| Title: | Tidy Schema Validation for Data Frames |
| Version: | 0.1.2 |
| Description: | Validate data.frames against schemas to ensure that data matches expectations. Define schemas using 'tidyselect' and predicate functions for type consistency, nullability, and more. Schema failure messages can be tailored for non-technical users and are ideal for user-facing applications such as in 'shiny' or 'plumber'. |
| Maintainer: | Will Hipson <will.e.hipson@gmail.com> |
| License: | MIT + file LICENSE |
| URL: | https://github.com/whipson/schematic, https://whipson.github.io/schematic/ |
| BugReports: | https://github.com/whipson/schematic/issues |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Imports: | cli, glue, purrr, rlang, tidyselect |
| Suggests: | knitr, rmarkdown, quarto, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| Depends: | R (≥ 4.1.0) |
| VignetteBuilder: | knitr, quarto |
| NeedsCompilation: | no |
| Packaged: | 2025-05-28 11:59:46 UTC; willhipson |
| Author: | Will Hipson  [aut,
cre, cph] |
| Repository: | CRAN |
| Date/Publication: | 2025-05-28 12:10:01 UTC |
Validate a data.frame against a schema
Description
Validate a data.frame against a schema
Usage
check_schema(data, schema)
Arguments
data |
A data.frame to check |
schema |
A Schema object created with 'schema()' |
Value
invisible if validation passes, otherwise stops with error
Examples
my_schema <- schema(
mpg ~ is.numeric
)
check_schema(mtcars, my_schema)
Check if all values in a vector are distinct
Description
Check if all values in a vector are distinct
Usage
is_all_distinct(x)
Arguments
x |
A vector |
Value
TRUE if the vector has all unique values
Examples
is_all_distinct(c(1:5)) # TRUE
is_all_distinct(c(1, 1, 2)) # FALSE
Check if the vector is sorted numerically or alphanumerically
Description
'NA's are not ignored and any vector with 'NA's will fail unless the whole vector is 'NA'.
Usage
is_incrementing(x)
Arguments
x |
A vector |
Value
TRUE if the vector is sorted
Examples
is_incrementing(1:5) # TRUE
is_incrementing(letters[1:5]) # TRUE
is_incrementing(c(4, 3, 0)) # FALSE
Check if all values are not NA
Description
Check if all values are not NA
Usage
is_non_null(x)
Arguments
x |
A vector |
Value
TRUE if the vector has no NA values
Examples
is_non_null(1:5) # TRUE
is_non_null(c(1, NA, 3)) # FALSE
Check if a vector has all positive integers
Description
A positive integer is a whole number that is greater than 0.
Usage
is_positive_integer(x)
Arguments
x |
A vector |
Details
This check requires 'is.integer(x)' to be true. If you want a more flexible check that allows for numbers of type 'numeric' but still want them to be integers, then use 'is_whole_number()'.
'NA's are ignored as long as they are 'NA_integer'.
Value
TRUE if all elements are positive integers (NA ignored)
Examples
is_positive_integer(c(1L, 2L, 4L)) # TRUE
is_positive_integer(2.4) # FALSE
is_positive_integer(-3) # FALSE
Check if a vector is text-based (character or factor)
Description
'NA's are ignored as long as they are 'NA_character_'.
Usage
is_text(x)
Arguments
x |
A vector |
Value
TRUE if vector is either character or factor
Examples
is_text(letters[1:4]) # TRUE
is_text(as.factor(letters[1:4])) # TRUE
is_text(1) # FALSE
Check if a vector has all whole numbers
Description
Similar to 'is_positive_integer()' but without the constraint that the underlying data type is actually integer. Useful if the numbers are stored as 'numeric' but you want to check that they are whole.
Usage
is_whole_number(x)
Arguments
x |
A vector |
Details
'NA's are ignored.
Value
TRUE if all elements are whole numbers (NA ignored)
Examples
is_whole_number(c(2.0, 4.0)) # TRUE
is_whole_number(c(-1.4)) # FALSE
Retrieve latest schematic run time errors
Description
Predicates that error will store the error messages internally and these can be accessed here.
Usage
last_check_errors()
Value
error messages
Examples
last_check_errors()
Ignore infinite values in a predicate
Description
This modifies a predicate function to ignore Inf.
Usage
mod_infinitable(pred)
Arguments
pred |
A predicate function |
Value
A new predicate that ignores infinites
Examples
# The `is_incrementing` predicate will fail here
x <- c(1, Inf, 3)
is_incrementing(x) # FALSE
is_incrementing_inf <- mod_infinitable(is_incrementing)
is_incrementing_inf(x) # TRUE
Allow NA in a predicate
Description
This modifies a predicate function to ignore NAs.
Usage
mod_nullable(pred)
Arguments
pred |
A predicate function |
Value
A new predicate that allows NAs
Examples
# The `is_incrementing` predicate will fail if there are NAs
x <- c(1, NA, 3)
is_incrementing(x) # FALSE
is_incrementing_null <- mod_nullable(is_incrementing)
is_incrementing_null(x) # TRUE
Print method for Schema
Description
Print method for Schema
Usage
## S3 method for class 'Schema'
print(x, ...)
Arguments
x |
Object of class Schema |
... |
Other arguments passed to 'print()' |
Value
invisible
Create a schema object
Description
Create a schema object
Usage
schema(...)
Arguments
... |
Formulae of the form tidyselect_expr ~ predicate |
Value
A Schema object
Examples
# Simple schema with one declared column
my_schema <- schema(
mpg ~ is.double
)
# Multiple columns
my_schema <- schema(
Sepal.Length ~ is.numeric,
Species ~ is.factor
)
# Use tidyselect syntax and anonymous functions
my_schema <- schema(
starts_with("Sepal") ~ is.numeric,
c(Petal.Length, Petal.Width) ~ function(x) all(x > 0)
)
# Use named arguments to customize error messages
my_schema <- schema(
`Must be a positive number` = cyl ~ function(x) all(x > 0)
)