我在试图解决这个看似简单的问题时感到很沮丧。我有一个像这样的数据集（df）：
structure(list(Year = c(2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L), Unknown = c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), Temp = c(21L, 21L, 21L, 23L, 23L, 21L, 21L, 22L, 21L, 23L,
23L, 22L, 21L, 21L, 22L, 22L, 21L, 21L, 23L, 23L), Obs = structure(c(1L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L), .Label = c("mdk", "sde"), class = "factor"), State = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = "ma", class = "factor"), Zone = c(2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L), Segment = c(8L, 7L, 4L, 17L, 18L, 7L, 2L, 12L, 1L, 17L,
18L, 12L, 9L, 7L, 13L, 11L, 8L, 9L, 17L, 18L), Subseg = c(1L,
3L, 3L, 2L, 2L, 2L, 4L, 0L, 10L, 4L, 2L, 0L, 1L, 1L, 3L, 1L,
2L, 2L, 1L, 1L), Wdir = structure(c(2L, 2L, 1L, 3L, 3L, 2L, 2L,
1L, 2L, 3L, 3L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L), .Label = c("na",
"ne", "nw"), class = "factor"), Wvel = structure(c(1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,
2L), .Label = c("5", "na"), class = "factor"), Clouds = structure(c(1L,
1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 3L, 1L,
1L, 3L, 3L), .Label = c("1", "4", "na"), class = "factor"), Temp.1 = structure(c(1L,
1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 3L, 1L,
1L, 3L, 3L), .Label = c("20", "25", "na"), class = "factor"),
Species = structure(c(7L, 21L, 1L, 21L, 16L, 4L, 16L, 6L,
1L, 17L, 5L, 7L, 5L, 1L, 1L, 6L, 7L, 7L, 24L, 5L), .Label = c("ABDU",
"ABDU", "ABDU", "ABDU", "ABDU", "CAGO", "CAGO", "CAGO", "CAGO",
"CAGO", "GOLD", "GOLD", "GOLD", "GOLD", "GOLD", "MERG", "MERG",
"MERG", "MERG", "MERG", "SCOT", "SCOT", "SCOT", "SCOT",
"SCOT", "SCOT", "SCOT"), class = "factor"), Count = c(5L,
1L, 150L, 3L, 20L, 8L, 5L, 10L, 5L, 1L, 20L, 10L, 2L, 2L,
80L, 40L, 1L, 1000L, 2L, 20L)), .Names = c("Year", "Unknown",
"Temp", "Obs", "State", "Zone", "Segment", "Subseg", "Wdir",
"Wvel", "Clouds", "Temp.1", "Species", "Count"), row.names = c(666L,
614L, 2060L, 1738L, 1459L, 536L, 197L, 2467L, 98L, 1794L, 1449L,
2464L, 696L, 483L, 2644L, 2350L, 686L, 844L, 2989L, 2934L), class = "data.frame")
标题如下：
Year Unknown Temp Obs State Zone Segment Subseg Wdir Wvel
666 2015 1 21 mdk ma 2 8 1 ne 5
614 2015 1 21 mdk ma 2 7 3 ne 5
2060 2015 1 21 sde ma 2 4 3 na na
1738 2015 1 23 mdk ma 2 17 2 nw 5
1459 2015 1 23 mdk ma 2 18 2 nw 5
536 2015 1 21 mdk ma 2 7 2 ne 5
Clouds Temp.1 Species Count
666 1 20 CAGO 5
614 1 20 SCOT 1
2060 na na ABDU 150
1738 1 20 SCOT 3
1459 1 20 MERG 20
536 1 20 ABDU 8
在dplyr中的其他内容中，当我按分段分组时，我希望得到每个物种的总和作为新列。这是我尝试过的最终代码，有很多变化。
df_group = df %>%
group_by(Segment) %>%
summarise(temp = round(mean(Temp)),
WDir = round(mean(Wdir)),
ABDU = sum(which(Species=="ABDU"),Count),
CAGO = sum(which(Species=="CAGO"),Count),
GOLD = sum(which(Species=="GOLD"),Count),
MERG = sum(which(Species=="MERG"),Count),
SCOT = sum(which(Species=="SCOT"),Count))
这就是我得到的（显示正确的格式）：
Segment temp WDir ABDU CAGO GOLD MERG SCOT
1 1 21 2 6 5 5 5 5
2 2 21 2 5 5 5 6 5
3 4 21 1 151 150 150 150 150
4 7 21 2 16 11 11 11 12
5 8 21 2 6 9 6 6 6
6 9 21 2 1003 1004 1002 1002 1002
格式和一般想法是我想要的，但数字并没有按我希望的方式加起来。我确信它很简单但需要一些帮助！感谢。
答案 0 :(得分：3)
问题在于返回位置的向量，但是您没有使用这些位置的子集。因此，除了count变量之外，您得到的总和是真实的位置。 e.g。
x <- c("a", "b", "b")
count <- c(10, 11, 12)
sum(which(c("a", "b", "b") == "b"), count)
# 38 because it is 2 + 3 + 10 + 11 + 12
我相信你想要的是（或至少有一种写作方式）：
sum(ifelse(x == "b", count, 0))
# 23 because it is equal to 0 + 11 + 12
转换为dplyr语法，您的示例可能如下所示：
df_group = df %>%
group_by(Segment) %>%
summarise(temp = round(mean(Temp)),
WDir = round(mean(Wdir)),
ABDU = sum(ifelse(Species=="ABDU", Count, 0L)),
CAGO = sum(ifelse(Species=="CAGO", Count, 0L)),
GOLD = sum(ifelse(Species=="GOLD", Count, 0L)),
MERG = sum(ifelse(Species=="MERG", Count, 0L)),
SCOT = sum(ifelse(Species=="SCOT", Count, 0L)))
答案 1 :(得分：1)
另一种方法，如果您不想输入所有物种的总和：
library(reshape2)
library(dplyr)
# I had a problem with duplicate factor levels from your dput,
# so I re-factored species
df$Species = as.factor(as.character(df$Species))
species.counts = select(df, Segment, Species, Count) %>%
dcast(formula = Segment ~ Species, value.var = "Count", fun.aggregate = sum)
> head(species.counts)
Segment ABDU CAGO MERG SCOT
1 1 5 0 0 0
2 2 0 0 5 0
3 4 150 0 0 0
4 7 10 0 0 1
5 8 0 6 0 0
6 9 2 1000 0 0
df %>% group_by(Segment) %>%
summarise(temp = round(mean(Temp))) %>%
left_join(species.counts)
Source: local data frame [11 x 6]
Segment temp ABDU CAGO MERG SCOT
1 1 21 5 0 0 0
2 2 21 0 0 5 0
3 4 21 150 0 0 0
4 7 21 10 0 0 1
5 8 21 0 6 0 0
6 9 21 2 1000 0 0
我也无法进行风向平均值，因为您的dput数据仅将其作为方向的因素，而不是您展示的head()，但该技术可以概括。