How can I summarize and reuse a complex dataset - eclipse-plugin

How can I re-use a single complex dataset across a number of tables?
The dataset has a number of computed columns that needs to be reported both in detail and in summary. Here's a very simplified example dataset:
is_food sale_association food_type total_sold total_associations percent_total
1 Before Movie Popcorn 50 3 x BirtMath.safeDivide(...)
0 Before Movie Soda 10 2 x BirtMath.safeDivide(...)
1 During Movie Jujubee 10 1 x BirtMath.safeDivide(...)
0 After Movie Soda 15 2 x BirtMath.safeDivide(...)
From this one dataset, I'd want to create a detailed summary of all food types while rolling up non food (using the 'is_food' column), another summary of all food types, another detailed summary of food with rolled up non-food by sale_association, etc. etc.
The report would also contain a number of percentages (6 in the most complex table) that need to be calculated (some across a row, others across all rows in a given group), all of which can have a zero value for the denominator and so need to be guarded against with safeDivide (which is a PITA to do in the source SQL query which itself is doing aggregation -- checking for divide by zero when both the numerator and denominator are sums leads to hairy queries).
Obviously I can do this by focusing the() SQL query as appropriate, but it seems like a waste of time and effort to create 12 or 15 queries that are very similar when I've already managed to create the monster query for the most detailed table.
What doesn't seem straightforward is how to perform the rollups in a table. I managed to hack something together by hiding rows that would later be summed up (e.g. "is_food == 0" in the example) and then creating custom data bindings that are displayed in a footer row. Not only does it feel like a hack, it also interferes with the ability to naturally order rows. Again, going back to the example, if I was ordering by total_sold and summarizing rows with is_food == 0, the natural order should be Popcorn, Non-food, Jujubee.
There's nothing in the BIRT wiki about this, nor does "BIRT: A Field Guide, 3rd E." really delve into the topic.

This seems like a fairly open-ended question (although I agree that re-using a single dataset makes much more sense than having multiple queries retrieving the same data in slightly different ways). A few general suggestions:
Use the most detailed version of the data required as a common dataset for each BIRT report item (typically BIRT tables)
Where summary-only level reporting is required, add groups to the BIRT table at the desired level, add data items as required to the group headers/footers and delete the detail level row(s) from the BIRT table.
Where detail-level reporting is required in some cases (eg. for food items but not for non-food items), add groups to the BIRT table as above, and set the visibility of the detail row (in Property Editor - Properties - Visibility) to check Hide Element, then specify the appropriate expression to suppress the non-required rows (non-food items, in this example).
Aggregations (ie. summary expressions) can be added to tables by selecting the whole table, selecting the Binding tab within the Property Editor and clicking the Add Aggregation... button.

Related

Is it possible to use SQL to show the average of some values in one column and then in subsequent columns display the individual values?

I have a bunch of data and I want the output to display an average of all the data points but also the individual data points in subsequent columns. Ideally it would look something like this:
Compound | Subject | Avg datapoint | Datapoint Experiment 1 | Datapoint Exp 2 | ...
..........XYZ......|.....ABC....|............40...............|...............20..............................|...............60...............|......
..........TUV......|.....ABC....|............30...............|...............20..............................|...............40...............|......
..........TUV......|.....DEF....|............20...............|...............10..............................|...............30...............|......
One problem I'm running in to is that I get repetitive lines of information. Another is that I have some rows pulling in info that doesn't apply, such that some of the individual datapoints in, say, row 2 would have info from subject DEF when I only want it to have info from subject ABC.
I hope this makes sense! I'm currently using inner join with a ton of where qualifiers. I'm close but not quite there. Any help is appreciate and let me know if I can provide additional info to help you help me.
The SQL language has a very strict rule requiring you to know the exact number of columns for your result set in advance, before looking at any data in your tables.
Therefore, if this average is based off a known fixed number of columns, or if the number of potential columns is reasonably small, where you can manually setup placeholders, then this will be possible. The key search terms to learn how to do this is "conditional aggregation", where you may also need to join the table to itself for each field.
Otherwise, you will need to pivot and aggregate your data in your client code or reporting tool.

TABLEAU Totals not matching what's in view

I've been dealing with this issue in various ways throughout my time with this dataset in Tableau.
As you can see, the Total count of properties for each city is including properties that have been successfully filtered out of view. Why? The dyn.RANKED Profitable Investments (grouped) variable on the Filter shelf is an attempt to double down on the same as the first line of the Calculated Field - to ignore the unwanted properties in each city. The view ignores them, but the totals do not.
If the Watershed Property pill is removed from the Rows shelf, then the dyn.NumProps_in_City results shown on the table are each the same as the Totals you see here (i.e., despite the first line of the calculated field, properties that do not meet that opening condition are being counted)...despite the view with the Watershed pill knowing not to show them.
Also if the Watershed Property pill is removed from the Rows shelf, then the dyn.RANKED Profitable Investments (grouped) variable on the Filter shelf suddenly only has one category to choose from (i.e., 'INVEST') if you go to edit the filter. Which would be great since that's the category I care about, but not if the counts are including things that are not in that category despite the filter.
Messing around with Include, Exclude, and Fixed in the calculated field doesn't seem to work here since I can't figure out how to get around various aggregate/non-aggregate and/or ATTR errors no matter where I place them. Plus, my incorrect counts are not suffering from an LOD issue - the LOD is correct - it's an issue of not consistently filtering out the unwanted rows at the desired LOD.
Please advise!
Thanks,
Christian
It seems that dyn.Ranked calculated field calculates the value prior to filtering. This may happen if you have used any LOD calculations in the syntax.
Simply right click such fields on filters shelf and click add to context. This will cause LOD calculations to calculate after the filtering.
see this link, the context filters are above the LOD calculations, in order of precedence; but measure filters are below the LOD calcs. Therefore if measures are used as filters, these have to be added to context so that their order of precedence is above such calculations

How to populate all possible combination of values in columns, using Spark/normal SQL

I have a scenario, where my original dataset looks like below
Data:
Country,Commodity,Year,Type,Amount
US,Vegetable,2010,Harvested,2.44
US,Vegetable,2010,Yield,15.8
US,Vegetable,2010,Production,6.48
US,Vegetable,2011,Harvested,6
US,Vegetable,2011,Yield,18
US,Vegetable,2011,Production,3
Argentina,Vegetable,2010,Harvested,15.2
Argentina,Vegetable,2010,Yield,40.5
Argentina,Vegetable,2010,Production,2.66
Argentina,Vegetable,2011,Harvested,15.2
Argentina,Vegetable,2011,Yield,40.5
Argentina,Vegetable,2011,Production,2.66
Bhutan,Vegetable,2010,Harvested,7
Bhutan,Vegetable,2010,Yield,35
Bhutan,Vegetable,2010,Production,5
Bhutan,Vegetable,2011,Harvested,2
Bhutan,Vegetable,2011,Yield,6
Bhutan,Vegetable,2011,Production,3
Image of the above csv:
Now there is a very small country lookup table which has all possible countries the source data can come with, listed. PFB:
I want to have the output data's number of columns always fixed (this is to ensure the reporting/visualization tool doesn't get dynamic number columns with every day's new source data ingestions depending on the varying distinct number of countries present).
So, I've to somehow join the source data with the country_lookup csv and populate all those columns with default value as F. Every country column would be binary with T or F being the possible values.
The original dataset from the above has to be converted into below:
Data (I've kept the Amount field unsolved for column Type having Derived Yield as is, rather than calculating them below for a better understanding and for you to match with the formulae):
Country,Commodity,Year,Type,Amount,US,Argentina,Bhutan,India,Nepal,Bangladesh
US,Vegetable,2010,Harvested,2.44,T,F,F,F,F,F
US,Vegetable,2010,Yield,15.8,T,F,F,F,F,F
US,Vegetable,2010,Production,6.48,T,F,F,F,F,F
US,Vegetable,2010,Derived Yield,(2.44+15.2)/(6.48+2.66),T,T,F,F,F,F
US,Vegetable,2010,Derived Yield,(2.44+7)/(6.48+5),T,F,T,F,F,F
US,Vegetable,2010,Derived Yield,(2.44+15.2+7)/(6.48+2.66+5),T,T,T,F,F,F
US,Vegetable,2011,Harvested,6,T,F,F,F,F,F
US,Vegetable,2011,Yield,18,T,F,F,F,F,F
US,Vegetable,2011,Production,3,T,F,F,F,F,F
US,Vegetable,2011,Derived Yield,(6+10)/(3+9),T,T,F,F,F,F
US,Vegetable,2011,Derived Yield,(6+2)/(3+3),T,F,T,F,F,F
US,Vegetable,2011,Derived Yield,(6+10+2)/(3+9+3),T,T,T,F,F,F
Argentina,Vegetable,2010,Harvested,15.2,F,T,F,F,F,F
Argentina,Vegetable,2010,Yield,40.5,F,T,F,F,F,F
Argentina,Vegetable,2010,Production,2.66,F,T,F,F,F,F
Argentina,Vegetable,2010,Derived Yield,(2.44+15.2)/(6.48+2.66),T,T,F,F,F,F
Argentina,Vegetable,2010,Derived Yield,(15.2+7)/(2.66+5),F,T,T,F,F,F
Argentina,Vegetable,2010,Derived Yield,(2.44+15.2+7)/(6.48+2.66+5),T,T,T,F,F,F
Argentina,Vegetable,2011,Harvested,10,F,T,F,F,F,F
Argentina,Vegetable,2011,Yield,90,F,T,F,F,F,F
Argentina,Vegetable,2011,Production,9,F,T,F,F,F,F
Argentina,Vegetable,2011,Derived Yield,(6+10)/(3+9),T,T,F,F,F,F
Argentina,Vegetable,2011,Derived Yield,(10+2)/(9+3),F,T,T,F,F,F
Argentina,Vegetable,2011,Derived Yield,(6+10+2)/(3+9+3),T,T,T,F,F,F
Bhutan,Vegetable,2010,Harvested,7,F,F,T,F,F,F
Bhutan,Vegetable,2010,Yield,35,F,F,T,F,F,F
Bhutan,Vegetable,2010,Production,5,F,F,T,F,F,F
Bhutan,Vegetable,2010,Derived Yield,(2.44+7)/(6.48+5),T,F,T,F,F,F
Bhutan,Vegetable,2010,Derived Yield,(15.2+7)/(2.66+5),F,T,T,F,F,F
Bhutan,Vegetable,2010,Derived Yield,(2.44+15.2+7)/(6.48+2.66+5),T,T,T,F,F,F
Bhutan,Vegetable,2011,Harvested,2,F,F,T,F,F,F
Bhutan,Vegetable,2011,Yield,6,F,F,T,F,F,F
Bhutan,Vegetable,2011,Production,3,F,F,T,F,F,F
Bhutan,Vegetable,2011,Derived Yield,(2.44+7)/(6.48+5),T,F,T,F,F,F
Bhutan,Vegetable,2011,Derived Yield,(10+2)/(9+3),F,T,T,F,F,F
Bhutan,Vegetable,2011,Derived Yield,(6+10+2)/(3+9+3),T,T,T,F,F,F
The image of the above expected output data for a structured look at it:
Part 1 -
Part 2 -
Formulae for populating Amount Field for Derived Type:
Derived Amount = Sum of Harvested of all countries with T (True) grouped by Year and Commodity columns divided by Sum of Production of all countries with T (True)grouped by Year and Commodity columns.
So, the target is to have a combination of all the countries from source and calculate the sum of respective Harvested and Production values which then has to be divided. The commodity can be more than one in the actual scenario for any given country, but that should not bother as the summation of amount happens on grouped commodity and year.
Note: The users in the frontend can select any combination of countries. The sole purpose of doing it in the backend rather than dynamically doing it in the frontend is because AWS QuickSight (our visualisation tool), even though can populate sum on selected column filters but doesn't yet support calculation on those derived summed fields. Hence, the entire calculation of all combination of countries has to be pre-populated (very naive approach) in order to make it available in report on dynamic users selection of countries.
Also if you've any better approach (than the above naive approach mentioned in note) to solve this problem, you are most welcome to guide me. I've also posted a question on the same problem without writing my expected approach for experts to show me the path on how we can solve this kind of a problem better than this naive approach. If you want to help solve it with some other technique, you're most welcome, here is the link to that question.
Any help shall be greatly acknowledged.

Merge two CSV and collate data

I have two CSV files, the first like so:
Book1:
ID,TITLE,SUBJECT
0001,BLAH,OIL
0002,BLAH,HAMSTER
0003,BLAH,HAMSTER
0004,BLAH,PLANETS
0005,BLAH,JELLO
0006,BLAH,OIL
0007,BLAH,HAMSTER
0008,BLAH,JELLO
0009,BLAH,JELLO
0010,BLAH,HAMSTER
0011,BLAH,OIL
0012,BLAH,OIL
0013,BLAH,OIL
0014,BLAH,JELLO
0015,BLAH,JELLO
0016,BLAH,HAMSTER
0017,BLAH,PLANETS
0018,BLAH,PLANETS
0019,BLAH,HAMSTER
0020,BLAH,HAMSTER
And then a second CSV with items associated with the first list, with ID being the common attribute between the two.
Book2:
ID,ITEM
0001,PURSE
0001,STEAM
0001,SEASHELL
0002,TRUMPET
0002,TRAMPOLINE
0003,PURSE
0003,DOLPHIN
0003,ENVELOPE
0004,SEASHELL
0004,SERPENT
0004,TRUMPET
0005,CAR
0005,NOODLE
0006,CANNONBALL
0006,NOODLE
0006,ORANGE
0006,SEASHELL
0007,CREAM
0007,CANNONBALL
0007,GUM
0008,SERPENT
0008,NOODLE
0008,CAR
0009,CANNONBALL
0009,SERPENT
0009,GRAPE
0010,SERPENT
0010,CAR
0010,TAPE
0011,CANNONBALL
0011,GRAPE
0012,ORANGE
0012,GUM
0012,SEASHELL
0013,NOODLE
0013,CAR
0014,STICK
0014,ORANGE
0015,GUN
0015,GRAPE
0015,STICK
0016,BASEBALL
0016,SEASHELL
0017,CANNONBALL
0017,ORANGE
0017,TRUMPET
0018,GUM
0018,STICK
0018,GRAPE
0018,CAR
0019,CANNONBALL
0019,TRUMPET
0019,ORANGE
0020,TRUMPET
0020,CHERRY
0020,ORANGE
0020,GUM
The real datasets are millions of records, so I'm sorry in advance for my simple example.
The problem I need to solve is getting the data merged and collated in a way where I can see which item groupings most commonly appear together on the same ID. (e.g. GRAPE,GUM,SEASHELL appear together 340 times, ORANGE and STICK 89 times, etc...)
Then I need to see if there is any change/deviation to the general results in common appearance when grouped by SUBJECT.
Tools I'm familiar with are Excel and SQL, but I also have PowerBI and Alteryx at my disposal.
Full disclosure: Not homework, or work, but a volunteer project, thus my unfamiliarity with this kind of data manipulation.
Thanks in advance.
An Alteryx solution:
Drag the two .csv files onto your canvas (seen as book1.csv and book2.csv in my picture; Alteryx will create "Input" tools for you.
Drag a "Join" tool on and connect the two .csv files to its inputs; select "ID" as the join field; unselect the "Right_ID" as output since it's merely a duplicate of "ID"
Drag a "Summary" tool on and connect the Join tool's output to the Summary tool's input; select all three of the outputs and add as a "group by"... then add the ID column with a "count"
Drag a browse tool on and connect the summary's output to the browse tool's input.
run the workflow
After all that, click on the browse tool and you should see what is seen in my screenshot: (which is showing just the first ten rows of output):
+1 for taking on a volunteer project - I think anyone who knows data can have a big impact in support of their favourite group or cause.
I would just pull the 2 files into Power BI as 2 separate tables (Get Data / From File). Create a relationship between the 2 tables based on ID (it might get auto-generated). It should be one to many.
Then I would add a Calculated Column to the Book1 table to Concatenate the related ITEM values, eg.
Items =
CALCULATE (
CONCATENATEX (
DISTINCT ( 'Book2'[ITEM] ),
'Book2'[ITEM],
", ",
'Book2'[ITEM], ASC
)
)
Now you can use that Items field in visuals (e.g. a Table), along with Count of ID to get the frequency.
Adding Subject to a copy of the table (e.g. to the Columns well of a Matrix) will produce your grouped scenario, or you could add a Subject Slicer.
As you will be comparing subsets of varying size, I would change Count of ID to Show value as - % of grand total.
Little different solution using Alteryx.
With this dataset, there are very few repeating 3 or 4 item groups. You can do the two item affinity analysis and get a probability of 3 or 4 item groups, or you can count the 3 and 4 item groups individually. I believe what you want is the latter as your probability of getting grapes with oranges may be altered by whether you have bananas in the cart or not.
Anyway, I did not join in the subject until after finding all of my combinations. I found all the combinations by taking the Cartesian join of two, then three, then four of the original set. I then removed all duplicates by ensuring items were always in alphabetical order in each row. I then counted occurrences of each combination. More joins can be added in the same pattern to count groups of 5,6,7...
Once you have the counts of occurrences, then I would join back with the subjects and perform this analysis on each group and compare to the overall results.
I'm supposed to disclose that I work for Alteryx.
first of all if you are using windows
just navigate to the directory which contains the CSV and write the following command:
copy pattern newfileName.csv
#example
copy *.csv merged.csv
now you created one csv file, the file is too large now you can't process it once, depending on your programming language you can use appropriate way, for python you can use generators to process line by line, or pandas you can read chunk by chunk it will be easy.
I hope this help you.

Excel VBA - Random extra grouping levels

This is not really an issue that affects the code but rather a question of the table's appearance.
So, the table is the summary of records for income and expenses of different business departments. Let's call each department a type of the record. Each of those types has subtype1. Each subtype1 has subtypes2 and each subtype2 has subtypes3.
So the sample data would be something like this.
1, Type1, sum of subtypes1
1.1, Subtype1, sum of subtypes2
1.1.1 Subtype2, sum of subtypes3
1.1.1.1 Subtype3, amount
1.1.1.2 Subtype3, amount
1.2, Subtype1, sum of subtypes2
1.2.1, Subtype2, sum of subtypes3
1.2.1.1, Subtype3, amount
Each subtype can have different number of "children subtypes". Children subtypes can't go further than subtype3.
Then I am using VBA script to group the records of the same subtype under their direct parent up to the main type. Everything works fine, I can expand or hide every single level of this structure.
However, logically the group outline on the left side of the table for rows should show 4 levels. Instead it shows 8 levels of groups. First 4 do exactly what you would expect, show or hide respective subtypes while the other 4 levels do absolutely nothing which is also expected because I don't see a reason for them to be there.
Any ideas why extra levels have been created and how to get rid of them?
I might have explained this in a not very clear way so feel free to ask for further information.
Try stepping through your code in trace mode to watch the groups being set up. (open the VBA window and use the F8 key to loop one line at a time)
This may reveal why the extra groups are being defined and suggest what to change.