---
title: "DelayedDataFrame: an on-disk represention of DataFrame"
author:
- name: Qian Liu
affiliation: Roswell Park Comprehensive Cancer Center, Buffalo, NY
- name: Hervé Pagès
affiliation: Fred Hutchinson Cancer Research Center, Seattle, WA
- name: Martin Morgan
affiliation: Roswell Park Comprehensive Cancer Center, Buffalo, NY
date: "last edit: 10/15/2021"
output:
BiocStyle::html_document:
toc: true
toc_float: true
package: DelayedDataFrame
vignette: |
%\VignetteIndexEntry{DelayedDataFrame}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "##"
)
```
```{r options, eval=TRUE, echo=FALSE}
options(showHeadLines=3)
options(showTailLines=3)
```
# Introduction
As the genetic/genomic data are having increasingly larger profile,
the annotation file are also getting much bigger than expected. the
memory space in _R_ has been an obstable for fast and efficient data
processing, because most available _R_ or _Bioconductor_ packages are
developed based on in-memory data manipulation. With some newly
developed data structure as [HDF5][] or [GDS][], and the _R_ interface
of [DelayedArray][] to represent on-disk data structures with
different back-end in _R_-user-friendly array data structure (e.g.,
[HDF5Array][],[GDSArray][]), the high-throughput genetic/genomic data
are now being able to easily loaded and manipulated within
_R_. However, the annotation files for the samples and features inside
the high-through data are also getting unexpectedly larger than
before. With an ordinary `data.frame` or `DataFrame`, it is still
getting more and more challenging for any analysis to be done within
_R_. So here we have developed the `DelayedDataFrame`, which has the
very similar characteristics as `data.frame` and `DataFrame`. But at
the same time, all column data could be optionally saved on-disk
(e.g., in [DelayedArray][] structure with any back-end). Common
operations like constructing, subsetting, splitting, combining could
be done in the same way as `DataFrame`. This feature of
`DelayedDataFrame` could enable efficient on-disk reading and
processing of the large-scale annotation files, and at the same,
signicantly saves memory space with common `DataFrame` metaphor in _R_
and _Bioconductor_.
[HDF5]: https://www.hdfgroup.org/solutions/hdf5/
[GDS]: http://corearray.sourceforge.net/
[DelayedArray]: https://bioconductor.org/packages/DelayedArray
[GDSArray]: https://bioconductor.org/packages/GDSArray
[HDF5Array]: https://bioconductor.org/packages/HDF5Array
# Installation
Download the package from _Bioconductor_:
```{r getPackage, eval=FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("DelayedDataFrame")
```
The development version is also available to download through github:
```{r getDevel, eval=FALSE}
BiocManager::install("Bioconductor/DelayedDataFrame")
```
Load the package into _R_ session before using:
```{r Load, message=FALSE, warning=FALSE}
library(DelayedDataFrame)
```
# DelayedDataFrame class
## class extension
`DelayedDataFrame` extends the `DataFrame` data structure, with an
additional slot called `lazyIndex`, which saves all the mapping
indexes for each column of the data inside `DelayedDataFrame`. It is
similar to `data.frame` in terms of construction, subsetting,
splitting, combining... The `rownames` are having same feature as
`DataFrame`. It will not be given automatically, but only by
explicitly specify in the constructor function `DelayedDataFrame(,
row.names=...)` or using the slot setter function `rownames()<-`.
Here we use the [GDSArray][] data as example to show the
`DelayedDataFrame` characteristics. [GDSArray][] is a _Bioconductor_
package that represents GDS files as objects derived from the
[DelayedArray][] package and `DelayedArray` class. It carries the
on-disk data path and represent the GDS nodes in a
`DelayedArray`-derived data structure.
The `GDSArray()` constructor takes 2 arguments: the file path and the
GDS node name inside the GDS file.
```{r, GDSArray}
library(GDSArray)
file <- SeqArray::seqExampleFileName("gds")
gdsnodes(file)
varid <- GDSArray(file, "annotation/id")
DP <- GDSArray(file, "annotation/info/DP")
```
We use an ordinary character vector and the `GDSArray` objects to
construct a `DelayedDataFrame` object.
```{r, construction}
ddf <- DelayedDataFrame(varid, DP) ## only accommodate 1D GDSArrays with same length
```
## slot accessors
The slots of `DelayedDataFrame` could be accessed by `lazyIndex()`,
`nrow()`, `rownames()` (if not NULL) functions. With a newly
constructed `DelayedDataFrame` object, the initial value of
`lazyIndex` slot will be NULL for all columns.
```{r, accessors}
lazyIndex(ddf)
nrow(ddf)
rownames(ddf)
```
## `lazyIndex` slot
The `lazyIndex` slot is in `LazyIndex` class, which is defined in the
`DelayedDataFrame` package and extends the `SimpleList` class. The
`listData` slot saves unique indexes for all the columns, and the
`index` slots saves the position of index in `listData` slot for each
column in `DelayedDataFrame` object. In the above example, with an
initial construction of `DelayedDataFrame` object, the index for each
column will all be NULL, and all 3 columns points the NULL values
which sits in the first position in `listData` slot of `lazyIndex`.
```{r}
lazyIndex(ddf)@listData
lazyIndex(ddf)@index
```
Whenever an operation is done (e.g., subsetting), the `listData` slot
inside the `DelayedDataFrame` stays the same, but the `lazyIndex` slot
will be updated, so that the show method, further statistical
calculation will be applied to the subsetting data set. For example,
here we subset the `DelayedDataFrame` object `ddf` to keep only the
first 5 rows, and see how the `lazyIndex` works. As shown in below,
after subsetting, the `listData` slot in `ddf1` stays the same as
`ddf`. But the subsetting operation was recorded in the `lazyIndex`
slot, and the slots of `lazyIndex`, `nrows` and `rownames` (if not
NULL) are all updated. So the subsetting operation is kind of
`delayed`.
```{r, lazyIndex}
ddf1 <- ddf[1:20,]
identical(ddf@listData, ddf1@listData)
lazyIndex(ddf1)
nrow(ddf1)
```
Only when functions like `DataFrame()`, or `as.list()`, the
`lazyIndex` will be realized and `DelayedDataFrame` returned.
We will show the realization in the following coercion method section.
# DelayedDataFrame methods
The common methods on `data.frame` or `DataFrame` are also defined on
`DelayedDataFrame` class, so that they behave similarily on
`DelayedDataFrame` objects.
## Coercion methods
Coercion methods between `DelayedDataFrame` and other data structures
are defined. When coercing from `ANY` to `DelayedDataFrame`, the
`lazyIndex` slot will be added automatically, with the initial NULL
value of indexes for each column.
- From vector
```{r}
as(letters, "DelayedDataFrame")
```
- From DataFrame
```{r}
as(DataFrame(letters), "DelayedDataFrame")
```
- From list
```{r}
(a <- as(list(a=1:5, b=6:10), "DelayedDataFrame"))
lazyIndex(a)
```
When coerce `DelayedDataFrame` into other data structure, the
`lazyIndex` slot will be realized and the new data structure
returned. For example, when `DelayedDataFrame` is coerced into a
`DataFrame` object, the `listData` slot will be updated according to
the `lazyIndex` slot.
```{r}
df1 <- as(ddf1, "DataFrame")
df1@listData
dim(df1)
```
## Subsetting methods
### subsetting by `[`
two-dimensional `[` subsetting on `DelayedDataFrame` objects by
integer, character, logical values all work.
- integer subscripts.
```{r, singleSB1}
ddf[, 1, drop=FALSE]
```
- character subscripts (column names).
```{r, singleSB2}
ddf[, "DP", drop=FALSE]
```
- logical subscripts.
```{r, singleSB3}
ddf[, c(TRUE,FALSE), drop=FALSE]
```
When subsetting using `[` on an already subsetted `DelayedDataFrame`
object, the `lazyIndex`, `nrows` and `rownames`(if not NULL) slot will
be updated.
```{r, singleSB4}
(a <- ddf1[1:10, 2, drop=FALSE])
lazyIndex(a)
nrow(a)
```
### subsetting by `[[`
The `[[` subsetting will take column subscripts for integer or
character values, and return corresponding columns in it's original
data format.
```{r, doubleSB}
ddf[[1]]
ddf[["varid"]]
identical(ddf[[1]], ddf[["varid"]])
```
## `rbind/cbind`
When doing `rbind`, the `lazyIndex` of input arguments will be
realized and a new `DelayedDataFrame` with NULL lazyIndex will be
returned.
```{r, rbind}
ddf2 <- ddf[21:40, ]
(ddfrb <- rbind(ddf1, ddf2))
lazyIndex(ddfrb)
```
`cbind` of `DelayedDataFrame` objects will keep all existing
`lazyIndex` of input arguments and carry into the new
`DelayedDataFrame` object.
```{r, cbind, error=FALSE}
(ddfcb <- cbind(varid = ddf1[,1, drop=FALSE], DP=ddf1[, 2, drop=FALSE]))
lazyIndex(ddfcb)
```
# sessionInfo
```{r, sessioninfo}
sessionInfo()
```