---
title: "Chart Stack (Scrolling)"
Author: "Hyunsu Ju"
output: 
  flexdashboard::flex_dashboard:
    theme: sandstone
    vertical_layout: scroll
    social: ["facebook","twitter","menu"]
    source_code: embed
    navbar:
      - {title: "About", href: "https://pkgs.rstudio.com/flexdashboard/", align: left}
---

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

Page 1
===========
### Chart A

```{r, fig.width=15, fig.height=7}
plot(cars)
```


### Chart B

```{r, fig.width=5, fig.height=5}
plot(pressure)
```

### Chart C

```{r, fig.width=5, fig.height=5}
plot(airmiles)
```

### Simple Table
```{r}
knitr::kable(mtcars)
```

### Data Table
```{r}
DT::datatable(mtcars,options=list(bPaginate=FALSE))
```

### Leaflet Package
```{r}
library(leaflet)
m<-leaflet() %>%
  addTiles() %>%
  addMarkers(lng=174.768, lat=-36.852, popup="The birthplace of R")
m
```

### Plotly
`Plotly` interactive chart R package

```{r}
library(ggplot2)
library(plotly)
p<-ggplot(data =diamonds, aes(x=cut, fill=clarity))+
   geom_bar(position="dodge")
ggplotly(p)

```

### Rbokeh
```{r}
library(rbokeh)
p <- figure() %>%
  ly_points(Sepal.Length, Sepal.Width, data = iris,
    color = Species, glyph = Species,
    hover = list(Sepal.Length, Sepal.Width))
p
```
### Crude map of the world with capital cities:
```{r}
library(maps)
data(world.cities)
caps <- subset(world.cities, capital == 1)
caps$population <- prettyNum(caps$pop, big.mark = ",")
figure(width = 800, height = 450, padding_factor = 0) %>%
  ly_map("world", col = "gray") %>%
  ly_points(long, lat, data = caps, size = 5,
    hover = c(name, country.etc, population))
```

### HighChart
`HighCharts` dynamic chart

```{r}
library(highcharter)
data(penguins, package = "palmerpenguins")
hchart(penguins, "scatter", hcaes(x=flipper_length_mm, y=bill_length_mm, group=species))
```
Page 2
===========

Row1
-----------------

### Articles per Day

```{r}
articles<-100
valueBox(articles, icon="fa-pencil")
```

### Comments per Day

```{r}
comments<-200
valueBox(comments, icon="fa-comments")
```

### Spam per Day

```{r}
spam<-300
valueBox(spam, icon="fa-trash",
         color=ifelse(spam>10,"warning","primary"))
```

Row2
--------

### Contact Rate

```{r}
gauge(70, min = 0, max = 100, symbol = '%', gaugeSectors(
  success = c(80, 100), warning = c(40, 79), danger = c(0, 39)
))
```

### Average Rating

```{r}
gauge(23, min = 0, max = 50, gaugeSectors(
  success = c(41, 50), warning = c(21, 40), danger = c(0, 20)
))
```

### Cancellations

```{r}
gauge(7, min = 0, max = 10, gaugeSectors(
  success = c(0, 2), warning = c(3, 6), danger = c(7, 10)
))
```
Page3
===========

Row
---------
```{r}
library(tidyverse) 
library(data.table)
library(dplyr)
library(ggplot2)
library(lubridate)
library(stringr)
library(plotly)
## Reading in files
library(igraph)
library(threejs)
library(ggraph)
library(tidygraph)
# You can access files from datasets you've added to this kernel in the "../input/" directory.
# You can see the files added to this kernel by running the code below. 
start.watch.date <- as.Date('2020-03-01')
watched.countries.count <- 12
covid <- data.table(read.csv('covid_19_data.csv'))
krcovid <- data.table(read.csv('PatientInfo.csv'))
krcovid[,confirmed_date:=as.Date(gsub('2002','2020',as.character(confirmed_date)))]
krcovid[,symptom_onset_date:=as.Date(as.character(symptom_onset_date))]
krcovid[,released_date:=as.Date(as.character(released_date))]
krcovid[,deceased_date:=as.Date(as.character(deceased_date),format='%Y-%m-%d')]
#krcovid[,table(symptom_onset_date==confirmed_date)]
#summary(krcovid)
#head(covid)



krcovid[,closed_date:=coalesce(released_date,deceased_date)]
krcovid[,closed.days:=as.integer(difftime(closed_date,confirmed_date,unit='days'))]
ggplot(krcovid[,.(n=.N,median_age=2020-median(birth_year,na.rm=T)),.(closed.days,confirmed_date,state)][,state:=ifelse(state=='deceased','death','recovered')])+
  geom_point(aes(y=closed.days,x=confirmed_date,size=n,col=state))+
  #geom_text(aes(y=closed.days,x=confirmed_date,label=median_age,col=state,hjust='left'))+
  scale_x_date(date_labels = '%m/%d',date_breaks = '3 days')+
  theme(axis.text.x=element_text(angle = 60, vjust = 0.5))+
  scale_color_brewer(palette = 'Set1')+
  labs(title='Days to close the cases in Korea')
```

Row
---------
```{r}
library(conflicted)
covid[,Last.Update:=as.Date(Last.Update,format='%m/%d/%Y')]
covid[str_length(ObservationDate)==8,ObservationDate1:=as.Date(ObservationDate,format='%m/%d/%y',origin = orgin)]
covid[str_length(ObservationDate)==10,ObservationDate1:=as.Date(ObservationDate,format='%m/%d/%Y',origin = orgin)]
covid[,ObservationDate:=ObservationDate1]
covid$ObservationDate1=NULL
covid[grep('Korea',Country.Region),Country.Region:='Republic of Korea']
covid[grep('Iran',Country.Region),Country.Region:='Iran']
covid[grep('Taiwan',Province.State),Country.Region:='Taiwan']
covid[ grepl('Diamon',Province.State),Country.Region:='Diamond Princess']
#str(covid)
# The JHU modified the Country name of Taiwan which is suck both for analysis and human right

## quick summary the cases by country
conflict_prefer("lag", "dplyr")
conflict_prefer("setdiff", "dplyr")
covid.by.country <- covid[,.(Confirmed=sum(Confirmed),Recovered=sum(Recovered),Deaths=sum(Deaths)),.(ObservationDate,Country.Region)]
setorder(covid.by.country,Country.Region,ObservationDate)
covid.by.country[,Daily.Confirmed:=Confirmed-lag(Confirmed,1),.(Country.Region)]
covid.by.country[,Daily.Recovered:=Recovered-lag(Recovered,1),.(Country.Region)]
covid.by.country[,Daily.Deaths:=Deaths-lag(Deaths,1),.(Country.Region)]
lookup.countries <- covid.by.country[order(-ObservationDate,Country.Region)][,head(.SD,1),.(Country.Region)][order(-Deaths),Country.Region]
lookup.countries <- setdiff(lookup.countries,grep('Diamon',lookup.countries,value=T))

ggplot(covid.by.country[grep('Italy|Korea',Country.Region)][,Daily.Confirmed:=ifelse(Daily.Confirmed>5000,2000,Daily.Confirmed)])+
  geom_line(aes(x=ObservationDate,y=Daily.Recovered,col='Recovered'))+
  geom_line(aes(x=ObservationDate,y=Daily.Deaths,col='Deaths'))+
  geom_line(aes(x=ObservationDate,y=Daily.Confirmed,col='Confirmed'))+
  facet_wrap(~Country.Region,scales = 'free',ncol=1)+
  scale_x_date(date_labels = '%m/%d',date_breaks = '7 days')
```

Row
-------

```{r}
ggplot(covid.by.country[grep('Italy|Korea',Country.Region)][ObservationDate>'2020-03-01'])+
  geom_bar(aes(x=ObservationDate,y=Daily.Deaths,fill='Deaths'),stat = 'identity')+
  geom_line(aes(x=ObservationDate,y=Daily.Confirmed),col='blue')+
  facet_wrap(~Country.Region,scales = 'free',ncol=1)+
  scale_x_date(date_labels = '%m/%d',date_breaks = '2 days')
```

Row
-------

```{r}
ggplot(covid.by.country[Country.Region %in% lookup.countries[1:watched.countries.count]],aes(group=1))+
  geom_line(aes(x=ObservationDate,y=Daily.Recovered,col='Recovered'))+
  geom_line(aes(x=ObservationDate,y=Daily.Deaths,col='Deaths'))+
  geom_line(aes(x=ObservationDate,y=Daily.Confirmed,col='Confirmed'))+
  facet_wrap(~Country.Region,ncol=3,scale='free_y')+
  scale_color_brewer(palette = 'Set1',name='Value Type')+
  scale_x_date(date_breaks = '7 days',date_labels = '%m/%d',)+
  theme(axis.text.x=element_text(angle = 90, vjust = 0.5))+
  labs(title='COVID-19 daily cases')
```