I am working with a very large dataset containing the same columns several times, but with different column names (both character and numeric).
Does anyone know how to find and delete these identical columns?
Example
A B C D E F G
12 ab 12 ab 8 h 12
14 cd 14 cd 65 j 14
6 fs 6 fs 3 g 6
. . . . 4 q .
3 d 3 d 5 d 3
A-G are variable names, and I want to be able to see that A, C and G are identical and then remove all except one.
Also B and D are identical. I want to keep only one.
Is this even possible?
Here is example using technique proposed by Shenglin Chen in the comments.
data have ;
input A B $ C D $ E F $ G ;
cards;
12 ab 12 ab 8 h 12
14 cd 14 cd 65 j 14
6 fs 6 fs 3 g 6
. . . . 4 q .
3 d 3 d 5 d 3
;;;;
Find the unique numeric columns.
proc transpose data=have out=tall_numbers ;
var _numeric_;
run;
proc sort data=tall_numbers nodupkey out=keep_numbers(keep=_name_);
by col: ;
run;
Find the unique character columns.
proc transpose data=have out=tall_characters ;
var _character_;
run;
proc sort data=tall_characters nodupkey out=keep_characters(keep=_name_);
by col: ;
run;
Get the combined list of columns.
proc sql noprint ;
select _name_
into :keep_list separated by ' '
from (select _name_ from keep_characters
union select _name_ from keep_numbers)
order by 1
;
quit;
Make new table with only the unique columns.
data want ;
set have ;
keep &keep_list ;
run;
Related
I am trying to compute the frequency of observation in a group.
My dataset looks like:
Date Account C_group Age ...
1 152627 A 28
2 152627 B 28
1 163718 B 32
3 163628 D 12
4 163717 C 41
.
.
I would like to determine the percentage of accounts in the different groups.
Do you know how I could that?
Thanks
The following should get you close to what you are looking for:
data dset ;
input
freqgroup $
subgroup ;
datalines ;
A 12
B 12
C 12
C 21
C 23
A 12
A 21
B 12
B 21
B 21
;
run;
proc sort data=dset;
by freqgroup;
run;
proc freq data=dset ;
table freqgroup ;
run ;
proc freq data=dset ;
by freqgroup ;
table subgroup ;
run ;
I have 2 sas output tables. First table has a,b,c columns and second table has d,e,f columns
First table is :
a b c
1 2 3
4 5 6
Second table is :
d e f
7 8 9
Is it possible to append them in one sheet with desired output
a b c
1 2 3
4 5 6
d e f
7 8 9
Yes, you can append the second table to the first using proc append , you just need to rename the columns in second table before appending.
proc append base=table1 data=table2(rename=(d=a e=b f=c)) ;run;
Full Code:
data table1;
input a b c;
datalines;
1 2 3
4 5 6
;
run;
data table2;
input d e f;
datalines;
7 8 9
;
run;
proc append base=table1 data=table2(rename=(d=a e=b f=c)) ;run;
Table1 will look like this after appending:
a=1 b=2 c=3
a=4 b=5 c=6
a=7 b=8 c=9
Another Option: If all your data is characters, you just need a create a third table to hold the column names you want to append.
Full Code:
data table1;
input a $ b $ c $;
datalines;
1 2 3
4 5 6
;
run;
data table2;
input d $ e $ f $;
datalines;
7 8 9
;
run;
data table2_names;
input d $ e $ f $;
datalines;
d e f
;
run;
proc append base=table1 data=table2_names(rename=(d=a e=b f=c)) ;run;
proc append base=table1 data=table2(rename=(d=a e=b f=c)) ;run;
Output:
a=1 b=2 c=3
a=4 b=5 c=6
a=d b=e c=f
a=7 b=8 c=9
I have observations with column ID, a, b, c, and d. I want to count the number of unique values in columns a, b, c, and d. So:
I want:
I can't figure out how to count distinct within each row, I can do it among multiple rows but within the row by the columns, I don't know.
Any help would be appreciated. Thank you
********************************************UPDATE*******************************************************
Thank you to everyone that has replied!!
I used a different method (that is less efficient) that I felt I understood more. I am still going to look into the ways listed below however to learn the correct method. Here is what I did in case anyone was wondering:
I created four tables where in each table I created a variable named for example ‘abcd’ and placed a variable under that name.
So it was something like this:
PROC SQL;
CREATE TABLE table1_a AS
SELECT
*
a as abcd
FROM table_I_have_with_all_columns
;
QUIT;
PROC SQL;
CREATE TABLE table2_b AS
SELECT
*
b as abcd
FROM table_I_have_with_all_columns
;
QUIT;
PROC SQL;
CREATE TABLE table3_c AS
SELECT
*
c as abcd
FROM table_I_have_with_all_columns
;
QUIT;
PROC SQL;
CREATE TABLE table4_d AS
SELECT
*
d as abcd
FROM table_I_have_with_all_columns
;
QUIT;
Then I stacked them (this means I have duplicate rows but that ok because I just want all of the variables in 1 column and I can do distinct count.
data ALL_STACK;
set
table1_a
table1_b
table1_c
table1_d
;
run;
Then I counted all unique values in ‘abcd’ grouped by ID
PROC SQL ;
CREATE TABLE count_unique AS
SELECT
My_id,
COUNT(DISTINCT abcd) as Count_customers
FROM ALL_STACK
GROUP BY my_id
;
RUN;
Obviously, it’s not efficient to replicate a table 4 times just to put a variables under the same name and then stack them. But my tables were somewhat small enough that I could do it and then immediately delete them after the stack. If you have a very large dataset this method would most certainly be troublesome. I used this method over the others because I was trying to use Procs more than loops, etc.
A linear search for duplicates in an array is O(n2) and perfectly fine for small n. The n for a b c d is four.
The search evaluates every pair in the array and has a flow very similar to a bubble sort.
data have;
input id a b c d; datalines;
11 2 3 4 4
22 1 8 1 1
33 6 . 1 2
44 . 1 1 .
55 . . . .
66 1 2 3 4
run;
The linear search for duplicates will occur on every row, and the count_distinct will be initialized automatically in each row to a missing (.) value. The sum function is used to increment the count when a non-missing value is not found in any prior array indices.
* linear search O(N**2);
data want;
set have;
array x a b c d;
do i = 1 to dim(x) while (missing(x(i)));
end;
if i <= dim(x) then count_distinct = 1;
do j = i+1 to dim(x);
if missing(x(j)) then continue;
do k = i to j-1 ;
if x(k) = x(j) then leave;
end;
if k = j then count_distinct = sum(count_distinct,1);
end;
drop i j k;
run;
Try to transpose dataset, each ID becomes one column, frequency each ID column by option nlevels, which count frequency of value, then merge back with original dataset.
Proc transpose data=have prefix=ID out=temp;
id ID;
run;
Proc freq data=temp nlevels;
table ID:;
ods output nlevels=count(keep=TableVar NNonMisslevels);
run;
data count;
set count;
ID=compress(TableVar,,'kd');
drop TableVar;
run;
data want;
merge have count;
by id;
run;
one more way using sortn and using conditions.
data have;
input id a b c d; datalines;
11 2 3 4 4
22 1 8 1 1
33 6 . 1 2
44 . 1 1 .
55 . . . .
66 1 2 3 4
77 . 3 . 4
88 . 9 5 .
99 . . 2 2
76 . . . 2
58 1 1 . .
50 2 . 2 .
66 2 . 7 .
89 1 1 1 .
75 1 2 3 .
76 . 5 6 7
88 . 1 1 1
43 1 . . 1
31 1 . . 2
;
data want;
set have;
_a=a; _b=b; _c=c; _d=d;
array hello(*) _a _b _c _d;
call sortn(of hello(*));
if a=. and b = . and c= . and d =. then count=0;
else count=1;
do i = 1 to dim(hello)-1;
if hello(i) = . then count+ 0;
else if hello(i)-hello(i+1) = . then count+0;
else if hello(i)-hello(i+1) = 0 then count+ 0;
else if hello(i)-hello(i+1) ne 0 then count+ 1;
end;
drop i _:;
run;
You could just put the unique values into a temporary array. Let's convert your photograph into data.
data have;
input id a b c d;
datalines;
11 2 3 4 4
22 1 8 1 1
33 6 . 1 2
44 . 1 1 .
;
So make an array of the input variables and another temporary array to hold the unique values. Then loop over the input variables and save the unique values. Finally count how many unique values there are.
data want ;
set have ;
array unique (4) _temporary_;
array values a b c d ;
call missing(of unique(*));
do _n_=1 to dim(values);
if not missing(values(_n_)) then
if not whichn(values(_n_),of unique(*)) then
unique(_n_)=values(_n_)
;
end;
count=n(of unique(*));
run;
Output:
Obs id a b c d count
1 11 2 3 4 4 3
2 22 1 8 1 1 2
3 33 6 . 1 2 3
4 44 . 1 1 . 1
I have a series of string values with missing observations. I would like to use flat substitution. For instance variable x has 3 available values. There should be a 33.333% chance that a missing value will be assigned to the available values for x under this substitution method. How would I do this?
DATA have;
INPUT id a $ b $ c $ x;
CARDS;
1 Y Male . 5
2 Y Female . 4
3 . Female Tall 4
4 Y . Short 2
5 N Male Tall 1
;
Run;
You could use temporary arrays to store the possible values. Then generate a random index into the array.
DATA have;
INPUT id a $ b $ c $ x;
CARDS;
1 Y Male . 5
2 Y Female . 4
3 . Female Tall 4
4 Y . Short 2
5 N Male Tall 1
;
data want ;
set have ;
array possible_b (2) $8 ('Male','Female') ;
if missing(b) then b=possible_b(1+int(rand('uniform')*dim(possible_b)));
run;
I did this with generating random numbers and hard coding the limits. There should be an easier way to do this, but for the purposes of the question this should work.
option missing='';
data begin;
input a $;
cards;
a
.
b
c
.
e
.
f
g
h
.
.
j
.
;
run;
data intermediate;
set begin;
if a EQ '' then help= rand("uniform");
else help=.;
run;
data wanted;
set intermediate;
format help populated.;
if a EQ '' then do;
if 0<=help<0.33 then a='V1';
else if 0.33<=help<0.66 then a='V2';
else if 0.66<=help then a='V3';
end;
drop help;
run;
Suppose the data set have contains various outliers which have been identified in an outliers data set. These outliers need to be replaced with missing values, as demonstrated below.
Have
Obs group replicate height weight bp cholesterol
1 1 A 0.406 0.887 0.262 0.683
2 1 B 0.656 0.700 0.083 0.836
3 1 C 0.645 0.711 0.349 0.383
4 1 D 0.115 0.266 666.000 0.015
5 2 A 0.607 0.247 0.644 0.915
6 2 B 0.172 333.000 555.000 0.924
7 2 C 0.680 0.417 0.269 0.499
8 2 D 0.787 0.260 0.610 0.142
9 3 A 0.406 0.099 0.263 111.000
10 3 B 0.981 444.000 0.971 0.894
11 3 C 0.436 0.502 0.563 0.580
12 3 D 0.814 0.959 0.829 0.245
13 4 A 0.488 0.273 0.463 0.784
14 4 B 0.141 0.117 0.674 0.103
15 4 C 0.152 0.935 0.250 0.800
16 4 D 222.000 0.247 0.778 0.941
Want
Obs group replicate height weight bp cholesterol
1 1 A 0.4056 0.8870 0.2615 0.6827
2 1 B 0.6556 0.6995 0.0829 0.8356
3 1 C 0.6445 0.7110 0.3492 0.3826
4 1 D 0.1146 0.2655 . 0.0152
5 2 A 0.6072 0.2474 0.6444 0.9154
6 2 B 0.1720 . . 0.9241
7 2 C 0.6800 0.4166 0.2686 0.4992
8 2 D 0.7874 0.2595 0.6099 0.1418
9 3 A 0.4057 0.0988 0.2632 .
10 3 B 0.9805 . 0.9712 0.8937
11 3 C 0.4358 0.5023 0.5626 0.5799
12 3 D 0.8138 0.9588 0.8293 0.2448
13 4 A 0.4881 0.2731 0.4633 0.7839
14 4 B 0.1413 0.1166 0.6743 0.1032
15 4 C 0.1522 0.9351 0.2504 0.8003
16 4 D . 0.2465 0.7782 0.9412
The "get it done" approach is to manually enter each variable/value combination in a conditional which replaces with missing when true.
data have;
input group replicate $ height weight bp cholesterol;
datalines;
1 A 0.4056 0.8870 0.2615 0.6827
1 B 0.6556 0.6995 0.0829 0.8356
1 C 0.6445 0.7110 0.3492 0.3826
1 D 0.1146 0.2655 666 0.0152
2 A 0.6072 0.2474 0.6444 0.9154
2 B 0.1720 333 555 0.9241
2 C 0.6800 0.4166 0.2686 0.4992
2 D 0.7874 0.2595 0.6099 0.1418
3 A 0.4057 0.0988 0.2632 111
3 B 0.9805 444 0.9712 0.8937
3 C 0.4358 0.5023 0.5626 0.5799
3 D 0.8138 0.9588 0.8293 0.2448
4 A 0.4881 0.2731 0.4633 0.7839
4 B 0.1413 0.1166 0.6743 0.1032
4 C 0.1522 0.9351 0.2504 0.8003
4 D 222 0.2465 0.7782 0.9412
;
run;
data outliers;
input parameter $ 11. group replicate $ measurement;
datalines;
cholesterol 3 A 111
height 4 D 222
weight 2 B 333
weight 3 B 444
bp 2 B 555
bp 1 D 666
;
run;
EDIT: Updated outliers so that parameter avoids truncation and changed measurement to be numeric type so as to match the corresponding height, weight, bp, cholesterol. This shouldn't change the responses.
data want;
set have;
if group = 3 and replicate = 'A' and cholesterol = 111 then cholesterol = .;
if group = 4 and replicate = 'D' and height = 222 then height = .;
if group = 2 and replicate = 'B' and weight = 333 then weight = .;
if group = 3 and replicate = 'B' and weight = 444 then weight = .;
if group = 2 and replicate = 'B' and bp = 555 then bp = .;
if group = 1 and replicate = 'D' and bp = 666 then bp = .;
run;
This, however, doesn't utilize the outliers data set. How can the replacement process be made automatic?
I immediately think of the IN= operator, but that won't work. It's not the entire row which needs to be matched. Perhaps an SQL key matching approach would work? But to match the key, don't I need to use a where statement? I'd then effectively be writing everything out manually again. I could probably create macro variables which contain the various if or where statements, but that seems excessive.
I don't think generating statements is excessive in this case. The complexity arises here because your outlier dataset cannot be merged easily since the parameter values represent variable names in the have dataset. If it is possible to reorient the outliers dataset so you have a 1 to 1 merge, this logic would be simpler.
Let's assume you cannot. There are a few ways to use a variable in a dataset that corresponds to a variable in another.
You could use an array like array params{*} height -- cholesterol; and then use the vname function as you loop through the array to compare to the value in the parameter variable, but this gets complicated in your case because you have a one to many merge, so you would have to retain the replacements and only output the last record for each by group... so it gets complicated.
You could transpose the outliers data using proc transpose, but that will get lengthy because you will need a transpose for each parameter, and then you'd need to merge all the transposed datasets back to the have dataset. My main issue with this method is that code with a bunch of transposes like that gets unwieldy.
You create the macro variable logic you are thinking might be excessive. But compared to the other ways of getting the values of the parameter variable to match up with the variable names in the have dataset, I don't think something like this is excessive:
data _null_;
set outliers;
call symput("outlierstatement"||_n_,"if group = "||group||" and replicate = '"||replicate||"' and "||parameter||" = "||measurement||" then "|| parameter ||" = .;");
call symput("outliercount",_n_);
run;
%macro makewant();
data want;
set have;
%do i = 1 %to &outliercount;
&&outlierstatement&i;
%end;
run;
%mend;
Lorem:
Transposition is the key to a fully automatic programmatic approach. The transposition that will occur is of the filter data, not the original data. The transposed filter data will have fewer rows than the original. As John indicated, transposition of the want data can create a very tall table and has to be transposed back after applying the filters.
As to the the filter data, the presence of a filter row for a specific group, replicate and parameter should be enough to mark a cell for filtering. This is on the presumption that you have a system for automatic outlier detection and the filter values will always be in concordance with the original values.
So, what has to be done to automate the filter application process without code generating a wall of test and assign statements ?
Transpose filter data into same form as want data, call it Filter^
Merge Want and Filter^ by record key (which is the by group of Group and Replicate)
Array process the data elements, looking for filtering conditions.
For your consideration, try the following SAS code. There is an erroneous filter record added to the mix.
data have;
input group replicate $ height weight bp cholesterol;
datalines;
1 A 0.4056 0.8870 0.2615 0.6827
1 B 0.6556 0.6995 0.0829 0.8356
1 C 0.6445 0.7110 0.3492 0.3826
1 D 0.1146 0.2655 666 0.0152
2 A 0.6072 0.2474 0.6444 0.9154
2 B 0.1720 333 555 0.9241
2 C 0.6800 0.4166 0.2686 0.4992
2 D 0.7874 0.2595 0.6099 0.1418
3 A 0.4057 0.0988 0.2632 111
3 B 0.9805 444 0.9712 0.8937
3 C 0.4358 0.5023 0.5626 0.5799
3 D 0.8138 0.9588 0.8293 0.2448
4 A 0.4881 0.2731 0.4633 0.7839
4 B 0.1413 0.1166 0.6743 0.1032
4 C 0.1522 0.9351 0.2504 0.8003
4 D 222 0.2465 0.7782 0.9412
5 E 222 0.2465 0.7782 0.9412 /* test record for filter value misalignment test */
;
run;
data outliers;
length parameter $32; %* <--- widened parameter so it can transposed into column via id;
input parameter $ group replicate $ measurement ; %* <--- changed measurement to numeric variable;
datalines;
cholesterol 3 A 111
height 4 D 222
height 5 E 223 /* test record for filter value misalignment test */
weight 2 B 333
weight 3 B 444
bp 2 B 555
bp 1 D 666
;
run;
data want;
set have;
if group = 3 and replicate = 'A' and cholesterol = 111 then cholesterol = .;
if group = 4 and replicate = 'D' and height = 222 then height = .;
if group = 2 and replicate = 'B' and weight = 333 then weight = .;
if group = 3 and replicate = 'B' and weight = 444 then weight = .;
if group = 2 and replicate = 'B' and bp = 555 then bp = .;
if group = 1 and replicate = 'D' and bp = 666 then bp = .;
run;
/* Create a view with 1st row having all the filtered parameters
* This is necessary so that the first transposed filter row
* will have the parameters as columns in alphabetic order;
*/
proc sql noprint;
create view outliers_transpose_ready as
select distinct parameter from outliers
union
select * from outliers
order by group, replicate, parameter
;
/* Generate a alphabetic ordered list of parameters for use
* as a variable (aka column) list in the filter application step */
select distinct parameter
into :parameters separated by ' '
from outliers
order by parameter
;
quit;
%put NOTE: &=parameters;
/* tranpose the filter data
* The ID statement pivots row data into column names.
* The prefix=_filter_ ensure the new column names
* will not collide with the original data, and can be
* the shortcut listed with _filter_: in an array statement.
*/
proc transpose data=outliers_transpose_ready out=outliers_apply_ready prefix=_filter_;
by group replicate notsorted;
id parameter;
var measurement;
run;
/* Robust production code should contain a bin for
* data that does not conform to the filter application conditions
*/
data
want2(label="Outlier filtering applied" drop=_i_ _filter_:)
want2_warnings(label="Outlier filtering: misaligned values")
;
merge have outliers_apply_ready(keep=group replicate _filter_:);
by group replicate;
/* The arrays are for like named columns
* due to the alphabetic ordering enforced in data and codegen preparation
*/
array value_filter_check _filter_:;
array value ¶meters;
if group ne .;
do _i_ = 1 to dim(value);
if value(_i_) EQ value_filter_check(_i_) then
value(_i_) = .;
else
if not missing(value_filter_check(_i_)) AND
value(_i_) NE value_filter_check(_i_)
then do;
put 'WARNING: Filtering expected but values do not match. ' group= replicate= value(_i_)= value_filter_check(_i_)=;
output want2_warnings;
end;
end;
output want2;
run;
Confirm your want and automated want2 agree.
proc compare noprint data=want compare=want2 outnoequal out=diffs;
by group replicate;
run;
Enjoy your SAS
You could use a hash table. Load a hash table with the outlier dataset, with parameter-group-replicate defined as the key. Then read in the data, and as you read each record, check each of the variables to see if that combination of parameter-group-replicate can be found in the hash table. I think below works (I'm no hash expert):
data want;
if 0 then set outliers (keep=parameter group replicate);
if _N_ = 1 then
do;
declare hash h(dataset:'outliers') ;
h.defineKey('parameter', 'group', 'replicate') ;
h.defineDone() ;
end;
set have ;
array vars {*} height weight bp cholesterol ;
do i=1 to dim(vars);
parameter=vname(vars{i});
if h.check()=0 then call missing(vars{i});
end;
drop i parameter;
run;
I like #John's suggestion:
You could use an array like array params{*} height -- cholesterol; and
then use the vname function as you loop through the array to compare
to the value in the parameter variable, but this gets complicated in
your case because you have a one to many merge, so you would have to
retain the replacements and only output the last record for each by
group... so it gets complicated.
Generally in a one to many merge I would avoid recoding variables from the dataset that is unique, because variables are retained within BY groups. But in this case, it works out well.
proc sort data=outliers;
by group replicate;
run;
data want (keep=group replicate height weight bp cholesterol);
merge have (in=a)
outliers (keep=group replicate parameter in=b)
;
by group replicate;
array vars {*} height weight bp cholesterol ;
do i=1 to dim(vars);
if vname(vars{i})=parameter then call missing(vars{i});
end;
if last.replicate;
run;
Thank you #John for providing a proof of concept. My implementation is a little different and I think worth making a separate entry for posterity. I went with a macro variable approach because I feel it is the most intuitive, being a simple text replacement. However, since a macro variable can contain only 65534 characters, it is conceivable that there could be sufficient outliers to exceed this limit. In such a case, any of the other solutions would make fine alternatives. Note that it is important that the put statement use something like best32. Too short a width will truncate the value.
If you desire to have a dataset containing the if statements (perhaps for verification), simply remove the into : statement and place a create table statements as line at the beginning of the PROC SQL step.
data have;
input group replicate $ height weight bp cholesterol;
datalines;
1 A 0.4056 0.8870 0.2615 0.6827
1 B 0.6556 0.6995 0.0829 0.8356
1 C 0.6445 0.7110 0.3492 0.3826
1 D 0.1146 0.2655 666 0.0152
2 A 0.6072 0.2474 0.6444 0.9154
2 B 0.1720 333 555 0.9241
2 C 0.6800 0.4166 0.2686 0.4992
2 D 0.7874 0.2595 0.6099 0.1418
3 A 0.4057 0.0988 0.2632 111
3 B 0.9805 444 0.9712 0.8937
3 C 0.4358 0.5023 0.5626 0.5799
3 D 0.8138 0.9588 0.8293 0.2448
4 A 0.4881 0.2731 0.4633 0.7839
4 B 0.1413 0.1166 0.6743 0.1032
4 C 0.1522 0.9351 0.2504 0.8003
4 D 222 0.2465 0.7782 0.9412
;
run;
data outliers;
input parameter $ 11. group replicate $ measurement;
datalines;
cholesterol 3 A 111
height 4 D 222
weight 2 B 333
weight 3 B 444
bp 2 B 555
bp 1 D 666
;
run;
proc sql noprint;
select
cat('if group = '
, strip(put(group, best32.))
, " and replicate = '"
, strip(replicate)
, "' and "
, strip(parameter)
, ' = '
, strip(put(measurement, best32.))
, ' then '
, strip(parameter)
, ' = . ;')
into : listIfs separated by ' '
from outliers
;
quit;
%put %quote(&listIfs);
data want;
set have;
&listIfs;
run;