shrysr / rbase

Docker for rbase

rbase

This layer contains all the basic R packages required for datascience and ML. A bunch of packages were added to the already extensive default list of packages in MatrixDS’s docker file.

The packages are defined in an R script called packages.R.

This layer takes a tremendously long time to build. A couple of hours on a Macbook Pro 2019, with 6 cores and 32 GB of RAM. One should be careful in assessing whether this layer has to be disturbed. Automated builds on Dockerhub are likely to take even longer.

Note: As such the dockerfile indicates that the packages are called in the last 2 layers only. It may be possible that subsequent image builds do not take as much time as I imagine.

• [ ] It may be easier to find a way to keep the additional packages specified in the rstudio and shiny package list to be in sync.

R package list - BASE

This is a list of the basic packages being installed. These conver many commonly used libraries for data science. This layer will take a Long time to install.

Do not install custom libraries to this layer. Install in the next layer.

#Script for common package installation on MatrixDS docker image
p<-c('nnet','kknn','randomForest','xgboost','tidyverse','plotly','shiny','shinydashboard',
	  'devtools','FinCal','googleVis','DT', 'kernlab','earth',
     'htmlwidgets','rmarkdown','lubridate','leaflet','sparklyr','magrittr','openxlsx',
     'packrat','roxygen2','knitr','readr','readxl','stringr','broom','feather',
     'forcats','testthat','plumber','RCurl','rvest','mailR','nlme','foreign','lattice',
     'expm','Matrix','flexdashboard','caret','mlbench','plotROC','RJDBC','rgdal',
     'highcharter','tidyquant','timetk','quantmod','PerformanceAnalytics','scales',
     'tidymodels','C50', 'parsnip','rmetalog','reticulate','umap', 'glmnet', 'easypackages', 'drake', 'shinythemes', 'shinyjs', 'recipes', 'rsample', 'rpart.plot', 'remotes', 'DataExplorer', 'inspectdf', 'janitor', 'mongolite', 'jsonlite', 'config' )


install.packages(p,dependencies = TRUE)

R Package list - CUSTOM

Add your custom packages to this layer. In this way, only the additional packages are installed in a new layer.

#Script for common package installation on MatrixDS docker image
PKGS <- c(
      "tidyverse", "mapproj", "maps", "genius"
)

install.packages(PKGS, dependencies = TRUE)
devtools::install_github("tidyverse/googlesheets4", dependencies = TRUE)
devtools::install_github("tidyverse/googletrendsR", dependencies = TRUE)

Dockerfile

FROM shrysr/asmith:v1

LABEL maintainer="Shreyas Ragavan <[email protected]>" \
	version="1.0"

#install some helper python packages
RUN pip install sympy numpy

# R Repo, see https://cran.r-project.org/bin/linux/ubuntu/README.html
RUN echo 'deb https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/' >> /etc/apt/sources.list
RUN apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
RUN add-apt-repository ppa:marutter/c2d4u3.5

# R-specific packages
RUN apt-get update \
  && apt-get install -y --no-install-recommends \
    r-base \
    r-base-core \
    r-recommended \
    r-base-dev \
    r-cran-boot \
    r-cran-class \
    r-cran-cluster \
    r-cran-codetools \
    r-cran-foreign \
    r-cran-kernsmooth \
    r-cran-matrix \
    r-cran-rjava \
    r-cran-rpart \
    r-cran-spatial \
    r-cran-survival

COPY packages.R /usr/local/lib/R/packages.R
COPY custom_packages.R /usr/local/lib/R/custom_packages.R

# Install Basic R packages for datascience and ML
RUN R CMD javareconf && \
    Rscript /usr/local/lib/R/packages.R

# Install custom set of R packages. This is on a separate layer for efficient image construction
RUN Rscript /usr/local/lib/R/custom_packages.R

*

YAML for CI

name: Docker Image CI

on: [push]

jobs:

  build:

    runs-on: ubuntu-18.04

    steps:
    - uses: shrysr/rbase@v2
    - name: shiny
      run:  docker build . --file Dockerfile --tag my-image-name:$(date +%s)