英文:
Creating a Triangle Corrlation Matrix Style plot for Species Pairwise comparison
问题
我已经进行了一系列的两两卡方检验，得到了每对物种的p值数据框。我想使用ggplot创建一种类似于相关矩阵的可视化，以便我们可以看到哪些物种对具有显著的p值。对于大于0.05的值使用一种颜色，对于小于或等于0.05的值使用另一种颜色。类似于这个示例：（https://www.researchgate.net/publication/293654442_cooccur_Probabilistic_Species_Co-Occurrence_Analysis_in_R）。
我已经尝试了以下这些指南：
但没有太大的成功。我在以下两个方面遇到了问题：1）将其放入三角形中，2）将颜色设为二进制而不是渐变色。我希望保持物种的顺序与数据框中的顺序相同。任何建议都将非常有帮助。我一直在为此而烦恼。
我已经附上了数据框的dputs，分别是宽格式（Wide.df）和长格式（long.df）的数据。
英文:
I have been doing a series of pairwise chi-squared comparisons of species, and have a data frame with the p_values for each species pair. I would like to create a visualization using ggplot similar to a correlation matrix so we can see what species pairs have significant p-values. With values >.05 in one color and values < or = 0.5 in another. Something similar to this: (https://www.researchgate.net/publication/293654442_cooccur_Probabilistic_Species_Co-Occurrence_Analysis_in_R)
I have tried following these guide:
https://www.youtube.com/watch?v=E3De2A73ako
Without much success. I am having trouble 1) getting it into a triangle and 2) having the colors be binary rather than a gradient. I would prefer to maintain the order of the species as I have them in my data frames. Any advice would be amazing. I have been driving myself bandannas with this one.
I have attached dputs of data frames both in wide and long formats!
Thanks very much!
Wide.df:
structure(list(Species1 = c(NA, 8.29661485364936e-14, 0.197328035330918,
3.73447143215061e-05, 0.0200245227839388, 0.000158518795659732,
0.999999999999999, 0.999999999999999, 0.200661174254572, 0.0259711510883781,
0.624415639985824, 0.999999999999995, 0.999999999999991), Species2 = c(8.29661485364936e-14,
NA, 0.911548596172133, 0.0808672833723648, 0.00232408080140171,
0.00257625010714883, 0.999999999999777, 0.999999999999777, 0.789829339795786,
0.198077829941541, 0.768466049890891, 0.999999999999885, 0.999999999999994
), Species3 = c(0.197328035330918, 0.911548596172133, NA, 0.181696316252709,
0.575238254258972, 0.939866701307512, 0.999999999999999, 0.999999999999999,
0.999999999999999, 0.507611967012476, 0.673527222144056, 0.999999999999996,
1), Species4 = c(3.73447143215061e-05, 0.0808672833723648, 0.181696316252709,
NA, 1.40996305374498e-10, 0.405410680293625, 0.999999999999999,
0.999999999999999, 1.56797538624063e-08, 8.50447159522988e-05,
0.00644405295214749, 0.88381428087806, 0.99999999999993), Species5 = c(0.0200245227839388,
0.00232408080140171, 0.575238254258972, 1.40996305374498e-10,
NA, 0.622095128306733, 0.999999999999959, 0.999999999999959,
0.000115476294641169, 1.60499061530966e-19, 4.67520836455185e-05,
0.528580893876124, 0.795044191844885), Species6 = c(0.000158518795659732,
0.00257625010714883, 0.939866701307512, 0.405410680293625, 0.622095128306733,
NA, 0.999999999999999, 0.999999999999999, 0.200661174254572,
0.410636112084533, 0.999999999999929, 0.999999999999995, 0.999999999999991
), Species7 = c(0.999999999999999, 0.999999999999777, 0.999999999999999,
0.999999999999999, 0.999999999999959, 0.999999999999999, NA,
0.999999999999299, 0.779426832974571, 0.999999999999933, 0.999999999999999,
0.999999999999998, 0.999999999999687), Species8 = c(0.999999999999999,
0.999999999999777, 0.999999999999999, 0.999999999999999, 0.999999999999959,
0.999999999999999, 0.999999999999299, NA, 0.999999999999999,
0.611136265859179, 0.999999999999999, 0.999999999999998, 0.999999999999687
), Species9 = c(0.200661174254572, 0.789829339795786, 0.999999999999999,
1.56797538624063e-08, 0.000115476294641169, 0.200661174254572,
0.779426832974571, 0.999999999999999, NA, 0.0311037604732729,
0.0122054515551129, 0.999999999999984, 0.999999999999999), Species10 = c(0.0259711510883781,
0.198077829941541, 0.507611967012476, 8.50447159522988e-05, 1.60499061530966e-19,
0.410636112084533, 0.999999999999933, 0.611136265859179, 0.0311037604732729,
NA, 0.0403275386741277, 0.508244635418544, 0.999999999999999),
Species11 = c(0.624415639985824, 0.768466049890891, 0.673527222144056,
0.00644405295214749, 4.67520836455185e-05, 0.999999999999929,
0.999999999999999, 0.999999999999999, 0.0122054515551129,
0.0403275386741277, NA, 1, 0.999999999999823), Species12 = c(0.999999999999995,
0.999999999999885, 0.999999999999996, 0.88381428087806, 0.528580893876124,
0.999999999999995, 0.999999999999998, 0.999999999999998,
0.999999999999984, 0.508244635418544, 1, NA, 0.999999999998991
), Species13 = c(0.999999999999991, 0.999999999999994, 1,
0.99999999999993, 0.795044191844885, 0.999999999999991, 0.999999999999687,
0.999999999999687, 0.999999999999999, 0.999999999999999,
0.999999999999823, 0.999999999998991, NA)), row.names = c("Species1",
"Species2", "Species3", "Species4", "Species5", "Species6", "Species7",
"Species8", "Species9", "Species10", "Species11", "Species12",
"Species13"), class = "data.frame")
long.df:
structure(list(SpeciesA = c("Species1", "Species2", "Species3",
"Species4", "Species5", "Species6", "Species7", "Species8", "Species9",
"Species10", "Species11", "Species12", "Species13", "Species1",
"Species2", "Species3", "Species4", "Species5", "Species6", "Species7",
"Species8", "Species9", "Species10", "Species11", "Species12",
"Species13", "Species1", "Species2", "Species3", "Species4",
"Species5", "Species6", "Species7", "Species8", "Species9", "Species10",
"Species11", "Species12", "Species13", "Species1", "Species2",
"Species3", "Species4", "Species5", "Species6", "Species7", "Species8",
"Species9", "Species10", "Species11", "Species12", "Species13",
"Species1", "Species2", "Species3", "Species4", "Species5", "Species6",
"Species7", "Species8", "Species9", "Species10", "Species11",
"Species12", "Species13", "Species1", "Species2", "Species3",
"Species4", "Species5", "Species6", "Species7", "Species8", "Species9",
"Species10", "Species11", "Species12", "Species13", "Species1",
"Species2", "Species3", "Species4", "Species5", "Species6", "Species7",
"Species8", "Species9", "Species10", "Species11", "Species12",
"Species13", "Species1", "Species2", "Species3", "Species4",
"Species5", "Species6", "Species7", "Species8", "Species9", "Species10",
"Species11", "Species12", "Species13", "Species1", "Species2",
"Species3", "Species4", "Species5", "Species6", "Species7", "Species8",
"Species9", "Species10", "Species11", "Species12", "Species13",
"Species1", "Species2", "Species3", "Species4", "Species5", "Species6",
"Species7", "Species8", "Species9", "Species10", "Species11",
"Species12", "Species13", "Species1", "Species2", "Species3",
"Species4", "Species5", "Species6", "Species7", "Species8", "Species9",
"Species10", "Species11", "Species12", "Species13", "Species1",
"Species2", "Species3", "Species4", "Species5", "Species6", "Species7",
"Species8", "Species9", "Species10", "Species11", "Species12",
"Species13", "Species1", "Species2", "Species3", "Species4",
"Species5", "Species6", "Species7", "Species8", "Species9", "Species10",
"Species11", "Species12", "Species13"), SpeciesB = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L), .Label = c("Species1", "Species2", "Species3",
"Species4", "Species5", "Species6", "Species7", "Species8", "Species9",
"Species10", "Species11", "Species12", "Species13"), class = "factor"),
p_value = c(NA, 8.29661485364936e-14, 0.197328035330918,
3.73447143215061e-05, 0.0200245227839388, 0.000158518795659732,
0.999999999999999, 0.999999999999999, 0.200661174254572,
0.0259711510883781, 0.624415639985824, 0.999999999999995,
0.999999999999991, 8.29661485364936e-14, NA, 0.911548596172133,
0.0808672833723648, 0.00232408080140171, 0.00257625010714883,
0.999999999999777, 0.999999999999777, 0.789829339795786,
0.198077829941541, 0.768466049890891, 0.999999999999885,
0.999999999999994, 0.197328035330918, 0.911548596172133,
NA, 0.181696316252709, 0.575238254258972, 0.939866701307512,
0.999999999999999, 0.999999999999999, 0.999999999999999,
0.507611967012476, 0.673527222144056, 0.999999999999996,
1, 3.73447143215061e-05, 0.0808672833723648, 0.181696316252709,
NA, 1.40996305374498e-10, 0.405410680293625, 0.999999999999999,
0.999999999999999, 1.56797538624063e-08, 8.50447159522988e-05,
0.00644405295214749, 0.88381428087806, 0.99999999999993,
0.0200245227839388, 0.00232408080140171, 0.575238254258972,
1.40996305374498e-10, NA, 0.622095128306733, 0.999999999999959,
0.999999999999959, 0.000115476294641169, 1.60499061530966e-19,
4.67520836455185e-05, 0.528580893876124, 0.795044191844885,
0.000158518795659732, 0.00257625010714883, 0.939866701307512,
0.405410680293625, 0.622095128306733, NA, 0.999999999999999,
0.999999999999999, 0.200661174254572, 0.410636112084533,
0.999999999999929, 0.999999999999995, 0.999999999999991,
0.999999999999999, 0.999999999999777, 0.999999999999999,
0.999999999999999, 0.999999999999959, 0.999999999999999,
NA, 0.999999999999299, 0.779426832974571, 0.999999999999933,
0.999999999999999, 0.999999999999998, 0.999999999999687,
0.999999999999999, 0.999999999999777, 0.999999999999999,
0.999999999999999, 0.999999999999959, 0.999999999999999,
0.999999999999299, NA, 0.999999999999999, 0.611136265859179,
0.999999999999999, 0.999999999999998, 0.999999999999687,
0.200661174254572, 0.789829339795786, 0.999999999999999,
1.56797538624063e-08, 0.000115476294641169, 0.200661174254572,
0.779426832974571, 0.999999999999999, NA, 0.0311037604732729,
0.0122054515551129, 0.999999999999984, 0.999999999999999,
0.0259711510883781, 0.198077829941541, 0.507611967012476,
8.50447159522988e-05, 1.60499061530966e-19, 0.410636112084533,
0.999999999999933, 0.611136265859179, 0.0311037604732729,
NA, 0.0403275386741277, 0.508244635418544, 0.999999999999999,
0.624415639985824, 0.768466049890891, 0.673527222144056,
0.00644405295214749, 4.67520836455185e-05, 0.999999999999929,
0.999999999999999, 0.999999999999999, 0.0122054515551129,
0.0403275386741277, NA, 1, 0.999999999999823, 0.999999999999995,
0.999999999999885, 0.999999999999996, 0.88381428087806, 0.528580893876124,
0.999999999999995, 0.999999999999998, 0.999999999999998,
0.999999999999984, 0.508244635418544, 1, NA, 0.999999999998991,
0.999999999999991, 0.999999999999994, 1, 0.99999999999993,
0.795044191844885, 0.999999999999991, 0.999999999999687,
0.999999999999687, 0.999999999999999, 0.999999999999999,
0.999999999999823, 0.999999999998991, NA)), row.names = c(NA,
-169L), class = "data.frame")
答案1
得分: 1
以下是翻译好的部分：
-
使用
cut将数据分组成简单的分组热图，但需要经过几个阶段： -
使用
cut对数据进行分组；我随意将它们分成了0、0.25、0.5和1的分组，您可以根据自己的需要自由更改这些数字； -
为了根据类似数字排序的方式保留物种的顺序，我们需要将它们转换为
factor，为了正确排序它们，我们需要解析出数字，因此需要使用as.integer(gsub(..))然后稍后使用factor。 -
为了获得半三角形，我确保 x 轴上的
num*值大于 y 轴上的值；这是任意的，但与您的示例图匹配。 -
为了在对角线上显示标签，我们保留了进入
ggplot(.)的数据中的numA == numB，然后将对角线用于瓷砖，然后仅保留标签的对角线部分（geom_text）。 -
为了扩展画布以容纳对角线的标签，我们需要同时使用
expand=扩展两个轴；实际的数字可能取决于画布（pdf、html等）。
我使用了基本的 R（R-4）转换/子集，这很容易转换为使用 dplyr 的 mutate/filter。
英文:
Here's a crude start. The basic plot is a simple binned heatmap, but there are several stages we need to go through:
- bin the data with
cut; I arbitrarily cut them into bins of 0, 0.25, 0.5, and 1, feel free to change the numbers for your own purpose; - to preserve order based on the numeric-like sorting of the species, we need to convert them to
factors, and to order them right we need to parse out the numbers, ergo theas.integer(gsub(..))thenfactorlater. - to get a half-triangle, I make sure that the x-axis
num*value is greater than the y-axis; this is arbitrary but matched your sample plot. - in order to show the labels on the diagonal, though, we keep
numA == numBin the data that goes intoggplot(.), subset out the diagonal for the tiles, then keep only the diagonal for the labels (geom_text). - to expand the canvas a little to allow for the diagonal's labels, we need to
expand=both axes; the actual numbers may depend on the canvas (pdf, html, etc).
I used base-R (R-4) transform/subset, this is easily converted to use dplyr's mutate/filter.
long.df |>
transform(
bin = cut(p_value, c(0, 0.25, 0.5, 1), labels = c("low", "moderate", "high")),
numA = as.integer(gsub("\\D", "", SpeciesA)),
numB = as.integer(gsub("\\D", "", SpeciesB))
) |> transform(
SpeciesA = factor(SpeciesA, levels = unique(SpeciesA[order(numA)])),
SpeciesB = factor(SpeciesB, levels = unique(SpeciesB[order(numB)]))
) |>
subset(numA >= numB) |>
ggplot(aes(SpeciesA, SpeciesB)) +
geom_tile(aes(fill = bin), data = ~ subset(., numA > numB)) +
geom_text(aes(label = SpeciesA), data = ~ subset(., numA == numB),
hjust = 1.1, angle = -30) +
scale_x_discrete(name = NULL, expand = expansion(0, c(2.5, 0)), drop = FALSE) +
scale_y_discrete(name = NULL, expand = expansion(0, c(0, 2)), drop = FALSE) +
theme(
axis.text = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank()
)
答案2
得分: 1
这段代码是使用r2evans的帮助解决的！我最终使用的代码如下：
test.plot1 <- long.df1 %>%
mutate(
color = ifelse(test = p_value > 0.05,
yes = "#440154FF",
no = (ifelse(test = p_value <= 0.05,
yes = "#FDE725FF",
no = NA)
)
)
) %>%
mutate(
Species1 = factor(Species1, levels = unique(Species1[order(order_species1)])),
Species2 = factor(Species2, levels = unique(Species2[order(order_species2)]))
) %>%
subset(order_species1 >= order_species2) %>%
ggplot(aes(Species1, Species2)) +
geom_tile(aes(fill = color), data = ~ subset(., order_species1 > order_species2), color = "white") +
geom_text(aes(label = Species1), data = ~ subset(., order_species1 == order_species2),
hjust = 1.1, angle = -30) +
scale_x_discrete(name = NULL, expand = expansion(0, c(2.5, 0)), drop = FALSE) +
scale_y_discrete(name = NULL, expand = expansion(0, c(0, 2)), drop = FALSE) +
theme_minimal() +
theme(
axis.text = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank()
) + scale_fill_identity()
test.plot1
这段代码生成了上述图片中的图表。
英文:
Solved with the help of r2evans! The code I ended up using is below!
test.plot1 <- long.df1 %>%
mutate(
color = # get colors of graphs
ifelse(test = p_value > 0.05,
yes = "#440154FF",
no = (ifelse(test = p_value <= 0.05,
yes = "#FDE725FF",
no = NA)
)
)
) %>%
mutate(
Species1 = factor(Species1, levels = unique(Species1[order(order_species1)])),
Species2 = factor(Species2 , levels = unique(Species2 [order(order_species2)]))
) %>%
subset(order_species1>=order_species2) %>%
ggplot(aes(Species1, Species2)) +
geom_tile(aes(fill = color), data = ~ subset(., order_species1> order_species2), color = "white") +
geom_text(aes(label = Species1), data = ~ subset(., order_species1 == order_species2),
hjust = 1.1, angle = -30) +
scale_x_discrete(name = NULL, expand = expansion(0, c(2.5, 0)), drop = FALSE) +
scale_y_discrete(name = NULL, expand = expansion(0, c(0, 2)), drop = FALSE) +
theme_minimal() +
theme(
axis.text = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank()
) + scale_fill_identity()
test.plot1
That produces this plot:
Thanks all!
Dput for data frame is here:
structure(list(Species1 = c("SpeciesA", "SpeciesB", "SpeciesC",
"SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI",
"SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM", "SpeciesA", "SpeciesB",
"SpeciesC", "SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH",
"SpeciesI", "SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM", "SpeciesA",
"SpeciesB", "SpeciesC", "SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG",
"SpeciesH", "SpeciesI", "SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM",
"SpeciesA", "SpeciesB", "SpeciesC", "SpeciesD", "SpeciesE", "SpeciesF",
"SpeciesG", "SpeciesH", "SpeciesI", "SpeciesJ", "SpeciesK", "SpeciesL",
"SpeciesM", "SpeciesA", "SpeciesB", "SpeciesC", "SpeciesD", "SpeciesE",
"SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI", "SpeciesJ", "SpeciesK",
"SpeciesL", "SpeciesM", "SpeciesA", "SpeciesB", "SpeciesC", "SpeciesD",
"SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI", "SpeciesJ",
"SpeciesK", "SpeciesL", "SpeciesM", "SpeciesA", "SpeciesB", "SpeciesC",
"SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI",
"SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM", "SpeciesA", "SpeciesB",
"SpeciesC", "SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH",
"SpeciesI", "SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM", "SpeciesA",
"SpeciesB", "SpeciesC", "SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG",
"SpeciesH", "SpeciesI", "SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM",
"SpeciesA", "SpeciesB", "SpeciesC", "SpeciesD", "SpeciesE", "SpeciesF",
"SpeciesG", "SpeciesH", "SpeciesI", "SpeciesJ", "SpeciesK", "SpeciesL",
"SpeciesM", "SpeciesA", "SpeciesB", "SpeciesC", "SpeciesD", "SpeciesE",
"SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI", "SpeciesJ", "SpeciesK",
"SpeciesL", "SpeciesM", "SpeciesA", "SpeciesB", "SpeciesC", "SpeciesD",
"SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI", "SpeciesJ",
"SpeciesK", "SpeciesL", "SpeciesM", "SpeciesA", "SpeciesB", "SpeciesC",
"SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI",
"SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM"), Species2 = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L), .Label = c("SpeciesA", "SpeciesB", "SpeciesC",
"SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI",
"SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM"), class = "factor"),
p_value = c(NA, 8.29661485364936e-14, 0.197328035330918,
3.73447143215061e-05, 0.0200245227839388, 0.000158518795659732,
0.999999999999999, 0.999999999999999, 0.200661174254572,
0.0259711510883781, 0.624415639985824, 0.999999999999995,
0.999999999999991, 8.29661485364936e-14, NA, 0.911548596172133,
0.0808672833723648, 0.00232408080140171, 0.00257625010714883,
0.999999999999777, 0.999999999999777, 0.789829339795786,
0.198077829941541, 0.768466049890891, 0.999999999999885,
0.999999999999994, 0.197328035330918, 0.911548596172133,
NA, 0.181696316252709, 0.575238254258972, 0.939866701307512,
0.999999999999999, 0.999999999999999, 0.999999999999999,
0.507611967012476, 0.673527222144056, 0.999999999999996,
1, 3.73447143215061e-05, 0.0808672833723648, 0.181696316252709,
NA, 1.40996305374498e-10, 0.405410680293625, 0.999999999999999,
0.999999999999999, 1.56797538624063e-08, 8.50447159522988e-05,
0.00644405295214749, 0.88381428087806, 0.99999999999993,
0.0200245227839388, 0.00232408080140171, 0.575238254258972,
1.40996305374498e-10, NA, 0.622095128306733, 0.999999999999959,
0.999999999999959, 0.000115476294641169, 1.60499061530966e-19,
4.67520836455185e-05, 0.528580893876124, 0.795044191844885,
0.000158518795659732, 0.00257625010714883, 0.939866701307512,
0.405410680293625, 0.622095128306733, NA, 0.999999999999999,
0.999999999999999, 0.200661174254572, 0.410636112084533,
0.999999999999929, 0.999999999999995, 0.999999999999991,
0.999999999999999, 0.999999999999777, 0.999999999999999,
0.999999999999999, 0.999999999999959, 0.999999999999999,
NA, 0.999999999999299, 0.779426832974571, 0.999999999999933,
0.999999999999999, 0.999999999999998, 0.999999999999687,
0.999999999999999, 0.999999999999777, 0.999999999999999,
0.999999999999999, 0.999999999999959, 0.999999999999999,
0.999999999999299, NA, 0.999999999999999, 0.611136265859179,
0.999999999999999, 0.999999999999998, 0.999999999999687,
0.200661174254572, 0.789829339795786, 0.999999999999999,
1.56797538624063e-08, 0.000115476294641169, 0.200661174254572,
0.779426832974571, 0.999999999999999, NA, 0.0311037604732729,
0.0122054515551129, 0.999999999999984, 0.999999999999999,
0.0259711510883781, 0.198077829941541, 0.507611967012476,
8.50447159522988e-05, 1.60499061530966e-19, 0.410636112084533,
0.999999999999933, 0.611136265859179, 0.0311037604732729,
NA, 0.0403275386741277, 0.508244635418544, 0.999999999999999,
0.624415639985824, 0.768466049890891, 0.673527222144056,
0.00644405295214749, 4.67520836455185e-05, 0.999999999999929,
0.999999999999999, 0.999999999999999, 0.0122054515551129,
0.0403275386741277, NA, 1, 0.999999999999823, 0.999999999999995,
0.999999999999885, 0.999999999999996, 0.88381428087806, 0.528580893876124,
0.999999999999995, 0.999999999999998, 0.999999999999998,
0.999999999999984, 0.508244635418544, 1, NA, 0.999999999998991,
0.999999999999991, 0.999999999999994, 1, 0.99999999999993,
0.795044191844885, 0.999999999999991, 0.999999999999687,
0.999999999999687, 0.999999999999999, 0.999999999999999,
0.999999999999823, 0.999999999998991, NA), order_species1 = c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L
), order_species2 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L)), row.names = c(NA, -169L
), class = "data.frame")
通过集体智慧和协作来改善编程学习和解决问题的方式。致力于成为全球开发者共同参与的知识库，让每个人都能够通过互相帮助和分享经验来进步。
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