## Information Density and Custom Chart Designs

I’ve been doodling today with a some charts for the Wrangling F1 Data With R living book, trying to see how much information I can start trying to pack into a single chart.

The initial impetus came simply from thinking about a count of laps led in a particular race by each drive; this morphed into charting the number of laps in each position for each driver, and then onto a more comprehensive race summary chart (see More Shiny Goodness – Tinkering With the Ergast Motor Racing Data API for an earlier graphical attempt at producing a race summary chart).

The chart shows:

grid position: identified using an empty grey square;
race position after the first lap: identified using an empty grey circle;
race position on each driver’s last lap: y-value (position) of corresponding pink circle;
points cutoff line: a faint grey dotted line to show which positions are inside – or out of – the points;
number of laps completed by each driver: size of pink circle;
total laps completed by driver: greyed annotation at the bottom of the chart;
whether a driver was classified or not: the total lap count is displayed using a bold font for classified drivers, and in italics for unclassified drivers;
finishing status of each driver: classification statuses other than *Finished* are also recorded at the bottom of the chart.

The chart also shows drivers who started the race but did not complete the first lap.

What the chart doesn’t show is what stage of the race the driver was in each position, and how long for. But I have an idea for another chart that could help there, as well as being able to reuse elements used in the chart shown here.

FWIW, the following fragment of R code shows the ggplot function used to create the chart. The data came from the ergast API, though it did require a bit of wrangling to get it into a shape that I could use to power the chart.

```#Reorder the drivers according to a final ranked position
g=ggplot(finalPos,aes(x=reorder(driverRef,finalPos)))
#Highlight the points cutoff
g=g+geom_hline(yintercept=10.5,colour='lightgrey',linetype='dotted')
#Highlight the position each driver was in on their final lap
g=g+geom_point(aes(y=position,size=lap),colour='red',alpha=0.15)
#Highlight the grid position of each driver
g=g+geom_point(aes(y=grid),shape=0,size=7,alpha=0.2)
#Highlight the position of each driver at the end of the first lap
g=g+geom_point(aes(y=lap1pos),shape=1,size=7,alpha=0.2)
#Provide a count of how many laps each driver held each position for
g=g+geom_text(data=posCounts,
aes(x=driverRef,y=position,label=poscount,alpha=alpha(poscount)),
size=4)
#Number of laps completed by driver
g=g+geom_text(aes(x=driverRef,y=-1,label=lap,fontface=ifelse(is.na(classification), 'italic' , 'bold')),size=3,colour='grey')
#Record the status of each driver
g=g+geom_text(aes(x=driverRef,y=-2,label=ifelse(status!='Finished', status,'')),size=2,angle=30,colour='grey')
#Styling - tidy the chart by removing the transparency legend
g+theme_bw()+xRotn()+xlab(NULL)+ylab(&quot;Race Position&quot;)+guides(alpha=FALSE)
```

The fully worked code can be found in forthcoming update to the Wrangling F1 Data With R living book.

## Connecting RStudio and MySQL Docker Containers – an example using the ergast db

building on Dockerising Open Data Databases – First Fumblings and my Book Extras – Data Files, Code Files and a Dockerised Application, I just figured out how to get the ergast db into a MySQL docker container and then query it from RStudio:

• install these docker-mysql-scripts
• run boot2docker
• from the boot2docker shell, start up a MySQL server (ergastdb) with password f1: dmysql-server ergastdb f1 By default, this exposes port 3306
• create an new empty database (f1db): dmysql-create-database ergastdb f1db
• add the ergast data to it: dmysql-import-database ergastdb /path/to/ergastdb/f1db.sql --database f1db
• fire up a copy of RStudio, in this case using my psychemedia/wranglingf1data container, linking it to the MySQL database which has the alias db: docker run --name f1djd -p 8788:8787 --link ergastdb:db -d psychemedia/wranglingf1data
• in RStudio, import the RMySQL library: library(RMySQL)
• in RStudio, connect to the database: con=dbConnect(MySQL(),user='root',password='f1',host='db',port=3306,dbname='f1db')
• in RStudio, run a test query: dbQuery(con,'SHOW TABLES');

I guess what I need to do now is pull the various bits into another script to make it a one-liner, perhaps with a few switches? For example, to create the database if it doesn’t exist, to download the ergast database file automatically, to populate the database for the first time, or update it with a more recent copy of the database, to fire up both containers and make sure they are appropriately linked etc. This would dramatically simplify things for use in the context of the Wrangling F1 Data With R book, for example. (If you beat me to it, please post details in the comments below.)

PS Hmm…. seems I get a UTF-8 encoding issue:

Not sure if this is with the database, or the RMySQL connector? Anyone got any ideas of a fix?

Ah ha – sort of via SO:

Running dbGetQuery(con,'SET NAMES utf8;') before querying seems to do the trick…

## Rediscovering Formula One Race Battlemaps

A couple of days ago, I posted a recipe on the F1DataJunkie blog that described how to calculate track position from laptime data.

Using that information, as well as additional derived columns such as the identity of, and time to, the cars immediately ahead of and behind a particular selected driver, both in terms of track position and race position, I revisited a chart type I first started exploring several years ago – race battle charts.

The main idea behind the battlemaps is that they can help us search for stories amidst the runners.

```dirattr=function(attr,dir='ahead') paste(attr,dir,sep='')

#We shall find it convenenient later on to split out the initial data selection
battlemap_df_driverCode=function(driverCode){
lapTimes[lapTimes['code']==driverCode,]
}

car_X=dirattr('car_',dir)
code_X=dirattr('code_',dir)
factor_X=paste('factor(position_',dir,'<position)',sep='')
code_race_X=dirattr('code_race',dir)

g=g+geom_hline(aes_string(yintercept=drs),linetype=5,col='grey')

#Plot the offlap cars that aren't directly being raced
g=g+geom_text(data=df[df[dirattr('code_',dir)]!=df[dirattr('code_race',dir)],],
aes_string(x='lap',
y=car_X,
label=code_X,
col=factor_X),
angle=45,size=2)
#Plot the cars being raced directly
g=g+geom_text(data=df,
aes_string(x='lap',
y=diff_X,
label=code_race_X),
angle=45,size=2)
g+guides(col=guide_legend(title='Intervening car'))

}

battle_WEB=battlemap_df_driverCode('WEB')
battlemap_core_chart(battle_WEB,g,dir='behind')
```

In this first sketch, from the 2012 Australian Grand Prix, I show the battlemap for Mark Webber:

We see how at the start of the race Webber kept pace with Alonso, albeit around about a second behind, at the same time as he drew away from Massa. In the last third of the race, he was closely battling with Hamilton whilst drawing away from Alonso. Coloured labels are used to highlight cars on a different lap (either ahead (aqua) or behind (orange)) that are in a track position between the selected driver and the car one place ahead or behind in terms of race position (the black labels). The y-axis is the time delta in milliseconds between the selected car and cars ahead (y > 0) or behind (y < 0). A dashed line at the +/- one second mark identifies cars within DRS range.

As well as charting the battles in the vicinity of a particular driver, we can also chart the battle in the context of a particular race position. We can reuse the chart elements and simply need to redefine the filtered dataset we are charting.

For example, if we filter the dataset to just get the data for the car in third position at the end of each lap, we can then generate a battle map of this data.

```battlemap_df_position=function(position){
lapTimes[lapTimes['position']==position,]
}

battleForThird=battlemap_df_position(3)

g=battlemap_core_chart(battleForThird,ggplot(),dir='behind')+xlab(NULL)+theme_bw()
g```

For more details, see the original version of the battlemap chapter. For updates to the chapter, I recommend that you invest in a copy Wrangling F1 Data With R book if you haven’t already done so:-)

## Segmenting F1 Qualifying Session Laptimes

I’ve started scraping some FIA timing sheets again, including practice and qualifying session laptimes. One of the things I’d like to do is explore various ways of looking at the qualifying session laptimes, which means identifying which qualifying session each laptime falls into, using some sort of clustering algorithm… or other means…:

For looking at session utilisation charts I’ve been making use of accumulated time into session to help display the data, as the following session utilisation chart (including green and purple laptimes) shows:

The horizontal x-axis is time into session from a basetime of the first time-of-day timestamp recorded on the timing sheets for the session.

If we look at the distribution of qualifying session laptimes for the 2015 Malaysian Grand Prix, we get something like this:

We can see a big rain delay gap, and also a tighter gap between the first and second sessions.

If we try to run a k-means clustering algorithm on the data, using 3 means for the three sessions, we see that in this case it isn’t managing to cluster the laptimes into actual sessions:

```# Attempt to identify qualifying session using K-Means Cluster Analysis around 3 means
clusters &amp;amp;lt;- kmeans(f12015test['cuml'], 3)

f12015test = data.frame(f12015test, clusters\$cluster)

ggplot(f12015test)+geom_text(aes(x=cuml, y=stime,
label=code, colour=factor(clusters.cluster)) ,angle=45,size=3)```

In particular, so of the Q1 laptimes are being grouped with Q2 laptimes.

However, we know that there is at least a 2 minute gap between sessions (regulations suggest 7 minutes, though if this is the time between lights going red then green again, we might need to knock a couple of minutes off the gap to account to for drivers who start their last lap just before the lights go red on a session) so if we assume that the only times there will be a two minute gap between recorded laptimes during the whole of qualifying session will be in the periods between the qualifying sessions, we can can generate a flag on those gaps, and then generate session number counts by counting on those flags.

```#Look for a two minute gap
f12015test=arrange(f12015test,cuml)
f12015test['gap']=c(0,diff(f12015test[,'cuml']))
f12015test['gapflag']= (f12015test['gap']&amp;amp;gt;=120)
f12015test['qsession']=1+cumsum(f12015test[,'gapflag'])

ggplot(f12015test)+ geom_text(aes(x=cuml, y=stime, label=code), angle=45,size=3
+facet_wrap(~qsession, scale=&amp;amp;quot;free&amp;amp;quot;)
```

(To tighten this up, we might also try to factor in the number of cars in the pits at any particular point in time…)

This chart clearly shows how the first qualifying session saw cars trialling evenly throughout the session, whereas in Q2 and Q3 they were far more bunched up (which perhaps creates more opportunities for cars to get in each others’ way on flying laps…)

One of the issues with this chart is that we don’t get to zoom in to actual flying laps. If all the flying lap times were about the same time, we could simply generate y-axis limits based on purple laptimes:

```minl=min(f12015test\$purple)*0.95
maxl=min(f12015test\$purple)*1.3

#Use these values in ylim()...```

However, where the laptimes differ significantly across sessions as they do in this case due to a dramatic change in weather conditions, we probably need to filter the data for each session separately.

Another crib we might use is to identify PIT lap and out-laps (laps immediately following a PIT event) and filter those out of the laptime traces.

Versions of these recipes will soon be added to the Wrangling F1 Data With R book. Once you buy into the book, you get all future updates to it for no additional cost, even in the case of the minimum book price increasing over time.

## Spotting Potential Battles in F1 Races

Over the last couple of races, I’ve started trying to review a variety of battlemaps for various drivers in each race. Prompted by an email request for more info around the battlemaps, I generated a new sketch charting the on track gaps between each driver and the lap leader for each lap of the race (How the F1 Canadian Grand Prix Race Evolved on Track).

Colour is used to identify cars on lead lap compared to lapped drivers. For lapped drivers, a count of how many laps they are behind the leader is displayed. I additionally overplot with a highlight for specified driver, as well as adding in a mark that shows the on track position of the leader of the next lap, along with their driver code.

Battles can be identified through the close proximity of two or more drivers within a lap, across several laps. The ‘next-lap-leader’ time at the far right shows how close the leader on the next lead lap is to the backmarker (on track) on the current lead lap.

By highlighting two particular drivers, we could compare how their races evolved, perhaps highlighting different strategies used within a race that eventually bring the drivers into a close competitive battle in the last few laps of a race.

The unchanging leader-on-track-delta-of-0 line is perhaps missing an informational opportunity? For example, should we set the leader’s time to be the delta compared to the lap time for the leader laps from the previous lead lap? Or a delta compared to the fastest laptime on the previous lead lap? And if we do start messing about with an offset to the leader’s lap time, we presumably need to apply the same offset to the laptime of everyone else on the lap so we can still see the comparative on-track gaps to leader?

On the to-do list are various strategies for automatically identifying potential battles based on a variety of in-lap and across-lap heuristics.

Here’s the code:

```#Grab some data
lapTimes =lapsData.df(2015,7)

#Process the laptimes
lapTimes=battlemap_encoder(lapTimes)

#Find the accumulated race time at the start of each leader's lap

#Find the on-track gap to leader
lapTimes['trackdiff']=lapTimes['acctime']-lapTimes['lstart']

#Construct a dataframe that contains the difference between the
#leader accumulated laptime on current lap and next lap
ll=data.frame(t=diff(lapTimes[lapTimes['position']==1,'acctime']))
#Generate a de facto lap count
ll['n']=1:nrow(ll)
#Grab the code of the lap leader on the next lap
ll['c']=lapTimes[lapTimes['position']==1 & lapTimes['lap']>1,'code']