# A Checkpoint Of Spanish Football League

I am an absolute beginner, but I am absolutely sane (Absolute Beginners, David Bowie)

Some time ago I wrote this post, where I predicted correctly the winner of the Spanish Football League several months before its ending. After thinking intensely about taking the risk of ruining my reputation repeating the analysis, I said “no problem, Antonio, do it again: in the end you don’t have any reputation to keep”. So here we are.

From a technical point of view there are many differences between both analysis. Now I use webscraping to download data, dplyr and pipes to do transformations and interactive D3.js graphs to show results. I think my code is better now and it makes me happy.

As I did the other time, Bradley-Terry Model gives an indicator of  the power of each team, called ability, which provides a natural mechanism for ranking teams. This is the evolution of abilities of each team during the championship (last season was played during the past weekend):

Although it is a bit messy, the graph shows two main groups of teams: on the one hand, Barcelona, Atletico de Madrid, Real Madrid and Villareal; on the other hand, the rest. Let’s have a closer look to evolution of the abilities of the top 4 teams:

While Barcelona, Atletico de Madrid and Real Madrid walk in parallel,  Villareal seems to be a bit stacked in the last seasons; the gap between them and Real Madrid is increasing little by little. Maybe is the Zidane’s effect. It is quite interesting discovering what teams are increasing their abilities: they are Malaga, Eibar and Getafe. They will probably finish the championship in a better position than they have nowadays (Eibar could reach fifth position):

What about Villareal? Will they go up some position? I don’t think so. This plot shows the probability of winning any of the top 3:

As you can see, probability is decreasing significantly. And what about Barcelona? Will win? It is a very difficult question. They are almost tied with Atletico de Madrid, and only 5 and 8 points above Real Madrid and Villareal. But it seems Barcelona keep them at bay. This plot shows the evolution of the probability of be beaten by Atletico, Real Madrid and Villareal:

All probabilities are under 50% and decreasing (I supposed a scoring of 2-0 for Barcelona in the match against Sporting of season 16 that was postponed to next February 17th).

Data science is a profession for brave people so it is time to do some predictions. These are mine, ordered by likelihood:

• Barcelona will win, followed by Atletico (2), Real Madrid (3), Villareal (4) and Eibar (5)
• Malaga and Getafe will go up some positions
• Next year I will do the analysis again

Here you have the code:

```library(rvest)
library(stringr)
library(dplyr)
library(reshape)
library(rCharts)
nseasons=20
results=data.frame()
for (i in 1:nseasons)
{
html(webpage) %>%
html_nodes("table") %>%
.[[1]] %>%
mutate(X4=i) %>%
rbind(results)->results
}
colnames(results)=c("home", "score", "visiting", "season")
results %>%
mutate(home     = iconv(home,     from="UTF8",to="ASCII//TRANSLIT"),
visiting = iconv(visiting, from="UTF8",to="ASCII//TRANSLIT")) %>%
#filter(grepl("-", score)) %>%
mutate(score=replace(score, score=="18:30 - 17/02/2016", "0-2")) %>% # resultado fake para el Barcelona
mutate(score_home     = as.numeric(str_split_fixed(score, "-", 2)[,1])) %>%
mutate(score_visiting = as.numeric(str_split_fixed(score, "-", 2)[,2])) %>%
mutate(points_home     =ifelse(score_home > score_visiting, 3, ifelse(score_home < score_visiting, 0, 1))) %>%
mutate(points_visiting =ifelse(score_home > score_visiting, 0, ifelse(score_home < score_visiting, 3, 1))) -> data
prob_BT=function(x, y) {exp(x-y) / (1 + exp(x-y))}
BTabilities=data.frame()
for (i in 13:nseasons)
{
data %>% filter(season<=i) %>%
BTm(cbind(points_home, points_visiting), home, visiting, data=.) -> footballBTModel
BTabilities(footballBTModel) %>%
as.data.frame()  -> tmp
cbind(tmp, as.character(rownames(tmp)), i) %>%
mutate(ability=round(ability, digits = 2)) %>%
rbind(BTabilities) -> BTabilities
}
colnames(BTabilities)=c("ability", "s.e.", "team", "season")
sort(unique(BTabilities[,"team"])) -> teams
BTprobabilities=data.frame()
for (i in 13:nseasons)
{
BTabilities[BTabilities\$season==i,1] %>% outer( ., ., prob_BT) -> tmp
colnames(tmp)=teams
rownames(tmp)=teams
cbind(melt(tmp),i) %>% rbind(BTprobabilities) -> BTprobabilities
}
colnames(BTprobabilities)=c("team1", "team2", "probability", "season")
BTprobabilities %>%
filter(team1=="Villarreal") %>%
mutate(probability=round(probability, digits = 2)) %>%
filter(team2 %in% c("R. Madrid", "Barcelona", "Atletico")) -> BTVillareal
BTprobabilities %>%
filter(team2=="Barcelona") %>%
mutate(probability=round(probability, digits = 2)) %>%
filter(team1 %in% c("R. Madrid", "Villarreal", "Atletico")) -> BTBarcelona
AbilityPlot <- nPlot(
ability ~ season,
data = BTabilities,
group = "team",
type = "lineChart")
AbilityPlot\$yAxis(axisLabel = "Estimated Ability", width = 62)
AbilityPlot\$xAxis(axisLabel = "Season")
VillarealPlot <- nPlot(
probability ~ season,
data = BTVillareal,
group = "team2",
type = "lineChart")
VillarealPlot\$yAxis(axisLabel = "Probability of beating", width = 62)
VillarealPlot\$xAxis(axisLabel = "Season")
BarcelonaPlot <- nPlot(
probability ~ season,
data = BTBarcelona,
group = "team1",
type = "lineChart")
BarcelonaPlot\$yAxis(axisLabel = "Probability of being beaten", width = 62)
BarcelonaPlot\$xAxis(axisLabel = "Season")
```

# Visualising The Evolution Of Migration Flows With rCharts

Heaven we hope is just up the road (Atlas, Coldplay)

Following with the analysis of migration flows, I have done next two visualizations. These charts are called bump charts and are very suitable to represent rankings. This is what I have done:

• Obtaining top 20 countries of the world according to % of migrants respect its population
• To do this, I divide total number of migrants between 1960 and 2009 by the mean population in the same period.
• I do the same to obtain top 20 countries of the world according to % of immigrants.
• In both cases, I only consider countries with population greater than 2 million.
• For these countries, I calculate % of migrants in each decade (60’s, 70’s, 80’s, 90’s and 00’s), dividing total number of migrants by mean population each decade
• I do the same in the case of immigrants.
• Instead of representing directly % of migrants and immigrants, I represent the ranking of countries according these indicators by decade

This is the bump chart of migrants:

And this is the one of immigrants:

• There is a permanent exodus in Puerto Rico: all decades (except 70’s) is located in the top 1 of countries with most migrants respect its population
• Ireland is also living a diaspora although in the 00’s decade has lost some positions
• Albania, Georgia and Bosnia and Herzegovina are gaining positions. Is East Europe gradually becoming uncomfortable?
• Jamaica is also moving up in this sad competition.
• On the other hand, Hong Kong and Israel are persistently leaders as receivers
• Saudi Arabia has presented an impressive growth receiving immigrants since 70’s
• United States does not appear in the immigrants ranking
• Singapore is gaining positions: in the 00’s decade is the third receiver country
• Also in the 00s, Switzerland is the first European country in the ranking, holding the fifth position

I like using rCharts as well as using Enigma data sets, as I have done previously. This is the code:

```library(data.table)
library(rCharts)
library(dplyr)
population %>%
filter(indicator_name=="Population, total") %>%
as.data.frame %>%
summarise(population=mean(value)) %>%
plyr::rename(., c("country_name"="country")) -> population2
populflows %>% filter(!is.na(total_migrants)) %>%
group_by(migration_year, destination_country) %>%
summarise(inmigrants = sum(total_migrants))  %>%
populflows %>% filter(!is.na(total_migrants)) %>%
group_by(migration_year, country_of_origin) %>%
summarise(migrants = sum(total_migrants)) %>%
# Join of data sets
migrants %>%
merge(inmigrants, by = c("country", "decade")) %>%
merge(population2, by = c("country", "decade")) %>%
mutate(p_migrants=migrants/population, p_inmigrants=inmigrants/population) -> populflows2
# Global Indicators
populflows2 %>%
group_by(country) %>%
summarise(migrants=sum(migrants), inmigrants=sum(inmigrants), population=mean(population)) %>%
mutate(p_migrants=migrants/population, p_inmigrants=inmigrants/population)  %>%
filter(population > 2000000)  %>%
mutate(rank_migrants = dense_rank(desc(p_migrants)), rank_inmigrants = dense_rank(desc(p_inmigrants))) -> global
# Migrants dataset
global %>%
filter(rank_migrants<=20) %>%
select(country) %>%
merge(populflows2, by = "country") %>%
plyr::ddply("decade", transform, rank = dense_rank(p_migrants)) -> migrants_rank
# Migrants dataset
global %>%
filter(rank_inmigrants<=20) %>%
select(country) %>%
merge(populflows2, by = "country") %>%
plyr::ddply("decade", transform, rank = dense_rank(p_inmigrants)) -> inmigrants_rank
# Function for plotting
plotBumpChart <- function(df){
bump_chart = Rickshaw\$new()
mycolors = ggthemes::tableau_color_pal("tableau20")(20)
bump_chart\$layer(rank ~ decade2, group = 'country_code', data = df, type = 'line', interpolation = 'none', colors = mycolors)
bump_chart\$set(slider = TRUE, highlight = TRUE, legend=TRUE)
bump_chart\$yAxis(tickFormat = "#!  function(y) { if (y == 0) { return '' } else { return String((21-y)) } } !#")
bump_chart\$hoverDetail(yFormatter = "#! function(y){return (21-y)} !#")
return(bump_chart)
}
plotBumpChart(migrants_rank)
plotBumpChart(inmigrants_rank)
```

# A Visualization Of The 100 Greatest Love Songs ft. D3.js

What would you do? If my heart was torn in two (More Than Words, Extreme)

Playing with `rCharts` package I had the idea of representing the list of 100 best love songs as a connected set of points which forms a heart. Songs can be seen putting mouse cursor over each dot:

You can reproduce it with this simple code:

```library(dplyr)
library(rCharts)
library(rvest)
heart <- function(r,x) {ifelse(abs(x)<2, ifelse(r%%2==0, sqrt(1-(abs(x)-1)^2), acos(1-abs(x))-pi), 0)} data.frame(x=seq(from=-3, to=3, length.out=100)) %>%
mutate(y=jitter(heart(row_number(), x), amount=.1)) -> df
love_songs <- html("http://www.cs.ubc.ca/~davet/music/list/Best13.html") love_songs %>%
html_nodes("table") %>%
.[[2]] %>%
cbind(df) -> df
m1=mPlot(x = "x",  y = "y",  data = df,  type = "Line")
m1\$set(pointSize = 5,
lineColors = c('red', 'red'),
width = 850,
height = 600,
lineWidth = 2,
hoverCallback = "#! function(index, options, content){
var row = options.data[index]
return '<b>' + row.ARTIST + '</b>' + '<br/>' + row.TITLE} !#",
grid=FALSE,
axes=FALSE)
m1\$save('Top_100_Greatest_Love_Songs.html', standalone = TRUE)
```