将具有多个唯一变量的长格式转换为宽格式，再转换为R中的其他唯一变量

我试图将长格式转换为宽格式，但多列表示唯一的行。 在下面的示例中，

块、密度、物种

列表示唯一的个体。每个人都有2或3行与面积和大小关联。我想将面积和大小转换为宽格式

这是我的数据集

block <- c(1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2)
species <- c("A","A","A","A","B","B","B","B","A","A","A","A","B","B","B","B","B")
den <- c("20","20","50","50","20","20","50","50","20","20","50","50","20","20","50","50","50")


block <- as.factor(block)
den <- as.factor(den)
species <- as.factor(species)

area <- c(1:17)
size <- c(17:33)

df <- data.frame(block, species, den, area, size)

---

注意：我所讨论的其他答案不使用多列来表示行的唯一性

我们可以在按组创建序列列后使用

pivot\u wide

library(dplyr)
library(tidyr)
df %>% 
    group_by(block, species, den) %>% 
    mutate(rn = row_number()) %>% 
    ungroup %>% 
    pivot_wider(names_from = rn, values_from = c(area, size), names_sep = ".")
# A tibble: 8 x 9
#  block species den   area.1 area.2 area.3 size.1 size.2 size.3
#  <fct> <fct>   <fct>  <int>  <int>  <int>  <int>  <int>  <int>
#1 1     A       20         1      2     NA     17     18     NA
#2 1     A       50         3      4     NA     19     20     NA
#3 1     B       20         5      6     NA     21     22     NA
#4 1     B       50         7      8     NA     23     24     NA
#5 2     A       20         9     10     NA     25     26     NA
#6 2     A       50        11     12     NA     27     28     NA
#7 2     B       20        13     14     NA     29     30     NA
#8 2     B       50        15     16     17     31     32     33

库（dplyr）
图书馆（tidyr）
df%>%
分组依据（区块、物种、巢穴）%>%
变异（rn=行数（））%>%
解组%>%
pivot_加宽（名称_from=rn，值_from=c（面积、大小），名称_sep=“.”）
#一个tibble:8x9
#区块物种巢穴区域。1个区域。2个区域。3个大小。1个大小。2个大小。3
#                  
#1 1 A 20 1 2 NA 17 18 NA
#2 1 A 50 3 4 NA 19 20 NA
#3 1 B 20 5 6 NA 21 22 NA
#4 1 B 50 7 8 NA 23 24 NA
#52A20910NA 2526NA
#6 2 A 50 11 12 NA 27 28 NA
#7 2 B 20 13 14 NA 29 30 NA
#82B5015173233

使用

data.table

：

库（data.table）；setDT（df）；dcast（df，block+species+den~rowid（block，species，den），value.var=c（“面积”，“大小”）

library(dplyr)
library(tidyr)
df %>% 
    group_by(block, species, den) %>% 
    mutate(rn = row_number()) %>% 
    ungroup %>% 
    pivot_wider(names_from = rn, values_from = c(area, size), names_sep = ".")
# A tibble: 8 x 9
#  block species den   area.1 area.2 area.3 size.1 size.2 size.3
#  <fct> <fct>   <fct>  <int>  <int>  <int>  <int>  <int>  <int>
#1 1     A       20         1      2     NA     17     18     NA
#2 1     A       50         3      4     NA     19     20     NA
#3 1     B       20         5      6     NA     21     22     NA
#4 1     B       50         7      8     NA     23     24     NA
#5 2     A       20         9     10     NA     25     26     NA
#6 2     A       50        11     12     NA     27     28     NA
#7 2     B       20        13     14     NA     29     30     NA
#8 2     B       50        15     16     17     31     32     33