具有成对距离矩阵输出的列名和行名
[英]colnames and rownames with pairwise distance matrix outputs
问题描述
EDIT:
编辑：
I am trying to collect these values/Cols/Rows我正在尝试收集这些值/列/行
** The numbers have changed slightly. ** 数字略有变化。
I am trying to extract the pairwise result of the following matrix.我正在尝试提取以下矩阵的成对结果。
ID1_2001 ID2_2001 ID3_2001 ID1_2000 ID2_2000
ID2_2001 0.96747537
ID3_2001 0.96850817 0.67983338
ID1_2000 0.11324889 0.97507292 0.97586446
ID2_2000 1.00000000 0.75336751 0.83321843 1.00000000
ID3_2000 1.00000000 0.76556229 0.81577353 1.00000000 0.05728332
That is the values of 0.1132489 , 0.7533675 , 0.8157735 .即0.1132489 、 0.7533675 、 0.8157735的值。
Thanks to another user on this site I know of the following function proxy::dist(m[1:3,], m[4:6,], pairwise=TRUE, method="cosine") which gives me just the following results 0.1132489 0.7533675 0.8157735 .感谢本网站上的另一位用户，我知道以下函数proxy::dist(m[1:3,], m[4:6,], pairwise=TRUE, method="cosine")它给了我以下内容结果0.1132489 0.7533675 0.8157735 。
However I would also like the column and row names from where the result comes from.但是，我也想要结果来自的列名和行名。 So 0.1132489 would be assigned to ID1_2000_ID1_2001 , and 0.7533675 assigned to ID2_2000_ID2_2001 , and finally 0.81577353 assigned to ID3_2000_ID3_2001 .所以0.1132489将分配给ID1_2000_ID1_2001 ， 0.7533675分配给ID2_2000_ID2_2001 ，最后0.81577353分配给ID3_2000_ID3_2001 。 However I cannot put this distance matrix into a data frame to Access/extract row_names and colnames.但是，我无法将此距离矩阵放入数据框中以访问/提取 row_names 和 colnames。
It would be most optimal to run just the following proxy::dist(m[1:3,], m[4:6,], pairwise=TRUE, method="cosine") and obtain the pairwise results along with their colnames and rownames (saving on computational time).最好只运行以下proxy::dist(m[1:3,], m[4:6,], pairwise=TRUE, method="cosine")并获得成对结果及其列名和行名（节省计算时间）。
How can I replace the m[1:3] with "groups", ie take 2001 group and then take 2000 group.如何用“组”替换m[1:3] ，即取2001组，然后取2000组。 Since I hope to scale this up to more years/IDs and I cannot count the rows 1:3 and 4:6 for all years/IDs.由于我希望将其扩展到更多年份/ID，并且我无法计算所有年份/ID 的1:3和4:6行。
library(tidyr)
x <- m %>%
data.frame() %>%
tibble::rownames_to_column("rownames") %>%
separate(rownames, c("id", "year"), "_")
Other:其他：
dist.matrix = proxy::dist(m, pairwise = TRUE, method = "cosine")
proxy::dist(m[1:3,], m[4:6,], pairwise=TRUE, method="cosine")
Data:数据：
data <- structure(c(0.96747537487273, 0.968508167135111, 0.113248890901578,
1, 1, 0.67983337671352, 0.97507292188601, 0.753367507803825,
0.765562291938692, 0.975864460398726, 0.833218430412641, 0.815773525411265,
1, 1, 0.0572833227621783), Size = 6L, Labels = c("ID1_2001",
"ID2_2001", "ID3_2001", "ID1_2000", "ID2_2000", "ID3_2000"), class = "dist", Diag = FALSE, Upper = FALSE, method = "cosine", call = proxy::dist(x = m,
method = "cosine", pairwise = TRUE))
Data 2 ( m )数据 2 ( m )
m <- structure(c(0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,
0, 0, 2, 2, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 1, 3, 3, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,
0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,
0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 2, 2, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 8, 0,
0, 12, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,
0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 1, 0,
1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1,
0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0,
0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1,
0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,
1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0,
0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
0, 0, 0, 1, 1, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 2, 2, 0,
1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0,
3, 4, 0, 1, 3, 0, 1, 1, 0, 2, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0,
0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 3, 0, 0, 3, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,
0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 3, 0, 0, 2, 2, 0, 0, 0, 0,
1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 2, 0, 0, 2, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 2, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0,
2, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 4, 2, 0, 1, 1, 0,
1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 0, 2, 0, 0, 0,
0, 0, 0, 1, 1), .Dim = c(6L, 196L), .Dimnames = list(Docs = c("ID1_2001",
"ID2_2001", "ID3_2001", "ID1_2000", "ID2_2000", "ID3_2000"),
Terms = c("-field", "(22-yard)", "(doubles).", "(either",
"(known", "(singles)", "(specifically", "20-metre", "able",
"across", "activity", "adjudicated", "aided", "although",
"american", "appears", "appears.", "around", "association",
"australian", "badminton", "bails", "bails,", "balanced",
"ball", "bat--ball", "bat,", "batting", "beach", "bowled",
"bowled,", "bowling", "called", "can", "canadian", "casual",
"catching", "centre", "certain", "codes", "common", "commonly",
"communicate", "comprising", "context", "cord", "countries",
"countries);", "court", "court.", "covered", "cricket", "degrees",
"degrees,", "different", "dislodges", "dismiss", "dismissal",
"dismissed,", "doubles", "each", "either", "eleven", "end,",
"ends", "family", "felt", "field", "fielding", "football",
"football);", "football.[1][2]", "football;", "football12",
"form", "formal", "forms", "gaelic", "gain", "game", "games",
"goal", "goal.", "gridiron", "ground.", "half", "hit", "hits",
"hollow", "include", "individually", "indoor", "information.",
"innings", "international", "involve", "involve,", "kicking",
"known", "landing", "larger", "league", "maneuver", "match",
"match's", "matches.", "may", "means", "net", "object", "often",
"one", "opponent", "opponent's", "opposing", "opposite",
"outdoor", "per", "pitch", "places", "play", "played", "player",
"players", "point,", "points", "popular", "prevent", "racket",
"racquet", "racquets", "record", "rectangular", "refer",
"referee", "regional", "return", "return.", "roles.", "rubber",
"rugby", "rules", "runs", "score", "scored", "scorers", "scores",
"shuttlecock", "side", "sides", "single", "singles", "soccer",
"specifically", "sport", "sports", "statistical", "strike",
"striking", "strung", "stumps", "stumps.", "swap", "team",
"teams", "ten", "tennis", "the", "these", "they", "third",
"three", "tries", "two", "umpire", "umpires,", "unable",
"understood", "union", "union);", "unqualified", "unqualified,",
"uses", "using", "valid", "variations", "varying", "way",
"when", "whichever", "wicket", "will", "will.", "within",
"word", "yard")))
EDIT:编辑：
I found this workaround to put into a data frame.我发现这个解决方法可以放入数据框中。 Not sure how efficient it will be on the large matrix不确定它在大型矩阵上的效率如何
x <- data.matrix(dist.matrix)
x <- as.data.frame(x)
EDIT2:编辑2：
> data.frame(rownames(dist.matrix), colnames(dist.matrix), as.vector(dist.matrix))
rownames.dist.matrix. colnames.dist.matrix. as.vector.dist.matrix.
1 ID1_2001 ID2_2001 0.97192896
2 ID1_2001 ID2_2001 0.97288923
3 ID1_2001 ID2_2001 0.01505221
4 ID1_2001 ID2_2001 1.00000000
5 ID1_2001 ID2_2001 1.00000000
6 ID1_2001 ID2_2001 0.69527190
7 ID1_2001 ID2_2001 0.97565046
8 ID1_2001 ID2_2001 0.75908178
9 ID1_2001 ID2_2001 0.77099402
10 ID1_2001 ID2_2001 0.97648342
11 ID1_2001 ID2_2001 0.77840308
12 ID1_2001 ID2_2001 0.76921180
13 ID1_2001 ID2_2001 1.00000000
14 ID1_2001 ID2_2001 1.00000000
15 ID1_2001 ID2_2001 0.05728332
EDIT 3:编辑 3：
I run the following;我运行以下；
dist.matrix = as.matrix(dist.matrix)
df <- data.frame(row = rownames(dist.matrix),
col = colnames(dist.matrix),
value = as.vector(dist.matrix))
Which gives me the following output:这给了我以下输出：
row col value
1 ID1_2001 ID1_2001 0.00000000
2 ID2_2001 ID2_2001 0.97192896
3 ID3_2001 ID3_2001 0.97288923
4 ID1_2000 ID1_2000 0.01505221
5 ID2_2000 ID2_2000 1.00000000
6 ID3_2000 ID3_2000 1.00000000
7 ID1_2001 ID1_2001 0.97192896
8 ID2_2001 ID2_2001 0.00000000
9 ID3_2001 ID3_2001 0.69527190
10 ID1_2000 ID1_2000 0.97565046
11 ID2_2000 ID2_2000 0.75908178
12 ID3_2000 ID3_2000 0.77099402
13 ID1_2001 ID1_2001 0.97288923
14 ID2_2001 ID2_2001 0.69527190
15 ID3_2001 ID3_2001 0.00000000
16 ID1_2000 ID1_2000 0.97648342
17 ID2_2000 ID2_2000 0.77840308
18 ID3_2000 ID3_2000 0.76921180
19 ID1_2001 ID1_2001 0.01505221
20 ID2_2001 ID2_2001 0.97565046
21 ID3_2001 ID3_2001 0.97648342
22 ID1_2000 ID1_2000 0.00000000
23 ID2_2000 ID2_2000 1.00000000
24 ID3_2000 ID3_2000 1.00000000
25 ID1_2001 ID1_2001 1.00000000
26 ID2_2001 ID2_2001 0.75908178
27 ID3_2001 ID3_2001 0.77840308
28 ID1_2000 ID1_2000 1.00000000
29 ID2_2000 ID2_2000 0.00000000
30 ID3_2000 ID3_2000 0.05728332
31 ID1_2001 ID1_2001 1.00000000
32 ID2_2001 ID2_2001 0.77099402
33 ID3_2001 ID3_2001 0.76921180
34 ID1_2000 ID1_2000 1.00000000
35 ID2_2000 ID2_2000 0.05728332
36 ID3_2000 ID3_2000 0.00000000
EDIT 4:编辑 4：
x <- data.matrix(dist.matrix)
x <- as.data.frame(x)
library(tibble)
library(tidyr)
y <- x %>%
rownames_to_column("row") %>%
separate(row, c("id_row", "year_row"), "_")
z <- melt(y)
z
w <- z %>%
separate(variable, c("id_col", "year_col"), "_")
w
Which seems to give这似乎给
> head(w)
id_row year_row id_col year_col value
1 ID1 2001 ID1 2001 0.00000000
2 ID2 2001 ID1 2001 0.97192896
3 ID3 2001 ID1 2001 0.97288923
4 ID1 2000 ID1 2001 0.01505221
5 ID2 2000 ID1 2001 1.00000000
6 ID3 2000 ID1 2001 1.00000000
1 个解决方案
解决方案1
1 已采纳 2019-04-18 20:41:01
Just stick the rownames and colnames in a dataframe alongside the data itself.只需将行名和列名与数据本身一起放在数据框中。 "Unraveling" the matrix as a vector (and vector recycling for the names) will take care of the rest:将矩阵“解开”为向量（以及名称的向量回收）将处理其余部分：
# example data
mat <- matrix(1:100, 10, 10)
rownames(mat) <- paste0("row",1:10)
colnames(mat) <- paste0("col",1:10)
# what you want
df <- data.frame(row = rownames(mat),
col = colnames(mat),
value = as.vector(mat) )
# take a look at the result
head(df)
# row col value
# row1 col1 1
# row2 col2 2
# row3 col3 3
# row4 col4 4
# row5 col5 5
# row6 col6 6
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