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The best way to learn how to program is to do something useful, so this introduction to Julia is built around a common scientific task: data analysis.
Our real goal isn't to teach you Julia, but to teach you the fundamental concepts that all programming depends on. We use Julia in this lesson because:
- of it's expressive syntax;
- it's free, open-source, and designed for high-performance computing;
- it is increasingly popular in scientific computing and data analysis; and
- it has a growing ecosystem of external packages for diverse tasks.
We are studying inflammation in patients who have been given a new treatment for arthritis, and need to analyze the first dozen data sets of their daily inflammation. The data sets are stored in CSV format (comma-separated values): each row holds information for a single patient, and the columns represent successive days. The first few rows of our first file look like this:
0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0
0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1
0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1
0,0,2,0,4,2,2,1,6,7,10,7,9,13,8,8,15,10,10,7,17,4,4,7,6,15,6,4,9,11,3,5,6,3,3,4,2,3,2,1
0,1,1,3,3,1,3,5,2,4,4,7,6,5,3,10,8,10,6,17,9,14,9,7,13,9,12,6,7,7,9,6,3,2,2,4,2,0,1,1
We want to:
- Calculate the average inflammation per day across all patients.
- Plot the result to discuss and share with colleagues.
To do all that, we'll have to learn a little bit about programming.
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You need to understand the concepts of files and directories and how to start a Julia interpreter before tackling this lesson. This lesson sometimes references Jupyter Notebook although you can use any Julia interpreter mentioned in the Setup.
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