set_csfmt_rts_data_v2 converts a data.table to csfmt_rts_data_v2 by reference.
csfmt_rts_data_v2 creates a new csfmt_rts_data_v2 (not by reference) from either a data.table or data.frame.
Usage
set_csfmt_rts_data_v2(x, create_unified_columns = TRUE, heal = TRUE)
csfmt_rts_data_v2(x, create_unified_columns = TRUE, heal = TRUE)Arguments
- x
The data.table to be converted to csfmt_rts_data_v2
- create_unified_columns
Do you want it to create unified columns?
- heal
Do you want to impute missing values on creation?
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_v2 in place.
Returns a duplicated csfmt_rts_data_v2.
Details
For more details see the vignette:
vignette("csfmt_rts_data_v2", package = "cstidy")
Smart assignment
csfmt_rts_data_v2 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
isoquarter
isoyearquarter
season
seasonweek
calyear
calmonth
calyearmonth
date
isoyearweek:
granularity_time
isoyear
isoweek
isoquarter
isoyearquarter
season
seasonweek
calyear
calmonth
calyearmonth
date
date:
granularity_time
isoyear
isoweek
isoyearweek
isoquarter
isoyearquarter
season
seasonweek
calyear
calmonth
calyearmonth
Unified columns
csfmt_rts_data_v2 contains 16 unified columns:
granularity_time
granularity_geo
country_iso3
location_code
border
age
sex
isoyear
isoweek
isoyearweek
isoquarter
isoyearquarter
season
seasonweek
calyear
calmonth
calyearmonth
date
See also
Other csfmt_rts_data:
expand_time_to(),
identify_data_structure(),
remove_class_csfmt_rts_data(),
set_csfmt_rts_data_v1(),
unique_time_series()
Examples
# Create some fake data as data.table
d <- cstidy::generate_test_data(fmt = "csfmt_rts_data_v2")
d <- d[1:5]
# convert to csfmt_rts_data_v2 by reference
cstidy::set_csfmt_rts_data_v2(d, create_unified_columns = TRUE)
#
d[1, isoyearweek := "2021-01"]
#> granularity_time granularity_geo country_iso3 location_code border age sex
#> 1: isoyearweek county nor county_nor42 NA <NA> <NA>
#> 2: isoyearweek county nor county_nor32 NA <NA> <NA>
#> 3: isoyearweek county nor county_nor33 NA <NA> <NA>
#> 4: isoyearweek county nor county_nor56 NA <NA> <NA>
#> 5: isoyearweek county nor county_nor34 NA <NA> <NA>
#> isoyear isoweek isoyearweek isoquarter isoyearquarter season seasonweek
#> 1: 2021 1 2021-01 1 2021-Q1 2020/2021 24
#> 2: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> 3: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> 4: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> 5: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> calyear calmonth calyearmonth date deaths_n
#> 1: NA NA <NA> 2021-01-10 0
#> 2: NA NA <NA> 2022-01-23 6
#> 3: NA NA <NA> 2022-01-23 3
#> 4: NA NA <NA> 2022-01-23 4
#> 5: NA NA <NA> 2022-01-23 5
d
#> granularity_time granularity_geo country_iso3 location_code border age sex
#> 1: isoyearweek county nor county_nor42 NA <NA> <NA>
#> 2: isoyearweek county nor county_nor32 NA <NA> <NA>
#> 3: isoyearweek county nor county_nor33 NA <NA> <NA>
#> 4: isoyearweek county nor county_nor56 NA <NA> <NA>
#> 5: isoyearweek county nor county_nor34 NA <NA> <NA>
#> isoyear isoweek isoyearweek isoquarter isoyearquarter season seasonweek
#> 1: 2021 1 2021-01 1 2021-Q1 2020/2021 24
#> 2: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> 3: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> 4: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> 5: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> calyear calmonth calyearmonth date deaths_n
#> 1: NA NA <NA> 2021-01-10 0
#> 2: NA NA <NA> 2022-01-23 6
#> 3: NA NA <NA> 2022-01-23 3
#> 4: NA NA <NA> 2022-01-23 4
#> 5: NA NA <NA> 2022-01-23 5
d[2, isoyear := 2019]
#> granularity_time granularity_geo country_iso3 location_code border age sex
#> 1: isoyearweek county nor county_nor42 NA <NA> <NA>
#> 2: isoyear county nor county_nor32 NA <NA> <NA>
#> 3: isoyearweek county nor county_nor33 NA <NA> <NA>
#> 4: isoyearweek county nor county_nor56 NA <NA> <NA>
#> 5: isoyearweek county nor county_nor34 NA <NA> <NA>
#> isoyear isoweek isoyearweek isoquarter isoyearquarter season seasonweek
#> 1: 2021 1 2021-01 1 2021-Q1 2020/2021 24
#> 2: 2019 52 2019-52 1 2022-Q1 <NA> NA
#> 3: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> 4: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> 5: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> calyear calmonth calyearmonth date deaths_n
#> 1: NA NA <NA> 2021-01-10 0
#> 2: NA NA <NA> 2019-12-29 6
#> 3: NA NA <NA> 2022-01-23 3
#> 4: NA NA <NA> 2022-01-23 4
#> 5: NA NA <NA> 2022-01-23 5
d
#> granularity_time granularity_geo country_iso3 location_code border age sex
#> 1: isoyearweek county nor county_nor42 NA <NA> <NA>
#> 2: isoyear county nor county_nor32 NA <NA> <NA>
#> 3: isoyearweek county nor county_nor33 NA <NA> <NA>
#> 4: isoyearweek county nor county_nor56 NA <NA> <NA>
#> 5: isoyearweek county nor county_nor34 NA <NA> <NA>
#> isoyear isoweek isoyearweek isoquarter isoyearquarter season seasonweek
#> 1: 2021 1 2021-01 1 2021-Q1 2020/2021 24
#> 2: 2019 52 2019-52 1 2022-Q1 <NA> NA
#> 3: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> 4: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> 5: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> calyear calmonth calyearmonth date deaths_n
#> 1: NA NA <NA> 2021-01-10 0
#> 2: NA NA <NA> 2019-12-29 6
#> 3: NA NA <NA> 2022-01-23 3
#> 4: NA NA <NA> 2022-01-23 4
#> 5: NA NA <NA> 2022-01-23 5
d[3, date := as.Date("2020-01-01")]
#> granularity_time granularity_geo country_iso3 location_code border age sex
#> 1: isoyearweek county nor county_nor42 NA <NA> <NA>
#> 2: isoyear county nor county_nor32 NA <NA> <NA>
#> 3: date county nor county_nor33 NA <NA> <NA>
#> 4: isoyearweek county nor county_nor56 NA <NA> <NA>
#> 5: isoyearweek county nor county_nor34 NA <NA> <NA>
#> isoyear isoweek isoyearweek isoquarter isoyearquarter season seasonweek
#> 1: 2021 1 2021-01 1 2021-Q1 2020/2021 24
#> 2: 2019 52 2019-52 1 2022-Q1 <NA> NA
#> 3: 2020 1 2020-01 1 2020-Q1 2019/2020 24
#> 4: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> 5: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> calyear calmonth calyearmonth date deaths_n
#> 1: NA NA <NA> 2021-01-10 0
#> 2: NA NA <NA> 2019-12-29 6
#> 3: 2020 1 2020-M01 2020-01-01 3
#> 4: NA NA <NA> 2022-01-23 4
#> 5: NA NA <NA> 2022-01-23 5
d
#> granularity_time granularity_geo country_iso3 location_code border age sex
#> 1: isoyearweek county nor county_nor42 NA <NA> <NA>
#> 2: isoyear county nor county_nor32 NA <NA> <NA>
#> 3: date county nor county_nor33 NA <NA> <NA>
#> 4: isoyearweek county nor county_nor56 NA <NA> <NA>
#> 5: isoyearweek county nor county_nor34 NA <NA> <NA>
#> isoyear isoweek isoyearweek isoquarter isoyearquarter season seasonweek
#> 1: 2021 1 2021-01 1 2021-Q1 2020/2021 24
#> 2: 2019 52 2019-52 1 2022-Q1 <NA> NA
#> 3: 2020 1 2020-01 1 2020-Q1 2019/2020 24
#> 4: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> 5: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> calyear calmonth calyearmonth date deaths_n
#> 1: NA NA <NA> 2021-01-10 0
#> 2: NA NA <NA> 2019-12-29 6
#> 3: 2020 1 2020-M01 2020-01-01 3
#> 4: NA NA <NA> 2022-01-23 4
#> 5: NA NA <NA> 2022-01-23 5
d[4, c("isoyear", "isoyearweek") := .(2021, "2021-01")]
#> Warning: Multiple time variables specified. Smart-assignment disabled.
#> granularity_time granularity_geo country_iso3 location_code border age sex
#> 1: isoyearweek county nor county_nor42 NA <NA> <NA>
#> 2: isoyear county nor county_nor32 NA <NA> <NA>
#> 3: date county nor county_nor33 NA <NA> <NA>
#> 4: isoyearweek county nor county_nor56 NA <NA> <NA>
#> 5: isoyearweek county nor county_nor34 NA <NA> <NA>
#> isoyear isoweek isoyearweek isoquarter isoyearquarter season seasonweek
#> 1: 2021 1 2021-01 1 2021-Q1 2020/2021 24
#> 2: 2019 52 2019-52 1 2022-Q1 <NA> NA
#> 3: 2020 1 2020-01 1 2020-Q1 2019/2020 24
#> 4: 2021 3 2021-01 1 2022-Q1 2021/2022 26
#> 5: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> calyear calmonth calyearmonth date deaths_n
#> 1: NA NA <NA> 2021-01-10 0
#> 2: NA NA <NA> 2019-12-29 6
#> 3: 2020 1 2020-M01 2020-01-01 3
#> 4: NA NA <NA> 2022-01-23 4
#> 5: NA NA <NA> 2022-01-23 5
d
#> granularity_time granularity_geo country_iso3 location_code border age sex
#> 1: isoyearweek county nor county_nor42 NA <NA> <NA>
#> 2: isoyear county nor county_nor32 NA <NA> <NA>
#> 3: date county nor county_nor33 NA <NA> <NA>
#> 4: isoyearweek county nor county_nor56 NA <NA> <NA>
#> 5: isoyearweek county nor county_nor34 NA <NA> <NA>
#> isoyear isoweek isoyearweek isoquarter isoyearquarter season seasonweek
#> 1: 2021 1 2021-01 1 2021-Q1 2020/2021 24
#> 2: 2019 52 2019-52 1 2022-Q1 <NA> NA
#> 3: 2020 1 2020-01 1 2020-Q1 2019/2020 24
#> 4: 2021 3 2021-01 1 2022-Q1 2021/2022 26
#> 5: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> calyear calmonth calyearmonth date deaths_n
#> 1: NA NA <NA> 2021-01-10 0
#> 2: NA NA <NA> 2019-12-29 6
#> 3: 2020 1 2020-M01 2020-01-01 3
#> 4: NA NA <NA> 2022-01-23 4
#> 5: NA NA <NA> 2022-01-23 5
d[5, c("location_code") := .("norge")]
#> granularity_time granularity_geo country_iso3 location_code border age sex
#> 1: isoyearweek county nor county_nor42 NA <NA> <NA>
#> 2: isoyear county nor county_nor32 NA <NA> <NA>
#> 3: date county nor county_nor33 NA <NA> <NA>
#> 4: isoyearweek county nor county_nor56 NA <NA> <NA>
#> 5: isoyearweek nation nor norge NA <NA> <NA>
#> isoyear isoweek isoyearweek isoquarter isoyearquarter season seasonweek
#> 1: 2021 1 2021-01 1 2021-Q1 2020/2021 24
#> 2: 2019 52 2019-52 1 2022-Q1 <NA> NA
#> 3: 2020 1 2020-01 1 2020-Q1 2019/2020 24
#> 4: 2021 3 2021-01 1 2022-Q1 2021/2022 26
#> 5: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> calyear calmonth calyearmonth date deaths_n
#> 1: NA NA <NA> 2021-01-10 0
#> 2: NA NA <NA> 2019-12-29 6
#> 3: 2020 1 2020-M01 2020-01-01 3
#> 4: NA NA <NA> 2022-01-23 4
#> 5: NA NA <NA> 2022-01-23 5
d
#> granularity_time granularity_geo country_iso3 location_code border age sex
#> 1: isoyearweek county nor county_nor42 NA <NA> <NA>
#> 2: isoyear county nor county_nor32 NA <NA> <NA>
#> 3: date county nor county_nor33 NA <NA> <NA>
#> 4: isoyearweek county nor county_nor56 NA <NA> <NA>
#> 5: isoyearweek nation nor norge NA <NA> <NA>
#> isoyear isoweek isoyearweek isoquarter isoyearquarter season seasonweek
#> 1: 2021 1 2021-01 1 2021-Q1 2020/2021 24
#> 2: 2019 52 2019-52 1 2022-Q1 <NA> NA
#> 3: 2020 1 2020-01 1 2020-Q1 2019/2020 24
#> 4: 2021 3 2021-01 1 2022-Q1 2021/2022 26
#> 5: 2022 3 2022-03 1 2022-Q1 2021/2022 26
#> calyear calmonth calyearmonth date deaths_n
#> 1: NA NA <NA> 2021-01-10 0
#> 2: NA NA <NA> 2019-12-29 6
#> 3: 2020 1 2020-M01 2020-01-01 3
#> 4: NA NA <NA> 2022-01-23 4
#> 5: NA NA <NA> 2022-01-23 5
# Investigating the data structure of one column inside a dataset
cstidy::generate_test_data() %>%
cstidy::set_csfmt_rts_data_v2() %>%
cstidy::identify_data_structure("deaths_n") %>%
plot()
# Investigating the data structure via summary
cstidy::generate_test_data() %>%
cstidy::set_csfmt_rts_data_v2() %>%
summary()
#>
#> granularity_time
#> ✅ No errors
#>
#> granularity_geo
#> ✅ No errors
#>
#> country_iso3
#> ✅ No errors
#>
#> location_code
#> ✅ No errors
#>
#> border
#> ❌ Errors:
#> - NA exists (not allowed)
#>
#> age
#> ✅ No errors
#>
#> sex
#> ✅ No errors
#>
#> isoyear
#> ✅ No errors
#>
#> isoweek
#> ✅ No errors
#>
#> isoyearweek
#> ✅ No errors
#>
#> isoquarter
#> ✅ No errors
#>
#> isoyearquarter
#> ✅ No errors
#>
#> season
#> ✅ No errors
#>
#> seasonweek
#> ✅ No errors
#>
#> calyear
#> ✅ No errors
#>
#> calmonth
#> ✅ No errors
#>
#> calyearmonth
#> ✅ No errors
#>
#> date
#> ✅ No errors
#> granularity_time (character):
#> - isoyearweek (n = 45)
#> granularity_geo (character):
#> - county (n = 45)
#> country_iso3 (character):
#> - nor (n = 45)
#> location_code (character)
#> border (integer):
#> - <NA> (n = 45)
#> age (character):
#> - 000_005 (n = 15)
#> - <NA> (n = 15)
#> - total (n = 15)
#> sex (character):
#> - <NA> (n = 15)
#> - total (n = 30)
#> isoyear (integer):
#> - 2022 (n = 45)
#> isoweek (integer)
#> isoyearweek (character)
#> isoquarter (integer)
#> isoyearquarter (character)
#> season (character):
#> - 2021/2022 (n = 45)
#> seasonweek (numeric)
#> calyear (integer)
#> calmonth (integer)
#> calyearmonth (character)
#> date (Date)
#> deaths_n (integer)
#>