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Docker is a set of platform as a service (PaaS) products that use OS-level virtualization to deliver software in packages called containers. So it is useful for people who want to share the code, data, and even analysis environment with other people to repeat their analysis and results.

We provide a docker version of tidymass, all the packages in tidymass and the dependent packages have been installed.

Install docker

Please refer to the offical website to download and install docker. And then run docker.

Pull the tidymass image

Open you terminal and then type code below:

docker pull jaspershen/tidymass:latest

Run tidymass docker image

In you terminal, run the code below:

docker run -e PASSWORD=tidymass -p 8787:8787 jaspershen/tidymass:latest

The below command will link the RStudio home folder with the desktop of the local machine running the container. Anything saved or edited in the home folder when using the container will be stored on the local desktop.

docker run -e PASSWORD=tidymass -v ~/Desktop:/home/rstudio/ -p 8787:8787 jaspershen/tidymass:latest

Open the Rstudio server

Then open the browser and visit http://localhost:8787 to power on RStudio server. The user name is rstudio and the password is tidymass.

Demo data and example analysis code

In this tidymass docker image, a folder named “demo_data” is included to help users to learn how to use tidymass.

Open the tidymass_demo.Rmd file in demo_data folder, and then run it code chunk by chunk or just render it by clicking Knit on Rstudio, you will get a reporting result (HTML format) of all the whole workflow.

# Session information

#> R version 4.1.2 (2021-11-01)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur 10.16
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> loaded via a namespace (and not attached):
#>  [1] rstudioapi_0.13   knitr_1.37        magrittr_2.0.2    R6_2.5.1         
#>  [5] ragg_1.2.1        rlang_1.0.1       fastmap_1.1.0     stringr_1.4.0    
#>  [9] tools_4.1.2       xfun_0.29         cli_3.2.0         jquerylib_0.1.4  
#> [13] htmltools_0.5.2   systemfonts_1.0.3 yaml_2.3.4        digest_0.6.29    
#> [17] rprojroot_2.0.2   pkgdown_2.0.2     crayon_1.5.0      textshaping_0.3.6
#> [21] purrr_0.3.4       sass_0.4.0        fs_1.5.2          memoise_2.0.1    
#> [25] cachem_1.0.6      evaluate_0.15     rmarkdown_2.11    stringi_1.7.6    
#> [29] compiler_4.1.2    bslib_0.3.1       desc_1.4.0        jsonlite_1.7.3