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Many of you will be familiar with clinical projects reliant on unwieldy spreadsheets containing hand-curated data, needing laborious processing by a dedicated but time-poor clinician. Wouldn't it be nice if a machine could do the work for you? The good news is that computer programming is more accessible than ever before. This article aims to point the reader towards getting started: there is no single ‘right’ way to learn. We will focus mainly on open-source, free software, but paid alternatives do exist. In this article we discuss steps 1 and 2 of 5.
Step 1: choose a language (or two)
Two popular, free and well documented languages for analysis are Python and R.1 Python is described as a ‘general purpose’ language—it can be applied to many computing applications. R describes itself as for ‘statistical computing and graphics’. It is not unusual to use different languages for different applications. I use Python for ECG processing and R for final statistical analysis prior to publication—but this is purely …
Footnotes
Contributors JC wrote the article and code snippets displayed in the figures.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Patient consent for publication Not required.
Provenance and peer review Commissioned; externally peer reviewed.
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