borealis | Hardware accelerated , controller and TV oriented UI library | User Interface library

I have some data that's unfortunately PHI and I can't share (to make reproducible) but I'd like to display it in a PDF document from R markdown.

Currently my results will display, but they don't look great, i.e.:

I'd love to display the exact same numbers, but just "prettier". No "##" along the side, and just a nice table in R markdown

I've obviously googled it, and I ran across sites such as this one or this one

But there seems to be drawbacks to using many of these methods. Either it uses a different code to initially conduct the anova (I used aov(), some of these sites use lm() and it doesn't seem to work with aov() ), or it doesn't help with the Tukey results.... or when I load the package it hides an important function I need in a different package (library(papeR) masks summarise, and summarize from dplyr() ).

Is there a simply way I'm missing in order to display those results 'cleanly'?

I have compiled two tables with plant names from different sources (A and B), which I want to merge into a third table (C). Table A and B both use the species name as a unique value, and contain Dutch names as well. Like this:

I offered to help a friend with a problem, and am quickly realizing it is beyond my skills. I am interested in filtering to delete records of a group which fall on or after the first record of another group.

I am grouping by 'species', 'year', and 'sex', and would like to remove any records where 'sex' is "f" which occur after the first 'observation_doy' of the "m". In this example, the records I would like to delete are indicated in bold.

I have a dataset with oppurtunistic species-observations per square kilometer per year (ranging from 1900 to 2019).

There are 139 different sites (square kilomters) in my dataset. I want to make a dataset where for each species for every year for every site, its presence or absence is stated with 1 or 0.

I think this is the appropriate format for including the length of the species-list per year per site in a GLM, to try and account for repeated visits to sites within years (See Szabo et al. 2010 sci-hub.tw/10.1890/09-0877.1 for application of this method).

Data now looks like: