Publishing analyses + Peer code review

Reproducing analyses from a persistant project repository

Author

Affiliation

Daniela Palleschi

Humboldt-Universität zu Berlin

Published

June 24, 2024

Learning objectives

Today we will…

set-up our first OSF project
share necessary files to make our OSF project reproducible
conduct a code review of our own project
conduct a peer code review

Resources

DeBruine (2022) and accompanying slides

1 Open and FAIR data

recall the FAIR principles we discussed at the beginning of term
- data should be Findable, Accessible, Interoperable, and Reusable
- we’re extending these principles to our analyses as well
so far, our data and analyses are stored locally on our machines
- we need to share them with persistent public storage
- e.g., GitHub or GitLab, the Open Science Framework (OSF) or Zenodo

Figure 1: Source: National Library of Medicine (all rights reserved)

2 OSF: Open Science Framework

we’ll use the OSF (https://osf.io/), which is a user-friendly project management platform
- provides persistant URLs
- user-friendly (drag-and-drop)
- popular for open storage of data, materials, and analyses
also offers pre-registration and pre-print storage
can also be connected to Dropbox, Google Drive, GitHub and GitLab
- but this requires you to have your data and analyses stored on these services, the security of which cannot be guaranteed long-term
if you don’t already have an OSF account, click the ‘sign up’ button at the top right of the OSF homepage

3 Our first OSF repo

we’ll start by creating a new OSF project

Sign in to the OSF
Click on ‘Create new project’
- provide a name such as ‘Open Science Practices: Reproducibility coursework (SoSe2024)’
- Important: set storage location to Germany - Frankfurt
- add some concise description
Navigate to your project and explore the page and tabs

3.1 Private or public

you should notice near the top right corner a button ‘Make Public’
- this tells you that your project is currently private
- this means nobody can see it but you (or any collaborators you add)
typically you would make a repository public when it has been accepted for publication, or if you publish a pre-print
- you can also make it public before this, but this is something to discuss with your collaborators

3.2 Contributors

repository contributors are typically co-authors or collaborators for a project
click on the ‘Contributors’ tab (top right of the screen)
- click on ‘+ Add’, find my account, and add me as a collaborator with ‘Read’ rights
- make sure the ‘Bibliographic Contributor’ button is checked
- this just means that I will be included as bibliographic author if this repo is ever cited
go back to the project page, do you see any changes?

3.3 Adding files

our purpose for creating an OSF project was to share our data and materials
- to do this, we navigate to the ‘Files’ tab
rather unfortunately, we can only upload files (i.e., not entire folders)
- this has the benefit of meaning our folder structure must be intentional
- but the drawback that it’s quite tedious if you want to share a large project
let’s start by adding our data and scripts
- add a folder called data
- and another folder called scripts or code, or whatever you prefer

3.4 Adding data

under data, add the three csv files you presumably have in the same folder in your project (drag and drop them, or select the big green +)
- data_lifetime_pilot.csv
- tidy_data_lifetime_pilot.csv
- tidy_data_lifetime_pilot_dictionary.csv

3.5 Adding scripts

under the scripts folder add the scripts where we worked with the data:
- our wrangling script
- our tables script
- our data visualisation script

3.5.1 Adding PDF output

optionally, you can also upload the PDF output produced by each script
- this means that anybody viewing your project doesn’t have to download the Quarto scripts to see what was done (as they aren’t viewable in-browser)

3.5.2 Structuring your `scripts/` folder

unlike the data folder, how you organise and name this folder on OSF is more flexible
- because we (likely) aren’t accessing these scripts from somewhere else in the project (unlike loading data from the data folder)
you can include them in sub-folders if you prefer
- the structure of this folder is organisational, and not fundamental to reproducibility
- more organised folders make it easier to navigate for someone not familiar with the project structure
- keeping this structure identical to your actual project structure is also ideal for on-going larger projects, but it’s up to you

Checklist: Share data and code

At this point, your OSF project should

be private (this is the default for a new project)
have me as a collaborator
contain the folders data/ and scripts/
- which in turn contain the 3 CSV files and 3 Quarto scripts
- ideally scripts/ will also contain the 3 PDF script outputs

4 Checking reproducibility

a code review refers to when somebody else checks your code
- this should also include a check for reproducibility
- as well as validity and good coding practices (not our focus right now)
why should we do it?
- firstly, everybody makes mistakes! increases the chances they’ll be fixed
- tests reproducibility
let’s do a quick code review of our own OSF repos, checking to see if we can download and re-run our own analyses

4.1 Download OSF repo

let’s start by downloading our OSF repo
- from the project overview page, go to the ‘Files’ pane
- click on ‘OSF Storage (Germany - Frankfurt)’
- Click ‘Download as zip’ button and store somewhere useful/rename as needed

4.2 Reproduce your analyses

first, close all R projects you currently have open
- this is because RStudio might try to open your downloaded scripts in an already open RProject, which we don’t want
now, navigate to the zip you just downloaded and decompress it (double-click)
- select a Quarto script from scripts/
- try to run the script, does it run?
- probably not…let’s discuss why

5 Revisiting reproducibility

we’ve shared the code, not just the data
- this has been strongly encouraged in the reproducibility research as of late (e.g., the title Share the code, not just the data…, Laurinavichyute et al. (2017))
- but is this sufficient to ensure long-term reproducibility?
- Laurinavichyute et al. (2017) (among others) suggest many more steps that should be taken to improve reproducibility
our focus is on sharing data and analyses with the aim of reproducibility, not just documenting what was done
- so we have to share what is necessary to make our project reproducible
- e.g., that it can be run with the same environment on another machine?
so what should we share?

5.1 Improving code reproducibility

what structural dependecies do our scripts have?
- e.g., filepaths and folder names
consider, for example, how we accessed the data from our scripts
- did we use setwd()?
- did we use filepaths?
- no, we used the here() package within an R project
- this meant we used our project root directory as our working directory
so, we should, at minimum, also include the .Rproj file at the project root directory

5.2 Reproducibility spectrum

reproducibility is on a continuum, referred to as the reproducibility spectrum in Peng (2011) (Figure 3)
- linked means “all data, metadata, and code [is] stored and linked with each other and with corresponding publications” (Peng, 2011, p. 1227)
- executable is not explained, and is more difficult to guarantee long-term as it depends on software versions
- but at minimum we can assume it refers to code running on someone else’s machine

5.3 Reproducibility iceberg

a more detailed description of this continuum is givin in Rodrigues (2023)’s reproducibility iceberg (Figure 4)
- our project is currently somewhere near the top-middle of the iceberg
- we’re not using GitHub, with is a developer platform and useful for version control and is beyond the scope of this course
  - GitHub and OSF serve some overlapping, but not identical, purposes
importantly, the iceberg explicitly mentioned the renv package
- this reminds us that we should be sharing some files generating by renv

5.4 Packages

included the .Rproj file won’t mean that the person who downloads it will also have our packages
- e.g., they might not have the here package, and won’t even be able to use our code to load in the data
as we discussed some weeks ago, this is what the renv package does
- we created project library, i.e., a lockfile (renv.lock) which is a project

Checklist: Share renv.lock

Add the renv.lock file to your OSF project repo (in the project root directory).

5.5 Auto-loaders

we also need to include our .Rprofile file
- this is our auto-loader
- whenever we open the R project, this file will be run
it currently contains just source("renv/activate.R")
- this means: when you open the R project, run the renv/activate.R file
so, we need to include our renv/activate.R file in our OSF repo
- including these two will automatically install the relevant version of renv

.Rprofile in Finder

Some files are usually invisible on a Mac, such as those that start with dot (like .Rprofile). This makes it difficult to simply drag and drop the .Rprofile file to the OSF. To make such files viewable in Finder, navigate to the relevant project folder and use the keyboard shortcut Ctrl + Shift + Dot. These files will then appear greyed out.

Figure 5: Use the shortcut `Ctrl + Shift + Dot` to view hidden files on a Mac

5.6 README

remember to update your README accordingly!
- this can be updated as you add more to your project
the project README.md will ideally have information that is useful once the project is downloaded in its entirety
- e.g., brief info about the project/data
- description of the folder/file structure
- any info required for reproducibility
e.g., anybody who downloads the project will need to run renv::restore() to restore your project library (but this will only work if they’re using the same R version!)
- so be sure to include that info!

For example, you could add something like this:

**Reproduce analyses**: Once you open this project, `renv` will automatically be installed. After this is complete, you please run `renv::restore()` to restore the project package library. This will not affect the package versions you have already installed globally on your machine.

Checklist: Share .Rprofile and renv/activate.R

Your project should now have

.Rprofile in the project root directory
renv/activate.R in the project root directory
- i.e., a folder renv/ in the root directory
- and the activate.R file in this renv/ folder
a README.md file in the root directory

5.7 OSF repo structure

your OSF project should look something like Figure 6
- N.B., there are more files under scripts/

Figure 6: Your OSF should now look like this

6 Code review

we’ll again try to reproduce our own analyses before sharing the OSF project with a peer
again, close all R projects you currently have open
now, navigate to the zip you just downloaded
- select a Quarto script from scripts/
- try to run the script, does it run?

6.2 Swapping project URLs

go to Moodle and add your OSF repo URL
go to someone else’s OSF repo and download their project
- inspect the project metafiles (e.g., README)
- try to reproduce the analyses, can you?

Anonymising your scripts (optional)

If you have a relatively large project with your name at the beginning of multiple scripts, it can be tedious to manually remove it. And you might not be sure you actually took your name out of everything!

This can be used using RStudio’s Global Find:

press Cmd+Shift+F
in the pop-up, choose your filepath (for me: the OSF folder only) and hit enter
then toggle to ‘Replace’ when a tab pops up next to the Terminal
type in your replacement (e.g., [Anonymized for peer review]), and hit “Replace All”

Important: this will work for HTML and R/Quarto/Rmd scripts, but not for PDFs! so you might want to re-render all PDFs. As far as I can tell you have to re-render each PDF. If you’re working in a Quarto project (and not an .Rproj), then you can use quarto render subfoldername --to pdf in the Terminal to re-render only the OSF PDFs. We didn’t discuss Quarto projects in this course, however.

After the manuscript is accepted, you can then reverse this step: use the Global Find to replace [Anonymized for peer review] with your name! This is why I suggest surrounding the phrase with [], it ensures you don’t accidentally replace the string ‘anonymized for peer review’ elsewhere in your files (e.g., maybe you wrote in some analysis plan “all scripts will be anonymized for peer review”, which would then be changed to “all scripts will be Daniela Palleschi” if I had replaced Anonymized for peer review with my name).

7 Quiz 2: Code Review Q&A

there is a Code Review Q&A on Moodle, which counts towards course credit
- consists of mostly true/false questions about your code review
do this quiz by next week
- nobody is penalised if the code isn’t reproducible
the point is to learn how to do a code review and how to improve our own projects to improve reproducibility

Learning objectives 🏁

Today we…

set-up our first OSF projectc ✅
shared necessary files to make our OSF project reproducible ✅
conducted a code review of our own project ✅
conducted a peer code review ✅

Session Info

print(sessionInfo(), locale = F)

R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS Ventura 13.2.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

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

loaded via a namespace (and not attached):
 [1] digest_0.6.35     fastmap_1.2.0     xfun_0.44         magrittr_2.0.3   
 [5] knitr_1.47        htmltools_0.5.8.1 rmarkdown_2.27    cli_3.6.2        
 [9] renv_1.0.7        compiler_4.4.0    rprojroot_2.0.4   here_1.0.1       
[13] rstudioapi_0.16.0 tools_4.4.0       evaluate_0.23     Rcpp_1.0.12      
[17] yaml_2.3.8        magick_2.8.3      rlang_1.1.4       jsonlite_1.8.8   
[21] htmlwidgets_1.6.4

References

DeBruine, L. (2022). Intro to code review. https://debruine.github.io/code-review/

Laurinavichyute, A., Yadav, H., & Vasishth, S. (2017). Share the code, not just the data: A case study of the reproducibility of JML articles published under the open data policy. Preprint, 1–77.

Peng, R. D. (2011). Reproducible Research in Computational Science. Science, 334(6060), 1226–1227. https://doi.org/10.1126/science.1213847

Rodrigues, B. (2023). Building reproducible analytical pipelines with R.

--- title: "Publishing analyses + Peer code review" subtitle: "Reproducing analyses from a persistant project repository" author: "Daniela Palleschi" institute: Humboldt-Universität zu Berlin lang: en date: 2024-06-24 format: html: output-file: osf.html number-sections: true toc: true code-overflow: wrap code-tools: true self-contained: true pdf: output-file: osf.pdf toc: true number-sections: true colorlinks: true code-overflow: wrap revealjs: output-file: osf_slides.html include-in-header: ../../mathjax.html # for multiple equation hyperrefs code-overflow: wrap theme: [dark] width: 1600 height: 900 # chalkboard: # src: chalkboard.json progress: true scrollable: true # smaller: true slide-number: c/t code-link: true incremental: true # number-sections: true toc: false toc-depth: 2 toc-title: 'Overview' navigation-mode: linear controls-layout: bottom-right fig-cap-location: top font-size: 0.6em slide-level: 4 self-contained: true title-slide-attributes: data-background-size: 15% data-background-position: 50% 92% fig-align: center fig-dpi: 300 editor_options: chunk_output_type: console bibliography: references.bib csl: ../../apa.csl execute: echo: true eval: true --- ```{r} #| eval: false #| echo: false # should be run manually rbbt::bbt_update_bib(here::here("slides/11-osf/osf.qmd")) ``` # Learning objectives {.unlisted .unnumbered} Today we will... - set-up our first OSF project - share necessary files to make our OSF project reproducible - conduct a code review of our own project - conduct a peer code review # Resources {.unnumbered .unlisted} - @debruine_intro_2022 and accompanying [slides](https://debruine.github.io/code-review/#/title-slide) # Open and FAIR data ::: {.content-visible when-format="revealjs"} :::: columns ::: {.column width="60%"} - recall the FAIR principles we discussed at the beginning of term + data should be Findable, Accessible, Interoperable, and Reusable + we're extending these principles to our *analyses* as well - so far, our data and analyses are stored locally on our machines + we need to share them with persistent public storage + e.g., GitHub or GitLab, the Open Science Framework (OSF) or Zenodo ::: ::: {.column width="40%"} ```{r} #| echo: false #| out-width: "95%" #| fig-align: center #| label: fig-FAIR-slides #| fig-cap: "Source: [National Library of Medicine](https://www.nlm.nih.gov/oet/ed/cde/tutorial/02-300.html) (all rights reserved)" magick::image_negate(magick::image_read(here::here("media/FAIR_nlm_nih.png"))) ``` ::: :::: ::: ::: {.content-hidden when-format="revealjs"} - recall the FAIR principles we discussed at the beginning of term + data should be Findable, Accessible, Interoperable, and Reusable + we're extending these principles to our *analyses* as well - so far, our data and analyses are stored locally on our machines + we need to share them with persistent public storage + e.g., GitHub or GitLab, the Open Science Framework (OSF) or Zenodo ```{r} #| echo: false #| out-width: "50%" #| fig-align: center #| label: fig-FAIR #| fig-cap: "Source: [National Library of Medicine](https://www.nlm.nih.gov/oet/ed/cde/tutorial/02-300.html) (all rights reserved)" magick::image_read(here::here("media/FAIR_nlm_nih.png")) ``` ::: # OSF: Open Science Framework - we'll use the OSF ([https://osf.io/](https://osf.io/)), which is a user-friendly project management platform + provides persistant URLs + user-friendly (drag-and-drop) + popular for open storage of data, materials, and analyses - also offers pre-registration and pre-print storage - can also be connected to Dropbox, Google Drive, GitHub and GitLab + but this requires you to have your data and analyses stored on these services, the security of which cannot be guaranteed long-term - if you don't already have an OSF account, click the 'sign up' button at the top right of the [OSF homepage](https://osf.io/) # Our first OSF repo - we'll start by creating a new OSF project 1. Sign in to the OSF 2. Click on 'Create new project' + provide a name such as 'Open Science Practices: Reproducibility coursework (SoSe2024)' + **Important**: set storage location to `Germany - Frankfurt` + add some concise description 3. Navigate to your project and explore the page and tabs ## Private or public - you should notice near the top right corner a button 'Make Public' + this tells you that your project is currently private + this means nobody can see it but you (or any collaborators you add) - typically you would make a repository public when it has been accepted for publication, or if you publish a pre-print + you can also make it public before this, but this is something to discuss with your collaborators ## Contributors - repository contributors are typically co-authors or collaborators for a project - click on the 'Contributors' tab (top right of the screen) + click on '+ Add', find my account, and add me as a collaborator with 'Read' rights + make sure the 'Bibliographic Contributor' button is checked + this just means that I will be included as bibliographic author if this repo is ever cited - go back to the project page, do you see any changes? ## Adding files - our purpose for creating an OSF project was to share our data and materials + to do this, we navigate to the 'Files' tab - rather unfortunately, we can only upload files (i.e., not entire folders) + this has the benefit of meaning our folder structure must be *intentional* + but the drawback that it's quite tedious if you want to share a large project - let's start by adding our data and scripts + add a folder called `data` + and another folder called `scripts` or `code`, or whatever you prefer ## Adding data - under `data`, add the three `csv` files you presumably have in the same folder in your project (drag and drop them, or select the big green `+`) + `data_lifetime_pilot.csv` + `tidy_data_lifetime_pilot.csv` + `tidy_data_lifetime_pilot_dictionary.csv` ## Adding scripts - under the `scripts` folder add the scripts where we worked with the data: + our wrangling script + our tables script + our data visualisation script ### Adding PDF output - optionally, you can also upload the PDF output produced by each script + this means that anybody viewing your project doesn't have to download the Quarto scripts to see what was done (as they aren't viewable in-browser) ### Structuring your `scripts/` folder - unlike the `data` folder, how you organise and name this folder on OSF is more flexible + because we (likely) aren't accessing these scripts from somewhere else in the project (unlike loading data from the `data` folder) - you can include them in sub-folders if you prefer + the structure of this folder is organisational, and not fundamental to reproducibility + more organised folders make it easier to navigate for someone not familiar with the project structure + keeping this structure identical to your actual project structure is also ideal for on-going larger projects, but it's up to you ::: {.content-visible when-format="revealjs"} ## {-} ::: ::: {.callout-note} ## Checklist: Share data and code ::: nonincremental At this point, your OSF project should - be private (this is the default for a new project) - have me as a collaborator - contain the folders `data/` and `scripts/` + which in turn contain the 3 `CSV` files and 3 Quarto scripts + ideally `scripts/` will also contain the 3 PDF script outputs ::: ::: # Checking reproducibility - a code review refers to when somebody else checks your code + this should also include a check for reproducibility + as well as validity and good coding practices (not our focus right now) - why should we do it? + firstly, everybody makes mistakes! increases the chances they'll be fixed + tests reproducibility - let's do a quick code review of our own OSF repos, checking to see if we can download and re-run our own analyses ## Download OSF repo - let's start by downloading our OSF repo + from the project overview page, go to the 'Files' pane + click on 'OSF Storage (Germany - Frankfurt)' + Click 'Download as zip' button and store somewhere useful/rename as needed ```{r} #| echo: false #| out-width: "100%" #| fig-align: center #| label: fig-osf_download_zip #| fig-cap: "How to download an OSF repo" magick::image_read(here::here("media/osf_download_zip.png")) ``` ## Reproduce your analyses - first, close all R projects you currently have open + this is because RStudio might try to open your downloaded scripts in an already open RProject, which we don't want - now, navigate to the zip you just downloaded and decompress it (double-click) + select a Quarto script from `scripts/` + try to run the script, does it run? + probably not...let's discuss why # Revisiting reproducibility - we've shared the code, not just the data + this has been strongly encouraged in the reproducibility research as of late (e.g., the title *Share the code, not just the data...*, @laurinavichyute_share_2017) + but is this sufficient to ensure long-term reproducibility? + @laurinavichyute_share_2017 (among others) suggest many more steps that should be taken to improve reproducibility - our focus is on sharing data and analyses with the aim of reproducibility, not just documenting what was done + so we have to share what is necessary to make our project reproducible + e.g., that it can be run with the same environment on another machine? - so what should we share? ## Improving code reproducibility - what structural dependecies do our scripts have? + e.g., filepaths and folder names - consider, for example, how we accessed the data from our scripts + did we use `setwd()`? + did we use filepaths? + no, we used the `here()` package within an R project + this meant we used our project root directory as our working directory - so, we should, at minimum, also include the `.Rproj` file at the project root directory ## Reproducibility spectrum - reproducibility is on a continuum, referred to as the *reproducibility spectrum* in @peng_reproducible_2011 (@fig-peng_2011) + *linked* means "*all data, metadata, and code [is] stored and linked with each other and with corresponding publications*" [@peng_reproducible_2011, p. 1227] + *executable* is not explained, and is more difficult to guarantee long-term as it depends on software versions + but at minimum we can assume it refers to code running on someone else's machine ```{r} #| echo: false #| out-width: "100%" #| fig-align: center #| label: fig-peng_2011 #| fig-cap: "Source: @peng_reproducible_2011" magick::image_read(here::here("media/peng_2011_reproducibility_spectrum.png")) ``` ::: {.content-visible when-format="revealjs"} ## Reproducibility iceberg {.smaller} :::: columns ::: {.column width="50%"} ```{r} #| echo: false #| out-width: "90%" #| fig-align: center #| label: fig-rodrigues_2023_slides #| fig-cap: "Source: @rodrigues_building_nodate" magick::image_read(here::here("media/rodrigues_2023_reproducibility_iceberg.png")) ``` ::: ::: {.column width="50%"} - a more detailed description of this continuum is givin in @rodrigues_building_nodate's *reproducibility iceberg* (@fig-rodrigues_2023_slides) + our project is currently somewhere near the top-middle of the iceberg + we're not using GitHub, with is a developer platform and useful for version control and is beyond the scope of this course + GitHub and OSF serve some overlapping, but not identical, purposes - importantly, the iceberg explicitly mentioned the `renv` package + this reminds us that we should be *sharing* some files generating by `renv` ::: :::: ::: ::: {.content-hidden when-format="revealjs"} ## Reproducibility iceberg - a more detailed description of this continuum is givin in @rodrigues_building_nodate's *reproducibility iceberg* (@fig-rodrigues_2023) + our project is currently somewhere near the top-middle of the iceberg + we're not using GitHub, with is a developer platform and useful for version control and is beyond the scope of this course + GitHub and OSF serve some overlapping, but not identical, purposes - importantly, the iceberg explicitly mentioned the `renv` package + this reminds us that we should be *sharing* some files generating by `renv` ```{r} #| echo: false #| out-width: "75%" #| fig-align: center #| label: fig-rodrigues_2023 #| fig-cap: "Source: @rodrigues_building_nodate" magick::image_read(here::here("media/rodrigues_2023_reproducibility_iceberg.png")) ``` ::: ## Packages - included the `.Rproj` file won't mean that the person who downloads it will also have our packages + e.g., they might not have the `here` package, and won't even be able to use our code to load in the data - as we discussed some weeks ago, this is what the `renv` package does + we created project library, i.e., a lockfile (`renv.lock`) which is a project ::: {.content-visible when-format="revealjs"} ## {-} ::: ::: {.callout-note} ## Checklist: Share `renv.lock` Add the `renv.lock` file to your OSF project repo (in the project root directory). ::: ## Auto-loaders - we also need to include our `.Rprofile` file + this is our auto-loader + whenever we open the R project, this file will be run - it currently contains just `source("renv/activate.R")` + this means: when you open the R project, run the `renv/activate.R` file - so, we need to include our `renv/activate.R` file in our OSF repo + including these two will automatically install the relevant version of `renv` ::: {.content-visible when-format="revealjs"} ## {.smaller} ::: ::: {.callout-tip} ## `.Rprofile` in Finder Some files are usually invisible on a Mac, such as those that start with dot (like `.Rprofile`). This makes it difficult to simply drag and drop the `.Rprofile` file to the OSF. To make such files viewable in Finder, navigate to the relevant project folder and use the keyboard shortcut `Ctrl + Shift + Dot`. These files will then appear greyed out. ```{r} #| echo: false #| out-width: "100%" #| fig-align: center #| label: fig-Rprofile #| fig-cap: "Use the shortcut `Ctrl + Shift + Dot` to view hidden files on a Mac" magick::image_read(here::here("media/finder_Rprofile.png")) ``` ::: ## README - remember to update your README accordingly! + this can be updated as you add more to your project - the project `README.md` will ideally have information that is useful once the project is downloaded in its entirety + e.g., brief info about the project/data + description of the folder/file structure + any info required for reproducibility - e.g., anybody who downloads the project will need to run `renv::restore()` to restore your project library (but this will only work if they're using the same R version!) + so be sure to include that info! ::: {.content-visible when-format="revealjs"} ## REAMDE renv explanation ::: For example, you could add something like this: ```{markdown} **Reproduce analyses**: Once you open this project, `renv` will automatically be installed. After this is complete, you please run `renv::restore()` to restore the project package library. This will not affect the package versions you have already installed globally on your machine. ``` ::: {.content-visible when-format="revealjs"} ## {-} ::: ::: {.callout-note} ## Checklist: Share `.Rprofile` and `renv/activate.R` ::: nonincremental Your project should now have - `.Rprofile` in the project root directory - `renv/activate.R` in the project root directory + i.e., a folder `renv/` in the root directory + and the `activate.R` file in this `renv/` folder - a `README.md` file in the root directory ::: ::: ## OSF repo structure ::: {.content-hidden when-format="revealjs"} - your OSF project should look something like @fig-osf_download_full_zip + N.B., there are more files under `scripts/` ::: ```{r} #| echo: false #| out-width: "75%" #| fig-align: center #| label: fig-osf_download_full_zip #| fig-cap: "Your OSF should now look like this" magick::image_read(here::here("media/osf_download_full_zip.png")) ``` # Code review - we'll again try to reproduce our own analyses before sharing the OSF project with a peer - again, close all R projects you currently have open - now, navigate to the zip you just downloaded + select a Quarto script from `scripts/` + try to run the script, does it run? ## Sharing your project - we need to share our project with others + your project is still private + so you need to produce a link because the URL won't work for non-contributors - produce a View-only link + you can do this in Settings (top right) + give an informative name (so you remember why you created this link) - if you select `Anonymize`, your name will be removed from the project + this is useful for e.g., blind peer review + but will *not* remove your name from your scripts! ## Swapping project URLs - go to Moodle and add your OSF repo URL - go to someone else's OSF repo and download their project + inspect the project metafiles (e.g., README) + try to reproduce the analyses, can you? ::: {.content-visible when-format="revealjs"} ## {-} ::: ::: {.callout-tip} # Anonymising your scripts (optional) ::: nonincremental If you have a relatively large project with your name at the beginning of multiple scripts, it can be tedious to manually remove it. And you might not be sure you actually took your name out of everything! This can be used using RStudio's [Global Find](https://posit.co/blog/rstudio-1-3-the-little-things/): + press `Cmd+Shift+F` + in the pop-up, choose your filepath (for me: the OSF folder *only*) and hit enter + then toggle to 'Replace' when a tab pops up next to the Terminal + type in your replacement (e.g., `[Anonymized for peer review]`), and hit "Replace All" **Important**: this will work for HTML and R/Quarto/Rmd scripts, but *not* for PDFs! so you might want to re-render all PDFs. As far as I can tell you have to re-render each PDF. If you're working in a Quarto project (and not an `.Rproj`), then you can use `quarto render subfoldername --to pdf` in the Terminal to re-render only the OSF PDFs. We didn't discuss Quarto projects in this course, however. After the manuscript is accepted, you can then reverse this step: use the Global Find to replace `[Anonymized for peer review]` with your name! This is why I suggest surrounding the phrase with `[]`, it ensures you don't accidentally replace the string 'anonymized for peer review' elsewhere in your files (e.g., maybe you wrote in some analysis plan "all scripts will be anonymized for peer review", which would then be changed to "all scripts will be Daniela Palleschi" if I had replaced `Anonymized for peer review` with my name). ::: ::: # Quiz 2: Code Review Q&A ::: nonincremental - there is a Code Review Q&A on Moodle, which *counts towards course credit* + consists of mostly true/false questions about your code review - do this quiz by next week + nobody is penalised if the code isn't reproducible - the point is to learn how to do a code review and how to improve our own projects to improve reproducibility ::: # Learning objectives 🏁 {.unnumbered .unlisted .uncounted} Today we... - set-up our first OSF projectc ✅ - shared necessary files to make our OSF project reproducible ✅ - conducted a code review of our own project ✅ - conducted a peer code review ✅ # Session Info {.unnumbered} ```{r} print(sessionInfo(), locale = F) ``` # References {.unlisted .unnumbered visibility="uncounted"} --- nocite: | @rodrigues_building_nodate @debruine_intro_2022 --- ::: {#refs custom-style="Bibliography"} :::