dplyr modifying operations

See dplyr::mutate(), dplyr::add_count(), dplyr::add_tally(), dplyr::transmute(), dplyr::select(), dplyr::relocate(), dplyr::rename() dplyr::rename_with(), dplyr::arrange() for more details on underlying functions. dtrackr provides equivalent functions for mutating, selecting and renaming a data set which act in the same way as dplyr. mutate / select / rename generally don't add anything in terms of provenance of data so the default behaviour is to miss these out of the dtrackr history. This can be overridden with the .messages, or .headline values in which case they behave just like a comment().

Usage

p_add_count(x, ..., .messages = "", .headline = "", .tag = NULL)

Arguments

x

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).

...

<data-masking> Variables to group by. Named arguments passed on to dplyr::add_count

wt

<data-masking> Frequency weights. Can be NULL or a variable:

If NULL (the default), counts the number of rows in each group.
If a variable, computes sum(wt) for each group.

sort

If TRUE, will show the largest groups at the top.

name

The name of the new column in the output.

If omitted, it will default to n. If there's already a column called n, it will use nn. If there's a column called n and nn, it'll use nnn, and so on, adding ns until it gets a new name.

.drop

Handling of factor levels that don't appear in the data, passed on to group_by().

For count(): if FALSE will include counts for empty groups (i.e. for levels of factors that don't exist in the data).

For add_count(): deprecated since it can't actually affect the output.

.messages

a set of glue specs. The glue code can use any global variable, grouping variable, {.new_cols} or {.dropped_cols} for changes to columns, {.cols} for the output column names, or {.strata}. Defaults to nothing.

.headline

a headline glue spec. The glue code can use any global variable, grouping variable, {.new_cols}, {.dropped_cols}, {.cols} or {.strata}. Defaults to nothing.

.tag

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

Value

the .data dataframe after being modified by the dplyr equivalent function, but with the history graph updated with a new stage if the .messages or .headline parameter is not empty.

Examples

library(dplyr)
library(dtrackr)

# mutate and other functions are unitary operations that generally change
# the structure but not size of a dataframe. In dtrackr these are by ignored
# by default but we can change that so that their behaviour is obvious.

# add_count
# adding in a count or tally column as a new column
iris %>%
  track() %>%
  add_count(Species, name="new_count_total",
            .messages="{.new_cols}",
            # .messages="{.cols}",
            .headline="New columns from add_count:") %>%
  history()
#> dtrackr history:
#> number of flowchart steps: 2 (approx)
#> tags defined: <none>
#> items excluded so far: <not capturing exclusions>
#> last entry / entries:
#> └ "New columns from add_count:", "new_count_total"

# add_tally
iris %>%
  track() %>%
  group_by(Species) %>%
  dtrackr::add_tally(wt=Petal.Length, name="new_tally_total",
            .messages="{.new_cols}",
            .headline="New columns from add_tally:") %>%
  history()
#> dtrackr history:
#> number of flowchart steps: 3 (approx)
#> tags defined: <none>
#> items excluded so far: <not capturing exclusions>
#> last entry / entries:
#> ├ [setosa]: "New columns from add_tally:", "new_tally_total"
#> ├ [versicolor]: "New columns from add_tally:", "new_tally_total"
#> └ [virginica]: "New columns from add_tally:", "new_tally_total"

Usage

Arguments

Value

See also

Examples