All posts (Page 117 of 130)
There are two things that make R such a wonderful programming environment - the vast number of packages to access, process and interpret data, and the enthusiastic individuals and subcommunities (of which rOpenSci is a great example). One, of course, flows from the other: R programmers write R packages to provide language users with more features, which makes everyone’s jobs easier and (hopefully!) attracts more users and more contributions.
But what if you have an idea, or a need, but not the time or confidence to write a package for it? I can’t speak for this blog’s readers, but I’ve been writing R for about two years and it took a good long while before I felt comfortable contributing upstream to CRAN. Or, what if you do have the time, and do have the confidence, but want to spend that time well, on things that you know other people will find useful, and don’t know what that is?
...rOpenSci specializes in creating R libraries for accessing data resources on the web from R. Most times you request data from the web in R with our packages, you should have no problem. However, you evenutally will run into problems. In addition, there are advanced things you can do modifying requests to web resources that fall in the advanced stuff category.
Underlying almost all of our packages are requests to web resources served over the http protocol via curl. curl is a command line tool and library for transferring data with URL syntax, supporting (lots of protocols) . curl has many options that you may not know about.
Key to the success of rOpenSci is our community and we want to hear more regularly from our members, and foster new interactions among the group. In addition, community calls are a way for us to give important updates, and get feedback on them.
We tentatively plan on doing community calls once per month. The format of rOpenSci community calls could be of various types. We could have community members show off software they’ve been working on, or users demo use cases. Instead, we could focus more on conversations. For this first call, we’ll be doing a combination of demonstration and discussion. We would like to experiment with the call format over the next few months before we decide on one or more approaches that work best.
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Why open data growth
At rOpenSci we try to make it easier for people to use open data and contribute open data to the community. The question often arises: How much open data do we have? Another angle on this topic is: How much is open data growing?
We provide access to dozens of data respositories through our various packages. We asked many of them to share numbers on the amount of data they have, and if possible, growth of their data holdings through time. Many of our partners came through with some data. Note that the below is biased towards those data sources we were able to get data from, and those that we were able to get growth through time data. In addition, note that much of the data we use below was from fall of 2013 (last year) - so the below is based on somewhat old data, but surely the trends are likely still the same now.
...You only know two things about Docker. First, it uses Linux containers. Second, the Internet won’t shut up about it.
– attributed to Solomon Hykes, Docker CEO
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So what is Docker?
Docker is a relatively new open source application and service, which is seeing interest across a number of areas. It uses recent Linux kernel features (containers, namespaces) to shield processes. While its use (superficially) resembles that of virtual machines, it is much more lightweight as it operates at the level of a single process (rather than an emulation of an entire OS layer). This also allows it to start almost instantly, require very little resources and hence permits an order of magnitude more deployments per host than a virtual machine.
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