如何使用R从具有多列的数据框中计算（共）发生矩阵？

Question

我是 R 的新手，目前正在处理边缘列表形式的协作数据，该列表具有 32 列和大约 200.000 行。 我想根据国家之间的相互作用创建一个（共）现矩阵。 但是，我想通过对象的总数来计算交互次数。

期望结果的基本示例

如果在一行中“England”出现了 3 次而“China”只出现了一次，结果应该是下面的矩阵。

         England  China
England    3        3
China      3        1

可重现的例子

df <- data.frame(ID = c(1,2,3,4), 
 V1 = c("England", "England", "China", "England"),
 V2 = c("Greece", "England", "Greece", "England"),
V32 = c("USA", "China", "Greece", "England"))

因此，示例数据框当前看起来像这样：

ID  V1       V2       ...   V32
1   England  Greece         USA
2   England  England        China
3   China    Greece         Greece
4   England  England        England
.
.
.

期望的结果

我想逐行计算（共）出现并且与顺序无关，以获得一个（共）出现矩阵，该矩阵说明边缘循环（例如英格兰 - 英格兰）的低频，这导致以下结果：

         China   England   Greece   USA

China    2        2         2        0

England  2        6         1        1

Greece   2        1         3        1

USA      0        1         1        1

到目前为止已经尝试了什么

我已经使用igraph来获得一个共现的邻接矩阵。 然而，它计算 - 正如预期的那样 - 相同两个对象的不超过两次交互，在某些情况下，我的值​​远低于按行/发布的对象的实际频率。

df <- data.frame(ID = c(1,2,3,4), 
 V1 = c("England", "England", "China", "England"),
 V2 = c("Greece", "England", "Greece", "England"),
V32 = c("USA", "China", "Greece", "England"))

# remove ID column

df[1] <- list(NULL)

# calculate co-occurrences and return as dataframe

library(igraph)
library(Matrix)

countrydf <- graph.data.frame(df)
countrydf2 <- as_adjacency_matrix(countrydf, type = "both", edges = FALSE)
countrydf3 <- as.data.frame(as.matrix(forceSymmetric(countrydf2)))

         China   England   Greece   USA

China    0        0         1        0

England  0        2         1        0

Greece   1        1         0        0

USA      0        0         0        0

我认为必须有一个使用base和/或dplyr和/或table和/或reshape2类似于[1] 、 [2] 、 [3] 、 [4]或[5]的简单解决方案，但没有任何效果到目前为止，我无法根据我的需要调整代码。 我也尝试使用[6]作为基础，但是，同样的问题也适用于此。

library(tidry)
library(dplyr)
library(stringr)


# collapse observations into one column

df2 <- df %>% unite(concat, V1:V32, sep = ",")

# calculate weights

df3 <- df2$concat %>%
  str_split(",") %>%
  lapply(function(x){
    expand.grid(x,x,x,x, w = length(x), stringsAsFactors = FALSE)
  }) %>%
  bind_rows

df4 <- apply(df3[, -5], 1, sort) %>%
  t %>%
  data.frame(stringsAsFactors = FALSE) %>%
  mutate(w = df3$w)

如果有人能指出我正确的方向，我会很高兴。

Answer 1

可能有更好的方法来做到这一点，但请尝试：

library(tidyverse)

df1 <- df %>%
pivot_longer(-ID, names_to = "Category", values_to = "Country") %>%
xtabs(~ID + Country, data = ., sparse = FALSE) %>% 
crossprod(., .) 

df_diag <- df %>% 
pivot_longer(-ID, names_to = "Category", values_to = "Country") %>%
mutate(Country2 = Country) %>%
xtabs(~Country + Country2, data = ., sparse = FALSE) %>% 
diag()

diag(df1) <- df_diag 

df1

Country   China England Greece USA
  China       2       2      2   0
  England     2       6      1   1
  Greece      2       1      3   1
  USA         0       1      1   1

Answer 2

这是使用 dplyr 和 tidyr 包的一种方法，整个想法在于创建一个数据帧，每个国家/地区按行出现，然后将其加入自身。

library(dplyr)

# Create dataframe sammple
df <- data.frame(ID = c(1,2,3,4), 
                 V1 = c("England", "England", "China", "England"),
                 V2 = c("Greece", "England", "Greece", "England"),
                 V32 = c("USA", "China", "Greece", "England"),
                 stringsAsFactors = FALSE)

# Get the occurance of each country in every row.
row_occurance <- 
  df %>%
  tidyr::gather(key = "identifier", value = "country", -ID) %>%
  group_by(ID, country) %>%
  count()

row_occurance %>%
  # Join row_occurance on itself to simulate the matrix
  left_join(row_occurance, by = "ID") %>%
  # Get the highest occurance row wise, this to handle when country
  # name is repeated within same row
  mutate(Occurance = pmax(n.x, n.y)) %>%
  # Group by 2 countries
  group_by(country.x, country.y) %>%
  # Sum the occurance of 2 countries together
  summarise(Occurance = sum(Occurance)) %>%
  # Spread the data to make it in matrix format
  tidyr::spread(key = "country.y", value = "Occurance", fill = 0)

# # A tibble: 4 x 5
# # Groups:   country.x [4]
# country.x China England Greece   USA
# <chr>     <dbl>   <dbl>  <dbl> <dbl>
# China         2       2      2     0
# England       2       6      1     1
# Greece        2       1      3     1
# USA           0       1      1     1

Answer 3

使用base::table的选项：

df <- data.frame(ID = c(1,2,3,4), 
    V1 = c("England", "England", "China", "England"),
    V2 = c("Greece", "England", "Greece", "England"),
    V3 = c("USA", "China", "Greece", "England"))

#get paired combi and remove those from same country
pairs <- as.data.frame(do.call(rbind, 
    by(df, df$ID, function(x) t(combn(as.character(x[-1L]), 2L)))))
pairs <- pairs[pairs$V1!=pairs$V2, ]

#repeat data frame with columns swap so that 
#upper and lower tri have same numbers and all countries are shown
pairs <- rbind(pairs, data.frame(V1=pairs$V2, V2=pairs$V1))

#tabulate pairs
tab <- table(pairs)

#set diagonals to be the count of countries
cnt <- c(table(unlist(df[-1L])))
diag(tab) <- cnt[names(diag(tab))]

tab

输出：

         V2
V1        China England Greece USA
  China       2       2      2   0
  England     2       6      1   1
  Greece      2       1      3   1
  USA         0       1      1   1