library(qtkit)
library(ggplot2)
library(dplyr)
library(knitr)
library(kableExtra)

Introduction

The {qtkit} package provides several functions to help publish analysis artifacts in a standardized way:

write_kbl(): Save kable/kableExtra tables to files
write_gg(): Save ggplot2 plots to files
write_obj(): Save R objects to files

These functions handle common tasks like:

Automatic file naming based on code chunk labels
Directory creation
Multiple output formats
Consistent styling and theming

Writing Tables

Basic Table Export

Let’s create and save a simple table using write_kbl():

# Create a basic table
mtcars_table <- mtcars[1:5, 1:4] |>
  kable(format = "html") |>
  kable_styling(bootstrap_options = "striped")

# Save to file
write_kbl(
  kbl_obj = mtcars_table,
  file = "mtcars_table",
  target_dir = "artifacts/",
  device = "png"
)

Customizing Table Output

You can customize the output format and styling:

mtcars_table <- mtcars[1:5, 1:4] |>
  kable(format = "html") |>
  kable_styling(
    bootstrap_options = c("striped", "hover"),
    full_width = FALSE
  ) |>
  row_spec(0, bold = TRUE)

write_kbl(
  kbl_obj = mtcars_table,
  file = "mtcars_styled",
  target_dir = "artifacts/",
  device = "png",
  bs_theme = "flatly"
)

Writing Plots

Basic Plot Export

Save ggplot2 plots with write_gg():

# Create a basic plot
p <- ggplot(mtcars, aes(wt, mpg)) +
  geom_point() +
  theme_minimal()

# Save to file
write_gg(
  gg_obj = p,
  file = "mtcars_plot",
  target_dir = "artifacts/",
  device = "png"
)

Customizing Plot Output

Add custom themes and formatting:

p <- ggplot(mtcars, aes(wt, mpg, color = factor(cyl))) +
  geom_point(size = 3) +
  theme_minimal() +
  labs(
    title = "Car Weight vs Fuel Efficiency",
    x = "Weight (1000 lbs)",
    y = "Miles per Gallon",
    color = "Cylinders"
  )

write_gg(
  gg_obj = p,
  file = "mtcars_plot_styled",
  target_dir = "artifacts/",
  device = "png",
  width = 8,
  height = 6,
  dpi = 300
)

Writing R Objects

Saving Data Objects

Use write_obj() to save R objects for later use:

# Create a filtered dataset
mtcars_subset <- mtcars |>
  filter(cyl == 4) |>
  select(mpg, wt, hp)

# Save to file
write_obj(
  obj = mtcars_subset,
  file = "mtcars_4cyl",
  target_dir = "artifacts/"
)
#> Directory created: artifacts/

Reading Saved Artifacts

Tables and plots saved with {qtkit} can be easily restored from the directory where they have been saved (in this case artifacts/). For image formats, you can use the knitr::include_graphics() function, markdown, or HTML:

knitr::include_graphics("artifacts/mtcars_plot.png")

![mtcars_plot](artifacts/mtcars_plot.png)

<img src="artifacts/mtcars_plot.png" alt="mtcars_plot" style="display: block; margin: auto;" />

Objects can be read back using dget():

# Read the saved object
restored_data <- dget("artifacts/mtcars_4cyl")

# Check the restored object
glimpse(restored_data)
#> Rows: 11
#> Columns: 3
#> $ mpg <dbl> 22.8, 24.4, 22.8, 32.4, 30.4, 33.9, 21.5, 27.3, 26.0, 30.4, 21.4
#> $ wt  <dbl> 2.320, 3.190, 3.150, 2.200, 1.615, 1.835, 2.465, 1.935, 2.140, 1.5…
#> $ hp  <dbl> 93, 62, 95, 66, 52, 65, 97, 66, 91, 113, 109

Tip Note that tables and plots need not be saved with the write_kbl() and write_gg() functions to be restored. These objects can be saved and restored using the write_obj() and dget() function as well.

Best Practices

Use consistent naming conventions
Create organized directory structures
Document output formats and locations
Include version control for outputs when appropriate
Validate restored objects match originals

Additional Resources

Package documentation: help(package = "qtkit")
Related packages: {kableExtra}, {ggplot2}, {webshot2}
Project homepage

Publishing Analysis Artifacts