I've tried looking through some of the posts and I'm having trouble finding something that will help me in this situation.
I have a spreadsheet that has Total Sales, Retail Price, and Inventory for each week in a year for a list of 100 or so projects. These three pieces of info are displayed as columns repeated for the year, with a row for each item.
I was able to add up the total annual cells (every 3rd column) using SUMPRODUCT((MOD(COLUMN(D3:L3),3)=0)*D3:L3)
The next goal is to get a formula to calculate the weighted average retail. I basically need to find a formula that will end up with the SUMPRODUCT of an array of Sales data and Retail data.
I have tried to use some layering of MMULT and SUMPRODUCT but keep getting #VALUE! errors. Particularly with SUMPRODUCT(TRANSPOSE(MMULT(TRANSPOSE((MOD(COLUMN(D3,L3),3)=0)),D3,L3)),MMULT(TRANSPOSE((MOD(COLUMN(D3,L3),3)=1)),D3,L3)) and with putting braces in there as well =SUMPRODUCT({TRANSPOSE(MMULT(TRANSPOSE((MOD(COLUMN(D3,L3),3)=0)),D3,L3))},{MMULT(TRANSPOSE((MOD(COLUMN(D3,L3),3)=1)),D3,L3)})
Does anyone have any experience with this type of issue? I feel like it should be something that Excel can do without having to have separate sheets to calculate.
For your weighted average:
=SUMPRODUCT(($D$2:$FD$2="Sales")*$D3:$FD3*$E3:$FE3)/SUMIF($D$2:$FD$2,"Sales",$D3:$FD3)
And also, to add up the Sales, you could consider:
=SUMIF($D$2:$FD$2,"Sales",$D3:$FD3)
I am assuming FD is the last column of data for the year, but change it if that is not the case.
I'm trying to create a function that takes the weighted average of an array. I am creating a presentation that shows rates ($) and revenue by market for a client with a weighted average rate per product at the bottom. I could manually find each markets % share of total revenue and then multiply each market's % factor by it's rate and then add all of these values up to find the weighted average rate, but I want to create a function to do it for me. I want to do the following:
For the following client data (fake):
-Asia $16 $200,000
-Europe $9 $50,000
-N. America $21 $100,000
-Africa $25 $250,000
I need to find the weighted average rate across all markets.
Function WeightedAverage(array, weightarray)
#"array" being {$16,$9,$21,$25} and "weightarray" being {$200,000, $50,000, $100,000, $250,000}
WeightedAverage = SumProduct(array, weightvalues)
weightvalues = an array of values like so {$200,000/sum(weightarray), $50,000/sum(weightarray), $100,000/sum(weightarray), $250,000/sum(weightarray)}
End Function
This should return a weighted average rate of $20.
Can someone help me accomplish this?
You don't need VBA -- a simple formula works. If your data is in cells A1:C4, enter the following formula:
=SUMPRODUCT(B1:B4,C1:C4/SUM(C1:C4))
In this version of the formula I am treating C1:C4/SUM(C1:C4) much like an array formula, which converts the array of numbers (200,000, 50,000, etc.) into the corresponding array of weights (0.33, 0.083, etc.). In many contexts, this would require explicitly treating the expression as an array formula, but sumproduct is a rather powerful function which can apply whole-array formulas to its arguments. See this for a nice discussion.
In this particular case
=SUMPRODUCT(B1:B4,C1:C4)/SUM(C1:C4)
also works and corresponds to Merely Useful's answer in the comments, but I'll leave my answer as it is since it is useful to know that sumproduct can in effect evaluate array formulas in its arguments, even if the overall sumproduct isn't entered as an array formula itself.
Well, you don't need a VBA function for this, an array formula will do:
=SUM(A1:A4*B1:B4)/SUM(B1:B4)
(Press Ctrl+Enter when entering the formula)
I am quite new to R statistics, and I one you can help me. I have tried finding the answer to my question by searching the forum and so on, and I apologize in advance if my question is trivial or stupid.
I have spent the last month collecting my first data set. And my dataset is now ready to be analyzed. I have spent some time learning the most basic function of the R statistics.
My dataset deals with adverse drug reaction reports. Each report may contain several suspect drugs and several adverse reactions. A case can therefore contain several drugs and adverse reaction (drug-ADR) combinations. Some cases contain just one combination and others contain several.
And now my question is: How do I make calculations that are “case-specific”?
I want to calculate a Completeness Score for the percentage of completed data fields for each drug-ADR combination, and then I would like to calculate the average for the entire case/report.
I want to calculate a Completness Score (C) for each drug-ADR combination expressed as:
C = (1-Pi) = (1-P1) x (1-P 2) x (1-P3) …. (1-Pn)
, where Pi refers to the penalty deducted, if the data field is not complete (ex 0.50 for 50%). If the information is not missing the panalty 0. The max score will then be 1. n is the number of parameters / variables.
Ultimately I want to calculate an overall Completness score for the overall case/report. The total score is should be calculated from the average of each drug-ADR combination.
C = Cj / m
, where j denotes the current drug-ADR combination, and m is the total number of combinations of drug-ADR in the full report.
Can anyone help me?
Thanke you for your attention!! I will be very grateful for any help that I can get.
This is more of a challenge question than something I urgently need, so don't spend all day on it guys.
I built a dating site (long gone) back in 2000 or so, and one of the challenges was calculating the distance between users so we could present your "matches" within an X mile radius. To just state the problem, given the following database schema (roughly):
USER TABLE
UserId
UserName
ZipCode
ZIPCODE TABLE
ZipCode
Latitude
Longitude
With USER and ZIPCODE being joined on USER.ZipCode = ZIPCODE.ZipCode.
What approach would you take to answer the following question: What other users live in Zip Codes that are within X miles of a given user's Zip Code.
We used the 2000 census data, which has tables for zip codes and their approximate lattitude and longitude.
We also used the Haversine Formula to calculate distances between any two points on a sphere... pretty simple math really.
The question, at least for us, being the 19 year old college students we were, really became how to efficiently calculate and/store distances from all members to all other members. One approach (the one we used) would be to import all the data and calculate the distance FROM every zip code TO every other zip code. Then you'd store and index the results. Something like:
SELECT User.UserId
FROM ZipCode AS MyZipCode
INNER JOIN ZipDistance ON MyZipCode.ZipCode = ZipDistance.MyZipCode
INNER JOIN ZipCode AS TheirZipCode ON ZipDistance.OtherZipCode = TheirZipCode.ZipCode
INNER JOIN User AS User ON TheirZipCode.ZipCode = User.ZipCode
WHERE ( MyZipCode.ZipCode = 75044 )
AND ( ZipDistance.Distance < 50 )
The problem, of course, is that the ZipDistance table is going to have a LOT of rows in it. It isn't completely unworkable, but it is really big. Also it requires complete pre-work on the whole data set, which is also not unmanageable, but not necessarily desireable.
Anyway, I was wondering what approach some of you gurus might take on something like this. Also, I think this is a common issue programmers have to tackle from time to time, especially if you consider problems that are just algorithmically similar. I'm interested in a thorough solution that includes at least HINTS on all the pieces to do this really quickly end efficiently. Thanks!
Ok, for starters, you don't really need to use the Haversine formula here. For large distances where a less accurate formula produces a larger error, your users don't care if the match is plus or minus a few miles, and for closer distances, the error is very small. There are easier (to calculate) formulas listed on the Geographical Distance Wikipedia article.
Since zip codes are nothing like evenly spaced, any process that partitions them evenly is going to suffer mightily in areas where they are clustered tightly (east coast near DC being a good example). If you want a visual comparison, check out http://benfry.com/zipdecode and compare the zipcode prefix 89 with 07.
A far better way to deal with indexing this space is to use a data structure like a Quadtree or an R-tree. This structure allows you to do spatial and distance searches over data which is not evenly spaced.
Here's what an Quadtree looks like:
To search over it, you drill down through each larger cell using the index of smaller cells that are within it. Wikipedia explains it more thoroughly.
Of course, since this is a fairly common thing to do, someone else has already done the hard part for you. Since you haven't specified what database you're using, the PostgreSQL extension PostGIS will serve as an example. PostGIS includes the ability to do R-tree spatial indexes which allow you to do efficient spatial querying.
Once you've imported your data and built the spatial index, querying for distance is a query like:
SELECT zip
FROM zipcode
WHERE
geom && expand(transform(PointFromText('POINT(-116.768347 33.911404)', 4269),32661), 16093)
AND
distance(
transform(PointFromText('POINT(-116.768347 33.911404)', 4269),32661),
geom) < 16093
I'll let you work through the rest of the tutorial yourself.
http://unserializableone.blogspot.com/2007/02/using-postgis-to-find-points-of.html
Here are some other references to get you started.
http://www.bostongis.com/PrinterFriendly.aspx?content_name=postgis_tut02
http://www.manning.com/obe/PostGIS_MEAPCH01.pdf
http://postgis.refractions.net/docs/ch04.html
I'd simply just create a zip_code_distances table and pre-compute the distances between all 42K zipcodes in the US which are within a 20-25 mile radius of each other.
create table zip_code_distances
(
from_zip_code mediumint not null,
to_zip_code mediumint not null,
distance decimal(6,2) default 0.0,
primary key (from_zip_code, to_zip_code),
key (to_zip_code)
)
engine=innodb;
Only including zipcodes within a 20-25 miles radius of each other reduces the number of rows you need to store in the distance table from it's maximum of 1.7 billion (42K ^ 2) - 42K to a much more manageable 4 million or so.
I downloaded a zipcode datafile from the web which contained the longitudes and latitudes of all the official US zipcodes in csv format:
"00601","Adjuntas","Adjuntas","Puerto Rico","PR","787","Atlantic", 18.166, -66.7236
"00602","Aguada","Aguada","Puerto Rico","PR","787","Atlantic", 18.383, -67.1866
...
"91210","Glendale","Los Angeles","California","CA","818","Pacific", 34.1419, -118.261
"91214","La Crescenta","Los Angeles","California","CA","818","Pacific", 34.2325, -118.246
"91221","Glendale","Los Angeles","California","CA","818","Pacific", 34.1653, -118.289
...
I wrote a quick and dirty C# program to read the file and compute the distances between every zipcode but only output zipcodes that fall within a 25 mile radius:
sw = new StreamWriter(path);
foreach (ZipCode fromZip in zips){
foreach (ZipCode toZip in zips)
{
if (toZip.ZipArea == fromZip.ZipArea) continue;
double dist = ZipCode.GetDistance(fromZip, toZip);
if (dist > 25) continue;
string s = string.Format("{0}|{1}|{2}", fromZip.ZipArea, toZip.ZipArea, dist);
sw.WriteLine(s);
}
}
The resultant output file looks as follows:
from_zip_code|to_zip_code|distance
...
00601|00606|16.7042215574185
00601|00611|9.70353520976393
00601|00612|21.0815707704904
00601|00613|21.1780461311929
00601|00614|20.101431539283
...
91210|90001|11.6815708119899
91210|90002|13.3915723402714
91210|90003|12.371251171873
91210|90004|5.26634939906721
91210|90005|6.56649623829871
...
I would then just load this distance data into my zip_code_distances table using load data infile and then use it to limit the search space of my application.
For example if you have a user whose zipcode is 91210 and they want to find people who are within a 10 mile radius of them then you can now simply do the following:
select
p.*
from
people p
inner join
(
select
to_zip_code
from
zip_code_distances
where
from_zip_code = 91210 and distance <= 10
) search
on p.zip_code = search.to_zip_code
where
p.gender = 'F'....
Hope this helps
EDIT: extended radius to 100 miles which increased the number of zipcode distances to 32.5 million rows.
quick performance check for zipcode 91210 runtime 0.009 seconds.
select count(*) from zip_code_distances
count(*)
========
32589820
select
to_zip_code
from
zip_code_distances
where
from_zip_code = 91210 and distance <= 10;
0:00:00.009: Query OK
You could shortcut the calculation by just assuming a box instead of a circular radius. Then when searching you simply calculate the lower/upper bound of lat/lon for a given point+"radius", and as long as you have an index on the lat/lon columns you could pull back all records that fall within the box pretty easily.
I know that this post is TOO old, but making some research for a client I've found some useful functionality of Google Maps API and is so simple to implement, you just need to pass to the url the origin and destination ZIP codes, and it calculates the distance even with the traffic, you can use it with any language:
origins = 90210
destinations = 93030
mode = driving
http://maps.googleapis.com/maps/api/distancematrix/json?origins=90210&destinations=93030&mode=driving&language=en-EN&sensor=false%22
following the link you can see that it returns a json. Remember that you need an API key to use this on your own hosting.
source:
http://stanhub.com/find-distance-between-two-postcodes-zipcodes-driving-time-in-current-traffic-using-google-maps-api/
You could divide your space into regions of roughly equal size -- for instance, approximate the earth as a buckyball or icosahedron. The regions could even overlap a bit, if that's easier (e.g. make them circular). Record which region(s) each ZIP code is in. Then you can precalculate the maximum distance possible between every region pair, which has the same O(n^2) problem as calculating all the ZIP code pairs, but for smaller n.
Now, for any given ZIP code, you can get a list of regions that are definitely within your given range, and a list of regions that cross the border. For the former, just grab all the ZIP codes. For the latter, drill down into each border region and calculate against individual ZIP codes.
It's certainly more complex mathematically, and in particular the number of regions would have to be chosen for a good balance between the size of the table vs. the time spent calculating on the fly, but it reduces the size of the precalculated table by a good margin.
I would use latitude and longitude. For example, if you have a latitude of 45 and a longitude of 45 and were asked to find matches within 50 miles, then you could do it by moving 50/69 ths up in latitude and 50/69 ths down in latitude (1 deg latitude ~ 69 miles). Select zip codes with latitudes in this range. Longitudes are a little different, because they get smaller as you move closer to the poles.
But at 45 deg, 1 longitude ~ 49 miles, so you could move 50/49ths left in latitude and 50/49ths right in latitude, and select all zip codes from the latitude set with this longitude. This gives you all zip codes within a square with lengths of a hundred miles. If you wanted to be really precise, you could then use the Haversine formula witch you mentioned to weed out zips in the corners of the box, to give you a sphere.
Not every possible pair of zip codes are going to be used. I would build zipdistance as a 'cache' table. For each request calculate the distance for that pair and save it in the cache. When a request for a distance pair comes, first look in the cache, then compute if it's not available.
I do not know the intricacies of distance calculations, so I would also check whether computing on the fly is cheaper than looking up (also taking into consideration how often you have to compute).
I have the problem running great, and pretty much everyone's answer got used. I was thinking about this in terms of the old solution instead of just "starting over." Babtek gets the nod for stating in in simplest terms.
I'll skip the code because I'll provide references to derive the needed formulas, and there is too much to cleanly post here.
Consider Point A on a sphere, represented by latitude and longitude. Figure out North, South, East, and West edges of a box 2X miles across with Point A at the center.
Select all point within the box from the ZipCode table. This includes a simple WHERE clause with two Between statements limiting by Lat and Long.
Use the haversine formula to determine the spherical distance between Point A and every point B returned in step 2.
Discard all points B where distance A -> B > X.
Select users where ZipCode is in the remaining set of points B.
This is pretty fast for > 100 miles. Longest result was ~ 0.014 seconds to calculate the match, and trivial to run the select statement.
Also, as a side note, it was necessary to implement the math in a couple of functions and call them in SQL. Once I got past a certain distance the matching number of ZipCodes was too large to pass back to SQL and use as an IN statement, so I had to use a temp table and join the resulting ZipCodes to User on the ZipCode column.
I suspect that using a ZipDistance table will not provide a long-term performance gain. The number of rows just gets really big. If you calculate the distance from every zip to to every other zip code (eventually) then the resultant row count from 40,000 zip codes would be ~ 1.6B. Whoah!
Alternately, I am interested in using SQL's built in geography type to see if that will make this easier, but good old int/float types served fine for this sample.
So... final list of online resources I used, for your easy reference:
Maximum Difference, Latitude and Longitude.
The Haversine Formula.
Lengthy but complete discussion of the whole process, which I found from Googling stuff in your answers.