Title: Helpful Functions for Cleaning Surveillance Data
Version: 2023.5.24
Description: Helpful functions for the cleaning and manipulation of surveillance data, especially with regards to the creation and validation of panel data from individual level surveillance data.
Depends: R (≥ 3.5.0)
License: MIT + file LICENSE
URL: https://www.csids.no/cstidy/, https://github.com/csids/cstidy
BugReports: https://github.com/csids/cstidy/issues
Encoding: UTF-8
LazyData: true
Imports: data.table, magrittr, ggplot2, csdata, cstime, crayon, digest, stringr, methods
Suggests: testthat, knitr, rmarkdown, rstudioapi, glue, gt, dplyr, purrr
RoxygenNote: 7.2.3
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2023-05-23 08:15:56 UTC; raw996
Author: Richard Aubrey White ORCID iD [aut, cre], CSIDS [cph]
Maintainer: Richard Aubrey White <hello@rwhite.no>
Repository: CRAN
Date/Publication: 2023-05-24 07:40:02 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling rhs(lhs).

Field contents validator (csfmt_rts_data_v1) An example (schema) validator of database data used in csfmt_rts_data_v1

Description

Field contents validator (csfmt_rts_data_v1) An example (schema) validator of database data used in csfmt_rts_data_v1

Usage

csdb_validator_field_contents_csfmt_rts_data_v1(data)

Arguments

data

data passed to schema

Value

Boolean, corresponding to where or not the validator is passed.

Field types validator (csfmt_rts_data_v1) An example (schema) validator of field_types used in csfmt_rts_data_v1

Description

Field types validator (csfmt_rts_data_v1) An example (schema) validator of field_types used in csfmt_rts_data_v1

Usage

csdb_validator_field_types_csfmt_rts_data_v1(db_field_types)

Arguments

db_field_types

db_field_types passed to schema

Value

Boolean, corresponding to where or not the validator is passed.

Expand time to

Description

Attempts to expand the dataset to include more time

A time series is defined as a unique combination of:

Usage

expand_time_to(
  x,
  max_isoyear = NULL,
  max_isoyearweek = NULL,
  max_date = NULL,
  ...
)

Arguments

x

An object of type csfmt_rts_data_v1

max_isoyear

Maximum isoyear

max_isoyearweek

Maximum isoyearweek

max_date

Maximum date

...

Not used.

Value

csfmt_rts_data_v1, a larger dataset that includes more rows corresponding to more time.

See Also

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

Generate test data

Description

Generates some test data

Usage

generate_test_data(fmt = "csfmt_rts_data_v1")

Arguments

fmt

Data format (csfmt_rts_data_v1)

Value

csfmt_rts_data_v1, a dataset containing fake data.

Examples

cstidy::generate_test_data("csfmt_rts_data_v1")

Provides corresponding healed times

Description

Provides corresponding healed times

Usage

heal_time_csfmt_rts_data_v1(x, cols, granularity_time = "date")

Arguments

x

A vector containing either dates, isoyearweek, or isoyear.

cols

Columns to restrict the output to.

granularity_time

date, isoyearweek, or isoyear, depending on the values contained in x.

Value

data.table, a dataset with time columns corresponding to the values given in x.

Hash the data structure of a dataset for a given column

Description

Reduces the data structure of a column inside a dataset into something that describes

Usage

identify_data_structure(x, col, ...)

## S3 method for class 'csfmt_rts_data_v1'
identify_data_structure(x, col, ...)

## S3 method for class ''tbl_Microsoft SQL Server''
identify_data_structure(x, col, ...)

Arguments

x

An object

col

Column name to hash

...

Arguments passed to or from other methods

Value

csfmt_rts_data_structure_hash_v1, a summary object.

See Also

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

Examples

cstidy::generate_test_data() %>%
  cstidy::set_csfmt_rts_data_v1() %>%
  cstidy::identify_data_structure("deaths_n") %>%
  plot()

Covid-19 data for PCR-confirmed cases in Norway (nation and county)

Description

This data comes from the Norwegian Surveillance System for Communicable Diseases (MSIS). The date corresponds to when the PCR-test was taken.

Usage

nor_covid19_cases_by_time_location_csfmt_rts_v1

Format

A csfmt_rts_data_v1 with 11028 rows and 18 variables:

granularity_time

day/isoweek

granularity_geo

nation, county

country_iso3

nor

location_code

norge, 11 counties

border

2020

age

total

isoyear

Isoyear of event

isoweek

Isoweek of event

isoyearweek

Isoyearweek of event

season

Season of event

seasonweek

Seasonweek of event

calyear

Calyear of event

calmonth

Calmonth of event

calyearmonth

Calyearmonth of event

date

Date of event

covid19_cases_testdate_n

Number of confirmed covid19 cases

covid19_cases_testdate_pr100000

Number of confirmed covid19 cases per 100.000 population

Details

The raw number of cases and cases per 100.000 population are recorded.

This data was extracted on 2022-05-04.

Source

https://github.com/folkehelseinstituttet/surveillance_data/blob/master/covid19/_DOCUMENTATION_data_covid19_msis_by_time_location.txt

Norwegian Covid-19 data for ICU and hospitalization

Description

This data was extracted on 2022-05-04.

Usage

nor_covid19_icu_and_hospitalization_csfmt_rts_v1

Format

A csfmt_rts_data_v1 with 919 rows and 18 variables:

granularity_time

day/isoweek

granularity_geo

nation

country_iso3

nor

location_code

norge

border

2020

age

total

isoyear

Isoyear of event

isoweek

Isoweek of event

isoyearweek

Isoyearweek of event

season

Season of event

seasonweek

Seasonweek of event

calyear

Calyear of event

calmonth

Calmonth of event

calyearmonth

Calyearmonth of event

date

Date of event

icu_with_positive_pcr_n

Number of new admissions to the ICU with a positive PCR test

hospitalization_with_covid19_as_primary_cause_n

Number of new hospitalizations with Covid-19 as the primary cause

Source

https://github.com/folkehelseinstituttet/surveillance_data/blob/master/covid19/_DOCUMENTATION_data_covid19_hospital_by_time.txt

Remove class csfmt_rts_data_*

Description

Remove class csfmt_rts_data_*

Usage

remove_class_csfmt_rts_data(x)

Arguments

x

data.table

Value

No return value, called for the side effect of removing the csfmt_rts_data class from x.

See Also

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

Examples

x <- cstidy::generate_test_data() %>%
  cstidy::set_csfmt_rts_data_v1()
class(x)
cstidy::remove_class_csfmt_rts_data(x)
class(x)

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:

isoyear:

isoyearweek:

date:

Unified columns

csfmt_rts_data_v1 contains 16 unified columns:

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()

Unique time series

Description

Attempts to identify the unique time series that exist in this dataset.

A time series is defined as a unique combination of:

Usage

unique_time_series(x, set_time_series_id = FALSE, ...)

Arguments

x

An object of type csfmt_rts_data_v1

set_time_series_id

If TRUE, then x will have a new column called 'time_series_id'

...

Not used.

Value

data.table, a dataset that lists all the unique time series in x.

See Also

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