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Quick start: substitute for NSE

Francois Collin

29.10.2020

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 5, and the substitute(a) returns the code to obtain the value: a.

This is the principle teal relies on to:

  1. generate expressions.
  2. return the result of the expression in the result panel of the app.
  3. 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:

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
## a + b
eval(non_evaluated_expression)
## Error in eval(non_evaluated_expression): object 'b' not found

What happened?

non_evaluated_expression <- substitute(expr = a + b)
a <- 1
b <- 5
eval(non_evaluated_expression)
## [1] 6

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
## 5 + -2
eval(non_evaluated_expression)
## [1] 3

What happened?

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
## 5 + 5
eval(non_evaluated_expression)
## [1] 10

What happened?

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
## plot(x = 0:10, y = exp(0:10), main = "A graph")
eval(non_evaluated_expression)

Note that:

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 in eval(expr, envir, enclos): object 'Sepal.Length' not found
# 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
## plot("Sepal.Width" ~ "Sepal.Length", data = iris, main = "Iris, again ...")
eval(plot_expr)
## Error in terms.formula(formula, data = data): invalid term in model formula

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
## plot(Sepal.Width ~ Sepal.Length, data = iris, main = "Iris, again ...")
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)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

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
## list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4), Sepal.Width = c(3.5, 
## 3, 3.2, 3.1, 3.6, 3.9), Petal.Length = c(1.4, 1.4, 1.3, 1.5, 
## 1.4, 1.7), Petal.Width = c(0.2, 0.2, 0.2, 0.2, 0.2, 0.4), Species = c(1L, 
## 1L, 1L, 1L, 1L, 1L)) %>% plot(Sepal.Width ~ Sepal.Length, data = ., 
##     main = "Iris, again ...")

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
## iris %>% plot(Sepal.Width ~ Sepal.Length, data = ., main = "Iris, again ...")
eval(plot_expr)

In a nutshell

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)
##                        A: Drug X                    B: Placebo        
##                   Value         Change         Value         Change   
## ——————————————————————————————————————————————————————————————————————
## WEEK 1 DAY 8                                                          
##   n                69             69            73             73     
##   Mean (SD)    20.8 (4.1)     1.6 (6.1)     20.2 (4.1)     -0.2 (5.6) 
##   Median          20.4           2.4           20.0           -0.2    
##   Min - Max    12.8 - 34.6   -11.3 - 14.2   12.6 - 29.0   -12.8 - 10.8

… but not easily readable …:

rtables_expr
## basic_table() %>% split_cols_by("ARM", split_fun = drop_split_levels) %>% 
##     split_rows_by("AVISIT", split_fun = drop_split_levels) %>% 
##     split_cols_by_multivar(vars = c("AVAL", "CHG"), varlabels = c("Value", 
##         "Change")) %>% summarize_colvars() %>% build_table(df = adlb_f)

… 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)
## basic_table() %>%
##   split_cols_by("ARM", split_fun = drop_split_levels) %>%
##   split_rows_by("AVISIT", split_fun = drop_split_levels) %>%
##   split_cols_by_multivar(vars = c("AVAL", "CHG"), varlabels = c(
##     "Value",
##     "Change"
##   )) %>%
##   summarize_colvars() %>%
##   build_table(df = adlb_f)

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)
## basic_table() %>%
##   split_cols_by("ARM", split_fun = drop_split_levels) %>%
##   split_rows_by("AVISIT", split_fun = drop_split_levels) %>%
##   split_cols_by_multivar(vars = c("AVAL", "CHG"), varlabels = c(
##     "Value",
##     "Change"
##   )) %>%
##   summarize_colvars() %>%
##   build_table(df = adlb_f)
eval(result)
##                        A: Drug X                    B: Placebo        
##                   Value         Change         Value         Change   
## ——————————————————————————————————————————————————————————————————————
## WEEK 1 DAY 8                                                          
##   n                69             69            73             73     
##   Mean (SD)    20.8 (4.1)     1.6 (6.1)     20.2 (4.1)     -0.2 (5.6) 
##   Median          20.4           2.4           20.0           -0.2    
##   Min - Max    12.8 - 34.6   -11.3 - 14.2   12.6 - 29.0   -12.8 - 10.8
result <- rtables_expr(df = adlb_f, arm = "ARMCD", visit = "AVISITN")
eval(result)
## Split var [AVISITN] was not character or factor. Converting to factor
##                         ARM A                        ARM B           
##                  Value         Change         Value         Change   
## —————————————————————————————————————————————————————————————————————
## 1                                                                    
##   n               69             69            73             73     
##   Mean (SD)   20.8 (4.1)     1.6 (6.1)     20.2 (4.1)     -0.2 (5.6) 
##   Median         20.4           2.4           20.0           -0.2    
##   Min - Max   12.8 - 34.6   -11.3 - 14.2   12.6 - 29.0   -12.8 - 10.8
styled_expr(result)
## basic_table() %>%
##   split_cols_by("ARMCD", split_fun = drop_split_levels) %>%
##   split_rows_by("AVISITN", split_fun = drop_split_levels) %>%
##   split_cols_by_multivar(vars = c("AVAL", "CHG"), varlabels = c(
##     "Value",
##     "Change"
##   )) %>%
##   summarize_colvars() %>%
##   build_table(df = adlb_f)

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
## df %>% head
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)
}
result <- rtables_expr(df = adlb_f, arm = "ARM", visit = "AVISIT")
styled_expr(result)
## basic_table() %>%
##   split_cols_by("ARM", split_fun = drop_split_levels) %>%
##   split_rows_by("AVISIT", split_fun = drop_split_levels) %>%
##   split_cols_by_multivar(vars = c("AVAL", "CHG"), varlabels = c(
##     "Value",
##     "Change"
##   )) %>%
##   summarize_colvars() %>%
##   build_table(df = adlb_f)
eval(result)
##                        A: Drug X                    B: Placebo        
##                   Value         Change         Value         Change   
## ——————————————————————————————————————————————————————————————————————
## WEEK 1 DAY 8                                                          
##   n                69             69            73             73     
##   Mean (SD)    20.8 (4.1)     1.6 (6.1)     20.2 (4.1)     -0.2 (5.6) 
##   Median          20.4           2.4           20.0           -0.2    
##   Min - Max    12.8 - 34.6   -11.3 - 14.2   12.6 - 29.0   -12.8 - 10.8
result <- rtables_expr(
  df = adlb_f, arm = "ARM", visit = "AVISIT",
  .stats = c("n", "mean_sd")
)
styled_expr(result)
## basic_table() %>%
##   split_cols_by("ARM", split_fun = drop_split_levels) %>%
##   split_rows_by("AVISIT", split_fun = drop_split_levels) %>%
##   split_cols_by_multivar(vars = c("AVAL", "CHG"), varlabels = c(
##     "Value",
##     "Change"
##   )) %>%
##   summarize_colvars(.stats = c("n", "mean_sd")) %>%
##   build_table(df = adlb_f)
eval(result)
##                      A: Drug X                B: Placebo       
##                  Value       Change       Value        Change  
## ———————————————————————————————————————————————————————————————
## WEEK 1 DAY 8                                                   
##   n                69          69           73           73    
##   Mean (SD)    20.8 (4.1)   1.6 (6.1)   20.2 (4.1)   -0.2 (5.6)

Including pre-processing

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

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$data)
## adlb <- adlb %>% filter(PARAMCD == "CRP" & ARMCD %in% c(
##   "ARM A",
##   "ARM B"
## ) & AVISIT == "WEEK 1 DAY 8")
styled_expr(result$rtables)
## basic_table() %>%
##   split_cols_by("ARM", split_fun = drop_split_levels) %>%
##   split_rows_by("AVISIT", split_fun = drop_split_levels) %>%
##   split_cols_by_multivar(vars = c("AVAL", "CHG"), varlabels = c(
##     "Value",
##     "Change"
##   )) %>%
##   summarize_colvars(.stats = c("n", "mean_sd")) %>%
##   build_table(df = adlb)

The two expressions can be executed and return the rtables:

result_exec <- mapply(eval, result)
result_exec$rtables
##                      A: Drug X              B: Placebo      
##                  Value      Change       Value      Change  
## ————————————————————————————————————————————————————————————
## WEEK 1 DAY 8                                                
##   n               69          69          73          73    
##   Mean (SD)    1.0 (0.2)   0.0 (0.3)   1.0 (0.2)   0.0 (0.3)

In a nutshell

At this point, it is then possible to:

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