Cannot comment from my personal impressions, as I have almost zero knowledge of R, compared to several years of using Python for writing apps and working with data. I like R's focus on functional programming, though.
However, a couple of years ago, my wife tried to transition from business consulting to a data analytics / data science role. She started with taking an R course. She was put off by R's complexity and the course's early focus on the details of R syntax, function definitions, closures etc. and abandoned it.
The year after, she decided to try again and enrolled in a course that used Python (with numpy+pandas+scipy as data science stack) and she reported it to be much simpler, more intuitive and easier to learn compared to her previous experience with R. Now she has successfully completed the program and is employed as a data analyst.
We ran into this problem often teaching R when I was in grad school. However, in the years since, the tidyverse has put strong focus on getting users manipulating data even if they don't know how to define a function, etc..!
Here's a useful post, comparing the classic approach you mention to an alternative
R has a number of features that are intended to facilitate interactive use, which despite being very convenient can be confusing to someone who is trying to learn the language. With Python, on the other hand, it is easier for a novice to build a mental model of how things work. However Python is pretty awful as an interactive language due to the way it interprets white space. Personally I think taking the time to learn R is well worth it.
I guess that's more an issue with the courses than the language per se. Sometimes it is a good idea to begin the course with direct application, instead of focusing on the language.
I have encountered a lot of really terrible R learning materials. One data viz course I took (a very, very reputable and widely-used course on a major MOOC platform) taught how to make several simple chart types in each of base R, a library called lattice that I've never encountered since, and ggplot2. I think a lot of it comes from R instructors who started out back before the tidyverse trying to teach the path they _took_ to learning the language, rather than the quickest path to being proficient in the language as it exists today.
The tidyverse is incredibly controversial in parts of the R community; it's essentially an opinionated set of packages that basically comes with its own "standard" library. But I think that wholeheartedly embracing it, and hiding the way to do things in R that you would do them without the affordances that the tidyverse offers, is absolutely the right way to teach R these days. Unfortunately, a lot of courses and books haven't caught up to that yet.
You only have to go through the learning process once. You are able to use the language for a lifetime. I find it so strange how much emphasis we tend to put on things being simple to learn and pick up.
However, a couple of years ago, my wife tried to transition from business consulting to a data analytics / data science role. She started with taking an R course. She was put off by R's complexity and the course's early focus on the details of R syntax, function definitions, closures etc. and abandoned it.
The year after, she decided to try again and enrolled in a course that used Python (with numpy+pandas+scipy as data science stack) and she reported it to be much simpler, more intuitive and easier to learn compared to her previous experience with R. Now she has successfully completed the program and is employed as a data analyst.