RStudio is only an IDE for R, and R is a programming language. It is surely possible in R, but that includes "programming".
Let the numeric vector x contain your data.
define the density, possibly as a weighted mix of a gaussian and a poisson:
dens <- function(x, mu, sigma, rate, p) p*dnorm(x, mu, sigma) + (1-p)*dpois(x,rate)
The parameters can be found by maximizing the likelihood of the data. A convenient function doing this job ist the function fitDist from the MASS package, so you get the parameter values with
MASS::fitDist(dens, start=c(mu=a, sigma=b, rate=c, p=d))
where a,b,c,d are sensible starting values for the parameters. Note that b>0, c>0 and 0<1
There could be problems with the fit because of the restrictions of some of the parameters. This can be avoided by using transformations that are not restricted, like the logarithms of sigma and rate and the log odds ratio of p.