让我们阅读数据，记录所有内容，看看你的错误出现在哪里。
一般来说，您应该有一个指向原始数据集的链接或提供一个缩短版本以遵循可重复性原则。我在 Kaggle 上找到了1990-2015 年的飞机野生动物袭击数据集，我将在这里使用它。注意：您需要有一个 Kaggle 帐户才能下载数据。它也可以在data.gov上获得。
读入数据
library(dplyr)
df <- read.csv("~/../Downloads/database.csv", stringsAsFactors = F)
> df$Species.Name[grepl("Canada goose", df$Species.Name, ignore.case = T)][1]
[1] "CANADA GOOSE"
> names(df)
[1] "Record.ID" "Incident.Year" "Incident.Month"
[4] "Incident.Day" "Operator.ID" "Operator"
[7] "Aircraft" "Aircraft.Type" "Aircraft.Make"
[10] "Aircraft.Model" "Aircraft.Mass" "Engine.Make"
[13] "Engine.Model" "Engines" "Engine.Type"
[16] "Engine1.Position" "Engine2.Position" "Engine3.Position"
[19] "Engine4.Position" "Airport.ID" "Airport"
[22] "State" "FAA.Region" "Warning.Issued"
[25] "Flight.Phase" "Visibility" "Precipitation"
[28] "Height" "Speed" "Distance"
[31] "Species.ID" "Species.Name" "Species.Quantity"
[34] "Flight.Impact" "Fatalities" "Injuries"
[37] "Aircraft.Damage" "Radome.Strike" "Radome.Damage"
[40] "Windshield.Strike" "Windshield.Damage" "Nose.Strike"
[43] "Nose.Damage" "Engine1.Strike" "Engine1.Damage"
[46] "Engine2.Strike" "Engine2.Damage" "Engine3.Strike"
[49] "Engine3.Damage" "Engine4.Strike" "Engine4.Damage"
[52] "Engine.Ingested" "Propeller.Strike" "Propeller.Damage"
[55] "Wing.or.Rotor.Strike" "Wing.or.Rotor.Damage" "Fuselage.Strike"
[58] "Fuselage.Damage" "Landing.Gear.Strike" "Landing.Gear.Damage"
[61] "Tail.Strike" "Tail.Damage" "Lights.Strike"
[64] "Lights.Damage" "Other.Strike" "Other.Damage"
[67] "totalKills"
请注意，物种名称全部大写。除非您确定您逐字了解该名称，否则请使用grepl代替。==
没有total_kills变量，该Fatalities变量代表人类死亡人数，因此我将忽略该过滤器变量。我确实找到了Species.Quantity，这可能是您正在寻找的，在事件中死亡的物种总数。
> unique(df$Species.Quantity)
[1] "1" "2-10" "" "11-100" "Over 100"
对于本例，我们可以将这些值转换为数字。
> dictNames <- unique(df$Species.Quantity)
> dict <- c(1, 2, 0, 11, 100)
> names(dict) <- dictNames
> dict['1']
1
1
> dict['2-10']
2-10
2
> df <- df %>% mutate(totalKills = dict[Species.Quantity])
> table(df$totalKills, useNA = "always")
1 2 11 100 <NA>
146563 21852 1166 46 4477
太好了，现在让我们看看你的代码。
试用您的代码并找出问题所在
> df %>%
+ group_by(State) %>%
+ filter(Species.Name == "CANADA GOOSE" & totalKills > 1) %>%
+ mutate(fall_mig_kills = ifelse(Species.Name == "CANADA GOOSE" &
+ Incident.Month %in% c(9,10,11),
+ totalKills,
+ 0)
+ ) %>%
+ summarise(
+ pct_mig_kills = fall_mig_kills/totalKills
+ )
Error in summarise_impl(.data, dots) :
Column `pct_mig_kills` must be length 1 (a summary value), not 19
嗯，让我们看看为什么会这样。?summarise通过在控制台中输入来阅读帮助菜单会说：
总结 {dplyr} R 文档将多个值减少为单个值
描述
summarise() 通常用于由 group_by() 创建的分组数据。每个组的输出将有一行。
好的，所以每个组的输出将有一行。由于您已经对变量进行了分组，因此我们需要对总击杀数求和。此外，您可能想要创建一个新变量“inSeason”，它可以让您适当地总结您的数据。
因此，要解决您的问题，您只需添加sum：
+ summarise(
+ pct_mig_kills = sum(fall_mig_kills)/sum(totalKills)
+ )
# A tibble: 49 x 2
State pct_mig_kills
<chr> <dbl>
1 0.70212766
2 AK 0.50000000
3 AL 0.00000000
4 AR 1.00000000
5 CA 0.06185567
重写你的代码没有错误
现在让我们将其更改为更容易阅读。你关心的是季节，而不是状态。
> df %>%
+ # inSeason = seasons we care about monitoring
+ # totalKills has NA values, we choose to put deaths at 0
+ mutate(inSeason = ifelse(Incident.Month %in% 9:11, "in", "out"),
+ totalKills = ifelse(is.na(totalKills), 0, totalKills)) %>%
+ # canadian geese only
+ filter(grepl("canada goose", Species.Name, ignore.case = T)) %>%
+ # collect data by inSeason
+ group_by(inSeason) %>%
+ # sum them up
+ summarise(totalDead = sum(totalKills)) %>%
+ # add a ratio value
+ mutate(percentDead = round(100*totalDead/sum(totalDead),0))
# A tibble: 2 x 3
inSeason totalDead percentDead
<chr> <dbl> <dbl>
1 in 838 34
2 out 1620 66
现在你有季节与淡季、总死亡人数和百分比。如果要添加状态，请将该变量添加到分组中。
另一个注释，group_by带有 asummarise会自动删除其他列，因此您不需要select在末尾使用。
> df %>%
+ mutate(inSeason = ifelse(Incident.Month %in% 9:11, "in", "out"),
+ totalKills = ifelse(is.na(totalKills), 0, totalKills)) %>%
+ filter(grepl("canada goose", Species.Name, ignore.case = T)) %>%
+ group_by(State, inSeason) %>%
+ summarise(totalDead = sum(totalKills)) %>%
+ mutate(percentDead = round(100*totalDead/sum(totalDead),0))
# A tibble: 98 x 4
# Groups: State [51]
State inSeason totalDead percentDead
<chr> <chr> <dbl> <dbl>
1 in 52 52
2 out 48 48
3 AB in 1 50
4 AB out 1 50
5 AK in 13 33
6 AK out 26 67
7 AL in 2 40
8 AL out 3 60
9 AR in 6 100
10 CA in 13 8