This article is a practical guide to how to set particular options on R startup. General information on how to manage R package environments is available at solutions.posit.co , and a deeper treatment of R process startup is available in this article.

Here is a summary table of how to control R options and environment variables on startup. More details are below. File Who Controls Level Limitations .Rprofile User or Admin User or Project None, sourced as R code. .Renviron User or Admin User or Project Set environment variables only. Rprofile.site Admin Version of R None, sourced as R code. Renviron.site Admin Version of R Set environment variables only. rsession.conf Admin Server Only RStudio IDE settings, only single repository. repos.conf Admin Server Only for setting repositories.

.Rprofile .Rprofile files are user-controllable files to set options and environment variables. .Rprofile files can be either at the user or project level. User-level .Rprofile files live in the base of the user's home directory, and project-level .Rprofile files live in the base of the project directory.  R will source only one .Rprofile file. So if you have both a project-specific .Rprofile file and a user .Rprofile file that you want to use, you explicitly source the user-level .Rprofile at the top of your project-level .Rprofile with source("~/.Rprofile"). .Rprofile files are sourced as regular R code, so setting environment variables must be done inside a Sys.setenv(key = "value") call.  One easy way to edit your .Rprofile file is to use the usethis::edit_r_profile() function from within an R session. You can specify whether you want to edit the user or project level .Rprofile.

.Renviron .Renviron is a user-controllable file that can be used to create environment variables. This is especially useful to avoid including credentials like API keys inside R scripts. This file is written in a key-value format, so environment variables are created in the format: Key1=value1 Key2=value2 ... And then Sys.getenv("Key1") will return "value1" in an R session. Like with the .Rprofile file, .Renviron files can be at either the user or project level. If there is a project-level .Renviron, the user-level file will not be sourced. The usethis package includes a helper function for editing .Renviron files from an R session with usethis::edit_r_environ().

Rprofile.site and Renviron.site Both .Rprofile and .Renviron files have equivalents that apply server wide. Rprofile.site andRenviron.site (no leading dot) files are managed by admins on Posit Workbench or RStudio Server, and are specific to a particular version of R. The most common settings for these files involve access to package repositories. For example, using the shared-baseline package management strategy is generally done from an Rprofile.site. Users can override settings in these files with their individual .Rprofile files. These files are set for each version of R and should be located in R_HOME/etc/. You can find R_HOME by running the command R.home(component = "home") in a session of that version of R. So, for example, if you find that R_HOME is /opt/R/3.6.2/lib/R, the Rprofile.site for R 3.6.2 would go in /opt/R/3.6.2/lib/R/etc/Rprofile.site.

rsession.conf and repos.conf Posit Workbench and RStudio Server allows server admins to configure particular server-wide R package repositories via the rsession.conf and repos.conf files. Only one repository can be configured in rsession.conf. If multiple repositories are needed, repos.conf should be used. Details on configuring Posit Workbench and RStudio Server with these files are in this support article.

Nowadays, it’s pretty much expected that software comes with an HTTP API interface. Every programming language out there offers a way to expose APIs or make GET/POST/PUT requests, including R. In this post, I’ll show you how to create an API using the plumber package. Plus, I’ll give you tips on how to make it more production ready - I’ll tackle scalability, statelessness, caching, and load balancing. You’ll even see how to consume your API with other tools like python, curl, and the R own httr package

# When an API is started it might take some time to initialize # this function stops the main execution and wait until # plumber API is ready to take queries. wait_for_api <- function(log_path, timeout = 60, check_every = 1) { times <- timeout / check_every for(i in seq_len(times)) { Sys.sleep(check_every) if(any(grepl(readLines(log_path), pattern = "Running plumber API"))) { return(invisible()) } } stop("Waiting timed!") }

Oh, in some examples I am using redis. So, before you dive in, make sure to fire up a simple redis server. At the end of the script, I’ll be turning redis off, so you don’t want to be using it for anything else at the same time. I just want to remind you that this code isn’t meant to be run on a production server.

redis is launched in a background, , so you might want to wait a little bit to make sure it’s fully up and running before moving on.

wait_for_redis <- function(timeout = 60, check_every = 1) { times <- timeout / check_every for(i in seq_len(times)) { Sys.sleep(check_every) status <- suppressWarnings(system2("redis-cli", "PING", stdout = TRUE, stderr = TRUE) == "PONG") if(status) { return(invisible()) } } stop("Redis waiting timed!") }

First off, let’s talk about logging. I try to log as much as possible, especially in critical areas like database accesses, and interactions with other systems. This way, if there’s an issue in the future (and trust me, there will be), I should be able to diagnose the problem just by looking at the logs alone. Logging is like “print debugging” (putting print(“I am here”), print(“I am here 2”) everywhere), but done ahead of time. I always try to think about what information might be needed to make a correct diagnosis, so logging variable values is a must. The logger and glue packages are your best friends in that area.

Next, it might also be useful to add a unique request identifier ((I am doing that in setuuid filter)) to be able to track it across the whole pipeline (since a single request might be passed across many functions). You might also want to add some other identifiers, such as MACHINE_ID - your API might be deployed on many machines, so it could be helpful for diagnosing if the problem is associated with a specific instance or if it’s a global issue.

In general you shouldn’t worry too much about the size of the logs. Even if you generate ~10KB per request, it will take 100000 requests to generate 1GB. And for the plumber API, 100000 requests generated in a short time is A LOT. In such scenario you should look into other languages. And if you have that many requests, you probably have a budget for storing those logs:)

It might also be a good idea to setup some automatic system to monitor those logs (e.g. Amazon CloudWatch if you are on AWS). In my example I would definitely monitor Error when reading key from cache string. That would give me an indication of any ongoing problems with API cache.

Speaking of cache, you might use it to save a lot of resources. Caching is a very broad topic with many pitfalls (what to cache, stale cache, etc) so I won’t spend too much time on it, but you might want to read at least a little bit about it. In my example, I am using redis key-value store, which allows me to save the result for a given request, and if there is another requests that asks for the same data, I can read it from redis much faster.

Note that you could use memoise package to achieve similar thing using R only. However, redis might be useful when you are using multiple workers. Then, one cached request becomes available for all other R processes. But if you need to deploy just one process, memoise is fine, and it does not introduce another dependency - which is always a plus.

info <- function(req, ...) { do.call( log_info, c( list("MachineId: {MACHINE_ID}, ReqId: {req$request_id}"), list(...), .sep = ", " ), envir = parent.frame(1) ) }

#* Log some information about the incoming request #* https://www.rplumber.io/articles/routing-and-input.html - this is a must read! #* @filter setuuid function(req) { req$request_id <- UUIDgenerate(n = 1) plumber::forward() }

#* Log some information about the incoming request #* @filter logger function(req) { if(!grepl(req$PATH_INFO, pattern = "PATH_INFO")) { info( req, "REQUEST_METHOD: {req$REQUEST_METHOD}", "PATH_INFO: {req$PATH_INFO}", "HTTP_USER_AGENT: {req$HTTP_USER_AGENT}", "REMOTE_ADDR: {req$REMOTE_ADDR}" ) } plumber::forward() }

To run the API in background, one additional file is needed. Here I am creating it using a simple bash script.

library(plumber) library(optparse) library(uuid) library(logger) MACHINE_ID <- "MAIN_1" PORT_NUMBER <- 8761 log_level(logger::TRACE) pr("tmp/api_v1.R") %>% pr_run(port = PORT_NUMBER)