Prelimaries
-
We will show two initial and basic examples for a dense and sparse array simply to create array data on disk to refer to later in examples that follow.
+
We will show two initial and basic examples for a dense and sparse
+array simply to create array data on disk to refer to later in examples
+that follow.
diff --git a/docs/articles/index.html b/docs/articles/index.html
index 8ed99b0597..94f2b64e56 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -1,5 +1,5 @@
-
Articles • tiledbArticles • tiledb
@@ -10,7 +10,7 @@
tiledb
-
0.17.0
+
0.17.1
diff --git a/docs/articles/installation-options.html b/docs/articles/installation-options.html
index 79418ade4d..ebd7971210 100644
--- a/docs/articles/installation-options.html
+++ b/docs/articles/installation-options.html
@@ -8,10 +8,10 @@
This document introduces TileDB via several simple examples. A corresponding document with more complete API documentation is also available.
+This document introduces TileDB via several simple examples. A
+corresponding document with more complete API documentation is also available.
Getting started
-
Once the TileDB R package is installed, it can be loaded via library(tiledb). Installation is supported for Windows, Linux and macOS via the official CRAN package, on Linux and macOS via the conda package as well as from source.
-
Documentation for the TileDB R package is available via the help() function from within R as well as via the package documentation and an introductory notebook. Documentation about TileDB itself is also available.
-
Several “quickstart” examples that are discussed on the website are available in the examples directory. This vignette discusses similar examples.
-
In the following examples, the URIs describing arrays point to local file system object. When TileDB has been built with S3 support, and with proper AWS credentials in the usual environment variables, URIs such as s3://some/data/bucket can be used where a local file would be used. See the script ex_S3.R for an example.
+
Once the TileDB R package is installed, it can be loaded via
+library(tiledb). Installation is supported for Windows,
+Linux and macOS via the official CRAN
+package, on Linux and macOS via the conda package as well as
+from source.
+
Documentation for the TileDB R package is available via the
+help() function from within R as well as via the package documentation
+and an introductory
+notebook. Documentation about TileDB itself is also available.
+
Several “quickstart” examples that are discussed on the website are
+available in the examples
+directory. This vignette discusses similar examples.
+
In the following examples, the URIs describing arrays point to local
+file system object. When TileDB has been built with S3 support, and with
+proper AWS credentials in the usual environment variables, URIs such as
+s3://some/data/bucket can be used where a local file would
+be used. See the script ex_S3.R
+for an example.
Basic Reading of Dense Arrays
-
The file ex_1.R in the examples directory is a simple yet complete example extending quickstart_dense.R by adding a second and third attribute. In this as well as the following examples we will use tiledb_array() to access the array; the older variants tiledb_dense() and tiledb_sparse() remain supported but are deprecated and may be removed at some point in the future.
+
The file ex_1.R
+in the examples directory is a simple yet complete example extending quickstart_dense.R
+by adding a second and third attribute. In this as well as the following
+examples we will use tiledb_array() to access the array;
+the older variants tiledb_dense() and
+tiledb_sparse() remain supported but are deprecated and may
+be removed at some point in the future.
Read 1-D
-
The first example extracts rows 1 to 2 and column 2 from an array. It also limits the selection to just one attribute (via attrs), asks for the return to be a data.frame (instead of a simpler list) and for the (row and column, if present as here) indices to not be printed (via extended=FALSE).
+
The first example extracts rows 1 to 2 and column 2 from an array. It
+also limits the selection to just one attribute (via
+attrs), asks for the return to be a data.frame
+(instead of a simpler list) and for the (row and column, if present as
+here) indices to not be printed (via extended=FALSE).
> A <- tiledb_array(uri = uri, attrs = "b",
+ as.data.frame=TRUE, extended=FALSE))
> A[1:2,2]
[1] 101.5 104.0
>
-
Note that the examples create three two-dimensional attributes. The attributes can be selected via the attrs argument, or the attrs() method on the array object. The square-bracket indexing then selects with in the 2-d attribute object.
-
If multiple objects are returned (as list or data.frame), subsetting on the returned object works via [[var]] or $var. A numeric index also works (but needs to account for rows and cols).
+
Note that the examples create three two-dimensional attributes. The
+attributes can be selected via the attrs argument, or the
+attrs() method on the array object. The square-bracket
+indexing then selects with in the 2-d attribute object.
+
If multiple objects are returned (as list or
+data.frame), subsetting on the returned object works via
+[[var]] or $var. A numeric index also works
+(but needs to account for rows and cols).
> A <- tiledb_array(uri = uri, attrs = c("a","b"),
+ as.data.frame = TRUE)
> A[1:2,2][["a"]]
@@ -132,7 +168,9 @@ Basic Reading of Dense Arrays[1] 2 7
>
Read 2-D
-
This works analogously. Note that the results are generally returned as vectors, or as a columns of a data.frame object in case that option was set.
+
This works analogously. Note that the results are generally returned
+as vectors, or as a columns of a data.frame object in case
+that option was set.
> A[6:9,3:4]
$a
[1] 28 29 33 34 38 39 43 44
@@ -147,7 +185,8 @@
Simple Examples
-Basic reading returns the coordinates and any attributes. The following examples use the array created by the quickstart_sparse example.
+Basic reading returns the coordinates and any attributes. The
+following examples use the array created by the quickstart_sparse
+example.
> A <- tiledb_array(uri = uri, is.sparse = TRUE)
> A[]
$rows
@@ -200,14 +249,16 @@ Basic Reading and Writing of
[1] 1 3 2
>
We can also request a data.frame object, either when opening or by changing this object characteristic on the fly:
+We can also request a data.frame object, either when
+opening or by changing this object characteristic on the fly:
> return.data.frame(A) <- TRUE
> A[]
a rows cols
1 1 1 1
2 3 2 3
3 2 2 4
For sparse arrays, the return type is by default ‘extended’ showing rows and column but this can be overridden.
+For sparse arrays, the return type is by default ‘extended’ showing
+rows and column but this can be overridden.
Assignment works similarly:
> A[4,2] <- 42L
> A[]
@@ -232,7 +283,10 @@ Basic Reading and Writing of
Date(time) Attributes
-
Similar to the dense array case described earlier, the file ex_2.R illustrates some basic operations on sparse arrays. It also shows date and datetime types instead of just integer and double precision floats.
+
Similar to the dense array case described earlier, the file ex_2.R
+illustrates some basic operations on sparse arrays. It also shows date
+and datetime types instead of just integer and double precision
+floats.
> A <- tiledb_array(uri = uri, as.data.frame = TRUE)
> A[1577858403:1577858408]
rows cols a b d e
@@ -243,7 +297,10 @@ Date(time) Attributes5 1577858407 1 7 107 2020-01-25 2020-01-09 04:29:56.309
6 1577858408 1 8 108 2020-01-26 2020-01-07 13:55:10.240
>
-
The row coordinate is currently a floating point representation of the underlying time type. We can both select attributes (here we excluded the “a” column) and select rows by time (as the time stamps get converted to the required floating point value).
+
The row coordinate is currently a floating point representation of
+the underlying time type. We can both select attributes (here we
+excluded the “a” column) and select rows by time (as the time stamps get
+converted to the required floating point value).
> attrs(A) <- c("b", "d", "e")
> A[as.POSIXct("2020-01-01"):as.POSIXct("2020-01-01 00:00:03")]
rows cols b d e
@@ -251,14 +308,22 @@ Date(time) Attributes2 1577858402 1 102 2020-01-10 2020-01-02 21:02:19.747
3 1577858403 1 103 2020-01-11 2020-01-02 18:24:33.844
>
-
More extended examples are available showing indexing by date(time) as well as character dimension.
+
More extended examples are available showing indexing by date(time)
+as well as character dimension.
diff --git a/docs/articles/tiledb-mariadb-examples.html b/docs/articles/tiledb-mariadb-examples.html
index ae38e5ccd6..fe35ab6f46 100644
--- a/docs/articles/tiledb-mariadb-examples.html
+++ b/docs/articles/tiledb-mariadb-examples.html
@@ -8,10 +8,10 @@
TileDB and (R)MariaDB Examples • tiledb
-
-
+
+
-
+
Authors and Citation • tiledbAuthors and Citation • tiledb
@@ -10,7 +10,7 @@
tiledb
-
0.17.0
+
0.17.1
@@ -96,7 +96,7 @@