toc | A Table of Contents of all Gruntwork Code | Infrastructure Automation library

There are several ways, but the easiest might be to use developer tools. You can do this in RStudio or your browser. After knitting, right-click and select "Inspect Element," "Inspect," or something along those lines (different browsers use slightly different names).

Either your screen will shift to fit this new view, or you'll see a new window open.

In RStudio, the top left of the window has a box/arrow symbol. Click it on (it turns green for me).

That icon looks like this:

Then move your cursor to the viewer pane, to the element in your RMD output that you want to know more about. Select the element by clicking on it two times (not double-click, more like 'select' and 'ya, I'm sure...').

Return to the inspector window. You're going to see something is highlighted. That's the HTML element for the element in your RMD output.

The style pane's content (on the right) has the styling—AKA CSS.

Next to the "Styles" header over the styles pane is "Computed." If you select "Computed," you'll get a summary of the applied styles.

My example RMD title is black:

This could look different depending on your browser and your OS, but the functionality is similar. If you have any questions, let me know.

From your comments, here are more specifics.

I started with the default RMD for bslib templates but replaced the YAML with the one provided in your question. I added some additional TOC elements and deleted a bit of the template. This is what I started with (hovering on one of the TOC elements to show the contrast).

Then I went to the inspector and selected the TOC two times. Then I returned to the inspector window to see what element was highlighted.

Next, I went to the Computed tab, and scrolled to the element background-color.

This told me what to look for and where to look. I could go to the inspector pane 'Source', but since you provided the link to the code in Github, I went there. I searched for "list-group." This is what I found there:

I can go back to the RMD and update my YAML now, like this:

The link to the article abstract appears to be a relative link (from the exception). /doi/abs/10.1080/03066150.2021.1956473 doesn't start with https:// or http://.

You should append this relative URL to the base URL of the website (i.e. if the base URL is "https://www.tandfonline.com", you can

You're rather abusing NumPy there. Especially the += with an array element seems to take almost all of the time.

This is how I'd do it, takes about 0.5 seconds:

There are too many issues in your code. We need to work from scratch. In general, here are some major concerns:

1. Don't do expensive operations so many times. Things like pivot_* and *_join are not cheap since they change the structure of the entire dataset. Don't use them so freely as if they come with no cost.

2. Do not repeat yourself. I saw filter(Id == idd, Category == ...) several times in your function. The rows that are filtered out won't come back. This is just a waste of computational power and makes your code unreadable.

3. Think carefully before you code. It seems that you want the regression results for multiple idd, date2 and Category. Then, should the function be designed to only take scalar inputs so that we can run it many times each involving several expensive data operations on a relatively large dataset, or should it be designed to take vector inputs, do fewer operations, and return them all at once? The answer to this question should be clear.

Now I will show you how I would approach this problem. The steps are

1. Find the relevant subset for each group of idd, dmda and CategoryChosse at once. We can use one or two joins to find the corresponding subset. Since we also need to calculate the median for each Week group, we would also want to find the corresponding dates that are in the same Week group for each dmda.

2. Pivot the data from wide to long, once and for all. Use row id to preserve row relationships. Call the column containing those "DRMXX" day and the column containing values value.

3. Find if trailing zeros exist for each row id. Use rev(cumsum(rev(x)) != 0) instead of a long and inefficient pipeline.

4. Compute the median-adjusted values by each group of "Id", "Category", ..., "day", and "Week". Doing things by group is natural and efficient in a long data format.

5. Aggregate the Week group. This follows directly from your code, while we will also filter out days that are smaller than the difference between each dmda and the corresponding date1 for each group.

6. Run lm for each group of Id, Category and dmda identified.

7. Use data.table for greater efficiency.

8. (Optional) Use a different median function rewritten in c++ since the one in base R (stats::median) is a bit slow (stats::median is a generic method considering various input types but we only need it to take numerics in this case). The median function is adapted from here.

Below shows the code that demonstrates the steps

I found a book on the Papaja package. That link will bring you to the YAML section. I think this is what you were looking for.

First, there doesn't appear to be an association for APA 7, unless you have the development version of Papaja. That being said, there is a ticket on this in their main github.

If you set the output to apa6_docx, this "and &" error won't be there. (And even if it were, you could have deleted it.) You can save it as a PDF from there. I'm not sure if this is an option for you, but I thought I would mention it.

This is what it states about subsequent authors (and much more).

tic/toc should be fine, but it looks like the timing is being skewed by memory pre-allocation.

I can reproduce similar timings to your MATLAB example, however

• On first run (clear workspace)

  • Loop approach takes 2.08 sec
  • Vectorised approach takes 1.04 sec
  • Vectorisation saves 50% execution time

• On second run (workspace not cleared)

  • Loop approach takes 2.55 sec
  • Vectorised approach takes 0.065 sec
  • Vectorisation "saves" 97.5% execution time

My guess would be that since the loop approach explicitly creates a new matrix via zeros, the memory is reallocated from scratch on every run and you don't see the speed improvement on subsequent runs.

However, when HH remains in memory and the HH=___ line outputs a matrix of the same size, I suspect MATLAB is doing some clever memory allocation to speed up the operation.

We can prove this theory with the following test:

This is not something weird pgadmin does, but rather it is something weird Windows itself does when needing to represent such file names in a DOS-like setting. Like when the name is more than 8 chars, or extension more than 3.

In my hands the weird presentation is only there in the logs and status messages. If I use the GUI file chooser, the file names look normal, and replay successfully.

If you really want to know what Windows is doing, I think that is a better question for superuser with a Windows tag. I don't know why you can't restore these files. Are you using the pgAdmin GUI file chooser or trying to type the names in directly to something?

This part of your code: