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Validate individual level data

Source: R/st_validate.R
st_validate.Rd

[Experimental] Locates the required and optional columns in your data and validates them.

Usage

st_validate(
  data,
  dataset_id,
  id,
  adm0,
  adm1 = NULL,
  adm2 = NULL,
  collection_start_date,
  collection_end_date,
  test_id,
  result,
  result_cat = NULL,
  age_group = NULL,
  age = NULL,
  sex = NULL,
  include_others = TRUE,
  rmd_safe = FALSE
)

Arguments

data

a data.frame.

dataset_id

An unquoted column name or a length-one vector that differentiates the data collection event(s).

id

column for anonymized individual level IDs. This column will be used to generate aggregate estimates.

adm0, adm1, adm2

a string or an unquoted name of a character column that contains the country (adm0), state/province (adm1), or district/municipality (adm2) codes. Use serotrackr::regions to select these. Only one adm0 is acceptable. adm1 and adm2 can be more.

collection_start_date, collection_end_date

Unquoted name of a date or character column or a date or string scalar (vector of length one) for sampling start and end dates. lubridate::parse_date_time2() is used to parse dates. Only yyyy-mm-dd or dd-mm-yyyy structures are acceptable. It recognize arbitrary non-digit separators as well as no separator. Month Can be entered as a digit or a full or abbreviated name.

test_id

a string or an unquoted name of a character column that contains the test IDs. Use serotrackr::assays to select these.

result

Unquoted name of a numeric column containing test results.

result_cat

Unquoted name of a character column with values of positive, borderline, or negative, ignoring case. A single string is also acceptable.

age_group

Unquoted name of a character column or a string containing age group(s). The only structures acceptable are number-number or number+. E.g. 18-64, and 65+.

age

Unquoted name of a numeric column or a single number. Acceptable values are between 0 and 120 inclusive.

sex

Unquoted name of a character column or a string. Acceptable values are: f, m, o, female, male, or other ignoring case.

include_others

include additional columns or not

rmd_safe

Logical. If TRUE, the output message will be appropriate for R markdown, i.e. progress indicators are removed and all the messages are printed at the same time, making only one chunk in the R markdown's knitted output. If FALSE (default), the progress indicators and messages are printed for each argument one by one, making it appropriate for interactive use.

Value

A validated data.frame

Examples

st_validate(
  sample_raw_data,
  dataset_id = dataset_id,
  id = id,
  age_group = "12-17",
  sex = "m",
  adm0 = regions$adm0$Canada,
  adm1 = regions$adm1$Canada$Alberta,
  adm2 = regions$adm2$Canada$Alberta$Calgary,
  collection_start_date = "2023-01-01",
  collection_end_date = "2023-02-01",
  test_id = assays$`SARS-CoV-2`$`AAZ LMB - IgG, IgM - COVID-PRESTO®`,
  result = result,
  result_cat = "negative",
  include_others = TRUE,
  rmd_safe = TRUE
)
#> ── Mapping columns and validating data ─────────────────────────────────────────
#> ✔ age_group is a valid string. [22ms]
#> ✔ sex is a valid string. [10ms]
#> ✔ adm0 is a valid string. [9ms]
#> ✔ adm1 is a valid string. [9ms]
#> ✔ adm2 is a valid string. [12ms]
#> ✔ collection_start_date is a valid scalar. [12ms]
#> ✔ collection_end_date is a valid scalar. [19ms]
#> ✔ test_id is a valid string. [6ms]
#> ✔ result is a valid column. [9ms]
#> ✔ result_cat is a valid string. [7ms]
#> ✔ dataset_id is a valid column. [3ms]
#> ✔ id is a valid column. [12ms]
#> ── Validation finished ─────────────────────────────────────────────────────────
#> Success! Validated data created.
#> # A tibble: 100 × 16
#>    dataset_id    id age_group sex   adm1             adm2  collection_start_date
#>         <int> <int> <chr>     <chr> <chr>            <chr> <date>               
#>  1          1     1 12-17     Male  4576071B9681799… 7649… 2023-01-01           
#>  2          1     2 12-17     Male  4576071B9681799… 7649… 2023-01-01           
#>  3          1     3 12-17     Male  4576071B9681799… 7649… 2023-01-01           
#>  4          1     4 12-17     Male  4576071B9681799… 7649… 2023-01-01           
#>  5          1     5 12-17     Male  4576071B9681799… 7649… 2023-01-01           
#>  6          1     6 12-17     Male  4576071B9681799… 7649… 2023-01-01           
#>  7          1     7 12-17     Male  4576071B9681799… 7649… 2023-01-01           
#>  8          1     8 12-17     Male  4576071B9681799… 7649… 2023-01-01           
#>  9          1     9 12-17     Male  4576071B9681799… 7649… 2023-01-01           
#> 10          1    10 12-17     Male  4576071B9681799… 7649… 2023-01-01           
#> # ℹ 90 more rows
#> # ℹ 9 more variables: collection_end_date <date>, test_id <chr>, result <dbl>,
#> #   result_cat <chr>, country <chr>, state <chr>, city <chr>, start_date <chr>,
#> #   end_date <chr>