I was wondering if is it possible to add annotations to a Plotly Animated Gapminder plot? I mean, I don't need that all the bubbles have annotations, but I would like that some of them had.
Here's an example, the only country that I want to display the name is China.
library(plotly)
library(gapminder)
m <- gapminder[gapminder$country == "China", ]
a <- list(
x = m$gdpPercap,
y = m$lifeExp,
text = m$country,
xref = "x",
yref = "y",
showarrow = TRUE,
arrowhead = 7,
ax = 20,
ay = -40
)
df <- gapminder
fig <- df %>%
plot_ly(
x = ~gdpPercap,
y = ~lifeExp,
size = ~pop,
color = ~continent,
frame = ~year,
text = ~country,
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
) %>% add_markers() %>% layout(annotations = a)
I need that the annotation to follow the China marker. Do you know if this is possible?
Thanks!
Bruno
Related
I am trying to replicate the following stacked bar chart with plotly. I attach one screenshot for every hover text I get when hovering on a bar. As you will see there are 2 issues. First I cannot achieve 3 colors, besides the fact that I create them in the legend and secondly I cannot put First dose as top bar besides the fact that I use factor() based on the levels. Maybe there is an issue with the way I have created my dataset. I have no problem if you have to reform it instead of fix the plotly code to replicate the chart.
library(plotly)
Category<-c("First dose","Full vaccination")
`Uptake first dose`<-c(19.8,7.6)
`Uptake full vaccination`<-c(0,0)
`Not vaccinated`<-c(80.2,92.4)
ch5<-data.frame(Category,`Uptake first dose`,`Uptake full vaccination`,`Not vaccinated`)
ch5$Category <- factor(ch5$Category, levels = ch5[["Category"]])
ax <- list(
title = "",
showticklabels = FALSE,
showgrid = FALSE
)
fig <- plot_ly(ch5, y = ~Category, x = ~`Uptake first dose`,
type = 'bar', name = 'Uptake first dose',marker = list(color = 'lightgreen'))
fig <- fig %>% add_trace(x = ~`Uptake full vaccination`, name = 'Uptake full vaccination',marker = list(color = 'green'))
fig <- fig %>% add_trace(x = ~`Not vaccinated`, name = 'Not vaccinated',marker = list(color = 'gray'))
fig <- fig %>% layout(yaxis = ax,xaxis=list(title="",showgrid=F), barmode = 'stack')
fig
There might be a problem with your dataset. The 7.6% of full vaccination is listed under first doese. Therefore your coloring might not work.
Furthermore I transformed the data into a long format for an easy way to create hovertemplates.
library(plotly)
library(tidyverse)
# data
Category<-c("First dose","Full vaccination")
`Uptake first dose`<-c(19.8,0)
`Uptake full vaccination`<-c(0,7.6)
`Not vaccinated`<-c(80.2,92.4)
ch5<-data.frame(Category,`Uptake first dose`,`Uptake full vaccination`,`Not vaccinated`)
# transform data
data.long <- ch5 %>%
pivot_longer(cols = -Category,
names_to = "vac",
values_to = "percent") %>%
mutate(vac = str_replace_all(vac, "\\.", " "),
vac = fct_rev(factor(vac)))
# add plot
plot_ly(data.long) %>%
add_bars(y = ~Category,
x = ~percent,
color = ~vac,
text = ~vac,
colors = c("darkgreen", "green", "gray"),
hovertemplate = paste('<b>%{y}</b>',
'<br>%{text}: %{x} ',
'<extra></extra>')) %>%
layout(barmode = "stack",
yaxis = list(autorange="reversed"),
hoverlabel = list(bgcolor = "black",
bordercolor = "black",
font = list(color = "white")),
shapes = list(type = "line",
y0 = 0, y1 = 1, yref = "paper",
x0 = 70, x1 = 70),
annotations = list(text = "Target (70.0%)",
showarrow = FALSE,
x = 70,
y = 1.05,
yref = "paper"))
I want to prepare a subplot where each facet is a separate dual y-axis plot of one variable against the others. So I make a base plot p and add secondary y-axis variable in a loop:
library(rlang)
library(plotly)
library(tibble)
dual_axis_lines <- function(data, x, y_left, ..., facets = FALSE, axes = NULL){
x <- rlang::enquo(x)
y_left <- rlang::enquo(y_left)
y_right <- rlang::enquos(...)
y_left_axparms <- list(
title = FALSE,
tickfont = list(color = "#1f77b4"),
side = "left")
y_right_axparms <- list(
title = FALSE,
overlaying = "y",
side = "right",
zeroline = FALSE)
p <- plotly::plot_ly(data , x = x) %>%
plotly::add_trace(y = y_left, name = quo_name(y_left),
yaxis = "y1", type = 'scatter', mode = 'lines',
line = list(color = "#1f77b4"))
p_facets <- list()
for(v in y_right){
p_facets[[quo_name(v)]] <- p %>%
plotly::add_trace(y = v, name = quo_name(v),
yaxis = "y2", type = 'scatter', mode = 'lines') %>%
plotly::layout(yaxis = y_left_axparms,
yaxis2 = y_right_axparms)
}
p <- subplot(p_facets, nrows = length(y_right), shareX = TRUE)
return(p)
}
mtcars %>%
rowid_to_column() %>%
dual_axis_lines(rowid, mpg, cyl, disp, hp, facets = TRUE)
However, the resulting plots have all the secondary y-axis variables cluttered in the first facet.
The issue seems to be absent when I return p_facets lists that goes into subplot as each plot looks like below:
How can I fix this issue?
Okay, I followed the ideas given in this github issue about your bug.
library(rlang)
library(plotly)
library(tibble)
dual_axis_lines <- function(data, x, y_left, ..., facets = FALSE, axes = NULL){
x <- rlang::enquo(x)
y_left <- rlang::enquo(y_left)
y_right <- rlang::enquos(...)
## I removed some things here for simplicity, and because we want overlaying to vary between subplots.
y_left_axparms <- list(
tickfont = list(color = "#1f77b4"),
side = "left")
y_right_axparms <- list(
side = "right")
p <- plotly::plot_ly(data , x = x) %>%
plotly::add_trace(y = y_left, name = quo_name(y_left),
yaxis = "y", type = 'scatter', mode = 'lines',
line = list(color = "#1f77b4"))
p_facets <- list()
## I needed to change the for loop so that i can have which plot index we are working with
for(v in 1:length(y_right)){
p_facets[[quo_name(y_right[[v]])]] <- p %>%
plotly::add_trace(y = y_right[[v]], x = x, name = quo_name(y_right[[v]]),
yaxis = "y2", type = 'scatter', mode = 'lines') %>%
plotly::layout(yaxis = y_left_axparms,
## here is where you can assign each extra line to a particular subplot.
## you want overlaying to be: "y", "y3", "y5"... for each subplot
yaxis2 = append(y_right_axparms, c(overlaying = paste0(
"y", c("", as.character(seq(3,100,by = 2)))[v]))))
}
p <- subplot(p_facets, nrows = length(y_right), shareX = TRUE)
return(p)
}
mtcars %>%
rowid_to_column() %>%
dual_axis_lines(rowid, mpg, cyl, disp, hp, facets = TRUE)
Axis text the same color as the lines.
For this you would need two things. You would need to give a palette to your function outside of your for-loop:
color_palette <- colorRampPalette(RColorBrewer::brewer.pal(10,"Spectral"))(length(y_right))
If you don't like the color palette, you'd change it!
I've cleaned up the for-loop so it's easier to look at. This is what it would now look like now so that lines and axis text share the same color:
for(v in 1:length(y_right)){
## here is where you can assign each extra line to a particular subplot.
## you want overlaying to be: "y", "y3", "y5"... for each subplot
overlaying_location = paste0("y", c("", as.character(seq(3,100,by = 2)))[v])
trace_name = quo_name(y_right[[v]])
trace_value = y_right[[v]]
trace_color = color_palette[v]
p_facets[[trace_name]] <- p %>%
plotly::add_trace(y = trace_value,
x = x,
name = trace_name,
yaxis = "y2",
type = 'scatter',
mode = 'lines',
line = list(color = trace_color)) %>%
plotly::layout(yaxis = y_left_axparms,
## We can build the yaxis2 right here.
yaxis2 = eval(
parse(
text = "list(side = 'right',
overlaying = overlaying_location,
tickfont = list(color = trace_color))")
)
)
}
When using plotly (in R), after combining subplots there remains an unused and blank subplot. I've recreated the issue using the ggplot2 dataset mpg below.
library(dplyr)
library(ggplot2)
library(plotly)
audi <- mpg %>%
filter(manufacturer == "audi")
chevy <- mpg %>%
filter(manufacturer == "chevrolet")
fig1 <- plot_ly(audi, x = ~hwy, y = ~year, name = "", type = 'scatter',
mode = "markers", marker = list(color = "blue", symbol = 'x-dot'))
fig2 <- plot_ly(chevy, x = ~hwy, y = ~year, name = "", type = 'scatter',
mode = "markers", marker = list(color = "red", symbol = 'circle'))
fig <- subplot(fig1, fig2)
fig <- fig %>% subplot(shareX = TRUE,shareY = TRUE,which_layout = "merge")
fig <- fig %>% layout(
title = "Audi and Chevy",
xaxis = list(title = "Highway MPG"),
yaxis = list(title = "Year"),
margin = list(l = 100)
)
The only solution I've been able to find is tinkering with the width of the used subplot, but this leaves quite a bit of unused white space on the right and causes the title to be far off to the right (as it adjusts into the center of the used and unused subplots).
Is there a way to remove the unused subplot? If not, is there a way to organize/subset the dataframe such that only one plot needs to be used in the first place?
Thanks!
You can assign the colours based on the manufacturer column:
data.subs <- mpg %>%
filter(manufacturer == "audi" | manufacturer == "chevrolet")
fig <- plot_ly(data.subs, x = ~hwy, y = ~year, name = "",
type = 'scatter', mode = "markers",
marker = list(color = factor(data.subs$manufacturer,
labels = c("red", "blue")),
symbol = 'circle'),
text = factor(data.subs$manufacturer,
labels = c("audi", "chevy")), hoverinfo = 'text'))
fig <- fig %>% layout(
title = "Audi and Chevy",
xaxis = list(title = "Highway MPG"),
yaxis = list(title = "Year"),
margin = list(l = 100)
)
fig
This makes generating multiple subplots unnecessary.
I am trying to split the attached grouped bar chart by the variable spec. Two thoughts on best way to do this are by adding facet_grid() or if a filter can be applied to the static output? Can either be done? Any advice appreciated.
a sample is below:
period <- c('201901', '201901', '201904', '201905')
spec <- c('alpha', 'bravo','bravo', 'charlie')
c <- c(5,6,3,8)
e <- c(1,2,4,5)
df <- data.frame(period, spec, c,e)
library(tidyverse)
library(plotly)
plot_ly(df, x =~period, y = ~c, type = 'bar', name = "C 1", marker = list(color = 'lightsteelblue3'))
%>%
add_trace(y = ~e, name = "E 1", marker = list(color = 'Gray')) %>%
layout(xaxis = list(title="", tickangle = -45),
yaxis = list(title=""),
margin= list(b=100),
barmode = 'group'
)
I am not sure if you are plotting what you actually want to achieve? My suggestion is to create your plot using standard ggplot and then use ggplotly.
For this, you also need to reshape your data and make it a bit longer.
library(tidyverse)
library(plotly)
period <- c('201901', '201901', '201904', '201905')
spec <- c('alpha', 'bravo','bravo', 'charlie')
c <- c(5,6,3,8)
e <- c(1,2,4,5)
df <- data.frame(period, spec, c,e) %>%
pivot_longer(cols = c(c,e), names_to = 'var', values_to = 'val')
p <- ggplot(df, aes(period, val, fill = var)) +
geom_col(position = position_dodge()) +
facet_grid(~spec)
ggplotly(p)
It's probably easier to use facets here, but a more "interactive" option would be to use a filter transforms which gives you a drop-down menu in the top left corner of your plot.
spec.val <- unique(df$spec)
plot_ly(
df %>% pivot_longer(-c(period, spec)),
x = ~period, y = ~value, color = ~name,
type = "bar",
transforms = list(
list(
type = "filter",
target = ~spec,
operation = "=",
value = spec.val[1]))) %>%
layout(
updatemenus = list(
list(
type = "drowdown",
active = 0,
buttons = map(spec.val, ~list(
method = "restyle",
args = list("transforms[0].value", .x),
label = .x)))))
I want to rename labels in a heatmap. for example:
instead of the label says "x:", I want the label to say "Hour:"
instead of the label says "y:", I want the label to say "Day:"
Library(plotly)
p <- plot_ly(z = volcano, colors = colorRamp(c("red", "green")), type = "heatmap")
furthermore, it would be useful, for example if we use a transformation of data in order to intensify contrast, still the html interactive label show real data.
Example
What about
library(plotly)
dat <- expand.grid(x = 1:nrow(volcano), y = 1:ncol(volcano))
dat$z <- c(volcano)
plot_ly(height = 500) %>%
layout(autosize = FALSE,
xaxis=list(title = "Hour", titlefont = list(size=20)),
yaxis=list(title = "Day", titlefont = list(size=20))) %>%
add_trace(data = dat, x = ~x, y = ~y, z = ~z, type = "heatmap",
hoverinfo = 'text',
text = ~paste("Hour:", dat$x,
"<br> Day:", dat$y,
"<br> z:", dat$z))