---
title: "COVID-19 Dashboard"
author: "dataSTAPLES"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(xml2)
library(tm)
library(rtweet)
# plotting and pipes - tidyverse!
library(ggplot2)
library(plotly)
library(plyr)
library(wordcloud2)
library(syuzhet)
library(lubridate)
library(scales)
library(reshape2)
library(dplyr)
library(rvest)
library(shiny)
library(NLP)






# text mining library
library(tidytext)
twitter_token <- create_token(
  app = "iSabiWey",
  consumer_key = "kwXvLHNXVFzM6qtHnPV2EZuan",
  consumer_secret = "FBODVaiqPjjVxQzGn33aWYCoKFdbP01oxUUQEhwGkqZHWDdZpm",
  access_token = "122035719-7yILa8vgOYssuFwvpII3jYvIxZ5Oqh80mXRiFkqI",
  access_secret = "l7RZGmToy6WcS9jSZ0OH2iTOqrMW0NazmEqKWcNNRjwxJ")
ww3 <- search_tweets(q = "COVID-19",
                               n = 1000)


# Make some noisily increasing data
world_war_3 <- ww3$text
class(world_war_3)
corpus <- Corpus(VectorSource(world_war_3))

corpus <- tm_map(corpus, tolower)

corpus <- tm_map(corpus, removePunctuation)

corpus <- tm_map(corpus, removeNumbers)
cleanset <- tm_map(corpus, removeWords,stopwords('english'))

removeURL <- function(x) gsub('http[[:alnum:]]*', '', x)
cleanset <- tm_map(cleanset, content_transformer(removeURL))
inspect(cleanset[1:5])


cleanset <- tm_map(cleanset, stripWhitespace)
tdm <- TermDocumentMatrix(cleanset)
tdm
tdm <- as.matrix(tdm)


w <- rowSums(tdm)
w <- subset(w, w>=10)
w <- data.frame(names(w), w)
colnames(w) <- c('word', 'freq')
s <- get_nrc_sentiment(world_war_3)


url <- "https://www.worldometers.info/coronavirus/"
globalcovid <- url %>%
xml2::read_html() %>%
html_nodes(xpath='//*[@id="main_table_countries_today"]') %>%
html_table()
globalcovid <- globalcovid[1]

x <- (globalcovid[[1]][["TotalCases"]][1:10]) 
x <- as.numeric(gsub(",","",x,fixed=TRUE))
ac <- (globalcovid[[1]][["ActiveCases"]][1:10]) 
ac <- as.numeric(gsub(",","",ac,fixed=TRUE))
y <- (globalcovid[[1]][["TotalRecovered"]][1:10])
y <- as.numeric(gsub(",","",y,fixed=TRUE))
d <- (globalcovid[[1]][["TotalDeaths"]][1:10])
d <- as.numeric(gsub(",","",d,fixed=TRUE))
a <- (globalcovid[[1]][["ActiveCases"]][1:10])
a <- as.numeric(gsub(",","",a,fixed=TRUE))
r <- (globalcovid[[1]][["Country,Other"]][1:10])

maxa <- (globalcovid[[1]][["ActiveCases"]][1:10])
maxa <- as.numeric(gsub(",","",maxa,fixed=TRUE))
maxal <- max(maxa)

maxalcountry <- r[maxa == maxal]
maxalcountry


maxn <- (globalcovid[[1]][["NewCases"]][1:10])
maxn <- as.numeric(gsub(",","",maxn,fixed=TRUE))
maxn[is.na(maxn[1:10])] = 0
maxnl <- max(maxn)

maxncountry <- r[maxn == maxnl]
maxncountry

maxd <- (globalcovid[[1]][["NewDeaths"]][1:10])
maxd <- as.numeric(gsub(",","",maxd,fixed=TRUE))
maxd[is.na(maxd[1:10])] = 0
maxdl <- max(maxd)

maxdcountry <- r[maxd == maxdl]
maxdcountry



Confirmed <- globalcovid[[1]][["TotalCases"]][207]
Deceased <- globalcovid[[1]][["TotalDeaths"]][207]
Recovered <- globalcovid[[1]][["TotalRecovered"]][207]
Active <- globalcovid[[1]][["ActiveCases"]][207]



```
# Introduction {.sidebar}

This dashboard shows the latest updates on the COVID-19 outbreak. Data was scrapped from two sources using R. 
The data collected was analyzed using NLP, sentiment analysis and data visualization with R.

Data Sources:

* https://twitter.com/
* https://www.worldometers.info/coronavirus/

P.S. You can scroll across the multiple VIZ (GLOBAL,TOP 10,AFRICA...)

This dashboard was last updated at

```{r}

Sys.time()

```


GLOBAL
=======================================================================

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

### Total Confirmed Cases

```{r}

valueBox(Confirmed, icon = "fa-bed")

```

### Total Death Cases

```{r}

valueBox(Deceased, icon = "fa-bed")

```


### Total Recovery Cases

```{r}

valueBox(Recovered, icon = "fa-users")

```

### Total Active Cases

```{r}

valueBox(Active, icon = "fa-bed")

```



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

### Emotions on Twitter towards COVID-19



```{r}
barplot(colSums(s),
        las = 2,
        col = rainbow(10),cex.names=0.7,
        ylab = 'Count',
        )
```


### COVID-19 WordCloud

```{r}
wordcloud2(w,
           size = 0.7,
           shape = 'square',
          
           minSize = 1)

```

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

### Highest New Death Cases Today

```{r}

valueBox(maxdl, icon = "fa-bed")

```
```{r}

valueBox(maxdcountry)

```



### Highest New Confirmed Cases Today

```{r}

valueBox(maxnl, icon = "fa-bed")

```

```{r}

valueBox(maxncountry)

```

### Highest Active Cases Today

```{r}

valueBox(maxal, icon = "fa-bed")

```

```{r}

valueBox(maxalcountry)

```


TOP 10
=======================================================================

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

### Confirmed Cases In the Most Affected Regions

```{r}
barplot(height=x,
        col="#69b3a9",
        names.arg=r,cex.names=0.7,
        las=2, horiz=TRUE )

```

### Death Cases In the Most Affected Regions

```{r}
barplot(height=d,
        col="#69b3a9",
        names.arg=r,cex.names=0.7,
        las=2,  horiz=TRUE )

```


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

### Active Cases In the Most Affected Regions

```{r}
barplot(height=ac,
        col="#69b3a9",
        names.arg=r,cex.names=0.7,
        las=2, horiz=TRUE )

```

### Recovery Cases In the Most Affected Regions

```{r}
barplot(height=y,
        col="#69b3a9",
        names.arg=r,cex.names=0.7,
        las=2,  horiz=TRUE )

```



AFRICA
=======================================================================

Coming Soon
-----------------------------------------------------------------------

### VIZ is Coming Soon

```{r}


```