TDT | Topic Detection and Tracking | Topic Modeling library

Goal: Work flow diagram displays underneath the header text with standard spacing
Actual Results: Work flow diagram displays underneath header with a standardized amount of white space
Errors: No errors messages
Troubleshooting Steps: I attempted to follow the solution steps in this article but it makes the mermaid diagram no longer display and produces formatting error codes.

I've got 2 tables

I want to perform univariate assessment on a dataset. I came up with the code below:

I have a problem with ngstyle in Angular.

When I have a condition that ngstyle has to satisfy and it does, it either changes the background of the html element or not, and it's completely random.

ex. for 122 it will load a GIF once and not once, and it's random

The process is that first it draws a number from this.data, then ngstyle checks the condition and if the condition is true it should change the background of the html element. But unfortunately it changes sometimes and not.

Proccess

1. https://ibb.co/s9WRhg1
2. https://ibb.co/kyPXrfy

here is my column definition for the react data table. in the last column, I'm trying to trigger btnClickedHandler function from outside the class. but now it's throwing error like this -> [TypeError: undefined has no properties].

that means this keyword is not working. because it's not inside the class. I have found that issue. but I really need to trigger that function from the outside in the class.

sorry for the language issue. Thanks in advance!

I need read a image and store it into a unsigned char array and use the array to construct a class. The class construction is device function. so I need read the image and copy to device. The code is similar to below.

How to overlay a vector annotation image on the top of a cartopy map?

This is part of a project to switch from SPSS to R. While there are good tools to import SPSS files into R (expss) what this question is part of is attempting to get the benefits of SPSS style labeling when data originates from CSV sources. This is to help bridge the staff training gap between SPSS and R by providing a common format for data.tables irrespective of file format origin.

Whilst CSV does a reasonable job of storing data it is hopeless for providing meaningful data. This inevitably means variable and factor levels and labels have to come from somewhere else. In most short examples of this (e.g. in documentation) it is practical to simply hard code the meta data in. But for larger projects it makes more sense to store this meta data in a second csv file.

Example data file

ID,varone,vartwo,varthree,varfour,varfive,varsix,varseven,vareight,varnine,varten 1,1,34,1,,1,,1,1,4, 2,1,21,0,1,,1,3,14,3,2 3,1,54,1,,,1,3,6,4,4 4,2,32,1,1,1,,3,7,4, 5,3,66,0,,,1,3,9,3,3 6,2,43,1,,1,,1,12,2,1 7,2,26,0,,,1,2,11,1, 8,3,,1,1,,,2,15,1,4 9,1,34,1,,1,,1,12,3,4 10,2,46,0,,,,3,13,2, 11,3,39,1,1,1,,3,7,1,2 12,1,28,0,,,1,1,6,5,1 13,2,64,0,,1,,2,11,,3 14,3,34,1,1,,,3,10,1,1 15,1,52,1,,1,1,1,8,6,

Example metadata file

Rowlabels,ID,varone,vartwo,varthree,varfour,varfive,varsix,varseven,vareight,varnine,varten varlabel,,Question one,Question two,Question three,Question four,Question five,Question six,Question seven,Question eight,Question nine,Question ten varrole,Unique,Attitude,Unique,Filter,Filter,Filter,Filter,Attitude,Filter,Attitude,Attitude Missing,Error,Error,Ignored,Error,Unchecked,Unchecked,Unchecked,Error,Error,Error,Ignored vallable,,One,,No,Checked,Checked,Checked,x,One,A,Support vallable,,Two,,Yes,,,,y,Two,B,Neutral vallable,,Three,,,,,,z,Three,C,Oppose vallable,,,,,,,,,Four,D,Dont know vallable,,,,,,,,,Five,E, vallable,,,,,,,,,Six,F, vallable,,,,,,,,,Seven,G, vallable,,,,,,,,,Eight,, vallable,,,,,,,,,Nine,, vallable,,,,,,,,,Ten,, vallable,,,,,,,,,Eleven,, vallable,,,,,,,,,Twelve,, vallable,,,,,,,,,Thirteen,, vallable,,,,,,,,,Fourteen,, vallable,,,,,,,,,Fifteen,,

SO the common elements are the column names which are the key to both files

The first column of the metadata file describes the role of the row for the data file so varlabel provides the variable label for each column varrole describes the analytic purpose of the variable missing describes how to treat missing data varlabel describes the label for a factor level starting at one on up to as many labels as there are.

Right! Here's the code that works:

Applying labels is an important part of making survey data comprehensible when reported

So the best example I can find uses expss::apply_labels() e.g the famous mtcars example https://cran.r-project.org/web/packages/expss/vignettes/tables-with-labels.html

as input this requires a data.table and a list of comma separated assignment pairs e.g