9/3/2023 0 Comments R studio commandsFrom the R point of view, the external program is basically a black box. Also complications with signal handling can occur when the command is executed from a parallel (multicore) process in R (which can cause programs to die unexpectedly in some cases).īecause we have no shared memory, there is also no good way for R to inspect or control the command line program while it is running. This means that if we interface with a CLI tool in R and an error appears, the best we can do is to show the output text from the program to the user we cannot programmatically handle errors. Hence, there is no return object, or exception handling if a problem appears in called program. This is all that we get back from executing a system command. When invoked from R, we can capture these text streams, which results in two large strings, in addition to the exit code. The output of a command line program only consists of an exit code (an integer indicating if the execution was successful) and two text streams which the program prints to the screen. This can again lead to bugs when they get joined into the shell command that is invoked by R. You have to consider these names could contain spaces or non-ascii characters. When the user has to provide the command arguments (such as filenames) that are to be passed to the command line program, Most programs that need actual input data will need to read this from file(s) on disk, in some particular format, which can lead to performance overhead or read/write errors. On the other hand, the only input to a shell command is a single string with the command line arguments. You can call such a C/C++ API from an R package, passing data from R objects from memory into the library and back, in a similar way as when calling functions from the package itself. □Ī C/C++ library has formal functions with one or more arguments and a return value, each consisting of typed data structures. Let’s explore these things in a bit more detail. This lack of structure makes it cumbersome to exchange data, handle exceptions, and control the program execution from R. The core issue is that each CLI execution starts a new process.īecause processes don’t share memory, the only interaction with the executing program is via free text streams (pipes) between R and the child process. To programmatically interface with external software, a foreign language API is typically more robust and performant. When possible, it is usually better to use a C, C++ (or even JS, Python…) interface to interact with external software, rather than calling a command-line interface (CLI).Ī CLI is mainly intended for interactive use by a human, with free-form text as input and output. In particular, we compare the base R functions system() and system2(), the sys package and the processx package.īut we start with some words of caution about the limitations of this approach, and explaining why it may be better to not shell out, and try to use an alternative interface if possible. In this post, we have a look at a few options for writing such CLI wrappers in R. some sort of scientific software providing a specific functionality made available though a command-line interface (CLI)? Have you ever found a command-line tool that’s perfect for getting your job done, and wanted to use it from an R script or package?Į.g.
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