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R validate package for data validation. Use for defining and checking data validation rules.
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| name | validate |
| description | R validate package for data validation. Use for defining and checking data validation rules. |
Data validation infrastructure.
library(validate)
# Create validator
rules <- validator(
age >= 0,
age <= 120,
income >= 0,
!is.na(name)
)
# From expressions
rules <- validator(
positive_age = age >= 0,
valid_income = income > 0,
has_name = nchar(name) > 0
)
# Confront data with rules
result <- confront(df, rules)
# Summary
summary(result)
# Values (TRUE/FALSE/NA)
values(result)
# As data frame
as.data.frame(result)
rules <- validator(
# Range checks
age %in% 0:120,
# Pattern matching
grepl("^[A-Z]", name),
# Cross-field validation
end_date >= start_date,
# Aggregates
mean(income) > 0,
# Uniqueness
is_unique(id),
# Completeness
is_complete(name, age)
)
# Define indicators (metrics)
ind <- indicator(
mean_age = mean(age, na.rm = TRUE),
pct_missing = mean(is.na(income)) * 100,
n_records = .N
)
# Add to confrontation
add_indicator(result, ind)
# Export to YAML
export_yaml(rules, "rules.yaml")
# Import from YAML
rules <- validator(.file = "rules.yaml")
# Export to data frame
as.data.frame(rules)
# Barplot of results
barplot(result)
# Aggregate by rule
aggregate(result)
# Aggregate by record
aggregate(result, by = "record")
# Find erroneous values
errors <- values(result)
df[!errors[, "positive_age"], ]
# Add descriptions
rules <- validator(
age >= 0,
.description = "Age must be non-negative"
)
# Get rule info
meta(rules)