我正在使用一些跟踪数据制作二维密度图。
我的目标是为每个实验单元绘制图
我的 df 看起来像这样:
head(df)
Caja x y t
1 4 0 0 0:00:00:00
2 4 -2 10 0:00:00:14
3 4 3 26 0:00:00:28
4 4 7 54 0:00:00:42
5 4 9 75 0:00:00:57
6 4 9 92 0:00:00:71
第一列(Caja)是我用于分面的因子(因为每个级别都是不同的实验单元)。 x 和 y 是我感兴趣的变量，t 只是采取测量的时间。
由于df是一个很长的data.frane并且我稍后将使用caja4我提供它
dput(caja4)
structure(list(Caja = c(4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), x = c(0L, -2L,
3L, 7L, 9L, 9L, 11L, 6L, -12L, -23L, -33L, -32L, -36L, -28L,
-15L, -2L, 2L, 6L, 6L, 4L, 4L, 2L, 1L, 0L, -1L, 2L, 2L, 4L, 6L,
11L, 9L, 11L, 13L, 12L, 0L, 3L, 10L, 5L, -14L, -10L, 5L, 10L,
12L, 12L, 11L, 11L, 9L, 9L, 11L, 9L, 10L, 11L, 12L, 7L, 1L, -6L,
-11L, -18L, -25L, -17L, -7L, -2L, -5L, -4L, -24L, -29L, -37L,
-39L, -41L, -41L, -41L, -41L, -41L, -41L, -41L, -39L, -39L, -39L,
-39L, -39L, -39L, -39L, -39L, -39L, -28L, -17L, -11L, -6L, -9L,
-16L, -27L, -37L, -38L, -42L, -42L, -42L, -42L, -42L, -42L, -42L,
-42L, -42L, -42L, -36L, -28L, -17L, -6L, 1L, 3L, 3L, 2L, 2L,
2L, 2L, -2L, 1L, 0L, 0L, 2L, 4L, 5L, 4L, 2L, 3L, -1L, -1L, -9L,
-12L, -30L, -38L, -37L, -36L, -42L, -42L, -42L, -42L, -42L, -43L,
-43L, -43L, -36L, -35L, -29L, -26L, -29L, -37L, -38L, -38L, -38L,
-37L, -31L, -18L, 5L, 7L, 7L, 8L, 7L, 4L, 7L, 2L, 5L, 10L, 13L,
12L, -13L, -32L, -41L, -31L, -14L, 0L, 10L, 2L, -8L, -29L, -37L,
-37L, -37L, -37L, -37L, -37L, -37L, -28L, -14L, -3L, 4L, 3L,
1L, -7L, -15L, -11L, -11L, -15L, -16L, -27L, -37L, -43L, -43L,
-43L, -43L, -43L, -43L, -43L, -24L, -1L, 0L, 3L, 4L, 3L, 2L,
1L, 3L, 4L, -9L, -31L, -45L, -45L, -34L, -32L, -4L, -12L, -8L,
-6L, -17L, -31L, -36L, -42L, -37L, -39L, -36L, -36L, -36L, -37L,
-37L, -36L, -37L, -32L, -19L, -5L, 2L, 7L, 9L, 9L, 7L, 10L, 9L,
10L, 11L, 11L, 11L, 8L, 8L, 10L, 12L, -1L, -14L, -32L, -17L,
-2L, -14L, -26L, -44L, -44L, -44L, -44L, -44L, -44L, -45L, -45L,
-33L, -21L, -12L, -11L, -5L, 2L, 5L, 4L, 2L, 6L, 6L, 6L, 6L,
6L, 6L, 7L, -7L, -29L, -27L, -10L, -2L, 5L, 7L, 7L, 8L, 13L),
y = c(0L, 10L, 26L, 54L, 75L, 92L, 113L, 116L, 117L, 114L,
119L, 115L, 114L, 108L, 99L, 90L, 76L, 57L, 37L, 21L, 12L,
7L, 8L, 2L, -2L, 5L, 7L, 17L, 37L, 52L, 67L, 86L, 100L, 118L,
122L, 125L, 122L, 122L, 129L, 129L, 126L, 124L, 125L, 123L,
106L, 92L, 70L, 49L, 27L, 17L, 5L, 6L, 10L, 7L, 3L, -3L,
-3L, -6L, -8L, -13L, -13L, -14L, -13L, -13L, -5L, 1L, 3L,
7L, 8L, 9L, 10L, 25L, 42L, 44L, 57L, 87L, 101L, 107L, 110L,
102L, 96L, 94L, 81L, 68L, 57L, 50L, 61L, 76L, 96L, 97L, 96L,
98L, 99L, 95L, 79L, 64L, 53L, 46L, 31L, 12L, 4L, -7L, -16L,
-17L, -17L, -17L, -17L, -17L, -17L, -16L, -17L, -17L, -17L,
-17L, -17L, -17L, -17L, -17L, -6L, 15L, 31L, 50L, 71L, 95L,
105L, 119L, 125L, 119L, 117L, 116L, 127L, 112L, 94L, 77L,
55L, 26L, 0L, -1L, -2L, 1L, -4L, -5L, -5L, -6L, -10L, -11L,
-11L, -11L, -10L, -9L, -10L, -8L, 4L, 9L, 15L, 25L, 41L,
57L, 71L, 84L, 100L, 117L, 133L, 134L, 137L, 134L, 134L,
135L, 139L, 135L, 131L, 120L, 125L, 132L, 131L, 127L, 140L,
143L, 144L, 143L, 143L, 142L, 139L, 138L, 131L, 124L, 104L,
80L, 61L, 45L, 23L, 9L, -4L, -4L, -8L, -13L, -13L, -13L,
-13L, -13L, -13L, -13L, -10L, -2L, 0L, 7L, 24L, 36L, 56L,
77L, 108L, 123L, 137L, 139L, 129L, 130L, 117L, 128L, 123L,
117L, 126L, 125L, 124L, 145L, 145L, 107L, 104L, 92L, 83L,
75L, 62L, 47L, 32L, 17L, 1L, -7L, -8L, -10L, -8L, -5L, -6L,
-3L, -1L, 10L, 20L, 28L, 32L, 33L, 34L, 52L, 74L, 98L, 114L,
116L, 116L, 121L, 122L, 123L, 120L, 117L, 114L, 107L, 109L,
87L, 69L, 37L, 12L, 8L, -14L, -14L, -14L, -11L, 2L, 17L,
29L, 25L, 12L, -9L, 0L, 12L, 28L, 54L, 84L, 96L, 105L, 114L,
114L, 111L, 112L, 111L, 116L, 120L, 120L, 127L), t = structure(1:294, .Label = c("0:00:00:00",
"0:00:00:14", "0:00:00:28", "0:00:00:42", "0:00:00:57", "0:00:00:71",
"0:00:00:85", "0:00:01:00", "0:00:01:14", "0:00:01:28", "0:00:01:42",
"0:00:01:57", "0:00:01:71", "0:00:01:85", "0:00:02:00", "0:00:02:14",
"0:00:02:28", "0:00:02:42", "0:00:02:57", "0:00:02:71", "0:00:02:85",
"0:00:03:00", "0:00:03:14", "0:00:03:28", "0:00:03:42", "0:00:03:57",
"0:00:03:71", "0:00:03:85", "0:00:04:00", "0:00:04:14", "0:00:04:28",
"0:00:04:42", "0:00:04:57", "0:00:04:71", "0:00:04:85", "0:00:05:00",
"0:00:05:14", "0:00:05:28", "0:00:05:42", "0:00:05:57", "0:00:05:71",
"0:00:05:85", "0:00:06:00", "0:00:06:14", "0:00:06:28", "0:00:06:42",
"0:00:06:57", "0:00:06:71", "0:00:06:85", "0:00:07:00", "0:00:07:14",
"0:00:07:28", "0:00:07:42", "0:00:07:57", "0:00:07:71", "0:00:07:85",
"0:00:08:00", "0:00:08:14", "0:00:08:28", "0:00:08:42", "0:00:08:57",
"0:00:08:71", "0:00:08:85", "0:00:09:00", "0:00:09:14", "0:00:09:28",
"0:00:09:42", "0:00:09:57", "0:00:09:71", "0:00:09:85", "0:00:10:00",
"0:00:10:14", "0:00:10:28", "0:00:10:42", "0:00:10:57", "0:00:10:71",
"0:00:10:85", "0:00:11:00", "0:00:11:14", "0:00:11:28", "0:00:11:42",
"0:00:11:57", "0:00:11:71", "0:00:11:85", "0:00:12:00", "0:00:12:14",
"0:00:12:28", "0:00:12:42", "0:00:12:57", "0:00:12:71", "0:00:12:85",
"0:00:13:00", "0:00:13:14", "0:00:13:28", "0:00:13:42", "0:00:13:57",
"0:00:13:71", "0:00:13:85", "0:00:14:00", "0:00:14:14", "0:00:14:28",
"0:00:14:42", "0:00:14:57", "0:00:14:71", "0:00:14:85", "0:00:15:00",
"0:00:15:14", "0:00:15:28", "0:00:15:42", "0:00:15:57", "0:00:15:71",
"0:00:15:85", "0:00:16:00", "0:00:16:14", "0:00:16:28", "0:00:16:42",
"0:00:16:57", "0:00:16:71", "0:00:16:85", "0:00:17:00", "0:00:17:14",
"0:00:17:28", "0:00:17:42", "0:00:17:57", "0:00:17:71", "0:00:17:85",
"0:00:18:00", "0:00:18:14", "0:00:18:28", "0:00:18:42", "0:00:18:57",
"0:00:18:71", "0:00:18:85", "0:00:19:00", "0:00:19:14", "0:00:19:28",
"0:00:19:42", "0:00:19:57", "0:00:19:71", "0:00:19:85", "0:00:20:00",
"0:00:20:14", "0:00:20:28", "0:00:20:42", "0:00:20:57", "0:00:20:71",
"0:00:20:85", "0:00:21:00", "0:00:21:14", "0:00:21:28", "0:00:21:42",
"0:00:21:57", "0:00:21:71", "0:00:21:85", "0:00:22:00", "0:00:22:14",
"0:00:22:28", "0:00:22:42", "0:00:22:57", "0:00:22:71", "0:00:22:85",
"0:00:23:00", "0:00:23:14", "0:00:23:28", "0:00:23:42", "0:00:23:57",
"0:00:23:71", "0:00:23:85", "0:00:24:00", "0:00:24:14", "0:00:24:28",
"0:00:24:42", "0:00:24:57", "0:00:24:71", "0:00:24:85", "0:00:25:00",
"0:00:25:14", "0:00:25:28", "0:00:25:42", "0:00:25:57", "0:00:25:71",
"0:00:25:85", "0:00:26:00", "0:00:26:14", "0:00:26:28", "0:00:26:42",
"0:00:26:57", "0:00:26:71", "0:00:26:85", "0:00:27:00", "0:00:27:14",
"0:00:27:28", "0:00:27:42", "0:00:27:57", "0:00:27:71", "0:00:27:85",
"0:00:28:00", "0:00:28:14", "0:00:28:28", "0:00:28:42", "0:00:28:57",
"0:00:28:71", "0:00:28:85", "0:00:29:00", "0:00:29:14", "0:00:29:28",
"0:00:29:42", "0:00:29:57", "0:00:29:71", "0:00:29:85", "0:00:30:00",
"0:00:30:14", "0:00:30:28", "0:00:30:42", "0:00:30:57", "0:00:30:71",
"0:00:30:85", "0:00:31:00", "0:00:31:14", "0:00:31:28", "0:00:31:42",
"0:00:31:57", "0:00:31:71", "0:00:31:85", "0:00:32:00", "0:00:32:14",
"0:00:32:28", "0:00:32:42", "0:00:32:57", "0:00:32:71", "0:00:32:85",
"0:00:33:00", "0:00:33:14", "0:00:33:28", "0:00:33:42", "0:00:33:57",
"0:00:33:71", "0:00:33:85", "0:00:34:00", "0:00:34:14", "0:00:34:28",
"0:00:34:42", "0:00:34:57", "0:00:34:71", "0:00:34:85", "0:00:35:00",
"0:00:35:14", "0:00:35:28", "0:00:35:42", "0:00:35:57", "0:00:35:71",
"0:00:35:85", "0:00:36:00", "0:00:36:14", "0:00:36:28", "0:00:36:42",
"0:00:36:57", "0:00:36:71", "0:00:36:85", "0:00:37:00", "0:00:37:14",
"0:00:37:28", "0:00:37:42", "0:00:37:57", "0:00:37:71", "0:00:37:85",
"0:00:38:00", "0:00:38:14", "0:00:38:28", "0:00:38:42", "0:00:38:57",
"0:00:38:71", "0:00:38:85", "0:00:39:00", "0:00:39:14", "0:00:39:28",
"0:00:39:42", "0:00:39:57", "0:00:39:71", "0:00:39:85", "0:00:40:00",
"0:00:40:14", "0:00:40:28", "0:00:40:42", "0:00:40:57", "0:00:40:71",
"0:00:40:85", "0:00:41:00", "0:00:41:14", "0:00:41:28", "0:00:41:42",
"0:00:41:57", "0:00:41:71", "0:00:41:85"), class = "factor")), .Names = c("Caja",
"x", "y", "t"), row.names = c(NA, 294L), class = "data.frame")
对于我使用的情节
ggplot(data=df,aes(x,y)) + facet_wrap(~Caja) +
stat_density2d(aes(fill=..level..,alpha=..level..),geom='polygon',colour='black') +
scale_fill_continuous(low="green",high="red") +
geom_point()+guides(alpha="none")
当我仅绘制 caja4 数据时
ggplot(data=caja4,aes(x,y)) + facet_wrap(~Caja) +
stat_density2d(aes(fill=..level..,alpha=..level..),geom='polygon',colour='black') +
scale_fill_continuous(low="green",high="red") +
geom_point()+guides(alpha="none")
这使得解释更容易，但如果你看一下线图，情况会有点不同
ggplot(caja4,aes(x,y))+geom_point()+geom_path(col="red")+theme_classic()
我想要面的自由密度标度，但有一些更依赖于线图的东西。
最佳答案
一般评论
好吧，我想这里有很多要讨论的事情:
您可以在构面调用中使用
scales = 'free'来更好地将绘图空间与数据对齐。您无法改变面之间的密度比例。这是因为这些面通常旨在使用常见美学映射来表示不同的组。您可以制作单独的图形，然后将它们粘在一起(例如使用
cowplot包)。绘图中的多边形被切断，结果令人失望。我更喜欢使用
geom_tile来可视化点的密度正如 MLavoie 所指出的，您需要使用
geom_path而不是geom_line。色阶不太好。例如，它对于色盲者来说表现非常差。我推荐
viridis色标(见下文)。如果
x和y轴的单位相同且有意义(可能是厘米或像素)，您需要确保它们在同样的方式。您可以使用coord_equal()来实现此目的。
为一次试验制作一个漂亮的情节
这里是一些示例代码和一个将其组合起来的图(我只有 Caja 4 的数据):
library(viridis)
ggplot(caja4, aes(x,y)) +
stat_density2d(geom = 'tile', aes(fill = ..density..), contour = FALSE) +
geom_point() +
geom_path() +
scale_fill_viridis() +
coord_equal() +
theme_minimal()
如果您确实想使用轮廓多边形，则必须手动扩展轴以避免多边形被剪裁 ( or you have close the polygons yourself ):
ggplot(caja4, aes(x,y)) +
stat_density2d(geom = 'polygon', aes(fill = ..level..), contour = TRUE) +
geom_point() +
geom_path() +
scale_fill_viridis() +
coord_equal() +
theme_minimal() +
xlim(-60, 40) +
ylim(-50, 170)
结合不同的试验
我们可以使用图形面板来代替面(参见上面的第 2 点)。我发现cowplot 包对此非常有用。下面是一些示例代码，可轻松创建复合图形。 (请注意，我在这里使用了 log(..密度..) 来对颜色进行不同的缩放。)
# Make data with three cajas (which only differ in x and y range here)
library(dplyr)
xy <- select(caja4, x, y)
cajas <- bind_rows('1' = xy, '2' = xy * 2, '3' = xy / 2, .id = 'Caja')
# Create ggplot objects for each caja
plots <- lapply(split(cajas, cajas$Caja),
function(XX) {
plot = ggplot(XX, aes(x,y)) +
stat_density2d(geom = 'tile', aes(fill = log(..density..)), contour = FALSE) +
geom_point() +
geom_path() +
scale_fill_viridis() +
coord_equal() +
theme_minimal()
} )
plot_grid(plotlist = plots, nrow = 1, labels = 1:length(plots))
结果是:
关于r - 2d 密度面尺度，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/34314967/