---
title: "Getting started with stenographer"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting started with stenographer}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>"
)
```

## Introduction

The `stenographer` package provides a flexible and powerful logging system for R applications. It includes a `Stenographer` class for creating customisable loggers, as well as helper functions for debugging and error reporting. This vignette will guide you through the basics of using the `stenographer` package and demonstrate how to leverage its features to improve your R workflows.

## Installation

You can install the released version of stenographer from CRAN:

```{r, eval = FALSE}
install.packages("stenographer")
```

You can install stenographer from [www.github.com/dereckmezquita/stenographer](https://github.com/dereckmezquita/stenographer) with:

```{r, eval = FALSE}
remotes::install_github("dereckmezquita/stenographer")
```

## Basic Usage

First, let's load the package and create a basic stenographer:

```{r}
box::use(stenographer[Stenographer, LogLevel, messageParallel])

# Create a basic logger
steno <- Stenographer$new()

# Log some messages
steno$info("This is an informational message")
steno$warn("This is a warning")
steno$error("This is an error")
```

## Customising the Stenographer

You can customise the `Stenographer` by specifying the minimum log level, output file, and custom print function:

```{r}
# Create a custom stenographer
custom_steno <- Stenographer$new(
    level = LogLevel$WARNING,
    file_path = "app.log",
    print_fn = message
)

custom_steno$info("This won't be logged")
custom_steno$warn("This will be logged to console and file")
custom_steno$error("This is an error message")
```

## Logging to a Database

The `Stenographer` class supports logging to a `SQLite` database. Here's how you can set it up:

```{r}
box::use(RSQLite[ SQLite ])
box::use(DBI[ dbConnect, dbDisconnect, dbGetQuery ])

# Create a database connection
db <- dbConnect(SQLite(), "log.sqlite")

# Create a Stenographer that logs to the database
db_steno <- Stenographer$new(
    db_conn = db,
    table_name = "app_logs"
)

# Log some messages
db_steno$info("This is logged to the database")
db_steno$warn("This is a warning", data = list(code = 101))
db_steno$error("An error occurred", error = "Division by zero")

# Example of querying the logs
query <- "SELECT * FROM app_logs WHERE level = 'ERROR'"
result <- dbGetQuery(db, query)
print(result)
```

## Using Context

The `Stenographer` class supports a context feature, which allows you to add persistent information to your log entries:

```{r}
context_steno <- Stenographer$new(
    db_conn = db,
    table_name = "context_logs",
    context = list(app_name = "MyApp", version = "1.0.0")
)

context_steno$info("Application started")

# Update context
context_steno$update_context(list(user_id = "12345"))
context_steno$info("User logged in")

# Log an error with context
context_steno$error("Operation failed", data = list(operation = "data_fetch"))

# Example of querying logs with context
query <- "SELECT * FROM context_logs WHERE json_extract(context, '$.user_id') = '12345'"
result <- dbGetQuery(db, query)
print(result)

# Clear context
context_steno$clear_context()
context_steno$info("Context cleared")
```

## Combining Features

You can combine various features of the `Stenographer` class to create a powerful logging system:

```{r}
# Create a combined Stenographer
combined_steno <- Stenographer$new(
    level = LogLevel$INFO,
    file_path = "combined_app.log",
    db_conn = db,
    table_name = "combined_logs",
    context = list(app_name = "CombinedApp", version = "2.0.0"),
    print_fn = messageParallel,
    format_fn = function(level, msg) {
        # manipulate the message before logging
        msg <- gsub("API_KEY=[^\\s]+", "API_KEY=***", msg)
        return(paste(level, msg))
    }
)

# Log some messages
combined_steno$info("Application started")
combined_steno$warn("Low memory", data = list(available_mb = 100))
combined_steno$error("Database connection failed", error = "Connection timeout")

# Update context
combined_steno$update_context(list(user_id = "67890"))
combined_steno$info("User action", data = list(action = "button_click"))

# Example of a more complex query using context and data
query <- "
SELECT *
FROM combined_logs
WHERE json_extract(context, '$.app_name') = 'CombinedApp'
  AND json_extract(data, '$.available_mb') < 200
"
result <- dbGetQuery(db, query)
print(result)

# Don't forget to close the database connection when you're done
dbDisconnect(db)
```

## Using Helper Functions

The `Stenographer` package includes several helper functions that can be used in conjunction with the `Stenographer` class to provide more detailed information in your logs. Let's explore how to use these functions effectively.

### Finding and Logging Data Issues

Suppose we have a dataset with some problematic values, and we want to log where these issues occur. We can use the `valueCoordinates` function to locate the problematic values and include this information in our log messages.

```{r}
box::use(stenographer[valueCoordinates])

# Create a sample dataset with some issues
df <- data.frame(
    a = c(1, NA, 3, 4, 5),
    b = c(2, 4, NA, 8, 10),
    c = c(3, 6, 9, NA, 15)
)

# Create a Stenographer
steno <- Stenographer$new()

# Find coordinates of NA values
na_coords <- valueCoordinates(df)

if (nrow(na_coords) > 0) {
    steno$warn(
        "NA values found in the dataset",
        data = list(
            na_locations = na_coords
        )
    )
}
```

This will produce a log entry like:

### Logging Errors with Context

When an error occurs, it's often useful to catch and log not just the error message, but also the context in which the error occurred. Here's an example of how to do this using the `Stenographer` class and helper functions:

```{r}
box::use(stenographer[tableToString])

steno <- Stenographer$new()

process_data <- function(df) {
    tryCatch({
        result <- df$a / df$b
        if (any(is.infinite(result))) {
            inf_coords <- valueCoordinates(data.frame(result), Inf)
            steno$error(
                "Division by zero occurred",
                data = list(
                    infinite_values = inf_coords,
                    dataset_preview = tableToString(df)
                )
            )
            cat("Division by zero error")
        }
        return(result)
    }, error = function(e) {
        steno$error(
            paste("An error occurred while processing data:", e$message),
            data = list(dataset_preview = tableToString(df)),
            error = e
        )
        cat(e)
    })
}

# Test the function with problematic data
df <- data.frame(a = c(1, 2, 3), b = c(0, 2, 0))
process_data(df)
```

## Logging in Parallel Environments

When working with parallel processing, standard logging functions might not work as expected. The stenographer package provides a `messageParallel` function to ensure messages are properly logged from parallel processes:

```{r}
box::use(future)
box::use(future.apply[future_lapply])

steno <- Stenographer$new(print_fn = messageParallel)

future::plan(future::multisession, workers = 2)

result <- future_lapply(1:5, function(i) {
    messageParallel(sprintf("Processing item %d", i))
    if (i == 3) {
        steno$warn(sprintf("Warning for item %d", i))
    }
    return(i * 2)
})

future::plan(future::sequential)
```

This ensures that messages from parallel processes are properly captured and logged.

## Conclusion

The stenographer package provides a robust and flexible logging system for R applications. With features like file logging, database logging, and context management, you can create informative and context-rich log messages that greatly aid in debugging and monitoring your R scripts and applications.

Moreover, by using helper functions like `valueCoordinates` and `tableToString` you can more easily track down and log data issues and errors, providing valuable information for troubleshooting and analysis.

Remember to adjust the log level, output file, database settings, and other parameters to suit your specific needs. The ability to query logs using SQL, especially with context-based filtering, makes it easy to analyze and troubleshoot issues in your applications.
