Resolves issues #39, #40, and #42

DESCRIPTION

@@ -19,7 +19,9 @@ Description: The open sourced data management software 'Integrated Rule-Oriented
     with R. Storage of annotated files and R objects in 'iRODS' ensures 
     findability, accessibility, interoperability, and reusability of data.
 License: MIT + file LICENSE
-URL: https://github.com/irods/irods_client_library_rirods
+URL: 
+    https://github.com/irods/irods_client_library_rirods,
+    https://rirods.irods4r.org
 BugReports: https://github.com/irods/irods_client_library_rirods/issues
 Depends: 
     R (>= 4.1)
@@ -50,3 +52,4 @@ Encoding: UTF-8
 Language: en-US
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.3
+SystemRequirements: iRODS C++ REST API 0.9.3 (https://github.com/irods/irods_client_rest_cpp)

README.Rmd

@@ -20,7 +20,7 @@ knitr::opts_knit$set(
 )
 ```
 
-# rirods <img src="man/figures/logo.png" align="right" />
+# rirods <a href="https://dplyr.tidyverse.org"><img src="man/figures/logo.png" align="right" height="138" /></a>
 
 <!-- badges: start -->
 [![Codecov test coverage](https://codecov.io/gh/irods/irods_client_library_rirods/branch/main/graph/badge.svg)](https://app.codecov.io/gh/irods/irods_client_library_rirods?branch=main)

README.md

@@ -1,6 +1,7 @@
+
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# rirods <img src="man/figures/logo.png" align="right" />
+# rirods <a href="https://dplyr.tidyverse.org"><img src="man/figures/logo.png" align="right" height="138" /></a>
 
 <!-- badges: start -->
 
@@ -15,27 +16,33 @@ The rirods package is an R client for iRODS.
 
 You can install the development version of rirods like so:
 
-    install.packages("rirods")
+``` r
+install.packages("rirods")
+```
 
 Or, the development version from GitHub, like so:
 
-    # install.packages("devtools")
-    devtools::install_github("irods/irods_client_library_rirods")
+``` r
+# install.packages("devtools")
+devtools::install_github("irods/irods_client_library_rirods")
+```
 
 ## Prerequisites
 
 This package connects to the iRODS C++ REST API -
-<https://github.com/irods/irods_client_rest_cpp>.
+https://github.com/irods/irods_client_rest_cpp.
 
 Launch a local demonstration iRODS service (including the REST API):
 
-    # load
-    library(rirods)
-    # setup a mock iRODS server (https://github.com/irods/irods_demo)
-    use_irods_demo("alice", "passWORD")
+``` r
+# load
+library(rirods)
+# setup a mock iRODS server (https://github.com/irods/irods_demo)
+use_irods_demo("alice", "passWORD")
+```
 
 This will result in the demonstration REST API running at
-<http://localhost/irods-rest/0.9.3> (or later version).
+http://localhost/irods-rest/0.9.3 (or later version).
 
 These Docker containers are designed to easily stand up a
 **DEMONSTRATION** of the iRODS server. It is intended for education and
@@ -49,8 +56,10 @@ To connect to the REST API endpoint of your choice, load `rirods`,
 connect with `create_irods()`, and authenticate with your iRODS
 credentials:
 
-    # connect
-    create_irods("http://localhost/irods-rest/0.9.3", "/tempZone/home")
+``` r
+# connect
+create_irods("http://localhost/irods-rest/0.9.3", "/tempZone/home")
+```
 
 ### Authentication
 
@@ -59,53 +68,44 @@ herself with `iauth()`. This prompts a dialog where you can enter your
 username and password without hardcoding this information in your
 scripts.
 
-    # login as alice with password "passWORD"
-    iauth() # or iauth("alice", "passWORD")
+``` r
+# login as alice with password "passWORD"
+iauth() # or iauth("alice", "passWORD")
+```
 
 ### Save R objects
 
 Suppose Alice would like to upload an R object from her current R
 session to an iRODS collection. For this, use the `isaveRDS()` command:
 
-    # some data
-    foo <- data.frame(x = c(1, 8, 9), y = c("x", "y", "z"))
+``` r
+# some data
+foo <- data.frame(x = c(1, 8, 9), y = c("x", "y", "z"))
 
-    # check where we are in the iRODS namespace
-    ipwd()
-    #> [1] "/tempZone/home/alice"
+# check where we are in the iRODS namespace
+ipwd()
 
-    # store data in iRODS
-    isaveRDS(foo, "foo.rds")
+# store data in iRODS
+isaveRDS(foo, "foo.rds")
+```
 
 ### Metadata
 
 To truly appreciate the strength of iRODS, we can add some metadata that
 describes the data object “foo”:
 
-    # add some metadata
-    imeta(
-      "foo.rds", 
-      "data_object", 
-      operations = 
-        data.frame(operation = "add", attribute = "foo", value = "bar", units = "baz")
-    )
-
-    # check if file is stored with associated metadata
-    ils(metadata = TRUE)
-    #> 
-    #> ========
-    #> metadata
-    #> ========
-    #> /tempZone/home/alice/foo.rds :
-    #>  attribute value units
-    #>        foo   bar   baz
-    #> 
-    #> 
-    #> ==========
-    #> iRODS Zone
-    #> ==========
-    #>                  logical_path        type
-    #>  /tempZone/home/alice/foo.rds data_object
+``` r
+# add some metadata
+imeta(
+  "foo.rds", 
+  "data_object", 
+  operations = 
+    data.frame(operation = "add", attribute = "foo", value = "bar", units = "baz")
+)
+
+# check if file is stored with associated metadata
+ils(metadata = TRUE)
+```
 
 For more on using metadata, check out `vignette("metadata")`.
 
@@ -114,91 +114,73 @@ For more on using metadata, check out `vignette("metadata")`.
 If Alice wanted to copy the foo R object from an iRODS collection to her
 current R session, she would use `ireadRDS()`:
 
-    # retrieve in native R format
-    ireadRDS("foo.rds")
-    #>   x y
-    #> 1 1 x
-    #> 2 8 y
-    #> 3 9 z
+``` r
+# retrieve in native R format
+ireadRDS("foo.rds")
+```
 
 ### Other file formats
 
 Possibly Alice does not want a native R object to be stored on iRODS but
 a file type that can be accessed by other programs. For this, use the
 `iput()` command:
 
-    library(readr)
+``` r
+library(readr)
 
-    # creates a csv file of foo
-    write_csv(foo, "foo.csv")
+# creates a csv file of foo
+write_csv(foo, "foo.csv")
 
-    # send file
-    iput("foo.csv", "foo.csv")
+# send file
+iput("foo.csv", "foo.csv")
 
-    # check whether it is stored
-    ils()
-    #> 
-    #> ==========
-    #> iRODS Zone
-    #> ==========
-    #>                  logical_path        type
-    #>  /tempZone/home/alice/foo.csv data_object
-    #>  /tempZone/home/alice/foo.rds data_object
+# check whether it is stored
+ils()
+```
 
 Later on somebody else might want to download this file again and store
 it locally:
 
-    # retrieve it again later
-    iget("foo.csv", "foo.csv")
-    read_csv("foo.csv")
-    #> Rows: 3 Columns: 2
-    #> ── Column specification ────────────────────────────────────────────────────────
-    #> Delimiter: ","
-    #> chr (1): y
-    #> dbl (1): x
-    #> 
-    #> ℹ Use `spec()` to retrieve the full column specification for this data.
-    #> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
-    #> # A tibble: 3 × 2
-    #>       x y    
-    #>   <dbl> <chr>
-    #> 1     1 x    
-    #> 2     8 y    
-    #> 3     9 z
+``` r
+# retrieve it again later
+iget("foo.csv", "foo.csv")
+read_csv("foo.csv")
+```
 
 ### Query
 
 By adding metadata you and others can more easily discover data in
 future projects. Objects can be searched with General Queries and
 `iquery()`:
 
-    # look for objects in the home collection with a wildcard `%`
-    iquery("SELECT COLL_NAME, DATA_NAME WHERE COLL_NAME LIKE '/tempZone/home/%'")
-    #>              COLL_NAME DATA_NAME
-    #> 1 /tempZone/home/alice   foo.csv
-    #> 2 /tempZone/home/alice   foo.rds
+``` r
+# look for objects in the home collection with a wildcard `%`
+iquery("SELECT COLL_NAME, DATA_NAME WHERE COLL_NAME LIKE '/tempZone/home/%'")
+```
 
-    # or for data objects with a name that starts with "foo"
-    iquery("SELECT COLL_NAME, DATA_NAME WHERE DATA_NAME LIKE 'foo%'")
-    #>              COLL_NAME DATA_NAME
-    #> 1 /tempZone/home/alice   foo.csv
-    #> 2 /tempZone/home/alice   foo.rds
+``` r
+# or for data objects with a name that starts with "foo"
+iquery("SELECT COLL_NAME, DATA_NAME WHERE DATA_NAME LIKE 'foo%'")
+```
 
 For more on querying, check out `vignette("metadata")`.
 
 ### Cleanup
 
 Finally, we can clean up Alice’s home collection:
 
-    # delete object
-    irm("foo.rds", force = TRUE)
-    irm("foo.csv", force = TRUE)
-
-    # check if objects are removed
-    ils()
-    #> This collection does not contain any objects or collections.
-
-    # close the server
-    stop_irods_demo()
-    # optionally remove the Docker images
-    # irods:::remove_docker_images()
+``` r
+# delete object
+irm("foo.rds", force = TRUE)
+irm("foo.csv", force = TRUE)
+
+# check if objects are removed
+ils()
+```
+
+``` r
+# close the server
+stop_irods_demo()
+# optionally remove the Docker images
+# irods:::remove_docker_images()
+```

_pkgdown.yml

@@ -1,7 +1,9 @@
 url: https://irods.github.io/irods_client_library_rirods/
 template:
   bootstrap: 5
-  bootswatch: minty
+  bslib:
+    bg: "#ffffff"
+    fg: "#005e56"
 
 reference:
 - title: "Connecting and authentication"

inst/WORDLIST

@@ -1,31 +1,49 @@
+ATTR
 AVU
 AVUs
 CMD
+COLN
 CONFIG
 Codecov
+GenQuery
 GeneralQuery
 Hostname
 IRODS
+POSIXct
+RStudio
+WSL
 arg
+behaviour
 childtoresc
 cpp
 csv
+datetime
 devtools
+dir
+ectory
+findability
 github
+hange
 hardcoding
 http
 https
 iCAT
+iCommand
 iCommands
 iRODS
 icommands
 iquest
+irectory
 irods
 macOS
 mockfiles
-findability
-accessibility
+modelled
+modelling
+myfile
+nrow
+oved
 reusability
-interoperability
+submodules
 tempZone
+unstage
 usethis

vignettes/demo.Rmd

@@ -40,7 +40,7 @@ sudo dockerd
 
 Make sure you don't need `sudo` for `docker`, though, by [adding yourself to the `docker` group](https://docs.docker.com/engine/install/linux-postinstall/#manage-docker-as-a-non-root-user). You can check that this is the case by running `docker run hello-world` on the command line.
 
-If you use WSL, you can start RStudio Server with `sudo rstudio-server start` and run it from your browser (http://localhost:8787). [The Posit site](https://posit.co/download/rstudio-server/) has installation instructions for R and RStudio Server in Linux.
+If you use WSL, you can start RStudio Server with `sudo rstudio-server start` and run it from your browser (`http://localhost:8787`). [The Posit site](https://posit.co/download/rstudio-server/) has installation instructions for R and RStudio Server in Linux.
 
 Then, in your script or console, load `{rirods}` and run `use_irods_demo()`:
 

vignettes/local-irods.Rmd

@@ -141,12 +141,11 @@ ils("data")
 ```
 
 Note that the file name need not stay the same in the local and iRODS systems.
-Now, let's say that we have processed our data with some linear regression modelling and plotting.
+Now, let's say that we have processed our data with some linear regression modelling.
 
 ```{r}
 m <- lm(y ~ x, data = fake_data)
 m
-p <- plot(fake_data)
 ```
 
 We could certainly store the output locally, but we could also decide to only store it in iRODS if we save it in RDS format. So let's save it in the "analysis" collection.
@@ -157,7 +156,6 @@ change_state()
 
 ```{r}
 isaveRDS(m, "analysis/linear_model.rds")
-isaveRDS(p, "analysis/plot.rds")
 ils("analysis")
 dir("analysis") # nothing was saved locally
 ```