In general, survival analysis can be said to be composed of two steps; Cox regression, with which you calculate the "hazard ratio" based on your variables, and a "Kaplan-Meier (KM) estimate", which is used to visuazlize the data.

Here is a [nice tutorial][1] for doing survival analysis with the `survival` and `survminer` packages. The latter includes `ggplot2` kind visualization of the KM curves.

For the Cox regression, you can use continuous or discrete variables, in your case these would be *the normalized expression value of your gene* and *categories of your gene* respectively. You can categorize the expression level of a gene as *low vs high*, or *low vs medium vs high*, based on different quantiles.  Another approach to categorize your expression data would be to find an "optimal" cut point with [maximally selected rank statistics][2]; from the link: 

> This is an outcome-oriented methods providing a value of a cutpoint
> that correspond to the most significant relation with outcome (here,
> survival)

Basically you search for the cut-point where your data is split into two such that the two resulting parts of the data show the most separation/difference in terms of survival.

  [1]: http://www.sthda.com/english/wiki/survival-analysis-basics
  [2]: https://rpkgs.datanovia.com/survminer/reference/surv_cutpoint.html