In pssguy/epldataBackUp: Datasets from English Premier League 1992-2018

knitr::opts_chunk$set(
  collapse = TRUE,
  warning = FALSE,
  message = FALSE,
  comment = "#>"
)

We have previously covered basic usage of datasets within the package including combining them to produce answers to questions and creating derived tables.

We will next look at more interesting output in the form of

• Tables
• Charts
• Maps

This package is particularly suited to the first two options though there is some geographic data to play around with

You will need the the data.frames created earlier so if it they are not in your environment either load a saved version or re-run the code

### Tables

I tend to use the DT package, but there are alternatives if you have another preference

Let's use the match_summary_full dataframe to calculate each team's head to head record. Over and above the current data, we need to create and sum the results

match_summary_full %>% 
  ungroup() %>%  #match_summary_full is grouped tbl_df
  #filter(team=="Arsenal",opponents=="Chelsea") %>% 
  group_by(team,opponents) %>% 
 # select(GF,GA) %>% 
  mutate(result = case_when(
    GF > GA ~ "W", #win
    GF == GA ~ "D", #draw/tie
    GF < GA ~ "L" # loss
  )) %>% 
  select(team,opponents,result,GF,GA,points) %>%
  mutate(yesno = 1) %>%
  distinct %>%
  spread(result, yesno, fill = 0) %>% 
  summarize(P=n(),W=sum(W),D=sum(D),L=sum(L),ppg=round(sum(points)/P,2))%>%
  arrange(desc(ppg)) %>% 
   DT::datatable(class='compact stripe hover row-border order-column',rownames=FALSE,options= list(paging = TRUE, searching = TRUE,info=FALSE))

This provides a sortable, searchable table

---

Charts

Let's turn attention to players. Firstly I will create a data.frame for the goals and assists for a specified player

For ease of use below, I have created it as a function and slipped in an example player_id

player_game_data <- function(player) {
# collect goal information for specific player
df_goals <- players %>% 
  left_join(player_team) %>% 
  left_join(player_game) %>% 
  left_join(goals) %>% 
  filter(start==TRUE|time_on>0) %>% 
  select(player_id,last_name,player_game_id,goal_id,team_game_id) %>% 
  mutate(goal=ifelse(!is.na(goal_id),1,0)) %>% 
  group_by(player_id,last_name,team_game_id) %>% 
  summarize(tot_goals=sum(goal)) %>% 
   filter(player_id==player)

# likewise with assists
df_assists <- players %>% 
  left_join(player_team) %>% 
  left_join(player_game) %>% 
  left_join(assists) %>% 
  filter(start==TRUE|time_on>0) %>% 
  select(player_id,last_name,team_game_id,assist_id,player_game_id) %>% 
  mutate(assist=ifelse(!is.na(assist_id),1,0)) %>% 
  group_by(player_id,last_name,team_game_id) %>% 
  summarize(tot_assists=sum(assist)) %>% 
   filter(player_id==player)

# combine
df_all <- df_goals %>% 
  inner_join(df_assists) %>% 
# create a game order  
  left_join(game_team) %>%
  left_join(game) %>% 
  arrange(game_date) %>% 
  mutate(player_game_order=row_number()) %>% 
   ungroup() %>% #removes unwanted name and PLAYERID
  select(player_game_order,tot_goals,tot_assists) %>% 
  # gather into narrow format for plotting
  gather(category,count,-player_game_order) 


}

player_df <-player_game_data("SALAHM")
head(player_df)

You can see why you might want to create a derived player table first if you want to do varied detailed analyses particularly where the raw data is only updated annually .saves time and enhances user interactivity experience

Now just choose your plotting package of choice to display the data. I will use plotly as this allows for ease of infoactivity with feature susch as panning/zooming, hover tooltips etc

library(plotly)

player_df %>%
  plot_ly(x=~player_game_order, y= ~count) %>%
  add_bars(color= ~category, colors=c("red","blue")) %>%
  layout(barmode="stack")

Lots of customization is available within the package.

---

Interactivity

Lets use the data to create some interactive output

Shiny

Lets say we use the match_summary_full data to plot a histogram of the goals scored by a team in the Premier League

library(shiny)
library(glue)

shinyApp(



  ui = fluidPage(

    ## calculate an ordered vector of teams to  select from
     teams <- match_summary_full %>%
      pull(team) %>%
      unique() %>% 
      sort(),

    selectInput("team", "Select Team:", teams),

    plotlyOutput("goals_for")
  ),
  server = function(input, output) {

    output$goals_for <- renderPlotly({

      match_summary_full %>%
        filter(team == input$team) %>%
        plot_ly %>%
        add_histogram(x =  ~ GF) %>%
        layout(title = glue("Distribution of Goals scored by {input$team}"))

    })
  }
)

Crosstalk

This is an alternative -- dont want to go to server interaction by brushing

Filtering

library(crosstalk)

msf  <- SharedData$new(match_summary_full)
bscols(
  widths = c(12), # forces components into rows
filter_select(id="team",label="Select a Team",sharedData=msf, group =  ~team, multiple = FALSE),
plot_ly(msf, x = ~GF, showlegend = FALSE) %>% 
    add_histogram(color = ~team, colors = "red")
)

Animation

see plotlyfunctionsextended.RMD which I think I did as blog post

We can use the standings dataset prepared earlier

Let's look at how arch-rivals, Brighton and Crystal Palace, fared last season

# function to add cumulative line
# courtesy Carson Sievert

accumulate_by <- function(dat, var) {
  var <- lazyeval::f_eval(var, dat)
  lvls <- plotly:::getLevels(var)
  dats <- lapply(seq_along(lvls), function(x) {
    cbind(dat[var %in% lvls[seq(1, x)], ], frame = lvls[[x]])
  })
  dplyr::bind_rows(dats)
}

# select team(s) to display 
teams <- c("Brighton","Crystal P")

# add function to base data and year of interest
df <- standings %>% 
  filter(season=="2017/2018"&team %in% teams) %>% 
  accumulate_by(~round)

# static plot - scatter plot- uncolored
base <- df %>% 
  plot_ly(x=~round,y=~position) %>% 
layout(
    xaxis=list(title="Games Played"),
   yaxis=list(title="League Standing",range=c(20.5,0.5))
  ) %>% 
  config(displayModeBar = F,showLink = F) 


# add animation options and color-blind safe colors
 base %>%
   add_lines(color = ~team, colors="Set2", frame = ~frame, ids = ~team) %>%
  animation_opts(500, easing = "linear",mode='immediate') %>%
  animation_button(
    x = 1, xanchor = "right", y = 0, yanchor = "middle", font = list(color="red"), bgcolor="lightblue"
  ) %>%
  animation_slider(
    currentvalue = list(prefix = "Game ")
  ) 

Brighton, a promoted club, were expected to be struggle but Crystal Palace spent more of the season in danger of relegation. In the end, they both survived relegation by placing higher than 18th

pssguy/epldataBackUp documentation built on May 23, 2019, 1:24 p.m.