### Inline Documentation Example

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/README.md

Demonstrates how to use the documentation reference for inline code assistance by showing example R commands to look up function signatures, types, or error details.

```r
# Looking for localize_beach() signature?
# See: api-reference-delta.md or api-reference-echo.md

# Need to understand a type?
# See: types.md

# Debugging an error?
# See: errors.md
```

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

### Example File Not Found Error

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/errors.md

Illustrates the error message when the 'beach-lookup-table.csv' file is not found. Ensure the delta package is installed and the file is not manually moved or deleted.

```R
Error: Cannot open file '/nonexistent/path/beach-lookup-table.csv'. No such file or directory.
```

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

### Install Echo Package

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/quick-start.md

Installs the echo package from the r-pkgs repository using devtools.

```r
devtools::install("package-within-files/echo")
```

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

### Install Delta Package

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/quick-start.md

Installs the delta package from the r-pkgs repository using devtools.

```r
devtools::install("package-within-files/delta")
```

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

### Echo DESCRIPTION File with Imports

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/module-structure.md

Example of a DESCRIPTION file for an R package named 'echo', explicitly declaring an import dependency on 'dplyr'.

```r
Package: echo
Title: What the Package Does (One Line, Title Case)
Version: 0.0.0.9000
Authors@R: person("Jennifer", "Bryan", ...)
Description: What the package does (one paragraph).
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
Imports: 
    dplyr
```

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

### Delta DESCRIPTION File

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/module-structure.md

Example of a DESCRIPTION file for an R package named 'delta'. It includes package name, title, version, author information, description, license, encoding, and Roxygen settings.

```r
Package: delta
Title: What the Package Does (One Line, Title Case)
Version: 0.0.0.9000
Authors@R: person("Jennifer", "Bryan", ...)
Description: What the package does (one paragraph).
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
```

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

### Load Tidyverse Package

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/module-structure.md

This snippet shows how to load the tidyverse package. It is a soft dependency and may cause the package to fail if tidyverse is not installed.

```r
library(tidyverse)
```

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

### Load devtools package in R

Source: https://github.com/hadley/r-pkgs/blob/main/data/colophon.txt

Loads the devtools package, which provides tools for package development. Ensure the package is installed before loading.

```r
library(devtools)
#> Loading required package: usethis
```

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

### Get R session information with devtools

Source: https://github.com/hadley/r-pkgs/blob/main/data/colophon.txt

Retrieves detailed information about the current R session, including R version, OS, and loaded packages. This is crucial for ensuring reproducibility.

```r
devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.2 (2022-10-31)
#>  os       macOS Big Sur ... 10.16
#>  system   x86_64, darwin17.0
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       America/Vancouver
#>  date     2023-06-06
#>  pandoc   2.19.2 @ /Applications/RStudio.app/.../bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package     * version date (UTC) lib source
#>  brio          1.1.3   2021-11-30 [1] CRAN (R 4.2.0)
#>  cachem        1.0.8   2023-05-01 [1] CRAN (R 4.2.0)
#>  callr         3.7.3   2022-11-02 [1] CRAN (R 4.2.0)
#>  cli           3.6.1   2023-03-23 [1] CRAN (R 4.2.0)
#>  crayon        1.5.2   2022-09-29 [1] CRAN (R 4.2.0)
#>  devtools    * 2.4.5   2022-10-11 [1] CRAN (R 4.2.0)
#>  digest        0.6.31  2022-12-11 [1] CRAN (R 4.2.0)
#>  ellipsis      0.3.2   2021-04-29 [1] CRAN (R 4.2.0)
#>  evaluate      0.21    2023-05-05 [1] CRAN (R 4.2.0)
#>  fastmap       1.1.1   2023-02-24 [1] CRAN (R 4.2.0)
#>  fs            1.6.2   2023-04-25 [1] CRAN (R 4.2.0)
#>  glue          1.6.2   2022-02-24 [1] CRAN (R 4.2.0)
#>  htmltools     0.5.5   2023-03-23 [1] CRAN (R 4.2.2)
#>  htmlwidgets   1.6.2   2023-03-17 [1] CRAN (R 4.2.0)
#>  httpuv        1.6.9   2023-02-14 [1] CRAN (R 4.2.0)
#>  knitr         1.43    2023-05-25 [1] CRAN (R 4.2.0)
#>  later         1.3.0   2021-08-18 [1] CRAN (R 4.2.0)
#>  lifecycle     1.0.3   2022-10-07 [1] CRAN (R 4.2.0)
#>  magrittr      2.0.3   2022-03-30 [1] CRAN (R 4.2.0)
#>  memoise       2.0.1   2021-11-26 [1] CRAN (R 4.2.0)
#>  mime          0.12    2021-09-28 [1] CRAN (R 4.2.0)
#>  miniUI        0.1.1.1 2018-05-18 [1] CRAN (R 4.2.0)
#>  pkgbuild      1.4.0   2022-11-27 [1] CRAN (R 4.2.0)
#>  pkgload       1.3.2   2022-11-16 [1] CRAN (R 4.2.0)
#>  prettyunits   1.1.1   2020-01-24 [1] CRAN (R 4.2.0)
#>  processx      3.8.1   2023-04-18 [1] CRAN (R 4.2.0)
#>  profvis       0.3.7   2020-11-02 [1] CRAN (R 4.2.0)
#>  promises      1.2.0.1 2021-02-11 [1] CRAN (R 4.2.0)
#>  ps            1.7.5   2023-04-18 [1] CRAN (R 4.2.0)
#>  purrr         1.0.1   2023-01-10 [1] CRAN (R 4.2.0)
#>  R.cache       0.16.0  2022-07-21 [1] CRAN (R 4.2.0)
#>  R.methodsS3   1.8.2   2022-06-13 [1] CRAN (R 4.2.0)
#>  R.oo          1.25.0  2022-06-12 [1] CRAN (R 4.2.0)
#>  R.utils       2.12.2  2022-11-11 [1] CRAN (R 4.2.0)
#>  R6            2.5.1   2021-08-19 [1] CRAN (R 4.2.0)
#>  Rcpp          1.0.10  2023-01-22 [1] CRAN (R 4.2.0)
#>  remotes       2.4.2   2021-11-30 [1] CRAN (R 4.2.0)
#>  reprex        2.0.2   2022-08-17 [1] CRAN (R 4.2.0)
#>  rlang         1.1.1   2023-04-28 [1] CRAN (R 4.2.0)
#>  rmarkdown     2.22    2023-06-01 [1] CRAN (R 4.2.0)
#>  roxygen2    * 7.2.3   2022-12-08 [1] CRAN (R 4.2.0)
#>  rstudioapi    0.14    2022-08-22 [1] CRAN (R 4.2.0)
#>  sessioninfo   1.2.2   2021-12-06 [1] CRAN (R 4.2.0)
#>  shiny         1.7.4   2022-12-15 [1] CRAN (R 4.2.0)
#>  stringi       1.7.12  2023-01-11 [1] CRAN (R 4.2.0)
#>  stringr       1.5.0   2022-12-02 [1] CRAN (R 4.2.0)
#>  styler        1.10.1  2023-06-05 [1] CRAN (R 4.2.2)
#>  testthat    * 3.1.8   2023-05-04 [1] CRAN (R 4.2.0)
#>  urlchecker    1.0.1   2021-11-30 [1] CRAN (R 4.2.0)
#>  usethis     * 2.2.0   2023-06-06 [1] CRAN (R 4.2.2)
#>  vctrs         0.6.2   2023-04-19 [1] CRAN (R 4.2.0)
#>  withr         2.5.0   2022-03-03 [1] CRAN (R 4.2.0)
#>  xfun          0.39    2023-04-20 [1] CRAN (R 4.2.0)
#>  xml2          1.3.4   2023-04-27 [1] CRAN (R 4.2.0)
#>  xtable        1.8-4   2019-04-21 [1] CRAN (R 4.2.0)
#>  yaml          2.3.7   2023-01-23 [1] CRAN (R 4.2.0)
#> 
#>  [1] /Users/jenny/Library/R/x86_64/4.2/library
#>  [2] /Library/Frameworks/R.framework/Versions/4.2/Resources/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────
```

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

### Basic Quarto Callout

Source: https://github.com/hadley/r-pkgs/blob/main/README.md

Demonstrates the basic syntax for a Quarto callout of type 'tip'.

```markdown
:::
callout-tip
Here's a super handy nifty thing!
:::
```

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

### Package Load-Time Configuration

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/configuration.md

Captures the current system time when the package is loaded. This timestamp is reused for all outfile_path() calls within the session.

```r
now <- Sys.time()
```

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

### Minimal Data Cleaning with Delta

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/quick-start.md

Demonstrates a minimal data cleaning pipeline using the delta package, including localization and temperature conversion.

```r
library(delta)

# Create sample data with beach terminology and temperature
data <- data.frame(
  where = "beach",
  temp = 72,
  stringsAsFactors = FALSE
)

# Add region localization
data <- localize_beach(data)

# Convert temperature from Fahrenheit to Celsius
data <- celsify_temp(data)

# Generate output filename
output_file <- outfile_path("input.csv")

# Write cleaned data
write.csv(data, output_file, row.names = FALSE)
```

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

### Import Delta or Echo Package

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/quick-start.md

Loads the delta or echo package into the current R session.

```r
library(delta)  # or library(echo)
```

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

### Package Structure Overview

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/INDEX.md

Illustrates the directory structure and key files for two R packages, 'delta' and 'echo'. Shows source code locations, exported functions, and data files.

```tree
delta/
├── R/cleaning-helpers.R          # Source code (24 lines)
│   ├── localize_beach()          # Exported
│   ├── celsify_temp()            # Exported
│   ├── outfile_path()            # Exported
│   ├── f_to_c()                  # Private
│   └── timestamp()               # Private
├── DESCRIPTION                   # Metadata (no explicit imports)
├── NAMESPACE                     # Exports: localize_beach, celsify_temp, outfile_path
└── beach-lookup-table.csv        # External data

echo/
├── R/cleaning-helpers.R          # Source code (27 lines)
│   ├── lookup_table (tribble)    # Private data
│   ├── localize_beach()          # Exported
│   ├── celsify_temp()            # Exported
│   ├── outfile_path()            # Exported
│   ├── f_to_c()                  # Private
│   └── timestamp()               # Private
├── DESCRIPTION                   # Metadata (dplyr in Imports)
└── NAMESPACE                     # Exports: localize_beach, celsify_temp, outfile_path
```

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

### Quarto Callout Types

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/book-chapters.md

Demonstrates the usage of different Quarto callout types for structuring special content within documentation. Use these to highlight tips, warnings, notes, and important information.

```markdown
::: callout-tip
Helpful hints and best practices
:::

::: callout-warning
Things to be careful about, especially for CRAN
:::

::: callout-note
Additional information and context
:::

::: callout-important
Critical information
:::
```

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

### Stage 5: Echo Best Practices Package

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/book-chapters.md

This stage presents an improved R package following best practices, including explicit dependency declarations (e.g., dplyr) in the DESCRIPTION file, embedded data, and standard package structure files.

```text
package-within-files/echo/
├── R/cleaning-helpers.R  # Source code
├── DESCRIPTION           # Package metadata with dplyr dependency
├── NAMESPACE             # Export declarations
├── LICENSE               # License file
└── echo.Rproj            # RStudio project
```

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

### RStudio Specific Quarto Callout

Source: https://github.com/hadley/r-pkgs/blob/main/README.md

Shows how to create a Quarto callout specifically for RStudio tips, using a level 2 header for the caption.

```markdown
:::
callout-tip
## RStudio
Here's a super handy nifty thing about RStudio specifically.
:::
```

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

### Enable Markdown in Roxygen2

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/configuration.md

Configure roxygen2 to use markdown syntax for documentation comments. This setting should be placed in the DESCRIPTION file.

```R
Roxygen: list(markdown = TRUE)
```

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

### Output Data After Cleaning

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/book-chapters.md

CSV data after applying the cleaning workflow, including localized terms and converted temperatures.

```csv
beach,temp,where,english
beach,22.22,beach,US
coast,21.11,coast,US
seashore,18.33,seashore,UK
seaside,20.00,seaside,UK
```

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

### Stage 4: Delta Real Package Structure

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/book-chapters.md

This stage introduces the first real R package, showcasing the basic structure including R source code, package metadata (DESCRIPTION, NAMESPACE), and license files. It also includes associated data and an RStudio project file.

```text
package-within-files/delta/
├── R/cleaning-helpers.R  # Source code
├── DESCRIPTION           # Package metadata
├── NAMESPACE             # Export declarations
├── LICENSE               # License file
├── beach-lookup-table.csv
└── delta.Rproj           # RStudio project
```

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

### Delta Package Dependencies

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/module-structure.md

Illustrates the dependencies of the 'cleaning-helpers.R' module in the Delta package, including external files and exported/internal functions.

```text
cleaning-helpers.R (exports 3 functions)
    ↓ (imports)
    - library(tidyverse)
    - beach-lookup-table.csv (external file)    
Exported API:
    - localize_beach()   [public]
    - celsify_temp()     [public]
    - outfile_path()     [public]

Internal:
    - f_to_c()           [private]
    - timestamp()        [private]
```

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

### R Packages Book DESCRIPTION File

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/book-chapters.md

This is the DESCRIPTION file for the R Packages book, used to declare site dependencies. It lists required and suggested packages.

```r
Package: rpkgs
Type: Book
Title: R Packages
Version: 0.1
Authors@R: person("Hadley", "Wickham", role = c("aut", "cre"))
Description: This is not a package, but we just use this file to declare
    the dependencies of the site.
URL: https://r-pkgs.org/
```

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

### Echo R/cleaning-helpers.R File Breakdown

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/module-structure.md

Details the structure of the 'cleaning-helpers.R' file within the Echo package, outlining private data, exported functions, and configuration sections.

```text
Lines | Content | Export Status
|-------|---------|----------------
| 1-7 | lookup_table (tribble) | Private data 
| 8-12 | localize_beach() | **EXPORTED** 
| 13-14 | f_to_c() | Private 
| 15-19 | celsify_temp() | **EXPORTED** 
| 20-22 | Load-time configuration | — 
| 23-27 | outfile_path() | **EXPORTED** 
```

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

### RStudio-Specific Callout

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/book-chapters.md

Shows how to use a Quarto callout to specifically highlight RStudio-related features and workflows. This is useful for documentation targeting RStudio users.

```markdown
::: callout-tip
## RStudio
RStudio-specific features and workflows
:::
```

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

### Delta R/cleaning-helpers.R File Breakdown

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/module-structure.md

Provides a line-by-line breakdown of the 'cleaning-helpers.R' file in the Delta package, categorizing sections by content and export status.

```text
Lines | Content | Export Status
|-------|---------|----------------
| 1 | Library import | — 
| 2 | (blank) | — 
| 3-10 | localize_beach() | **EXPORTED** 
| 11-12 | f_to_c() | Private 
| 13-17 | celsify_temp() | **EXPORTED** 
| 18-20 | Load-time configuration | — 
| 21-24 | outfile_path() | **EXPORTED** 
```

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

### Generate Timestamped Output File Path with outfile_path

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/api-reference-echo.md

Generates a timestamped output file path by prepending a formatted timestamp to the input filename. Note that the timestamp is captured at load time, so all calls within a session use the same timestamp.

```r
library(echo)

output <- outfile_path("swim.csv")
# Result: "2024-June-29_14-35-42_swim_clean.csv" (exact timestamp varies)
```

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

### Echo Package Dependencies

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/module-structure.md

Details the dependencies of the 'cleaning-helpers.R' module in the Echo package, specifying explicit DESCRIPTION imports and internal data structures.

```text
cleaning-helpers.R (exports 3 functions)
    ↓ (imports)
    - dplyr (explicit in DESCRIPTION)
    - lookup_table (internal tribble)    
Exported API:
    - localize_beach()   [public]
    - celsify_temp()     [public]
    - outfile_path()     [public]

Internal:
    - f_to_c()           [private]
    - timestamp()        [private]
    - lookup_table       [private data]
```

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

### Echo Package DESCRIPTION File

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/configuration.md

Full configuration for the echo R package as defined in its DESCRIPTION file. This configuration includes package metadata and an explicit import of the dplyr package.

```r
Package: echo
Title: What the Package Does (One Line, Title Case)
Version: 0.0.0.9000
Authors@R: 
    person("Jennifer", "Bryan", , "jenny@posit.co", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-6983-2759"))
Description: What the package does (one paragraph).
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
Imports: 
    dplyr
```

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

### Localize Beach Data and Celsify Temperature

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/quick-start.md

Always call `localize_beach()` before `celsify_temp()` to ensure the 'english' column is available. This sequence is crucial for correct data processing.

```r
data <- localize_beach(data)
data <- celsify_temp(data)
```

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

### Delta Package DESCRIPTION File

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/configuration.md

Full configuration for the delta R package as defined in its DESCRIPTION file. This configuration specifies package metadata and dependencies.

```r
Package: delta
Title: What the Package Does (One Line, Title Case)
Version: 0.0.0.9000
Authors@R: 
    person("Jennifer", "Bryan", , "jenny@posit.co", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-6983-2759"))
Description: What the package does (one paragraph).
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
```

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

### outfile_path()

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/api-reference-delta.md

Generates a timestamped output file path by prepending a formatted timestamp to the input filename. The timestamp is captured at module load time.

```APIDOC
## outfile_path()

### Description
Generates a timestamped output file path by prepending a formatted timestamp to the input filename.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Method
None (R function)

### Endpoint
None (R function)

### Parameters
- **infile** (character) - Required - Input file name (typically with .csv extension).

### Returns
character - Output file path with format `YYYY-Month-DD_HH-MM-SS_original_clean.csv`

### Example
```r
library(delta)

# Generate output filename
output <- outfile_path("swim.csv")
# Result: "2024-June-29_14-35-42_swim_clean.csv" (exact timestamp varies)
```
```

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

### Stage 3: Charlie Helper Functions

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/book-chapters.md

This stage demonstrates the extraction of reusable helper functions into a separate script, used by the main data cleaning script. It includes helper functions, the main script, and associated data files.

```text
package-within-files/charlie/
├── cleaning-helpers.R   # Helper functions
├── data-cleaning.R      # Main script using helpers
├── beach-lookup-table.csv
├── swim.csv
└── swim_clean.csv
```

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

### Defensive Input Preparation Function in R

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/errors.md

This pattern involves creating a dedicated function to prepare input data by ensuring it is a data frame, contains necessary columns, and has correct data types. This function should be called before any processing.

```r
library(delta)

# Prepare data defensively
prepare_for_cleaning <- function(dat) {
  # Ensure data frame
  if (!is.data.frame(dat)) {
    dat <- as.data.frame(dat)
  }
  
  # Ensure required columns
  if (!("temp" %in% colnames(dat))) {
    stop("Input must have 'temp' column")
  }
  
  # Ensure correct types
  if (!is.numeric(dat$temp)) {
    dat$temp <- as.numeric(dat$temp)
  }
  
  return(dat)
}

dat <- data.frame(english = "US", temp = "72")
dat <- prepare_for_cleaning(dat)
dat <- celsify_temp(dat)
```

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

### Stage 1: Alfa Minimal Script

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/book-chapters.md

This stage shows a basic R script for data cleaning before it is structured into a package. It includes input and output data files.

```text
package-within-files/alfa/
├── data-cleaning.R      # Basic script without package structure
├── swim.csv             # Input data
└── swim_clean.csv       # Output data
```

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

### Input Data for Cleaning

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/book-chapters.md

CSV data representing beach and temperature readings, used as input for the data cleaning workflow.

```csv
beach,temp
beach,72
coast,70
seashore,65
seaside,68
```

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

### Recovering from Missing Columns in celsify_temp()

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/errors.md

Demonstrates how to ensure the input data frame has the required 'english' and 'temp' columns before calling celsify_temp(). This typically involves calling localize_beach() first and then adding or ensuring the 'temp' column.

```R
# Ensure data has required columns
dat <- localize_beach(dat)  # Adds 'english' column
dat$temp <- c(72, 32)       # Add or ensure 'temp' column
dat <- celsify_temp(dat)    # Now safe to call
```

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

### Generate Timestamped Output Filename with outfile_path

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/quick-start.md

Generates a timestamped output filename. Takes a filename as input and returns a path with a timestamp prepended.

```r
output <- outfile_path("input.csv")
```

```r
output <- outfile_path("swim.csv")
# Returns: "2024-June-29_14-35-42_swim_clean.csv"

```

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

### outfile_path()

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/api-reference-echo.md

Generates a timestamped output file path by prepending a formatted timestamp to the input filename. The output path includes a timestamp and '_clean' before the extension.

```APIDOC
## outfile_path()

### Description
Generates a timestamped output file path by prepending a formatted timestamp to the input filename.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
- **infile** (character) - Required - Input file name (typically with .csv extension)

### Returns
character - Output file path with format `YYYY-Month-DD_HH-MM-SS_original_clean.csv`

### Details
Uses the internal `timestamp()` function to format the current system time. The timestamp format is `%Y-%B-%d_%H-%M-%S` (4-digit year, full month name, day, hour-minute-second).

Applies regex substitution `"(.*)([.]csv$)"` to insert `_clean` before the `.csv` extension. For example, `swim.csv` becomes `2024-June-29_14-35-42_swim_clean.csv`.

### Limitations
The timestamp (`now`) is captured at load time via `now <- Sys.time()`, not at function call time. All invocations of `outfile_path()` within a session will use the same timestamp value.

### Example
```r
library(echo)

output <- outfile_path("swim.csv")
# Result: "2024-June-29_14-35-42_swim_clean.csv" (exact timestamp varies)
```
```

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

### Handle Non-String Input for outfile_path()

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/errors.md

When providing non-character input to outfile_path(), an error will occur because no automatic string coercion is performed. Ensure all inputs are character strings.

```r
outfile_path(123)  # Error in paste0(...): invalid 'x' argument
```

```r
outfile_path(as.character(123))  # Convert to string first
```

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

### Function Signatures for Delta/Echo

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/INDEX.md

These are the function signatures provided by both the Delta and Echo R packages. They cover localization, temperature conversion, and output file path generation.

```r
# Localize beach terminology by joining with region data
localize_beach(dat)

# Convert temperatures to Celsius based on region
celsify_temp(dat)

# Generate timestamped output filename
outfile_path(infile)
```

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

### Call outfile_path function

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/api-reference-delta.md

Illustrates generating a timestamped output file path using the outfile_path function. The function takes a base filename and returns a formatted string with a timestamp and '_clean' appended before the extension.

```r
library(delta)

# Generate output filename
output <- outfile_path("swim.csv")
# Result: "2024-June-29_14-35-42_swim_clean.csv" (exact timestamp varies)
```

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

### CRAN Submission Quarto Callout

Source: https://github.com/hadley/r-pkgs/blob/main/README.md

Illustrates a Quarto callout for warnings related to submitting packages to CRAN, with a descriptive header.

```markdown
:::
callout-warning
## Submitting to CRAN
Here's something to be super careful about.
:::
```

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

### Load Delta Package

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/quick-start.md

Loads the Delta package and implicitly uses tidyverse functions. The lookup table is loaded from a CSV file.

```r
library(delta)

# Uses tidyverse functions implicitly
# Loads lookup table from CSV file

```

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

### Delta Package Structure

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/module-structure.md

Illustrates the R code structure for the Delta package, including library loading, private helper functions, load-time configuration, and the public API. It shows implicit dependency on tidyverse.

```r
# Package load-time execution
library(tidyverse)

# External dependency on tidyverse functions
# Private helper function
f_to_c <- function(x) (x - 32) * 5/9

# Load-time configuration
now <- Sys.time()

# Private helper function
timestamp <- function(time) format(time, "%Y-%B-%d_%H-%M-%S")

# Public API - 3 exported functions
# 1. localize_beach(dat)        - joins with lookup table
# 2. celsify_temp(dat)          - converts temperature
# 3. outfile_path(infile)       - generates timestamped filename

```

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

### Data Cleaning Pipeline with Delta or Echo

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/INDEX.md

This R code demonstrates a common data cleaning pipeline using either the Delta or Echo package. It includes reading data, localizing beach terminology, converting temperatures, generating an output filename, and saving the processed data.

```r
library(delta)  # or library(echo)

# Read raw data
data <- read.csv("input.csv")

# Step 1: Add region information
data <- localize_beach(data)

# Step 2: Standardize temperatures
data <- celsify_temp(data)

# Step 3: Generate output filename
output <- outfile_path("input.csv")

# Step 4: Save
write.csv(data, output, row.names = FALSE)
```

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

### Load testthat package in R

Source: https://github.com/hadley/r-pkgs/blob/main/data/colophon.txt

Loads the testthat package, a framework for testing R code. It may mask functions from other packages like devtools.

```r
library(testthat)
#> 
#> Attaching package: 'testthat'
#> The following object is masked from 'package:devtools':
#> 
#>     test_file
```

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

### Load Echo Package

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/quick-start.md

Loads the Echo package and uses explicit dplyr function calls. The lookup table is embedded within the code.

```r
library(echo)

# Uses explicit dplyr function calls
# Lookup table embedded in code

```

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

### Handle Unusual File Extensions with outfile_path()

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/errors.md

The outfile_path() function's regex pattern expects a '.csv' extension. Inputs with different extensions or case variations may not be processed as expected, leading to unexpected output. Convert extensions to lowercase '.csv' before passing to the function.

```r
outfile_path("data.txt")   # Returns "2024-June-29_14-35-42_data.txt" (no _clean)
```

```r
outfile_path("data.CSV")   # Returns "2024-June-29_14-35-42_data.CSV" (case-sensitive)
```

```r
# Convert to lowercase .csv
infile <- tolower(sub("\.CSV$", ".csv", infile))
outfile_path(infile)
```

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

### Load roxygen2 package in R

Source: https://github.com/hadley/r-pkgs/blob/main/data/colophon.txt

Loads the roxygen2 package, used for documenting R code. This is a common dependency for package development workflows.

```r
library(roxygen2)
```

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

### Call localize_beach function

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/api-reference-delta.md

Demonstrates how to use the localize_beach function to add localization information to a data frame. This function requires a data frame as input and adds 'where' and 'english' columns.

```r
library(delta)

# Create sample data
swim_data <- data.frame(
  location = "beach",
  temp = 72,
  stringsAsFactors = FALSE
)

# Add localization information
localized <- localize_beach(swim_data)
# Result includes columns: location, temp, where, english
```

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

### localize_beach()

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/api-reference-echo.md

Adds localization information to a data frame by joining with a region lookup table. It augments the input data with 'where' and 'english' columns based on location names.

```APIDOC
## localize_beach()

### Description
Adds localization information to a data frame by joining with a region lookup table.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
- **dat** (data.frame or tibble) - Required - Input data to augment with localization information

### Returns
data.frame or tibble - Input data joined with the lookup table, adding `where` and `english` columns.

### Details
This function joins the input data with an internally-defined lookup table created using `dplyr::tribble()`. Unlike the delta version, the lookup table is embedded in the package code rather than loaded from an external CSV file.

### Example
```r
library(echo)

# Create sample data
swim_data <- data.frame(
  location = "beach",
  temp = 72,
  stringsAsFactors = FALSE
)

# Add localization information
localized <- localize_beach(swim_data)
# Result includes: location, temp, where, english columns
```
```

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

### Complete Data Cleaning Pipeline with Delta

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/quick-start.md

Executes a comprehensive data cleaning pipeline using the delta package, from reading raw data to saving cleaned output.

```r
library(delta)

# Read raw data
raw_data <- read.csv("swim.csv")

# Step 1: Add region information
data <- localize_beach(raw_data)

# Step 2: Standardize temperatures to Celsius
data <- celsify_temp(data)

# Step 3: Generate output path
output_path <- outfile_path("swim.csv")

# Step 4: Save cleaned data
write.csv(data, output_path, row.names = FALSE)

print(paste("Cleaned data saved to:", output_path))
```

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

### Data Cleaning Pipeline with Error Handling in Delta

Source: https://github.com/hadley/r-pkgs/blob/main/_autodocs/quick-start.md

Applies a data cleaning pipeline with robust error handling using tryCatch to manage potential issues during data processing.

```r
library(delta)

# Safe version with validation
tryCatch({
  # Load data
  data <- read.csv("swim.csv")
  
  # Validate structure
  if (!is.data.frame(data)) {
    stop("Input must be a data frame")
  }
  
  # Apply transformations
  data <- localize_beach(data)
  
  if (!("english" %in% colnames(data))) {
    stop("localize_beach() failed to add region information")
  }
  
  data <- celsify_temp(data)
  
  # Save output
  output_file <- outfile_path("swim.csv")
  write.csv(data, output_file, row.names = FALSE)
  
  cat("Success! Cleaned data saved to:", output_file, "\n")
  
}, error = function(e) {
  cat("Error in cleaning pipeline:\n", as.character(e), "\n")
})
```