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

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

twitter_token <- create_token(
  app = "iSabiWey",
  consumer_key = "kwXvLHNXVFzM6qtHnPV2EZuan",
  consumer_secret = "FBODVaiqPjjVxQzGn33aWYCoKFdbP01oxUUQEhwGkqZHWDdZpm",
  access_token = "122035719-7yILa8vgOYssuFwvpII3jYvIxZ5Oqh80mXRiFkqI",
  access_secret = "l7RZGmToy6WcS9jSZ0OH2iTOqrMW0NazmEqKWcNNRjwxJ")
ww3 <- search_tweets(q = "Virus",
                               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://ncov2019.live/data"
globalcovid <- url %>%
xml2::read_html() %>%
html_nodes(xpath='//*[@id="sortable_table_global"]') %>%
html_table()
globalcovid <- globalcovid[1]

x <- (globalcovid[[1]][["Confirmed"]][2:11]) 
x <- as.numeric(gsub(",","",x,fixed=TRUE))
y <- (globalcovid[[1]][["Recovered"]][2:11])
y <- as.numeric(gsub(",","",y,fixed=TRUE))
d <- (globalcovid[[1]][["Deceased"]][2:11])
d <- as.numeric(gsub(",","",d,fixed=TRUE))
ac <- (globalcovid[[1]][["Serious"]][2:11])
ac <- as.numeric(gsub(",","",ac,fixed=TRUE))
r <- (globalcovid[[1]][["Name"]][2:11])
r <- gsub("[^a-zA-Z]", "", r)


maxa <- (globalcovid[[1]][["Confirmed"]][2:11])
maxa <- as.numeric(gsub(",","",maxa,fixed=TRUE))
maxal <- max(maxa)

maxalcountry <- r[maxa == maxal]
maxalcountry <- gsub("[^a-zA-Z]", "", maxalcountry)



maxr <- (globalcovid[[1]][["Recovered"]][2:11])
maxr <- as.numeric(gsub(",","",maxr,fixed=TRUE))
maxrl <- max(maxr)

maxrlcountry <- r[maxr == maxrl]
maxrlcountry <- gsub("[^a-zA-Z]", "", maxrlcountry)



maxd <- (globalcovid[[1]][["Deceased"]][2:11])
maxd <- as.numeric(gsub(",","",maxd,fixed=TRUE))
maxdl <- max(maxd)

maxdcountry <- r[maxd == maxdl]
maxdcountry <- gsub("[^a-zA-Z]", "", maxdcountry)



Confirmed <- globalcovid[[1]][["Confirmed"]][1]
Deceased <- globalcovid[[1]][["Deceased"]][1]
Recovered <- globalcovid[[1]][["Recovered"]][1]


url1 <- "https://ncov2019.live/data"
africacovid <- url1 %>%
xml2::read_html() %>%
html_nodes(xpath='//*[@id="sortable_table_africa"]') %>%
html_table()
africacovid <- africacovid[1]

afx <- (africacovid[[1]][["Confirmed"]][2:16]) 
afx <- as.numeric(gsub(",","",afx,fixed=TRUE))
afy <- (africacovid[[1]][["Recovered"]][2:16])
afy <- as.numeric(gsub(",","",afy,fixed=TRUE))
afd <- (africacovid[[1]][["Deceased"]][2:16])
afd <- as.numeric(gsub(",","",afd,fixed=TRUE))
afac <- (africacovid[[1]][["Serious"]][2:16])
afac <- as.numeric(gsub(",","",afac,fixed=TRUE))
afr <- (africacovid[[1]][["Name"]][2:16])
afr <- gsub("[^a-zA-Z]", "", afr)


afmaxa <- (africacovid[[1]][["Confirmed"]][2:16])
afmaxa <- as.numeric(gsub(",","",afmaxa,fixed=TRUE))
afmaxal <- max(afmaxa)

afmaxalcountry <- afr[afmaxa == afmaxal]
afmaxalcountry <- gsub("[^a-zA-Z]", "", afmaxalcountry)



afmaxr <- (africacovid[[1]][["Recovered"]][2:16])
afmaxr <- as.numeric(gsub(",","",afmaxr,fixed=TRUE))
afmaxrl <- max(afmaxr)

afmaxrlcountry <- afr[afmaxr == afmaxrl]
afmaxrlcountry <- gsub("[^a-zA-Z]", "", afmaxrlcountry)



afmaxd <- (africacovid[[1]][["Deceased"]][2:16])
afmaxd <- as.numeric(gsub(",","",afmaxd,fixed=TRUE))
afmaxdl <- max(afmaxd)

afmaxdcountry <- afr[afmaxd == afmaxdl]
afmaxdcountry <- gsub("[^a-zA-Z]", "", afmaxdcountry)



afConfirmed <- africacovid[[1]][["Confirmed"]][1]
afDeceased <- africacovid[[1]][["Deceased"]][1]
afRecovered <- africacovid[[1]][["Recovered"]][1]






```
# 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://ncov2019.live/

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")

```



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 Confirmed

```{r}

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

```
```{r}

valueBox(maxalcountry)

```



### Highest Recovered

```{r}

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

```

```{r}

valueBox(maxrlcountry)

```

### Highest Death

```{r}

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

```

```{r}

valueBox(maxdcountry)

```


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=FALSE )

```

### Death Cases In the Most Affected Regions

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

```


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

### Serious Cases In the Most Affected Regions

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

```

### Recovery Cases In the Most Affected Regions

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

```



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

-----------------------------------------------------------------------

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

### Total Confirmed Cases in Africa

```{r}

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

```

### Total Death Cases in Africa

```{r}

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

```


### Total Recovery Cases in Africa

```{r}

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

```

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

### Confirmed Cases In Africa

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

```

### Recovery Cases In Africa

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

```




AFRICA Cont
=======================================================================

-----------------------------------------------------------------------

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

### Highest Confirmed Cases In Africa

```{r}

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

```
```{r}

valueBox(afmaxalcountry)

```



### Highest Recovered Cases In Africa

```{r}

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

```

```{r}

valueBox(afmaxrlcountry)

```

### Highest Death Cases In Africa

```{r}

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

```

```{r}

valueBox(afmaxdcountry)

```




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

### Death Cases In Africa

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

```

### Serious Cases In Africa

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

```