---
title: "Creating An ExperimentHub Package"
author: "Valerie Obenchain and Lori Shepherd"
date: "Modified: November 2017. Compiled: `r format(Sys.Date(), '%d %b %Y')`"
output:
  BiocStyle::html_document:
    toc: true
    toc_depth: 2
vignette: >
  %\VignetteIndexEntry{Creating An ExperimentHub Package}
  %\VignetteEngine{knitr::rmarkdown}
---

# Overview

`ExperimentHubData` provides tools to add or modify resources in
Bioconductor's `ExperimentHub`. This 'hub' houses curated data from courses,
publications or experiments.  The resources are generally not files of raw data
(as can be the case in `AnnotationHub`) but instead are `R` / `Bioconductor`
objects such as GRanges, SummarizedExperiment, data.frame etc.  Each resource
has associated metadata that can be searched through the `ExperimentHub` client interface.

# New resources

Resources are contributed to `ExperimentHub` in the form of a package.  The
package contains the resource metadata, man pages, vignette and any supporting
`R` functions the author wants to provide.  This is a similar design to the
existing `Bioconductor` experimental data packages except the data are
stored in AWS S3 buckets instead of the data/ directory of the package.

Below are the steps required for adding new resources.

## Notify `Bioconductor` team member

The man page and vignette examples in the data experiment package will not work until
the data are available in `ExperimentHub`. Adding the data to AWS S3 and the
metadata to the production database involves assistance from a `Bioconductor`
team member.  Please read the section "Uploading Data to S3".

## Building the data experiment package

When a resource is downloaded from `ExperimentHub` the associated data experiment
package is loaded in the workspace making the man pages and vignettes readily
available. Because documentation plays an important role in understanding these
curated resources please take the time to develop clear man pages and a
detailed vignette. These documents provide essential background to the user and
guide appropriate use the of resources.

Below is an outline of package organization. The files listed are required
unless otherwise stated.

### `inst/extdata/`

- `metadata.csv`:
  This file contains the metadata in the format of one row per resource
  to be added to the `ExperimentHub` database. The file should be generated
  from the code in inst/scripts/make-metadata.R where the final data are
  written out with `write.csv(..., row.names=FALSE)`. The required column
  names and data types are specified in
  `ExperimentHubData::makeExperimentHubMetadata` See
  ?`ExperimentHubData::makeExperimentHubMetadata` for details.

  An example data experiment package metadata.csv file can be found [here](https://github.com/Bioconductor/GSE62944/blob/master/inst/extdata/metadata.csv)

### `inst/scripts/`

- `make-data.R`:
  A script describing the steps involved in making the data object(s). This
  includes where the original data were downloaded from, pre-processing,
  and how the final R object was made. Include a description of any
  steps performed outside of `R` with third party software. It is encouraged to
  serialize Data objects with `save()` with the .rda extension on the filename
  but not strictly necessary. If the data is provided in another format an
  appropriate loading method may need to be implemented. Please advise when
  reaching out for "Uploading Data to S3".

- `make-metadata.R`:
  A script to make the metadata.csv file located in inst/extdata of the
  package. See ?`ExperimentHubData::makeExperimentHubMetadata` for a description
  of expected fields and data types.
  `ExperimentHubData::makeExperimentHubMetadata()` can be used to validate the
  metadata.csv file before submitting the package.

### `vignettes/`

- One or more vignettes describing analysis workflows.

### `R/`

- `zzz.R`: Optional. You can include a `.onLoad()` function in a zzz.R file that
  exports each resource name (i.e., title) into a function. This allows the data
  to be loaded by name, e.g., `resouce123()`.

    ```{r, eval=FALSE}
    .onLoad <- function(libname, pkgname) {
	fl <- system.file("extdata", "metadata.csv", package=pkgname)
	titles <- read.csv(fl, stringsAsFactors=FALSE)$Title
	createHubAccessors(pkgname, titles)
    }
    ```

    `ExperimentHub::createHubAccessors()` and
    `ExperimentHub:::.hubAccessorFactory()` provide internal
    detail. The resource-named function has a single 'metadata'
    argument. When metadata=TRUE, the metadata are loaded (equivalent
    to single-bracket method on an ExperimentHub object) and when
    FALSE the full resource is loaded (equivalent to double-bracket
    method).

- `R/*.R`: Optional. Functions to enhance data exploration.

### `man/`

- package man page:
  The package man page serves as a landing point and should briefly describe
  all resources associated with the package. There should be an \alias
  entry for each resource title either on the package man page or individual
  man pages.

- resource man pages:
  Resources can be documented on the same page, grouped by common type
  or have their own dedicated man pages.

- document how data are loaded:
  Data can be accessed via the standard ExperimentHub interface with
  single and double-bracket methods, e.g.,

    ```{r, eval=FALSE}
    library(ExperimentHub)
    eh <- ExperimentHub()
    myfiles <- query(eh, "PACKAGENAME")
    myfiles[[1]]        ## load the first resource in the list
    myfiles[["EH123"]]  ## load by EH id
    ```

- If a `.onLoad()` function is used to export each resource as a function
  also document that method of loading, e.g.,

    ```{r, eval=FALSE}
    resourceA(metadata = FALSE) ## data are loaded
    resourceA(metadata = TRUE)  ## metadata are displayed
    ```

### `DESCRIPTION` / `NAMESPACE`

- The package should depend on and fully import ExperimentHub. If using the
  suggested `.onLoad()` function, import the utils package in the DESCRIPTION
  file and selectively importFrom(utils, read.csv) in the NAMESPACE.

- Package authors are encouraged to use the `ExperimentHub::listResources()` and
  `ExperimentHub::loadResource()` functions in their man pages and vignette.
  These helpers are designed to facilitate data discovery within a specific
  package vs within all of ExperimentHub.

## Data objects

Data are not formally part of the software package and are stored separately in
AWS S3 buckets.  The author should follow instructions in the
section "Uploading Data to S3".


## Metadata

When you are satisfied with the representation of your resources in
make-metadata.R (which produces metadata.csv) the `Bioconductor` team
member will add the metadata to the production database.

## Package review

Once the data are in AWS S3 and the metadata have been added to the
production database the man pages and vignette can be finalized. When the
package passes R CMD build and check it can be submitted to the
[package tracker](https://github.com/Bioconductor/Contributions) for
review. The package should be submitted without any of the data that is now
located on S3; This keeps the package light weight and minimual size while still
providing access to key large data files now stored on S3. If the data files
were added to the github repository please see [removing large data files and
clean git
tree](http://bioconductor.org/developers/how-to/git/remove-large-data/) to
remove the large files and reduce package size.

Many times these data package are created as a suppliment to a software
package. There is a process for submitting [mulitple package under the same
issue](https://github.com/Bioconductor/Contributions#submitting-related-packages).



# Add additional resources

Metadata for new versions of the data can be added to the same package as they
become available.

* The titles for the new versions must be unique and not match the title of
  any resource currently in ExperimentHub. Good practice would be to
  include the version and / or genome build in the title.

*  Make data available: see section on "Uploading Data to S3"

* Update make-metadata.R with the new metadata information

* Generate a new metadata.csv file. The package should contain
  metadata for all versions of the data in ExperimentHub so the old file should
  remain.  When adding a new version it might be helpful to write a new csv file
  named by version, e.g., metadata_v84.csv, metadata_85.csv etc.

* Bump package version and commit to git

* Notify Lori.Shepherd@Roswellpark.org that an update is ready and
  a team member will add the new metadata to the production database;
  new resources will not be visible in ExperimentHub until
  the metadata are added to the database.


Contact  Lori.Shepherd@roswellpark.org or maintainer@bioconductor.org with any
questions.

# Bug fixes

A bug fix may involve a change to the metadata, data resource or both.

## Update the resource

* The replacement resource must have the same name as the original and be at the
  same location (path)

* Notify Lori.Shepherd@roswellpark.org that you want to replace the data
  and make the files available: see section "Uploading Data to S3".

## Update the metadata

New metadata records can be added for new resources but modifying existing
records is discouraged. Record modification will only be done in the case of
bug fixes.

* Notify Lori.Shepherd@roswellpark.org that you want to change the metadata

* Update make-metadata.R with modified information and regenerate the
  metadata.csv file if necessary

* Bump the package version and commit to git

# Remove resources

Removing resources should be done with caution. The intent is that
ExperimentHub be a 'reproducible' resource by providing a stable snapshot
of the data. Data made available in Bioconductor version x.y.z should be
available for all versions greater than x.y.z. Unfortunately this is not
always possible. If you find it necessary to remove data from ExperimentHub
please contact Lori.shepherd@roswellpark.org or maintainer@bioconductor.org for
assistance.

When a resource is removed from ExperimentHub the 'status' field in the
metadata is modified to explain why they are no longer available. Once
this status is changed the `ExperimentHub()` constructor will not list the
resource among the available ids. An attempt to extract the resource with
'[[' and the EH id will return an error along with the status message.

# Uploading Data to S3

Instead of providing the data files via dropbox, ftp, etc. we will grant
temporary access to an S3 bucket where you can upload your data. Please
email Lori.Shepherd@roswellpark.org for access.

You will be given access to the 'AnnotationContributor' user. Ensure that the
`AWS CLI` is installed on your machine. See instructions for installing `AWS
CLI` [here](https://aws.amazon.com/cli/). Once you  have requested access you
will be emailed a set of keys. There are two options to set the profile up for
AnnotationContributor

1.  Update your `.aws/config` file to include the following updating the keys
accordingly:

```
[profile AnnotationContributor]
output = text
region = us-east-1
aws_access_key_id = ****
aws_secret_access_key = ****
```
2. If you can't find the `.aws/config` file,  Run the following command entering
appropriate information from above

```
aws configure --profile AnnotationContributor
```

After the configuration is set you should be able to upload resources using

```
aws --profile AnnotationContributor s3 cp test_file.txt s3://annotation-contributor/test_file.txt --acl public-read

# to upload directory

aws --profile AnnotationContributor s3 cp test_dir s3://annotation-contributor/teset_dir --recursive --acl public-read
```

Please upload the data with the appropriate directory structure, including
subdirectories as necessary (i.e. top directory must be software package name,
then if applicable, subdirectories of versions, ...)

Once the upload is complete, email Lori.Shepherd@roswellpark.org to continue the
process


# Validating

The best way to validate record metadata is to  read inst/extdata/metadata.csv
with `ExperimentHubData::makeExperimentHubMetadata()`. If that is successful the
metadata are ready to go.
