Problems with the abundance table by group

[LailaF]

Hello everyone! I'm trying to create a table with relative abundance of phyla and genre by groups of patient I have in my dataset. The groups are HPV, CT, and healthy. The problem is when I subset my phyloseq object "ps1" by group I get the same results. Like there is no difference in the number of phyla in ps1 and e.g. the healthy group. I'm going to leave the code and the output below. The healthy group is composed of
The code:
tab <- table(tax_table(ps1)[, "Phylum"], exclude = NULL)

tab

#subset phyloseq object
Healthy_group <- subset_samples (ps1, Group=="Healthy")

tab1 <- table(tax_table(Healthy_group)[, "Phylum"], exclude = NULL)
tab1

I tried checking Healthy_group and ps1:

They have the same taxa number. Btw it's the same result for the other 2 groups.
Can someone please help me out? I don't know what I'm doing wrong.
Thank you!

[M_AcostaCH]

Remember put a reproducible example for better help all the community.

FAQ: What's a reproducible example (`reprex`) and how do I create one?

Why reprex? Getting unstuck is hard. Your first step here is usually to create a reprex, or reproducible example. The goal of a reprex is to package your code, and information about your problem so that others can run it and feel your pain. Then, hopefully, folks can more easily provide a solution. What's in a Reproducible Example? Parts of a reproducible example: background information - Describe what you are trying to do. What have you already done? complete set up - include any library() calls and data to reproduce your issue. data for a reprex: Here's a discussion on setting up data for a reprex make it run - include the minimal code required to reproduce your error on the data…

Im saw that you select only the Phylum column in both cases.

[LailaF]

Yes, I wanted to compare phylum abundance between different groups. But I get the same result everytime. I'll try to post a reproducible example. Thank you.

[olibravo]

Please, attach data so we can see the structure of it. You can use dput function if the dataset is not too big