Concept
The concept behind org is fairly simple - most analyses have three main sections:
- code
- results
- data
Yet each of these sections have extremely different requirements.
Code should:
- Be version controlled
- Be publically accessible
- Have 1 analysis pipeline that logically and sequentially details all steps of the data cleaning, analysis, and result generation
Results should:
- Be immediately shared with close collaborators
- Have each set of results saved and accessible, so that you can see how your results have changed over time (i.e. “if we run the code today, do we get similar results to yesterday?”)
Data should:
- Be encrypted (if sensitive)
- Not stored on the cloud (if sensitive)
Code
org::initialize_project
org::initialize_project takes in 2+ arguments. It then saves its results (i.e. folder locations) in org::project, which you will use in all of your subsequent code.
home is the location of run.R and the R/ folder. It will be accessible via org::project$home (but you will rarely need this).
results is your results folder. It will create a folder inside results with today’s date and it will be accessible via org::project$results_today (this is where you will store all of your results). Because the folder’s name is today’s date, this means that it automatically archives one copy of results per day. This allows you and your collaborators to easily check and see how the results have changed over time.
... you can specify other folders if you want, and they will also be accessible via org::project. Some suggested ones are raw (raw data) and clean (clean data).
run.R
run.R will be your masterfile. It will sequentially run through every part of your analysis:
- data cleaning
- analysis
- result generation
You will not have any other masterfiles, as this creates confusion when you need to return to your code 2 years later.
All sections of code (e.g. data cleaning, analyses for table 1, analyses for table 2, result generation for table 1, sensitivity analysis 1, etc) will be encapsulated in functions that live in the R folder.
An example run.R might look like this:
# Define the following 'special' locations:
# 'home' (i.e. run.R and R/ folder)
# 'results' (i.e. A folder with today's date will be created here)
# Define any other locations you want:
# 'raw' (i.e. raw data)
org::initialize_project(
env = .GlobalEnv,
home = "/git/analyses/2019/analysis3/",
results = "/dropbox/analyses_results/2019/analysis3/"
raw = "/data/analyses/2019/analysis3/"
)
# It is a good practice to describe how the archived
# results are changing from day-to-day.
#
# We recommend saving this information as a text file
# stored in the `org::project$results` folder.
txt <- glue::glue("
2019-01-01:
Included:
- Table 1
- Table 2
2019-02-02:
Changed Table 1 from mean -> median
", .trim=FALSE)
org::write_text(
txt = txt,
file = fs::path(org::project$results, "info.txt")
)
library(data.table)
library(ggplot2)
# This function would access data located in org::project$raw
d <- clean_data()
# These functions would save results to org::project$results_today
table_1(d)
figure_1(d)
figure_2(d)R/
All of your functions should be defined in org::project$home/R. The function org::initialize_project automatically sources all of the R scripts in org::project$home/R, making them available for use.
Research Article Versioning (Submission/Resubmission)
When writing a research article, multiple versions are often required (initial submission, followed by multiple resubmission variants based on the recommendations of peer reviewers).
To account for this, on the initial submission of the article it is recommended to rename run.R and R/ to run_YYYY_MM_DD_submission_1.R and R_YYYY_MM_DD_submission_1/ (YYYY_MM_DD should be replaced with year, month, and date).
When making adjustments for the resubmission, the relevant files/folders should be called run_YYYY_MM_DD_submission_2.R and R_YYYY_MM_DD_submission_2/ (YYYY_MM_DD should be replaced with year, month, and date).
In doing so, you successfully preserve all of your code that produced the results for every submission, allowing you to easily ensure that all changes are deliberate and intentional.
Solutions
Best Practice
Our very opinionated solution is as follows:
Code should be stored in GitHub. Data should be stored locally in an encrypted volume. Results should be stored on Dropbox, with the results for each day stored in a different folder.
# code goes here:
git
+-- analyses
+-- 2018
| +-- analysis_1 <- org::project$home
| | +-- run.R
| | +-- R
| | +-- clean_data.R
| | +-- descriptives.R
| | +-- analysis.R
| | +-- figure_1.R
| +-- analysis_2 <- org::project$home
| +-- run.R
| +-- R
| +-- clean_data.R
| +-- descriptives.R
| +-- analysis.R
| +-- figure_1.R
+-- 2019
+-- analysis_3 <- org::project$home
+-- run.R
+-- R
+-- clean_data.R
+-- descriptives.R
+-- analysis.R
+-- figure_1.R
# results goes here:
dropbox
+-- analyses_results
+-- 2018
| +-- analysis_1 <- org::project$results
| | +-- 2018-03-12 <- org::project$results_today
| | | +-- table_1.xlsx
| | | +-- figure_1.png
| | +-- 2018-03-15 <- org::project$results_today
| | | +-- table_1.xlsx
| | | +-- figure_1.png
| | +-- 2018-03-18 <- org::project$results_today
| | | +-- table_1.xlsx
| | | +-- figure_1.png
| | | +-- figure_2.png
| | +-- 2018-06-18 <- org::project$results_today
| | | +-- table_1.xlsx
| | | +-- table_2.xlsx
| | | +-- figure_1.png
| | | +-- figure_2.png
| +-- analysis_2 <- org::project$results
| | +-- 2018-06-09 <- org::project$results_today
| | | +-- table_1.xlsx
| | | +-- figure_1.png
| | +-- 2018-12-15 <- org::project$results_today
| | | +-- table_1.xlsx
| | | +-- figure_1.png
| | +-- 2019-01-18 <- org::project$results_today
| | | +-- table_1.xlsx
| | | +-- figure_1.png
| | | +-- figure_2.png
+-- 2019
+-- analysis_3 <- org::project$results
| +-- 2019-06-09 <- org::project$results_today
| | +-- table_1.xlsx
| | +-- figure_1.png
| +-- 2019-12-15 <- org::project$results_today
| | +-- table_1.xlsx
| | +-- figure_1.png
| +-- 2020-01-18 <- org::project$results_today
| | +-- table_1.xlsx
| | +-- figure_1.png
| | +-- figure_2.png
# data goes here:
data
+-- analyses
+-- 2018
| +-- analysis_1 <- org::project$raw
| | +-- data.xlsx
| +-- analysis_2 <- org::project$raw
| | +-- data.xlsx
+-- 2019
+-- analysis_3 <- org::project$raw
+-- data.xlsx
Suggested code for run.R:
org::initialize_project(
env = .GlobalEnv,
home = "/git/analyses/2019/analysis_3/",
results = "/dropbox/analyses_results/2019/analysis_3/",
raw = "/data/analyses/2019/analysis_3/"
)Quarto
If you only have one folder on a shared network drive, without access to GitHub or Dropbox, and you would like to create an RMarkdown document, then we propose the following solution:
# code goes here:
project_name <- org::project$home
+-- run.R
+-- R
| +-- CleanData.R
| +-- Descriptives.R
| +-- Analysis1.R
| +-- Graphs1.R
+-- paper
| +-- paper.Rmd
+-- results <- org::project$results
| +-- 2018-03-12 <- org::project$results_today
| | +-- table1.xlsx
| | +-- figure1.png
| +-- 2018-03-12 <- org::project$results_today
| | +-- table1.xlsx
| | +-- figure1.png
+-- data_raw <- org::project$raw
| +-- data.xlsxSuggested code for run.R:
# Initialize the project
org::initialize_project(
env = .GlobalEnv,
home = "/network/project_name/",
results = "/network/project_name/results/",
paper = "/network/paper/",
raw = "/network/project_name/data_raw/"
)
# do some analyses here
# render the paper
rmarkdown::render(
input = fs::path(org::project$paper,"paper.Rmd"),
output_dir = org::project$results_today,
quiet = F
)One Folder on a Shared Network Drive Without GitHub Access
If you only have one folder on a shared network drive, without access to GitHub or Dropbox, then we propose the following solution:
# code goes here:
project_name <- org::project$home
+-- run.R
+-- R
| +-- clean_data.R
| +-- descriptives.R
| +-- analysis.R
| +-- figure_1.R
+-- results <- org::project$results
| +-- 2018-03-12 <- org::project$results_today
| | +-- table_1.xlsx
| | +-- figure_1.png
| +-- 2018-03-12 <- org::project$results_today
| | +-- table_1.xlsx
| | +-- figure_1.png
+-- data_raw <- org::project$raw
| +-- data.xlsxSuggested code for run.R:
org::initialize_project(
env = .GlobalEnv,
home = "/network/project_name/",
results = "/network/project_name/results/",
raw = "/network/project_name/data_raw/"
)