I have a dataset with fields targeted and opens and I need to add calculated field opens per targeted which essentially means doing simple devision of those 2 values.
My calculated field is as follows
{opens}/{targeted}
but then displaying simple table with values they are completely incorrect
If I try any other operator like + * etc calculations are correct.
I'm completely out of ideas on how to debug this. I've simplified the dataset to just columns of targeted and opens, can't get any simpler.
Had the same problem, I fixed it by wrapping the columns with the sum() function. Like this:
sum({opens})/sum({targeted})
I think you need to make AWS understand that you are working with float numbers.
1.0*{opens}/{targeted}
if still not working try also
(1.0*{opens})/({targeted}*1.0)
it should give you the desired output (not tested, let me know if it doesnt work)
Related
Working with basketball data, I'm trying to get the time on court for the players (there are some columns that have information about a player or players).
I tried to obtain the value with a calculated column, named "TimeOnCourt". The code works for most cases but there is a case that, due to a mistake in the data entry team, there are different values of the players columns for the same "TimeOnCourt" so, when I try to visualize the information, the data entry mistake comes out.
I guess I could use the column "Index" to add a piece of code to choose the MIN value for the "TimeOnCourt" column but, after trying some options, I don't know where to put it or if I have to change the full code.
I also tried with Test_Flags but not working for all cases (but could fix 2 of the 4 cases).
Add you the link with the pbix file and the Test_Flag measures I tried: Link to pbix file v3
And the image with the mistake marked. The expected time in the right visualization should be 0:40:00 instead of 0:43:03 (it's due to the duplicate in Full Quarter = 2Q and Time_Def = 0:04:00. This could happen again although I talked with them so the solution should be general, not filtering this specific case.
Problem
I'm making a time-spending tracker based on the work I do every hour of the day.
Now, suppose I have 28 types of work listed in my tracker (which I also have to increase from time to time), and I have about 8 significance values that I have decided to relate to these 28 types of work, predefined.
I want that, as soon as I enter a type of work in cell 1 - I want the adjacent cell 2 to get automatically populated with a significance value (from a range of 8 values) that is pre-definitely set by me.
Every time I input a new or old occurrence of a type of work, the adjacent cell should automatically get matched with its relevant significance value & automatically get populated in real-time.
I know how to do it using IF, IFS, and IF_OR conditions, but I feel that based on the ever-expanding types of work & significance values, the above formulas will be very big, complicated, and repetitive in the future. I feel there's a more efficient way to achieve it. Also, I don't want it to be selected from a drop-down list.
Guys, please help me out with the most efficient way to handle this. TUIA :)
Also, I've added a snapshot and a sample sheet describing the problem.
Sample sheet
XLOOKUP() may work. Try-
=XLOOKUP(D2,A2:A,B2:B)
Or FILTER() function like-
=FILTER(B2:B,A2:A=D2)
You can use this formula for a whole column:
=INDEX(IFERROR(VLOOKUP(C14:C,A2:B9,2,0)))
Adapt the ranges to your actual tables in order to include in the second argument all the potential values and their significances
This is the formula, that worked for me (for anybody's reference):
I created another reference sheet, stating the types of work & their significance. From that sheet, I'm using either vlookup, filter, xlookup.Using gforms for inputting my data.
=ARRAYFORMULA(IFS(ROW(D:D)=1,"Significance",A:A="","",TRUE,VLOOKUP(D:D,Reference!$A:$B,2,0)))
I'm kind of new to DAX and I'm basically learning as I'm using it in my work. We are building reports in PowerBI and we have data model that gets data from Oracle database. So I'm using DAX to create measures in this data model.
I need to substract 2 numbers from each other. So I created simple measure which looked like this:
[MEASURE1] - [MEASURE2]
Whether it works or it doesn't depends on my Period filter which uses another table. I don't know how could period be related to any of this. So when I change filter to some values, I get normal number. However, when I switch it to different values, I get numbers like 2,27483058473905E-13.
Weird thing is that if I check those two measures that I'm subtracting, they have exactly the same numbers, so the difference should be 0.
I know this is not the best explanation, but it is impossible to describe entire data model here. So I'm just looking for some ideas what could possibly be causing this and what should I check.
I have literally no idea what could be causing this.
Floating point precision.
Either use fixed decimal data types, specify the format string of the measure, or wrap your measure in ROUND, e.g.:
Diff =
ROUND (
[Measure 1] - [Measure 2] ,
2
)
2,27483058473905E-13 is not a huge number, but as close as a decimal calculator can get to zero.
I built a pymc3 model using the DensityDist distribution. I have four parameters out of which 3 use Metropolis and one uses NUTS (this is automatically chosen by the pymc3). However, I get two different UserWarnings
1.Chain 0 contains number of diverging samples after tuning. If increasing target_accept does not help try to reparameterize.
MAy I know what does reparameterize here mean?
2. The acceptance probability in chain 0 does not match the target. It is , but should be close to 0.8. Try to increase the number of tuning steps.
Digging through a few examples I used 'random_seed', 'discard_tuned_samples', 'step = pm.NUTS(target_accept=0.95)' and so on and got rid of these user warnings. But I couldn't find details of how these parameter values are being decided. I am sure this might have been discussed in various context but I am unable to find solid documentation for this. I was doing a trial and error method as below.
with patten_study:
#SEED = 61290425 #51290425
step = pm.NUTS(target_accept=0.95)
trace = sample(step = step)#4000,tune = 10000,step =step,discard_tuned_samples=False)#,random_seed=SEED)
I need to run these on different datasets. Hence I am struggling to fix these parameter values for each dataset I am using. Is there any way where I give these values or find the outcome (if there are any user warnings and then try other values) and run it in a loop?
Pardon me if I am asking something stupid!
In this context, re-parametrization basically is finding a different but equivalent model that it is easier to compute. There are many things you can do depending on the details of your model:
Instead of using a Uniform distribution you can use a Normal distribution with a large variance.
Changing from a centered-hierarchical model to a
non-centered
one.
Replacing a Gaussian with a Student-T
Model a discrete variable as a continuous
Marginalize variables like in this example
whether these changes make sense or not is something that you should decide, based on your knowledge of the model and problem.
Usually we use the following statement
model y/n = block variety / dist=binomial solution;
However, if I have already calculate proprtion=y/n by EXCEL and directly use "proportion" into SAS. i.e
model proportion = block variety /dist=binomial solution;
I get the same result.
However, is there anything wrong with my second code?
You are suppose to get the same result as you have said too. So it is not clear what went wrong with your code!