dev/README.md
In D-Se/ML: Machine Leanring targets-bookdown pipeline
title: "Onboarding"
output:
rmarkdown::html_vignette:
toc: true
number_sections: true
body .main-container {
max-width: 1280px !important;
width: 1280px !important;
}
body {
max-width: 1280px !important;
}
h1 {
text-align: center;
}
table {
width: 50%;
text-align: center;
}
Welcome!
Hi there! Glad to have you on board. This is a guide to help you help us. If anything is unclear, questions can be asked by posting on Github Discussions, or by sending an email to r paste0(gsub("[<>]", "", strsplit(maintainer("ML"), "<")[[1]] |> trimws()), collapse = " at: ").
Project Goals
-
Deliver a scalable, reproducible and continuously integrated data analysis compendium that:
-
Deploys Machine Learning algorithms,
-
Visualizes model performances & statistical inference,
-
Communicates findings to target audiences in multiple formats.
-
Display best practices at the edge of machine learning knowledge using R, placing importance on:
-
Packaging approaches using version control,
-
Deploying computationally efficient algorithms,
-
Interfacing modelling & reporting API's,
-
Adopting a functional programming mindset.
Project deliverables
-
A data analysis report in pdf format,
-
An R package containing target-based tidymodels approach,
-
A pipeline-generated bookdown book,
-
Github repository of mock firm & analysis.
Meet the team
```{=html}
div.green { background-color:#93ed99; border-radius: 5px; padding: 20px;}
::: green
<center>
We are currently <B> recruiting! </B>
</center>
:::
+---------------+------------------------+------------------------------------------------------------------------------------+
| Name | School | Responsibilities |
+:=============:+:======================:+:==================================================================================:+
| Donald Seinen | Information Management | Project Management \| Quality Assurance \| Modelling \| Visualization \| Reporting |
+---------------+------------------------+------------------------------------------------------------------------------------+
| ... | ... | ... |
+---------------+------------------------+------------------------------------------------------------------------------------+
| ... | ... | ... |
+---------------+------------------------+------------------------------------------------------------------------------------+
| ... | ... | ... |
+---------------+------------------------+------------------------------------------------------------------------------------+
| ... | ... | ... |
+---------------+------------------------+------------------------------------------------------------------------------------+
## Getting started
1. Sign the [non-disclose agreement](https://github.com/D-Se/ML/tree/master/inst) (Only in effect until **August 31, 2022**).
2. Install project tools and dependencies by running `devtools::install_github("D-Se/ML")` in the R console.
3. Connect RStudio to github ([guide](https://happygitwithr.com/rstudio-git-github.html)).
4. Fork the Github repository.
5. Find [issues](https://github.com/D-Se/ML/issues) to solve (picked by you or assigned to you).
# About the project
## Tools
This project uses **R & C++** for core development, **HTML, CSS & JavaScript** for reporting.
## R packages
This is a (partial) list of packages used in the project at various stages:
### Data wrangling
| Package | Description | Resource |
|--------------|--------------------|----------|
| `dplyr` | Data wrangling | |
| `data.table` | Speedy data.frames | |
### Reporting packages
+--------------+---------------------------+-----------------------------------------------+
| Package | Description | Resources |
+==============+===========================+===============================================+
| `ggplot2` | Data visualization | |
+--------------+---------------------------+-----------------------------------------------+
| `plotly` | Interactive plots | |
+--------------+---------------------------+-----------------------------------------------+
| `visNetwork` | Ntwork graphs | |
+--------------+---------------------------+-----------------------------------------------+
| `rmarkdown` | Markdown with an R accent | [Book](https://bookdown.org/yihui/rmarkdown/) |
+--------------+---------------------------+-----------------------------------------------+
| `bookdown` | | |
+--------------+---------------------------+-----------------------------------------------+
| `shiny` | Web application framework | |
+--------------+---------------------------+-----------------------------------------------+
### Modelling packages
+--------------+------------------------------------------+------------------------------------------------------------------------+
| Package | Description | Resources |
+==============+==========================================+========================================================================+
| `keras` | APi to `tensorflow` neural nets | [keras](https://keras.io/) ; [TensorFlow](https://www.tensorflow.org/) |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `rpart` | Regression trees | |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `glmnet` | Penalized maximum likelihood models | |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `earth` | Multivariate Adaptive Regression Splines | |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `rsample` | Data sampling | |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `recipes` | Pre-processor specification | [Book](https://www.tmwr.org/) |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `parsnip` | Model specification | |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `tune` | Hyper parameter tuning | |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `workflows` | | |
+--------------+------------------------------------------+------------------------------------------------------------------------+
### Package development
+---------------+--------------------------------+----------------------------------------------------------------------+
| Package | Description | Resources |
+===============+================================+======================================================================+
| `targets` | Make-like pipeline toolkit | [Book](https://books.ropensci.org/targets/) |
+---------------+--------------------------------+----------------------------------------------------------------------+
| `tarchetypes` | Function-oriented `targets` | |
+---------------+--------------------------------+----------------------------------------------------------------------+
| `rlang` | Tidyverse programming toolbox | |
+---------------+--------------------------------+----------------------------------------------------------------------+
| `testthat` | Unit testing | [Book](https://r-pkgs.org/tests.html) |
+---------------+--------------------------------+----------------------------------------------------------------------+
| `lifecycle` | function life cycle management | |
+---------------+--------------------------------+----------------------------------------------------------------------+
| `future` | Parallel processing framework | |
+---------------+--------------------------------+----------------------------------------------------------------------+
| `promises` | Asynchronous programming | [website](https://rstudio.github.io/promises/articles/overview.html) |
+---------------+--------------------------------+----------------------------------------------------------------------+
| `crayon` | Pretty console printing | |
+---------------+--------------------------------+----------------------------------------------------------------------+
## Github repo
The [Github repo](https://github.com/D-Se/ML) is where the project lives. In it, you can find the following files. This may change in future releases.
```{verbatim}
├── .github >>> github workflows, automated checking
├── R/ >>> directory for ML package functions.
├──── ... >>> regular R package functions. o sub-directory allowed.
├── inst/ >>> supplementary materials for ML package
├── man/ >>> automatically generated package documentation [NO EDIT]
├──── figures/ >>> images used
├── src/ >>> compiled (C++) code used in ML package
├── tests/ >>> unit tests for package functions
├──── testthat/ >>> directory of unit test
├── vignettes/ >>> long-form documentation
├── .Rbuildignore/ >>> package utility: instruction set to ignore non-standard files
├── .gitignore >>> instruction set to RStudio to ignore files in directory for version control
├── DESCRIPTION >>> R package metadata file
├── ML.Rproj >>> RStudio file [NO EDIT]
├── NAMESPACE >>> automatically generated R package build file [NO EDIT]
├── README.Rmd >>> Github introduction file with executable R code
├── README.md >>> automatically generated Github introduction file [NO EDIT]
├── run.sh >>> shell script to execture pipeline
├── run.R >>>
├── _targets.R # core pipeline
└── report.Rmd
The project is split up into segments, each ending with finishing a deliverable. These steps can be found in the milestones section of the repo. The milestones are made up of collections of issues. These are tasks assigned to project members, and can be added by any person with access rights to the repo. labels are placed on issues to make it easy to find and assign tasks. Each issue is allocated a difficulty and priority label, and further labels were appropriate.
External Resources - learning & finding help
R resources
Visualization
Modelling
Reporting
Productivity tips
A list of useful version controlled R project commands:
-
devtools::document() to update (locally) the package using roxygen2.
-
lintr::lint_dir() to use static code analysis for code formatting checks.
lintr is useful to check code style line-by-line and make adjustments on the fly. It will open an interface in the RStudio Markers pane where individual lines can be clicked. On a click, the relevant code snippet will be opened in the editor. This command is also run automatically on commits to the master branch of Github repo, viewable as comments in the Github Actions pane.
-
covr::report() for visual inspection of unit test coverage.
-
usethis::edit_r_profile() to run functions on startup.
An example of Donald's ML-project specific R profile line: runs library(devtools) and imports specific targets functions as an alias for brevity.
if (tryCatch(endsWith(readLines("DESCRIPTION", 1), "ML"), warning = function(x) F)) {
#library(devtools, quietly = T)
cat("loading devtools & select targets functions\n")
box::use(
devtools[...],
targets[make = tar_make,
make_future = tar_make_future,
grab = tar_read]
)
}
```{=html}
Written by Donald Seinen
2869830202@qq.com
```
D-Se/ML documentation built on April 1, 2022, 10:53 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
title: "Onboarding" output: rmarkdown::html_vignette: toc: true number_sections: true
body .main-container { max-width: 1280px !important; width: 1280px !important; } body { max-width: 1280px !important; } h1 { text-align: center; } table { width: 50%; text-align: center; }Welcome!
Hi there! Glad to have you on board. This is a guide to help you help us. If anything is unclear, questions can be asked by posting on Github Discussions, or by sending an email to r paste0(gsub("[<>]", "", strsplit(maintainer("ML"), "<")[[1]] |> trimws()), collapse = " at: ").
Project Goals
-
Deliver a scalable, reproducible and continuously integrated data analysis compendium that:
-
Deploys Machine Learning algorithms,
-
Visualizes model performances & statistical inference,
-
Communicates findings to target audiences in multiple formats.
-
-
Display best practices at the edge of machine learning knowledge using R, placing importance on:
-
Packaging approaches using version control,
-
Deploying computationally efficient algorithms,
-
Interfacing modelling & reporting API's,
-
Adopting a functional programming mindset.
-
Project deliverables
-
A data analysis report in pdf format,
-
An R package containing
target-basedtidymodelsapproach, -
A pipeline-generated
bookdownbook, -
Github repository of mock firm & analysis.
Meet the team
```{=html} div.green { background-color:#93ed99; border-radius: 5px; padding: 20px;}
::: green
<center>
We are currently <B> recruiting! </B>
</center>
:::
+---------------+------------------------+------------------------------------------------------------------------------------+
| Name | School | Responsibilities |
+:=============:+:======================:+:==================================================================================:+
| Donald Seinen | Information Management | Project Management \| Quality Assurance \| Modelling \| Visualization \| Reporting |
+---------------+------------------------+------------------------------------------------------------------------------------+
| ... | ... | ... |
+---------------+------------------------+------------------------------------------------------------------------------------+
| ... | ... | ... |
+---------------+------------------------+------------------------------------------------------------------------------------+
| ... | ... | ... |
+---------------+------------------------+------------------------------------------------------------------------------------+
| ... | ... | ... |
+---------------+------------------------+------------------------------------------------------------------------------------+
## Getting started
1. Sign the [non-disclose agreement](https://github.com/D-Se/ML/tree/master/inst) (Only in effect until **August 31, 2022**).
2. Install project tools and dependencies by running `devtools::install_github("D-Se/ML")` in the R console.
3. Connect RStudio to github ([guide](https://happygitwithr.com/rstudio-git-github.html)).
4. Fork the Github repository.
5. Find [issues](https://github.com/D-Se/ML/issues) to solve (picked by you or assigned to you).
# About the project
## Tools
This project uses **R & C++** for core development, **HTML, CSS & JavaScript** for reporting.
## R packages
This is a (partial) list of packages used in the project at various stages:
### Data wrangling
| Package | Description | Resource |
|--------------|--------------------|----------|
| `dplyr` | Data wrangling | |
| `data.table` | Speedy data.frames | |
### Reporting packages
+--------------+---------------------------+-----------------------------------------------+
| Package | Description | Resources |
+==============+===========================+===============================================+
| `ggplot2` | Data visualization | |
+--------------+---------------------------+-----------------------------------------------+
| `plotly` | Interactive plots | |
+--------------+---------------------------+-----------------------------------------------+
| `visNetwork` | Ntwork graphs | |
+--------------+---------------------------+-----------------------------------------------+
| `rmarkdown` | Markdown with an R accent | [Book](https://bookdown.org/yihui/rmarkdown/) |
+--------------+---------------------------+-----------------------------------------------+
| `bookdown` | | |
+--------------+---------------------------+-----------------------------------------------+
| `shiny` | Web application framework | |
+--------------+---------------------------+-----------------------------------------------+
### Modelling packages
+--------------+------------------------------------------+------------------------------------------------------------------------+
| Package | Description | Resources |
+==============+==========================================+========================================================================+
| `keras` | APi to `tensorflow` neural nets | [keras](https://keras.io/) ; [TensorFlow](https://www.tensorflow.org/) |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `rpart` | Regression trees | |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `glmnet` | Penalized maximum likelihood models | |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `earth` | Multivariate Adaptive Regression Splines | |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `rsample` | Data sampling | |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `recipes` | Pre-processor specification | [Book](https://www.tmwr.org/) |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `parsnip` | Model specification | |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `tune` | Hyper parameter tuning | |
+--------------+------------------------------------------+------------------------------------------------------------------------+
| `workflows` | | |
+--------------+------------------------------------------+------------------------------------------------------------------------+
### Package development
+---------------+--------------------------------+----------------------------------------------------------------------+
| Package | Description | Resources |
+===============+================================+======================================================================+
| `targets` | Make-like pipeline toolkit | [Book](https://books.ropensci.org/targets/) |
+---------------+--------------------------------+----------------------------------------------------------------------+
| `tarchetypes` | Function-oriented `targets` | |
+---------------+--------------------------------+----------------------------------------------------------------------+
| `rlang` | Tidyverse programming toolbox | |
+---------------+--------------------------------+----------------------------------------------------------------------+
| `testthat` | Unit testing | [Book](https://r-pkgs.org/tests.html) |
+---------------+--------------------------------+----------------------------------------------------------------------+
| `lifecycle` | function life cycle management | |
+---------------+--------------------------------+----------------------------------------------------------------------+
| `future` | Parallel processing framework | |
+---------------+--------------------------------+----------------------------------------------------------------------+
| `promises` | Asynchronous programming | [website](https://rstudio.github.io/promises/articles/overview.html) |
+---------------+--------------------------------+----------------------------------------------------------------------+
| `crayon` | Pretty console printing | |
+---------------+--------------------------------+----------------------------------------------------------------------+
## Github repo
The [Github repo](https://github.com/D-Se/ML) is where the project lives. In it, you can find the following files. This may change in future releases.
```{verbatim}
├── .github >>> github workflows, automated checking
├── R/ >>> directory for ML package functions.
├──── ... >>> regular R package functions. o sub-directory allowed.
├── inst/ >>> supplementary materials for ML package
├── man/ >>> automatically generated package documentation [NO EDIT]
├──── figures/ >>> images used
├── src/ >>> compiled (C++) code used in ML package
├── tests/ >>> unit tests for package functions
├──── testthat/ >>> directory of unit test
├── vignettes/ >>> long-form documentation
├── .Rbuildignore/ >>> package utility: instruction set to ignore non-standard files
├── .gitignore >>> instruction set to RStudio to ignore files in directory for version control
├── DESCRIPTION >>> R package metadata file
├── ML.Rproj >>> RStudio file [NO EDIT]
├── NAMESPACE >>> automatically generated R package build file [NO EDIT]
├── README.Rmd >>> Github introduction file with executable R code
├── README.md >>> automatically generated Github introduction file [NO EDIT]
├── run.sh >>> shell script to execture pipeline
├── run.R >>>
├── _targets.R # core pipeline
└── report.Rmd
The project is split up into segments, each ending with finishing a deliverable. These steps can be found in the milestones section of the repo. The milestones are made up of collections of issues. These are tasks assigned to project members, and can be added by any person with access rights to the repo. labels are placed on issues to make it easy to find and assign tasks. Each issue is allocated a difficulty and priority label, and further labels were appropriate.
External Resources - learning & finding help
R resources
Visualization
Modelling
Reporting
Productivity tips
A list of useful version controlled R project commands:
-
devtools::document()to update (locally) the package usingroxygen2. -
lintr::lint_dir()to use static code analysis for code formatting checks.
lintr is useful to check code style line-by-line and make adjustments on the fly. It will open an interface in the RStudio Markers pane where individual lines can be clicked. On a click, the relevant code snippet will be opened in the editor. This command is also run automatically on commits to the master branch of Github repo, viewable as comments in the Github Actions pane.
-
covr::report()for visual inspection of unit test coverage. -
usethis::edit_r_profile()to run functions on startup.
An example of Donald's ML-project specific R profile line: runs library(devtools) and imports specific targets functions as an alias for brevity.
if (tryCatch(endsWith(readLines("DESCRIPTION", 1), "ML"), warning = function(x) F)) {
#library(devtools, quietly = T)
cat("loading devtools & select targets functions\n")
box::use(
devtools[...],
targets[make = tar_make,
make_future = tar_make_future,
grab = tar_read]
)
}
```{=html}
Written by Donald Seinen
2869830202@qq.com
```
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.