Matrices and Arrays

HDF5 is an excellent format for storing large, multi-dimensional numerical arrays. h5lite simplifies the process of reading and writing matrices and arrays by handling the complex memory layout differences between R and HDF5 automatically.

This vignette covers writing matrices, preserving dimension names (dimnames), and understanding how h5lite manages dimension ordering.

library(h5lite)
file <- tempfile(fileext = ".h5")

Writing Matrices

In R, matrices are simply 2-dimensional arrays. You can write them directly using h5_write(). h5lite preserves the dimensions exactly as they appear in R.

# Create a 3x4 matrix
mat <- matrix(1:12, nrow = 3, ncol = 4)

# Write to file
h5_write(mat, file, "linear_algebra/mat_a")

# Read back
mat_in <- h5_read(file, "linear_algebra/mat_a")

# Verify
all.equal(mat, mat_in)
#> [1] TRUE

Writing N-Dimensional Arrays

The same logic applies to arrays with 3 or more dimensions.

# Create a 3D array (e.g., spatial data over time: x, y, time)
vol <- array(runif(24), dim = c(4, 3, 2))

h5_write(vol, file, "spatial/volume")

# Check dimensions without reading the full data
h5_dim(file, "spatial/volume")
#> [1] 4 3 2

Dimension Names (dimnames)

R objects often carry metadata in the form of dimnames (row names, column names, etc.). HDF5 does not have a native “row name” concept for numerical arrays, but it supports Dimension Scales.

h5lite automatically converts R dimnames into HDF5 Dimension Scales. This allows your row and column names to survive the round-trip to disk and back.

# Create a matrix with row and column names
data <- matrix(rnorm(6), nrow = 2)
rownames(data) <- c("Sample_A", "Sample_B")
colnames(data) <- c("Gene_1", "Gene_2", "Gene_3")

h5_write(data, file, "genetics/expression")

# Read back
data_in <- h5_read(file, "genetics/expression")

print(data_in)
#>             Gene_1     Gene_2      Gene_3
#> Sample_A 1.3613766 -1.0888527 -0.01808857
#> Sample_B 0.4309375  0.1311432  1.62414883

Technical Note: In the HDF5 file, the names are stored as separate datasets (e.g., _rownames, _colnames) and linked to the main dataset using HDF5 Dimension Scale attributes.

Dimension Ordering (Row-Major vs. Column-Major)

One of the most confusing aspects of HDF5 for R users is dimension ordering.

How h5lite handles it

To ensure that a 3x4 matrix in R looks like a 3x4 dataset in HDF5 tools (like h5dump or HDFView), h5lite physically transposes the data during read/write operations.

  1. Writing: h5lite converts R’s column-major memory layout to HDF5’s row-major layout.
  2. Reading: h5lite converts the data back to column-major for R.

This ensures that indexing is preserved. x[2, 1] in R refers to the exact same value after reading it back from HDF5.

Interoperability with Python

Because h5lite writes the data in C-order (Row-Major) to match the HDF5 specification, files created with h5lite are perfectly readable by Python (h5py or pandas).

Note: Some other R packages create HDF5 files by swapping the dimensions (writing a 3x4 matrix as 4x3) to avoid the cost of transposing data. h5lite prioritizes correctness and interoperability over raw write speed.

Compression and Chunking

Matrices and arrays benefit significantly from compression. When you enable compression, h5lite automatically “chunks” the dataset (breaks it into smaller tiles).

# Large matrix of zeros (highly compressible)
sparse_mat <- matrix(0, nrow = 1000, ncol = 1000)
sparse_mat[1:10, 1:10] <- 1

# Write with compression (zlib level 5)
h5_write(sparse_mat, file, "compressed/matrix", compress = TRUE)

# Write with high compression (zlib level 9)
h5_write(sparse_mat, file, "compressed/matrix_max", compress = 9)

Partial I/O

h5lite is designed for simplicity and currently reads/writes full datasets at once. It does not support partial I/O (hyperslabs), such as reading only rows 1-10 of a 1,000,000 row matrix.

If you need to read specific subsets of data that are too large to fit in memory, you should consider using the rhdf5 or hdf5r packages.