Standard dplyr verbs dispatched on an mrgsimsds object. Each verb extracts
the underlying Arrow Dataset and forwards all arguments to the corresponding
dplyr generic, returning a lazy Arrow query that can be materialized with
dplyr::collect().
Usage
# S3 method for class 'mrgsimsds'
group_by(.data, ..., .add = FALSE, .drop = TRUE)
# S3 method for class 'mrgsimsds'
select(.data, ...)
# S3 method for class 'mrgsimsds'
mutate(.data, ...)
# S3 method for class 'mrgsimsds'
filter(.data, ..., .preserve = FALSE)
# S3 method for class 'mrgsimsds'
arrange(.data, ..., .by_group = FALSE)
# S3 method for class 'mrgsimsds'
rename(.data, ...)
# S3 method for class 'mrgsimsds'
summarise(.data, ..., .groups = NULL)
# S3 method for class 'mrgsimsds'
distinct(.data, ..., .keep_all = FALSE)
# S3 method for class 'mrgsimsds'
relocate(.data, ..., .before = NULL, .after = NULL)
# S3 method for class 'mrgsimsds'
count(x, ..., wt = NULL, sort = FALSE, name = NULL)
# S3 method for class 'mrgsimsds'
pull(.data, var = -1, name = NULL, as_vector = TRUE, ...)Arguments
- .data, x
an mrgsimsds object.
- ...
passed to the corresponding dplyr generic.
- .add, .drop
passed to
dplyr::group_by().- .preserve
passed to
dplyr::filter().- .by_group
passed to
dplyr::arrange().- .groups
passed to
dplyr::summarise().- .keep_all
passed to
dplyr::distinct().- .before, .after
passed to
dplyr::relocate().- wt, sort
passed to
dplyr::count().- name
passed to
dplyr::pull().- var
passed to
dplyr::pull().- as_vector
passed to
dplyr::pull().
Value
A lazy Arrow query object. Use dplyr::collect() to materialize the result
into a tibble. pull() is an exception — it collects immediately and returns
a vector.
Examples
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
mod <- house_ds(end = 24)
data <- evd_expand(amt = c(100, 300), ID = 1:10)
out <- mrgsim_ds(mod, data)
out |> filter(TIME > 0) |> select(ID, TIME, CP) |> collect()
#> # A tibble: 1,920 × 3
#> ID TIME CP
#> <dbl> <dbl> <dbl>
#> 1 1 0.25 1.29
#> 2 1 0.5 2.23
#> 3 1 0.75 2.90
#> 4 1 1 3.39
#> 5 1 1.25 3.74
#> 6 1 1.5 3.98
#> 7 1 1.75 4.14
#> 8 1 2 4.25
#> 9 1 2.25 4.31
#> 10 1 2.5 4.34
#> # ℹ 1,910 more rows
out |> group_by(ID) |> summarise(auc = sum(CP)) |> collect()
#> # A tibble: 20 × 2
#> ID auc
#> <dbl> <dbl>
#> 1 1 275.
#> 2 2 825.
#> 3 3 275.
#> 4 4 825.
#> 5 5 275.
#> 6 6 825.
#> 7 7 275.
#> 8 8 825.
#> 9 9 275.
#> 10 10 825.
#> 11 11 275.
#> 12 12 825.
#> 13 13 275.
#> 14 14 825.
#> 15 15 275.
#> 16 16 825.
#> 17 17 275.
#> 18 18 825.
#> 19 19 275.
#> 20 20 825.
out |> mutate(WEEK = TIME / 168) |> collect()
#> # A tibble: 1,960 × 8
#> ID TIME GUT CENT RESP DV CP WEEK
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 0 0 50 0 0 0
#> 2 1 0 100 0 50 0 0 0
#> 3 1 0.25 74.1 25.7 48.7 1.29 1.29 0.00149
#> 4 1 0.5 54.9 44.5 46.2 2.23 2.23 0.00298
#> 5 1 0.75 40.7 58.1 43.6 2.90 2.90 0.00446
#> 6 1 1 30.1 67.8 41.4 3.39 3.39 0.00595
#> 7 1 1.25 22.3 74.7 39.6 3.74 3.74 0.00744
#> 8 1 1.5 16.5 79.6 38.2 3.98 3.98 0.00893
#> 9 1 1.75 12.2 82.8 37.1 4.14 4.14 0.0104
#> 10 1 2 9.07 85.0 36.4 4.25 4.25 0.0119
#> # ℹ 1,950 more rows