r-novice-gapminder | R for Reproducible Scientific Analysis | Data Visualization library

In c (prior to c11), a given "function call" cannot be overloaded. If you want a function that operates on a vector or a function that operates on an element, those functions should have different names.

With c11, _Generic and macros let you dispatch based on argument type. See this SO answer. That would permit sin(x) to do a scalar operation if x was a double, or a vector operation if x was not.

In c++ functions can be overloaded. The same function (or operation) can do scalar operations on single elements and vector operations on multiple elements. You can also store results in auto type variables, so you can be agnostic to the return type.

Writing the glue code to convert a scalar operation into a vector one still has to be done somewhere, and C++ has only limited ability to automate writing that glue code.

Now, you could write c style tagged unions that could contain either vectors or scalars and have the code that operates on them dynamically switch between the two modes.

In c++ you could write template code that statically switches between vector and scalar implementations.

Both solutions are not something a beginner in either language would be able to successfully do.

c++ has valarray, which does limited vectorization for you, but it isn't well supported by compilers, nor does it extend well.

Various libraries support efficient vectorization of a limited set of operations; any good matrix library, for example.

Most higher level (than C/C++) languages end up implementing their lower level high speed code in C or C++ or (in some cases) more directly in assembly. Usually C/C++ with assembly or "intrinsics" augmentation is enough to get the most of the performance speedup they want.

To filter out years 1952 and 1957 you can use:

You are very close to what you wanted. scale_x_continuous can be set manually to deal with visibility. You have commented it in your code.

Try scale_x_continuous(limits = c(-5000,40000)) and you can adjust based on your satisfaction.

To make your code work use rlang::as_label(enquo(bench_country)) in geom_label instead of !!bench_country or the other options you have tried. While enquo quotes the argument rlang::as_label converts the argument or the expression to a string which can then be used as label.

With this change I get:

EDIT For reference here is the full code for the plotting function. I also adjusted the code to take make the title and axis label "dynamic". To avoid duplicating code I add new variable at the beginning of the function which I assign the country label as a character:

As far as I get it at least your second approach worked fine. Also to make the first approach work use .f = ~ filter(.x, complete.cases(.x)).

Both approaches give me the same result as your final approach using map2

Do not update variables from inside the function. The approach which you are taking is correct so no need to use <<-, keep fn_benchmark_country as it is. Try saving the returned dataframe in the function.

When you use enquo() you also need to use !! when you call the variable in question within the function. This works:

Thanks for providing your data to this question. I believe your typeError is coming from the labels array your code is creating for the annotation. Based on the function's built-in annotate properties, I actually don't think you need this extra work and it's modifying your data in a way that errors out when plotting.

I took a stab at re-writing your project to produce a heatmap that shows the pivot table of country and year of lifeExp. I'm also assuming that it is important for you to keep this number a float.