Parse and reformat JSON data

[confooseddesi89]

I agree that JSON is terrible and I was unfortunately forced to use it.

My apologies, earlier I shared with you a sample of my data; I tried your code with my much larger dataset and ran into some errors. I was trying to make the task easier for any responders by sharing only a sample, but unfortunately now the code is not working on my actual dataset (45 columns, 847 rows, and 20 variables in each "" JSON segment). (Getting a code for a sample and then applying it to a larger dataset has always worked before on this forum for me).

I used the replace_crlf on my larger dataset (which has the same structure - I tried to attach the file here but it exceeds the size limit; it's uploaded here) and my output csv looked like this below, where everything was in one column separated by commas, and each record was cut with a carriage return in the wrong place (only displaying first 2 rows since it's so large):

id,anx_1_date,anx_1_PA,dep_1_date,dep_1_PA,dep_2_date,dep_2_PA,dep_3_date,dep_3_PA,dep_4_date,dep_4_PA,dep_5_date,dep_5_PA,dep_6_date,dep_6_PA,dep_7_date,dep_7_PA,dep_8_date,dep_8_PA,dep_9_date,dep_9_PA,dep_10_date,dep_10_PA,dep_11_date,dep_11_PA,dep_12_date,dep_12_PA,dep_13_date,dep_13_PA,dep_14_date,dep_14_PA,dep_15_date,dep_15_PA,dep_16_date,dep_16_PA,dep_17_date,dep_17_PA,dep_18_date,dep_18_PA,dep_19_date,dep_19_PA,dep_20_date,dep_20_PA,dep_21_date,dep_21_PA,,,,,

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Then I imported the data into SAS and "have" looks like this (first two rows and 11 columns shown).

Obs	id	anx_1_date	anx_1_PA	dep_1_date	dep_1_PA	dep_2_date	dep_2_PA	dep_3_date	dep_3_PA	dep_4_date	dep_4_PA
1	1346	1/19/2019	['{"dep_count":2,"dep_duration":2,"happy_count":12,"qual":90,"end_datetime":"2019-01-15T10:15:00Z","date":"2019-01-15","mid":"2019-01-15T07:35:00Z","counter_report":0,"counter_nap":289,"counter_dep":32,"counter_to_eat":0,"stress_count":10,"stress_duration":30,"start_datetime":"2019-01-15T04:54:00Z","act_deep":20,"act_light":231,"act_rem":26,"act_wake":44,"time_sedentary":321,"id":1346}'| '{"dep_count":1,"dep_duration":1,"happy_count":10,"qual":97,"end_datetime":"2019-01-16T11:27:30Z","date":"2019-01-16","mid":"2019-01-16T07:42:30Z","counter_report":3,"counter_nap":433,"counter_dep":14,"counter_to_eat":0,"stress_count":9,"stress_duration":16,"start_datetime":"2019-01-16T03:57:30Z","act_deep":100,"act_light":233,"act_rem":77,"act_wake":40,"time_sedentary":450,"id":1346}'| '{"dep_count":4,"dep_duration":9,"happy_count":15,"qual":89,"end_datetime":"2019-01-18T13:35:30Z","date":"2019-01-18","mid":"2019-01-18T10:32:00Z","counter_report":0,"counter_nap":325,"counter_dep":42,"counter_to_eat":0,"stress_count":11,"stress_duration":33,"start_datetime":"2019-01-18T07:28:00Z","act_deep":28,"act_light":261,"act_rem":18,"act_wake":60,"time_sedentary":367,"id":1346}'| '{"dep_count":2,"dep_duration":2,"happy_count":15,"qual":96,"end_datetime":"2019-01-19T13:42:30Z","date":"2019-01-19","mid":"2019-01-19T09:57:00Z","counter_report":0,"counter_nap":432,"counter_dep":18,"counter_to_eat":0,"stress_count":13,"stress_duration":16,"start_datetime":"2019-01-19T06:12:00Z","act_deep":87,"act_light":229,	1/20/2019	['{"dep_count":2,"dep_duration":2,"happy_count":12,"qual":90,"end_datetime":"2019-01-15T10:15:00Z","date":"2019-01-15","mid":"2019-01-15T07:35:00Z","counter_report":0,"counter_nap":289,"counter_dep":32,"counter_to_eat":0,"stress_count":10,"stress_duration":30,"start_datetime":"2019-01-15T04:54:00Z","act_deep":20,"act_light":231,"act_rem":26,"act_wake":44,"time_sedentary":321,"id":1346}'| '{"dep_count":1,"dep_duration":1,"happy_count":10,"qual":97,"end_datetime":"2019-01-16T11:27:30Z","date":"2019-01-16","mid":"2019-01-16T07:42:30Z","counter_report":3,"counter_nap":433,"counter_dep":14,"counter_to_eat":0,"stress_count":9,"stress_duration":16,"start_datetime":"2019-01-16T03:57:30Z","act_deep":100,"act_light":233,"act_rem":77,"act_wake":40,"time_sedentary":450,"id":1346}'| '{"dep_count":4,"dep_duration":9,"happy_count":15,"qual":89,"end_datetime":"2019-01-18T13:35:30Z","date":"2019-01-18","mid":"2019-01-18T10:32:00Z","counter_report":0,"counter_nap":325,"counter_dep":42,"counter_to_eat":0,"stress_count":11,"stress_duration":33,"start_datetime":"2019-01-18T07:28:00Z","act_deep":28,"act_light":261,"act_rem":18,"act_wake":60,"time_sedentary":367,"id":1346}'| '{"dep_count":2,"dep_duration":2,"happy_count":15,"qual":96,"end_datetime":"2019-01-19T13:42:30Z","date":"2019-01-19","mid":"2019-01-19T09:57:00Z","counter_report":0,"counter_nap":432,"counter_dep":18,"counter_to_eat":0,"stress_count":13,"stress_duration":16,"start_datetime":"2019-01-19T06:12:00Z","act_deep":87,"act_light":229,	2/19/2019	['{"dep_count":0,"dep_duration":0,"happy_count":15,"qual":96,"end_datetime":"2019-02-04T12:25:30Z","date":"2019-02-04","mid":"2019-02-04T09:01:30Z","counter_report":0,"counter_nap":392,"counter_dep":17,"counter_to_eat":0,"stress_count":15,"stress_duration":17,"start_datetime":"2019-02-04T05:36:30Z","act_deep":77,"act_light":191,"act_rem":86,"act_wake":55,"time_sedentary":409,"id":1346}'| '{"dep_count":4,"dep_duration":7,"happy_count":13,"qual":93,"end_datetime":"2019-02-05T09:09:30Z","date":"2019-02-05","mid":"2019-02-05T06:13:00Z","counter_report":0,"counter_nap":327,"counter_dep":26,"counter_to_eat":0,"stress_count":9,"stress_duration":19,"start_datetime":"2019-02-05T03:16:00Z","act_deep":53,"act_light":227,"act_rem":30,"act_wake":43,"time_sedentary":353,"id":1346}'| '{"dep_count":7,"dep_duration":8,"happy_count":26,"qual":74,"end_datetime":"2019-02-06T15:09:00Z","date":"2019-02-06","mid":"2019-02-06T13:01:00Z","counter_report":0,"counter_nap":189,"counter_dep":67,"counter_to_eat":0,"stress_count":19,"stress_duration":59,"start_datetime":"2019-02-06T10:53:00Z","act_deep":44,"act_light":152,"act_rem":6,"act_wake":54,"time_sedentary":256,"id":1346}'| '{"dep_count":5,"dep_duration":23,"happy_count":60,"qual":88,"end_datetime":"2019-02-07T15:33:30Z","date":"2019-02-07","mid":"2019-02-07T05:23:30Z","counter_report":1,"counter_nap":1071,"counter_dep":148,"counter_to_eat":0,"stress_count":55,"stress_duration":126,"start_datetime":"2019-02-06T19:13:30Z","act_deep":201,"act_light":7	3/22/2019	"['{""dep_count"":4	.	""happy_count"":25
2	1498	1/28/2019	['{"dep_count":4,"dep_duration":4,"happy_count":10,"qual":95,"end_datetime":"2019-01-25T06:44:30Z","date":"2019-01-25","mid":"2019-01-25T04:08:00Z","counter_report":0,"counter_nap":297,"counter_dep":15,"counter_to_eat":0,"stress_count":6,"stress_duration":11,"start_datetime":"2019-01-25T01:32:00Z","act_deep":52,"act_light":175,"act_rem":55,"act_wake":30,"time_sedentary":312,"id":1498}'| '{"dep_count":0,"dep_duration":0,"happy_count":5,"qual":96,"end_datetime":"2019-01-26T07:15:30Z","date":"2019-01-26","mid":"2019-01-26T04:39:30Z","counter_report":0,"counter_nap":300,"counter_dep":13,"counter_to_eat":0,"stress_count":5,"stress_duration":13,"start_datetime":"2019-01-26T02:02:30Z","act_deep":54,"act_light":179,"act_rem":50,"act_wake":30,"time_sedentary":313,"id":1498}'| '{"dep_count":0,"dep_duration":0,"happy_count":3,"qual":97,"end_datetime":"2019-01-27T06:30:00Z","date":"2019-01-27","mid":"2019-01-27T04:33:00Z","counter_report":0,"counter_nap":228,"counter_dep":7,"counter_to_eat":0,"stress_count":3,"stress_duration":7,"start_datetime":"2019-01-27T02:35:00Z","act_deep":33,"act_light":162,"act_rem":20,"act_wake":20,"time_sedentary":235,"id":1498}'| '{"dep_count":0,"dep_duration":0,"happy_count":3,"qual":98,"end_datetime":"2019-01-28T06:37:30Z","date":"2019-01-28","mid":"2019-01-28T04:25:00Z","counter_report":0,"counter_nap":258,"counter_dep":6,"counter_to_eat":0,"stress_count":3,"stress_duration":6,"start_datetime":"2019-01-28T02:13:00Z","act_deep":0,"act_light":0,"act_rem":0,"a	1/30/2019	['{"dep_count":4,"dep_duration":4,"happy_count":10,"qual":95,"end_datetime":"2019-01-25T06:44:30Z","date":"2019-01-25","mid":"2019-01-25T04:08:00Z","counter_report":0,"counter_nap":297,"counter_dep":15,"counter_to_eat":0,"stress_count":6,"stress_duration":11,"start_datetime":"2019-01-25T01:32:00Z","act_deep":52,"act_light":175,"act_rem":55,"act_wake":30,"time_sedentary":312,"id":1498}'| '{"dep_count":0,"dep_duration":0,"happy_count":5,"qual":96,"end_datetime":"2019-01-26T07:15:30Z","date":"2019-01-26","mid":"2019-01-26T04:39:30Z","counter_report":0,"counter_nap":300,"counter_dep":13,"counter_to_eat":0,"stress_count":5,"stress_duration":13,"start_datetime":"2019-01-26T02:02:30Z","act_deep":54,"act_light":179,"act_rem":50,"act_wake":30,"time_sedentary":313,"id":1498}'| '{"dep_count":0,"dep_duration":0,"happy_count":3,"qual":97,"end_datetime":"2019-01-27T06:30:00Z","date":"2019-01-27","mid":"2019-01-27T04:33:00Z","counter_report":0,"counter_nap":228,"counter_dep":7,"counter_to_eat":0,"stress_count":3,"stress_duration":7,"start_datetime":"2019-01-27T02:35:00Z","act_deep":33,"act_light":162,"act_rem":20,"act_wake":20,"time_sedentary":235,"id":1498}'| '{"dep_count":0,"dep_duration":0,"happy_count":3,"qual":98,"end_datetime":"2019-01-28T06:37:30Z","date":"2019-01-28","mid":"2019-01-28T04:25:00Z","counter_report":0,"counter_nap":258,"counter_dep":6,"counter_to_eat":0,"stress_count":3,"stress_duration":6,"start_datetime":"2019-01-28T02:13:00Z","act_deep":0,"act_light":0,"act_rem":0,"a	2/28/2019	['{"dep_count":0,"dep_duration":0,"happy_count":7,"qual":99,"end_datetime":"2019-02-13T10:41:30Z","date":"2019-02-13","mid":"2019-02-13T06:12:30Z","counter_report":0,"counter_nap":530,"counter_dep":8,"counter_to_eat":0,"stress_count":7,"stress_duration":8,"start_datetime":"2019-02-13T01:43:30Z","act_deep":0,"act_light":0,"act_rem":0,"act_wake":0,"time_sedentary":538,"id":1498}'| '{"dep_count":1,"dep_duration":1,"happy_count":4,"qual":99,"end_datetime":"2019-02-14T06:46:00Z","date":"2019-02-14","mid":"2019-02-14T04:13:30Z","counter_report":6,"counter_nap":296,"counter_dep":2,"counter_to_eat":0,"stress_count":3,"stress_duration":7,"start_datetime":"2019-02-14T01:41:30Z","act_deep":49,"act_light":198,"act_rem":31,"act_wake":26,"time_sedentary":304,"id":1498}'| '{"dep_count":0,"dep_duration":0,"happy_count":7,"qual":97,"end_datetime":"2019-02-15T06:36:00Z","date":"2019-02-15","mid":"2019-02-15T04:00:00Z","counter_report":6,"counter_nap":299,"counter_dep":8,"counter_to_eat":0,"stress_count":7,"stress_duration":14,"start_datetime":"2019-02-15T01:23:00Z","act_deep":0,"act_light":0,"act_rem":0,"act_wake":0,"time_sedentary":313,"id":1498}'| '{"dep_count":1,"dep_duration":1,"happy_count":7,"qual":98,"end_datetime":"2019-02-16T07:40:00Z","date":"2019-02-16","mid":"2019-02-16T04:52:30Z","counter_report":4,"counter_nap":322,"counter_dep":8,"counter_to_eat":0,"stress_count":6,"stress_duration":11,"start_datetime":"2019-02-16T02:05:30Z","act_deep":63,"act_light":179,"act_rem":49,"act_wake":	4/1/2019	"['{""dep_count"":2	.	""happy_count"":12

Obviously, there are some formatting errors here (you can see this clearly in the dep_3_PA, dep_4_date, and dep_4_PA columns). Note that it goes up to dep_21_date/PA.

After running the code from have to tall, below is a preview of "tall" (first  2 rows) - most of the  columns are empty:

Obs	id	anx_1_date	dep_1_date	dep_2_date	dep_3_date	dep_4_date	dep_5_date	dep_6_date	dep_7_date	dep_8_date	dep_9_date	dep_10_date	dep_11_date	dep_12_date	dep_13_date	dep_14_date	dep_15_date	dep_16_date	dep_17_date	dep_18_date	dep_19_date	dep_20_date	dep_21_date	col	line	row	name	value	term	record
1	1346	1/19/2019	1/20/2019	2/19/2019	3/22/2019	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	1	1	dep_count	2	"dep_count":2	['{"dep_count":2,"dep_duration":2,"happy_count":12,"qual":90,"end_datetime":"2019-01-15T10:15:00Z","date":"2019-01-15","mid":"2019-01-15T07:35:00Z","counter_report":0,"counter_nap":289,"counter_dep":32,"counter_to_eat":0,"stress_count":10,"stress_duration":30,"start_datetime":"2019-01-15T04:54:00Z","act_deep":20,"act_light":231,"act_rem":26,"act_wake":44,"time_sedentary":321,"id":1346}'
2	1346	1/19/2019	1/20/2019	2/19/2019	3/22/2019	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	1	2	dep_duration	2	"dep_duration":2	['{"dep_count":2,"dep_duration":2,"happy_count":12,"qual":90,"end_datetime":"2019-01-15T10:15:00Z","date":"2019-01-15","mid":"2019-01-15T07:35:00Z","counter_report":0,"counter_nap":289,"counter_dep":32,"counter_to_eat":0,"stress_count":10,"stress_duration":30,"start_datetime":"2019-01-15T04:54:00Z","act_deep":20,"act_light":231,"act_rem":26,"act_wake":44,"time_sedentary":321,"id":1346}'

Could you please assist (again)? I've worked on this all day and can't figure out why the sample worked and not my larger dataset.

Many thanks in advance for your help, and apologies again for not providing a proper sample!

[Tom]

Your file has lines that are over 200,000 bytes long.  So make sure to use a large enough LRECL when you read the CSV file into a dataset.

 69         data _null_;
 70           infile fixed lrecl=1000000;
 71           input;
 72         run;
 
 NOTE: The infile FIXED is:
       Filename=/saswork/SAS_workCEC800005E34_odaws01-usw2.oda.sas.com/#LN00019,
       Owner Name=tom.abernathy,Group Name=oda,
       Access Permission=-rw-r--r--,
       Last Modified=16Aug2023:12:58:11,
       File Size (bytes)=102385714
 
 NOTE: 1047 records were read from the infile FIXED.
       The minimum record length was 92.
       The maximum record length was 288873.
 NOTE: DATA statement used (Total process time):
       real time           0.03 seconds
       cpu time            0.03 seconds

You appear to now have one variable for ANX and 21 variables for DEP.  So just read in the CSV file that way to begin with and eliminate one level of looping you will need later.

The embedded line breaks in this version of the file are bare LINEFEED characters and the actual end of lines are CR + LF pairs.

So this data step seems to read in the  ORIGINAL CSV file into something that is more reasonable.

data json ;
  length id 8 date 8 type $3 rep 8 json $32000;
  informat date mmddyy.;
  format date yymmdd10.;
  infile '~/json_example.zip' zip member='dep_PA for forum 2.csv'
    dsd truncover fistosb=2 termstr=crlf lrecl=1000000
  ;
  input id @ ;
  type = 'anx'; rep=1;
  input date json @;
  if json='[]' then json=' ';
  else json=translate(json,'|','0A'x);
  output;
  type= 'dep';
  do rep=1 by 1 until(date=. and json=' ');
    input date ?? json @ ;
    if json='[]' then json=' ';
    else json=translate(json,'|','0A'x);
    if not (date=. and json=' ') then output;
  end;
run;

[Tom · Accepted solution]

Once you have the JSON data properly extracted from the CSV file you can finally turn to trying to parse it.

data tall;
  set json(keep=id type rep json);
  where json ne ' ';
  length line row 8 name $32 value $200 term $300 record $1500;
  do line=1 to countw(json,'|');
     record=left(scan(json,line,'|'));
     do row=1 by 1 until(term=' ');
        term=scan(record,row,"[{,'}]"||'A0'x);
        term=translate(term,' ','A0'x);
        name=dequote(scan(term,1,':','q'));
        value=dequote(scan(term,2,':','q'));     
        if term ne ' ' then output;
    end;
  end;
  drop json term record;
run;

proc sort;
  by id type rep line row;
run;

proc transpose data=tall out=wide(drop=_name_) ;
  where name ne 'id';
  by id type rep line ;
  id name;
  var value;
run;

Looks like you also might need to do a little more cleaning.  It appears some of the JSON strings have MID instead of MIDPOINT for one the timestamp variables.

[confooseddesi89]

Thanks, this worked perfectly!

I have one remaining issue: most of the variables are character; they should be numeric. I tried the "input" function but the datetime and date variables are throwing an error of "invalid argument at line XX column XX" - the structure of the datetime variables is YYYY-MM-DDTHH:MM:SSZ (e.g., 2021-08-17T06:46:30Z) and the date variable is YYYY-MM-DD (e.g., 2021-08-17). Can you provide any help or should I make another post for this issue? Thanks again!

[Tom]

You can use iNPUT() to make a numeric variable from the character variable VALUE before transposing.  Then use that variable in the PROC TRANSPOSE step instead.

Just use IF/THEN logic to use a different INFORMAT based on the variable type.  Since you you have at least one DATE variable and  a number of DATETIME variables you will need to use IF/THEN/ELSE logic to use the right informat.

32. will work for the normal numbers.

You might be able to use ANYDTDTE and ANYDTDTM for the others.

You will need to attach formats to the date and datetime variables in the resulting WIDE dataset so they print in a way that humans can understand.

[confooseddesi89]

Thanks - for some reason the "if then do" statements did not work on the "format" statements, so I applied "input" and if-then-do statements before transposing, and formatted each time/date variable after the data were transposed.

[Tom]

---

@confooseddesi89 wrote:

Thanks - for some reason the "if then do" statements did not work on the "format" statements, so I applied "input" and if-then-do statements before transposing, and formatted each time/date variable after the data were transposed.

---

Of course not.  There is nothing to execute for a FORMAT statement.  It just set one of the many attributes of the variable.  Just like a LABEL statement.

[ballardw]

---

@confooseddesi89 wrote:

Thanks, this worked perfectly!

I have one remaining issue: most of the variables are character; they should be numeric. I tried the "input" function but the datetime and date variables are throwing an error of "invalid argument at line XX column XX" - the structure of the datetime variables is YYYY-MM-DDTHH:MM:SSZ (e.g., 2021-08-17T06:46:30Z) and the date variable is YYYY-MM-DD (e.g., 2021-08-17). Can you provide any help or should I make another post for this issue? Thanks again!

---

The informat to read that value with the T in the middle is e8601dz20.  If you want to display it with the time zone the use the same named Format for display.

The informat for the date is yymmdd10.

Show your code with the error messages from the log.