Include 'job_info_df' as part of the package, and describe its creati…

…on in a conventional way as recommended at https://r-pkgs.org/data.html#sec-data-data-raw

.Rbuildignore

@@ -3,3 +3,4 @@
 ^README\.Rmd$
 ^\.github$
 ^codecov\.yml$
+^data-raw$

DESCRIPTION

@@ -31,3 +31,6 @@ Imports:
     stringr,
     purrr,
     utils
+Depends: 
+    R (>= 2.10)
+LazyData: true

dev/interactive_testing/clean_job_info.R → data-raw/job_info_df.R

@@ -1,29 +1,26 @@
-library(here)
 library(dplyr)
-library(stringr)
-source(here('R', 'job_info.R'))
 
 #   Randomly grab 100 jobs running now on the 'shared' partition
-job_df = job_info(user = NULL) |>
+job_info_df = job_info(user = NULL) |>
     sample_n(size = 100) |>
     arrange(job_id)
 
 #   A vector whose values are anonymous usernames and whose names are the
 #   original usernames
-user_map = paste0('user', 1:length(unique(job_df$user)))
-names(user_map) = unique(job_df$user)
+user_map = paste0('user', 1:length(unique(job_info_df$user)))
+names(user_map) = unique(job_info_df$user)
 
 #   Similarly for job names, though we'll keep the generic name for interactive
 #   jobs ('bash')
-name_map = paste0('my_job_', 1:length(unique(job_df$name)))
-names(name_map) = unique(job_df$name)
+name_map = paste0('my_job_', 1:length(unique(job_info_df$name)))
+names(name_map) = unique(job_info_df$name)
 name_map['bash'] = 'bash'
 
 #   Anonymize username and job name
-job_df = job_df  |>
+job_info_df = job_info_df  |>
     mutate(
         user = user_map[user],
         name = name_map[name]
     )
 
-saveRDS(job_df, here('inst', 'extdata', 'job_info_df.rds'))
+usethis::use_data(job_info_df, overwrite = TRUE)

vignettes/slurmjobs.Rmd

@@ -178,22 +178,19 @@ array_submit("my_array_job.sh", submit = FALSE)
 
 The `job_info()` function provides wrappers around the `squeue` and `sstat` utilities SLURM provides for monitoring specific jobs and how busy partitions are. The general idea is to provide the information output from `squeue` into a `tibble`, while retrieving memory-utilization information that ordinarily must be retrieved manually on a job-by-job basis with `sstat -j [specific job ID]`.
 
-On a SLURM system, you'd run `job_df = job_info(user = NULL, partition = "shared")` here, to get every user's jobs running on the "shared" partition. We'll load an example output directly here.
+On a SLURM system, you'd run `job_info_df = job_info(user = NULL, partition = "shared")` here, to get every user's jobs running on the "shared" partition. We'll load an example output directly here.
 
 ```{r "job_info_quick_look"}
 #   On a real SLURM system
-job_df <- readRDS(
-    system.file("extdata", "job_info_df.rds", package = "slurmjobs")
-)
-print(job_df)
+print(job_info_df)
 ```
 
 The benefit to having this data in R, now, is to be able to trivially ask summarizing questions. First, "how much memory and how many CPUs am I currently using?" Knowing this answer can help ensure fair and civil use of shared computing resources, for example on a computing cluster.
 
 ```{r "job_info_total_resources"}
-job_df |>
+job_info_df |>
     #   Or your username here
-    filter(user == "user17") |>
+    filter(user == "user21") |>
     #   Get the number of CPUs requested and the memory requested in GB
     summarize(
         total_mem_req = sum(requested_mem_gb),