#Packages

#Data ### Data was pulled from most recent 200 tweets that mentioned the Buffalo Bills that were not retweets. Note for Miami Dolphins data.The Dolphins were mentioned so many times today that there offical twitter tweets were not in the lastest 200

BuffaloBills_Tweets <- read_csv("https://myxavier-my.sharepoint.com/:x:/g/personal/helbiga_xavier_edu/EU6YSv5c8YxAlGBtXkNkf-YBdPG4pPTKmZB-qoUOaZw4dA?download=1")
## Parsed with column specification:
## cols(
##   .default = col_character(),
##   created_at = col_datetime(format = ""),
##   display_text_width = col_double(),
##   is_quote = col_logical(),
##   is_retweet = col_logical(),
##   favorite_count = col_double(),
##   retweet_count = col_double(),
##   quote_count = col_logical(),
##   reply_count = col_logical(),
##   symbols = col_logical(),
##   ext_media_type = col_logical(),
##   quoted_created_at = col_datetime(format = ""),
##   quoted_favorite_count = col_double(),
##   quoted_retweet_count = col_double(),
##   quoted_followers_count = col_double(),
##   quoted_friends_count = col_double(),
##   quoted_statuses_count = col_double(),
##   quoted_verified = col_logical(),
##   retweet_status_id = col_logical(),
##   retweet_text = col_logical(),
##   retweet_created_at = col_logical()
##   # ... with 21 more columns
## )
## See spec(...) for full column specifications.
MianiDolphins_Tweets <- read_csv("https://myxavier-my.sharepoint.com/:x:/g/personal/helbiga_xavier_edu/EdL4_5ObprdAhRoYEKLnpDwBltyyJOsvIP3aRmQhIPAlTA?download=1")
## Parsed with column specification:
## cols(
##   .default = col_character(),
##   created_at = col_datetime(format = ""),
##   display_text_width = col_double(),
##   is_quote = col_logical(),
##   is_retweet = col_logical(),
##   favorite_count = col_double(),
##   retweet_count = col_double(),
##   quote_count = col_logical(),
##   reply_count = col_logical(),
##   symbols = col_logical(),
##   ext_media_type = col_logical(),
##   quoted_created_at = col_datetime(format = ""),
##   quoted_favorite_count = col_double(),
##   quoted_retweet_count = col_double(),
##   quoted_followers_count = col_double(),
##   quoted_friends_count = col_double(),
##   quoted_statuses_count = col_double(),
##   quoted_verified = col_logical(),
##   retweet_status_id = col_logical(),
##   retweet_text = col_logical(),
##   retweet_created_at = col_logical()
##   # ... with 21 more columns
## )
## See spec(...) for full column specifications.

###Option 1

#Introduction

How do the the tweets compare between two rival Football team franchises throughout a normal day. The teams I select were the Buffalo Bills and Miami Dolphins. By using the Buffalo Bills offical twitter page and the Miami Dolphins offical Twitter page. Plus tweets that either team was mentioned in. As I am a Buffalo Bills fans, I wanted to see how the Bills were mentioned compared to there afc rivals the Miami Dolphins. I would of looked at the New England Patriots, but too much of the content was appropriate enough and related to the football team. They were talking about people being patriots over the acutal team.

###Question 1

#What sources do fans tweet more often with a higher average tweet lengths?

The results thsese graphs show what type of source fans are tweeting from with longer descriptions mentioning either franchise team. This could be good to identifying what type operating systems the major of your fans use. An implication of finding out what operating system your fan use is future paid advertisemnt for franchise merchandise. You could could figuire out what is the best way to target paid adds based on what operating system they use. Which could also save the franchise money if they realize there fans use a maajority of one device. Plus you could make franchise merchandise for the most common operation system your fan base uses.

#Question 2 ### Do the Franchise Team or the people mentioning the teams name get more favorites

#You could use the screen names to block or reward fans for getting a lot of favorites with the franchise team mentioned. For positive tweets, you can choose to retweet it or send a personal note to this fan to reward them for their loyalty to your franchise. If its a negative tweet about your franchise, you could chose to block the usernames or use them to understand why your franchise team is upset with a decesion or move your organizaiton made. To make the screen names more appropriate, if they had more than 5 favorites, the tweets had more appropriate screen names.

##Note for Miami Dolphins data.The Dolphins were mentioned so many times today that there offical twitter tweets were not in the lastest 200

#Question 3

###What was the most popular hashtage associated with the Franchise team?(count had to be greater than 1)

##For this question, I wanted to see what hashtags were popular with the franchsie. As both of these NFL rivals have had a good off season so far with the NFL Drafts and Free Agency. I wanted to see what were the trending hashtags. By chosing a hashtag that was more than 1 we could weed out inappropriate hashtags associated with teams. THe results for the Buffalo Bills were all positive as all the hashtags. The Miami Dolphins also had positive hashtags associated with the team as they got good new that there drafted rookie quarterback signed with the team.