gender | Predict Gender from Names Using Historical Data | Development Tools library

I have two dataframes, df1 and df2. I would like to join the two with the following conditions:

1. merge df1 and df2 on gender and Test
2. TestDate in df1 need to be within Date1 and Date2 from df2
3. all.x = TRUE (keep df1 records)

How can I handle the second part?

I Am having the following data in my SSAS cube.

My need is to get the value of the measure based on two conditions with two different dimensions using the MDX.

In this example data, I need to get the Reseller Sales Amount value where the value of Title dimension is equal to Sales Representative and the value of the Genderdimension is equal to Male condition.

I have tried to achieve the requirement with the Case statement and IIF() function available in the MDX but it is not working.

Please find the queries I have tried with different functions.

Using Case statement:

I've been working on some survey data using the survey package. I read the documentation available on post-stratification and calibration, however I got stuck trying to calibrate the sampling weights on a total known for the population that is not the population total.

To make my self clear I prepared an example: Let's say I have income information for a sample stratified by sex, which lets me create the svydesign object:

I have the following table in R which lists a person race, gender, age, and cholesterol test. age and cholesterol test are displayed as dummy variables. age can be categorized as low, medium, or high, while cholesterol tests can be categorized as low or high. I want to transform the age and cholesterol columns to be single columns where low is categorized as 1, medium is categorized as 2, and high is categorized as 3. Cholesterol test can be neigh low or high if a person never took one and should be N/A in the expected output. I want the solution to be dynamic so that if I have multiple columns in this format, the code would still work (i.e. there may be some new tests, which can be categorized as high, low, or medium as dummy variables).

How can I do this in R?

input:

I am a bit stuck, I have a working function that can be utilised using .apply(), however, I cannot seem to get it to work with .assign(). I'd like this to work with assign, so I can chain a number of transformations together.

Could anyone point me in the right direction to resolving the issue?

This works

I have a controller that accepts ObjectNode as @RequestBody.

That ObjectNode represents json with some user data

I am trying to create an R Shiny app that calculates a score using ridge regression and then uses that in a random forest model. I saved both models as RDS and kept them in the same folder where the app.R is.

Then I read the models and data of predicted probabilities and define some functions:

I'm working on a chat application using node js that displays a list of online users. Each user has the following attributes:

ipv4, rank, score, login time

ipv4 is used when accessing a user.

Users should be listed according to this sorting strategy:

• Users with higher rank should be at the top of the list.
• If the user rank is equal, the users with the highest score should be at the top of the list after high-rank users.
• If the user score is equal, users with less login time should be at the top of the list after high-score users.

for example:

1. Login userA (ipv4:1.1.1.1, rank:0, score:3000, loginTime:1631966424070)

• list: [userA]

2. Login userB (ipv4:2.2.2.2, rank:1, score:2000, loginTime:1631966424080)

• list: [userB, userA]

3. Login userC (ipv4:3.3.3.3, rank:1, score:1000, loginTime:1631966424090)

• list: [userB, userC, userA]

4. Login userD (ipv4:4.4.4.4, rank:0, score:3000, loginTime:1631966424100)

• list: [userB, userC, userA, userD]

Access example:

Get user(ipv4:3.3.3.3) -> return userC

There are many users and I use the functional-red-black-tree data structure to improve performance. In this data structure, each item has a key and a value. The constructor of this tree takes a comparator function -like array sort comparator- with two parameters, each of which are keys, as a parameter.

I have defined the user as follows:

I am using Angular, Passport and sessions for authentication. I can create a new user, login but,

Problem

when checking if the user is logged using Passport's isAuthenticated() function always returns false. I followed the instructions from various online resources and I am unsure if:

Question

I can use passport and sessions with my Angular app? or I am just doing something wrong in my code? I would really appreciate it if you guys have any idea/hint why it's not working. Thank you.

Backend code