JSON versus YAML

Last updated: 2024-06-10

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Rmd	c7dcdf2	Dave Tang	2023-06-14	Parsing JSON
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Rmd	50c92d7	Dave Tang	2023-05-19	JSON and YAML formats

JSON and YAML are popular serialisation formats.

In computing, serialization (or serialisation) is the process of translating a data structure or object state into a format that can be stored (e.g. files in secondary storage devices, data buffers in primary storage devices) or transmitted (e.g. data streams over computer networks) and reconstructed later (possibly in a different computer environment).

Install the following packages:

jsonlite for parsing JSON
yaml for parsing YAML
tidyjson for converting JSON to tidy data frames
rjson for parsing JSON

install.packages(c("jsonlite", "yaml", "tidyjson", "rjson"))

Load libraries.

library(jsonlite)
library(yaml)
library(tidyjson)


Attaching package: 'tidyjson'

The following object is masked from 'package:jsonlite':

    read_json

The following object is masked from 'package:stats':

    filter

library(rjson)


Attaching package: 'rjson'

The following objects are masked from 'package:jsonlite':

    fromJSON, toJSON

As a first example, we will convert the women data set, which is a small data set with 15 observations for 2 variables.

women

   height weight
1      58    115
2      59    117
3      60    120
4      61    123
5      62    126
6      63    129
7      64    132
8      65    135
9      66    139
10     67    142
11     68    146
12     69    150
13     70    154
14     71    159
15     72    164

Convert women to JSON using jsonlite.

women_json <- jsonlite::toJSON(women, pretty = TRUE)
women_json

[
  {
    "height": 58,
    "weight": 115
  },
  {
    "height": 59,
    "weight": 117
  },
  {
    "height": 60,
    "weight": 120
  },
  {
    "height": 61,
    "weight": 123
  },
  {
    "height": 62,
    "weight": 126
  },
  {
    "height": 63,
    "weight": 129
  },
  {
    "height": 64,
    "weight": 132
  },
  {
    "height": 65,
    "weight": 135
  },
  {
    "height": 66,
    "weight": 139
  },
  {
    "height": 67,
    "weight": 142
  },
  {
    "height": 68,
    "weight": 146
  },
  {
    "height": 69,
    "weight": 150
  },
  {
    "height": 70,
    "weight": 154
  },
  {
    "height": 71,
    "weight": 159
  },
  {
    "height": 72,
    "weight": 164
  }
]

read_json does not parse the output of toJSON.

jsonlite::write_json(x = women_json, path = "women.json")
tidyjson::read_json(path = "women.json")

# A tbl_json: 1 x 2 tibble with a "JSON" attribute
  ..JSON                   document.id
  <chr>                          <int>
1 "[\"[\\n  {\\n    \\..."           1

Converts into list.

str(rjson::fromJSON(women_json))

List of 15
 $ :List of 2
  ..$ height: num 58
  ..$ weight: num 115
 $ :List of 2
  ..$ height: num 59
  ..$ weight: num 117
 $ :List of 2
  ..$ height: num 60
  ..$ weight: num 120
 $ :List of 2
  ..$ height: num 61
  ..$ weight: num 123
 $ :List of 2
  ..$ height: num 62
  ..$ weight: num 126
 $ :List of 2
  ..$ height: num 63
  ..$ weight: num 129
 $ :List of 2
  ..$ height: num 64
  ..$ weight: num 132
 $ :List of 2
  ..$ height: num 65
  ..$ weight: num 135
 $ :List of 2
  ..$ height: num 66
  ..$ weight: num 139
 $ :List of 2
  ..$ height: num 67
  ..$ weight: num 142
 $ :List of 2
  ..$ height: num 68
  ..$ weight: num 146
 $ :List of 2
  ..$ height: num 69
  ..$ weight: num 150
 $ :List of 2
  ..$ height: num 70
  ..$ weight: num 154
 $ :List of 2
  ..$ height: num 71
  ..$ weight: num 159
 $ :List of 2
  ..$ height: num 72
  ..$ weight: num 164

Convert women to YAML.

women_yaml <- as.yaml(women, indent = 3)
writeLines(women_yaml)

height:
- 58.0
- 59.0
- 60.0
- 61.0
- 62.0
- 63.0
- 64.0
- 65.0
- 66.0
- 67.0
- 68.0
- 69.0
- 70.0
- 71.0
- 72.0
weight:
- 115.0
- 117.0
- 120.0
- 123.0
- 126.0
- 129.0
- 132.0
- 135.0
- 139.0
- 142.0
- 146.0
- 150.0
- 154.0
- 159.0
- 164.0

To data frame

JSON to data frame.

jsonlite::fromJSON(women_json)

   height weight
1      58    115
2      59    117
3      60    120
4      61    123
5      62    126
6      63    129
7      64    132
8      65    135
9      66    139
10     67    142
11     68    146
12     69    150
13     70    154
14     71    159
15     72    164

YAML to data frame. This does not work for more complex data structures (see below).

yaml.load(women_yaml, handlers = list(map = function(x) as.data.frame(x) ))

   height weight
1      58    115
2      59    117
3      60    120
4      61    123
5      62    126
6      63    129
7      64    132
8      65    135
9      66    139
10     67    142
11     68    146
12     69    150
13     70    154
14     71    159
15     72    164

Non-tidy data frame

A data frame containing lists.

my_df <- data.frame(
  id = 1:3,
  title = letters[1:3]
)
my_df$keywords = list(
    c('aa', 'aaa', 'aaaa'),
    c('bb', 'bbb'),
    c('cc', 'ccc', 'cccc', 'ccccc')
)

my_df

  id title             keywords
1  1     a        aa, aaa, aaaa
2  2     b              bb, bbb
3  3     c cc, ccc, cccc, ccccc

Convert my_df to JSON.

my_df_json <- jsonlite::toJSON(my_df, pretty = TRUE)
my_df_json

[
  {
    "id": 1,
    "title": "a",
    "keywords": ["aa", "aaa", "aaaa"]
  },
  {
    "id": 2,
    "title": "b",
    "keywords": ["bb", "bbb"]
  },
  {
    "id": 3,
    "title": "c",
    "keywords": ["cc", "ccc", "cccc", "ccccc"]
  }
]

Convert my_df to YAML.

my_df_yaml <- as.yaml(my_df, indent = 3)
writeLines(my_df_yaml)

id:
- 1
- 2
- 3
title:
- a
- b
- c
keywords:
-  - aa
   - aaa
   - aaaa
-  - bb
   - bbb
-  - cc
   - ccc
   - cccc
   - ccccc

JSON to YAML and vice versa

Converting from JSON to YAML is easy.

identical(writeLines(as.yaml(jsonlite::fromJSON(my_df_json))), writeLines(my_df_yaml))

id:
- 1
- 2
- 3
title:
- a
- b
- c
keywords:
- - aa
  - aaa
  - aaaa
- - bb
  - bbb
- - cc
  - ccc
  - cccc
  - ccccc

id:
- 1
- 2
- 3
title:
- a
- b
- c
keywords:
-  - aa
   - aaa
   - aaaa
-  - bb
   - bbb
-  - cc
   - ccc
   - cccc
   - ccccc

[1] TRUE

Converting from YAML to JSON for my_df is not as straight-forward because of the different number of keywords.

my_df_list <- yaml.load(my_df_yaml)
my_df_list

$id
[1] 1 2 3

$title
[1] "a" "b" "c"

$keywords
$keywords[[1]]
[1] "aa"   "aaa"  "aaaa"

$keywords[[2]]
[1] "bb"  "bbb"

$keywords[[3]]
[1] "cc"    "ccc"   "cccc"  "ccccc"

This conversion is different from the original data frame to JSON conversion because this creates a single object, where as the original conversion creates an array with three objects.

jsonlite::toJSON(my_df_list, pretty = TRUE)

{
  "id": [1, 2, 3],
  "title": ["a", "b", "c"],
  "keywords": [
    ["aa", "aaa", "aaaa"],
    ["bb", "bbb"],
    ["cc", "ccc", "cccc", "ccccc"]
  ]
}

my_df_json

[
  {
    "id": 1,
    "title": "a",
    "keywords": ["aa", "aaa", "aaaa"]
  },
  {
    "id": 2,
    "title": "b",
    "keywords": ["bb", "bbb"]
  },
  {
    "id": 3,
    "title": "c",
    "keywords": ["cc", "ccc", "cccc", "ccccc"]
  }
]

I could probably write a hacky function to make the conversion but I won’t.

Parsing JSON

The ffq tool generates metadata in JSON:

ffq SRX079566 > data/SRX079566.json

ffq_json <- jsonlite::read_json(path = "data/SRX079566.json", simplifyVector = TRUE)
str(ffq_json)

List of 1
 $ SRX079566:List of 5
  ..$ accession : chr "SRX079566"
  ..$ title     : chr "Illumina Genome Analyzer IIx paired end sequencing; RNA-Seq (polyA+) analysis of DLBCL cell line HS0798"
  ..$ platform  : chr "ILLUMINA"
  ..$ instrument: chr "Illumina Genome Analyzer IIx"
  ..$ runs      :List of 2
  .. ..$ SRR292241:List of 7
  .. .. ..$ accession : chr "SRR292241"
  .. .. ..$ experiment: chr "SRX079566"
  .. .. ..$ study     : chr "SRP020237"
  .. .. ..$ sample    : chr "SRS212581"
  .. .. ..$ title     : chr "Illumina Genome Analyzer IIx paired end sequencing; RNA-Seq (polyA+) analysis of DLBCL cell line HS0798"
  .. .. ..$ attributes:List of 6
  .. .. .. ..$ RUN             : chr "94367"
  .. .. .. ..$ instrument_model: chr "Illumina Genome Analyzer II"
  .. .. .. ..$ ENA-SPOT-COUNT  : int 9721384
  .. .. .. ..$ ENA-BASE-COUNT  : int 699939648
  .. .. .. ..$ ENA-FIRST-PUBLIC: chr "2011-07-05"
  .. .. .. ..$ ENA-LAST-UPDATE : chr "2019-10-07"
  .. .. ..$ files     :List of 4
  .. .. .. ..$ ftp :'data.frame':   2 obs. of  8 variables:
  .. .. .. .. ..$ accession : chr [1:2] "SRR292241" "SRR292241"
  .. .. .. .. ..$ filename  : chr [1:2] "SRR292241_1.fastq.gz" "SRR292241_2.fastq.gz"
  .. .. .. .. ..$ filetype  : chr [1:2] "fastq" "fastq"
  .. .. .. .. ..$ filesize  : int [1:2] 387227151 395115704
  .. .. .. .. ..$ filenumber: int [1:2] 1 2
  .. .. .. .. ..$ md5       : chr [1:2] "a5e0d2d51550127ea9ce3a0219deb375" "e9ce7abd3bce9d5ff194d6e045a36c1c"
  .. .. .. .. ..$ urltype   : chr [1:2] "ftp" "ftp"
  .. .. .. .. ..$ url       : chr [1:2] "ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR292/SRR292241/SRR292241_1.fastq.gz" "ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR292/SRR292241/SRR292241_2.fastq.gz"
  .. .. .. ..$ aws :'data.frame':   2 obs. of  8 variables:
  .. .. .. .. ..$ accession : chr [1:2] "SRR292241" "SRR292241"
  .. .. .. .. ..$ filename  : chr [1:2] "Run94367Lane6.srf" "SRR292241"
  .. .. .. .. ..$ filetype  : chr [1:2] "sra" "sra"
  .. .. .. .. ..$ filesize  : logi [1:2] NA NA
  .. .. .. .. ..$ filenumber: int [1:2] 1 1
  .. .. .. .. ..$ md5       : logi [1:2] NA NA
  .. .. .. .. ..$ urltype   : chr [1:2] "aws" "aws"
  .. .. .. .. ..$ url       : chr [1:2] "s3://sra-pub-src-13/SRR292241/Run94367Lane6.srf" "https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR292241/SRR292241"
  .. .. .. ..$ gcp :'data.frame':   1 obs. of  8 variables:
  .. .. .. .. ..$ accession : chr "SRR292241"
  .. .. .. .. ..$ filename  : chr "SRR292241.3"
  .. .. .. .. ..$ filetype  : chr "sra"
  .. .. .. .. ..$ filesize  : logi NA
  .. .. .. .. ..$ filenumber: int 1
  .. .. .. .. ..$ md5       : logi NA
  .. .. .. .. ..$ urltype   : chr "gcp"
  .. .. .. .. ..$ url       : chr "gs://sra-pub-crun-3/SRR292241/SRR292241.3"
  .. .. .. ..$ ncbi:'data.frame':   1 obs. of  8 variables:
  .. .. .. .. ..$ accession : chr "SRR292241"
  .. .. .. .. ..$ filename  : chr "SRR292241.3"
  .. .. .. .. ..$ filetype  : chr "sra"
  .. .. .. .. ..$ filesize  : logi NA
  .. .. .. .. ..$ filenumber: int 1
  .. .. .. .. ..$ md5       : logi NA
  .. .. .. .. ..$ urltype   : chr "ncbi"
  .. .. .. .. ..$ url       : chr "https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos5/sra-pub-run-32/SRR000/292/SRR292241/SRR292241.3"
  .. ..$ SRR390728:List of 7
  .. .. ..$ accession : chr "SRR390728"
  .. .. ..$ experiment: chr "SRX079566"
  .. .. ..$ study     : chr "SRP020237"
  .. .. ..$ sample    : chr "SRS212581"
  .. .. ..$ title     : chr "Illumina Genome Analyzer IIx paired end sequencing; RNA-Seq (polyA+) analysis of DLBCL cell line HS0798"
  .. .. ..$ attributes:List of 6
  .. .. .. ..$ RUN             : chr "94367"
  .. .. .. ..$ assembly        : chr "NCBI36_BCCAGSC_variant"
  .. .. .. ..$ ENA-SPOT-COUNT  : int 7178576
  .. .. .. ..$ ENA-BASE-COUNT  : int 516857472
  .. .. .. ..$ ENA-FIRST-PUBLIC: chr "2011-12-23"
  .. .. .. ..$ ENA-LAST-UPDATE : chr "2016-06-28"
  .. .. ..$ files     :List of 4
  .. .. .. ..$ ftp :'data.frame':   2 obs. of  8 variables:
  .. .. .. .. ..$ accession : chr [1:2] "SRR390728" "SRR390728"
  .. .. .. .. ..$ filename  : chr [1:2] "SRR390728_1.fastq.gz" "SRR390728_2.fastq.gz"
  .. .. .. .. ..$ filetype  : chr [1:2] "fastq" "fastq"
  .. .. .. .. ..$ filesize  : int [1:2] 170346275 168836179
  .. .. .. .. ..$ filenumber: int [1:2] 1 2
  .. .. .. .. ..$ md5       : chr [1:2] "9a3d37cbb3e47cf8930ed2ba6c8d2cef" "bc4e6304170876186522f4175ee39a8f"
  .. .. .. .. ..$ urltype   : chr [1:2] "ftp" "ftp"
  .. .. .. .. ..$ url       : chr [1:2] "ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR390/SRR390728/SRR390728_1.fastq.gz" "ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR390/SRR390728/SRR390728_2.fastq.gz"
  .. .. .. ..$ aws :'data.frame':   2 obs. of  8 variables:
  .. .. .. .. ..$ accession : chr [1:2] "SRR390728" "SRR390728"
  .. .. .. .. ..$ filename  : chr [1:2] "30KWMAAXX_6.sorted_withJunctionsOnGenome_dupsFlagged.bam.1" "SRR390728"
  .. .. .. .. ..$ filetype  : chr [1:2] "bam" "sra"
  .. .. .. .. ..$ filesize  : logi [1:2] NA NA
  .. .. .. .. ..$ filenumber: int [1:2] 1 1
  .. .. .. .. ..$ md5       : logi [1:2] NA NA
  .. .. .. .. ..$ urltype   : chr [1:2] "aws" "aws"
  .. .. .. .. ..$ url       : chr [1:2] "s3://sra-pub-src-15/SRR390728/30KWMAAXX_6.sorted_withJunctionsOnGenome_dupsFlagged.bam.1" "https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR390728/SRR390728"
  .. .. .. ..$ gcp :'data.frame':   1 obs. of  8 variables:
  .. .. .. .. ..$ accession : chr "SRR390728"
  .. .. .. .. ..$ filename  : chr "SRR390728.lite.2"
  .. .. .. .. ..$ filetype  : chr "sra"
  .. .. .. .. ..$ filesize  : logi NA
  .. .. .. .. ..$ filenumber: int 1
  .. .. .. .. ..$ md5       : logi NA
  .. .. .. .. ..$ urltype   : chr "gcp"
  .. .. .. .. ..$ url       : chr "gs://sra-pub-zq-5/SRR390728/SRR390728.lite.2"
  .. .. .. ..$ ncbi:'data.frame':   1 obs. of  8 variables:
  .. .. .. .. ..$ accession : chr "SRR390728"
  .. .. .. .. ..$ filename  : chr "SRR390728.lite.2"
  .. .. .. .. ..$ filetype  : chr "sra"
  .. .. .. .. ..$ filesize  : logi NA
  .. .. .. .. ..$ filenumber: int 1
  .. .. .. .. ..$ md5       : logi NA
  .. .. .. .. ..$ urltype   : chr "ncbi"
  .. .. .. .. ..$ url       : chr "https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos2/sra-pub-zq-20/SRR000/390/SRR390728/SRR390728.lite.2"

Use a recursive apply to create a named character vector, which is convenient for plucking values.

test <- rapply(object = ffq_json, f = function(x) x)
class(test)

[1] "character"

Subset the FTP links.

test[grepl("ftp.url\\d+$", names(test))]

                                   SRX079566.runs.SRR292241.files.ftp.url1 
"ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR292/SRR292241/SRR292241_1.fastq.gz" 
                                   SRX079566.runs.SRR292241.files.ftp.url2 
"ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR292/SRR292241/SRR292241_2.fastq.gz" 
                                   SRX079566.runs.SRR390728.files.ftp.url1 
"ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR390/SRR390728/SRR390728_1.fastq.gz" 
                                   SRX079566.runs.SRR390728.files.ftp.url2 
"ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR390/SRR390728/SRR390728_2.fastq.gz"

Appendix

YAML defined

What is YAML? The YML File Format.

YAML created in 2001 stands for YAML Ain’t Markup Language, but it originally stood for Yet Another Markup Language.
YAML is a human-readable data serialisation language, just like XML and JSON.
- YAML can do everything that JSON can and more. A valid YAML file can contain JSON, and JSON can transform into YAML.
Serialisation is a process where one application or service, which has different data structures and is written in a different set of technologies, can transfer data to another application using a standard format.
In other words, serialisation is about translating, converting, and wrapping up a data structure in another format.
The data in the new format can be stored in a file or transmitted to another application or service over a network.
YAML is a widely used format for writing configuration files for different DevOps tools, programs, and applications because of its human-readable and intuitive syntax.
YAML uses indentation to define structure in the file, which is helpful if you are used to writing Python code and are familiar with the indentation style the language uses.

To create a YAML file, use either the .yaml or .yml file extension. Before writing any YAML code, you can add three dashes (---) at the start of the file to allow having multiple YAML documents in a single YAML file, making file organisation much easier; separate each document with three dashes (---). You can use three dots (...) to mark the end of the document.

In YAML, there is an emphasis on indentation and line separation to denote levels and structure in data. The indentation system is quite similar to the one Python uses.
YAML doesn’t use symbols such as curly braces, square brackets, or opening or closing tags - just indentation.
YAML doesn’t allow you to use any tabs when creating indentation - use spaces instead.
Whitespace doesn’t matter as long as child elements are indented inside the parent element.
To add a comment to comment out a line of code, use the # character

Although YAML auto-detects the data types in a file, you can specify the type of data you want to use. To explicitly specify the type of data, use the !! symbol and the name of the data type before the value:

# parse this value as a string
date: !!str 2022-11-11

## parse this value as a float (it will be 1.0 instead of 1)
fave_number: !!float 1

In YAML, strings in some cases can be left unquoted, but you can also wrap them in single (') or double (") quotation marks.

If you want to write a string that spans across multiple lines and you want to preserve the line breaks, use the pipe symbol (|) and make sure that the message is indented!

|
 I am message that spans multiple lines
 I go on and on across lines
 and lines
 and more lines

If you have a string in a YAML file that spans across multiple lines for readability, but you want the parser to interpret it as a single line string, you can use the > character instead of |, which will replace each line break with a space.

Numbers express numerical data, and in YAML, these include integers (whole numbers), floats (numbers with a decimal point), exponentials, octals, and hexadecimals.

# integer
19

# float 
8.7

# exponential
4.5e+13

# octal 
0o23

# hexadecimal
0xFF

Booleans in YAML, and other programming languages, have one of two states and are expressed with either true or false. Words like true and false are keywords in YAML, so don’t surround them with quotation marks if you want them interpreted as Booleans. Null values are expressed with the keyword null or the tilde character, ~.

Collections in YAML can be:

Sequences (lists/arrays)
Mappings (dictionaries/hashes)

To write a sequence, use a dash (-) followed by a space:

- HTML
- CSS
- JavaScript

Each item in the sequence (list) is placed on a separate line, with a dash in front of the value and each item in the list is on the same level.

You can create a nested sequence (remember, use spaces - not tabs - to create the levels of indentation):

- HTML
- CSS
- JavaScript
 - React
 - Angular
 - Vue

Mappings allow you to list keys with values. Key/value pairs are the building blocks of YAML documents. Use a colon (:) followed by a space to create key/value pairs:

Employees:
 name: John Doe
 age: 23
 country: USA

Combining the two to create a list of objects:

Employees:
- name: John Doe
  department: Engineering
  country: USA
- name: Kate Kateson
  department: IT support
  country: United Kingdom

Read YAML

Read a test YAML file.

dat <- yaml::read_yaml("data/nested_list.yml")
dat$root

$simple_list
[1] "one"   "two"   "three"

$simple_list_2
[1] "four" "five" "six" 

$nested_list
$nested_list$one
[1] "one"   "two"   "three"

$nested_list$two
[1] "four" "five" "six" 


$nested_list_2
$nested_list_2$one
$nested_list_2$one$two
[1] "one"   "two"   "three"

$nested_list_2$one$three
[1] "four" "five" "six"

Return only specific “level” by using names().

names(dat$root)

[1] "simple_list"   "simple_list_2" "nested_list"   "nested_list_2"

names(dat$root$nested_list)

[1] "one" "two"

JSON defined

Notes from JSON Defined.

JavaScript Object Notation (JSON) is a data-exchange format that makes it possible to transfer populated data structures between different languages/tools.

JSON can be used in JavaScript programs without any need for parsing or serialising. It is a text-based way of representing JavaScript object literals, arrays, and scalar data.

JSON is relatively easy to read and write, while also easy for software to parse and generate. It is often used for serialising structured data and exchanging it over a network, typically between a server and web applications.

At the granular level, JSON consists of data types.

String - composed of Unicode characters, with backslash (\) escaping.

{ “name” : “Bob” }
Number - a JSON number follows JavaScript’s double-precision floating-point format.

{
   "number_1" : 210,
   "number_2" : 215,
   "number_3" : 21.05,
   "number_4" : 10.05
}

Boolean - either true or false, not surrounded with quotes, and are treated as string values.

{ “AllowPartialShipment” : false }
Null - empty value and can be used when there is no value assigned to a key.

{ “Special Instructions” : null }
Object - a set of name or value pairs inserted between curly braces ({}). The keys must be strings and should be unique, and separated by comma/s.

{
  "Influencer" : { "name" : "Jaxon" , "age" : "42" , "city" , "New York" }
}

Array - an ordered collection of values. Array values must be of type string, number, object, array, Boolean, or null.

{
   "Influencers" : [ 
      {
         "name" : "Jaxon", 
         "age" : 42, 
         "Works At" : "Tech News"
      }
      {
         "name" : "Miller", 
         "age" : 35
         "Works At" : "IT Day"
      }
   ] 
}

Use cases

Generating a JSON object from user-generated data

JSON is perfect for storing temporary data. For example, temporary data can be user-generated data, such as a submitted form on a website. JSON can also be used as a data format for any programming language to provide a high level of interoperability.

Transferring data between systems

A website database has a customer’s mailing address, but the address needs to be verified via an API to make sure it is valid. Send the address data in JSON format to the address validation service API.

Configuring data for applications

When developing applications, each application needs the credentials to connect to a database as well as a log file path. The credentials and the file path can be specified in a JSON file.

Simplifying complex data models

JSON simplifies complex documents down to the components that have been identified as being meaningful by converting the process of data extraction to a predictable and human readable JSON file.

Why JSON is popular

JSON has gained momentum in API code programming and web services because it helps in faster data interchange and web service results.

It is text-based, lightweight, and has an easy-to-parse data format requiring no additional code for parsing. For web services, the need to return and display a lot of data makes JSON the ideal choice.

What is a document database?

A document database is a type of nonrelational database designed to store, retrieve, and manage document-oriented information. Rather than having a schema defined upfront, document databases allow for storing data in collections consisting of documents. NoSQL databases and JSON databases are types of document databases.

Document databases are often popular among developers because they store data in a document-model format (semi-structured) rather than relational (structured).

Document databases offer more flexibility, because developers do not have to plan out the schemas ahead of time and they can use the same format they are using in their application code. This means the careful planning of a SQL database is not as necessary, which makes document databases useful for rapidly evolving schemas, which can be common in software development. However, this can come at the cost of speed, size, and specificity.

What is a JSON document database?

Applications that use different JSON data types and JSON-oriented query language can interact with data stored in a JSON document database. The JSON document database also provides native support for JSON.

Characterisitics that define a JSON document database:

Non-relational database designed to store and query as a JSON document.
Data is in plain text but can be stored using the data types BLOB, VARCHAR2, CLOB, or binary JSON in 21c.
Accessing JSON data stored in the database is similar to accessing other database data, including using OCI, .NET, and JDBC.
A schema is not necessary

Storing JSON data in a JSON document database makes use of columns whose data types are VARCHAR2, CLOB, BLOB, or binary JSON in 21c. The choice of which to use is usually determined by the size of the JSON documents. Storing JSON data in the database using standard SQL data types means that JSON data can be manipulated like any other data type.

JSON data can be managed and manipulated with tables in a JSON document database, regardless of the data type. The choice of which table to use is typically motivated by the size of the JSON documents.

sessionInfo()

R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] rjson_0.2.21    tidyjson_0.3.2  yaml_2.3.8      jsonlite_1.8.8 
[5] workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] dplyr_1.1.4       compiler_4.4.0    promises_1.3.0    tidyselect_1.2.1 
 [5] Rcpp_1.0.12       stringr_1.5.1     git2r_0.33.0      assertthat_0.2.1 
 [9] tidyr_1.3.1       callr_3.7.6       later_1.3.2       jquerylib_0.1.4  
[13] fastmap_1.2.0     R6_2.5.1          generics_0.1.3    knitr_1.46       
[17] tibble_3.2.1      rprojroot_2.0.4   bslib_0.7.0       pillar_1.9.0     
[21] rlang_1.1.3       utf8_1.2.4        cachem_1.1.0      stringi_1.8.4    
[25] httpuv_1.6.15     xfun_0.44         getPass_0.2-4     fs_1.6.4         
[29] sass_0.4.9        cli_3.6.2         magrittr_2.0.3    ps_1.7.6         
[33] digest_0.6.35     processx_3.8.4    rstudioapi_0.16.0 lifecycle_1.0.4  
[37] vctrs_0.6.5       evaluate_0.23     glue_1.7.0        whisker_0.4.1    
[41] fansi_1.0.6       purrr_1.0.2       rmarkdown_2.27    httr_1.4.7       
[45] tools_4.4.0       pkgconfig_2.0.3   htmltools_0.5.8.1