### Installing Development Version of tarchetypes (R)

Source: https://github.com/ropensci/tarchetypes/blob/main/CONTRIBUTING.md

This R command installs the latest development version of the `tarchetypes` package directly from its GitHub repository. It is recommended for bug reporting to verify if an issue persists in the most recent unreleased code. Requires the `remotes` package.

```R
remotes::install_github("ropensci/tarchetypes")
```

--------------------------------

### Installing tarchetypes R Package

Source: https://github.com/ropensci/tarchetypes/blob/main/README.md

This snippet provides commands for installing the `tarchetypes` R package from different sources: CRAN for the release version, GitHub for the development version, and rOpenSci's development repository for another development option.

```R
install.packages("tarchetypes")
```

```R
remotes::install_github("ropensci/tarchetypes")
```

```R
install.packages("tarchetypes", repos = "https://dev.ropensci.org")
```

--------------------------------

### Defining a targets Pipeline with tar_plan in R

Source: https://github.com/ropensci/tarchetypes/blob/main/README.md

This snippet illustrates an alternative, `drake`-like syntax for defining a `targets` pipeline using `tar_plan()`. It allows users to specify targets as name/command pairs, simplifying the pipeline definition. It includes examples of file targets, data processing, plot generation, statistical modeling, and R Markdown report rendering.

```R
tar_plan(
  tar_file(raw_data_file, "data/raw_data.csv", format = "file"),
  # Simple drake-like syntax:
  raw_data = read_csv(raw_data_file, col_types = cols()),
  data =raw_data %>%
    mutate(Ozone = replace_na(Ozone, mean(Ozone, na.rm = TRUE))),
  hist = create_plot(data),
  fit = biglm(Ozone ~ Wind + Temp, data),
  # Needs tar_render() because it is a target archetype:
  tar_render(report, "report.Rmd")
)
```

--------------------------------

### Checking Code Formatting with lintr (R)

Source: https://github.com/ropensci/tarchetypes/blob/main/CONTRIBUTING.md

This R function, part of the `lintr` package, is used to check the formatting of the package's code against the tidyverse style guide. It helps ensure code consistency and readability for contributions. Requires the `lintr` package.

```R
lint_package()
```

--------------------------------

### Running Good Practice Checks with goodpractice (R)

Source: https://github.com/ropensci/tarchetypes/blob/main/CONTRIBUTING.md

This R command evaluates the package against a set of good practice recommendations using the `goodpractice` package. It helps identify potential issues related to code quality, documentation, and testing, ensuring high-quality contributions. Requires the `goodpractice` package.

```R
goodpractice::gp()
```

--------------------------------

### Executing and Inspecting Grouped Data Targets in R

Source: https://github.com/ropensci/tarchetypes/blob/main/README.md

This R console output demonstrates the execution of a `targets` pipeline that utilizes `tarchetypes` for grouped data frames. It shows the `tar_make()` command initiating the pipeline, followed by the dispatch and completion of individual data branches. The `tar_read()` calls illustrate how to access specific row groups from the branched target, confirming the successful grouping and processing of data subsets.

```R
library(targets)
tar_make()
#> ▶ dispatched target data
#> ● completed target data [0.007 seconds]
#> ▶ dispatched branch group_b3d7d010
#> ● completed branch group_b3d7d010 [0 seconds]
#> ▶ dispatched branch group_6a76c5c0
#> ● completed branch group_6a76c5c0 [0 seconds]
#> ▶ dispatched branch group_164b16bf
#> ● completed branch group_164b16bf [0 seconds]
#> ▶ dispatched branch group_f5aae602
#> ● completed branch group_f5aae602 [0 seconds]
#> ● completed pattern group
#> ▶ completed pipeline [0.104 seconds]

# First row group:
tar_read(group, branches = 1)
#> # A tibble: 3 × 4
#>   var1  var2    rep tar_group
#>   <fct> <fct> <dbl>     <int>
#> 1 a     c         1         1
#> 2 a     c         2         1
#> 3 a     c         3         1

# Second row group:
tar_read(group, branches = 2)
#> # A tibble: 3 × 4
#>   var1  var2    rep tar_group
#>   <fct> <fct> <dbl>     <int>
#> 1 a     d         1         2
#> 2 a     d         2         2
#> 3 a     d         3         2
```

--------------------------------

### Defining a targets Pipeline with tar_target and tar_render in R

Source: https://github.com/ropensci/tarchetypes/blob/main/README.md

This snippet demonstrates a basic `targets` pipeline definition using `list()` to combine `tar_target()` for data objects and `tar_render()` for R Markdown reports. `tar_render()` automatically scans the R Markdown file for dependencies on other targets, ensuring they are processed in the correct order.

```R
library(targets)
library(tarchetypes)
list(
  tar_target(dataset, data.frame(x = letters)),
  tar_render(report, "report.Rmd")
)
```

--------------------------------

### Checking Package Code Coverage with covr (R)

Source: https://github.com/ropensci/tarchetypes/blob/main/CONTRIBUTING.md

This R command calculates the code coverage for the entire package using the `covr` package. Contributors should ensure that automated tests cover all new or changed functionality introduced in their pull requests. Requires the `covr` package.

```R
covr::package_coverage()
```

--------------------------------

### Citing tarchetypes R Package

Source: https://github.com/ropensci/tarchetypes/blob/main/README.md

This snippet demonstrates how to obtain citation information for the `tarchetypes` R package using the `citation()` function. It outputs the recommended citation format for publications, including a BibTeX entry for LaTeX users.

```R
citation("tarchetypes")
```

--------------------------------

### Manually Rendering R Markdown Reports in targets (Without tarchetypes) in R

Source: https://github.com/ropensci/tarchetypes/blob/main/README.md

This R code snippet demonstrates how to manually define a `targets` pipeline entry for rendering an R Markdown report. It explicitly lists dependencies (like `dataset`), handles file paths, and configures `rmarkdown::render()` parameters such as `knit_root_dir`. This approach requires careful management of file tracking and dependencies, highlighting the complexity `tarchetypes` aims to simplify.

```R
library(targets)
list(
  tar_target(dataset, data.frame(x = letters)),
  tar_target(
    report, {
      # Explicitly mention the symbol `dataset`.
      list(dataset)
      # Return relative paths to keep the project portable.
      fs::path_rel(
        # Need to return/track all input/output files.
        c( 
          rmarkdown::render(
            input = "report.Rmd",
            # Always run from the project root
            # so the report can find _targets/.
            knit_root_dir = getwd(),
            quiet = TRUE
          ),
          "report.Rmd"
        )
      )
    },
    # Track the input and output files.
    format = "file",
    # Avoid building small reports on HPC.
    deployment = "main"
  )
)
```

--------------------------------

### Running Overall Package Checks with devtools (R)

Source: https://github.com/ropensci/tarchetypes/blob/main/CONTRIBUTING.md

This R command performs comprehensive checks on the package, including R CMD check, using the `devtools` package. It's a crucial step to ensure the package adheres to CRAN policies and best practices before submitting contributions. Requires the `devtools` package.

```R
devtools::check()
```

--------------------------------

### Defining Grouped Data Targets with tar_group_by in R

Source: https://github.com/ropensci/tarchetypes/blob/main/README.md

This R code snippet defines a `targets` pipeline (`_targets.R`) that uses `tarchetypes::tar_group_by()` to create row groups from a generated dataset. Downstream dynamic targets will automatically branch over these defined groups, enabling efficient parallel processing of data subsets. It demonstrates how to set up a data production function and then group its output by specified variables.

```R
library(targets)
library(tarchetypes)
produce_data <- function() {
  expand.grid(var1 = c("a", "b"), var2 = c("c", "d"), rep = c(1, 2, 3))
}
list(
  tar_group_by(data, produce_data(), var1, var2),
  tar_target(group, data, pattern = map(data))
)
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.