Exporting data with labels


A few emails have come in requesting support for questions as headings in data exports. We have heeded your call and are glad to announce this is now possible. When exporting CSV, Excel and CSV Zip files, you can choose whether labels and/or names show up as column headers.

Using XLSForm syntax, the label column contains the actual text of the question you see in the form. The name column in the survey worksheet defines the unique variable name for the question.

Exporting data with labels is helpful when using statistical software for advanced data analysis, because it minimizes the work of replacing your variable names with labels. Also, if your variable names are not descriptive enough or are codified, having labels as column headers means you will no longer have to refer to the XLSForm to reference questions.

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Writing Python Code to Decide an Election


The long awaited video from Ona’s keynote presentation at PyConZA 2014.

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Automating Style In Clojure


project settings snippet

We do everything we can to improve code quality. Our process includes rigorous code reviews focused on getting the correct level of abstraction, modularity, and reusability. We quickly realized that nitpicking code format and line length was distracting us from our goals. It isn’t that those aspects aren’t important, but that they should be standardized so we can spend our time on more interesting questions and problems.

To keep ourselves focused on the meaningful aspects of the code, we run a suite of automated analysis tools in our CI. This gives us a higher level of consistency and predictability before a branch gets to code review.

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Squashing Commits with an Interactive Git Rebase



There are plenty of reasons to get familiar with and start using git’s interactive rebase. You might want to edit a commit message, delete commits, reorder commits, or edit commits.

Here we will talk about using it to “squash” (as in combine, merge, or meld) multiple commits into a single commit.

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Making an Impact with Lean Data


Ona Collect

Lean data, or applying lean processes to data collection and use, is an important tool for creating impact in global development programs. It was created as a response costly traditional M&E (monitoring and evaluation) practices that aren’t about the end consumer or user and only applicable to large-scale programs. The core tenet of lean data is focusing on a bite-sized goal and using customer impact data to drive your decision-making towards that goal.

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