我想显示包含实际值和累积值的面积图。虽然我希望实际值显示得低于累积值，但相反的情况却发生了，并且图表也以一种非常奇怪的方式显示。
Tar<-structure(list(Week = structure(c(1L, 1L, 15L, 15L, 20L, 20L,
8L, 8L, 3L, 3L, 18L, 18L, 16L, 16L, 14L, 14L, 5L, 5L, 14L, 14L,
15L, 15L, 8L, 8L, 10L, 10L, 19L, 19L, 5L, 5L, 17L, 17L, 20L,
20L, 18L, 18L, 18L, 18L, 2L, 2L, 3L, 3L, 8L, 8L, 20L, 20L, 20L,
20L, 7L, 7L, 5L, 5L, 2L, 2L, 18L, 18L, 16L, 16L, 7L, 7L, 20L,
20L, 17L, 17L, 20L, 20L, 20L, 20L, 5L, 5L, 15L, 15L, 16L, 16L,
6L, 6L, 14L, 14L, 20L, 20L, 15L, 15L, 8L, 8L, 18L, 18L, 14L,
14L, 16L, 16L, 18L, 18L, 5L, 5L, 5L, 5L, 20L, 20L, 20L, 20L,
20L, 20L, 1L, 1L, 16L, 16L, 7L, 7L, 9L, 9L, 15L, 15L, 18L, 18L,
20L, 20L, 15L, 15L, 3L, 3L, 19L, 19L, 14L, 14L, 17L, 17L, 10L,
10L, 20L, 20L, 9L, 9L, 18L, 18L, 18L, 18L, 14L, 14L, 5L, 5L,
18L, 18L, 14L, 14L, 9L, 9L, 17L, 17L, 16L, 16L, 9L, 9L, 10L,
10L, 14L, 14L, 15L, 15L, 7L, 7L, 20L, 20L, 20L, 20L, 10L, 10L,
18L, 18L, 10L, 10L, 20L, 20L, 11L, 11L, 8L, 8L, 17L, 17L, 15L,
15L, 20L, 20L, 15L, 15L, 11L, 11L, 8L, 8L, 5L, 5L, 16L, 16L,
7L, 7L, 14L, 14L, 15L, 15L, 14L, 14L, 17L, 17L, 14L, 14L, 20L,
20L, 14L, 14L, 15L, 15L, 14L, 14L, 5L, 5L, 19L, 19L, 18L, 18L,
14L, 14L, 3L, 3L, 14L, 14L, 8L, 8L, 14L, 14L, 15L, 15L, 3L, 3L,
20L, 20L, 5L, 5L, 20L, 20L, 17L, 17L, 19L, 19L, 8L, 8L, 8L, 8L,
9L, 9L, 14L, 14L, 3L, 3L, 20L, 20L, 19L, 19L, 17L, 17L, 3L, 3L,
14L, 14L, 1L, 1L, 16L, 16L, 18L, 18L, 18L, 18L, 20L, 20L, 18L,
18L, 16L, 16L, 16L, 16L, 7L, 7L, 15L, 15L, 20L, 20L, 17L, 17L,
8L, 8L, 16L, 16L, 15L, 15L, 3L, 3L, 19L, 19L, 15L, 15L, 17L,
17L, 2L, 2L, 20L, 20L, 10L, 10L, 16L, 16L, 14L, 14L, 8L, 8L,
18L, 18L, 6L, 6L, 10L, 10L, 17L, 17L, 3L, 3L, 17L, 17L, 18L,
18L, 18L, 18L, 14L, 14L, 15L, 15L, 14L, 14L, 14L, 14L, 18L, 18L,
16L, 16L, 14L, 14L, 4L, 4L, 18L, 18L, 13L, 13L, 6L, 6L, 14L,
14L, 15L, 15L, 14L, 14L, 3L, 3L, 16L, 16L, 18L, 18L, 4L, 4L,
2L, 2L, 8L, 8L, 3L, 3L, 14L, 14L, 5L, 5L, 18L, 18L, 8L, 8L, 19L,
19L, 17L, 17L, 14L, 14L, 17L, 17L, 20L, 20L, 17L, 17L, 15L, 15L,
18L, 18L, 10L, 10L, 2L, 2L, 15L, 15L, 8L, 8L, 14L, 14L, 16L,
16L, 14L, 14L, 5L, 5L, 19L, 19L, 5L, 5L, 4L, 4L, 17L, 17L, 6L,
6L, 6L, 6L, 4L, 4L, 13L, 13L, 18L, 18L, 2L, 2L, 17L, 17L, 14L,
14L, 20L, 20L, 6L, 6L, 3L, 3L, 15L, 15L, 18L, 18L, 6L, 6L, 3L,
3L, 20L, 20L, 11L, 11L, 20L, 20L, 16L, 16L, 8L, 8L, 18L, 18L,
7L, 7L, 14L, 14L, 1L, 1L, 4L, 4L, 20L, 20L, 20L, 20L, 8L, 8L,
18L, 18L, 1L, 1L, 14L, 14L, 4L, 4L, 14L, 14L, 18L, 18L, 4L, 4L,
5L, 5L, 6L, 6L, 20L, 20L, 2L, 2L, 8L, 8L, 18L, 18L, 18L, 18L,
15L, 15L, 7L, 7L, 17L, 17L, 20L, 20L, 8L, 8L, 5L, 5L, 16L, 16L,
13L, 13L, 12L, 12L, 16L, 16L, 17L, 17L, 20L, 20L, 9L, 9L, 4L,
4L, 14L, 14L, 15L, 15L, 20L, 20L, 5L, 5L, 18L, 18L, 4L, 4L, 16L,
16L, 2L, 2L, 6L, 6L, 7L, 7L, 3L, 3L, 13L, 13L, 13L, 13L, 20L,
20L, 18L, 18L, 17L, 17L, 14L, 14L, 18L, 18L, 12L, 12L, 7L, 7L,
20L, 20L, 4L, 4L, 13L, 13L, 6L, 6L, 5L, 5L, 6L, 6L, 20L, 20L,
20L, 20L, 14L, 14L, 6L, 6L, 5L, 5L, 4L, 4L, 2L, 2L, 17L, 17L,
9L, 9L, 15L, 15L, 16L, 16L, 18L, 18L, 16L, 16L, 4L, 4L, 6L, 6L,
13L, 13L, 17L, 17L, 8L, 8L, 17L, 17L, 7L, 7L, 5L, 5L, 15L, 15L,
1L, 1L, 6L, 6L, 4L, 4L, 20L, 20L, 5L, 5L, 3L, 3L, 6L, 6L, 20L,
20L, 13L, 13L, 8L, 8L, 18L, 18L, 4L, 4L, 7L, 7L, 5L, 5L, 6L,
6L, 16L, 16L, 18L, 18L, 20L, 20L, 20L, 20L, 20L, 20L, 6L, 6L,
13L, 13L, 5L, 5L, 16L, 16L, 17L, 17L, 6L, 6L, 13L, 13L, 8L, 8L,
15L, 15L, 6L, 6L, 4L, 4L, 8L, 8L, 13L, 13L, 3L, 3L, 6L, 6L, 20L,
20L, 18L, 18L, 6L, 6L, 13L, 13L, 14L, 14L, 11L, 11L, 8L, 8L,
7L, 7L, 4L, 4L, 16L, 16L, 16L, 16L, 17L, 17L, 1L, 1L, 5L, 5L,
17L, 17L, 5L, 5L, 20L, 20L, 20L, 20L, 4L, 4L, 6L, 6L, 15L, 15L,
7L, 7L, 18L, 18L, 17L, 17L, 17L, 17L, 3L, 3L, 5L, 5L, 18L, 18L,
16L, 16L, 18L, 18L, 18L, 18L, 20L, 20L, 6L, 6L, 16L, 16L, 2L,
2L, 8L, 8L, 20L, 20L, 16L, 16L, 6L, 6L, 8L, 8L, 3L, 3L, 15L,
15L, 16L, 16L, 19L, 19L, 16L, 16L, 18L, 18L, 5L, 5L, 17L, 17L,
4L, 4L, 6L, 6L, 16L, 16L, 6L, 6L, 20L, 20L, 17L, 17L, 7L, 7L,
7L, 7L, 2L, 2L, 18L, 18L, 18L, 18L, 20L, 20L, 7L, 7L, 16L, 16L,
2L, 2L, 15L, 15L, 20L, 20L, 15L, 15L, 4L, 4L, 5L, 5L, 16L, 16L,
6L, 6L, 19L, 19L, 3L, 3L, 18L, 18L, 6L, 6L, 7L, 7L, 18L, 18L,
20L, 20L, 17L, 17L, 18L, 18L, 6L, 6L, 9L, 9L), .Label = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20"), class = "factor"),
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"Actual target", "Cumilative target", "Actual target", "Cumilative target",
"Actual target", "Cumilative target", "Actual target", "Cumilative target",
"Actual target", "Cumilative target", "Actual target", "Cumilative target",
"Actual target", "Cumilative target", "Actual target", "Cumilative target",
"Actual target", "Cumilative target", "Actual target", "Cumilative target",
"Actual target", "Cumilative target", "Actual target", "Cumilative target",
"Actual target", "Cumilative target", "Actual target", "Cumilative target",
"Actual target", "Cumilative target", "Actual target", "Cumilative target",
"Actual target", "Cumilative target", "Actual target", "Cumilative target",
"Actual target", "Cumilative target", "Actual target", "Cumilative target",
"Actual target", "Cumilative target", "Actual target", "Cumilative target",
"Actual target", "Cumilative target", "Actual target", "Cumilative target",
"Actual target", "Cumilative target", "Actual target"), Count = c(5,
7, 323, 29, 448, 52, 148, 25, 28, 19, 398, 47, 348, 33, 298,
37, 73, 27, 298, 37, 323, 29, 148, 25, 198, 8, 423, 10, 73,
27, 373, 31, 448, 52, 398, 47, 398, 47, 13, 12, 28, 19, 148,
25, 448, 52, 448, 52, 123, 18, 73, 27, 13, 12, 398, 47, 348,
33, 123, 18, 448, 52, 373, 31, 448, 52, 448, 52, 73, 27,
323, 29, 348, 33, 98, 29, 298, 37, 448, 52, 323, 29, 148,
25, 398, 47, 298, 37, 348, 33, 398, 47, 73, 27, 73, 27, 448,
52, 448, 52, 448, 52, 5, 7, 348, 33, 123, 18, 173, 8, 323,
29, 398, 47, 448, 52, 323, 29, 28, 19, 423, 10, 298, 37,
373, 31, 198, 8, 448, 52, 173, 8, 398, 47, 398, 47, 298,
37, 73, 27, 398, 47, 298, 37, 173, 8, 373, 31, 348, 33, 173,
8, 198, 8, 298, 37, 323, 29, 123, 18, 448, 52, 448, 52, 198,
8, 398, 47, 198, 8, 448, 52, 223, 4, 148, 25, 373, 31, 323,
29, 448, 52, 323, 29, 223, 4, 148, 25, 73, 27, 348, 33, 123,
18, 298, 37, 323, 29, 298, 37, 373, 31, 298, 37, 448, 52,
298, 37, 323, 29, 298, 37, 73, 27, 423, 10, 398, 47, 298,
37, 28, 19, 298, 37, 148, 25, 298, 37, 323, 29, 28, 19, 448,
52, 73, 27, 448, 52, 373, 31, 423, 10, 148, 25, 148, 25,
173, 8, 298, 37, 28, 19, 448, 52, 423, 10, 373, 31, 28, 19,
298, 37, 5, 7, 348, 33, 398, 47, 398, 47, 448, 52, 398, 47,
348, 33, 348, 33, 123, 18, 323, 29, 448, 52, 373, 31, 148,
25, 348, 33, 323, 29, 28, 19, 423, 10, 323, 29, 373, 31,
13, 12, 448, 52, 198, 8, 348, 33, 298, 37, 148, 25, 398,
47, 98, 29, 198, 8, 373, 31, 28, 19, 373, 31, 398, 47, 398,
47, 298, 37, 323, 29, 298, 37, 298, 37, 398, 47, 348, 33,
298, 37, 48, 19, 398, 47, 273, 12, 98, 29, 298, 37, 323,
29, 298, 37, 28, 19, 348, 33, 398, 47, 48, 19, 13, 12, 148,
25, 28, 19, 298, 37, 73, 27, 398, 47, 148, 25, 423, 10, 373,
31, 298, 37, 373, 31, 448, 52, 373, 31, 323, 29, 398, 47,
198, 8, 13, 12, 323, 29, 148, 25, 298, 37, 348, 33, 298,
37, 73, 27, 423, 10, 73, 27, 48, 19, 373, 31, 98, 29, 98,
29, 48, 19, 273, 12, 398, 47, 13, 12, 373, 31, 298, 37, 448,
52, 98, 29, 28, 19, 323, 29, 398, 47, 98, 29, 28, 19, 448,
52, 223, 4, 448, 52, 348, 33, 148, 25, 398, 47, 123, 18,
298, 37, 5, 7, 48, 19, 448, 52, 448, 52, 148, 25, 398, 47,
5, 7, 298, 37, 48, 19, 298, 37, 398, 47, 48, 19, 73, 27,
98, 29, 448, 52, 13, 12, 148, 25, 398, 47, 398, 47, 323,
29, 123, 18, 373, 31, 448, 52, 148, 25, 73, 27, 348, 33,
273, 12, 248, 2, 348, 33, 373, 31, 448, 52, 173, 8, 48, 19,
298, 37, 323, 29, 448, 52, 73, 27, 398, 47, 48, 19, 348,
33, 13, 12, 98, 29, 123, 18, 28, 19, 273, 12, 273, 12, 448,
52, 398, 47, 373, 31, 298, 37, 398, 47, 248, 2, 123, 18,
448, 52, 48, 19, 273, 12, 98, 29, 73, 27, 98, 29, 448, 52,
448, 52, 298, 37, 98, 29, 73, 27, 48, 19, 13, 12, 373, 31,
173, 8, 323, 29, 348, 33, 398, 47, 348, 33, 48, 19, 98, 29,
273, 12, 373, 31, 148, 25, 373, 31, 123, 18, 73, 27, 323,
29, 5, 7, 98, 29, 48, 19, 448, 52, 73, 27, 28, 19, 98, 29,
448, 52, 273, 12, 148, 25, 398, 47, 48, 19, 123, 18, 73,
27, 98, 29, 348, 33, 398, 47, 448, 52, 448, 52, 448, 52,
98, 29, 273, 12, 73, 27, 348, 33, 373, 31, 98, 29, 273, 12,
148, 25, 323, 29, 98, 29, 48, 19, 148, 25, 273, 12, 28, 19,
98, 29, 448, 52, 398, 47, 98, 29, 273, 12, 298, 37, 223,
4, 148, 25, 123, 18, 48, 19, 348, 33, 348, 33, 373, 31, 5,
7, 73, 27, 373, 31, 73, 27, 448, 52, 448, 52, 48, 19, 98,
29, 323, 29, 123, 18, 398, 47, 373, 31, 373, 31, 28, 19,
73, 27, 398, 47, 348, 33, 398, 47, 398, 47, 448, 52, 98,
29, 348, 33, 13, 12, 148, 25, 448, 52, 348, 33, 98, 29, 148,
25, 28, 19, 323, 29, 348, 33, 423, 10, 348, 33, 398, 47,
73, 27, 373, 31, 48, 19, 98, 29, 348, 33, 98, 29, 448, 52,
373, 31, 123, 18, 123, 18, 13, 12, 398, 47, 398, 47, 448,
52, 123, 18, 348, 33, 13, 12, 323, 29, 448, 52, 323, 29,
48, 19, 73, 27, 348, 33, 98, 29, 423, 10, 28, 19, 398, 47,
98, 29, 123, 18, 398, 47, 448, 52, 373, 31, 398, 47, 98,
29, 173, 8)), row.names = c(NA, -858L), class = "data.frame")
library(ggplot2)
library(dplyr)
library(plotly)
p <-
ggplot(Tar, aes(x = Week, y = Count, fill = Type))+
geom_area(alpha = 0.6 , size = 0.5, colour = "white", stat = "identity", orientation = "x") +
labs(fill = NULL)+
theme(legend.position = "bottom")
p <- p+labs(title = "Figure 1: Weekly Cumulative Projected Enrollment vs Weekly Cumulative Actual Enrollment",
subtitle = "Cum Weekly Projected Enrollment/Cum Weekly Actual Enrollment")
# not printed
ggplotly(p)
它应该是这样的:
最佳答案
数据中每个组和周都有重复的条目，因此看起来很困惑。此外，为了不堆叠当前和累积(这是相当误导的)，您可以设置 position = "identity"。我通过因子转换将实际值带到前面，但可以按照您的喜好进行处理。
library(ggplot2)
library(dplyr)
library(plotly)
clean_data <- Tar %>%
distinct() %>%
mutate(Type = ordered(Type, levels = unique(Type)[2:1]))
p <- ggplot(clean_data, aes(x = Week, y = Count, fill = Type, group = Type)) +
geom_area(alpha = 0.6 , size = 0.5, colour = "white", position = "identity", orientation = "x") +
labs(fill = NULL)+
theme(legend.position = "bottom")
p <- p+labs(title = "Figure 1: Weekly Cumulative Projected Enrollment vs Weekly Cumulative Actual Enrollment",
subtitle = "Cum Weekly Projected Enrollment/Cum Weekly Actual Enrollment")
p
# not printed
ggplotly(p)
关于r - 面积图显示较大的值低于较小的值，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/69178320/