suggested_dependent_pkgs <- c("dplyr") knitr::opts_chunk$set( collapse = TRUE, comment = "#>", eval = all(vapply( suggested_dependent_pkgs, requireNamespace, logical(1), quietly = TRUE )) )
knitr::opts_chunk$set(comment = "#")
We saw in the previous intermediate
portion of this tour that a well engineered library of analysis, group
summary, and split functions can combine to support a massive array of
different individual tables. Whether we are tasked with maintaining
and extending those libraries or simply with creating custom tables
outside of that supported space, we sometimes need to write new custom
functions. By the end of this portion of the tour we will have the
tools necessary to do that. While gaining those tools, we will also
become more familiar with the structure of TableTree objects (how
rtables models tables) and how to interact with them once they are
created.
Upon learning the material in this portion of the training, users will
be able to fully exploit the flexibility and power of the rtables
layout and table engines to create virtually any desired table in
cases when their existing function library falls short.
afuns With .spl_context
Creating afun/cfun behavior conditional on location within the
table structure using .spl_context and other optional arguments.afuns Within Custom afuns Details
about what in_rows returns and how we can use that to wrap or
combine existing afuns or cfunsafuns Examples of
prototypical behaviors which can be reused and combined when
writing custom afunsmake_split_fun Effectively make_split_fun and
recognizing when to specify pre, core, and post behavior
customizationsrtables and how to use and
combine themTableTree
Objects Understanding how rtables
models tables and how to interact with them after creationsort_at_pathprune_tableAny scripts or data that you put into this service are public.
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