condathis

R-CMD-check CRAN status

Run system command line interface (CLI) tools in a reproducible and isolated environment within R.

Get started

Install the release version of the package from CRAN:

install.packages("condathis")

Install package from R-Universe:

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

Installing the development version

remotes::install_github("luciorq/condathis")
# or
pak::pkg_install("github::luciorq/condathis")

Motivation

One of the main disadvantages of calling CLI tools within R is that they are system-specific. This affects the replicability of your code, making it dependent on the system it’s run on. Additionally, using multiple CLI tools increases the likelihood of encountering version conflicts, where different tools require different versions of the same library. Therefore, relying on system-specific tools within R is generally not recommended.

The package {condathis} lets you call CLI tools within R while keeping things reproducible and isolated.

This means you can use R alongside other tools without the drawback of having system-specific code. It opens up the possibility of creating code and pipelines in R that integrate multiple CLI tools. This is especially useful for bioinformatics and other fields that rely on many software tools for conducting complex analysis.

Reproducibility: An Example

The issue with system

Suppose you’re writing a pipeline or just a script for some analysis, and you want to use fastqc — a program to check the quality of FASTQ files. You’ve installed fastqc and use system2 to run it.

The fastqc command synopsis is fastqc <path-to-fastq-file> -o <output-dir>. The output directory is where fastqc saves its quality control reports.

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

system2(command = "fastqc", args = c(fastq_file, "-o", temp_out_dir))

The fastqc program generates several output files, including a zip file that is 424KB in size. To get information about one of the output files, we can use:

library(fs)
library(dplyr)

file_info(fs::dir_ls(temp_out_dir, glob = "*zip")) |>
  mutate(file_name = path_file(path)) |>
  select(file_name, size)
fastq_file <- system.file("extdata", "sample1_L001_R1_001.fastq.gz", package = "condathis")
temp_out_dir <- file.path(tempdir(), "output")
condathis::create_env(packages = "fastqc==0.11.2", env_name = "fastqc-0.11.2")
condathis::run("fastqc", fastq_file, "-o", temp_out_dir, env_name = "fastqc-0.11.2")

library(fs)
library(dplyr)

file_info(fs::dir_ls(temp_out_dir, glob = "*zip")) |>
  mutate(file_name = path_file(path)) |>
  select(file_name, size)
#> # A tibble: 1 × 2
#>   file_name                             size
#>   <chr>                          <fs::bytes>
#> 1 sample1_L001_R1_001_fastqc.zip        424K

Now, let’s consider the scenario where you share your code with someone else or revisit it yourself after a year. There’s no guarantee the code will run because it relies on a specific CLI tool installed on the system. In the worst case, it might run without throwing any errors but produce different results, so you might not even realize that.

The exact same code run on the same system but with an updated version of fastqc (0.12.1 instead of 0.11.2) generates a different file, and its size is different as well: 446k instead of 424k.

temp_out_dir_2 <- file.path(tempdir(), "output")

condathis::create_env(packages = "fastqc==0.12.1", env_name = "fastqc-0.12.1")
condathis::run("fastqc", fastq_file, "-o", temp_out_dir, env_name = "fastqc-0.12.1")

condathis::remove_env("fastqc-0.12.1")

file_info(fs::dir_ls(temp_out_dir_2, glob = "*zip")) |>
  mutate(file_name = path_file(path)) |>
  select(file_name, size)
#> # A tibble: 1 × 2
#>   file_name                             size
#>   <chr>                          <fs::bytes>
#> 1 sample1_L001_R1_001_fastqc.zip        446K

This discrepancy limits the workflow, pipelines, and scripts to using only R packages!

What can we do about it? We can use {condathis}!

The package {condathis} ensures that the code you share and the results from running fastqc will be consistent across different systems and over time!

The solution with {condathis}

We would first create an isolated environment containing a specific version of the package fastqc (0.12.1). The command automatically manages all the library dependencies of fastqc, making sure that they are compatible with the specific operating system.

condathis::create_env(packages = "fastqc==0.12.1", env_name = "fastqc-env", verbose = "output")
#> ! Environment fastqc-env succesfully created.

Then we run the command inside the environment just created which contains a version 0.12.1 of fastqc.

# dir of output files
temp_out_dir_2 <- file.path(tempdir(), "output")

out <- condathis::run(
  "fastqc", fastq_file, "-o", temp_out_dir_2, # command
  env_name = "fastqc-env" # environment
)

The out object contains info regarding the exit status, standard error, standard output, and timeout if any.

print(out)
#> $status
#> [1] 0
#> 
#> $stdout
#> [1] "application/gzip\nAnalysis complete for sample1_L001_R1_001.fastq.gz\n"
#> 
#> $stderr
#> [1] "Started analysis of sample1_L001_R1_001.fastq.gz\nApprox 90% complete for sample1_L001_R1_001.fastq.gz\n"
#> 
#> $timeout
#> [1] FALSE

In the output temporary directory, fastqcgenerated the output files as expected.

fs::dir_ls(temp_out_dir_2) |>
  basename()
#> [1] "sample1_L001_R1_001_fastqc.html" "sample1_L001_R1_001_fastqc.zip"

The code that we created with {condathis} uses a system CLI tool but is reproducible.

Isolation: an example

Another key feature of {condathis} is the ability to run CLI tools in independent, isolated environments. This allows you to run packages within R that would have conflicting dependencies. This makes it possible for {condathis} to run two versions of the same CLI tool simultaneously!

For example, the system’s curl is of a specific version:

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"

However, we can choose to use a different version of curl run in a different environment. Here, for example, we are installing a different version of curl in a separate environment, and checking the version of the newly installed curl.

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

out <- condathis::run(
  "curl", "--version",
  env_name = "curl-env" # environment
)

message(out$stdout)
#> curl 8.10.1 (aarch64-apple-darwin20.0.0) libcurl/8.10.1 OpenSSL/3.5.0 (SecureTransport) zlib/1.3.1 zstd/1.5.7 libssh2/1.11.1 nghttp2/1.64.0
#> 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 isolation feature of {condathis} allows not only running different versions of the same CLI tools but also different tools that have incompatible dependencies. One common example is CLI tools that rely on different versions of Python.

Details

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. The package processx runs external processes and manages their input and output, ensuring that commands to micromamba are executed correctly from within R. The package withr temporarily modifies environment variables and settings, allowing micromamba to run smoothly without permanently altering your R environment.

Known limitations

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