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On this page

+
  • Reproducibility Best Practices Summary
  • Additional Resources
    • Papers & Documents
    • @@ -405,13 +407,17 @@

      Fundamental Structur
    • Organize content with sub-folders
    • Putting files that share a purpose or source into logical sub-folders is a great idea! This makes it easy to figure out where to put new content and reduces the effort of documenting project organization. Don’t feel like you need to use an intricate web of sub-folders either! Just one level of sub-folders is enough for many projects.

      +
        +
      1. Craft informative file names
      2. +
      +

      An ideal file name should give some information about the file’s contents, purpose, and relation to other project files. Some of that may be reinforced by folder names, but the file name itself should be inherently meaningful. This lets you change folder names without fear that files would also need to be re-named.

      -Discussion: Folder Structure +Discussion: Project Structure
      @@ -427,10 +433,6 @@

      Fundamental Structur

    -
      -
    1. Craft informative file names
    2. -
    -

    An ideal file name should give some information about the file’s contents, purpose, and relation to other project files. Some of that may be reinforced by folder names, but the file name itself should be inherently meaningful. This lets you change folder names without fear that files would also need to be re-named.

    Naming Tips

    We’ve brought up the importance of naming several times already but haven’t actually discussed the specifics of what makes a “good” name for a file or folder. Consider the adopting some (or all!) of the file name tips we outline below.

    @@ -557,9 +559,33 @@

    Areas for Improvem

    Documentation

    -

    Documenting a project can feel daunting but it is often not as hard as one might imagine and always well worth the effort! One simple practice you can adopt to dramatically improve the reproducibility of your project is to create a “README” file in the top-level of your project’s folder system. This file can be formatted however you’d like but generally READMEs should include (1) a project overview written in plain language, (2) a basic table of contents for the primary folders in your project folder, and (3) a brief description of the file naming scheme you’ve adopted for this project.

    +

    Documenting a project can feel daunting but it is often not as hard as one might imagine and always well worth the effort! One simple practice you can adopt to dramatically improve the reproducibility of your project is to create a “README” file in the top-level of your project’s folder system. This file can be formatted however you’d like but generally READMEs should include:

    +
      +
    1. Project overview written in plain language
    2. +
    3. Basic table of contents for the primary folders in your project folder
    4. +
    5. Brief description of the file naming scheme you’ve adopted for this project.
    6. +

    Your project’s README becomes the ‘landing page’ for those navigating your repository and makes it easy for team members to know where documentation should go (in the README!). You may also choose to create a README file for some of the sub-folders of your project. This can be particularly valuable for your “data” folder(s) as it is an easy place to store data source/provenance information that might be overwhelming to include in the project-level README file.

    Finally, you should choose a place to keep track of ideas, conversations, and decisions about the project. While you can take notes on these topics on a piece of paper, adopting a digital equivalent is often helpful because you can much more easily search a lengthy document when it is machine readable. We will discuss GitHub during the Version Control module but GitHub offers something called Issues that can be a really effective place to record some of this information.

    +
    +
    +
    + +
    +
    +Activity: Create a README +
    +
    +
    +

    Create a draft README for one of your research projects. If all of your projects already have READMEs (very impressive!) revisit the one with the least detail.

    +
      +
    • Include a 2-4 sentence description of the project objectives / hypotheses
    • +
    • Identify and describe (in 1 sentence) the primary sub-folders in the project
    • +
    • If your chosen project includes scripts, summarize each and indicate which script(s) they depend on and which depend on them
    • +
    +

    Feel free to put your personal flair on the README! If there is other information you feel would be relevant to an outsider looking at your project, you can definitely add that.

    +
    +

    Organization Recommendations

    @@ -591,58 +617,199 @@

    Reproducible Coding

    Packages, Namespacing, and Software Versions

    -

    One of the first things that every script should begin with is an explicit loading of all libraries that script need (these are called “dependencies). Scripts that don’t specify which libraries are needed are unlikely to run on anyone’s computer. Unfortunately, many R packages need to be installed by each user before they can be loaded with the library function. You may find it simpler to use the librarian package which automatically detects and installs needed packages if they are not already present. Note that users would still need to install librarian itself!

    -

    It is also strongly recommended to “namespace” functions everywhere you use them. In R this is technically optional (Python requires this) but it is a really good practice to adopt, particularly for functions that may appear in multiple packages with the same name but do very different operations depending on their source. Namespacing in R is done by adding the package name and two colons before the function name (e.g., dplyr::mutate). This prevents accidental use of functions from the ‘wrong’ package for a given context.

    -

    You may also need to consider the version of the packages that you’re using and the version of R. The sessionInfo function (from the utils package loaded into R by default) is a good way of capturing some of this information but it is relatively high level and lacks sufficient detail for many contexts. For a more complete amount of information, consider using the renv or packrat packages.

    +

    An under-appreciated facet of reproducible coding is a record of what code packages are used in a particular script and the version number of those packages. Packages evolve over time and code that worked when using one version of a given package may not work for future versions of that same package. Perpetually updating your code to work with the latest package versions is not sustainable but recording key information can help users set up the code environment that does work for your project.

    +
    +

    Load Libraries Explicitly

    +

    It is important to load libraries at the start of every script. In some languages (like Python) this step is required but in others (like R) this step is technically “optional” but disastrous to skip. It is safe to skip including the installation step in your code because the library step should tell code-literate users which packages they need to install.

    +

    For instance you might begin each script with something like:

    +
    # Load needed libraries
    +library(dplyr); library(magrittr); library(ggplot2)
    +
    +# Get to actual work
    +. . .
    +

    In R the semicolon allows you to put multiple code operations in the same line of the script. Listing the needed libraries in this way thus lets everyone reading the code know exactly which packages they will need to have installed.

    +

    If you are feeling generous you could use the librarian R package to install packages that are not yet installed and simultaneously load all needed libraries. Note that users would still need to install librarian itself but this at least limits possible errors to one location. This is done like so:

    +
    # Load `librarian` package
    +library(librarian)
    +
    +# Install missing packages and load needed libraries
    +shelf(dplyr, magrittr, ggplot2)
    +
    +# Get to actual work
    +. . .
    +
    +
    +

    Function Namespacing

    +

    It is also strongly recommended to “namespace” functions everywhere you use them. In R this is technically optional but it is a really good practice to adopt, particularly for functions that may appear in multiple packages with the same name but do very different operations depending on their source. In R the ‘namespacing operator’ is two colons.

    +
    # Use the `mutate` function from the `dplyr` package
    +dplyr::mutate(. . .)
    +

    An ancillary benefit of namespacing is that namespaced functions don’t need to have their respective libraries loaded. Still good practice to load the library though!

    +
    +
    +

    Package Versions

    +

    While working on a project you should use the latest version of every needed package. However, as you prepare to publish or otherwise publicize your code, you’ll need to record package versions. R provides the sessionInfo function (from the utils package included in “base” R) which neatly summarizes some high level facets of your code environment. Note that for this method to work you’ll need to actually run the library-loading steps of your scripts.

    +

    For more in-depth records of package versions and environment preservation–in R–you might also consider the renv package or the packrat package.

    +

    Script Organization

    Every change to the data between the initial raw data and the finished product should be scripted. The ideal would be that you could hand someone your code and the starting data and have them be able to perfectly retrace your steps. This is not possible if you make unscripted modifications to the data at any point!

    You may wish to break your scripted workflow into separate, modular files for ease of maintenance and/or revision. This is a good practice so long as each file fits clearly into a logical/thematic group (e.g., data cleaning versus analysis).

    -

    Finally, your code should never use absolute file paths. Absolute file paths are those that begin at the root of your entire computer (“C:…” on Windows and “~…” on Mac). Such paths are inherently not reproducible as the odds of anyone having the exact same absolute file path are extremely slim. Instead, using relative file paths that begin at the project folder is preferable. These are transferable among users. You can even use R’s file.path function to automatically detect the correct direction of slashes between folders to make it easier to collaborate across operating systems! Note in the above figure from Trisovic et al. (2022) that many scripts that set the working directory manually had errors until that bit was removed. Avoid setting the working directory explicitly and instead structure your project such that relative paths within the project folder will always succeed.

    +
    +
    +

    File Paths

    +

    When importing inputs or exporting outputs we need to specify “file paths”. These are the set of folders between where your project is ‘looking’ and where the input/output should come from/go. The figure from Trisovic et al. (2022) shows that file path and working directory errors are a substantial barrier to code that can be re-run in clean coding environments. Consider the following ways of specifying file paths from least to most reproducible.

    +
    + +
    +
    +
    +

    Absolute Paths

    +

    The worst way of specifying a file path is to use the “absolute” file path. This is the path from the root of your computer to a given file. There are many issues here but the primary one is that absolute paths only work for one computer! Given that only one person can even run lines of code that use absolute paths, it’s not really worth specifying the other issues.

    +
    +
    +

    Example

    +
    # Read in bee community data
    +my_df <- read.csv(file = "~/Users/lyon/Documents/Grad School/Thesis (Chapter 1)/Data/bees.csv")
    +
    +
    +
    +
    +

    Manually Setting the Working Directory

    +

    Marginally better than using the absolute path is to set the working directory to some location. This may look neater than the absolute path option but it actually has the same point of failure: Both methods only work for one computer!

    +
    +
    +

    Example

    +
    # Set working directory
    +setwd(dir = "~/Users/lyon/Documents/Grad School/Thesis (Chapter 1)")
    +
    +# Read in bee community data
    +my_df <- read.csv(file = "Data/bees.csv")
    +
    +
    +
    +
    +

    Relative Paths

    +

    Instead of using absolute paths or manually setting the working directory you can use “relative” file paths! Relative paths assume all project content lives in the same folder.

    +

    This is a safe assumption because it is the most fundamental tenet of reproducible project organization! The strength of relative paths is actually a serious contributing factor for why it is good practice to use a single folder.

    +
    +
    +

    Example

    +
    # Read in bee community data
    +my_df <- read.csv(file = "Data/bees.csv")
    +
    +
    1
    +
    +Parts of file path specific to each user are automatically recognized by the computer +
    +
    +
    +
    +
    +
    +

    Operating System-Flexible Relative Paths

    +

    The “better” example is nice but has a serious limitation: it hard coded the type of slash between file path elements. This means that only computers of the same operating system as the code author could run that line.

    +

    We can use functions to automatically detect and insert the correct slashes though!

    +
    +
    +

    Example

    +
    # Read in bee community data
    +my_df <- read.csv(file = file.path("Data", "bees.csv"))
    +
    +
    +
    +

    Code Style

    -

    When it comes to code style, the same ‘rule of thumb’ applies here that applied to project organization: virtually any system will work so long as you (and your team) are consistent! Thtat said, there are a few principles worth adopting if you have not already done so.

    -

    1. Use concise and descriptive object names

    +

    When it comes to code style, the same ‘rule of thumb’ applies here that applied to project organization: virtually any system will work so long as you (and your team) are consistent! That said, there are a few principles worth adopting if you have not already done so.

    +
    +

    Use concise and descriptive object names

    +

    It can be difficult to balance these two imperatives but short object names are easier to re-type and visually track through a script. Descriptive object names on the other hand are useful because they help orient people reading the script to what the object contains.

    -

    2. Don’t be afraid of space!

    +
    +

    Don’t be afraid of empty space!

    +

    Scripts are free to write regardless of the number of lines so do not feel as though there is a strict character limit you need to keep in mind. Cramped code is difficult to read and thus can be challenging to share with others or debug on your own. Inserting an empty line between coding lines can help break up sections of code and putting spaces before and after operators can make reading single lines much simpler.

    Meme-style image where someone puts on progressively more clown makeup as they explain why they don't need to leave code comments

    -


    -
    -
    -
    -
    -

    Code Comments

    A “comment” in a script is a human readable, non-coding line that helps give context for the code. In R (and Python), comment lines start with a hashtag (#). Including comments is a low effort way of both (A) creating internal documentation for the script and (B) increasing the reproducibility of the script. It is difficult to include “too many” comments, so when in doubt: add more comments!

    There are two major strategies for comments and either or both might make sense for your project.

    -
    - -
    -
    +
    +

    “What” Comments

    Comments describe what the code is doing.

    • Benefits: allows team members to understand workflow without code literacy
    • Risks: rationale for code not explicit
    -
    # Remove all pine trees from dataset
    -no_pine_df <- dplyr::filter(full_df, genus != "Pinus")
    -
    -
    +
    # Remove all pine trees from dataset
    +no_pine_df <- dplyr::filter(full_df, genus != "Pinus")
    +
    +
    +

    “Why” Comments

    Comments describe rationale and/or context for code.

    • Benefits: built-in documentation for team decisions
    • Risks: assumes everyone can read code
    -
    # Cone-bearing plants are not comparable with other plants in dataset
    -no_pine_df <- dplyr::filter(full_df, genus != "Pinus")
    +
    # Cone-bearing plants are not comparable with other plants in dataset
    +no_pine_df <- dplyr::filter(full_df, genus != "Pinus")
    +
    +
    +
    +
    +
    +Discussion: Comment on Comments
    +
    +

    With a partner discuss the following questions:

    +
      +
    • When you write comments, do you focus more on the “what” or the “why”?
    • +
    • What would you estimate is the ratio of code to comment lines in your code? +
        +
      • 1:1 being every code line has one comment line
      • +
    • +
    • If you have revisited old code, were your comments helpful? +
        +
      • How could you make them more helpful?
      • +
    • +
    • In what ways do you think you would need to change your commenting style for a team project?
    • +
    +
    +
    +
    +
    +
    + +
    +
    +Activity: Make Comments +
    +
    +
    +

    Revisit a script from an old project (ideally one you haven’t worked on recently). Once you’ve opened the script:

    +
      +
    • Scan through the script +
        +
      • Can you identify the main purpose(s) of the code?
      • +
    • +
    • Identify any areas where you’re not sure either (A) what the code is doing or (B) why that section of code exists +
        +
      • Add comments to these areas to document what they’re up to
      • +
    • +
    • Share the commented version of one of these trouble areas with a partner +
        +
      • Do they understand the what and/or why of your code?
      • +
      • If not, revise the comments and repeat
      • +
    • +
    +
    +
    +

    Consider Custom Functions

    @@ -654,17 +821,17 @@

    Function Recommen

    - If a given operation is duplicated 3 or more times within a project, write a custom function

    Functions written in this case can be extremely specific and–though documentation is always a good idea–can be a little lighter on documentation. Note that the reason you can reduce the emphasis on documentation is only because of the assumption that you won’t be sharing the function widely. If you do decide the function could be widely valuable you would need to add the needed documentation post hoc.

    - Write functions defensively

    -

    When you write custom functions, it is really valuable to take the time to write them defensively. In this context, “defensively” means that you anticipate likely errors and write your own informative/human readable error messages. Let’s consider a simplified version of a function rom the ltertools R package for calculating the coefficient of variation (CV).

    -

    The coefficient of variation is equal to the standard deviation divided by the mean. Fortunately, R provides functions for calculating both of these already and both expect numeric vectors. If either of those functions is given a non-number you get the following warning message: In mean.default(x = "...") : argument is not numeric or logical: returning NA.

    +

    When you write custom functions, it is really valuable to take the time to write them defensively. In this context, “defensively” means that you anticipate likely errors and write your own informative/human readable error messages. Let’s consider a simplified version of a function from the ltertools R package for calculating the coefficient of variation (CV).

    +

    The coefficient of variation is equal to the standard deviation divided by the mean. Fortunately, R provides functions for calculating both of these already and both expect numeric vectors. If either of those functions is given a non-number you get the following warning message: “In mean.default(x =”…“) : argument is not numeric or logical: returning NA”.

    Someone with experience in R may be able to interpret this error but for many users this error message is completely opaque. In the function included below however we can see that there is a simpler, more human readable version of the error message and the function is stopped before it can ever reach the part of the code that would throw the warning message included above.

    -
    cv <- function(x){
    -  
    -  # Error out if x is not numeric
    -  if(is.numeric(x) != TRUE)
    -    stop("`x` must be numeric")
    -  
    -  # Calculate CV
    -  sd(x = x) / mean(x = x)
    +
    cv <- function(x){
    +  
    +  # Error out if x is not numeric
    +  if(is.numeric(x) != TRUE)
    +    stop("`x` must be numeric")
    +  
    +  # Calculate CV
    +  sd(x = x) / mean(x = x)

    The key to defensive programming is to try to get functions to fail fast and fail informatively as soon as a problem is detected! This is easier to debug and understand for coders with a range of coding expertise and–for complex functions–can save a ton of useless processing time when failure is guaranteed at a later step.

    - If a given operation is duplicated 3 or more times across projects, consider creating an R package

    Creating an R package can definitely seem like a daunting task but duplication across projects carries the same weaknesses of excessive duplication within a project. However, when duplication is across projects, not even writing a custom function saves you because you need to duplicate that function’s script for each project that needs the tool.

    @@ -732,6 +899,7 @@

    FAIR

    CARE

    While making data and code more FAIR is often a good ideal the philosophy behind those four criteria come from a perspective that emphasizes data sharing as a good in and of itself. This approach can ignore historical context and contemporary power differentials and thus be insufficient as the sole tool to use in evaluating how data/code are shared and stored. The Global Indigenous Data Alliance (GIDA) created the CARE principles with these ethical considerations explicitly built into their tenets. Before making your data widely available and transparent (ideally before even beginning your research), it is crucial to consider this ethical dimension.

    +

    Patterned image reading 'Be FAIR and CARE' with the letters of both acronyms defined beneath each letter

    CARE stands for Collective Benefit, Authority to Control, Responsibility, and Ethics. Ensuring that your data meet these criteria helps to advance Indigenous data sovereignty and respects those who have been–and continue to be–collecting knowledge about the world around us for millennia. The following actions are adapted from Jennings et al. 2023 (linked at the bottom of this page).

    Collective Benefit

      @@ -761,11 +929,15 @@

      CARE

    • Work to maximize benefits from the perspectives of Indigenous Peoples by clear and transparent dialogue with communities and individuals
    • Engage with community guidelines for the use and reuse of data (including facilitating data removal and/or disposal requests from aggregated datasets)
    +
    +
    +
    +

    Reproducibility Best Practices Summary

    +

    Making sure that your project is reproducible requires a handful of steps before you begin, some actions during the life of the project, and then a few finishing touches when the project nears its conclusion. The following diagram may prove helpful as a coarse roadmap for how these steps might be followed in a general project setting.

    -Patterned image reading 'Be FAIR and CARE' with the letters of both acronyms defined beneath each letter +General steps for creating and maintaining a reproducible project. Steps follow the major headings of this section from starting on the 'right foot' with well thought out documentation, flowing through to consistent maintenance, and ending with some of the decisions needed for publication

    -

    Additional Resources

    @@ -1292,25 +1464,64 @@

    Websites

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That is definitely an important facet but before we write even a single line it is vital to consider project-wide reproducibility. “Perfect” code in a project that isn’t structured thoughtfully can still result in a project that isn’t reproducible. On the other hand, “bad” code can be made more intelligible when it is placed in a well-documented/organized project!\n\nFundamental Structure\n\nThe simplest way of beginning a reproducible project is adopting a good file organization system. There is no single “best” way of organizing your projects’ files as long as you are consistent. Consistency will make your system–whatever that consists of–understandable to others.\nHere are some rules to keep in mind as you decide how to organize your project:\n\nUse one folder per project\n\nKeeping all inputs, outputs, and documentation in a single folder makes it easier to collaborate and share all project materials. Also, most programming applications (RStudio, VS Code, etc.) work best when all needed files are in the same folder.\n\nOrganize content with sub-folders\n\nPutting files that share a purpose or source into logical sub-folders is a great idea! This makes it easy to figure out where to put new content and reduces the effort of documenting project organization. Don’t feel like you need to use an intricate web of sub-folders either! Just one level of sub-folders is enough for many projects.\n\n\n\n\n\n\nDiscussion: Folder Structure\n\n\n\nWith a partner discuss (some of) the following questions:\n\nHow do you typically organize your projects’ files?\nWhat benefits do you see of your current approach?\nWhat–if any–limitations to your system have you experienced?\nDo you think your structure would work well in a team environment?\n\nIf not, what changes might you make to better fit that context?\n\n\n\n\n\nCraft informative file names\n\nAn ideal file name should give some information about the file’s contents, purpose, and relation to other project files. Some of that may be reinforced by folder names, but the file name itself should be inherently meaningful. This lets you change folder names without fear that files would also need to be re-named.\n\nNaming Tips\nWe’ve brought up the importance of naming several times already but haven’t actually discussed the specifics of what makes a “good” name for a file or folder. Consider the adopting some (or all!) of the file name tips we outline below.\n\nNames should be sorted by a computer and human in the same way\n\nComputers sort files/folders alphabetically and numerically. Sorting alphabetically rarely matches the order scripts in a workflow should be run. If you add step numbers to the start of each file name the computer will sort the files in an order that makes sense for the project. You may also want to “zero pad” numbers so that all numbers have the same number of digits (e.g., “01” and “10” vs. “1” and “10”).\n\nNames should avoid spaces and special characters\n\nSpaces and special characters (e.g., é, ü, etc.) cause errors in some computers (particularly Windows operating systems). You can replace spaces with underscores or hyphens to increase machine readability. Avoid using special characters as much as possible. You should also be consistent about casing (i.e., lower vs. uppercase).\n\nNames should use consistent delimiters\n\nDelimiters are characters used to separate pieces of information in otherwise plain text. Underscores are a commonly used example of this. If a file/folder name has multiple pieces of information, you can separate these with a delimiter to make them more readable to people and machines. For example, you could name a folder “coral_reef_data” which would be more readable than “coralreefdata”.\nYou may also want to use multiple delimiters to indicate different things. For instance, you could use underscores to differentiate categories and then use hyphens instead of spaces between words.\n\nNames should use “slugs” to connect inputs and outputs\n\nSlugs are human-readable, unique pieces of file names that are shared between files and the outputs that they create. Maybe a script is named “02_tidy.R” and all of the data files it creates are named “02_…”. Weird or unlikely outputs are easily traced to the scripts that created them because of their shared slug.\n\n\n\nOrganizing Example\nThese tips are all worthwhile but they can feel a little abstract without a set of files firmly in mind. Let’s consider an example synthesis project where we incrementally change the project structure to follow increasing more of the guidelines we suggest above.\n\nVersion 1Version 2Version 3Version 4\n\n\n\n\n\n\n\nPositives\n\nAll project files are in one folder\n\n\n\nAreas for Improvement\n\nNo use of sub-folders to divide logically-linked content\nFile names lack key context (e.g., workflow order, inputs vs. outputs, etc.)\nInconsistent use of delimiters\n\n\n\n\n\n\n\n\n\n\n\nPositives\n\nSub-folders used to divide content\nProject documentation included in top level (README and license files)\n\n\n\nAreas for Improvement\n\nFile names still inconsistent\n\nFile names contain different information in different order\nMixed use of delimiters\nMany file names include spaces\n\n\n\n\n\n\n\n\n\n\n\n\nPositives\n\nMost file names contain context\nStandardized use of casing and–within sub-folder–consistent delimiters used\n\n\n\nAreas for Improvement\n\nWorkflow order “guessable” but not explicit\nUnclear which files are inputs / outputs (and of which scripts)\n\n\n\n\n\n\n\n\n\n\n\nPositives\n\nScripts include zero-padded numbers indicating order of operations\nInputs / outputs share zero padded slug with source script\nReport file names machine sorted from least to most recent (top to bottom)\n\n\n\nAreas for Improvement\n\nDepending on sub-folder complexity, could add sub-folder specific README files\nGraph file names still include spaces\n\n\n\n\n\n\n\n\n\nDocumentation\nDocumenting a project can feel daunting but it is often not as hard as one might imagine and always well worth the effort! One simple practice you can adopt to dramatically improve the reproducibility of your project is to create a “README” file in the top-level of your project’s folder system. This file can be formatted however you’d like but generally READMEs should include (1) a project overview written in plain language, (2) a basic table of contents for the primary folders in your project folder, and (3) a brief description of the file naming scheme you’ve adopted for this project.\nYour project’s README becomes the ‘landing page’ for those navigating your repository and makes it easy for team members to know where documentation should go (in the README!). You may also choose to create a README file for some of the sub-folders of your project. This can be particularly valuable for your “data” folder(s) as it is an easy place to store data source/provenance information that might be overwhelming to include in the project-level README file.\nFinally, you should choose a place to keep track of ideas, conversations, and decisions about the project. While you can take notes on these topics on a piece of paper, adopting a digital equivalent is often helpful because you can much more easily search a lengthy document when it is machine readable. We will discuss GitHub during the Version Control module but GitHub offers something called Issues that can be a really effective place to record some of this information.\n\n\nOrganization Recommendations\nIf you integrate any of the concepts we’ve covered above you will find the reproducibility and transparency of your project will greatly increase. However, if you’d like additional recommendations we’ve assembled a non-exhaustive set of additional “best practices” that you may find helpful.\n\nNever Edit Raw Data\nFirst and foremost, it is critical that you never edit the raw data directly. If you do need to edit the raw data, use a script to make all needed edits and save the output of that script as a separate file. Editing the raw data directly without a script or using a script but overwriting the raw data are both incredibly risky operations because your create a file that “looks” like the raw data (and is likely documented as such) but differs from what others would have if they downloaded the ‘real’ raw data personally.\n\n\nSeparate Raw and Processed Data\nIn the same vein as the previous best practice, we recommend that you separate the raw and processed data into separate folders. This will make it easier to avoid accidental edits to the raw data and will make it clear what data are created by your project’s scripts; even if you choose not to adopt a file naming convention that would make this clear.\n\n\nQuarantine External Outputs\nThis can sound harsh, but it is often a good idea to “quarantine” outputs received from others until they can be carefully vetted. This is not at all to suggest that such contributions might be malicious! As you embrace more of the project organization recommendations we’ve described above outputs from others have more and more opportunities to diverge from the framework you establish. Quarantining inputs from others gives you a chance to rename files to be consistent with the rest of your project as well as make sure that the style and content of the code also match (e.g., use or exclusion of particular packages, comment frequency and content, etc.)", + "text": "Project Organization & Documentation\nMuch of the popular conversation around reproducibility centers on reproducibility as it pertains to code. That is definitely an important facet but before we write even a single line it is vital to consider project-wide reproducibility. “Perfect” code in a project that isn’t structured thoughtfully can still result in a project that isn’t reproducible. On the other hand, “bad” code can be made more intelligible when it is placed in a well-documented/organized project!\n\nFundamental Structure\n\nThe simplest way of beginning a reproducible project is adopting a good file organization system. There is no single “best” way of organizing your projects’ files as long as you are consistent. Consistency will make your system–whatever that consists of–understandable to others.\nHere are some rules to keep in mind as you decide how to organize your project:\n\nUse one folder per project\n\nKeeping all inputs, outputs, and documentation in a single folder makes it easier to collaborate and share all project materials. Also, most programming applications (RStudio, VS Code, etc.) work best when all needed files are in the same folder.\n\nOrganize content with sub-folders\n\nPutting files that share a purpose or source into logical sub-folders is a great idea! This makes it easy to figure out where to put new content and reduces the effort of documenting project organization. Don’t feel like you need to use an intricate web of sub-folders either! Just one level of sub-folders is enough for many projects.\n\nCraft informative file names\n\nAn ideal file name should give some information about the file’s contents, purpose, and relation to other project files. Some of that may be reinforced by folder names, but the file name itself should be inherently meaningful. This lets you change folder names without fear that files would also need to be re-named.\n\n\n\n\n\n\nDiscussion: Project Structure\n\n\n\nWith a partner discuss (some of) the following questions:\n\nHow do you typically organize your projects’ files?\nWhat benefits do you see of your current approach?\nWhat–if any–limitations to your system have you experienced?\nDo you think your structure would work well in a team environment?\n\nIf not, what changes might you make to better fit that context?\n\n\n\n\n\nNaming Tips\nWe’ve brought up the importance of naming several times already but haven’t actually discussed the specifics of what makes a “good” name for a file or folder. Consider the adopting some (or all!) of the file name tips we outline below.\n\nNames should be sorted by a computer and human in the same way\n\nComputers sort files/folders alphabetically and numerically. Sorting alphabetically rarely matches the order scripts in a workflow should be run. If you add step numbers to the start of each file name the computer will sort the files in an order that makes sense for the project. You may also want to “zero pad” numbers so that all numbers have the same number of digits (e.g., “01” and “10” vs. “1” and “10”).\n\nNames should avoid spaces and special characters\n\nSpaces and special characters (e.g., é, ü, etc.) cause errors in some computers (particularly Windows operating systems). You can replace spaces with underscores or hyphens to increase machine readability. Avoid using special characters as much as possible. You should also be consistent about casing (i.e., lower vs. uppercase).\n\nNames should use consistent delimiters\n\nDelimiters are characters used to separate pieces of information in otherwise plain text. Underscores are a commonly used example of this. If a file/folder name has multiple pieces of information, you can separate these with a delimiter to make them more readable to people and machines. For example, you could name a folder “coral_reef_data” which would be more readable than “coralreefdata”.\nYou may also want to use multiple delimiters to indicate different things. For instance, you could use underscores to differentiate categories and then use hyphens instead of spaces between words.\n\nNames should use “slugs” to connect inputs and outputs\n\nSlugs are human-readable, unique pieces of file names that are shared between files and the outputs that they create. Maybe a script is named “02_tidy.R” and all of the data files it creates are named “02_…”. Weird or unlikely outputs are easily traced to the scripts that created them because of their shared slug.\n\n\n\nOrganizing Example\nThese tips are all worthwhile but they can feel a little abstract without a set of files firmly in mind. Let’s consider an example synthesis project where we incrementally change the project structure to follow increasing more of the guidelines we suggest above.\n\nVersion 1Version 2Version 3Version 4\n\n\n\n\n\n\n\nPositives\n\nAll project files are in one folder\n\n\n\nAreas for Improvement\n\nNo use of sub-folders to divide logically-linked content\nFile names lack key context (e.g., workflow order, inputs vs. outputs, etc.)\nInconsistent use of delimiters\n\n\n\n\n\n\n\n\n\n\n\nPositives\n\nSub-folders used to divide content\nProject documentation included in top level (README and license files)\n\n\n\nAreas for Improvement\n\nFile names still inconsistent\n\nFile names contain different information in different order\nMixed use of delimiters\nMany file names include spaces\n\n\n\n\n\n\n\n\n\n\n\n\nPositives\n\nMost file names contain context\nStandardized use of casing and–within sub-folder–consistent delimiters used\n\n\n\nAreas for Improvement\n\nWorkflow order “guessable” but not explicit\nUnclear which files are inputs / outputs (and of which scripts)\n\n\n\n\n\n\n\n\n\n\n\nPositives\n\nScripts include zero-padded numbers indicating order of operations\nInputs / outputs share zero padded slug with source script\nReport file names machine sorted from least to most recent (top to bottom)\n\n\n\nAreas for Improvement\n\nDepending on sub-folder complexity, could add sub-folder specific README files\nGraph file names still include spaces\n\n\n\n\n\n\n\n\n\nDocumentation\nDocumenting a project can feel daunting but it is often not as hard as one might imagine and always well worth the effort! One simple practice you can adopt to dramatically improve the reproducibility of your project is to create a “README” file in the top-level of your project’s folder system. This file can be formatted however you’d like but generally READMEs should include:\n\nProject overview written in plain language\nBasic table of contents for the primary folders in your project folder\nBrief description of the file naming scheme you’ve adopted for this project.\n\nYour project’s README becomes the ‘landing page’ for those navigating your repository and makes it easy for team members to know where documentation should go (in the README!). You may also choose to create a README file for some of the sub-folders of your project. This can be particularly valuable for your “data” folder(s) as it is an easy place to store data source/provenance information that might be overwhelming to include in the project-level README file.\nFinally, you should choose a place to keep track of ideas, conversations, and decisions about the project. While you can take notes on these topics on a piece of paper, adopting a digital equivalent is often helpful because you can much more easily search a lengthy document when it is machine readable. We will discuss GitHub during the Version Control module but GitHub offers something called Issues that can be a really effective place to record some of this information.\n\n\n\n\n\n\nActivity: Create a README\n\n\n\nCreate a draft README for one of your research projects. If all of your projects already have READMEs (very impressive!) revisit the one with the least detail.\n\nInclude a 2-4 sentence description of the project objectives / hypotheses\nIdentify and describe (in 1 sentence) the primary sub-folders in the project\nIf your chosen project includes scripts, summarize each and indicate which script(s) they depend on and which depend on them\n\nFeel free to put your personal flair on the README! If there is other information you feel would be relevant to an outsider looking at your project, you can definitely add that.\n\n\n\n\nOrganization Recommendations\nIf you integrate any of the concepts we’ve covered above you will find the reproducibility and transparency of your project will greatly increase. However, if you’d like additional recommendations we’ve assembled a non-exhaustive set of additional “best practices” that you may find helpful.\n\nNever Edit Raw Data\nFirst and foremost, it is critical that you never edit the raw data directly. If you do need to edit the raw data, use a script to make all needed edits and save the output of that script as a separate file. Editing the raw data directly without a script or using a script but overwriting the raw data are both incredibly risky operations because your create a file that “looks” like the raw data (and is likely documented as such) but differs from what others would have if they downloaded the ‘real’ raw data personally.\n\n\nSeparate Raw and Processed Data\nIn the same vein as the previous best practice, we recommend that you separate the raw and processed data into separate folders. This will make it easier to avoid accidental edits to the raw data and will make it clear what data are created by your project’s scripts; even if you choose not to adopt a file naming convention that would make this clear.\n\n\nQuarantine External Outputs\nThis can sound harsh, but it is often a good idea to “quarantine” outputs received from others until they can be carefully vetted. This is not at all to suggest that such contributions might be malicious! As you embrace more of the project organization recommendations we’ve described above outputs from others have more and more opportunities to diverge from the framework you establish. Quarantining inputs from others gives you a chance to rename files to be consistent with the rest of your project as well as make sure that the style and content of the code also match (e.g., use or exclusion of particular packages, comment frequency and content, etc.)", "crumbs": [ "Quantitative Modules", "Reproducibility" @@ -392,7 +392,7 @@ "href": "mod_reproducibility.html#reproducible-coding", "title": "Reproducibility Best Practices", "section": "Reproducible Coding", - "text": "Reproducible Coding\nNow that you’ve organized your project in a reasonable way and documented those choices, we can move on to principles of reproducible coding! Doing your data operations with scripts is more reproducible than doing those operations without a programming language (i.e., with Microsoft Excel, Google Sheets, etc.). However, scripts are often written in a way that is not reproducible. A recent study aiming to run 2,000 project’s worth of R code found that 74% of the associated R files failed to complete without error (Trisovic et al. 2022). Many of those errors involve coding practices that hinder reproducibility but are easily preventable by the original code authors.\n\nWhen your scripts are clear and reproducibly-written you will reap the following benefits:\n\nReturning to your code after having set it down for weeks/months is much simpler\nCollaborating with team members requires less verbal explanation\nSharing methods for external result validation is more straightforward\nIn cases where you’re developing a novel method or workflow, structuring your code in this way will increase the odds that someone outside of your team will adopt your strategy\n\n\nPackages, Namespacing, and Software Versions\nOne of the first things that every script should begin with is an explicit loading of all libraries that script need (these are called “dependencies). Scripts that don’t specify which libraries are needed are unlikely to run on anyone’s computer. Unfortunately, many R packages need to be installed by each user before they can be loaded with the library function. You may find it simpler to use the librarian package which automatically detects and installs needed packages if they are not already present. Note that users would still need to install librarian itself!\nIt is also strongly recommended to “namespace” functions everywhere you use them. In R this is technically optional (Python requires this) but it is a really good practice to adopt, particularly for functions that may appear in multiple packages with the same name but do very different operations depending on their source. Namespacing in R is done by adding the package name and two colons before the function name (e.g., dplyr::mutate). This prevents accidental use of functions from the ‘wrong’ package for a given context.\nYou may also need to consider the version of the packages that you’re using and the version of R. The sessionInfo function (from the utils package loaded into R by default) is a good way of capturing some of this information but it is relatively high level and lacks sufficient detail for many contexts. For a more complete amount of information, consider using the renv or packrat packages.\n\n\nScript Organization\nEvery change to the data between the initial raw data and the finished product should be scripted. The ideal would be that you could hand someone your code and the starting data and have them be able to perfectly retrace your steps. This is not possible if you make unscripted modifications to the data at any point!\nYou may wish to break your scripted workflow into separate, modular files for ease of maintenance and/or revision. This is a good practice so long as each file fits clearly into a logical/thematic group (e.g., data cleaning versus analysis).\nFinally, your code should never use absolute file paths. Absolute file paths are those that begin at the root of your entire computer (“C:…” on Windows and “~…” on Mac). Such paths are inherently not reproducible as the odds of anyone having the exact same absolute file path are extremely slim. Instead, using relative file paths that begin at the project folder is preferable. These are transferable among users. You can even use R’s file.path function to automatically detect the correct direction of slashes between folders to make it easier to collaborate across operating systems! Note in the above figure from Trisovic et al. (2022) that many scripts that set the working directory manually had errors until that bit was removed. Avoid setting the working directory explicitly and instead structure your project such that relative paths within the project folder will always succeed.\n\n\nCode Style\nWhen it comes to code style, the same ‘rule of thumb’ applies here that applied to project organization: virtually any system will work so long as you (and your team) are consistent! Thtat said, there are a few principles worth adopting if you have not already done so.\n1. Use concise and descriptive object names\nIt can be difficult to balance these two imperatives but short object names are easier to re-type and visually track through a script. Descriptive object names on the other hand are useful because they help orient people reading the script to what the object contains.\n2. Don’t be afraid of space!\nScripts are free to write regardless of the number of lines so do not feel as though there is a strict character limit you need to keep in mind. Cramped code is difficult to read and thus can be challenging to share with others or debug on your own. Inserting an empty line between coding lines can help break up sections of code and putting spaces before and after operators can make reading single lines much simpler.\n\n\n\n\n\n\n\n\n\nCode Comments\nA “comment” in a script is a human readable, non-coding line that helps give context for the code. In R (and Python), comment lines start with a hashtag (#). Including comments is a low effort way of both (A) creating internal documentation for the script and (B) increasing the reproducibility of the script. It is difficult to include “too many” comments, so when in doubt: add more comments!\nThere are two major strategies for comments and either or both might make sense for your project.\n\n“What” Comments“Why” Comments\n\n\nComments describe what the code is doing.\n\nBenefits: allows team members to understand workflow without code literacy\nRisks: rationale for code not explicit\n\n# Remove all pine trees from dataset\nno_pine_df <- dplyr::filter(full_df, genus != \"Pinus\")\n\n\nComments describe rationale and/or context for code.\n\nBenefits: built-in documentation for team decisions\nRisks: assumes everyone can read code\n\n# Cone-bearing plants are not comparable with other plants in dataset\nno_pine_df <- dplyr::filter(full_df, genus != \"Pinus\")\n\n\n\n\n\nConsider Custom Functions\nIn most cases, duplicating code is not good practice. Such duplication risks introducing a typo in one copy but not the others. Additionally, if a decision is made later on that requires updating this section of code, you must remember to update each copy separately.\nInstead of taking this copy/paste approach, you could consider writing a “custom” function that fits your purposes. All instances where you would have copied the code now invoke this same function. Any error is easily tracked to the single copy of the function and changes to that step of the workflow can be accomplished in a centralized location.\n\nFunction Recommendations\nWe have the following ‘rules of thumb’ for custom function use:\n- If a given operation is duplicated 3 or more times within a project, write a custom function\nFunctions written in this case can be extremely specific and–though documentation is always a good idea–can be a little lighter on documentation. Note that the reason you can reduce the emphasis on documentation is only because of the assumption that you won’t be sharing the function widely. If you do decide the function could be widely valuable you would need to add the needed documentation post hoc.\n- Write functions defensively\nWhen you write custom functions, it is really valuable to take the time to write them defensively. In this context, “defensively” means that you anticipate likely errors and write your own informative/human readable error messages. Let’s consider a simplified version of a function rom the ltertools R package for calculating the coefficient of variation (CV).\nThe coefficient of variation is equal to the standard deviation divided by the mean. Fortunately, R provides functions for calculating both of these already and both expect numeric vectors. If either of those functions is given a non-number you get the following warning message: In mean.default(x = \"...\") : argument is not numeric or logical: returning NA.\nSomeone with experience in R may be able to interpret this error but for many users this error message is completely opaque. In the function included below however we can see that there is a simpler, more human readable version of the error message and the function is stopped before it can ever reach the part of the code that would throw the warning message included above.\ncv <- function(x){\n \n # Error out if x is not numeric\n if(is.numeric(x) != TRUE)\n stop(\"`x` must be numeric\")\n \n # Calculate CV\n sd(x = x) / mean(x = x)\nThe key to defensive programming is to try to get functions to fail fast and fail informatively as soon as a problem is detected! This is easier to debug and understand for coders with a range of coding expertise and–for complex functions–can save a ton of useless processing time when failure is guaranteed at a later step.\n- If a given operation is duplicated 3 or more times across projects, consider creating an R package\nCreating an R package can definitely seem like a daunting task but duplication across projects carries the same weaknesses of excessive duplication within a project. However, when duplication is across projects, not even writing a custom function saves you because you need to duplicate that function’s script for each project that needs the tool.\nHadley Wickham and Jenny Bryan have written a free digital book on this subject that demystifies a lot of this process and may make you feel more confident to create your own R package if/when one is needed.\nIf you do take this path, you can simply install your package as you would any other in order to have access to the operations rather than creating duplicates by hand.", + "text": "Reproducible Coding\nNow that you’ve organized your project in a reasonable way and documented those choices, we can move on to principles of reproducible coding! Doing your data operations with scripts is more reproducible than doing those operations without a programming language (i.e., with Microsoft Excel, Google Sheets, etc.). However, scripts are often written in a way that is not reproducible. A recent study aiming to run 2,000 project’s worth of R code found that 74% of the associated R files failed to complete without error (Trisovic et al. 2022). Many of those errors involve coding practices that hinder reproducibility but are easily preventable by the original code authors.\n\nWhen your scripts are clear and reproducibly-written you will reap the following benefits:\n\nReturning to your code after having set it down for weeks/months is much simpler\nCollaborating with team members requires less verbal explanation\nSharing methods for external result validation is more straightforward\nIn cases where you’re developing a novel method or workflow, structuring your code in this way will increase the odds that someone outside of your team will adopt your strategy\n\n\nPackages, Namespacing, and Software Versions\nAn under-appreciated facet of reproducible coding is a record of what code packages are used in a particular script and the version number of those packages. Packages evolve over time and code that worked when using one version of a given package may not work for future versions of that same package. Perpetually updating your code to work with the latest package versions is not sustainable but recording key information can help users set up the code environment that does work for your project.\n\nLoad Libraries Explicitly\nIt is important to load libraries at the start of every script. In some languages (like Python) this step is required but in others (like R) this step is technically “optional” but disastrous to skip. It is safe to skip including the installation step in your code because the library step should tell code-literate users which packages they need to install.\nFor instance you might begin each script with something like:\n# Load needed libraries\nlibrary(dplyr); library(magrittr); library(ggplot2)\n\n# Get to actual work\n. . .\nIn R the semicolon allows you to put multiple code operations in the same line of the script. Listing the needed libraries in this way thus lets everyone reading the code know exactly which packages they will need to have installed.\nIf you are feeling generous you could use the librarian R package to install packages that are not yet installed and simultaneously load all needed libraries. Note that users would still need to install librarian itself but this at least limits possible errors to one location. This is done like so:\n# Load `librarian` package\nlibrary(librarian)\n\n# Install missing packages and load needed libraries\nshelf(dplyr, magrittr, ggplot2)\n\n# Get to actual work\n. . .\n\n\nFunction Namespacing\nIt is also strongly recommended to “namespace” functions everywhere you use them. In R this is technically optional but it is a really good practice to adopt, particularly for functions that may appear in multiple packages with the same name but do very different operations depending on their source. In R the ‘namespacing operator’ is two colons.\n# Use the `mutate` function from the `dplyr` package\ndplyr::mutate(. . .)\nAn ancillary benefit of namespacing is that namespaced functions don’t need to have their respective libraries loaded. Still good practice to load the library though!\n\n\nPackage Versions\nWhile working on a project you should use the latest version of every needed package. However, as you prepare to publish or otherwise publicize your code, you’ll need to record package versions. R provides the sessionInfo function (from the utils package included in “base” R) which neatly summarizes some high level facets of your code environment. Note that for this method to work you’ll need to actually run the library-loading steps of your scripts.\nFor more in-depth records of package versions and environment preservation–in R–you might also consider the renv package or the packrat package.\n\n\n\nScript Organization\nEvery change to the data between the initial raw data and the finished product should be scripted. The ideal would be that you could hand someone your code and the starting data and have them be able to perfectly retrace your steps. This is not possible if you make unscripted modifications to the data at any point!\nYou may wish to break your scripted workflow into separate, modular files for ease of maintenance and/or revision. This is a good practice so long as each file fits clearly into a logical/thematic group (e.g., data cleaning versus analysis).\n\n\nFile Paths\nWhen importing inputs or exporting outputs we need to specify “file paths”. These are the set of folders between where your project is ‘looking’ and where the input/output should come from/go. The figure from Trisovic et al. (2022) shows that file path and working directory errors are a substantial barrier to code that can be re-run in clean coding environments. Consider the following ways of specifying file paths from least to most reproducible.\n\nWorstBadBetterBest!\n\n\n\nAbsolute Paths\nThe worst way of specifying a file path is to use the “absolute” file path. This is the path from the root of your computer to a given file. There are many issues here but the primary one is that absolute paths only work for one computer! Given that only one person can even run lines of code that use absolute paths, it’s not really worth specifying the other issues.\n\n\nExample\n# Read in bee community data\nmy_df <- read.csv(file = \"~/Users/lyon/Documents/Grad School/Thesis (Chapter 1)/Data/bees.csv\")\n\n\n\n\nManually Setting the Working Directory\nMarginally better than using the absolute path is to set the working directory to some location. This may look neater than the absolute path option but it actually has the same point of failure: Both methods only work for one computer!\n\n\nExample\n# Set working directory\nsetwd(dir = \"~/Users/lyon/Documents/Grad School/Thesis (Chapter 1)\")\n\n# Read in bee community data\nmy_df <- read.csv(file = \"Data/bees.csv\")\n\n\n\n\nRelative Paths\nInstead of using absolute paths or manually setting the working directory you can use “relative” file paths! Relative paths assume all project content lives in the same folder.\nThis is a safe assumption because it is the most fundamental tenet of reproducible project organization! The strength of relative paths is actually a serious contributing factor for why it is good practice to use a single folder.\n\n\nExample\n# Read in bee community data\n1my_df <- read.csv(file = \"Data/bees.csv\")\n\n1\n\nParts of file path specific to each user are automatically recognized by the computer\n\n\n\n\n\n\nOperating System-Flexible Relative Paths\nThe “better” example is nice but has a serious limitation: it hard coded the type of slash between file path elements. This means that only computers of the same operating system as the code author could run that line.\nWe can use functions to automatically detect and insert the correct slashes though!\n\n\nExample\n# Read in bee community data\nmy_df <- read.csv(file = file.path(\"Data\", \"bees.csv\"))\n\n\n\n\n\n\nCode Style\nWhen it comes to code style, the same ‘rule of thumb’ applies here that applied to project organization: virtually any system will work so long as you (and your team) are consistent! That said, there are a few principles worth adopting if you have not already done so.\n\nUse concise and descriptive object names\n\nIt can be difficult to balance these two imperatives but short object names are easier to re-type and visually track through a script. Descriptive object names on the other hand are useful because they help orient people reading the script to what the object contains.\n\nDon’t be afraid of empty space!\n\nScripts are free to write regardless of the number of lines so do not feel as though there is a strict character limit you need to keep in mind. Cramped code is difficult to read and thus can be challenging to share with others or debug on your own. Inserting an empty line between coding lines can help break up sections of code and putting spaces before and after operators can make reading single lines much simpler.\n\n\n\nCode Comments\nA “comment” in a script is a human readable, non-coding line that helps give context for the code. In R (and Python), comment lines start with a hashtag (#). Including comments is a low effort way of both (A) creating internal documentation for the script and (B) increasing the reproducibility of the script. It is difficult to include “too many” comments, so when in doubt: add more comments!\nThere are two major strategies for comments and either or both might make sense for your project.\n\n“What” Comments\nComments describe what the code is doing.\n\nBenefits: allows team members to understand workflow without code literacy\nRisks: rationale for code not explicit\n\n# Remove all pine trees from dataset\nno_pine_df <- dplyr::filter(full_df, genus != \"Pinus\")\n\n\n“Why” Comments\nComments describe rationale and/or context for code.\n\nBenefits: built-in documentation for team decisions\nRisks: assumes everyone can read code\n\n# Cone-bearing plants are not comparable with other plants in dataset\nno_pine_df <- dplyr::filter(full_df, genus != \"Pinus\")\n\n\n\n\n\n\nDiscussion: Comment on Comments\n\n\n\nWith a partner discuss the following questions:\n\nWhen you write comments, do you focus more on the “what” or the “why”?\nWhat would you estimate is the ratio of code to comment lines in your code?\n\n1:1 being every code line has one comment line\n\nIf you have revisited old code, were your comments helpful?\n\nHow could you make them more helpful?\n\nIn what ways do you think you would need to change your commenting style for a team project?\n\n\n\n\n\n\n\n\n\nActivity: Make Comments\n\n\n\nRevisit a script from an old project (ideally one you haven’t worked on recently). Once you’ve opened the script:\n\nScan through the script\n\nCan you identify the main purpose(s) of the code?\n\nIdentify any areas where you’re not sure either (A) what the code is doing or (B) why that section of code exists\n\nAdd comments to these areas to document what they’re up to\n\nShare the commented version of one of these trouble areas with a partner\n\nDo they understand the what and/or why of your code?\nIf not, revise the comments and repeat\n\n\n\n\n\n\n\nConsider Custom Functions\nIn most cases, duplicating code is not good practice. Such duplication risks introducing a typo in one copy but not the others. Additionally, if a decision is made later on that requires updating this section of code, you must remember to update each copy separately.\nInstead of taking this copy/paste approach, you could consider writing a “custom” function that fits your purposes. All instances where you would have copied the code now invoke this same function. Any error is easily tracked to the single copy of the function and changes to that step of the workflow can be accomplished in a centralized location.\n\nFunction Recommendations\nWe have the following ‘rules of thumb’ for custom function use:\n- If a given operation is duplicated 3 or more times within a project, write a custom function\nFunctions written in this case can be extremely specific and–though documentation is always a good idea–can be a little lighter on documentation. Note that the reason you can reduce the emphasis on documentation is only because of the assumption that you won’t be sharing the function widely. If you do decide the function could be widely valuable you would need to add the needed documentation post hoc.\n- Write functions defensively\nWhen you write custom functions, it is really valuable to take the time to write them defensively. In this context, “defensively” means that you anticipate likely errors and write your own informative/human readable error messages. Let’s consider a simplified version of a function from the ltertools R package for calculating the coefficient of variation (CV).\nThe coefficient of variation is equal to the standard deviation divided by the mean. Fortunately, R provides functions for calculating both of these already and both expect numeric vectors. If either of those functions is given a non-number you get the following warning message: “In mean.default(x =”…“) : argument is not numeric or logical: returning NA”.\nSomeone with experience in R may be able to interpret this error but for many users this error message is completely opaque. In the function included below however we can see that there is a simpler, more human readable version of the error message and the function is stopped before it can ever reach the part of the code that would throw the warning message included above.\ncv <- function(x){\n \n # Error out if x is not numeric\n if(is.numeric(x) != TRUE)\n stop(\"`x` must be numeric\")\n \n # Calculate CV\n sd(x = x) / mean(x = x)\nThe key to defensive programming is to try to get functions to fail fast and fail informatively as soon as a problem is detected! This is easier to debug and understand for coders with a range of coding expertise and–for complex functions–can save a ton of useless processing time when failure is guaranteed at a later step.\n- If a given operation is duplicated 3 or more times across projects, consider creating an R package\nCreating an R package can definitely seem like a daunting task but duplication across projects carries the same weaknesses of excessive duplication within a project. However, when duplication is across projects, not even writing a custom function saves you because you need to duplicate that function’s script for each project that needs the tool.\nHadley Wickham and Jenny Bryan have written a free digital book on this subject that demystifies a lot of this process and may make you feel more confident to create your own R package if/when one is needed.\nIf you do take this path, you can simply install your package as you would any other in order to have access to the operations rather than creating duplicates by hand.", "crumbs": [ "Quantitative Modules", "Reproducibility" @@ -403,7 +403,18 @@ "href": "mod_reproducibility.html#fair-care-data-principles", "title": "Reproducibility Best Practices", "section": "FAIR & CARE Data Principles", - "text": "FAIR & CARE Data Principles\nData availability, data size, and demand for transparency by government and funding agencies are all steadily increasing. While ensuring that your project and code practices are reproducible is important, it is also important to consider how open and reproducible your data are as well. Synthesis projects are in a unique position here because synthesis projects use data that may have been previously published on and/or be added to a public data repository by the original data collectors. However, synthesis projects take data from these different sources and wrangle it such that the different data sources are comparable to one another. These ‘synthesis data products’ can be valuable to consider archiving in a public repository to save other researchers from needing to re-run your entire wrangling workflow in order to get the data product. In either primary or synthesis research contexts there are several valuable frameworks to consider as data structure and metadata are being decided. Among these are the FAIR and CARE data principles.\n\nFAIR\nFAIR is an acronym for Findable Accessible Interpoerable and Reusable. Each element of the FAIR principles can be broken into a set of concrete actions that you can take throughout the lifecycle of your project to ensure that your data are open and transparent. Perhaps most importantly, FAIR data are most easily used by other research teams in the future so the future impact of your work is–in some ways–dependent upon how thoroughly you consider these actions.\nConsider the following list of actions you might take to make your data FAIR. Note that not all actions may be appropriate for all types of data (see our discussion of the CARE principles below), but these guidelines are still important to consider–even if you ultimately choose to reject some of them. Virtually all of the guidelines considered below apply to metadata (i.e., the formal documentation describing your data) and the ‘actual’ data but for ease of reference we will call both of these resources “data.”\n\nFindability\n\nEnsure that data have a globally unique and persistent identifier\nCompletely fill out all metadata details\nRegister/index data in a searchable resource\n\nAccessibility\n\nStore data in a file format that is open, free, and universally implementable (e.g., CSV rather than MS Excel, etc.)\nEnsure that metadata will be available even if the data they describe are not\n\nInteroperability\n\nUse formal, shared, and broadly applicable language for knowledge representation\n\nThis can mean using full species names rather than codes or shorthand that may not be widely known\n\nUse vocabularies that are broadly used and that themselves follow FAIR principles\nInclude explicit references to other FAIR data\n\nReusability\n\nDescribe your data with rich detail that covers a plurality of relevant attributes\nAttach a clear data usage license so that secondary data users know how they are allowed to use your data\nInclude detailed provenance information about your data\nEnsure that your data meet discipline-specific community standards\n\n\n\n\n\n\n\nDiscussion: Consider Data FAIRness\n\n\n\nConsider the first data chapter of your thesis or dissertation. On a scale of 1-5, how FAIR do you think your data and metadata are? What actions could you take to make your data more FAIR compliant? If it helps, feel free to rate your (meta)data based on each FAIR criterion separately!\nFeel free to use these questions to guide your thinking\n\nHow are the data for that project stored?\nWhat metadata exists to document those data?\nHow easy would it be for someone in your lab group to pick up and use your data?\nHow easy would it be for someone not in your lab group?\n\n\n\n\n\nCARE\nWhile making data and code more FAIR is often a good ideal the philosophy behind those four criteria come from a perspective that emphasizes data sharing as a good in and of itself. This approach can ignore historical context and contemporary power differentials and thus be insufficient as the sole tool to use in evaluating how data/code are shared and stored. The Global Indigenous Data Alliance (GIDA) created the CARE principles with these ethical considerations explicitly built into their tenets. Before making your data widely available and transparent (ideally before even beginning your research), it is crucial to consider this ethical dimension.\nCARE stands for Collective Benefit, Authority to Control, Responsibility, and Ethics. Ensuring that your data meet these criteria helps to advance Indigenous data sovereignty and respects those who have been–and continue to be–collecting knowledge about the world around us for millennia. The following actions are adapted from Jennings et al. 2023 (linked at the bottom of this page).\nCollective Benefit\n\nDemonstrate how your research and its potential results are relevant and of value to the interests of the community at large and its individual members\nInclude and value local community experts in the research team\nUse classifications and categories in ways defined by Indigenous communities and individuals\nDisaggregate large geographic scale data to increase relevance for place-specific Indigenous priorities\nCompensate community experts throughout the research process (proposal development through to community review of pre-publication manuscripts)\n\nAuthority to Control\n\nEstablish institutional principles or protocoles that explicitly recognize Indigenous Peoples’ rights to and interests in their knowledges/data\nEnsure data collection and distribution are consistent with individual and community consent provisions and that consent is ongoing (including the right to withdraw or refuse)\nEnsure Indigenous communities have access to the (meta)data in usable format\n\nResponsibility\n\nCreate and expand opportunities for community capacity\nRecord the Traditional Knowledge and biocultural labels of the Local Contexts Hub in metadata\nEnsure review of draft publications before publication\nUse the languages of Indigenous Peoples in the (meta)data\n\nEthics\n\nAccess research using Indigenous ethical frameworks\nUse community-defined review processes with appropriate reviewers for activities delineated in data management plans\nWork to maximize benefits from the perspectives of Indigenous Peoples by clear and transparent dialogue with communities and individuals\nEngage with community guidelines for the use and reuse of data (including facilitating data removal and/or disposal requests from aggregated datasets)", + "text": "FAIR & CARE Data Principles\nData availability, data size, and demand for transparency by government and funding agencies are all steadily increasing. While ensuring that your project and code practices are reproducible is important, it is also important to consider how open and reproducible your data are as well. Synthesis projects are in a unique position here because synthesis projects use data that may have been previously published on and/or be added to a public data repository by the original data collectors. However, synthesis projects take data from these different sources and wrangle it such that the different data sources are comparable to one another. These ‘synthesis data products’ can be valuable to consider archiving in a public repository to save other researchers from needing to re-run your entire wrangling workflow in order to get the data product. In either primary or synthesis research contexts there are several valuable frameworks to consider as data structure and metadata are being decided. Among these are the FAIR and CARE data principles.\n\nFAIR\nFAIR is an acronym for Findable Accessible Interpoerable and Reusable. Each element of the FAIR principles can be broken into a set of concrete actions that you can take throughout the lifecycle of your project to ensure that your data are open and transparent. Perhaps most importantly, FAIR data are most easily used by other research teams in the future so the future impact of your work is–in some ways–dependent upon how thoroughly you consider these actions.\nConsider the following list of actions you might take to make your data FAIR. Note that not all actions may be appropriate for all types of data (see our discussion of the CARE principles below), but these guidelines are still important to consider–even if you ultimately choose to reject some of them. Virtually all of the guidelines considered below apply to metadata (i.e., the formal documentation describing your data) and the ‘actual’ data but for ease of reference we will call both of these resources “data.”\n\nFindability\n\nEnsure that data have a globally unique and persistent identifier\nCompletely fill out all metadata details\nRegister/index data in a searchable resource\n\nAccessibility\n\nStore data in a file format that is open, free, and universally implementable (e.g., CSV rather than MS Excel, etc.)\nEnsure that metadata will be available even if the data they describe are not\n\nInteroperability\n\nUse formal, shared, and broadly applicable language for knowledge representation\n\nThis can mean using full species names rather than codes or shorthand that may not be widely known\n\nUse vocabularies that are broadly used and that themselves follow FAIR principles\nInclude explicit references to other FAIR data\n\nReusability\n\nDescribe your data with rich detail that covers a plurality of relevant attributes\nAttach a clear data usage license so that secondary data users know how they are allowed to use your data\nInclude detailed provenance information about your data\nEnsure that your data meet discipline-specific community standards\n\n\n\n\n\n\n\nDiscussion: Consider Data FAIRness\n\n\n\nConsider the first data chapter of your thesis or dissertation. On a scale of 1-5, how FAIR do you think your data and metadata are? What actions could you take to make your data more FAIR compliant? If it helps, feel free to rate your (meta)data based on each FAIR criterion separately!\nFeel free to use these questions to guide your thinking\n\nHow are the data for that project stored?\nWhat metadata exists to document those data?\nHow easy would it be for someone in your lab group to pick up and use your data?\nHow easy would it be for someone not in your lab group?\n\n\n\n\n\nCARE\nWhile making data and code more FAIR is often a good ideal the philosophy behind those four criteria come from a perspective that emphasizes data sharing as a good in and of itself. This approach can ignore historical context and contemporary power differentials and thus be insufficient as the sole tool to use in evaluating how data/code are shared and stored. The Global Indigenous Data Alliance (GIDA) created the CARE principles with these ethical considerations explicitly built into their tenets. Before making your data widely available and transparent (ideally before even beginning your research), it is crucial to consider this ethical dimension.\n\nCARE stands for Collective Benefit, Authority to Control, Responsibility, and Ethics. Ensuring that your data meet these criteria helps to advance Indigenous data sovereignty and respects those who have been–and continue to be–collecting knowledge about the world around us for millennia. The following actions are adapted from Jennings et al. 2023 (linked at the bottom of this page).\nCollective Benefit\n\nDemonstrate how your research and its potential results are relevant and of value to the interests of the community at large and its individual members\nInclude and value local community experts in the research team\nUse classifications and categories in ways defined by Indigenous communities and individuals\nDisaggregate large geographic scale data to increase relevance for place-specific Indigenous priorities\nCompensate community experts throughout the research process (proposal development through to community review of pre-publication manuscripts)\n\nAuthority to Control\n\nEstablish institutional principles or protocoles that explicitly recognize Indigenous Peoples’ rights to and interests in their knowledges/data\nEnsure data collection and distribution are consistent with individual and community consent provisions and that consent is ongoing (including the right to withdraw or refuse)\nEnsure Indigenous communities have access to the (meta)data in usable format\n\nResponsibility\n\nCreate and expand opportunities for community capacity\nRecord the Traditional Knowledge and biocultural labels of the Local Contexts Hub in metadata\nEnsure review of draft publications before publication\nUse the languages of Indigenous Peoples in the (meta)data\n\nEthics\n\nAccess research using Indigenous ethical frameworks\nUse community-defined review processes with appropriate reviewers for activities delineated in data management plans\nWork to maximize benefits from the perspectives of Indigenous Peoples by clear and transparent dialogue with communities and individuals\nEngage with community guidelines for the use and reuse of data (including facilitating data removal and/or disposal requests from aggregated datasets)", + "crumbs": [ + "Quantitative Modules", + "Reproducibility" + ] + }, + { + "objectID": "mod_reproducibility.html#reproducibility-best-practices-summary", + "href": "mod_reproducibility.html#reproducibility-best-practices-summary", + "title": "Reproducibility Best Practices", + "section": "Reproducibility Best Practices Summary", + "text": "Reproducibility Best Practices Summary\nMaking sure that your project is reproducible requires a handful of steps before you begin, some actions during the life of the project, and then a few finishing touches when the project nears its conclusion. The following diagram may prove helpful as a coarse roadmap for how these steps might be followed in a general project setting.", "crumbs": [ "Quantitative Modules", "Reproducibility" diff --git a/sitemap.xml b/sitemap.xml index 89fb8b5..20ce334 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,78 +2,78 @@ https://lter.github.io/ssecr/mod_version-control.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/mod_thinking.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/mod_wrangle.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/mod_credit.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/mod_findings.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/mod_data-disc.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/topic_interactivity.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/mod_facilitation.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/mod_data-viz.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/mod_team-sci.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/index.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/mod_reports.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/topic_spatial.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/mod_stats.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/mod_reproducibility.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/mod_spatial.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/mod_logic-models.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z https://lter.github.io/ssecr/CONTRIBUTING.html - 2024-02-28T16:53:44.377Z + 2024-02-29T16:53:11.320Z https://lter.github.io/ssecr/mod_project-mgmt.html - 2024-02-28T16:53:44.401Z + 2024-02-29T16:53:11.348Z