Using Crosstalk to Add User-Interactivity

The goal is to link the reactable table I created to a plotly chart and provide additional filter options that control both the table and the chart.

An important note: in order to use crosstalk, you must create a shared dataset and call that dataset within both plotly and reactable. Otherwise, your dataset will not communicate and filter with eachother. The code to do this is SharedData$new(dataset).

If you expand the code below, you’ll see that the code to build a table in reactable is quite extensive. I will not go into the details in this post, but do recommend a couple great tutorials that I used to create the interactive table such as this tutorial from Greg Lin, and this from Tom Mock which really helped me understand how to use CSS and Google fonts to enhance the visual appeal of the table (see the “Additional CSS Used for Table” section below for more info).

If you have ever built something in Shiny before, you’ll notice that the crosstalk filters are very similar. You can add a filter to any existing column in the dataset. As you can see in the code below, I used a mixture of filter_checkbox and filter_select depending on how many unique options were available in the column you’re filtering. My rule of thumb is if there are more than five options to choose from it’s probably better to put them into a list in filter_select like I did with the Division filtering as to not take up too much space on the page.

For the layout of the data visualization, I used bscols to place the crosstalk filters side-by-side with the interactive plotly chart. I then placed the reactable table underneath and added a legend to the table using tags from the htmltools package. The final result is shown below. Feel free to click around and the filters and you will notice that both the plot and the table will filter accordingly. Another option is to drag and click on the plot and you will see the table underneath mimic the teams shown.

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)