set_csfmt_rts_data_v1 {cstidy}

Convert data.table to csfmt_rts_data_v1

Description

set_csfmt_rts_data_v1 converts a data.table to csfmt_rts_data_v1 by reference. csfmt_rts_data_v1 creates a new csfmt_rts_data_v1 (not by reference) from either a data.table or data.frame.

Usage

set_csfmt_rts_data_v1(x, create_unified_columns = TRUE, heal = TRUE)

csfmt_rts_data_v1(x, create_unified_columns = TRUE, heal = TRUE)

Arguments

x

The data.table to be converted to csfmt_rts_data_v1

create_unified_columns

Do you want it to create unified columns?

heal

Do you want to impute missing values on creation?

Details

For more details see the vignette: vignette("csfmt_rts_data_v1", package = "cstidy")

Value

An extended data.table, which has been modified by reference and returned (invisibly).

No return value, called for side effect of replacing the current data.table with a csfmt_rts_data_v1 in place.

Returns a duplicated csfmt_rts_data_v1.

Smart assignment

csfmt_rts_data_v1 contains the smart assignment feature for time and geography.

When the variables in bold are assigned using ⁠:=⁠, the listed variables will be automatically imputed.

location_code:

• granularity_geo

• country_iso3

isoyear:

• granularity_time

• isoweek

• isoyearweek

• season

• seasonweek

• calyear

• calmonth

• calyearmonth

• date

isoyearweek:

• granularity_time

• isoyear

• isoweek

• season

• seasonweek

• calyear

• calmonth

• calyearmonth

• date

date:

• granularity_time

• isoyear

• isoweek

• isoyearweek

• season

• seasonweek

• calyear

• calmonth

• calyearmonth

Unified columns

csfmt_rts_data_v1 contains 16 unified columns:

• granularity_time

• granularity_geo

• country_iso3

• location_code

• border

• age

• sex

• isoyear

• isoweek

• isoyearweek

• season

• seasonweek

• calyear

• calmonth

• calyearmonth

• date

See Also

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

Examples

# Create some fake data as data.table
d <- cstidy::generate_test_data(fmt = "csfmt_rts_data_v1")
d <- d[1:5]

# convert to csfmt_rts_data_v1 by reference
cstidy::set_csfmt_rts_data_v1(d, create_unified_columns = TRUE)

#
d[1, isoyearweek := "2021-01"]
d
d[2, isoyear := 2019]
d
d[3, date := as.Date("2020-01-01")]
d
d[4, c("isoyear", "isoyearweek") := .(2021, "2021-01")]
d
d[5, c("location_code") := .("norge")]
d

# Investigating the data structure of one column inside a dataset
cstidy::generate_test_data() %>%
  cstidy::set_csfmt_rts_data_v1() %>%
  cstidy::identify_data_structure("deaths_n") %>%
  plot()
# Investigating the data structure via summary
cstidy::generate_test_data() %>%
  cstidy::set_csfmt_rts_data_v1() %>%
  summary()