sorbus | experimental reimplementation of rowan , focused on size | Dataset library

I have a dataset with climate suitability values (0-1) for tree species for both present and future. I would like to visualise the data in a pyramid plot with the ggplot2 package, whereas present should be displayed on the left side of the plot and future on the right side and the tree species in the according order given in my raw dataset.

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:

I want to use some basic exploratory commands for my dataset, but can't figure this one out: i want to be able to have the ten (or any other number) most abundant species returned to me based on my data-matrix.

So, species that are present in all sites (rows) end up high in the ranking, species that are present in only one site end up low (and may even be excluded based on the ten most abundant species).

Furthermore, each species has a number that corresponds with its abundacy within the site (row). I also want to be able to get a top ten most abundant species based on abundancy rahter then presence absesence.

Here i used dput to give you a part of my dataset.

I have created a data nightmare using Excel to do our project data entry. I have two spreadsheets with a total of 20 tabs. The data structure is identical in each tab. I have managed to create a CSV file with the data from one of the tabs in one of the spreadsheets merged into a structure I hope can then be imported into a R dataframe and then re-structured into a meaningful format.

Each tab has approximately 120 columns (depending on the number of sampling dates) and 100 rows (depends on the number of species observed). Each row is a species of plant and each column contains the data for a specific date and specific location.

I would like to convert my (sample data) five by five (rows and columns) dataframe into a single column with 25 rows.

Then I would like to take the data from each row (record, for example: "08/10/2017,2 to 3,Solidago virgaurea ssp. alpestris,V,vt") and convert it into the following columns:

Date, Segment, Species, Code 1, Code 2, ...

Each observation on a date at a segment for a species contains between 1 and 7 different codes, so the number of columns for each record will generate between 4 and 10 columns. Note the result will follow the above structure. Each observation is enclosed in quotes and the data is delimited with commas.

Thank you for your help!