I have a dataset with over 50,000 records that looks like the following; ID, season(either high or low), bed_time and triage_time in minutes:
ID Season Bed_time Triage_time
1 high 34 68
2 low 44 20
3 high 90 14
4 low 71 88
5 low 27 54
I would like to create a GROUPED VERTICAL BAR CHART where both then Bed_time Triage_time are reflected as a median and grouped by season on the X-axis:
| | |
| | | |
| | | | |
|-------------------------------
BT TT BT TT
High Low
I reckon I have to transpose the data and then plug into an SGPLOT, but I'm not quite sure how to do that to ensure that data can then be graphed.
proc sgplot data=mysas.projects;
vbar season/ stat=median
group=[Bed_time Triage_time] /*NEED FROM TRANSPOSED DATA*/
groupdisplay=cluster;
run;
quit;
Indeed, you will need to reshape data from wide to long to use group call. Additionally, you will need to include a response call. Consider proc transpose for reshaping:
*** POSTED DATA
data time_data;
length Season $ 5;
input ID Season $ Bed_time Triage_time;
cards;
1 high 34 68
2 low 44 20
3 high 90 14
4 low 71 88
5 low 27 54
;
run;
*** RESHAPE LONG TO WIDE;
proc transpose
data = time_data
out = time_data_long
name = time_group;
by ID Season;
run;
*** CLEAN UP OUTPUT;
data time_data_long;
set time_data_long;
label time_group = "Time Group";
rename col1 = value;
run;
proc sgplot data=time_data_long;
vbar season / response=value stat=median
group = time_group
groupdisplay=cluster;
run;
Related
I want to transform my SAS table from data Have to data want.
I feel I need to use Proc transpose but could not figure it out how to do it.
data Have;
input Stat$ variable_1 variable_2 variable_3 variable_4;
datalines;
MAX 6 7 11 23
MIN 0 1 3 5
SUM 29 87 30 100
;
data Want;
input Variable $11.0 MAX MIN SUM;
datalines;
Variable_1 6 0 29
Variable_2 7 1 87
Variable_3 11 3 87
Variable_4 23 5 100
;
You are right, proc transpose is the solution
data Have;
input Stat$ variable_1 variable_2 variable_3 variable_4;
datalines;
MAX 6 7 11 23
MIN 0 1 3 5
SUM 29 87 30 100
;
/*sort it by the stat var*/
proc sort data=Have; by Stat; run;
/*id statement will keep the column names*/
proc transpose data=have out=want name=Variable;
id stat;
run;
proc print data=want; run;
Say I have a dataset like this
day product sales
1 a 1 48
2 a 2 55
3 a 3 88
4 b 2 33
5 b 3 87
6 c 1 97
7 c 2 95
On day "b" there were no sales for product 1, so there is no row where day = b and product = 1. Is there an easy way to add a row with day = b, product = 1 and sales = 0, and similar "missing" rows to get a dataset like this?
day product sales
1 a 1 48
2 a 2 55
3 a 3 88
4 b 1 0
5 b 2 33
6 b 3 87
7 c 1 97
8 c 2 95
9 c 3 0
In R you can do complete(df, day, product, fill = list(sales = 0)). I realize you can accomplish this with a self-join in proc sql, but I'm wondering if there is a procedure for this.
In this particular example you can also use the SPARSE option in PROC FREQ. It tells SAS to generate all the complete types with every value from DAY included with PRODUCT, so similar to a cross join between those elements. If you do not have the value in the table already it cannot add the value. You would need a different method in that case.
data have;
input n day $ product sales;
datalines;
1 a 1 48
2 a 2 55
3 a 3 88
4 b 2 33
5 b 3 87
6 c 1 97
7 c 2 95
;;;;
run;
proc freq data=have noprint;
table day*product / out=want sparse;
weight sales;
run;
proc print data=want;run;
There are, as usual in SAS, about a dozen ways to do this. Here's my favorite.
data have;
input n day $ product sales;
datalines;
1 a 1 48
2 a 2 55
3 a 3 88
4 b 2 33
5 b 3 87
6 c 1 97
7 c 2 95
;;;;
run;
proc means data=have completetypes;
class day product;
types day*product;
var sales;
output out=want sum=;
run;
completetypes tells SAS to put out rows for every class combination, including missing ones. You could then use proc stdize to get them to be 0's (if you need them to be 0). It's possible you might be able to do this in the first place with proc stdize, I'm not as familiar unfortunately with that proc.
You can do this with proc freq using the sparse option.
Code:
proc freq data=have noprint;
table day*product /sparse out=freq (drop=percent);
run;
Output:
day=a product=1 COUNT=1
day=a product=2 COUNT=1
day=a product=3 COUNT=1
day=b product=1 COUNT=0
day=b product=2 COUNT=1
day=b product=3 COUNT=1
day=c product=1 COUNT=1
day=c product=2 COUNT=1
day=c product=3 COUNT=0
I am trying to match max daily data within a month to a monthly data.
data daily;
input permno $ date ret;
datalines;
1000 19860101 88
1000 19860102 90
1000 19860201 70
1000 19860202 55
1001 19860201 97
1001 19860202 74
1001 19860203 79
1002 19860301 55
1002 19860302 100
1002 19860301 10
;
run;
data monthly;
input permno $ date ret;
datalines;
1000 19860131 1
1000 19860228 2
1000 19860331 5
1001 19860331 3
1002 19860430 4
;
run;
The result I want is the following; (I want to match daily max data to one month lag monthly data. )
1000 19860102 90 1000 19860228 2
1000 19860201 70 1000 19860331 5
1001 19860201 97 1001 19860331 3
1002 19860302 100 1002 19860430 4
Below is what I have tried so far.
I want to have maximum ret value within a month so I have created yrmon to assign same yyyymm data for the same month daily data
data a1; set daily;
yrmon=year(date)*100 + month(date);
run;
In order to choose the maximum value(here, ret) within same yrmon group for the same permno, I used code below
proc means data=a1 noprint;
class permno yrmon ;
var ret;
output out= a2 max=maxret;
run;
However, it only got me permno yrmon ret data, leaving the original date data away.
data a3;
set a2;
new=intnx('month',yrmon,1);
format date new yymmn6.;
run;
But it won't work since yrmon is no longer date format.
Thank you in advance.
Hello
I am trying to match two different sets by permno(same company) but with one month lag (eg. daily9 dataset yrmon=198601 and monthly2 dataset yrmon=198602)
it is pretty difficult to handle for me because if I just add +1 in yrmon, 198612 +1 will not be 198701 and I am confused with handling these issues.
Can anyone help?
1) informat date1/date2 yymmn6. is used to read the date in yyyymm format
2) format date1/date2 yymmn6. is used to view the date in yyyymm format
3) intnx("months",b.date2,-1) is used to join the dates with lag of 1 month
data data1;
input date1 value1;
informat date1 yymmn6.;
format date1 yymmn6.;
cards;
200101 200
200212 300
200211 400
;
run;
data data2;
input date2 value2;
informat date2 yymmn6.;
format date2 yymmn6.;
cards;
200101 3000000
200102 4000000
200301 2000000
200212 2000000
;
run;
proc sql;
create table result as
select a.*,b.date2,b.value2 from
data1 a
left join
data2 b
on a.date1 = intnx("months",b.date2,-1);
quit;
My Output:
date1 |value1 |date2 |value2
200101 |200 |200102 |4000000
200211 |400 |200212 |2000000
200212 |300 |200301 |2000000
Let me know in case of any queries.
I don't know where to start with this. I've tried listing the columns in every possible order but they are always listed horizontally. The dataset is:
data job2;
input year apply_count interviewed_count hired_count interviewed_mean hired_mean;
datalines;
2012 349 52 12 0.149 0.23077
2013 338 69 20 0.20414 0.28986
2014 354 70 18 0.19774 0.25714
;
run;
Here's an example of the proc report code for just one analysis variable:
proc report data = job2;
columns apply_count year;
define year / across " ";
define apply_count / analysis "Applied" format = comma8.;
run;
Ideally the final report would look like this:
2012 2013 2014
Applied 349 338 354
Interv. 52 69 70
Hired 12 20 18
Inter % 15% 20% 20%
Hired % 23% 29% 26%
I don't know if this is the best way to do this.
data job2;
input year apply_count interviewed_count hired_count interviewed_mean hired_mean;
datalines;
2012 349 52 12 0.149 0.23077
2013 338 69 20 0.20414 0.28986
2014 354 70 18 0.19774 0.25714
;;;;
run;
proc transpose data=job2 out=job3;
by year;
run;
data job3;
set job3;
length y atype $8;
y = propcase(scan(_name_,1,'_'));
atype = scan(_name_,-1,'_');
if atype eq 'mean' then substr(y,8,1)='%';
run;
proc print;
run;
proc report data=job3 list;
columns atype y year, col1 dummy;
define atype / group noprint;
define y / group order=data ' ';
define year / across ' ';
define dummy / noprint;
define col1 / format=12. ' ';
compute before atype;
xatype = atype;
endcomp;
compute after atype;
line ' ';
endcomp;
compute col1;
if xatype eq 'mean' then do;
call define('_C3_','format','percent12.');
call define('_C4_','format','percent12.');
call define('_C5_','format','percent12.');
end;
endcomp;
run;
I have a data with patientID and the next column with illness, which has more than one category seperated by commas. I need to find the total number of patients per each illness category and the percentage of patients per category. I tried the normal way, it gives the frequency correct but not the percent.
The data looks like this.
ID Type_of_illness
4 lf13
5 lf5,lf11
63
13 lf12
85
80
15
20
131 lf6,lf7,lf12
22
24
55 lf12
150 lf12
34 lf12
49 lf12
151 lf12
60
74
88
64
82 lf13
5 lf5,lf7
112
87 lf17
78
79 lf16
83 lf11
where the empty spaces represent no illness. I first separated the illnesses into separate columns, but then got stuck there not knowing how to process to find out the percent.
The code I wrote is as below:
Data new;
set old;
array P(3) L1 L2 L3;
do i to dim(p);
p(i)=scan(type_of_illness,i,',');
end;
run;
Then I created a new column to copy all the illnesses to it so I thought it would give me the correct frequency, but it did not give me the correct percent.
data new;
set new;
L=L1;output;
L=L2;output;
L=L3;output;
run;
proc freq data=new;
tables L;run;
I have to create a table something like
*Total numer of patients Percent*
.......................................
lf5
lf7
lf6
lf11
lf12
lf13
Please help.
You're trying to output percentages on non-mutually exclusive groups (each illness). It isn't obvious in SAS how to do this.
The following takes Joe's input code but takes an alternative route in determining percentages from event data (a 'long' dataset, if you will). I prefer this to creating a binary variable for each illness at the patient level (a 'wide' dataset) as, for me, this soon gets unwieldy. That said, if you then go on to do some modelling then a 'wide' dataset is usually more useful.
The following code produces output as follows:-
| | Pats | Pats | | | Mean | | |
| | with 0 |with 1+ | % with | Num | events | | |
| |records | record | record | Events |per pat |Std Dev | Median |
|-----------------------|--------|--------|--------|--------|--------|--------|---------
|lf11 | 24| 2| 8| 2| 1.0| 0.00| 1|
|-----------------------|--------|--------|--------|--------|--------|--------|---------
|lf12 | 19| 7| 27| 7| 1.0| 0.00| 1|
|-----------------------|--------|--------|--------|--------|--------|--------|---------
|lf13 | 24| 2| 8| 2| 1.0| 0.00| 1|
|-----------------------|--------|--------|--------|--------|--------|--------|---------
|lf16 | 25| 1| 4| 1| 1.0| .| 1|
|-----------------------|--------|--------|--------|--------|--------|--------|---------
|lf17 | 25| 1| 4| 1| 1.0| .| 1|
|-----------------------|--------|--------|--------|--------|--------|--------|---------
|lf5 | 25| 1| 4| 1| 1.0| .| 1|
|-----------------------|--------|--------|--------|--------|--------|--------|---------
|lf6 | 25| 1| 4| 1| 1.0| .| 1|
|-----------------------|--------|--------|--------|--------|--------|--------|---------
|lf7 | 24| 2| 8| 2| 1.0| 0.00| 1|
---------------------------------------------------------------------------------------|
Note that patient 5 is repeated in your data for illness lf5. My code only counts this record once. This is fine if a chronic illness but not if acute. Also, my code includes patients in the denominator who do not have an event.
Finally, you can see another example of this code using dates - with test data - here at the mycodestock.com code sharing site => https://mycodestock.com/public/snippet/11251
Here's the code for the table above:-
options nodate nonumber nocenter pageno=1 obs=max nofmterr ps=52 ls=100 formchar="|----||---|-/\<>*";
data have;
format type_of_illness $30.;
infile datalines truncover;
input ID Type_of_illness $;
datalines;
4 lf13
5 lf5,lf11
63
13 lf12
85
80
15
20
131 lf6,lf7,lf12
22
24
55 lf12
150 lf12
34 lf12
49 lf12
151 lf12
60
74
88
64
82 lf13
5 lf5,lf7
112
87 lf17
78
79 lf16
83 lf11
;;;;
proc sort;
by id;
run;
** Create patient level data;
proc sort data = have(keep = id) out = pat_data nodupkey;
by id;
run;
** Create event table (1 row per patient*event);
** NOTE: Patients without events are dropped (as is usual in events data);
data events(drop = i type_of_illness);
set have;
attrib grp length = $5 label = 'Illness';
do i = 1 to countc(type_of_illness, ',') + 1;
grp = scan(type_of_illness, i, ',');
if grp ne '' then output;
end;
run;
** Count the number of events each patient had for each grp;
** NOTE: The NODUPKEY in the PROC SORT remove duplicate records (within PAT & GRP);
** NOTE: The use of CLASSDATA and COMPLETETYPES ensures zero counts for all patients and grps;
proc sort in = events out = perc2_summ_grp_pat nodupkey;
by grp id;
proc summary data = perc2_summ_grp_pat nway missing classdata = pat_data completetypes;
by grp;
class id;
output out = perc2_summ_grp_pat(rename=(_freq_ = num_events) drop=_type_);
run;
** Add a denominator variable - value '1' for each row.;
** Ensure when num_events = 0 the value is set to missing;
** Create a flag variable - set to 1 - if a patient has a record (no matter how many);
data perc2_summ_grp_pat;
set perc2_summ_grp_pat;
denom = 1;
if num_events = 0 then num_events = .;
flg_scripts = ifn(num_events, 1, .);
run;
proc tabulate data = perc2_summ_grp_pat format=comma8.;
title1 bold "Table 1: N, % and basic statistics of events within non-mutually exclusive groups";
title2 "Units: Patients - within each group level";
title3 "The statistics summarises the number of events (not whether a patient had at least 1 event)";
title4 "This means, for the statistics, only patients with 1+ record are included in the denominator";
class grp;
var denom flg_scripts num_events;
table grp='', flg_scripts=''*(nmiss='Pats with 0 records' n='Pats with 1+ record' pctsum<denom>='% with record')
num_events=''*(sum='Num Events' mean='Mean events per pat'*f=8.1 stddev='Std Dev'*f=8.2 p50='Median');
run; title; footnote;
You're going about this right, but you need to pick percent differently. Normally percent is 'percent of whole dataset', which means that it is going to triplicate your base. You want the percent based to the illness. This means you need a 1/0 for each illness.
The one downside is you have the 0's in your automatic tables; you would have to output the table to a dataset and remove them, then proc print/report the resulting dataset to get the 1's only - or use PROC SQL to generate the table.
data have;
format type_of_illness $30.;
infile datalines truncover;
input ID Type_of_illness $;
datalines;
4 lf13
5 lf5,lf11
63
13 lf12
85
80
15
20
131 lf6,lf7,lf12
22
24
55 lf12
150 lf12
34 lf12
49 lf12
151 lf12
60
74
88
64
82 lf13
5 lf5,lf7
112
87 lf17
78
79 lf16
83 lf11
;;;;
run;
data want;
set have;
array L[8] lf5-lf7 lf11-lf13 lf16 lf17;
do _t = 1 to dim(L);
if find(type_of_illness,trim(vname(L[_t]))) then L[_t]=1;
else L[_t]=0;
end;
run;
proc tabulate data=want;
class lf:;
tables lf:,n pctn;
run;
The multilabel format solution is interesting, so I present it separately.
Using the same have, we create a format that takes every combination of illnesses and outputs a row for each illness in it, ie, if you have "1,2,3", it outputs rows
1,2,3 = 1
1,2,3 = 2
1,2,3 = 3
Enabling multilabel formats and using a class-enabled proc like proc tabulate, you can then use this to allow each respondent to count in each of the label values, but not be counted more than once against the total.
data for_procformat;
set have;
start=type_of_illness; *start is the input to the format;
hlo=' m'; *m means multilabel, adding a space
here to leave room for the o later;
type='c'; *character format - n is numeric;
fmtname='$ILLF'; *whatever name you like;
do _t = 1 to countw(type_of_illness,','); *for each 'word' do this once;
label=scan(type_of_illness,_t,','); *label is the 'result' of the format;
if not missing(label) then output;
end;
if _n_=1 then do; *this block adds a row to deal with values;
hlo='om'; *not defined (in this case, just missings);
label='No Illness'; *the o means 'other';
output;
end;
run;
proc sort data=for_procformat nodupkey; *remove duplicates (which there will be many);
by start label;
run;
proc format cntlin=for_procformat; *import the formats;
quit;
proc tabulate data=have;
class type_of_illness/mlf missing ; *mlf means multilabel formats;
format type_of_illness $ILLF.; *apply said format;
tables type_of_illness,n pctn; *and your table;
run;