I am hoping to find someone with some (social) network analysis knowledge in R.
I have been trying to create an edgelist in order to perform an network analysis/visualize a network. In short, I am studying whether membership of memberships in social clubs contributes to social integration.
I have 14 variables (sm04 tm sm14) all recoded and consisting of either a 'yes' or 'no' to membership. I am struggling to transform this into the right format and get an edge list which I can work with. Basically, I need all respondents (ID's) to be listed in rows under each other. If one is member of more clubs it should have a row for each club he or she is member of.
I have tried the 'list' option in R and the 'cbind' but I didn't get the list I wanted.
Additionally, I am struggling to get the right 'nodes' list. If I understand everything correctly nodes will represent the respondents, in my case 115 migrants. Is the nodelist then simply 115 rows with the ID number of each respondent?
I hope someone is able to help me! If anything is unclear please ask. Your help is highly appreciated!
Related
I've set up a Google Sheets workbook that synthesizes data from a few different sources via manual input, IMPORTHTML and IMPORTRANGE. Once the data is populated, I'm using INDEX MATCH to filter and compare the information and to RANK each data set.
Since I have multiple data inputs, I'm running into a persistent issue of names not being written exactly the same between sources, even though they're the same person. First names are the primary culprit (i.e. Mary Lou vs Marylou vs Mary-Lou vs Mary Louise) but some last names with special symbols (umlauts, accents, tildes) are also causing errors. When Sheets can't recognize a match, the INDEX MATCH and RANK functions both break down.
I'm wondering how to better unify the data automatically so my Sheet understands that each occurrence is actually the same person (or "value").
Since you can't edit the results of an IMPORTHTML directly, I've set up "helper columns" and used functions like TRIM and SPLIT to try and fix instances as I go, but it seems like there must be a simpler path.
It feels like IFS could work but I can't figure how to integrate it. Also thinking this may require a script, which I'm just beginning to study.
Here's a simplified example of what I'm trying to achieve and the corresponding errors: Sample Spreadsheet
The first tab is attempting to pull and RANK data from tabs 2 and 3. Sample formulas from the Summary tab, row 3 (Amelia Rose):
Cell B3: =INDEX('Q1 Sales'!B:B, MATCH(A3,'Q1 Sales'!A:A,0))
Cell C3: =RANK(B3,$B$2:B,1)
Cell D3: =INDEX('Q2 Sales'!B:B, MATCH(A3,'Q2 Sales'!A:A,0))
Cell E3: =RANK(D3,$D$2:D,1)
I'd be grateful for any insight on how to best index 'Q2Sales'!B3 as the correct value for 'Summary'!D3. Thanks in advance - the thoughtful answers on Stack Overflow have gotten me this far!
to counter every possible scenario do it like this:
=ARRAYFORMULA(IFERROR(VLOOKUP(LOWER(REGEXREPLACE(A2:A, "-|\s", )),
{REGEXEXTRACT(LOWER(REGEXREPLACE('Q2 Sales'!A2:A, "-|\s", )),
TEXTJOIN("|", 1, LOWER(REGEXREPLACE(A2:A, "-|\s", )))), 'Q2 Sales'!B2:B}, 2, 0)))
I am currently working with a large data set using R. So, I have a column called "Offers". This column contains text describing 'promotions' that companies offer on their products. I am trying to extract numeric values from these. While, for most cases, I am able to do so well using a combination of regex and functions in R packages, I am unable to deal with a couple of specific cases of text shown below. I would really appreciate any help on these.
"Buying this ensures Savings of $50. Online Credit worth 35$ is also available. So buy soon!"
1a. I want to get both the numeric values out but in 2 different columns. How
do I go about that?
1b. For another problem that I have to solve, I only need to take the value associated with the credit. It is always the case that for texts like above, the second numeric value in the text, if it exists, is the one associated with the credit.
"Get 50% off on your 3 night stay along with 25 credits, offer available on 3 December 2016"
(How should I only take the value associated with credits?)
Note: Efficiency would be important as well because I am dealing with about 14 million rows.
I have tried looking online for a solution but have not found anything very satisfactory.
I am not 100% sure about what you want but this may help you.
A <- "do 50% and whatever 23"
B <- gregexpr("\\d+",A)[[1]]
firstNum <- substr(A,B[1],B[1]+attr(B,"match.length")[1]-1)
secondNum <- substr(A,B[2],B[2]+attr(B,"match.length")[2]-1)
Hope this helps.
I'm developing a Weka app like Akinator by using the j48 method.
Sample:
http://jbossews-vdoctor.rhcloud.com/doctor
The following is the app's table definition and sample data
qa means question id(Please refer the master which can be set by user) + answer(1:Yes, 2: I don't know, 3: No).
1 line per 1 question & answer.
id,qa,class
A,13,1
A,23,1
B,13,2
B,21,2
The point is to find a way to select the question which can maximize the entropy.
Currently this app is regarding first node id of decision tree as the best question.
And then it narrows down the options by this elimination way.
But the accuracy was too bad to run correctly so I'd like to improve it.
I noticed that the qa column was identified as numeric so it could not build the correct decision tree.
I am confused what I should do for improvement. Dataset? Table definition? Logic?
This is quite a broad question that you are asking, and without code or a clear understanding of the problem it is quite difficult to answer, but I'll give some tips for improvement:
Table Definition
What may have made more sense here is to have an attribute for each question, instead of using a single instance per question. For Example, instead of id, qa and class, you could have A, B, C, D, E, F and Disease. (I believe there were six questions, and naming each attribute would be recommended instead of A-F)
Dataset
You will need at least as many cases as there are diseases, if not more for defining multiple subsets of the problem space for the same disease. There are likely cases where some questions are irrelevant or missing, and the model may need to handle such situations.
Logic
In such a case, you might be able to do the questionnaire by starting with the root node and asking questions until you reach the estimated class. This way, you can ask from node to node until a class is reached.
I hope this helps in improving your existing model.
NOTE: I tried your questionnaire and answered No to all of your questions, and I strangely ended up with Trichomoniasis. Perhaps there could be a 'No Disease' category for your training data also.
My nominal qa data is building such a decision tree by binary split.
actually this structure won't make sense because there is tree at only one side. When qa equal 23 it would be always '3' answer. It's irrational.
http://www.fastpic.jp/viewer.php?file=2693704973.jpg
You should first reformat your features to get all possible questions A,B,C,D... as binary features and your final answer (ie. what to guess) as target class if you want your tree to get a sequence of questions reaching to your answer. Your data will certainly be sparse (many questions without data/answer).
By the way, a binary tree is not the right ML structure and algorithm to build an Akinator like or 20Q/Guess-who. Please look some suggestions here: https://stats.stackexchange.com/questions/6074/akinator-com-and-naive-bayes-classifier
I need to create a stats sheet that records the amount of touchdowns performed by a certain team. I have it working for the first round when it records all 8 teams, however in the semifinals since only 4 teams make it, it does not keep track of what index the winning teams were on and just couts the stats in a regular order from 0 - 4. Ive been thinking for a couple days now on how I could possibly overcome this but I havent been able to find a solution yet.
the stats table that is outputted
Please let me know if i can contribute anymore information to make my question less vague and easier for you to understand. I appreciate all the help, thank you.
Having implemented an algorithm to recommend products with some success, I'm now looking at ways to calculate the initial input data for this algorithm.
My objective is to calculate a score for each product that a user has some sort of history with.
The data I am currently collecting:
User order history
Product pageview history for both anonymous and registered users
All of this data is timestamped.
What I'm looking for
There are a couple of things I'm looking for suggestions on, and ideally this question should be treated more for discussion rather than aiming for a single 'right' answer.
Any additional data I can collect for a user that can directly imply an interest in a product
Algorithms/equations for turning this data into scores for each product
What I'm NOT looking for
Just to avoid this question being derailed with the wrong kind of answers, here is what I'm doing once I have this data for each user:
Generating a number of user clusters (21 at the moment) using the k-means clustering algorithm, using the pearsons coefficient for the distance score
For each user (on demand) calculating their a graph of similar users by looking for their most and least similar users within their cluster, and repeating for an arbitrary depth.
Calculating a score for each product based on the preferences of other users within the user's graph
Sorting the scores to return a list of recommendations
Basically, I'm not looking for ideas on what to do once I have the input data (I may need further help with that later, but it's not the point of this question), just for ideas on how to generate this input data in the first place
Here's a haymaker of a response:
time spent looking at a product
semantic interpretation of comments left about the product
make a discussion page about a product, brand, or product category and semantically interpret the comments
if they Shared a product page (email, del.icio.us, etc.)
browser (mobile might make them spend less time on the page vis-à-vis laptop while indicating great interest) and connection speed (affects amt. of time spent on the page)
facebook profile similarity
heatmap data (e.g. à la kissmetrics)
What kind of products are you selling? That might help us answer you better. (Since this is an old question, I am addressing both #Andrew Ingram and anyone else who has the same question and found this thread through search.)
You can allow users to explicitly state their preferences, the way netflix allows users to assign stars.
You can assign a positive numeric value for all the stuff they bought, since you say you do have their purchase history. Assign zero for stuff they didn't buy
You could do some sort of weighted value for stuff they bought, adjusted for what's popular. (if nearly everybody bought a product, it doesn't tell you much about a person that they also bought it) See "term frequency–inverse document frequency"
You could also assign some lesser numeric value for items that users looked at but did not buy.