cohorts | Cohort visualizer – A handy tool | Data Visualization library

the app function have a router and i well use them in the components thanks for help ....

Currently, after logging in I'm able to get the JWT to the frontend. My app currently has a logging page as the landing page and as soon as the user logins the route checks for authentication to redirect to the guarded home path.

My first intuition was to send a boolean from the backend (Django) and use that to create a guard. But I keep seeing that seems to be better practice to handle this in the front end.

What I did was create an auth.service.ts and an auth.guard.ts. In the service, I try to retrieve the token from the browser and then verify that it hasn't expired. Then I call that method on the guard and return a boolean. Problem is that every time I look for the token in the local storage, I get back null.

Is there any better way to get achieve this?

auth.guard.ts

I want to run a script that searches if each snp (list of snps are contained in the variable $snplist) is in all GWAS cohorts (all cohorts are in separate files ending with *renamed_snp_search.txt). If a snp is in all the cohorts then the snp goes into a log file and I want the loop to terminate after 10 snps are found. I thought that redefining the $total_snp variable toward the end of the while loop would help with this but it appears that the loop just keeps going after using sample data.

I have an inner for-loop in R which I have identified as significant bottleneck in my code. The script simulates the effect of a time-varying policy on individuals prior to adulthood. The outer loop runs over a list of cohorts (yob = 1910,...,1930 etc.) that I would like to study. The inner loop counts from ages from a = 5 to a = 17. CSL.details is a data.table that contains the details of each law that I am studying in form of the variables I grab, which vary by year = birthyear + a. To understand the overall effects of the policy by birth cohort, I need to track ca_years1, ca_years2, ca_years3, and ca_years4 for each a.

I have a data frame that looks like this:

I am trying to find a formula that will generate the total profit for a number of cohorts that generate a different periodic profit per unit, without having to create a line item for each cohort.

In this example, the profit contributed by each widget over time is shown in row 3, and the number of widgets issued in each cohort is shown vertically in column B. Each unit will contribute $25 in the first period, $60 in the second period, and so on. So year 1 total profit would be 100 x $25 = $2,500. Then in year 2, the Y1 cohort would generate 100 x $60 and the Y2 cohort would generate 200 x $25 for a total year 2 profit of $11,000.

Does someone know of a method in Excel that would work to consolidate the total profit calculation each year into a single formula? I am trying to model multiple line items over many periods, so looking for a more efficient solution.

Edit: In case this helps clarify the question, below is an image showing an example of another inefficient way to solve the problem in one line for year 4 total profits, but this is still not scalable. Also shown in text below.

This is a tricky one, and I'm making it trickier by basically asking two questions concurrently. But they're related (in practice, if not in theory).

Basically I want to use one script and one data frame to create a faceted plot with arrows:

• at unique locations on each plot and
• at consistent angles regardless of the scale.

The goal is to use the arrows to indicate dosing of a therapeutic, which might change for individuals or treatment cohorts.

Here's an example of the sort of data which I might need to plot with arrows:

I'm trying to find the biggest cohort in a dataset of about 1000 candidates and 100 test questions. Every candidate is asked 15 questions out of a pool of 100 test questions. People in different cohorts make the same set of randomly sampled questions. I'm trying to find the largest group of candidates who all make the same test.

I'm working in R. The data.frame has about a 1000 rows, and 100 columns. Each column indicates which test question we're working with. For each row (candidate) all column entries are NA apart from the ones where a candidate filled in a particular question he or she was shown. The input in these question instances are either 0 or 1. (see picture)

Is there an elegant way to solve this? The only thing I could think of was using dplyer and filter per 15 question subset, and check how many rows still remain. However, with 100 columns this means it has to check (i think) 15 choose 100 different possibilities. Many thanks!

data.frame structure

Since the ggplot2 version 3.3.5 update, setting breaks to an arbitrary number to limit the number of keys displayed in the legend does no longer work and set the missing breaks to the new NA = gray default instead: