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Repository Details

R interface to jq

jqr

R-CMD-check codecov cran checks rstudio mirror downloads cran version

R interface to jq, a JSON processor http://stedolan.github.io/jq/

jqr makes it easy to process large amounts of json without having to convert from json to R, or without using regular expressions. This means that the eventual loading into R can be quicker.

Quickstart Tutorial

The jq command line examples from the jq tutorial work exactly the same in R!

library(curl)
library(jqr)
curl('https://api.github.com/repos/ropensci/jqr/commits?per_page=5') %>%
  jq('.[] | {message: .commit.message, name: .commit.committer.name}')
#> [
#>     {
#>         "message": "update cran comments and codemeta.json",
#>         "name": "Scott Chamberlain"
#>     },
#>     {
#>         "message": "change license to \"jqr authors\"",
#>         "name": "Scott Chamberlain"
#>     },
#>     {
#>         "message": "fix ci",
#>         "name": "Jeroen Ooms"
#>     },
#>     {
#>         "message": "Windows: update to libjq 1.6",
#>         "name": "Jeroen Ooms"
#>     },
#>     {
#>         "message": "Small tweak for autobrew",
#>         "name": "Jeroen Ooms"
#>     }
#> ]

Try running some of the other examples.

Installation

Binary packages for OS-X or Windows can be installed directly from CRAN:

install.packages("jqr")

Installation from source on Linux or OSX requires libjq. On Ubuntu 14.04 and 16.04 lower use libjq-dev from Launchpad:

sudo add-apt-repository -y ppa:cran/jq
sudo apt-get update -q
sudo apt-get install -y libjq-dev

More recent Debian or Ubuntu install libjq-dev directly from Universe:

sudo apt-get install -y libjq-dev

On Fedora we need jq-devel:

sudo yum install jq-devel

On CentOS / RHEL we install jq-devel via EPEL:

sudo yum install epel-release
sudo yum install jq-devel

On OS-X use jq from Homebrew:

brew install jq

On Solaris we can have libjq_dev from OpenCSW:

pkgadd -d http://get.opencsw.org/now
/opt/csw/bin/pkgutil -U
/opt/csw/bin/pkgutil -y -i libjq_dev
library(jqr)

Interfaces

low level

There's a low level interface in which you can execute jq code just as you would on the command line:

str <- '[{
    "foo": 1,
    "bar": 2
  },
  {
    "foo": 3,
    "bar": 4
  },
  {
    "foo": 5,
    "bar": 6
}]'
jq(str, ".[]")
#> [
#>     {
#>         "foo": 1,
#>         "bar": 2
#>     },
#>     {
#>         "foo": 3,
#>         "bar": 4
#>     },
#>     {
#>         "foo": 5,
#>         "bar": 6
#>     }
#> ]
jq(str, "[.[] | {name: .foo} | keys]")
#> [
#>     [
#>         "name"
#>     ],
#>     [
#>         "name"
#>     ],
#>     [
#>         "name"
#>     ]
#> ]

Note that we print the output to look like a valid JSON object to make it easier to look at. However, it's a simple character string or vector of strings. A trick you can do is to wrap your jq program in brackets like [.[]] instead of .[], e.g.,

jq(str, ".[]") %>% unclass
#> [1] "{\"foo\":1,\"bar\":2}" "{\"foo\":3,\"bar\":4}" "{\"foo\":5,\"bar\":6}"
# vs.
jq(str, "[.[]]") %>% unclass
#> [1] "[{\"foo\":1,\"bar\":2},{\"foo\":3,\"bar\":4},{\"foo\":5,\"bar\":6}]"

Combine many jq arguments - they are internally combined with a pipe |

(note how these are identical)

jq(str, ".[] | {name: .foo} | keys")
#> [
#>     [
#>         "name"
#>     ],
#>     [
#>         "name"
#>     ],
#>     [
#>         "name"
#>     ]
#> ]
jq(str, ".[]", "{name: .foo}", "keys")
#> [
#>     [
#>         "name"
#>     ],
#>     [
#>         "name"
#>     ],
#>     [
#>         "name"
#>     ]
#> ]

Also accepts many JSON inputs now

jq("[123, 456]   [77, 88, 99]", ".[]")
#> [
#>     123,
#>     456,
#>     77,
#>     88,
#>     99
#> ]
jq('{"foo": 77} {"bar": 45}', ".[]")
#> [
#>     77,
#>     45
#> ]
jq('[{"foo": 77, "stuff": "things"}] [{"bar": 45}] [{"n": 5}]', ".[] | keys")
#> [
#>     [
#>         "foo",
#>         "stuff"
#>     ],
#>     [
#>         "bar"
#>     ],
#>     [
#>         "n"
#>     ]
#> ]

# if you have jsons in a vector
jsons <- c('[{"foo": 77, "stuff": "things"}]', '[{"bar": 45}]', '[{"n": 5}]')
jq(paste0(jsons, collapse = " "), ".[]")
#> [
#>     {
#>         "foo": 77,
#>         "stuff": "things"
#>     },
#>     {
#>         "bar": 45
#>     },
#>     {
#>         "n": 5
#>     }
#> ]

high level

The other is higher level, and uses a suite of functions to construct queries. Queries are constucted, then excuted internally with jq() after the last piped command.

You don't have to use pipes though. See examples below.

Examples:

Index

x <- '[{"message": "hello", "name": "jenn"}, {"message": "world", "name": "beth"}]'
x %>% index()
#> [
#>     {
#>         "message": "hello",
#>         "name": "jenn"
#>     },
#>     {
#>         "message": "world",
#>         "name": "beth"
#>     }
#> ]

Sort

'[8,3,null,6]' %>% sortj
#> [
#>     null,
#>     3,
#>     6,
#>     8
#> ]

reverse order

'[1,2,3,4]' %>% reverse
#> [
#>     4,
#>     3,
#>     2,
#>     1
#> ]

Show the query to be used using peek()

'[1,2,3,4]' %>% reverse %>% peek
#> <jq query>
#>   query: reverse

get multiple outputs for array w/ > 1 element

x <- '{"user":"stedolan","titles":["JQ Primer", "More JQ"]}'
jq(x, '{user, title: .titles[]}')
#> [
#>     {
#>         "user": "stedolan",
#>         "title": "JQ Primer"
#>     },
#>     {
#>         "user": "stedolan",
#>         "title": "More JQ"
#>     }
#> ]
x %>% index()
#> [
#>     "stedolan",
#>     [
#>         "JQ Primer",
#>         "More JQ"
#>     ]
#> ]
x %>% build_object(user, title = `.titles[]`)
#> [
#>     {
#>         "user": "stedolan",
#>         "title": "JQ Primer"
#>     },
#>     {
#>         "user": "stedolan",
#>         "title": "More JQ"
#>     }
#> ]
jq(x, '{user, title: .titles[]}') %>% jsonlite::toJSON() %>% jsonlite::validate()
#> [1] TRUE

string operations

join

'["a","b,c,d","e"]' %>% join
#> "a, b,c,d, e"
'["a","b,c,d","e"]' %>% join(`;`)
#> "a; b,c,d; e"

ltrimstr

'["fo", "foo", "barfoo", "foobar", "afoo"]' %>% index() %>% ltrimstr(foo)
#> [
#>     "fo",
#>     "",
#>     "barfoo",
#>     "bar",
#>     "afoo"
#> ]

rtrimstr

'["fo", "foo", "barfoo", "foobar", "foob"]' %>% index() %>% rtrimstr(foo)
#> [
#>     "fo",
#>     "",
#>     "bar",
#>     "foobar",
#>     "foob"
#> ]

startswith

'["fo", "foo", "barfoo", "foobar", "barfoob"]' %>% index %>% startswith(foo)
#> [
#>     false,
#>     true,
#>     false,
#>     true,
#>     false
#> ]
'["fo", "foo"] ["barfoo", "foobar", "barfoob"]' %>% index %>% startswith(foo)
#> [
#>     false,
#>     true,
#>     false,
#>     true,
#>     false
#> ]

endswith

'["fo", "foo", "barfoo", "foobar", "barfoob"]' %>% index %>% endswith(foo)
#> [
#>     false,
#>     true,
#>     true,
#>     false,
#>     false
#> ]

tojson, fromjson, tostring

'[1, "foo", ["foo"]]' %>% index
#> [
#>     1,
#>     "foo",
#>     [
#>         "foo"
#>     ]
#> ]
'[1, "foo", ["foo"]]' %>% index %>% tostring
#> [
#>     "1",
#>     "foo",
#>     "[\"foo\"]"
#> ]
'[1, "foo", ["foo"]]' %>% index %>% tojson
#> [
#>     "1",
#>     "\"foo\"",
#>     "[\"foo\"]"
#> ]
'[1, "foo", ["foo"]]' %>% index %>% tojson %>% fromjson
#> [
#>     1,
#>     "foo",
#>     [
#>         "foo"
#>     ]
#> ]

contains

'"foobar"' %>% contains("bar")
#> true

unique

'[1,2,5,3,5,3,1,3]' %>% uniquej
#> [
#>     1,
#>     2,
#>     3,
#>     5
#> ]

filter

With filtering via select() you can use various operators, like ==, &&, ||. We translate these internally for you to what jq wants to see (==, and, or).

Simple, one condition

'{"foo": 4, "bar": 7}' %>% select(.foo == 4)
#> {
#>     "foo": 4,
#>     "bar": 7
#> }

More complicated. Combine more than one condition; combine each individual filtering task in parentheses

x <- '{"foo": 4, "bar": 2} {"foo": 5, "bar": 4} {"foo": 8, "bar": 12}'
x %>% select((.foo < 6) && (.bar > 3))
#> {
#>     "foo": 5,
#>     "bar": 4
#> }
x %>% select((.foo < 6) || (.bar > 3))
#> [
#>     {
#>         "foo": 4,
#>         "bar": 2
#>     },
#>     {
#>         "foo": 5,
#>         "bar": 4
#>     },
#>     {
#>         "foo": 8,
#>         "bar": 12
#>     }
#> ]

types

get type information for each element

'[0, false, [], {}, null, "hello"]' %>% types
#> [
#>     "number",
#>     "boolean",
#>     "array",
#>     "object",
#>     "null",
#>     "string"
#> ]
'[0, false, [], {}, null, "hello", true, [1,2,3]]' %>% types
#> [
#>     "number",
#>     "boolean",
#>     "array",
#>     "object",
#>     "null",
#>     "string",
#>     "boolean",
#>     "array"
#> ]

select elements by type

'[0, false, [], {}, null, "hello"]' %>% index() %>% type(booleans)
#> false

key operations

get keys

str <- '{"foo": 5, "bar": 7}'
str %>% keys()
#> [
#>     "bar",
#>     "foo"
#> ]

delete by key name

str %>% del(bar)
#> {
#>     "foo": 5
#> }

check for key existence

str3 <- '[[0,1], ["a","b","c"]]'
str3 %>% haskey(2)
#> [
#>     false,
#>     true
#> ]
str3 %>% haskey(1,2)
#> [
#>     true,
#>     false,
#>     true,
#>     true
#> ]

Build an object, selecting variables by name, and rename

'{"foo": 5, "bar": 7}' %>% build_object(a = .foo)
#> {
#>     "a": 5
#> }

More complicated build_object(), using the included dataset commits

commits %>%
  index() %>%
  build_object(sha = .sha, name = .commit.committer.name)
#> [
#>     {
#>         "sha": [
#>             "110e009996e1359d25b8e99e71f83b96e5870790"
#>         ],
#>         "name": [
#>             "Nicolas Williams"
#>         ]
#>     },
#>     {
#>         "sha": [
#>             "7b6a018dff623a4f13f6bcd52c7c56d9b4a4165f"
#>         ],
#>         "name": [
#>             "Nicolas Williams"
#>         ]
#>     },
#>     {
#>         "sha": [
#>             "a50e548cc5313c187483bc8fb1b95e1798e8ef65"
#>         ],
#>         "name": [
#>             "Nicolas Williams"
#>         ]
#>     },
#>     {
#>         "sha": [
#>             "4b258f7d31b34ff5d45fba431169e7fd4c995283"
#>         ],
#>         "name": [
#>             "Nicolas Williams"
#>         ]
#>     },
#>     {
#>         "sha": [
#>             "d1cb8ee0ad3ddf03a37394bfa899cfd3ddd007c5"
#>         ],
#>         "name": [
#>             "Nicolas Williams"
#>         ]
#>     }
#> ]

Maths

'{"a": 7}' %>%  do(.a + 1)
#> 8
'{"a": [1,2], "b": [3,4]}' %>%  do(.a + .b)
#> [
#>     1,
#>     2,
#>     3,
#>     4
#> ]
'{"a": [1,2], "b": [3,4]}' %>%  do(.a - .b)
#> [
#>     1,
#>     2
#> ]
'{"a": 3}' %>%  do(4 - .a)
#> 1
'["xml", "yaml", "json"]' %>%  do('. - ["xml", "yaml"]')
#> ". - [\"xml\", \"yaml\"]"
'5' %>%  do(10 / . * 3)
#> 6

comparisons

'[5,4,2,7]' %>% index() %>% do(. < 4)
#> [
#>     false,
#>     false,
#>     true,
#>     false
#> ]
'[5,4,2,7]' %>% index() %>% do(. > 4)
#> [
#>     true,
#>     false,
#>     false,
#>     true
#> ]
'[5,4,2,7]' %>% index() %>% do(. <= 4)
#> [
#>     false,
#>     true,
#>     true,
#>     false
#> ]
'[5,4,2,7]' %>% index() %>% do(. >= 4)
#> [
#>     true,
#>     true,
#>     false,
#>     true
#> ]
'[5,4,2,7]' %>% index() %>% do(. == 4)
#> [
#>     false,
#>     true,
#>     false,
#>     false
#> ]
'[5,4,2,7]' %>% index() %>% do(. != 4)
#> [
#>     true,
#>     false,
#>     true,
#>     true
#> ]

length

'[[1,2], "string", {"a":2}, null]' %>% index %>% lengthj
#> [
#>     2,
#>     6,
#>     1,
#>     0
#> ]

sqrt

'9' %>% sqrtj
#> 3

floor

'3.14159' %>% floorj
#> 3

find minimum

'[5,4,2,7]' %>% minj
#> 2
'[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% minj
#> {
#>     "foo": 2,
#>     "bar": 3
#> }
'[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% minj(foo)
#> {
#>     "foo": 1,
#>     "bar": 14
#> }
'[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% minj(bar)
#> {
#>     "foo": 2,
#>     "bar": 3
#> }

find maximum

'[5,4,2,7]' %>% maxj
#> 7
'[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% maxj
#> {
#>     "foo": 1,
#>     "bar": 14
#> }
'[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% maxj(foo)
#> {
#>     "foo": 2,
#>     "bar": 3
#> }
'[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% maxj(bar)
#> {
#>     "foo": 1,
#>     "bar": 14
#> }

Combine into valid JSON

jq sometimes creates pieces of JSON that are valid in themselves, but together are not. combine() is a way to make valid JSON.

This outputs a few pieces of JSON

(x <- commits %>%
  index() %>%
  build_object(sha = .sha, name = .commit.committer.name))
#> [
#>     {
#>         "sha": [
#>             "110e009996e1359d25b8e99e71f83b96e5870790"
#>         ],
#>         "name": [
#>             "Nicolas Williams"
#>         ]
#>     },
#>     {
#>         "sha": [
#>             "7b6a018dff623a4f13f6bcd52c7c56d9b4a4165f"
#>         ],
#>         "name": [
#>             "Nicolas Williams"
#>         ]
#>     },
#>     {
#>         "sha": [
#>             "a50e548cc5313c187483bc8fb1b95e1798e8ef65"
#>         ],
#>         "name": [
#>             "Nicolas Williams"
#>         ]
#>     },
#>     {
#>         "sha": [
#>             "4b258f7d31b34ff5d45fba431169e7fd4c995283"
#>         ],
#>         "name": [
#>             "Nicolas Williams"
#>         ]
#>     },
#>     {
#>         "sha": [
#>             "d1cb8ee0ad3ddf03a37394bfa899cfd3ddd007c5"
#>         ],
#>         "name": [
#>             "Nicolas Williams"
#>         ]
#>     }
#> ]

Use combine() to put them together.

combine(x)
#> [
#>     {
#>         "sha": [
#>             "110e009996e1359d25b8e99e71f83b96e5870790"
#>         ],
#>         "name": [
#>             "Nicolas Williams"
#>         ]
#>     },
#>     {
#>         "sha": [
#>             "7b6a018dff623a4f13f6bcd52c7c56d9b4a4165f"
#>         ],
#>         "name": [
#>             "Nicolas Williams"
#>         ]
#>     },
#>     {
#>         "sha": [
#>             "a50e548cc5313c187483bc8fb1b95e1798e8ef65"
#>         ],
#>         "name": [
#>             "Nicolas Williams"
#>         ]
#>     },
#>     {
#>         "sha": [
#>             "4b258f7d31b34ff5d45fba431169e7fd4c995283"
#>         ],
#>         "name": [
#>             "Nicolas Williams"
#>         ]
#>     },
#>     {
#>         "sha": [
#>             "d1cb8ee0ad3ddf03a37394bfa899cfd3ddd007c5"
#>         ],
#>         "name": [
#>             "Nicolas Williams"
#>         ]
#>     }
#> ]

Meta

  • Please report any issues or bugs.
  • License: MIT
  • Get citation information for jqr in R doing citation(package = 'jqr')
  • Please note that this package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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hunspell

High-Performance Stemmer, Tokenizer, and Spell Checker for R
C++
106
star
51

crul

R6 based http client for R (made for developers)
R
101
star
52

gistr

Interact with GitHub gists from R
R
101
star
53

spelling

Tools for Spell Checking in R
R
101
star
54

rfishbase

R interface to the fishbase.org database
R
100
star
55

weathercan

R package for downloading weather data from Environment and Climate Change Canada
R
99
star
56

git2rdata

An R package for storing and retrieving data.frames in git repositories.
R
98
star
57

gutenbergr

Search and download public domain texts from Project Gutenberg
R
97
star
58

bib2df

Parse a BibTeX file to a tibble
R
97
star
59

ckanr

R client for the CKAN API
R
97
star
60

rsvg

SVG renderer for R based on librsvg2
C
95
star
61

UCSCXenaTools

πŸ“¦ An R package for accessing genomics data from UCSC Xena platform, from cancer multi-omics to single-cell RNA-seq https://cran.r-project.org/web/packages/UCSCXenaTools/
R
95
star
62

EML

Ecological Metadata Language interface for R: synthesis and integration of heterogenous data
R
94
star
63

nasapower

API Client for NASA POWER Global Meteorology, Surface Solar Energy and Climatology in R
R
93
star
64

cyphr

:shipit: Humane encryption
R
91
star
65

FedData

Functions to Automate Downloading Geospatial Data Available from Several Federated Data Sources
R
91
star
66

av

Working with Video in R
C
88
star
67

mapscanner

R package to print maps, draw on them, and scan them back in
R
87
star
68

opencage

🌐 R package for the OpenCage API -- both forward and reverse geocoding 🌐
R
86
star
69

tidync

NetCDF exploration and data extraction
R
85
star
70

GSODR

API Client for Global Surface Summary of the Day ('GSOD') Weather Data Client in R
R
84
star
71

rzmq

R package for ZMQ
C++
82
star
72

gittargets

Data version control for reproducible analysis pipelines in R with {targets}.
R
80
star
73

openalexR

Getting bibliographic records from OpenAlex
R
80
star
74

bikedata

🚲 Extract data from public hire bicycle systems
R
79
star
75

historydata

Datasets for Historians
R
78
star
76

dittodb

dittodb: A Test Environment for DB Queries in R
R
78
star
77

arkdb

Archive and unarchive databases as flat text files
R
78
star
78

fingertipsR

R package to interact with Public Health England’s Fingertips data tool
R
78
star
79

vcr

Record HTTP calls and replay them
R
77
star
80

rebird

Wrapper to the eBird API
R
77
star
81

smapr

An R package for acquisition and processing of NASA SMAP data
R
77
star
82

nodbi

Document DBI connector for R
R
75
star
83

CoordinateCleaner

Automated flagging of common spatial and temporal errors in biological and palaeontological collection data, for the use in conservation, ecology and palaeontology.
HTML
74
star
84

opentripplanner

An R package to set up and use OpenTripPlanner (OTP) as a local or remote multimodal trip planner.
R
73
star
85

nlrx

nlrx NetLogo R
R
71
star
86

rb3

A bunch of downloaders and parsers for data delivered from B3
R
69
star
87

tidyhydat

An R package to import Water Survey of Canada hydrometric data and make it tidy
R
69
star
88

robotstxt

robots.txt file parsing and checking for R
R
68
star
89

slopes

Package to calculate slopes of roads, rivers and trajectories
R
65
star
90

tradestatistics

R package to access Open Trade Statistics API
R
65
star
91

terrainr

Get DEMs and orthoimagery from the USGS National Map, georeference your images and merge rasters, and visualize with Unity 3D
R
64
star
92

unconf17

Website for 2017 rOpenSci Unconf
JavaScript
64
star
93

NLMR

πŸ“¦ R package to simulate neutral landscape models πŸ”
R
63
star
94

roadoi

Use Unpaywall with R
R
63
star
95

parzer

Parse geographic coordinates
R
63
star
96

tiler

Generate geographic and non-geographic map tiles from R
R
63
star
97

rWBclimate

R interface for the World Bank climate data
R
62
star
98

codemetar

an R package for generating and working with codemeta
R
62
star
99

comtradr

Functions for Interacting with the UN Comtrade API
R
60
star
100

aRxiv

Programmatic interface to the Arxiv API
R
58
star