Visualizing Strikeouts¶

Data¶

The first step is to get data on strikeouts by team across years. The NY Times graphic uses data scraped from baseball-reference, using the XML package in R. However, I will be using data from the R package Lahman, which provides tables from Sean Lahman’s Baseball Database as a set of data frames.

The data processing step involves using the plyr package to create two data frames:

1. team_data containing SOG (strikeouts per game) by yearID and team name
2. league_data containing SOG by yearID averaged across the league.

require(Lahman) ; require(plyr); library(ascii)
dat = Teams[,c('yearID', 'name', 'G', 'SO')]
team_data = na.omit(transform(dat, SOG = round(SO/G, 2)))
league_data = ddply(team_data, .(yearID), summarize, SOG = mean(SOG))
ascii(head(team_data), type = 'rst')

Charts¶

We will start by first creating a scatterplot of SOG by yearID across all teams. We use the rPlot function which uses the PolyChartsJS library to create interactive visualizations. The formula interface specifies the x and y variables, the data to use and the type of plot. We also specify a size and color argument to style the points. Finally, we pass a tooltip argument, which is a javascript function that overrides the default tooltip to display the information we require. You will see below the R code and the resulting chart.

require(rCharts)
p1 <- rPlot(SOG ~ yearID, data = team_data,
  type = "point",
  size = list(const = 2),
  color = list(const = "#888"),
  tooltip = "#! function(item){
    return item.SOG + ' ' + item.name + ' ' + item.yearID
   } !#"
)
p1

Standalone

Now, we need to add a line plot of the average SOG for the league by yearID. We do this by adding a second layer to the chart, which copies the elements of the previous layer and overrides the data, type, color and tooltip arguments. The R code is shown below and you will note that the resulting chart now shows a blue line chart corresponding to the league average SOG.

p1$layer(data = league_data, type = 'line',
  color = list(const = 'blue'), copy_layer = T, tooltip = NULL)
p1

Standalone

Finally, we will overlay a line plot of SOG by yearID for a specific team name. Later, while building the shiny app, we will turn this into an input variable that a user can choose from a dropdown menu. We use the layer approach used earlier and this time override the data and color arguments so that the line plot for the team stands out from the league average.

myteam = "Boston Red Sox"
p1$layer(data = team_data[team_data$name == myteam,],
  color = list(const = 'red'),
  copy_layer = T)
p1$set(dom = 'chart3')
p1

Standalone

Let us add a little more interactivity to the chart. To keep it simple, we will use handlers in PolychartJS to initiate an action when a user clicks on a point. The current handler is a simple one, which just displays the name of the team clicked on. If you are familiar with Javascript event handlers, the code should be self explanatory.

p2 <- p1$copy()
p2$setTemplate(afterScript = '
  <script>
    graph_chart3.addHandler(function(type, e) {
      var data;
      data = e.evtData;
      if (type === "click") {
        return alert("You clicked on the team: " + data.name["in"][0]);
      }
    });
  </script>
')
p2

Standalone

Application¶

Now it is time to convert this into a Shiny App. We will throw the data processing code into global.R so that it can be accessed both by ui.R and server.R. For the dropdown menu allowing users to choose a specific team, we will restrict the choices to only those which have data for more than 30 years. Accordingly, we have the following global.R.

## global.R
require(Lahman); require(plyr)
dat = Teams[,c('yearID', 'name', 'G', 'SO')]
team_data = na.omit(transform(dat, SOG = round(SO/G, 2)))
league_data = ddply(team_data, .(yearID), summarize, SOG = mean(SOG))
THRESHOLD = 30
team_appearances = count(team_data, .(name))
teams_in_menu = subset(team_appearances, freq > THRESHOLD)$name

For the UI, we will use a bootstrap page with controls being displayed in the sidebar. Shiny makes it really easy to create a page like this. See the annotated graphic below and the ui.R code that accompanies it to understand how the different pieces fit together.

We now need to write the server part of the shiny app. Thankfully, this is the easiest part, since it just involves wrapping the charting code inside renderChart and replacing user inputs to enable reactivity. We add a few more lines of code to set the height and title and remove the axis titles, since they are self explanatory.