Create a sparse confrontation query

Create a sparse confrontation query. Only errors and missing are stored. This stores all results of a tbl validation in a table with length(rules) columns and nrow(tbl) rows. Note that the result of this function is a (lazy) query object that still needs to be executed in the database, e.g. with dplyr::collect(), dplyr::collapse() or dplyr::compute().

Usage

confront_tbl_sparse(tbl, x, key, union_all = TRUE, check_rules = TRUE)

Arguments

tbl: dbplyr::tbl_dbi() table in a database, retrieved with tbl()
x: validate::validator() object with validation rules.
key: character with key column name, must be specified
union_all: if FALSE each rule is a separate query.
check_rules: if TRUE it is checked which rules 'work' on the db.

Value

A object with the necessary information: see details

Details

The return value of the function is a list with:

$query: A dbplyr::tbl_dbi() object that refers to the confrontation query.
$errors: The validation rules that are not working on the database
$working: A logical with which expression are working on the database.
$exprs: All validation expressions.

Examples

# create a table in a database
income <- data.frame(id = letters[1:2], age=c(12,35), salary = c(1000,NA))
con <- dbplyr::src_memdb()
tbl_income <- dplyr::copy_to(con, income, overwrite=TRUE)
print(tbl_income)
#> # Source:   table<income> [?? x 3]
#> # Database: sqlite 3.38.5 [:memory:]
#>   id      age salary
#>   <chr> <dbl>  <dbl>
#> 1 a        12   1000
#> 2 b        35     NA

# Let's define a rule set and confront the table with it:
rules <- validator( is_adult   = age >= 18
                  , has_income = salary > 0
                  , mean_age   = mean(age,na.rm=TRUE) > 20
                  )

# and confront! (we have to use a key, because a db...)
cf <- confront(tbl_income, rules, key = "id")
print(cf)
#> Object of class 'tbl_validation'
#> Call:
#>     confront.tbl_sql(tbl = dat, x = x, ref = ref, key = key, sparse = sparse)
#> 
#> Confrontations: 3
#> Tbl           : income (:memory:)
#> Key column    : id
#> Sparse        : FALSE
#> Fails         : [??] (see `values`, `summary`)
#> Errors        : 0
summary(cf)
#>                  name items npass nfail nNA warning error
#> is_adult     is_adult     2     1     1   0   FALSE FALSE
#> has_income has_income     2     1     0   1   FALSE FALSE
#> mean_age     mean_age     1     1     0   0   FALSE FALSE
#>                              expression
#> is_adult             age - 18 >= -1e-08
#> has_income                   salary > 0
#> mean_age   mean(age, na.rm = TRUE) > 20

# Values (i.e. validations on the table) can be retrieved like in `validate` 
# with`type="matrix"` (simplify = TRUE)
values(cf, type = "matrix")
#> [[1]]
#>      is_adult has_income
#> [1,]    FALSE       TRUE
#> [2,]     TRUE         NA
#> 
#> [[2]]
#>      mean_age
#> [1,]     TRUE
#> 

# But often this seems more handy:
values(cf, type = "tbl")
#> # Source:   lazy query [?? x 4]
#> # Database: sqlite 3.38.5 [:memory:]
#>   id    is_adult has_income mean_age
#>   <chr>    <int>      <int>    <int>
#> 1 a            0          1        1
#> 2 b            1         NA        1

# We can see the sql code by using `show_query`:
show_query(cf)
#> <SQL>
#> SELECT `id`, CAST(`is_adult` AS BOOLEAN) AS `is_adult`, CAST(`has_income` AS BOOLEAN) AS `has_income`, CAST(`mean_age` AS BOOLEAN) AS `mean_age`
#> FROM (SELECT `id`, NULLIF(`is_adult`, -1) AS `is_adult`, NULLIF(`has_income`, -1) AS `has_income`, NULLIF(`mean_age`, -1) AS `mean_age`
#> FROM (SELECT `id`, MIN(`is_adult`) AS `is_adult`, MIN(`has_income`) AS `has_income`, MIN(`mean_age`) AS `mean_age`
#> FROM (SELECT `id`, CASE `rule` WHEN ('is_adult') THEN (COALESCE(1 - CAST(`fail` AS INTEGER), -1)) ELSE (1) END AS `is_adult`, CASE `rule` WHEN ('has_income') THEN (COALESCE(1 - CAST(`fail` AS INTEGER), -1)) ELSE (1) END AS `has_income`, CASE `rule` WHEN ('mean_age') THEN (COALESCE(1 - CAST(`fail` AS INTEGER), -1)) ELSE (1) END AS `mean_age`
#> FROM (SELECT `LHS`.`id` AS `id`, `rule`, `fail`
#> FROM (SELECT `id`
#> FROM `income`) AS `LHS`
#> LEFT JOIN (SELECT `id`, 'is_adult' AS `rule`, 1 AS `fail`
#> FROM (SELECT `id`, `age`
#> FROM `income`)
#> WHERE (`age` - 18.0 < -1e-08)
#> UNION ALL
#> SELECT `id`, 'is_adult' AS `rule`, NULL AS `fail`
#> FROM (SELECT `id`, `age`
#> FROM `income`)
#> WHERE (((`age`) IS NULL))
#> UNION ALL
#> SELECT `id`, 'has_income' AS `rule`, 1 AS `fail`
#> FROM (SELECT `id`, `salary`
#> FROM `income`)
#> WHERE (`salary` <= 0.0)
#> UNION ALL
#> SELECT `id`, 'has_income' AS `rule`, NULL AS `fail`
#> FROM (SELECT `id`, `salary`
#> FROM `income`)
#> WHERE (((`salary`) IS NULL))
#> UNION ALL
#> SELECT `id`, 'mean_age' AS `rule`, 1 AS `fail`
#> FROM (SELECT `id`, `age`
#> FROM (SELECT `id`, `age`, AVG(`age`) OVER () AS `q01`
#> FROM (SELECT `id`, `age`
#> FROM `income`))
#> WHERE (`q01` <= 20.0))
#> UNION ALL
#> SELECT `id`, 'mean_age' AS `rule`, NULL AS `fail`
#> FROM (SELECT `id`, `age`
#> FROM `income`)
#> WHERE (((`age`) IS NULL))) AS `RHS`
#> ON (`LHS`.`id` = `RHS`.`id`)
#> ))
#> GROUP BY `id`))

# identical
show_query(values(cf, type = "tbl"))
#> <SQL>
#> SELECT `id`, CAST(`is_adult` AS BOOLEAN) AS `is_adult`, CAST(`has_income` AS BOOLEAN) AS `has_income`, CAST(`mean_age` AS BOOLEAN) AS `mean_age`
#> FROM (SELECT `id`, NULLIF(`is_adult`, -1) AS `is_adult`, NULLIF(`has_income`, -1) AS `has_income`, NULLIF(`mean_age`, -1) AS `mean_age`
#> FROM (SELECT `id`, MIN(`is_adult`) AS `is_adult`, MIN(`has_income`) AS `has_income`, MIN(`mean_age`) AS `mean_age`
#> FROM (SELECT `id`, CASE `rule` WHEN ('is_adult') THEN (COALESCE(1 - CAST(`fail` AS INTEGER), -1)) ELSE (1) END AS `is_adult`, CASE `rule` WHEN ('has_income') THEN (COALESCE(1 - CAST(`fail` AS INTEGER), -1)) ELSE (1) END AS `has_income`, CASE `rule` WHEN ('mean_age') THEN (COALESCE(1 - CAST(`fail` AS INTEGER), -1)) ELSE (1) END AS `mean_age`
#> FROM (SELECT `LHS`.`id` AS `id`, `rule`, `fail`
#> FROM (SELECT `id`
#> FROM `income`) AS `LHS`
#> LEFT JOIN (SELECT `id`, 'is_adult' AS `rule`, 1 AS `fail`
#> FROM (SELECT `id`, `age`
#> FROM `income`)
#> WHERE (`age` - 18.0 < -1e-08)
#> UNION ALL
#> SELECT `id`, 'is_adult' AS `rule`, NULL AS `fail`
#> FROM (SELECT `id`, `age`
#> FROM `income`)
#> WHERE (((`age`) IS NULL))
#> UNION ALL
#> SELECT `id`, 'has_income' AS `rule`, 1 AS `fail`
#> FROM (SELECT `id`, `salary`
#> FROM `income`)
#> WHERE (`salary` <= 0.0)
#> UNION ALL
#> SELECT `id`, 'has_income' AS `rule`, NULL AS `fail`
#> FROM (SELECT `id`, `salary`
#> FROM `income`)
#> WHERE (((`salary`) IS NULL))
#> UNION ALL
#> SELECT `id`, 'mean_age' AS `rule`, 1 AS `fail`
#> FROM (SELECT `id`, `age`
#> FROM (SELECT `id`, `age`, AVG(`age`) OVER () AS `q01`
#> FROM (SELECT `id`, `age`
#> FROM `income`))
#> WHERE (`q01` <= 20.0))
#> UNION ALL
#> SELECT `id`, 'mean_age' AS `rule`, NULL AS `fail`
#> FROM (SELECT `id`, `age`
#> FROM `income`)
#> WHERE (((`age`) IS NULL))) AS `RHS`
#> ON (`LHS`.`id` = `RHS`.`id`)
#> ))
#> GROUP BY `id`))

# sparse results in db (that the default)
values(cf, type="tbl", sparse=TRUE)
#> # Source:   lazy query [?? x 3]
#> # Database: sqlite 3.38.5 [:memory:]
#>   id    rule        fail
#>   <chr> <chr>      <int>
#> 1 a     is_adult       1
#> 2 b     has_income    NA

# or if you like data.frames
values(cf, type="data.frame", sparse=TRUE)
#>   id       rule fail
#> 1  a   is_adult TRUE
#> 2  b has_income   NA

Usage

Arguments

Value

Details

See also

Examples