我有一个数据框，包括国家/年进出口，对其他国家。与示例数据集中一样，二元导入和导出的数据不会完全重叠。
EG
library(tidyverse)
df <- data.frame("Reporter" = c("USA", "USA", "USA", "USA", "USA", "USA", "USA", "USA", "Africa","Africa", "Africa","Africa", "Africa","Africa", "Africa","Africa", "EU", "EU","EU", "EU", "EU", "EU","EU", "EU"),
"Partner" = c("Africa","Africa", "Africa","Africa","EU", "EU","EU", "EU", "USA", "USA", "USA", "USA", "EU", "EU","EU", "EU","USA", "USA", "USA", "USA","Africa","Africa", "Africa","Africa"),
"Year" = c(1970, 1970, 1980, 1980, 1970, 1970, 1980, 1980, 1970, 1970, 1980, 1980, 1970, 1970, 1980, 1980, 1970, 1970, 1980, 1980, 1970, 1970, 1980, 1980),
"Flow" = c("Import", "Export","Import", "Export","Import", "Export","Import", "Export","Import", "Export","Import", "Export","Import", "Export","Import", "Export","Import", "Export","Import", "Export","Import", "Export","Import", "Export"),
"Val" = runif(24, min=0, max=100), stringsAsFactors = FALSE)
我创建了这个数据的宽版本。
wide_df <- df %>% spread ("Flow", "Val")
我能够为二元组创建方向ID。
wide_df$ReporterID <- as.numeric(factor(wide_df$Reporter, levels=unique(wide_df$Reporter)))
然而，由此产生的数据被认为是不同的，例如，美国和非洲，以及非洲和美国。
任何人都可以想到一种方法，允许我将这些二元组折叠成一个ID代码
谢谢你的考虑！
library(tidyverse)
# vectorised function to order and combine values
f = function(x,y) paste(sort(c(x, y)), collapse="_")
f = Vectorize(f)
df %>%
spread ("Flow", "Val") %>%
mutate(ID1 = f(Reporter, Partner),
ID2 = as.numeric(as.factor(ID1)))
# Reporter Partner Year Export Import ID1 ID2
# 1 Afica EU 1970 56.6 98.9 Afica_EU 1
# 2 Afica EU 1980 95.3 2.25 Afica_EU 1
# 3 Afica USA 1970 50.4 10.3 Afica_USA 2
# 4 Afica USA 1980 29.4 3.08 Afica_USA 2
# 5 EU Afica 1970 88.8 56.3 Afica_EU 1
# 6 EU Afica 1980 53.6 48.0 Afica_EU 1
# 7 EU USA 1970 4.50 83.8 EU_USA 3
# 8 EU USA 1980 79.1 0.473 EU_USA 3
# 9 USA Afica 1970 61.9 37.2 Afica_USA 2
#10 USA Afica 1980 9.88 39.6 Afica_USA 2
#11 USA EU 1970 10.4 29.3 EU_USA 3
#12 USA EU 1980 21.1 35.3 EU_USA 3
一个选项是ID1，它结合了实际值。
另一种选择是ID2，它创建了一个基于ID1的数字。
这些ID2数背后的逻辑是factor变量ID1的水平顺序（即在这种情况下的字母顺序）。
如果您不需要原始列Reporter和Partner，您可以在流程结束时使用unite(ID1, Reporter, Partner, remove = T)或select(-Reporter, -Partner)将它们排除。
我们通过pasteing每个行（pmin，pmax）的'Reporter'，'Partner'的相应元素的最小值和最大值创建独特的'id'，将其转换为factor并强制转换为numeric
or usingtidyverse`
library(tidyverse)
wide_df %>%
mutate(newid = as.numeric(factor(paste(pmin(Reporter, Partner),
pmax(Reporter, Partner), sep="_"))))
# Reporter Partner Year Export Import newid
#1 Afica EU 1970 23.494073 62.50156 1
#2 Afica EU 1980 18.808975 52.17495 1
#3 Afica USA 1970 23.679063 37.02527 2
#4 Afica USA 1980 2.346382 21.69631 2
#5 EU Afica 1970 73.075570 78.00496 1
#6 EU Afica 1980 69.620370 60.24295 1
#7 EU USA 1970 89.163190 80.78952 3
#8 EU USA 1980 77.462146 48.51146 3
#9 USA Afica 1970 18.285198 99.99596 2
#10 USA Afica 1980 26.119664 40.51762 2
#11 USA EU 1970 78.307579 70.91757 3
#12 USA EU 1980 41.067151 84.06877 3