Analyzing the NFL Divisions | Data Wrangling in R

For this homework, my goal was to analyze and accurately show the number of Super Bowl wins from each division. Through this visualization, my hope was to portray which division was historically the “best” and then draw conclusions based on previous events. I first imported the data set, nfl_standings and chose the three variables I wanted to utilize. Those three variables are as follows:

• division: Portrays the division to which each team belongs (on the x-axis as my bins).
• bin_sb: This variable is a binary variable that takes the value of one if the team had won the Super Bowl in a given year or zero if the team did not win the Super Bowl. My goal was to utilize the count of this variable over the course of the years.
• team_name: This variable was used in the legend of the visualization to represent specific teams.

I was able to use the geom_col() to visualize the above intended result. I also utilized ggtitle() to title my graphic, xlab() to name my x-axis variable, ylab() to name my y-axis values, and labs() to properly name my legend.

nfl_standings <- readr::read_csv("standings.csv")
nfl_standings

## # A tibble: 638 x 17
##    team  division  year  wins  loss points_for points_against points_differen~
##    <chr> <chr>    <dbl> <dbl> <dbl>      <dbl>          <dbl>            <dbl>
##  1 Miam~ AFC East  2000    11     5        323            226               97
##  2 Indi~ AFC Sou~  2000    10     6        429            326              103
##  3 New ~ AFC East  2000     9     7        321            321                0
##  4 Buff~ AFC East  2000     8     8        315            350              -35
##  5 New ~ AFC East  2000     5    11        276            338              -62
##  6 Tenn~ AFC Sou~  2000    13     3        346            191              155
##  7 Balt~ AFC Nor~  2000    12     4        333            165              168
##  8 Pitt~ AFC Nor~  2000     9     7        321            255               66
##  9 Jack~ AFC Sou~  2000     7     9        367            327               40
## 10 Cinc~ AFC Nor~  2000     4    12        185            359             -174
## # ... with 628 more rows, and 9 more variables: margin_of_victory <dbl>,
## #   strength_of_schedule <dbl>, simple_rating <dbl>, offensive_ranking <dbl>,
## #   defensive_ranking <dbl>, playoffs <chr>, sb_winner <chr>,
## #   bin_playoffs <dbl>, bin_sb <dbl>

library(ggplot2)
ggplot(data = nfl_standings, 
       aes(division, bin_sb, col = team)) + geom_col() +
        ggtitle("Super Bowl Winners by Division") +
        xlab("Division") + ylab("Count of Super Bowl Championships Won") + 
        labs(col = "Team Name")

As evident in the above visualization created used the ggplot2 package, the AFC East has had the most Super Bowl wins between 2000-2019. This can be largely attributed to the New England Patriots’ former quarterback Tom Brady and current head coach Bill Belichick bringing home championships in 2002, 2004, 2005, 2015, 2017, and 2019. Additionally, the second-best division appears to be the AFC North, with both the Pittsburgh Steelers and Baltimore Ravens winning at least one Super Bowl Championship each.

Conversely, it appears the AFC South, NFC North, and NFC West have all only won one Super Bowl over the past two decades. Using the ggplot2 package, I was able to effectively create and interpret a visualization, and I look forward to learning more ways to manipulate my data sets. Using the ggplot feature, I will be able to use a variety of geoms such as geom_bar, geom_histogram, and geom_point, among others moving forward.