On older systems, UCRT has to be installed manually from here.
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This build requires UCRT, which is part of Windows since Windows 10 and Windows Server 2016. README on the Windows binary distribution.driver.Download R-4.2.1 for Windows (79 megabytes, 64 bit) We can use the config package to handle having different cache paths accross development, staging, and production environments. To avoid losing the _targets folder, you need to configure a specific path for the targets cache in RStudio Connect that lives outside of the folder where the application is deployed ( E.g /mnt/data). This re-execution means that we lose the _targets folder where the cache lives and, thus, we lose the main functionality of targets. One of the caveats of deploying an RMarkdown document to RStudio Connect is that whenever the document is re-executed, it spawns a new process that only contains the files included in the deployment bundle. In our example, this file is called driver.Rmd and, in line 39, it calls the tar_make() command to execute the pipeline. To host a targets pipeline in RStudio Connect, you will need to add an RMarkdown document that executes the pipeline. Deploying a targets pipeline to RStudio Connect # We will see how we will use this flexibility to store the cache to our advantage when deploying a pipeline to RStudio Connect. However, the cache can live in other places like a separate folder or an S3 bucket. If you don't configure the location, targets will create the cache locally in the _targets folder. By default, each target will get stored in the cache as an R object. The _targets cache is how targets handles this problem. However, how does targets know what is out-of-date and what isn't? When executing the pipeline, targets looks at the dependency graph before executing a step so that the pipeline only computes the steps that are out-of-date. targets uses all of these dependency relationships to build a dependency graph. In the second target, we use the raw_data_url string to read the data and store this output in a target named raw_data.
In the first target, we specify that we want a target with the name raw_data_url which, holds the value of a URL string. List ( tar_target ( raw_data_url, "", format = "url" ), tar_target ( raw_data, read_csv ( raw_data_url, col_types = cols ()) ). The first four lines of the _targets.R file in the example repository look as follows: This file specifies the dependencies of the project in addition to the steps that the pipeline will execute. Targets has one main file named _targets.R. To showcase how targets work we will discuss an example of a pipeline that reads data from a CSV file, trains a linear regression model using tidymodels, and deploys an RMarkdown report with the information about the model.
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shows tangible evidence that the results match the underlying code and data.runs only the necessary computation, supports implicit parallel computing.skips costly runtime for tasks that are already up to date.
With targets, you can maintain a reproducible workflow without repeating yourself. The targets package is a Make-like pipeline toolkit for Statistics and data science in R. Targets can be described as an orchestration package for R. Example GitHub repository: What is targets? #
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