Styjun

I'm generating scatter plot of the comparison of 2 variables in a data frame, where the colors of the points were determined by their type (another column in the data frame). However, there are many points that overlap each other and therefore I want an estimation of how many points there are in a given part of the grid. (for example: In case of hexbin package, one can estimate how many values there are in a given hexagon on the grid)

My question is: how can I use the hexbin package where for every group I will have another color scale, so in that way it will be possible to distinguish between the groups and get an evaluation of how many values there are.

I tried to google it but didn't find any satisfying solution.
All the options that I found were focused only on the distinction between the groups.

My code so far:

ggplot(data_for_scatter_plot,aes(x=Log2FoldChange, y=TT_frequency, color=factor(Type))) +
geom_point(alpha = 0.6)

where my data_for_scatter_plot data frame is:

 Gene Log2FoldChange length TT_frequency Type
 ENSG00000007968 1.928153 24791 0.05623008 up_regulated
 ENSG00000009724 2.209263 20711 0.05842306 down_regulated
 ENSG00000010219 1.794972 53099 0.08250626 other_genes
 ENSG00000053438 3.815411 2479 0.10851150 up_regulated

And the graph that I get is:

While I want to get the following graph for each group in a different color scale:

While this is not exactly what you asked for it might be a push in the right direction.

I don't think multiple color/cont scales are possible at the same time. You can change the color of the lines around the hexes, and increase line thickness.

First some sample data (please provide this yourself in the future)

library(tidyverse)
library(hexbin)

set.seed(1)

data_for_scatter_plot <- 
 crossing(
 tibble(Trial =seq(1:100)),
 tibble(Extra = seq(1:10)),
 tribble( 
 ~Gene, ~Log2FoldChange, ~length, ~TT_frequency, ~Type, 
 "ENSG00000007968", 1.928153, 24791, 0.05623008, "up_regulated",
 "ENSG00000009724", 2.209263, 20711, 0.05842306, "down_regulated",
 "ENSG00000010219", 1.794972, 53099, 0.08250626, "other_genes",
 "ENSG00000053438", 3.815411, 2479 , 0.10851150, "up_regulate")) %>% 
 mutate(
 Log2FoldChange = Log2FoldChange*0.001*Trial+rnorm(n=n(), mean=0, sd = 0.1),
 TT_frequency = TT_frequency-0.00001*Trial+rnorm(n=n(), mean=0, sd = 0.005)
 )


 data_for_scatter_plot 

 # A tibble: 4,000 x 7
 Trial Extra Gene Log2FoldChange length TT_frequency Type 
 <int> <int> <chr> <dbl> <dbl> <dbl> <chr> 
 1 1 1 ENSG00000007968 -0.0607 24791 0.0505 up_regulated 
 2 1 1 ENSG00000009724 0.0206 20711 0.0622 down_regulated
 3 1 1 ENSG00000010219 -0.0818 53099 0.0853 other_genes 
 4 1 1 ENSG00000053438 0.163 2479 0.102 up_regulate 
 5 1 2 ENSG00000007968 0.0349 24791 0.0461 up_regulated 
 6 1 2 ENSG00000009724 -0.0798 20711 0.0614 down_regulated
 7 1 2 ENSG00000010219 0.0505 53099 0.0754 other_genes 
 8 1 2 ENSG00000053438 0.0776 2479 0.117 up_regulate 
 9 1 3 ENSG00000007968 0.0595 24791 0.0654 up_regulated 
10 1 3 ENSG00000009724 -0.0283 20711 0.0653 down_regulated
# ... with 3,990 more rows

Now the plot:

data_for_scatter_plot %>% 
 ggplot(aes(x=Log2FoldChange, y=TT_frequency, color=factor(Type)))+
 geom_hex(size = 1)

I hope this helps.

---EDIT after comment---

I think a better solution for you is to facet. This will also make you able to distinguish overlapping groups

data_for_scatter_plot %>% 
 ggplot(aes(x=Log2FoldChange, y=TT_frequency, color=factor(Type)))+
 geom_hex()+
 facet_grid(cols = vars(factor(Type)))