The Bachelorette Eps. 9 to Finale – The Final – Data and Drama in R

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Wow, this post is months behind…

The combination of a crazy 2020 and a surge in business, The Bachelorette blog posts took the back seat. To be honest, we didn’t even watch episodes 9 to the finale but we know the final results.

Some people might say:

  • You’re not true fans if you can’t sit through 3 hours of The Bachelorette followed by another 3 hours the night after for 3 weeks in a row…

  • You have no business writing a blog post about something you didn’t watch…

  • You’re a normal person who can’t sit through trash TV for that long…

All of those statements are accurate, which isn’t a surprise, considering the fact that I wrote them. Let’s get down to the final charts because they’re kind of fun. If we look at a chart to see the “winners” vs the rest, we notice that the only true winners are the MLM companies.

Screen Shot 2021-01-12 at 11.48.52 AM.png

On a more serious note, it’s truly impressive to see that on the very date the show ended, the curves for followers flattens out immediately. What does this mean? Once you’re off TV, your ability to attract new followers tanks. While it’s not surprising, I never thought it would be so dramatic.

For the “data drive decision makers” entering The Bachelorette world “for the wrong reasons” it makes it clear that you need to do two things:

  1. Find a way to get past the 4th episode

  2. Sacrifice dignity at all costs in order to be more dramatic or memorable

These simple steps provide for unlimited upside potential in Instagram growth until the show stops airing. Contestants need to be positive and their sights high enough to become a social media “influencer” or they could find themselves living a tough life as a “micro-influencer” who push products like a new toothbrush made with bristles made from organic boars hair (if you make this product, I reserve the right to claim half of your earnings).

Here is the same chart as above but modified to show growth of follower count for each person.

Rplot.png

Moving on, I wanted to showcase the same summary table as last time which is just so much fun in R!

Leaderboard.png

Conclusion

Thank you for reading all of these blogs. I received great responses over this set of posts and truly appreciate the fact that you have taken the time to appreciate the fact that you can gather data about almost anything and bring it to life with just a little bit of effort. I won’t be continuing to follow the upcoming seasons in order to preserve what brain cells I have left!

# Code for the table

insta_changes %>%
  mutate(pic = '') %>%
  mutate(followers_at_start = scales::number(followers_at_start, accuracy = 1, big.mark = ','), 
         followers_at_departure = scales::number(followers_at_departure, accuracy = 1, big.mark = ','), 
         followers_latest = scales::number(followers_latest, accuracy = 1, big.mark = ','),
         `% Change` = scales::number(change_latest_pct, accuracy=1, big.mark = ',')) %>% 
  select(` ` = pic, Name = name, Start = followers_at_start, Latest = followers_latest, `% Change`) %>%
  kbl(booktabs = TRUE, longtable = TRUE, align = 'c') %>%
  kable_styling(latex_options = c("hold_position", "repeat_header"), bootstrap_options = c('striped')) %>%
  kable_paper(full_width = FALSE) %>%
  column_spec(1, image = spec_image(insta_changes$pic_filename, 200, 200))


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