This vignette provides a quick tour of the osfr package.
What is OSF?
OSF is a free and open source web application that provides a space for researchers to collaboratively store, manage, and share their research materials (e.g. data, code, protocols).
Most work on OSF is organized around projects, which include a cloud-based storage bucket where files can be stored and organized into directories. Note there is no storage limit on the size of projects but individual files must be < 5Gb. Projects can be kept private, shared with a specific group of collaborators, or made publicly available with citable DOIs so you can get credit for their work.
If you’d like to learn more about OSF the Center for Open Science has published an excellent series of guides to help get you started. We’ll provide links to specific guides throughout this vignette. Here are a few relevant topics:
Accessing OSF projects
Let’s check out an example project containing materials for an analysis of the 2012 American National Election Survey (ANES). You can access the OSF project in your browser by navigating to its URL: https://osf.io/jgyxm/.
Let’s load this project into R with
osfr::osf_retrieve_node():
anes_project <- osf_retrieve_node("https://osf.io/jgyxm")
anes_project
#> # A tibble: 1 × 3
#> name id meta
#> <chr> <chr> <list>
#> 1 Political identification and gender jgyxm <named list [3]>This returns an osf_tbl object, which is the
data.frame-like class osfr uses to represent items
retrieved from OSF. You can now use anes_project to perform
a variety of project related tasks by passing it to different osfr
functions.
Downloading files
Let’s list all of the files that have been uploaded to the project:
anes_files <- osf_ls_files(anes_project)
anes_files
#> # A tibble: 5 × 3
#> name id meta
#> <chr> <chr> <list>
#> 1 cleaning.R 5e20d22bedceab002d82e0f1 <named list [3]>
#> 2 Questionnaire.docx 5e20d22bedceab002b82dc3f <named list [3]>
#> 3 raw_data.csv 5e20d22c675e0e00096b4de8 <named list [3]>
#> 4 Data Dictionary.docx 5e20d22c675e0e000e6b4b18 <named list [3]>
#> 5 analyses.R 5e20d22c675e0e000a6b4bd3 <named list [3]>This returns another osf_tbl but this one contains 5
rows; one for each of the project files stored on OSF. A nice
feature of OSF is it provides rendered views for a wide variety of file
formats, so it’s not necessary to actually download and open a file if
you just want to quickly examine it. Let’s open the Word Document
containing the project’s data dictionary by extracting the relevant row
from anes_tbl and passing it to
osf_open():
osf_open(anes_files[4, ])Because osf_tbls are just specialized
data.frames, we could also subset() or
dplyr::filter() to achieve the same result.
Note: If an osf_tbl with multiple
entities is passed to an non-vectorized osfr function like
osf_open(), the default behavior is to use the entity in
the first row and warn that all other entities are ignored.
We can also download local copies of these files by passing
anes_files to osf_download().
osf_download(anes_files)
#> # A tibble: 5 × 4
#> name id local_path meta
#> <chr> <chr> <chr> <list>
#> 1 cleaning.R 5e20d22bedceab002d82e0f1 ./cleaning.R <named list>
#> 2 Questionnaire.docx 5e20d22bedceab002b82dc3f ./Questionnaire.do… <named list>
#> 3 raw_data.csv 5e20d22c675e0e00096b4de8 ./raw_data.csv <named list>
#> 4 Data Dictionary.docx 5e20d22c675e0e000e6b4b18 ./Data Dictionary.… <named list>
#> 5 analyses.R 5e20d22c675e0e000a6b4bd3 ./analyses.R <named list>We’ll use these files in the next section for creating a new project.
Pipes
As you’ve likely noticed, osf_tbl objects are central to
osfr’s functionality. Indeed, nearly all of its functions both expect an
osf_tbl as input and return an osf_tbl as
output. As such, osfr functions can be chained together using the pipe operator
(%>%), allowing for the creation of pipelines to
automate OSF-based tasks.
Here is a short example that consolidates all of the steps we’ve performed so far:
osf_retrieve_node("jgyxm") %>%
osf_ls_files() %>%
osf_download()Project management
Now let’s see how to use osfr to create and manage your own projects.
The goal for this section is to create your own version of the
Political Identification and Gender project but with a better
organizational structure. To follow along with this section you’ll need
to authenticate osfr using a personal access token (PAT). See the
?osf_auth() function documentation or the auth
vignette for more information.
Creating a project
First you will need to create a new private project on OSF to store all the files related to the project. Here, we’re giving the new project a title (required) and description (optional).
my_project <- osf_create_project(
title = "Political Identification and Gender: Re-examined",
description = "A re-analysis of the original study's results."
)
my_project
#> # A tibble: 1 × 3
#> name id meta
#> <chr> <chr> <list>
#> 1 Political Identification and Gender: Re-examined f7bgz <named list [3]>The GUID for this new project is f7bgz, but yours will
be something different. You can check out the project on OSF by opening
it’s URL (https://www.osf.io/<GUID>), or, more
conveniently: osf_open(my_project).
Adding structure with components
A key organizational feature of OSF is the ability to augment a project’s structure with sub-projects, which are referred to as components on OSF. Like top-level projects, every component is assigned a unique URL and contains its own cloud-based storage bucket. They can also have different privacy settings from the parent project.
We are going to create two nested components, one for the raw data and one for the analysis scripts.
data_comp <- osf_create_component(my_project, title = "Raw Data")
script_comp <- osf_create_component(my_project, title = "Analysis Scripts")
# verify the components were created
# osf_open(my_project)If you refresh the OSF project in your browser the Components widget should now contain two entries for each of our newly created components.
Uploading files
Now that our project components are in place we can start to populate them with files. Let’s start with the csv file containing our raw data.
data_file <- osf_upload(my_project, path = "raw_data.csv")
data_file
#> # A tibble: 1 × 3
#> name id meta
#> <chr> <chr> <list>
#> 1 raw_data.csv 63309f3e18f4581162429679 <named list [3]>Oh no! Instead of uploading raw_data.csv to the Raw
Data component, we uploaded it to the parent project instead.
Fear not. We can easily fix this contrived mistake by simply moving the file to its intended location.
data_file <- osf_mv(data_file, to = data_comp)Crisis averted. Now if you open Raw Data on OSF
(osf_open(data_comp)), it should contain the csv file.
Our next step is to upload the R scripts into the Analysis
Scripts component. Rather than upload each file individually, we’ll
take advantage of osf_upload()’s ability to handle multiple
files/directories and use list.files() to identify all
.R files in the working directory:
r_files <- osf_upload(script_comp, path = list.files(pattern = ".R$"))
r_files
#> # A tibble: 3 × 3
#> name id meta
#> <chr> <chr> <list>
#> 1 analyses.R 63309f47408a27127e7637de <named list [3]>
#> 2 cleaning.R 63309f4a555fe211977a9017 <named list [3]>
#> 3 precompile.R 63309f4c18f4581167428d70 <named list [3]>Putting it all together
Finally, let’s repeat the process for the 2 .docx file
containing the survey and accompanying data dictionary. This time we’ll
use a more succinct approach that leverages pipes to create and populate
the component in one block:
my_project %>%
osf_create_component("Research Materials") %>%
osf_upload(path = list.files(pattern = "\\.docx$"))
#> # A tibble: 2 × 3
#> name id meta
#> <chr> <chr> <list>
#> 1 Data Dictionary.docx 63309f526c2401128550a2bc <named list [3]>
#> 2 Questionnaire.docx 63309f5418f4581150428ef4 <named list [3]>We can verify the project is now structured the way we wanted by listing the components we have under the main project.
osf_ls_nodes(my_project)
#> # A tibble: 3 × 3
#> name id meta
#> <chr> <chr> <list>
#> 1 Research Materials dg79a <named list [3]>
#> 2 Analysis Scripts fquzh <named list [3]>
#> 3 Raw Data 6urqv <named list [3]>which gives us an osf_tbl with one row for each of the
project’s components.
Updating files
OSF provides automatic and unlimited file versioning. Let’s see how
this works with osfr. Make a small edit to your local copy of
cleaning.R and save. Now, if we attempt to upload this new
version to the Analysis Scripts component, osfr will throw a
conflict error:
osf_upload(script_comp, path = "cleaning.R")Error: Can't upload file 'cleaning.R'.
* A file with the same name already exists at the destination.
* Use the `conflicts` argument to avoid this error in the future.As the error indicates, we need to use the conflicts
argument to instruct osf_upload() how to handle the
conflict. In this case, we want to overwrite the original copy with our
new version:
osf_upload(script_comp, path = "cleaning.R", conflicts = "overwrite")Learn more about file versioning on OSF here.
A few details about files on OSF
On OSF, files can exist in projects, components, and/or directories.
Files can be stored on OSF’s Storage or in another service that
is connected to an OSF project (e.g. GitHub, Dropbox, or Google Drive).
However, osfr currently only supports interacting with
files on OSF Storage.
We can download files from any public or private node that we have access to and can identify files to download in two different ways:
-
If we know where the file is located, but don’t remember its GUID, you can use the
osf_ls_filesfunction to filter by filename within a specified node and then pipe the results toosf_download().anes_project %>% osf_ls_files(pattern = ) %>% osf_download(conflicts = "overwrite") For a public file that was referenced in a published article, you may already have the GUID, and so can retrieve the file directly before downloading it. For example, let’s download Daniel Laken’s helpful spreadsheet for calculating effect sizes (available from https://osf.io/vbdah/).
osf_retrieve_file("vbdah") %>%
osf_download(excel_file)