Slice operations

Slice operations behave as in dplyr, except the history graph can be updated with tracked dataframe with the before and after sizes of the dataframe. See dplyr::slice(), dplyr::slice_head(), dplyr::slice_tail(), dplyr::slice_min(), dplyr::slice_max(), dplyr::slice_sample(), for more details on the underlying functions.

p_slice_max(
  .data,
  ...,
  .messages = c("{.count.in} before", "{.count.out} after"),
  .headline = "slice data"
)

Arguments

.data

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.

...

Arguments passed on to dplyr::slice_max

n,prop

Provide either n, the number of rows, or prop, the proportion of rows to select. If neither are supplied, n = 1 will be used. If n is greater than the number of rows in the group (or prop > 1), the result will be silently truncated to the group size. prop will be rounded towards zero to generate an integer number of rows.

A negative value of n or prop will be subtracted from the group size. For example, n = -2 with a group of 5 rows will select 5 - 2 = 3 rows; prop = -0.25 with 8 rows will select 8 * (1 - 0.25) = 6 rows.

order_by

<data-masking> Variable or function of variables to order by. To order by multiple variables, wrap them in a data frame or tibble.

with_ties

Should ties be kept together? The default, TRUE, may return more rows than you request. Use FALSE to ignore ties, and return the first n rows.

na_rm

Should missing values in order_by be removed from the result? If FALSE, NA values are sorted to the end (like in arrange()), so they will only be included if there are insufficient non-missing values to reach n/prop.

.messages

a set of glue specs. The glue code can use any global variable, {.count.in}, {.count.out} for the input and output dataframes sizes respectively and {.excluded} for the difference

.headline

a glue spec. The glue code can use any global variable, {.count.in}, {.count.out} for the input and output dataframes sizes respectively.

Value

the sliced dataframe with the history graph updated.

Examples

library(dplyr)
library(dtrackr)


# Subset the data by the maximum of a given value
iris %>% track() %>% group_by(Species) %>%
  slice_max(prop=0.5, order_by = Sepal.Width,
            .messages="{.count.out} / {.count.in} = {prop} (with ties)",
            .headline="Widest 50% Sepals") %>%
  history()
#> dtrackr history:
#> number of flowchart steps: 3 (approx)
#> tags defined: <none>
#> items excluded so far: <not capturing exclusions>
#> last entry / entries:
#> ├ [Species:setosa]: "Widest 50% Sepals", "31 / 50 = 0.5 (with ties)"
#> ├ [Species:versicolor]: "Widest 50% Sepals", "29 / 50 = 0.5 (with ties)"
#> └ [Species:virginica]: "Widest 50% Sepals", "29 / 50 = 0.5 (with ties)"


# The narrowest 25% of the iris data set by group can be calculated in the
# slice_min() function. Recording this is a matter of tracking and
# using glue specs.
iris %>%
  track() %>%
  group_by(Species) %>%
  slice_min(prop=0.25, order_by = Sepal.Width,
            .messages="{.count.out} / {.count.in} (with ties)",
            .headline="narrowest {sprintf('%1.0f',prop*100)}% {Species}") %>%
  history()
#> dtrackr history:
#> number of flowchart steps: 3 (approx)
#> tags defined: <none>
#> items excluded so far: <not capturing exclusions>
#> last entry / entries:
#> ├ [Species:setosa]: "narrowest 25% setosa", "12 / 50 (with ties)"
#> ├ [Species:versicolor]: "narrowest 25% versicolor", "13 / 50 (with ties)"
#> └ [Species:virginica]: "narrowest 25% virginica", "19 / 50 (with ties)"

Arguments

Value

See also

Examples