# orderly

## Introduction

orderly is a package designed to help make analysis more reproducible. Its principal aim is to automate a series of basic steps in the process of writing analyses, making it easy to:

• track all inputs into an analysis (packages, code, and data resources)
• store multiple versions of an analysis where it is repeated
• track outputs of an analysis
• create analyses that depend on the outputs of previous analyses

With orderly we have two main hopes:

• analysts can write code that will straightforwardly run on someone else’s machine (or a remote machine)
• when an analysis that is run several times starts behaving differently it will be easy to see when the outputs started changing, and what inputs started changing at the same time

orderly requires a few conventions around organisation of a project, and after that tries to keep out of your way. However, these requirements are designed to make collaborative development with git easier by minimising conflicts and making backup easier by using an append-only storage system.

### The problem

One often-touted goal of R over point-and-click analyses packages is that if an analysis is scripted it is more reproducible. However, essentially all analyses depend on external resources - packages, data, code, and R itself; any change in these external resources might change the results. Preventing such changes in external resources is not always possible, but tracking changes should be straightforward - all we need to know is what is being used.

For example, while reproducible research has become synonymous with literate programming this approach often increases the number of external resources. A typical knitr document will depend on:

• the source file (.Rmd or .Rnw)
• templates used for styling
• data that is read in for the analysis
• code that is directly read in with source

The orderly package helps by

• collecting external resources before an analysis
• ensuring that all required external resources are identified
• removing any manual work in tracking information about these external resources
• allowing running reports multiple times and making it easy to see what changed and why

The core problem is that analyses have no general interface. Consider in contrast the role that functions take in programming. All functions have a set of arguments (inputs) and a return value (outputs). With orderly, we borrow this idea, and each piece of analysis will require that the user describes what is needed and what will be produced.

### The process

The user describes the inputs of their analysis, including:

• SQL queries (if using databases)
• Required R sources
• External resource files (e.g., csv data files, Rmd files, templates)
• Packages required to run the analysis
• Dependencies on previously run analyses

The user also provides a list of “artefacts” (file-based results) that they will produce.

Then orderly:

1. creates a new empty directory
2. copies over only the declared file resources
3. loads only the declared packages
4. loads the declared R sources
5. evaluates any sql queries to create R objects
6. then runs the analysis
7. verifies that the declared artefacts are produced

It then stores metadata alongside the analysis including md5 hashes of all inputs and outputs, copies of data extracted from the database, a record of all R packages loaded at the end of the session, and (if using git) information about the git state (hash, branch and status).

Then if one of the dependencies of a report changes (the used data, code, etc), we have metadata that can be queried to identify the likely source of the change.

## Example

To illustrate, we will start with a minimal example (you can use orderly::orderly_init to create a similar structure directly), and we will build it up to demonstrate orderly features. In the most minimal example, we want to run a script that creates a graph. It uses no external resources.

.
├── orderly_config.yml
└── src
└── example
├── orderly.yml
└── script.R

In this example, the orderly_config.yml file is completely empty, but serves to mark the root of the orderly project. We have one report, called example, and its configuration is within orderly.yml:

script: script.R

artefacts:
- staticgraph:
description: A graph of things
filenames: mygraph.png

There are two keys here:

• script the path of the script to run, script.R
• artefacts a description of the artefacts (files) that will be produced by running this script. In this case it is a graph with the filename mygraph.png

The script is plain R code:

png("mygraph.png")
plot(1:10)
dev.off()

The R code can be as long or as short as needed and can use whatever packages it needs. orderly does not do anything with the script apart from run it so it can be formatted freely (there are no magic comments, etc). There are no restrictions on what can be done except that it must produce the artefacts listed in orderly.yml. If not, an error will be thrown describing what was missing.

### Running the report

To run the report, use orderly::orderly_run (typically one would be in the orderly root and so the root directory could be omitted, but within this vignette we use a temporary directory):

id <- orderly::orderly_run("example", root = path)
## [ info       ]  Writing initial orderly archive version as 0.7.15
## [ name       ]  example
## Registered S3 method overwritten by 'openssl':
##   method      from
##   print.bytes Rcpp
## [ id         ]  20191028-121940-be36d760
## [ start      ]  2019-10-28 12:19:40
##
## > png("mygraph.png")
##
## > plot(1:10)
##
## > dev.off()
## quartz_off_screen
##                 2
## [ end        ]  2019-10-28 12:19:40
## [ elapsed    ]  Ran report in 0.05906796 secs
## [ artefact   ]  mygraph.png: 483e336c4c4c9205ecc45b4789a24a9c

The return value is the id of the report (also printed on the third line of log output) and is always in the format YYYYMMDD-HHMMSS-abcdef01 where the last 8 characters are hex digits (i.e., 4 random bytes). This means reports will automatically sort nicely but we’ll have some collision resistance.

id
## [1] "20191028-121940-be36d760"

Having run the report, the directory layout now looks like:

.
├── archive
├── data
│   ├── csv
│   └── rds
├── draft
│   └── example
│       └── 20191028-121940-be36d760
│           ├── mygraph.png
│           ├── orderly.yml
│           ├── orderly_run.rds
│           └── script.R
├── orderly_config.yml
└── src
└── example
├── orderly.yml
└── script.R

Within drafts, the directory example/20191028-121940-be36d760 has been created which contains the result of running the report. In here there are the files:

• orderly.yml: this is an exact copy of the input file
• script.R: this is an exact copy of the script used for the analysis
• mygraph.png: the artefact created by the report
• orderly_run.rds: this is metadata about the run and includes hashes of input files, of the data used, and of the output etc, along with details about the packages used and the state of git. It is stored in R’s internal data format.

Every time a report is run it will create a new directory at this level with a new id. Running the report again now might create the directory example/20191028-121941-06a08548

We store the copies of files as run by orderly so that even if the input files change we can still easily get back to previous versions of the inputs, alongside the outputs, and these are safe from any changes to the underlying source.

You can see the list of draft reports like so:

orderly::orderly_list_drafts(root = path)
##      name                       id
## 1 example 20191028-121940-be36d760

Once you’re happy with a report, then “commit” it with

orderly::orderly_commit(id, root = path)
## [ commit     ]  example/20191028-121940-be36d760
## [ copy       ]
## [ import     ]  example:20191028-121940-be36d760
## [ success    ]  :)
## [1] "/private/var/folders/z7/c2kx_kt96zn2tt_6179bkc4m0000gp/T/RtmpS0PUsv/filee70727db0347/archive/example/20191028-121940-be36d760"

After this step our directory structure looks like:

.
├── archive
│   └── example
│       └── 20191028-121940-be36d760
│           ├── mygraph.png
│           ├── orderly.yml
│           ├── orderly_run.rds
│           └── script.R
├── data
│   ├── csv
│   └── rds
├── draft
│   └── example
├── orderly.sqlite
├── orderly_config.yml
└── src
└── example
├── orderly.yml
└── script.R

This looks very like the previous, but files have been moved from being within draft to being within archive. The other difference is that the index orderly.sqlite has been created. This is a machine-readable index to all the orderly metadata that can be used to build applications around orderly (for example OrderlyWeb, a web portal for orderly - see the “remotes” vignette). The documentation for the database format is available on the orderly website.

## Creating a report

First, run orderly::orderly_new to create a directory within src. The name is important and should not contain spaces (nor should it change as this will change the key report id and you’ll lose a chain of history), then edit the file orderly.yml within that directory.

orderly::orderly_new("new", root = path)
## Created report at '/private/var/folders/z7/c2kx_kt96zn2tt_6179bkc4m0000gp/T/RtmpS0PUsv/filee70727db0347/src/new'
## Edit the file 'orderly.yml' within this directory

which results in a directory structure like:

.
├── archive
│   └── example
│       └── 20191028-121940-be36d760
│           ├── mygraph.png
│           ├── orderly.yml
│           ├── orderly_run.rds
│           └── script.R
├── data
│   ├── csv
│   └── rds
├── draft
│   └── example
├── orderly.sqlite
├── orderly_config.yml
└── src
├── example
│   ├── orderly.yml
│   └── script.R
└── new
└── orderly.yml

## Resources, sources and artefacts

Resources to a report are expected to be read-only files that are used by the script to produce the report. Examples of the sort of files that should be used as resources are:

• Moderately sized data files (large datasets should be accessed from a database),
• A markdown file used to create a report,
• Documentation for the report.

“Resources” cannot be modified by the report; if orderly detects that a resource has been changed an error will be thrown.

orderly will automatically detect any files named README.md in a report’s source directory and copy them to the new directory too.

resources:
- years.csv
- data_dictionary.xlsx
- report.Rmd
- code_documentation.md

“Sources” are files containing R code that will be sourced (via the R function source()) before the main script is run. Often this file contains functions or variables used by the main script. All of the copying and sourcing will be handled by orderly itself so there is no need to explicitly source the files in the main script.

“Artefacts” are the output of the report. At least one artefact must be listed and files created during the running of the script must be included as artefacts (or deleted before the script finishes) or an error will be returned.

Examples of artefacts fields in orderly.yml:

artefacts:
- report:
filenames: report.html
description: a simple report
artefacts:
- report:
filenames: report.html
description: a simple report
- data:
description:
- associated data sets
filenames:
- data_one.csv
- data_two.csv
- data_three.csv
- data_four.csv

When declaring an artefact we have to specify what format the artefact is. Currently supported formats are :data, report, staticgraph, interactivegraph and interactivehtml. These tags reflect the intent of use of the file, they have no special meaning within orderly itself.

## Using artefacts from other reports

It is often the case that we would like to write a report that depends on an earlier report, e.g. one report produces a large dataset and a later report produces a high level summary. orderly allows a report to directly copy an artefact file from an existing report without having to manually copy it into the report source directory. This is handled in the depends block of the report’s orderly.yml.

To use a file as a dependency it must be explicitly listed as an artefact.

An simple example might look like:

depends:
- big-data-report:
id: 20190425-163691-b8451bbf
use:
data.rds: huge-data-set.rds
draft: false

This will copy the file the huge-data-set.rds from the report big-data-report with id 20190425-163691-b8451bbf and rename it data.rds. This file can then be used by the report as if it were in the source directory. The field draft tells orderly to only use completed reports in the archive as opposed to drafts. Setting this to true allows uncommitted reports in draft to be used. This can be useful when developing a chain of related reports.

If we want a report to always use the latest version of a report big-data-report we can set the id field to latest, e.g.:

depends:
- big-data-report:
id: latest
use:
data.rds: huge-data-set.rds
draft: false

This will find the most recent version of the report big-data-report and copy files from that directory.

To use multiple artefacts from a single report add the files into the use block e.g.:

depends:
- big-data-report:
id: latest
use:
data.rds: huge-data-set.rds
pop.csv: population_data.csv
draft: false

To use artefacts from multiple reports we add multiple entries to the depends field e.g.:

depends:
- big-data-report:
id: latest
use:
data.rds: huge-data-set.rds
pop.csv: population_data.csv
draft: false
- report_two:
id: latest
use:
data_b.rds: filename.rds
draft: false

We can also use the same artefact from different versions of the same report. This might come up if we want to write a report that compares the output from different versions of another report. The yaml pattern for this is:

depends:
- big-data-report:
id: 20190425-163691-b8451bbf
draft: false
use:
data_latest.rds: huge-data-set.rds
- big-data-report:
id: 20181225-172991-34c91ef1
draft: false
use:
data_old: huge-data-set.rds

The important feature in this example is the dashes before the report name. When all the report names are different these dashes can be omitted, but they are necessary when the report depends on different versions of the same report. Since including the dashes will never cause a problem but omitting them might, we advise that they should always be included.

## Parameterised reports

Sometimes it can be useful to control how a report runs by a parameter. This could be the name of a country that an analysis applies to (though we hope to develop a better interface for this soon) through to controlling the number of iterations that an analysis runs for. Parameters are declared in the orderly.yml like:

parameters:
a:
default: 1
b: ~

This would declare that a report takes two parameters a (with a default of 1), and b (with no default). Running the report would then look like:

orderly::orderly_run("reportname", list(a = 10, b = 100))

These parameters are then present in the environment of the report, so the code can use values a and b.

The parameters will also be interpolated into any SQL queries before they are run, so if the orderly.yml contains:

data:
cars:
query: SELECT * FROM mtcars WHERE cyl > ?a

then this will be evaluated on the SQL server with a substituted in where the query says ?a (this is done with DBI::sqlInterpolate).

## Using global resources

There might be files that are used in (almost) every report. Examples of these sorts of files might be document templates or organisation logos. To set up a global resource create a directory your_global_dir in <root> and the following to the orderly_config.yml:

global_resources:
your_global_dir

Then to use any file in your_global_dir in your report add a global_resources field to that report’s orderly.yml:

global_resources:
logo.jpg: org_logo.jpg
latex_class.cls: org_latex_class.cls
styles.css: org_styles.css

Currently code i.e. R source code cannot be sourced from the global resources directory. So for example utility functions common across multiple reports must be included in each report directory separately. The functionality to include global source code may be added in future versions.

## Using SQL databases

One of the original aims of orderly was to provide a set of tools for use of SQL databases within reproducible reporting. Because the SQL database is an external global resource it is difficult to work with any concept of “versioning” from R (there is no git history, no way of easily rolling back to previous versions etc). If using a central SQL server, there is configuration that should be kept out of any analysis, particularly things like passwords. Configuration problems multiply when using both “production” and “staging” systems as we would like to be able to switch between different configurations.

### Configuration

The root orderly_config.yml configuration specifies the locations of databases (there can be any number), for example:

database:
source:
driver: RPostgres::Postgres
args:
host: dbhost.example.org
port: 5432
user: myusername
password: s3cret
dbname: mydb

This database will be referred to elsewhere as source and it will be connected with the RPostgres::Postgres driver (from the RPostgres package). Arguments within the args block will be passed to the driver, in this case being the equivalent of:

DBI::dbConnect(RPostgres::Postgres, host = "dbhost.example.org", port = 5432,
user = "myusername", password = "s3cret", dbname = "mydb")

The values used in the args blocks can be environment values (e.g., password: $DB_PASSWORD) in which case they will be resolved from the environment before connecting. This will be useful for keeping secrets out of source control. For SQLite databases, the args block will typically contain only dbname which is the path to the database file. ### Use within a report A report configuration (orderly.yml) can contain a data block, which contains sql queries, such as: data: cars: query: SELECT * FROM mtcars WHERE cyl = 4 database: source In this case, the query SELECT * FROM mtcars WHERE cyl = 4 will be run against the source database to create an object cars in the report environment. The actual report code can use that object without having ever created the database connection or evaluating the query. Further, the data used in the query will be captured in orderly’s data directory, and hashes of the data will be stored alongside the results. This means that even if the data in the database is a constantly moving target we can still detect if changes to the data are responsible for changes in the result of a report. ### Advanced use If you need to perform complicated SQL queries, then you can export the database connection directly by adding a block: connection: con: source which will save the connection to the source database as the R object con. We have used this where a report requires running queries in a loop that depend on the results of a previous query or additional data loaded into a report, or where the result of the query will be very large and we do not want to save it to disk. Note that this reduces the amount of tracking that orderly can do, as we have no way of knowing what is done with the connection once passed to the script. ### Customising the database configuration The contents of orderly_config.yml may contain things like secrets (passwords) or hostnames that vary depending on deployment (e.g., testing locally vs running on a remote system). To customise this, you can use environment variables within the configuration. So rather than writing database: source: driver: RPostgres::Postgres args: host: localhost port: 5432 user: myuser dbname: databasename password: p4ssw0rd you might write database: source: driver: RPostgres::Postgres args: host:$MY_DBHOST
port: $MY_DBPORT user:$MY_DBUSER
dbname: $MY_DBNAME password:$MY_PASSWORD

environment variables, as used this way must begin with a dollar sign and consist only of uppercase letters, numbers and the underscore character. You can then set the environment variables in an .Renviron (either within the project or in your home directory) file or your .profile file. Alternatively, you can create a file orderly_envir.yml in the same directory as orderly_config.yml with key-value pairs, such as

MY_DBHOST: localhost
MY_DBPORT: 5432
MY_DBUSER: myuser
MY_DBNAME: databasename
MY_PASSWORD: p4ssw0rd

This will be read every time that orderly_config.yml is read (in contrast with .Renviron which is read-only at the start of a session). This will likely be more pleasant to work with.

The advantage of using environment variables is that you can add the orderly_envir.yml file to your .gitignore and avoid committing system-dependent data to the central repository.

To avoid leaving passwords in plain text, you can use vault (along with the R client vaultr) to retrieve them.

To do this, you should include the address of your vault server in the orderly_config.yml as

vault_server: https://example.com:8200

Then, for values that you want to retrieve from the vault, set the value of the field to VAULT:<path>:<field>, where <path> is the name of a vault secret path (probably beginning with /secret/ and field is the name of the field at that path. So, for example:

      password: VAULT:/secret/users/database_user:password

would look up the field password at the path /secret/users/database_user. This can be stored in orderly_config.yml, in the contents of an environment variable or in orderly_envir.yml (currently this only uses the vault version 1 key-value storage)

## Debugging a report

As a report becomes more complex, the function orderly::orderly_test_start will become useful; this function creates the isolated environment that orderly uses to run a report, but then leaves you to interactively work with your report.