Convert a data.table to csfmt_rts_data_v3 (clean csfmt; explicit healing)

Eleven unified columns, taken from the 18 of set_csfmt_rts_data_v2: granularity_geo, country_iso3, location_code, age, sex, isoyear, isoweek, isoyearweek, season, seasonweek and date. It drops the self-healing [ override (healing is explicit) and gives time_series_id a content hash.

Usage

set_csfmt_rts_data_v3(x, create_unified_columns = TRUE, heal = TRUE)

csfmt_rts_data_v3(x, create_unified_columns = TRUE, heal = TRUE)

Arguments

x

The data.table to convert (by reference).

create_unified_columns

Create the unified columns?

heal

Derive the missing time and geography columns on creation? These are deterministically looked up from isoyearweek and location_code; nothing is statistically imputed and no count is invented.

Value

x, modified by reference, invisibly.

A new csfmt_rts_data_v3 (not by reference).

See also

No vignette covers csfmt_rts_data_v3. The data-format vignette documents the csfmt_rts_data_v2 columns that this format takes its own from: vignette("csfmt_rts_data_v2", package = "cstidy").

Other csfmt_rts_data: expand_time_to(), identify_data_structure(), remove_class_csfmt_rts_data(), set_csfmt_rts_data_v1(), set_csfmt_rts_data_v2(), unique_time_series()

Other csfmt format converters: set_csfmt_rts_data_v1(), set_csfmt_rts_data_v2()

Examples

# v3 is weekly-only: the other time columns come from isoyearweek alone.
d <- data.table::data.table(
  isoyearweek = c("2020-34", "2020-35"),
  location_code = "nation_nor",
  deaths_n = c(1L, 2L)
)

# set_csfmt_rts_data_v3() converts d in place and returns it invisibly.
cstidy::set_csfmt_rts_data_v3(d)
d[]
#>    granularity_geo country_iso3 location_code    age    sex isoyear isoweek
#>             <char>       <char>        <char> <char> <char>   <int>   <int>
#> 1:          nation          nor    nation_nor   <NA>   <NA>    2020      34
#> 2:          nation          nor    nation_nor   <NA>   <NA>    2020      35
#>    isoyearweek    season seasonweek       date deaths_n
#>         <char>    <char>      <num>     <Date>    <int>
#> 1:     2020-34 2019/2020         52 2020-08-23        1
#> 2:     2020-35 2020/2021          1 2020-08-30        2
class(d)
#> [1] "csfmt_rts_data_v3" "data.table"        "data.frame"       

# csfmt_rts_data_v3() copies instead, so e is left as it was.
e <- data.table::data.table(
  isoyearweek = c("2020-34", "2020-35"),
  location_code = "nation_nor",
  deaths_n = c(1L, 2L)
)
y <- cstidy::csfmt_rts_data_v3(e)
class(y)
#> [1] "csfmt_rts_data_v3" "data.table"        "data.frame"       
class(e)
#> [1] "data.table" "data.frame"
names(e)
#> [1] "isoyearweek"   "location_code" "deaths_n"