R Markdown

In this study the team at five thirt eight wanted to take Super Bowl ads from the 10 most aired brands from the year 2000 to present and create a data set. The team created 7 criteria that an ad was “graded” against. These criterea show as columns 5 through 11 and contain “TRUE” or “FALSE”.

#Duplicated this code block and now it appears in RPubs. Do this going forward... 

library (readr)

urlfile="https://raw.githubusercontent.com/Nhodgkinson/superbowl-ads/main/superbowl-ads.csv"

mydata<-read_csv(url(urlfile))
## Rows: 244 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): brand, superbowl_ads_dot_com_url, youtube_url
## dbl (1): year
## lgl (7): funny, show_product_quickly, patriotic, celebrity, danger, animals,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
SBA <- mydata[, c(1,2,5:11)]
SBA
## # A tibble: 244 × 9
##     year brand     funny show_product_q…¹ patri…² celeb…³ danger animals use_sex
##    <dbl> <chr>     <lgl> <lgl>            <lgl>   <lgl>   <lgl>  <lgl>   <lgl>  
##  1  2018 Toyota    FALSE FALSE            FALSE   FALSE   FALSE  FALSE   FALSE  
##  2  2020 Bud Light TRUE  TRUE             FALSE   TRUE    TRUE   FALSE   FALSE  
##  3  2006 Bud Light TRUE  FALSE            FALSE   FALSE   TRUE   TRUE    FALSE  
##  4  2018 Hynudai   FALSE TRUE             FALSE   FALSE   FALSE  FALSE   FALSE  
##  5  2003 Bud Light TRUE  TRUE             FALSE   FALSE   TRUE   TRUE    TRUE   
##  6  2020 Toyota    TRUE  TRUE             FALSE   TRUE    TRUE   TRUE    FALSE  
##  7  2020 Coca-Cola TRUE  FALSE            FALSE   TRUE    FALSE  TRUE    FALSE  
##  8  2020 Kia       FALSE FALSE            FALSE   TRUE    FALSE  FALSE   FALSE  
##  9  2020 Hynudai   TRUE  TRUE             FALSE   TRUE    FALSE  TRUE    FALSE  
## 10  2020 Budweiser FALSE TRUE             TRUE    TRUE    TRUE   FALSE   FALSE  
## # … with 234 more rows, and abbreviated variable names ¹​show_product_quickly,
## #   ²​patriotic, ³​celebrity
## # ℹ Use `print(n = ...)` to see more rows

Conclusion

The team found some interesting clusters and provided some historical context. For example, they recal and a Pepsi ad featuring American politician Bob Dole comparing the bevarage to Viagra and preforming backflips, this ad would not make sense to todays viewing audience but would’ve to the 2001 audience who would’ve remembered a Viagra ad with Bob Dole in 1998. Exploring this concept further, I believe we could use this data to view larger pop culture trends in the United States.