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)
#> [1] 1 3 4 2 1 3 4 2
The fbind()
function glues two factors together and returns factor.
fbind(a, b)
#> [1] character hits your eyeballs but integer where it
#> [8] counts
#> Levels: but character counts eyeballs hits integer where it your
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)
#> x
#> a b c d e
#> 19 19 21 22 19
The fcount()
function returns a frequency table as a tibble with a
column of factor levels and another of frequencies:
fcount(x)
#> # A tibble: 5 x 2
#> f n
#> <fct> <int>
#> 1 d 22
#> 2 c 21
#> 3 a 19
#> 4 b 19
#> 5 e 19
What is special about using README.Rmd
instead of just README.md
?
You can include R chunks like so:
summary(cars)
#> speed dist
#> Min. : 4.0 Min. : 2.00
#> 1st Qu.:12.0 1st Qu.: 26.00
#> Median :15.0 Median : 36.00
#> Mean :15.4 Mean : 42.98
#> 3rd Qu.:19.0 3rd Qu.: 56.00
#> Max. :25.0 Max. :120.00
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:
In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub and CRAN.
Add the following code to your website.
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