from abstract of Data organization in spreadsheets. By Broman and Woo 2017. The American Statiscian >"Spreadsheets are widely used software tools for data entry, storage, analysis, and visualization. Focusing on the data entry and storage aspects, this paper offers practical recommendations for organizing spreadsheet data to reduce errors and ease later analyses. The basic principles are: be consistent, write dates like YYYY-MM-DD, don't leave any cells empty, put just one thing in a cell, organize the data as a single rectangle (with subjects as rows and variables as columns, and with a single header row), create a data dictionary, don't include calculations in the raw data files, don't use font color or highlighting as data, choose good names for things, make b
Wayne's Bioinformatics Code Portal fomightez
| %matplotlib notebook | |
| # use `%matplotlib notebook` if you are using current JupyterLab | |
| from vpython import * | |
| import matplotlib.pyplot as plt | |
| plt.style.use('ggplot') | |
| # based on "AtomicSolid" by Bruce Sherwood | |
| # adapted to include realtime matplotlib by Wayne Decatur |
It seems you are asking about a few related things here. There is no short answer as some of this depends on your motivation, time, resources, skills, target audience, journal, etc. Plus a lot of the best practices are subjective or in flux.
I'd suggest looking at some of the resources below, find some direction and examples you find admirable, and see what you'd like to implement for this time. And what is possible for next time. Someone suggested using Jupyter notebooks which is great especially since you can now share live ones using Binder, see https://github.com/fomightez/methods_in_yeast_genetics/tree/master/cell_density_estimator for an example. Alternatively or as a companion site/documentation, I suggest using read the docs (https://readthedocs.org/) and Github for a making a step by step version that a biologist with limited computational skills could use it (or youself maybe a year from now) and then you can adapt that into a paper. That way you have a long version that the paper can refer read
images for another gist
See here for technical information relating to making and using this type of gist.
Scripts work the same as with VPython, just not as pretty. Falls back gracefully.
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Open the script you wish to run in your favorite text editor. Wordpad on the Windows system or TextWrangler or TextEdit on the Mac system will work. Microsoft Word is best avoided. In this case, it may work for copying ad long as you don't try saving it.
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Go to tmpnb.org or try.jupyter.org in your browser. A Jupyter dashboard page will come up after a moment and it will look similar to below.
Information good as of early 2017
In the listing below they are ordered by ranking with the most stable and full-featured being first. The ranking emphasizes stability overall.
Because the impetus for this list involves VPython, that is the most strongly considered feature.
Step-by-step guides are linked to for each one:
images for another gist
See here for technical information relating to making and using this type of gist.
Scripts will run with VPython on the full-featured Domino Data Science Platform.
You need to remember to conclude session to have generated files be saved to your account, plus stop the company from complaining in your email. All final results of each session are archived for easy retrieval.
Text list ===> Python-code list object
Quick example of how to paste a list extracted from text elsewhere with each item on a separate line and convert it to a Python list easily. Good for small lists and eliminates need for separate file or for running running regex in editor
to add commas and then format to Python-code list by hand.
You can get this entire thing as Python code in the raw gist.
This markdown renders nicely here or here.
STEP 1: Paste list as a docstring below first line and in front of ```.
list_as_string ='''
RF2