Prepare v0.2.0 release. (#38)

* Use native pipe and wallis prefix.

* Remove comments from _targets.R.

* Add a Makefile.

* Add instructions to the README.

* Add a NEWS.md file.

Makefile

@@ -0,0 +1,2 @@
+run:
+	Rscript -e "targets::tar_make()"

NEWS.md

@@ -0,0 +1,9 @@
+# maximal-room-squares (development version)
+
+# maximal-room-squares (v0.2.0)
+
+* greedy1 and greedy2 implementations from Meszka and Rosa (2021)
+* Based on wallis (v0.0.0.9006)
+* renv for dependency installation (renv::restore())
+* targets pipeline to generate all plots (targets::tar_make())
+* Tested on Ubuntu 20.04.6 LTS (Focal Fossa) with R4.4.0, renv 1.0.7 and wallis 0.0.0.9006

R/greedy1.R

@@ -1,13 +1,13 @@
 greedy1 <- function(n) {
 
-  R <- empty_room(n) |>
+  R <- wallis::empty_room(n) |>
     dplyr::mutate(avail = list(0:(n - 1))) |>
     dplyr::mutate(visit = as.numeric(NA))
 
   # pairs to be used in order
-  P <- unused_pairs(R)
+  P <- wallis::unused_pairs(R)
   # empty cells to be visited in order
-  E <- empty_cells(R)
+  E <- wallis::empty_cells(R)
 
   t <- 1
 

R/greedy2.R

@@ -1,13 +1,13 @@
 greedy2 <- function(n) {
 
-  R <- empty_room(n) %>%
-    dplyr::mutate(avail = list(0:(n - 1))) %>%
+  R <- wallis::empty_room(n) |>
+    dplyr::mutate(avail = list(0:(n - 1))) |>
     dplyr::mutate(visit = as.numeric(NA))
 
   # pairs to be used in order
-  P <- unused_pairs(R)
+  P <- wallis::unused_pairs(R)
   # empty cells to be visited in order
-  E <- empty_cells(R)
+  E <- wallis::empty_cells(R)
 
   t <- 1
 
@@ -52,5 +52,5 @@ greedy2 <- function(n) {
     }
 
   }
-  return(R %>% select(-avail))
+  return(R |> select(-avail))
 }

README.md

@@ -2,7 +2,20 @@
 
 # Generating Maximal Partial Room Squares in R
 
-Two different greedy procedures for building maximal partial Room squares in R inspired by Meszka and Rosa (2021).
+Two different greedy procedures for building maximal partial Room squares in R inspired by Meszka and Rosa (2021) using functions from the [wallis](https://mhenderson.r-universe.dev/wallis) R package.
+
+Tested on R4.4.0 with renv 1.0.7, targets 1.7.1 and wallis 0.0.0.9006.
+
+To create the plots below, first install `renv` and install package dependencies:
+```
+install.packages("renv")
+renv::restore()
+```
+
+Now run the `targets` pipeline:
+```
+targets::tar_make()
+```
 
 ## greedy1
 

_targets.R

@@ -1,54 +1,11 @@
-# Created by use_targets().
-# Follow the comments below to fill in this target script.
-# Then follow the manual to check and run the pipeline:
-#   https://books.ropensci.org/targets/walkthrough.html#inspect-the-pipeline
-
-# Load packages required to define the pipeline:
 library(targets)
-# library(tarchetypes) # Load other packages as needed.
 
-# Set target options:
 tar_option_set(
-  packages = c("dplyr", "ggplot2", "glue", "here", "patchwork", "tibble", "wallis") # Packages that your targets need for their tasks.
-  # format = "qs", # Optionally set the default storage format. qs is fast.
-  #
-  # Pipelines that take a long time to run may benefit from
-  # optional distributed computing. To use this capability
-  # in tar_make(), supply a {crew} controller
-  # as discussed at https://books.ropensci.org/targets/crew.html.
-  # Choose a controller that suits your needs. For example, the following
-  # sets a controller that scales up to a maximum of two workers
-  # which run as local R processes. Each worker launches when there is work
-  # to do and exits if 60 seconds pass with no tasks to run.
-  #
-  #   controller = crew::crew_controller_local(workers = 2, seconds_idle = 60)
-  #
-  # Alternatively, if you want workers to run on a high-performance computing
-  # cluster, select a controller from the {crew.cluster} package.
-  # For the cloud, see plugin packages like {crew.aws.batch}.
-  # The following example is a controller for Sun Grid Engine (SGE).
-  # 
-  #   controller = crew.cluster::crew_controller_sge(
-  #     # Number of workers that the pipeline can scale up to:
-  #     workers = 10,
-  #     # It is recommended to set an idle time so workers can shut themselves
-  #     # down if they are not running tasks.
-  #     seconds_idle = 120,
-  #     # Many clusters install R as an environment module, and you can load it
-  #     # with the script_lines argument. To select a specific verison of R,
-  #     # you may need to include a version string, e.g. "module load R/4.3.2".
-  #     # Check with your system administrator if you are unsure.
-  #     script_lines = "module load R"
-  #   )
-  #
-  # Set other options as needed.
+  packages = c("dplyr", "ggplot2", "glue", "here", "patchwork", "tibble", "wallis")
 )
 
-# Run the R scripts in the R/ folder with your custom functions:
 tar_source()
-# tar_source("other_functions.R") # Source other scripts as needed.
 
-# Replace the target list below with your own:
 list(
   tar_target(
     name = R6_g1,

maximal-room-squares.Rproj

@@ -11,3 +11,5 @@ Encoding: UTF-8
 
 RnwWeave: Sweave
 LaTeX: pdfLaTeX
+
+BuildType: Makefile