I have a formula that do almost what I need. I'm trying to get a list of values with a condition depending about one value, is objetive 1 is equal or over to 80 show me the list of objetives equal or over 80. My formula is this one:
=ARRAYFORMULA(IF(('Product Prioritization Matrix'!C7:C >= 80), 'Product Prioritization Matrix'!B7:B,""))
My problem comes when I try to put this in just one cell in the last image will show what I need visualy.
The next images will show the sheets:
My formula
Expected result
I think a JOIN(... , FILTER( structure will work for this:
=JOIN(", ",FILTER(Sheet1!B:B,Sheet1!C:C>=80))
I have calculated column Importance_Weight of which I need to create measure that sum questions for related line number.
Importance_Weight = (ColumnX * Average(ColumnY) )/100
Calculated column values for each question is correct as below but when aggrigated by line number the sum is wrong as blow
I used following measure to calculate sum of questions (q) i.e. L1
LT_WeightPerQuestion:=SUMX(DimQuestion, AVERAGE(Survey[Importance_Weight]))
Thanks in advance.
Use measure as below
LT_WeightPerQuestion = SUM(Survey[Importance_Weight])
I have a confusing mystery...
Simple DIVIDE formula works correctly. However blank rows are not displayed.
I attempted a different method using IF, and now the blank row is correctly displayed.
However this line is only displayed if I include the IF formula (which gives a zero value I don't want).
Formula 1:
Completion % =
DIVIDE(SUM(Courses[Completed]),SUM(Courses[Attended]),BLANK())
Formula 2:
Completion % with IF =
IF(SUM(Courses[Attended])=0,0,DIVIDE(SUM(Courses[Completed]),SUM(Courses[Attended])))
With only the DIVIDE formula:
Including the IF formula:
It appears that Power BI is capable of showing this row without error, but only if I inlude the additional IF formula. I'm guessing it's because there is now a value (0) to display.
However I want to be able show all courses, including those that have no values, without the inaccurate zero value.
I don't understand why the table doesn't include these lines. Can anyone explain/help?
The point is very simple, by default Power BI shows only elements for which there is at least one non-blank measure.
The DIVIDE operator under-the-hood execute the following:
IF(ISBLANK(B), BLANK(), A / B))
You can change its behaviour by defining the optimal parameter in order to show 0 instead of BLANK:
DIVIDE(A, B, 0) will be translated in the following:
IF(ISBLANK(B), 0, A/B))
Proposed solution
Those mentioned avobe might all be possible solutions to your problem, however, my personal suggestion is to simply enable the option "show item with no data" in your visualization.
While DIVIDE(A, B, 0) will return zero when when B is zero or blank, I think a blank A will still return a blank.
One possibility is to simply append +0 (or prepend 0+) to your measure so that it always returns a numeric value.
DIVIDE ( SUM ( Courses[Completed] ), SUM ( Courses[Attended] ) ) + 0
The reason I am posting this question is that combining Index and Match functions only searches for first qualifying row from top-down and I am needing to find next row up from current that matches as part of my formula.
The complete formula I am trying to construct is to return TRUE in cell "C4" if equal to the row that has the lowest value in column "A" from just above a value (nonblank) in column "C" to just before numbers in column "B" go above 55. So in this case, it would return TRUE in cell "C4" because for the blue highlighted area value 28.28 is lowest in column "A".
Secondarily not sure if INDIRECT function is best to use since I have a few hundred of these in my sheet. Is this a resource hog when I need these to calculate quickly???
I have it posted here and am posting it here because I am trying to get this to work in Sheets which I know is often different than Excel.
https://answers.microsoft.com/en-us/msoffice/forum/msoffice_excel-mso_win10-mso_2016/formula-to-return-true-if-criteria-matches/02834e93-c29f-449d-ace0-98722c399e63?tm=1568399215380
perhaps like this:
=ARRAYFORMULA(IF(A1:A=MIN(FILTER(A1:A, B1:B<55)), TRUE, ))
I have an unbalanced panel data set (countries and years). For simplicity let's say I have one variable, x, that I am measuring. The panel data sorted first by country (a 3-digit numeric country-code) and then by year. I would like to write a .do file that generates a new variable, z_x, containing the standardized values of the variable x. The variables should be standardized by subtracting the mean from the preceding (exclusive) m time periods, and then dividing by the standard deviation from those same time periods. If this is not possible, return a missing value.
Currently, the code I am using to accomplish this is the following (edited now for clarity)
xtset weocountrycode year
sort weocountrycode year
local win_len = 5 // Defining rolling window length.
quietly: rolling sd_x=r(sd) mean_x=r(mean), window(`win_len') saving(stats_x, replace): sum x
use stats_x, clear
rename end year
save, replace
use all_data_PROCESSED_FINAL.dta, clear
quietly: merge 1:1 (weocountrycode year) using stats_x
replace sd_x = . if `x'[_n-`win_len'+1] == . | weocountrycode[_n-`win_len'+1] != weocountrycode[_n] // This and next line are for deleting values that rolling calculates when I actually want missing values.
replace mean_`x' = . if `x'[_n-`win_len'+1] == . | weocountrycode[_n-`win_len'+1] != weocountrycode[_n]
gen z_`x' = (`x' - mean_`x'[_n-1])/sd_`x'[_n-1] // calculate z-score
UPDATE:
My struggle with rolling is that when rolling is set up to use a window length 5 rolling mean, it automatically does window length 1,2,3,4 means for the first, second, third and fourth entries (when there are not 5 preceding entries available to average out). In fact, it does this in general - if the first non-missing value is on entry 5, it will do a length 1 rolling average on entry 5, length 2 rolling average on entry 6, ..... and then finally start doing length 5 moving averages on entry 9. My issue is that I do not want this, so I would like to avoid performing these calculations. Until now, I have only been able to figure out how to delete them after they are done, which is both inefficient and bothersome.
I tried adding an if clause to the -rolling- statement:
quietly: rolling sd_x=r(sd) mean_x=r(mean) if x[_n-`win_len'+1] != . & weocountrycode[_n-`win_len'+1] != weocountrycode[_n], window(`win_len') saving(stats_x, replace): sum x
But it did not fix the problem and the output is "weird" in the sense that
1) If `win_len' is equal to, say, 10, there are 15 missing values in the resulting z_x variable, instead of 9.
2) Even though there are "extra" missing values in z_x, the observations still start out as window length 1 means, then window length 2 means, etc. which makes no sense to me.
Which leads me to believe I fundamentally don't understand 1) what -rolling- is doing and 2) how an if clause works in the context of -rolling-.
Does this help?
Thanks!
I'm not sure I understand completely but I'll try to answer based on what I think your problem is, and based on a comment by #NickCox.
You say:
... when rolling is set up to use a window length 5 rolling mean...
if the first non-missing value is
on entry 5, it will do a length 1 rolling average on entry 5, length 2
rolling average on entry 6, ...
This is expected. help rolling states:
The window size refers to calendar periods, not the number of
observations. If there
are missing data (for example, because of weekends), the actual number of observations used by command may be less than
window(#).
It's not actually doing a "length 1 rolling average", but I get to that later.
Below some examples to see what rolling does:
clear all
set more off
*-------------------------- example data -----------------------------
set obs 92
gen dat = _n - 1
format dat %tq
egen seq = fill(1 1 1 1 2 2 2 2)
tsset dat
tempfile main
save "`main'"
list in 1/12, separator(4)
*------------------- Example 1. None missing ------------------------
rolling mean=r(mean), window(4) stepsize(4) clear: summarize seq, detail
list in 1/12, separator(0)
*------- Example 2. All but one value, missing in first window ------
use "`main'", clear
replace seq = . in 1/3
list in 1/8
rolling mean=r(mean), window(4) stepsize(4) clear: summarize seq, detail
list in 1/12, separator(0)
*------------- Example 3. All missing in first window --------------
use "`main'", clear
replace seq = . in 1/4
list in 1/8
rolling mean=r(mean), window(4) stepsize(4) clear: summarize seq, detail
list in 1/12, separator(0)
Note I use the stepsize option to make things much easier to follow. Because the date variable is in quarters, I set windowsize(4) and stepsize(4) so rolling is just computing averages by year. I hope that's easy to see.
Example 1 does as expected. No problem here.
Example 2 on the other hand, should be more interesting for you. We've said that what matters are calendar periods, so the mean is computed for the whole year (four quarters), even though it contains missings. There are three missings and one non-missing. summarize is computing the mean over the whole year, but summarize ignores missings, so it just outputs the mean of non-missings, which in this case is just one value.
Example 3 has missings for all four quarters of the year. Therefore, summarize outputs . (missing).
Your problem, as I understand it, is that when you face a situation like Example 2, you'd like the output to be missing. This is where I think Nick Cox's advice comes in. You could try something like:
rolling mean=r(mean) N=r(N), window(4) stepsize(4) clear: summarize seq, detail
replace mean = . if N != 4
list in 1/12, separator(0)
This says: if the number of non-missings for the window (r(N), also computed by summarize), is not the same as the window size, then replace it with missing.