strata | Strata : A Cross Media File System

I have three large dataframes and I want to append some of the elements from one onto another based on several criteria. I looked up similar questions in Stack Overflow but they don't seem to work for my dataframe format (or I'm not skilled enough to adapt it properly).

What needs to happen is:

1. Filter by sex in maindf1
2. Search for the same ZCTA value in maindf1 in a rowname (first column) in maledflookup
3. Also search for the right age strata from a row in maindf1 in the column name of maledflookup
4. Add a new column of data to maindf1 row with matching ZCTA that has the census population value for that sex and age strata taken from maledflookup
5. Repeat with femaledflookup
6. End result is maindf1 having a censuspop value for every row that was matched by sex, ZCTA, and age strata

maindf1 is raw data where each row is an individual and columns are survey responses or collected data on individuals

The lookup table from the census website I had to use is in weird formatting so the easiest solution for me to fix one of the issues with it was to separate the lookup tables by sex first.

I had no luck in writing successful code as I'm not very experienced with coding in R yet. I tried some for & if loops and failed at adapting fuzzyjoin code for this task. I appreciate your help!

Example data:

I have the following dataset:

I was trying to obtain the expected utility for each individual using R's survival package (clogit function) and I was not able to find a simple solution such as mlogit's logsum.

Below I set an example of how one would do it using the mlogit package. It is pretty straight forward: it just requires regressing the variables with the mlogit function, save the output and use it as an argument in the logsum function -- if needed, there is a short explanation in this vignette. And what I want is to know the similar method for clogit. I've read the package's manual but I have failed to grasp what would be the most adequate function to perform the analsysis.

Note1: My preference for a function like mlogit's is related to the fact that I might need to perform tons of regressions later on and being able to perform the correct estimation in different scenarios would be helpful.

Note2: I do not intend that the dataset created below be representative of how data should behave. I've set the example solely for the purpose of perfoming the function after the logit regressions.

**

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