如果table_selection是一个data.table（如示例数据所示），则可以创建一个新的单列wt，根据variable中的值保存弹出权重值

table_selection[
  ,
  wt:=.SD[[paste0(variable,"_pop_weights")]][1],
  by = 1:nrow(table_selection),
  .SDcols = patterns("ha_pop_weights")
]

下面是使用dplyr（rowwise()和cacross()）的相同方法

# helper function
f <- function(d,v) d[[paste0(v,"_pop_weights")]][1]

# vector of wt variable names
ha_wts = names(table_selection)[grepl("ha_pop_weights$", names(table_selection))]

# mutate the `wt` column
table_selection %>% 
  rowwise() %>% 
  mutate(wt = f(setNames(c_across(all_of(ha_wts)), ha_wts),variable))

无论使用哪种方法，您都可以在上面对summarize()的调用中使用w=wt。

如果你想要一个tidyverse的解决方案，我认为最好的方法是使用tidyr把数据转换成长格式。我的计算机不知道函数'weighted_mean'或'weighted_se'，所以我不能100%肯定这会工作。

library(magrittr)
table_selection %>% 
  tidyr::pivot_longer(cols = tidyselect::contains("weights"),
                      values_to = "pop_values",
                      names_to = "NAMES") %>% 
  dplyr::group_by(year, variable, NAMES) %>%
  dplyr::summarize(weighted_mean = weighted_mean(value, w = pop_values, na.rm=TRUE),
weighted_se = weighted_se(value, w = pop_values , na.rm=TRUE))

但是使用统计数据包中的加权平均值...

table_selection %>% 
  tidyr::pivot_longer(cols = tidyselect::contains("weights"),
                      values_to = "pop_values",
                      names_to = "NAMES") %>% 
  dplyr::group_by(year, variable, NAMES) %>%
  dplyr::summarize(weighted_mean = stats::weighted.mean(value, w = pop_values , na.rm=TRUE),
                   #weighted_se = weighted_se(value, w = pop_values , na.rm=TRUE))

退货：

# A tibble: 24 x 4
# Groups:   year, variable [3]
    year variable NAMES              weighted_mean
   <dbl> <fct>    <chr>                      <dbl>
 1  2006 A_ha     A_ha_pop_weights            5.63
 2  2006 A_ha     B_ha_pop_weights            5.63
 3  2006 A_ha     C_ha_pop_weights            5.63
 4  2006 A_ha     D_ha_pop_weights            5.63
 5  2006 A_ha     h10_pop_weights             5.63
 6  2006 A_ha     Totaal_pop_weights          5.63
 7  2006 A_ha     Y01_pop_weights             5.63
 8  2006 A_ha     Y02_pop_weights             5.63
 9  2006 B_ha     A_ha_pop_weights            2   
10  2006 B_ha     B_ha_pop_weights            2   
# ... with 14 more rows