I want to generate a 3 column table by flattening the given multi column table using google sheets formula. The formula should scale to any sized input table.
The sample file is here
https://docs.google.com/spreadsheets/d/1qTvQ58hh1jJEMVWqhQ833jjDBpuudJambmvdJemESPI/edit?usp=sharing
I have tried with
={ARRAYFORMULA(B2:B6),ARRAYFORMULA(D2:D6)}
Disaggregating is not a simple process. You can do it in different ways. Here is one with REDUCE that doesn't involve joining and splitting process. I've moved your input chart some columns to the right so the formula is "scalable" to as many companies you may have:
=QUERY(
REDUCE({"","",""},SEQUENCE(COUNTA(D2:D)),LAMBDA(a,c,{a;
REDUCE({"","",""},SEQUENCE(COUNTA(F1:1)),LAMBDA(d,e,{d;INDEX(D2:D,c),INDEX(F1:1,1,e),INDEX(F2:1000,c,e)}))})),
"Where Col1 is not null",)
You may try:
=hstack(tocol(map(B2:B6,lambda(z,wraprows(z,counta(D1:I1),z))),1,1),tocol(bycol(D1:I1,lambda(z,index(text(SEQUENCE(counta(B2:B6)),"")&z))),1,1),tocol(D2:I6,,1))
Related
I'm an average user of Google sheets and I've tried writing/looking up the formula I'm going for, but I haven't had any luck yet.
I have a spreadsheet that details multiple values that I need to display in a single cell the average of a certain set of values derived from a specific set of those values from multiple columns.
The flow of information would look something along the lines of:
if value in Column D=L
then
if value in Column J<$1.20
then
Find Avg of all Values in Column N
I'd need the formula to narrow it's field of data each time so the final result was the average of all the values in Column N that had a value in column J<$1.20 with a value in Column D=L.
I feel like a dummy over here because I just can't narrow down how I should write this flow and get it to work right without adding multiple extra hidden columns. Can anyone help on this one?
I've tried writing the formula multiple different ways but haven't kept it written down to pass on.
Forgive me if I am not using the correct terminology, I short of crash-coursed myself in Google sheets a few days ago.
Is there a way that I could using IMPORTRANGE to import a data range from spreadsheet 2 into spreadsheet 1, where the range selected from spreadsheet 2 can be matched against criteria in spreadsheet 1 that corresponds to criteria in spreadsheet 2? I have a specific set of data in spreadsheet 1 that, while the same in content, is not in the same order as spreadsheet 2 (which I don't myself maintain) or spreadsheet 3 (which is maintained by someone other than myself or the person that maintains spreadsheet 2), but am being given access to spreadsheet 2 and spreadsheet 3 data that I didn't previously have.
EXAMPLE:
https://docs.google.com/spreadsheets/d/1ByN9Ju8QiiHTfFgow7lDF4VN-zBRqP1gzpAK73ZRBNg/edit?usp=sharing
You work with IMPORTRANGE content the same way as you do with any range within your spreadsheet. Good practice is to use columns with unique content as ID's for searching, filtering, etc.
If you want put the content of somebody's spreadsheet into yours, you can control it.
For example:
In order to get REGISTRATION number from sheet3
Think of VLOOKUP construction:
=VLOOKUP(key,table with key value on the leftmost column;number of column to take value from,false)
You use vlookup formula that takes name in your table as a key (first parameter of formula), then you must rebuild your importrange to have key in leftmost column.
2nd parameter of VLOOKUP will look like this:
{importrange("Sheet3url";"Sheet!Columnwithname"),importrange("Sheet3url";"Sheet!Columnwithregistration")}
This is your temporary table made of 2 importranges.
You want 2nd column of this construction - which is column with registration.
Whole vlookup looks like this:
=VLOOKUP(key,{importrange("Sheet3url";"Sheet!Columnwithname"),importrange("Sheet3url";"Sheet!Columnwithregistration")},2,false)
It's much easier when key is on the left. If you want to extract SEX and DOB you use:
=VLOOKUP(key,importrange("Sheet3url";"Sheet!Columnsfromname to DOB"),2 and then 3,false)
Beware - using multiple importrange makes your sheet slow.
If you have hundreds of rows, you should wrap it around with arrayformula to work with all rows in one go.
Also you can first importrange somebodys table into your sheet on a side and operate inside your sheet.
It's advised when using big datasets and not that many files.
My company has tasked with slicing the information on turnover and to create different graphs.
My source data looks like this: Relevant columns are: Voluntary/Involuntary, Termination Reason, Country, Production, and TermDateKey
I am trying to get counts using different filters on the data. I managed to get the basic monthly total using the formula:
Term Month Count = GROUPBY('Turnover Source','Turnover Source'[TermDateKey],"Turnover Total Count", COUNTX(CURRENTGROUP(),'Turnover Source'[TermDateKey]))
This gave me a new sheet with the counts for each month.
Table that shows TermDateKey on Column 1, and Counts on column 2
I am trying to add onto this table by adding counts but using different filters.
For example, I am trying to add another column that gives me the monthly count but filtered for 'Turnover Source'[Voluntary/Involuntary]=="Voluntary". Then another column for 'Turnover Source'[Voluntary/Involuntary]=="Involuntary" and so on. I have not found anywhere that shows me how to do this and when I add in the FILTER function it says that GROUPBY(...) can only work on CURRENTGROUP().
Can some one point me to a resource that will give me the solution I need? I am at a loss, thank you all.
It looks like you may not be aware that you don't have to calculate all possible groupings with DAX formulas.
The very nature of Power BI is that you use a column like "Termination Reason" on an X axis or in the legend of a visual. Any measure that you have created on values of another column, for e.g. a count of all rows, will then automatically be calculated to be grouped by the values in "Termination Reason", giving you a count of each of the values in the column.
You do NOT need DAX functions to calculate the grouping values for each measure for each column value combination.
Here is some simple sample data that has been grouped into dates and colours, one chart showing a count of each colour and one chart showing a sum of the Value column. No DAX was written for that.
If your scenario is different, please explain.
I have 2 columns - Matches(Integer), Accounts_type(String). And i want to create a third column where i want to get proportions of matches played by different account types. I am new to Talend & am facing issue with this for past 2 days & did a lot of research but to no avail. Please help..
You can do it like this:
You need to read your source data twice (I used tFixedFlowInput_1 and tFixedFlowInput_2 with the same data). The idea is to calculate the total of your matches in tAggregateRow_1, it simply does a sum of all Matches without a group by column, then use that as a lookup.
The tMap then joins your source data with the calculated total. Since the total will always be one record, you don't need any join column. You then simply divide Matches by Total as required.
This is supposing you have unique values in Account_type; if you don't, you need to add another tAggregateRow between your source and tMap_1, in order to get sum of Matches for each Account_type (group by Account_type).
I have two datasets in my birt report :
Lesson (date)
Student (name)
and I would like to know how to create a cross table using the date (red) as the column names and name (blue) as the row names as shown below :
The cells will stay empty.
I have try to use the Cross Tab but it seems that I can only use one dataset.
For information I am stuck with the version 2.5.2. I say this in case someone writes about a practical functionality available in the later version of birt... :-)
Where both datasets are coming from the same relational data source, the simplest way to achieve this would normally be:
Replace the existing two datasets with a single dataset, in which the two original datasets are cross-joined to each other;
create a crosstab from the new dataset, with the new dataset columns as the data cube groups.