R Plotly - Assign the color of a marker by its size

I tried to make this as repeatable as possible. This does assume that you have only one trace and at least 1 trace (the pie plot). If so, you can you use this to identify all the colors in the gradient and assign the color map to the plot. I used the 'high' and 'low' colors you originally chose for your chart for the gradient in this function.

library(shades)
library(tidyverse)
library(plotly)

updater <- function(plt) {
  plt <- plotly_build(plt)
  if(length(plt$x$data) < 2) { # if there is only 1 trace
    df1 <- data.frame(labs = plt$x$data[[1]]$labels,
                      vals = plt$x$data[[1]]$values)
    vals <- df1 %>% group_by(labs) %>%  # find value
      summarise(perc = sum(df1$vals/sum(vals))) %>% 
      arrange(perc)            # assign colors to values/labels
    vals$colr <- gradient(c('#003f5c', '#955196'), steps = nrow(vals))
    # establish color order by data order in length of data
    df1 <- inner_join(df1, vals, by = "labs", keep = F) 
    # change the plot
    plt$x$data[[1]]$marker$colors <- df1$colr %>% unlist(use.names = F)
    plt # return the updated plot
  }
}

Here are some examples of how you might incorporate this.

plot_ly(iris, labels = ~Species, values = ~Sepal.Length, type = 'pie',
        textinfo = 'label+percent') %>% 
  updater()

plot_ly(diamonds, labels = ~cut, values = ~price, type = "pie",
        textinfo = 'label+percent') %>% 
  updater()

Now when it comes to your original plot, whether you designate color or not, this will work. (It will override whatever you designate for colors.)

plot_ly(df, labels = ~Product, values = ~Value, type = 'pie', 
        textinfo = 'label+percent',
        marker = list(colors = ~color,
                      line = list(color = '#FFFFFF', width = 0.5))) %>%
  updater() %>% 
  layout(xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

You may not be aware, but all of the arguments you designated in layout are the defaults for a pie plot. Whether you include them or not, you're getting the same plot.

plot_ly(df, labels = ~Product, values = ~Value, type = 'pie', 
        textinfo = 'label+percent') %>% updater()

Last but not least, if you assign your plot to an object name like plt1 <- plot_ly(..., you can call this function, as well.

updater(plt1)         # an alternative
plt2 <- updater(plt1) # an alternative

------- Update --------

From your comments, @Brani brought up a great point. Perhaps you could swap your real labels for letters and share some of the values you're seeing? I'm not able to reproduce your issue, unfortunately. I'm going to guess it's either an issue with negative values, a grouping issue, or both.

Is your data clean? Is your label a factor?

In the function updater, I group by labels (labs). You could add print(df1) immediately following the creation of the vals data frame to find out what is getting sent to the creation of vals$colr.

Check out what happens with negatives.

set.seed(354)
dfn <- data.frame(letters = sample(
  rep(LETTERS[1:7], 10) %>% as.factor(), 50),
  vals = rnorm(50, 0, 10) %>% round(1))

plot_ly(dfn, labels = ~letters, values = ~vals, type = 'pie',
        textinfo = 'label+percent') %>% updater()

See how a slice is unlabeled? Where did E & C go? Let's take a closer look at this data.

dfn2 <- dfn %>% group_by(letters) %>% 
  summarise(vals = sum(vals))
# # A tibble: 7 × 2
#   letters  vals
#   <fct>   <dbl>
# 1 A        25  
# 2 B         6.6
# 3 C        -5  
# 4 D        47.2
# 5 E       -29.7
# 6 F         7.7
# 7 G        19.8 

The negatives are missing. That still doesn't account for why D didn't get a label.

At the beginning of this update, I said you could add print(vals) to updater. I added that call before running this last plot. This is what was printed in the console:

# A tibble: 7 × 2
  labs    perc
  <fct>  <dbl>
1 C     -14.3 
2 E      -2.41
3 D       1.52
4 A       2.86
5 G       3.62
6 F       9.30
7 B      10.8 

If I add the lowest possible value (so that the lowest value is 0), see what happens.

dfn2 <- dfn2 %>% 
  mutate(vals = vals + 29.7)

plot_ly(dfn2, labels = ~letters, values = ~vals, type = 'pie',
        textinfo = 'label+percent') %>% updater()

For this plot, this is what was printed in the console.

# A tibble: 7 × 2
  labs    perc
  <fct>  <dbl>
1 D       3.63
2 A       5.11
3 G       5.65
4 F       7.47
5 B       7.70
6 C      11.3 
7 E     NaN   

Since you're getting more than one slice with the same color, I'm curious about what you see if you add print.