group_by and related methods for epi_archive, grouped_epi_archive
Source: R/archive.R, R/grouped_epi_archive.R
group_by.epi_archive.Rdgroup_by and related methods for epi_archive, grouped_epi_archive
Usage
# S3 method for class 'epi_archive'
group_by(.data, ..., .add = FALSE, .drop = dplyr::group_by_drop_default(.data))
# S3 method for class 'grouped_epi_archive'
group_by(.data, ..., .add = FALSE, .drop = dplyr::group_by_drop_default(.data))
# S3 method for class 'grouped_epi_archive'
group_by_drop_default(.tbl)
# S3 method for class 'grouped_epi_archive'
groups(x)
# S3 method for class 'grouped_epi_archive'
ungroup(x, ...)
is_grouped_epi_archive(x)Arguments
- .data
An
epi_archiveorgrouped_epi_archive- ...
Similar to
dplyr::group_by(see "Details:" for edge cases);For
group_by: unquoted variable name(s) or other "data masking" expression(s). It's possible to usedplyr::mutate-like syntax here to calculate new columns on which to perform grouping, but note that, if you are regrouping an already-grouped.dataobject, the calculations will be carried out ignoring such grouping (same as in dplyr).For
ungroup: eitherempty, in order to remove the grouping and output an
epi_archive; orvariable name(s) or other "tidy-select" expression(s), in order to remove the matching variables from the list of grouping variables, and output another
grouped_epi_archive.
- .add
Boolean. If
FALSE, the default, the output will be grouped by the variable selection from...only; ifTRUE, the output will be grouped by the current grouping variables plus the variable selection from....- .drop
As described in
dplyr::group_by; determines treatment of factor columns.- .tbl
A
grouped_epi_archiveobject.- x
For
groupsorungroup: agrouped_epi_archive; foris_grouped_epi_archive: any object
Details
To match dplyr, group_by allows "data masking" (also referred to as
"tidy evaluation") expressions ..., not just column names, in a way similar
to mutate. Note that replacing or removing key columns with these
expressions is disabled.
archive %>% group_by() and other expressions that group or regroup by zero
columns (indicating that all rows should be treated as part of one large
group) will output a grouped_epi_archive, in order to enable the use of
grouped_epi_archive methods on the result. This is in slight contrast to
the same operations on tibbles and grouped tibbles, which will not output a
grouped_df in these circumstances.
Using group_by with .add=FALSE to override the existing grouping is
disabled; instead, ungroup first then group_by.
group_by_drop_default on (ungrouped) epi_archives is expected to dispatch
to group_by_drop_default.default (but there is a dedicated method for
grouped_epi_archives).
Examples
grouped_archive <- archive_cases_dv_subset %>% group_by(geo_value)
# `print` for metadata and method listing:
grouped_archive %>% print()
#> A `grouped_epi_archive` object:
#> * Groups: geo_value
#> It wraps an ungrouped `epi_archive`, with metadata:
#> ℹ Min/max time values: 2020-06-01 / 2021-11-30
#> ℹ First/last version with update: 2020-06-02 / 2021-12-01
#> ℹ Versions end: 2021-12-01
#> ℹ A preview of the table (129638 rows x 5 columns):
#> Key: <geo_value, time_value, version>
#> geo_value time_value version percent_cli case_rate_7d_av
#> <char> <Date> <Date> <num> <num>
#> 1: ca 2020-06-01 2020-06-02 NA 6.628329
#> 2: ca 2020-06-01 2020-06-06 2.140116 6.628329
#> 3: ca 2020-06-01 2020-06-07 2.140116 6.628329
#> 4: ca 2020-06-01 2020-06-08 2.140379 6.628329
#> 5: ca 2020-06-01 2020-06-09 2.114430 6.628329
#> ---
#> 129634: tx 2021-11-26 2021-11-29 1.858596 7.957657
#> 129635: tx 2021-11-27 2021-11-28 NA 7.174299
#> 129636: tx 2021-11-28 2021-11-29 NA 6.834681
#> 129637: tx 2021-11-29 2021-11-30 NA 8.841247
#> 129638: tx 2021-11-30 2021-12-01 NA 9.566218
# The primary use for grouping is to perform a grouped `epix_slide`:
archive_cases_dv_subset %>%
group_by(geo_value) %>%
epix_slide(
f = ~ mean(.x$case_rate_7d_av),
before = 2,
ref_time_values = as.Date("2020-06-11") + 0:2,
new_col_name = "case_rate_3d_av"
) %>%
ungroup()
#> # A tibble: 12 × 3
#> geo_value time_value case_rate_3d_av
#> <chr> <date> <dbl>
#> 1 ca 2020-06-11 7.19
#> 2 fl 2020-06-11 5.71
#> 3 ny 2020-06-11 4.59
#> 4 tx 2020-06-11 5.62
#> 5 ca 2020-06-12 7.52
#> 6 fl 2020-06-12 5.82
#> 7 ny 2020-06-12 4.34
#> 8 tx 2020-06-12 5.91
#> 9 ca 2020-06-13 7.62
#> 10 fl 2020-06-13 6.11
#> 11 ny 2020-06-13 4.14
#> 12 tx 2020-06-13 6.03
# -----------------------------------------------------------------
# Advanced: some other features of dplyr grouping are implemented:
library(dplyr)
toy_archive <-
tribble(
~geo_value, ~age_group, ~time_value, ~version, ~value,
"us", "adult", "2000-01-01", "2000-01-02", 121,
"us", "pediatric", "2000-01-02", "2000-01-03", 5, # (addition)
"us", "adult", "2000-01-01", "2000-01-03", 125, # (revision)
"us", "adult", "2000-01-02", "2000-01-03", 130 # (addition)
) %>%
mutate(
age_group = ordered(age_group, c("pediatric", "adult")),
time_value = as.Date(time_value),
version = as.Date(version)
) %>%
as_epi_archive(other_keys = "age_group")
# The following are equivalent:
toy_archive %>% group_by(geo_value, age_group)
#> A `grouped_epi_archive` object:
#> * Groups: geo_value, age_group
#> * Drops groups formed by factor levels that don't appear in the data
#> It wraps an ungrouped `epi_archive`, with metadata:
#> ℹ Non-standard DT keys: age_group
#> ℹ Min/max time values: 2000-01-01 / 2000-01-02
#> ℹ First/last version with update: 2000-01-02 / 2000-01-03
#> ℹ Versions end: 2000-01-03
#> ℹ A preview of the table (4 rows x 5 columns):
#> Key: <geo_value, time_value, age_group, version>
#> geo_value age_group time_value version value
#> <char> <ord> <Date> <Date> <num>
#> 1: us adult 2000-01-01 2000-01-02 121
#> 2: us adult 2000-01-01 2000-01-03 125
#> 3: us pediatric 2000-01-02 2000-01-03 5
#> 4: us adult 2000-01-02 2000-01-03 130
toy_archive %>%
group_by(geo_value) %>%
group_by(age_group, .add = TRUE)
#> A `grouped_epi_archive` object:
#> * Groups: geo_value, age_group
#> * Drops groups formed by factor levels that don't appear in the data
#> It wraps an ungrouped `epi_archive`, with metadata:
#> ℹ Non-standard DT keys: age_group
#> ℹ Min/max time values: 2000-01-01 / 2000-01-02
#> ℹ First/last version with update: 2000-01-02 / 2000-01-03
#> ℹ Versions end: 2000-01-03
#> ℹ A preview of the table (4 rows x 5 columns):
#> Key: <geo_value, time_value, age_group, version>
#> geo_value age_group time_value version value
#> <char> <ord> <Date> <Date> <num>
#> 1: us adult 2000-01-01 2000-01-02 121
#> 2: us adult 2000-01-01 2000-01-03 125
#> 3: us pediatric 2000-01-02 2000-01-03 5
#> 4: us adult 2000-01-02 2000-01-03 130
grouping_cols <- c("geo_value", "age_group")
toy_archive %>% group_by(across(all_of(grouping_cols)))
#> A `grouped_epi_archive` object:
#> * Groups: geo_value, age_group
#> * Drops groups formed by factor levels that don't appear in the data
#> It wraps an ungrouped `epi_archive`, with metadata:
#> ℹ Non-standard DT keys: age_group
#> ℹ Min/max time values: 2000-01-01 / 2000-01-02
#> ℹ First/last version with update: 2000-01-02 / 2000-01-03
#> ℹ Versions end: 2000-01-03
#> ℹ A preview of the table (4 rows x 5 columns):
#> Key: <geo_value, time_value, age_group, version>
#> geo_value age_group time_value version value
#> <char> <ord> <Date> <Date> <num>
#> 1: us adult 2000-01-01 2000-01-02 121
#> 2: us adult 2000-01-01 2000-01-03 125
#> 3: us pediatric 2000-01-02 2000-01-03 5
#> 4: us adult 2000-01-02 2000-01-03 130
# And these are equivalent:
toy_archive %>% group_by(geo_value)
#> A `grouped_epi_archive` object:
#> * Groups: geo_value
#> It wraps an ungrouped `epi_archive`, with metadata:
#> ℹ Non-standard DT keys: age_group
#> ℹ Min/max time values: 2000-01-01 / 2000-01-02
#> ℹ First/last version with update: 2000-01-02 / 2000-01-03
#> ℹ Versions end: 2000-01-03
#> ℹ A preview of the table (4 rows x 5 columns):
#> Key: <geo_value, time_value, age_group, version>
#> geo_value age_group time_value version value
#> <char> <ord> <Date> <Date> <num>
#> 1: us adult 2000-01-01 2000-01-02 121
#> 2: us adult 2000-01-01 2000-01-03 125
#> 3: us pediatric 2000-01-02 2000-01-03 5
#> 4: us adult 2000-01-02 2000-01-03 130
toy_archive %>%
group_by(geo_value, age_group) %>%
ungroup(age_group)
#> A `grouped_epi_archive` object:
#> * Groups: geo_value
#> It wraps an ungrouped `epi_archive`, with metadata:
#> ℹ Non-standard DT keys: age_group
#> ℹ Min/max time values: 2000-01-01 / 2000-01-02
#> ℹ First/last version with update: 2000-01-02 / 2000-01-03
#> ℹ Versions end: 2000-01-03
#> ℹ A preview of the table (4 rows x 5 columns):
#> Key: <geo_value, time_value, age_group, version>
#> geo_value age_group time_value version value
#> <char> <ord> <Date> <Date> <num>
#> 1: us adult 2000-01-01 2000-01-02 121
#> 2: us adult 2000-01-01 2000-01-03 125
#> 3: us pediatric 2000-01-02 2000-01-03 5
#> 4: us adult 2000-01-02 2000-01-03 130
# To get the grouping variable names as a `list` of `name`s (a.k.a. symbols):
toy_archive %>%
group_by(geo_value) %>%
groups()
#> [[1]]
#> geo_value
#>
toy_archive %>%
group_by(geo_value, age_group, .drop = FALSE) %>%
epix_slide(f = ~ sum(.x$value), before = 20) %>%
ungroup()
#> # A tibble: 4 × 4
#> geo_value age_group time_value slide_value
#> <chr> <ord> <date> <dbl>
#> 1 us pediatric 2000-01-02 0
#> 2 us adult 2000-01-02 121
#> 3 us pediatric 2000-01-03 5
#> 4 us adult 2000-01-03 255