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ggplot2 geom_raster() in R: Fast Heatmap From Regular Grid

The geom_raster() function in ggplot2 draws heatmaps from data on a regular grid. It is FASTER than geom_tile() when the grid is evenly spaced.

By Selva Prabhakaran  ·  Published May 12, 2026  ·  Last updated May 12, 2026
⚡ Quick Answer
ggplot(df, aes(x, y, fill = value)) + geom_raster()
geom_raster(interpolate = TRUE)        # smooth interpolation
geom_tile()                             # equivalent, slower for regular grids

Need explanation? Read on for examples and pitfalls.

📊 Is geom_raster() the right tool?
STARTregular grid heatmap (equal spacing)geom_raster() (fast)irregular gridgeom_tile() (slower but works)contour lines instead of fillgeom_contour()hexagonal binninggeom_hex()

What geom_raster() does in one sentence

geom_raster() is a fast version of geom_tile() for evenly spaced grid data. Each (x, y) cell is filled with the corresponding fill aesthetic value.

Syntax

geom_raster(interpolate = FALSE, ...). Requires aes(x, y, fill).

Run live
Run live, no install needed. Every R block on this page runs in your browser. Click Run, edit the code, re-run instantly. No setup.
RHeatmap of correlation matrix
library(ggplot2) library(tidyr) cor_mat <- cor(mtcars[, 1:5]) cor_long <- as_tibble(cor_mat, rownames = "x") |> tidyr::pivot_longer(-x, names_to = "y", values_to = "cor") ggplot(cor_long, aes(x, y, fill = cor)) + geom_raster() + scale_fill_viridis_c()

  

  





Tip
Use geom_raster when your grid is REGULAR (equal x and y spacing). For irregular grids, geom_tile is more flexible.



Five common patterns



1. Correlation heatmap




  

    
RStandard usage

    
ggplot(cor_long, aes(x, y, fill = cor)) +
  geom_raster() +
  scale_fill_viridis_c()

    

      
      
    

    

  

  





2. With interpolation




  

    
RSmooth blending

    
ggplot(df, aes(x, y, fill = z)) +
  geom_raster(interpolate = TRUE)

    

      
      
    

    

  

  





3. Image data




  

    
RImage-like raster

    
matrix_data <- matrix(rnorm(100), 10, 10)
df_grid <- as_tibble(matrix_data) |>
  mutate(row = row_number()) |>
  tidyr::pivot_longer(-row, names_to = "col", values_to = "z")

ggplot(df_grid, aes(col, row, fill = z)) +
  geom_raster()

    

      
      
    

    

  

  





4. With theme cleanup




  

    
RPure heatmap

    
ggplot(df, aes(x, y, fill = z)) +
  geom_raster() +
  theme_void() +
  scale_fill_viridis_c()

    

      
      
    

    

  

  





5. Compare to geom_tile




  

    
RSame output for regular grid

    
ggplot(df, aes(x, y, fill = z)) + geom_raster()
ggplot(df, aes(x, y, fill = z)) + geom_tile()
# Same visual; raster is faster

    

      
      
    

    

  

  





Key Insight
geom_raster is geom_tile optimized for REGULAR grids. It exploits the regularity to render faster (especially useful for large grids 100x100+).



geom_raster() vs geom_tile() vs geom_hex()







Function
Grid type
Best for






geom_raster()
Regular
Fast heatmaps




geom_tile()
Any
Heatmaps with irregular cells




geom_hex()
Hexagonal binning
2D density




geom_bin2d()
Rectangular binning
2D density







A practical workflow



Heatmaps for matrices, correlation matrices, and per-cell aggregations.




  

    
RInteractive R

    
df_grid |>
  ggplot(aes(x, y, fill = value)) +
  geom_raster() +
  scale_fill_viridis_c() +
  coord_fixed() +
  theme_minimal()

    

      
      
    

    

  

  





Common pitfalls



Pitfall 1: irregular grid. geom_raster expects evenly-spaced x and y. Irregular spacing produces visual artifacts.



Pitfall 2: missing values. Empty cells appear gray (NA color). Fill with 0 or NA explicitly via complete().



Warning
geom_raster() is NOT for non-rectangular layouts. For hex bins, use geom_hex; for bins, use geom_bin2d.



Try it yourself





Try it: Plot a heatmap of mtcars correlation matrix. Save to ex_plot.



  

    
RYour turn: correlation heatmap

    
cor_mat <- cor(mtcars)
cor_long <- as_tibble(cor_mat, rownames = "x") |>
  tidyr::pivot_longer(-x, names_to = "y", values_to = "cor")

ex_plot <- cor_long |>
  # your code here

    

      
      
    

    

  

  





Click to reveal solution



  

    
RSolution

    
ex_plot <- ggplot(cor_long, aes(x, y, fill = cor)) +
  geom_raster() +
  scale_fill_viridis_c() +
  theme_minimal()

    

      
      
    

    

  

  





Explanation: geom_raster fills each (x, y) cell with the correlation value; viridis colors highlight values.










After mastering geom_raster, look at:



    


  • geom_tile(): more flexible for irregular grids
    • 


  • geom_hex(): hexagonal 2D bins
    • 


  • geom_bin2d(): rectangular 2D bins
    • 


  • geom_contour(): contour lines
    • 


  • scale_fill_viridis_c(): perceptually uniform colors
    • 





FAQ



What does geom_raster do in ggplot2?



geom_raster() draws a fast heatmap from regular-grid data. Each (x, y) cell is filled with the fill aesthetic value.



What is the difference between geom_raster and geom_tile?



geom_raster is faster but requires a REGULAR grid. geom_tile works for any grid but is slower for large regular grids.



Should I use geom_raster or geom_hex?



geom_raster for grid data you already have. geom_hex for 2D density of raw points (it bins them automatically).



How do I add interpolation?



Pass interpolate = TRUE to geom_raster. Smooths transitions between cells.



Can I use geom_raster for image data?



Yes. It is the standard tool for matrix/image-like display. Use coord_fixed() to keep the aspect ratio.



          

        


        
      


      

        

        
© 2016-2026 Selva Prabhakaran.
          This work is licensed under the Creative Commons License.
        

        

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