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I am trying to follow this tutorial here - https://juliasilge.com/blog/xgboost-tune-volleyball/

I am using it on the most recent Tidy Tuesday dataset about great lakes fishing - trying to predict agency based on many other values.

ALL of the code below works except the final row where I get the following error:

Edit: It looks like this is a known issue with the "cascade" method. Results that return NA values after the first attempt don't like being converted to doubles when subsequent methods return lat/lons.

Data: I have a list of addresses that I need to geocode. I'm using lapply() to split-apply-combine, which works, but very slowly. My thought to split (further)-apply-combine is returning errors about dim names and sizes that are confusing to me.

I just noticed that read_csv() somehow uses random numbers which is unexpected (at least to me). The corresponding base R function read.csv() does not do that. So, what does read_csv() use the random numbers for? I looked into the documentation but could not find a clear answer to that. Are the random numbers related to the guess_max argument?

How can I convert my column "payment" from long to wide format while keeping the other columns unchanged?

For each level of "letter", when the cell is before the value of "payment", then when in the wide format this row of the corresponding new variable "e.g., dollar" will have "0"; otherwise "1".

I tried output_format_test<-input_format%>%tidyr::pivot_wider(names_from = age, values_from = payment), but it does not produce the intended result.

##Input format

I am looping to load multiple xlsx files. This I am doing well. But when I want to add the name of the columns of the documents (the same names for all files) I have not managed to do it.

I'm trying to come up with a neat/fast way to read files delimited by newline (\n) characters into more than one column.

Essentially in a given input file, multiple rows in the input file should become a single row in the output, however most file reading functions sensibly interpret the newline character as signifying a new row, and so they end up as a data frame with a single column. Here's an example:

The input files look like this:

Here is 1 row of data we are fetching from a sports API that comes into us as a nested list. Our fetch_results$data is a list with a nested lists like this for each of many games, as this data is for many soccer matches. The list-of-list nesting can go 3-4 layers deep, with inner lists for scores, and time, and visitorTeam below, and more.

I am working with the current tidytuesday data about salaries and trying to create a model with tidymodels and recipes. I want to predict salary with many of the other factors present using the recipes code, but I run into an issue.

Issue 1 - My recipe says there are empty rows, but I do not know how to figure out how. This does not give an error, so maybe it is not a problem.

Issue 2 - Understanding what my models actually did and how to visualize the performance. I want to plot the models performance on the initial data. Here is an example of my goal: https://indescribled.files.wordpress.com/2021/05/image-17.png?w=782

I do not understand exactly how to use the predict function with my recipe. juice(rec) is less than 1000 rows while the testing data is about 6000. Perhaps I am reading it backwards, but can someone try to point me in the right direction?

The code below should be an exact reproduction of mine.

I am able to read in a subset of columns defined in cols_only like so: