Package index • 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

reframe(<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.