OrienteeRing!Sheffield R user group

Pete Dodd

March, 2015

This talk

Using R to grab, visualize, and analyse data from a mini mountainmarathon

I getting data from web tables with rvestI manipulating the dataI visualisation with ggplot2I mapping with ggmapI analysis of speeds and leg lengthsI genetic algorithms for optimisation with GA

Mountain marathons: what are they?

Figure 1: A real event map

The data

Figure 2: The data at sportident.co.uk

rvest

library(rvest)library(ggplot2)rurl <- 'http://www.sportident.co.uk/results/....html'rurl %>% html() %>% html_table() %>% as.data.frame() %>%

ggplot(.,aes(x=Score)) %>%+geom_histogram(binwidth=10) %>% print

rvest

Figure 3: Distribution of scores.

Data manipulationdata frame sdf with position, score and route:

## [1] "Pos" "Name"## [3] "Team" "Category"## [5] "Score" "Time"## [7] "Points" "Bonus.Penalty"## [9] "Manual.Adj" "Controls..in.order.visited"

data frame sdf splits:

## [1] "Pos" "Name" "Category" "Score"## [5] "Time" "X.U.00A0." "X.U.00A0..1" "X.U.00A0..2"## [9] "X.U.00A0..3" "X.U.00A0..4" "X.U.00A0..5" "X.U.00A0..6"## [13] "X.U.00A0..7" "X.U.00A0..8" "X.U.00A0..9" "X.U.00A0..10"## [17] "X.U.00A0..11" "X.U.00A0..12" "X.U.00A0..13" "X.U.00A0..14"## [21] "X.U.00A0..15" "X.U.00A0..16" "X.U.00A0..17" "X.U.00A0..18"## [25] "X.U.00A0..19"

Data manipulation

CN <- c('S1',paste0('10',1:9),paste0('1',10:24),'F1')

getpath <- function(pos){tt <- tdf$Controls..in.order.visited[pos]tt2 <- unlist(strsplit(tt,', '))tt3 <- sapply(X=tt2,FUN = function(x)which(CN==x))g <- G[tt3,] #df of controlsg$Pos <- posg$Pos <- factor(g$Pos)tt <- gettimez(sdf[pos,6:ncol(sdf)]) #splitsg$split <- tt[!is.na(tt)]return(g)

}

Data manipulationlibrary(grid) #for arrowBP <- ggplot(data=G,aes(x=LON,y=LAT,label=Value)) +

geom_point(shape=1,size=3) +geom_text(size=4,hjust=-.5,color='red') +

scale_shape_discrete(solid=FALSE)

for(i in 1:nrow(tdf)){GP <- BP+

geom_segment(data=getpath(i),aes(xend=c(tail(LON,n=-1),NA),yend=c(tail(LAT,n=-1),NA),color=split),arrow = arrow(angle = 15, ends = "last", length = unit(0.2, "inches")),alpha=.9)+ggtitle(paste0(i,': ',tdf[i,2],'\nScore: ',tdf$Score[i],', penalty: ',tdf$Bonus.Penalty[i]))+scale_colour_gradient(limits=c(0,45),high='red',low='blue') + theme(plot.title = element_text(hjust = 0))

ggsave(GP,file=paste0('graphs/GP',sprintf("%03d",i),'.png'))}

Use FFMPEG to make movie!

Example routes

Example routes

Example routes

Example routes

Example routes

Mapping

library(ggmap)bbox <- make_bbox(LON, LAT, G, f = .8)map <- get_map(location = bbox,maptype='terrain')mp <- ggmap(map) + geom_point(data=G,aes(x=LON,y=LAT),shape=1,size=3)+

geom_text(data=G,aes(x=LON,y=LAT,label=Value),size=4,hjust=-.5,color='red')+scale_shape_discrete(solid=FALSE) + ylim(c(min(G$LAT)-eps,max(G$LAT)+eps)) +

xlim(c(min(G$LON)-eps,max(G$LON)+eps))

NB geom_leg needed rather than arrow for some reason.

Example routes

Example routes

Example routes

Example routes

Example routes

Mapping

Lessons:

I easy to use & integrates well with ggplot2I osm can be used rather than google, but service only runs in

middle of night!I discrete zoom levels can be awkward if you’re in betweenI bbox’s are square; although you can trim the display with xlim

etc.I usual shenanigans with different coordinate systems

Analysis

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−1.64 −1.62 −1.60 −1.58 −1.56LON

LAT

Analysis

Need a way of standardizing the leg durations, given splits fromdifferent runners.

One might imagine for leg i and runner j , split Tij

Tij = di/sj

where di is an inherent leg duration, and sj is a relative speed. Then

logTij = log di − log sj

Suggests linear regression:

lm(log(split) ~ leg + Pos + 0)

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0 50 100 150position

Spe

ed r

elat

ive

to w

inne

r

Optimization problem

I Combinatorial optimisation problem, similar to the travellingsalesman problem

I These problems are (NP) hardI Need to encode routes and their fitness (including penalties)

I sample a route as a permutation of 1:no_checkpoints,extracting the points between 1 and no_checkpoints

I 26! ≈ 4× 1026 (cf Avogadro’s number)I =⇒ need heuristics; not true solutions

Genetic algorithms

A class of optimization algorithms inspired by evolution.

There is a population of solutions with:

I a notion of fitnessI generationsI mutationI crossover (sex)

Package GA

Makes using these techniques pretty easy:

GA <- ga(type = "permutation", fitness = fitness,min = 1, max = length(ptz),popSize = 1e2, maxiter = 1e4,run = 5e3, pmutation = 0.2)

Package GA

0 2000 4000 6000 8000 10000

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020

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Generation

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ess

valu

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Final Score: 463, Raw Score: 470Time: 246mins, penalty: 7

Figure 4: Cf winner’s score of 440

Winner’s actual route