R: replacement has 1 row, data has 0

Question

I am using the R programming language. I am trying to replicate the answer provided in this stackoverflow post (visualizing the optimization path till convergence in R) for visualizing the results of an optimization algorithm.

First, I load the libraries and create some data:

#load libraries
library(dplyr)
library(optimization)


# create some data for this example
a1 = rnorm(1000,100,10)
b1 = rnorm(1000,100,5)
c1 = sample.int(1000, 1000, replace = TRUE)
train_data = data.frame(a1,b1,c1)

Then, I define the function I want to optimize :

fitness <- function(x) {
    #bin data according to random criteria
    train_data <- train_data %>%
        mutate(cat = ifelse(a1 <= x[1] & b1 <= x[3], "a",
                            ifelse(a1 <= x[2] & b1 <= x[4], "b", "c")))
    
    train_data$cat = as.factor(train_data$cat)
    
    #new splits
    a_table = train_data %>%
        filter(cat == "a") %>%
        select(a1, b1, c1, cat)
    
    b_table = train_data %>%
        filter(cat == "b") %>%
        select(a1, b1, c1, cat)
    
    c_table = train_data %>%
        filter(cat == "c") %>%
        select(a1, b1, c1, cat)
    
    
    
    
    #calculate  quantile ("quant") for each bin
    
    table_a = data.frame(a_table%>% group_by(cat) %>%
                             mutate(quant = ifelse(c1 > x[5],1,0 )))
    
    table_b = data.frame(b_table%>% group_by(cat) %>%
                             mutate(quant = ifelse(c1 > x[6],1,0 )))
    
    table_c = data.frame(c_table%>% group_by(cat) %>%
                             mutate(quant = ifelse(c1 > x[7],1,0 )))
    
    
    
    
    #group all tables
    
    final_table = rbind(table_a, table_b, table_c)
    # calculate the total mean : this is what needs to be optimized
    mean = mean(final_table$quant)
}

From here, I run the optimization algorithm:

#optimization algorithm:
Output <- optim_nm(fitness, k = 7, trace = TRUE)

From the optimization algorithm, I extract the necessary information needed for the visualization, and place this information into a data frame:

#extract information from the optimization and place into data frame
a = Output$trace
b = a[,c(1,2,3,5)]
b = data.frame(b)

Problem : Here is where things start to go wrong. When I try to plot the "static" visualization, several warnings are produced along with an incomplete plot:

#first part of the visualization
p <- ggplot(b, aes(x_1, x_3)) + 
    geom_raster(aes(fill = function_value)) +
    geom_contour(aes(z = function_value), col = 'grey40') +
    geom_path(data = b, col = 'white') +
    geom_point(data = b, col = 'white') +
    scale_fill_viridis_c() +
    coord_cartesian(expand = FALSE)

#view plot
p

Warning messages:
1: stat_contour(): Zero contours were generated 
2: In min(x) : no non-missing arguments to min; returning Inf
3: In max(x) : no non-missing arguments to max; returning -Inf
4: Raster pixels are placed at uneven horizontal intervals and will be shifted. Consider using geom_tile() instead. 
5: Raster pixels are placed at uneven vertical intervals and will be shifted. Consider using geom_tile() instead.

Then, when I try to animate the results, the following error is produced:

library(gganimate)
final <- p + transition_reveal(iteration)

#view animation
final

Error in `$<-.data.frame`(`*tmp*`, "group", value = "") : 
  replacement has 1 row, data has 0

A list of warnings is also produced:

#view warnings 
warnings()

Warning messages:
1: stat_contour(): Zero contours were generated
2: In min(x) : no non-missing arguments to min; returning Inf
3: In max(x) : no non-missing arguments to max; returning -Inf

Can someone please tell me what I am doing wrong? Is there a way to fix this error?

Thanks