CODEWITHSUNDEEP
rggplot2plotly
firmo23
firmo23

Reputation: 8404

Area chart displays bigger values lower than smaller values

I want to display an area chart with Actual and Cumulative values. While I would expect that the Actual values would be displayed lower than Cumulative the opposite happens and also displays the chart in a very strange way.

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"), 
    Type = c("Cumilative target", "Actual target", "Cumilative target", 
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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", "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)

This is how it should look like:

enter image description here

Upvotes: 2

Views: 76

Answers (2)

mnist
mnist

Reputation: 6956

You have duplicate entries for each group and week in your data, hence the messy look. Additionally, to not stack current and cumulative, which is quite misleading, you can set position = "identity". And I brought the actual values to the front via the factor transformation but handle this as you like.

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)

enter image description here

Upvotes: 2

patL
patL

Reputation: 2299

mnist's answer is complete and solves the problem. He mentioned that you have duplicate records on your dataset. You can either remove them or calculate the mean or total (not sure now if the duplicates are supposed to be there). Here's an alternative that reorders Type and also removes extra lines (labs(fill = NULL)+) by adding legend.title = element_blank() to the theme part. You can also remove the stat = identity in this specific case:

Edit:

Because Original Post was updated with the whole dataset, I'm updating my answer. Now you have to use group in order to make it work:

library(tidyverse)
library(plotly)
p <- Tar %>% 
  group_by(Week, Type) %>% 
  summarise(mean_count = mean(Count)) %>% 
  mutate(Type = factor(Type, levels = rev(unique(Type)))) %>% 
  ggplot(aes(x = Week, y = mean_count,
             group = Type)) +
  geom_area(aes(fill = Type),
            alpha = 0.6 , 
            size = 0.5, 
            stat = "identity",
            colour = "white", 
            orientation = "x") +
  theme(legend.position = "bottom",
        legend.title = element_blank())

p <- p +
  labs(title = "Figure 1: Weekly Cumulative Projected Enrollment vs Weekly Cumulative Actual Enrollment",
       subtitle = "Cum Weekly Projected Enrollment/Cum Weekly Actual Enrollment")

enter image description here

Old answer:


library(tidyverse)
library(plotly)

p <- Tar %>%
  group_by(Week, Type) %>% 
  summarise(mean_count = mean(Count)) %>% 
  mutate(Type = factor(Type, levels = rev(unique(Type)))) %>% 
  ggplot(aes(x = Week, 
             y = mean_count, 
             fill = Type)) +
  geom_area(alpha = 0.6 , 
            size = 0.5, 
            colour = "white", 
            orientation = "x") +
  theme(legend.position = "bottom",
        legend.title = element_blank())

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

enter image description here

Please note that you have a typo on your data: you wrote Cumilative and not Cumulative.

Upvotes: 1

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