Function Reference

Function	Description
%within%	Test interval membership
abs	Absolute value
acos	Inverse cosine
acosh	Inverse hyperbolic cosine
add_diagnostics	Augment Data with Model Calculations
all_of	Select an explicit set of columns
am	Check whether a time is before noon
anova	Analysis of Variance (ANOVA)
anti_join	Filter rows lacking matches
any_of	Select columns that exist
apropos	Search for functions by keyword
args	Get function arguments and their types
arrange	Arrange rows
arrange_node	Arrange Pipeline Nodes
asin	Inverse sine
asinh	Inverse hyperbolic sine
assert	Assert Condition
assert_dir_exists	Assert Directory Exists
assert_file_exists	Assert File Exists
assert_non_empty_file	Assert File Is Non-Empty
assert_size_of_file	Assert File Size
atan	Inverse tangent
atan2	Two-argument arctangent
atanh	Inverse hyperbolic tangent
bind_cols	Combine DataFrames by columns
bind_rows	Stack DataFrames by rows
body	Get function body
build_log	Retrieve Build Log for Pipeline
build_log_history	Retrieve Build Log History for Pipeline
build_log_to_frame	Tabulate Build Log as DataFrame
build_pipeline	Build Pipeline
build_pipeline_internal	Build Pipeline Internally
case_when	Vectorized Case-When
cat	Print values without escaping
cbind	Column bind matrices
ceiling	Ceiling function
ceiling_date	Round dates up
chain	Chain Two Pipelines
char_at	Get character at index
check	Inline assertion wrapper
clean_colnames	Clean DataFrame Column Names
coef	Model Coefficients
col_lens	Create a Column Lens
collect_exceptions	Gather Pipeline Node Exceptions and Warnings
colnames	Get column names
compare	Compare Models
compare_native_vs_pmml_scores	Compare native T scoring vs JPMML scoring
complete	Complete a data frame
compose	Compose Lenses
conf_int	Confidence Intervals for Model Coefficients
contains	Check if string contains substring
cor	Correlation
cos	Cosine
cosh	Hyperbolic cosine
count	Count rows by group
cov	Covariance
cross_pattern	Cross pattern stub
crossing	Create a data frame from all combinations of inputs
cumall	Cumulative All
cumany	Cumulative Any
cume_dist	Cumulative Distribution
cummax	Cumulative Maximum
cummean	Cumulative Mean
cummin	Cumulative Minimum
cumsum	Cumulative Sum
cut	Discretize numeric vector
cv	Coefficient of variation
day	Extract the day of month
days_in_month	Get the number of days in a month
debug_node	Interactively Debug a Pipeline Node
dense_rank	Dense Rank
deserialize	Deserialize Value
deviance	Model Deviance
df_residual	Residual Degrees of Freedom
diag	Create or extract diagonal
diff_summary	Summarize Output Changes Across Builds
difference	Subtract one pipeline from another
dir_exists	Check if directory exists
dispersion	Dispersion Parameter
distinct	Keep unique rows
downstream_of	Extract Downstream Subgraph
drop_na	Remove rows with missing values
ends_with	Check if string ends with suffix
enquo	Capture a function argument’s expression (non-standard evaluation)
enquos	Capture variadic argument expressions (non-standard evaluation)
env	Get environment variable
error	Raise Error
error_chain	Chain errors to preserve provenance
error_code	Get error code
error_context	Get error context
error_msg	Get error message
errored_nodes	Get Errored Pipeline Nodes
eval	Evaluate a quoted expression or quosure
everything	Select every column
exit	Exit the interpreter
exp	Exponential function
expand	Create all combinations of values
expand_pipeline	Expand pattern-based branching in a pipeline.
expect_between	Closed range numerical bounds assertion
expect_colnames	DataFrame column names assertion
expect_column_types	DataFrame column types assertion
expect_computed	Computed node assertion
expect_dependency	Node dependency assertion
expect_deserializer	Node deserializer assertion
expect_empty	Empty container / string assertion
expect_equal	Compare two values for testing
expect_error	Error assertion with optional class and message filtering
expect_fail	Check whether an Expect value failed
expect_false	Strict boolean false assertion
expect_falsy	Loose falsiness assertion
expect_fields	Dict key / named List label assertion
expect_gt	Numeric greater-than assertion
expect_gte	Numeric greater-than-or-equal assertion
expect_has_colnames	DataFrame / Dict subset column names assertion
expect_has_pattern	Node dynamic branching pattern assertion
expect_in	Set membership assertion
expect_length	Container length assertion
expect_lt	Numeric less-than assertion
expect_lte	Numeric less-than-or-equal assertion
expect_match	Regex string match assertion
expect_msg	Get the diagnostic message from a failing Expect value
expect_ncol	DataFrame column count assertion
expect_no_na	Absence of NA values assertion
expect_nodes	Pipeline nodes assertion
expect_noop	Node noop assertion
expect_nrow	DataFrame row count assertion
expect_pass	Check whether an Expect value passed
expect_pipeline	Pipeline assertion
expect_range	DataFrame numeric column closed range bounds assertion
expect_runtime	Node runtime assertion
expect_serializer	Node serializer assertion
expect_set_equal	Order-independent set equality assertion
expect_str_contains	Substring search assertion
expect_summary	Expectation test suite summary report
expect_table_equal	DataFrame table equality assertion
expect_true	Strict boolean true assertion
expect_truthy	Loose truthiness assertion
expect_type	Type name assertion
expect_unique	Element uniqueness assertion
expect_values	DataFrame column allowed values assertion
expect_warning	Assert that a pipeline node produced a warning diagnostic
explain	Explain Value
explain_json	Explain Value as JSON
export_artifacts	Export Pipeline Artifacts
fct_c	Concatenate factor vectors
fct_collapse	Collapse multiple levels
fct_drop	Drop unused factor levels
fct_expand	Add explicit factor levels
fct_infreq	Order factor levels by frequency
fct_lump_min	Lump factor levels below a minimum count
fct_lump_n	Keep the most frequent factor levels
fct_lump_prop	Lump factor levels below a minimum proportion
fct_other	Replace unlisted levels with Other
fct_recode	Rename factor levels
fct_relevel	Move selected levels to the front
fct_reorder	Order factor levels by another vector
fct_rev	Reverse factor levels
fetchurl	Fetch a URL
file_exists	Check if file exists
fill	Fill missing values
filter	Filter rows
filter_lens	Filter Lens
filter_node	Filter Pipeline Nodes
fit_stats	Model Goodness-of-Fit Statistics
fivenum	Five-number summary
float_seq	Generate a sequence of evenly-spaced floats
floor	Floor function
floor_date	Round dates down
force_tz	Retag a datetime with a timezone
format_date	Format dates as strings
format_datetime	Format datetimes as strings
full_join	Join all rows from both tables
get	Get Value via Lens
getwd	Get current working directory
glimpse	Glimpse DataFrame
greet	Greet someone
group_by	Group by columns
head	Get the first n rows/items
head_pattern	Head pattern stub
help	Display documentation for a function
hour	Extract the hour
huber_loss	Huber loss
identical	Deep Equality Check
idx_lens	Index Lens
ifelse	Vectorized If-Else
import_artifacts	Import Pipeline Artifacts
index_of	Find index of substring
inner_join	Join matching rows
inspect_artifacts	Inspect Artifact Archive
inspect_log	Inspect Pipeline Logs (Dynamic)
inspect_node	Inspect Pipeline Node Metadata
inspect_pipeline	Inspect Pipeline Schema (Static)
intent_fields	Get All Intent Fields
intent_get	Get Intent Field
intersect	Keep shared pipeline nodes
interval	Create an interval
inv	Matrix inverse
iota	Create a vector of ones
iqr	Interquartile range
is_character	Check for character columns
is_empty	Check if string is empty
is_error	Check if a value is an Error
is_factor	Check for to_factor columns
is_leap_year	Check for leap years
is_logical	Check for logical columns
is_na	Check for NA
is_numeric	Check for numeric columns
isoweek	Extract the ISO week number
isoyear	Extract the ISO week-based year
jln	Configure a Julia Pipeline Node
kron	Kronecker product
kurtosis	Excess kurtosis
lag	Lag values
last_index_of	Find last index of substring
lead	Lead values
left_join	Join rows from the left table
length	Get length
lens	Lens Library
levels	Get factor levels
list_files	List files in directory
list_logs	List Pipeline Logs
lm	Linear Model
log	Natural logarithm
mad	Median absolute deviation
make_date	Construct a Date value
make_datetime	Construct a Datetime value
make_period	Create a period value
map	Map a function over a list
map_pattern	Map pattern stub
matches	Match columns by regex
matmul	Matrix multiplication
max	Maximum value
mean	Compute arithmetic mean of numeric values
median	Median
meta_flatten	Flatten MetaPipeline into Standard Pipeline
min	Minimum value
min_rank	Minimum Rank
minute	Extract the minute
mode	Mode
modify	Multiple Lens Transformations
month	Extract or label the month
mutate	Mutate DataFrame
mutate_node	Mutate Pipeline Node Metadata
n	Group size aggregation
n_distinct	Count distinct values
na	Generic NA
na_bool	Boolean NA
na_float	Float NA
na_int	Integer NA
na_string	String NA
ncol	Number of columns
ndarray	Create an N-dimensional array
ndarray_data	Get NDArray data
nest	Nest columns into sub-dataframes
nesting	Helper to find combinations present in data
nobs	Number of Observations
node	Configure a Pipeline Node
node_diff	Compare Node Outputs Across Builds
node_fork	Static pipeline multi-way branch
node_lens	Pipeline Node Lens
node_meta_lens	Pipeline Metadata Lens
node_when	Static pipeline node conditional
normalize	Normalize values
now	Get the current datetime
nrow	Number of rows
ntile	N-tiles
ordered	Create ordered factors
over	Transform Focused Value
package_info	Get package information
packages	List available packages
parallel	Combine Pipelines in Parallel
parse_date	Parse dates from strings
parse_datetime	Parse datetimes from strings
parse_file	Parse T-Doc Comments
patch	Overlay one pipeline onto another
path_abs	Resolve relative path to absolute
path_basename	Get filename component of a path
path_dirname	Get directory portion of a path
path_ext	Get file extension
path_join	Join multiple path segments
path_stem	Get filename without extension
pchisq	Chi-squared distribution CDF
percent_rank	Percent Rank
pf	F distribution CDF
pipeline_assert	Assert Pipeline Validity
pipeline_cache_status	Check Pipeline Cache Status
pipeline_config_to_frame	Convert Pipeline Config to DataFrame
pipeline_copy	Copy Pipeline Node Artifacts to Local Directory
pipeline_cycles	Detect Pipeline Cycles
pipeline_deps	List Node Dependencies
pipeline_depth	Maximum Topological Depth
pipeline_diff	Compare Pipeline Structures
pipeline_edges	Pipeline Dependency Edges
pipeline_gc	Garbage Collect Pipeline Nodes
pipeline_leaves	Pipeline Leaf Nodes
pipeline_node	Get Pipeline Node
pipeline_node_options	Get Pipeline Node Options (read-back)
pipeline_nodes	List Pipeline Nodes
pipeline_print	Pretty-Print a Pipeline
pipeline_report	Generate Pipeline Report
pipeline_roots	Pipeline Root Nodes
pipeline_run	Run Pipeline
pipeline_to_dot	Export Pipeline/MetaPipeline as DOT Graph
pipeline_to_drv	Introspect Node Derivation Paths
pipeline_to_frame	Convert Pipeline to DataFrame
pipeline_to_ga	Export Pipeline as GitHub Actions Workflow
pipeline_to_mermaid	Export Pipeline/MetaPipeline as Mermaid Graph
pipeline_to_store	Introspect Node Store Paths
pipeline_validate	Validate a Pipeline
pivot_longer	Pivot longer
pivot_wider	Pivot wider
pm	Check whether a time is after noon
pnorm	Normal distribution CDF
poly	Polynomial basis expansion
populate_pipeline	Prepare Pipeline Infrastructure
pow	Power function
predict	Model Prediction
prefetch	Prefetch a URL and compute its SHA-256 hash
pretty_print	Pretty-print a value
print	Print values to standard output
prop_for_all	Check a property over generated values
prop_gen_between	Generate a random Int within domain bounds
prop_gen_bool	Generate a random Bool
prop_gen_choice	Generate a value chosen from several generators
prop_gen_date_range	Generate a Date or Datetime in a range
prop_gen_df	Generate a random DataFrame
prop_gen_df_from	Generate a DataFrame matching an existing sample
prop_gen_dict	Generate a Dict
prop_gen_factor	Generate a random Factor
prop_gen_float_range	Generate a random Float in a range
prop_gen_fn	Generate a value via a custom function
prop_gen_frequency	Generate a value from weighted generators
prop_gen_int	Generate a random Int
prop_gen_int_range	Generate a random Int in a fixed range
prop_gen_list	Generate a random List
prop_gen_one_of	Generate a value chosen from a fixed set
prop_gen_string_from	Generate a random String
prop_gen_vector	Generate a random Vector
prop_gen_ymd	Generate a Date within a year span
prop_map_gen	Transform a generated value
prop_named	Name a reusable property
prop_resize	Resize a generator
prop_show_spec	Render a generator spec back to T source
prop_stats	Probe a generator’s behaviour
prop_such_that	Filter generated values by a predicate
prop_test	Run a named property
prune	Prune Pipeline Leaf Nodes
pt	Student t distribution CDF
pull	Extract column as vector
pyn	Configure a Python Pipeline Node
qchisq	Chi-squared distribution quantile (inverse CDF)
qf	F distribution quantile (inverse CDF)
qn	Configure a Quarto Pipeline Node
qnorm	Normal distribution quantile (inverse CDF)
qt	Student t distribution quantile (inverse CDF)
quantile	Quantiles
quarter	Extract the quarter
quo	Capture an expression with its lexical environment (quosure)
quos	Capture multiple expressions with their lexical environment (quosures)
range	Range
read_csv	Read CSV file
read_file	Read file contents
read_ipc	Read an Arrow IPC (Feather) file
read_log	Read Node Build Log
read_node	Read Pipeline Node Artifact
read_parquet	Read Parquet file
read_past_node	Read Pipeline Node from a Past Build Run
read_pipeline	Read Pipeline Metadata
rebuild_node	Rebuild a Pipeline Node
relocate	Move columns to a new position
rename	Rename DataFrame columns
rename_node	Rename a Pipeline Node
replace_first	Replace first occurrence
replace_na	Replace missing values
reshape	Reshape an NDArray
residuals	Model Residuals
rewire	Rewire a Node’s Dependencies
rm	Remove objects from the environment
rn	Configure an R Pipeline Node
round	Round values
round_date	Round dates to the nearest unit
row_lens	Row Lens
row_number	Row Number
run	Run a shell command
run_doctor	Run Package/Project Doctor
sample	Random sample from a vector or list
sample_pattern	Sample pattern stub
scaffold_package	Scaffold a new T package
scaffold_project	Scaffold a new T project
scale	Scale values
score	Model Scoring
sd	Standard Deviation
second	Extract the second
select	Select columns
select_node	Select Node Metadata Fields
semester	Extract the semester
semi_join	Filter rows using matches in another table
separate	Separate a character column into multiple columns
separate_rows	Split delimited values into rows
seq	Generate a sequence of integers
serialize	Serialize Value
set	Set Focused Value
set_nix_defaults	Set Global Nix Orchestration Defaults
set_pipeline_global_options	Set Pipeline Global Options (pure)
set_seed	Set random seed for reproducibility
shape	Get NDArray dimensions
shn	Configure a Shell Pipeline Node
show_plot	Render a plot node and open it locally
sigma	Residual Standard Deviation
sign	Sign of number
signif	Significant-digit rounding
sin	Sine
sinh	Hyperbolic sine
skewness	Skewness
slice	Extract slice
slice_max	Keep rows with the largest values
slice_min	Keep rows with the smallest values
slice_pattern	Slice pattern stub
slice_sample	Randomly sample rows from a DataFrame
source	Get function source code
sqrt	Square root
standardize	Standardize values
starts_with	Check if string starts with prefix
str_count	Count regex matches
str_detect	Test whether a regex matches
str_extract	Extract the first regex match
str_extract_all	Extract all regex matches
str_flatten	Flatten a collection of strings
str_format	Named string interpolation
str_join	Join strings with a separator
str_lines	Split string into lines
str_nchar	Get character count
str_pad	Pad strings to a target width
str_repeat	Repeat a string
str_replace	Replace all occurrences
str_split	Split a string on a delimiter
str_sprintf	Format a string
str_substring	Extract substring
str_trim	Trim whitespace
str_trunc	Truncate strings for display
str_words	Split string into words
subgraph	Extract Connected Subgraph
sum	Sum of numeric values
summarize	Summarize data
summary	Model Summary
suppress_warnings	Suppress Diagnostics for a Node
swap	Swap a Pipeline Node Implementation
t_check	Check a T Script for Errors
t_diff	Compare Two Builds of a Pipeline
t_doc	Generate Documentation
t_fix	Mechanically Apply Suggested Fixes
t_gc	Run System Garbage Collection
t_make	Build and run a pipeline file
t_read_json	Read Value from JSON
t_read_onnx	Read an ONNX model file
t_read_pmml	Read a PMML model file
t_run	Run a T script
t_score_pmml	Score a PMML model using JPMML
t_test	Run tests
t_write_json	Write Value to JSON
t_write_onnx	Write an ONNX model file
t_write_pmml	Write a PMML model file
tail	Get the last n rows/items
tail_pattern	Tail pattern stub
tan	Tangent
tanh	Hyperbolic tangent
to_array	Convert to NDArray
to_bool	Convert to Boolean
to_dataframe	Create a DataFrame
to_date	Convert values to Date
to_datetime	Convert values to Datetime
to_expr	Capture an expression
to_exprs
to_factor	Create factor values
to_float	Convert to Float
to_integer	Convert to Integer
to_lower	Convert to lowercase
to_string	Convert to string
to_symbol	Convert a string to a Symbol
to_upper	Convert to uppercase
today	Get the current date
trace_nodes	Trace Pipeline Nodes
transpose	Transpose matrix
trim_end	Trim trailing whitespace
trim_start	Trim leading whitespace
trimmed_mean	Trimmed mean
trunc	Truncate values
type	Get the type name of a value
tz	Extract the timezone label
uncount	Expand rows by weight
ungroup	Remove grouping
union	Combine two pipelines
unite	Combine multiple columns into one character column
unnest	Expand nested columns
update_flake_lock	Update Dependencies
upstream_of	Extract Upstream Subgraph
var	Variance
vcov	Variance-Covariance Matrix
wald_test	Joint Wald Test
warning_msg	Get warning message
wday	Extract or label the weekday
week	Extract the week number
where	Select columns by predicate
which_nodes	Filter Readable Pipeline Node Records
winsorize	Winsorize values
with_seed	Run a thunk with a scoped random seed
with_tz	Convert a datetime to a new timezone
write_csv	Write CSV file
write_ipc	Write Arrow IPC file
write_parquet	Write Parquet file
write_text	Write text to a file
yday	Extract the day of year
year	Extract the year component