将新的（较短的）列添加到 tibble 并扩展 tibble 以保持整洁答案

【问题标题】：Add new (shorter) column to tibble and expand tibble to remain tidy将新的（较短的）列添加到 tibble 并扩展 tibble 以保持整洁
【发布时间】：2018-09-13 04:10:58
【问题描述】：

我有一个数据框，我需要添加一列以包含对应于现有数据框每一行的 3 个物种。希望下面的例子能说明问题：

Site    Year    Trt
A       2016    bowl
A       2016    vane
A       2017    target
A       2017    stick
B       2016    bowl
B       2016    vane
B       2017    target
B       2017    stick

species<-c("species1", "species2", "species3")

那我想去

Site    Year    Trt     Species
A       2016    bowl    species1
A       2016    vane    species1
A       2017    target  species1
A       2017    stick   species1
A       2016    bowl    species2
A       2016    vane    species2
A       2017    target  species2
A       2017    stick   species2
A       2016    bowl    species3
A       2016    vane    species3
A       2017    target  species3
A       2017    stick   species3
B       2016    bowl    species1
B       2016    vane    species1
B       2017    target  species1
B       2017    stick   species1
B       2016    bowl    species2
B       2016    vane    species2
B       2017    target  species2
B       2017    stick   species2
B       2016    bowl    species3
B       2016    vane    species3
B       2017    target  species3
B       2017    stick   species3

我想一些rep 方法可以，但真正的数据集有两个额外的列和141 个物种。我对所有匹配正确的事情都感到偏执。

【问题讨论】：

tibble 应该如何知道重复物种值的次数？它应该在每个“碗”值处重置吗？目前尚不清楚您希望它如何工作。
@MrFlick 似乎在每个站点的bowl, vane, target, stick 电池之后，会生成一个新物种。也许最简单的事情是按站点拆分数据并应用每个出现 4 个条目的物种的逻辑。但我同意，一些进一步的澄清、尝试和可重复的例子将是非常有益的。

标签： r dataframe dplyr tidyr

【解决方案1】：

利用rep 方法的基本 R 解决方案：

transform(
    do.call('rbind', rep(list(df), length(species))), 
    Species = rep(species, each=nrow(df))
)

#   Site Year    Trt  Species
#1     A 2016   bowl species1
#2     A 2016   vane species1
#3     A 2017 target species1
#4     A 2017  stick species1
#5     B 2016   bowl species1
#6     B 2016   vane species1
#7     B 2017 target species1
#8     B 2017  stick species1
#9     A 2016   bowl species2
#10    A 2016   vane species2
#11    A 2017 target species2
#12    A 2017  stick species2
#13    B 2016   bowl species2
#14    B 2016   vane species2
#15    B 2017 target species2
#16    B 2017  stick species2
#17    A 2016   bowl species3
#18    A 2016   vane species3
#19    A 2017 target species3
#20    A 2017  stick species3
#21    B 2016   bowl species3
#22    B 2016   vane species3
#23    B 2017 target species3
#24    B 2017  stick species3

【讨论】：

【解决方案2】：

如何使用tidyr::expand 将数据扩展为：

library(tidyverse)
expand(data, nesting(Site, Year, Trt), species) %>% as.data.frame()

#     Site Year    Trt  species
# 1     A 2016   bowl species1
# 2     A 2016   bowl species2
# 3     A 2016   bowl species3
# 4     A 2016   vane species1
# 5     A 2016   vane species2
# 6     A 2016   vane species3
# 7     A 2017  stick species1
# 8     A 2017  stick species2
# 9     A 2017  stick species3
# 10    A 2017 target species1
# 11    A 2017 target species2
# 12    A 2017 target species3
# 13    B 2016   bowl species1
# 14    B 2016   bowl species2
# 15    B 2016   bowl species3
# 16    B 2016   vane species1
# 17    B 2016   vane species2
# 18    B 2016   vane species3
# 19    B 2017  stick species1
# 20    B 2017  stick species2
# 21    B 2017  stick species3
# 22    B 2017 target species1
# 23    B 2017 target species2
# 24    B 2017 target species3

数据

data <- read.table(text = 
"Site    Year    Trt
A       2016    bowl
A       2016    vane
A       2017    target
A       2017    stick
B       2016    bowl
B       2016    vane
B       2017    target
B       2017    stick",
header = TRUE, stringsAsFactors = FALSE)

species<-c("species1", "species2", "species3")

【讨论】：

【解决方案3】：

另一个base-R 解决方案是这样的（您可以稍后sort 基于Site 列）：

cbind(dat, Species = rep(species, each = nrow(dat)))

#    Site Year    Trt  Species
# 1     A 2016   bowl species1
# 2     A 2016   vane species1
# 3     A 2017 target species1
# 4     A 2017  stick species1
# 5     B 2016   bowl species1
# 6     B 2016   vane species1
# 7     B 2017 target species1
# 8     B 2017  stick species1
# 9     A 2016   bowl species2
# 10    A 2016   vane species2
# 11    A 2017 target species2
# 12    A 2017  stick species2
# 13    B 2016   bowl species2
# 14    B 2016   vane species2
# 15    B 2017 target species2
# 16    B 2017  stick species2
# 17    A 2016   bowl species3
# 18    A 2016   vane species3
# 19    A 2017 target species3
# 20    A 2017  stick species3
# 21    B 2016   bowl species3
# 22    B 2016   vane species3
# 23    B 2017 target species3
# 24    B 2017  stick species3

【讨论】：