Google Sheets formula for summing/averaging with specific conditions - if-statement

I am hoping for a formula to take hours from the name columns and sum/average them by week, into a separate table like the 2nd one below. The formulas need to update upon changing the start and end week cells.
Body Part
Start Week
End Week
Arnold (hours)
Usain (hours)
Bob (hours)
Arms
1
3
6
3
0
Legs
1
6
12
36
20
Chest
2
4
6
2
2
Booty
4
6
9
12
3
Core
1
5
10
5
5
Formula Needed:
Hours
Arnold
Usian
Bob
Week 1
6
8
4.33
Week 2
8
8.67
5
Week 3
8
8.67
5
Week 4
9
11.67
6
Week 5
7
11
5.33
Week 6
5
10
4.33
Bonus if there is a way to also quickly average hours by body parts if for example there are multiple Arms rows.

try:
=ARRAYFORMULA(LAMBDA(a, b, QUERY(SPLIT(FLATTEN(BYCOL(D1:F1, LAMBDA(xx, FLATTEN(IF(
IF(a>=SEQUENCE(1, MAX(a)), "Week "&TEXT(SEQUENCE(1, MAX(a))+b, "00"), )="",,
REGEXEXTRACT(OFFSET(xx,,,1), "(.+) \(")&"×"&
IF(a>=SEQUENCE(1, MAX(a)), "Week "&TEXT(SEQUENCE(1, MAX(a))+b, "00"), )&"×"&
QUERY({REGEXEXTRACT(OFFSET(xx,,,1), "(.+) \("); OFFSET(xx,1,,9^9)/(a)}, "offset 1", )))))), "×"),
"select Col2,sum(Col3) where Col3>0 group by Col2 pivot Col1"))
(C2:INDEX(C:C, MAX(ROW(C:C)*(C:C<>"")))-B2:INDEX(B:B, MAX(ROW(B:B)*(B:B<>"")))+1,
B2:INDEX(B:B, MAX(ROW(B:B)*(B:B<>"")))-1))

Related

DAX equation to average data with different timespans

I have data for different companies. The data stops at day 10 for one of the companies (Company 1), day 6 for the others. If Company 1 is selected with other companies, I want to show the average so that the data runs until day 10, but using day 7, 8, 9, 10 values for Company 1 and day 6 values for others.
I'd want to just fill down days 8-10 for other companies with the day 6 value, but that would look misleading on the graph. So I need a DAX equation with some magic in it.
As an example, I have companies:
Company 1
Company 2
Company 3
etc. as a filter
And a table like:
Company
Date
Day of Month
Count
Company 1
1.11.2022
1
10
Company 1
2.11.2022
2
20
Company 1
3.11.2022
3
21
Company 1
4.11.2022
4
30
Company 1
5.11.2022
5
40
Company 1
6.11.2022
6
50
Company 1
7.11.2022
7
55
Company 1
8.11.2022
8
60
Company 1
9.11.2022
9
62
Company 1
10.11.2022
10
70
Company 1
11.11.2022
11
NULL
Company 2
1.11.2022
1
15
Company 2
2.11.2022
2
25
Company 2
3.11.2022
3
30
Company 2
4.11.2022
4
34
Company 2
5.11.2022
5
45
Company 2
6.11.2022
6
100
Company 2
7.11.2022
7
NULL
Every date has a row, but for days over 6/10 the count is NULL. If Company 1 or Company 2 is chosen separately, I'd like to show the count as is. If they are chosen together, I'd like the average of the two so that:
Day 5: AVG(40,45)
Day 6: AVG(50,100)
Day 7: AVG(55,100)
Day 8: AVG(60,100)
Day 9: AVG(62,100)
Day 10: AVG(70,100)
Any ideas?
You want something like this?
Create a Matriz using your:
company_table_dim (M)
calendar_Days_Table(N)
So you will have a new table of MXN Rows
Go to PowerQuery Order DATA and FillDown your QTY column
(= Table.FillDown(#"Se expandió Fact_Table",{"QTY"}))
So your last known QTY will de filled til the end of Time_Table for any company filters
Cons: Consider your new Matriz MXN it could be millions of rows to calculate
Greetings
enter image description here

Subtract Value at Aggregate by Quarter

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:

cumulative average powerbi by month

I have below dataset.
Math Literature Biology date student
4 2 5 2019-08-25 A
4 5 4 2019-08-08 A
5 4 5 2019-08-23 A
5 5 5 2019-08-15 A
5 5 5 2019-07-19 A
5 5 5 2019-07-15 A
5 5 5 2019-07-03 A
5 5 5 2019-06-26 A
1 1 2 2019-06-18 A
2 3 3 2019-06-14 A
5 5 5 2019-05-01 A
2 1 3 2019-04-26 A
I need to develop a solution in powerbi so in output I have cumulative average per subject per month
For example
April May June July August
Math | 2 3.5 3 3.75 4
Literature | 1 3 3 3.75 3.83
Biology | 3 4 3.6 4.125 4.33
Can you help?
You can use a matrix visualization for this.
Create a month-year variable and use it in the columns.
Use Average of Math,Literature and Biology in values
Under the format pane --> Values --> Show on rows --> Select this
This should give the view you are looking for. You can edit the value headers to your requirement.

Retain the cluster number for each member of a cluster within an id variable

I would like to label how many unique clusters of data are in a longitudinal dataset and have each member of the cluster carry the cluster count. Distinct clusters are those sharing a set of dates within an id. The order of those distinct cluster relative to previous (earlier) clusters creates the desired result. This coding is necessary to address the problem of event ordering required for a time-dependent covariate analysis.
input id date
1 28jan2015
1 28jan2015
2 26nov2015
3 19oct2015
4 26dec2015
5 23dec2015
6 22may2015
6 23sep2015
6 23sep2015
7 14jan2015
7 27feb2015
7 30may2015
8 16apr2015
8 16apr2015
8 16apr2015
8 16apr2015
8 16apr2015
9 17jul2015
9 03oct2015
9 03oct2015
10 27jul2015
end
I have attempted:
bys id (date): gen count_obs = [_n]
bys id date: gen count_interval_obs = [_n]
egen n_interval = group(id date)
resulting in accurate counts of the total number of observations per id and enumeration of the number of observations within a date. However, the egen function group() results in identifying each unique set of dates, but numbers the groups without regard to id, giving:
id wrong_cluster correct_cluster
1 28jan2015 1 1
1 28jan2015 1 1
2 26nov2015 2 1
3 19oct2015 3 1
4 26dec2015 4 1
5 23dec2015 5 1
6 22may2015 6 1
6 23sep2015 7 2
6 23sep2015 7 2
etc.
egen, group() cannot be used with the by: prefix.
Any assistance would be appreciated.
Todd
Edit: Added an explanation of why the cluster identification is necessary. Clarified what rule defines a cluster.
#Roberto Ferrer has given a direct approach. It follows from the logic he uses that there is also a route using egen's group() function:
egen group = group(id date2)
bysort id (group): gen clust2 = sum(group != group[_n-1])
For each id, when the date is different than the preceding observation, add 1 to the running sum. The 1 is realized when the condition inside sum() is met.
clear
set more off
input id str15 date
1 28jan2015
1 28jan2015
2 26nov2015
3 19oct2015
4 26dec2015
5 23dec2015
6 22may2015
6 23sep2015
6 23sep2015
7 14jan2015
7 27feb2015
7 30may2015
8 16apr2015
8 16apr2015
8 16apr2015
8 16apr2015
8 16apr2015
9 17jul2015
9 03oct2015
9 03oct2015
10 27jul2015
end
gen date2 = date(date, "DMY")
format %td date2
drop date
list, sepby(id)
*----- what you want -----
bysort id (date2) : gen clust = sum(date2 != date2[_n-1])
list, sepby(id)

Stata: how to duplicate observations under certain conditions

Please help me duplicate a variable under certain conditions? My original dataset looks like this:
week category averageprice
1 1 5
1 2 6
2 1 4
2 2 7
This table says that for each week, there is a unique average price for each category of goods.
I need to create the following variables:
averageprice1 (av. price for category 1)
averageprice2 (av. price for category 2)
such that:
week category averageprice1 averageprice2
1 1 5 6
1 2 5 6
2 1 4 7
2 2 4 7
meaning that for week 1, average price for category 1 stayed at $5, and av. price for cater 2 stayed at 6. Similar logic applies to week 2.
As you could see that the new variables are duplicated depending on a week.
I am still learning Stata. I tried:
bysort week: replace averageprice1=averageprice if categ==1
but it doesn't work as expected.
You are not duplicating observations (meaning here in the Stata sense, i.e. cases or records) here at all, as (1) the number of observations remains the same (2) you are copying certain values, not the contents of observations. Similar comment on "duplicating variables". However, that's just loose use of terminology.
Taking your example very literally
clear
input week category averageprice
1 1 5
1 2 6
2 1 4
2 2 7
end
bysort week (category) : gen averageprice1 = averageprice[1]
by week: gen averageprice2 = averageprice[2]
l
+--------------------------------------------------+
| week category averag~e averag~1 averag~2 |
|--------------------------------------------------|
1. | 1 1 5 5 6 |
2. | 1 2 6 5 6 |
3. | 2 1 4 4 7 |
4. | 2 2 7 4 7 |
+--------------------------------------------------+
This is a standard application of subscripting with by:. Your code didn't work because it did not oblige Stata to look in other observations when that is needed. In fact your use of bysort week did not affect how the code applied at all.
EDIT:
A generalization is
egen averageprice1 = mean(averageprice / (category == 1)), by(week)
egen averageprice2 = mean(averageprice / (category == 2)), by(week)