Function reference • dtrackr

Controlling dtrackr Functions that control `dtrackr`
`track()`	Start tracking the dtrackr history graph
`untrack()`	Remove tracking from the dataframe
`pause()`	Pause tracking the data frame.
`resume()`	Resume tracking the data frame.
`print(<trackr_graph>)`	Print a history graph to the console
`plot(<trackr_graph>)`	Plots a history graph as html
`flowchart()`	Flowchart output
`history()`	Get the dtrackr history graph
`capture_exclusions()`	Start capturing exclusions on a tracked dataframe.
`excluded()`	Get the dtrackr excluded data record
`tagged()`	Retrieve tagged data in the history graph
Pipeline annotations `dtrackr` has functions that are specific additions to a data pipeline. These are novel data pipeline functions that don’t have a direct equivalent in `dplyr` or `tidyr`.
`comment()`	Add a generic comment to the dtrackr history graph
`status()`	Add a summary to the dtrackr history graph
`count_subgroup()`	Add a subgroup count to the dtrackr history graph
`exclude_all()`	Exclude all items matching one or more criteria
`include_any()`	Include any items matching a criteria
S3 dplyr function extensions S3 functions extend the behaviour of a subset of the `dplyr` or `tidyr` commands that operate on dataframes, and return dataframes, to include a history graph. In general these work the same as the normal `dplyr` functions but allow additional parameters that allow us to configure how the history is captured as they are executed. `dplyr` functions that are not listed here can still be used as normal with a tracked dataframe as input and give you the same output but simply do not give you the additional functionality to store a entry in the history graph. This may be because the operations are terminal such as `count()`, `tally()`, `glimpse()`, `pull()`, or they modify subsequent behaviour (e.g `rowwise()`), or they do not output dataframes (e.g. `group_map()` and `group_walk()`). If a function is not supported in `dtrackr` it can still be used, but that step of the pipeline will not be captured in the history graph.
`add_count(<trackr_df>)`	dplyr modifying operations
`add_tally()`	dplyr modifying operations
`arrange(<trackr_df>)`	dplyr modifying operations
`distinct(<trackr_df>)`	Distinct values of data
`filter(<trackr_df>)`	Filtering data
`group_by(<trackr_df>)`	Stratifying your analysis
`group_modify(<trackr_df>)`	Group-wise modification of data and complex operations
`mutate(<trackr_df>)`	dplyr modifying operations
`relocate(<trackr_df>)`	dplyr modifying operations
`rename(<trackr_df>)`	dplyr modifying operations
`rename_with(<trackr_df>)`	dplyr modifying operations
`select(<trackr_df>)`	dplyr modifying operations
`summarise(<trackr_df>)`	Summarise a data set
`transmute(<trackr_df>)`	dplyr modifying operations
`ungroup(<trackr_df>)`	Remove a stratification from a data set
`anti_join(<trackr_df>)`	Anti join
`full_join(<trackr_df>)`	Full join
`inner_join(<trackr_df>)`	Inner joins
`left_join(<trackr_df>)`	Left join
`right_join(<trackr_df>)`	Right join
`semi_join(<trackr_df>)`	Semi join
`nest_join(<trackr_df>)`	Nest join
`slice(<trackr_df>)`	Slice operations
`slice_head(<trackr_df>)`	Slice operations
`slice_tail(<trackr_df>)`	Slice operations
`slice_min(<trackr_df>)`	Slice operations
`slice_max(<trackr_df>)`	Slice operations
`slice_sample(<trackr_df>)`	Slice operations
`bind_rows()`	Set operations
`bind_cols()`	Set operations
`intersect(<trackr_df>)`	Set operations
`union(<trackr_df>)`	Set operations
`union_all(<trackr_df>)`	Set operations
`setdiff(<trackr_df>)`	Set operations
S3 function extensions in other packages `dtrackr` support for functions from other tidyverse packages is evolving. The focus is on functions that take a dataframe as input and produce a dataframe as output, and naturally fit within a data pipeline. `pivot_longer()` and `pivot_wider()` are good examples which are already implemented. Tracking of `nest()` and `unnest()` is not yet implemented (but this does not stop you from using these functions in a pipeline), and `purrr` functions such as `map_df()`, `map_dfc()`, `map_dfr()`, `pmap()`, `pmap_dfr()`, `pmap_dfc()` are potential candidates for future implementation, as are functions that acquire data such as those from the `readr` package.
`pivot_longer(<trackr_df>)`	Reshaping data using `tidyr::pivot_longer`
`pivot_wider(<trackr_df>)`	Reshaping data using `tidyr::pivot_wider`
Dot graph rendering. Rendering of a `dtrackr` history once converted to `GraphViz` dot format. These can be also used for non-dtrackr dot content
`dot2svg()`	Convert `Graphviz` dot content to a SVG
`save_dot()`	Save DOT content to a file
`std_size`	Standard paper sizes
Legacy interface The following operations are all aliases for functions above. They are generally backend functions, and should not be used for new projects.
`p_add_count()`	dplyr modifying operations
`p_add_tally()`	dplyr modifying operations
`p_anti_join()`	Anti join
`p_arrange()`	dplyr modifying operations
`p_bind_cols()`	Set operations
`p_bind_rows()`	Set operations
`p_capture_exclusions()`	Start capturing exclusions on a tracked dataframe.
`p_clear()`	Clear the dtrackr history graph
`p_comment()`	Add a generic comment to the dtrackr history graph
`p_copy()`	Copy the dtrackr history graph from one dataframe to another
`p_count_if()`	Simple count_if dplyr summary function
`p_count_subgroup()`	Add a subgroup count to the dtrackr history graph
`p_distinct()`	Distinct values of data
`p_exclude_all()`	Exclude all items matching one or more criteria
`p_excluded()`	Get the dtrackr excluded data record
`p_filter()`	Filtering data
`p_flowchart()`	Flowchart output
`p_full_join()`	Full join
`p_get()`	Get the dtrackr history graph
`p_get_as_dot()`	DOT output
`p_group_by()`	Stratifying your analysis
`p_group_modify()`	Group-wise modification of data and complex operations
`p_include_any()`	Include any items matching a criteria
`p_inner_join()`	Inner joins
`p_intersect()`	Set operations
`p_left_join()`	Left join
`p_mutate()`	dplyr modifying operations
`p_nest_join()`	Nest join
`p_pause()`	Pause tracking the data frame.
`p_pivot_longer()`	Reshaping data using `tidyr::pivot_longer`
`p_pivot_wider()`	Reshaping data using `tidyr::pivot_wider`
`p_relocate()`	dplyr modifying operations
`p_rename()`	dplyr modifying operations
`p_rename_with()`	dplyr modifying operations
`p_resume()`	Resume tracking the data frame.
`p_right_join()`	Right join
`p_select()`	dplyr modifying operations
`p_semi_join()`	Semi join
`p_set()`	Set the dtrackr history graph
`p_setdiff()`	Set operations
`p_slice()`	Slice operations
`p_slice_head()`	Slice operations
`p_slice_max()`	Slice operations
`p_slice_min()`	Slice operations
`p_slice_sample()`	Slice operations
`p_slice_tail()`	Slice operations
`p_status()`	Add a summary to the dtrackr history graph
`p_summarise()`	Summarise a data set
`p_tagged()`	Retrieve tagged data in the history graph
`p_track()`	Start tracking the dtrackr history graph
`p_transmute()`	dplyr modifying operations
`p_ungroup()`	Remove a stratification from a data set
`p_union()`	Set operations
`p_union_all()`	Set operations
`p_untrack()`	Remove tracking from the dataframe

Reference

Controlling dtrackr

Pipeline annotations

S3 dplyr function extensions

S3 function extensions in other packages

Dot graph rendering.

Legacy interface