28.11 Formatting Tables Using Kable
REVIEW Including a typeset table based on a
dataset can be accomplished using knitr::kable(). Here we
will use the larger dataset from
rattle (G. Williams 2026) setting it up as a tibble::tibble()
courtesy of (Wickham, François, et al. 2026). We will then choose specific columns and a
random selection of rows to include in the table. The source text we
include in our .Rnw file is listed in the following code
block.
<<example_kable, echo=TRUE, results="asis">>=
set.seed(42)
library(rattle)
nobs <- nrow(weatherAUS)
obs <- sample(nobs, 5)
vars <- 2:6
ds <- weatherAUS[obs, vars]
kable(ds)
@The result (also showing the R code since we specified
echo=TRUE) is then:
# Set the seed so that results are repeatable.
set.seed(42)
# Load the package from the local library into the R session.
library(rattle)
# Record metadata for a sample of the dataset.
nobs <- nrow(weatherAUS)
obs <- sample(nobs, 5)
vars <- 2:6
ds <- weatherAUS[obs, vars]
# Generate the appropriate LaTeX code to display the data.
kable(ds)| Location | MinTemp | MaxTemp | Rainfall | Evaporation |
|---|---|---|---|---|
| Sydney | 13.2 | 29.5 | 0.0 | 6.4 |
| Richmond | 10.5 | 27.6 | 0.0 | NA |
| MountGinini | -4.1 | -1.0 | 4.6 | NA |
| Williamtown | 7.0 | 18.4 | NA | NA |
| Penrith | 12.1 | 18.2 | 0.0 | NA |
Since we are working with a random sample and we would like the sampling to be repeatable we have used base::set.seed() to initialise the random number generator to a fixed value.
References
Wickham, Hadley, Romain François, Lionel Henry, Kirill Müller, and Davis Vaughan. 2026. Dplyr: A Grammar of Data Manipulation. https://dplyr.tidyverse.org.
Williams, Graham. 2026. Rattle: R Data Science Supporting Rattle. https://togaware.com/projects/rattle/.
If you find this curated material useful then you can consider a donation to support it's ongoing availability and give you access to the PDF version of this book. The material has been scoped up by Generative AI without permission or any kind of recompense so do consider a donation if you can afford it. Unlike Generative AI your access to this materials is freely given. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Togaware has a 30 year tradition of making popular open source software which includes sold privacy preserving productivity apps, rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0
