Commit 665cb2a3 authored by Frank Hellmann's avatar Frank Hellmann
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# swc workshop 2017 Fall
## Open questions
Frank: formulate targets (Python specific)
Stefan/Marvin/Peter/Martin: how(+why) to integrate shell usage in later R sessions
?: prepare time series files
Stefan/Marvin: if + where to introduce tidyverse
## DAY 1 AM --- both
### Session AM1 (get started, know what we're doing these days)
- intro
- lightning talks participants
- course outline
### Session AM2 (basic concepts, reasons why, basis for later use cases)
- shell
- git
Each shorter than spring 2017, will be picked up in later sessions
## DAY 1 PM --- Python
### Session PM1 Python Intro (basic overview)
- python+anaconda+conda+jupyter+history
- operators, data structures (tuples, lists, dicts, nparrays, panda.df, etc)
- object types (string, number, boolean, complex, etc)
- provided functions + methods, atrributes
- control flows: loops + conditions
- numpy
- read file, simple plot (or in session PM2)
### Session PM2 Python (structuring code, defensive programming, using prepared ts files)
- write function + module
- put under version control
- simple test (assert)
- documentation
- write script + call from bash
- traceback, errors
not covered: closures, argpars, numba
## DAY 1 PM --- R
### Session PM1 R Intro (basic intro)
- R+Rstudio+CRAN+history
- operators, vector, df, object structures (vector,df,list,other), read file, subset df
- object class (string, number, boolean, complex, etc)
- simple plot
- read help for functions
- install.packages, library + fun vs pack::fun
### Session PM2 R (overview of code structuring)
- conditions, loops, *apply
- write function (with comments for documentation)
- put under version control
- simple test (testthat)
not covered: package with structured documentation with devtools/Roxygen/sinew (but link to
## DAY 2 AM --- both
### Session AM1: Time series, functions, plotting
- reading + understanding documentation of functions
Global temperature average development 1750-2010
- download file (optional: call shell with wget from python/R)
- read, process
- moving average
- plot with uncertainty band
- write function (exercise!)
### Session AM2:
using shell:
- if possible: get list of available files in
- with wget: Get all textfiles (country temp development) starting with B (or with >3 consonants or whatever)
- apply function to several textfiles
- Python: APIs: numpy, pandas, xarray
- R: Tidyverse?
- BUFFER (previous sessions potentially too full)
## DAY 2 PM --- both
### Session PM1: higher dimensional (spatial) data, advanced plotting + cartography, application of aquired knowledge
gridded data Europe:
- read netcdf file
- plot map of one time slice
- now compare plot with given cuteplot function by Joachim
- extract time series Potsdam
- compare with global average from Berkeley
### Session PM2: Git in practice (optional if we ran out of time)
- use git: final commit
- create github/gitlab account + create repos + add remote
- git push + inspect online
- github/gitlab release
- outro, feedback
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