Savoir compter, savoir coder - Bonnes pratiques du statisticien en programmation
R. Le Saout and
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E. L'Hour: Insee
R. Le Saout: Insee
B. Rouppert: Insee
Documents de Travail de l'Insee - INSEE Working Papers from Institut National de la Statistique et des Etudes Economiques
Clean statistics come with clean code : Statistics come from computer programs. In a published study, these figures are more readable when editorial techniques are well used. But few rules govern the upstream coding tasks. As a consequence, to run or to change code written by someone else can prove to be a strenuous burden. A readable code increases individual and collective work efficiency by limiting errors, by making review easier, and by making the code more sustainable. For someone preparing a study, both conducting a literature search and identifying key messages are crucial tasks. Similarly, expressing the relevant business need, identifying the available means and planning adequate actions can save valuable time when coding. Moreover, short sentences built around “one idea – one sentence” and no useless jargon make a message clearer. Likewise, readable programs are short, well structured, and use meaningful names. Finally, in the same manner as a published study has been reviewed several times, code reviews and defining tests seem critical.
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Persistent link: https://EconPapers.repec.org/RePEc:nse:doctra::m2016-04
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