My initial dream personal foldering - python and r template mix for data science
project/ Project Name
data/ Data
external/ External Data
interim/ Interim processed Data
processed/ Processed Data
raw/ Raw Data
notebooks/ Notebooks rmarkdown/jupyter
references/ Document useful development links
docs/ Store documents
reports/ Reporting including figures and final reports
graphs/ Graphs
src/ Scripts
munge/ Data Wrangling code
test/ Testing dev code
model/ Model code
visualisation/ Visualise code
lib/
logs/
README
TODO
install.packages('ProjectTemplate')
library('ProjectTemplate')
setwd('')
create.project('ProjectName')
load.project(ProjectName)Project Folders from create.project(minimal = TRUE)
- data where store raw data files (called in load.projects())
- doc store any docs here
- graphs store graphs
- munge preprocessing or data munging code
- src store final scripts (dev script move to munge folder)
R folder that are confusing
- cache means where you store your data (need to use the cache() function for it to be useful)
- config means what are your settings (need to use DCF " format read.dcf())
- diagnostics means scripts to deal with problems in data
- lib files useful for functionality
- logs logging work (package log4r)
- profiling benchmarking and timing code
- report output report HTML / latex
- tests test cases
References