How to keep same environment setting among different compter/platform?

[sguo2]

Dear All,

I have performed a great analysis on a HPC cluster platform. I can use sessionInfo() to record all the package version and settings. Now I need to do same work in another platform AWS-clound. Is there any way to easily set as same environment as HPC? I need to keep same setting before the R-version matters for my results.

Conda ? YAML?

Thanks.

Shicheng

> sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 8 (Core)

Matrix products: default
BLAS/LAPACK: /home/sguo2/miniconda3/lib/libopenblasp-r0.3.12.so

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] stats4    parallel  stats     graphics  grDevices utils     datasets
[8] methods   base

other attached packages:
 [1] ape_5.5                biomaRt_2.48.3         mygene_1.28.0
 [4] GenomicFeatures_1.44.2 AnnotationDbi_1.54.1   Biobase_2.52.0
 [7] GenomicRanges_1.44.0   GenomeInfoDb_1.28.2    IRanges_2.26.0
[10] S4Vectors_0.30.0       BiocGenerics_0.38.0    officer_0.4.0
[13] officedown_0.2.2       knitr_1.35             readxl_1.3.1
[16] UniprotR_2.1.0         forcats_0.5.1          stringr_1.4.0
[19] dplyr_1.0.7            purrr_0.3.4            readr_2.0.2
[22] tidyr_1.1.4            tibble_3.1.5           ggplot2_3.3.5
[25] tidyverse_1.3.1

loaded via a namespace (and not attached):
  [1] utf8_1.2.2                  proto_1.0.0
  [3] tidyselect_1.1.1            RSQLite_2.2.8
  [5] htmlwidgets_1.5.4           grid_4.1.0
  [7] BiocParallel_1.26.2         devtools_2.4.2
  [9] airr_1.3.0                  munsell_0.5.0
 [11] chron_2.3-56                withr_2.4.2
 [13] colorspace_2.0-2            filelock_1.0.2
 [15] highr_0.9                   uuid_0.1-4
 [17] rstudioapi_0.13             ggsignif_0.6.3
 [19] MatrixGenerics_1.4.3        GenomeInfoDbData_1.2.6
 [21] bit64_4.0.5                 rprojroot_2.0.2
 [23] vctrs_0.3.8                 generics_0.1.0
 [25] xfun_0.24                   BiocFileCache_2.0.0
 [27] R6_2.5.1                    bitops_1.0-7
 [29] cachem_1.0.6                DelayedArray_0.18.0

[nirgrahamuk]

If you still have access to the original environment this would be the best way
Project Environments • renv (rstudio.github.io)
if you don't have access, it probably still is the best way, but more headaches/manual labour for you.

[sguo2]

Awesome! Thank you, I think renv package is exactly what I need!! @nirgrahamuk

[sguo2]

Any ideas how renv will conflict with conda? or is there any suggestion to avoid conflict since I also use Conda to manage some R-packages. Thanks @nirgrahamuk