Category: Open Source

Digitize you charts with Engauge Digitizer

A few words of appreciation for an open source software that can help you a lot in your work, Engauge Digitizer (ED) from Mark Mitchell. ED is a simple, straightforward curve digitizer: it takes images with graphs like the one below and transform them (with a little help) in data you can use later on.


Continue reading “Digitize you charts with Engauge Digitizer”

Fedora 23 on a Dell XPS13 (part 1)

Taking advantage of a trip to Canada and a very favourable CAN$:€ exchange rate, I bought a Dell XPS13 (9350 or “late 2015”), following excellent reviews from around the web. Dell sold a ‘developer edition‘ of this laptop (shipping with Ubuntu Linux) but unfortunately it was out of stock on Dell US and I couldn’t find the item on the Dell Canada website. So I bought the Windows version with a touchscreen (it was Black Friday :-)).

fedora_infinity_140x140Here is how to install Fedora 23 on it (and probably most other Linux distribution) … I will focus on three aspects (in brief: everything works out of the box, except the wireless card that needed some additional action):

  1. How to boot and install Fedora Workstation
  2. What works and what doesn’t work out of the box
  3. Some things to do after installation (additional software)

Continue reading “Fedora 23 on a Dell XPS13 (part 1)”

Happy to use Zotero since a few weeks

Source Material - by Josh DiMauroFor my work I need to reference a lot of statements, mainly with papers and books in the biological / medical literature. Usually “professionals” use two proprietary software, Reference Manager or EndNote (both owned by Thomson Reuters). But there are a few very interesting free alternatives (see this comparison of reference management software).

I switched from Mendeley to Zotero a few weeks ago and I’m very happy. Here is why … Continue reading “Happy to use Zotero since a few weeks”

How to write data from R to Excel (even if you don’t have Excel)

Following my previous posts on how to read/write Excel files from Matlab here is the way I use to read/write Excel files from R. Again it seems the Apache POI java library made developers’life easy. I use here the simple-yet-powerful xlsx package (documentation here in PDF; project website).

Here you don’t need to install any additional files, installing the xlsx package from R does all the dirty work that for you. Then, reading an Excel file is very easy:

inData <- read.xlsx2("input.xls", sheetName="Contactmatrix", header=FALSE)

I usually use read.xlsx2 instead of read.xlsx. It is said to be faster with large matrices and I had the opportunity to experience it – so I stick with this. You can read xls, xlsx and xlsm files without issue (well, with the simple formatting I usually use).

Writing to an Excel file is also very easy:

write.xlsx2(outData, "output.xls", sheetName="Random2", col.names=FALSE, row.names=FALSE)

Easy, isn’t it?

Map of GAVI eligible countries in R

I was trying to reproduce the map of the GAVI Alliance eligible countries (btw I was surprised India is eligible – but that’s the beauty of relying on numbers only and not assumptions) in R. This is the original map (there are 57 countries eligible):


I started to use the R package rworldmap because it seemed the most appropriate for this task. Everything went fine. Most of the time was spent converting the list of countries from plain English to plain “ISO3” code as required (ISO3 is in fact ISO 3166-1 alpha-3). I took my source from Wikipedia.

Well, that was until joinCountryData2Map gave me this reply:

54 codes from your data successfully matched countries in the map
3 codes from your data failed to match with a country code in the map
189 codes from the map weren’t represented in your data

I should have better simply read the documentation: there is another small command that needs not to be overlooked, rwmGetISO3. What are the three codes that failed to match?

Although you can compare visually the map produced with the map above, R (and rworldmap) can indirectly give you the culprits:

tC2 = matrix(c("Afghanistan", "Bangladesh", "Benin", "Burkina Faso", "Burundi", "Cambodia", "Cameroon", "Central African Republic", "Chad", "Comoros", "Congo, Dem Republic of", "Côte d'Ivoire", "Djibouti", "Eritrea", "Ethiopia", "Gambia", "Ghana", "Guinea", "Guinea Bissau", "Haiti", "India", "Kenya", "Korea, DPR", "Kyrgyz Republic", "Lao PDR", "Lesotho", "Liberia", "Madagascar", "Malawi", "Mali", "Mauritania", "Mozambique", "Myanmar", "Nepal", "Nicaragua", "Niger", "Nigeria", "Pakistan", "Papua New Guinea", "Rwanda", "São Tomé e Príncipe", "Senegal", "Sierra Leone", "Solomon Islands", "Somalia", "Republic of Sudan", "South Sudan", "Tajikistan", "Tanzania", "Timor Leste", "Togo", "Uganda", "Uzbekistan", "Viet Nam", "Yemen", "Zambia", "Zimbabwe"), nrow=57, ncol=1)
apply(tC2, 1, rwmGetISO3)

In the results, some countries are actually given in a slightly different way by GAVI than in R. For instance “Congo, Dem Republic of” should be changed for rworldmap in “Democratic Republic of the Congo” (ISO3 code: COD). Or “Côte d’Ivoire” should be changed for rworldmap in “Ivory Coast” (ISO3 code: CIV). An interesting resource for country names recognised by rworld map is the UN Countries or areas, codes and abbreviations. Once you correct this, you can have your map of GAVI-eligible countries:


And here is the code:

# Displays map of GAVI countries
theCountries <- c("AFG", "BGD", "BEN", "BFA", "BDI", "KHM", "CMR", "CAF", "TCD", "COM", "COD", "CIV", "DJI", "ERI", "ETH", "GMB", "GHA", "GIN", "GNB", "HTI", "IND", "KEN", "PRK", "KGZ", "LAO", "LSO", "LBR", "MDG", "MWI", "MLI", "MRT", "MOZ", "MMR", "NPL", "NIC", "NER", "NGA", "PAK", "PNG", "RWA", "STP", "SEN", "SLE", "SLB", "SOM", "SDN", "SSD", "TJK", "TZA", "TLS", "TGO", "UGA", "UZB", "VNM", "YEM", "ZMB", "ZWE")
GaviEligibleDF <- data.frame(country = c("AFG", "BGD", "BEN", "BFA", "BDI", "KHM", "CMR", "CAF", "TCD", "COM", "COD", "CIV", "DJI", "ERI", "ETH", "GMB", "GHA", "GIN", "GNB", "HTI", "IND", "KEN", "PRK", "KGZ", "LAO", "LSO", "LBR", "MDG", "MWI", "MLI", "MRT", "MOZ", "MMR", "NPL", "NIC", "NER", "NGA", "PAK", "PNG", "RWA", "STP", "SEN", "SLE", "SLB", "SOM", "SDN", "SSD", "TJK", "TZA", "TLS", "TGO", "UGA", "UZB", "VNM", "YEM", "ZMB", "ZWE"),
GAVIeligible = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1))
GAVIeligibleMap <- joinCountryData2Map(GaviEligibleDF, joinCode = "ISO3", nameJoinColumn = "country") mapCountryData(GAVIeligibleMap, nameColumnToPlot="GAVIeligible", catMethod = "categorical", missingCountryCol = gray(.8))

Android is catching up iOS

121221-android-mba-rWell, there is nothing new in this statement. The smartphone OS Android is catching up and even overtaking its rival iOS in many domains:

  • more activated products per day and per year in 2011,
  • more Samsung Galaxy S3 (running Android) sold in Q3 2012 than iPhone4 and 5S (running iOS),
  • more devices worldwide,
  • catching up Apple’s market share in tablets,

All this is summarised in an infographics MBA Online designed (the original address is here: at your own risk). It is sweet and colorful, with lots of numbers and some references in the end. Unfortunately these references are embedded in the image so you cannot click on them if you ever want to read more info.

Also as I mentioned previously (for an infographics coming from a similar type of website), I didn’t like much the fact it was very, very long (see reduced copy on the right). It makes things easily read while scrolling down. But ymmv I would have like something a bit more different. For instance I would have seen this more as a succession of slides, a-la Pechakucha maybe (except there is a lot of text). But the restrictive license (CC-by-nc-nd) prohibits derivative works.

So I like my Android device. I like when people promote it, are proud that Android is a success and talk about it. And the web is full of these infographics: a similar story about taking over the world, the successive Android versions (again very long), tastes of Android users (versus iOS users’), a broader smartphone comparison (again very long), a Google search for it, … Choose the one you like!