我正在尝试找出最有效的方法来实现一系列目标,以对数据进行分组、汇总列并根据摘要更改新列。
通过下面的示例数据,我想要:
- 变异一个新列“sum”,它将是“count”、group_by(site, trmt, id,species)的总和
- 计算每个物种的相对丰度,group_by(id)。
这篇文章几乎可以帮助我,但我并不想总结(跨())多列:dplyr: group_by, sum various columns, and apply a function based on grouped row sums?
您将如何使用 dplyr 中的管道来解决此问题,以从“df_have”到“df_want”?
谢谢!
site <- c("X", "Y", "Y", "X", "X", "X", "Y", "X", "Y", "X", "Y", "Y", "X", "X", "X", "Y", "X", "Y")
trmt <- c("yes", "yes", "no", "no", "yes", "no", "no", "yes", "yes", "yes", "yes", "no", "no", "yes", "no", "no", "yes", "yes")
id <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9)
species <- c("a", "b", "a", "c", "d", "a", "e", "b", "d", "a", "b", "m", "c", "p", "a", "q", "r", "d")
count <- c(28, 17, 7, 8, 2, 9, 1, 5, 3, 12, 4, 18, 3, 30, 12, 21, 18, 6)
extra <- c("A", "A", "A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B")
df_have <- cbind(site, trmt, id, species, count, extra)
df_have <- as.data.frame(df_have)
df_have
site1 <- c("X", "Y", "Y", "X", "X", "Y", "Y", "X", "X", "Y" )
trmt1 <- c("yes", "yes", "no", "yes", "no", "no", "no", "yes", "yes", "yes" )
id1 <- c(1, 2, 3, 3, 4, 5, 5, 6, 7, 7, 8, 8, 9)
species1 <- c("a", "b", "a", "m", "c", "d", "p", "a", "e", "q", "b", "r", "d" )
sum <- c(40, 21, 7, 18, 11, 2, 30, 21, 1, 21, 5, 18, 9)
relabund <- c(100, 100, 38.9, 61.1, 100, 6.25, 93.75, 100, 4.54, 95.45, 27.74, 78.26, 100)
df_want <- cbind(site1, trmt1, id1, species1, sum, relabund)
df_want <- as.data.frame(df_want)
df_want
最佳答案
这是一个 dplyr
选项
library(dplyr)
df_have %>%
group_by(site, trmt, id, species) %>%
summarise(sum = sum(as.integer(count)), .groups = "drop") %>%
group_by(id) %>%
mutate(relabund = sum / sum(sum) * 100) %>%
ungroup() %>%
arrange(id, species)
## A tibble: 13 x 6
# site trmt id species sum relabund
# <chr> <chr> <chr> <chr> <int> <dbl>
# 1 X yes 1 a 40 100
# 2 Y yes 2 b 21 100
# 3 Y no 3 a 7 28
# 4 Y no 3 m 18 72
# 5 X no 4 c 11 100
# 6 X yes 5 d 2 6.25
# 7 X yes 5 p 30 93.8
# 8 X no 6 a 21 100
# 9 Y no 7 e 1 4.55
#10 Y no 7 q 21 95.5
#11 X yes 8 b 5 21.7
#12 X yes 8 r 18 78.3
#13 Y yes 9 d 9 100
最后一个 arrange()
命令只是为了匹配您的预期输出;如果顺序无关紧要,您可以省略。另请注意,count
列中的数据是字符
,因此我们需要先转换为整数
;这可能应该在上游修复。
关于r - dplyr:子集、总结和变异新函数的工作流程,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/73315935/