mice | Multivariate Imputation by Chained Equations

Unfortunately I have a problem and would have to ask for your support.

I have a list of the most popular pet names. From the dataframe x, it appears that "Jerry" is the most popular mouse name, "Garfield" is the second most popular cat name, and so on.

I would like to extract the first observations of different groups from a dataframe. The number of observations comes from another dataframe.

Here is an example: In dataframe "x" there are different groups of animals with their names.

I have two objects that I need to combine somehow to achieve a desired output. Here's a toy example.

Let's say that I'm running an experiment about a new mice diet. I gathered 6 mice and fed each of them with a different diet. After several weeks, I weighted all mice and recorded each one's weight. Those weights are given in the following object:

I have a text A and a text B. I wish to find the percentage of words in text B (counting all occurrences) not present in the vocabulary (i.e., the list of all unique words) of text A.

E.g.,

A = "a cat and a cat and a mouse"

B = "a plump cat and some more cat and some more mice too"

B has 12 words. Plump, some, more, mice and too are not in A. Plumb is not in A and occurs once, some twice, more twice, mice once, too once. 7 out of 12 words in B are not in A. --> 58 % of B is not in A.

I think we can use Tensorflow's Tokenizer. We can probably also use something else, plain python or another tokenizer and other solutions are welcome.

With tf.Tokenizer I get the word_index for the text A

I have imputed data saved as a mids object and am trying to adapt my usual workflow around imputed data. However, I cannot figure out how to use sjPlot's tab_corr() and tab_df() and psych's describe on a mids object.

My goal is to generate a table of descriptive statistics and a correlation matrix without averaging the imputed datasets together. I was able to generate correlations using miceadds::micombine.cor, but the output isn't formatted like a typical correlation matrix. I also can individually compute means, SDs, etc. of variables from the mids object, but I'm looking for something that will generate a table.

In a ramda pipe, I want to subtract the values of two array keys, to ultimately end up with an array of those differences.

For example, consider the following mice_weights array. I want to get an array with the differences weight_post minus weight_pre, for the male mice only.

I am trying to add r-squared and the number of observations to my regression tables using gtsummary and multiply imputed data from mice. I can still extract r-squared using glance if I pool the model, but mipo objects are not compatible with tbl_regression since pooling is already part of the tidying process. Any help with this issue is appreciated.

Given this somewhat tortured set-up:

I am trying to write a function that can be used within a dplyr pipeline. It should take an arbitrary number of columns as arguments, and replace certain substrings in only those columns. Below is a simple example of what I have so far.