have the following table :
EmpId DeptId WeekNumber Month NumberofCalls
1 3 4 1 34
2 3 2 3 59
I created a measure to calculate the average of number of calls :
AvgCalls = AVG('MyTable'[NumberofCalls])
now I want to get the max average calls by month, week.
I will be having 3 filters :
Month
Week
Once I select all of them, the result in the histogram bar will be the employee having the max average calls.
Once I select the Month and the Week I want the histogram to display the code of the Employee (W1,W2,W3...) having the maximum average, in my case I get the following result all the employees but not the employee having the max average.
Here is my solution:
I tested it with some random datasets, Here is my data:
EmpId DeptId WeekNumber Month NumberofCalls
Emp01 3 W4 1 34
Emp01 3 W2 3 59
Emp02 3 W5 4 68
Emp02 3 W6 4 76
Emp03 3 W10 5 90
Emp04 4 W10 6 98
Emp04 4 W11 6 45
Emp05 4 W12 7 56
Emp06 4 W13 7 23
Emp07 4 W15 9 45
Emp08 4 W34 8 56
Emp09 4 W52 8 44
Emp05 4 W36 9 23
Emp01 4 W17 10 51
Emp02 4 W23 9 67
Emp06 4 W29 11 28
Emp05 4 W34 12 34
Emp07 4 W41 11 21
Emp04 4 W37 12 33
I wrote this measure using Iterator Function (ADDCOLUMNS):
MaxAverageEmployer =
VAR TAvgCalls =
ADDCOLUMNS(
SUMMARIZE(MyTable,MyTable[EmpId],MyTable[Month ],MyTable[WeekNumber ]),
"AvgCall",CALCULATE(AVERAGE('MyTable'[NumberofCalls]))
)
VAR TMaxAvgCalls =
ADDCOLUMNS(
TAvgCalls,
"MaxAvg",CALCULATE(MAXX(TAvgCalls,[AvgCall]))
)
VAR MaxEmpID =
ADDCOLUMNS(
TMaxAvgCalls,
"MaxEmp",CALCULATE(VALUES(MyTable[EmpId]),FILTER(TMaxAvgCalls,[AvgCall] = [MaxAvg]))
)
RETURN
MAXX(MaxEmpID,[MaxEmp])
Here is the part:
It showed nothing when I tried to show it on histogram (or Bar Chart Visual); but It gave me correct values on a table visual:
WeekNumber : I put in on Rows
MonthNumber : I put it on Slicer to filter it!
Here is the final solution, and I hope It is what you are looking for!
Related
I've a lot of material on Stack about this, but i'm still not able to reproduce it.
Sample data set.
Asset
Value
Index
A
10
1
B
15
1
C
20
1
A
11
2
B
17
2
C
24
2
A
18
3
B
25
3
C
30
3
What i want to do is, subtract the Asset values individually based on the index column.
Ex:
Asset A [1] -> 10
Asset A [2] -> 11
11 - 10 = 1
So the table would be like this.
Asset
Value
Index
Diff
A
10
1
0
B
15
1
0
C
20
1
0
A
11
2
1
B
17
2
2
C
24
2
4
A
18
3
7
B
25
3
8
C
30
3
6
This need's to be done using DAX.
Can you guys help me ?
Best Regards!
I just did this and it worked.
Diff =
var Assets = 'Table'[Asset]
var Ind = 'Table'[Index] - 1
Return
IF(Ind = -1, 0, 'Table'[Value] - CALCULATE(SUM('Table'[Value]),FILTER('Table','Table'[Asset] = Assets && 'Table'[Index] = Ind)))
I have a dataset that has to be grouped by number as follows.
ID dept count
1 10 2
2 10 2
3 20 4
4 20 4
5 20 4
6 20 4
7 30 4
8 30 4
9 30 4
10 30 4
so for every 3rd row I need a new level the output should be as follows.
ID dept count Level
1 10 2 1
2 10 2 1
3 20 4 1
4 20 4 1
5 20 4 2
6 20 4 2
7 30 4 1
8 30 4 1
9 30 4 2
10 30 4 2
I have tried counting the number of rows based on the dept and count.
data want;
set have;
by dept count;
if first.count then level=1;
else level+1;
run;
this generates a count but not what exactly I am looking for
ID dept count Level
1 10 2 1
2 10 2 1
3 20 4 1
4 20 4 1
5 20 4 2
6 20 4 2
7 30 4 1
8 30 4 1
9 30 4 2
10 30 4 2
It isn't quite clear what output you want. I've extended your input data a bit - please
could you clarify what output you'd expect for this input and what the logic is for generating it?
I've made a best guess at roughly what you might be aiming for - incrementing every 3 rows with the same dept and count - perhaps this will be enough for you to get to the answer you want?
data have;
input ID dept count;
cards;
1 10 2
2 10 2
3 20 4
4 20 4
5 20 4
6 20 4
7 30 4
8 30 4
9 30 4
10 30 4
11 30 4
12 30 4
13 30 4
14 30 4
;
run;
data want;
set have;
by dept count;
if first.count then do;
level = 0;
dummy = 0;
end;
if mod(dummy,3) = 0 then level + 1;
dummy + 1;
drop dummy;
run;
Output:
ID dept count level
1 10 2 1
2 10 2 1
3 20 4 1
4 20 4 1
5 20 4 1
6 20 4 2
7 30 4 1
8 30 4 1
9 30 4 1
10 30 4 2
11 30 4 2
12 30 4 2
13 30 4 3
14 30 4 3
One way to do this is to nest the SET statement inside a DO loop. Or in this case two DO loops. One to generate the LEVEL (within DEPT) and the second to count by twos. Use the LAST.DEPT flag to handle odd number of observations.
So if I modify the input to include odd number of observations in some groups.
data have;
input ID dept count;
cards;
1 10 2
2 10 2
3 20 4
4 20 4
5 20 4
6 20 4
7 20 4
8 30 4
9 30 4
10 30 4
;
Then can use this step to assign the LEVEL variable.
data want ;
do level=1 by 1 until(last.dept);
do sublevel=1 to 2 until(last.dept);
set have;
by dept;
output;
end;
end;
run;
Results:
Obs level sublevel ID dept count
1 1 1 1 10 2
2 1 2 2 10 2
3 1 1 3 20 4
4 1 2 4 20 4
5 2 1 5 20 4
6 2 2 6 20 4
7 3 1 7 20 4
8 1 1 8 30 4
9 1 2 9 30 4
10 2 1 10 30 4
I am working with a spell dataset that has the following form:
clear all
input persid start end t_start t_end spell_type year spell_number event
1 8 9 44 45 1 1999 1 0
1 12 12 60 60 1 2000 1 0
1 1 1 61 61 1 2001 1 0
1 7 11 67 71 1 2001 2 0
1 1 4 85 88 2 2003 1 0
1 5 7 89 91 1 2003 2 1
1 8 11 92 95 2 2003 3 0
1 1 1 97 97 2 2004 1 0
1 1 3 121 123 1 2006 1 1
1 4 5 124 125 2 2006 2 0
1 6 9 126 129 1 2006 3 1
1 10 11 130 131 2 2006 4 0
1 12 12 132 132 1 2006 5 1
1 1 12 157 168 1 2009 1 0
1 1 12 169 180 1 2010 1 0
1 1 12 181 192 1 2011 1 0
1 1 12 193 204 1 2012 1 0
1 1 12 205 216 1 2013 1 0
end
lab define lab_spelltype 1 "unemployment spell" 2 "employment spell"
lab val spell_type lab_spelltype
where persid is the id of the person; start and end are the months when the yearly unemployment/employment spell starts and ends, respectively; t_start and t_end are the same measures but starting to count from 1st January 1996; event is equal to 1 for the employment entries for which the previous row was an unemployment spell.
The data is such that there are no overlapping spells during a given year, and each year contiguous spells of the same type have been merged together.
My goal is, for each row such that event is 1, to compute the number of months spent as employed in the last 6 months and 24 months.
In this specific example, what I would like to get is:
clear all
input persid start end t_start t_end spell_type year spell_number event empl_6 empl_24
1 8 9 44 45 1 1999 1 0 . .
1 12 12 60 60 1 2000 1 0 . .
1 1 1 61 61 1 2001 1 0 . .
1 7 11 67 71 1 2001 2 0 . .
1 1 4 85 88 2 2003 1 0 . .
1 5 7 89 91 1 2003 2 1 0 5
1 8 11 92 95 2 2003 3 0 . .
1 1 1 97 97 2 2004 1 0 . .
1 1 3 121 123 1 2006 1 1 0 0
1 4 5 124 125 2 2006 2 0 . .
1 6 9 126 129 1 2006 3 1 3 3
1 10 11 130 131 2 2006 4 0 . .
1 12 12 132 132 1 2006 5 1 4 7
1 1 12 157 168 1 2009 1 0 . .
1 1 12 169 180 1 2010 1 0 . .
1 1 12 181 192 1 2011 1 0 . .
1 1 12 193 204 1 2012 1 0 . .
1 1 12 205 216 1 2013 1 0 . .
end
So the idea is that I have to go back to rows preceding each event==1 entry and count how many months the individual was employed.
Can you suggest a way to obtain this final result?
Some suggested to expand the dataset, but perhaps there are better ways to tackle the problem (especially because the dataset is quite large).
EDIT
The correct labeling of the employment status is:
lab define lab_spelltype 1 "employment spell" 2 "unemployment spell"
The number of past months spent in employment (empl_6 and empl_24) and the definition of event are now correct with this label.
A solution to the problem is to:
expand the data so to have it monthly,
fill in the gap months with tsfill and finally,
use sum() and lag operators to get the running sum for the last 6 and 24 months.
See also Robert solution for some ideas I borrowed.
Important: this is almost surely not an efficient way to solve the issue, especially if the data is large (as in my case).
However, the plus is that one actually "sees" what happens in background to make sure the final result is the one desired.
Also, importantly, this solution takes into account cases where 2 (or more) events happen within 6 (or 24) months from each other.
clear all
input persid start end t_start t_end spell_type year spell_number event
1 8 9 44 45 1 1999 1 0
1 12 12 60 60 1 2000 1 0
1 1 1 61 61 1 2001 1 0
1 7 11 67 71 1 2001 2 0
1 1 4 85 88 2 2003 1 0
1 5 7 89 91 1 2003 2 1
1 8 11 92 95 2 2003 3 0
1 1 1 97 97 2 2004 1 0
1 1 3 121 123 1 2006 1 1
1 4 5 124 125 2 2006 2 0
1 6 9 126 129 1 2006 3 1
1 10 11 130 131 2 2006 4 0
1 12 12 132 132 1 2006 5 1
1 1 12 157 168 1 2009 1 0
1 1 12 169 180 1 2010 1 0
1 1 12 181 192 1 2011 1 0
1 1 12 193 204 1 2012 1 0
1 1 12 205 216 1 2013 1 0
end
lab define lab_spelltype 1 "employment" 2 "unemployment"
lab val spell_type lab_spelltype
list
* generate Stata monthly dates
gen spell_start = ym(year,start)
gen spell_end = ym(year,end)
format %tm spell_start spell_end
list
* expand to monthly data
gen n = spell_end - spell_start + 1
expand n, gen(expanded)
sort persid year spell_number (expanded)
bysort persid year spell_number: gen month = spell_start + _n - 1
by persid year spell_number: replace event = 0 if _n > 1
format %tm month
* xtset, fill months gaps with "empty" rows, use lags and cumsum to count past months in employment
xtset persid month, monthly // %tm format
tsfill
bysort persid (month): gen cumsum = sum(spell_type) if spell_type==1
bysort persid (month): replace cumsum = cumsum[_n-1] if cumsum==.
bysort persid (month): gen m6 = cumsum-1 - L7.cumsum if event==1 // "-1" otherwise it sums also current empl month
bysort persid (month): gen m24 = cumsum-1 - L25.cumsum if event==1
drop if event==.
list persid start end year m* if event
The posted example is of little utility in developing and testing a solution so I made up fake data that has the same properties. It's bad practice to use 1 and 2 as values for an indicator so I replaced the employed indicator with 1 meaning employed, 0 otherwise. Using month and year separately is also useless so Stata monthly dates are used.
The first solution uses tsegen (from SSC) after expanding each spell to one observation per month. With panel data, all you need to do is to sum the employment indicator for the desired time window.
The second solution uses rangestat (also from SSC) and does the same computations without expanding the data at all. The idea is simple, just add the duration of previous employment spells if the end of the spell falls within the desired window. Of course if the end of the spell falls within the window but not the start, days outside the window must be subtracted.
* fake data for 100 persons, up to 10 spells with no overlap
clear
set seed 123423
set obs 100
gen long persid = _n
gen spell_start = ym(runiformint(1990,2013),1)
expand runiformint(1,10)
bysort persid: gen spellid = _n
by persid: gen employed = runiformint(0,1)
by persid: gen spell_avg = int((ym(2015,12) - spell_start) / _N) + 1
by persid: replace spell_start = spell_start[_n-1] + ///
runiformint(1,spell_avg) if _n > 1
by persid: gen spell_end = runiformint(spell_start, spell_start[_n+1]-1)
replace spell_end = spell_start + runiformint(1,12) if mi(spell_end)
format %tm spell_start spell_end
* an event is an employment spell that immediately follow an unemployment spell
by persid: gen event = employed & employed[_n-1] == 0
* expand to one obs per month and declare as panel data
expand spell_end - spell_start + 1
bysort persid spellid: gen ym = spell_start + _n - 1
format %tm ym
tsset persid ym
* only count employement months; limit results to first month event obs
tsegen m6 = rowtotal(L(1/6).employed)
tsegen m24 = rowtotal(L(1/24).employed)
bysort persid spellid (ym): replace m6 = . if _n > 1 | !event
bysort persid spellid (ym): replace m24 = . if _n > 1 | !event
* --------- redo using rangestat, without any monthly expansion ----------------
* return to original obs but keep first month results
bysort persid spellid: keep if _n == 1
* employment end and duration for employed observations only
gen e_end = spell_end if employed
gen e_len = spell_end - spell_start + 1 if employed
foreach target in 6 24 {
// define interval bounds but only for event observations
// an out-of-sample [0,0] interval will yield no results for non-events
gen low`target' = cond(event, spell_start-`target', 0)
gen high`target' = cond(event, spell_start-1, 0)
// sum employment lengths and save earliest employment spell info
rangestat (sum) empl`target'=e_len ///
(firstnm) firste`target'=e_end firste`target'len=e_len, ///
by(persid) interval(spell_end low`target' high`target')
// remove from the count months that occur before lower bound
gen e_start = firste`target' - firste`target'len + 1
gen outside = low`target' - e_start
gen empl`target'final = cond(outside > 0, empl`target'-outside, empl`target')
replace empl`target'final = 0 if mi(empl`target'final) & event
drop e_start outside
}
* confirm that we match the -tsegen- results
assert m24 == empl24final
assert m6 == empl6final
I have a tricky question about conditional sum in SAS. Actually, it is very complicated for me and therefore, I cannot explain it by words. Therefore I want to show an example:
A B
5 3
7 2
8 6
6 4
9 5
8 2
3 1
4 3
As you can see, I have a datasheet that has two columns. First of all, I calculated the conditional cumulative sum of column A ( I can do it by myself-So no need help for that step):
A B CA
5 3 5
7 2 12
8 6 18
6 4 8 ((12+8)-18)+6
9 5 17
8 2 18
3 1 10 (((17+8)-18)+3
4 3 14
So my condition value is 18. If the cumulative more than 18, then it equal 18 and next value if sum of the first value after 18 and exceeds amount over 18. ( As I said I can do it by myself )
So the tricky part is I have to calculate the cumulative sum of column B according to column A:
A B CA CB
5 3 5 3
7 2 12 5
8 6 18 9.5 (5+(6*((18-12)/8)))
6 4 8 5.5 ((5+6)-9.5)+4
9 5 17 10.5 (5.5+5)
8 2 18 10.75 (10.5+(2*((18-7)/8)))
3 1 10 2.75 ((10.5+2)-10.75)+1
4 3 14 5.75 (2.75+3)
As you can see from example the cumulative sum of column B is very specific. When column CA is equal to our condition value (18), then we calculate the proportion of the last value for getting our condition value (18) and then use this proportion for computing cumulative sum of column B.
Looks like when the sum of A reaches 18 or more you want to split the values of A and B between the current and the next record. One way is to remember the left over values for A and B and carry them forward in your new cumulative variables. Just make sure to output the observation before resetting those variables.
data want ;
set have ;
ca+a;
cb+b;
if ca >= 18 then do;
extra_a=ca - 18;
extra_b=b - b*((a - extra_a)/a) ;
ca=18;
cb=cb-extra_b ;
end;
output;
if ca=18 then do;
ca=extra_a;
cb=extra_b;
end;
drop extra_a extra_b ;
run;
I have a table (allsales) with a column for time (sale_time). I want to group the data by sale_time. But I want to be able to bucket this. ex any data where time is between 00:00:00-03:00:00 should be grouped together, 03:00:00-06:00:00 should be grouped together and so on. Is there a way to write such a query?
xbar is useful for rounding to interval values e.g.
q)5 xbar 1 3 5 8 10 11 12 14 18
0 0 5 5 10 10 10 10 15
We can then use this to group rows into time groups, for your example:
q)s:([] t:13:00t+00:15t*til 24; v:til 24)
q)s
t v
--------------
13:00:00.000 0
13:15:00.000 1
13:30:00.000 2
13:45:00.000 3
14:00:00.000 4
14:15:00.000 5
..
q)select count i,sum v by xbar[`int$03:00t;t] from s
t | x v
------------| ------
12:00:00.000| 8 28
15:00:00.000| 12 162
18:00:00.000| 4 86
"by xbar[`int$03:00t;t]" rounds the time column t to the nearest three hour value, then this is used as the group by.
There are few more ways to achieve the same results.
q)select count i , sum v by t:01:00u*3 xbar t.hh from s
q)select count i , sum v by t:180 xbar t.minute from s
t | x v
-----| ------
12:00| 8 28
15:00| 12 162
18:00| 4 86
But in all cases, be careful of the date column if present in the table, otherwise same time window across different dates will generate the wrong results.
q)s:([] d:24#2013.05.07 2013.05.08; t:13:00t+00:15t*til 24; v:til 24)
q)select count i , sum v by d, t:180 xbar t.minute from s
d t | x v
----------------| ----
2013.05.07 12:00| 4 12
2013.05.07 15:00| 6 78
2013.05.07 18:00| 2 42
2013.05.08 12:00| 4 16
2013.05.08 15:00| 6 84
2013.05.08 18:00| 2 44