Last updated: 2019-04-10
Checks: 6 0
Knit directory: dsc-linreg/analysis/
This reproducible R Markdown analysis was created with workflowr (version 1.2.0.9000). The Report tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(1) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .sos/
Ignored: analysis/.sos/
Ignored: dsc/.sos/
Unstaged changes:
Modified: dsc/linreg.dsc
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | bc841c9 | Peter Carbonetto | 2019-04-10 | wflow_publish(“usage_instructions.Rmd”) |
| html | 9b1d649 | Peter Carbonetto | 2019-04-10 | Fixed formatting in usage_instructions page. |
| Rmd | 1abb0e4 | Peter Carbonetto | 2019-04-10 | wflow_publish(“usage_instructions.Rmd”) |
| Rmd | f809da9 | Peter Carbonetto | 2019-04-10 | wflow_publish(“usage_instructions.Rmd”) |
| html | 93d37ac | Peter Carbonetto | 2019-04-09 | Adjusted links in workflowr pages. |
| Rmd | f8959f4 | Peter Carbonetto | 2019-04-09 | wflow_publish(c(“index.Rmd”, “usage_instructions.Rmd”)) |
| html | e54452c | Peter Carbonetto | 2019-04-09 | Made a few small revisions to the usage instructions. |
| Rmd | 070f004 | Peter Carbonetto | 2019-04-09 | wflow_publish(“usage_instructions.Rmd”) |
| Rmd | d4d0415 | Peter Carbonetto | 2019-04-09 | Re-organized some files, and moved usage instructions to workflowr page. |
Please follow these steps to start using the linear regression DSC.
First, install DSC.
Next, install R, as well as the R packages used. The devtools and glmnet packages can be installed from CRAN:
install.packages("devtools")
install.packages("glmnet")
install.packages("ggplot2")
install.packages("cowplot")The susieR, varbvs and dscrutils packages can be installed from GitHub using devtools:
library(devtools)
install_github("stephenslab/susieR")
install_github("pcarbo/varbvs",subdir = "varbvs-R")
install_github("stephenslab/dsc",subdir = "dscrutils")Finally, clone or download this git repository.
Now you should have everything you need to run the DSC. Navigate to the “dsc” directory inside your local copy of the git repository, and run it with this command (here we have chosen 4 threads, but you may want to adjust the number of threads to better suit your computer):
dsc -c 4 linreg.dscGo grab a coffee while you wait for the DSC to run. It may take some time, perhaps as long as 30 minutes, for all the DSC pipelines to complete.
Once the DSC has finished running, you may want to work through this short analysis to explore the results of the DSC in R or RStudio. When running the R code, make sure your working directory is set to the “analysis” directory in the git repository.
To recreate and commit the website from the R Markdown files, this can be done using the wflow_publish function from the workflowr package, after setting your working directory to the “analysis” directory:
library(workflowr)
wflow_publish("index.Rmd")
wflow_publish("usage_instructions.Rmd")
wflow_publish("results_overview.Rmd")These lines of R code will populate the “docs” directory with the newly rendered webpages, and commit the changes to the git repository.
sessionInfo()R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/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] workflowr_1.2.0.9000 Rcpp_1.0.0 digest_0.6.17
[4] rprojroot_1.3-2 backports_1.1.2 git2r_0.23.3
[7] magrittr_1.5 evaluate_0.11 stringi_1.2.4
[10] fs_1.2.6 whisker_0.3-2 rmarkdown_1.10
[13] tools_3.4.3 stringr_1.3.1 glue_1.3.0
[16] yaml_2.2.0 compiler_3.4.3 htmltools_0.3.6
[19] knitr_1.20