Ggplotly with tooltip has problems using geom_rect() - r

I am trying to plot some data in a ggplotly plot.
The x-axis contains dates. Ggplotly doesn't work well with dates as when I hover over a point, the date is displayed as a number.
I solved this by setting a tooltip like below.
Some sample data:
x <- data.frame(Date = as.Date(seq(Sys.Date(), Sys.Date() + 29, by = "days")), Amount = seq(-10000, 19000, by = 1000),
stringsAsFactors = FALSE)
The plot:
ggplotly(ggplot(x, aes(x = Date, y = Amount, group = 1, text = paste("Date: ", Date, "<br>Amount: ", Amount))) + geom_line() + geom_point()
, tooltip = "text")
Now I want to use geom_rect() to get some background colors depending on the value of the y-axis. This gives me problems as the rectangles seem to be placed on top of the geom_line(). Also, the rectangles are labeled by ggplotly too, which I don't want either.
Here is the code I tried (the background coloring works fine when I am not using a custom tooltip, but then the problem with the dates in the labels occurs):
ggplotly(ggplot(x, aes(x = Date, y = Amount, group = 1, text = paste("Date: ", Date, "<br>Amount: ", Amount))) + geom_line() + geom_point()
+
geom_rect(aes(xmin = as.Date(Sys.Date()),
xmax = as.Date(Sys.Date() + 30),
ymin = 10000, ymax = max(max(x$Amount) + 1000, 11000), fill = "1")) +
geom_rect(aes(xmin = as.Date(Sys.Date()),
xmax = as.Date(Sys.Date() + 30),
ymin = 0, ymax = 10000, fill = "2")) +
geom_rect(aes(xmin = as.Date(Sys.Date()),
xmax = as.Date(Sys.Date() + 30),
ymin = min(min(x$Amount) - 1000, 0), ymax = 0, fill = "3"))
+
scale_fill_manual(values = alpha(c("green", "orange", "red"), 0.2))
, tooltip = "text")
Result
Any help would be appreciated, thanks!
EDIT:
The following code results in working geom_rect():
ggplotly(ggplot(x, aes(x = Date, y = Amount)) + geom_line() + geom_point()
+
geom_rect(aes(xmin = as.Date(Sys.Date()),
xmax = as.Date(Sys.Date() + 30),
ymin = 10000, ymax = max(max(x$Amount) + 1000, 11000), fill = "1")) +
geom_rect(aes(xmin = as.Date(Sys.Date()),
xmax = as.Date(Sys.Date() + 30),
ymin = 0, ymax = 10000, fill = "2")) +
geom_rect(aes(xmin = as.Date(Sys.Date()),
xmax = as.Date(Sys.Date() + 30),
ymin = min(min(x$Amount) - 1000, 0), ymax = 0, fill = "3"))
+
scale_fill_manual(values = alpha(c("green", "orange", "red"), 0.2)))
Result

You could try this:
ggplotly(ggplot() +
geom_rect(data = x, aes(xmin = as.Date(Sys.Date()),
xmax = as.Date(Sys.Date() + 30),
ymin = 10000, ymax = max(max(x$Amount) + 1000, 11000), fill = "1")) +
geom_rect(data = x, aes(xmin = as.Date(Sys.Date()),
xmax = as.Date(Sys.Date() + 30),
ymin = 0, ymax = 10000, fill = "2")) +
geom_rect(data = x, aes(xmin = as.Date(Sys.Date()),
xmax = as.Date(Sys.Date() + 30),
ymin = min(min(x$Amount) - 1000, 0), ymax = 0, fill = "3")) +
geom_line(data = x, aes(x = Date, y = Amount, group = 1, text = paste("Date: ", Date, "<br>Amount: ", Amount))) +
geom_point(data = x, aes(x = Date, y = Amount, text = paste("Date: ", Date, "<br>Amount: ", Amount))) +
scale_fill_manual(values = alpha(c("green", "orange", "red"), 0.2))
, tooltip = "text")

Related

R plotly::subplot adjust title placement

Based on the code below how can I adjust the title such that it does not get trimmed.
Data (df):
structure(list(year = 1980:2021, AvgTMean = c(24.2700686838937,
23.8852956598276, 25.094446596092, 24.1561175050287, 24.157183605977,
24.3047482638362, 24.7899738481466, 24.5756232655603, 24.5833086228592,
24.7344695534483, 25.3094451071121, 25.2100615173707, 24.3651692293534,
24.5423890611494, 25.2492166633908, 24.7005097837931, 24.2491591827443,
25.0912281781322, 25.0779264303305, 24.403294248319, 24.4983991453592,
24.4292324356466, 24.8179824927011, 24.7243948463075, 24.5086534543966,
24.2818632071983, 24.4567195220259, 24.8402224356034, 24.6574465515086,
24.5440715673563, 23.482670620977, 24.9979594684914, 24.5452453980747,
24.9271462811494, 24.7443215819253, 25.8929839790805, 25.1801908261063,
25.2079308058908, 25.0722425561207, 25.4554644289799, 25.4548979078736,
25.0756772250287), AvgTMin = c(19.6018663372126, 18.9935718486724,
20.8351710187356, 19.7723002680316, 19.8097384811782, 19.7280847671034,
20.2907499842098, 20.1950373662931, 20.1812715311494, 20.1808865070833,
21.0320272801006, 21.1252427976293, 20.1712830368678, 20.407655174727,
21.5430646243391, 20.6760574525862, 20.0822658237356, 21.0735574619397,
21.0871494406322, 20.1311178414224, 20.3191250001149, 20.3474683732557,
20.668169553204, 20.3772270269296, 20.2330157893678, 19.9486551337931,
20.1114496908333, 20.5816350393966, 20.4033879191236, 20.1582514856897,
19.2288879223678, 20.8451063140805, 20.4878865041092, 21.0259712576437,
20.5510100674138, 22.0143793370977, 21.3529094881753, 21.1688506012213,
21.040550304569, 21.4923981385632, 21.6580430460057, 21.2433069288506
), AvgTMax = c(28.9392198638937, 28.778245693046, 29.3549223685201,
28.5411393752011, 28.5058118063649, 28.8825532046983, 29.2903534709195,
28.9574051835776, 28.9865201368247, 29.2891997662069, 29.5881379007328,
29.2960976760201, 28.5602557685057, 28.6782844806753, 28.9566034394684,
28.7262054694971, 28.4171896994397, 29.1100747038649, 29.0698836095546,
28.6766350461063, 28.6788764437787, 28.5122026355891, 28.9690143596839,
29.0727844759914, 28.7854971337931, 28.6163189712069, 28.8032270024138,
29.1000460207471, 28.9127356101149, 28.9310646744109, 27.7376810545833,
29.1520129070402, 28.6037845089512, 28.8295359311638, 28.9388276133764,
29.7726939654598, 29.0086407880029, 29.2482097613937, 29.1050890698132,
29.4187571974569, 29.2519238543247, 28.9081913630029)), class = "data.frame", row.names = c(NA,
-42L))
Code:
library(tidyverse)
library(plotly)
AvgTMeanYearFP = ggplot(df, aes(year, AvgTMean)) +
geom_smooth(method = 'lm', fullrange = TRUE) +
annotate('rect', xmin = -Inf, xmax = 2021, ymin = -Inf, ymax = Inf,
fill = 'gray92') +
geom_vline(xintercept = seq(1980, 2020, 5), color = 'white') +
geom_hline(yintercept = seq(23.5, 25.5, 0.5), color = 'white') +
geom_line() +
scale_x_continuous(limits = c(1980, 2030)) +
labs(y = "Avg. Mean T (C)", x = "Year") +
geom_text(aes(x = 2000 , y = 25.5, label = "Historic Trend")) +
geom_text(aes(x = 2025 , y = 25.5, label = "Forecast Trend"))
AvgTMinYearFP = ggplot(df, aes(year, AvgTMin)) +
geom_smooth(method = 'lm', fullrange = TRUE) +
annotate('rect', xmin = -Inf, xmax = 2021, ymin = -Inf, ymax = Inf,
fill = 'gray92') +
geom_vline(xintercept = seq(1980, 2020, 5), color = 'white') +
geom_hline(yintercept = seq(23.5, 25.5, 0.5), color = 'white') +
geom_line() +
scale_x_continuous(limits = c(1980, 2030)) +
ylim(18, 23) +
labs(y = "Avg. Min. T (C)", x = "Year")
AvgTMaxYearFP = ggplot(df, aes(year, AvgTMax)) +
geom_smooth(method = 'lm', fullrange = TRUE) +
annotate('rect', xmin = -Inf, xmax = 2021, ymin = -Inf, ymax = Inf,
fill = 'gray92') +
geom_vline(xintercept = seq(1980, 2020, 5), color = 'white') +
geom_hline(yintercept = seq(23.5, 25.5, 0.5), color = 'white') +
geom_line() +
scale_x_continuous(limits = c(1980, 2030)) +
ylim(27, 30) +
labs(y = "Avg. Max. T (C)", x = "Year")
# Combine plots
p = subplot(AvgTMeanYearFP, AvgTMinYearFP, AvgTMaxYearFP, titleY = TRUE, shareX = TRUE, nrows = 3) %>%
layout(title ="Historic Average Temperature And Future Temperature Projection")
# Add a box around the forecast trend
p$x$layout$shapes = list(type = "rect",
fillcolor = "red",
opacity = 0.1,
x0 = 2021,
x1 = 2030,
xref = "x",
y0 = 0,
y1 = 1,
yref = "paper")
p
Current output:
You can add margin to the layout, so e.g.
p = subplot(AvgTMeanYearFP, AvgTMinYearFP, AvgTMaxYearFP, titleY = TRUE, shareX = TRUE, nrows = 3) %>%
layout(title ="Historic Average Temperature And Future Temperature Projection",margin = list(t = 50))
with the t standing for top.

Draw a dashed transparent box around forecast lines

I have 3 forecast plots that are combined together by plotly::subplot. The next step is draw a transparent box (or 3 separate boxes) with red dashed lines around the forecast line of each plot so that they stand out to the reader.
How can I do this ?
Desired Output:
Data (df):
structure(list(year = 1980:2021, AvgTMean = c(24.2700686838937,
23.8852956598276, 25.094446596092, 24.1561175050287, 24.157183605977,
24.3047482638362, 24.7899738481466, 24.5756232655603, 24.5833086228592,
24.7344695534483, 25.3094451071121, 25.2100615173707, 24.3651692293534,
24.5423890611494, 25.2492166633908, 24.7005097837931, 24.2491591827443,
25.0912281781322, 25.0779264303305, 24.403294248319, 24.4983991453592,
24.4292324356466, 24.8179824927011, 24.7243948463075, 24.5086534543966,
24.2818632071983, 24.4567195220259, 24.8402224356034, 24.6574465515086,
24.5440715673563, 23.482670620977, 24.9979594684914, 24.5452453980747,
24.9271462811494, 24.7443215819253, 25.8929839790805, 25.1801908261063,
25.2079308058908, 25.0722425561207, 25.4554644289799, 25.4548979078736,
25.0756772250287), AvgTMin = c(19.6018663372126, 18.9935718486724,
20.8351710187356, 19.7723002680316, 19.8097384811782, 19.7280847671034,
20.2907499842098, 20.1950373662931, 20.1812715311494, 20.1808865070833,
21.0320272801006, 21.1252427976293, 20.1712830368678, 20.407655174727,
21.5430646243391, 20.6760574525862, 20.0822658237356, 21.0735574619397,
21.0871494406322, 20.1311178414224, 20.3191250001149, 20.3474683732557,
20.668169553204, 20.3772270269296, 20.2330157893678, 19.9486551337931,
20.1114496908333, 20.5816350393966, 20.4033879191236, 20.1582514856897,
19.2288879223678, 20.8451063140805, 20.4878865041092, 21.0259712576437,
20.5510100674138, 22.0143793370977, 21.3529094881753, 21.1688506012213,
21.040550304569, 21.4923981385632, 21.6580430460057, 21.2433069288506
), AvgTMax = c(28.9392198638937, 28.778245693046, 29.3549223685201,
28.5411393752011, 28.5058118063649, 28.8825532046983, 29.2903534709195,
28.9574051835776, 28.9865201368247, 29.2891997662069, 29.5881379007328,
29.2960976760201, 28.5602557685057, 28.6782844806753, 28.9566034394684,
28.7262054694971, 28.4171896994397, 29.1100747038649, 29.0698836095546,
28.6766350461063, 28.6788764437787, 28.5122026355891, 28.9690143596839,
29.0727844759914, 28.7854971337931, 28.6163189712069, 28.8032270024138,
29.1000460207471, 28.9127356101149, 28.9310646744109, 27.7376810545833,
29.1520129070402, 28.6037845089512, 28.8295359311638, 28.9388276133764,
29.7726939654598, 29.0086407880029, 29.2482097613937, 29.1050890698132,
29.4187571974569, 29.2519238543247, 28.9081913630029)), class = "data.frame", row.names = c(NA,
-42L))
Code
library(tidyverse)
library(plotly)
AvgTMeanYearFP = ggplot(df, aes(year, AvgTMean)) +
geom_smooth(method = 'lm', fullrange = TRUE) +
annotate('rect', xmin = -Inf, xmax = 2021, ymin = -Inf, ymax = Inf,
fill = 'gray92') +
geom_vline(xintercept = seq(1980, 2020, 5), color = 'white') +
geom_hline(yintercept = seq(23.5, 25.5, 0.5), color = 'white') +
geom_line() +
scale_x_continuous(limits = c(1980, 2030)) +
labs(y = "Avg. Mean T (C)", x = "Year") +
geom_text(aes(x = 2000 , y = 25.5, label = "Historic Trend")) +
geom_text(aes(x = 2025 , y = 25.5, label = "Forecast Trend"))
AvgTMinYearFP = ggplot(df, aes(year, AvgTMin)) +
geom_smooth(method = 'lm', fullrange = TRUE) +
annotate('rect', xmin = -Inf, xmax = 2021, ymin = -Inf, ymax = Inf,
fill = 'gray92') +
geom_vline(xintercept = seq(1980, 2020, 5), color = 'white') +
geom_hline(yintercept = seq(23.5, 25.5, 0.5), color = 'white') +
geom_line() +
scale_x_continuous(limits = c(1980, 2030)) +
ylim(18, 23) +
labs(y = "Avg. Min. T (C)", x = "Year")
AvgTMaxYearFP = ggplot(df, aes(year, AvgTMax)) +
geom_smooth(method = 'lm', fullrange = TRUE) +
annotate('rect', xmin = -Inf, xmax = 2021, ymin = -Inf, ymax = Inf,
fill = 'gray92') +
geom_vline(xintercept = seq(1980, 2020, 5), color = 'white') +
geom_hline(yintercept = seq(23.5, 25.5, 0.5), color = 'white') +
geom_line() +
scale_x_continuous(limits = c(1980, 2030)) +
ylim(27, 30) +
labs(y = "Avg. Max. T (C)", x = "Year")
# Combine plots
subplot(AvgTMeanYearFP, AvgTMinYearFP, AvgTMaxYearFP, titleY = TRUE, shareX = TRUE, nrows = 3) %>%
layout(title ="Historic Average Temperature And Future Temperature Projection")
I actually like one box over all plots more aesthetically. Had a hard time doing this, because there seems to be a known issue with using ggplotly and the layout() function. That's why the shapes are put in p$x$layout$shapes like this.
# Combine plots
p <- subplot(AvgTMeanYearFP, AvgTMinYearFP, AvgTMaxYearFP, titleY = TRUE, shareX = TRUE, nrows = 3) %>%
layout(title ="Historic Average Temperature And Future Temperature Projection")
p$x$layout$shapes <- list(type = "rect",
line = list(color = "red",
dash = 'dash'),
x0 = 2021,
x1 = 2030,
xref = "x",
y0 = 0,
y1 = 1,
yref = "paper")
p
An alternative to a dashed box could be using the opacity.
list(type = "rect",
fillcolor = "red",
opacity = 0.1,
x0 = 2021,
x1 = 2030,
xref = "x",
y0 = 0,
y1 = 1,
yref = "paper")
I can also get you some of the way there - by making a red box in each figure, but putting a single box across the whole plot is going to be more challenging.
library(tidyverse)
library(plotly)
add_box <- function(p, start=2022, stop=NULL, prop_in=.05, ...){
pb <- ggplot_build(p)
rgy <- pb$layout$panel_params[[1]]$y.range
rgx <- pb$layout$panel_params[[1]]$x.range
px1 <- diff(rgx)*prop_in
py1 <- diff(rgy)*prop_in
rgx <- c(1,-1)*px1 + rgx
rgy <- c(1,-1)*py1 + rgy
rgx[1] <- start
if(!is.null(stop)){
rgx[2] <- stop
}
boxdf <- data.frame(x = rgx[c(1,2,2,1,1)],
y=rgy[c(1,1,2,2,1)])
p + geom_path(data=boxdf,
aes(x=x,
y=y),
col="red",
linetype=2)
}
AvgTMeanYearFP = ggplot(df, aes(year, AvgTMean)) +
geom_smooth(method = 'lm', fullrange = TRUE) +
annotate('rect', xmin = -Inf, xmax = 2021, ymin = -Inf, ymax = Inf,
fill = 'gray92') +
geom_vline(xintercept = seq(1980, 2020, 5), color = 'white') +
geom_hline(yintercept = seq(23.5, 25.5, 0.5), color = 'white') +
geom_line() +
scale_x_continuous(limits = c(1980, 2030)) +
labs(y = "Avg. Mean T (C)", x = "Year") +
geom_text(aes(x = 2000 , y = 25.5, label = "Historic Trend")) +
geom_text(aes(x = 2025 , y = 25.5, label = "Forecast Trend"))
AvgTMinYearFP = ggplot(df, aes(year, AvgTMin)) +
geom_smooth(method = 'lm', fullrange = TRUE) +
annotate('rect', xmin = -Inf, xmax = 2021, ymin = -Inf, ymax = Inf,
fill = 'gray92') +
geom_vline(xintercept = seq(1980, 2020, 5), color = 'white') +
geom_hline(yintercept = seq(23.5, 25.5, 0.5), color = 'white') +
geom_line() +
scale_x_continuous(limits = c(1980, 2030)) +
ylim(18, 23) +
labs(y = "Avg. Min. T (C)", x = "Year")
AvgTMaxYearFP = ggplot(df, aes(year, AvgTMax)) +
geom_smooth(method = 'lm', fullrange = TRUE) +
annotate('rect', xmin = -Inf, xmax = 2021, ymin = -Inf, ymax = Inf,
fill = 'gray92') +
geom_vline(xintercept = seq(1980, 2020, 5), color = 'white') +
geom_hline(yintercept = seq(23.5, 25.5, 0.5), color = 'white') +
geom_line() +
scale_x_continuous(limits = c(1980, 2030)) +
ylim(27, 30) +
labs(y = "Avg. Max. T (C)", x = "Year")
# Combine plots
subplot(AvgTMeanYearFP %>% add_box(stop=2030, prop_in=.05),
AvgTMinYearFP %>% add_box(stop=2030, prop_in=.05),
AvgTMaxYearFP %>% add_box(stop=2030, prop_in=.05),
titleY = TRUE, shareX = TRUE, nrows = 3) %>%
layout(title ="Historic Average Temperature And Future Temperature Projection")
The add_box() function does a few different things. First, it builds your plot so I can grab the ranges of the x and y axes. If you try to plot the box all the way to the end of the range, the top, bottom and right side lines don't print. So, I have it pull the those edges prop_in toward the interior of the plot. I found that .05 is about the smallest that worked. Then, I change the rgx and rgy objects accordingly. Then, I replace the first and optionally second value of rgx with the start and stop arguments from the function call. I take the range values and make them into a data frame that will be amenable to plot with geom_path() and then I add the appropriate geom_path() function to your existing plot.

using a grouping variable and geom_rect

I want to show covered ranges (including overlaps) and (after some failures with stacked bar plots) I chose geom_rect. The following code works well for one type.
library(tidyverse)
# create dummy data
foo <- tibble(start = c(1, 150, 140, 75, 300),
end = c(150, 180, 170, 160, 400))
ggplot() +
geom_rect(data = foo, aes(xmin = start, xmax = end, ymin = 0, ymax = 1), fill = "green", linetype = "blank", alpha = 0.3) +
geom_rect(data = foo, aes(xmin = 1, xmax = max(end), ymin = 0, ymax = 1), fill = NA, colour = "black") +
scale_y_continuous(name = "", breaks = NULL, limits = c(0, 1)) +
scale_x_continuous(name = "", breaks = NULL) +
theme_minimal() +
theme(panel.grid = element_blank())
If I add more data (only one more type, but in the original data I do have some more) like below, I can add the data "by hand", i.e. add two lines of code for each type, but I'm looking for a way to do this by grouping, but didn't succeed.
foo <- foo %>%
mutate(type = "A", .before = 1)
bar <- tibble(type = "B",
start = c(1, 30, 40, 100, 150, 200, 310),
end = c(20, 50, 100, 120, 200, 300, 380))
foo <- bind_rows(foo, bar)
ggplot() +
geom_rect(data = foo %>% filter(type == "A"), aes(xmin = start, xmax = end, ymin = 0, ymax = 1), fill = "green", linetype = "blank", alpha = 0.3) +
geom_rect(data = foo, aes(xmin = 1, xmax = max(end), ymin = 0, ymax = 1), fill = NA, colour = "black") +
geom_rect(data = foo %>% filter(type == "B"), aes(xmin = start, xmax = end, ymin = 2, ymax = 3), fill = "green", linetype = "blank", alpha = 0.3) +
geom_rect(data = foo, aes(xmin = 1, xmax = max(end), ymin = 2, ymax = 3), fill = NA, colour = "black") +
scale_y_continuous(name = "", breaks = NULL, limits = c(0, 3)) +
scale_x_continuous(name = "", breaks = NULL) +
geom_text(aes(x = c(0, 0), y = c(0.5, 2.5), label = c("A", "B")), size = 4, hjust = 2) +
theme_minimal() +
theme(panel.grid = element_blank())
So, the graph already looks the way I want, but I'd prefer to get here by using grouping (or any other non-manual way).
Maybe there's also a different geom or method to get this kind of graph?
You can write a small helper function that positions a categorical value in continuous space. Example below.
helper <- function(x) {(match(x, sort(unique(x))) - 1) * 2}
ggplot(foo) +
geom_rect(
aes(xmin = start, xmax = end,
ymin = helper(type),
ymax = helper(type) + 1),
fill = "green", linetype = "blank", alpha = 0.3
) +
geom_rect(
aes(xmin = min(start), xmax = max(end),
ymin = helper(type),
ymax = helper(type) + 1),
fill = NA, colour = "black"
) +
scale_y_continuous(name = "", breaks = NULL, limits = c(0, 3)) +
scale_x_continuous(name = "", breaks = NULL) +
annotate(
"text", x = c(0, 0), y = c(0.5, 2.5), label = c("A", "B"),
size = 4, hjust = 2
) +
theme_minimal() +
theme(panel.grid = element_blank())

How to overlay a box on an existing plot?

I am trying to draw a box on top of the plot on a specific x = Date and y = Price.
I have multiple Date entries stored in specificDates, but even though the code can be ran and doesn't output any errors, the box doesn't show on the plot.
dataDate <- as.Date(c("2015-01-01","2016-03-01","2018-06-01","2020-08-01"))
dataPrice <- c(170, 320, 7000,8000)
dummyData <- data.frame(dataDate, dataPrice)
specificDates <- as.Date(c("2016-07-15", "2020-05-20"))
plot_linPrice <- ggplot(data = dummyData,
mapping = aes(x = dataDate, y = dataPrice)) +
geom_line() +
scale_y_log10() +
geom_vline(xintercept = as.numeric(specificDates), color = "blue", lwd = .5) #+ #uncommenting + brings up error
geom_rect(aes(xmin = "2015-01-01", xmax = "2015-06-01", ymin = 5000, ymax = 8000), fill = "blue")
print(plot_linPrice)
Try with this:
library(ggplot2)
#Data
dataDate <- as.Date(c("2015-01-01","2016-03-01","2018-06-01","2020-08-01"))
dataPrice <- c(170, 320, 7000,8000)
dummyData <- data.frame(dataDate, dataPrice)
specificDates <- as.Date(c("2016-07-15", "2020-05-20"))
#Code
ggplot(data = dummyData,
mapping = aes(x = dataDate, y = dataPrice)) +
geom_line() +
scale_y_log10() +
geom_vline(xintercept = as.numeric(specificDates), color = "blue", lwd = .5)+
geom_rect(aes(xmin = as.Date("2015-01-01"), xmax = as.Date("2015-06-01"), ymin = 5000, ymax = 8000), fill = "blue")
Output:

waterfall chart in ggplot2 doesnt seem to render right

I am trying to recreate the waterfall chart as shown in
https://vita.had.co.nz/papers/ggplot2-wires.pdf
I am reproducing the code from the link
balance <- data.frame(event = c("Starting\nCash", "Sales", "Refunds",
"Payouts", "Court\nLosses", "Court\nWins", "Contracts", "End\nCash"),
change = c(2000, 3400, -1100, -100, -6600, 3800, 1400, -2800))
balance$balance <- cumsum(c(0, balance$change[-nrow(balance)]))
balance$time <- 1:nrow(balance)
balance$flow <- factor(sign(balance$change))
ggplot(balance) +
geom_hline(yintercept = 0, colour = "white", size = 2) +
geom_rect(aes(fill= 'red'),xmin = time - 0.45, xmax = time + 0.45, ymin = balance, ymax = balance)
geom_text(aes(x = time, y = pmin(balance, balance + change) - 50, label = dollar(change)),
hjust = 0.5, vjust = 1, size = 3)
scale_x_continuous( breaks = balance$time, labels = balance$event) +
scale_y_continuous("Balance") +
scale_fill_manual(values = c("-1" = "red", "1" = "black"))
it throws an error :Error in scale_x_continuous(breaks = balance$time, labels = balance$event) + : non-numeric argument to binary operator
THe final output, per the pdf should look like the image below
ggplot(balance %>%
mutate(flow = factor(flow, labels = c("Negative", "Positive")))) +
geom_hline(yintercept = 0, colour = "white", size = 2) +
geom_rect(aes(fill= flow, xmin = time - 0.45, xmax = time+0.45, ymin = change, ymax = balance),
color = "black") +
geom_text(aes(x = time, y = pmin(balance, balance + change) - 50, label = change),
hjust = 0.5, vjust = 1, size = 3) +
scale_x_continuous(breaks = balance$time, labels = balance$event) +
scale_y_continuous("Balance") +
scale_fill_manual(values = c("Negative" = "red", "Positive" = "green"))

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