library(dplyr)
library(tidyr)
#install.packages("devtools")
devtools::install_github("cardiomoon/moonBook")
devtools::install_github("cardiomoon/moonBook2")
require(moonBook)
require(ggplot2)
require(ggiraph)
#require(plyr)
require(reshape2)
require(scales)
require(moonBook2)setwd("/Users/mitchell/R/R_MarkDowns")
# DF1 is the table I am using for the Spider.Plot function.
DF1 <- read.csv("ESPN.TIME.TABLE1.csv", stringsAsFactors = F) %>% mutate(POS = trimws(POS))
NFLterms <- read.csv("FBall.Terms.csv", stringsAsFactors = F)This is my Spider.Plot Function (or radar plot) for multiple players in a given year. Simply entering the player names and year will generate a spider plot as depicted below…
SpiderPlot.Func <- function(Category, Position, YEAR, Player1, Player2, Player3, Player4, Player5, Player6)
{
p <<- DF1 %>% filter(POS == Position) %>%
filter(Year == YEAR) %>%
filter(Web.Cat == Category) %>%
group_by(Web.Cat, POS, Year, Pro.Record) %>%
mutate(Max = max(Value, na.rm = T)
,T.Value = (Value / Max)) %>%
ungroup() %>%
select(POS, PLAYER, Pro.Record, T.Value) %>%
filter(PLAYER == Player1 | PLAYER == Player2 | PLAYER == Player3
| PLAYER == Player4 | PLAYER == Player5 | PLAYER == Player6) %>%
spread(Pro.Record, T.Value)
write.csv(p, "p.csv", row.names = F)
p1 <<- p
ggRadar(p, groupvar= "PLAYER", interactive = TRUE)
}
SpiderPlot.Func(Category = "Passing", Position = "QB", YEAR = "2006"
, Player1 = "Peyton Manning", Player2 = "Drew Brees", Player3 = "Carson Palmer"
, Player4 = "Brett Favre", Player5 = "Tom Brady", Player6 = "Aaron Rodgers")This series of radar plots is created by a seperate df “p1”, also created in the above function…
pA<-ggRadar(p1[1:6,],rescale=F,groupvar="PLAYER",legend.position="none")+
ylim(0,1)+facet_wrap(~PLAYER)
ggiraph(code=print(pA))All of the plots above are created from the below table. This table is also a product of the Spider.Plot Function…
| POS | PLAYER | ATT | COMP | INT | LONG | PCT | RATE | SACK | TD | YDS | YDS.A | YDS.G |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| QB | Aaron Rodgers | 0.0244698 | 0.0161290 | NA | 0.1797753 | 0.400 | 0.3505455 | 0.0476190 | NA | 0.0104120 | 0.2971926 | 0.0833333 |
| QB | Brett Favre | 1.0000000 | 0.9220430 | 0.7826087 | 0.9213483 | 0.560 | 0.5287273 | 0.3333333 | 0.5806452 | 0.8793572 | 0.6137464 | 0.8804348 |
| QB | Carson Palmer | 0.8482871 | 0.8709677 | 0.5652174 | 0.8314607 | 0.623 | 0.6829091 | 0.5714286 | 0.9032258 | 0.9133092 | 0.7512101 | 0.9130435 |
| QB | Drew Brees | 0.9037520 | 0.9569892 | 0.4782609 | 0.9662921 | 0.643 | 0.6996364 | 0.2857143 | 0.8387097 | 1.0000000 | 0.7725073 | 1.0000000 |
| QB | Peyton Manning | 0.9086460 | 0.9731183 | 0.3913043 | 0.7640449 | 0.650 | 0.7345455 | 0.2222222 | 1.0000000 | 0.9952467 | 0.7637948 | 0.9963768 |
| QB | Tom Brady | 0.8417618 | 0.8575269 | 0.5217391 | 0.6966292 | 0.618 | 0.6392727 | 0.4126984 | 0.7741935 | 0.7987777 | 0.6621491 | 0.8007246 |
This radar plot displays a single player’s recorded performance over multiple / selected years…
SpiderPlot.Year.Func <- function(Category, Position, Player, YEAR1, YEAR2, YEAR3, YEAR4, YEAR5, YEAR6)
{
pY <<- DF1 %>% filter(POS == Position) %>%
filter(Web.Cat == Category) %>%
group_by(Web.Cat, POS, Year, Pro.Record) %>%
mutate(Max = max(Value, na.rm = T)
,T.Value = (Value / Max)) %>%
ungroup() %>%
select(POS, PLAYER, Year, Pro.Record, T.Value) %>%
filter(PLAYER == Player) %>%
filter(Year == YEAR1 | Year == YEAR2 | Year == YEAR3
| Year == YEAR4 | Year == YEAR5 | Year == YEAR6) %>%
spread(Pro.Record, T.Value)
write.csv(pY, "pYear.csv", row.names = F)
p2 <<- pY
ggRadar(pY, groupvar= "Year", interactive = TRUE)
}
SpiderPlot.Year.Func(Category = "Passing", Position = "QB", Player = "Aaron Rodgers"
, YEAR1 = "2007", YEAR2 = "2008", YEAR3 = "2009"
, YEAR4 = "2010", YEAR5 = "2013", YEAR6 = "2014")Even with 6 years displayed in this radar plot you can still clearly see his performance levels change over the course of time. For example, notice how there is a level of consistency throughout his career that drastically changes for 2013, the year sustained a broken collarbone. Still, if this plot appears too congested a plot for every year can be created instead…
pB<-ggRadar(p2[1:6,],rescale=F,groupvar="Year",legend.position="none")+
ylim(0,1)+facet_wrap(~Year)
ggiraph(code=print(pB))Here’s a (hokey) little ifelse function that pulls the terminology for each stats category (e.g. Rushing, Passing, etc.)
CatTable <- function(Category)
{
BBB <- Category
ifelse(BBB == "Passing", x <- 1, ifelse(BBB == "Returning", x <- 3, ifelse(BBB == "Rushing", x <- 5
, ifelse(BBB == "Scoring", x <- 7, ifelse(BBB == "Receiving", x <- 9, ifelse(BBB == "Kicking", x <- 11
, ifelse(BBB == "Defense", x <- 13, ifelse(BBB == "Punting", x <- 15))))))))
y <- 1 + x
Categories <- NFLterms %>% select(x, y) %>% print()
}
CatTable(Category = "Passing")## Passing Passing.1
## 1 COMP Completions
## 2 ATT Passing attempts
## 3 PCT Completion percentage
## 4 YDS Passing yards
## 5 YDS.A Yards per pass attempt
## 6 LONG Longest pass
## 7 TD Passing touchdowns
## 8 INT Interceptions thrown
## 9 SACK Sacks
## 10 RATE Passer rating
## 11 YDS.G Passing yards per game
## 12
## 13I created a few other quick helpful functions. For instance, the below function will display the rank of players of a selected position, category of recorded stats (e.g. Passing, or Rushing, etc.), and chosen year. Furthermore, if a variable might disrupt this ranking, say for instance like a perfect percentage of completions score for a QB who only threw one pass in a season, said variable can be removed by name.
PlayerRank <- function(Position, YEAR, Category, Disruptive.Variable, Count)
{
pRnk <<- DF1 %>%
filter(POS == Position) %>%
filter(Year == YEAR) %>%
filter(Web.Cat == Category) %>%
filter(Pro.Record != Disruptive.Variable) %>%
group_by(Pro.Record) %>%
mutate(Avg = round(mean(Value2, na.rm = T), 2)
, Max = max(Value, na.rm = T)) %>%
ungroup() %>%
mutate(Grade = round(Value2/Max, 2) * 100) %>%
group_by(PLAYER) %>%
mutate(Average.Grade = mean(Grade, na.rm = T)) %>%
ungroup() %>%
mutate(Max.Avg = max(Average.Grade, na.rm = T))%>%
mutate(True.Grade = round(Average.Grade/Max.Avg, 2) * 100) %>%
arrange(desc(True.Grade)) %>%
select(1,2,3,4,15) %>%
distinct() %>%
slice(1:Count)
}
PlayerRank(Position = "QB", YEAR = "2006", Category = "Passing"
, Disruptive.Variable = "PCT", Count = "10")| PLAYER | POS | TEAM | Year | True.Grade |
|---|---|---|---|---|
| Peyton Manning | QB | IND | 2006 | 100 |
| Drew Brees | QB | NO | 2006 | 98 |
| Marc Bulger | QB | LA | 2006 | 87 |
| Carson Palmer | QB | CIN | 2006 | 85 |
| Brett Favre | QB | GB | 2006 | 80 |
| Tom Brady | QB | NE | 2006 | 79 |
| Donovan McNabb | QB | PHI | 2006 | 77 |
| Philip Rivers | QB | SD | 2006 | 76 |
| Steve McNair | QB | BAL | 2006 | 75 |
| Chad Pennington | QB | NYJ | 2006 | 70 |
This function identifies players from a partial spelling of their name…
PlayerFinder <- function(Partial_Player_name)
{
PFound <<- DF1 %>%
mutate(PlayerFound = ifelse(grepl(Partial_Player_name, DF1$PLAYER), PLAYER, "NA")) %>%
filter(PLAYER == PlayerFound) %>%
select(1,2,3,4) %>%
distinct()
}
PlayerFinder(Partial_Player_name = "Fav")
print(PFound)## PLAYER POS TEAM Year
## 1 Brett Favre QB GB 2005
## 2 Brett Favre QB GB 2006
## 3 Brett Favre QB GB 2007
## 4 Brett Favre QB NYJ 2008
## 5 Brett Favre QB MIN 2009
## 6 Brett Favre QB MIN 2010
## 7 Greg Favors LB JAX 2005This function creates a list of every player of a stats catagory, on a team, in a given year…
PlayerInfo.Func <- function(Category, Team, YEAR)
{PlayerInfo <<- DF1 %>%
filter(Web.Cat == Category) %>%
filter(TEAM == Team) %>%
filter(Year == YEAR) %>%
select(1,2) %>% distinct() %>%
print()
}
PlayerInfo.Func(Category = "Defense", Team = "PHI", YEAR = "2015")## PLAYER POS
## 1 Malcolm Jenkins FS
## 2 Mychal Kendricks LB
## 3 Walter Thurmond III DB
## 4 Fletcher Cox DE
## 5 Byron Maxwell CB
## 6 Nolan Carroll II CB
## 7 Bennie Logan DT
## 8 Connor Barwin LB
## 9 Brandon Graham LB
## 10 Jordan Hicks LB
## 11 DeMeco Ryans LB
## 12 E.J. Biggers CB
## 13 Kiko Alonso LB
## 14 Cedric Thornton DT
## 15 Eric Rowe DB
## 16 Beau Allen DT
## 17 Taylor Hart DE
## 18 Ed Reynolds S
## 19 Chris Maragos FS
## 20 Vinny Curry DE
## 21 Brandon Bair DE
## 22 Najee Goode LB
## 23 Marcus Smith II LB
## 24 Bryan Braman LB
## 25 Brad Jones LB
## 26 Jerome Couplin III S
## 27 Jaylen Watkins CBThis is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.