profile_emissions_upstream.Rmd
Source:vignettes/articles/profile_emissions_upstream.Rmd
profile_emissions_upstream.RmdThis workflow runs in chunks of the *companies data and
caches intermediate results. This saves memory, completes faster, and
allows you to resume after interruptions.
Setup
library(dplyr, warn.conflicts = FALSE)
library(readr, warn.conflicts = FALSE)
library(rappdirs)
library(future)
library(fs)
# Masking `tiltIndicatorAfter::profile*()` to use `chunks`
library(tiltWorkflows)
#> Loading required package: tiltIndicatorAfter
#> Loading required package: tiltToyData
#>
#> Attaching package: 'tiltWorkflows'
#> The following objects are masked from 'package:tiltIndicatorAfter':
#>
#> profile_emissions, profile_emissions_upstream, profile_sector,
#> profile_sector_upstreamIf the parameter chunks is NULL (default)
your *companies dataset is automatically chunked to
distribute its companies across available cores. This uses your computer
resources efficiently but may not be enough. Consider adjusting the
chunks parameter manually. Aim to balance memory-usage and
speed. A small number of chunks makes each chunk bigger and
may overwhelm your memory. A large number of chunks may
take longer because of the overhead of caching each chunk.
Parameters
params
#> $chunks
#> [1] ""
#>
#> $order
#> [1] "sample"
#>
#> $cache_dir
#> [1] ""
#>
#> $input
#> [1] "input"
#>
#> $output
#> [1] "output"
#>
#> $europages_companies
#> [1] "europages_companies.csv"
#>
#> $ecoinvent_activities
#> [1] "ecoinvent_activities.csv"
#>
#> $ecoinvent_europages
#> [1] "ecoinvent_europages.csv"
#>
#> $isic
#> [1] "isic.csv"
#>
#> $emissions_profile_any_companies
#> [1] "emissions_profile_any_companies.csv"
#>
#> $emissions_profile_upstream_products
#> [1] "emissions_profile_upstream_products.csv"
#>
#> $ecoinvent_inputs
#> [1] "ecoinvent_inputs.csv"
options(
# Determines the number of chunks
tiltWorkflows.chunks = params$chunks,
# Determines the order in which the chunks run
tiltWorkflows.order = params$order,
# Determines where to store the cache
tiltWorkflows.cache_dir = params$cache_dir,
# Read data quietly
readr.show_col_types = FALSE,
# Make printed output wider
width = 500
)
# Enable computing over multiple workers in parallel
plan(multisession)
# Ensure input/ and output/ directories
if (!dir_exists(params$input)) use_toy_input()
#> Writing input/ with toy datasets.
if (!dir_exists(params$output)) dir_create(params$output)Session information
getwd()
#> [1] "/home/runner/work/tiltWorkflows/tiltWorkflows/vignettes/articles"
availableCores()
#> system
#> 4
dir_tree(params$input)
#> input
#> ├── ecoinvent_activities.csv
#> ├── ecoinvent_europages.csv
#> ├── ecoinvent_inputs.csv
#> ├── emissions_profile_any_companies.csv
#> ├── emissions_profile_products.csv
#> ├── emissions_profile_upstream_products.csv
#> ├── europages_companies.csv
#> ├── isic.csv
#> ├── sector_profile_any_scenarios.csv
#> ├── sector_profile_companies.csv
#> ├── sector_profile_upstream_companies.csv
#> └── sector_profile_upstream_products.csv
dir_tree(params$output)
#> output
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.4.1 (2024-06-14)
#> os Ubuntu 22.04.5 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language en
#> collate C.UTF-8
#> ctype C.UTF-8
#> tz UTC
#> date 2024-10-09
#> pandoc 3.1.11 @ /opt/hostedtoolcache/pandoc/3.1.11/x64/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
#> archive 1.1.9 2024-09-12 [1] RSPM
#> bslib 0.8.0 2024-07-29 [1] RSPM
#> cachem 1.1.0 2024-05-16 [1] RSPM
#> cli 3.6.3 2024-06-21 [1] RSPM
#> codetools 0.2-20 2024-03-31 [3] CRAN (R 4.4.1)
#> crayon 1.5.3 2024-06-20 [1] RSPM
#> dchunkr 0.0.0.9001 2024-10-09 [1] Github (maurolepore/dchunkr@9748350)
#> desc 1.4.3 2023-12-10 [1] RSPM
#> digest 0.6.37 2024-08-19 [1] RSPM
#> dplyr * 1.1.4 2023-11-17 [1] RSPM
#> evaluate 1.0.0 2024-09-17 [1] RSPM
#> fansi 1.0.6 2023-12-08 [1] RSPM
#> fastmap 1.2.0 2024-05-15 [1] RSPM
#> fs * 1.6.4 2024-04-25 [1] RSPM
#> furrr 0.3.1 2022-08-15 [1] RSPM
#> future * 1.34.0 2024-07-29 [1] RSPM
#> generics 0.1.3 2022-07-05 [1] RSPM
#> globals 0.16.3 2024-03-08 [1] RSPM
#> glue 1.8.0 2024-09-30 [1] RSPM
#> hms 1.1.3 2023-03-21 [1] RSPM
#> htmltools 0.5.8.1 2024-04-04 [1] RSPM
#> htmlwidgets 1.6.4 2023-12-06 [1] RSPM
#> httpuv 1.6.15 2024-03-26 [1] RSPM
#> jquerylib 0.1.4 2021-04-26 [1] RSPM
#> jsonlite 1.8.9 2024-09-20 [1] RSPM
#> knitr 1.48 2024-07-07 [1] RSPM
#> later 1.3.2 2023-12-06 [1] RSPM
#> lifecycle 1.0.4 2023-11-07 [1] RSPM
#> listenv 0.9.1 2024-01-29 [1] RSPM
#> magrittr 2.0.3 2022-03-30 [1] RSPM
#> memoise 2.0.1 2021-11-26 [1] RSPM
#> mime 0.12 2021-09-28 [1] RSPM
#> parallelly 1.38.0 2024-07-27 [1] RSPM
#> pillar 1.9.0 2023-03-22 [1] RSPM
#> pkgconfig 2.0.3 2019-09-22 [1] RSPM
#> pkgdown 2.1.1 2024-09-17 [1] RSPM
#> promises 1.3.0 2024-04-05 [1] RSPM
#> purrr 1.0.2 2023-08-10 [1] RSPM
#> R6 2.5.1 2021-08-19 [1] RSPM
#> ragg 1.3.3 2024-09-11 [1] RSPM
#> rappdirs * 0.3.3 2021-01-31 [1] RSPM
#> Rcpp 1.0.13 2024-07-17 [1] RSPM
#> readr * 2.1.5 2024-01-10 [1] RSPM
#> rlang 1.1.4 2024-06-04 [1] RSPM
#> rmarkdown 2.28 2024-08-17 [1] RSPM
#> sass 0.4.9 2024-03-15 [1] RSPM
#> sessioninfo 1.2.2 2021-12-06 [1] RSPM
#> shiny 1.9.1 2024-08-01 [1] RSPM
#> stringi 1.8.4 2024-05-06 [1] RSPM
#> stringr 1.5.1 2023-11-14 [1] RSPM
#> systemfonts 1.1.0 2024-05-15 [1] RSPM
#> textshaping 0.4.0 2024-05-24 [1] RSPM
#> tibble 3.2.1 2023-03-20 [1] RSPM
#> tidyr 1.3.1 2024-01-24 [1] RSPM
#> tidyselect 1.2.1 2024-03-11 [1] RSPM
#> tiltAddCO2 0.0.0.9002 2024-10-09 [1] Github (2DegreesInvesting/tiltAddCO2@dc7eacd)
#> tiltIndicator 0.0.0.9228 2024-10-09 [1] Github (2DegreesInvesting/tiltIndicator@25abe9a)
#> tiltIndicatorAfter * 0.0.0.9060 2024-10-09 [1] Github (2DegreesInvesting/tiltIndicatorAfter@e952dae)
#> tiltPolish 0.0.0.9006 2024-10-09 [1] Github (2degreesinvesting/tiltPolish@d8bdff6)
#> tiltToyData * 0.0.0.9204 2024-10-09 [1] Github (2DegreesInvesting/tiltToyData@3a2417a)
#> tiltTransitionRisk 0.0.0.9003 2024-10-09 [1] Github (2degreesinvesting/tiltTransitionRisk@ee013b8)
#> tiltWorkflows * 0.0.0.9033 2024-10-09 [1] local
#> tzdb 0.4.0 2023-05-12 [1] RSPM
#> utf8 1.2.4 2023-10-22 [1] RSPM
#> vctrs 0.6.5 2023-12-01 [1] RSPM
#> withr 3.0.1 2024-07-31 [1] RSPM
#> xfun 0.48 2024-10-03 [1] RSPM
#> xtable 1.8-4 2019-04-21 [1] RSPM
#> yaml 2.3.10 2024-07-26 [1] RSPM
#>
#> [1] /home/runner/work/_temp/Library
#> [2] /opt/R/4.4.1/lib/R/site-library
#> [3] /opt/R/4.4.1/lib/R/library
#>
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────Data
This example defaults to using toy datasets but you may use the parameters of this file to instead use your own data.
europages_companies <- read_csv(path(params$input, params$europages_companies))
ecoinvent_activities <- read_csv(path(params$input, params$ecoinvent_activities))
ecoinvent_europages <- read_csv(path(params$input, params$ecoinvent_europages))
isic <- read_csv(path(params$input, params$isic))The “upstream” workflows also need this dataset.
Data specific to this indicator.
emissions_profile_any_companies <- read_csv(path(params$input, params$emissions_profile_any_companies))
# FIXME User toy_emissions_profile_upstream_products_ecoinvent()
# See https://github.com/2DegreesInvesting/tiltToyData/pull/12
# https://github.com/2DegreesInvesting/tiltWorkflows/issues/9
emissions_profile_upstream_products <- read_csv(path(params$input, params$emissions_profile_upstream_products))Emissions profile upstream
For this TILT indicator, compute results both at product and company level.
emissions_profile_upstream <- profile_emissions_upstream(
companies = emissions_profile_any_companies,
co2 = emissions_profile_upstream_products,
europages_companies = europages_companies,
ecoinvent_activities = ecoinvent_activities,
ecoinvent_inputs = ecoinvent_inputs,
ecoinvent_europages = ecoinvent_europages,
isic = isic
)
#> Warning: Splitting `companies` into 4 chunks.Results
Overview and save results at each level.
emissions_profile_upstream |>
unnest_product() |>
print() |>
write_csv(path(params$output, "emissions_profile_upstream_at_product_level.csv"))
#> # A tibble: 3,966 × 31
#> companies_id company_name country emission_upstream_pr…¹ benchmark ep_product matched_activity_name matched_reference_pr…² unit multi_match matching_certainty matching_certainty_c…³ input_name input_unit input_tilt_sector input_tilt_subsector input_isic_4digit input_isic_4digit_name company_city postcode address main_activity activity_uuid_produc…⁴ profile_ranking ei_input_geography min_headcount max_headcount ei_geography input_activity_uuid_…⁵ co2e_lower co2e_upper
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 asteria_megalotomusquinquespinosus asteria_megalo… austria high all tent market for shed, lar… shed, large, wood, no… m2 FALSE low low shed, lar… m2 construction construction reside… '4100' Construction of build… wilhelmsburg 3150 flesch… wholesaler 76269c17-78d6-420b-99… 0.938 tilt_land 1 10 tilt_world 44e5e288-4f81-40d0-88… 0.0256 587680710.
#> 2 asteria_megalotomusquinquespinosus asteria_megalo… austria high input_is… tent market for shed, lar… shed, large, wood, no… m2 FALSE low low shed, lar… m2 construction construction reside… '4100' Construction of build… wilhelmsburg 3150 flesch… wholesaler 76269c17-78d6-420b-99… 1 tilt_land 1 10 tilt_world 44e5e288-4f81-40d0-88… -206. 867.
#> 3 asteria_megalotomusquinquespinosus asteria_megalo… austria high input_ti… tent market for shed, lar… shed, large, wood, no… m2 FALSE low low shed, lar… m2 construction construction reside… '4100' Construction of build… wilhelmsburg 3150 flesch… wholesaler 76269c17-78d6-420b-99… 1 tilt_land 1 10 tilt_world 44e5e288-4f81-40d0-88… -268. 412.
#> 4 asteria_megalotomusquinquespinosus asteria_megalo… austria high input_un… tent market for shed, lar… shed, large, wood, no… m2 FALSE low low shed, lar… m2 construction construction reside… '4100' Construction of build… wilhelmsburg 3150 flesch… wholesaler 76269c17-78d6-420b-99… 1 tilt_land 1 10 tilt_world 44e5e288-4f81-40d0-88… -185. 535.
#> 5 asteria_megalotomusquinquespinosus asteria_megalo… austria high input_un… tent market for shed, lar… shed, large, wood, no… m2 FALSE low low shed, lar… m2 construction construction reside… '4100' Construction of build… wilhelmsburg 3150 flesch… wholesaler 76269c17-78d6-420b-99… 1 tilt_land 1 10 tilt_world 44e5e288-4f81-40d0-88… 220. 880.
#> 6 asteria_megalotomusquinquespinosus asteria_megalo… austria high input_un… tent market for shed, lar… shed, large, wood, no… m2 FALSE low low shed, lar… m2 construction construction reside… '4100' Construction of build… wilhelmsburg 3150 flesch… wholesaler 76269c17-78d6-420b-99… 1 tilt_land 1 10 tilt_world 44e5e288-4f81-40d0-88… 106. 410.
#> 7 skarn_gallinule skarn_gallinule austria high all sheds, co… market for shed, lar… shed, large, wood, no… m2 FALSE low low shed, lar… m2 construction construction reside… '4100' Construction of build… wiener neud… 2355 iz nö-… wholesaler 76269c17-78d6-420b-99… 0.938 tilt_land 1 10 tilt_world 44e5e288-4f81-40d0-88… 0.0256 587680710.
#> 8 skarn_gallinule skarn_gallinule austria high input_is… sheds, co… market for shed, lar… shed, large, wood, no… m2 FALSE low low shed, lar… m2 construction construction reside… '4100' Construction of build… wiener neud… 2355 iz nö-… wholesaler 76269c17-78d6-420b-99… 1 tilt_land 1 10 tilt_world 44e5e288-4f81-40d0-88… -206. 867.
#> 9 skarn_gallinule skarn_gallinule austria high input_ti… sheds, co… market for shed, lar… shed, large, wood, no… m2 FALSE low low shed, lar… m2 construction construction reside… '4100' Construction of build… wiener neud… 2355 iz nö-… wholesaler 76269c17-78d6-420b-99… 1 tilt_land 1 10 tilt_world 44e5e288-4f81-40d0-88… -268. 412.
#> 10 skarn_gallinule skarn_gallinule austria high input_un… sheds, co… market for shed, lar… shed, large, wood, no… m2 FALSE low low shed, lar… m2 construction construction reside… '4100' Construction of build… wiener neud… 2355 iz nö-… wholesaler 76269c17-78d6-420b-99… 1 tilt_land 1 10 tilt_world 44e5e288-4f81-40d0-88… -185. 535.
#> # ℹ 3,956 more rows
#> # ℹ abbreviated names: ¹emission_upstream_profile, ²matched_reference_product, ³matching_certainty_company_average, ⁴activity_uuid_product_uuid, ⁵input_activity_uuid_product_uuid
emissions_profile_upstream |>
unnest_company() |>
print() |>
write_csv(path(params$output, "emissions_profile_upstream_at_company_level.csv"))
#> # A tibble: 1,728 × 13
#> companies_id company_name company_city country emission_upstream_profile_share emission_upstream_profile benchmark matching_certainty_company_average postcode address main_activity profile_ranking_avg co2_avg
#> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 asteria_megalotomusquinquespinosus asteria_megalotomusquinquespinosus wilhelmsburg austria 1 high all low 3150 fleschplatz 2, top 5 | 3150 wilhelmsburg wholesaler 0.938 298.
#> 2 asteria_megalotomusquinquespinosus asteria_megalotomusquinquespinosus wilhelmsburg austria 0 medium all low 3150 fleschplatz 2, top 5 | 3150 wilhelmsburg wholesaler 0.938 298.
#> 3 asteria_megalotomusquinquespinosus asteria_megalotomusquinquespinosus wilhelmsburg austria 0 low all low 3150 fleschplatz 2, top 5 | 3150 wilhelmsburg wholesaler 0.938 298.
#> 4 asteria_megalotomusquinquespinosus asteria_megalotomusquinquespinosus wilhelmsburg austria 0 NA all low 3150 fleschplatz 2, top 5 | 3150 wilhelmsburg wholesaler 0.938 298.
#> 5 asteria_megalotomusquinquespinosus asteria_megalotomusquinquespinosus wilhelmsburg austria 1 high input_isic_4digit low 3150 fleschplatz 2, top 5 | 3150 wilhelmsburg wholesaler 1 298.
#> 6 asteria_megalotomusquinquespinosus asteria_megalotomusquinquespinosus wilhelmsburg austria 0 medium input_isic_4digit low 3150 fleschplatz 2, top 5 | 3150 wilhelmsburg wholesaler 1 298.
#> 7 asteria_megalotomusquinquespinosus asteria_megalotomusquinquespinosus wilhelmsburg austria 0 low input_isic_4digit low 3150 fleschplatz 2, top 5 | 3150 wilhelmsburg wholesaler 1 298.
#> 8 asteria_megalotomusquinquespinosus asteria_megalotomusquinquespinosus wilhelmsburg austria 0 NA input_isic_4digit low 3150 fleschplatz 2, top 5 | 3150 wilhelmsburg wholesaler 1 298.
#> 9 asteria_megalotomusquinquespinosus asteria_megalotomusquinquespinosus wilhelmsburg austria 1 high input_tilt_subsector low 3150 fleschplatz 2, top 5 | 3150 wilhelmsburg wholesaler 1 298.
#> 10 asteria_megalotomusquinquespinosus asteria_megalotomusquinquespinosus wilhelmsburg austria 0 medium input_tilt_subsector low 3150 fleschplatz 2, top 5 | 3150 wilhelmsburg wholesaler 1 298.
#> # ℹ 1,718 more rowsThe results at product and company level are now saved in the output/ directory.
# NOTE: If other workflows run before this one, this shows the results of all
params$output |> dir_tree()
#> output
#> ├── emissions_profile_upstream_at_company_level.csv
#> └── emissions_profile_upstream_at_product_level.csvCleanup
Here is the cache that allows you to resume after interruptions.
- The number of files is determined by
params$chunks.
# NOTE: If other workflows run before this one, this shows the cache of all
cache_info()
#> # A tibble: 16 × 18
#> modification_time path type size permissions user group device_id hard_links special_device_id inode block_size blocks flags generation access_time change_time birth_time
#> <dttm> <fs::path> <fct> <fs::bytes> <fs::perms> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dttm> <dttm> <dttm>
#> 1 2024-10-09 11:00:07 /home/runner/.cache/tiltWorkflows/profile_emissions/1.rds file 186.1K rw-r--r-- runner docker 2049 1 0 547925 4096 376 0 0 2024-10-09 11:00:08 2024-10-09 11:00:07 2024-10-09 11:00:07
#> 2 2024-10-09 11:00:07 /home/runner/.cache/tiltWorkflows/profile_emissions/2.rds file 185.5K rw-r--r-- runner docker 2049 1 0 547926 4096 376 0 0 2024-10-09 11:00:08 2024-10-09 11:00:07 2024-10-09 11:00:07
#> 3 2024-10-09 11:00:08 /home/runner/.cache/tiltWorkflows/profile_emissions/3.rds file 190.7K rw-r--r-- runner docker 2049 1 0 547927 4096 384 0 0 2024-10-09 11:00:08 2024-10-09 11:00:08 2024-10-09 11:00:08
#> 4 2024-10-09 11:00:08 /home/runner/.cache/tiltWorkflows/profile_emissions/4.rds file 191K rw-r--r-- runner docker 2049 1 0 547928 4096 384 0 0 2024-10-09 11:00:08 2024-10-09 11:00:08 2024-10-09 11:00:08
#> 5 2024-10-09 11:00:09 /home/runner/.cache/tiltWorkflows/profile_emissions_upstream/1.rds file 911.9K rw-r--r-- runner docker 2049 1 0 547930 4096 1824 0 0 2024-10-09 11:00:10 2024-10-09 11:00:09 2024-10-09 11:00:09
#> 6 2024-10-09 11:00:09 /home/runner/.cache/tiltWorkflows/profile_emissions_upstream/2.rds file 190.9K rw-r--r-- runner docker 2049 1 0 547931 4096 384 0 0 2024-10-09 11:00:10 2024-10-09 11:00:09 2024-10-09 11:00:09
#> 7 2024-10-09 11:00:10 /home/runner/.cache/tiltWorkflows/profile_emissions_upstream/3.rds file 922.8K rw-r--r-- runner docker 2049 1 0 547932 4096 1848 0 0 2024-10-09 11:00:10 2024-10-09 11:00:10 2024-10-09 11:00:10
#> 8 2024-10-09 11:00:10 /home/runner/.cache/tiltWorkflows/profile_emissions_upstream/4.rds file 921.3K rw-r--r-- runner docker 2049 1 0 547933 4096 1848 0 0 2024-10-09 11:00:10 2024-10-09 11:00:10 2024-10-09 11:00:10
#> 9 2024-10-09 11:00:13 /home/runner/.cache/tiltWorkflows/profile_sector/1.rds file 91.6K rw-r--r-- runner docker 2049 1 0 547946 4096 184 0 0 2024-10-09 11:00:14 2024-10-09 11:00:13 2024-10-09 11:00:13
#> 10 2024-10-09 11:00:14 /home/runner/.cache/tiltWorkflows/profile_sector/2.rds file 88.4K rw-r--r-- runner docker 2049 1 0 547947 4096 184 0 0 2024-10-09 11:00:14 2024-10-09 11:00:14 2024-10-09 11:00:14
#> 11 2024-10-09 11:00:14 /home/runner/.cache/tiltWorkflows/profile_sector/3.rds file 89.8K rw-r--r-- runner docker 2049 1 0 547948 4096 184 0 0 2024-10-09 11:00:14 2024-10-09 11:00:14 2024-10-09 11:00:14
#> 12 2024-10-09 11:00:14 /home/runner/.cache/tiltWorkflows/profile_sector/4.rds file 87.9K rw-r--r-- runner docker 2049 1 0 547949 4096 176 0 0 2024-10-09 11:00:14 2024-10-09 11:00:14 2024-10-09 11:00:14
#> 13 2024-10-09 11:00:15 /home/runner/.cache/tiltWorkflows/profile_sector_upstream/1.rds file 100.5K rw-r--r-- runner docker 2049 1 0 547951 4096 208 0 0 2024-10-09 11:00:15 2024-10-09 11:00:15 2024-10-09 11:00:15
#> 14 2024-10-09 11:00:15 /home/runner/.cache/tiltWorkflows/profile_sector_upstream/2.rds file 119.8K rw-r--r-- runner docker 2049 1 0 547952 4096 240 0 0 2024-10-09 11:00:15 2024-10-09 11:00:15 2024-10-09 11:00:15
#> 15 2024-10-09 11:00:15 /home/runner/.cache/tiltWorkflows/profile_sector_upstream/3.rds file 101.2K rw-r--r-- runner docker 2049 1 0 547953 4096 208 0 0 2024-10-09 11:00:15 2024-10-09 11:00:15 2024-10-09 11:00:15
#> 16 2024-10-09 11:00:15 /home/runner/.cache/tiltWorkflows/profile_sector_upstream/4.rds file 135.9K rw-r--r-- runner docker 2049 1 0 547954 4096 272 0 0 2024-10-09 11:00:15 2024-10-09 11:00:15 2024-10-09 11:00:15If you want to recompute some result, you must first delete the relevant cache:
library(fs)
library(rappdirs)
dir_delete(user_cache_dir("tiltWorkflows/PROFILE-DIRECTORY-YOU-WANT-TO-DELETE"))
# DANGER: Or delete the entire default cache directory with
cache_delete()