| Title: | Client for 'jq', a 'JSON' Processor |
|---|---|
| Description: | Client for 'jq', a 'JSON' processor (<https://jqlang.github.io/jq/>), written in C. 'jq' allows the following with 'JSON' data: index into, parse, do calculations, cut up and filter, change key names and values, perform conditionals and comparisons, and more. |
| Authors: | Rich FitzJohn [aut], Jeroen Ooms [aut, cre], Scott Chamberlain [aut], Stefan Milton Bache [aut] |
| Maintainer: | Jeroen Ooms <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.3.5 |
| Built: | 2024-11-04 06:21:52 UTC |
| Source: | CRAN |
Format strings and escaping
at(.data, ...) at_(.data, ..., .dots)at(.data, ...) at_(.data, ..., .dots)
.data |
input. This can be JSON input, or an object of class
|
... |
Comma separated list of unquoted variable names |
.dots |
Used to work around non-standard evaluation |
dots |
dots |
x <- '{"user":"jqlang","titles":["JQ Primer", "More JQ"]}' x %>% at(base64) %>% peek x %>% at(base64) x %>% index() %>% at(base64) y <- '["fo", "foo", "barfoo", "foobar", "barfoob"]' y %>% index() %>% at(base64) ## prepare for shell use y %>% index() %>% at(sh) ## rendered as csv with double quotes z <- '[1, 2, 3, "a"]' z %>% at(csv) ## rendered as csv with double quotes z %>% index() z %>% index() %>% at(text) ## % encode for URI's #### DOESNT WORK -------------------------- ## html escape #### DOESNT WORK -------------------------- ## serialize to json #### DOESNT WORK --------------------------x <- '{"user":"jqlang","titles":["JQ Primer", "More JQ"]}' x %>% at(base64) %>% peek x %>% at(base64) x %>% index() %>% at(base64) y <- '["fo", "foo", "barfoo", "foobar", "barfoob"]' y %>% index() %>% at(base64) ## prepare for shell use y %>% index() %>% at(sh) ## rendered as csv with double quotes z <- '[1, 2, 3, "a"]' z %>% at(csv) ## rendered as csv with double quotes z %>% index() z %>% index() %>% at(text) ## % encode for URI's #### DOESNT WORK -------------------------- ## html escape #### DOESNT WORK -------------------------- ## serialize to json #### DOESNT WORK --------------------------
Build arrays and objects
build_array(.data, ...) build_array_(.data, ..., .dots) build_object(.data, ...) build_object_(.data, ..., .dots)build_array(.data, ...) build_array_(.data, ..., .dots) build_object(.data, ...) build_object_(.data, ..., .dots)
.data |
input. This can be JSON input, or an object of class
|
... |
Comma separated list of unquoted variable names |
.dots |
Used to work around non-standard evaluation |
dots |
dots |
## BUILD ARRAYS x <- '{"user":"jqlang", "projects": ["jq", "wikiflow"]}' jq(x, "[.user, .projects[]]") x %>% build_array(.user, .projects[]) jq('[1, 2, 3]', '[ .[] | . * 2]') '[1, 2, 3]' %>% build_array(.[] | . * 2) ## BUILD OBJECTS '{"foo": 5, "bar": 7}' %>% build_object(a = .foo) %>% peek '{"foo": 5, "bar": 7}' %>% build_object(a = .foo) # using json dataset, just first element x <- commits %>% index(0) x %>% build_object(message = .commit.message, name = .commit.committer.name) x %>% build_object(sha = .commit.tree.sha, author = .author.login) # using json dataset, all elements x <- index(commits) x %>% build_object(message = .commit.message, name = .commit.committer.name) x %>% build_object(sha = .sha, name = .commit.committer.name) # many JSON inputs '{"foo": 5, "bar": 7} {"foo": 50, "bar": 7} {"foo": 500, "bar": 7}' %>% build_object(hello = .foo)## BUILD ARRAYS x <- '{"user":"jqlang", "projects": ["jq", "wikiflow"]}' jq(x, "[.user, .projects[]]") x %>% build_array(.user, .projects[]) jq('[1, 2, 3]', '[ .[] | . * 2]') '[1, 2, 3]' %>% build_array(.[] | . * 2) ## BUILD OBJECTS '{"foo": 5, "bar": 7}' %>% build_object(a = .foo) %>% peek '{"foo": 5, "bar": 7}' %>% build_object(a = .foo) # using json dataset, just first element x <- commits %>% index(0) x %>% build_object(message = .commit.message, name = .commit.committer.name) x %>% build_object(sha = .commit.tree.sha, author = .author.login) # using json dataset, all elements x <- index(commits) x %>% build_object(message = .commit.message, name = .commit.committer.name) x %>% build_object(sha = .sha, name = .commit.committer.name) # many JSON inputs '{"foo": 5, "bar": 7} {"foo": 50, "bar": 7} {"foo": 500, "bar": 7}' %>% build_object(hello = .foo)
Combine json pieces
combine(x)combine(x)
x |
Input, of class json |
x <- '{"foo": 5, "bar": 7}' %>% select(a = .foo) combine(x) (x <- commits %>% index() %>% select(sha = .sha, name = .commit.committer.name)) combine(x)x <- '{"foo": 5, "bar": 7}' %>% select(a = .foo) combine(x) (x <- commits %>% index() %>% select(sha = .sha, name = .commit.committer.name)) combine(x)
GitHub Commits Data
A character string of json github commits data for the jq repo.
dot and related functions
dot(.data) dot_(.data, dots = ".") dotstr(.data, ...) dotstr_(.data, ..., .dots)dot(.data) dot_(.data, dots = ".") dotstr(.data, ...) dotstr_(.data, ..., .dots)
.data |
input. This can be JSON input, or an object of class
|
dots |
dots |
... |
Comma separated list of unquoted variable names |
.dots |
Used to work around non-standard evaluation |
str <- '[{"name":"JSON", "good":true}, {"name":"XML", "good":false}]' str %>% dot str %>% index %>% dotstr(name) '{"foo": 5, "bar": 8}' %>% dot '{"foo": 5, "bar": 8}' %>% dotstr(foo) '{"foo": {"bar": 8}}' %>% dotstr(foo.bar)str <- '[{"name":"JSON", "good":true}, {"name":"XML", "good":false}]' str %>% dot str %>% index %>% dotstr(name) '{"foo": 5, "bar": 8}' %>% dot '{"foo": 5, "bar": 8}' %>% dotstr(foo) '{"foo": {"bar": 8}}' %>% dotstr(foo.bar)
Define and use functions
funs(.data, fxn, action)funs(.data, fxn, action)
.data |
input |
fxn |
A function definition, without |
action |
What to do with the function on the data |
jq("[1,2,10,20]", 'def increment: . + 1; map(increment)') "[1,2,10,20]" %>% funs('increment: . + 1', 'map(increment)') "[1,2,10,20]" %>% funs('increment: . / 100', 'map(increment)') "[1,2,10,20]" %>% funs('increment: . / 100', 'map(increment)') '[[1,2],[10,20]]' %>% funs('addvalue(f): f as $x | map(. + $x)', 'addvalue(.[0])') "[1,2]" %>% funs('f(a;b;c;d;e;f): [a+1,b,c,d,e,f]', 'f(.[0];.[1];.[0];.[0];.[0];.[0])') "[1,2,3,4]" %>% funs('fac: if . == 1 then 1 else . * (. - 1 | fac) end', '[.[] | fac]')jq("[1,2,10,20]", 'def increment: . + 1; map(increment)') "[1,2,10,20]" %>% funs('increment: . + 1', 'map(increment)') "[1,2,10,20]" %>% funs('increment: . / 100', 'map(increment)') "[1,2,10,20]" %>% funs('increment: . / 100', 'map(increment)') '[[1,2],[10,20]]' %>% funs('addvalue(f): f as $x | map(. + $x)', 'addvalue(.[0])') "[1,2]" %>% funs('f(a;b;c;d;e;f): [a+1,b,c,d,e,f]', 'f(.[0];.[1];.[0];.[0];.[0];.[0])') "[1,2,3,4]" %>% funs('fac: if . == 1 then 1 else . * (. - 1 | fac) end', '[.[] | fac]')
index and related functions
index(.data, ...) index_(.data, ..., .dots) indexif(.data, ...) indexif_(.data, ..., .dots) dotindex(.data, ...) dotindex_(.data, ..., .dots)index(.data, ...) index_(.data, ..., .dots) indexif(.data, ...) indexif_(.data, ..., .dots) dotindex(.data, ...) dotindex_(.data, ..., .dots)
.data |
input. This can be JSON input, or an object of class
|
... |
Comma separated list of unquoted variable names |
.dots |
Used to work around non-standard evaluation |
dots |
dots |
index/index_ - queries like: .[], .[0],
.[1:5],
.["foo"]
indexif/indexif_ - queries like: .["foo"]?
dotindex/dotindex_ - queries like: .[].foo,
.[].foo.bar
str <- '[{"name":"JSON", "good":true}, {"name":"XML", "good":false}]' str %>% index '{"name":"JSON", "good":true}' %>% indexif(name) '{"name":"JSON", "good":true}' %>% indexif(good) '{"name":"JSON", "good":true}' %>% indexif(that) '{"a": 1, "b": 1}' %>% index '[]' %>% index '[{"name":"JSON", "good":true}, {"name":"XML", "good":false}]' %>% index(0) '["a","b","c","d","e"]' %>% index(2) '["a","b","c","d","e"]' %>% index('2:4') '["a","b","c","d","e"]' %>% index('2:5') '["a","b","c","d","e"]' %>% index(':3') '["a","b","c","d","e"]' %>% index('-2:') str %>% index %>% select(bad = .name) '[{"name":"JSON", "good":true}, {"name":"XML", "good":false}]' %>% dotindex(name) '[{"name":"JSON", "good":true}, {"name":"XML", "good":false}]' %>% dotindex(good) '[{"name":"JSON", "good":{"foo":5}}, {"name":"XML", "good":{"foo":6}}]' %>% dotindex(good) '[{"name":"JSON", "good":{"foo":5}}, {"name":"XML", "good":{"foo":6}}]' %>% dotindex(good.foo)str <- '[{"name":"JSON", "good":true}, {"name":"XML", "good":false}]' str %>% index '{"name":"JSON", "good":true}' %>% indexif(name) '{"name":"JSON", "good":true}' %>% indexif(good) '{"name":"JSON", "good":true}' %>% indexif(that) '{"a": 1, "b": 1}' %>% index '[]' %>% index '[{"name":"JSON", "good":true}, {"name":"XML", "good":false}]' %>% index(0) '["a","b","c","d","e"]' %>% index(2) '["a","b","c","d","e"]' %>% index('2:4') '["a","b","c","d","e"]' %>% index('2:5') '["a","b","c","d","e"]' %>% index(':3') '["a","b","c","d","e"]' %>% index('-2:') str %>% index %>% select(bad = .name) '[{"name":"JSON", "good":true}, {"name":"XML", "good":false}]' %>% dotindex(name) '[{"name":"JSON", "good":true}, {"name":"XML", "good":false}]' %>% dotindex(good) '[{"name":"JSON", "good":{"foo":5}}, {"name":"XML", "good":{"foo":6}}]' %>% dotindex(good) '[{"name":"JSON", "good":{"foo":5}}, {"name":"XML", "good":{"foo":6}}]' %>% dotindex(good.foo)
jq is meant to work with the high level interface in this package.
jq also provides access to the low level interface in which you can
use jq query strings just as you would on the command line. Output gets
class of json, and pretty prints to the console for easier viewing.
jqr doesn't do pretty printing.
jq(x, ...) ## S3 method for class 'jqr' jq(x, ...) ## S3 method for class 'character' jq(x, ..., flags = jq_flags()) ## S3 method for class 'json' jq(x, ..., flags = jq_flags()) ## S3 method for class 'connection' jq(x, ..., flags = jq_flags(), out = NULL)jq(x, ...) ## S3 method for class 'jqr' jq(x, ...) ## S3 method for class 'character' jq(x, ..., flags = jq_flags()) ## S3 method for class 'json' jq(x, ..., flags = jq_flags()) ## S3 method for class 'connection' jq(x, ..., flags = jq_flags(), out = NULL)
x |
|
... |
character specification of jq query. Each element in |
flags |
See |
out |
a filename, callback function, connection object to stream output. Set to 'NULL' to buffer all output and return a character vector. |
'{"a": 7}' %>% do(.a + 1) '[8,3,null,6]' %>% sortj x <- '[{"message": "hello", "name": "jenn"}, {"message": "world", "name": "beth"}]' jq(index(x)) jq('{"a": 7, "b": 4}', 'keys') jq('[8,3,null,6]', 'sort') # many json inputs jq(c("[123, 456]", "[77, 88, 99]", "[41]"), ".[]") # Stream from connection tmp <- tempfile() writeLines(c("[123, 456]", "[77, 88, 99]", "[41]"), tmp) jq(file(tmp), ".[]") ## Not run: # from a url x <- 'http://jeroen.github.io/data/diamonds.json' jq(url(x), ".[]") # from a file file <- file.path(tempdir(), "diamonds_nd.json") download.file(x, destfile = file) jq(file(file), ".carat") jq(file(file), "select(.carat > 1.5)") jq(file(file), 'select(.carat > 4 and .cut == "Fair")') ## End(Not run)'{"a": 7}' %>% do(.a + 1) '[8,3,null,6]' %>% sortj x <- '[{"message": "hello", "name": "jenn"}, {"message": "world", "name": "beth"}]' jq(index(x)) jq('{"a": 7, "b": 4}', 'keys') jq('[8,3,null,6]', 'sort') # many json inputs jq(c("[123, 456]", "[77, 88, 99]", "[41]"), ".[]") # Stream from connection tmp <- tempfile() writeLines(c("[123, 456]", "[77, 88, 99]", "[41]"), tmp) jq(file(tmp), ".[]") ## Not run: # from a url x <- 'http://jeroen.github.io/data/diamonds.json' jq(url(x), ".[]") # from a file file <- file.path(tempdir(), "diamonds_nd.json") download.file(x, destfile = file) jq(file(file), ".carat") jq(file(file), "select(.carat > 1.5)") jq(file(file), 'select(.carat > 4 and .cut == "Fair")') ## End(Not run)
The flags function is provided for the high-level DSL
approach, whereas the jq_flags function is used to provide
the low-level jq with the appropriate flags.
jq_flags( pretty = FALSE, ascii = FALSE, color = FALSE, sorted = FALSE, stream = FALSE, seq = FALSE ) flags( .data, pretty = FALSE, ascii = FALSE, color = FALSE, sorted = FALSE, stream = FALSE, seq = FALSE )jq_flags( pretty = FALSE, ascii = FALSE, color = FALSE, sorted = FALSE, stream = FALSE, seq = FALSE ) flags( .data, pretty = FALSE, ascii = FALSE, color = FALSE, sorted = FALSE, stream = FALSE, seq = FALSE )
pretty |
Pretty print the json (different to jsonlite's pretty printing). |
ascii |
Force jq to produce pure ASCII output with non-ASCII characters replaced by equivalent escape sequences. |
color |
Add ANSI escape sequences for coloured output |
sorted |
Output fields of each object with keys in sorted order |
stream |
Parse the input in streaming fashion, outputing arrays
of path and leaf values like |
seq |
Use the application/json-seq MIME type scheme for separating
JSON like the |
.data |
A |
'{"a": 7, "z":0, "b": 4}' %>% flags(sorted = TRUE) '{"a": 7, "z":0, "b": 4}' %>% dot %>% flags(sorted = TRUE) jq('{"a": 7, "z":0, "b": 4}', ".") %>% flags(sorted = TRUE) jq('{"a": 7, "z":0, "b": 4}', ".", flags = jq_flags(sorted = TRUE))'{"a": 7, "z":0, "b": 4}' %>% flags(sorted = TRUE) '{"a": 7, "z":0, "b": 4}' %>% dot %>% flags(sorted = TRUE) jq('{"a": 7, "z":0, "b": 4}', ".") %>% flags(sorted = TRUE) jq('{"a": 7, "z":0, "b": 4}', ".", flags = jq_flags(sorted = TRUE))
An R client for the C library jq
Low level interface, in which you can execute 'jq' code just as you
would on the command line. Available via jq
High-level, uses a suite of functions to construct queries. Queries
are constucted, then excuted internally with jq
The high level DSL supports piping, though you don't have to use pipes.
Most DSL functions have NSE (non-standard evaluation) and SE
(standard evaluation) versions, which make jqr easy to use
for interactive use as well as programming.
We link to jq through the installed version on your system,
so the version can vary. Run jq --version to get your jq version
note that jq indexing starts at 0, whereas R indexing
starts at 1. So when you want the first thing in an array using
jq, for example, you want 0, not 1
Note that with both the low level interface and the high level DSL, we
print the output to look like a valid JSON object to make it easier to
look at. However, it's important to know that the output is really just a
simple character string or vector of strings - it's just the print function
that pretty prints it and makes it look like a single JSON object. What jq
is giving you often is a stream of valid JSON objects, each one of which is
valid, but altogether are not valid. However, a trick you can do is to
wrap your jq program in brackets like [.[]] instead of .[]
to give a single JSON object
Related to above, you can use the function provided string
with the high level DSL to get back a character string instead of
pretty printed version
Maintainer: Jeroen Ooms [email protected]
Authors:
Rich FitzJohn [email protected]
Scott Chamberlain [email protected]
Stefan Milton Bache [email protected]
Useful links:
Report bugs at https://github.com/ropensci/jqr/issues
Low level JQ API. First create a program using a 'query' and 'flags' and then feed pieces of data.
jqr_new(query, flags = jq_flags()) jqr_feed(jqr_program, json, unlist = TRUE, finalize = FALSE)jqr_new(query, flags = jq_flags()) jqr_feed(jqr_program, json, unlist = TRUE, finalize = FALSE)
query |
string with a valid jq program |
flags |
See |
jqr_program |
object returned by [jqr_new] |
json |
character vector with json data. If the JSON object is incomplete, you must set 'finalize' to 'FALSE' otherwise you get an error. |
unlist |
if 'TRUE' returns a single character vector with all output for each each string in 'json' input |
finalize |
completes the parsing and verifies that the JSON string is valid. Set this to 'TRUE' when feeding the final piece of data. |
program <- jqr_new(".[]") jqr_feed(program, c("[123, 456]", "[77, 88, 99]")) jqr_feed(program, c("[41, 234]")) jqr_feed(program, "", finalize = TRUE)program <- jqr_new(".[]") jqr_feed(program, c("[123, 456]", "[77, 88, 99]")) jqr_feed(program, c("[41, 234]")) jqr_feed(program, "", finalize = TRUE)
keys takes no input, and retrieves keys. del deletes
provided keys. haskey checks if a json string has a key, or the
input array has an element at the given index.
keys(.data) del(.data, ...) del_(.data, ..., .dots) haskey(.data, ...) haskey_(.data, ..., .dots)keys(.data) del(.data, ...) del_(.data, ..., .dots) haskey(.data, ...) haskey_(.data, ..., .dots)
.data |
input. This can be JSON input, or an object of class
|
... |
Comma separated list of unquoted variable names |
.dots |
Used to work around non-standard evaluation |
dots |
dots |
# get keys str <- '{"foo": 5, "bar": 7}' jq(str, "keys") str %>% keys() # delete by key name jq(str, "del(.bar)") str %>% del(bar) # check for key existence str3 <- '[[0,1], ["a","b","c"]]' jq(str3, "map(has(2))") str3 %>% haskey(2) jq(str3, "map(has(1,2))") str3 %>% haskey(1,2) ## many JSON inputs '{"foo": 5, "bar": 7} {"hello": 5, "world": 7}' %>% keys '{"foo": 5, "bar": 7} {"hello": 5, "bar": 7}' %>% del(bar)# get keys str <- '{"foo": 5, "bar": 7}' jq(str, "keys") str %>% keys() # delete by key name jq(str, "del(.bar)") str %>% del(bar) # check for key existence str3 <- '[[0,1], ["a","b","c"]]' jq(str3, "map(has(2))") str3 %>% haskey(2) jq(str3, "map(has(1,2))") str3 %>% haskey(1,2) ## many JSON inputs '{"foo": 5, "bar": 7} {"hello": 5, "world": 7}' %>% keys '{"foo": 5, "bar": 7} {"hello": 5, "bar": 7}' %>% del(bar)
Logical tests
allj(.data) anyj(.data)allj(.data) anyj(.data)
.data |
input. This can be JSON input, or an object of class
|
# any '[true, false]' %>% anyj '[false, false]' %>% anyj '[]' %>% anyj # all '[true, false]' %>% allj '[true, true]' %>% allj '[]' %>% allj ## many JSON inputs '[true, false] [true, true] [false, false]' %>% anyj '[true, false] [true, true] [false, false]' %>% allj# any '[true, false]' %>% anyj '[false, false]' %>% anyj '[]' %>% anyj # all '[true, false]' %>% allj '[true, true]' %>% allj '[]' %>% allj ## many JSON inputs '[true, false] [true, true] [false, false]' %>% anyj '[true, false] [true, true] [false, false]' %>% allj
Manipulation operations
join(.data, ...) join_(.data, ..., .dots) splitj(.data, ...) splitj_(.data, ..., .dots) ltrimstr(.data, ...) ltrimstr_(.data, ..., .dots) rtrimstr(.data, ...) rtrimstr_(.data, ..., .dots) startswith(.data, ...) startswith_(.data, ..., .dots) endswith(.data, ...) endswith_(.data, ..., .dots) index_loc(.data, ...) index_loc_(.data, ..., .dots) rindex_loc(.data, ...) rindex_loc_(.data, ..., .dots) indices(.data, ...) indices_(.data, ..., .dots) tojson(.data) fromjson(.data) tostring(.data) tonumber(.data) contains(.data, ...) contains_(.data, ..., .dots) uniquej(.data, ...) uniquej_(.data, ..., .dots) group(.data, ...) group_(.data, ..., .dots)join(.data, ...) join_(.data, ..., .dots) splitj(.data, ...) splitj_(.data, ..., .dots) ltrimstr(.data, ...) ltrimstr_(.data, ..., .dots) rtrimstr(.data, ...) rtrimstr_(.data, ..., .dots) startswith(.data, ...) startswith_(.data, ..., .dots) endswith(.data, ...) endswith_(.data, ..., .dots) index_loc(.data, ...) index_loc_(.data, ..., .dots) rindex_loc(.data, ...) rindex_loc_(.data, ..., .dots) indices(.data, ...) indices_(.data, ..., .dots) tojson(.data) fromjson(.data) tostring(.data) tonumber(.data) contains(.data, ...) contains_(.data, ..., .dots) uniquej(.data, ...) uniquej_(.data, ..., .dots) group(.data, ...) group_(.data, ..., .dots)
.data |
input. This can be JSON input, or an object of class
|
... |
Comma separated list of unquoted variable names |
.dots |
Used to work around non-standard evaluation |
dots |
dots |
# join str <- '["a","b,c,d","e"]' jq(str, 'join(", ")') str %>% join str %>% join(`;`) str %>% join(`yep`) ## many JSON inputs '["a","b,c,d","e"] ["a","f,e,f"]' %>% join(`---`) # split jq('"a, b,c,d, e"', 'split(", ")') # ltrimstr jq('["fo", "foo", "barfoo", "foobar", "afoo"]', '[.[]|ltrimstr("foo")]') '["fo", "foo", "barfoo", "foobar", "afoo"]' %>% index() %>% ltrimstr(foo) # rtrimstr jq('["fo", "foo", "barfoo", "foobar", "foob"]', '[.[]|rtrimstr("foo")]') '["fo", "foo", "barfoo", "foobar", "foob"]' %>% index() %>% rtrimstr(foo) # startswith str <- '["fo", "foo", "barfoo", "foobar", "barfoob"]' jq(str, '[.[]|startswith("foo")]') str %>% index %>% startswith(foo) ## many JSON inputs '["fo", "foo"] ["barfoo", "foobar", "barfoob"]' %>% index %>% startswith(foo) # endswith jq(str, '[.[]|endswith("foo")]') str %>% index %>% endswith(foo) str %>% index %>% endswith_("foo") str %>% index %>% endswith(bar) str %>% index %>% endswith_("bar") ## many JSON inputs '["fo", "foo"] ["barfoo", "foobar", "barfoob"]' %>% index %>% endswith(foo) # get index (location) of a character ## input has to be quoted str <- '"a,b, cd, efg, hijk"' str %>% index_loc(", ") str %>% index_loc(",") str %>% index_loc("j") str %>% rindex_loc(", ") str %>% indices(", ") # tojson, fromjson, tostring, tonumber '[1, "foo", ["foo"]]' %>% index %>% tostring '[1, "1"]' %>% index %>% tonumber '[1, "foo", ["foo"]]' %>% index %>% tojson '[1, "foo", ["foo"]]' %>% index %>% tojson %>% fromjson # contains '"foobar"' %>% contains("bar") '["foobar", "foobaz", "blarp"]' %>% contains(`["baz", "bar"]`) '["foobar", "foobaz", "blarp"]' %>% contains(`["bazzzzz", "bar"]`) str <- '{"foo": 12, "bar":[1,2,{"barp":12, "blip":13}]}' str %>% contains(`{foo: 12, bar: [{barp: 12}]}`) str %>% contains(`{foo: 12, bar: [{barp: 15}]}`) # unique '[1,2,5,3,5,3,1,3]' %>% uniquej str <- '[{"foo": 1, "bar": 2}, {"foo": 1, "bar": 3}, {"foo": 4, "bar": 5}]' str %>% uniquej(foo) str %>% uniquej_("foo") '["chunky", "bacon", "kitten", "cicada", "asparagus"]' %>% uniquej(length) # group x <- '[{"foo":1, "bar":10}, {"foo":3, "bar":100}, {"foo":1, "bar":1}]' x %>% group(foo) x %>% group_("foo")# join str <- '["a","b,c,d","e"]' jq(str, 'join(", ")') str %>% join str %>% join(`;`) str %>% join(`yep`) ## many JSON inputs '["a","b,c,d","e"] ["a","f,e,f"]' %>% join(`---`) # split jq('"a, b,c,d, e"', 'split(", ")') # ltrimstr jq('["fo", "foo", "barfoo", "foobar", "afoo"]', '[.[]|ltrimstr("foo")]') '["fo", "foo", "barfoo", "foobar", "afoo"]' %>% index() %>% ltrimstr(foo) # rtrimstr jq('["fo", "foo", "barfoo", "foobar", "foob"]', '[.[]|rtrimstr("foo")]') '["fo", "foo", "barfoo", "foobar", "foob"]' %>% index() %>% rtrimstr(foo) # startswith str <- '["fo", "foo", "barfoo", "foobar", "barfoob"]' jq(str, '[.[]|startswith("foo")]') str %>% index %>% startswith(foo) ## many JSON inputs '["fo", "foo"] ["barfoo", "foobar", "barfoob"]' %>% index %>% startswith(foo) # endswith jq(str, '[.[]|endswith("foo")]') str %>% index %>% endswith(foo) str %>% index %>% endswith_("foo") str %>% index %>% endswith(bar) str %>% index %>% endswith_("bar") ## many JSON inputs '["fo", "foo"] ["barfoo", "foobar", "barfoob"]' %>% index %>% endswith(foo) # get index (location) of a character ## input has to be quoted str <- '"a,b, cd, efg, hijk"' str %>% index_loc(", ") str %>% index_loc(",") str %>% index_loc("j") str %>% rindex_loc(", ") str %>% indices(", ") # tojson, fromjson, tostring, tonumber '[1, "foo", ["foo"]]' %>% index %>% tostring '[1, "1"]' %>% index %>% tonumber '[1, "foo", ["foo"]]' %>% index %>% tojson '[1, "foo", ["foo"]]' %>% index %>% tojson %>% fromjson # contains '"foobar"' %>% contains("bar") '["foobar", "foobaz", "blarp"]' %>% contains(`["baz", "bar"]`) '["foobar", "foobaz", "blarp"]' %>% contains(`["bazzzzz", "bar"]`) str <- '{"foo": 12, "bar":[1,2,{"barp":12, "blip":13}]}' str %>% contains(`{foo: 12, bar: [{barp: 12}]}`) str %>% contains(`{foo: 12, bar: [{barp: 15}]}`) # unique '[1,2,5,3,5,3,1,3]' %>% uniquej str <- '[{"foo": 1, "bar": 2}, {"foo": 1, "bar": 3}, {"foo": 4, "bar": 5}]' str %>% uniquej(foo) str %>% uniquej_("foo") '["chunky", "bacon", "kitten", "cicada", "asparagus"]' %>% uniquej(length) # group x <- '[{"foo":1, "bar":10}, {"foo":3, "bar":100}, {"foo":1, "bar":1}]' x %>% group(foo) x %>% group_("foo")
Math operations
do(.data, ...) do_(.data, ..., .dots) lengthj(.data) sqrtj(.data) floorj(.data) minj(.data, ...) minj_(.data, ..., .dots) maxj(.data, ...) maxj_(.data, ..., .dots) ad(.data) map(.data, ...) map_(.data, ..., .dots)do(.data, ...) do_(.data, ..., .dots) lengthj(.data) sqrtj(.data) floorj(.data) minj(.data, ...) minj_(.data, ..., .dots) maxj(.data, ...) maxj_(.data, ..., .dots) ad(.data) map(.data, ...) map_(.data, ..., .dots)
.data |
input. This can be JSON input, or an object of class
|
... |
Comma separated list of unquoted variable names |
.dots |
Used to work around non-standard evaluation |
dots |
dots |
# do math jq('{"a": 7}', '.a + 1') # adding null gives back same result jq('{"a": 7}', '.a + null') jq('{"a": 7}', '.a += 1') '{"a": 7}' %>% do(.a + 1) # '{"a": 7}' %>% do(.a += 1) # this doesn't work quite yet '{"a": [1,2], "b": [3,4]}' %>% do(.a + .b) '{"a": [1,2], "b": [3,4]}' %>% do(.a - .b) '{"a": 3}' %>% do(4 - .a) '["xml", "yaml", "json"]' %>% do('. - ["xml", "yaml"]') '5' %>% do(10 / . * 3) ## many JSON inputs '{"a": [1,2], "b": [3,4]} {"a": [1,5], "b": [3,10]}' %>% do(.a + .b) # comparisons '[5,4,2,7]' %>% index() %>% do(. < 4) '[5,4,2,7]' %>% index() %>% do(. > 4) '[5,4,2,7]' %>% index() %>% do(. <= 4) '[5,4,2,7]' %>% index() %>% do(. >= 4) '[5,4,2,7]' %>% index() %>% do(. == 4) '[5,4,2,7]' %>% index() %>% do(. != 4) ## many JSON inputs '[5,4,2,7] [4,3,200,0.1]' %>% index() %>% do(. < 4) # length '[[1,2], "string", {"a":2}, null]' %>% index %>% lengthj # sqrt '9' %>% sqrtj ## many JSON inputs '9 4 5' %>% sqrtj # floor '3.14159' %>% floorj ## many JSON inputs '3.14159 30.14 45.9' %>% floorj # find minimum '[5,4,2,7]' %>% minj '[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% minj '[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% minj(foo) '[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% minj(bar) ## many JSON inputs '[{"foo":1}, {"foo":14}] [{"foo":2}, {"foo":3}]' %>% minj(foo) # find maximum '[5,4,2,7]' %>% maxj '[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% maxj '[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% maxj(foo) '[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% maxj(bar) ## many JSON inputs '[{"foo":1}, {"foo":14}] [{"foo":2}, {"foo":3}]' %>% maxj(foo) # increment values ## requires special % operators, they get escaped internally '{"foo": 1}' %>% do(.foo %+=% 1) '{"foo": 1}' %>% do(.foo %-=% 1) '{"foo": 1}' %>% do(.foo %*=% 4) '{"foo": 1}' %>% do(.foo %/=% 10) '{"foo": 1}' %>% do(.foo %//=% 10) ### fix me - %= doesn't work # '{"foo": 1}' %>% do(.foo %%=% 10) ## many JSON inputs '{"foo": 1} {"foo": 2} {"foo": 3}' %>% do(.foo %+=% 1) # add '["a","b","c"]' %>% ad '[1, 2, 3]' %>% ad '[]' %>% ad ## many JSON inputs '["a","b","c"] ["d","e","f"]' %>% ad # map ## as far as I know, this only works with numbers, thus it's ## in the maths section '[1, 2, 3]' %>% map(.+1) '[1, 2, 3]' %>% map(./1) '[1, 2, 3]' %>% map(.*4) # many JSON inputs '[1, 2, 3] [100, 200, 300] [1000, 2000, 30000]' %>% map(.+1)# do math jq('{"a": 7}', '.a + 1') # adding null gives back same result jq('{"a": 7}', '.a + null') jq('{"a": 7}', '.a += 1') '{"a": 7}' %>% do(.a + 1) # '{"a": 7}' %>% do(.a += 1) # this doesn't work quite yet '{"a": [1,2], "b": [3,4]}' %>% do(.a + .b) '{"a": [1,2], "b": [3,4]}' %>% do(.a - .b) '{"a": 3}' %>% do(4 - .a) '["xml", "yaml", "json"]' %>% do('. - ["xml", "yaml"]') '5' %>% do(10 / . * 3) ## many JSON inputs '{"a": [1,2], "b": [3,4]} {"a": [1,5], "b": [3,10]}' %>% do(.a + .b) # comparisons '[5,4,2,7]' %>% index() %>% do(. < 4) '[5,4,2,7]' %>% index() %>% do(. > 4) '[5,4,2,7]' %>% index() %>% do(. <= 4) '[5,4,2,7]' %>% index() %>% do(. >= 4) '[5,4,2,7]' %>% index() %>% do(. == 4) '[5,4,2,7]' %>% index() %>% do(. != 4) ## many JSON inputs '[5,4,2,7] [4,3,200,0.1]' %>% index() %>% do(. < 4) # length '[[1,2], "string", {"a":2}, null]' %>% index %>% lengthj # sqrt '9' %>% sqrtj ## many JSON inputs '9 4 5' %>% sqrtj # floor '3.14159' %>% floorj ## many JSON inputs '3.14159 30.14 45.9' %>% floorj # find minimum '[5,4,2,7]' %>% minj '[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% minj '[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% minj(foo) '[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% minj(bar) ## many JSON inputs '[{"foo":1}, {"foo":14}] [{"foo":2}, {"foo":3}]' %>% minj(foo) # find maximum '[5,4,2,7]' %>% maxj '[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% maxj '[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% maxj(foo) '[{"foo":1, "bar":14}, {"foo":2, "bar":3}]' %>% maxj(bar) ## many JSON inputs '[{"foo":1}, {"foo":14}] [{"foo":2}, {"foo":3}]' %>% maxj(foo) # increment values ## requires special % operators, they get escaped internally '{"foo": 1}' %>% do(.foo %+=% 1) '{"foo": 1}' %>% do(.foo %-=% 1) '{"foo": 1}' %>% do(.foo %*=% 4) '{"foo": 1}' %>% do(.foo %/=% 10) '{"foo": 1}' %>% do(.foo %//=% 10) ### fix me - %= doesn't work # '{"foo": 1}' %>% do(.foo %%=% 10) ## many JSON inputs '{"foo": 1} {"foo": 2} {"foo": 3}' %>% do(.foo %+=% 1) # add '["a","b","c"]' %>% ad '[1, 2, 3]' %>% ad '[]' %>% ad ## many JSON inputs '["a","b","c"] ["d","e","f"]' %>% ad # map ## as far as I know, this only works with numbers, thus it's ## in the maths section '[1, 2, 3]' %>% map(.+1) '[1, 2, 3]' %>% map(./1) '[1, 2, 3]' %>% map(.*4) # many JSON inputs '[1, 2, 3] [100, 200, 300] [1000, 2000, 30000]' %>% map(.+1)
Outputs paths to all the elements in its input
paths(.data)paths(.data)
.data |
input |
'[1,[[],{"a":2}]]' %>% paths '[{"name":"JSON", "good":true}, {"name":"XML", "good":false}]' %>% paths'[1,[[],{"a":2}]]' %>% paths '[{"name":"JSON", "good":true}, {"name":"XML", "good":false}]' %>% paths
Prints the query resulting from jq all in one character string just
as you would execute it on the command line. Output gets class of json,
and pretty prints to the console for easier viewing.
peek(.data)peek(.data)
.data |
(list) input, using higher level interface |
jq.
'{"a": 7}' %>% do(.a + 1) %>% peek '[8,3,null,6]' %>% sortj %>% peek'{"a": 7}' %>% do(.a + 1) %>% peek '[8,3,null,6]' %>% sortj %>% peek
Produce range of numbers
rangej(x, array = FALSE)rangej(x, array = FALSE)
x |
Input, single number or number range. |
array |
(logical) Create array. Default: |
2:4 %>% rangej 2:1000 %>% rangej 1 %>% rangej 4 %>% rangej2:4 %>% rangej 2:1000 %>% rangej 1 %>% rangej 4 %>% rangej
Search through a recursive structure - extract data from all levels
recurse(.data, ...) recurse_(.data, ..., .dots)recurse(.data, ...) recurse_(.data, ..., .dots)
.data |
input. This can be JSON input, or an object of class
|
... |
Comma separated list of unquoted variable names |
.dots |
Used to work around non-standard evaluation |
dots |
dots |
x <- '{"name": "/", "children": [ {"name": "/bin", "children": [ {"name": "/bin/ls", "children": []}, {"name": "/bin/sh", "children": []}]}, {"name": "/home", "children": [ {"name": "/home/stephen", "children": [ {"name": "/home/stephen/jq", "children": []}]}]}]}' x %>% recurse(.children[]) %>% build_object(name) x %>% recurse(.children[]) %>% build_object(name) %>% stringx <- '{"name": "/", "children": [ {"name": "/bin", "children": [ {"name": "/bin/ls", "children": []}, {"name": "/bin/sh", "children": []}]}, {"name": "/home", "children": [ {"name": "/home/stephen", "children": [ {"name": "/home/stephen/jq", "children": []}]}]}]}' x %>% recurse(.children[]) %>% build_object(name) x %>% recurse(.children[]) %>% build_object(name) %>% string
The function select(foo) produces its input unchanged if
foo returns TRUE for that input, and produces no output otherwise
select(.data, ...) select_(.data, ..., .dots)select(.data, ...) select_(.data, ..., .dots)
.data |
input. This can be JSON input, or an object of class
|
... |
Comma separated list of unquoted variable names |
.dots |
Used to work around non-standard evaluation |
dots |
dots |
this function has changed what it does dramatically. we were
using this function for object construction, which is now done with
build_object
jq('[1,5,3,0,7]', 'map(select(. >= 2))') '[1,5,3,0,7]' %>% map(select(. >= 2)) '{"foo": 4, "bar": 7}' %>% select(.foo == 4) '{"foo": 5, "bar": 7} {"foo": 4, "bar": 7}' %>% select(.foo == 4) '[{"foo": 5, "bar": 7}, {"foo": 4, "bar": 7}]' %>% index() %>% select(.foo == 4) '{"foo": 4, "bar": 7} {"foo": 5, "bar": 7} {"foo": 8, "bar": 7}' %>% select(.foo < 6) x <- '{"foo": 4, "bar": 2} {"foo": 5, "bar": 4} {"foo": 8, "bar": 12}' jq(x, 'select((.foo < 6) and (.bar > 3))') jq(x, 'select((.foo < 6) or (.bar > 3))') x %>% select((.foo < 6) && (.bar > 3)) x %>% select((.foo < 6) || (.bar > 3)) x <- '[{"foo": 5, "bar": 7}, {"foo": 4, "bar": 7}, {"foo": 4, "bar": 9}]' jq(x, '.[] | select(.foo == 4) | {user: .bar}') x %>% index() %>% select(.foo == 4) %>% build_object(user = .bar)jq('[1,5,3,0,7]', 'map(select(. >= 2))') '[1,5,3,0,7]' %>% map(select(. >= 2)) '{"foo": 4, "bar": 7}' %>% select(.foo == 4) '{"foo": 5, "bar": 7} {"foo": 4, "bar": 7}' %>% select(.foo == 4) '[{"foo": 5, "bar": 7}, {"foo": 4, "bar": 7}]' %>% index() %>% select(.foo == 4) '{"foo": 4, "bar": 7} {"foo": 5, "bar": 7} {"foo": 8, "bar": 7}' %>% select(.foo < 6) x <- '{"foo": 4, "bar": 2} {"foo": 5, "bar": 4} {"foo": 8, "bar": 12}' jq(x, 'select((.foo < 6) and (.bar > 3))') jq(x, 'select((.foo < 6) or (.bar > 3))') x %>% select((.foo < 6) && (.bar > 3)) x %>% select((.foo < 6) || (.bar > 3)) x <- '[{"foo": 5, "bar": 7}, {"foo": 4, "bar": 7}, {"foo": 4, "bar": 9}]' jq(x, '.[] | select(.foo == 4) | {user: .bar}') x %>% index() %>% select(.foo == 4) %>% build_object(user = .bar)
Sort and related
sortj(.data, ...) sortj_(.data, ..., .dots) reverse(.data)sortj(.data, ...) sortj_(.data, ..., .dots) reverse(.data)
.data |
input. This can be JSON input, or an object of class
|
... |
Comma separated list of unquoted variable names |
.dots |
Used to work around non-standard evaluation |
dots |
dots |
# sort '[8,3,null,6]' %>% sortj '[{"foo":4, "bar":10}, {"foo":3, "bar":100}, {"foo":2, "bar":1}]' %>% sortj(foo) # reverse order '[1,2,3,4]' %>% reverse # many JSON inputs '[{"foo":7}, {"foo":4}] [{"foo":300}, {"foo":1}] [{"foo":2}, {"foo":1}]' %>% sortj(foo) '[1,2,3,4] [10,20,30,40] [100,200,300,4000]' %>% reverse# sort '[8,3,null,6]' %>% sortj '[{"foo":4, "bar":10}, {"foo":3, "bar":100}, {"foo":2, "bar":1}]' %>% sortj(foo) # reverse order '[1,2,3,4]' %>% reverse # many JSON inputs '[{"foo":7}, {"foo":4}] [{"foo":300}, {"foo":1}] [{"foo":2}, {"foo":1}]' %>% sortj(foo) '[1,2,3,4] [10,20,30,40] [100,200,300,4000]' %>% reverse
Give back a character string
string(.data)string(.data)
.data |
(list) input, using higher level interface |
'{"a": 7}' %>% do(.a + 1) %>% string '[8,3,null,6]' %>% sortj %>% string'{"a": 7}' %>% do(.a + 1) %>% string '[8,3,null,6]' %>% sortj %>% string
Types and related functions
types(.data) type(.data, ...) type_(.data, ..., .dots)types(.data) type(.data, ...) type_(.data, ..., .dots)
.data |
input. This can be JSON input, or an object of class
|
... |
Comma separated list of unquoted variable names |
.dots |
Used to work around non-standard evaluation |
dots |
dots |
# get type information for each element jq('[0, false, [], {}, null, "hello"]', 'map(type)') '[0, false, [], {}, null, "hello"]' %>% types '[0, false, [], {}, null, "hello", true, [1,2,3]]' %>% types # select elements by type jq('[0, false, [], {}, null, "hello"]', '.[] | numbers,booleans') '[0, false, [], {}, null, "hello"]' %>% index() %>% type(booleans)# get type information for each element jq('[0, false, [], {}, null, "hello"]', 'map(type)') '[0, false, [], {}, null, "hello"]' %>% types '[0, false, [], {}, null, "hello", true, [1,2,3]]' %>% types # select elements by type jq('[0, false, [], {}, null, "hello"]', '.[] | numbers,booleans') '[0, false, [], {}, null, "hello"]' %>% index() %>% type(booleans)
Variables
vars(.data, ...) vars_(.data, ..., .dots)vars(.data, ...) vars_(.data, ..., .dots)
.data |
input. This can be JSON input, or an object of class
|
... |
Comma separated list of unquoted variable names |
.dots |
Used to work around non-standard evaluation |
dots |
dots |
x <- '{ "posts": [ {"title": "Frist psot", "author": "anon"}, {"title": "A well-written article", "author": "person1"} ], "realnames": { "anon": "Anonymous Coward", "person1": "Person McPherson" } }' x %>% dotstr(posts[]) x %>% dotstr(posts[]) %>% string x %>% vars(realnames = names) %>% dotstr(posts[]) %>% build_object(title, author = "$names[.author]")x <- '{ "posts": [ {"title": "Frist psot", "author": "anon"}, {"title": "A well-written article", "author": "person1"} ], "realnames": { "anon": "Anonymous Coward", "person1": "Person McPherson" } }' x %>% dotstr(posts[]) x %>% dotstr(posts[]) %>% string x %>% vars(realnames = names) %>% dotstr(posts[]) %>% build_object(title, author = "$names[.author]")