README

Tired of system() calls that work on your machine but break everywhere else? condathis is here to help! It lets you create self-contained environments for your command-line tools, so your R code runs reliably for you, your colleagues, and your future self.

A Quick Example

Let’s have some fun with cowpy. With just two commands, we can install and run it in its own isolated environment:

# 1. Install 'cowpy' into an environment named 'cowpy-env'
condathis::create_env(packages = "cowpy", env_name = "cowpy-env")
#> ! Environment cowpy-env succesfully created.

# 2. Run it!
# Example showing redirection from a file to the Standard Input (STDIN)
temp_file <- tempfile()
writeLines("{condathis} is awesome!", temp_file)
condathis::run("cowpy", stdin = temp_file, env_name = "cowpy-env")
#>  _________________________
#> < {condathis} is awesome! >
#>  -------------------------
#>      \   ^__^
#>       \  (oo)\_______
#>          (__)\       )\/\
#>            ||----w |
#>            ||     ||

Maybe you want to try something fancier, like rich-cli for formatting messages?

condathis::create_env(packages = "rich-cli", env_name = "rich-cli-env")
#> ! Environment rich-cli-env succesfully created.

condathis::run(
  "rich", "[b]Condathis[/b] is awesome!", "-p", "-a", "heavy",
  env_name = "rich-cli-env"
)
#> ┏━━━━━━━━━━━━━━━━━━━━━━━┓
#> ┃ Condathis is awesome! ┃
#> ┗━━━━━━━━━━━━━━━━━━━━━━━┛

That’s it! You can now package any command-line tool with your R script, ensuring it works everywhere, every time.

Get Started

install.packages("condathis")

install.packages("condathis", repos = c("https://luciorq.r-universe.dev", getOption("repos")))

Why condathis?

R’s system() and system2() are powerful, but they depend on tools being installed on the host system. This creates a few problems:

{condathis} solves these issues by creating isolated environments for each tool.

Reproducibility: An Example

Let’s say you’re using fastqc for quality control in a bioinformatics pipeline. Different versions of fastqc can produce slightly different results.

fastq_file <- system.file("extdata", "sample1_L001_R1_001.fastq.gz", package = "condathis")
temp_out_dir <- file.path(tempdir(), "output")
fs::dir_create(temp_out_dir)

# Always use fastqc version 0.12.1
condathis::create_env(packages = "fastqc==0.12.1", channels = c("conda-forge", "bioconda"), env_name = "fastqc-0.12.1")
#> ! Environment fastqc-0.12.1 succesfully created.
condathis::run("fastqc", fastq_file, "-o", temp_out_dir, env_name = "fastqc-0.12.1")
#> application/gzip
#> Started analysis of sample1_L001_R1_001.fastq.gz
#> Approx 90% complete for sample1_L001_R1_001.fastq.gz
#> Analysis complete for sample1_L001_R1_001.fastq.gz

Now your analysis will produce the same output files, regardless of where or when it’s run.

Isolation: An Example

Need to use a specific version of a tool like curl that’s different from your system’s version? No problem.

libcurlVersion()
#> [1] "8.7.1"
#> attr(,"ssl_version")
#> [1] "SecureTransport (LibreSSL/3.3.6)"
#> attr(,"libssh_version")
#> [1] ""
#> attr(,"protocols")
#>  [1] "dict"    "file"    "ftp"     "ftps"    "gopher"  "gophers" "http"
#>  [8] "https"   "imap"    "imaps"   "ldap"    "ldaps"   "mqtt"    "pop3"
#> [15] "pop3s"   "rtsp"    "smb"     "smbs"    "smtp"    "smtps"   "telnet"
#> [22] "tftp"

condathis::create_env(
  packages = "curl==8.10.1",
  env_name = "curl-env"
)
#> ! Environment curl-env succesfully created.

# Handling output thorough R objects
out <- condathis::run(
  "curl", "--version",
  env_name = "curl-env",
  verbose = "silent"
)

message(out$stdout)
#> curl 8.10.1 (aarch64-apple-darwin20.0.0) libcurl/8.10.1 OpenSSL/3.5.4 (SecureTransport) zlib/1.3.1 zstd/1.5.7 libssh2/1.11.1 nghttp2/1.67.0
#> Release-Date: 2024-09-18
#> Protocols: dict file ftp ftps gopher gophers http https imap imaps ipfs ipns mqtt pop3 pop3s rtsp scp sftp smb smbs smtp smtps telnet tftp ws wss
#> Features: alt-svc AsynchDNS GSS-API HSTS HTTP2 HTTPS-proxy IPv6 Kerberos Largefile libz MultiSSL NTLM SPNEGO SSL threadsafe TLS-SRP UnixSockets zstd

This allows you to run tools with conflicting dependencies side-by-side without any issues.

How It Works

The package {condathis} relies on micromamba to bring reproducibility and isolation. micromamba is a lightweight, fast, and efficient package manager that “does not need a base environment and does not come with a default version of Python”.

The integration of micromamba into R is handled using the processx and withr packages.

Known Caveats

Special characters in CLI commands are interpreted as literals and not expanded.

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