AnVILAz 0.99.19
The package is not yet available from Bioconductor.
Install the development version of the AnVILAz package from GitHub with
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager", repos = "https://cran.r-project.org")
BiocManager::install("Bioconductor/AnVILAz")
Once installed, load the package with
library(AnVILAz)
For this tutorial we will refer to the Azure Blob Storage service as ABS. Within the ABS, we are given access to a Container. For more information, follow this link to Microsoft’s definition of containers and blobs.
avlist()
The avlist
command corresponds to a view of the files in the Blob
container on Azure. They can also be accessed via the
Microsoft Azure Storage Explorer.
Azure Storage Explorer
As an example, we load the internal mtcars
dataset and save it as an .Rda
file with save
. We can then upload this file to the ABS.
data("mtcars", package = "datasets")
test <- head(mtcars)
save(test, file = "mydata.Rda")
Now we can upload the data to the analyses/
folder in the Azure Blob Storage
(ABS) Container.
avcopy("mydata.Rda", "analyses/")
We can also use a small log file for demonstration purposes. The jupyter.log
file is already present in our workspace directory.
avcopy("jupyter.log", "analyses/")
We can remove the data with avremove
and the relative path to the .Rda
file.
avremove("analyses/mydata.Rda")
The reverse operation is also possible with a remote and local paths as the first and second arguments, respectively.
avcopy("analyses/jupyter.log", "./test/")
To upload an entire folder, we can use avbackup
. Note that the entire test
folder becomes a subfolder of the remote analyses
folder in this example.
avbackup("./test/", "analyses/")
By default, the entire source
directory will be copied to the current working
directory "."
, i.e., the base workspace directory.
avrestore("analyses/test")
You may also move this to another folder by providing a folder name as the second argument.
avrestore("analyses/test", "test")
DATA
tabmtcars
exampleFirst we create an example dataset for uploading to the DATA
tab. We create a
model_id
column from the rownames
.
library(dplyr)
mtcars_tbl <-
mtcars |>
as_tibble(rownames = "model_id") |>
mutate(model_id = gsub(" ", "-", model_id))
The avtable_import
command takes an existing R object (usually a tibble
) and
uploads to the DATA
tab in the AnVIL User Interface. The table
argument will
set the name of the table. We also need to provide the primaryKey
which
corresponds to the column name that uniquely identifies each row in the data.
Typically, the primaryKey
column provides a list of patient or UUID
identifiers and is in the first column of the data.
mtcars_tbl |> avtable_import(table = "testData", primaryKey = "model_id")
The avtable
function will pull the data from the DATA
tab and represent the
data locally as a tibble
. It works by using the same type
identifier (i.e.,
the table
argument) that was used when the data was uploaded.
model_data <- avtable(table = "testData")
head(model_data)
The API allows deletion of specific rows in the data using
avtable_delete_values
. To indicate which row to delete, provide the a value or
set of values that correspond to row identifiers in the primaryKey
. In this
example, we remove the AMC-Javelin
entry from the data. We are left with 31
records.
avtable_delete_values(table = "testData", values = "AMC-Javelin")
To remove the entire table from the DATA
tab, we can use the avtable_delete
method with the corresponding table name.
avtable_delete(table = "testData")
If you experience issues, please feel free to contact us with a reproducible example on GitHub:
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
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## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] AnVILAz_0.99.19 BiocStyle_2.33.1
##
## loaded via a namespace (and not attached):
## [1] vctrs_0.6.5 httr_1.4.7 cli_3.6.3
## [4] knitr_1.48 rlang_1.1.4 xfun_0.47
## [7] jsonlite_1.8.8 glue_1.7.0 rjsoncons_1.3.1
## [10] htmltools_0.5.8.1 BiocBaseUtils_1.7.2 sass_0.4.9
## [13] fansi_1.0.6 rmarkdown_2.28 rappdirs_0.3.3
## [16] evaluate_0.24.0 jquerylib_0.1.4 tibble_3.2.1
## [19] fastmap_1.2.0 yaml_2.3.10 lifecycle_1.0.4
## [22] httr2_1.0.3 bookdown_0.40 BiocManager_1.30.25
## [25] compiler_4.4.1 pkgconfig_2.0.3 digest_0.6.37
## [28] R6_2.5.1 utf8_1.2.4 pillar_1.9.0
## [31] magrittr_2.0.3 bslib_0.8.0 tools_4.4.1
## [34] AnVILBase_0.99.27 cachem_1.1.0