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:

creates a new empty directory
copies over only the declared file resources
loads only the declared packages
loads the declared R sources
evaluates any sql queries to create R objects
then runs the analysis
verifies that the declared artefacts are produced

It then stores metadata alongside the analysis including 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.

 [01;34m. [0m
├── orderly_config.yml
└──  [01;34msrc [0m
    └──  [01;34mexample [0m
        ├── orderly.yml
        └──  [32mscript.R [0m

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
  - data:
      description: Data that went into the plot
      filenames: mydata.csv

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:

dat <- data.frame(x = 1:10, y = runif(10))
write.csv(dat, "mydata.csv", row.names = FALSE)

png("mygraph.png")
plot(dat)
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 1.1.25

## [ name       ]  example

## [ id         ]  20230621-105048-bd3a2cb5

## [ start      ]  2023-06-21 10:50:48

## 
## > dat <- data.frame(x = 1:10, y = runif(10))
## 
## > write.csv(dat, "mydata.csv", row.names = FALSE)
## 
## > png("mygraph.png")
## 
## > plot(dat)

## 
## > dev.off()
## agg_png 
##       2

## [ end        ]  2023-06-21 10:50:48

## [ elapsed    ]  Ran report in 0.0185976 secs

## [ artefact   ]  mygraph.png: 4d7152b3aa39de3389a493eb4f2c5289
## [ ...        ]  mydata.csv: 51b694edbb6be70835ef9e0fbd932e01

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] "20230621-105048-bd3a2cb5"

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

 [01;34m. [0m
├──  [01;34marchive [0m
├──  [01;34mdraft [0m
│   └──  [01;34mexample [0m
│       └──  [01;34m20230621-105048-bd3a2cb5 [0m
│           ├── mydata.csv
│           ├──  [01;35mmygraph.png [0m
│           ├── orderly.yml
│           ├── orderly_run.rds
│           └──  [32mscript.R [0m
├── orderly_config.yml
└──  [01;34msrc [0m
    └──  [01;34mexample [0m
        ├── orderly.yml
        └──  [32mscript.R [0m

Within drafts, the directory example/20230621-105048-bd3a2cb5 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/20230621-105049-085007d6

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 20230621-105048-bd3a2cb5

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

orderly::orderly_commit(id, root = path)
## [ commit     ]  example/20230621-105048-bd3a2cb5
## [ copy       ]
## [ import     ]  example:20230621-105048-bd3a2cb5
## [ success    ]  :)
## [1] "/tmp/RtmpzlBf1Y/file3597c1dbe0b/archive/example/20230621-105048-bd3a2cb5"

After this step our directory structure looks like:

 [01;34m. [0m
├──  [01;34marchive [0m
│   └──  [01;34mexample [0m
│       └──  [01;34m20230621-105048-bd3a2cb5 [0m
│           ├── mydata.csv
│           ├──  [01;35mmygraph.png [0m
│           ├── orderly.yml
│           ├── orderly_run.rds
│           └──  [32mscript.R [0m
├──  [01;34mdraft [0m
│   └──  [01;34mexample [0m
├── orderly.sqlite
├── orderly_config.yml
└──  [01;34msrc [0m
    └──  [01;34mexample [0m
        ├── orderly.yml
        └──  [32mscript.R [0m

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 '/tmp/RtmpzlBf1Y/file3597c1dbe0b/src/new'

## Edit the file 'orderly.yml' within this directory

which results in a directory structure like:

 [01;34m. [0m
├──  [01;34marchive [0m
│   └──  [01;34mexample [0m
│       └──  [01;34m20230621-105048-bd3a2cb5 [0m
│           ├── mydata.csv
│           ├──  [01;35mmygraph.png [0m
│           ├── orderly.yml
│           ├── orderly_run.rds
│           └──  [32mscript.R [0m
├──  [01;34mdraft [0m
│   └──  [01;34mexample [0m
├── orderly.sqlite
├── orderly_config.yml
└──  [01;34msrc [0m
    ├──  [01;34mexample [0m
    │   ├── orderly.yml
    │   └──  [32mscript.R [0m
    └──  [01;34mnew [0m
        └── 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

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.

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

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

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
  - report_two:
      id: latest
      use:
        data_b.rds: filename.rds

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
      use:
        data_latest.rds: huge-data-set.rds
  - big-data-report:
      id: 20181225-172991-34c91ef1
      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 version control

orderly is designed to work well with git (or any other version control system). The general principle is that src/ and any configuration files should be added to git, while most of the generated files (data, draft, archive, and any SQLite databases such as orderly.sqlite) should be excluded, which can be done by creating a .gitignore file.

This process can be automated by running

orderly::orderly_use_gitignore(root = path, prompt = FALSE)

## Changes to '/tmp/RtmpzlBf1Y/file3597c1dbe0b/.gitignore'

##  1 | # These are directories where orderly stores (potentially large)
##  2 | # generated files - you definitely do not want these in git.
##  3 | data
##  4 | archive
##  5 | draft
##  6 | metadata
##  7 | 
##  8 | # Orderly will store data in a sqlite database, by default orderly.sqlite
##  9 | *.sqlite
## 10 | 
## 11 | # It is suggested to use environment variables, in either
## 12 | # orderly_envir.yml or .Renviron to store sensitive data to use with
## 13 | # orderly - keep those out of git
## 14 | .Renviron
## 15 | orderly_envir.yml
## 16 | 
## 17 | # Other good things to exclude
## 18 | .Rhistory
## 19 | .Rdata
## 20 | .Rproj.user
## 21 | 
## 22 | # Created by orderly.server
## 23 | runner
## 24 | backup

## Writing to '/tmp/RtmpzlBf1Y/file3597c1dbe0b/.gitignore'

(the default, prompt = TRUE will request user confirmation before writing the .gitignore file).

You should arrange to back up the entire orderly directory through some other means.

If your generated files are particularly small you might leave them in git, but in our experience this will result in a git repository that is unpleasant to use.

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 (see orderly::orderly_use_gitignore) to help automate this.

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:
  addr: 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)

If you need to control how the vault server is accessed, then you can pass additional arguments within an args block:

vault:
  addr: https://example.com:8200
  login: userpass
  username: alice
  password: secret
  mount: userpass

This is equivalent to connecting to the vault, using vaultr as

vaultr::vault_client(addr = "https://example.com:8200", login = "userpass",
                     username = "alice", password = "secret",
                     mount = "userpass")

Environment variables here will be respected so you could write:

vault:
  addr: https://example.com:8200
  login: userpass
  username: $VAULT_USER
  password: $VAULT_PASSWORD

and the username and password will be found from environment variables (the actual secret resolution uses vaultr::vault_resolve_secrets - see the documentation in vaultr for further details).

Advanced database configuration

In general, you can ignore this section if you only use one global database.

The above approach can be used to switch databases by using different environmental variables, but that can become tiresome. If you have multiple database “instances” corresponding to different realisations of the same logical database (e.g., production and staging), then you can configure and switch between these directly from orderly commands. At VIMC we have several copies of our main database: one called production, which is the canonical copy, and then several staging copies that we use for experimentation.

To configure this situation, list common arguments within the args block as before, then add logical databases as named entries in an instances field:

database:
  source:
    driver: RPostgres::Postgres
    args:
      port: 5432
      user: user
      dbname: mydb
    instances:
      production:
        host: production.example.org
        password: $PASSWORD_PRODUCTION
      staging:
        host: staging.example.org
        password: $PASSWORD_STAGING
    default_instance: $DEFAULT_INSTANCE

Here - staging and production have different hostnames (production.example.org and staging.example.org) and different passwords (retrieved using environment variables) and the default instance is set with another environment variable ($DEFAULT_INSTANCE, which must be one of production or staging). To switch between databases, you can set that variable, or pass the instance argument to orderly::orderly_run and friends, as:

orderly::orderly_run(name, instance = "production")

orderly::orderly_run(name, instance = "staging")

If there is more than one database configured, then the interpretation of instance is a little more nuanced. For example, suppose we have this (abridged) database configuration:

database:
  source:
    driver: RPostgres::Postgres
    args:
      port: 5432
    instances:
      production:
        host: production.example.org
      staging:
        host: staging.example.org
    default_instance: staging
  extra:
    driver: RPostgres::Postgres
    args:
      port: 5432
      host: extra.example.org

Then we can pass in a string like production in as the instance, e.g.,

orderly::orderly_run(name, instance = "production")

as the source database will select the production instance and as there are no instances configured for the extra database we will ignore the argument when connecting to extra.

However, if both databases had two instances, such as:

database:
  source:
    driver: RPostgres::Postgres
    args:
      port: 5432
    instances:
      production:
        host: production.example.org
      staging:
        host: staging.example.org
    default_instance: staging
  extra:
    driver: RPostgres::Postgres
    args:
      port: 15432
    instances:
      production:
        host: production.example.org
      staging:
        host: staging.example.org
    default_instance: staging

Then it is possible to select different instances for each database, such as:

orderly::orderly_run(name,
                      instance = c(source = "production", extra = "staging"))

Using environment variables and secrets

Environment variables declared in orderly.yml are made available in the report script (since orderly 1.1.11). For example, if your orderly.yml contains

environment:
  external_file: EXTERNAL_FILE_PATH
  variable_2: ENV_VAR_2

Then orderly will evaluate the variables and make them available in your script via the specified name. For example in your script you can use

external_data <- read.csv(external_file)

The expected use case for this is if you have data which you want to use in a report but do not want to be in the orderly repository (either because it is sensitive or particularly large). You can have the external files somewhere else on your machine and specify the path to them via an environment variable so they can then be accessed from the report script.

Environment variables can also be used in top-level orderly_envir.yml (since orderly 1.1.6) which can be accessed via Sys.getenv(). For example, if your orderly_envir.yml contains

DATA_URL: https://www.example.com/thedata

Then in your script you can use

url <- Sys.getenv("DATA_URL")
download.file(url)

If the values are sensitive, then this is not ideal, as you will store your values in plain text in the orderly_envir.yml. Instead, if using vault, you can use a secrets: section in orderly.yml, like:

secrets:
  password: /secret/myproject/login:value

and then an R variable password will be available to all the code in your report, containing the result of looking up /secret/myproject/login in your vault and getting the value field.

Developing a report

Because orderly works in a directory that is not the same as the source directory (e.g., draft/myreport/YYYYMMDD-HHMMSS-abcdefgh), because global resources and dependencies etc are copied in just as it is used, and because orderly takes control of things like parameters and sourcing files, it may not seem straightforward to develop a report as you would ordinarily.

In order to make this easier, orderly has a set of functions to help develop a report within the source directory. These functions are:

orderly::orderly_develop_start which copies files into your source copy
orderly::orderly_develop_status which reports on the status of your source
orderly::orderly_develop_clean which cleans up files that orderly copied

This section illustrates the idea, creating a new report that will depend on an artefact from a previous report. Here is the state of our orderly tree from before:

 [01;34m. [0m
├──  [01;34marchive [0m
│   └──  [01;34mexample [0m
│       └──  [01;34m20230621-105048-bd3a2cb5 [0m
│           ├── mydata.csv
│           ├──  [01;35mmygraph.png [0m
│           ├── orderly.yml
│           ├── orderly_run.rds
│           └──  [32mscript.R [0m
├──  [01;34mdraft [0m
│   └──  [01;34mexample [0m
├── orderly.sqlite
├── orderly_config.yml
└──  [01;34msrc [0m
    ├──  [01;34mexample [0m
    │   ├── orderly.yml
    │   └──  [32mscript.R [0m
    └──  [01;34mnew [0m
        ├── orderly.yml
        └──  [32mscript.R [0m

The new report has an orderly.yml containing

script: script.R

artefacts:
  - staticgraph:
      description: Mean of the values
      filenames: mean.txt

depends:
  example:
    id: latest
    use:
      data.csv: mydata.csv

In order to run this report, we need the file data.csv (which contains the output mydata.csv from the latest version of the example report) to exist. If we wanted to interactively develop the script.R we’d have to run orderly::orderly_run repeatedly, which will be annoying - and impractical if the report is slow to run. So we can run instead:

orderly::orderly_develop_start("new", root = path)

## [ name       ]  new

## [ depends    ]  example@20230621-105048-bd3a2cb5:mydata.csv -> data.csv

which copies in the required artefact and we can then change the directory (using something like setwd("src/new")) and start working on the report directly.

You can see the status of the directory by running

orderly::orderly_develop_status("new", root = path)

##      filename       type present derived
## 1 orderly.yml    orderly    TRUE   FALSE
## 2    script.R     script    TRUE   FALSE
## 3    data.csv dependency    TRUE    TRUE
## 4    mean.txt   artefact   FALSE    TRUE

(the output of this object is likely to change in future versions), which shows that data.csv is present, and that it is derived - by which means that orderly knows that it should not persist in the source tree. Files marked as derived as TRUE are liable for deletion by orderly::orderly_develop_clean. The output also shows that mean.txt is not present.

If you have changed directly into the path under development (via setwd(file.path(path, "src/new"))) the you can omit the arguments and simply call

orderly::orderly_develop_status()

##      filename       type present derived
## 1 orderly.yml    orderly    TRUE   FALSE
## 2    script.R     script    TRUE   FALSE
## 3    data.csv dependency    TRUE    TRUE
## 4    mean.txt   artefact   FALSE    TRUE

You can then run your script as if it was a normal R script:

source("script.R", echo = TRUE)

## 
## > data <- read.csv("data.csv")
## 
## > writeLines(as.character(mean(data$y)), "mean.txt")

(the above code assuming that we are within src/new with the report)

After which the data.csv is present

orderly::orderly_develop_status()

##      filename       type present derived
## 1 orderly.yml    orderly    TRUE   FALSE
## 2    script.R     script    TRUE   FALSE
## 3    data.csv dependency    TRUE    TRUE
## 4    mean.txt   artefact    TRUE    TRUE

If a newer version of the upstream dependency has become available, you can update the file by running orderly::orderly_develop_start() again

id <- orderly::orderly_run("example", root = path)

## [ name       ]  example

## [ id         ]  20230621-105050-7f4d9ff1

## [ start      ]  2023-06-21 10:50:50

## 
## > dat <- data.frame(x = 1:10, y = runif(10))
## 
## > write.csv(dat, "mydata.csv", row.names = FALSE)
## 
## > png("mygraph.png")
## 
## > plot(dat)

## 
## > dev.off()
## agg_png 
##       2

## [ end        ]  2023-06-21 10:50:50

## [ elapsed    ]  Ran report in 0.01168251 secs

## [ artefact   ]  mygraph.png: a9f715b3919f48edeaeea649d1693408
## [ ...        ]  mydata.csv: 9a8ea34959eb8172fd19dd78367d6cf9

orderly::orderly_commit(id, root = path)

## [ commit     ]  example/20230621-105050-7f4d9ff1

## [ copy       ]

## [ import     ]  example:20230621-105050-7f4d9ff1

## [ success    ]  :)

## [1] "/tmp/RtmpzlBf1Y/file3597c1dbe0b/archive/example/20230621-105050-7f4d9ff1"

orderly::orderly_develop_start()

## [ name       ]  new

## [ depends    ]  example@20230621-105050-7f4d9ff1:mydata.csv -> data.csv

(note that this has updated data.csv to use this new id 20230621-105050-7f4d9ff1).

Finally, you can delete the files that orderly has copied into the source tree:

orderly::orderly_develop_clean()
## [ remove     ]  data.csv
## [ ...        ]  mean.txt
orderly::orderly_develop_status()
##      filename       type present derived
## 1 orderly.yml    orderly    TRUE   FALSE
## 2    script.R     script    TRUE   FALSE
## 3    data.csv dependency   FALSE    TRUE
## 4    mean.txt   artefact   FALSE    TRUE

(this function also accepts name and root as above, required if not working in the source directory)

Rich FitzJohn

2023-06-21