Bump Chart with rCharts and Rickshaw

# Get Data from URL
# Reference: http://learnr.wordpress.com/2009/03/19/data-manipulation-football-league-table/
getData <- function(dataURL){
  require(reshape2); require(plyr)
  df <- read.csv(dataURL, stringsAsFactors = F)
  df <- df[, c(2, 3, 4, 7)]
  df <- rename(df, c(Date = "date", HomeTeam = "H", AwayTeam = "A"))
  df$date <- as.Date(df$date, format = "%d/%m/%y")

  dfm <- melt(df, measure.vars = c("H", "A"), 
    variable.name = "loc", value.name = 'team')
  dfm <- arrange(dfm, date)

  dfm <- mutate(dfm, 
    loc = as.character(loc), 
    points = 3*(loc == FTR) + 1*(FTR == 'D')
  )
  return(dfm)
}


# Compute the League Table on a Given Date
getLeagueTable <- function(date_, data_){
  dat <- subset(data_, date <= date_)
  dat2 <- ddply(dat, .(team), summarize,
    P = sum(points),
    G = length(points),
    W = length(points == 3),
    D = length(points == 1),
    L = length(points == 0)
  )
  dat3 <- arrange(dat2, P)
  dat3 <- transform(dat3, rank = rank(P, ties.method = 'min'))
  arrange(dat3, rank)
}


getBumpChartData <- function(url){
  require(plyr)
  #' Utility function to convert dates to seconds for javascript
  to_jsdate <- function(date_){
    val = as.POSIXct(as.Date(date_),origin="1970-01-01")
    as.numeric(val)
  }
  dfm <- getData(url)
  bumpChartData <- ldply(unique(dfm$date), function(date_){
    cbind(getLeagueTable(date_, dfm), date = to_jsdate(date_))
  })
  return(bumpChartData)
}

# Function to plot bump chart
plotBumpChart <- function(bData){
  require(rCharts);
  bump_chart <- Rickshaw$new()
  mycolors = ggthemes::tableau_color_pal("tableau20")(20)
  bump_chart$layer(rank ~ date, group = 'team', data = bData, 
    type = 'line', interpolation = 'basis', colors = mycolors)
  bump_chart$yAxis(orientation = 'left')
  bump_chart$xAxis(type = 'Time')
  bump_chart$set(slider = TRUE, highlight = TRUE)
  options(RCHART_TEMPLATE = 'Rickshaw.html');
  return(bump_chart)
}

# data url for 2012-2013 results
url <- 'http://www.football-data.co.uk/mmz4281/1213/E0.csv'
bData <- getBumpChartData(url)
b1 <- plotBumpChart(bData)
b1$yAxis(tickFormat = 'function(n){return(n == 0 ? "": String(21 - n))}')
b1$hoverDetail(formatter = 'function(series, x, y){
  return "<b>" + series.name + "</b>: " + String(21 - y)        
}')

<style>p {text-align: justify}</style>

This bump chart was created using [rCharts](http://github.com/rCharts) and [Rickshaw](http://code.shutterstock.com/rickshaw/). It was inspired by two excellents posts by [Kenan Davis](http://kenandavis.com/projects/a_season_visualized/) and [Anna Powell-Smith](http://thestoryoftheseason.com/). 

My objective with rCharts is to democratize interactive visualizations by creating charting components which are reusable. The source code for this bump chart is availabe [here](https://gist.github.com/ramnathv/5649396/raw/5029d2224c45545a7531c3928e029fca4478f7c0/bumpchart.R). You will notice that bulk of the code is getting the data into a format that can be fed into the `plotBumpChart` function. The code is currently geared towards making bump charts for [EPL Data](http://www.football-data.co.uk/englandm.php). However, it can be generalized so that one can create bump charts for any context.

If you have some feedback, please click on the comments link below.

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