I have a dataset that looks like below:

I want to Identify differentially expressed genes (for example, using p-value or fold change or both) for each treatment that includes direction of change, then I want to Identify the most important pathways impacted by the treatment. I also want to do visualizations that show the changes in gene expression as a function of treatment.

I also have a counts file, but since I already have this file with fold change and pvalues, I was hoping I could get my answers from this.

Any suggestions are appreciated.

If the p-values are already BH-adjusted, you can just define DE genes as those with p-values below a desired threshold, e.g., 5%. If not, you'll have to adjust them yourself using p.adjust with method="BH". After you identify DE genes, you can identify enriched pathways/processes using goana or kegga from the limma package. Check whether your organism is supported, though; the IDs aren't familiar to me.

What you mean by "visualizations" is vague. If the log-fold changes describe the effect of treatment, you can just plot them. They look a bit weird, though, usually there's more than 1 decimal point.