Related
I never have worked with json data in R and unfortunately, I was sent a sample of data as:
{
"task_id": "104",
"status": "succeeded",
"metrics": {
"requests_made": 2,
"network_errors": 0,
"unique_locations_visited": 0,
"requests_queued": 0,
"queue_items_completed": 2,
"queue_items_waiting": 0,
"issue_events": 9,
"caption": "",
"progress": 100
},
"message": "",
"issue_events": [
{
"id": "1234",
"type": "issue_found",
"issue": {
"name": "policy not enforced",
"type_index": 123456789,
"serial_number": "123456789183923712",
"origin": "https://test.com",
"path": "/robots.txt",
"severity": "low",
"confidence": "certain",
"caption": "/robots.txt",
"evidence": [
{
"type": "FirstOrderEvidence",
"detail": {
"band_flags": [
"in_band"
]
},
"request_response": {
"url": "https://test.com/robots.txt",
"request": [
{
"type": "DataSegment",
"data": "jaghsdjgasdgaskjdgasdgashdgsahdgasjkdgh==",
"length": 313
}
],
"response": [
{
"type": "DataSegment",
"data": "asudasjdgasaaasgdasgaksjdhgasjdgkjghKGKGgKJgKJgKJGKgh==",
"length": 303
}
],
"was_redirect_followed": false,
"request_time": "1234567890"
}
}
],
"internal_data": "jdfhgjhJHkjhdskfhkjhjs0sajkdfhKHKhkj=="
}
},
{
"id": "1235",
"type": "issue_found",
"issue": {
"name": "certificate",
"type_index": 12345845684,
"serial_number": "123456789165637150",
"origin": "https://test.com",
"path": "/",
"severity": "info",
"confidence": "certain",
"description": "The server description a valid, trusted certificate. This issue is purely informational.<br><br>The server presented the following certificates:<br><br><h4>Server certificate</h4><table><tr><td><b>Issued to:</b> </td><td>test.ie, test.com, www.test.com, www.test.ie</td></tr><tr><td><b>Issued by:</b> </td><td>GeoTrust EV RSA CA 2018</td></tr><tr><td><b>Valid from:</b> </td><td>Tue May 12 00:00:00 UTC 2020</td></tr><tr><td><b>Valid to:</b> </td><td>Tue May 17 12:00:00 UTC 2022</td></tr></table><h4>Certificate chain #1</h4><table><tr><td><b>Issued to:</b> </td><td>GeoTrust EV RSA CA 2018</td></tr><tr><td><b>Issued by:</b> </td><td> High Assurance EV Root CA</td></tr><tr><td><b>Valid from:</b> </td><td>Mon Nov 06 12:22:46 UTC 2017</td></tr><tr><td><b>Valid to:</b> </td><td>Sat Nov 06 12:22:46 UTC 2027</td></tr></table><h4>Certificate chain #2</h4><table><tr><td><b>Issued to:</b> </td><td> High Assurance EV Root CA</td></tr><tr><td><b>Issued by:</b> </td><td> High Assurance EV Root CA</td></tr><tr><td><b>Valid from:</b> </td><td>Fri Nov 10 00:00:00 UTC 2006</td></tr><tr><td><b>Valid to:</b> </td><td>Mon Nov 10 00:00:00 UTC 2031</td></tr></table>",
"caption": "/",
"evidence": [],
"internal_data": "sjhdgsajdggJGJHgjfgjhGJHgjhsdgfgjhGJHGjhsdgfjhsgfdsjfg098867hjhgJHGJHG=="
}
},
{
"id": "1236",
"type": "issue_found",
"issue": {
"name": "without flag set",
"type_index": 1254392,
"serial_number": "12345678965616",
"origin": "https://test.com",
"path": "/robots.txt",
"severity": "info",
"confidence": "certain",
"description": "my description text here....",
"caption": "/robots.txt",
"evidence": [
{
"type": "InformationListEvidence",
"request_response": {
"url": "https://test.com/robots.txt",
"request": [
{
"type": "DataSegment",
"data": "adjkhajksdhaskjdhkjHKJHjkhaskjdhkjasdhKHKJHkjsdhfkjsdhfkjsdhKHJKHjksdfhsdjkfhksdjhKHKJHJKhsdkfjhsdkfjhKHJKHjksdkfjhsdkjfhKHKJHjkhsdkfjhsdkjfhsdjkfhksdjfhKJHKjksdhfsdjkfhksdjfhsdkjhKHJKhsdkfhsdkjfhsdkfhdskjhKHKjhsdfkjhsdjkfh==",
"length": 313
}
],
"response": [
{
"type": "DataSegment",
"data": "adjkhajksdhaskjdhkjHKJHjkhaskjdhkjasdhKHKJHkjsdhfkjsdhfkjsdhKHJKHjksdfhsdjkfhksdjhKHKJHJKhsdkfjhsdkfjhKHJKHjksdkfjhsdkjfhKHKJHjkhsdkfjhsdkjfhsdjkfhksdjfhKJHKjksdhfsdjkfhksdjfhsdkjhKHJKhsdkfhsdkjfhsdkfhdskjhKHKjhsdfkjhsdjkfh=",
"length": 161
},
{
"type": "HighlightSegment",
"data": "adjkhajksdhaskjdhkjHKJHjkhaskjdhkjasdhKHKJHkjsdhfkjsdhfkjsdhKHJKHjksdfhsdjkfhksdjhKHKJHJKhsdkfjhsdkfjhKHJKHjksdkfjhsdkjfhKHKJHjkhsdkfjhsdkjfhsdjkfhksdjfhKJHKjksdhfsdjkfhksdjfhsdkjhKHJKhsdkfhsdkjfhsdkfhdskjhKHKjhsdf=",
"length": 119
},
{
"type": "DataSegment",
"data": "AasjkdhasjkhkjHKJSDHFJKSDFHKhjkHSKADJFHKhjkhjkh=",
"length": 23
}
],
"was_redirect_followed": false,
"request_time": "178454751191465"
},
"information_items": [
"Other: user_id"
]
}
],
"internal_data": "adjkhajksdhaskjdhkjHKJHjkhaskjdhkjasdhKHKJHkjsdhfkjsdhfkjsdhKHJKHjksdfhsdjkfhksdjhKHKJHJKhsdkfjhsdkfjhKHJKHjksdkfjhsdkjfhKHKJHjkhsdkfjhsdkjfhsdjkfhksdjfhKJHKjksdhfsdjkfhksdjfhsdkjhKHJKhsdkfhsdkjfhsdkfhdskjhKH=="
}
},
{
"id": "1237",
"type": "issue_found",
"issue": {
"name": "without flag set",
"type_index": 1234567,
"serial_number": "123456789056704",
"origin": "https://test.com",
"path": "/",
"severity": "info",
"confidence": "certain",
"description": "long description here zjkhasdjkh hsajkdhsajkd hasjkdhbsjkdash d",
"caption": "/",
"evidence": [
{
"type": "InformationListEvidence",
"request_response": {
"url": "https://test.com/",
"request": [
{
"type": "DataSegment",
"data": "adjkhajksdhaskjdhkjHKJHjkhaskjdhkjasdhKHKJHkjsdhfkjsdhfkjsdhKHJKHjksdfhsdjkfhksdjhKHKJHJKhsdkfjhsdkfjhKHJKHjksdkfjhsdkjfhKHKJHjkhsdkfjhsdkjfhsdjkfhksdjfhKJHKjksdhfsdjkfhksdjfhsdkjhKHJKhsdkfhsdkjfhsdkfhdskjhKHKjhsdfkjhsdjkfhsfdsfdsfdsfdsfdsfsdfdsf",
"length": 303
}
],
"response": [
{
"type": "DataSegment",
"data": "adjkhajksdhaskjdhkjHKJHjkhaskjdhkjasdhKHKJHkjsdhfkjsdhfkjsdhKHJKHjksdfhsdjkfhksdjhKHKJHJKhsdkfjhsdkfjhKHJKHjksdkfjhsdkjfhKHKJHjkhsdkfjhsdkjfhsdjkfhksdjfhKJHKjksdhfsdjkfhksdjfhsdkjhKHJKhsdkfhsdkjfhsdkfhdskjhKHKjhsdfkjhsdjkfh==",
"length": 151
},
{
"type": "HighlightSegment",
"data": "adjkhajksdhaskjdhkjHKJHjkhaskjdhkjasdhKHKJHkjsdhfkjsdhfkjsdhKHJKHjksdfhsdjkfhksdjhKHKJHJKhsdkfjhsdkfjhKHJKHjksdkfjhsdkjfhKHKJHjkhsdkfjhsdkjfhsdjkfhksdjfhKJHKjksdhfsdjkfhksdjfhsdkjhKHJKhsdkfhsdkjfhsdkfhdskjhKHKjhsdfkjhsdjkfh=",
"length": 119
},
{
"type": "DataSegment",
"data": "sdfdsfsdfSDFSDFdSFDS546SDFSDFDSFG657=",
"length": 23
}
],
"was_redirect_followed": false,
"request_time": "123541191466"
},
"information_items": [
"Other: user_id"
]
}
],
"internal_data": "adjkhajksdhaskjdhkjHKJHjkhaskjdhkjasdhKHKJHkjsdhfkjsdhfkjsdhKHJKHjksdfhsdjkfhksdjhKHKJHJKhsdkfjhsdkfjhKHJKHjksdkfjhsdkjfhKHKJHjkhsdkfjhsdkjfhsdjkfhksdjfhKJHKjksdhfsdjkfhksdjfhsd=="
}
},
{
"id": "1238",
"type": "issue_found",
"issue": {
"name": "parameter pollution",
"type_index": 4137000,
"serial_number": "123456789810290176",
"origin": "https://test.com",
"path": "/robots.txt",
"severity": "low",
"confidence": "firm",
"description": "very long description text here...",
"caption": "/robots.txt [URL path filename]",
"evidence": [
{
"type": "FirstOrderEvidence",
"detail": {
"payload": {
"bytes": "Q3jkeiZkcmg8MQ==",
"flags": 0
},
"band_flags": [
"in_band"
]
},
"request_response": {
"url": "https://test.com/%3fhdz%26drh%3d1",
"request": [
{
"type": "DataSegment",
"data": "W1QOIC8=",
"length": 5
},
{
"type": "HighlightSegment",
"data": "WRMnBGR6JTI2ZHJoJTNkMQ==",
"length": 16
},
{
"type": "DataSegment",
"data": "adjkhajksdhaskjdhkjHKJHjkhaskjdhkjasdhKHKJHkjsdhfkjsdhfkjsdhKHJKHjksdfhsdjkfhksdjhKHKJHJKhsdkfjhsdkfjhKHJKHjksdkfjhsdkjfhKHKJHjkhsdkfjhsdkjfhsdjkfhksdjfhKJHKjksdhfsdjkfhksdjfhsdkjhKHJKhsdkfhsdkjfhsdkfhdskjhKHKjhsdfkjhsdjkfhcvxxcvklxcvjkxclvjxclkvjxcklvjlxckjvlxckjvklxcjvxcklvjxcklvjxckljvlxckjvxcklvjxckljvxcklvjcklxjvcxkl==",
"length": 298
}
],
"response": [
{
"type": "DataSegment",
"data": "adjkhajksdhaskjdhkjHKJHjkhaskjdhkjasdhKHKJHkjsdhfkjsdhfkjsdhKHJKHjksdfhsdjkfhksdjhKHKJHJKhsdkfjhsdkfjhKHJKHjksdkfjhsdkjfhKHKJHjkhsdkfjhsdkjfhsdjkfhksdjfhKJHKjksdhfsdjkfhksdjfhsdkjhKHJKhsdkfhsdkjfhsdkfhdskjhKHKjhsdfkjhsdjkfh==",
"length": 130
},
{
"type": "HighlightSegment",
"data": "Q4jleiZkcmg9MQ==",
"length": 10
},
{
"type": "DataSegment",
"data": "adjkhajksdhaskjdhkjHKJHjkhaskjdhkjasdhKHKJHkjsdhfkjsdhfkjsdhKHJKHjksdfhsdjkfhksdjhKHKJHJKhsdkfjhsdkfjhKHJKHjksdkfjhsdkjfhKHKJHjkhsdkfjhsdkjfhsdjkfhksdjfhKJHKjksdhfsdjkfhksdjfhsdkjhKHJKhsdkfhsdkjfhsdkfhdskjhKHKjhsdfkjhsdjkfh==",
"length": 163
}
],
"was_redirect_followed": false,
"request_time": "51"
}
}
],
"internal_data": "adjkhajksdhaskjdhkjHKJHjkhaskjdhkjasdhKHKJHkjsdhfkjsdhfkjsdhKHJKHjksdfhsdjkfhksdjhKHKJHJKhsdkfjhsdkfjhKHJKHjksdkfjhsdkjfhKHKJHjkhsdkfjhsdkjfhsdjkfhksdjfhKJHKjksdhfsdjkfhksdjfhsdkjhKHJKhsdkfhsdkjfhsdkfhdskjhKHKjhsdfkjhsdjkfh="
}
}
],
"event_logs": [],
"audit_items": []
}
I read it in R using jsonlite:
df_orig <- fromJSON('dast_sample_output.json', flatten= T)
This gives a nested list type R object. I wish to convert this list to a data frame in a tidy format with all the arrays and sub arrays being unnested.
If you run the str(df_orig), you could see the nested data frames in there.
How do I convert it to tidy format?
I tried unnest(), purrr but struggling to get into the tidy format for analysis? Any pointers would be highly appreciated.
Cheers,
use the jsonlite package function fromJSON()
edit:
set option flatten=T
edit2:
use content( x, 'text') before flattening
here is a full example converting to data.table:
get.json <- GET( apicall.text )
get.json.text <- content( get.json , 'text')
get.json.flat <- fromJSON( get.json.text , flatten = T)
dt <- as.data.table( get.json.flat )
I've noticed quite severe inconsistency in result provided by HERE /geocode API endpoint. Some address parts have original special characters like in "Łódź" city and some don't.
When doing following request:
https://geocoder.cit.api.here.com/6.2/geocode.json?lon=19.4734111&lat=51.73771300000001&language=sv-SE&searchtext=sienkiewicza lodz&result_types=address,place&cs=pds&additionaldata=Country2,true
We get the result which is inconsistent
"Address": {
"Label": "ulica Henryka Sienkiewicza, 90-009 Lodz, Polen",
"Country": "POL",
"State": "Woj. Łódzkie",
"County": "Lodz",
"City": "Lodz",
"District": "Lodz",
"Subdistrict": "Śródmieście",
"Street": "ulica Henryka Sienkiewicza",
"PostalCode": "90-009",
"AdditionalData": [
{
"value": "PL",
"key": "Country2"
},
{
"value": "Polen",
"key": "CountryName"
},
{
"value": "Woj. Łódzkie",
"key": "StateName"
},
{
"value": "Lodz",
"key": "CountyName"
}
]
}
As we can see value for state contains polish characters "Woj. Łódzkie", but city is "Lodz" which is not ok.
All results should contain original letters like "Łódź". In other words such results shouldn't be latinized.
Thank you
When using a language code different than the one of the original data, like in your case sv-SE for data in Poland, you get exonyms "where available", which is why you may get a mix of alphabets.
If you remove the language parameter from the query, or set it to Polish explicitely with language=pl-PL, you get the following response for your example:
"Address": {
"Label": "ulica Henryka Sienkiewicza, 90-057 Łódź, Polska",
"Country": "POL",
"State": "Woj. Łódzkie",
"County": "Łódź",
"City": "Łódź",
"District": "Łódź",
"Subdistrict": "Śródmieście",
"Street": "ulica Henryka Sienkiewicza",
"PostalCode": "90-057",
"AdditionalData": [
{
"value": "PL",
"key": "Country2"
},
{
"value": "Polska",
"key": "CountryName"
},
{
"value": "Woj. Łódzkie",
"key": "StateName"
},
{
"value": "Łódź",
"key": "CountyName"
}
]
}
I've been using Forms Recognizer for some days now and can't get it to recognize the keys in my forms.
I want to use it to extract the answers given by students in a test...here is an example.
I can't change the structure of the sheet students fill because it is a national exam and I don't have access to who organizes it.
So I trained a model as recommended on Microsoft documentation and used it to "read" the forms and it gets most of the answers, but it all comes as values of a key "Tokens"
{
"key": [
{
"text": "__Tokens__",
"boundingBox": [
0,
0,
0,
0,
0,
0,
0,
0
]
}
],
"value": [
{
"text": "01",
"boundingBox": [
110.1,
826.6,
125.6,
826.6,
125.6,
816.8,
110.1,
816.8
],
"confidence": 1
},
{
"text": "A",
"boundingBox": [
148.2,
834.4,
160.6,
834.4,
160.6,
816.8,
148.2,
816.8
],
"confidence": 1
},
{
"text": "26",
"boundingBox": [
229.4,
828.6,
246,
828.6,
246,
816.8,
229.4,
816.8
],
"confidence": 1
},
{
"text": "B",
"boundingBox": [
268.6,
834.4,
277.8,
834.4,
277.8,
816.8,
268.6,
816.8
],
"confidence": 1
}
Then I recreated the structure on excel but with : after the numbers and trained another model. I also printed some copies of it and filled in to test and Form Recognizer understood the numbers as keys.
{
"key": [
{
"text": "01:",
"boundingBox": [
270.4,
1625.4,
313,
1625.4,
313,
1600.5,
270.4,
1600.5
]
}
],
"value": [
{
"text": "A",
"boundingBox": [
350.7,
1620.9,
368.8,
1620.9,
368.8,
1587,
350.7,
1587
],
"confidence": 1
}
]
},
{
"key": [
{
"text": "26:",
"boundingBox": [
520.2,
1624.2,
552.8,
1624.2,
552.8,
1600.5,
520.2,
1600.5
]
}
],
"value": [
{
"text": "E",
"boundingBox": [
604.6,
1618.8,
625.8,
1618.8,
625.8,
1587,
604.6,
1587
],
"confidence": 1
}
]
}
Does anyone know some way to recognize the number fields as keys without the : ?
Form Recognizer will not consider the row numbers as keys unless specifically marked as keys, hence it currently does not discover them as keys.
I need to be able to give a City/State or Postal Code and an Integer for the radius and return all cities/postal codes within the given radius using HERE but the Documentation seems to be unclear.
https://geocoder.api.here.com/6.2/reversegeocode.json?app_id=xxx&app_code=xxxx
The return is:
<ns2:Error xmlns:ns2="http://www.navteq.com/lbsp/Errors/1" type="PermissionError" subtype="InvalidCredentials">
<Details>invalid credentials for xxxxxxx</Details>
</ns2:Error>
But when I use
https://geocoder.api.here.com/6.2/geocode.json?app_id=xxx&app_code=xxxx
I get data back.
It is worth mentioning I am on the Freemium Package for the REST API
So first why can't I get data back from the Reverse Geo Code API?
And what is the appropriate string to accomplish the above?
Update:
Leaving the rest here in case someone else runs into this. To use Reverse Geo Code the API Route is actually
https://reverse.geocoder.api.here.com/6.2/reversegeocode.json
Though I still need some help on how to get the Radius Data
Update 2:
https://reverse.geocoder.api.here.com/6.2/reversegeocode.json?app_id=x&app_code=x&level=city&mode=retrieveAreas&prox=52.5309,13.3847,80467.2
Returns:
{
"Response": {
"MetaInfo": {
"Timestamp": "2019-04-27T17:47:41.043+0000"
},
"View": [
{
"_type": "SearchResultsViewType",
"ViewId": 0,
"Result": [
{
"Relevance": 1,
"Distance": 0,
"Direction": 0,
"MatchLevel": "district",
"MatchQuality": {
"Country": 1,
"State": 1,
"County": 1,
"City": 1,
"District": 1,
"PostalCode": 1
},
"Location": {
"LocationId": "NT_0ES-GaH3lZzJCuLQBrdw7C",
"LocationType": "point",
"DisplayPosition": {
"Latitude": 52.5309,
"Longitude": 13.3847
},
"MapView": {
"TopLeft": {
"Latitude": 52.54063,
"Longitude": 13.36566
},
"BottomRight": {
"Latitude": 52.50407,
"Longitude": 13.42964
}
},
"Address": {
"Label": "Mitte, Berlin, Deutschland",
"Country": "DEU",
"State": "Berlin",
"County": "Berlin",
"City": "Berlin",
"District": "Mitte",
"PostalCode": "10178",
"AdditionalData": [
{
"value": "Deutschland",
"key": "CountryName"
},
{
"value": "Berlin",
"key": "StateName"
},
{
"value": "Berlin",
"key": "CountyName"
}
]
},
"MapReference": {
"ReferenceId": "53500282",
"SideOfStreet": "neither",
"CountryId": "20147700",
"StateId": "20187401",
"CountyId": "20187402",
"CityId": "20187403",
"DistrictId": "20187417"
}
}
}
]
}
]
}
}
The documentation says to put in coordinates and a radius, that's 50 miles, so not sure why I am only receiving 1 city, Berlin, in the response.
Update 3:
https://reverse.geocoder.api.here.com/6.2/multi-reversegeocode.json?app_id=x&app_code=x&level=city&mode=retrieveAreas&prox=60.5544,-151.2583,80000
Tried with multi-reversegeocode
Return is: {}
Get cities in a proximity radius
Use the Reverse Geocode endpoint with the following parameters
https://reverse.geocoder.api.here.com/6.2/reversegeocode.json?
prox=52.5309,13.3847,80500 /* Note: 80.5 km around Berlin */
&app_id=YOUR_APP_ID
&app_code=YOUR_APP_CODE
&mode=retrieveAreas
&level=city
&gen=9
In short, your example in "Update 2" is correct, besides that it is missing the gen query parameter. Indeed, as per the API Reference, the query parameter level is valid only in combination with gen=2 or higher.
I have this file with JSON formatted data, but need this into a dataframe. Ultimately I would like to plot the geolocations onto a map, but can't seem to get this data into a df first.
json_to_df <- function(file){
file <- lapply(file, function(x) {
x[sapply(x, is.null)] <- NA
unlist(x)
})
df <- do.call("rbind", file)
return(df)
}
But I get only this error:
Error in fromJSON(file) :
STRING_ELT() can only be applied to a 'character vector', not a 'list'
The file structure looks like this (this is only part of the data):
{
"results": [
{
"utc_offset": 7200000,
"venue": {
"country": "nl",
"localized_country_name": "Netherlands",
"city": "Bergen",
"address_1": "16 Notweg",
"name": "FitClub Bergen",
"lon": 4.699218,
"id": 24632049,
"lat": 52.673046,
"repinned": false
},
"headcount": 0,
"distance": 22.46796989440918,
"visibility": "public",
"waitlist_count": 0,
"created": 1467149834000,
"rating": {
"count": 0,
"average": 0
},
"maybe_rsvp_count": 0,
"description": "<p>Start your week off right with a Monday Morning Bootcamp!!! The fresh air and peaceful dunes provide the perfect setting for a total body workout. Whether you are a beginner with brand spankin' new health goals and in need of some direction, or training for a race or competition, we're the trainers for you!!! See you at 8:50 for sign-in!</p>",
"event_url": "https://www.meetup.com/FitClubBergen/events/234936736/",
"yes_rsvp_count": 3,
"duration": 3600000,
"name": "Free Bootcamp in the Bergen Dunes",
"id": "glzqvlyvnbgc",
"time": 1477292400000,
"updated": 1477297999000,
"group": {
"join_mode": "open",
"created": 1441658286000,
"name": "FitClub Bergen Free Bootcamp in the Dunes",
"group_lon": 4.710000038146973,
"id": 18908751,
"urlname": "FitClubBergen",
"group_lat": 52.66999816894531,
"who": "FitClubbers"
},
"status": "past"
},
{
"utc_offset": 7200000,
"venue": {
"country": "nl",
"localized_country_name": "Netherlands",
"city": "Bergen",
"address_1": "16 Notweg",
"name": "FitClub Bergen",
"lon": 4.699218,
"id": 24632049,
"lat": 52.673046,
"repinned": false
},
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}
So I was wondering how I would put this in a dataframe or csv even to be able to extract geolocations later?
There is no need to write a parser yourself, there are a number of packages that can read JSON formatted data. The one I use, and #hrbrmstr linked, is jsonlite. This package provides a fromJSON function which can parse JSON into a data.frame:
fromJSON('file.json', flatten = TRUE)
note that the flatten argument here ensures the json is flattended into a nice data.frame.