I am trying to write a program that collects interests and availability from users and clusters them into groups based on similar interests and matching availability.
For example, four users could have the following availability and interests:
User 1:
Availability: Monday, Tuesday, Wednesday
Interest: Movies, Hiking
User 2:
Availability: Monday, Wednesday, Friday
Interest: Movies, Music
User 3:
Availability: Tuesday, Thursday, Saturday
Interest: Sports, Hiking
User 4:
Availability: Thursday, Friday
Interest: Hiking
Then the program could group the users into two clusters:
Cluster 1: User 1, User 2
Cluster 2: User 3, User 4
Since both User 1 and User 2 have the interest "movies" and at least one matching day of availability (Monday), they are placed in the same cluster. Similarly, Users 3 and 4 would be matched in a separate cluster
I am looking for suggestions on how to implement such a clustering algorithm. How can I ensure that each cluster has at least one common day of availability? Ideally, each cluster would share a maximal amount of interests and days of availability. Any other tips or suggestions would be greatly appreciated.
I am trying to determine the on time delivery rate of orders:
The column of interest is on time delivery orders, which contains a field of 0 (not on time) or 1 ( on time). How can I calculate in sql the on time rate for each person? Basically count the number of 0 / over total count(0's & 1's) for each person? Same thing for on time ( count 1/total count (0's & 1's)?
Heres a data example:
Week Delivery on time Person
1 0 sARAH
1 0 sARAH
1 1 sARAH
2 1 vIC
2 0 Vic
You may aggregate by person, and then take the average of the on time statistic:
SELECT Person, AVG(1.0*DeliveryOnTime) AS OnTime,
AVG(1.0 - DeliveryOnTime) AS NotOnTime
FROM yourTable
GROUP BY Person;
Demo
The demo given is for SQL Server, and the above syntax might have to change slightly depending on your actual database, which you did not reveal to us.
in the last days I have been working with markov chain for a multi touch (data driven) attribution model, I have found too much important information at the macro level, for example, the ChannelAttribution package gives me the attribution of each one of the channels of the process to achieve a conversion (either TV, search or call-center) but this attribute is done taking into account all customer journeys, and also the elimination effects for each channel. My question is the following, at a micro level of the analysis, can I obtain at the customer level, which was the channel that most attributed to their purchase decision? That is, which is the channel that had the greatest impact for each one of them customers to make their purchase? it does not matter if a conversion was not made or not.
For example, I imagine an output like the following:
Curtomer ID
Channel Atribution by curtomer
Conversion
1
TV
Conversion
2
TV
Conversion
3
Search
Non-Conversion
4
Call-center
Conversion
5
TV
Non-Conversion
6
Call-center
Conversion
I would be grateful, also sorry for my English I hope to be clear.
sorry for the late reply, maybe this will be helpful for someone else.
The first thing you'll need to do is get your data into shape, specifically a long shape. I've built a sample below for the first 3 Customer IDs in your output table:
|Customer ID|Channel |Conversion |
|----------|------------|--------------|
|1 |TV |Conversion |
|1 |TV |Conversion |
|1 |Call Centre |Conversion |
|2 |TV |Conversion |
|2 |TV |Conversion |
|3 |Search |Conversion |
|3 |Search |Non-conversion|
|3 |Call Centre |Non-conversion|
Notice that if look at the most popular channel for each Customer ID, that it will correspond to the 'Channel Atribution by curtomer' field in your output able?
You can do this by:
Grouping the Customer IDs and the Conversion (Conversion-Customer ID relationship should be 1 to 1)
Count the occasions the channel occurs for each Customer, this will give you
Customer ID
Channel
Conversion
Channel Count
1
TV
Conversion
2
1
TV
Conversion
2
1
Call Centre
Conversion
1
2
TV
Conversion
2
2
TV
Conversion
2
3
Search
Non-conversion
2
3
Search
Non-conversion
2
3
Call Centre
Non-conversion
1
There is some duplication on the conversion and count fields, ignore for now.
With the same grouping as above, filter on the max Channel Count column. This will give you:
Customer ID
Channel
Conversion
Channel Count
1
TV
Conversion
2
1
TV
Conversion
2
2
TV
Conversion
2
2
TV
Conversion
2
3
Search
Non-conversion
2
3
Search
Non-conversion
2
Run a distinct across the dataset, this will give you:
Customer ID
Channel
Conversion
Channel Count
1
TV
Conversion
2
2
TV
Conversion
2
3
Search
Non-conversion
2
Which corresponds to your imagined output.
Tie breakers
It will happen that a Customer ID will have two or more equal number of channels. For instance two TV and two Search. How do we manage this? If you really must have one row per customer, then depending on what you're planning on doing, you'll need to either:
Build some priority ranking logic where rules dictate which channel is counted as the attribution.
Build some logic that randomly attributes the channel in the case of tie breakers.
I hope that helps, I've kept the answer code free but had R/Python in the back of my mind. It could possible be implemented in Excel, but someone far smarter than I would need to contribute that answer.
I have a dataset where I am trying to get the sentiment by article. I have about 1000 articles. Each article is a string. This string has multiple sentences within it. I ideally would like to add another column that would summarise the sentiment for each article. Is there an efficient way to do this using dplyr?
Below is an example dataset with just 2 articles.
date<- as.Date(c('2020-06-24', '2020-06-24'))
text <- c('3 more cops recover as PNP COVID-19 infections soar to 519', 'QC suspends processing of PWD IDs after reports of abuse in issuance of cards')
link<- c('https://newsinfo.inquirer.net/1296981/3-more-cops-recover-as-pnps-covid-19-infections-soar-to-519,3,10,4,11,9,8', 'https://newsinfo.inquirer.net/1296974/qc-suspends-processing-of-pwd-ids-after-reports-of-abuse-in-issuance-of-cards')
V4 <-c('MANILA, Philippines — Three more police officers have recovered from the new coronavirus disease, increasing the total number of recoveries in the Philippine National Police to (PNP) 316., This developed as the total number of COVID-19 cases in the PNP rose to 519 with one new infection and nine deaths recorded., In a Facebook post on Wednesday, the PNP also recorded 676 probable and 876 suspects for the disease., PNP chief Gen. Archie Gamboa previously said the force would will intensify its health protocols among its personnel after recording a recent increase in deaths., The latest fatality of the ailment is a police officer in Cebu City, which is under enhanced community quarantine as COVID-19 cases continued to surge there., ATM, \r\n\r\nFor more news about the novel coronavirus click here.\r\nWhat you need to know about Coronavirus.\r\n\r\n\r\n\r\nFor more information on COVID-19, call the DOH Hotline: (02) 86517800 local 1149/1150.\r\n\r\n \r\n \r\n \r\n\r\n \r\n , The Inquirer Foundation supports our healthcare frontliners and is still accepting cash donations to be deposited at Banco de Oro (BDO) current account #007960018860 or donate through PayMaya using this link .',
'MANILA, Philippines — Quezon City will halt the processing of identification cards to persons with disability for two days starting Thursday, June 25, so it could tweak its guidelines after reports that unqualified persons had issued with the said IDs., In a statement on Wednesday, Quezon City Mayor Joy Belmonte said the suspension would the individual who issued PWD ID cards to six members of a family who were not qualified but who paid P2,000 each to get the IDs., Belmonte said the suspect, who is a local government employee, was already issued with a show-cause order to respond to the allegation., According to city government lawyer Nino Casimir, the suspect could face a grave misconduct case that could result in dismissal., The IDs are issued to only to persons qualified under the Act Expanding the Benefits and Privileges of Persons with Disability (Republic Act No. 10754)., The IDs entitle PWDs to a 20 percent discount and VAT exemption on goods and services., /atm')
df<-data.frame(date, text, link, V4)
head(df)
So I have been looking up how to do this using the sentimentr package and created below. However, this only outputs each sentences' sentiment (I do this by doing a strsplit of .,) and I want to instead aggregate everything at the full article level after applying this strsplit.
library(sentimentr)
full<-df %>%
group_by(V4) %>%
mutate(V2 = strsplit(as.character(V4), "[.],")) %>%
unnest(V2) %>%
get_sentences() %>%
sentiment()
The desired output I am looking for is to simply add an extra column my df dataframe with a summary sum(sentiment) for each article.
Additional info based on answer below:
date<- as.Date(c('2020-06-24', '2020-06-24'))
text <- c('3 more cops recover as PNP COVID-19 infections soar to 519', 'QC suspends processing of PWD IDs after reports of abuse in issuance of cards')
link<- c('https://newsinfo.inquirer.net/1296981/3-more-cops-recover-as-pnps-covid-19-infections-soar-to-519,3,10,4,11,9,8', 'https://newsinfo.inquirer.net/1296974/qc-suspends-processing-of-pwd-ids-after-reports-of-abuse-in-issuance-of-cards')
V4 <-c('MANILA, Philippines — Three more police officers have recovered from the new coronavirus disease, increasing the total number of recoveries in the Philippine National Police to (PNP) 316., This developed as the total number of COVID-19 cases in the PNP rose to 519 with one new infection and nine deaths recorded., In a Facebook post on Wednesday, the PNP also recorded 676 probable and 876 suspects for the disease., PNP chief Gen. Archie Gamboa previously said the force would will intensify its health protocols among its personnel after recording a recent increase in deaths., The latest fatality of the ailment is a police officer in Cebu City, which is under enhanced community quarantine as COVID-19 cases continued to surge there., ATM, \r\n\r\nFor more news about the novel coronavirus click here.\r\nWhat you need to know about Coronavirus.\r\n\r\n\r\n\r\nFor more information on COVID-19, call the DOH Hotline: (02) 86517800 local 1149/1150.\r\n\r\n \r\n \r\n \r\n\r\n \r\n , The Inquirer Foundation supports our healthcare frontliners and is still accepting cash donations to be deposited at Banco de Oro (BDO) current account #007960018860 or donate through PayMaya using this link .',
'MANILA, Philippines — Quezon City will halt the processing of identification cards to persons with disability for two days starting Thursday, June 25, so it could tweak its guidelines after reports that unqualified persons had issued with the said IDs., In a statement on Wednesday, Quezon City Mayor Joy Belmonte said the suspension would the individual who issued PWD ID cards to six members of a family who were not qualified but who paid P2,000 each to get the IDs., Belmonte said the suspect, who is a local government employee, was already issued with a show-cause order to respond to the allegation., According to city government lawyer Nino Casimir, the suspect could face a grave misconduct case that could result in dismissal., The IDs are issued to only to persons qualified under the Act Expanding the Benefits and Privileges of Persons with Disability (Republic Act No. 10754)., The IDs entitle PWDs to a 20 percent discount and VAT exemption on goods and services., /atm')
df<-data.frame(date, text, link, V4)
df %>%
group_by(V4) %>% # group by not really needed
mutate(V4 = gsub("[.],", ".", V4),
sentiment_score = sentiment_by(V4))
# A tibble: 2 x 5
# Groups: V4 [2]
date text link V4 sentiment_score$e~ $word_count $sd $ave_sentiment
<date> <chr> <chr> <chr> <int> <int> <dbl> <dbl>
1 2020-06-24 3 more cops recover as P~ https://newsinfo.inquirer.net/1296~ "MANILA, Philippines — Three more police officers ~ 1 172 0.204 -0.00849
2 2020-06-24 QC suspends processing o~ https://newsinfo.inquirer.net/1296~ "MANILA, Philippines — Quezon City will halt the p~ 1 161 0.329 -0.174
Warning message:
Can't combine <sentiment_by> and <sentiment_by>; falling back to <data.frame>.
x Some attributes are incompatible.
i The author of the class should implement vctrs methods.
i See <https://vctrs.r-lib.org/reference/faq-error-incompatible-attributes.html>.
If you need the sentiment over the whole text, there is no need to split the text first into sentences, the sentiment functions take care of this. I replaced the ., in your text back to periods as this is needed for the sentiment functions. The sentiment functions recognizes "mr." as not being the end of a sentence. If you use get_sentences() first, you get the sentiment per sentence and not over the whole text.
The function sentiment_by handles the sentiment over the whole text and averages it nicely. Check help with the option for the averaging.function if you need to change this. The by part of the function can deal with any grouping you want to apply.
df %>%
group_by(V4) %>% # group by not really needed
mutate(V4 = gsub("[.],", ".", V4),
sentiment_score = sentiment_by(V4))
# A tibble: 2 x 5
# Groups: V4 [2]
date text link V4 sentiment_score$~ $word_count $sd $ave_sentiment
<date> <chr> <chr> <chr> <int> <int> <dbl> <dbl>
1 2020-06-24 3 more cops recov~ https://newsinfo.inquire~ "MANILA, Philippines — Three~ 1 172 0.204 -0.00849
2 2020-06-24 QC suspends proce~ https://newsinfo.inquire~ "MANILA, Philippines — Quezo~ 1 161 0.329 -0.174
I tried to read some online pdf document in R. I used readRDF function. My script goes like this
safex <- readPDF(PdftotextOptions='-layout')(elem=list(uri='C:/Users/FCG/Desktop/NoteF7000.pdf'),language='en',id='id1')
R showed the message that running command has status 309. I tried different pdftotext options. however, it is the same message. and the text file created has no content.
Can anyone read this pdf
readPDF has bugs and probably isn't worth bothering with (check out this well-documented struggle with it).
Assuming that...
you've got xpdf installed (see here for details)
your PATHs are all in order (see here for details of how to do that) and you've restarted your computer.
Then you might be better off avoiding readPDF and instead using this workaround:
system(paste('"C:/Program Files/xpdf/pdftotext.exe"',
'"C:/Users/FCG/Desktop/NoteF7000.pdf"'), wait=FALSE)
And then read the text file into R like so...
require(tm)
mycorpus <- Corpus(URISource("C:/Users/FCG/Desktop/NoteF7001.txt"))
And have a look to confirm that it went well:
inspect(mycorpus)
A corpus with 1 text document
The metadata consists of 2 tag-value pairs and a data frame
Available tags are:
create_date creator
Available variables in the data frame are:
MetaID
[[1]]
Market Notice
Number: Date F7001 08 May 2013
New IDX SSF (EWJG) The following new IDX SSF contract will be added to the list and will be available for trade today.
Summary Contract Specifications Contract Code Underlying Instrument Bloomberg Code ISIN Code EWJG EWJG IShares MSCI Japan Index Fund (US) EWJ US EQUITY US4642868487 1 (R1 per point)
Contract Size / Nominal
Expiry Dates & Times
10am New York Time; 14 Jun 2013 / 16 Sep 2013
Underlying Currency Quotations Minimum Price Movement (ZAR) Underlying Reference Price
USD/ZAR Bloomberg Code (USDZAR Currency) Price per underlying share to two decimals. R0.01 (0.01 in the share price)
4pm underlying spot level as captured by the JSE.
Currency Reference Price
The same method as the one utilized for the expiry of standard currency futures on standard quarterly SAFEX expiry dates.
JSE Limited Registration Number: 2005/022939/06 One Exchange Square, Gwen Lane, Sandown, South Africa. Private Bag X991174, Sandton, 2146, South Africa. Telephone: +27 11 520 7000, Facsimile: +27 11 520 8584, www.jse.co.za
Executive Director: NF Newton-King (CEO), A Takoordeen (CFO) Non-Executive Directors: HJ Borkum (Chairman), AD Botha, MR Johnston, DM Lawrence, A Mazwai, Dr. MA Matooane , NP Mnxasana, NS Nematswerani, N Nyembezi-Heita, N Payne Alternate Directors: JH Burke, LV Parsons
Member of the World Federation of Exchanges
Company Secretary: GC Clarke
Settlement Method
Cash Settled
-
Clearing House Fees -
On-screen IDX Futures Trading: o 1 BP for Taker (Aggressor) o Zero Booking Fees for Maker (Passive) o No Cap o Floor of 0.01 Reported IDX Futures Trades o 1.75 BP for both buyer and seller o No Cap o Floor of 0.01
Initial Margin Class Spread Margin V.S.R. Expiry Date
R 10.00 R 5.00 3.5 14/06/2013, 16/09/2013
The above instrument has been designated as "Foreign" by the South African Reserve Bank
Should you have any queries regarding IDX Single Stock Futures, please contact the IDX team on 011 520-7399 or idx#jse.co.za
Graham Smale Director: Bonds and Financial Derivatives Tel: +27 11 520 7831 Fax:+27 11 520 8831 E-mail: grahams#jse.co.za
Distributed by the Company Secretariat +27 11 520 7346
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