diff --git a/.nojekyll b/.nojekyll index e94381c..c654a92 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -855481b6 \ No newline at end of file +40a0b9c0 \ No newline at end of file diff --git a/CHANGELOG.html b/CHANGELOG.html index 720f1de..a2f8557 100644 --- a/CHANGELOG.html +++ b/CHANGELOG.html @@ -106,7 +106,15 @@
  • - Reproduction + Reproduce Figures 2-4 and in-text results 1-3 +
    • +
  • + + Reproduce Figure 5 +
    • +
  • + + Reproduce supplementary figure
    • diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html index 7d1b43c..be92bc8 100644 --- a/CONTRIBUTING.html +++ b/CONTRIBUTING.html @@ -106,7 +106,15 @@
  • - Reproduction + Reproduce Figures 2-4 and in-text results 1-3 +
    • +
  • + + Reproduce Figure 5 +
    • +
  • + + Reproduce supplementary figure
    • diff --git a/evaluation/artefacts.html b/evaluation/artefacts.html index f41d866..f9c6586 100644 --- a/evaluation/artefacts.html +++ b/evaluation/artefacts.html @@ -126,7 +126,15 @@
  • - Reproduction + Reproduce Figures 2-4 and in-text results 1-3 +
    • +
  • + + Reproduce Figure 5 +
    • +
  • + + Reproduce supplementary figure
    • diff --git a/evaluation/badges.html b/evaluation/badges.html index b0881c8..b511cc4 100644 --- a/evaluation/badges.html +++ b/evaluation/badges.html @@ -160,7 +160,15 @@
  • - Reproduction + Reproduce Figures 2-4 and in-text results 1-3 +
    • +
  • + + Reproduce Figure 5 +
    • +
  • + + Reproduce supplementary figure
    • @@ -258,7 +266,7 @@

    Journal badges

    Caveat: Please note that these criteria are based on available information about each badge online, and that we have likely differences in our procedure (e.g. allowed troubleshooting for execution and reproduction, not under tight time pressure to complete). Moreover, we focus only on reproduction of the discrete-event simulation, and not on other aspects of the article. We cannot guarantee that the badges below would have been awarded in practice by these journals.

    Criteria

    -
    +
    Code 
    from IPython.display import display, Markdown
@@ -391,7 +399,7 @@ 
    Criteria

    Badges

    -
    +
    Code 
    # Full badge names
diff --git a/evaluation/reflections.html b/evaluation/reflections.html
index ae4b2a4..7d6a9df 100644
--- a/evaluation/reflections.html
+++ b/evaluation/reflections.html
@@ -106,7 +106,15 @@
     
         
  • 
     
- Reproduction
+ Reproduce Figures 2-4 and in-text results 1-3
+  
    •   
+        
  • 
+    
+ Reproduce Figure 5
+  
    •   
+        
  • 
+    
+ Reproduce supplementary figure
   
    •   
     
   
diff --git a/evaluation/reporting.html b/evaluation/reporting.html
index 9cd7a1c..19e45ca 100644
--- a/evaluation/reporting.html
+++ b/evaluation/reporting.html
@@ -129,7 +129,15 @@
     
         
  • 
     
- Reproduction
+ Reproduce Figures 2-4 and in-text results 1-3
+  
    •   
+        
  • 
+    
+ Reproduce Figure 5
+  
    •   
+        
  • 
+    
+ Reproduce supplementary figure
   
    •   
     
   
@@ -1056,7 +1064,7 @@ 
    DES c
    - + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + + + +
    + +
    +
    +

    Reproduce Figure 5

    +
    + + + +
    + + + + +
    + + + +
    + + +

    This is run in a separate script from the other figures due to issues with RStudio crashing when all scenarios were run from a single script.

    +

    Run time: 6.165 minutes (will vary between machines)

    +
    +

    Set up

    +
    +
    # Clear environment
+rm(list=ls())
+
+# Start timer
+start.time <- Sys.time()
+
+# Disable scientific notation
+options(scipen=999)
+
+# Get the model and helper functions (but hide loading warnings for each package)
+suppressMessages(source("model.R"))
+suppressMessages(source("helpers.R"))
    +
    +
    +
    # Set the seed and default dimensions for figures
+SEED = 200
+
+# Set file paths to save results
+folder = "../outputs"
+path_fig5 <- file.path(folder, "fig5.png")
    +
    +
    +
    +

    Run models

    +
    +
    run <- FALSE
    +
    +
    +
    if (isTRUE(run)) {
+  baseline_f5 <- run_model(seed = SEED, fig5=TRUE)
+  exclusive_f5 <- run_model(exclusive_use = TRUE, seed = SEED, fig5=TRUE)
+  twoangio_f5 <- run_model(angio_inr = 2, angio_ir=0, seed = SEED, fig5=TRUE)
+}
    +
    +
    +
    +

    Create figure

    +

    Currently depends on simmer.plot() function that doesn’t work on the imported results from the csv file, so need to allow to run model to produce this plot!

    +
    +
    if (isTRUE(run)) {
+  # Replace resource (which has been filtered to angioINR) with scenario
+  baseline_f5$resource <- "Baseline"
+  exclusive_f5$resource <-"Exclusive-use"
+  twoangio_f5$resource <- "Two angio INRs"
+  
+  # Combine into single object
+  fig5_df <- dplyr::bind_rows(baseline_f5, exclusive_f5, twoangio_f5)
+  
+  # Create figure using simmer's plot
+  p <- plot(fig5_df, metric="utilization") +
+    xlab("Scenarios") +
+    ylab("Utilisation") +
+    scale_y_continuous(labels = scales::percent, limits=c(0, 0.4)) +
+    ggtitle("") +
+    geom_text(aes(label=round(.data$Q50*100)), vjust=-1)
+  p
+  
+  # Save to provided path
+  ggsave(path_fig5, width=5, height=2.5)
+}
    +
    +
    +
    +

    Time elapsed

    +
    +
    if (isTRUE(run)) {
+  end.time <- Sys.time()
+  elapsed.time <- round((end.time - start.time), 3)
+  elapsed.time
+}
    +
    + + +
    + +
    + +
    + + + + + + \ No newline at end of file diff --git a/reproduction/scripts/reproduction_files/figure-html/fig2a_seeds_arrange-1.png b/reproduction/scripts/reproduction_files/figure-html/fig2a_seeds_arrange-1.png deleted file mode 100644 index 8f6f164..0000000 Binary files a/reproduction/scripts/reproduction_files/figure-html/fig2a_seeds_arrange-1.png and /dev/null differ diff --git a/reproduction/scripts/reproduction_supp.html b/reproduction/scripts/reproduction_supp.html new file mode 100644 index 0000000..d670dc4 --- /dev/null +++ b/reproduction/scripts/reproduction_supp.html @@ -0,0 +1,835 @@ + + + + + + + + + +Reproducing Huang et al. 2019 - Reproduce supplementary figure + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + + + +
    + +
    +
    +

    Reproduce supplementary figure

    +
    + + + +
    + + + + +
    + + + +
    + + +

    This is run in a separate script from the other figures due to issues with RStudio crashing when all scenarios were run from a single script.

    +

    If run is TRUE, it will run scenarios with double and triple the number of ECR patients.

    +

    To create the figure, it will use those files, as well as the baseline file created within reproduction.qmd.

    +

    Run time: 4.975 minutes (will vary between machines)

    +
    +

    Set up

    +
    +
    # Clear environment
+rm(list=ls())
+
+# Start timer
+start.time <- Sys.time()
+
+# Disable scientific notation
+options(scipen=999)
+
+# Get the model and helper functions (but hide loading warnings for each package)
+suppressMessages(source("model.R"))
+suppressMessages(source("helpers.R"))
+
+# Import other required libraries (if not otherwise import in R scripts below)
+library(ggpubr)
+library(tidyr, include.only = c("pivot_wider"))
    +
    +
    +
    # Set the seed and default dimensions for figures
+SEED = 200
+DEFAULT_WIDTH = 7
+DEFAULT_HEIGHT = 4
+
+# Set file paths to save results
+folder = "../outputs"
+
+path_baseline_f2 <- file.path(folder, "fig2_baseline.csv.gz")
+path_double_sup <- file.path(folder, "sup_baseline_double.csv.gz")
+path_triple_sup <- file.path(folder, "sup_baseline_triple.csv.gz")
+
+path_supfig <- file.path(folder, "supplementary_figure.png")
    +
    +
    +
    +

    Run models

    +

    Set to true or false, depending on whether you want to run everything.

    +
    +
    run <- FALSE
    +
    +

    Run baseline with double and triple the number of ECR patients, for the supplementary figure.

    +
    +
    if (isTRUE(run)) {
+  baseline_sup2 <- run_model(seed = SEED, ecr_pt = 58*2)
+  baseline_sup3 <- run_model(seed = SEED, ecr_pt = 58*3)
+}
    +
    +
    +
    if (isTRUE(run)) {
+  # Save results
+  data.table::fwrite(baseline_sup2, path_double_sup)
+  data.table::fwrite(baseline_sup3, path_triple_sup)
+
+  # Remove the dataframes from environment
+  rm(baseline_sup2, baseline_sup3)
+}
    +
    +
    +
    +

    Import results

    +

    Import the results, adding a column to each to indicate the scenario.

    +
    +
    base_f2 <- import_results(path_baseline_f2, "Baseline")
+base_sup_double <- import_results(path_double_sup, "Baseline (double)")
+base_sup_triple <- import_results(path_triple_sup, "Baseline (triple)")
    +
    +
    +
    +

    Supplementary figure

    +
    +
    # Create sub-plots
+p1 <- create_plot(base_f2,
+                  group="resource",
+                  title="Baseline",
+                  ylab="Standardised density of patient in queue")
+p2 <- create_plot(base_sup_double,
+                  group="resource",
+                  title="Doubling ECR patients",
+                  xlab="Patient wait time (min)",
+                  xlim=c(0, 300),
+                  breaks_width=100)
+p3 <- create_plot(base_sup_triple,
+                  group="resource",
+                  title="Tripling ECR patients",
+                  xlim=c(0, 300))
+
+# Arrange in a single figure
+ggarrange(p1, p2, p3, nrow=1,
+          common.legend=TRUE, legend="bottom",
+          labels=c("A", "B", "C"))
    +
    +
    Warning: Removed 1 row containing non-finite outside the scale range (`stat_density()`).
+Removed 1 row containing non-finite outside the scale range (`stat_density()`).
+Removed 1 row containing non-finite outside the scale range (`stat_density()`).
+Removed 1 row containing non-finite outside the scale range (`stat_density()`).
    +
    +
    +
    Warning: Removed 4 rows containing non-finite outside the scale range
+(`stat_density()`).
+Removed 4 rows containing non-finite outside the scale range
+(`stat_density()`).
    +
    +
    +
    Warning: Removed 3 rows containing non-finite outside the scale range
+(`stat_density()`).
+Removed 3 rows containing non-finite outside the scale range
+(`stat_density()`).
    +
    +
    +
    +
    +

    +
    +
    +
    +
    ggsave(path_supfig, width=DEFAULT_WIDTH, height=DEFAULT_HEIGHT)
    +
    +
    +
    +

    Time elapsed

    +
    +
    if (isTRUE(run)) {
+  end.time <- Sys.time()
+  elapsed.time <- round((end.time - start.time), 3)
+  elapsed.time
+}
    +
    + + +
    + +
    + +
    + + + + + + \ No newline at end of file diff --git a/reproduction/scripts/reproduction_files/figure-html/supplementary_figure-1.png b/reproduction/scripts/reproduction_supp_files/figure-html/supplementary_figure-1.png similarity index 100% rename from reproduction/scripts/reproduction_files/figure-html/supplementary_figure-1.png rename to reproduction/scripts/reproduction_supp_files/figure-html/supplementary_figure-1.png diff --git a/search.json b/search.json index 7694e2e..8496067 100644 --- a/search.json +++ b/search.json @@ -447,6 +447,41 @@ "section": "", "text": "This page evaluates the extent to which the original study meets the recommendations from the STARS framework for the sharing of code and associated materials from discrete-event simulation models (Monks, Harper, and Mustafee (2024)).\nOf the 8 essential STARS components:\n\n2 were met fully (✅)\n6 were not met (❌)\n\nOf the 5 optional STARS components:\n\n2 were met fully (✅)\n3 were not met (❌)\n\n\n\n\n\n\n\n\n\n\nComponent\nDescription\nMet by study?\nEvidence/location\n\n\n\n\nEssential components\n\n\n\n\n\nOpen license\nFree and open-source software (FOSS) license (e.g. MIT, GNU Public License (GPL))\n✅ Fully\nGPL-3.0\n\n\nDependency management\nSpecify software libraries, version numbers and sources (e.g. dependency management tools like virtualenv, conda, poetry)\n❌ Not met\n-\n\n\nFOSS model\nCoded in FOSS language (e.g. R, Julia, Python)\n✅ Fully\nR\n\n\nMinimum documentation\nMinimal instructions (e.g. in README) that overview (a) what model does, (b) how to install and run model to obtain results, and (c) how to vary parameters to run new experiments\n❌ Not met\nNo documentation provided\n\n\nORCID\nORCID for each study author\n❌ Not met\n-\n\n\nCitation information\nInstructions on how to cite the research artefact (e.g. CITATION.cff file)\n❌ Not met\n-\n\n\nRemote code repository\nCode available in a remote code repository (e.g. GitHub, GitLab, BitBucket)\n❌ Not met\n-\n\n\nOpen science archive\nCode stored in an open science archive with FORCE11 compliant citation and guaranteed persistance of digital artefacts (e.g. Figshare, Zenodo, the Open Science Framework (OSF), and the Computational Modeling in the Social and Ecological Sciences Network (CoMSES Net))\n❌ Not met\n-\n\n\nOptional components\n\n\n\n\n\nEnhanced documentation\nOpen and high quality documentation on how the model is implemented and works (e.g. via notebooks and markdown files, brought together using software like Quarto and Jupyter Book). Suggested content includes:• Plain english summary of project and model• Clarifying license• Citation instructions• Contribution instructions• Model installation instructions• Structured code walk through of model• Documentation of modelling cycle using TRACE• Annotated simulation reporting guidelines• Clear description of model validation including its intended purpose\n❌ Not met\n-\n\n\nDocumentation hosting\nHost documentation (e.g. with GitHub pages, GitLab pages, BitBucket Cloud, Quarto Pub)\n❌ Not met\n-\n\n\nOnline coding environment\nProvide an online environment where users can run and change code (e.g. BinderHub, Google Colaboratory, Deepnote)\n❌ Not met\n-\n\n\nModel interface\nProvide web application interface to the model so it is accessible to less technical simulation users\n✅ Fully\nShiny application that allows you to modify parameters and produces graphs showing waiting times for each patient type at the angioINR (boxplots grouped into <20, 20-40 and 40+ minutes), and resource utilisation. There is also a linked CLOUDES model of the simulation to aid user understanding.\n\n\nWeb app hosting\nHost web app online (e.g. Streamlit Community Cloud, ShinyApps hosting)\n✅ Fully\nHosted with ShinyApps at https://compneuro.shinyapps.io/desECR11/\n\n\n\n\n\n\n\nReferences\n\nMonks, Thomas, Alison Harper, and Navonil Mustafee. 2024. “Towards Sharing Tools and Artefacts for Reusable Simulations in Healthcare.” Journal of Simulation 0 (0): 1–20. https://doi.org/10.1080/17477778.2024.2347882." }, + { + "objectID": "reproduction/scripts/reproduction_fig5.html", + "href": "reproduction/scripts/reproduction_fig5.html", + "title": "Reproduce Figure 5", + "section": "", + "text": "This is run in a separate script from the other figures due to issues with RStudio crashing when all scenarios were run from a single script.\nRun time: 6.165 minutes (will vary between machines)" + }, + { + "objectID": "reproduction/scripts/reproduction_fig5.html#set-up", + "href": "reproduction/scripts/reproduction_fig5.html#set-up", + "title": "Reproduce Figure 5", + "section": "Set up", + "text": "Set up\n\n# Clear environment\nrm(list=ls())\n\n# Start timer\nstart.time <- Sys.time()\n\n# Disable scientific notation\noptions(scipen=999)\n\n# Get the model and helper functions (but hide loading warnings for each package)\nsuppressMessages(source(\"model.R\"))\nsuppressMessages(source(\"helpers.R\"))\n\n\n# Set the seed and default dimensions for figures\nSEED = 200\n\n# Set file paths to save results\nfolder = \"../outputs\"\npath_fig5 <- file.path(folder, \"fig5.png\")" + }, + { + "objectID": "reproduction/scripts/reproduction_fig5.html#run-models", + "href": "reproduction/scripts/reproduction_fig5.html#run-models", + "title": "Reproduce Figure 5", + "section": "Run models", + "text": "Run models\n\nrun <- FALSE\n\n\nif (isTRUE(run)) {\n baseline_f5 <- run_model(seed = SEED, fig5=TRUE)\n exclusive_f5 <- run_model(exclusive_use = TRUE, seed = SEED, fig5=TRUE)\n twoangio_f5 <- run_model(angio_inr = 2, angio_ir=0, seed = SEED, fig5=TRUE)\n}" + }, + { + "objectID": "reproduction/scripts/reproduction_fig5.html#create-figure", + "href": "reproduction/scripts/reproduction_fig5.html#create-figure", + "title": "Reproduce Figure 5", + "section": "Create figure", + "text": "Create figure\nCurrently depends on simmer.plot() function that doesn’t work on the imported results from the csv file, so need to allow to run model to produce this plot!\n\nif (isTRUE(run)) {\n # Replace resource (which has been filtered to angioINR) with scenario\n baseline_f5$resource <- \"Baseline\"\n exclusive_f5$resource <-\"Exclusive-use\"\n twoangio_f5$resource <- \"Two angio INRs\"\n \n # Combine into single object\n fig5_df <- dplyr::bind_rows(baseline_f5, exclusive_f5, twoangio_f5)\n \n # Create figure using simmer's plot\n p <- plot(fig5_df, metric=\"utilization\") +\n xlab(\"Scenarios\") +\n ylab(\"Utilisation\") +\n scale_y_continuous(labels = scales::percent, limits=c(0, 0.4)) +\n ggtitle(\"\") +\n geom_text(aes(label=round(.data$Q50*100)), vjust=-1)\n p\n \n # Save to provided path\n ggsave(path_fig5, width=5, height=2.5)\n}" + }, + { + "objectID": "reproduction/scripts/reproduction_fig5.html#time-elapsed", + "href": "reproduction/scripts/reproduction_fig5.html#time-elapsed", + "title": "Reproduce Figure 5", + "section": "Time elapsed", + "text": "Time elapsed\n\nif (isTRUE(run)) {\n end.time <- Sys.time()\n elapsed.time <- round((end.time - start.time), 3)\n elapsed.time\n}" + }, { "objectID": "quarto_site/study_publication.html", "href": "quarto_site/study_publication.html", @@ -594,80 +629,108 @@ "section": "", "text": "This repository is licensed under the [license].\n\n\n\n\n\n\nView license\n\n\n\n\n\nGNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright © 2007 Free Software Foundation, Inc. http://fsf.org/\nEveryone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.\nPreamble\nThe GNU General Public License is a free, copyleft license for software and other kinds of works.\nThe licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program–to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too.\nWhen we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for them if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs, and that you know you can do these things.\nTo protect your rights, we need to prevent others from denying you these rights or asking you to surrender the rights. Therefore, you have certain responsibilities if you distribute copies of the software, or if you modify it: responsibilities to respect the freedom of others.\nFor example, if you distribute copies of such a program, whether gratis or for a fee, you must pass on to the recipients the same freedoms that you received. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights.\nDevelopers that use the GNU GPL protect your rights with two steps: (1) assert copyright on the software, and (2) offer you this License giving you legal permission to copy, distribute and/or modify it.\nFor the developers’ and authors’ protection, the GPL clearly explains that there is no warranty for this free software. For both users’ and authors’ sake, the GPL requires that modified versions be marked as changed, so that their problems will not be attributed erroneously to authors of previous versions.\nSome devices are designed to deny users access to install or run modified versions of the software inside them, although the manufacturer can do so. This is fundamentally incompatible with the aim of protecting users’ freedom to change the software. The systematic pattern of such abuse occurs in the area of products for individuals to use, which is precisely where it is most unacceptable. Therefore, we have designed this version of the GPL to prohibit the practice for those products. If such problems arise substantially in other domains, we stand ready to extend this provision to those domains in future versions of the GPL, as needed to protect the freedom of users.\nFinally, every program is threatened constantly by software patents. States should not allow patents to restrict development and use of software on general-purpose computers, but in those that do, we wish to avoid the special danger that patents applied to a free program could make it effectively proprietary. To prevent this, the GPL assures that patents cannot be used to render the program non-free.\nThe precise terms and conditions for copying, distribution and modification follow.\nTERMS AND CONDITIONS\n\nDefinitions.\n\n“This License” refers to version 3 of the GNU General Public License.\n“Copyright” also means copyright-like laws that apply to other kinds of works, such as semiconductor masks.\n“The Program” refers to any copyrightable work licensed under this License. Each licensee is addressed as “you”. “Licensees” and “recipients” may be individuals or organizations.\nTo “modify” a work means to copy from or adapt all or part of the work in a fashion requiring copyright permission, other than the making of an exact copy. The resulting work is called a “modified version” of the earlier work or a work “based on” the earlier work.\nA “covered work” means either the unmodified Program or a work based on the Program.\nTo “propagate” a work means to do anything with it that, without permission, would make you directly or secondarily liable for infringement under applicable copyright law, except executing it on a computer or modifying a private copy. Propagation includes copying, distribution (with or without modification), making available to the public, and in some countries other activities as well.\nTo “convey” a work means any kind of propagation that enables other parties to make or receive copies. Mere interaction with a user through a computer network, with no transfer of a copy, is not conveying.\nAn interactive user interface displays “Appropriate Legal Notices” to the extent that it includes a convenient and prominently visible feature that (1) displays an appropriate copyright notice, and (2) tells the user that there is no warranty for the work (except to the extent that warranties are provided), that licensees may convey the work under this License, and how to view a copy of this License. If the interface presents a list of user commands or options, such as a menu, a prominent item in the list meets this criterion.\n\nSource Code. The “source code” for a work means the preferred form of the work for making modifications to it. “Object code” means any non-source form of a work.\n\nA “Standard Interface” means an interface that either is an official standard defined by a recognized standards body, or, in the case of interfaces specified for a particular programming language, one that is widely used among developers working in that language.\nThe “System Libraries” of an executable work include anything, other than the work as a whole, that (a) is included in the normal form of packaging a Major Component, but which is not part of that Major Component, and (b) serves only to enable use of the work with that Major Component, or to implement a Standard Interface for which an implementation is available to the public in source code form. A “Major Component”, in this context, means a major essential component (kernel, window system, and so on) of the specific operating system (if any) on which the executable work runs, or a compiler used to produce the work, or an object code interpreter used to run it.\nThe “Corresponding Source” for a work in object code form means all the source code needed to generate, install, and (for an executable work) run the object code and to modify the work, including scripts to control those activities. However, it does not include the work’s System Libraries, or general-purpose tools or generally available free programs which are used unmodified in performing those activities but which are not part of the work. 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If not, see http://www.gnu.org/licenses/.\nAlso add information on how to contact you by electronic and paper mail.\nIf the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:\n Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type show w'. This is free software, and you are welcome to redistribute it under certain conditions; typeshow c’ for details.\nThe hypothetical commands show w' andshow c’ should show the appropriate parts of the General Public License. Of course, your program’s commands might be different; for a GUI interface, you would use an “about box”.\nYou should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see http://www.gnu.org/licenses/.\nThe GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read http://www.gnu.org/philosophy/why-not-lgpl.html.\n\n\n\n\nThis is aligned with the original study, who shared their code under [license].\n\n\n\n\n\n\nView license\n\n\n\n\n\n\n[Embedded license]\n\n\n\n\nThe original study was published in the journal “[Journal name]”. They distributed the article under [Add more details about license]\n\n\n\n\n\n\nView copyright statement from journal" }, + { + "objectID": "reproduction/scripts/reproduction_supp.html", + "href": "reproduction/scripts/reproduction_supp.html", + "title": "Reproduce supplementary figure", + "section": "", + "text": "This is run in a separate script from the other figures due to issues with RStudio crashing when all scenarios were run from a single script.\nIf run is TRUE, it will run scenarios with double and triple the number of ECR patients.\nTo create the figure, it will use those files, as well as the baseline file created within reproduction.qmd.\nRun time: 4.975 minutes (will vary between machines)" + }, + { + "objectID": "reproduction/scripts/reproduction_supp.html#set-up", + "href": "reproduction/scripts/reproduction_supp.html#set-up", + "title": "Reproduce supplementary figure", + "section": "Set up", + "text": "Set up\n\n# Clear environment\nrm(list=ls())\n\n# Start timer\nstart.time <- Sys.time()\n\n# Disable scientific notation\noptions(scipen=999)\n\n# Get the model and helper functions (but hide loading warnings for each package)\nsuppressMessages(source(\"model.R\"))\nsuppressMessages(source(\"helpers.R\"))\n\n# Import other required libraries (if not otherwise import in R scripts below)\nlibrary(ggpubr)\nlibrary(tidyr, include.only = c(\"pivot_wider\"))\n\n\n# Set the seed and default dimensions for figures\nSEED = 200\nDEFAULT_WIDTH = 7\nDEFAULT_HEIGHT = 4\n\n# Set file paths to save results\nfolder = \"../outputs\"\n\npath_baseline_f2 <- file.path(folder, \"fig2_baseline.csv.gz\")\npath_double_sup <- file.path(folder, \"sup_baseline_double.csv.gz\")\npath_triple_sup <- file.path(folder, \"sup_baseline_triple.csv.gz\")\n\npath_supfig <- file.path(folder, \"supplementary_figure.png\")" + }, + { + "objectID": "reproduction/scripts/reproduction_supp.html#run-models", + "href": "reproduction/scripts/reproduction_supp.html#run-models", + "title": "Reproduce supplementary figure", + "section": "Run models", + "text": "Run models\nSet to true or false, depending on whether you want to run everything.\n\nrun <- FALSE\n\nRun baseline with double and triple the number of ECR patients, for the supplementary figure.\n\nif (isTRUE(run)) {\n baseline_sup2 <- run_model(seed = SEED, ecr_pt = 58*2)\n baseline_sup3 <- run_model(seed = SEED, ecr_pt = 58*3)\n}\n\n\nif (isTRUE(run)) {\n # Save results\n data.table::fwrite(baseline_sup2, path_double_sup)\n data.table::fwrite(baseline_sup3, path_triple_sup)\n\n # Remove the dataframes from environment\n rm(baseline_sup2, baseline_sup3)\n}" + }, + { + "objectID": "reproduction/scripts/reproduction_supp.html#import-results", + "href": "reproduction/scripts/reproduction_supp.html#import-results", + "title": "Reproduce supplementary figure", + "section": "Import results", + "text": "Import results\nImport the results, adding a column to each to indicate the scenario.\n\nbase_f2 <- import_results(path_baseline_f2, \"Baseline\")\nbase_sup_double <- import_results(path_double_sup, \"Baseline (double)\")\nbase_sup_triple <- import_results(path_triple_sup, \"Baseline (triple)\")" + }, + { + "objectID": "reproduction/scripts/reproduction_supp.html#supplementary-figure", + "href": "reproduction/scripts/reproduction_supp.html#supplementary-figure", + "title": "Reproduce supplementary figure", + "section": "Supplementary figure", + "text": "Supplementary figure\n\n# Create sub-plots\np1 <- create_plot(base_f2,\n group=\"resource\",\n title=\"Baseline\",\n ylab=\"Standardised density of patient in queue\")\np2 <- create_plot(base_sup_double,\n group=\"resource\",\n title=\"Doubling ECR patients\",\n xlab=\"Patient wait time (min)\",\n xlim=c(0, 300),\n breaks_width=100)\np3 <- create_plot(base_sup_triple,\n group=\"resource\",\n title=\"Tripling ECR patients\",\n xlim=c(0, 300))\n\n# Arrange in a single figure\nggarrange(p1, p2, p3, nrow=1,\n common.legend=TRUE, legend=\"bottom\",\n labels=c(\"A\", \"B\", \"C\"))\n\nWarning: Removed 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\n\n\nWarning: Removed 4 rows containing non-finite outside the scale range\n(`stat_density()`).\nRemoved 4 rows containing non-finite outside the scale range\n(`stat_density()`).\n\n\nWarning: Removed 3 rows containing non-finite outside the scale range\n(`stat_density()`).\nRemoved 3 rows containing non-finite outside the scale range\n(`stat_density()`).\n\n\n\n\n\n\n\n\nggsave(path_supfig, width=DEFAULT_WIDTH, height=DEFAULT_HEIGHT)" + }, + { + "objectID": "reproduction/scripts/reproduction_supp.html#time-elapsed", + "href": "reproduction/scripts/reproduction_supp.html#time-elapsed", + "title": "Reproduce supplementary figure", + "section": "Time elapsed", + "text": "Time elapsed\n\nif (isTRUE(run)) {\n end.time <- Sys.time()\n elapsed.time <- round((end.time - start.time), 3)\n elapsed.time\n}" + }, { "objectID": "reproduction/scripts/reproduction.html", "href": "reproduction/scripts/reproduction.html", - "title": "Reproduction", + "title": "Reproduce Figures 2-4 and in-text results 1-3", "section": "", - "text": "# Clear environment\nrm(list=ls())\n\n# Start timer\nstart.time <- Sys.time()\n\n# Disable scientific notation\noptions(scipen=999)\n\n# Import required libraries (if not otherwise import in model.R)\nlibrary(ggplot2)\nlibrary(data.table)\nlibrary(ggpubr)\nlibrary(tidyr, include.only = c(\"pivot_wider\"))\n\n# Get the model and helper functiosn (but hide loading warnings for each package)\nsuppressMessages(source(\"model.R\"))\nsource(\"helpers.R\")\n\n\n# Set the seed\nSEED = 200\n\n# Set default dimensions for figures\nDEFAULT_WIDTH = 7\nDEFAULT_HEIGHT = 4\n\n# Set file paths to save results\n\nfolder = \"../outputs\"\n\npath_baseline_f2 <- file.path(folder, \"fig2_baseline.csv.gz\")\npath_exclusive_f2 <- file.path(folder, \"fig2_exclusive.csv.gz\")\npath_twoangio_f2 <- file.path(folder, \"fig2_twoangio.csv.gz\")\n\npath_baseline_f3 <- file.path(folder, \"fig3_baseline.csv.gz\")\npath_exclusive_f3 <- file.path(folder, \"fig3_exclusive.csv.gz\")\npath_twoangio_f3 <- file.path(folder, \"fig3_twoangio.csv.gz\")\npath_baseline_f3_seed500 <- file.path(folder, \"fig3_baseline_seed500.csv.gz\")\npath_baseline_f3_seed700 <- file.path(folder, \"fig3_baseline_seed700.csv.gz\")\npath_twoangio_f3_seed500 <- file.path(folder, \"fig3_twoangio_seed500.csv.gz\")\npath_twoangio_f3_seed700 <- file.path(folder, \"fig3_twoangio_seed700.csv.gz\")\n\npath_baseline_f5 <- file.path(folder, \"fig5_baseline.csv.gz\")\npath_exclusive_f5 <- file.path(folder, \"fig5_exclusive.csv.gz\")\npath_twoangio_f5 <- file.path(folder, \"fig5_twoangio.csv.gz\")\n\npath_double_sup <- file.path(folder, \"sup_baseline_double.csv.gz\")\npath_triple_sup <- file.path(folder, \"sup_baseline_triple.csv.gz\")\n\npath_txt2 <- file.path(folder, \"txt2.csv\")\npath_txt3 <- file.path(folder, \"txt3.csv\")\npath_txt3_seeds <- file.path(folder, \"txt3_seeds.csv\")\npath_fig2 <- file.path(folder, \"fig2.png\")\npath_fig2a_seeds <- file.path(folder, \"fig2a_seeds.png\")\npath_fig3 <- file.path(folder, \"fig3.png\")\npath_fig4 <- file.path(folder, \"fig4.png\")\npath_fig5 <- file.path(folder, \"fig5.png\")\npath_supfig <- file.path(folder, \"supplementary_figure.png\")" + "text": "The majority of the items in the model scope are reproduced in this file, but Figure 5 and the supplementary figure are created in seperate .qmd files.\nThis decision was primarily due to issues with RStudio crashing when running all scenarios from a single .Rmd file.\nRun time: 18.024 minutes (will vary between machines)" }, { "objectID": "reproduction/scripts/reproduction.html#set-up", "href": "reproduction/scripts/reproduction.html#set-up", - "title": "Reproduction", - "section": "", - "text": "# Clear environment\nrm(list=ls())\n\n# Start timer\nstart.time <- Sys.time()\n\n# Disable scientific notation\noptions(scipen=999)\n\n# Import required libraries (if not otherwise import in model.R)\nlibrary(ggplot2)\nlibrary(data.table)\nlibrary(ggpubr)\nlibrary(tidyr, include.only = c(\"pivot_wider\"))\n\n# Get the model and helper functiosn (but hide loading warnings for each package)\nsuppressMessages(source(\"model.R\"))\nsource(\"helpers.R\")\n\n\n# Set the seed\nSEED = 200\n\n# Set default dimensions for figures\nDEFAULT_WIDTH = 7\nDEFAULT_HEIGHT = 4\n\n# Set file paths to save results\n\nfolder = \"../outputs\"\n\npath_baseline_f2 <- file.path(folder, \"fig2_baseline.csv.gz\")\npath_exclusive_f2 <- file.path(folder, \"fig2_exclusive.csv.gz\")\npath_twoangio_f2 <- file.path(folder, \"fig2_twoangio.csv.gz\")\n\npath_baseline_f3 <- file.path(folder, \"fig3_baseline.csv.gz\")\npath_exclusive_f3 <- file.path(folder, \"fig3_exclusive.csv.gz\")\npath_twoangio_f3 <- file.path(folder, \"fig3_twoangio.csv.gz\")\npath_baseline_f3_seed500 <- file.path(folder, \"fig3_baseline_seed500.csv.gz\")\npath_baseline_f3_seed700 <- file.path(folder, \"fig3_baseline_seed700.csv.gz\")\npath_twoangio_f3_seed500 <- file.path(folder, \"fig3_twoangio_seed500.csv.gz\")\npath_twoangio_f3_seed700 <- file.path(folder, \"fig3_twoangio_seed700.csv.gz\")\n\npath_baseline_f5 <- file.path(folder, \"fig5_baseline.csv.gz\")\npath_exclusive_f5 <- file.path(folder, \"fig5_exclusive.csv.gz\")\npath_twoangio_f5 <- file.path(folder, \"fig5_twoangio.csv.gz\")\n\npath_double_sup <- file.path(folder, \"sup_baseline_double.csv.gz\")\npath_triple_sup <- file.path(folder, \"sup_baseline_triple.csv.gz\")\n\npath_txt2 <- file.path(folder, \"txt2.csv\")\npath_txt3 <- file.path(folder, \"txt3.csv\")\npath_txt3_seeds <- file.path(folder, \"txt3_seeds.csv\")\npath_fig2 <- file.path(folder, \"fig2.png\")\npath_fig2a_seeds <- file.path(folder, \"fig2a_seeds.png\")\npath_fig3 <- file.path(folder, \"fig3.png\")\npath_fig4 <- file.path(folder, \"fig4.png\")\npath_fig5 <- file.path(folder, \"fig5.png\")\npath_supfig <- file.path(folder, \"supplementary_figure.png\")" + "title": "Reproduce Figures 2-4 and in-text results 1-3", + "section": "Set up", + "text": "Set up\n\n# Clear environment\nrm(list=ls())\n\n# Start timer\nstart.time <- Sys.time()\n\n# Disable scientific notation\noptions(scipen=999)\n\n# Import required libraries (if not otherwise import in R scripts below)\nlibrary(ggpubr)\n\nLoading required package: ggplot2\n\nlibrary(tidyr, include.only = c(\"pivot_wider\"))\n\n# Get the model and helper functions (but hide loading warnings for each package)\nsuppressMessages(source(\"model.R\"))\nsuppressMessages(source(\"helpers.R\"))\n\n\n# Set the seed and default dimensions for figures\nSEED = 200\nDEFAULT_WIDTH = 7\nDEFAULT_HEIGHT = 4\n\n# Set file paths to save results\n\nfolder = \"../outputs\"\n\npath_baseline_f2 <- file.path(folder, \"fig2_baseline.csv.gz\")\npath_exclusive_f2 <- file.path(folder, \"fig2_exclusive.csv.gz\")\npath_twoangio_f2 <- file.path(folder, \"fig2_twoangio.csv.gz\")\n\npath_baseline_f3 <- file.path(folder, \"fig3_baseline.csv.gz\")\npath_exclusive_f3 <- file.path(folder, \"fig3_exclusive.csv.gz\")\npath_twoangio_f3 <- file.path(folder, \"fig3_twoangio.csv.gz\")\n\npath_txt2 <- file.path(folder, \"txt2.csv\") # Used for results 1 and 2\npath_txt3 <- file.path(folder, \"txt3.csv\")\npath_fig2 <- file.path(folder, \"fig2.png\")\npath_fig3 <- file.path(folder, \"fig3.png\")\npath_fig4 <- file.path(folder, \"fig4.png\")" }, { "objectID": "reproduction/scripts/reproduction.html#run-models", "href": "reproduction/scripts/reproduction.html#run-models", - "title": "Reproduction", + "title": "Reproduce Figures 2-4 and in-text results 1-3", "section": "Run models", - "text": "Run models\nRun model scenarios.\n\nif (isTRUE(run)) {\n # Run model\n baseline <- run_model(seed = SEED)\n baseline_6pm <- run_model(shifts = c(8,18), seed = SEED)\n baseline_7pm <- run_model(shifts = c(8,19), seed = SEED)\n\n exclusive <- run_model(exclusive_use = TRUE, seed = SEED)\n exclusive_6pm <- run_model(shifts = c(8,18), exclusive_use = TRUE, seed = SEED)\n exclusive_7pm <- run_model(shifts = c(8,19), exclusive_use = TRUE, seed = SEED)\n\n twoangio <- run_model(angio_inr = 2, angio_ir=0, seed = SEED)\n twoangio_6pm <- run_model(shifts = c(8,18), angio_inr = 2, angio_ir=0, seed = SEED)\n twoangio_7pm <- run_model(shifts = c(8,19), angio_inr = 2, angio_ir=0, seed = SEED)\n}\n\n\n# (in seperate cell to above as otherwise seemed to crash)\nif (isTRUE(run)) {\n # Save results for Figure 2\n data.table::fwrite(baseline, path_baseline_f2)\n data.table::fwrite(exclusive, path_exclusive_f2)\n data.table::fwrite(twoangio, path_twoangio_f2)\n\n # Process and save results for Figure 3\n process_f3_data(baseline, baseline_6pm, baseline_7pm, path_baseline_f3)\n process_f3_data(exclusive, exclusive_6pm, exclusive_7pm, path_exclusive_f3)\n process_f3_data(twoangio, twoangio_6pm, twoangio_7pm, path_twoangio_f3)\n}\n\nRe-run models but return different output from standard hours for use in Figure 5\n\nif (isTRUE(run)) {\n baseline_f5 <- run_model(seed = SEED, fig5=TRUE)\n exclusive_f5 <- run_model(exclusive_use = TRUE, seed = SEED, fig5=TRUE)\n twoangio_f5 <- run_model(angio_inr = 2, angio_ir=0, seed = SEED, fig5=TRUE)\n}\n\n\nif (isTRUE(run)) {\n data.table::fwrite(baseline_f5, path_baseline_f5)\n data.table::fwrite(exclusive_f5, path_exclusive_f5)\n data.table::fwrite(twoangio_f5, path_twoangio_f5)\n}\n\nRun baseline with double and triple the number of ECR patients, for the supplementary figure.\n\nif (isTRUE(run)) {\n baseline_sup2 <- run_model(seed = SEED, ecr_pt = 58*2)\n baseline_sup3 <- run_model(seed = SEED, ecr_pt = 58*3)\n}\n\n\nif (isTRUE(run)) {\n data.table::fwrite(baseline_sup2, path_double_sup)\n data.table::fwrite(baseline_sup3, path_triple_sup)\n}\n\nRun baseline and double angioINR with varying hours and seeds.\n\nif (isTRUE(run)) {\n # Baseline with varying hours and seed 500\n s500_baseline <- run_model(seed = 500)\n s500_baseline_6pm <- run_model(shifts = c(8,18), seed = 500)\n s500_baseline_7pm <- run_model(shifts = c(8,19), seed = 500)\n\n # Baseline with varying hours and seed 700\n s700_baseline <- run_model(seed = 700)\n s700_baseline_6pm <- run_model(shifts = c(8,18), seed = 700)\n s700_baseline_7pm <- run_model(shifts = c(8,19), seed = 700)\n\n # Double AngioINR with varying hours and seed 500\n s500_twoangio <- run_model(angio_inr = 2, angio_ir=0, seed = 500)\n s500_twoangio_6pm <- run_model(shifts = c(8,18), angio_inr = 2, angio_ir=0, seed = 500)\n s500_twoangio_7pm <- run_model(shifts = c(8,19), angio_inr = 2, angio_ir=0, seed = 500)\n\n # Double AngioINR with varying hours and seed 700\n s700_twoangio <- run_model(angio_inr = 2, angio_ir=0, seed = 700)\n s700_twoangio_6pm <- run_model(shifts = c(8,18), angio_inr = 2, angio_ir=0, seed = 700)\n s700_twoangio_7pm <- run_model(shifts = c(8,19), angio_inr = 2, angio_ir=0, seed = 700)\n}\n\n\n# Save results (in seperate cell to above as otherwise seemed to crash)\nif (isTRUE(run)) {\n process_f3_data(s500_baseline, s500_baseline_6pm, s500_baseline_7pm,\n path_baseline_f3_seed500)\n process_f3_data(s700_baseline, s700_baseline_6pm, s700_baseline_7pm,\n path_baseline_f3_seed700)\n process_f3_data(s500_twoangio, s500_twoangio_6pm, s500_twoangio_7pm,\n path_twoangio_f3_seed500)\n process_f3_data(s700_twoangio, s700_twoangio_6pm, s700_twoangio_7pm,\n path_twoangio_f3_seed700)\n}\n\nRun baseline with several different seeds\n\nbaseline_seeds <- function(seed, run, folder) {\n #' Run baseline model with different seeds\n #' \n #' @param seed integer, starting seed for replications\n #' @param run boolean, whether to run model\n #' @param folder string, folder to store results in\n if (isTRUE(run)) {\n seed_baseline <- run_model(seed = seed)\n seed_path <- file.path(folder, paste(\n \"fig2_baseline_seed\", seed, \".csv.gz\", sep=\"\"))\n data.table::fwrite(seed_baseline, seed_path)\n }\n}\n\nbaseline_seeds(0, run, folder)\nbaseline_seeds(50, run, folder)\nbaseline_seeds(100, run, folder)\nbaseline_seeds(150, run, folder)\nbaseline_seeds(450, run, folder)\nbaseline_seeds(700, run, folder)\nbaseline_seeds(750, run, folder)" + "text": "Run models\nSet to true or false, depending on whether you want to run everything.\n\nrun <- FALSE\n\nRun model scenarios.\n\nif (isTRUE(run)) {\n # Run model\n baseline <- run_model(seed = SEED)\n baseline_6pm <- run_model(shifts = c(8,18), seed = SEED)\n baseline_7pm <- run_model(shifts = c(8,19), seed = SEED)\n\n exclusive <- run_model(exclusive_use = TRUE, seed = SEED)\n exclusive_6pm <- run_model(shifts = c(8,18), exclusive_use = TRUE, seed = SEED)\n exclusive_7pm <- run_model(shifts = c(8,19), exclusive_use = TRUE, seed = SEED)\n\n twoangio <- run_model(angio_inr = 2, angio_ir=0, seed = SEED)\n twoangio_6pm <- run_model(shifts = c(8,18), angio_inr = 2, angio_ir=0, seed = SEED)\n twoangio_7pm <- run_model(shifts = c(8,19), angio_inr = 2, angio_ir=0, seed = SEED)\n}\n\n\n# (in seperate cell to above as otherwise seemed to crash)\nif (isTRUE(run)) {\n # Save results for Figure 2\n data.table::fwrite(baseline, path_baseline_f2)\n data.table::fwrite(exclusive, path_exclusive_f2)\n data.table::fwrite(twoangio, path_twoangio_f2)\n\n # Process and save results for Figure 3\n process_f3_data(baseline, baseline_6pm, baseline_7pm, path_baseline_f3)\n process_f3_data(exclusive, exclusive_6pm, exclusive_7pm, path_exclusive_f3)\n process_f3_data(twoangio, twoangio_6pm, twoangio_7pm, path_twoangio_f3)\n\n # Remove the dataframes from environment\n rm(baseline, baseline_6pm, baseline_7pm,\n exclusive, exclusive_6pm, exclusive_7pm,\n twoangio, twoangio_6pm, twoangio_7pm)\n}" }, { "objectID": "reproduction/scripts/reproduction.html#import-results", "href": "reproduction/scripts/reproduction.html#import-results", - "title": "Reproduction", + "title": "Reproduce Figures 2-4 and in-text results 1-3", "section": "Import results", - "text": "Import results\nImport the results, adding a column to each to indicate the scenario.\n\nbase_f2 <- import_results(path_baseline_f2,\n \"Baseline\")\nexc_f2 <- import_results(path_exclusive_f2,\n \"Exclusive use\")\ntwo_f2 <- import_results(path_twoangio_f2,\n \"Two AngioINRs\")\n\nbase_f3 <- import_results(path_baseline_f3,\n \"Baseline\")\nexc_f3 <- import_results(path_exclusive_f3,\n \"Exclusive use\")\ntwo_f3 <- import_results(path_twoangio_f3,\n \"Two AngioINRs\")\n\nbase_f3_s500 <- import_results(path_baseline_f3_seed500,\n \"Baseline (seed 500)\")\nbase_f3_s700 <- import_results(path_baseline_f3_seed700,\n \"Baseline (seed 700)\")\ntwo_f3_s500 <- import_results(path_twoangio_f3_seed500,\n \"Two AngioINRs (seed 500)\")\ntwo_f3_s700 <- import_results(path_twoangio_f3_seed700,\n \"Two AngioINRs (seed 700)\")\n\nbase_f5 <- data.table::fread(path_baseline_f5)\nexc_f5 <- data.table::fread(path_exclusive_f5)\ntwo_f5 <- data.table::fread(path_twoangio_f5)\n\nbase_sup_double <- import_results(path_double_sup, \"Baseline (double)\")\nbase_sup_triple <- import_results(path_triple_sup, \"Baseline (triple)\")" + "text": "Import results\nImport the results, adding a column to each to indicate the scenario.\n\nbase_f2 <- import_results(path_baseline_f2,\n \"Baseline\")\nexc_f2 <- import_results(path_exclusive_f2,\n \"Exclusive use\")\ntwo_f2 <- import_results(path_twoangio_f2,\n \"Two AngioINRs\")\n\nbase_f3 <- import_results(path_baseline_f3,\n \"Baseline\")\nexc_f3 <- import_results(path_exclusive_f3,\n \"Exclusive use\")\ntwo_f3 <- import_results(path_twoangio_f3,\n \"Two AngioINRs\")" }, { "objectID": "reproduction/scripts/reproduction.html#in-text-results", "href": "reproduction/scripts/reproduction.html#in-text-results", - "title": "Reproduction", + "title": "Reproduce Figures 2-4 and in-text results 1-3", "section": "In-text results", - "text": "In-text results\nIn-text results 1 and 2\n\ntxt2 <- dplyr::bind_rows(base_f2, exc_f2, two_f2) %>%\n filter(resource==\"angio_inr\") %>%\n group_by(scenario) %>%\n summarize(mean = mean(wait_time)) %>%\n mutate(diff_from_baseline = round(mean - mean[1], 2))\n\n# Save and display result\ndata.table::fwrite(txt2, path_txt2)\ntxt2\n\n# A tibble: 3 × 3\n scenario mean diff_from_baseline\n <chr> <dbl> <dbl>\n1 Baseline 14.0 0 \n2 Exclusive use 8.12 -5.84\n3 Two AngioINRs 9.62 -4.34\n\n\nIn-text result 3\n\ntxt3 <- dplyr::bind_rows(base_f3, exc_f3, two_f3) %>%\n filter(resource==\"angio_inr\") %>%\n group_by(scenario, shift) %>%\n summarize(mean = mean(wait_time)) %>%\n mutate(diff_from_5pm = round(mean - mean[1], 2))\n\n`summarise()` has grouped output by 'scenario'. You can override using the\n`.groups` argument.\n\n# Save and display result\ndata.table::fwrite(txt3, path_txt3)\ntxt3\n\n# A tibble: 9 × 4\n# Groups: scenario [3]\n scenario shift mean diff_from_5pm\n <chr> <chr> <dbl> <dbl>\n1 Baseline 5pm 14.0 0 \n2 Baseline 6pm 12.5 -1.47\n3 Baseline 7pm 12.5 -1.47\n4 Exclusive use 5pm 8.12 0 \n5 Exclusive use 6pm 7.80 -0.31\n6 Exclusive use 7pm 6.43 -1.69\n7 Two AngioINRs 5pm 9.62 0 \n8 Two AngioINRs 6pm 9.22 -0.4 \n9 Two AngioINRs 7pm 8.70 -0.92\n\n\n\nDemonstrate variation between model runs\nWe can see the importance of seed control here. For example, with seed 700, we see a broader range of results, with the result for 6pm (13.32) is much higher than for the other two seeds and, compared with their 5pm results, we would’ve seen less of a reduction. Similarly, if we compared the 5pm seed 700 result with the 6pm seed 500 result, we would see a much greater reduction.\nThe same applies to the prior scenarios for Figure 2. I’ve held the seeds still between them, so the only change is the scenario.\nHowever, as we are not sure, it is also reasonable to assume that a seed might have been used for the paper, but just not mentioned in the article or included in the code.\n\n# Dataframes to analyse\nvar_list <- c(list(base_f3, base_f3_s500, base_f3_s700),\n list(two_f3, two_f3_s500, two_f3_s700))\n\n# For loop through those two lists\ndf_seeds_list <- list()\ni <- 0\nfor (l in var_list) {\n i <- i + 1\n\n # Process results\n df_seeds <- dplyr::bind_rows(l) %>%\n filter(resource==\"angio_inr\") %>%\n group_by(scenario, shift) %>%\n summarize(mean = round(mean(wait_time),2)) %>%\n ungroup()\n \n # Convert to wide format and display\n df_seeds_list[[i]] <- pivot_wider(df_seeds, id_cols=\"scenario\",\n values_from=c(\"mean\"), names_from=\"shift\")\n}\n\n`summarise()` has grouped output by 'scenario'. You can override using the\n`.groups` argument.\n`summarise()` has grouped output by 'scenario'. You can override using the\n`.groups` argument.\n`summarise()` has grouped output by 'scenario'. You can override using the\n`.groups` argument.\n`summarise()` has grouped output by 'scenario'. You can override using the\n`.groups` argument.\n`summarise()` has grouped output by 'scenario'. You can override using the\n`.groups` argument.\n`summarise()` has grouped output by 'scenario'. You can override using the\n`.groups` argument.\n\n# Combine into single dataframe, save and display\ncombined_seeds <- dplyr::bind_rows(df_seeds_list)\ndata.table::fwrite(combined_seeds, path_txt3_seeds)\ncombined_seeds\n\n# A tibble: 6 × 4\n scenario `5pm` `6pm` `7pm`\n <chr> <dbl> <dbl> <dbl>\n1 Baseline 14.0 12.5 12.5 \n2 Baseline (seed 500) 14.0 12.2 12.2 \n3 Baseline (seed 700) 15.5 13.3 12.3 \n4 Two AngioINRs 9.62 9.22 8.7 \n5 Two AngioINRs (seed 500) 11.2 10.6 10.1 \n6 Two AngioINRs (seed 700) 10.4 9.96 9.63" + "text": "In-text results\nIn-text results 1 and 2\n\ntxt2 <- dplyr::bind_rows(base_f2, exc_f2, two_f2) %>%\n filter(resource==\"angio_inr\") %>%\n group_by(scenario) %>%\n summarize(mean = mean(wait_time)) %>%\n mutate(diff_from_baseline = round(mean - mean[1], 2))\n\n# Save and display result\ndata.table::fwrite(txt2, path_txt2)\ntxt2\n\n# A tibble: 3 × 3\n scenario mean diff_from_baseline\n <chr> <dbl> <dbl>\n1 Baseline 14.0 0 \n2 Exclusive use 8.12 -5.84\n3 Two AngioINRs 9.62 -4.34\n\n\nIn-text result 3\n\ntxt3 <- dplyr::bind_rows(base_f3, exc_f3, two_f3) %>%\n filter(resource==\"angio_inr\") %>%\n group_by(scenario, shift) %>%\n summarize(mean = mean(wait_time)) %>%\n mutate(diff_from_5pm = round(mean - mean[1], 2))\n\n`summarise()` has grouped output by 'scenario'. You can override using the\n`.groups` argument.\n\n# Save and display result\ndata.table::fwrite(txt3, path_txt3)\ntxt3\n\n# A tibble: 9 × 4\n# Groups: scenario [3]\n scenario shift mean diff_from_5pm\n <chr> <chr> <dbl> <dbl>\n1 Baseline 5pm 14.0 0 \n2 Baseline 6pm 12.5 -1.47\n3 Baseline 7pm 12.5 -1.47\n4 Exclusive use 5pm 8.12 0 \n5 Exclusive use 6pm 7.80 -0.31\n6 Exclusive use 7pm 6.43 -1.69\n7 Two AngioINRs 5pm 9.62 0 \n8 Two AngioINRs 6pm 9.22 -0.4 \n9 Two AngioINRs 7pm 8.70 -0.92" }, { "objectID": "reproduction/scripts/reproduction.html#figure-2", "href": "reproduction/scripts/reproduction.html#figure-2", - "title": "Reproduction", + "title": "Reproduce Figures 2-4 and in-text results 1-3", "section": "Figure 2", - "text": "Figure 2\n\n# Create sub-plots\np1 <- create_plot(base_f2,\n group=\"resource\",\n title=\"Baseline\",\n ylab=\"Standardised density of patient in queue\")\np2 <- create_plot(exc_f2,\n group=\"resource\",\n title=\"Exclusive-use\",\n xlab=\"Patient wait time (min)\",\n xlim=c(0, 250))\np3 <- create_plot(two_f2,\n group=\"resource\",\n title=\"Double angio INRs\")\n\n# Arrange in a single figure\nggarrange(p1, p2, p3, nrow=1,\n common.legend=TRUE, legend=\"bottom\",\n labels=c(\"A\", \"B\", \"C\"))\n\nWarning: Removed 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\n\n\n\n\n\n\n\n\nggsave(path_fig2, width=DEFAULT_WIDTH, height=DEFAULT_HEIGHT)\n\n\nDemonstrate that geom_density scaled is scaling against density of 0 wait time\n\n# Create figure as usual\np <- create_plot(base_f2,\n group=\"resource\",\n title=\"Baseline\",\n ylab=\"Standardised density of patient in queue\")\n\n# Get data from the plot\nplot_data <- ggplot_build(p)$data[[1]]\n\nWarning: Removed 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\n\n# Create dataframe with the densities for when the waitimes are 0\nno_wait <- plot_data %>% filter(x==0) %>% select(colour, density, scaled)\n\n# Loop through each of the colours (which reflect the resource groups)\nfor (c in no_wait$colour) {\n # Filter the plot data to that resource group, then divide the densities by\n # the density from wait time 0\n d <- plot_data %>%\n filter(colour == c) %>%\n mutate(scaled2 = density / no_wait[no_wait$colour==c, \"density\"]) %>%\n ungroup() %>%\n select(scaled, scaled2)\n\n # Find the number of rows where these values match the scaled values\n n_match <- sum(apply(d, 1, function(x) length(unique(x)) == 1))\n n_total <- nrow(d)\n print(sprintf(\"%s out of %s results match\", n_match, n_total))\n}\n\n[1] \"512 out of 512 results match\"\n[1] \"512 out of 512 results match\"\n[1] \"512 out of 512 results match\"\n[1] \"512 out of 512 results match\"\n[1] \"512 out of 512 results match\"\n\n\n\n\nFigure 2 (baseline only) with various different seeds\nThis imports the files too (as several files)\n\nplot_list <- list()\ni <- 0\nseeds <- c(0, 50, 100, 150, 450, 700, 750)\nfor (s in seeds) {\n i <- i + 1\n seed_path <- file.path(folder, paste(\n \"fig2_baseline_seed\", s, \".csv.gz\", sep=\"\"))\n seed_baseline <- import_results(seed_path, \"Baseline\")\n plot_list[[i]] <- create_plot(seed_baseline, group=\"resource\", title=s)\n}\n\n\nggarrange(plotlist=plot_list, common.legend=TRUE, legend=\"bottom\")\n\nWarning: Removed 4 rows containing non-finite outside the scale range\n(`stat_density()`).\nRemoved 4 rows containing non-finite outside the scale range\n(`stat_density()`).\nRemoved 4 rows containing non-finite outside the scale range\n(`stat_density()`).\nRemoved 4 rows containing non-finite outside the scale range\n(`stat_density()`).\n\n\nWarning: Removed 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\n\n\nWarning: Removed 4 rows containing non-finite outside the scale range\n(`stat_density()`).\nRemoved 4 rows containing non-finite outside the scale range\n(`stat_density()`).\n\n\nWarning: Removed 5 rows containing non-finite outside the scale range\n(`stat_density()`).\nRemoved 5 rows containing non-finite outside the scale range\n(`stat_density()`).\n\n\n\n\n\n\n\n\nggsave(path_fig2a_seeds, width=13, height=20)" + "text": "Figure 2\n\n# Create sub-plots\np1 <- create_plot(base_f2,\n group=\"resource\",\n title=\"Baseline\",\n ylab=\"Standardised density of patient in queue\")\np2 <- create_plot(exc_f2,\n group=\"resource\",\n title=\"Exclusive-use\",\n xlab=\"Patient wait time (min)\",\n xlim=c(0, 250))\np3 <- create_plot(two_f2,\n group=\"resource\",\n title=\"Double angio INRs\")\n\n# Arrange in a single figure\nggarrange(p1, p2, p3, nrow=1,\n common.legend=TRUE, legend=\"bottom\",\n labels=c(\"A\", \"B\", \"C\"))\n\nWarning: Removed 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\n\n\n\n\n\n\n\n\nggsave(path_fig2, width=DEFAULT_WIDTH, height=DEFAULT_HEIGHT)\n\n\nDemonstrate that geom_density scaled is scaling against density of 0 wait time\n\n# Create figure as usual\np <- create_plot(base_f2,\n group=\"resource\",\n title=\"Baseline\",\n ylab=\"Standardised density of patient in queue\")\n\n# Get data from the plot\nplot_data <- ggplot_build(p)$data[[1]]\n\nWarning: Removed 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\n\n# Create dataframe with the densities for when the waitimes are 0\nno_wait <- plot_data %>% filter(x==0) %>% select(colour, density, scaled)\n\n# Loop through each of the colours (which reflect the resource groups)\nfor (c in no_wait$colour) {\n # Filter the plot data to that resource group, then divide the densities by\n # the density from wait time 0\n d <- plot_data %>%\n filter(colour == c) %>%\n mutate(scaled2 = density / no_wait[no_wait$colour==c, \"density\"]) %>%\n ungroup() %>%\n select(scaled, scaled2)\n\n # Find the number of rows where these values match the scaled values\n n_match <- sum(apply(d, 1, function(x) length(unique(x)) == 1))\n n_total <- nrow(d)\n print(sprintf(\"%s out of %s results match\", n_match, n_total))\n}\n\n[1] \"512 out of 512 results match\"\n[1] \"512 out of 512 results match\"\n[1] \"512 out of 512 results match\"\n[1] \"512 out of 512 results match\"\n[1] \"512 out of 512 results match\"" }, { "objectID": "reproduction/scripts/reproduction.html#figure-3", "href": "reproduction/scripts/reproduction.html#figure-3", - "title": "Reproduction", + "title": "Reproduce Figures 2-4 and in-text results 1-3", "section": "Figure 3", "text": "Figure 3\n\n# Create sub-plots\np1 <- create_plot(base_f3,\n group=\"shift\",\n title=\"Baseline\",\n ylab=\"Standardised density of patient in queue\")\np2 <- create_plot(exc_f3,\n group=\"shift\",\n title=\"Exclusive-use\",\n xlab=\"Patient wait time (min)\",\n xlim=c(0, 300),\n breaks_width=100)\np3 <- create_plot(two_f3,\n group=\"shift\",\n title=\"Double angio INRs\",\n xlim=c(0, 250))\n\n# Arrange in a single figure\nggarrange(p1, p2, p3, nrow=1,\n common.legend=TRUE, legend=\"bottom\",\n labels=c(\"A\", \"B\", \"C\"))\n\nWarning: Removed 5 rows containing non-finite outside the scale range\n(`stat_density()`).\nRemoved 5 rows containing non-finite outside the scale range\n(`stat_density()`).\nRemoved 5 rows containing non-finite outside the scale range\n(`stat_density()`).\nRemoved 5 rows containing non-finite outside the scale range\n(`stat_density()`).\n\n\nWarning: Removed 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\n\n\nWarning: Removed 2 rows containing non-finite outside the scale range\n(`stat_density()`).\nRemoved 2 rows containing non-finite outside the scale range\n(`stat_density()`).\n\n\n\n\n\n\n\n\nggsave(path_fig3, width=DEFAULT_WIDTH, height=DEFAULT_HEIGHT)" }, { "objectID": "reproduction/scripts/reproduction.html#figure-4", "href": "reproduction/scripts/reproduction.html#figure-4", - "title": "Reproduction", + "title": "Reproduce Figures 2-4 and in-text results 1-3", "section": "Figure 4", "text": "Figure 4\n\n# Get the relevant results from in-text results 1, 2 and 3\n# Then calculate difference from baseline\nfig4 <- dplyr::bind_rows(txt2 %>% select(scenario, mean),\n txt3 %>%\n filter(scenario==\"Exclusive use\", shift==\"6pm\") %>%\n mutate(scenario=\"Exclusive use (+1h)\") %>%\n select(scenario, mean)) %>%\n mutate(diff = mean - mean[1]) %>%\n filter(scenario!=\"Baseline\") %>%\n mutate(dis_free_gain = abs(diff)*4.2)\n\n# Set order of the bars, and give full labels\nfig4_col <- c(\"Exclusive use\", \"Two AngioINRs\", \"Exclusive use (+1h)\")\nfig4_col_l <- c(\"Exclusive-use\", \"Two angio INRs\", \"Exclusive-use and +1hr work\")\nfig4$scenario <- factor(fig4$scenario, levels=fig4_col)\nfig4$scenario_lab <- plyr::mapvalues(fig4$scenario, from=fig4_col, to=fig4_col_l)\n\nggplot(fig4, aes(x=scenario_lab, y=dis_free_gain)) +\n geom_bar(stat=\"identity\") +\n ylim(0, 32) +\n xlab(\"Scenarios\") +\n ylab(\"Mean disability-free life added (days)\")\n\n\n\n\n\n\n\nggsave(path_fig4, width=5, height=3)" }, - { - "objectID": "reproduction/scripts/reproduction.html#figure-5", - "href": "reproduction/scripts/reproduction.html#figure-5", - "title": "Reproduction", - "section": "Figure 5", - "text": "Figure 5\nCurrently depends on simmer.plot() function that doesn’t work on the imported results from the csv file, so need to allow to run model to produce this plot.\n\nif (isTRUE(run)) {\n baseline_f5 <- run_model(seed = SEED, fig5=TRUE)\n exclusive_f5 <- run_model(exclusive_use = TRUE, seed = SEED, fig5=TRUE)\n twoangio_f5 <- run_model(angio_inr = 2, angio_ir=0, seed = SEED, fig5=TRUE)\n \n # Replace resource (which has been filtered to angioINR) with scenario\n baseline_f5$resource <- \"Baseline\"\n exclusive_f5$resource <-\"Exclusive-use\"\n twoangio_f5$resource <- \"Two angio INRs\"\n \n # Combine into single object\n fig5_df <- dplyr::bind_rows(baseline_f5, exclusive_f5, twoangio_f5)\n \n # Create figure using simmer's plot\n p <- plot(fig5_df, metric=\"utilization\") +\n xlab(\"Scenarios\") +\n ylab(\"Utilisation\") +\n scale_y_continuous(labels = scales::percent, limits=c(0, 0.4)) +\n ggtitle(\"\") +\n geom_text(aes(label=round(.data$Q50*100)), vjust=-1)\n p\n \n # Save to provided path\n ggsave(path_fig5, width=5, height=2.5)\n}" - }, - { - "objectID": "reproduction/scripts/reproduction.html#supplementary-figure", - "href": "reproduction/scripts/reproduction.html#supplementary-figure", - "title": "Reproduction", - "section": "Supplementary figure", - "text": "Supplementary figure\n\n# Create sub-plots\np1 <- create_plot(base_f2,\n group=\"resource\",\n title=\"Baseline\",\n ylab=\"Standardised density of patient in queue\")\np2 <- create_plot(base_sup_double,\n group=\"resource\",\n title=\"Doubling ECR patients\",\n xlab=\"Patient wait time (min)\",\n xlim=c(0, 300),\n breaks_width=100)\np3 <- create_plot(base_sup_triple,\n group=\"resource\",\n title=\"Tripling ECR patients\",\n xlim=c(0, 300))\n\n# Arrange in a single figure\nggarrange(p1, p2, p3, nrow=1,\n common.legend=TRUE, legend=\"bottom\",\n labels=c(\"A\", \"B\", \"C\"))\n\nWarning: Removed 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\nRemoved 1 row containing non-finite outside the scale range (`stat_density()`).\n\n\nWarning: Removed 4 rows containing non-finite outside the scale range\n(`stat_density()`).\nRemoved 4 rows containing non-finite outside the scale range\n(`stat_density()`).\n\n\nWarning: Removed 3 rows containing non-finite outside the scale range\n(`stat_density()`).\nRemoved 3 rows containing non-finite outside the scale range\n(`stat_density()`).\n\n\n\n\n\n\n\n\nggsave(path_supfig, width=DEFAULT_WIDTH, height=DEFAULT_HEIGHT)" - }, { "objectID": "reproduction/scripts/reproduction.html#time-elapsed", "href": "reproduction/scripts/reproduction.html#time-elapsed", - "title": "Reproduction", + "title": "Reproduce Figures 2-4 and in-text results 1-3", "section": "Time elapsed", "text": "Time elapsed\n\nif (isTRUE(run)) {\n end.time <- Sys.time()\n elapsed.time <- round((end.time - start.time), 3)\n elapsed.time\n}" }, @@ -963,7 +1026,7 @@ "href": "logbook/posts/2024_07_14/index.html#untimed-research-compendium", "title": "Day 10", "section": "Untimed: Research compendium", - "text": "Untimed: Research compendium\n\nTried adding parallel processing in model.R to speed it up\n\nAdd future.apply to the environment\nplan(multisession, workers=max(availableCores()-5, 1))\nfuture_lapply()\nHowever, it took longer than usual! So I removed it\n\nReorganising\n\nMoved scripts into a scripts/ folder\nMoved help functions from reproduction.Rmd into seperate R script (primarily so can reuse in tests more easily)\n\nSet ggsave() image width as realised it otherwise varied with window size when running" + "text": "Untimed: Research compendium\n\nTried adding parallel processing in model.R to speed it up\n\nAdd future.apply to the environment\nplan(multisession, workers=max(availableCores()-5, 1))\nfuture_lapply()\nHowever, it took longer than usual! So I removed it\n\nReorganising\n\nMoved scripts into a scripts/ folder\nMoved help functions from reproduction.Rmd into seperate R script (primarily so can reuse in tests more easily)\n\nSet ggsave() image width as realised it otherwise varied with window size when running\n\nCreate tests to check model results are consistent\n\nStarted with creating a basic test saving tempfile csv and loading it to compare to another dataframe\nThen made a test with two example models being run for 3 replications and comparing results\nThen, set up with two files, as testthat can run files in parallel, and configured parallel processing. This involved:\n\nAdding Config/testthat/parallel: true to DESCRIPTION\nCreate project-specific environment file with nano reproduction/.Renviron and setting TESTTHAT_CPUS=4\n\nRan testthat::test_dir(\"tests\"), although seemed to just run sequentially. Confirmed by checking testthat::isparallel() which returned FALSE.\nTried adding Config/testthat/start-first: shifts, model to DESCRIPTION and it ignored the order, so it appears the issue is it is not using info from the DESCRIPTION file\nChecked version and it is correct for running in parallel (testthat>=3.0.0)\nTried instead running testthat::test_local(), and moving tests into a folder testthat/, and this returned an error Could not find a root 'DESCRIPTION' file that starts with '^Package' in /home/amy/Documents/stars/stars-reproduce-huang-2019/reproduction.\nChanged DESCRIPTION to add Package and re-run - but this had error that installation of renv failed. Same error occurs if run testthat::test_dir(). It says to Try removing ‘/home/amy/.cache/R/renv/library/reproduction-0912b448/linux-ubuntu-jammy/R-4.4/x86_64-pc-linux-gnu/00LOCK-renv’. I deleted this file (navigated there than rm -r 00LOCK-renv) then re-ran. However, this kept getting the same error message with that same file being created.\nTried removing Package from DESCRIPTION and running testthat::test_dir(\"tests/testthat\", load_package=\"none\") - but that ignores the order in DESCRIPTION\nTried testthat::test_dir(\"tests/testthat\", load_package=\"source\") which had error that Field 'Version' not found. Once I had this and re-ran, it ran the tests in the specified order! From Config/testthat/start-first: shifts, model\nI then add in Config/testthat/parallel: true and Config/testthat/edition: 3 but it had the same renv error as before\nThen decided to just run without parallel for now, so removed those lines from DESCRIPTION, deleted the .Renviron file, and put tests in a single file\n\nPackage: huang2019\nVersion: 0.1\nConfig/testthat/start-first: shifts, model\nConfig/testthat/parallel: true\nConfig/testthat/edition: 3\n\nCreated function to simplify testing, then wrote tests fora selection of scenarios (not all scenarios, to minimise run time).\nTest was failing with error of Length mismatch: comparison on first 2 components. I tried changing from expect_equal to using all.equal() and then expect_true(is_true()) on result. But this returned the same error!\nI tried running everything manually in the console so I could inspect the dataframes myself.\n\nfile = \"tests/testthat/expected_results/fig2_baseline.csv.gz\"\nexp <- as.data.frame(data.table::fread(file))\ninputs=list(seed=200)\nresult <- do.call(run_model, inputs)\n\nI realised the issue was that the expected result included a column shift where value throughout was 5pm. This was likely due to changing it at some point but not having re-run the whole script since, so I did that (and timed it!). I removed some of the model variants that aren’t to produce results from the paper (E.g. varying seeds)\n\nIt takes a while to run and, midway through, the R session encountered a fatal error and aborted. Tried again, and it failed again on exclusive_f5 <- run_model(exclusive_use = TRUE, seed = SEED, fig5=TRUE).\nI’m suspecting this might be due to the size of the dataframes produced? So tried removing them from the environment after saving and ran again - but it still crashed, this time on the next run_model() statement\nI considered trying again with parallelisation but, given I hadn’t had much luck with that before, and given that the issue here is with R crashing (and so parallelisation actually may not help), I decided to instead split up reproduction.rmd into a few smaller files.\n\n\nDocker\n\nUsed the RStudio documentation and this tutorial to write a Dockerfile." }, { "objectID": "original_study/desECR/figure_description.html", diff --git a/sitemap.xml b/sitemap.xml index 32c2323..442da88 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -44,6 +44,10 @@ https://pythonhealthdatascience.github.io/stars-reproduce-huang-2019/evaluation/artefacts.html 2024-07-12T10:15:58.709Z + + https://pythonhealthdatascience.github.io/stars-reproduce-huang-2019/reproduction/scripts/reproduction_fig5.html + 2024-07-16T14:56:22.223Z + https://pythonhealthdatascience.github.io/stars-reproduce-huang-2019/quarto_site/study_publication.html 2024-07-04T14:45:18.063Z @@ -64,9 +68,13 @@ https://pythonhealthdatascience.github.io/stars-reproduce-huang-2019/quarto_site/license.html 2024-07-03T11:37:23.942Z + + https://pythonhealthdatascience.github.io/stars-reproduce-huang-2019/reproduction/scripts/reproduction_supp.html + 2024-07-16T17:24:57.094Z + https://pythonhealthdatascience.github.io/stars-reproduce-huang-2019/reproduction/scripts/reproduction.html - 2024-07-16T08:41:53.076Z + 2024-07-16T17:24:52.594Z https://pythonhealthdatascience.github.io/stars-reproduce-huang-2019/evaluation/reporting.html @@ -102,7 +110,7 @@ https://pythonhealthdatascience.github.io/stars-reproduce-huang-2019/logbook/posts/2024_07_14/index.html - 2024-07-16T08:19:12.554Z + 2024-07-16T14:20:28.264Z https://pythonhealthdatascience.github.io/stars-reproduce-huang-2019/original_study/desECR/figure_description.html