jmw86069 / jamsession

Manage R sessions, objects, project-specific functions

jamsession

The goal of jamsession is to help manage

• multiple R sessions,
• associated R data objects, and
• custom R functions.

Overview

Load custom R functions on the fly.

refresh_functions("project")

Functions are loaded inside a temporary R package, separate from your R workspace.

Save and re-use R objects across projects.

save_object("some_fancy_object")

load_object("some_fancy_object")

Objects are loaded into the user environment, but can be loaded into a separate environment if needed.

Save R session for rapid re-use in future.

save_jamsession("myproject")

load_jamsession("myproject")

The full R session and R history is loaded (if available) into the user workspace. It can be loaded into a separate environment if needed, in which case the history is not loaded.

How to install

# install.packages(remotes)
remotes::install_github("jmw86069/jamsession")

Who might use jamsession?

Have you ever been asked a follow-up question about your R analysis results?

• Did it involve repeating the R workflow, or loading the R session from an .RData file?

Do you sometimes create useful R data objects that you later re-use in other R analysis projects?

• And did that R object data not fit comfortably into a text file?

Do you create and modify custom functions for your projects, before they may (or may not) become part of an R package?

• And do you need a safe method of refreshing the function while you make updates to the function?

My answers are YES, YES, YES! (And YES for each of the sub-questions.)

The solution

R sessions

• Central place to store R sessions.
• Convenient way to find past R sessions, re-load, then perform follow-up analysis work.
• Option to load an R session inside an environment (protect your workspace as needed)

R data objects

• Central place to store important, re-usable R data objects.
• Convenient way to load saved R data object into any active R session.
• Option to load R object inside an environment (protect your workspace as needed)

Custom R functions

• Central place to store R files with custom functions.
• Convenient way to load and refresh R functions.
• Option to document functions using roxygen2.

Package Reference

A full online function reference is available via the pkgdown documentation:

Full jamsession command reference

Background

R projects

For me, it is routine to get asked follow-up questions, sometimes a year or more after the original analysis. Sometimes a manuscript reviewer asked us to re-cluster data with different distance metric; or make the font size larger on a figure; or try a new method on the results…

I need a rapid way to re-load a past R session, with all the supporting R objects in that session, so I can continue work right where I left off.

First time analyzing data for a new project…

library(jamsession);
## Some analysis work
lotty_dah <- data.frame(A=1:10, B=LETTERS[1:10]);

## Save the project at the end
save_jamsession("LottyDah");
#> ##------ Fri Jun 12 14:09:02 2020 ------##
#> ##  (14:09:02) 12Jun2020:  save_jamsession(): Saved jamsession 'LottyDah' to sessions_path '~/Projects/R-sessions' 
#> ##  (14:09:03) 12Jun2020:  save_jamsession(): session_file:/Users/wardjm/Projects/R-sessions/inProgress_LottyDah_12jun2020.RData

Deliver results to Dr. Dah, who is very excited, reviews results for a few weeks, then responds, “Hey what about C?” Meanwhile, I’ve been working on other projects, but I can answer with confidence: “Sure thing, let me just load the project and look into C.”

## Look for Lotty's project
grep_jamsessions("Lotty");

	session	save_date	days_old	file_size	session_path	session_file
LottyDah	LottyDah	12jun2020	0	1.1 kb	/Users/wardjm/Projects/R-sessions	inProgress_LottyDah_12jun2020.RData

#> "LottyDah"

load_jamsession("LottyDah");
#> ##  (14:09:03) 12Jun2020:  load_jamsession(): Loading session: /Users/wardjm/Projects/R-sessions/inProgress_LottyDah_12jun2020.RData 
#> ##  (14:09:03) 12Jun2020:  load_jamsession(): Loaded session:LottyDah into environment 'R_GlobalEnv' 
#> ##  (14:09:03) 12Jun2020:  load_jamsession(): jamba::setPrompt()

## booyah
lotty_dah$C <- letters[1:10];

## Save the updated project
save_jamsession();
#> ##------ Fri Jun 12 14:09:03 2020 ------##
#> ##  (14:09:03) 12Jun2020:  save_jamsession(): Saved jamsession 'LottyDah' to sessions_path '~/Projects/R-sessions' 
#> ##  (14:09:03) 12Jun2020:  save_jamsession(): session_file:/Users/wardjm/Projects/R-sessions/inProgress_LottyDah_12jun2020.RData

Dr. Lotty Dah I’m sure is thinking: “Wow that was fast, thanks so much!”

R data objects

I also very often create very useful reference data, that is not easy to save in a text file.

For example, Bioconductor package GenomicRanges defines an object GenomicRangesList that can store gene exons, genomic coordiantes, and associated annotations at the gene, and gene-exon level. It takes non-trivial time to build a proper data object, and is not easily stored in a text file. (Ask a Bioinformatician about GTF and GFF3 files…)

Once the data is built, I really just want to save the R object directly, and re-use it in subsequent R sessions. I do this a lot.

The code below is not evaluated, but used as an example.

## Load Bioconductor package GenomicFeatures
library(GenomicFeatures)

## This step takes a few minutes
txdb_hg19_v32 <- GenomicFeatures::makeTxDbFromGFF("Gencode_hg19_v32.gtf")

## This step may also take a few minutes
exonsByGene_hg19_v32 <- GenomicFeatures::exonsBy(txdb_hg19_v32, by="gene")

## Add gene_id to each exon
values(exonsByGene_hg19_v32@unlistData)$gene_id <- rep(names(exonsByGene_hg19_v32),
   lengths(exonsByGene_hg19_v32));

## Some more fancy annotation stuff

## Whoa finally, save this result!
save_object("exonsByGene_hg19_v32");

For another project, I confidently say to myself: “Hey let me just re-use that cool gene-exon data I spent the time to make. Hmm… What did I call that?”

## Let me look for exonsByGene
grep_objects("exonsByGene");
#> "exonsByGene_hg19_v27"
#> "exonsByGene_hg19_v28"
#> "exonsByGene_hg19_v32"
#> "exonsByGene_mm10_vM17"
#> "exonsByGene_mm10_vM24"

## Oh yeah I need hg19 version 32
load_object("exonsByGene_hg19_v32");

## Data is loaded in a few seconds

Custom R functions

Like many R analysts, I realize that a custom R function can make a lot of workflows much cleaner and more consistent. For a complex series of steps, I usually create an R function to perform those steps, then call the R function each time it’s needed.

Sadly, I never create the perfect function the first time. I usually add features as they occur to me, or as I notice the many bugs in that function. (The majority of my code seems to be error-checking.) So I need a fast, reliable way to update the R function, refresh in my environment, then try again.

I create a file, "myproject_functions.R", and a custom function mydim() to that file.

Note that for this example, I save the file in tempdir(), then I point that path with argument functions_path=tempdir(). For routine use, functions_path is already defined, and I save all my R functions to one known file path.

## Create custom function
mydim <- function(x){dim(x)}

## Save it to a file
fn_file <- file.path(tempdir(), "myproject_functions.R");
dump(c("mydim"), file=fn_file);
rm(mydim);

## Now I can refresh_functions() to load that file
refresh_functions("myproject", functions_path=tempdir());
#> ##  (14:09:04) 12Jun2020:  refresh_functions(): Loaded temporary package as 'myproject'

Now what’s nice is that you can see the package using search(), and use find() to find the function to confirm.

## See "myproject" as one of the packages
print(search())
#>  [1] ".GlobalEnv"           "devtools_shims"       "package:myproject"   
#>  [4] "package:S4Vectors"    "package:BiocGenerics" "package:parallel"    
#>  [7] "package:stats4"       "package:matrixStats"  "package:colorspace"  
#> [10] "package:farver"       "package:rstudioapi"   "package:crayon"      
#> [13] "package:jamsession"   "package:stats"        "package:graphics"    
#> [16] "package:grDevices"    "package:utils"        "package:datasets"    
#> [19] "package:methods"      "Autoloads"            "package:base"

## use find() to locate mydim()
find("mydim")
#> [1] "package:myproject"

## Run the function
mydim(10);
#> NULL

## OOPS I forgot dim() doesn't work on vectors...

Oops, I realize mydim() should handle vector different than data.frame. So I edit "myproject_functions.R" and edit the mydim() function.

mydim <- function(x){adim <- dim(x);if (length(adim) == 0) {length(x)} else {adim}}
fn_file <- file.path(tempdir(), "myproject_functions.R");
dump(c("mydim"), file=fn_file);
rm(mydim);

refresh_functions("myproject", functions_path=tempdir());
#> ##  (14:09:05) 12Jun2020:  refresh_functions(): Loaded temporary package as 'myproject'

mydim(10);
#> [1] 1

mydim(matrix(1:4, ncol=2))
#> [1] 2 2

There are other convenient benefits. If you include roxygen2 help documentation for your function, it can be accessed with ? mydim

What if you rename the function? Not a problem, the package is reloaded and the previous function name is no longer present.

mysize <- function(x){adim <- dim(x);if (length(adim) == 0) {length(x)} else {adim}}
dump(c("mysize"), file=fn_file);
rm(mysize);

refresh_functions("myproject", functions_path=tempdir());
#> ##  (14:09:05) 12Jun2020:  refresh_functions(): Loaded temporary package as 'myproject'

find("mydim");
#> character(0)

find("mysize");
#> [1] "package:myproject"

Notice that mydim() is no longer found, but mysize() is found.