munsell | munsell colour system for R | Data Visualization library

I have never used R before, and I am after the hex codes from library("munsell"). I have skimmed through the notes, but have resorted to the following tedious method:

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 am stuck on this small problem. This is how my Excel data looks like:

I have a UserForm which asks for the "Hue", "Munsell Value" and "Munsell Chroma" values, and it should give the value in column D (the color name) as output in a Message Box. I am trying to loop until the last non-empty row and check A, B, C columns for matching data and then give out the corresponding 4th column as output.

This is my VBA code:

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?

Upon opening a project on rstudio i have the following Warning:

I'm new to HTML and interactive tables, and I'm having trouble building a table container to add headers for my data set. Is there an easy way to insert table headers for my data set? I want columns 2-5 (excluding date in column 1) and columns 6-9 to have header 'Sector' and 'Industry' respectively.

I've included an extract of the dataset below.

Thank you!

I've a data set with two variables consisting of full names (name and surname). However, these two variables are ordered in a different sequence:

• variable1 is ordered by
• variable2 is ordered by

How do I filter the rows such that variable1 = variable2? Or can I modify the order of variable2 to match that of variable1?

I created a small sample to replicate the dataset(to note, some full names contain 3 or more words):

I've put together a data preprocessing recipe for the recent coffee dataset featured on TidyTuesday. My intention is to generate a workflow, and then from there tune a hyperparameter. I'm specifically interesting in manually declaring predictors and outcomes through the various update_role() functions, rather than using a formula, since I have some great plans for this style of variable selection (it's a really great idea!).

The example below produces a recipe that works just fine with prep and bake(coffee_test). It even works if I deselect the outcome column, eg. coffee_recipe %>% bake(select(coffee_test, -cupper_points)). However, when I run the workflow through tune_grid I get the errors as shown. It looks like tune_grid can't find the variables that don't have the "predictor" role, even though bake does just fine.

Now, if I instead do things the normal way with a formula and step_rm the variables I don't care about, then things mostly work --- I get a few warnings for rows with missing country_of_origin values, which I find strange since I should be imputing those. It's entirely possible I've misunderstood the purpose of roles and how to use them.

This is a direct follow up to How to interpret ggplot2::stat_density2d.

bins has been re-added as an argument see this thread and the corresponding github issue, but it remains a mistery to me how to interpret those bins.

This answer (answer 1) suggests a way to calculate contour lines based on probabilities, and this answer argues that the current use of kde2d in stat_density_2d would not mean that the bins can be interpreted as percentiles.

So the question. When trying both approaches in order to get estimate quintile probabilities of the data, I get four lines as expected using the approach from answer 1, but only three lines with bins = 5 in stat_density_2d. (which, as I believe, would give 4 bins!)

The fifth bin could be this tiny little dot in the centre which appears (maybe the centroid??)???

Is one of the ways utterly wrong? Or both? Or just two ways of estimating probabilities with their very own imprecision?