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https://github.com/tidylab/ddd
Domain-driven Design (DDD) in R. Put domain experts and developers on a level playing field, which produces software that makes perfect sense to the business, not just the coders.
https://github.com/tidylab/ddd
ddd r
Last synced: 8 days ago
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Domain-driven Design (DDD) in R. Put domain experts and developers on a level playing field, which produces software that makes perfect sense to the business, not just the coders.
- Host: GitHub
- URL: https://github.com/tidylab/ddd
- Owner: tidylab
- License: other
- Created: 2020-10-01T04:45:56.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2021-10-17T04:58:56.000Z (about 3 years ago)
- Last Synced: 2024-08-13T07:13:21.348Z (4 months ago)
- Topics: ddd, r
- Language: R
- Homepage: https://tidylab.github.io/ddd/
- Size: 1.59 MB
- Stars: 8
- Watchers: 2
- Forks: 0
- Open Issues: 7
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - tidylab/ddd - Domain-driven Design (DDD) in R. Put domain experts and developers on a level playing field, which produces software that makes perfect sense to the business, not just the coders. (R)
README
---
output: github_document
bibliography: [./inst/REFERENCES.bib]
biblio-style: apalike
link-citations: yes
notice: |
@Evans2004, @Vernon2013, @Wlaschin2018, @Percival2020
editor_options:
canonical: true
markdown:
wrap: 80
references:
location: section
---
```{r, include = FALSE}
source(file.path(usethis::proj_get(), "vignettes", "_common.R"))
```
# `ddd` 
[](https://CRAN.R-project.org/package=ddd)
[](https://github.com/tidylab/ddd/actions)
[](https://codecov.io/gh/tidylab/ddd)
## `Domain-driven Design (ddd)`
```{r, echo = FALSE, out.width = "100%"}
knitr::include_graphics("https://i.imgur.com/RLEQkhe.png")
```
## Hurry please, I have a short attention span, what is `ddd`?
Sorry, most likely `ddd` is not for you.
## Who is `ddd` for?
Here are a few assumptions about you:
- You are an R user
- You are an experienced Data Scientist
- You have seen and created clunkers, that is, analytic applications with an
abundance of cruft
- You have a passion for producing analytic applications of high-quality every
day
If you belong to the intersection of all the sets above, then `ddd` may benefit
you.
## What `ddd` is (but first, what it is not)
Because `ddd` is an R package that caters for Data Scientists, it is natural to
think it may contain a new learning algorithm, statistical method, a
visualisation tool, or data processing toolkit. `ddd` offers none of these
things. Instead, `ddd` automates repetitive tasks that arise during software
development in R. In that sense, `ddd` is akin to the
[`usethis`](https://usethis.r-lib.org/) package.
`ddd` is an acronym of a software development approach called [***domain-driven
design***](https://en.wikipedia.org/wiki/Domain-driven_design), or DDD for
short. `ddd` facilitates the implementation of key DDD patterns including
Entity, Value Object and Domain Services. Together these building blocks form a
domain model.
Before getting into specifics about **domain-driven design**, lets first discuss
the importance of software design and how it is inevitable.
## Why should I use software design?
When you develop software to solve a new business problem, what guides your
software design? Obviously, you strive to eliminate unintended behaviours (bugs)
from the software. Perhaps you are using tests and even a Test-Driven
Development (TDD) approach. However, guiding your design with tests is analogous
to driving a car by using the guardrails. It keeps you safe from the bar ditches
while you drive, though it has no navigation utility.
All too often, Data Scientists produce software without thoughtful design. On
the one hand, Data Scientists are typically not Software Engineers, nor are they
taught good practices of software engineering. On the other hand, Data
Scientists often take the role of Software Engineers when they develop analytic
applications. Perhaps, the lack of software engineering knowledge, leads Data
Scientists to overlook the significance of good design.
No design is a fallacy. This is because design still flows from the brains of
the Data Scientists through their fingertips as they wrangle with the code.
Thus, questions about whether a design is necessary or affordable are quite
beside the point: design is inevitable. The alternative to good design is bad
design, not no design at all.
## Installation
You can install `ddd` by using:
```{r, echo=TRUE, eval=FALSE}
install.packages("remotes")
remotes::install_github("tidylab/ddd")
```
Optionally, you can make a project independent of the `ddd` package by calling `use_ddd()`.
`ddd` is needed for software development and has no added value in production.
`use_ddd()` will copy the necessary R classes and functions into your project.
## References