JSON versus YAML

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Last updated: 2023-06-14

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Knit directory: muse/

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File	Version	Author	Date	Message
Rmd	c7dcdf2	Dave Tang	2023-06-14	Parsing JSON
html	ca51b55	Dave Tang	2023-05-24	Build site.
Rmd	b72b5b3	Dave Tang	2023-05-24	Check out additional packages
html	3bb3245	Dave Tang	2023-05-19	Build site.
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"))

Installing packages into '/packages'
(as 'lib' is unspecified)

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" 

Session information

sessionInfo()

R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 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_2.3.7      jsonlite_1.8.5 
[5] workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] dplyr_1.1.2      compiler_4.3.0   promises_1.2.0.1 tidyselect_1.2.0
 [5] Rcpp_1.0.10      stringr_1.5.0    git2r_0.32.0     assertthat_0.2.1
 [9] tidyr_1.3.0      callr_3.7.3      later_1.3.0      jquerylib_0.1.4 
[13] fastmap_1.1.1    R6_2.5.1         generics_0.1.3   knitr_1.42      
[17] tibble_3.2.1     rprojroot_2.0.3  bslib_0.4.2      pillar_1.9.0    
[21] rlang_1.1.0      utf8_1.2.3       cachem_1.0.7     stringi_1.7.12  
[25] httpuv_1.6.9     xfun_0.39        getPass_0.2-2    fs_1.6.2        
[29] sass_0.4.5       cli_3.6.1        magrittr_2.0.3   ps_1.7.5        
[33] digest_0.6.31    processx_3.8.1   rstudioapi_0.14  lifecycle_1.0.3 
[37] vctrs_0.6.2      evaluate_0.20    glue_1.6.2       whisker_0.4.1   
[41] fansi_1.0.4      purrr_1.0.1      rmarkdown_2.21   httr_1.4.5      
[45] tools_4.3.0      pkgconfig_2.0.3  htmltools_0.5.5