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
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
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.
Lets use the data to create some interactive output
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}")) }) } )
This is an alternative -- dont want to go to server interaction by brushing
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") )
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
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