在R中为不同变量指定不同聚合函数（FUN）实现多变量分组聚合

Absolutely! You can easily apply different aggregation functions to separate variables when grouping your data in R. Let's use your sample dataset to demonstrate a few common, practical approaches:

First, let's recreate your sample data (so anyone following along can run the code):

dat <- data.frame(
  ID = rep(letters[1:3], each =3), 
  Plot = rep(1:3,3),
  Val1 = (1:9)*10, 
  Val2 = (1:9)*20
)

Here's what the dataset looks like:

ID Plot Val1 Val2
1  a    1   10   20
2  a    2   20   40
3  a    3   30   60
4  b    1   40   80
5  b    2   50  100
6  b    3   60  120
7  c    1   70  140
8  c    2   80  160
9  c    3   90  180

1. Using dplyr (Tidyverse Approach) #

This is the most readable method for most R users, especially if you're working with the tidyverse ecosystem. Use group_by() to define your grouping variable(s), then summarize() to specify exactly which function to apply to each variable:

library(dplyr)

dat_summary <- dat %>%
  group_by(ID) %>%
  summarize(
    Total_Val1 = sum(Val1),  # Sum Val1 for each ID group
    Avg_Val2 = mean(Val2)   # Take the mean of Val2 for each ID group
  )

# View the result
dat_summary

Output:

# A tibble: 3 × 3
  ID    Total_Val1 Avg_Val2
  <chr>      <dbl>    <dbl>
1 a             60       40
2 b            150      100
3 c            240      160

2. Using data.table (Fast for Large Datasets) #

If you're working with big datasets and need speed, data.table is the way to go. The syntax is concise and efficient:

library(data.table)

# Convert data.frame to data.table
setDT(dat)

dat_summary <- dat[, .(
  Total_Val1 = sum(Val1),
  Avg_Val2 = mean(Val2)
), by = ID]

# View the result
dat_summary

Output:

ID Total_Val1 Avg_Val2
1:  a         60       40
2:  b        150      100
3:  c        240      160

3. Base R Approach with aggregate() #

You can also do this with base R's aggregate() function. Note that you'll need to pass a list of functions if you want different behavior per variable:

# For this method, we'll use a named list to map variables to functions
dat_summary <- aggregate(
  x = list(Val1 = dat$Val1, Val2 = dat$Val2),
  by = list(ID = dat$ID),
  FUN = function(x) c(sum = sum(x), mean = mean(x))
)

# Unnest the result for a cleaner table
dat_summary <- do.call(data.frame, dat_summary)
dat_summary

Output:

ID Val1.sum Val1.mean Val2.sum Val2.mean
1  a       60        20      120        40
2  b      150        50      300       100
3  c      240        80      480       160

(This returns both sum and mean for each variable, but you can adjust the function to only return what you need.)

内容的提问来源于stack exchange，提问作者theforestecologist