映射到特定的组，在R中使用purrr

purrr新问题： 我试图通过使用map（）包在R中的组之间应用一个函数来对代码进行矢量化，但我似乎做得不对。下面是我的示例，跨越复制粘贴、for循环，然后映射示例

species <- rep(seq(1:3), times = 10)
bio_data <- bind_cols(length = rnorm(n = 30, mean = 50, sd = 25), 
                      weight = rnorm(n = 30, mean = 100, sd = 35))

dat <- bind_cols(species = species, bio_data)

# test --------------------------------------------------------------------

# manually get mean by species:
dat %>% filter(species == 1) %>% summarize(mean_wt = mean(weight))
dat %>% filter(species == 2) %>% summarize(mean_wt = mean(weight))
dat %>% filter(species == 3) %>% summarize(mean_wt = mean(weight))

# create function and loop over function:
get_mean <- function(data, group){
  data %>% 
    dplyr::filter(species == group) %>% 
    summarise(mean_wt = mean(weight))
}

for(i in unique(species)){
  print(get_mean(dat, i))
}

# use purrr map() package
results <- map(dat, get_mean, group = unique(species))

---

如何让我的代码生成每个物种的平均权重？

循环应为

唯一（物种）

-输出

#[[1]]
# A tibble: 1 x 1
#  mean_wt
#     <dbl>
#1    88.2

#[[2]]
# A tibble: 1 x 1
#  mean_wt
#    <dbl>
#1    105.

#[[3]]
# A tibble: 1 x 1
#  mean_wt
#    <dbl>
#1    124.

# A tibble: 3 x 2
#  species mean_wt
#*   <int>   <dbl>
#1       1    88.2
#2       2   105. 
#3       3   124. 

-输出

#[[1]]
# A tibble: 1 x 1
#  mean_wt
#     <dbl>
#1    88.2

#[[2]]
# A tibble: 1 x 1
#  mean_wt
#    <dbl>
#1    105.

#[[3]]
# A tibble: 1 x 1
#  mean_wt
#    <dbl>
#1    124.

# A tibble: 3 x 2
#  species mean_wt
#*   <int>   <dbl>
#1       1    88.2
#2       2   105. 
#3       3   124. 

#一个tible:3 x 2
#物种平均值
#*      
#1       1    88.2
#2       2   105. 
#3       3   124. 

Aha！如此接近，却又如此遥远。谢谢你的帮助！

# A tibble: 3 x 2
#  species mean_wt
#*   <int>   <dbl>
#1       1    88.2
#2       2   105. 
#3       3   124.