Quick start: `substitute` for NSE

In teal.modules.clinical: 'teal' Modules for Standard Clinical Outputs

knitr::opts_chunk$set(
  purl = TRUE,
  collapse = TRUE,
  dev = "svg",
  fig.ext = "svg"
)

Introduction

Considering an expression, R usually evaluates it and returns its value. Instead of focusing on the value, it is also possible to work with the code which generated the value. This is where non standard evaluation, or NSE, starts. The function substitute is an important element of non-standard evaluation. For instance, if we consider a defined as a <- 5, then the expression a returns r a <- 5; a, and the substitute(a) returns the code to obtain the value: a.

This is the principle teal relies on to:

i. generate expressions. ii. return the result of the expression in the result panel of the app. iii. return the corresponding code (or expression) with Show R Code.

The expression returning the displayed value must be reactive. The information in the encoding on one hand, and the filtering panel on the other hand modify the expression and the displayed value. As such, teal needs to work both on expressions and values and relies heavily on NSE.

The NSE is an advanced notion and mixing it with Shiny app development is a source of difficulties such as:

• hindered coding efficiency as the Shiny app must be run in order to check the correct execution of the code.
• limited possibilities for testing.

As an alternative, it is possible to focus first on the NSE aspects in plain R, and only once ready, integrate it in the Shiny App. The following are a few practical examples demonstrating how NSE works. The choice was made to focus on substitute.

The Basics

NSE Principle

non_evaluated_expression <- substitute(expr = a + b)
non_evaluated_expression
eval(non_evaluated_expression)

What happened?

• substitute returns the code and not the value,
• it does not attempt to run the code, therefore it is possible to return an expression which does not make sense (yet), for instance involving two non defined objects.
• If the values of a and b exist, the expression can run without error:

non_evaluated_expression <- substitute(expr = a + b)
a <- 1
b <- 5
eval(non_evaluated_expression)

Now, the function name substitute is for a reason. Not only returning the expression, it also operates substitutions of some terms within a given expression.

fun <- function(a, b) {
  substitute(expr = a + b)
}
non_evaluated_expression <- fun(5, -2)
non_evaluated_expression
eval(non_evaluated_expression)

What happened?

• the objects a and b exist in the function environment where substitute is called.
• the terms of the expression within substitute were replaced by the values of a and b.

Indeed, before returning the expression, substitute verifies if a and b don't have any value existing in the evaluation environment. If so, values of a and b are used in the expression.

It is also possible to use the second argument of substitute, env, an environment (or a list) containing objects. If the expression submitted in substitute has corresponding objects in env, the terms within the expression will be substituted with provided values:

non_evaluated_expression <- substitute(
  expr = a + b,
  env = list(a = 5, b = 5)
)
non_evaluated_expression
eval(non_evaluated_expression)

What happened?

• The environment in which the values of a and b were taken from was directly declared within the substitute expression (argument expr) and the values were substituted (argument env).
• substitute returned a non-evaluated expression, use eval() to evaluate it.

With a slightly more elaborate expression:

non_evaluated_expression <- substitute(
  expr = plot(x = x, y = exp(x), main = text),
  env = list(x = 0:10, text = "A graph")
)
non_evaluated_expression
eval(non_evaluated_expression)

Note that:

• x as an argument name in plot has been preserved, while x as an object has been replaced.

Replace an object name

In formulas, character strings are not accepted, how do we execute the substitution?

# Error expected:
plot_expr <- substitute(
  expr = plot(y ~ x, data = iris, main = text),
  env = list(
    x = Sepal.Length,
    y = Sepal.Width,
    text = "Iris, again ..."
  )
)

# Error expected:
plot_expr <- substitute(
  expr = plot(y ~ x, data = iris, main = text),
  env = list(
    x = "Sepal.Length",
    y = "Sepal.Width",
    text = "Iris, again ..."
  )
)
plot_expr
eval(plot_expr)

The object names have a specific class (name); as.names coerces a character string to an object name (alternatively, as.symbol provides an identical result):

plot_expr <- substitute(
  expr = plot(y ~ x, data = iris, main = text),
  env = list(
    x = as.name("Sepal.Length"),
    y = as.symbol("Sepal.Width"),
    text = "Iris, again ..."
  )
)
plot_expr
eval(plot_expr)

What about dataframe names?

Lets imagine a pipe-flavored expression, with df being the term corresponding to the dataframe which should be substituted: df %>% plot(y ~ x, data = ., main = text).

The principle exposed above can work directly without addition. However, df in the expression is then replaced directly by the value of the object provided and not the expression generating the dataframe: the pipeline is working but not humanly readable.

library(dplyr)

short_iris <- head(iris)
plot_expr <- substitute(
  expr = df %>% plot(y ~ x, data = ., main = text),
  env = list(
    df = short_iris,
    x = as.name("Sepal.Length"),
    y = as.symbol("Sepal.Width"),
    text = "Iris, again ..."
  )
)
eval(plot_expr)
plot_expr

How can we replace the value by the expression generating this value?

That is pretty much the topic of the vignette: substitute.

plot_expr <- substitute(
  expr = df %>% plot(y ~ x, data = ., main = text),
  env = list(
    df = substitute(iris),
    x = as.name("Sepal.Length"),
    y = as.symbol("Sepal.Width"),
    text = "Iris, again ..."
  )
)
plot_expr
eval(plot_expr)

In a nutshell

• substitute is relevant when the expression needs to be modified. It takes 2 arguments:
  • expr the expression to be (eventually) substituted.
  • env the environment in which potential replacement value might be needed.
• If the replacement value should be slightly more special like:
  • an object name (like in formulas e.g. y ~ x) then, use as.name or as.symbol.
  • a data frame name (like iris) then, use substitute.

rtables

Direct use of substitute

The substitute approach can be used with the rtables pipelines.

Lets prepare an example for reporting data from the LB domain. The example is based on the template LBT01; the target is to report in columns the lab test result per study arm, as values (AVAL) and changes from baseline (CHG), per analysis visit in rows.

The data can be prepared as follows:

library(teal.modules.clinical)
library(dplyr)

adlb <- tmc_ex_adlb
adlb_f <- adlb %>%
  filter(
    PARAM == "Alanine Aminotransferase Measurement" &
      ARMCD %in% c("ARM A", "ARM B") & AVISIT == "WEEK 1 DAY 8"
  )

And the rtables expression is obtained as:

rtables_expr <- substitute(
  expr = basic_table() %>%
    split_cols_by(arm, split_fun = drop_split_levels) %>%
    split_rows_by(visit, split_fun = drop_split_levels) %>%
    split_cols_by_multivar(
      vars = c("AVAL", "CHG"),
      varlabels = c("Value", "Change")
    ) %>%
    summarize_colvars() %>%
    build_table(df = df),
  env = list(
    df = substitute(adlb_f),
    arm = "ARM",
    visit = "AVISIT"
  )
)

The expression is valid ... :

eval(rtables_expr)

... but not easily readable ...:

rtables_expr

... but that can be arranged:

library(teal)
library(styler)

#' Stylish code
#'
#' Deparse an expression and display the code following NEST conventions.
#'
#' @param expr (`call`)\cr or possibly understood as so.
#'
styled_expr <- function(expr) {
  print(
    styler::style_text(text = deparse(expr)),
    colored = FALSE
  )
}
#'
#' @examples
styled_expr(rtables_expr)

substitute in a function

Moving further, substitute can actually be wrapped in a function, this way the rtables pipelines are programmatically obtained:

rtables_expr <- function(df,
                         arm,
                         visit) {
  substitute(
    expr = basic_table() %>%
      split_cols_by(arm, split_fun = drop_split_levels) %>%
      split_rows_by(visit, split_fun = drop_split_levels) %>%
      split_cols_by_multivar(
        vars = c("AVAL", "CHG"),
        varlabels = c("Value", "Change")
      ) %>%
      summarize_colvars() %>%
      build_table(df = df),
    env = list(
      df = substitute(df),
      arm = arm,
      visit = visit
    )
  )
}
result <- rtables_expr(df = adlb_f, arm = "ARM", visit = "AVISIT")
styled_expr(result)
eval(result)

• The same results as before are obtained ...
• while, fine tuning is easier.
• For instance, the variable designating the study arm and the visit can be changed, which is an expected feature in teal module encoding panel.

result <- rtables_expr(df = adlb_f, arm = "ARMCD", visit = "AVISITN")
eval(result)
styled_expr(result)

Chain expressions in a pipeline

It is also possible to manipulate expressions, for instance, expressions might be chained in a pipeline.

#' Expressions as a pipeline
#'
#' Accepts expressions to be chained using the `magrittr` pipeline-flavor.
#' @param ... (`call`)\cr or object which can be interpreted as so.
#'    (e.g. `name`)
#'
pipe_expr <- function(...) {
  exprs <- unlist(list(...))
  exprs <- lapply(
    exprs,
    function(x) {
      x <- deparse(x)
      paste(x, collapse = " ")
    }
  )
  exprs <- unlist(exprs)
  exprs <- paste(exprs, collapse = " %>% ")
  str2lang(exprs)
}

#' @examples
result <- pipe_expr(
  expr1 = substitute(df),
  expr2 = substitute(head)
)
result

• Expressions can be arranged in a list, this way, it is possible to have conditional editing of expressions.
• In the context of rtables, layers enclosing analyze call handle .stats option. The lean expression should include the .stats option, only when the default value is changed.
• This is again an expected feature in teal module when rendering the code with Show R Code:

rtables_expr <- function(df,
                         arm,
                         visit,
                         .stats = NULL) {
  # The rtables layout is decomposed into a list of expressions.
  lyt <- list()
  # 1. First the columns and rows:
  lyt$structure <- substitute(
    expr = basic_table() %>%
      split_cols_by(arm, split_fun = drop_split_levels) %>%
      split_rows_by(visit, split_fun = drop_split_levels) %>%
      split_cols_by_multivar(
        vars = c("AVAL", "CHG"),
        varlabels = c("Value", "Change")
      ),
    env = list(
      arm = arm,
      visit = visit
    )
  )
  # 2. The analyze layer which depends on the use of .stats.
  lyt$analyze <- if (is.null(.stats)) {
    substitute(
      summarize_colvars()
    )
  } else {
    substitute(
      summarize_colvars(.stats = .stats),
      list(.stats = .stats)
    )
  }
  # 3. And finishing with rtables::build_table.
  lyt$build <- substitute(
    build_table(df = df),
    list(df = substitute(df))
  )
  # As previously demonstrated, expressions can be manipulated and
  # chained in a pipeline.
  pipe_expr(lyt)
}

• First application with standard statistics:

result <- rtables_expr(df = adlb_f, arm = "ARM", visit = "AVISIT")
styled_expr(result)
eval(result)

• Then with statistics specifications:

result <- rtables_expr(
  df = adlb_f, arm = "ARM", visit = "AVISIT",
  .stats = c("n", "mean_sd")
)
styled_expr(result)
eval(result)

Including pre-processing

Finally, it would also be possible to wrap several expressions into a single function.

• For instance, the teal module generally includes a pre-processing section:

rtables_expr <- function(df,
                         paramcd,
                         arm,
                         visit,
                         .stats = NULL) {
  # y is a list which will collect two expressions:
  # 1. y$data with the preprocessing steps.
  # 2. y$rtables the table layout and build.
  y <- list()
  # 1. Preprocessing ---
  y$data <- substitute(
    df <- df %>%
      filter(
        PARAMCD == paramcd &
          ARMCD %in% c("ARM A", "ARM B") & AVISIT == "WEEK 1 DAY 8"
      ),
    list(
      df = substitute(df),
      paramcd = paramcd
    )
  )
  # 2. rtables layout ---
  lyt <- list()
  lyt$structure <- substitute(
    expr = basic_table() %>%
      split_cols_by(arm, split_fun = drop_split_levels) %>%
      split_rows_by(visit, split_fun = drop_split_levels) %>%
      split_cols_by_multivar(
        vars = c("AVAL", "CHG"),
        varlabels = c("Value", "Change")
      ),
    env = list(
      arm = arm,
      visit = visit
    )
  )
  lyt$analyze <- if (is.null(.stats)) {
    substitute(
      summarize_colvars()
    )
  } else {
    substitute(
      summarize_colvars(.stats = .stats),
      list(.stats = .stats)
    )
  }
  lyt$build <- substitute(
    build_table(df = df),
    list(df = substitute(df))
  )
  y$rtables <- pipe_expr(lyt)
  # Finally returns y as a list with two expressions.
  y
}

It is now possible to modify the studied parameter (PARAMCD) in addition to the study arm and visit variables names.

adlb <- tmc_ex_adlb
result <- rtables_expr(
  df = adlb, paramcd = "CRP", arm = "ARM", visit = "AVISIT",
  .stats = c("n", "mean_sd")
)

The two expressions are consistent:

styled_expr(result$data)
styled_expr(result$rtables)

The two expressions can be executed and return the rtables:

result_exec <- mapply(eval, result)
result_exec$rtables

In a nutshell

At this point, it is then possible to:

• generate rtables pipelines.
• chain expressions in a pipeline (e.g. pipe_expr)
• decompose a rtables pipeline to add conditional layers (e.g. .stats).
• group expressions into a single list and control both pre-processing and rtables pipeline.

teal.modules.clinical documentation built on April 4, 2025, 12:35 a.m.