I have a simple data set with training sessions for some athletes. Let's say I want to visualize how many training sessions are done as an average of the number of athletes, either in total or divided by the clubs that exist. I hope the data set is somewhat self-describing.
To norm the number of activities by the number of athletes I use two measures:
TotalSessions = COUNTA(Tab_Sessions[Session key])
AvgAthlete = AVERAGEX(VALUES(Tab_Sessions[Athlete]),[TotalSessions])
I give AvgAthlete as the desired value in both visuals shown below. If I make a filter on the clubs the values are as expected, but with no filter applied I get some strange values
What I guess happens is that since Athlete B doesn't do any strength, Athlete B is not included in the norming factor for strength. Is there a DAX function that can solve this?
If I didn't have the training sessions as a hierarchy (Type-Intensity), it would be pretty straightforward to do some kind of workaround with a calculated column, but it won't work with hierarchical categories. The expected results calculated in excel are shown below:
Data set as csv:
Session key;Club;Athlete;Type;Intensity
001;Fast runners;A;Cardio;High
002;Fast runners;A;Strength;Low
003;Fast runners;B;Cardio;Low
004;Fast runners;B;Cardio;High
005;Fast runners;B;Cardio;High
006;Brutal boxers;C;Cardio;High
007;Brutal boxers;C;Strength;High
If you specifically want to aggregate this across whatever choice you have made in your Club selection, then you simply write out a simple measure that does that:
AvgAthlete =
VAR _athletes =
CALCULATE (
DISTINCTCOUNT ( 'Table'[Athlete] ) ,
ALLEXCEPT ( 'Table' , 'Table'[Club] )
)
RETURN
DIVIDE (
[Sessions] ,
_athletes
)
Here we use a distinct count of values in the Athlete column, with all filters removed apart from on the Club column. This is, as far as I interpret your question, the denominator you are after.
Divide the total number of sessions on this number of athletes. Here is the result:
Related
I have a riport, in which I need to make a measure, which counts the 'true' values of another measure.
My partner's sales prices are calculated with a measure, which is then put into a matrix visual.
Column 'A' contains the partner name (as there are several) Column 'B' is the item name, 'C' is their sales price and 'D' is the price they should sell the product for.
Example_1 matrix visual with open hierarchy:
What I need, is a measure, that I can put into the matrix, which then calculates those items, that are not sold for the given price, so when I close the table hieararchy and I only see the partner's name, I should have the info of how many products they sell for lower than given sales price (making it easier to rank them)
What I'm having trouble with, is to count the 'true' values of a measure, with another measure.
It's important, that I cannot make a new colmn into the source tables. I must have a measure, which counts 'True' values of another, as their sales prices are also calculated.
Example_2 matrix visual with closed hieararchy with the needed result value:
The first two items from Example_1 were sold for a lower than given price. My first measure will determine this by doing a true/false logical test.
My second measure which gives back two, should calculate the 'true' values of the first measure.
Practically the measures should look like this:
Measure_1 = if([measure_salesprice] < sum('given_pricelist'[Price] , "Lower" , "Not lower")
--> this one works perfectly
Measure_2 = Calculate(DISTINCTCOUNTX('Sales table', 'Sales table[Item name]),[measure_lowerpricetruorfalse] = "Lower")
--> now, this doesn't
Is this possible somehow?
I've tried several DAX combinatains like:
Calculate --> DISTINCTCOUNT, COUNTROWS, COUNTA, COUNTAX, COUNTX( with filter)
Always the same true/false error.
Please be informed that There is no function in DAX called DISTINCTCOUNTX ----> It is DISTINCTCOUNT only as of 2 Nov 2022:
Regarding your question: Use this DAX Code As Measure:
You don't need to categorize them as Lower or not Lower if you don't need this info to use later.
YourMeasure =
VAR GroupLower = FILTER(
'Sales table', [Partner sales price] < [Given sales price])
RETURN
COUNTX(
GroupLower,[Item name])
If you try to test it on a matrix visual:
I have a big dataset (around 100k rows) with 2 columns referencing a device_id and a date and the rest of the columns being attributes (e.g. device_repaired, device_replaced).
I'm building a ML algorithm to predict when a device will have to be maintained. To do so, I want to calculate certain features (e.g. device_reparations_on_last_3days, device_replacements_on_last_5days).
I have a function that subsets my dataset and returns a calculation:
For the specified device,
That happened before the day in question,
As long as there's enough data (e.g. if I want last 3 days, but only 2 records exist this returns NA).
Here's a sample of the data and the function outlined above:
data = data.frame(device_id=c(rep(1,5),rep(2,10))
,day=c(1:5,1:10)
,device_repaired=sample(0:1,15,replace=TRUE)
,device_replaced=sample(0:1,15,replace=TRUE))
# Exaxmple: How many times the device 1 was repaired over the last 2 days before day 3
# => getCalculation(3,1,data,"device_repaired",2)
getCalculation <- function(fday,fdeviceid,fdata,fattribute,fpreviousdays){
# Subset dataset
df = subset(fdata,day<fday & day>(fday-fpreviousdays-1) & device_id==fdeviceid)
# Make sure there's enough data; if so, make calculation
if(nrow(df)<fpreviousdays){
calculation = NA
} else {
calculation = sum(df[,fattribute])
}
return(calculation)
}
My problem is that the amount of attributes available (e.g. device_repaired) and the features to calculate (e.g. device_reparations_on_last_3days) has grown exponentially and my script takes around 4 hours to execute, since I need to loop over each row and calculate all these features.
I'd like to vectorize this logic using some apply approach which would also allow me to parallelize its execution, but I don't know if/how it's possible to add these arguments to a lapply function.
I am working with a large federal dataset with thousands of observations and thousands of variables. Replicate weights are provided. I am using the "survey" package in R to apply these weights:
els.weighted=svrepdesign(data=els, repweights = ~els$F3F1PNLWT,
combined.weights = TRUE).
I am interested in some categorical descriptive characteristics of a subset of the population, such as family living arrangements. I want to get these sorted out into a contingency table that shows frequency. I would like to sort people based on four variables (none of which are binary, but all of which are numeric) This is what I would like to get:
.
The blank boxes are where the cross-tabulation/frequency counts would show. (I only put in 3 columns beneath F1COMP for brevity's sake, but it has 9 outcomes – indexed 1-9)
My current code: svyby(~F1FCOMP, ~F1RTRCC +BYS33C +F1A10 +byurban, els.weighted, svytotal)
This code does sort the data, but it sorts every single combination, by default. I want them pared down to represent only specific subpopulations of each variable. I tried:
svyby(~F1FCOMP, ~F1RTRCC==2 |F1RTRCC==3 +BYS33C==1 +F1A10==2 | F1A10==3 +byurban==3, els.weighted, svytotal)
But got stopped:
Error: unexpected '==' in "svyby(~F1FCOMP, ~F1RTRCC==2 |F1RTRCC==3 +BYS33C=="
Additionally, my current version of the code tells me how many cases occur for each combination, This is a picture of what my current output looks like. There are hundreds more rows, 1 for each combination, when I keep scrolling down.
This is a picture of what my current output looks like. There are hundreds more rows, 1 for each combination, when I keep scrolling down
.
You can see in that picture that I only get one number for F1FCOMP per row – the number of cases who fit the specified combination – a specific subpopulation. I want to know more about that subpopulation. That is, F1COMP has nine different outcomes (indexed 1-9), and I want to see how many of each subpopulation fits into each of the 9 outcomes of F1COMP.
I have a model generated by the function lda.collapsed.gibbs.sampler, from the lda package, and i need to know the "relevance" of the top words.
When using the
top.topic.words(result$topics, 10, by.score=TRUE)
i get a list of top 10 words for each topic, but i'd like to see the percentage of the topic that those 10 words represent. I guess the information exists, because there is a "score", but I'm not really familiar with the statistical methods of the Gibbs sampler.
Thanks in advance!
I think something like this may be what you want:
for (ii in 1:nrow(result$topics)) {
print(
head(
cumsum(
sort(result$topics[ii,], decreasing=TRUE)
),
n = 20
) / result$topic_sums[ii]
)
}
Let's break it down. If you want the fraction of Gibbs assignments, then that is easy. The LDA routine returns the number of assignments to each (word, topic) pair. So all you have to do is sort each row of the result$topics to get the top words (this is essentially what top.topic.words does if you set by.score=FALSE). Once you have it in sorted order you can just see, for each topic, how many counts occur for that word versus for the entire topic. To do that I divide by result$topic_sums which contains the total number of assignments of that topic. Finally, I use cumsum so you can see the running total weight for words in that topic.
I am working on EBS, Forex market Limit Order Book(LOB): here is an example of LOB in a 100 millisecond time slice:
datetime|side(0=Bid,1=Ask)| distance(1:best price, 2: 2nd best, etc.)| price
2008/01/28,09:11:28.000,0,1,1.6066
2008/01/28,09:11:28.000,0,2,1.6065
2008/01/28,09:11:28.000,0,3,1.6064
2008/01/28,09:11:28.000,0,4,1.6063
2008/01/28,09:11:28.000,0,5,1.6062
2008/01/28,09:11:28.000,1,1,1.6067
2008/01/28,09:11:28.000,1,2,1.6068
2008/01/28,09:11:28.000,1,3,1.6069
2008/01/28,09:11:28.000,1,4,1.6070
2008/01/28,09:11:28.000,1,5,1.6071
2008/01/28,09:11:28.500,0,1,1.6065 (I skip the rest)
To summarize the data, They have two rules(I have changed it a bit for simplicity):
If there is no change in LOB in Bid or Ask side, they will not record that side. Look at the last line of the data, millisecond was 000 and now is 500 which means there was no change at LOB in either side for 100, 200, 300 and 400 milliseconds(but those information are important for any calculation).
The last price (only the last) is removed from a given side of the order book. In this case, a single record with nothing in the price field. Again there will be no record for whole LOB at that time.
Example:2008/01/28,09:11:28.800,0,1,
I want to calculate minAsk-maxBid(1.6067-1.6066) or weighted average price (using sizes of all distances as weights, there is size column in my real data). I want to do for my whole data. But as you see the data has been summarized and this is not routine. I have written a code to produce the whole data (not just summary). This is fine for small data set but for a large one I am creating a huge file. I was wondering if you have any tips how to handle the data? How to fill the gaps while it is efficient.
You did not give a great reproducible example so this will be pseudo/untested code. Read the docs carefully and make adjustments as needed.
I'd suggest you first filter and split your data into two data.frames:
best.bid <- subset(data, side == 0 & distance == 1)
best.ask <- subset(data, side == 1 & distance == 1)
Then, for each of these two data.frames, use findInterval to compute the corresponding best ask or best bid:
best.bid$ask <- best.ask$price[findInterval(best.bid$time, best.ask$time)]
best.ask$bid <- best.bid$price[findInterval(best.ask$time, best.bid$time)]
(for this to work you might have to transform date/time into a linear measure, e.g. time in seconds since market opening.)
Then it should be easy:
min.spread <- min(c(best.bid$ask - best.bid$price,
best.ask$bid - best.ask$price))
I'm not sure I understand the end of day particularity but I bet you could just compute the spread at market close and add it to the final min call.
For the weighted average prices, use the same idea but instead of the two best.bid and best.ask data.frames, you should start with two weighted.avg.bid and weighted.avg.ask data.frames.