At first, it looks like Rails.env is a special kind of object that has methods defined on it to check for the environment properties. However, upon closer inspection, it looks like it is really just a String, but not quite:
“The Rails 4 Way”, by Obie Fernandez and Kevin Faustino is a great reference book that covers most of what a Rails developer is likely to need on a daily basis. It covers the various DSLs and idioms (i.e. route definition, controller filter declaration, ActiveModel association and validations, etc) without getting into the details of Rails internals and how those features are implemented. The explanations are clear and the code examples relevant.
Just like Rails itself, “The Rails 4 Way” is opinionated and occasionally differs from the omakase1 way; Most notoriously, but hardly controversial, using Haml as a template engine and Rspec for testing.
Most of the book can be read cover-to-cover or used as a reference on particular topics. The exception is section about rails helpers (Chapter 11) which, as the author themselves point out, is really just an alphabetical listing of the methods available, like the one usually found on appendices or online documentation.
I recommend this book to new Rails developers (maybe after trying out an online tutorial) and for experienced Rails developers who are still working on Rails 3 (or 2!) and are expecting to make the jump to Rails 4 in the near future.
Last night I had the pleasure of giving a talk at the SDRuby monthly meeting on practical uses of UNIX
command line programs for Ruby and Rails developers. Check out the slides! and thanks everyone for the words of encouragment.
Back in February I wrote about Atom. At the time, I felt Atom showed promise, but was still a bit lacking. After Github announced that Atom is now completely open source in May, I decided to take another look. Most of what I use every day for development is open source, especially the tools with which I make my living: Linux, zsh, Ruby, Rails, etc that I find the idea of my editor being open source very appealing.
Atom uses ctags, as does other Unix-y editors. Support for jumping back from declarations has now been added, wich was crucial for my workflow. Another issue I had was the lack of context when multiple symbols were listed when navigating, but I managed to wrestle my way through coffeescript to fix that. Now, it works just like I expect it to.
Atom has much better performance now and it is clear that the development team consider this a priority. Overall, I find that the speed while working in the editor (opening files, editing files, jumping between files, searching) is acceptable and I do not notice and lag. Opening the editor, however is another matter. It is very sluggish, even when opening directories that are not deep and with a small number of files. For example, I enjoy using Atom for my git commit messages since I am already familiar with the navigation and it has a nice syntax formatting. However, some times it takes several seconds to open and makes me want to tear my hair out.
I have been using Atom as my main editor for the last 3 months. For being a beta version, I find the editor more than usable. Sublime Text 2 is a great editor and a lot of Atom is modeled after it (just as Sublime is modeled after TextMate). However, I believe Atom is here to stay and is my main editor for the foreseeable future.
I love watching the World Cup: It’s more soccer than you could hope for, mixed with national rivalries. What could be better. Now that I am older than most of the players, it dawned on me the intense pressure that they are under, to perform for their country and a question came to me: Just how old are this kids? Let’s find out.
With a quick google search, I came up with what seemed like a good source of data for the task at hand. I simply copy and pasted the data from my browser into a text editor to get a file that looks like this:
Alan PULIDO Mexico 08/03/1991 5 4
Adam TAGGART Australia 02/06/1993 4 3
Reza GHOOCHANNEJAD Iran 20/09/1987 13 9
NEYMAR Brazil 05/02/1992 48 31
Didier DROGBA Ivory Coast 11/03/1978 100 61
David VILLA Spain 03/12/1981 95 56
Abel HERNANDEZ Uruguay 08/08/1990 12 7
Javier HERNANDEZ Mexico 01/06/1988 61 35
Islam SLIMANI Algeria 18/06/1988 19 10
Shinji OKAZAKI Japan 16/04/1986 75 38
Cutting and Slicing
Using the power of unix pipes, we can easily extract the data we want from the data. Let’s start by getting all birthdates:
In this case, we are cutting again, this time using / as a delimiter. Now we have a list of all the players' birth years.
I searched around for some quick utilities that would generate a histogram and the most promising seemed a python utility called data hacks. Unfortunetly, I did not install for me and I didn’t have the inclination to mess with my python installation. I did however, find something similar to what I needed in a blog post about visualizing your shell history. After adapting it a bit to my purposes, I created a small bash function that now lives in my profile:
This function leverages awk very heavily. awk is a pattern-directed scanning and processing language. I am not very familiar with it, but after seeing how powerful it is, I am definitely want to get acquainted with it.
With this function, we can now get a full histogram:
Notice that the final sort is needed, because histogram returns the values ordered by the number of times it appeared in the data, but since we are talking about birth years, I believe the graph is more telling if it is in ascending order.
With all that in place, it is relatively easy to make a histogram of other data. I remember Malcom Gladwell’s theory in his book Outliers about how most professional hockey players are born in the first part of the year, because of how the developmental leagues work in Canada. With a database of 735 professional soccer players, chosen to be then best 23 of each country, I would expect 61.25 players to be born on each month. Let’s find out how many really are:
Notice that to arrive at this graph, the only thing we changed was the field we extracted from the data, in this case the month of the birthday. Is there a trend here? It does suggest that players born in the first part of the year are favored, but I do not know if it’s statistically significant.
Quick and dirty data analysis on the command line is pretty easy if you know a bit of unix and some awk!