我正在使用一个面板数据集，并打算将其建模为使用SAOMs的动态关联网络。不幸的是，数据非常混乱，处理起来很麻烦。
我已经设法为每个面板波创建邻接矩阵。然而，随着时间的推移，小组的规模越来越大/人们离开了。我需要每个矩阵的行数是相同的，并且根据唯一id的顺序是相同的，当检查r中的对象时，存在唯一id。应该在整行显示10。
这里有一个可重复的例子，应该使问题清楚，也表明了我的目标。我认为这可以通过巧妙地使用merge()函数来解决，但我无法让它工作:
wave1 <- matrix(c(0,0,1,1,0,1,1,0,1,1), nrow = 5, ncol = 2, dimnames = list(c("1","2","4","5","9"), c("group1","group2")))
wave2 <- matrix(c(0,1,1,0,1,0,1,1), nrow = 4, ncol = 2, dimnames = list(c("1","4","8","9"), c("group1","group2")))
wave1_c <- matrix(c(0,0,1,1,10,0,1,1,0,0,10,1), nrow = 6, ncol = 2, dimnames = list(c("1","2","4","5","8","9"), c("group1","group2")))
wave2_c <- matrix(c(0,10,1,10,1,0,1,10,0,10,1,1), nrow = 6, ncol = 2, dimnames = list(c("1","2","4","5","8","9"), c("group1","group2")))
提前感谢。除10外，矩阵中的数字是任意的。
base R中使用数据框架和合并的解决方案
合并和外连接。
dwave1_c <- merge(wave1, wave2, by = 'row.names', all = TRUE, suffixes="")[2:3]
dwave2_c <- merge(wave2, wave1, by = 'row.names', all = TRUE, suffixes="")[2:3]
dwave1_c[is.na(dwave1_c)] <- 10
dwave2_c[is.na(dwave2_c)] <- 10
as.matrix(dwave1_c)
as.matrix(dwave2_c)
更新。
both <- merge(wave1, wave2, by = 'row.names', all = TRUE)
输出。
Row.names group1.x group2.x group1.y group2.y
1 1 0 1 0 1
2 2 0 1 NA NA
3 4 1 0 1 0
4 5 1 1 NA NA
5 8 NA NA 1 1
6 9 0 1 0 1
dwave1_c <- both[,2:3]; colnames(dwave1_c) <- colnames(wave1)
dwave2_c <- both[,4:5]; colnames(dwave2_c) <- colnames(wave2)
dwave1_c[is.na(dwave1_c)] <- 10
dwave2_c[is.na(dwave2_c)] <- 10
显示结果。
as.matrix(dwave1_c)
as.matrix(dwave2_c)
第一次尝试。
## Convert matrix to dataframe.
df1 <- as.data.frame(wave1)
df2 <- as.data.frame(wave2)
## Merge df1 and df2 by row name.
m_df1_df2 <- merge(df1, df2, by = 'row.names', all = TRUE)
rownames(m_df1_df2) <- m_df1_df2$Row.names
# Rows not in df1, but in df2,
# rows not in df2, but in df1
not1_2 <- m_df1_df2[is.na(m_df1_df2$group1.x),][c("group1.x", "group2.x")] # not in df1, in df2
not2_1 <- m_df1_df2[is.na(m_df1_df2$group1.y),][c("group1.y", "group2.y")] # not in df2, in df1
## Same column names.
colnames(not1_2) <- colnames(df1)
colnames(not2_1) <- colnames(df2)
## append
df1_c <- rbind(df1, not1_2)
df2_c <- rbind(df2, not2_1)
## order by row name
df1_c <- df1_c[order(row.names(df1_c)), ]
df2_c <- df2_c[order(row.names(df2_c)), ]
## replace NA by 10
df1_c[is.na(df1_c)] <- 10
df2_c[is.na(df2_c)] <- 10
as.matrix(df1_c)
as.matrix(df2_c)
在我的第一次尝试中将wave1,2转换为数据帧是多余的，可以省略。但是以隐式强制值为代价。
## merge wave1 and wave2 by row name.
m_df1_df2 <- merge(wave1, wave2, by = 0, all = TRUE)
rownames(m_df1_df2) <- m_df1_df2$Row.names
# rows not in set 1, but in set 2,
# rows not in set 2, but in set 1.
not1_2 <- m_df1_df2[is.na(m_df1_df2$group1.x),][c("group1.x", "group2.x")]
not2_1 <- m_df1_df2[is.na(m_df1_df2$group1.y),][c("group1.y", "group2.y")]
## Same column names.
colnames(not1_2) <- colnames(wave1)
colnames(not2_1) <- colnames(wave2)
## append.
wave1_c <- rbind(wave1, not1_2)
wave2_c <- rbind(wave2, not2_1)
## order by row name.
wave1_c <- wave1_c[order(row.names(wave1_c)), ]
wave2_c <- wave2_c[order(row.names(wave2_c)), ]
## replace NA by 10.
wave1_c[is.na(wave1_c)] <- 10
wave2_c[is.na(wave2_c)] <- 10
## show result.
wave1_c
wave2_c
使用setdiff解决方案。
## rownames not in set 1, but in set 2,
## rownames not in set 2, but in set 1.
rn_not2_1 <- setdiff(rownames(wave1), rownames(wave2))
rn_not1_2 <- setdiff(rownames(wave2), rownames(wave1))
## missing rows to add.
add_to_1 <- wave2[rn_not1_2,,drop=FALSE]
add_to_2 <- wave1[rn_not2_1,,drop=FALSE]
add_to_1[,] <- 10
add_to_2[,] <- 10
## append.
wave1_c <- rbind(wave1, add_to_1)
wave2_c <- rbind(wave2, add_to_2)
## order by row name.
wave1_c <- wave1_c[order(row.names(wave1_c)), ]
wave2_c <- wave2_c[order(row.names(wave2_c)), ]
## show result.
wave1_c
wave2_c