---
title: "Trump & Joe The latest 100 Tweets"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
    social: ["menu"]
    theme: paper
---
```{r include=FALSE}
library(flexdashboard)
library(dplyr)
library(rtweet)
library(SentimentAnalysis) #sentiment analysis
library(ggplot2)


# To connect to Twitter: 

api_key <- "SpGXGfb7kRZGdWul8T7L4H0u8"
api_secret_key <- "7aPRstWMTQvQYLScNGvxSwItf9U6bR5LzHrZTyOqJElIbj0qnl"
access_token <- "1263464116510998529-jzwUhaFHLRnKbLkRNgNFDHWP0KgCyl"
access_token_secret <- "YevdjVCwvvFlYPiSTmqWUcbsB0xMo8QhlOSPSYyP850bU"

## authenticate via web browser
token <- create_token(
  app = "JOE-DON-in-100-tweets",
  consumer_key = api_key,
  consumer_secret = api_secret_key,
  access_token = access_token,
  access_secret = access_token_secret)
```

```{r data gathering, include=FALSE}
Don <- get_timelines("realdonaldtrump", n = 100)
JOE <- get_timelines("JoeBiden", n = 100)
```

```{r popularity}
popularity <- bind_rows(
  tibble(ID = "TRUMP", likes = Don$favorite_count, Retweets = Don$retweet_count, time = Don$created_at),
  tibble(ID = "BIDEN", likes = JOE$favorite_count, Retweets = JOE$retweet_count, time = JOE$created_at)
)

```

Sentiment Analysis
======================================================================

Row
-----------------------------------------------------------------
### Who Has Tracktion On Twitter?

```{r likes and Rts}
popularity %>% arrange(desc(time)) %>% ggplot(aes(x= likes, y = time , size = Retweets, color = ID)) + facet_grid(~ID)+
    geom_point(alpha=0.7) +
    scale_size(range = c(.1, 9)) +
    theme_classic()+
    theme(axis.text.x = element_text(angle = 90))+
  labs( title = "Who Has Tracktion On Twitter?", subtitle = "Last 100 Tweets", caption = "\nSource: Data collected from Twitter's REST API via rtweet")

```


### How Are They Feeling? 

The Redline in the density plot represents the median sentiment of the tweets. 
A score of 0 is normal langage. A negative score indicates the use of negative words. Positive sentiments indicates the use of positive words. 

```{r include=FALSE}
library(textdata)
library(tidytext)
library(purrr) # to use the map function
```

```{r funtion to get the sentiments out, include=FALSE}
sentiment.1 <- function (twt){
  
 twt_tbl = tibble(text = twt) %>%
   mutate(stripped_text = gsub("http\\s+","",text)) %>%
   unnest_tokens(word, stripped_text) %>%
   anti_join(stop_words) %>%
   inner_join(get_sentiments("bing")) %>%
   count(word,sentiment, sort = TRUE) %>%
   ungroup() %>% 
  mutate(score = case_when(
      sentiment == 'negative'~ n*(-1),
      sentiment == 'positive' ~ n*1)
  )
  
  sent.score = case_when(
    nrow(twt_tbl)== 0~0,
    nrow(twt_tbl)> 0 ~ sum(twt_tbl$score)
  )
  list(score = sent.score , twt_tbl = twt_tbl)
}
```

```{r tesing and gathering the data, include=FALSE}
Don_tested <- lapply(Don$text, function(x){sentiment.1(x)})
JOE_tested <- lapply(JOE$text, function(x){sentiment.1(x)})

sentiments <- bind_rows(
  tibble(ID = "TRUMP", score = unlist(map(Don_tested,'score'))),
  tibble(ID = "BIDEN", score = unlist(map(JOE_tested,'score')))
)

```

```{r plot}
ggplot(sentiments, aes(x = score)) +facet_grid(~ID) + geom_density(bw=.5)+ geom_vline(xintercept = median(sentiments$score), color = "red", linetype = "dashed") + 
  labs( title = "Sentimet Analysis for Biden & Trump  ", subtitle = "Based On The Last 100 Tweets", caption = "\nSource: Data collected from Twitter's REST API via rtweet")+  ylab(label = "Density") + theme_classic()
```

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

### Total Retweets Trump

```{r}
Retweets.D <- sum(Don$retweet_count)
valueBox(Retweets.D, icon = "fa-comments")
```

### Total likes Trump

```{r}
Likes.D <- sum(Don$favorite_count)
valueBox(Likes.D, icon = "fa-comments", color = ifelse(sum(Don$favorite_count) > sum(JOE$favorite_count), "success","primary"))
```

### Total Retweets JOE

```{r}
Retweets.J <- sum(JOE$retweet_count)
valueBox(Retweets.J, icon = "fa-comments")
```

### Total likes JOE

```{r}
likes.J <- sum(JOE$favorite_count)
valueBox(likes.J, icon = "fa-comments",color = ifelse(sum(JOE$favorite_count) > sum(Don$favorite_count), "success","primary"))
```



What Words Do They Use Most Frequently? 
============================================================

```{r include=FALSE}
library(wordcloud2)
```


```{r data filtering again, include=FALSE}
data("stop_words")
J.TBL <- JOE %>% unnest_tokens(word, text)

J.TBL<- J.TBL %>% anti_join(stop_words)

J.TBL <- J.TBL %>% count(word, sort = TRUE)

J.TBL <- J.TBL %>% filter(!word %in% c('t.co', 'https', 'president', 'i’ll','it’s'), n > 3)
```

```{r data filtering, include=FALSE}
D.TBL <- Don %>% unnest_tokens(word, text)

D.TBL<- D.TBL %>% anti_join(stop_words)

D.TBL <- D.TBL %>% count(word, sort = TRUE)

D.TBL <- D.TBL %>% filter(n>=3 , !word %in% c('t.co', 'https', 'president', 'it`s',"realdonaldtrump","amp"))

```
Column
-------------------------------------
### JOE's Words
```{r message=FALSE, warning=FALSE}

J.TBL %>%
 filter(n >= 4) %>%
 ggplot() +
 aes(x = word, weight = n) +
 geom_bar(fill = "#0c4c8a") +
 labs(x = "WORD", y = "COUNT", title = "Joe's Most Used Words", subtitle = "Based on His Last 100 Tweets") +
 coord_flip() +
 theme_classic()
geom_bar()
```
Column
---------------------------------------------------------------------------------------------------
### Don's Words
```{r message=FALSE, warning=FALSE}
D.TBL %>%
 filter(n >= 5 ) %>%
 ggplot() +
 aes(x = word, weight = n) +
 geom_bar(fill = "#cb181d") +
 labs(x = "WORD", y = "COUNT", title = "Don's Most Used Words", subtitle = "Based on His Last 100 Tweets") +
 coord_flip() +
 theme_classic()
geom_bar()
```

Word Cloud {data-width=350}
===================================================================================================

Sadly at the moment it isn't possible to show both word clouds at the same time. It appears to be an issue with [flexdashboard](https://github.com/Lchiffon/wordcloud2/issues/60#issuecomment-608360195) . 
They are working on it. 

So since Don is far more active on Twitter than Joe is (afterall he is a slightly younger man) I included only wordcloud of his words for now. 

```{r include=FALSE}
library(wordcloud2)

```

### Wordcloud Don 

```{r don words}

wordcloud2(D.TBL, size= 0.7)

```

```{r joe words, eval=FALSE, include=FALSE}
### wordcloud Joe {.no-padding}

wordcloud2(J.TBL, size= 0.7 )
```