The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

qryflow

Overview

Execute multi-step ‘SQL’ statements using specially formatted comments that define and control execution.

qryflow lets you define multi-step SQL workflows using comment-based tags in your SQL code. These tags tell R how to execute each SQL chunk and what to name the results. This allows you to:

• Keep multiple SQL statements in the same file.

• Control how each SQL “chunk” is executed.

• Return results as named R objects.

• Extend behavior using custom tags, parsers, and handlers.

Install

You can install the released version of qryflow from CRAN with:

install.packages("qryflow")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("christian-million/qryflow")

Example

The code below demonstrates the primary use case for qryflow.

Basic Usage:

library(qryflow)

# Connection to In-Memory DB with table populated from mtcars
con <- example_db_connect(mtcars)

sql <- "
-- @exec: drop_cyl_6
DROP TABLE IF EXISTS cyl_6;

-- @exec: prep_cyl_6
CREATE TABLE cyl_6 AS
SELECT *
FROM mtcars
WHERE cyl = 6;

-- @query: df_mtcars
SELECT *
FROM mtcars;

-- @query: df_cyl_6
SELECT *
FROM cyl_6;
"

# Pass tagged SQL to `qryflow`
results <- qryflow(sql, con)

# Access the results from the chunk named `df_cyl_6`
head(results$df_cyl_6)
#>    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> 1 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> 2 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> 3 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> 4 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> 5 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
#> 6 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4

The path to a file containing SQL can also be passed:

filepath <- example_sql_path('mtcars.sql')

# Pass tagged SQL to `qryflow`
results <- qryflow(filepath, con)

# Access the results from the chunk named `df_cyl_6`
results$df_cyl_6 |>
  head()
#>    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> 1 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> 2 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> 3 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> 4 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> 5 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
#> 6 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4

Additional Learning

Consider the following vignettes for a more in depth understanding:

• Getting Started: Outlines available features, how to use qryflow, and provides an operational understanding of how it works (vignette("getting-started", package = "qryflow")).

• Advanced Usage: A look under the hood at the objects and classes that power qryflow so that you can get more out of the package (vignette("advanced-qryflow", package = "qryflow")).

• Extend qryflow: A guide to understanding how to implement custom tags, or override the built-in tags, using custom chunk parsers and handlers (vignette("extend-qryflow", package = "qryflow")).

Similar Packages

The functionality made available by qryflow exists in other packages. However, the scope and implementation of qryflow makes it distinct enough to justify a unique package.

I recommend reviewing these other packages to see which works best for your needs. If you feel this list is incomplete, please submit an issue:

• sqlhelper provides comprehensive tools for executing parameterized SQL scripts, managing database connections and configurations, supporting spatial data types, and statement-level control within SQL files.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.