摆脱 cat 命令.这太贵了.有了它，我得到了 384239.返回质数向量反而让我得到了 471617，这是一个显着的改进.

Get rid of the cat command. That's expensive. With it in place, I get to 384239. Returning the vector of primes instead gets me to 471617, a significant improvement.

改变 n >sqrt(counter) 到 n*n >counter 让我得到 477163，一个小小的改进.

Changing n > sqrt(counter) to n*n > counter gets me to 477163, a small improvement.

将 p_nums 和 counter 更改为 integer 类型使我得到 514859，这是另一个小改进.这是通过修改定义和调整这些行的行来实现的:

Changing p_nums and counter to be of type integer gets me to 514859, another small improvement. This is achieved by modifying the lines where these are defined and adjusted:

p_nums = c(2L)
counter = 2L
# ... and inside the loop:
  counter = counter +1L

请注意，您可以使用如下代码对确定某个值为素数的循环进行矢量化:

Note that you can vectorize the loop which determines that a value is prime, with code such as this:

isPRIME = !any(0 == (counter %% p_nums[1:sqrt(counter)]))

使用它代替 for 让我得到 451249，这是一个显着的回归(不使用 cat 并使用整数算法).这是因为 R 没有惰性列表评估，所以对每个值都取模数，然后对 0 进行测试.在这种情况下，这是 for 的一个优点.

Using that instead of for gets me to 451249, a significant regression (using no cat and using integer arithmetic). This is because R does not have lazy list evaluation, so the modulus is taken for every value, then they are tested against 0. This is an advantage of for in this case.