A ‘tidyr::complete()’ analogue for ‘epi_df’ objects. This function
can be used, for example, to add rows for missing combinations
of ‘geo_value’ and ‘time_value’, filling other columns with NAs.
See the examples for usage details.
Arguments
- data
an
epi_df- ...
see
tidyr::complete- fill
see
tidyr::complete- explicit
see
tidyr::complete
Examples
start_date <- as.Date("2020-01-01")
daily_edf <- tibble::tribble(
~geo_value, ~time_value, ~value,
1, start_date + 1, 1,
1, start_date + 3, 3,
2, start_date + 2, 2,
2, start_date + 3, 3,
) %>%
as_epi_df(as_of = start_date + 3)
# Complete without grouping puts all the geo_values on the same min and max
# time_value index
daily_edf %>%
complete(geo_value, time_value = full_seq(time_value, period = 1))
#> An `epi_df` object, 6 x 3 with metadata:
#> * geo_type = hhs
#> * time_type = day
#> * as_of = 2020-01-04
#>
#> # A tibble: 6 × 3
#> geo_value time_value value
#> * <dbl> <date> <dbl>
#> 1 1 2020-01-02 1
#> 2 1 2020-01-03 NA
#> 3 1 2020-01-04 3
#> 4 2 2020-01-02 NA
#> 5 2 2020-01-03 2
#> 6 2 2020-01-04 3
# Complete with grouping puts all the geo_values on individual min and max
# time_value indices
daily_edf %>%
group_by(geo_value) %>%
complete(time_value = full_seq(time_value, period = 1))
#> An `epi_df` object, 5 x 3 with metadata:
#> * geo_type = hhs
#> * time_type = day
#> * as_of = 2020-01-04
#>
#> # A tibble: 5 × 3
#> # Groups: geo_value [2]
#> geo_value time_value value
#> * <dbl> <date> <dbl>
#> 1 1 2020-01-02 1
#> 2 1 2020-01-03 NA
#> 3 1 2020-01-04 3
#> 4 2 2020-01-03 2
#> 5 2 2020-01-04 3
# Complete has explicit=TRUE by default, but if it's FALSE, then complete
# only fills the implicit gaps, not those that are explicitly NA
daily_edf <- tibble::tribble(
~geo_value, ~time_value, ~value,
1, start_date + 1, 1,
1, start_date + 2, NA,
1, start_date + 3, 3,
2, start_date + 2, 2,
2, start_date + 3, 3,
) %>%
as_epi_df(as_of = start_date + 3)
daily_edf %>%
complete(
geo_value,
time_value = full_seq(time_value, period = 1),
fill = list(value = 0),
explicit = FALSE
)
#> An `epi_df` object, 6 x 3 with metadata:
#> * geo_type = hhs
#> * time_type = day
#> * as_of = 2020-01-04
#>
#> # A tibble: 6 × 3
#> geo_value time_value value
#> * <dbl> <date> <dbl>
#> 1 1 2020-01-02 1
#> 2 1 2020-01-03 NA
#> 3 1 2020-01-04 3
#> 4 2 2020-01-02 0
#> 5 2 2020-01-03 2
#> 6 2 2020-01-04 3
# Complete works for weekly data and can take a fill value
# No grouping
weekly_edf <- tibble::tribble(
~geo_value, ~time_value, ~value,
1, start_date + 1, 1,
1, start_date + 15, 3,
2, start_date + 8, 2,
2, start_date + 15, 3,
) %>%
as_epi_df(as_of = start_date + 3)
weekly_edf %>%
complete(
geo_value,
time_value = full_seq(time_value, period = 7),
fill = list(value = 0)
)
#> An `epi_df` object, 6 x 3 with metadata:
#> * geo_type = hhs
#> * time_type = week
#> * as_of = 2020-01-04
#>
#> # A tibble: 6 × 3
#> geo_value time_value value
#> * <dbl> <date> <dbl>
#> 1 1 2020-01-02 1
#> 2 1 2020-01-09 0
#> 3 1 2020-01-16 3
#> 4 2 2020-01-02 0
#> 5 2 2020-01-09 2
#> 6 2 2020-01-16 3
# With grouping
weekly_edf %>%
group_by(geo_value) %>%
complete(
time_value = full_seq(time_value, period = 7),
fill = list(value = 0)
)
#> An `epi_df` object, 5 x 3 with metadata:
#> * geo_type = hhs
#> * time_type = week
#> * as_of = 2020-01-04
#>
#> # A tibble: 5 × 3
#> # Groups: geo_value [2]
#> geo_value time_value value
#> * <dbl> <date> <dbl>
#> 1 1 2020-01-02 1
#> 2 1 2020-01-09 0
#> 3 1 2020-01-16 3
#> 4 2 2020-01-09 2
#> 5 2 2020-01-16 3