使用dplyr和tidyr：

library(dplyr)
library(tidyr)
df1 %>% 
  pivot_longer(unique(df2$Lichen)) %>% 
  inner_join(df2, by = c("name" = "Lichen")) %>% 
  group_by(Tree_ID, Sensitivity) %>% 
  summarise(value = sum(value)) %>% 
  pivot_wider(names_from = "Sensitivity")

  Tree_ID   Low Medium
1 Tree1       3      3
2 Tree2       7      7

资料

df1 <- read.table(header = T,text = "Tree_ID    Lichen_A    Lichen_B    Lichen_C
Tree1   1   2   3
Tree2   2   5   7")

df2 <- read.table(header = T,text= "
Lichen  Sensitivity
Lichen_A    Low
Lichen_B    Low
Lichen_C    Medium")

只需很小的努力，您就可以将sapply的unique灵敏度转换为子集dat，并获得rowSums，我们将cbind转换为第一列。

cbind(dat[1], sapply(unique(ctgr$Sensitivity), \(x) {
  rowSums(dat[names(dat) %in% ctgr$Lichen[ctgr$Sensitivity == x]])
}))
#   Tree_ID Low Medium
# 1   Tree1   3      3
# 2   Tree2   7      7

• 数据：*

dat <- structure(list(Tree_ID = c("Tree1", "Tree2"), Lichen_A = 1:2, 
    Lichen_B = c(2L, 5L), Lichen_C = c(3L, 7L)), class = "data.frame", row.names = c(NA, 
-2L))

ctgr <- read.table(text='Lichen     Sensitivity
Lichen_A    Low
Lichen_B    Low
Lichen_C    Medium', header=TRUE)

使用data.table

library(data.table)
dcast(melt(setDT(df1), id.var = "Tree_ID", variable.name = "Lichen")[df2, 
 on = .(Lichen)], Tree_ID ~ Sensitivity, value.var = "value", sum)

• 输出

Tree_ID Low Medium
1:   Tree1   3      3
2:   Tree2   7      7