R is Producing an Empty Graph

[swaheera]

Can someone please help me understand why the following graph is "empty"?

#load libraries
library(Rtsne)
library(plotly)
library(dplyr)
library(data.table)

set.seed(123)

# 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)


#### generate data for tsne
results_table <- data.frame()

for (i in 1:1000 ) {
    
    #generate random numbers
    random_1 =  runif(1, 80, 120)
    random_2 =  runif(1, random_1, 120)
    random_3 =  runif(1, 85, 120)
    random_4 =  runif(1, random_3, 120)
    
    #bin data according to random criteria
    train_data <- train_data %>% mutate(cat = ifelse(a1 <= random_1 & b1 <= random_3, "a", ifelse(a1 <= random_2 & b1 <= random_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)
    
    split_1 =  runif(1,0, 1)
    split_2 =  runif(1, 0, 1)
    split_3 =  runif(1, 0, 1)
    
    #calculate random quantile ("quant") for each bin
    
    table_a = data.frame(a_table%>% group_by(cat) %>%
                             mutate(quant = quantile(c1, prob = split_1)))
    
    table_b = data.frame(b_table%>% group_by(cat) %>%
                             mutate(quant = quantile(c1, prob = split_2)))
    
    table_c = data.frame(c_table%>% group_by(cat) %>%
                             mutate(quant = quantile(c1, prob = split_3)))
    
    
    
    
    #create a new variable ("diff") that measures if the quantile is bigger tha the value of "c1"
    table_a$diff = ifelse(table_a$quant > table_a$c1,1,0)
    table_b$diff = ifelse(table_b$quant > table_b$c1,1,0)
    table_c$diff = ifelse(table_c$quant > table_c$c1,1,0)
    
    #group all tables
    
    final_table = rbind(table_a, table_b, table_c)
    
    #create a table: for each bin, calculate the average of "diff"
    final_table_2 = data.frame(final_table %>%
                                   group_by(cat) %>%
                                   summarize(
                                       mean = mean(diff)
                                   ))
    
    #add "total mean" to this table
    final_table_2 = data.frame(final_table_2 %>% add_row(cat = "total", mean = mean(final_table$diff)))
    
    #format this table: add the random criteria to this table for reference
    final_table_2$random_1 = random_1
    
    final_table_2$random_2 = random_2
    
    final_table_2$random_3 = random_3
    
    final_table_2$random_4 = random_4
    
    final_table_2$split_1 = split_1
    
    final_table_2$split_2 = split_2
    
    final_table_2$split_3 = split_3
    
    final_table_2$iteration_number = i
    
    
    results_table <- rbind(results_table, final_table_2)
    
    final_results = dcast(setDT(results_table), iteration_number + random_1 + random_2 + random_3 + random_4 + split_1 + split_2 + split_3 ~ cat, value.var = 'mean')
    
}


#format output
f = data.frame(final_results)
f$iteration_number = NULL
f = na.omit(f)

#perform tsne
m2 <- Rtsne(f[,-11], pca=TRUE, check_duplicates=FALSE, perplexity=20, theta=0.5, dims=2) 
a = m2$Y
a= data.frame(a)
a$total = f$total
DF = a 

#get ready for plot
#Compute z values at each grid point
DF$Z <- DF$total

#Compute values for color at each grid point (arbitrary transform)
DF$C <- 3.2*DF$X1 + 1.4*DF$X2 

#make a matrix of Z values
Mat <- matrix(DF$Z,nrow = 953)

#make a matrix for color values
Mat2 <- matrix(DF$C,nrow = 953)

#plot final graph
plot_ly(y=~DF$X2,x=DF$X1,  z=~Mat) %>%  
  add_surface(surfacecolor=~Mat2)

Thanks