geom_ribbon error: Aesthetics must either be length one
geom_ribbon error: Aesthetics must either be length one
我的问题类似于Fill region between two loess-smoothed lines in R with ggplot1
但是我有两个组。
g1<-ggplot(NVIQ_predict,aes(cogn.age, predict, color=as.factor(NVIQ_predict$group)))+
geom_smooth(aes(x = cogn.age, y = upper,group=group),se=F)+
geom_line(aes(linetype = group), size = 0.8)+
geom_smooth(aes(x = cogn.age, y = lower,group=group),se=F)
我想为每个组填充红色和蓝色。
我试过了：
gg1 <- ggplot_build(g1)
df2 <- data.frame(x = gg1$data[[1]]$x,
ymin = gg1$data[[1]]$y,
ymax = gg1$data[[3]]$y)
g1 + geom_ribbon(data = df2, aes(x = x, ymin = ymin, ymax = ymax),fill = "grey", alpha = 0.4)
但它给了我错误：美学必须是长度一，或者与数据问题的长度相同
每当我的 geom_ribbon() 数据和 ggplot() 数据不同时，我都会得到同样的错误。
有人可以帮我吗？太感谢了！
我的数据如下：
> NVIQ_predict
cogn.age predict upper lower group
1 7 39.04942 86.68497 18.00000 1
2 8 38.34993 82.29627 18.00000 1
3 10 37.05174 74.31657 18.00000 1
4 11 36.45297 70.72421 18.00000 1
5 12 35.88770 67.39555 18.00000 1
6 13 35.35587 64.32920 18.00000 1
7 14 34.85738 61.52322 18.00000 1
8 16 33.95991 56.68024 18.00000 1
9 17 33.56057 54.63537 18.00000 1
10 18 33.19388 52.83504 18.00000 1
11 19 32.85958 51.27380 18.00000 1
12 20 32.55752 49.94791 18.00000 1
13 21 32.28766 48.85631 18.00000 1
14 24 31.67593 47.09206 18.00000 1
15 25 31.53239 46.91136 18.00000 1
16 28 31.28740 48.01764 18.00000 1
17 32 31.36627 50.55201 18.00000 1
18 35 31.73386 53.19630 18.00000 1
19 36 31.91487 54.22624 18.00000 1
20 37 32.13026 55.25721 18.00000 1
21 38 32.38237 56.26713 18.00000 1
22 40 32.98499 58.36229 18.00000 1
23 44 34.59044 62.80187 18.00000 1
24 45 35.06804 64.01951 18.00000 1
25 46 35.57110 65.31888 18.00000 1
26 47 36.09880 66.64696 17.93800 1
27 48 36.72294 67.60053 17.97550 1
28 49 37.39182 68.49995 18.03062 1
29 50 38.10376 69.35728 18.10675 1
30 51 38.85760 70.17693 18.18661 1
31 52 39.65347 70.95875 18.27524 1
32 53 40.49156 71.70261 18.38020 1
33 54 41.35332 72.44006 17.90682 1
34 59 46.37849 74.91802 18.63206 1
35 60 47.53897 75.66218 19.64432 1
36 61 48.74697 76.43933 20.82346 1
37 63 51.30607 78.02426 23.73535 1
38 71 63.43129 86.05467 40.43482 1
39 72 65.15618 87.44794 42.72704 1
40 73 66.92714 88.95324 45.01966 1
41 84 89.42079 114.27939 68.03834 1
42 85 91.73831 117.44007 69.83676 1
43 7 33.69504 54.03695 15.74588 2
44 8 34.99931 53.96500 18.00533 2
45 10 37.61963 54.05684 22.43516 2
46 11 38.93493 54.21969 24.60049 2
47 12 40.25315 54.45963 26.73027 2
48 13 41.57397 54.77581 28.82348 2
49 14 42.89710 55.16727 30.87982 2
50 16 45.54954 56.17193 34.88453 2
51 17 46.87877 56.78325 36.83632 2
52 18 48.21025 57.46656 38.75807 2
53 19 49.54461 58.22266 40.65330 2
54 20 50.88313 59.05509 42.52505 2
55 21 52.22789 59.97318 44.36944 2
56 24 56.24397 63.21832 49.26963 2
57 25 57.55394 64.33850 50.76938 2
58 28 61.45282 68.05043 54.85522 2
59 32 66.44875 72.85234 60.04517 2
60 35 69.96560 76.06171 63.86949 2
61 36 71.09268 77.06821 65.11714 2
62 37 72.19743 78.04559 66.34927 2
63 38 73.28041 78.99518 67.56565 2
64 40 75.37861 80.81593 69.94129 2
65 44 79.29028 84.20275 74.37780 2
66 45 80.20272 85.00888 75.39656 2
67 46 81.08645 85.80180 76.37110 2
68 47 81.93696 86.57689 77.29704 2
69 48 82.75920 87.34100 78.17739 2
70 49 83.55055 88.09165 79.00945 2
71 50 84.30962 88.82357 79.79567 2
72 51 85.03743 89.53669 80.53817 2
73 52 85.73757 90.23223 81.24291 2
74 53 86.41419 90.91607 81.91232 2
75 54 87.05716 91.58632 82.52800 2
76 59 89.75923 94.58218 84.93629 2
77 60 90.18557 95.05573 85.31541 2
78 61 90.58166 95.51469 85.64864 2
79 63 91.27115 96.31107 86.23124 2
80 71 92.40983 98.35031 86.46934 2
81 72 92.36362 98.52258 86.20465 2
82 73 92.27734 98.67161 85.88308 2
83 84 88.66150 98.84699 78.47602 2
84 85 88.08846 98.73625 77.44067 2
根据 Gregor 的说法，我尝试了 inherit.aes = FALSE，错误消失了。但我的情节看起来像：
我们已获得所需的所有信息。现在我们只需要，咳咳，把这些点联系起来 ;-)
首先输入数据：
NVIQ_predict <- read.table(text = "
id cogn.age predict upper lower group
1 7 39.04942 86.68497 18.00000 1
2 8 38.34993 82.29627 18.00000 1
3 10 37.05174 74.31657 18.00000 1
4 11 36.45297 70.72421 18.00000 1
5 12 35.88770 67.39555 18.00000 1
6 13 35.35587 64.32920 18.00000 1
7 14 34.85738 61.52322 18.00000 1
8 16 33.95991 56.68024 18.00000 1
9 17 33.56057 54.63537 18.00000 1
10 18 33.19388 52.83504 18.00000 1
11 19 32.85958 51.27380 18.00000 1
12 20 32.55752 49.94791 18.00000 1
13 21 32.28766 48.85631 18.00000 1
14 24 31.67593 47.09206 18.00000 1
15 25 31.53239 46.91136 18.00000 1
16 28 31.28740 48.01764 18.00000 1
17 32 31.36627 50.55201 18.00000 1
18 35 31.73386 53.19630 18.00000 1
19 36 31.91487 54.22624 18.00000 1
20 37 32.13026 55.25721 18.00000 1
21 38 32.38237 56.26713 18.00000 1
22 40 32.98499 58.36229 18.00000 1
23 44 34.59044 62.80187 18.00000 1
24 45 35.06804 64.01951 18.00000 1
25 46 35.57110 65.31888 18.00000 1
26 47 36.09880 66.64696 17.93800 1
27 48 36.72294 67.60053 17.97550 1
28 49 37.39182 68.49995 18.03062 1
29 50 38.10376 69.35728 18.10675 1
30 51 38.85760 70.17693 18.18661 1
31 52 39.65347 70.95875 18.27524 1
32 53 40.49156 71.70261 18.38020 1
33 54 41.35332 72.44006 17.90682 1
34 59 46.37849 74.91802 18.63206 1
35 60 47.53897 75.66218 19.64432 1
36 61 48.74697 76.43933 20.82346 1
37 63 51.30607 78.02426 23.73535 1
38 71 63.43129 86.05467 40.43482 1
39 72 65.15618 87.44794 42.72704 1
40 73 66.92714 88.95324 45.01966 1
41 84 89.42079 114.27939 68.03834 1
42 85 91.73831 117.44007 69.83676 1
43 7 33.69504 54.03695 15.74588 2
44 8 34.99931 53.96500 18.00533 2
45 10 37.61963 54.05684 22.43516 2
46 11 38.93493 54.21969 24.60049 2
47 12 40.25315 54.45963 26.73027 2
48 13 41.57397 54.77581 28.82348 2
49 14 42.89710 55.16727 30.87982 2
50 16 45.54954 56.17193 34.88453 2
51 17 46.87877 56.78325 36.83632 2
52 18 48.21025 57.46656 38.75807 2
53 19 49.54461 58.22266 40.65330 2
54 20 50.88313 59.05509 42.52505 2
55 21 52.22789 59.97318 44.36944 2
56 24 56.24397 63.21832 49.26963 2
57 25 57.55394 64.33850 50.76938 2
58 28 61.45282 68.05043 54.85522 2
59 32 66.44875 72.85234 60.04517 2
60 35 69.96560 76.06171 63.86949 2
61 36 71.09268 77.06821 65.11714 2
62 37 72.19743 78.04559 66.34927 2
63 38 73.28041 78.99518 67.56565 2
64 40 75.37861 80.81593 69.94129 2
65 44 79.29028 84.20275 74.37780 2
66 45 80.20272 85.00888 75.39656 2
67 46 81.08645 85.80180 76.37110 2
68 47 81.93696 86.57689 77.29704 2
69 48 82.75920 87.34100 78.17739 2
70 49 83.55055 88.09165 79.00945 2
71 50 84.30962 88.82357 79.79567 2
72 51 85.03743 89.53669 80.53817 2
73 52 85.73757 90.23223 81.24291 2
74 53 86.41419 90.91607 81.91232 2
75 54 87.05716 91.58632 82.52800 2
76 59 89.75923 94.58218 84.93629 2
77 60 90.18557 95.05573 85.31541 2
78 61 90.58166 95.51469 85.64864 2
79 63 91.27115 96.31107 86.23124 2
80 71 92.40983 98.35031 86.46934 2
81 72 92.36362 98.52258 86.20465 2
82 73 92.27734 98.67161 85.88308 2
83 84 88.66150 98.84699 78.47602 2
84 85 88.08846 98.73625 77.44067 2", header = TRUE)
NVIQ_predict$id <- NULL
确保 group 列是因子变量，以便我们可以将其用作线型。
NVIQ_predict$group <- as.factor(NVIQ_predict$group)
然后构建情节。
library(ggplot2)
g1 <- ggplot(NVIQ_predict, aes(cogn.age, predict, color=group)) +
geom_smooth(aes(x = cogn.age, y = upper, group=group), method = loess, se = FALSE) +
geom_smooth(aes(x = cogn.age, y = lower, group=group), method = loess, se = FALSE) +
geom_line(aes(linetype = group), size = 0.8)
最后，提取第 1 组和第 2 组曲线的 (x,ymin) 和 (x,ymax) 坐标。这些对具有相同的 x 坐标，因此连接这些点模拟阴影之间的区域两条曲线。 Fill region between two loess-smoothed lines in R with ggplot 中对此进行了解释。这里唯一的区别是我们需要更加小心 select 并连接属于 正确 曲线的点...
gp <- ggplot_build(g1)
d1 <- gp$data[[1]]
d2 <- gp$data[[2]]
df1 <- data.frame(x = d1[d1$group == 1,]$x,
ymin = d2[d2$group == 1,]$y,
ymax = d1[d1$group == 1,]$y)
df2 <- data.frame(x = d1[d1$group == 2,]$x,
ymin = d2[d2$group == 2,]$y,
ymax = d1[d1$group == 2,]$y)
g1 + geom_ribbon(data = df1, aes(x = x, ymin = ymin, ymax = ymax), inherit.aes = FALSE, fill = "grey", alpha = 0.4) +
geom_ribbon(data = df2, aes(x = x, ymin = ymin, ymax = ymax), inherit.aes = FALSE, fill = "grey", alpha = 0.4)
结果如下所示：
我的问题类似于Fill region between two loess-smoothed lines in R with ggplot1
但是我有两个组。
g1<-ggplot(NVIQ_predict,aes(cogn.age, predict, color=as.factor(NVIQ_predict$group)))+
geom_smooth(aes(x = cogn.age, y = upper,group=group),se=F)+
geom_line(aes(linetype = group), size = 0.8)+
geom_smooth(aes(x = cogn.age, y = lower,group=group),se=F)
我想为每个组填充红色和蓝色。
我试过了：
gg1 <- ggplot_build(g1)
df2 <- data.frame(x = gg1$data[[1]]$x,
ymin = gg1$data[[1]]$y,
ymax = gg1$data[[3]]$y)
g1 + geom_ribbon(data = df2, aes(x = x, ymin = ymin, ymax = ymax),fill = "grey", alpha = 0.4)
但它给了我错误：美学必须是长度一，或者与数据问题的长度相同
每当我的 geom_ribbon() 数据和 ggplot() 数据不同时，我都会得到同样的错误。
有人可以帮我吗？太感谢了！
我的数据如下：
> NVIQ_predict
cogn.age predict upper lower group
1 7 39.04942 86.68497 18.00000 1
2 8 38.34993 82.29627 18.00000 1
3 10 37.05174 74.31657 18.00000 1
4 11 36.45297 70.72421 18.00000 1
5 12 35.88770 67.39555 18.00000 1
6 13 35.35587 64.32920 18.00000 1
7 14 34.85738 61.52322 18.00000 1
8 16 33.95991 56.68024 18.00000 1
9 17 33.56057 54.63537 18.00000 1
10 18 33.19388 52.83504 18.00000 1
11 19 32.85958 51.27380 18.00000 1
12 20 32.55752 49.94791 18.00000 1
13 21 32.28766 48.85631 18.00000 1
14 24 31.67593 47.09206 18.00000 1
15 25 31.53239 46.91136 18.00000 1
16 28 31.28740 48.01764 18.00000 1
17 32 31.36627 50.55201 18.00000 1
18 35 31.73386 53.19630 18.00000 1
19 36 31.91487 54.22624 18.00000 1
20 37 32.13026 55.25721 18.00000 1
21 38 32.38237 56.26713 18.00000 1
22 40 32.98499 58.36229 18.00000 1
23 44 34.59044 62.80187 18.00000 1
24 45 35.06804 64.01951 18.00000 1
25 46 35.57110 65.31888 18.00000 1
26 47 36.09880 66.64696 17.93800 1
27 48 36.72294 67.60053 17.97550 1
28 49 37.39182 68.49995 18.03062 1
29 50 38.10376 69.35728 18.10675 1
30 51 38.85760 70.17693 18.18661 1
31 52 39.65347 70.95875 18.27524 1
32 53 40.49156 71.70261 18.38020 1
33 54 41.35332 72.44006 17.90682 1
34 59 46.37849 74.91802 18.63206 1
35 60 47.53897 75.66218 19.64432 1
36 61 48.74697 76.43933 20.82346 1
37 63 51.30607 78.02426 23.73535 1
38 71 63.43129 86.05467 40.43482 1
39 72 65.15618 87.44794 42.72704 1
40 73 66.92714 88.95324 45.01966 1
41 84 89.42079 114.27939 68.03834 1
42 85 91.73831 117.44007 69.83676 1
43 7 33.69504 54.03695 15.74588 2
44 8 34.99931 53.96500 18.00533 2
45 10 37.61963 54.05684 22.43516 2
46 11 38.93493 54.21969 24.60049 2
47 12 40.25315 54.45963 26.73027 2
48 13 41.57397 54.77581 28.82348 2
49 14 42.89710 55.16727 30.87982 2
50 16 45.54954 56.17193 34.88453 2
51 17 46.87877 56.78325 36.83632 2
52 18 48.21025 57.46656 38.75807 2
53 19 49.54461 58.22266 40.65330 2
54 20 50.88313 59.05509 42.52505 2
55 21 52.22789 59.97318 44.36944 2
56 24 56.24397 63.21832 49.26963 2
57 25 57.55394 64.33850 50.76938 2
58 28 61.45282 68.05043 54.85522 2
59 32 66.44875 72.85234 60.04517 2
60 35 69.96560 76.06171 63.86949 2
61 36 71.09268 77.06821 65.11714 2
62 37 72.19743 78.04559 66.34927 2
63 38 73.28041 78.99518 67.56565 2
64 40 75.37861 80.81593 69.94129 2
65 44 79.29028 84.20275 74.37780 2
66 45 80.20272 85.00888 75.39656 2
67 46 81.08645 85.80180 76.37110 2
68 47 81.93696 86.57689 77.29704 2
69 48 82.75920 87.34100 78.17739 2
70 49 83.55055 88.09165 79.00945 2
71 50 84.30962 88.82357 79.79567 2
72 51 85.03743 89.53669 80.53817 2
73 52 85.73757 90.23223 81.24291 2
74 53 86.41419 90.91607 81.91232 2
75 54 87.05716 91.58632 82.52800 2
76 59 89.75923 94.58218 84.93629 2
77 60 90.18557 95.05573 85.31541 2
78 61 90.58166 95.51469 85.64864 2
79 63 91.27115 96.31107 86.23124 2
80 71 92.40983 98.35031 86.46934 2
81 72 92.36362 98.52258 86.20465 2
82 73 92.27734 98.67161 85.88308 2
83 84 88.66150 98.84699 78.47602 2
84 85 88.08846 98.73625 77.44067 2
根据 Gregor 的说法，我尝试了 inherit.aes = FALSE，错误消失了。但我的情节看起来像：
我们已获得所需的所有信息。现在我们只需要，咳咳，把这些点联系起来 ;-)
首先输入数据：
NVIQ_predict <- read.table(text = "
id cogn.age predict upper lower group
1 7 39.04942 86.68497 18.00000 1
2 8 38.34993 82.29627 18.00000 1
3 10 37.05174 74.31657 18.00000 1
4 11 36.45297 70.72421 18.00000 1
5 12 35.88770 67.39555 18.00000 1
6 13 35.35587 64.32920 18.00000 1
7 14 34.85738 61.52322 18.00000 1
8 16 33.95991 56.68024 18.00000 1
9 17 33.56057 54.63537 18.00000 1
10 18 33.19388 52.83504 18.00000 1
11 19 32.85958 51.27380 18.00000 1
12 20 32.55752 49.94791 18.00000 1
13 21 32.28766 48.85631 18.00000 1
14 24 31.67593 47.09206 18.00000 1
15 25 31.53239 46.91136 18.00000 1
16 28 31.28740 48.01764 18.00000 1
17 32 31.36627 50.55201 18.00000 1
18 35 31.73386 53.19630 18.00000 1
19 36 31.91487 54.22624 18.00000 1
20 37 32.13026 55.25721 18.00000 1
21 38 32.38237 56.26713 18.00000 1
22 40 32.98499 58.36229 18.00000 1
23 44 34.59044 62.80187 18.00000 1
24 45 35.06804 64.01951 18.00000 1
25 46 35.57110 65.31888 18.00000 1
26 47 36.09880 66.64696 17.93800 1
27 48 36.72294 67.60053 17.97550 1
28 49 37.39182 68.49995 18.03062 1
29 50 38.10376 69.35728 18.10675 1
30 51 38.85760 70.17693 18.18661 1
31 52 39.65347 70.95875 18.27524 1
32 53 40.49156 71.70261 18.38020 1
33 54 41.35332 72.44006 17.90682 1
34 59 46.37849 74.91802 18.63206 1
35 60 47.53897 75.66218 19.64432 1
36 61 48.74697 76.43933 20.82346 1
37 63 51.30607 78.02426 23.73535 1
38 71 63.43129 86.05467 40.43482 1
39 72 65.15618 87.44794 42.72704 1
40 73 66.92714 88.95324 45.01966 1
41 84 89.42079 114.27939 68.03834 1
42 85 91.73831 117.44007 69.83676 1
43 7 33.69504 54.03695 15.74588 2
44 8 34.99931 53.96500 18.00533 2
45 10 37.61963 54.05684 22.43516 2
46 11 38.93493 54.21969 24.60049 2
47 12 40.25315 54.45963 26.73027 2
48 13 41.57397 54.77581 28.82348 2
49 14 42.89710 55.16727 30.87982 2
50 16 45.54954 56.17193 34.88453 2
51 17 46.87877 56.78325 36.83632 2
52 18 48.21025 57.46656 38.75807 2
53 19 49.54461 58.22266 40.65330 2
54 20 50.88313 59.05509 42.52505 2
55 21 52.22789 59.97318 44.36944 2
56 24 56.24397 63.21832 49.26963 2
57 25 57.55394 64.33850 50.76938 2
58 28 61.45282 68.05043 54.85522 2
59 32 66.44875 72.85234 60.04517 2
60 35 69.96560 76.06171 63.86949 2
61 36 71.09268 77.06821 65.11714 2
62 37 72.19743 78.04559 66.34927 2
63 38 73.28041 78.99518 67.56565 2
64 40 75.37861 80.81593 69.94129 2
65 44 79.29028 84.20275 74.37780 2
66 45 80.20272 85.00888 75.39656 2
67 46 81.08645 85.80180 76.37110 2
68 47 81.93696 86.57689 77.29704 2
69 48 82.75920 87.34100 78.17739 2
70 49 83.55055 88.09165 79.00945 2
71 50 84.30962 88.82357 79.79567 2
72 51 85.03743 89.53669 80.53817 2
73 52 85.73757 90.23223 81.24291 2
74 53 86.41419 90.91607 81.91232 2
75 54 87.05716 91.58632 82.52800 2
76 59 89.75923 94.58218 84.93629 2
77 60 90.18557 95.05573 85.31541 2
78 61 90.58166 95.51469 85.64864 2
79 63 91.27115 96.31107 86.23124 2
80 71 92.40983 98.35031 86.46934 2
81 72 92.36362 98.52258 86.20465 2
82 73 92.27734 98.67161 85.88308 2
83 84 88.66150 98.84699 78.47602 2
84 85 88.08846 98.73625 77.44067 2", header = TRUE)
NVIQ_predict$id <- NULL
确保 group 列是因子变量，以便我们可以将其用作线型。
NVIQ_predict$group <- as.factor(NVIQ_predict$group)
然后构建情节。
library(ggplot2)
g1 <- ggplot(NVIQ_predict, aes(cogn.age, predict, color=group)) +
geom_smooth(aes(x = cogn.age, y = upper, group=group), method = loess, se = FALSE) +
geom_smooth(aes(x = cogn.age, y = lower, group=group), method = loess, se = FALSE) +
geom_line(aes(linetype = group), size = 0.8)
最后，提取第 1 组和第 2 组曲线的 (x,ymin) 和 (x,ymax) 坐标。这些对具有相同的 x 坐标，因此连接这些点模拟阴影之间的区域两条曲线。 Fill region between two loess-smoothed lines in R with ggplot 中对此进行了解释。这里唯一的区别是我们需要更加小心 select 并连接属于 正确 曲线的点...
gp <- ggplot_build(g1)
d1 <- gp$data[[1]]
d2 <- gp$data[[2]]
df1 <- data.frame(x = d1[d1$group == 1,]$x,
ymin = d2[d2$group == 1,]$y,
ymax = d1[d1$group == 1,]$y)
df2 <- data.frame(x = d1[d1$group == 2,]$x,
ymin = d2[d2$group == 2,]$y,
ymax = d1[d1$group == 2,]$y)
g1 + geom_ribbon(data = df1, aes(x = x, ymin = ymin, ymax = ymax), inherit.aes = FALSE, fill = "grey", alpha = 0.4) +
geom_ribbon(data = df2, aes(x = x, ymin = ymin, ymax = ymax), inherit.aes = FALSE, fill = "grey", alpha = 0.4)
结果如下所示：