fansi | js library for manipulating Fancy Ansi colored strings | Functional Programming library

I am trying to build a function wrapping over haven::read_dta() similar to the wrap_function() defined in the code below.

My wrap_function() has a default variables = NULL, which should be able to pass NULL to haven::read_dta()'s col_select argument if no values are specified. However, passing the NULL from variables to col_select throws an error (i.e. 'Error: Can't find any columns matching col_select in data.').

Can someone help me understand why this happens and how could I build a wrap_function capable of passing a NULL default value to the lower-level function?

Thanks!

When I knit any of the CV templates in the R package vitae I get a slightly different error for each one. I've made sure that all the files, including my Rmd file, are in the same directory and I haven't changed the template in any way. When I knit the modern CV template, for example, I get this error message:

The example below is a simple one which tries to assert the column y is always positive (y>0). How can I extract the errored data (row 3 with the negative value,into a dataframe maybe, or any convenient object) while allowing the workflow to continue with "cleaned" data?

When I try to use the new-ish ragg::agg_png() device with ggplot2::ggsave(), the image does not appear to save correctly.

Take the following reprex. I make a simple plot and then save it using the agg_png() function directly, and with ggsave(). The image saved with the agg_png() device directly comes out as expected. However, when I use ggsave(), it's almost like the units are being ignored. You can't tell, but there is a tiny image beneath the final code output. In that output, we can see that the image is only 7x7 px, even though inches have been specified by the units. From this blog post, it doesn't seem like anything extra should be required to make ggsave() work beyond setting device = agg_png.

Are there additional parameters I need to specify? Including session info in case there is something system-specific going on.

I'm trying to install the package TStools for R. I have tried all the suggestion I've found so far through googleing, but nothing works. I get every time the same exact error:

Is there a way to calculate the difference between each group efficiently? Ideally, I want to create a new column with mutate() function to show the difference (in one column, in a long format). I don't want to have to do compute the difference between each group individually.

i.e. I want to find the difference in values between each group, on a given date and hour:
arc1045 - arc1046,
arc1045 - arc1047,
arc1045 - arc1048,
arc1045 - arc1050,
arc1046 - arc1047,
arc1046 - arc1048,
.
.
.

The data frame can be retrieved using the code below.

Knitting the following Rmd file now takes ~2 minutes on a 2020 MacBook Pro:

Given a tibble that lists users, products, and product features, I am attempting to calculate the fraction of distinct product users who have a certain product feature:

This may be a usage misunderstanding, but I expect the following toy example to work. I want to have a lagged predictor in my recipe, but once I include it in the recipe, and try to predict on the same data using a workflow with the recipe, it doesn't recognize the column foo and cannot compute its lag.

Now, I can get this to work if I:

1. Pull the fit out of the workflow that has been fit.
2. Independently prep and bake the data I want to fit.

Which I code after the failed workflow fit, and it succeeds. According to the documentation, I should be able to put a workflow fit in the predict slot: https://www.tidymodels.org/start/recipes/#predict-workflow

I am probably fundamentally misunderstanding how workflow is supposed to operate. I have what I consider a workaround, but I do not understand why the failed statement isn't working in the way the workaround is. I expected the failed workflow construct to work under the covers like the workaround I have.

In short, if work_df is a dataframe, the_rec is a recipe based off work_df, rf_mod is a model, and you create the workflow rf_workflow, then should I expect the predict() function to work identically in the two predict() calls below?