- Import File and summarize
library(readr)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(magrittr)
library(curl)
## Using libcurl 7.64.1 with LibreSSL/2.8.3
##
## Attaching package: 'curl'
## The following object is masked from 'package:readr':
##
## parse_date
#nfl_2007 <- read.csv("NFL2007Standings.csv")
nfl_2007<-read.csv(curl("https://raw.githubusercontent.com/brsingh7/R-Bridge/main/NFL2007Standings.csv?token=GHSAT0AAAAAABQQZHIYIQNP2JNGHSYBIUXQYO7L3AQ"))
summary(nfl_2007) #summarize data set
## X Team Conference Division
## Min. : 1.00 Length:32 Length:32 Length:32
## 1st Qu.: 8.75 Class :character Class :character Class :character
## Median :16.50 Mode :character Mode :character Mode :character
## Mean :16.50
## 3rd Qu.:24.25
## Max. :32.00
## Wins Losses WinPct PointsFor PointsAgainst
## Min. : 1.0 Min. : 0.0 Min. :0.0630 Min. :219.0 Min. :262.0
## 1st Qu.: 6.5 1st Qu.: 6.0 1st Qu.:0.4068 1st Qu.:273.2 1st Qu.:299.2
## Median : 8.0 Median : 8.0 Median :0.5000 Median :341.0 Median :349.5
## Mean : 8.0 Mean : 8.0 Mean :0.5003 Mean :347.0 Mean :347.0
## 3rd Qu.:10.0 3rd Qu.: 9.5 3rd Qu.:0.6250 3rd Qu.:395.2 3rd Qu.:385.8
## Max. :16.0 Max. :15.0 Max. :1.0000 Max. :589.0 Max. :444.0
## NetPts TDs
## Min. :-175.0 Min. :24.00
## 1st Qu.: -99.0 1st Qu.:28.00
## Median : -0.5 Median :37.50
## Mean : 0.0 Mean :38.84
## 3rd Qu.: 73.5 3rd Qu.:46.25
## Max. : 315.0 Max. :75.00
#Group by division and summarize on average wins, points for and median points against
#sort desceneding on average wins
nfl_2007 %>% summarize(AvgWins=mean(Wins), AvgPts=mean(PointsFor), MedianAgainst=median(PointsAgainst)) %>% arrange(desc(AvgWins))
## AvgWins AvgPts MedianAgainst
## 1 8 347 349.5
- Subset of the data. NFC teams with wins, losses, points for and tds
#NFC <- nfl_2007[c(Conference=="NFC"),c("Team", "Division", "Wins", "Losses", "PointsFor", "PointsAgainst", "TDs")]
NFC <- subset(nfl_2007, Conference == "NFC", select = c(Team, Division, Wins, Losses, PointsFor, PointsAgainst, TDs))
head(NFC, n=10)
## Team Division Wins Losses PointsFor PointsAgainst TDs
## 2 Dallas Cowboys NCE 13 3 455 325 54
## 3 Green Bay Packers NCN 13 3 435 291 49
## 8 New York Giants NCE 10 6 373 351 44
## 10 Seattle Seahawks NCW 10 6 393 291 44
## 12 Tampa Bay Buccaneers NCS 9 7 334 270 36
## 13 Washington Redskins NCE 9 7 334 310 35
## 14 Arizona Cardinals NCW 8 8 404 399 49
## 16 Minnesota Vikings NCN 8 8 365 311 43
## 17 Philadelphia Eagles NCE 8 8 336 300 38
## 19 Carolina Panthers NCS 7 9 267 347 28
- Rename Columns
colnames(NFC) <- c("Team_Name","NFC_Division", "W", "L", "Scored", "Against", "Touchdowns")
head(NFC,n=10)
## Team_Name NFC_Division W L Scored Against Touchdowns
## 2 Dallas Cowboys NCE 13 3 455 325 54
## 3 Green Bay Packers NCN 13 3 435 291 49
## 8 New York Giants NCE 10 6 373 351 44
## 10 Seattle Seahawks NCW 10 6 393 291 44
## 12 Tampa Bay Buccaneers NCS 9 7 334 270 36
## 13 Washington Redskins NCE 9 7 334 310 35
## 14 Arizona Cardinals NCW 8 8 404 399 49
## 16 Minnesota Vikings NCN 8 8 365 311 43
## 17 Philadelphia Eagles NCE 8 8 336 300 38
## 19 Carolina Panthers NCS 7 9 267 347 28
- Summarize subset, print mean and median for attributes in #1. Based on the subset, the average wins remained the same, as within each conference, that will not change because there is an evenly number of games played by each team (16 games in 2007). However, you do see that the average points scored (by 3.5) as well as the median of points against (by 2.0). This is because the data from the teams in the AFC is not included, and the data is telling us that the NFC, on average, scored less points in 2007.
summary(NFC) #summarize data set
## Team_Name NFC_Division W L
## Length:16 Length:16 Min. : 3.00 Min. : 3.00
## Class :character Class :character 1st Qu.: 7.00 1st Qu.: 6.75
## Mode :character Mode :character Median : 8.00 Median : 8.00
## Mean : 8.00 Mean : 8.00
## 3rd Qu.: 9.25 3rd Qu.: 9.00
## Max. :13.00 Max. :13.00
## Scored Against Touchdowns
## Min. :219.0 Min. :270.0 Min. :24.00
## 1st Qu.:317.2 1st Qu.:307.5 1st Qu.:32.50
## Median :341.0 Median :347.5 Median :37.50
## Mean :343.5 Mean :349.4 Mean :38.44
## 3rd Qu.:382.5 3rd Qu.:390.8 3rd Qu.:44.75
## Max. :455.0 Max. :444.0 Max. :54.00
#Group by division and summarize on average wins, points for and median points against
#sort desceneding on average wins
NFC %>% summarize(AvgWins=mean(W), AvgPts=mean(Scored), MedianAgainst=median(Against)) %>% arrange(desc(AvgWins))
## AvgWins AvgPts MedianAgainst
## 1 8 343.5 347.5
- Change values within columns
nfc_rename <- NFC
nfc_rename$NFC_Division[NFC$NFC_Division=="NCE"] <- "NFC East"
nfc_rename$NFC_Division[NFC$NFC_Division=="NCN"] <- "NFC North"
nfc_rename$NFC_Division[NFC$NFC_Division=="NCS"] <- "NFC South"
nfc_rename$NFC_Division[NFC$NFC_Division=="NCW"] <- "NFC West"
head(nfc_rename,n=10)
## Team_Name NFC_Division W L Scored Against Touchdowns
## 2 Dallas Cowboys NFC East 13 3 455 325 54
## 3 Green Bay Packers NFC North 13 3 435 291 49
## 8 New York Giants NFC East 10 6 373 351 44
## 10 Seattle Seahawks NFC West 10 6 393 291 44
## 12 Tampa Bay Buccaneers NFC South 9 7 334 270 36
## 13 Washington Redskins NFC East 9 7 334 310 35
## 14 Arizona Cardinals NFC West 8 8 404 399 49
## 16 Minnesota Vikings NFC North 8 8 365 311 43
## 17 Philadelphia Eagles NFC East 8 8 336 300 38
## 19 Carolina Panthers NFC South 7 9 267 347 28