = list(color = recovered_color),
    marker = list(color = recovered_color)
  ) %>%
  add_annotations(
    x = as.Date("2020-03-11"),
    y = 3,
    text = paste("Kematian pertama"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = 0,
    ay = -90
  ) %>%
  layout(
    title = "",
    yaxis = list(title = "Jumlah kasus kumulatif"),
    xaxis = list(title = "Tanggal", range = c("2020-03-02", "2020-06-26")),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

### **Laporan kasus harian**
```{r message=FALSE, warning=FALSE}
plot_ly(data = df, x = ~date) %>% 
  add_trace(
    y = ~case_daily,
    name = "Kasus terkonfirmasi baru",
    type = "bar",
    marker = list(color = active_color)
  ) %>% 
  add_trace(
    y = ~death_daily,
    name = "Kasus meninggal",
    type = "bar",
    marker = list(color = death_color)
  ) %>%
  add_trace(
    y = ~recovered_daily,
    name = "Kasus sembuh",
    type = "bar",
    marker = list(color = recovered_color)
  ) %>%
  add_trace(
    y = ~test_daily,
    name = "Jumlah test yang dilakukan",
    type = "bar",
    marker = list(color = numtest_color)
  ) %>% 
  add_annotations(
    x = as.Date("2020-03-16"),
    y = 3,
    text = paste("Perubahan kriteria pemeriksaan"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = 0,
    ay = -90
  ) %>% 
  layout(
    title = "",
    yaxis = list(title = "Jumlah kasus harian"),
    xaxis = list(title = "Tanggal", range = c("2020-03-02", "2020-06-26")),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

Statistik Provinsi
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------
### Perbandingan data kumulatif antar-provinsi di Pulau Jawa
```{r}
plot_ly(data = provCaseCum, x = ~date) %>% 
  add_trace(
    y = ~Jakarta,
    name = "DKI Jakarta",
    type = "scatter",
    mode = "lines+markers"
  ) %>% 
  add_trace(
    y = ~Banten,
    name = "Banten",
    type = "scatter",
    mode = "lines+markers"
  ) %>% 
  add_trace(
    y = ~Jabar,
    name = "Jawa Barat",
    type = "scatter",
    mode = "lines+markers"
  ) %>% 
  add_trace(
    y = ~Jateng,
    name = "Jawa Tengah",
    type = "scatter",
    mode = "lines+markers"
  ) %>% 
  add_trace(
    y = ~DIY,
    name = "DI Yogyakarta",
    type = "scatter",
    mode = "lines+markers"
  ) %>% 
  add_trace(
    y = ~Jatim,
    name = "Jawa Timur",
    type = "scatter",
    mode = "lines+markers"
  ) %>% 
  layout(
    title = "",
    yaxis = list(title = "Jumlah Kasus Kumulatif"),
    xaxis = list(title = "Tanggal"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

### Perbandingan data harian antar-provinsi di Pulau Jawa
```{r}
plot_ly(data = provCaseDaily, x = ~date) %>% 
  add_trace(
    y = ~Jakarta,
    name = "DKI Jakarta",
    type = "bar"
  ) %>% 
  add_trace(
    y = ~Banten,
    name = "Banten",
    type = "bar"
  ) %>% 
  add_trace(
    y = ~Jabar,
    name = "Jawa Barat",
    type = "bar"
  ) %>% 
  add_trace(
    y = ~Jateng,
    name = "Jawa Tengah",
    type = "bar"
  ) %>% 
  add_trace(
    y = ~DIY,
    name = "DI Yogyakarta",
    type = "bar"
  ) %>% 
  add_trace(
    y = ~Jatim,
    name = "Jawa Timur",
    type = "bar"
  ) %>% 
  layout(
    title = "",
    yaxis = list(title = "Jumlah Kasus Kumulatif"),
    xaxis = list(title = "Tanggal"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

Google Mobility Data
=======================================================================

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

### Daerah Istimewa Yogyakarta
```{r}
plot_ly(data = mobility, x = ~date) %>% 
  add_trace(
    y = ~retail_and_recreation_percent_change_from_baseline,
    type = "scatter",
    mode = "lines",
    name = "Tempat rekreasi"
  ) %>%
  add_trace(
    y = ~grocery_and_pharmacy_percent_change_from_baseline,
    type = "scatter",
    mode = "lines",
    name = "Perbelanjaan dan Farmasi"
  ) %>%
  add_trace(
    y = ~parks_percent_change_from_baseline,
    type = "scatter",
    mode = "lines",
    name = "Taman"
  ) %>%
  add_trace(
    y = ~transit_stations_percent_change_from_baseline,
    type = "scatter",
    mode = "lines",
    name = "Transportasi"
  ) %>%
  add_trace(
    y = ~workplaces_percent_change_from_baseline,
    type = "scatter",
    mode = "lines",
    name = "Tempat kerja"
  ) %>%
  add_trace(
    y = ~residential_percent_change_from_baseline,
    type = "scatter",
    mode = "lines",
    name = "Pemukiman"
  ) %>%
  add_trace(
    data = provCaseDaily,
    x = ~date,
    y = ~DIY,
    type = "bar",
    name = "Kasus Harian Baru"
  ) %>%
  add_annotations(
    x = as.Date("2020-06-07"),
    y = 3,
    text = paste("Berita viral Malioboro padat pesepeda"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = 0,
    ay = -90
  ) %>% 
  layout(
    title = "Mobility Data Wilayah DIY Selama Pandemi",
    yaxis = list(title = "Intensitas"),
    xaxis = list(title = "Tanggal", rangeslider = list(type = "date"), range = c("2020-02-15", "2020-07-07")),
    legend = list(x = 0.01, y = -0.0001),
    hovermode = "compare"
  )
```


<!-- Kluster Map -->
<!-- ======================================================================= -->

<!-- Row {data-width=400} -->
<!-- ------------------------------------- -->

<!-- ### Visualisasi Cluster Infeksi -->

<!-- ```{r} -->
<!-- idn.covidtracing <- read.csv("https://docs.google.com/spreadsheets/d/1ma1T9hWbec1pXlwZ89WakRk-OfVUQZsOCFl4FwZxzVw/export?format=csv&gid=0", sep=",", skip = 1) -->
<!-- idn.covidtracing$Sumber.Kontak <- ifelse(idn.covidtracing$Jenis.kasus == "Impor", "Impor", as.character(idn.covidtracing$Sumber.Kontak)) -->
<!-- tracing.network <- idn.covidtracing %>% select(No, Sumber.Kontak) -->
<!-- tracing.network$Sumber.Kontak <- ifelse(tracing.network$Sumber.Kontak == "", tracing.network$No, as.character(tracing.network$Sumber.Kontak)) -->

<!-- simpleNetwork(tracing.network, fontSize = 12, zoom = TRUE) -->
<!-- ``` -->

Peta Kasus Seluruh Indonesia
=======================================================================

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

### **Jumlah kasus di seluruh dunia**

```{r message=FALSE, warning=FALSE}
cv_data_for_plot <- st_centroid(provCaseMap) %>%
  dplyr::mutate(log_cases = 2 * log(value))
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("Kasus", "Kematian", "Sembuh"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
  purrr::walk(function(df) {
    map_object <<- map_object %>%
      addCircleMarkers(
        data = cv_data_for_plot.split[[df]],
        color = ~pal(type),
        stroke = FALSE,
        fillOpacity = 0.8,
        radius = ~log_cases,
        popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
          feature.id = FALSE,
          row.numbers = FALSE,
          zcol = c("type", "value", "locname")
        ),
        group = df,
        labelOptions = labelOptions(
          noHide = F,
          direction = "auto"
        )
      )
  })

map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_for_plot.split),
    options = layersControlOptions(collapsed = FALSE)
  )
```

Tentang Dasbor
=======================================================================

**Dasbor Situasi Coronavirus di Indonesia**

Dasbor ini merupakan visualisasi data mengenai epidemi COVID-19 yang sedang berlangsung di Indonesia. Dasbor ini dikembangkan menggunakan piranti lunak R. Pengembangan dasbor ini diadaptasi dari [dasbor](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} yang dikembangkan oleh Rami Krispin.

**Data**

Data yang digunakan untuk pengembangan dasbor ini menggunakan data yang dikumpulkan oleh relawan [KawalCovID19](https://kawalcovid19.id){target="_blank"} dan juga dataset dari pustaka R [`{coronavirus}`](https://github.com/RamiKrispin/coronavirus){target="_blank"} 

Data akan selalu diupdate setiap hari.

Data mentah diambil dari data yang disediakan Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repositori](https://github.com/RamiKrispin/coronavirus-csv){target="_blank"}.

**Kontak**

Untuk mengetahui kode sumber dasbor ini dapat mengunjungi [Github](https://github.com/aldeetropolis/flexboard-covid-id){target="_blank"}.