R语言：多层嵌套列表中按层级统计1/2出现次数并求和

First, let's break down the problem into manageable steps:

1. Count 1s and 2s in a single data frame: Create a helper function that takes a data frame and returns the total number of entries that are either 1 or 2.
2. Sum counts per year: For each year (the middle level of your nested list), apply the helper function to all data frames in that year and sum the results.
3. Preserve the species-year hierarchy: Use nested iteration to keep the structure of your original list while computing the sums.

Base R Implementation #

First, define the helper function to handle individual data frames:

# Helper function to count occurrences of 1 or 2 in a data frame
count_12 <- function(df) {
  sum(df %in% c(1, 2))
}

Next, use nested lapply() calls to process the nested list structure:

# Calculate total counts per year, grouped by species
yearly_sums <- lapply(ll, function(species_data) {
  lapply(species_data, function(year_data) {
    # Get counts for each data frame in the year, then sum them
    df_counts <- sapply(year_data, count_12)
    sum(df_counts)
  })
})

Testing with Your Example #

Let's verify this works with your sample data. With set.seed(42):

• l1 contains [1, 0, 0, 0, 0], so count_12(l1) = 1
• l2 contains [2, 2, 2, 1, 0], so count_12(l2) = 4

Each year (e.g., ll[[1]][[1]]) is a list of l1 and l2, so the sum is 1 + 4 = 5. Running the code above will return:

yearly_sums
# [[1]]
# [[1]][[1]]
# [1] 5
# 
# [[1]][[2]]
# [1] 5
# 
# [[2]]
# [[2]][[1]]
# [1] 5
# 
# [[2]][[2]]
# [1] 5

Adding Readable Names (Optional) #

To make the output easier to interpret, you can add descriptive names to the result:

# Name species and year levels
names(yearly_sums) <- paste0("Species_", 1:length(yearly_sums))
for (i in seq_along(yearly_sums)) {
  names(yearly_sums[[i]]) <- paste0("Year_", 1:length(yearly_sums[[i]]))
}

# View the named result
yearly_sums
# $Species_1
# $Species_1$Year_1
# [1] 5
# 
# $Species_1$Year_2
# [1] 5
# 
# $Species_2
# $Species_2$Year_1
# [1] 5
# 
# $Species_2$Year_2
# [1] 5

Alternative Using purrr (For Tidyverse Users) #

If you prefer the tidyverse ecosystem, you can use purrr functions for a more pipe-friendly syntax:

library(purrr)

# Function to sum counts for all data frames in a year
sum_year_counts <- function(year_data) {
  year_data %>%
    map_dbl(count_12) %>%
    sum()
}

# Compute results with nested map calls
yearly_sums_purrr <- ll %>%
  map(map, sum_year_counts)

This will produce the same result as the base R version but with a more concise, readable structure for users familiar with tidyverse tools.

内容的提问来源于stack exchange，提问作者Simon