Jsperger LLM R Skills Tidy Evaluation

Tidy Evaluation Programming Patterns

Data masking lets you refer to data frame columns as if they were regular objects. Programming with data-masked functions requires special patterns to pass column references through your functions.

Quick Reference

What is Data Masking?

Data masking inserts a data frame at the bottom of the environment chain, giving columns precedence over user-defined variables:

# Without masking: must use $ notation
mean(mtcars$cyl + mtcars$am)

# With masking: columns are directly accessible
with(mtcars, mean(cyl + am))
dplyr::summarise(mtcars, mean(cyl + am))

Why Injection is Needed

Data-masking functions defuse their arguments. When you wrap them, you must inject the user's expression:

# Without injection: summarise sees literal "var1 + var2"
my_mean <- function(data, var1, var2) {
  dplyr::summarise(data, mean(var1 + var2))  # Error!
}

# With injection: summarise sees "cyl + am"
my_mean <- function(data, var1, var2) {
  dplyr::summarise(data, mean({{ var1 }} + {{ var2 }}))
}

Argument Behaviors

Documenting Argument Behaviors

#' @param var <[`data-masked`][dplyr::dplyr_data_masking]> Column to summarize.
#' @param cols <[`tidy-select`][dplyr::dplyr_tidy_select]> Columns to pivot.
#' @param ... <[`dynamic-dots`][rlang::dyn-dots]> Name-value pairs.

Forwarding Patterns

Single Argument with {{}}

The embrace operator forwards an argument, inheriting behavior from the wrapped function:

# Forwarding to data-masked context
my_summarise <- function(data, var) {
  data |> dplyr::summarise({{ var }})
}
mtcars |> my_summarise(mean(cyl))

# Forwarding to tidy-select context
my_pivot <- function(data, cols) {
  data |> tidyr::pivot_longer(cols = {{ cols }})
}
mtcars |> my_pivot(starts_with("c"))

Multiple Arguments with ...

Pass ... directly to data-masked functions:

my_group_by <- function(.data, ...) {
  .data |> dplyr::group_by(...)
}
mtcars |> my_group_by(cyl, am)

For tidy-select functions taking a single argument, wrap in c():

my_pivot <- function(.data, ...) {
  .data |> tidyr::pivot_longer(c(...))
}
mtcars |> my_pivot(cyl, am, vs)

Names Patterns

Use strings or character vectors instead of expressions. Your function becomes "regular" with no data-masking complications.

.data[[var]] for Single Column

my_mean <- function(data, var) {
  data |> dplyr::summarise(mean = mean(.data[[var]]))
}
my_mean(mtcars, "cyl")

# No masking surprises
am <- "cyl"
my_mean(mtcars, am)  # Uses "cyl", not masked

all_of() for Character Vectors

vars <- c("cyl", "am")
mtcars |> tidyr::pivot_longer(all_of(vars))
mtcars |> dplyr::select(all_of(vars))

Loop Pattern

vars <- c("cyl", "am", "vs")
for (var in vars) {
  result <- mtcars |>
    dplyr::summarise(mean = mean(.data[[var]]))
  print(result)
}

# Or with purrr
purrr::map(vars, ~ dplyr::summarise(mtcars, mean = mean(.data[[.x]])))

Bridge Patterns

Convert between argument behaviors when the wrapped function doesn't match your desired interface.

Selection to Data-Mask: across()

Give your function tidy-select behavior when wrapping a data-masked function:

my_group_by <- function(data, var) {
  data |> dplyr::group_by(across({{ var }}))
}
# Now supports selection helpers:
mtcars |> my_group_by(starts_with("c"))

For ..., wrap in c():

my_group_by <- function(.data, ...) {
  .data |> dplyr::group_by(across(c(...)))
}

Names to Data-Mask: across(all_of())

Accept character vectors for data-masked operations:

my_group_by <- function(data, vars) {
  data |> dplyr::group_by(across(all_of(vars)))
}
my_group_by(mtcars, c("cyl", "am"))

Data-Mask to Selection: transmute() Bridge

Three-step pattern for data-masked input to tidy-select functions:

my_pivot_longer <- function(data, ...) {
  # 1. Capture inputs in data-masked context, get names
  inputs <- dplyr::transmute(data, ...)
  names <- names(inputs)

  # 2. Update data with the expressions
  data <- dplyr::mutate(data, !!!inputs)

  # 3. Pass names to tidy-select
 tidyr::pivot_longer(data, cols = all_of(names))
}

mtcars |> my_pivot_longer(cyl, am_scaled = am * 100)

Transformation Patterns

Named Arguments: Code Around {{}}

Add code around embraced arguments:

my_mean <- function(data, var) {
  data |> dplyr::summarise(mean = mean({{ var }}, na.rm = TRUE))
}

... Arguments: Use across()

Map an expression across multiple columns:

my_mean <- function(data, ...) {
  data |> dplyr::summarise(
    across(c(...), ~ mean(.x, na.rm = TRUE))
  )
}
mtcars |> my_mean(cyl, disp, hp)

Filter with if_all() / if_any()

Combine logical conditions across columns:

filter_non_min <- function(.data, ...) {
  .data |> dplyr::filter(
    if_all(c(...), ~ .x != min(.x, na.rm = TRUE))
  )
}

filter_any_max <- function(.data, ...) {
  .data |> dplyr::filter(
    if_any(c(...), ~ .x == max(.x, na.rm = TRUE))
  )
}

Disambiguation: .data and .env Pronouns

Data masking can cause collisions when variable names exist in both the data and environment.

Column Collisions

x <- 100
df <- data.frame(x = 1:3, y = 4:6)

# Ambiguous: which x?
df |> dplyr::mutate(z = y / x)
#> Uses column x (data takes precedence)

# Explicit: use environment x
df |> dplyr::mutate(z = y / .env$x)

In Functions (Critical)

Always use .env for function parameters that might collide:

my_rescale <- function(data, var, factor = 10) {
  # Safe even if data has a 'factor' column
  data |> dplyr::mutate(
    "{{ var }}" := {{ var }} / .env$factor
  )
}

Full Disambiguation

df |> dplyr::mutate(
  result = .data$y / .env$x
)

Pitfalls

Double Evaluation

Expressions injected multiple times execute multiple times:

# BAD: times100() runs twice
summarise_stats <- function(data, var) {
  data |> dplyr::summarise(
    mean = mean({{ var }}),
    sd = sd({{ var }})
  )
}
# If var = times100(cyl), function executes twice

# GOOD: Evaluate once, reference result
summarise_stats <- function(data, var) {
  data |>
    dplyr::transmute(var = {{ var }}) |>
    dplyr::summarise(mean = mean(var), sd = sd(var))
}

Exception: Glue strings ("{{ var }}") don't suffer from double evaluation.

{{ Out of Context

Outside data-masking, {{ becomes literal double-braces and silently returns the value:

# In non-tidy-eval function:
f <- function(x) {{ x }}
f(1 + 1)
#> [1] 2  # No error, but not defuse-and-inject

!! and !!! Out of Context

Outside injection context, these become logical negation:

x <- TRUE
!!x    # Double negation: TRUE
!!!x   # Triple negation: FALSE

No error, just wrong semantics.

{{ on Non-Arguments

{{ should only wrap function arguments:

# Correct
my_fn <- function(arg) {
  summarise(data, {{ arg }})
}

# Problematic: x is not a defused argument
x <- expr(cyl)
summarise(data, {{ x }})  # Captures x's VALUE, not expression

Tidy Selection vs Data Masking

Tidy selection is not data masking. It interprets expressions rather than masking environments, so there's no ambiguity:

data <- data.frame(x = 1, data = 2)

# Tidy selection: no collision
data |> dplyr::select(data:ncol(data))
#> Works correctly

# Data masking: potential collision
data |> dplyr::mutate(y = data + 1)
#> Uses column 'data', not the data frame

See Also

• r-metaprogramming: Defusing, quosures, expression building mechanics
• designing-tidy-r-functions: Function API design principles
• rlang-conditions: Error handling with rlang

Vignettes

Access detailed rlang documentation via R:

# Data masking concepts
vignette("data-mask", package = "rlang")

# Programming with data masking
vignette("data-mask-programming", package = "rlang")

# Or browse all vignettes
browseVignettes("rlang")

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