Introduction to TileDB for R
Introduction to TileDB for R
This tutorial, led by instructors Dirk Eddelbuettel and Aaron Wolen, introduces the 'tiledb' R package on CRAN, which allows users to efficiently operate on large dense/sparse arrays using familiar R techniques and data structures. It also offers key features of the underlying TileDB Embedded library: parallelised read and write operations, multiple compression formats, time traveling (i.e., the ability to recover data stored at previous timepoints), flexible encryption, and Apache Arrow support. Several simple usage examples are provided.
Usage examples from Bioinformatics provide a more extended case study. Here, we illustrate how TileDB can be used to create a performant data store for results produced by Genome-Wide Association Studies, and demonstrate the BioConductor package, TileDBArray, which is built on top of the DelayedArray framework and has shown excellent performance relevant to existing (hdf5-based) solutions. Finally, usage of TileDB with cloud storage providers is illustrated. This covers both direct reads and writes to, for example, Amazon S3.
See the full useR! 2021 tutorial agenda at https://user2021.r-project.org/progra... and view the source footage at • Introduction to TileDB for R .
Contents of this video
00:00:00 – Welcome & installation
00:07:25 – Intro example: basic reads & writes
00:15:51 – Dense arrays
00:24:06 – Sparse arrays
00:44:17 – High-level API functions: fromDataFrame & tiledb_array
00:47:32 – Full API walkthrough: S3, time traveling, encryption
01:06:00 – TileDB SQL via MariaDB
01:16:58 – Flights data science example
01:41:15 – LiDAR example
01:50:01 – Finance time series example
01:57:18 – GWAS tutorial
02:26:33 – Wrap-up
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