Introduction

Let’s face it…

  • There are going to be files

  • LOTS of files

  • The files will change over time

  • The files will have relationships to each other

It’ll probably get complicated

Strategy against chaos

File organization and naming is a mighty weapon against chaos

  • Make a file’s name and location VERY INFORMATIVE about:
    • what it is,
    • why it exists,
    • how it relates to other things
  • The more things are self-explanatory, the better.

File naming


Names matter

What works, what doesn’t?

NO

myabstract.docx
Joe’s Filenames Use Spaces and Punctuation.xlsx
figure 1.png
fig 2.png
JW7d^(2sl@deletethisandyourcareerisoverWx2*.txt

YES

2014-06-08_abstract-for-sla.docx
joes-filenames-are-getting-better.xlsx
fig01_scatterplot-talk-length-vs-interest.png
fig02_histogram-talk-attendance.png
1986-01-28_raw-data-from-challenger-o-rings.txt

Three principles for good (file) names

  1. Machine readable

  2. Human readable

  3. Play well with default ordering

Machine readable

  • Regular expression and globbing friendly
    • Avoid spaces, punctuation, accented characters, case sensitivity
  • Easy to compute on
    • Deliberate use of delimiters

Filtering and search through Globbing

Excerpt of complete file listing:

Example of globbing to filter file listing:

Search using Mac OS Finder search facilities

Search using regex in R

Delimit information with punctuation

Deliberate use of "-" and "_" allows recovery of metadata from the filenames:

  • "_" underscore used to delimit units of metadata I want to access later
  • "-" hyphen used to delimit words so our eyes don’t bleed

Splitting filenames by delimiters

This happens to be R but also possible in the shell, Python, etc.

Include important metadata

e.g. I’m saving a number of files of temperature data extracted at different resolutions (res) and for a number of months (month). Including these parameters in the filename allows me to use them to target files to read in.

write.csv(df, paste("variable", res, month, sep ="_"))

df <- read.csv(paste("variable", res, month, sep ="_"))

Recap: machine readable

  • Easy to search for files later

  • Easy to filter file lists based on names

  • Easy to extract info from file names, e.g. by splitting

New to regular expressions and globbing? be kind to yourself and avoid + Spaces in file names + Punctuation + Accented characters

Human readable

  • Name contains info on content

  • Connects to concept of a slug from semantic URLs

Example

Which set of file(name)s do you want at 3 a.m. before a deadline?

Embrace the slug

Recap: Human readable

  • \(\rightarrow\) Easy to figure out what the heck something is, based on its name

Play well with default ordering

  • Put something numeric first
  • Use the ISO 8601 standard for dates
  • Left pad other numbers with zeros

Examples

Chronological order:

Logical order: Put something numeric first

Dates

Use the ISO 8601 standard for dates: YYYY-MM-DD

iso_psa

iso_psa

Left pad other numbers with zeros

If you don’t left pad, you get this:

10_final-figs-for-publication.R
1_data-cleaning.R
2_fit-model.R

which is just sad :(

Recap: Play well with default ordering

  • Put something numeric first

  • Use the ISO 8601 standard for dates

  • Left pad other numbers with zeros

Recap: Three principles for (file) names

  1. Machine readable

  2. Human readable

  3. Play well with default ordering

Go forth and use awesome file names :)