The 2017 NFL Regular season is weeks away and so are fantasy drafts! Using a few packages in R with NFL player data from Kaggle, you can easily explore this data visually with a few lines of code.
Below is a crosstalk widget that explores individual NFL players who played 5 or more games and who had 11 or more completed receptions, rushes, or passes in the 2016 NFL Regular Season. All components of the widets below are interactive and should update when you click on them. Double click the dots below to try it out!
R Code
library(data.table)
library(sqldf)
library(plotly)
library(crosstalk)
library(DT)
##Passing
Passing <- fread('Career_Stats_Passing.csv', stringsAsFactors = F)
#Passing <- subset(Passing, Year %in% 2016)
names(Passing) <- gsub(" ", "_", names(Passing))
Passing$Passes_Attempted<-as.numeric(Passing$Passes_Attempted)
Passing$Passes_Completed<-as.numeric(Passing$Passes_Completed)
Passing$Completion_Percentage<-as.numeric(Passing$Completion_Percentage)
Passing$Passing_Yards <- as.numeric(gsub(",", "", Passing$Passing_Yards))
Passing$TD_Passes<-as.numeric(Passing$TD_Passes)
Passing$Passing_Yards_Per_Game<-as.numeric(Passing$Passing_Yards_Per_Game)
Passing <- subset(Passing, Year %in% 2016 & Passes_Attempted > 10 & Games_Played>4)
##Rushing
Rushing <- fread('Career_Stats_Rushing.csv', stringsAsFactors = F)
#Rushing <- subset(Rushing, Year %in% 2016)
names(Rushing) <- gsub(" ", "_", names(Rushing))
Rushing$Rushing_Attempts<-as.numeric(Rushing$Rushing_Attempts)
Rushing$Rushing_Yards<- as.numeric(gsub(",", "", Rushing$Rushing_Yards))
Rushing$Rushing_TDs<-as.numeric(Rushing$Rushing_TDs)
Rushing <- subset(Rushing, Year %in% 2016 & Rushing_Attempts > 10 & Games_Played>4)
##Receiving
Receiving <- fread('Career_Stats_Receiving.csv', stringsAsFactors = F)
names(Receiving) <- gsub(" ", "_", names(Receiving))
Receiving$Receptions<-as.numeric(Receiving$Receptions)
Receiving$Receiving_Yards<- as.numeric(gsub(",", "", Receiving$Receiving_Yards))
Receiving$Receiving_TDs<-as.numeric(Receiving$Receiving_TDs)
Receiving <- subset(Receiving, Year %in% 2016 & Receptions > 10 & Games_Played>4)
all <- merge(Rushing, Receiving, by = "Player_Id", all = T)
all <- merge(all, Passing, by = "Player_Id", all = T)
all$Position <- ifelse(!is.na(all$Position.x), all$Position.x, all$Position.y)
all$Position <- ifelse(!is.na(all$Position), all$Position, all$Position)
all$Position <- ifelse(!is.na(all$Position.x), all$Position.x, all$Position.y)
all$MAINName <- ifelse(!is.na(all$Name.x), all$Name.x, all$Name.y)
all$MAINName <- ifelse(!is.na(all$MAINName), all$MAINName, all$Name)
all$Total_Games_Played <- ifelse(!is.na(all$Games_Played.x), all$Games_Played.x, all$Games_Played.y)
all$Total_Games_Played <- ifelse(!is.na(all$Total_Games_Played), all$Total_Games_Played, all$Games_Played)
all$MAINTeam <- ifelse(!is.na(all$Team.x), all$Team.x, all$Team.y)
all$MAINTeam <- ifelse(!is.na(all$MAINTeam), all$MAINTeam, all$Team)
all$Name <- all$MAINName
all$Team <- all$MAINTeam
#all[,c('Player_Id',56,57,58,7,9,12,25)]
df<-all[,c('Player_Id', 'Name', 'Position', 'Team','Total_Games_Played', 'Rushing_Attempts','Rushing_Yards',
'Rushing_TDs', 'Receptions', 'Receiving_Yards', 'Receiving_TDs',
'Passes_Completed', 'Passing_Yards', 'TD_Passes')]
df[is.na(df)] <- 0
df$Position <- gsub('0', 'QB', df$Position)
df$Position <- gsub('FB', 'RB', df$Position)
a <- sqldf("select Player_Id,
Name,
Position,
Team,
Total_Games_Played,
Rushing_Yards+Receiving_Yards+Passing_Yards as Yards,
(Rushing_Yards+Receiving_Yards+Passing_Yards)/Total_Games_Played as AvgYardsPerGame,
Rushing_TDs+Receiving_TDs+TD_Passes as Touchdowns,
(Rushing_TDs+Receiving_TDs+TD_Passes)/Total_Games_Played as AvgTouchdownsPerGame,
Passes_Completed+Receptions+Rushing_Attempts as Completions,
(Passes_Completed+Receptions+Rushing_Attempts)/Total_Games_Played as AvgCompletionsPerGame
from df")
##Create Shared Data element
sd <- SharedData$new(a)
##Create Chart 1
x <- list(
title = "Avg Yards Per Game (Rushing, Receiving, Passing)"
)
y <- list(
title = "Avg TDs Per Game (Rushing, Receiving, Passing)"
)
p<-plot_ly(sd, x = ~AvgYardsPerGame, y = ~AvgTouchdownsPerGame, color = ~Position,
#width="100%", height=400,
mode = 'markers', symbol = ~Position, symbols = c('circle','x','o', '+'), marker = list(size = 8),
text = ~paste('Name: ', Name,
'</br> Team: ', Team))%>%
layout(title = 'Avg Yards v Touchdowns',
xaxis = x, yaxis = y) %>%
config(displayModeBar = F)
##Create Chart 2
ppr<- plot_ly(sd, x = ~AvgYardsPerGame, y = ~AvgCompletionsPerGame, type = 'scatter', color = ~Position,
#width="100%", height=400,
mode = 'markers', symbol = ~Position, symbols = c('circle','x','o', '+'), marker = list(size = 8),
text = ~paste('Name: ', Name,
'</br> Team: ', Team)
#mode = 'text', text = ~Name, textposition = 'middle right', textfont = list(size = 8)
)%>%
layout( title = 'Avg Yards v Completions',
xaxis = list(title = 'Avg Yards Per Game (Rushing, Receiving, Passing)'),
yaxis = list(title = 'Avg Completions Per Game (Rushing, Receiving, Passing)')) %>%
config(displayModeBar = F)
##create datatable
dt <- datatable(sd, extensions="Scroller", style="bootstrap", class="compact",
options=list(initComplete = JS(
"function(settings, json) {",
"$(this.api().table().header()).css({'background-color': '#F8F8F8', 'color': '#000'});",
"}"), columnDefs = list(list(visible=FALSE, targets=c(1,7,9,11)))))
##Create Crosstalk Widget
bscols(
list(filter_checkbox("Position", "Position", sd, ~Position, inline = TRUE),
filter_select("Team", "Team", sd, ~Team,multiple=T))
)
p
ppr
datatable(sd, extensions="Scroller", style="bootstrap", class="compact",
#width="100%", height=400,
options=list(initComplete = JS(
"function(settings, json) {",
"$(this.api().table().header()).css({'background-color': '#F8F8F8', 'color': '#000'});",
"}"), columnDefs = list(list(visible=FALSE, targets=c(1,7,9,11)))))