Category Archives: Highcharts

Visualizing the Spanish Contribution to The Metropolitan Museum of Art

Well I walk upon the river like it’s easier than land
(Love is All, The Tallest Man on Earth)

The Metropolitan Museum of Art provides here a dataset with information on more than 450.000 artworks in its collection. You can do anything you want with these data: there are no restrictions of use. Each record contains information about the author, title, type of work, dimensions, date, culture and  geography of a particular piece.

I can imagine a bunch of things to do with these data but since I am a big fan of highcharter,  I have done a treemap, which is an artistic (as well as efficient) way to visualize hierarchical data. A treemap is useful to visualize frequencies. They can handle levels, allowing to navigate to go into detail about any category. Here you can find a good example of treemap.

To read data I use fread function from data.table package. I also use this package to do some data wrangling operations on the data set. After them, I filter it looking for the word SPANISH in the columns Artist Nationality and Culture and looking for the word SPAIN in the column Country. For me, any piece created by an Spanish artist (like this one), coming from Spanish culture (like this one) or from Spain (like this one) is Spanish (this is my very own definition and may do not match with any academical one). Once it is done, it is easy to extract some interesting figures:

  • There are 5.294 Spanish pieces in The Met, which means a 1,16% of the collection
  • This percentage varies significantly between departments: it raises to 9,01% in The Cloisters and to 4,83% in The Robert Lehman Collection; on the other hand, it falls to 0.52% in The Libraries and to 0,24% in Photographs.
  • The Met is home to 1.895 highlights and 44 of them (2,32%) are Spanish; It means that Spanish art is twice as important as could be expected (remember that represents a 1,16% of the entire collection)

My treemap represents the distribution of Spanish artworks by department (column Department) and type of work (column Classification). There are two important things to know before doing a treemap with highcharter:

  • You have to use treemap function from treemap package to create a list with your data frame that will serve as input for hctreemap function
  • hctreemap fails if some category name is the same as any of its subcategories. To avoid this, make sure that all names are distinct.

This is the treemap:

Here you can see a full size version of it.

There can be seen several things at a glance: most of the pieces are drawings and prints and european sculpture and decorative arts (in concrete, prints and textiles), there is also big number of costumes, arms and armor is a very fragmented department … I think treemap is a good way to see what kind of works owns The Met.

Mi favorite spanish piece in The Met is the stunning Portrait of Juan de Pareja by Velázquez, which illustrates this post: how nice would be to see it next to El Primo in El Museo del Prado!

Feel free to use my code to do your own experiments:


# Download data
if (!file.exists(file)) download.file(paste0("", file), 
# Read data
data=fread(file, sep=",", encoding="UTF-8")

# Modify column names to remove blanks
colnames(data)=gsub(" ", ".", colnames(data))

# Clean columns to prepare for searching
data[,`:=`(Artist.Nationality_aux=toupper(Artist.Nationality) %>% str_replace_all("\\[\\d+\\]", "") %>% 
             iconv(from='UTF-8', to='ASCII//TRANSLIT'),
           Culture_aux=toupper(Culture) %>% str_replace_all("\\[\\d+\\]", "") %>% 
             iconv(from='UTF-8', to='ASCII//TRANSLIT'),
           Country_aux=toupper(Country) %>% str_replace_all("\\[\\d+\\]", "") %>% 
             iconv(from='UTF-8', to='ASCII//TRANSLIT'))]

# Look for Spanish artworks
data[Artist.Nationality_aux %like% "SPANISH" | 
       Culture_aux %like% "SPANISH" | 
       Country_aux %like% "SPAIN"] -> data_spain

# Count artworks by Department and Classification
data_spain %>% 
  mutate(Classification=ifelse(Classification=='', "miscellaneous", Classification)) %>% 
         Classification1=str_match(Classification, "(\\w+)(-|,|\\|)")[,2],
                               tolower(Classification))) %>% 
  group_by(Department, Classification) %>% 
  summarize(Objects=n()) %>% 
  ungroup %>% 
  mutate(Classification=ifelse(Department==Classification, paste0(Classification, "#"), 
                               Classification)) %>% -> dfspain

# Do treemap without drawing
tm_dfspain <- treemap(dfspain, index = c("Department", "Classification"),
                      vSize = "Objects", 
                      vColor = "Objects",
                      type = "index")

# Do highcharter treemap 
  allowDrillToNode = TRUE,
  allowPointSelect = T,
  levelIsConstant = F,
  levels = list(
      level = 1,
      dataLabels = list (enabled = T, color = '#f7f5ed', style = list("fontSize" = "1em")),
      borderWidth = 1
      level = 2,
      dataLabels = list (enabled = F,  align = 'right', verticalAlign = 'top', 
                         style = list("textShadow" = F, "fontWeight" = 'light', "fontSize" = "1em")),
      borderWidth = 0.7
  )) %>% 
  hc_title(text = "Spanish Artworks in The Met") %>% 
  hc_subtitle(text = "Distribution by Department") -> plot


Bayesian Blood

The fourth, the fifth, the minor fall and the major lift (Hallelujah, Leonard Cohen)

Next problem is extracted from MacKay’s Information Theory, Inference and Learning Algorithms:

Two people have left traces of their own blood at the scene of a crime. A suspect, Oliver, is tested and found to have type ‘O’ blood. The blood groups of the two traces are found to be of type ‘O’ (a common type in the local population, having frequency 60%) and of type ‘AB’ (a rare type, with frequency 1%). Do these data give evidence in favor of the proposition that Oliver was one of the people who left blood at the scene?

To answer the question, let’s first remember the probability form of Bayes theorem:



  • p(H) is the probability of the hypothesis H before we see the data, called the prior
  • p(H|D) is the probablity of the hyothesis after we see the data, called the posterior
  • p(D|H) is the probability of the data under the hypothesis, called the likelihood
  • p(D)is the probability of the data under any hypothesis, called the normalizing constant

If we have two hypothesis, A and B, we can write the ratio of posterior probabilities like this:


If p(A)=1-p(B) (what means that A and B are mutually exclusive and collective exhaustive), then we can rewrite the ratio of the priors and the ratio of the posteriors as odds. Writing o(A) for odds in favor of A, we get the odds form of Bayes theorem:


Dividing through by o(A) we have:


The term on the left is the ratio of the posteriors and prior odds. The term on the right is the likelihood ratio, also called the Bayes factor. If it is greater than 1, that means that the data were more likely under A than under B. And since the odds ratio is also greater than 1, that means that the odds are greater, in light of the data, than they were before. If the Bayes factor is less than 1, that means the data were less likely under A than under B, so th odds in favor of A go down.

Let’s go back to our initial problem. If Oliver left his blood at the crime scene, the probability of the data is just the probability that a random member of the population has type ‘AB’ blood, which is 1%. If Oliver did not leave blood at the scene, what is the the chance of finding two people, one with type ‘O’ and one with type ‘AB’? There are two ways it might happen: the first person we choose might have type ‘O’ and the second ‘AB’, or the other way around. So the probability in this case is 2(0.6)(0.01)=1.2%. Dividing probabilities of both scenarios we obtain a Bayes factor of 0.83, and we conclude that the blood data is evidence against Oliver’s guilt.

Once I read this example, I decided to replicate it using real data of blood type distribution by country from here. After cleaning data, I have this nice data set to work with:

For each country, I get the most common blood type (the one which the suspect has) and the least common and replicate the previous calculations. For example, in the case of Spain, the most common type is ‘O+’ with 36% and the least one is ‘AB-‘ with 0.5%. The Bayes factor is 0.005/(2(0.36)(0.005))=1.39 so data support the hypothesis of guilt in this case. Next chart shows Bayes factor accross countries:

Just some comments:

  • Sometimes data consistent with a hypothesis are not necessarily in favor of the hypothesis
  • How different is the distribution of blood types between countries!
  • If you are a estonian ‘A+’ murderer, choose carefully your accomplice

This is the code of the experiment:


# Webscapring of the table with the distribution of blood types
url <- ""
blood <- url %>%
   read_html() %>%
   html_node(xpath='/html/body/center/table') %>%

# Some data cleansing
blood %>% slice(-c(66:68)) -> blood

blood[,-c(1:2)] %>% 
  sapply(gsub, pattern=",", replacement=".") %>% %>% 
  sapply(gsub, pattern=".79.2", replacement=".79") %>%> blood[,-c(1:2)]

blood %>% 
  sapply(gsub, pattern="%|,", replacement="") %>% -> blood

blood[,-1] = apply(blood[,-1], 2, function(x) as.numeric(as.character(x)))

blood[,-c(1:2)] %>% mutate_all(funs( . / 100)) -> blood[,-c(1:2)]

# And finally, we have a nice data set
          rownames = FALSE,
          options = list(
          searching = FALSE,
          pageLength = 10)) %>% 
  formatPercentage(3:10, 2)

# Calculate the Bayes factor
blood %>% 
  mutate(factor=apply(blood[,-c(1,2)], 1, function(x) {min(x)/(2*min(x)*max(x))})) %>% 
  arrange(factor)-> blood

# Data Visualization
highchart() %>% 
     hc_chart(type = "column") %>% 
     hc_title(text = "Bayesian Blood") %>%
     hc_subtitle(text = "An experiment about the Bayes Factor") %>%  
     hc_xAxis(categories = blood$Country, 
             labels = list(rotation=-90, style = list(fontSize = "12px")))  %>% 
     hc_yAxis(plotBands = list(list(from = 0, to = 1, color = "rgba(255,215,0, 0.8)"))) %>% 
     hc_add_series(data = blood$factor,
                   color = "rgba(255, 0, 0, 0.5)",
                   name = "Bayes Factor")%>% 
  hc_yAxis(min=0.5) %>% 
  hc_tooltip(pointFormat = "{point.y:.2f}") %>% 
  hc_legend(enabled = FALSE) %>% 
  hc_exporting(enabled = TRUE) %>%
  hc_chart(zoomType = "xy")

Visualizing the Daily Variability of Bitcoin with Quandl and Highcharts

Lay your dreams, little darling, in a flower bed; let that sunshine in your hair (Where the skies are blue, The Lumineers)

I discovered this nice visualization some days ago. The author is also the creator of Highcharter, an incredible R wrapper for Highcharts javascript libray and its modules. I am a big fan of him.

Inspired by his radial plot, I did a visualization of the daily evolution of Daily Bitcoin exchange rate (BTC vs. EUR) on Localbtc. Data is sourced from here and I used Quandl to obtain the data frame. Quandl is a marketplace for financial and economic data delivered in modern formats for today’s analysts. There is a package called Quandl to interact directly with the Quandl API to download data in a number of formats usable in R. You only need to locate the data you want in the Quandl site. In my case data are here.

After loading data, I do the folowing steps:

  • Filtering data to obtain last 12 complete months
  • Create a new variable with the difference between closing and opening price of Bitcoin (in Euros)
  • Create a color variable to distinguish between positive and negative differences
  • Create the graph using Fivethirtyeight theme for highcharts

This is the result:

Apart of its appealing, I think is a good way to to have a quick overview of the evolution of a stock price. This is the code to do the experiment:

bitcoin %>% 
  arrange(Date) %>% 
  mutate(tmstmp = datetime_to_timestamp(Date)) -> bitcoin
if (day(last_date+1)==1) date_to=last_date else 
  date_to=ymd(paste(year(last_date), month(last_date),1, sep="-"))-1
date_from=ymd(paste(year(date_to)-1, month(date_to)+1,1, sep="-"))
bitcoin %>% filter(Date>=date_from, Date<=date_to) -> bitcoin
var_bitcoin <- bitcoin %>% 
  mutate(Variation = Close - Open,
         color = ifelse(Variation>=0, "green", "red"),
         y = Variation) %>% 
  select(x = tmstmp,
         variation = Variation,
         name = Date,
         open = Open,
         close = Close) %>% 
x <- c("Open", "Close", "Variation")
y <- sprintf("{point.%s}", tolower(x))
tltip <- tooltip_table(x, y)
hc <- highchart() %>% 
  hc_title(text = "Bitcoin Exchange Rate (BTC vs. EUR)") %>% 
  hc_subtitle(text = "Daily Variation on Localbtc. Last 12 months")%>% 
    type = "column",
    polar = TRUE) %>%
    series = list(
      stacking = "normal",
      showInLegend = FALSE)) %>% 
    gridLineWidth = 0.5,
    type = "datetime",
    tickInterval = 30 * 24 * 3600 * 1000,
    labels = list(format = "{value: %b}")) %>% 
  hc_yAxis(showFirstLabel = FALSE) %>% 
  hc_add_series(data = var_bitcoin) %>% 
  hc_add_theme(hc_theme_538()) %>% 
  hc_tooltip(useHTML = TRUE,
    headerFormat = as.character(tags$small("{point.x:%d %B, %Y}")),
    pointFormat = tltip)