dtrackr (version 0.4.6)

resume: Resume tracking the data frame.

Description

This may reset the grouping of the tracked data if the grouping structure has changed since the data frame was paused. If you try and resume tracking a data frame with too many groups (as defined by options("dtrackr.max_supported_groupings"=XX)) then the resume will fail and the data frame will still be paused. This can be overridden by specifying a value for the .maxgroups parameter.

Usage

resume(.data, ...)

Value

the .data data frame with history graph tracking resumed

Arguments

.data

a tracked dataframe

...

Named arguments passed on to p_group_by

.messages

a set of glue specs. The glue code can use any global variable, or {.cols} which is the columns that are being grouped by.

.headline

a headline glue spec. The glue code can use any global variable, or {.cols}.

.tag

if you want the summary data from this step in the future then give it a name with .tag.

.maxgroups

the maximum number of subgroups allowed before the tracking is paused.

...

In group_by(), variables or computations to group by. Computations are always done on the ungrouped data frame. To perform computations on the grouped data, you need to use a separate mutate() step before the group_by(). Computations are not allowed in nest_by(). In ungroup(), variables to remove from the grouping. Named arguments passed on to dplyr::group_by

.add

When FALSE, the default, group_by() will override existing groups. To add to the existing groups, use .add = TRUE.

This argument was previously called add, but that prevented creating a new grouping variable called add, and conflicts with our naming conventions.

.drop

Drop groups formed by factor levels that don't appear in the data? The default is TRUE except when .data has been previously grouped with .drop = FALSE. See group_by_drop_default() for details.

x

A tbl()

Examples

library(dplyr)
library(dtrackr)
iris %>% track() %>% pause() %>% resume() %>% history()