Last updated: 2023-01-12

Checks: 7 0

Knit directory: bioinformatics_tips/

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Repository version: 82a105c

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The results in this page were generated with repository version 82a105c. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

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: 


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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/scripting.Rmd) and HTML (docs/scripting.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 82a105c Dave Tang 2023-01-12 Scripting
html 78910db Dave Tang 2022-04-13 Build site.
Rmd f80e28b Dave Tang 2022-04-13 Script as much as possible

It may already be obvious that writing a script, i.e. a file that runs a series of commands, is much more preferable than having to type those commands manually. For example, instead of manually typing the commands of various tools that process your data, you saved all those commands into a single file and can simply execute that file, which is called a script, to run all the steps. Now you can easily re-run your analysis.

Having all your commands saved into a script makes it easier to re-run your analysis with new data or new settings too. You could edit your script manually to specify the location of the new data or what you should do is write a script that accepts command line arguments. What this means is that instead of hardcoding some value in your script like /data/gene_exp.csv, you assign it as arguments/parameters to your script. If your script is called summarise.sh, you could write your script so that data is passed via the command line.

./summarise.sh /data/gene_exp.csv

You could also include settings/parameters that can be passed to your script, so you can easily re-run your analysis with different settings.

./summarise.sh /data/gene_exp.csv --alpha 0.5 --beta 3

After you have nicely scripted up this analysis, you start working on scripting up another analysis. However, you realise that some steps in your previous analysis are also needed in this analysis. You could use your previous script as a template and modify it for this analysis. But what you should do is include each step in its own separate command line argument accepting script. This way you don’t have to modify two analysis scripts when you need to make changes to an individual step. It may seem annoying to have to write ten separate scripts for an analysis pipeline that has ten steps. But this makes it much easier to maintain in the future, especially when you start building more and more analysis pipelines.

If you have gone this far to set up your work, you can go a bit further to tie everything together using a workflow management system. The benefits of such systems is that it makes it easier to manage your workflows. For example, you could execute your workflow via a queuing system or Google Cloud. You could set up limits for computational resources, restart jobs, cache results, and more.

Now that everything is nicely automated, it is time to include tests, which makes sure your analysis pipeline generates expected results. This should also be automated by using CI/CD, which means that each time you make a change to your pipeline, another pipeline is automatically run to see if your pipeline is running as expected.

Since everything is so nicely set up, you have more time to do what needs to be done! And it wouldn’t have been possible if you didn’t script everything up.


sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8.3     bslib_0.4.1      compiler_4.2.0   pillar_1.7.0    
 [5] later_1.3.0      git2r_0.30.1     jquerylib_0.1.4  tools_4.2.0     
 [9] getPass_0.2-2    digest_0.6.30    jsonlite_1.8.3   evaluate_0.17   
[13] tibble_3.1.7     lifecycle_1.0.3  pkgconfig_2.0.3  rlang_1.0.6     
[17] cli_3.4.1        rstudioapi_0.13  yaml_2.3.6       xfun_0.34       
[21] fastmap_1.1.0    httr_1.4.3       stringr_1.4.0    knitr_1.39      
[25] sass_0.4.1       fs_1.5.2         vctrs_0.5.0      rprojroot_2.0.3 
[29] glue_1.6.2       R6_2.5.1         processx_3.8.0   fansi_1.0.3     
[33] rmarkdown_2.17   callr_3.7.3      magrittr_2.0.3   whisker_0.4     
[37] ps_1.7.2         promises_1.2.0.1 htmltools_0.5.2  ellipsis_0.3.2  
[41] httpuv_1.6.6     utf8_1.2.2       stringi_1.7.6    cachem_1.0.6    
[45] crayon_1.5.2