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I've noticed twitter people search can come up with some weird results. Searching for match in screen_name twitter_name and bio is obvious, but they also do something different. I guess it has something to do with Triadic Closure but find its usage for search (instead of suggestions) weird. Wanted to hear your thoughts about this issue.
I think your question might be a little nonspecific, but here are my thoughts:
Suppose your search query was "Miley Cyrus", for instance. Now the top results will for sure include her real account, then fake ones, but then the results will get a little distorted.
I expect it ranks each account / person X in this manner (or something similar):
If person X follows accounts that has the search query in its bio / name, it has a higher rank than if that person didn't.
In our search, "Rock Mafia" is a good example; it doesn't have the term "Miley Cyrus" in its bio nor its name, but if you look at the people "Rock Mafia" is following, you'll find a lot of "similar" names / bios. Another ranking criteria would be this:
If person X has tweets that contains the search query in its content, it would also have a higher rank
A good example is the result "AnythingDisney" (#adljupdated), you can see that the 4th most recent tweet contains "Miley".
So basically the search prioritization looks like this:
Look in name / bio.
Need more results? Rank each person X by his followers and the people he follows, and by tweets that contain the query.
Need even more results? Look at "deeper" levels, rank each person X by the people being followed by the people X is following.
An so on, recursively.
I hope this helped in any manner!
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Currently, my line of code is really long and I was curious to know if there was a more efficient way of doing this.
As Nick has pointed out your question is missing most of the information that would make it answerable. Please read more here, and add more information to your question.
In the meantime, a useful approach is to merge your zipcode data with a dataframe (or dataset) with the state-zipcode link in it.
* first you need to get the zipcode data from somewhere.
* Here is one way:
!wget "https://www2.census.gov/geo/docs/maps-data/data/rel/zcta_county_rel_10.txt"
* now put this data in a frame
frame create zctaFrame
frame zctaFrame{
import delimited "zcta_county_rel_10.txt"
}
* now I'm making up a dataset (share some of yours with dataex from ssc
input str10 name zip
"sam" 55901
"sasha" 84101
"saul" 84111
end
frlink 1:1 zip, frame(zctaFrame zcta5)
frget state, from(zctaFrame)
If this doesn't match what you're trying to do, please add more detail to the question.
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I am working on a Power BI report. There are two dimensions DimWorkedClass and DimWorkedService. (The above snippet is obtained by exporting matrix values to csv.)
The requirement is to transform only the Worked Service Text5 into the Worked Class of Text5 as opposed to A (which is the current value).
It can be transformed at the backend, but is there any way to do it in Power BI?
This is trickier than it might appear, but it looks like this question has already been answered here:
Power Query Transform a Column based on Another Column
In your case, the M code would look something like this:
= Table.FromRecords(Table.TransformRows(#"[Source or Previous Step Here]",
(here) => Record.TransformFields(here, {"Worked Class",
each if here[Worked Service] = "text5" then "text5" else here[Worked Class]})))
(In the above, here represents the current row.)
Another answer points out a slightly cleaner way of doing this:
= Table.ReplaceValue(#"[Source or Previous Step Here]",
each [Worked Class],
each if [Worked Service] = "text5" then "text5" else [Worked Class],
Replacer.ReplaceText,{"Worked Class"})
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I'm practicing with classes and I'm given the task of creating employee management system. I'm given two .txt files. One (details.txt) has details of each employee with the following info: ID, name, DOB, SSN, department, and position. A sample of the file looks like such:
5 ali 6/24/1988 126-42-6989 support assistant
13 tim 2/10/1981 131-12-1034 logistics manager
The other .txt (timelog.txt) will contain a daily log of when employees clock in and clock out. The following format for this file is: ID, date, clock in time, and clock out time. Sample:
5 3/11 0800 1800
13 3/11 0830 1830
Firstly, I am to allow users to search up an employee by ID, name, department or position. Doing so will display all of the employees info (multiple employees if they have the same name, position or are from the same department) as well as show the total number of hours they have worked in the company.
Secondly, users are to be given another option to look up employee time logs by ID number. This will display the entire clock in/ clock out history of that employee as well as total hours worked each day.
I'm planning to read in the info from .txt files via ifstream and store them as an array of objects. I'm just wondering how many classes I should create. I'm thinking 2 classes- one for employee info (from details.txt) and one for time logs(timelogs.txt). Is there any other class I should create or should those 2 suffice?
Short answer: At least two.
Long answer: It depends on many things. Especially what part of code you can identify as potentially reusable.
If you asked for the highest possible amount of classes that could accomplish your task, I would think about a single class for:
Employee
EmployeeManager (Factory, Holder etc.) – creates, holds and deletes the Employee objects, provides search feature
DayWork – a row from timelog.txt, can calculate the amount of hours/minutes spent in work that day
WorkLog – a list of DayWork objects for one employee, can calculate the whole spent time
TextLineParser – encapsulation of std::ifstream
The right answer is most likely somewhere between. Keep in mind that C++ is a multi-paradigm language and you can perform some operations without having a class for them. Instead, they can be performed in a function or a set of functions in a C-like unit. That’s especially useful for one-time operations where the functions don’t share common data (potential properties).
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I'm working on a research project and am assigned to do a bit of data scraping and writing code in R that can help extract current temperature for a particular zip code from a site such as wunderground.com. Now this may be a bit of an abstract question but does anyone know how to do the following:
I can extract the current temperature of a particular zip code by doing this:
temps <- readLines("http://www.wunderground.com/q/zmw:20904.1.99999")
edit(temps)
temps //gives me the source code for the website where I can look at the line that contains the temperature
ldata <- temps[lnumber]
ldata
# then have a few gsub functions that basically extracts
# just the numerical data (57.8 for example) from that line of code
I have a cvs file that contains zip code of every city in the country and I have that imported in R. It is arranged in a table according to zip, city and state. My challenge now is to write a method (using java analogy here because I'm new to R) that basically extracts 6-7 consecutive zip codes (after a particular one specified) and runs the above code by modifying the link within the readLines function and putting in the respective zip code after the link segment zmw:XXXXX and running everything after that based on that link. Now I don't quite know how to extract the data from the table. Maybe with a for-loop function? But then I don't know how to use that to modify the link. I think that's where I'm really getting stuck on. I have a bit of Java background so I understand HOW to approach this problem, just not the knowledge of the syntax. I understand this is quite an abstract question as I didn't provide a lot of code but I just want to know they functions/syntax that will help me extract the data from the table and somehow use that to modify the link through a function rather than manually doing it.
So this is about the Weather Underground data.
You can download csv files from individual weather stations in wunderground, however you need to know the weather station identifier. Here is an example URL for a weather station in Kirkland, WA (KWAKIRKL8):
http://www.wunderground.com/weatherstation/WXDailyHistory.asp?ID=KWAKIRKL8&day=31&month=1&year=2014&graphspan=day&format=1
Here is some R code:
url <- 'http://www.wunderground.com/weatherstation/WXDailyHistory.asp?ID=KWAKIRKL8&day=31&month=1&year=2014&graphspan=day&format=1'
s <- getURL(url)
s <- gsub("<br>\n","",s)
wdf <- read.csv(con<-textConnection(s))
And here is a page with which you can manually find stations and their codes.
http://www.wunderground.com/wundermap/
Since you only need a few you can pick them out manually.
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I have a set of training data consisting of 20 multiple choice questions (A/B/C/D) answered by a hundred respondents. The answers are purely categorical and cannot be scaled to numerical values. 50 of these respondents were selected for free product trial. The selection process is not known. What interesting knowledge can be mined from this information?
The following is a list of what I have come up with so far-
A study of percentages (Example - Percentage of people who answered B on Qs.5 and got selected for free product trial)
Conditional probabilities (Example - What is the probability that a person will get selected for free product trial given that he answered B on Qs.5)
Naive Bayesian classifier (This can be used to predict whether a person will be selected or not for a given set of values for any subset of questions).
Can you think of any other interesting analysis or data-mining activities that can be performed?
The usual suspects like correlation can be eliminated as the response is not quantifiable/scoreable.
Is my approach correct?
It is kind of reverse engineering.
For each respondent, you have 20 answers and one label, which indicates whether this respondent gets the product trial or not.
You want to know which of the 20 questions are critical to give trial or not decision. I'd suggest you first build a decision tree model on the training data. And study the tree carefully to get some insights, e.g. the low level decision nodes contain most discriminant questions.
The answers can be made numeric for analysis purposes, example:
RespondentID IsSelected Q1AnsA Q1AnsB Q1AnsC Q1AnsD Q2AnsA...
12345 1 0 0 1 0 0
Use association analysis to see if there are patterns in the answers.
Q3AnsC + Q8AnsB -> IsSelected
Use classification (such as logistic regression or a decision tree) to model how users are selected.
Use clustering. Are there distinct groups of respondents? In what ways are they different? Use the "elbow" or scree method to determine the number of clusters.
Do you have other info about the respondents, such as demographics? Pivot table would be good in that case.
Is there missing data? Are there patterns in the way that people skipped questions?