---
title: "Introduction to jsonvalidate"
author: "Rich FitzJohn"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to jsonvalidate}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r echo = FALSE, results = "hide"}
knitr::opts_chunk$set(error = FALSE)
```

This package wraps
[is-my-json-valid](https://github.com/mafintosh/is-my-json-valid)
using [V8](https://cran.r-project.org/package=V8) to do JSON schema
validation in R.

You need a JSON schema file; see
[json-schema.org](https://json-schema.org/) for details on writing
these.  Often someone else has done the hard work of writing one
for you, and you can just check that the JSON you are producing or
consuming conforms to the schema.

The examples below come from the [JSON schema
website](https://json-schema.org//learn/getting-started-step-by-step.html)

They describe a JSON based product catalogue, where each product
has an id, a name, a price, and an optional set of tags.  A JSON
representation of a product is:

```json
{
    "id": 1,
    "name": "A green door",
    "price": 12.50,
    "tags": ["home", "green"]
}
```

The schema that they derive looks like this:

```json
{
    "$schema": "http://json-schema.org/draft-04/schema#",
    "title": "Product",
    "description": "A product from Acme's catalog",
    "type": "object",
    "properties": {
        "id": {
            "description": "The unique identifier for a product",
            "type": "integer"
        },
        "name": {
            "description": "Name of the product",
            "type": "string"
        },
        "price": {
            "type": "number",
            "minimum": 0,
            "exclusiveMinimum": true
        },
        "tags": {
            "type": "array",
            "items": {
                "type": "string"
            },
            "minItems": 1,
            "uniqueItems": true
        }
    },
    "required": ["id", "name", "price"]
}
```

This ensures the types of all fields, enforces presence of `id`,
`name` and `price`, checks that the price is not negative and
checks that if present `tags` is a unique list of strings.

There are two ways of passing the schema in to R; as a string or as
a filename.  If you have a large schema loading as a file will
generally be easiest!  Here's a string representing the schema
(watch out for escaping quotes):

```{r}
schema <- '{
    "$schema": "http://json-schema.org/draft-04/schema#",
    "title": "Product",
    "description": "A product from Acme\'s catalog",
    "type": "object",
    "properties": {
        "id": {
            "description": "The unique identifier for a product",
            "type": "integer"
        },
        "name": {
            "description": "Name of the product",
            "type": "string"
        },
        "price": {
            "type": "number",
            "minimum": 0,
            "exclusiveMinimum": true
        },
        "tags": {
            "type": "array",
            "items": {
                "type": "string"
            },
            "minItems": 1,
            "uniqueItems": true
        }
    },
    "required": ["id", "name", "price"]
}'
```

Create a schema object, which can be used to validate a schema:

```{r}
obj <- jsonvalidate::json_schema$new(schema)
```

If we'd saved the json to a file, this would work too:

```{r}
path <- tempfile()
writeLines(schema, path)
obj <- jsonvalidate::json_schema$new(path)
```

```{r include = FALSE}
file.remove(path)
```

The returned object is a function that takes as its first argument
a json string, or a filename of a json file.  The empty list will
fail validation because it does not contain any of the required fields:

```{r}
obj$validate("{}")
```

To get more information on why the validation fails, add `verbose = TRUE`:

```{r}
obj$validate("{}", verbose = TRUE)
```

The attribute "errors" is a data.frame and is present only when the
json fails validation.  The error messages come straight from
`ajv` and they may not always be that informative.

Alternatively, to throw an error if the json does not validate, add
`error = TRUE` to the call:

```{r error = TRUE}
obj$validate("{}", error = TRUE)
```

The JSON from the opening example works:

```{r}
obj$validate('{
    "id": 1,
    "name": "A green door",
    "price": 12.50,
    "tags": ["home", "green"]
}')
```

But if we tried to enter a negative price it would fail:

```{r}
obj$validate('{
    "id": 1,
    "name": "A green door",
    "price": -1,
    "tags": ["home", "green"]
}', verbose = TRUE)
```

...or duplicate tags:

```{r}
obj$validate('{
    "id": 1,
    "name": "A green door",
    "price": 12.50,
    "tags": ["home", "home"]
}', verbose = TRUE)
```

or just basically everything wrong:
```{r}
obj$validate('{
    "id": "identifier",
    "name": 1,
    "price": -1,
    "tags": ["home", "home", 1]
}', verbose = TRUE)
```

The names comes from within the `ajv` source, and may be annoying to work with programmatically.

There is also a simple interface where you take the schema and the
json at the same time:

```{r}
json <- '{
    "id": 1,
    "name": "A green door",
    "price": 12.50,
    "tags": ["home", "green"]
}'
jsonvalidate::json_validate(json, schema, engine = "ajv")
```

However, this will be much slower than building the schema object once and using it repeatedly.

Prior to 1.4.0, the recommended way of building a reusable validator object was to use `jsonvalidate::json_validator`; this is still supported but note that it has different defaults to `jsonvalidate::json_schema` (using imjv for backward compatibility).

```{r}
v <- jsonvalidate::json_validator(schema, engine = "ajv")
v(json)
```

While we do not intend on removing this old interface, new code should prefer both `jsonvalidate::json_schema` and the `ajv` engine.

## Combining schemas

You can combine schemas with `ajv` engine. You can reference definitions within one schema

```{r}
schema <- '{
  "$schema": "http://json-schema.org/draft-04/schema#",
  "definitions": {
    "city": { "type": "string" }
  },
  "type": "object",
  "properties": {
    "city": { "$ref": "#/definitions/city" }
  }
}'
json <- '{
    "city": "Firenze"
}'
jsonvalidate::json_validate(json, schema, engine = "ajv")
```
You can reference schema from other files

```{r}
city_schema <- '{
  "$schema": "http://json-schema.org/draft-07/schema",
  "type": "string",
  "enum": ["Firenze"]
}'
address_schema <- '{
  "$schema": "http://json-schema.org/draft-07/schema",
  "type":"object",
  "properties": {
    "city": { "$ref": "city.json" }
  }
}'

path <- tempfile()
dir.create(path)
address_path <- file.path(path, "address.json")
city_path <- file.path(path, "city.json")
writeLines(address_schema, address_path)
writeLines(city_schema, city_path)
jsonvalidate::json_validate(json, address_path, engine = "ajv")
```

You can combine schemas in subdirectories. Note that the `$ref` path needs to be relative to the schema path. You cannot use absolute paths in `$ref` and jsonvalidate will throw an error if you try to do so.

```{r}
user_schema = '{
  "$schema": "http://json-schema.org/draft-07/schema",
  "type": "object",
  "required": ["address"],
  "properties": {
    "address": {
      "$ref": "sub/address.json"
    }
  }
}'

json <- '{
  "address": {
    "city": "Firenze"
  }
}'

path <- tempfile()
subdir <- file.path(path, "sub")
dir.create(subdir, showWarnings = FALSE, recursive = TRUE)
city_path <- file.path(subdir, "city.json")
address_path <- file.path(subdir, "address.json")
user_path <- file.path(path, "schema.json")
writeLines(city_schema, city_path)
writeLines(address_schema, address_path)
writeLines(user_schema, user_path)
jsonvalidate::json_validate(json, user_path, engine = "ajv")
```
