Last updated: 2025-06-04

Checks: 7 0

Knit directory: muse/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(20200712) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 32deb8e. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rproj.user/
    Ignored:    data/1M_neurons_filtered_gene_bc_matrices_h5.h5
    Ignored:    data/293t/
    Ignored:    data/293t_3t3_filtered_gene_bc_matrices.tar.gz
    Ignored:    data/293t_filtered_gene_bc_matrices.tar.gz
    Ignored:    data/5k_Human_Donor1_PBMC_3p_gem-x_5k_Human_Donor1_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
    Ignored:    data/5k_Human_Donor2_PBMC_3p_gem-x_5k_Human_Donor2_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
    Ignored:    data/5k_Human_Donor3_PBMC_3p_gem-x_5k_Human_Donor3_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
    Ignored:    data/5k_Human_Donor4_PBMC_3p_gem-x_5k_Human_Donor4_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
    Ignored:    data/97516b79-8d08-46a6-b329-5d0a25b0be98.h5ad
    Ignored:    data/Parent_SC3v3_Human_Glioblastoma_filtered_feature_bc_matrix.tar.gz
    Ignored:    data/brain_counts/
    Ignored:    data/cl.obo
    Ignored:    data/cl.owl
    Ignored:    data/jurkat/
    Ignored:    data/jurkat:293t_50:50_filtered_gene_bc_matrices.tar.gz
    Ignored:    data/jurkat_293t/
    Ignored:    data/jurkat_filtered_gene_bc_matrices.tar.gz
    Ignored:    data/pbmc20k/
    Ignored:    data/pbmc20k_seurat/
    Ignored:    data/pbmc3k.h5ad
    Ignored:    data/pbmc3k/
    Ignored:    data/pbmc3k_bpcells_mat/
    Ignored:    data/pbmc3k_seurat.rds
    Ignored:    data/pbmc4k_filtered_gene_bc_matrices.tar.gz
    Ignored:    data/pbmc_1k_v3_filtered_feature_bc_matrix.h5
    Ignored:    data/pbmc_1k_v3_raw_feature_bc_matrix.h5
    Ignored:    data/refdata-gex-GRCh38-2020-A.tar.gz
    Ignored:    data/seurat_1m_neuron.rds
    Ignored:    data/t_3k_filtered_gene_bc_matrices.tar.gz
    Ignored:    r_packages_4.4.1/
    Ignored:    r_packages_4.5.0/

Untracked files:
    Untracked:  Nothobranchius_furzeri.Nfu_20140520.113.gtf.gz
    Untracked:  analysis/bioc_scrnaseq.Rmd
    Untracked:  bpcells_matrix/
    Untracked:  data/GCF_043380555.1-RS_2024_12_gene_ontology.gaf.gz
    Untracked:  m3/
    Untracked:  pbmc3k_before_filtering.rds
    Untracked:  pbmc3k_save_rds.rds
    Untracked:  rsem.merged.gene_counts.tsv

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/trace.Rmd) and HTML (docs/trace.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 32deb8e Dave Tang 2025-06-04 Tracing functions

?trace:

Interactive Tracing and Debugging of Calls to a Function or Method

A call to trace allows you to insert debugging code (e.g., a call to browser or recover) at chosen places in any function. A call to untrace cancels the tracing. Specified methods can be traced the same way, without tracing all calls to the generic function. Trace code (tracer) can be any R expression. Tracing can be temporarily turned on or off globally by calling tracingState.

trace() is a built-in R function that lets you temporarily insert code into another function. It’s useful for:

  • Debugging
  • Logging when a function is called
  • Peeking into function arguments
  • Finding out who called what, and when

It works even on functions inside packages without editing package source code.

Usage:

trace(what, tracer, exit, at, print, signature, where = topenv(parent.frame()), edit = FALSE)

You can get started by setting two arguments:

  • what: the function you want to trace
  • tracer: the code you want to run when that function is called

See when a function is called

The example below will print “sq was called!” when sq() is called. After printing, the function will operate as usual.

sq <- function(x){
  x^2
}

trace("sq", tracer = quote(print("sq was called!")))
[1] "sq"
sq(4)
Tracing sq(4) on entry 
[1] "sq was called!"
[1] 16

See what arguments were passed

You can inspect the function’s arguments inside the tracer by using args() or directly printing them.

trace("sq", tracer = quote({
  print(paste("x is", x))
}))
[1] "sq"
sq(5)
Tracing sq(5) on entry 
[1] "x is 5"
[1] 25

Trace a base R function

You can also trace functions from packages.

trace("mean", tracer = quote(print("mean() was called!")))
Tracing function "mean" in package "base"
[1] "mean"
mean(c(1, 2, 3))
Tracing mean(c(1, 2, 3)) on entry 
[1] "mean() was called!"
[1] 2

Calling mean() from another function.

trace("mean", tracer = quote(print("mean() was called!")))
Tracing function "mean" in package "base"
[1] "mean"
my_mean <- function(x){
  mean(x)
}

my_mean(1:10)
Tracing mean(x) on entry 
[1] "mean() was called!"
[1] 5.5

Calling base::mean().

trace("mean", tracer = quote(print("mean() was called!")))
Tracing function "mean" in package "base"
[1] "mean"
base::mean(1:10)
Tracing base::mean(1:10) on entry 
[1] "mean() was called!"
[1] 5.5

Trace a function inside a package namespace

trace("kmeans") with a namespace.

trace("kmeans", tracer = quote(print("stats::kmeans was called")), where = asNamespace("stats"))
Tracing function "kmeans" in package "namespace:stats"
[1] "kmeans"
k <- kmeans(iris[, 1:4], centers = 3)

Calling stats::kmeans().

k <- stats::kmeans(iris[, 1:4], centers = 3)
Tracing stats::kmeans(iris[, 1:4], centers = 3) on entry 
[1] "stats::kmeans was called"

Removing a trace

When done debugging, remove the trace.

untrace("sq")
untrace("mean")
Untracing function "mean" in package "base"
untrace("kmeans", where = asNamespace("stats"))
Untracing function "kmeans" in package "namespace:stats"

sessionInfo()
R version 4.5.0 (2025-04-11)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.2 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] lubridate_1.9.4 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
 [5] purrr_1.0.4     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
 [9] ggplot2_3.5.2   tidyverse_2.0.0 workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] sass_0.4.10        generics_0.1.4     stringi_1.8.7      hms_1.1.3         
 [5] digest_0.6.37      magrittr_2.0.3     timechange_0.3.0   evaluate_1.0.3    
 [9] grid_4.5.0         RColorBrewer_1.1-3 fastmap_1.2.0      rprojroot_2.0.4   
[13] jsonlite_2.0.0     processx_3.8.6     whisker_0.4.1      ps_1.9.1          
[17] promises_1.3.2     httr_1.4.7         scales_1.4.0       jquerylib_0.1.4   
[21] cli_3.6.5          rlang_1.1.6        withr_3.0.2        cachem_1.1.0      
[25] yaml_2.3.10        tools_4.5.0        tzdb_0.5.0         httpuv_1.6.16     
[29] vctrs_0.6.5        R6_2.6.1           lifecycle_1.0.4    git2r_0.36.2      
[33] fs_1.6.6           pkgconfig_2.0.3    callr_3.7.6        pillar_1.10.2     
[37] bslib_0.9.0        later_1.4.2        gtable_0.3.6       glue_1.8.0        
[41] Rcpp_1.0.14        xfun_0.52          tidyselect_1.2.1   rstudioapi_0.17.1 
[45] knitr_1.50         farver_2.1.2       htmltools_0.5.8.1  rmarkdown_2.29    
[49] compiler_4.5.0     getPass_0.2-4