drake is all lowercase

NEWS.md

@@ -272,8 +272,8 @@ across R sessions.
 - Add `"future_lapply"` parallelism: parallel backends supported by the [future](https://github.com/HenrikBengtsson/future) and [future.batchtools](https://github.com/HenrikBengtsson/future.batchtools) packages. See `?backend` for examples and the parallelism vignette for an introductory tutorial. More advanced instruction can be found in the `future` and `future.batchtools` packages themselves.
 - Cache diagnostic information of targets that fail and retrieve diagnostic info with `diagnose()`.
 - Add an optional `hook` argument to `make()` to wrap around `build()`. That way, users can more easily control the side effects of distributed jobs. For example, to redirect error messages to a file in `make(..., parallelism = "Makefile", jobs = 2, hook = my_hook)`, `my_hook` should be something like `function(code){withr::with_message_sink("messages.txt", code)}`.
-- Remove console logging for "parLapply" parallelism. `Drake` was previously using the `outfile` argument for PSOCK clusters to generate output that could not be caught by `capture.output()`. It was a hack that should have been removed before.
-- Remove console logging for "parLapply" parallelism. `Drake` was previously using the `outfile` argument for PSOCK clusters to generate output that could not be caught by `capture.output()`. It was a hack that should have been removed before.
+- Remove console logging for "parLapply" parallelism. `drake` was previously using the `outfile` argument for PSOCK clusters to generate output that could not be caught by `capture.output()`. It was a hack that should have been removed before.
+- Remove console logging for "parLapply" parallelism. `drake` was previously using the `outfile` argument for PSOCK clusters to generate output that could not be caught by `capture.output()`. It was a hack that should have been removed before.
 - If 'verbose' is 'TRUE' and all targets are already up to date (nothing to build), then `make()` and `outdated()` print "All targets are already up to date" to the console.
 - Add new examples in 'inst/examples', most of them demonstrating how to use the `"future_lapply"` backends.
 - New support for timeouts and retries when it comes to building targets.

R/dependencies.R

@@ -24,7 +24,7 @@
 #'   enclosing them in literal double quotes.
 #'   (For example, `file_store("report.Rmd")` is just `"\"report.Rmd\""`.)
 #'
-#'   `Drake` takes special precautions so that a target/import
+#'   `drake` takes special precautions so that a target/import
 #'   does not depend on itself. For example, `deps_code(f)`` might return
 #'   `"f"` if `f()` is a recursive function, but [make()] just ignores
 #'   this conflict and runs as expected. In other words, [make()]

R/deprecate.R

@@ -726,7 +726,7 @@ load_basic_example <- function(
 }
 
 #' @title Deprecated function
-#' @description Do not use this function. `Drake`'s parallel algorithm
+#' @description Do not use this function. `drake`'s parallel algorithm
 #'   has changed since version 5.1.2, so `max_useful_jobs()`
 #'   will give you the wrong idea of how many jobs to use. Instead,
 #'   use the [predict_runtime()] function with a sensible value
@@ -759,7 +759,7 @@ max_useful_jobs <- function(
     package = "drake",
     msg = c(
       "Do not use max_useful_jobs(). ",
-      "Drake's parallel scheduling algorithm has changed, ",
+      "drake's parallel scheduling algorithm has changed, ",
       "so max_useful_jobs() will give you the wrong idea about ",
       "how many jobs to assign to `make()`. For a better estimate, ",
       "play around with predict_runtime() with sensible values, ",
@@ -1381,7 +1381,7 @@ session <- function(
 #' @title Defunct function
 #' @description Staged parallelism is removed from drake,
 #' so this function is moot.
-#' Drake uses a much better parallel algorithm now.
+#' drake uses a much better parallel algorithm now.
 #' @details Made defunct on May 4, 2018.
 #' @export
 #' @keywords internal
@@ -1404,7 +1404,7 @@ parallel_stages <- function(
     msg = c(
       "Staged parallelism is removed from drake, ",
       "so the parallel_stages() function is moot. ",
-      "Drake uses a much better parallel algorithm now."
+      "drake uses a much better parallel algorithm now."
     )
   )
 }

R/diagnose.R

@@ -44,7 +44,7 @@
 #' failed() # List the failed targets from the last make() (my_target).
 #' # List targets that failed at one point or another
 #' # over the course of the project (my_target).
-#' # Drake keeps all the error logs.
+#' # drake keeps all the error logs.
 #' diagnose()
 #' # Get the error log, an object of class "error".
 #' error <- diagnose(my_target)$error # See also warnings and messages.

R/drake_plan.R

@@ -322,7 +322,7 @@ drake_plan_override <- function(target, field, config){
 #'   )
 #' )
 #' plan
-#' # Drake knows "\"mtcars.csv\"" is the first target
+#' # drake knows "\"mtcars.csv\"" is the first target
 #' # and a dependency of `contents`. See for yourself:
 #' make(plan)
 #' file.exists("mtcars.csv")
@@ -362,7 +362,7 @@ file_in <- function(...){
 #'   )
 #' )
 #' plan
-#' # Drake knows "\"mtcars.csv\"" is the first target
+#' # drake knows "\"mtcars.csv\"" is the first target
 #' # and a dependency of `contents`. See for yourself:
 #' make(plan)
 #' file.exists("mtcars.csv")

R/hash.R

@@ -95,7 +95,7 @@ short_hash <- function(
 #' the hash algorithm already chosen for the project
 #' in a previous `make()`. \cr \cr
 #'
-#' Drake uses both a short hash algorithm
+#' drake uses both a short hash algorithm
 #' and a long hash algorithm. The shorter hash has fewer characters,
 #' and it is used to generate the names of internal cache files
 #' and auxiliary files. The decision for short names is important
@@ -163,7 +163,7 @@ default_short_hash_algo <- function(cache = NULL) {
 #' the hash algorithm already chosen for the project
 #' in a previous `make()`. \cr \cr
 #'
-#' Drake uses both a short hash algorithm
+#' drake uses both a short hash algorithm
 #' and a long hash algorithm. The shorter hash has fewer characters,
 #' and it is used to generate the names of internal cache files
 #' and auxiliary files. The decision for short names is important

R/knitr.R

@@ -10,7 +10,7 @@
 #' `knit("your_report.Rmd")` or
 #' `knit("your_report.Rmd", quiet = TRUE)`.
 #' @return A character vector of the names of dependencies.
-#' @details Drake looks for dependencies in the document by
+#' @details drake looks for dependencies in the document by
 #' analyzing evaluated code chunks for other targets/imports
 #' mentioned in [loadd()] and [readd()].
 #' @param target file path to the file or name of the file target,
@@ -71,7 +71,7 @@ safe_get_tangled_frags <- function(target){
   error = function(e){
     warning(
       "Could not parse file '", file,
-      "'. Drake dependencies could not be extracted from code chunks: ",
+      "'. drake dependencies could not be extracted from code chunks: ",
       conditionMessage(e)
     )
     character(0)

R/package.R

@@ -1,4 +1,4 @@
-#' Drake is a pipeline toolkit
+#' drake is a pipeline toolkit
 #' (<https://github.com/pditommaso/awesome-pipeline>)
 #' and a scalable, R-focused solution for reproducibility
 #' and high-performance computing.

R/read.R

@@ -542,7 +542,7 @@ read_drake_plan <- function(
 #' digest::digest(.Random.seed) # Fingerprint of the current R session's seed.
 #' make(my_plan, cache = cache) # Run the project, build the targets.
 #' digest::digest(.Random.seed) # Your session's seed did not change.
-#' # Drake uses a hard-coded seed if you do not supply one.
+#' # drake uses a hard-coded seed if you do not supply one.
 #' read_drake_seed(cache = cache)
 #' readd(target2, cache = cache) # Randomly-generated target data.
 #' clean(target2, cache = cache) # Oops, I removed the data!

R/tips.R

@@ -19,7 +19,7 @@ drake_tip <- function() {
     "Check out the reference website (https://ropensci.github.io/drake/)
      and user manual (https://ropenscilabs.github.io/drake-manual/).",
 
-    "Drake quickstart:
+    "drake quickstart:
      load_mtcars_example();
      make(my_plan);
      readd(small)"

inst/hpc_template_files/sge_batchtools.tmpl

@@ -4,7 +4,7 @@
 # for the Sun/Univa Grid Engine.
 # It resembles https://github.com/mllg/batchtools/blob/master/inst/templates/sge-simple.tmpl,
 # but it is specifically tailored for drake.
-# Drake has some special requirements.
+# drake has some special requirements.
 
 # Each line beginning with `#$` contains flags to qsub.
 # In this case, we are going to write the equivalent of

paper.md

@@ -23,7 +23,7 @@ bibliography: paper.bib
 
 The [drake](https://github.com/ropensci/drake) R package [@drake] is a workflow manager and computational engine for data science projects. Its primary objective is to keep results up to date with the underlying code and data. When it runs a project, [drake](https://github.com/ropensci/drake) detects any pre-existing output and refreshes the pieces that are outdated or missing. Not every runthrough starts from scratch, and the final answers are reproducible. With a user-friendly R-focused interface, [comprehensive documentation](https://ropensci.github.io/drake), and extensive implicit parallel computing support, [drake](https://github.com/ropensci/drake) surpasses the analogous functionality in similar tools such as [Make](www.gnu.org/software/make/) [@Make], [remake](https://github.com/richfitz/remake) [@remake], [memoise](https://github.com/r-lib/memoise) [@memoise], and [knitr](https://github.com/yihui/knitr) [@knitr].
 
-In reproducible research, [drake](https://github.com/ropensci/drake)'s role is to provide tangible evidence that a project's results are re-creatable. [Drake](https://github.com/ropensci/drake) quickly detects when the code, data, and output are synchronized. In other words, [drake](https://github.com/ropensci/drake) helps determine if the starting materials would produce the expected output if the project were to start over and run from scratch. This approach decreases the time and effort it takes to evaluate research projects for reproducibility.
+In reproducible research, [drake](https://github.com/ropensci/drake)'s role is to provide tangible evidence that a project's results are re-creatable. [drake](https://github.com/ropensci/drake) quickly detects when the code, data, and output are synchronized. In other words, [drake](https://github.com/ropensci/drake) helps determine if the starting materials would produce the expected output if the project were to start over and run from scratch. This approach decreases the time and effort it takes to evaluate research projects for reproducibility.
 
 Regarding high-performance computing, [drake](https://github.com/ropensci/drake) interfaces with a variety of technologies and scheduling algorithms to deploy the steps of a data analysis project. Here, the parallel computing is implicit. In other words, [drake](https://github.com/ropensci/drake) constructs the directed acyclic network of the workflow and determines which steps can run simultaneously and which need to wait for dependencies. This automation eases the cognitive and computational burdens on the user, enhancing the readability of code and thus reproducibility.