Hello this is my data sample
coustmer_NO id
1 5
1 13
2 4
2 4
2 4
3 4
3 10
4 8
4 8
using SQL >> I Would like to count for each customer how many different ID They have.
the expected output is:
coustmer_NO total_id
1 2
2 1
3 2
4 1
I guess there is a typo in your data,
The result should be:
coustmer_NO total_id
1 2
2 1
3 2
4 1
You can do the following:
SELECT costumer_NO, count(distinct id) AS total_id FROM <table_name> GROUP BY costumer_NO;
Try this query in MYSQL:
select coustmer_NO, count(distinct id) as 'total_id' from table_name group by coustmer_NO;
Related
I have a dataset on multiple outcome for individuals in two groups that were treated (or not treated) by an intervention at two time points. However, not every individual has complete data for each measure at each time point.
id
outcome
outcome_value
group
time
1
depression
10
1
1
1
depression
8
1
2
2
depression
10
2
1
2
depression
.
2
2
1
anxiety
12
1
1
1
anxiety
8
1
2
2
anxiety
12
2
1
2
anxiety
6
2
2
How do I exclude IDs that do not have an outcome in both periods? I only want to see how outcomes changed between groups over time for observations have data in all periods. I am using the mixed command in Stata to conduct this analysis.
First drop the missing rows
keep if !missing(outcome_value)
Then, keep the ID/outcome combinations that have _N==2
bysort id outcome: keep if _N==2
Output:
id outcome outco~ue group time ct
1 anxiety 8 1 2 2
1 anxiety 12 1 1 2
1 depression 10 1 1 2
1 depression 8 1 2 2
2 anxiety 6 2 2 2
2 anxiety 12 2 1 2
As #NickCox has pointed out in the comments, while we cannot directly combine these two, there is still a one-line approach:
bysort id outcome (time) : keep if !missing(outcome_value[1], outcome_value[2])
Of note, we cannot do this:
bysort id outcome : keep if !missing(outcome_value) & _N==2
because _N is not reduced by group until after the rows with missing outcome have been removed.
I have two tables:
Table A
id
name
month_1
month_2
month_3
month_4
month_5
month_6
1
John
3
0
1
0
null
null
2
Mary
6
1
2
1
1
2
3
Angelo
1
5
null
null
null
null
4
Diane
3
2
0
1
null
null
Table B
id
name
LastYearTotal
CurrentYearTotal
1
John
2
4
2
Mary
6
13
3
Angelo
9
6
4
Diane
9
6
And then tables A and B will be side by side but not in the same table. Like there will be a separator between A and B. But when I use a filter, both tables will reflect the filter. In addition, there will only be one scroll for both tables so they move at the same time.
Thanks.
Values are for two groups by quarter.
In DAX, need to summarize all the data but also need to remove -3 from each quarter in 2021 for Group 1, without allowing the value to go below 0.
This only impacts:
Group 1 Only
2021 Only
However, I also need to retain the data details without the adjustment. So I can't do this in Power Query. My data detail is actually in months but I'm only listing one date per quarter for brevity.
Data:
Group
Date
Value
1
01/01/2020
10
1
04/01/2020
8
1
07/01/2020
18
1
10/01/2020
2
1
01/01/2021
12
1
04/01/2021
3
1
07/01/2021
7
1
10/01/2021
2
2
01/01/2020
10
2
04/01/2020
8
2
07/01/2020
18
2
10/01/2020
2
2
01/01/2021
12
2
04/01/2021
3
2
07/01/2021
7
2
10/01/2021
2
Result:
Group
Qtr/Year
Value
1
Q1-2020
10
1
Q2-2020
8
1
Q3-2020
18
1
Q4-2020
2
1
2020
38
1
Q1-2021
9
1
Q2-2021
0
1
Q3-2021
4
1
Q4-2021
0
1
2021
13
2
Q1-2020
10
2
Q2-2020
8
2
Q3-2020
18
2
Q4-2020
2
2
2020
2
2
Q1-2021
12
2
Q2-2021
3
2
Q3-2021
7
2
Q4-2021
2
2
2021
24
You issue can be solved by using Matrix Table, and also to add new column to process value before create the table:
First, add a new column using following formula:
Revised value =
var newValue = IF(YEAR(Sheet1[Date])=2021,Sheet1[Value]-3,Sheet1[Value])
return
IF(newValue <0,0,newValue)
Second, create the matrix table for the desired outcome:
I have a dataset that I am converting from wide to long format.
Currently I have 1 observation per patient, and each patient can have up to 5 aneurysms, currently recorded in wide format.
I am trying to re-arrange this dataset so that I have one observation per aneurysm instead. I have done so successfully, but now I need to label the aneurysms in a new variable called aneurysmIdentifier.
Here is a glimpse at the data. You can see how, when a patient has 4 aneurysms, I have successfully created 4 corresponding observations, however these are duplicates created via the expand function.
I am stuck at the next point, which, as mentioned, is creating a new variable aneurysmIdentifier that reads 1 if there is only one copy of the specific record_id, 1 and 2 if there are two copies and so forth all the way to 1-2-3-4-5. This would enable me to have a point of reference as to what I call aneurysm 1, 2, 3, 4 and 5 so I can keep re-arranging data to fit as such.
I have created this sketch hopefully showcasing what I mean. As you can see it counts how many duplicates there are and then counts forward up to the maximum of 5.
Can anyone push me in the right direction on how to achieve this?
Example of data:
* Example generated by -dataex-. To install: ssc install dataex
clear
input str32 record_id float aneurysmNumber
"007128de18ce5cb1635b8f27c5435ff3" 1
"00abd7bdb6283dd0ac6b97271608a122" 1
"0142103f84693c6eda416dfc55f65de1" 1
"0153826d93a58d7e1837bb98a3c21ba8" 1
"01c729ac4601e36f245fd817d8977917" 2
"01c729ac4601e36f245fd817d8977917" 2
"01dd90093fbf201a1f357e22eaff6b6a" 1
"0208e14dcabc43dd2b57e2e8b117de4d" 1
"0210f575075e5def7ffa77530ce17ef0" 1
"022cc7a9397e81cf58cd9111f9d1db0d" 1
"02afd543116a22fc7430620727b20bb5" 1
"0303ef0bd5d256cca1c836e2b70415ac" 2
"0303ef0bd5d256cca1c836e2b70415ac" 2
"041b2b0cac589d6e3b65bb924803cf1a" 1
"0536317a2bbb936e85c3eb8294b076da" 1
"06161d4668f217937cac0ac033d8d199" 1
"065e151f8bcebb27fabf8b052fd70566" 4
"065e151f8bcebb27fabf8b052fd70566" 4
"065e151f8bcebb27fabf8b052fd70566" 4
"065e151f8bcebb27fabf8b052fd70566" 4
"07196414cd6bf89d94a33e149983d102" 1
"0721c38f8275dab504fc53aebcc005ce" 4
"0721c38f8275dab504fc53aebcc005ce" 4
"0721c38f8275dab504fc53aebcc005ce" 4
"0721c38f8275dab504fc53aebcc005ce" 4
"07bef516d53279a3f5e477d56d552a2b" 1
"08678829b7e0ee6a01b17974b4d19cfa" 1
"08bb6c65e63c499ea19ac24d5113dd94" 1
"08f036417500c332efd555c76c4654a0" 1
"090c54d021b4b21c7243cec01efbeb91" 1
"09166bb44e4c5cdb8f40d402f706816e" 1
"0930159addcdc35e7dc18812522d4377" 1
"096844af91d2e266767775b0bee9105e" 1
"09884af1bb9d59803de0c74d6df57c23" 1
"09e03748da35e9d799dc5d8ddf1909b5" 1
"0a4ce4a7941ff6d1f5c217bf5a9a3bf9" 1
"0a5db40dc58e97927b407c9210aab7ba" 2
"0a5db40dc58e97927b407c9210aab7ba" 2
"0a73c992955231650965ed87e3bd52f6" 1
"0a84ab77fff74c247a525dfde8ce988c" 3
"0a84ab77fff74c247a525dfde8ce988c" 3
"0a84ab77fff74c247a525dfde8ce988c" 3
"0af333ae400f75930125bb0585f0dcf5" 1
"0af73334d9d2166191f3385de48f15d2" 1
"0b341ac8f396a8cdb88b7c658f66f653" 2
"0b341ac8f396a8cdb88b7c658f66f653" 2
"0b35cf4beb830b361d7c164371f25149" 2
"0b35cf4beb830b361d7c164371f25149" 2
"0b3e110c9765e14a5c41fadcc3cfc300" .
"0b6681f0f441e69c26106ab344ac0733" 1
"0b8d8253a8415275dbc2619e039985bb" 3
"0b8d8253a8415275dbc2619e039985bb" 3
"0b8d8253a8415275dbc2619e039985bb" 3
"0b92c26375117bf42945c04d8d6573d4" 2
"0b92c26375117bf42945c04d8d6573d4" 2
"0ba961f437f43105c357403c920bdef1" 1
"0bb601fabe1fdfa794a5272408997a2f" 1
"0c75b36e91363d596dc46bd563c3f5ef" 1
"0d461328a3bae7164ce7d3a10f366812" 1
"0d4cc4eb459301a804cbef22914f44a3" 1
"0d4e29e11bb94e922112089f3fec61ef" 2
"0d4e29e11bb94e922112089f3fec61ef" 2
"0d513c74d667f55c8f4a9836c304149c" 1
"0da25de126bb3b3ee565eff8888004c2" 2
"0da25de126bb3b3ee565eff8888004c2" 2
"0db9ae1f2201577f431b7603d0819fa6" 1
"0dd8a681f6a5d4c888831a591e57a747" 1
"0e05d6958d878368b5fb831211fad6a1" 1
"0e3ff41e0e2b2cb5ec336fd0b04e5d44" 1
"0f61e560ab56b8fea1f2593d7d3b2718" 2
"0f61e560ab56b8fea1f2593d7d3b2718" 2
"0f69f1f998984d37f133185179d63c60" 1
"1037032886a93e66406a4c910d1ef747" 2
"1037032886a93e66406a4c910d1ef747" 2
"1044b81b354b420e85ae835ea07de2d6" 1
"10620fc488346291281212a404681386" 1
"1074389c469944edf026d193a55b1148" 1
"1090d5a678119b03cddab609289a4d3c" 1
"111eebb45cef2211a2a2ff0219095e6a" 1
"11ddcbc8de8ef56cbc578fc81b602ffc" 1
"11f22488513cf717c333786c789b0289" 2
"11f22488513cf717c333786c789b0289" 2
"121552b22cee2a1eb4360b4d2534cd39" 1
"1251d707c5dc9243dc45d04beb7c3493" 1
"125689659bb3821fa81698dd72462773" 1
"127ba572433921c5bb408fc62eb9b5d7" 1
"129bea3f73e84e37d77d55fadfeb49dd" 1
"12e8dc6fb87822be26d6678cee9644f5" 1
"12f05a65f771c9675c2c5e9cdbfc33d1" 2
"12f05a65f771c9675c2c5e9cdbfc33d1" 2
"13d2bc86f1a19ed2959cd7354bc92d1d" 1
"13db5ede38e2ae1da17884c9a18df202" 1
"13f946e50df8ad74d7cf9fa05b4ad05b" 1
"146c4b8be7996a9789873fe55a47ab41" 1
"147fadd87da13a0271225d944d2a5e98" 1
"14a1dcfa015343bbefaac9a3a45769e5" 2
"14a1dcfa015343bbefaac9a3a45769e5" 2
"14d1377f74a63ffa29db2d99e7f6a1ce" 1
"150017d944a87b4c61f90034380c0659" 1
"150f6ca1ea453260eabf3472d3ebcad1" 1
end
You can go
bysort record_id: gen aneurysm_id = _n
but the results will be arbitrary unless there is some other information, say a date variable, to provide a rationale for the ordering. Let's suppose that there is a date variable date that is numeric and in good order. Then
bysort record_id (date) : gen aneurysm_id = _n
would be a suitable modification. For date read also date-time if time of day is noted and notable.
I would like to add summary record after each group of records connected with specific shop. So, I have this:
Shop_id Trans_id Count
1 1 10
1 2 23
1 3 12
2 1 8
2 2 15
And want to have this:
Shop_id Trans_id Count
1 1 10
1 2 23
1 3 12
. . 45
2 1 8
2 2 15
. . 23
I have done this using PROC SQL but I would like to do this using PROC REPORT as I have read that PROC REPORT should handle such cases.
Try this:
data have;
input shop_id Trans_id Count;
cards;
1 1 10
1 2 23
1 3 12
2 1 8
2 2 15
;
proc report data=have out=want(drop=_:);
define shop_id/group;
define trans_id/order;
define count/sum;
break after shop_id/summarize;
compute after shop_id;
if _break_='shop_id' then shop_id='';
endcomp;
run;