Consuming IFTTTed Twitter favs

‪I consider my Twitter favourites to be mainly bookmarks although endorsements happen too. Liking comes from Instagram and Facebook but because my social media communities do not really overlap, I sometimes want to send digital respect in Twitter too. I’ve tried to unlearn the classic liking in Twitter and instead reply or retweet, but old habits – you know.‬

‪There’s little point in bookmarking if you cannot find your bookmarks afterwards. The official Twitter archive that I unzip to my private website few times a year, does not include favourites, which is a pity. The archive looks nice out of the box, and there is a search facility. ‬

‪Since late 2013, I have an active IFTTT rule that writes metadata of the favourited tweet to a Google spreadsheet. IFTTT is a nifty concept but oftentimes there are delays and other hickups. ‬

‪My husband and I lunch together several times a week. Instead of emailing, phoning or IM’ing the usual 5 minutes and I’m there bye message, I had this idea of enhanced automation with IFTTT. Whenever I entered the inner circle around our regular meeting point, IFTTT sent me an email announcing Entered the area. This caused a predefined email rule to trigger which forwarded the message to the receiver. Basta! At first this simple digital helper was fun, but as soon as it didn’t deliver, trust started to erode rapidly.

– Where are you? Didn’t you get the message?
– What message?

Sometimes the email came doubled, sometimes it arrived the next day (or never). After few months I had to deactivate the rule.‬

‪With tweets it’s not so critical. In the beginning I checked few times that the spreadsheet do was populated, but then I let it be. From time to time Google (or IFTTT) opens up a new document but fortunately keeps the original file name, just adds a version number.‬

IFTTT rule

IFTTT rule

‪I appreciate Google’s back office services but don’t often use their user interfaces. Besides, my IFTTT’ed archive does not include the tweet status text, so without some preprocessing the archive is useless anyhow. In theory I could get the text by building calls myself to the Twitter API with the Google Query Language, or become a member of the seemingly happy TAGS community. TAGS is a long-lasting and sensible tool by Martin Hawksey. But what would blogging be without the NIH spirit, so here we go.‬

‪Because I have access to a instance, I made a searchable tweet interface as an R Shiny web app. For that I needed to‬

  • Install googlesheets and twitterR
  • Collect information on my tweet sheets‬, and download all data‬ in them
  • Expand short URLs‬
  • Fetch the Twitter status from the API‬
  • Write a row to the latest sheet to denote where to start next time‬
  • Build the Shiny app‬

The app acts now as a searchable table to my favourite tweets. While at it, I also plotted statistics on timeline.

Here is the app. The R code for gathering all data is in GitHub, likewise the code how I built the app.

A Finnish alien

On 17th April 1929, Aimo August Sonkkila, brother of my late grandpa, left Finland to London. He was 30 years old, son of a farmer in the then rural Laitila municipality in SW Finland.

In London, Aimo embarked S/S Orvieto. His target destination was Brisbane, Australia.

The same year, 589 males in his age emigrated. 11 of them were from the countryside of the same province, Turun ja Porin lääni.
© Gordy

The first stop was Gibraltar. Then, via Toulon and Neapel, over the Mediterranean Sea to Port Said, Egypt. From there via Suez Canal to Colombo, Sri Lanka. Finally, on the horizon, the West coast of Australia, Fremantle! But the trip was not over yet. Following the Australian coastline, Orvieto visited Adelaide and Melbourne before reaching Brisbane.

I haven’t found the date of the departure, so Orvieto’s exact travel time is unknown. Unlike the newspaper archive provided by National Library of Australia from where I found the route, the British Newspaper Archive is subscription-based. An unfortunate show-stopper for a random visitor like me, although I can understand the monetizing idea.

Anyway, there are hints that the voyage lasted several weeks, which is what you would expect, really. If we trust the computations of Wolfram Alpha, the travel time would’ve been around two weeks, had Orvieto managed 25 knots. Orvieto’s speed, however, was only 18 knots. Yet the globe-shaped map that Wolfram Alpha serves, wakes suspicions. Maybe they use a straight line of distance? In any case, given the fair number of waypoints on route, let us imagine a rough travelling time of one month.

As it happens, Orvieto’s voyage became one of its last ones. The ship was taken out of business in 1930.

Aimo travelled in the 3rd class with roughly 550 other passengers, whereas the 1st class only occupied 75. Among these lucky ones were few celebrities and other prominent figures, featured by The West Australian the day after Orvieto’s arrival to the Australian continent. Onboard was also mail and cargo.

On 28th May, Orvieto docked Fremantle. From the Incoming passenger list, on row 692, we find Aimo. A search by Sonkkila hits 0 because the name is mistyped as M. Sonkkilla. A non-English person, misspellings were to follow Aimo the rest of his life. In the scanned bundle of official records of him, Amio comes up just as often as Aimo. Maybe not a big deal. In Australia, with a hint of Italian, that version was perhaps more practical anyway.

Why did Aimo emigrate? We can only guess. Was he adventurous? Driven to believe in juicy stories of easy money, or official promises on steady income? Had someone he knew and emigrated before him, sent assuring letters to homeland, making him decide to follow suite? As a son of a farmer, he had prospects of taking care of the farm after his father. But, he was not the only son – always a problematic situation. Besides, what if farming was not something he looked forward to? Both push and pull may have played a role here.

We know now that 1929 was the year when Great Depression started. Still, it is difficult to judge in what way and how soon, individual lives are affected by economic fluctuations of such a global scale.

Emigration from Finland was by no means a sudden fad. Previously, the obvious target for the majority of people had been the North American continent. The Immigration Act of 1924 drastically changed this. People were still let in, but in much less quantities than earlier. Very much like in Europe at the moment, both the US and Canada had switched to a selective immigration policy.

This sankey diagram tries to visualize where Finns left between 1900 and 1945, aggregated over decades. Data come from Institute of Migration (Emigration 1870-1945). Note that all targets are not mutually exclusive. Between 1900 and 1923, Americas was recorded as one entity, but from then on, as separate countries. In addition, during that same period, statistics from other countries are scarce. [A technical side note: with Firefox, the diagram may appear very small. Chrome and Internet Explorer don’t have this issue.]

Life in Australia proved a challenging endeavour for Aimo, to say the least. The records are fragmented and don’t reveal much, but it is fairly easy to imagine what is in the gaps.

Work as a miner was incredibly tough. Some of it is captured in The Diminishing Sugar-Miners of Mount Isa, Australia by Greg Watson, linked to by Institute of Migration. I wonder if Aimo had any realistic idea beforehand what it was to be like. Yet, with his modest background, he had not much choice once he had arrived.

Then, after 12 laborious years, Second World War.

On 12th April 1942, Aimo is arrested in Townsville. He is still a Finnish citizen, and because Finland is Axis-aligned, he is a member of the enemy. The rest of the year Aimo would stay in an internment camp at Gaythorne (Enoggera), Brisbane. However, on the application by the Mt Isa Mining Company, his employer, he is allowed to work. Between a rock and a hard place is an idiom that must have been coined by Aimo himself.

At some stage, Aimo had married Impi Rapp. That’s basically all I know about her, the name. Few years after WWII, a son is born. His life would become totally different from that of his parents.

Finger print

National Archives of Australia, NAA: BP25/1, SONKKILA A A FINNISH. Digital copy, page 31

R code of the diagram is available here.

Seven Brothers

A movie that I’ve never seen myself but what apparently forms a Christmas Eve tradition in the US, inspired David Robinson to write a thorough and illustrative blog post about its network of characters. Like some commentators have already mentioned, his analysis is particularly interesting e.g. because of the way he parses and processes raw text with R.

I wanted to replicate David’s take. For it, I needed material that’s more familiar.

One of the well known group of characters in Finnish pre-1900 fiction is Seven Brothers by Aleksis Kivi. Published in 1870, the book is already copyright free, and available in plain text from Project Gutenberg.

Brothers form a tight group but how tight, actually? With whom did they speak? What else is there to find with some quick R plotting?

First I needed a list of all characters that say something.

In the text, dialog is marked by uppercase letters with a trailing full stop. Thanks to a compact synopsis I found (in Finnish) I noticed that the A-Z character class wouldn’t suffice in filtering all names.

names <- data_frame(raw = raw) %>%
  filter(grepl('^[A-ZÄÖ-]+\\.', raw)) %>%
  separate(raw, c("speaker","text"), sep = "\\.", extra = "drop") %>%
  group_by(speaker) %>%
  summarize(name = speaker[1]) %>%

Some of the names referred to a group of people (e.g. VELJEKSET) or other non-person (the false positive DAMAGE), so I excluded them. I also found one typo; KERO is not some person but in fact EERO.

This data frame I then wrote to file, added manually some descriptive text in English with help of the above mentioned synopsis, and read in again. Later on in the process, this information would be joined with the rest of the processed data.

Next, with some minor modifications to David’s script, the main parsing process.

What the script does is that it filters all-blank rows; detects rows that mark the beginning of a new chapter (luku in Finnish); keeps a cumulative count of chapters; separates the name of the speaker from the first line of his/her dialog; groups by chapter, and finally summarizes – which I found interesting, because I’d thought that summarize() would be of use only with numerical values.

lines <- data_frame(raw = raw) %>%
  filter(raw != "") %>%
  mutate(is_chap = str_detect(raw, " LUKU"),
         chapter = cumsum(is_chap)) %>%
  filter(!is_chap) %>%
  mutate(raw_speaker = gsub("^([A-ZÄÖ-]+)(\\.)(.*)", "\\1%\\3", raw, perl=TRUE)) %>%
  separate(raw_speaker, c("speaker", "dialogue"), sep = "%", extra = "drop", 
           fill = "left") %>%
  group_by(chapter, line = cumsum(! %>%
  summarize(name = speaker[1], dialogue = str_c(dialogue, collapse = " "))

Inner_join()‘ing lines with names.df by their common variable name, only the relevant rows are kept.

lines <- lines %>%
  inner_join(names.df) %>%
  mutate(character = paste0(name, " (", type, ")"))

How much do the brothers speak across the chapters?

by_name_chap <- lines %>%
  count(chapter, character)

ggplot(by_name_chap, aes(x=character, y=dialogs, fill=character))+
  geom_bar(stat = "identity") +
  facet_grid(. ~ chapter) +
  coord_flip() +

From the facetted bar chart we’ll notice that Juhani, the oldest brother, is also the most talkative one. He remains silent only in the very last chapter, the epilogue.

Facetted bar chart

Whenever we have a matrix, it’s worth trying to cluster it.

– says David, so let’s follow his advice.

Cluster dendrogram

Brothers are mostly together, which is not a surprise. Lauri does not talk much, and Timo has got his own chapter. These facts might have influenced to their having a separate branch each. The few other people that have a say in the book, form their own hierarchies.

Next David shows how this ordered tree can be transformed to a scatterplot. What a neat way to make a timeline! Because of the great number of different permutations of pairs, his example movie is visually more interesting in this respect than Seven Brothers. Still, even here the plot acts nicely as a snapshot of the storyline.

Scatterplot as a timeline

The network graph of brothers and their allies does not reveal anything overly exciting. This part of the analysis I took merely as an exercise in plotting the network with the new geomnet R package.

# Adjacency matrix
cooccur <- name_chap_matrix %*% t(name_chap_matrix)


# Define network from the matrix, plus few attributes
g <- graph.adjacency(cooccur, weighted = TRUE, mode = "undirected", diag = FALSE)
V(g)$lec_community <- as.character($membership)
V(g)$centrality <- igraph::betweenness(g, directed = F)
E(g)$weight <- runif(ecount(g))
V(g)$Label <- V(g)$name

# Plot network 

# From the igraph object, two dataframes: vertices and edges, respectively
gV <-, what=c("vertices"))
gE <-, what=c("edges"))

# Merge edges and vertices
gnet <- merge(
  gE, gV,
  by.x = "from", by.y = "Label", all = TRUE

# Add a new variable, a pretty-print variant of names
gnet$shortname <- sapply(gnet$name, function(x) {
  n <- strsplit(x, " \\(")[[1]][1]
  nwords <- strsplit(n, "\\-")[[1]]
  paste0(substring(nwords, 1, 1),
         tolower(substring(nwords, 2)),
         collapse = "-")

# Colour palette from Wes Anderson's movie Castell Cavalcanti
wesanderson.cavalcanti <- c("#D8B70A", "#02401B", "#A2A475", "#81A88D", "#972D15")

p <- ggplot(data = gnet,
            aes(from_id = from, to_id = to)) +
    ecolour = "lightyellow", # edge colour
      colour = lec_community, 
      group = lec_community,
      fontsize = 6,
      linewidth = weight * 10 / 5 + 0.2,
      size = centrality,
      label = shortname
    show.legend = F,
    vjust = -0.75, alpha = 0.4,
    layout = 'fruchtermanreingold'

p + theme_net() +
  theme(panel.background = element_rect(fill = "gray90"),
        plot.margin = unit(c(1, 1, 1, 1), "lines")) +
  scale_color_manual(values = wesanderson.cavalcanti[1:length(unique(gnet$lec_community))]) +
  guides(linetype = FALSE)

The community detection algorithm of igraph found four communities. In the network graph, these are shown with different colours. Most of the characters in Seven Brothers belong to the same community, but there are few loners.

The size of the node tells about the centrality of the person. Timo seems to be influential, probably because he is the only one from the brothers that shares a chapter with his wife and maid.

The thicker the edge, i.e. the line connecting two nodes, the more weight there is. I assume that here weight is simply a measure of co-appearance.

Network graph

Discussion on diabetes

A tweet by Peter Grabitz got my attention the other day.


This is worth of a brief investigation. It’s seldom you start an altmetrics project from a topic.

One obvious choice for getting article ID’s is either Web of Science or Scopus, but to go down that path you obviously need to have access to them to start with. Another solution is to query the PubMed API for a list of PMID’s.

Thanks to the helpful posting Hacking on the Pubmed API by Fred Trotter, you are led to the PubMed Advanced Search page. There, you can define your search with a MeSH topic, and filter articles by the publication year.

PubMed advanced search

PubMed knows of 4890 articles on diabetes mellitus, published this year.

As Fred explains, by URLencoding this Search details string and joining it to the base URL, you are ready to approach the API.

If you are familiar with R, here is one solution. From 4890 articles, Altmetric had metrics on 505, based on PMID. Note that there are probably also mentions that use the DOI.

The Altmetric result dataset is on Figshare.

About 43000 results

Since few days now, I’ve had my Google search archive with me. In my case, it’s a collection of 38 JSON files, containing search strings and timestamps. The oldest file dates back to mid-2006, which acts as a digital marriage certificate of us, me and the Internet giant.

JSON files of Google search archive

It took no more than 15 minutes for Google to fulfill my wish to get the archive as a zipped file. For more information on How & Where, see e.g. Google now lets you export your search history.

Now, this whole archive business started when I was led to a very nice blog posting by Lisa Charlotte Rost

Tweet about Lisa Charlotte Rost

I find it fascinating, what you can tell about a person just by looking at her searches. Or rather, what kind of narratives s/he builds upon them; to publish all search strings verbatim is not really an option.

Halfway in the 4-week course Intermediate D3 for Data Visualization, the theme is stacked charts. Maybe I could visualize, on a timeline, as a stacked area chart, some aspects of my search activity. But what aspects? What sort of person am I as a searcher?

Quite dull, I have to admit. No major or controversial hobbies, no burning desire to follow latest gadgets, only mildly hypocondriac, not much interest at all in self-help advisory. Wikipedia is probably my number one landing site. Very often I use Google simply as a text corpus, an evidence-based dictionary:”Has this English word/idiom been used in the UK or did I just made it up, or misspelled?” Unlike Lisa, who tells in episode #61 of the Data Stories podcast that now when she lives in a big city, Berlin, she often searches for directions – I do not. Well, compared to Berlin, Helsinki do is small, but we also have a superb web service for guiding us around here, Journey Planner. So instead of a search, I’ll go straight there.

One area of digital life I’ve been increasingly interested in – and what this blog and my job blog reflect, too, I hope – is coding. Note, “coding” not as in building software but as in scripting, mashupping, visualizing. Small-scale, proof-of-concept data wrangling. Learning by doing. Part of it is of course related to my day job at Aalto University. For example, now when we are setting up a CRIS system, I’ve been transforming, with XSLT, legacy publication metadata to XML. It needs to validate against the Elsevier Pure XML Schema before it can be imported.

A few years now, appart XSLT, the other languages I have been writing with, are R and Perl. Unix command line tools I use on a daily basis. Thanks to the D3 course, I’m also slowly starting to get familiar with JavaScript. Python has been on my list a longer time, but since the introductory course I took at CSC – IT Center for Science some time ago, I haven’t really touched it.

I’m not the only one that googles while coding. Mostly it’s about a specific problem: I need to accomplish something but cannot remember or don’t know, how. When you are not a full-time coder, you forget details easily. Or, you get an error message you cannot understand. Whatever.

Are my coding habits visible in the search history? If yes, in what way.

First thing to do with the JSON files, was to merge them into one. For this, I turned to R.

filenames <- list.files("Searches", pattern="*.json", full.names=TRUE) <- lapply(filenames, function(f) fromJSON(txt = f))
alljson <- toJSON(
write(alljson, file = "g.json")

Then, just as Lisa did, I fired up Google Refine, and opened a new project on g.json.

To do:

  • add Boolean value columns for JavaScript, XSLT (including XPath), Python, Perl and R by filtering the query column with the respective search string
  • convert Unix timestamps to Date/Time (Epoch time to Date/Time as String). For now, I'm only interested in date, not time of day
  • export all Boolean columns and Date to CSV

Google Refine new column

From the language names, R is the most tricky one to filter because it is just one character. Therefore, I need to build a longish Boolean or sentence for that.

Google Refine text facet

Here I'm ready with R and Date, and checking the results with a text facet on the column r.

Thanks to a clearly commented template by the D3 course leader, Scott Murray, the stacked area chart was easy to do, but only after I had figured out how to process and aggregate yearly counts by language. Guess what - I googled for a hint, and got it. The trick was, while looping over all rows by language, to define an object to store counts by year. Then, for every key (=year), I could push values to the dataset array.

Do the colors of the chart ring a bell? I'm a Wes Anderson fan, and have waited for an excuse to make use of some of the color palette implementations of his films. This 5-color selection represents The Life Aquatic With Steve Zissou. The blues and browns are perhaps a little too close to each other, especially when used as inline font color, but anyway.

Quite an R mountain there to climb, eh? It all started during the ELAG 2012 conference in Palma, Spain. Back then I was still working at the Aalto University Library. I had read a little about R before, but it was the pre-conference track An Introduction to R led by Harrison Dekker, that finally convinced me that I needed to learn this. I guess it was the easiness of installing packages (always a nightmare with Perl), reading in data, and quick plotting.

So what does the big amount of R searches tell? For one thing, it shows my active use of the language. At the same time though, it tells that I've needed a lot of help. A lot. I still do.

2015 on 1917

Kulosaari (Brändö in Swedish), an 1,8 square km island in Helsinki, detached itself from the Helsinki parish in early 1920’s, and became an independent municipality. The history of Kulosaari is an interesting chapter of Finnish National Romantic architecture and semi-urban development. It all began in 1907 when the company AB Brändö Villastad (Wikipage in Finnish) was established – but that’s another story. In 1949, the island was annexed again by Helsinki. Today, Kulosaari is cut in half by one of the busiest highways in Finland. The idealistic, tranquil village community is long gone. Since late 1997, Kulosaari has been my home suburb.

One of the open datasets provided by Helsinki Region Infoshare, is a scanned map of Kulosaari from 1917. Or rather, a scheme which became reality only in a limited extent. As long as I’ve known a little about what georeferencing is all about – thanks to the excellent Coursera MOOC Maps and the Geospatial Revolution by Dr. Anthony C. Robinson – I’ve had in mind to work with that map some day. That day dawned when I happened to read the blog posting Using custom tiles in an RStudio Leaflet map by Kyle Walker.

Unlike Kyle, I haven’t got any historical data to render upon the 1917 map but instead, there are a number of present day datasets available, courtesy of the City of Helsinki, e.g. roadmap and 3D models of buildings. How does the highway look like on top of the map? What about buildings and their whereabouts today? Note that I don’t aim particularly high here, or to more than two dimensions anyway; my intention is just to get an idea of how the face of the island has changed.

Georeferencing with QGIS is fun. I’m sure there are many good introductions out there in various languages. For Finnish speakers, I can recommend this one (PDF) by Latuviitta, a GIS treasure chamber.


The devil is in the detail, and I know I could’ve done more with the control points, but that’s a start. When QGIS was done with number-crunching, the result looked like this when I adjusted transparence for an easier quality check.


Not bad. Maybe hanging a tad high, but will do.

Next, I basically just followed Kyle’s footsteps and made tiles with the OSGeo4W shell. I even used the same five zoom layers than he. Then I uploaded the whole directory structure with PNG files (~300 MB) to my web domain where this blog resides, too.

Roadmap data is available both in ESRI Shapefile and Google KML. I downloaded the zipped Shapefile, unzipped it, and imported as new vector layer to QGIS. After some googling I found help on how to select an area – Kulosaari main island in my case – by rectangle, how to merge selected features, and how to save the selection as a new Shapefile.

Then, to RStudio and some R code.

In Kulosaari, there are 23 different kind of roads. Even steps (porras) and boat docks (venelaituri) are categorized as part of the city roadmap.

> unique(streets$Vaylatyypp)

 [1] "Asuntokatu"                             "Paikallinen kokoojakatu"                    
 [3] "Huoltoajo sallittu"                     "Moottoriväyläramppi"                        
 [5] "Alueellinen kokoojakatu"                "Silta tai ylikulku (katuverkolla)"          
 [7] "Moottoriväylä"                          "Pääkatu"                                    
 [9] "Silta tai ylikulku (jalkakäytävä, pyörä "Alikulku (jalkakäytävä, pyörätie)"          
[11] "Jalkakäytävä"                           "Porras"                                     
[13] "Yhdistetty jalkakäytävä ja pyörätie"    "Puistotie (hiekka)"                         
[15] "Ulkoilureitti"                          "Puistokäytävä (hiekka)"                     
[17] "Puistokäytävä (päällystetty)"           "Venelaituri"                                
[19] "Polku"                                  "Suojatie"                                   
[21] "Väylälinkki"                            "Pyöräkaista"                                
[23] "Pyörätie"                                  

From these, I extracted motorways, bridges, paths, steps, parkways, streets allowed for service drive, and underpasses.

Working with the 3D data wasn’t quite as easy (no surprise). By far the biggest challenge turned to be computing resources.

I decided to work with KMZ (zipped KML) files. The documentation explained that the data is divided into 1 x 1 km grids, and that the numbering of the grids follows the one used by Helsingin karttapalvelu (map service). The screenshot below shows one of the four grids I was mainly interested in: 675499 (NW), 674499 (SW), 675500 (NE) and 674500 (SE). These would leave out outer tips of the island in the East, and bring in a chunk of the Kivinokka recreation area in the North.

First I had in mind to continue using Shapefiles: imported one KML file to QGIS, saved as Shapefile, and added it as a polygon to the leaflet map. It worked, but I noticed that RStudio started to slow down immediately, and that the map in the Viewer became seemingly harder to manipulate. How about GeoJSON instead? Well, the file size do was reduced but still too much data. Still, I succeeded in getting all on the map, of which this screenshot acts as the evidence:

roadmap and 3D buildings

However, where I failed was to get the map transformed to a web page from the RStudio GUI. The problem: default Pandoc memory options.

Stack space overflow: current size 16777216 bytes.
Use `+RTS -Ksize -RTS' to increase it.
Error: pandoc document conversion failed with error 2

People seem to get over this situation by adding an appropiate command to the YAML metadata block of the RMarkdown file, but I’m not dealing with RMarkdown here. Couldn’t get the option work from the .Rprofile file either.

Anyway, here is the map without the buildings, so far: there is the motorway/highway (red), few bridges (blue), sandy parkways (green) here and there, a couple of underpasses (yellow), streets for service drive only (white) – and one path (brown) on the Southern coast of the neighbour island Mustikkamaa, as unbuilt as in 1917.

Note that interactivity in the map is limited to zooming and panning. No popups, for example.

I’ve heard many stories of the time when the highway was built. One detail mentioned by a neighbour is also visible on the map: it reduced the size of the big Storaängen outdoor sports area on the Southern side of the highway. The sports area is accessible from the Hertonäs Boulevarden – now Kulosaaren puistotie – by an underpass.

EDIT 26.3.2015: Thanks to the helpful comment by Yihui Xie, I realized that there is in fact several options to do a standalone HTML file from the RStudio GUI. With File > Compile Notebook... the result was combiled without problems, and now all buildings are rendered in the leaflet too. The file is a whopping 7 MB and therefore slow in its turns, but at least all data are now there. As a bonus, the R code is included as well! RStudios capabilities don’t stop to amaze me.

Birds on a map

Lintuatlas aka Finnish Breeding Bird Atlas is the flagship of longitudinal observations on avian fauna in Finland. And it’s not just one atlas but many. The first covers years 1974-79, second 1986-89, and third 2006–2010. Since February this year, the data from the first ones are open. Big news, and asks for an experiment on how to make use of the data.

One of the main ideas behind the Atlases is to give a tool for comparison, to visualize possible shifts in the population. I decided to do a simple old-school web app, a snapshot from a given species: select, and see observations plotted on a map.

The hardest part in the data were the coordinates. How to transform the KKJ Uniform Coordinate System values to something that a layman like me finds more familiar, like ETRS89? After a few hours of head-banging, I had to turn to the data provider. Thanks to advice from Mikko Heikkinen, the wizard behind many a nature-related web application in this country – including the Atlas pages – the batch transformation was easy. Excellent service!.

advice on Lintuatlas coordinates

All that was left was few joins between the datasets, and data was ready for an interactive R Shiny application. To reflect the reliability of observations on one particular area (scale from 1 to 4), I used four data classes from the PuBu ColorBrewer scheme to color the circles.

The application is here, and the code for those of you more inclined to R.

Note that the application is on a freemium Basic account of so I cannot guarantee its availability. There is a monthly 25 500 hour use limit.

Snow in Lapland

Finnish Meteorological Institute (FMI) Open Data API has been with us for over a year already. Like any other specialist data source, it takes some time before a lay person like me is able to get a grasp on it. Now, thanks to the fmi R package by the collaborative effort of Jussi Jousimo and other active contributors, the road ahead is much easier. A significant leap forward came just before New Year, when Joona Lehtomäki submitted a posting on fmi and FMI observation stations to the rOpengov blog.

Unlike many other Finns, I am relatively novice when it comes to Finnish Lapland. I’ve never been there during summertime, for example, and never farther North than the village of Inari. Yet, I count cross-country skiing in Lapland among the best memories of my adulthood years so far; pure fun in the scorchio April sun, but maybe even more memorable under the slowly shifting colors of the polar night.

Snow is of course a central element in skiing. Although warmer temperatures seem to be catching us up here, there has still been plenty of snow in Lapland during the core winter months. But how much, exactly, and when did it rain, when melt?

I followed Joona’s steps, and queried the FMI API of snow depth observations at three weather stations in Lapland, from the beginning of 2012 to the end of 2014: Kilpisjärvi, Saariselkä and Salla. Note that you have to repeat the query year-wise because the API doesn’t want to return all the years in one go.

Being lazy, I used the get_weather_data utility function by Joona as is, meaning I got more data than I needed. Here I filter it down to time and snow measurements, and also change the column name from ‘measurement’ to ‘snow’

snow.Salla.2014 <- salla.2014 %>%
  filter(variable == "snow") %>%
  mutate(snow = measurement) %>%
  select(time, snow)

and then combine all data rows of one station:

snow.Salla <- rbind(snow.Salla.2012, snow.Salla.2013, snow.Salla.2014)

One of the many interesting new R package suites out there is htmlwidgets. For my experiment of representing time-series and weather stations on a map, dygraphs and leaflet looked particularly useful.

Last time I was in Lapland was in mid-December 2014, in Inari, Saariselkä. BTW, over 40 cm of snow! During some trips I left Endomondo on to gather data about tracks, speed etc. I have to point out that I'm not into fitness gadgets as such, but it's nice to experiment with them. Endomondo is a popular app in its genre. Among other things it lets you export data in a standard GPX format, which is a friendly gesture.

For the sake of testing how to add GeoJSON to a leaflet map, I needed to convert the GPX files to GeoJSON. This turned out to be easy with the ogr2ogr command line tool that comes with the GDAL library, used by the fmi R package too. Here I convert the skiing ("hiihto") route of Dec 14th:

ogr2ogr -f "GeoJSON" hiihto1214.geojson hiihto1214.gpx tracks

One of the many aspects I like about dygraphs is how it lets you zoom into the graph. You can try it yourself in my shiny web application titled (a bit too grandiously I'm afraid) Snow Depth 2012-2014. Double-clicking resets. To demonstrate what one can do with the various options that the R shiny package provides, and how you can bind a value to a dygraphs event - pick a day from the calendar, and notice how it is drawn as a vertical line onto the graph.

The tiny, blue spot on the map denotes my skiing routes in Saariselkä. You have to zoom all the way in to see them properly.

The shiny application R code is here.

Edit 11.1: Winter and snow do not follow calendar years, so added data from the first leg of the 2012 winter period.

Network once again, now with YQL!

While fiddling with the Facebook network, GEXF and JSON parsing I remembered Yahoo! and its YQL Web Services. With it, you can get a JSON-formatted result from any, say, XML file out there. GEXF is XML.

The YQL query language isn’t that handy if you are interested only in a selection of nodes; the XPath filter is only for HTML files, curiosly enough. I wanted the whole story though, so no problem. Here is how the YQL Console shows the result:

YQL Console

With the REST query down below, you can e.g. transfer the JSON result to your local machine, in Unix like curl '*' > gexf.json

The structure is more deep than in the JSON that the Cytoscape D3.js Exporter returns, but the only bigger change the D3 code needs is to have new references from the links/edges to nodes.

Like the documentation of force.start() says,

On start, the layout initializes various attributes on the associated nodes. The index of each node is computed by iterating over the array, starting at zero.

This is fine, if the source and target attributes in the edge array apply to this. Here, they do not. Instead, the attributes reference the id attribute in the respective nodes. So I needed to change that, and excellent help was available.

So far so good, but using index numbers to access attribute values isn’t pretty and needs to be done differently. Maybe next time.