I have categorical variables like "Household income", "Urban/rural", "Gender", "Age". I want to find out how many people are all of (for example): Low household income, urban, male, and in age category two.
That is, I don't want to calculate the frequencies of each of these variables separately, but rather, I want to see how many people lie at the intersections of them.
Basically: How do I use SPSS to find out how many people in my dataset are low income urban males between ages 25-33?
Aggregating by the categorical variables can give you all the possible intersections:
dataset declare agg.
aggregate /out = agg /break = Household income Urban_rural Gender AgeGroup /Nintersect = n .
dataset activate agg.
The new dataset now has the count of cases in each intersection of all the categorical variables used as break in the aggregate command.
You could use the crosstab command, from Analyze/Descriptive statistics menu. This wor works best when you want to intersect 2 variables. You may also use the Layer, in you have a 3rd variable
For more than 3 variables, use an if recode:
if age=2 and gender=1... Recodevar=1'.
Fre recodevar.
Mind the overlapping scenarios, so you don't overwrite your recodevar
Related
I have created a matrix report that has a Row Group of Person and a column group of Subject with Child groups of SubmittedBy and Grading Name. The data that is being pulled through is a mixture of numerical and alphabetic data. I am needing to do a count per row of how many of a certain criteria appear. For example, I wish to count by person how many 1's they have achieved, or how many A* they have achieved (this is all grading data from a school). Is there a way to do this with matrix data?
If there is a way, can you then get a count using certain criteria, for example, if grading Name = focus count how many WB grades there are per person (row)?
This is the Design
This shows a subsection of data (as it is a very large dataset), but with SchoolID, Forename and Surname cut off to preserve anonymity.
A simple way would be to add Total to the Subject row group and use the count() function:
[
Tableau:
This may seem simple, but I ran out of the usual tricks I've used in other systems.
I want a variance column. Essentially adding a member 'Variance' to the Act/Plan dimension which only contains the members 'Actual' and 'Plan'
I've come in where the data structure and reporting is set up like so:
Actual | Plan
Profit measure
measure 2
measure 3
etc
The goal is to have a Variance column (calculated and not part of the Actual/Plan dimension)
Actual | Plan | Variance
Profit measure
measure 2
measure 3
etc
There are solutions where it works for one measure only, and I've looked into that.
ie, create calculated field as such
Profit_Actual | Profit_Plan | Variance
You put this on the columns, and you get a grid that I want... except a grid with only 1 measure.
This does not work if I want to run several measures on rows. Essentially the solution above will only display the Profit measure, not Measure 1_Actual , Measure 2_Plan etc.
So I tried a trick where I grouped a the 3 calculated measures, ie Profit_Actual | Profit_Plan | Profit_Variance as 'Profit_Measure'
Created a parameter list - 'Actual', 'Plan', 'Variance'
Now I can half achieve my goal, by having the parameter on columns and the 'Profit Measure' on Rows (so I can have Measure 123_group etc down on rows too). Trouble is, I found that parameters are single select only. Only if it can display all options in the custom paramater at once, I would've solved my problem.
Any ideas on how I can achieve the Variance column I want?
Virtually adding a member to a dimension/Calculated fieds/tricks/workaround
Thank you
Any leads is appreciated
Gemmo
Okay. First thing, I had a really hard time trying to understand how your data is organized, try to be more clear (say how each entry in your database looks like, and not how a specific view in Tableau looks like).
But I think I got it. I guess you have a collection of entries, and each entry has a number of measure fields (profits and etc.) and an Act/Plan field, to identify whether that entry is an actual value or a planned value. Is that correct?
Well, if that's the case, I'm sorry to say you have to calculate a variance field for each dimension. Think about it, how your original dataset is structured. Do you think you can add a single field "Variance" to represent the variance of each measure? Well, you can, store the values in a string, and then collect it back using some string functions, but it's not very practical. The problem is that each entry have many measures, if it had only 1 measure, than 1 single variance field would suffice.
So, if you can re-organize your data, what would be an easier to work set (but with many more entries) is something with the fields: Measure, Value, Actual/Plan. The measure field would have a string to identify what you're measuring in that entry. Value would be a number to represent the actual measure. And the Actual/Plan is the same. For instance:
Measure Value Actual/Plan
Profit 100 Actual
So, each line in your current model would become n entries, where n is the number of measures you have right now. So a larger dataset in a way, but easier to work with. Think about, now you can have a calculated field, and use some table calculations to calculate the variance only for that measure and/or Actual/Plan. Just use WINDOW_VAR, and put Measure and/or Actual/Plan in the partition.
Table calculations are awesome, take a look at this to understand it better. http://onlinehelp.tableausoftware.com/current/pro/online/en-us/help.htm#calculations_tablecalculations_understanding_addressing.html
I generally like to have my data staged such that Actual is its own column and Plan is its own column in the data being fed to Tableau. It makes calculations so much easier.
If your data is such that there is a column called "Actual/Plan" and every row is populated with either "Actual" or "Plan" and there is another column called "Value" or "Measure" that is populated with the values, you can force Tableau to make them columns assuming you can't or won't rearrange your data.
Create a calculated field called "Actual" with the following calc:
IF [Actual/Plan] = 'Actual' THEN [Value] END
Similarly, create a calculated field called "Plan" with the following calc:
IF [Actual/Plan] = 'Plan' THEN [Value] END
Now, you can finally create your "Variance" and "Variance %" calculations (respectively):
SUM([Actual]) - SUM([Plan])
[Variance] / SUM([Plan])
Data Structure:
User has many Profiles
(Limit - no more than one of each profile type per user, no duplicates)
Profiles has many Attribute Values
(A user can have as many or few attribute values as they like)
Attributes belong to a category
(No overlap. This controls which attribute values a profile can have)
Example/Context:
I believe with stack exchange you can have many profiles for one user, as they differ per exchange site? In this problem:
Profile: Video, so Video profile only contains Attributes of Video category
Attributes, so an Attribute in the Video category may be Genre
Attribute Values, e.g. Comedy, Action, Thriller are all Attribute Values
Profiles and Attributes are just ways of grouping Attribute Values on two levels.
Without grouping (which is needed for weighting in 2. onwards), the relationship is just User hasMany Attribute Values.
Problem:
Give each user a similarity rating against each other user.
Similarity based on All Attribute Values associated with the user.
Flat/one level
Unequal number of attribute values between two users
Attribute value can only be selected once per user, so no duplicates
Therefore, binary string/boolean array with Cosine Similarity?
1 + Weight Profiles
Give each profile a weight (totaling 1?)
Work out profile similarity, then multiply by weight, and sum?
1 + Weight Attribute Categories and Profiles
As an attribute belongs to a category, categories can be weighted
Similarity per category, weighted sum, then same by profile?
Or merge profile and category weights
3 + Distance between every attribute value
Table of similarity distance for every possible value vs value
Rather than similarity by value === value
'Close' attributes contribute to overall similarity.
No idea how to do this one
Fancy code and useful functions are great, but I'm really looking to fully understand how to achieve these tasks, so I think generic pseudocode is best.
Thanks!
First of all, you should remember that everything should be made as simple as possible, but not simpler. This rule applies to many areas, but in things like semantics, similarity and machine learning it is essential. Using several layers of abstraction (attributes -> categories -> profiles -> users) makes your model harder to understand and to reason about, so I would try to omit it as much as possible. This means that it's highly preferable to keep direct relation between users and attributes. So, basically your users should be represented as vectors, where each variable (vector element) represents single attribute.
If you choose such representation, make sure all attributes make sense and have appropriate type in this context. For example, you can represent 5 video genres as 5 distinct variables, but not as numbers from 1 to 5, since cosine similarity (and most other algos) will treat them incorrectly (e.g. multiply thriller, represented as 2, with comedy, represented as 5, which makes no sense actually).
It's ok to use distance between attributes when applicable. Though I can hardly come up with example in your settings.
At this point you should stop reading and try it out: simple representation of users as vector of attributes and cosine similarity. If it works well, leave it as is - overcomplicating a model is never good.
And if the model performs bad, try to understand why. Do you have enough relevant attributes? Or are there too many noisy variables that only make it worse? Or do some attributes should really have larger importance than others? Depending on these questions, you may want to:
Run feature selection to avoid noisy variables.
Transform your variables, representing them in some other "coordinate system". For example, instead of using N variables for N video genres, you may use M other variables to represent closeness to specific social group. Say, 1 for "comedy" variable becomes 0.8 for "children" variable, 0.6 for "housewife" and 0.9 for "old_people". Or anything else. Any kind of translation that seems more "correct" is ok.
Use weights. Not weights for categories or profiles, but weights for distinct attributes. But don't set these weights yourself, instead run linear regression to find them out.
Let me describe last point in a bit more detail. Instead of simple cosine similarity, which looks like this:
cos(x, y) = x[0]*y[0] + x[1]*y[1] + ... + x[n]*y[n]
you may use weighted version:
cos(x, y) = w[0]*x[0]*y[0] + w[1]*x[1]*y[1] + ... + w[2]*x[2]*y[2]
Standard way to find such weights is to use some kind of regression (linear one is the most popular). Normally, you collect dataset (X, y) where X is a matrix with your data vectors on rows (e.g. details of house being sold) and y is some kind of "correct answer" (e.g. actual price that the house was sold for). However, in you case there's no correct answer to user vectors. In fact, you can define correct answer to their similarity only. So why not? Just make each row of X be a combination of 2 user vectors, and corresponding element of y - similarity between them (you should assign it yourself for a training dataset). E.g.:
X[k] = [ user_i[0]*user_j[0], user_i[1]*user_j[1], ..., user_i[n]*user_j[n] ]
y[k] = .75 // or whatever you assign to it
HTH
I am in the process of merging two data sets together in Stata and came up with a potential concern.
I am planning on sorting each data set in exactly the same manner on several categorical variables that are common to both sets of data. HOWEVER, several of the categorical variables have more categories present in one data set over the other. I have been careful enough to ensure that the coding matches up in both data sets (e.g. Red is coded as 1 in both data set A and B, but data set A has only Red, Green and Blue whereas data set B has Red, Green, Blue, and Yellow).
If I were to sort each data set the same way and generate an id variable (gen id = _n) and merge on that, would I run into any problems?
There is no statistical question here, as this is purely about data management in Stata, so I too shall shortly vote for this to be migrated to Stack Overflow, where I would be one of those who might try to answer it, so I will do that now.
What you describe to generate identifiers is not how to think of merging data sets, regardless of any of the other details in your question.
Imagine any two data sets, and then in each data set, generate an identifier that is based on the observation numbers, as you propose. Generating such similar identifiers does not create a genuine merge key. You might as well say that four values "Alan" "Bill" "Christopher" "David" in one data set can be merged with "William" "Xavier" "Yulia" "Zach" in another data set because both can be labelled with observation numbers 1 to 4.
My advice is threefold:
Try what you are proposing with your data and try to understand the results.
Consider whether you have something else altogether, namely an append problem. It is quite common to confuse the two.
If both of those fail, come back with a real problem and real code and real results for a small sample, rather than abstract worries.
I think I may have solved my problem - I figured I would post an answer specifically relating to the problem in case anybody has the same issue.
~~
I have two data sets: One containing information about the amount of time IT help spent at a customer and another data set with how much product a customer purchased. Both data sets contain unique ID numbers for each company and the fiscal quarter and year that link the sets together (e.g. ID# 1001 corresponds to the same company in both data sets). Additionally, the IT data set contains unique ID numbers for each IT person and the customer purchases data set contains a unique ID number for each purchase made. I am not interested in analysis at the individual employee level, so I collapsed the IT time data set to the total sum of time spent at a given company regardless of who was there.
I was interested in merging both data sets so that I could perform analysis to estimate some sort of "responsiveness" (or elasticity) function linking together IT time spent and products purchased.
I am certain this is a case of "merging" data because I want to add more VARIABLES not OBSERVATIONS - that is, I wish to horizontally elongate not vertically elongate my final data set.
Stata 12 has many options for merging - one to one, many to one, and one to many. Supposing that I treat my IT time data set as my master and my purchases data set as my merging set, I would perform a "m:1" or many to one merge. This is because I have MANY purchases corresponding to one observation per quarter per company.
I have this dataset containing world bilateral trade data for a few years.
I would like to determine which goods were the most exported ones in the timespan considered by the dataset.
The dataset is composed by the following variables:
"year"
"hs2", containing a two-digit number that tells which good is exported
"exp_val", giving the value of the export in a certain year, for that good
"exp_qty", giving the exported quantity of the good in a certain year
Basically, I would like to get the total sum of the quantity exported for a certain good, so an output like
hs2 exp_qty
01 34892
02 54548
... ...
and so forth. Right now, the column "hs2" gives me a very large number of observations and, as you can understand, they repeat themselves multiple times (as the variables vary across both time and country of destination). So, the task would be to have every hs2 number just once, with the correspondent value of "total" exports.
Also (but that would be just a plus, I could just check the numbers by myself) it would be nice to get a result sorted by exp_qty, so to have a ranking of the most exported goods by quantity.
The following might be a start at what you need.
collapse (sum) exp_qty, by(hs2)
gsort -exp_qty
collapse summarizes the data in memory to one observation per value of hs2, summing the values of exp_qty. gsort then sorts the collapsed data by descending value of exp_qty so the first observation will be the largest. See help collapse and help gsort for further details.