将列比较成比较矩阵 - R 解决方案
[英]Comparing columns into comparison matrix - R solution
问题描述
Apologies if this is a really dumb question, but I'm trying to make a "matrix comparison" table from some data I have in an unfriendly format.
如果这是一个非常愚蠢的问题，我深表歉意，但我正试图从我拥有的一些不友好格式的数据中制作一个“矩阵比较”表。 I'm part the way there, but need some help at.我是那里的一部分，但需要一些帮助。 the last stage pls!最后阶段请！
Input data:输入数据：
library(tidyverse)
#input data
data <- data.frame(ID = c("A", "A", "A","A", "B", "B", "B", "C", "C", "D"),
colour = c("B", "C", "D", "E", "C", "D", "E", "D", "E", "E"),
age =c(7, 7, 5, 6, 3, 7, 9, 8, 3, 2))
Now how I'd like it to look:现在我希望它看起来如何：
output_desired <- data.frame(sample=c("A", "B", "C", "D", "E"),
A = c(NA, 7, 7, 5, 6),
B = c(7, NA, 3, 7, 9),
C = c(7, 3, NA, 8, 3),
D = c(5, 7, 8, NA, 2 ),
E = c(6, 9, 3, 2, NA))
Just to be clear, the two columns in data compare to each other.为了清楚起见， data中的两列相互比较。 A cannot be compared with A , B cannot be compared with B etc. A不能与A比较， B不能与B比较等。
This is my attempt:这是我的尝试：
output <- data %>% pivot_wider(sample_a, names_from=sample_b, values_from=comparison)
However, you'll see the output is not mirrored and I'm missing the column of A...但是，您会看到 output 未镜像，并且我缺少 A 列...
The above uses an example.上面使用了一个例子。 Sample of data attached:附上数据样本：
structure(list(sample_a = c("AD001", "AD001", "AD001", "AD001",
"AD001", "AD001", "AD001", "AD001", "AD001", "AD001", "AD001",
"AD001", "AD001", "AD001", "AD001", "AD001", "AD001", "AD001",
"AD001", "AD001", "AD001", "AD001", "AD001", "AD001", "AD001",
"AD001", "AD001", "AD002", "AD002", "AD002", "AD002", "AD002",
"AD002", "AD002", "AD002", "AD002", "AD002", "AD002", "AD002",
"AD002", "AD002", "AD002", "AD002", "AD002", "AD002", "AD002",
"AD002", "AD002", "AD002", "AD002", "AD002", "AD002", "AD002",
"AD003", "AD003", "AD003", "AD003", "AD003", "AD003", "AD003",
"AD003", "AD003", "AD003", "AD003", "AD003", "AD003", "AD003",
"AD003", "AD003", "AD003", "AD003", "AD003", "AD003", "AD003",
"AD003", "AD003", "AD003", "AD003", "AD004", "AD004", "AD004",
"AD004", "AD004", "AD004", "AD004", "AD004", "AD004", "AD004",
"AD004", "AD004", "AD004", "AD004", "AD004", "AD004", "AD004",
"AD004", "AD004", "AD004", "AD004", "AD004", "AD004", "AD004",
"AD005", "AD005", "AD005", "AD005", "AD005", "AD005", "AD005",
"AD005", "AD005", "AD005", "AD005", "AD005", "AD005", "AD005",
"AD005", "AD005", "AD005", "AD005", "AD005", "AD005", "AD005",
"AD005", "AD005", "AD006", "AD006", "AD006", "AD006", "AD006",
"AD006", "AD006", "AD006", "AD006", "AD006", "AD006", "AD006",
"AD006", "AD006", "AD006", "AD006", "AD006", "AD006", "AD006",
"AD006", "AD006", "AD006", "AD007", "AD007", "AD007", "AD007",
"AD007", "AD007", "AD007", "AD007", "AD007", "AD007", "AD007",
"AD007", "AD007", "AD007", "AD007", "AD007", "AD007", "AD007",
"AD007", "AD007", "AD007", "AD008", "AD008", "AD008", "AD008",
"AD008", "AD008", "AD008", "AD008", "AD008", "AD008", "AD008",
"AD008", "AD008", "AD008", "AD008", "AD008", "AD008", "AD008",
"AD008", "AD008", "AD009", "AD009", "AD009", "AD009", "AD009",
"AD009", "AD009", "AD009", "AD009", "AD009", "AD009", "AD009",
"AD009", "AD009", "AD009", "AD009", "AD009", "AD009", "AD009",
"AD010", "AD010", "AD010", "AD010", "AD010", "AD010", "AD010",
"AD010", "AD010", "AD010", "AD010", "AD010", "AD010", "AD010",
"AD010", "AD010", "AD010", "AD010", "AD011", "AD011", "AD011",
"AD011", "AD011", "AD011", "AD011", "AD011", "AD011", "AD011",
"AD011", "AD011", "AD011", "AD011", "AD011", "AD011", "AD011",
"AD012", "AD012", "AD012", "AD012", "AD012", "AD012", "AD012",
"AD012", "AD012", "AD012", "AD012", "AD012", "AD012", "AD012",
"AD012", "AD012", "AD013", "AD013", "AD013", "AD013", "AD013",
"AD013", "AD013", "AD013", "AD013", "AD013", "AD013", "AD013",
"AD013", "AD013", "AD013", "AD014", "AD014", "AD014", "AD014",
"AD014", "AD014", "AD014", "AD014", "AD014", "AD014", "AD014",
"AD014", "AD014", "AD014", "AD015", "AD015", "AD015", "AD015",
"AD015", "AD015", "AD015", "AD015", "AD015", "AD015", "AD015",
"AD015", "AD015", "AD016", "AD016", "AD016", "AD016", "AD016",
"AD016", "AD016", "AD016", "AD016", "AD016", "AD016", "AD016",
"AD017", "AD017", "AD017", "AD017", "AD017", "AD017", "AD017",
"AD017", "AD017", "AD017", "AD017", "AD018", "AD018", "AD018",
"AD018", "AD018", "AD018", "AD018", "AD018", "AD018", "AD018",
"AD019", "AD019", "AD019", "AD019", "AD019", "AD019", "AD019",
"AD019", "AD019", "AD020", "AD020", "AD020", "AD020", "AD020",
"AD020", "AD020", "AD020", "AD021", "AD021", "AD021", "AD021",
"AD021", "AD021", "AD021", "AD022", "AD022", "AD022", "AD022",
"AD022", "AD022", "AD023", "AD023", "AD023", "AD023", "AD023",
"AD024", "AD024", "AD024", "AD024", "AD025", "AD025", "AD025",
"AD026", "AD026", "AD027"), sample_b = c("AD002", "AD003", "AD004",
"AD005", "AD006", "AD007", "AD008", "AD009", "AD010", "AD011",
"AD012", "AD013", "AD014", "AD015", "AD016", "AD017", "AD018",
"AD019", "AD020", "AD021", "AD022", "AD023", "AD024", "AD025",
"AD026", "AD027", "AD028", "AD003", "AD004", "AD005", "AD006",
"AD007", "AD008", "AD009", "AD010", "AD011", "AD012", "AD013",
"AD014", "AD015", "AD016", "AD017", "AD018", "AD019", "AD020",
"AD021", "AD022", "AD023", "AD024", "AD025", "AD026", "AD027",
"AD028", "AD004", "AD005", "AD006", "AD007", "AD008", "AD009",
"AD010", "AD011", "AD012", "AD013", "AD014", "AD015", "AD016",
"AD017", "AD018", "AD019", "AD020", "AD021", "AD022", "AD023",
"AD024", "AD025", "AD026", "AD027", "AD028", "AD005", "AD006",
"AD007", "AD008", "AD009", "AD010", "AD011", "AD012", "AD013",
"AD014", "AD015", "AD016", "AD017", "AD018", "AD019", "AD020",
"AD021", "AD022", "AD023", "AD024", "AD025", "AD026", "AD027",
"AD028", "AD006", "AD007", "AD008", "AD009", "AD010", "AD011",
"AD012", "AD013", "AD014", "AD015", "AD016", "AD017", "AD018",
"AD019", "AD020", "AD021", "AD022", "AD023", "AD024", "AD025",
"AD026", "AD027", "AD028", "AD007", "AD008", "AD009", "AD010",
"AD011", "AD012", "AD013", "AD014", "AD015", "AD016", "AD017",
"AD018", "AD019", "AD020", "AD021", "AD022", "AD023", "AD024",
"AD025", "AD026", "AD027", "AD028", "AD008", "AD009", "AD010",
"AD011", "AD012", "AD013", "AD014", "AD015", "AD016", "AD017",
"AD018", "AD019", "AD020", "AD021", "AD022", "AD023", "AD024",
"AD025", "AD026", "AD027", "AD028", "AD009", "AD010", "AD011",
"AD012", "AD013", "AD014", "AD015", "AD016", "AD017", "AD018",
"AD019", "AD020", "AD021", "AD022", "AD023", "AD024", "AD025",
"AD026", "AD027", "AD028", "AD010", "AD011", "AD012", "AD013",
"AD014", "AD015", "AD016", "AD017", "AD018", "AD019", "AD020",
"AD021", "AD022", "AD023", "AD024", "AD025", "AD026", "AD027",
"AD028", "AD011", "AD012", "AD013", "AD014", "AD015", "AD016",
"AD017", "AD018", "AD019", "AD020", "AD021", "AD022", "AD023",
"AD024", "AD025", "AD026", "AD027", "AD028", "AD012", "AD013",
"AD014", "AD015", "AD016", "AD017", "AD018", "AD019", "AD020",
"AD021", "AD022", "AD023", "AD024", "AD025", "AD026", "AD027",
"AD028", "AD013", "AD014", "AD015", "AD016", "AD017", "AD018",
"AD019", "AD020", "AD021", "AD022", "AD023", "AD024", "AD025",
"AD026", "AD027", "AD028", "AD014", "AD015", "AD016", "AD017",
"AD018", "AD019", "AD020", "AD021", "AD022", "AD023", "AD024",
"AD025", "AD026", "AD027", "AD028", "AD015", "AD016", "AD017",
"AD018", "AD019", "AD020", "AD021", "AD022", "AD023", "AD024",
"AD025", "AD026", "AD027", "AD028", "AD016", "AD017", "AD018",
"AD019", "AD020", "AD021", "AD022", "AD023", "AD024", "AD025",
"AD026", "AD027", "AD028", "AD017", "AD018", "AD019", "AD020",
"AD021", "AD022", "AD023", "AD024", "AD025", "AD026", "AD027",
"AD028", "AD018", "AD019", "AD020", "AD021", "AD022", "AD023",
"AD024", "AD025", "AD026", "AD027", "AD028", "AD019", "AD020",
"AD021", "AD022", "AD023", "AD024", "AD025", "AD026", "AD027",
"AD028", "AD020", "AD021", "AD022", "AD023", "AD024", "AD025",
"AD026", "AD027", "AD028", "AD021", "AD022", "AD023", "AD024",
"AD025", "AD026", "AD027", "AD028", "AD022", "AD023", "AD024",
"AD025", "AD026", "AD027", "AD028", "AD023", "AD024", "AD025",
"AD026", "AD027", "AD028", "AD024", "AD025", "AD026", "AD027",
"AD028", "AD025", "AD026", "AD027", "AD028", "AD026", "AD027",
"AD028", "AD027", "AD028", "AD028"), rel = c(-0.03991, -0.0249,
-0.01788, -0.02618, -0.003831, -0.0003193, 0, 0.00447, -0.03768,
-0.02554, -0.0249, 0, -0.03512, -0.05268, -0.01948, -0.3177,
-0.01692, 0.004151, -0.03857, -0.02618, -0.008621, -0.00447,
-0.02778, -0.009898, -0.2722, 0.01054, 0.002235, -0.03303, 0.01615,
0.01119, 0.00713, -0.005846, -0.012, -0.01108, -0.001863, -0.02334,
0.00315, -0.01785, -0.02194, -0.002775, -0.01669, -0.2779, -0.01021,
-0.02185, -0.04339, -0.05598, -0.02432, -0.002786, -0.01186,
-0.02276, -0.2833, -0.02246, 0, 0.009972, 0.01122, 0.009037,
0.01932, 0.01215, -0.001246, 0.02586, 0.6049, 0.007874, 0.0162,
-0.01974, -0.005921, 0.002493, -0.3015, -0.008293, 0.002805,
0.001286, -0.01518, 0.005609, 0.01652, -0.002809, -0.01714, -0.2803,
0.03584, 0.004051, 0.01927, 0.02889, 0.03604, 0.0205, 0.01118,
0.02641, 0.0165, -0.005984, 0.03573, -0.00282, 0.00497, 0.001553,
-0.2583, 0.005742, 0.0174, 0.002893, -0.01233, 0.009009, 0.02578,
0.009363, -0.005299, -0.252, 0.01771, 0.02081, -0.01243, 0.0202,
0.01088, 0.0003108, -0.01336, -0.007781, -0.01449, 0.02735, -0.02068,
-0.003729, 0.01243, -0.3045, -0.03254, -0.01274, -0.03247, -0.01297,
-0.009323, -0.001554, -0.006554, -0.01465, -0.3005, 0.002797,
-0.0006215, 0.01457, 0.03007, 0.02821, 0.02732, 0.01587, 0.009764,
0.02728, 0.01661, 0.01209, 0.01457, -0.2398, -0.01372, 0.02108,
-0.01093, -0.01107, 0.02449, 0.02294, 0.008427, -0.007481, -0.2665,
0.04495, 0.03348, 0.03673, 0, 0.04471, 0.01961, 0.01134, 0.03367,
0.02946, 0.008017, 0.01793, -0.2552, 0.0236, 0.003061, -0.004179,
0.01139, 0.0157, 0.02477, -0.007803, 0.001247, -0.239, 0.03796,
0.02173, 0.02416, 0.04905, 0.01836, -0.004409, 0.04178, 0.02131,
0.0222, 0.009583, -0.01157, 0.02424, 0.01592, -0.009965, 0.0003163,
-0.008929, 0.0192, 0.0231, 0.01091, 0.01565, 0.02958, 0.03478,
0.02049, 0.02521, -0.008819, 0.02875, 0.01254, 0.02097, 0.01144,
-0.2556, -0.007337, 0.03061, -0.006107, 0.03226, -0.002771, 0.01579,
-0.009988, 0.01496, -0.2711, 0.0315, 0.01621, 0.02428, 0.01953,
0.03663, 0.01504, 0.008382, -0.001552, -0.2791, 0.007337, 0.007451,
-0.008679, -0.02309, 0.0326, 0.02391, -0.006554, 0.01029, -0.2375,
0.02763, 0.04222, 0.004094, 0.02583, -0.004701, -0.01961, 0.01587,
-0.2737, 0.0118, 0.01369, -0.007715, -0.006958, 0.007781, 0.01369,
0.0103, -0.006234, -0.2635, 0.03579, 0.02148, 0.01764, -0.008189,
-0.01134, -0.01858, -0.3096, -0.001595, -0.004724, -0.02732,
-0.02846, -0.02016, -0.004724, -0.001575, -0.02047, -0.3032,
0.01039, 0.008504, 0.03855, 0.02189, 0.02875, -0.2467, 0.01148,
0.02296, 0.01382, 0.006009, 0.02586, 0.03158, 0.03246, 0.007481,
-0.2593, 0.04971, 0.0421, -0.03071, -0.01128, -0.2722, 0.005423,
0.02382, -0.018, -0.04175, -0.004074, 0.006894, -0.01128, -0.01316,
-0.2665, 0.01943, 0.002194, -0.01051, -0.2868, -0.000638, -0.01418,
-0.02411, -0.01992, 0.004009, 0.01424, 0.006866, -0.03148, -0.2913,
0.0296, 0.01048, -0.2934, -0.01276, 0.01793, -0.01254, -0.004744,
0.003091, 0, -0.008739, -0.008416, -0.2787, 0.02566, 0.01855,
-0.2587, -0.2537, -0.3115, -0.2772, -0.3053, -0.2779, -0.3165,
-0.2945, 0.01157, -0.2695, -0.2325, 0.01021, -0.03118, -0.02392,
0.000638, 0.01085, 0.01818, -0.02073, -0.2635, 0.01786, 0.01212,
-0.02347, 0.006325, 0.002463, 0.02539, -0.002185, -0.0134, -0.2417,
0.02694, 0.008877, -0.02829, -0.02861, -0.03439, -0.03568, -0.02668,
-0.3028, 0.004822, 0.006429, 0.01265, 0.01107, -0.009171, -0.01044,
-0.2738, 0.009171, 0.01581, 0.006811, -0.01155, -0.004364, -0.2868,
0.03633, 0.0314, 0.009051, 0.003117, -0.2787, 0.02415, 0.04211,
-0.0362, -0.3043, 0.0009363, 0.02341, -0.2787, 0.02525, 0.01808,
-0.2482, -0.2497, 0.05003)), row.names = c(NA, -378L), class = "data.frame")
2 个解决方案
解决方案1
2 已采纳 2021-03-18 21:56:08
In base R , we could convert the columns to factor with levels specified as unique elements from unlist ing the 'colour' and 'ID' column.在base R中，我们可以将列转换为factor ，其levels指定为unlist 'color' 和 'ID' 列中的unique元素。 Then, we reshape to 'wide' format with xtabs and add the transpose of the output ('m1')然后，我们使用xtabs重塑为“宽”格式并添加 output ('m1') 的转置
un1 <- sort(unique(unlist(data[1:2])))
m1 <- xtabs(age ~ ID + colour, transform(data,
ID = factor(ID, levels = un1), colour = factor(colour, levels = un1)))
out <- m1 + t(m1)
In tidyverse , we can use complete to expand the rows based on the unique values ('un1')在tidyverse中，我们可以使用complete来根据唯一值 ('un1') 扩展行
library(dplyr)
library(tidyr)
library(purrr)
out2 <- data %>%
complete(ID = un1, colour = un1) %>%
pivot_wider(names_from = colour, values_from = age)
map2_dfc(data.table::transpose(out2, make.names = 'ID'),
out2[-1], coalesce) %>%
bind_cols(out2 %>% select(ID), .)
# A tibble: 5 x 6
# ID A B C D E
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 A NA 7 7 5 6
#2 B 7 NA 3 7 9
#3 C 7 3 NA 8 3
#4 D 5 7 8 NA 2
#5 E 6 9 3 2 NA
Update更新
Testing with OP's new dataset使用 OP 的新数据集进行测试
un2 <- sort(unique(unlist(df2[1:2])))
out2_new <- df2 %>%
complete(sample_a = un2, sample_b = un2) %>%
pivot_wider(names_from = sample_b, values_from = rel)
map2_dfc(data.table::transpose(out2_new, make.names = 'sample_a'),
out2_new[-1], coalesce) %>%
bind_cols(out2_new %>%
select(sample_a), .)
# A tibble: 28 x 29
# sample_a AD001 AD002 AD003 AD004 AD005 AD006 AD007 AD008 AD009 AD010 AD011 AD012 AD013 AD014
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 AD001 NA -0.0399 -0.0249 -0.0179 -2.62e-2 -0.00383 -3.19e-4 0 4.47e-3 -0.0377 -0.0255 -0.0249 0 -0.0351
# 2 AD002 -3.99e-2 NA -0.0330 0.0162 1.12e-2 0.00713 -5.85e-3 -0.012 -1.11e-2 -0.00186 -0.0233 0.00315 -0.0178 -0.0219
# 3 AD003 -2.49e-2 -0.0330 NA 0.00997 1.12e-2 0.00904 1.93e-2 0.0122 -1.25e-3 0.0259 0.605 0.00787 0.0162 -0.0197
# 4 AD004 -1.79e-2 0.0162 0.00997 NA 1.93e-2 0.0289 3.60e-2 0.0205 1.12e-2 0.0264 0.0165 -0.00598 0.0357 -0.00282
# 5 AD005 -2.62e-2 0.0112 0.0112 0.0193 NA -0.0124 2.02e-2 0.0109 3.11e-4 -0.0134 -0.00778 -0.0145 0.0274 -0.0207
# 6 AD006 -3.83e-3 0.00713 0.00904 0.0289 -1.24e-2 NA 1.46e-2 0.0301 2.82e-2 0.0273 0.0159 0.00976 0.0273 0.0166
# 7 AD007 -3.19e-4 -0.00585 0.0193 0.0360 2.02e-2 0.0146 NA 0.0367 0. 0.0447 0.0196 0.0113 0.0337 0.0295
# 8 AD008 0. -0.012 0.0122 0.0205 1.09e-2 0.0301 3.67e-2 NA 2.42e-2 0.0491 0.0184 -0.00441 0.0418 0.0213
# 9 AD009 4.47e-3 -0.0111 -0.00125 0.0112 3.11e-4 0.0282 0. 0.0242 NA 0.0205 0.0252 -0.00882 0.0288 0.0125
#10 AD010 -3.77e-2 -0.00186 0.0259 0.0264 -1.34e-2 0.0273 4.47e-2 0.0491 2.05e-2 NA 0.0243 0.0195 0.0366 0.0150
# … with 18 more rows, and 14 more variables: AD015 <dbl>, AD016 <dbl>, AD017 <dbl>, AD018 <dbl>, AD019 <dbl>, AD020 <dbl>, AD021 <dbl>,
# AD022 <dbl>, AD023 <dbl>, AD024 <dbl>, AD025 <dbl>, AD026 <dbl>, AD027 <dbl>, AD028 <dbl>
解决方案2
1 2021-03-18 22:02:49
Here is one option with igraph这是igraph的一个选项
data %>%
graph_from_data_frame(directed = FALSE) %>%
set_edge_attr("weight", value = data$age) %>%
as_adjacency_matrix(attr = "weight", sparse = FALSE) %>%
`diag<-`(NA) %>%
data.frame(sample = row.names(.)) %>%
relocate(sample, .before = 1) %>%
`row.names<-`(NULL)
which gives这使
sample A B C D E
1 A NA 7 7 5 6
2 B 7 NA 3 7 9
3 C 7 3 NA 8 3
4 D 5 7 8 NA 2
5 E 6 9 3 2 NA
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R矩阵行的比较 - R Comparison of Rows of a Matrix
R：比较矩阵中的字段 - R: Comparing fields in matrix
比较R中矩阵的向量 - Comparing vectors in a matrix in R
比较 R 中的多列 - Comparing multiple columns in R
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