knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
The goal of rat is to ...
You can install the released version of rat from CRAN with:
install.packages("rat")
And the development version from GitHub with:
# install.packages("devtools") devtools::install_github("katiepress/rat")
Binding two factors via fbind()
:
library(testpackage) ## basic example code a <- factor(c("character", "hits", "your", "eyeballs")) b <- factor(c("but", "integer", "where it", "counts"))
Simply catenating two factors leads to a result that most don't expect.
c(a, b)
The fbind()
function glues two factors together and returns factor.
fbind(a, b)
Often we want a table of frequencies for the levels of a factor. The base table()
function returns an object of class table
, which can be inconvenient for downstream work.
set.seed(1234) x <- factor(sample(letters[1:5], size = 100, replace = TRUE)) table(x)
The fcount()
function returns a frequency table as a tibble with a column of factor levels and another of frequencies:
fcount(x)
What is special about using README.Rmd
instead of just README.md
? You can include R chunks like so:
summary(cars)
You'll still need to render README.Rmd
regularly, to keep README.md
up-to-date. devtools::build_readme()
is handy for this. You could also use GitHub Actions to re-render README.Rmd
every time you push. An example workflow can be found here: https://github.com/r-lib/actions/tree/master/examples.
You can also embed plots, for example:
plot(pressure)
In that case, don't forget to commit and push the resulting figure files, so they display on GitHub and CRAN.
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