Awesome
The arsenal
Package <a href="https://mayoverse.github.io/arsenal/"><img src="man/figures/logo.png" alt="Arsenal logo" style="float:right;height:232.25px" align="right" height="232.25"></a>
<!-- badges: start -->
<!-- badges: end -->
Overview
The goal of library(arsenal)
is to make statistical reporting easy. It includes many functions which the useR will find useful to have
in his/her "arsenal" of functions. There are, at this time, 6 main functions, documented below. Each of these functions is
motivated by a local SAS macro or procedure of similar functionality.
Note that arsenal
v3.0.0 is not backwards compatible with previous versions (mainly because compare()
got renamed to comparedf()
).
See the NEWS
file for more details.
arsenal
now has a pkgdown
site: https://mayoverse.github.io/arsenal/
The tableby()
Function
tableby()
is a function to easily summarize a set of independent variables by one or more categorical variables.
Optionally, an appropriate test is performed to test the distribution of the independent variables across
the levels of the categorical variable. Options for this function are easily controlled using tableby.control()
.
The tableby()
output is easily knitted in an Rmarkdown document or displayed in the command line using the summary()
function.
Other S3 methods are implemented for objects from tableby()
, including print()
, [
, as.data.frame()
, sort()
, merge()
, padjust()
,
head()
, and tail()
.
The paired()
Function
paired()
is a function to easily summarize a set of independent variables across two time points.
Optionally, an appropriate test is performed to test the distribution of the independent variables across
the time points. Options for this function are easily controlled using paired.control()
.
The paired()
output is easily knitted in an Rmarkdown document or displayed in the command line using the summary()
function.
It has the same S3 methods as tableby()
, since it's a special case of the tableby()
object.
The modelsum()
Function
modelsum()
is a function to fit and summarize models for each independent variable with one or more response variables,
with options to adjust for covariates for each model. Options for this function are easily controlled using modelsum.control()
.
The modelsum
output is easily knitted in an Rmarkdown document or displayed in the command line using the summary()
function.
Other S3 methods are implemented for objects from modelsum()
, including print()
, [
, as.data.frame()
, and merge()
.
The freqlist()
Function
freqlist()
is a function to approximate the output from SAS's PROC FREQ
procedure when using the /list
option of the TABLE
statement.
Options for this function are easily controlled using freq.control()
.
The freqlist()
output is easily knitted in an Rmarkdown document or displayed in the command line using the summary()
function.
Other S3 methods are implemented for objects from freqlist()
, including print()
, [
, as.data.frame()
, sort()
, and merge()
.
Additionally, the summary()
output can be used with head()
or tail()
.
The comparedf()
Function
comparedf()
compares two data.frames and reporting any differences between them,
much like SAS's PROC COMPARE
procedure.
The comparedf()
output is easily knitted in an Rmarkdown document or displayed in the command line using the summary()
function.
Other S3 methods are implemented for objects of class "comparedf"
, including print()
, n.diffs()
, n.diff.obs()
, and diffs()
.
The write2*()
Family of Functions
write2word()
, write2pdf()
, and write2html()
are functions to output a table into a document, much like SAS's ODS
procedure.
The S3 method behind them is write2()
. There are methods implemented for tableby()
, modelsum()
, freqlist()
, and comparedf()
, and
also methods for knitr::kable()
, xtable::xtable()
, and pander::pander_return()
. Another option is to coerce an object using
verbatim()
to print out the results monospaced (as if they were in the terminal)--the default method does this automatically.
To output multiple tables into a document, simply make a list of them and call the same function as before. A YAML
header can be added using yaml()
. Code chunks can be written using code.chunk()
.
For more information, see vignette("write2")
.
Other Notable Functions
-
keep.labels()
keeps the'label'
attribute on an R object when subsetting.loosen.labels()
allows the labels to drop again. -
formulize()
is a shortcut to collapse variable names into a formula. -
mdy.Date()
andDate.mdy()
convert numeric dates for month, day, and year to Date object, and vice versa. -
is.Date
: tests if an object is a date. -
%nin%
tests for "not in", the negation of%in%
. -
allNA()
tests for all elements being NA, andincludeNA()
makes NAs explicit values.