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projects

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Authors

Nikolas I. Krieger, M.S.;<sup>1</sup> Adam T. Perzynski, Ph.D.;<sup>2</sup> and Jarrod E. Dalton, Ph.D.<sup>1,3</sup>

<sup>1</sup> Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue (JJN3), Cleveland, OH, 44195
<sup>2</sup> Center for Healthcare Research and Policy, Case Western Reserve University at MetroHealth, 2500 MetroHealth Drive, Cleveland, OH 44109
<sup>3</sup> Cleveland Clinic Lerner College of Medicine, Case Western Reserve University

Acknowledgements:
The authors of this package acknowledge the support provided by members of the Northeast Ohio Cohort for Atherosclerotic Risk Estimation (NEOCARE) investigative team: Claudia Coulton, Douglas Gunzler, Darcy Freedman, Neal Dawson, Michael Rothberg, David Zidar, David Kaelber, Douglas Einstadter, Alex Milinovich, Monica Webb Hooper, Kristen Hassmiller-Lich, Ye Tian (Devin), Kristen Berg, and Sandy Andrukat.

Funding:
This work was supported by The National Institute on Aging of the National Institutes of Health under award number R01AG055480. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Installation

You can install projects with:

install.packages("projects")

Introduction

The goal of the projects R package is to provide a set of tools that support an efficient project management workflow for statisticians and data scientists who perform reproducible research within team science environments. The projects package is built upon some existing tools for reproducible research, particularly RStudio, the R integrated development environment in which it dwells, and R Markdown, the file structure that allows users to assemble datasets, to perform analyses and to write manuscripts in a single file. The projects package is oriented towards efficient and reproducible academic research manuscript development and incorporates protocol and analysis templates based on widely-accepted reporting guidelines (viz., CONSORT and STROBE). When used on a shared file system (e.g., a server), the projects package provides infrastructure for collaborative research: multiple researchers can work on the same project and keep track of its progress without having to request updates.

The primary features of the projects R package are the following:

At its outset, the projects package creates a folder called /projects in a user-specified location. This directory will contain all research projects created and maintained by the user. The /projects folder will also contain a relational database of the research projects and the persons who contribute to them. The database allows for users to input important metadata about the projects and authors, including stage of research and contact information. Once this higher-level folder is created, users run R functions to create projects, each of which is given its own folder. New project folders automatically include aptly named subfolders and templates in order to guide the project workflow (e.g., a “data” subfolder; a “datawork” R Markdown template). Right away, users can begin working on the research project and edit the metadata of the project itself and its authors. To lessen the burden of the mundane details of manuscript writing, the projects package can output lines to the console that, when copied into an R Markdown file, generates a title page with all relevant authorship information of any given project. Finally, since users may create dozens of projects over time, users can run functions to organize their projects within grouping subfolders of the main /projects folder.

Conceptual Framework: Reproducible Research Workflows

Reproducibility in research is the focus of the much debated replication crisis and is therefore an increasingly central goal of the contemporary scientific process. In addition to a final report of study results, reproducible research processes include the entire workflow that the researchers used to generate those results. Actively maintaining and archiving this workflow is important to the evaluation and validation of the research. If other researchers can follow the same workflow to achieve the same results, it corroborates the results as scientific knowledge. When results are not produced by the same workflow, however, scientific knowledge is still advanced, as the workflow is shown not to yield generalizable results. (Baker 2016)

There exist today widely available tools that aid with reproducible research, such as R and other statistical programming languages, that allow for precise documentation of some of the most detail-oriented portions of a project workflow. Researchers can distribute their code scripts alongside their results in order to communicate the integrity of their data processing and analysis. Unfortunately, statistical programming languages per se only contribute to research reproducibility insofar as individual statistical programmers are able (1) to use these tools effectively and (2) to integrate their own use of these tools with their collaborators’ work—which may not necessarily be oriented towards reproducibility.

Although researchers of different disciplines may operate in nuanced ways, there are aspects of the project workflow that are common to most investigations. First, studies are conceptualized and designed according to a protocol that details the research questions and planned analyses. Data are collected, manipulated (or “tidied”) in order to make data analysis possible. The results of the analyses are compiled into a report, and ultimately an academic manuscript is drafted and submitted for wider distribution.

When navigating this workflow, researchers strive for reproducibility wherever possible, but especially during the intermediate, data-focused phases of the workflow. Readers of the final manuscript should have access to the study data in its most unrefined state possible: a frozen dataset. A frozen dataset is almost invariably a digital file or set of files that standard data analysis software can process. Whereas study data may have been initially collected in a non-digital manner, a frozen data set represents the study data’s earliest state of simultaneous digitization and consolidation. From this point forward through the reporting stage, total reproducibility is expected. Thanks to modern data analysis via statistical programming languages, a reader should be able to exactly reproduce all data-derived results from the frozen data set alone. With access to the exact scripts the researchers used to produce their results, readers can scrutinize every function call performed on the frozen data set and its descendants.

The middle stage of the assumed study workflow can be performed with near perfect reproducibility, but the beginning and ending stages may not. Researchers cannot document every thought process, literature probe and informal conversation that contributes to the development of the initial study protocol, but they should strive to document it as meticulously as possible. Databases tend to be dynamic such that a given analytic data set is merely a snapshot in time. As for the final stages of project development, journals require that manuscripts adhere to specific and unique stylistic guidelines and that they be digitally submitted with file types that are not independently conducive to reproducibility (e.g., .pdf). For instance, even as RStudio supports the creation of submission-ready documents directly from frozen datasets, the vast majority of project teams include experts who do not use RStudio; therefore, the collaborative manuscript editing process ultimately takes place in an environment (e.g., Microsoft Word) that only supports total reproducibility with extraordinary effort. In light of these realities, researchers must do their best during manuscript creation, keeping the process in reproducible environments for as long as possible and otherwise documenting significant changes and alterations.

The projects package

Initial Setup

All projects that the user creates with the projects package—as well as its infrastructure—reside in a main folder called /projects. Users need not manually create this directory, and in fact they are encouraged not to manually manipulate any folders that the projects package involves. Instead, users run the function setup_projects(), providing the full file path of the directory in which the user wants the /projects folder to reside.

Metadata

Data about authors, institutional and/or department affiliations and projects and tasks are stored in .rds files within the main /project directory, so that the user only needs to enter these details once (unless, for example, a co-author changes their name or affiliations). These data are also used to assemble title pages of reports, with automatically generated author lists and lists of author affiliations. We provide a complete example of this process below in the Demonstration section below.

The main metadata tables accessible to the user are projects(), tasks(), authors() and affiliations(), via functions thusly named. Two additional tables are internally created to keep track of associations between authors and projects and between authors and affiliations (see the Internal Tables section).

Projects Table Columns

Authors Table

Affiliations Table

Tasks Table

Internal Tables

In keeping with relational database theory, there are two .rds files that keep track of the many-to-many relationships between projects and authors and between authors and affiliations. Each has two columns, id1 and id2, that contain the id numbers of these items. Each row of this table describes an association. Furthermore, the projects package keeps track of the order in which these associations appear so that the automatically generated title pages list authors and affiliations in the correct order. Users are able to run functions to reorder these associations as needed.

Project File Structure

Users create individual project folders with the function new_project(). By default, the name of each project folder is of the form pXXXX, where XXXX is the project’s id padded with 0s on the left side. Its contents are copied from a template project folder within the .templates directory in the main /projects folder.

The default project folder template is structured as follows:

The included subfolders serve to organize the project, while the .Rmd files are templates that facilitate the user’s workflow. The style.css and styles.docx files are a cascading style sheets (CSS) file and a Word style template, which allow for custom styling of knitted HTML files and Word documents, respectively. The citations.bib file is an empty BibTeX file. The default 01_protocol.Rmd and 04_report.Rmd files already reference the .bib and style files, easing the implementation of these features for the user.

File Management

The goal of the projects package is to provide a comprehensive set of tools managing project files in a way that is self-contained in R and independent of the underlying operating system. On a daily basis, researchers make, move, copy, delete and archive files. Through the projects package, researchers can perform all these actions in an organized manner with an automated file structure. In fact, users are advised not to manipulate the /projects folder and its content with their operating system, so that the package does not lose track of these files. Multiuser application of projects requires a server or an otherwise shared directory where multiple users can access the /projects folder. File-managing functions—along with all functions—are demonstrated below in the Demonstration section.

Demonstration

Upon installation, the projects package must be set up using setup_projects(). The user is to input the file path of the directory wherein the /projects folder is to be located.

library(projects)

setup_projects("~")
#> projects folder created at
#> /tmp/RtmpjCNxOS/projects
#> 
#> Add affiliations with new_affiliation(),
#> then add authors with new_author(),
#> then create projects with new_project(),
#> and finally, create tasks with new_task()

As the message suggests, it is in the user’s best interest to add affiliations, followed by authors and projects.

new_affiliation(
  department_name  = "Department of Physics",
  institution_name = "University of North Science",
  address = "314 Newton Blvd, Springfield CT 06003"
)
#> New affiliation:
#> # A tibble: 1 x 4
#>      id department_name      institution_name        address                    
#>   <int> <chr>                <chr>                   <chr>                      
#> 1     1 Department of Physi… University of North Sc… 314 Newton Blvd, Springfie…

This affiliation has been successfully added to the “affiliations” table in the projects relational database. Now to create a few more affiliations:

new_affiliation(
  department_name  = "Impossibles Investigation Team",
  institution_name = "Creekshirebrook Academy of Thinks",
  address = "Let Gade 27182, 1566 Copenhagen, Denmark"
)
#> New affiliation:
#> # A tibble: 1 x 4
#>      id department_name         institution_name         address                
#>   <int> <chr>                   <chr>                    <chr>                  
#> 1     2 Impossibles Investigat… Creekshirebrook Academy… Let Gade 27182, 1566 C…

new_affiliation(
  department_name  = "Statistical Consulting Unit",
  institution_name = "Creekshirebrook Academy of Thinks",
  address = "196 Normal Ave, Columbus, OH ",
  id = 50
)
#> New affiliation:
#> # A tibble: 1 x 4
#>      id department_name         institution_name           address              
#>   <int> <chr>                   <chr>                      <chr>                
#> 1    50 Statistical Consulting… Creekshirebrook Academy o… "196 Normal Ave, Col…

Note that we chose a specific id number (50) for the affiliation called the “Impossibles Investigation Team.”

Now we are ready to add authors to the “authors” table of the projects database.

new_author(
  given_names = "Scott",
  last_name = "Bug",
  title = "Professor",
  affiliations = c(2, "Physics"),
  degree = "PhD",
  email = "scottbug@imPOSSible.net",
  phone = "965-555-5556"
)
#> New author:
#> # A tibble: 1 x 7
#>      id last_name given_names title     degree email                 phone      
#>   <int> <chr>     <chr>       <chr>     <chr>  <chr>                 <chr>      
#> 1     1 Bug       Scott       Professor PhD    scottbug@impossible.… 965-555-55…
#> 
#> New author's affiliations:
#> # A tibble: 2 x 4
#>   affiliation_id department_name       institution_name       address           
#>            <int> <chr>                 <chr>                  <chr>             
#> 1              2 Impossibles Investig… Creekshirebrook Acade… Let Gade 27182, 1…
#> 2              1 Department of Physics University of North S… 314 Newton Blvd, …

Notice that in creating associations between Scott Bug and his affiliations, we were able to enter both the id number of one of them (2) and a substring of the department_name of the other (“Physics”). Also notice that the email address was coerced to be lowercase.

Now we’ll add more authors.

new_author(
  given_names = "Marie",
  last_name = "Curie",
  title = "Chemist",
  affiliations = "Unit",
  phone = "553-867-5309",
  id = 86
)
#> New author:
#> # A tibble: 1 x 7
#>      id last_name given_names title   degree email phone       
#>   <int> <chr>     <chr>       <chr>   <chr>  <chr> <chr>       
#> 1    86 Curie     Marie       Chemist <NA>   <NA>  553-867-5309
#> 
#> New author's affiliations:
#> # A tibble: 1 x 4
#>   affiliation_id department_name       institution_name        address          
#>            <int> <chr>                 <chr>                   <chr>            
#> 1             50 Statistical Consulti… Creekshirebrook Academ… "196 Normal Ave,…

new_author(
  given_names = "George Washington",
  last_name = "Carver",
  title = "Astrophysicist",
  degree = "MA, MPhil, PhD",
  affiliations = c(1, 2, 50),
  id = 1337
)
#> New author:
#> # A tibble: 1 x 7
#>      id last_name given_names       title          degree         email phone
#>   <int> <chr>     <chr>             <chr>          <chr>          <chr> <chr>
#> 1  1337 Carver    George Washington Astrophysicist MA, MPhil, PhD <NA>  <NA>
#> 
#> New author's affiliations:
#> # A tibble: 3 x 4
#>   affiliation_id department_name       institution_name      address            
#>            <int> <chr>                 <chr>                 <chr>              
#> 1              1 Department of Physics University of North … "314 Newton Blvd, …
#> 2              2 Impossibles Investig… Creekshirebrook Acad… "Let Gade 27182, 1…
#> 3             50 Statistical Consulti… Creekshirebrook Acad… "196 Normal Ave, C…

new_author(last_name = "Archimedes", title = "Mathematician")
#> New author:
#> # A tibble: 1 x 7
#>      id last_name  given_names title         degree email phone
#>   <int> <chr>      <chr>       <chr>         <chr>  <chr> <chr>
#> 1     2 Archimedes <NA>        Mathematician <NA>   <NA>  <NA>
#> 
#> New author's affiliations:
#> None.

new_author(
  last_name = "Wu",
  given_names = "Chien-Shiung",
  title = "Physicist",
  affiliations = c("of North", "Statistical Consulting"),
  degree = "PhD",
  email = "wu@WU.wU"
)
#> New author:
#> # A tibble: 1 x 7
#>      id last_name given_names  title     degree email    phone
#>   <int> <chr>     <chr>        <chr>     <chr>  <chr>    <chr>
#> 1     3 Wu        Chien-Shiung Physicist PhD    wu@wu.wu <NA>
#> 
#> New author's affiliations:
#> # A tibble: 2 x 4
#>   affiliation_id department_name      institution_name       address            
#>            <int> <chr>                <chr>                  <chr>              
#> 1              1 Department of Physi… University of North S… "314 Newton Blvd, …
#> 2             50 Statistical Consult… Creekshirebrook Acade… "196 Normal Ave, C…

Now that some authors and affiliations have been created, we can view these tables:

authors()
#> # A tibble: 5 x 7
#>      id last_name  given_names     title      degree     email          phone   
#>   <int> <chr>      <chr>           <chr>      <chr>      <chr>          <chr>   
#> 1     1 Bug        Scott           Professor  PhD        scottbug@impo… 965-555…
#> 2     2 Archimedes <NA>            Mathemati… <NA>       <NA>           <NA>    
#> 3     3 Wu         Chien-Shiung    Physicist  PhD        wu@wu.wu       <NA>    
#> 4    86 Curie      Marie           Chemist    <NA>       <NA>           553-867…
#> 5  1337 Carver     George Washing… Astrophys… MA, MPhil… <NA>           <NA>
affiliations()
#> # A tibble: 3 x 4
#>      id department_name         institution_name         address                
#>   <int> <chr>                   <chr>                    <chr>                  
#> 1     1 Department of Physics   University of North Sci… "314 Newton Blvd, Spri…
#> 2     2 Impossibles Investigat… Creekshirebrook Academy… "Let Gade 27182, 1566 …
#> 3    50 Statistical Consulting… Creekshirebrook Academy… "196 Normal Ave, Colum…

Now we will showcase project creation:

new_project(
  title = "Achieving Cold Fusion",
  short_title = "ACF",
  authors = c("Bug", "Chien-Shiung", 86, 1337),
  current_owner = "Carver",
  corresp_auth = "Bug",
  stage = "1: design",
  deadline_type = "Pilot study",
  deadline = "2020-12-31"
)
#> 
#> Project 1 has been created at
#> /tmp/RtmpjCNxOS/projects/p0001
#> # A tibble: 1 x 6
#>      id title             stage     status     deadline_type deadline           
#>   <int> <chr>             <prjstg>  <chr>      <chr>         <dttm>             
#> 1     1 Achieving Cold F… 1: design just crea… Pilot study   2020-12-31 00:00:00
#> 
#> New project's authors:
#> # A tibble: 4 x 7
#>   author_id last_name given_names    title      degree    email         phone   
#>       <int> <chr>     <chr>          <chr>      <chr>     <chr>         <chr>   
#> 1         1 Bug       Scott          Professor  PhD       scottbug@imp… 965-555…
#> 2         3 Wu        Chien-Shiung   Physicist  PhD       wu@wu.wu      <NA>    
#> 3        86 Curie     Marie          Chemist    <NA>      <NA>          553-867…
#> 4      1337 Carver    George Washin… Astrophys… MA, MPhi… <NA>          <NA>    
#> # A tibble: 1 x 3
#>   current_owner corresp_auth creator   
#>   <prjaut>      <prjaut>     <prjaut>  
#> 1 1337: Carver  1: Bug       0: kriegen

Notice that since a creator was not specified, this field was populated with the value of Sys.info()["user"].

Notice also that the author order given to the authors argument in new_project() command has been preserved. Also notice that Scott Bug has been marked as the corresponding author, and his contact information has been included. When knitted, this will be a proper title page.

Now a few more projects will be created:

new_project(
  title = "Weighing the Crown",
  short_title = "Eureka!", 
  authors = "Archimedes", 
  current_owner = "Archimedes", 
  corresp_auth = "Archimedes", 
  stage = 4
)
#> 
#> Project 2 has been created at
#> /tmp/RtmpjCNxOS/projects/p0002
#> # A tibble: 1 x 6
#>      id title          stage         status    deadline_type deadline           
#>   <int> <chr>          <prjstg>      <chr>     <chr>         <dttm>             
#> 1     2 Weighing the … 4: manuscript just cre… <NA>          NA
#> 
#> New project's authors:
#> # A tibble: 1 x 7
#>   author_id last_name  given_names title         degree email phone
#>       <int> <chr>      <chr>       <chr>         <chr>  <chr> <chr>
#> 1         2 Archimedes <NA>        Mathematician <NA>   <NA>  <NA> 
#> # A tibble: 1 x 3
#>   current_owner corresp_auth  creator   
#>   <prjaut>      <prjaut>      <prjaut>  
#> 1 2: Archimedes 2: Archimedes 0: kriegen

new_project(
  title = "How I Learned to Stop Worrying and Love the Bomb",
  short_title = "Dr. Strangelove",
  authors = c("wu", 1),
  creator = "wu",
  current_owner = "George",
  corresp_auth = "George",
  stage = "under review",
  deadline_type = "2nd revision",
  deadline = "2030-10-8",
  id = 1945,
  status = "debating leadership changes",
  parent_directory = "top_secret",
  make_directories = TRUE
)
#> 
#> Project 1945 has been created at
#> /tmp/RtmpjCNxOS/projects/top_secret/p1945
#> # A tibble: 1 x 6
#>      id title        stage           status    deadline_type deadline           
#>   <int> <chr>        <prjstg>        <chr>     <chr>         <dttm>             
#> 1  1945 How I Learn… 5: under review debating… 2nd revision  2030-10-08 00:00:00
#> 
#> New project's authors:
#> # A tibble: 3 x 7
#>   author_id last_name given_names    title      degree    email         phone   
#>       <int> <chr>     <chr>          <chr>      <chr>     <chr>         <chr>   
#> 1         3 Wu        Chien-Shiung   Physicist  PhD       wu@wu.wu      <NA>    
#> 2         1 Bug       Scott          Professor  PhD       scottbug@imp… 965-555…
#> 3      1337 Carver    George Washin… Astrophys… MA, MPhi… <NA>          <NA>    
#> # A tibble: 1 x 3
#>   current_owner corresp_auth creator 
#>   <prjaut>      <prjaut>     <prjaut>
#> 1 1337: Carver  1337: Carver 3: Wu

new_project(
  title = "Understanding Radon",
  short_title = "Rn86",
  authors = 86,
  creator = 86,
  corresp_auth = 86,
  stage = "3",
  status = "Safety procedures"
)
#> 
#> Project 3 has been created at
#> /tmp/RtmpjCNxOS/projects/p0003
#> # A tibble: 1 x 6
#>      id title         stage       status       deadline_type deadline           
#>   <int> <chr>         <prjstg>    <chr>        <chr>         <dttm>             
#> 1     3 Understandin… 3: analysis Safety proc… <NA>          NA
#> 
#> New project's authors:
#> # A tibble: 1 x 7
#>   author_id last_name given_names title   degree email phone       
#>       <int> <chr>     <chr>       <chr>   <chr>  <chr> <chr>       
#> 1        86 Curie     Marie       Chemist <NA>   <NA>  553-867-5309
#> # A tibble: 1 x 3
#>   current_owner corresp_auth creator  
#>   <prjaut>      <prjaut>     <prjaut> 
#> 1 86: Curie     86: Curie    86: Curie

Here is the list of all projects that have been created:

projects()
#> # A tibble: 4 x 6
#>      id title                 current_owner status        stage           impact
#>   <int> <chr>                 <prjaut>      <chr>         <prjstg>         <dbl>
#> 1  1945 How I Learned to Sto… 1337: Carver  debating lea… 5: under review     NA
#> 2     2 Weighing the Crown    2: Archimedes just created  4: manuscript       NA
#> 3     3 Understanding Radon   86: Curie     Safety proce… 3: analysis         NA
#> 4     1 Achieving Cold Fusion 1337: Carver  just created  1: design           NA

Projects, authors, and affiliations can all be edited with their respective edit_*() functions. For example, we can add to and remove affiliations from an author with:

edit_author(author = "Bug", affiliations = ~ + 50 - impossibles)

When adding or removing affiliations/authors from an author/project, a one-sided formula is used: it must begin with a tilde ~, and elements are added with + and removed with -. Elements can be referred to by their id numbers or their names, as described above.

A formula is also used in the authors argument in edit_project():

edit_project(
  "Cold",
  title = "Cold Fusion Is Actually Impossible",
  authors = ~ "archi",
  stage = "accepted"
)
#> 
#> Header has changed. Reprint it with:
#> header(1)

Here, the title and stage of the project have also been edited.

Note that the default behavior when adding elements is to place them before the last author (unless there was only one author). This occurs after elements are removed, as specified by any minus signs (-) in the formula.

The reorder_authors() function allows for the user to change author order:

reorder_authors(project = "Cold Fusion", "George", "Bug", 86)
#> 
#> Header has changed. Reprint it with:
#> header(1)

Author order matters when obtaining title page YAML text to paste into a protocol or manuscript .Rmd file. Best practice is to do so late in the writing process, if, for example, the name of an affiliation changes in the middle of a research project:

edit_affiliation(
  affiliation     = "Impossibles",
  department_name = "Pseudoscience Debunking Unit"
)

The text to be pasted in the YAML can be obtained for any given project using header():

header(project = "Cold")

title: "Cold Fusion Is Actually Impossible"
author:
  - George Washington Carver, MA, MPhil, PhD;^1,2,3^ Scott Bug, PhD;^1,3^\* Marie Curie;^3^ Chien-Shiung Wu, PhD;^1,3^ and Archimedes
  - ^1^ Department of Physics, University of North Science, 314 Newton Blvd, Springfield CT 06003
  - ^2^ Pseudoscience Debunking Unit, Creekshirebrook Academy of Thinks, Let Gade 27182, 1566 Copenhagen, Denmark
  - ^3^ Statistical Consulting Unit, Creekshirebrook Academy of Thinks, 196 Normal Ave, Columbus, OH 
  - \* Corresponding author
  - 314 Newton Blvd, Springfield CT 06003
  - 965-555-5556
  - scottbug@impossible.net

In order to organize projects, users can create subdirectories within the main /projects folder where individual project folders can dwell. Among the examples above, this has already occurred with the project with the nickname (i.e., short_title) “Dr. Strangelove” because on its creation the arguments path = top_secret and make_directories = TRUE were included. The latter argument must be TRUE if the desired path does not already exist. Observe the path column among the existing projects (including the path column in projects() output requires the argument verbose = TRUE):

projects(verbose = TRUE) %>% select(id, short_title, path)
#> # A tibble: 3 x 3
#>      id short_title     path                                     
#>   <int> <chr>           <chr>                                    
#> 1  1945 Dr. Strangelove /tmp/RtmpjCNxOS/projects/top_secret/p1945
#> 2     2 Eureka!         /tmp/RtmpjCNxOS/projects/p0002           
#> 3     3 Rn86            /tmp/RtmpjCNxOS/projects/p0003

Users can also create subdirectories with the function new_project_group():

new_project_group("Greek_studies/ancient_studies")
#> 
#> The following directory was created:
#> /tmp/RtmpjCNxOS/projects/Greek_studies/ancient_studies

If a project has already been created, it can be moved not with edit_project() but move_project(). Users can also copy projects using copy_project(); everything in the copy will be the same except its id, folder name (which, again, is based on its id), path (which, again, is based on its folder name), and the name of its .Rproj file (which has the same name as the folder name).

move_project("Crown", path = "Greek_studies/ancient_studies")
#> # A tibble: 1 x 12
#>      id title short_title current_owner status deadline_type deadline           
#>   <int> <chr> <chr>       <prjaut>      <chr>  <chr>         <dttm>             
#> 1     2 Weig… Eureka!     2: Archimedes just … <NA>          NA                 
#> # … with 5 more variables: stage <prjstg>, impact <dbl>, path <chr>,
#> #   corresp_auth <prjaut>, creator <prjaut>
#> 
#> Project 2 moved so that its new path is
#> /tmp/RtmpjCNxOS/projects/Greek_studies/ancient_studies/p0002

copy_project(
  project_to_copy = "Radon",
  path = "dangerous_studies/radioactive_studies/radon_studies", 
  make_directories = TRUE
)
#> # A tibble: 1 x 12
#>      id title short_title current_owner status deadline_type deadline           
#>   <int> <chr> <chr>       <prjaut>      <chr>  <chr>         <dttm>             
#> 1     3 Unde… Rn86        86: Curie     Safet… <NA>          NA                 
#> # … with 5 more variables: stage <prjstg>, impact <dbl>, path <chr>,
#> #   corresp_auth <prjaut>, creator <prjaut>
#> 
#> Project 4 below is a copy of project 3 and is located at
#> /tmp/RtmpjCNxOS/projects/dangerous_studies/radioactive_studies/radon_studies/p0004
#> # A tibble: 1 x 12
#>      id title short_title current_owner status deadline_type deadline           
#>   <int> <chr> <lgl>       <prjaut>      <chr>  <chr>         <dttm>             
#> 1     4 Unde… NA          86: Curie     Safet… <NA>          NA                 
#> # … with 5 more variables: stage <prjstg>, impact <dbl>, path <chr>,
#> #   corresp_auth <prjaut>, creator <prjaut>
#> 
#> The .Rproj file
#> /tmp/RtmpjCNxOS/projects/dangerous_studies/radioactive_studies/radon_studies/p0004/p0003.Rproj
#> was renamed to
#> /tmp/RtmpjCNxOS/projects/dangerous_studies/radioactive_studies/radon_studies/p0004/p0004.Rproj
#> 
#> Be sure to change all instances of "p0003" to "p0004" as desired
#> (e.g., .bib files and references to them in YAML headers).

projects(c("Crown", "Radon"), verbose = TRUE) %>% select(id, title, path)
#> # A tibble: 3 x 3
#>      id title           path                                                    
#>   <int> <chr>           <chr>                                                   
#> 1     2 Weighing the C… /tmp/RtmpjCNxOS/projects/Greek_studies/ancient_studies/…
#> 2     4 Understanding … /tmp/RtmpjCNxOS/projects/dangerous_studies/radioactive_…
#> 3     3 Understanding … /tmp/RtmpjCNxOS/projects/p0003

Projects can also be archived; they are moved into a subdirectory called /archive that is at the same level as the project folder (/pXXXX) before it was run. If this /archive folder does not exist, it will be created.

archive_project("Strangelove")
#> # A tibble: 1 x 12
#>      id title short_title current_owner status deadline_type deadline           
#>   <int> <chr> <chr>       <prjaut>      <chr>  <chr>         <dttm>             
#> 1  1945 How … Dr. Strang… 1337: Carver  debat… 2nd revision  2030-10-08 00:00:00
#> # … with 5 more variables: stage <prjstg>, impact <dbl>, path <chr>,
#> #   corresp_auth <prjaut>, creator <prjaut>
#> 
#> The above project was archived and has the file path
#> /tmp/RtmpjCNxOS/projects/top_secret/archive/p1945

When a project is archived, it is no longer included in projects() output unless the user sets archived = TRUE.

projects(verbose = TRUE) %>% select(id, short_title, path)
#> # A tibble: 3 x 3
#>      id short_title path                                                        
#>   <int> <chr>       <chr>                                                       
#> 1     2 Eureka!     /tmp/RtmpjCNxOS/projects/Greek_studies/ancient_studies/p0002
#> 2     4 <NA>        /tmp/RtmpjCNxOS/projects/dangerous_studies/radioactive_stud…
#> 3     3 Rn86        /tmp/RtmpjCNxOS/projects/p0003

projects(verbose = TRUE, archived = TRUE) %>% select(id, short_title, path)
#> # A tibble: 4 x 3
#>      id short_title    path                                                     
#>   <int> <chr>          <chr>                                                    
#> 1  1945 Dr. Strangelo… /tmp/RtmpjCNxOS/projects/top_secret/archive/p1945        
#> 2     2 Eureka!        /tmp/RtmpjCNxOS/projects/Greek_studies/ancient_studies/p…
#> 3     4 <NA>           /tmp/RtmpjCNxOS/projects/dangerous_studies/radioactive_s…
#> 4     3 Rn86           /tmp/RtmpjCNxOS/projects/p0003

Project contributors can utilize the tasks() table to manage tasks associated with projects. Here, a few tasks are created, edited, and viewed.

new_task(
  project = "Eureka",
  task = "Retrieve the crown from the monarch",
  lead = "archimedes",
  effort = 2,
  status = "Waiting on the horse to get well."
)
#> 
#> New task was added to project 2:
#> # A tibble: 1 x 8
#>     PID   TID  done task             effort timing lead          status         
#>   <int> <dbl> <int> <chr>             <dbl>  <dbl> <prjaut>      <chr>          
#> 1     2     1     0 Retrieve the cr…      2     NA 2: Archimedes Waiting on the…

new_task(
  project = "Eureka",
  task = "Rehabilitate the horse",
  TID = 1,
  lead = "scott bug",
  effort = 22
)
#> 
#> New task was added to project 2:
#> # A tibble: 2 x 8
#>     PID   TID  done task             effort timing lead          status         
#>   <int> <dbl> <int> <chr>             <dbl>  <dbl> <prjaut>      <chr>          
#> 1     2     1     0 Rehabilitate th…     22     NA 1: Bug        <NA>           
#> 2     2     2     0 Retrieve the cr…      2     NA 2: Archimedes Waiting on the…

edit_task(project = "Crown", TID = 1, timing = pi)
#> 
#> Updated task list for project 2:
#> # A tibble: 2 x 8
#>     PID   TID  done task             effort timing lead          status         
#>   <int> <int> <int> <chr>             <dbl>  <dbl> <prjaut>      <chr>          
#> 1     2     1     0 Rehabilitate th…     22   3.14 1: Bug        <NA>           
#> 2     2     2     0 Retrieve the cr…      2  NA    2: Archimedes Waiting on the…

new_task(project = 4, task = "collect the radon", lead = "scott bug")
#> 
#> New task was added to project 4:
#> # A tibble: 1 x 8
#>     PID   TID  done task              effort timing lead     status
#>   <int> <dbl> <int> <chr>              <dbl>  <dbl> <prjaut> <chr> 
#> 1     4     1     0 collect the radon     NA     NA 1: Bug   <NA>

# This is a shortcut to set a task's "done" value to 1
finish(4, 1)
#> 
#> Updated task list for project 4:
#> # A tibble: 1 x 8
#>     PID   TID  done task              effort timing lead     status
#>   <int> <int> <int> <chr>              <dbl>  <dbl> <prjaut> <chr> 
#> 1     4     1     1 collect the radon     NA     NA 1: Bug   <NA>

tasks()
#> # A tibble: 3 x 11
#>     PID project     PI            impact   TID  done task          effort timing
#>   <int> <chr>       <prjaut>       <dbl> <int> <int> <chr>          <dbl>  <dbl>
#> 1     2 Weighing t… 2: Archimedes     NA     1     0 Rehabilitate…     22   3.14
#> 2     2 Weighing t… 2: Archimedes     NA     2     0 Retrieve the…      2  NA   
#> 3     4 Understand… 86: Curie         NA     1     1 collect the …     NA  NA   
#> # … with 2 more variables: lead <prjaut>, status <chr>

Like the other metadata-viewing functions, the user can filter tasks() by project, by lead, or both:

tasks(project = 4)
#> # A tibble: 1 x 11
#>     PID project  PI        impact   TID  done task   effort timing lead   status
#>   <int> <chr>    <prjaut>   <dbl> <int> <int> <chr>   <dbl>  <dbl> <prja> <chr> 
#> 1     4 Underst… 86: Curie     NA     1     1 colle…     NA     NA 1: Bug <NA>
tasks(lead = "bug")
#> # A tibble: 2 x 11
#>     PID project   PI            impact   TID  done task     effort timing lead  
#>   <int> <chr>     <prjaut>       <dbl> <int> <int> <chr>     <dbl>  <dbl> <prja>
#> 1     2 Weighing… 2: Archimedes     NA     1     0 Rehabil…     22   3.14 1: Bug
#> 2     4 Understa… 86: Curie         NA     1     1 collect…     NA  NA    1: Bug
#> # … with 1 more variable: status <chr>
tasks(project = "Eureka", lead = 1)
#> # A tibble: 1 x 11
#>     PID project   PI            impact   TID  done task     effort timing lead  
#>   <int> <chr>     <prjaut>       <dbl> <int> <int> <chr>     <dbl>  <dbl> <prja>
#> 1     2 Weighing… 2: Archimedes     NA     1     0 Rehabil…     22   3.14 1: Bug
#> # … with 1 more variable: status <chr>

Lastly, affiliations, authors, tasks and projects can be deleted with the delete_*() functions. Deleting an author is complete: doing so removes the author from the creator, current_owner and corresp_auth fields of all projects and from the lead field of all tasks. Furthermore, deleting a project also deletes the entire project folder. Use the delete_*() functions with caution.

delete_affiliation("north science")
#> # A tibble: 1 x 4
#>      id department_name      institution_name        address                    
#>   <int> <chr>                <chr>                   <chr>                      
#> 1     1 Department of Physi… University of North Sc… 314 Newton Blvd, Springfie…
#> # A tibble: 1 x 4
#>      id department_name      institution_name        address                    
#>   <int> <chr>                <chr>                   <chr>                      
#> 1     1 Department of Physi… University of North Sc… 314 Newton Blvd, Springfie…
#> The above affiliation was deleted.
delete_author(2)
#> # A tibble: 1 x 7
#>      id last_name  given_names title         degree email phone
#>   <int> <chr>      <chr>       <chr>         <chr>  <chr> <chr>
#> 1     2 Archimedes <NA>        Mathematician <NA>   <NA>  <NA> 
#> # A tibble: 1 x 7
#>      id last_name  given_names title         degree email phone
#>   <int> <chr>      <chr>       <chr>         <chr>  <chr> <chr>
#> 1     2 Archimedes <NA>        Mathematician <NA>   <NA>  <NA>
#> The above author was deleted.
delete_project("Crown")
#> # A tibble: 1 x 12
#>      id title short_title current_owner status deadline_type deadline           
#>   <int> <chr> <chr>       <prjaut>      <chr>  <chr>         <dttm>             
#> 1     2 Weig… Eureka!     NA            just … <NA>          NA                 
#> # … with 5 more variables: stage <prjstg>, impact <dbl>, path <chr>,
#> #   corresp_auth <prjaut>, creator <prjaut>
#> # A tibble: 1 x 12
#>      id title short_title current_owner status deadline_type deadline           
#>   <int> <chr> <chr>       <prjaut>      <chr>  <chr>         <dttm>             
#> 1     2 Weig… Eureka!     NA            just … <NA>          NA                 
#> # … with 5 more variables: stage <prjstg>, impact <dbl>, path <chr>,
#> #   corresp_auth <prjaut>, creator <prjaut>
#> 
#> The above project was deleted.
delete_task(project = 4, TID = 1)
#> # A tibble: 1 x 9
#>     PID   TID  done task           effort timing lead     status project        
#>   <int> <int> <int> <chr>           <dbl>  <dbl> <prjaut> <chr>  <chr>          
#> 1     4     1     1 collect the r…     NA     NA 1: Bug   <NA>   Understanding …
#> 
#> No more tasks remaining for project 4
#> # A tibble: 0 x 9
#> # … with 9 variables: PID <int>, TID <int>, done <int>, task <chr>,
#> #   effort <dbl>, timing <dbl>, lead <prjaut>, status <chr>, project <chr>

Conclusions

The projects package provides a comprehensive set of tools for reproducible team science workflows. Efficiency in project management, including manuscript development, is facilitated by an internal database that keeps record of project details as well as team members’ affiliations and contact information. For manuscripts, title pages are automatically generated from this database, and a selection of manuscript outlines compliant with reporting guidelines are available in R Markdown format. We believe that the projects package may be useful for teams that manage multiple collaborative research projects in various stages of development.

References

<div id="refs" class="references"> <div id="ref-baker20161">

Baker, Monya. 2016. “1,500 Scientists Lift the Lid on Reproducibility.” Nature News 533 (7604): 452.

</div> </div>