多次运行pop-gen模拟，并将每个结果存储在数据框的新列中

Question

我有一个基本的赖特 - 费舍尔模拟两个等位基因的工作非常好，并产生一个好看的情节显示等位基因按预期固定或消失。它将计算出来的每一代导出到数据帧d中，因此我掌握了每一代的值。我想要做的是运行整个事情20次，并将每个完整的模拟自动存储在一个新列中，这样我就可以在ggplot图上用颜色和所有好东西绘制所有这些。我最感兴趣的是获得一个整洁的框架，为项目制作好看的情节，而不是突破效率。

#Wright Fisher model Mk1

#Simulation Parameters
# n = pop.size
# f = frequency of focal allele
# x = number of focal allele, do not set by hand
# y = number of the other allele, do not set by hand
# g = generations desired
n = 200
f = 0.6
x = (n*f)
y = (n-x)
g = 200

#This creates a data frame of the correct size to store each generation

d = data.frame(f = rep(0,g))

#Creates the graph.
plot(1,0, type = "n", xlim = c(1,200), ylim = c(0,n),
     xlab = "Generation", ylab = "Frequency A")

#Creates the population, this model is limited to only two alleles, and can only plot one
alleles<- c(rep("A",x), rep("a",y))

#this is the loop that actually simulates the population
#It has code for plotting each generation on the graph as a point 
#Exports the number of focal allele A to the data frame
for (i in 1:g){ 
  alleles <- sample(alleles, n, replace = TRUE)
points(i, length(alleles[alleles=="A"]), pch = 19, col= "red")
F = sum(alleles == "A")
d[i, ] = c(F)
}

所以我想多次运行最后一位并以某种方式存储每个完整的迭代。我知道我可以通过嵌套来循环该函数，即使这很快且很脏，但这样做只会存储外循环的最后一次迭代的值。

Answer 1

这里有很多改进的机会，但这应该让你前进。我只展示了五个模拟，但你应该能够扩展。本质上，将大部分代码放在一个函数中然后你可以使用map包中的purrr函数，或者你也可以用replicate做一些事情：

library(tidyverse)

n = 200
f = 0.6
x = (n*f)
y = (n-x)
g = 200

d = data.frame(f = rep(0,g))

run_sim <- function() {
  alleles <- c(rep("A", x), rep("a", y))

  for (i in 1:g) { 
    alleles <- sample(alleles, n, replace = TRUE)
    cnt_A = sum(alleles == "A")
    d[i, ] = c(cnt_A)
  }

  return(d)
}

sims <- paste0("sim_", 1:5)

set.seed(4) # for reproducibility

sims %>%
  map_dfc(~ run_sim()) %>%
  set_names(sims) %>%
  gather(simulation, results) %>%
  group_by(simulation) %>%
  mutate(period = row_number()) %>%
  ggplot(., aes(x = period, y = results, group = simulation, color = simulation)) +
  geom_line()

由reprex package创建于2019-03-21（v0.2.1）

注意：您还可以为run_sim函数添加参数，例如x和y（即run_sim <- function(x, y) { ... }），这将允许您探索其他可能性。