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 4.4.2 (2024-10-31)
#> os Ubuntu 24.04.1 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language en
#> collate C.UTF-8
#> ctype C.UTF-8
#> tz UTC
#> date 2025-02-19
#> pandoc 3.1.11 @ /opt/hostedtoolcache/pandoc/3.1.11/x64/ (via rmarkdown)
#> quarto NA
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
#> archive 1.1.11 2025-02-05 [1] RSPM
#> bslib 0.9.0 2025-01-30 [1] RSPM
#> cachem 1.1.0 2024-05-16 [1] RSPM
#> cli 3.6.4 2025-02-13 [1] RSPM
#> codetools 0.2-20 2024-03-31 [3] CRAN (R 4.4.2)
#> crayon 1.5.3 2024-06-20 [1] RSPM
#> dchunkr 0.0.0.9001 2025-02-19 [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 1.0.3 2025-01-10 [1] RSPM
#> fastmap 1.2.0 2024-05-15 [1] RSPM
#> fs * 1.6.5 2024-10-30 [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 1.49 2024-11-08 [1] RSPM
#> later 1.4.1 2024-11-27 [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 1.42.0 2025-01-30 [1] RSPM
#> pillar 1.10.1 2025-01-07 [1] RSPM
#> pkgconfig 2.0.3 2019-09-22 [1] RSPM
#> pkgdown 2.1.1 2024-09-17 [1] RSPM
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#> purrr 1.0.4 2025-02-05 [1] RSPM
#> R6 2.6.1 2025-02-15 [1] RSPM
#> ragg 1.3.3 2024-09-11 [1] RSPM
#> rappdirs * 0.3.3 2021-01-31 [1] RSPM
#> Rcpp 1.0.14 2025-01-12 [1] RSPM
#> readr * 2.1.5 2024-01-10 [1] RSPM
#> rlang 1.1.5 2025-01-17 [1] RSPM
#> rmarkdown 2.29 2024-11-04 [1] RSPM
#> sass 0.4.9 2024-03-15 [1] RSPM
#> sessioninfo 1.2.3 2025-02-05 [1] RSPM
#> shiny 1.10.0 2024-12-14 [1] RSPM
#> stringi 1.8.4 2024-05-06 [1] RSPM
#> stringr 1.5.1 2023-11-14 [1] RSPM
#> systemfonts 1.2.1 2025-01-20 [1] RSPM
#> textshaping 1.0.0 2025-01-20 [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 2025-02-19 [1] Github (2DegreesInvesting/tiltAddCO2@ff86d35)
#> tiltIndicator 0.0.0.9230 2025-02-19 [1] Github (2DegreesInvesting/tiltIndicator@bff1cd9)
#> tiltIndicatorAfter * 0.0.0.9062 2025-02-19 [1] Github (2DegreesInvesting/tiltIndicatorAfter@158246b)
#> tiltToyData * 0.0.0.9204 2025-02-19 [1] Github (2DegreesInvesting/tiltToyData@3a2417a)
#> tiltWorkflows * 0.0.0.9033 2025-02-19 [1] local
#> tzdb 0.4.0 2023-05-12 [1] RSPM
#> vctrs 0.6.5 2023-12-01 [1] RSPM
#> withr 3.0.2 2024-10-28 [1] RSPM
#> xfun 0.50 2025-01-07 [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/4.4.2/lib/R/site-library
#> [3] /opt/R/4.4.2/lib/R/library
#> * ── Packages attached to the search path.
#>
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────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: 3,431 × 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… -165. 379.
#> 6 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.
#> 7 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.
#> 8 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.
#> 9 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.
#> 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… -165. 379.
#> # ℹ 3,421 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 2025-02-19 22:41:13 /home/runner/.cache/tiltWorkflows/profile_emissions/1.rds file 196.9K rw-r--r-- runner docker 2065 1 0 1660577 4096 400 0 0 2025-02-19 22:41:14 2025-02-19 22:41:13 2025-02-19 22:41:13
#> 2 2025-02-19 22:41:13 /home/runner/.cache/tiltWorkflows/profile_emissions/2.rds file 195.8K rw-r--r-- runner docker 2065 1 0 1660578 4096 392 0 0 2025-02-19 22:41:14 2025-02-19 22:41:13 2025-02-19 22:41:13
#> 3 2025-02-19 22:41:14 /home/runner/.cache/tiltWorkflows/profile_emissions/3.rds file 202K rw-r--r-- runner docker 2065 1 0 1660579 4096 408 0 0 2025-02-19 22:41:14 2025-02-19 22:41:14 2025-02-19 22:41:14
#> 4 2025-02-19 22:41:14 /home/runner/.cache/tiltWorkflows/profile_emissions/4.rds file 201.9K rw-r--r-- runner docker 2065 1 0 1660580 4096 408 0 0 2025-02-19 22:41:14 2025-02-19 22:41:14 2025-02-19 22:41:14
#> 5 2025-02-19 22:41:15 /home/runner/.cache/tiltWorkflows/profile_emissions_upstream/1.rds file 803.6K rw-r--r-- runner docker 2065 1 0 1660582 4096 1608 0 0 2025-02-19 22:41:16 2025-02-19 22:41:15 2025-02-19 22:41:15
#> 6 2025-02-19 22:41:15 /home/runner/.cache/tiltWorkflows/profile_emissions_upstream/2.rds file 178.6K rw-r--r-- runner docker 2065 1 0 1660583 4096 360 0 0 2025-02-19 22:41:16 2025-02-19 22:41:15 2025-02-19 22:41:15
#> 7 2025-02-19 22:41:16 /home/runner/.cache/tiltWorkflows/profile_emissions_upstream/3.rds file 813.2K rw-r--r-- runner docker 2065 1 0 1660584 4096 1632 0 0 2025-02-19 22:41:16 2025-02-19 22:41:16 2025-02-19 22:41:16
#> 8 2025-02-19 22:41:16 /home/runner/.cache/tiltWorkflows/profile_emissions_upstream/4.rds file 812.4K rw-r--r-- runner docker 2065 1 0 1660585 4096 1632 0 0 2025-02-19 22:41:16 2025-02-19 22:41:16 2025-02-19 22:41:16
#> 9 2025-02-19 22:41:19 /home/runner/.cache/tiltWorkflows/profile_sector/1.rds file 97.8K rw-r--r-- runner docker 2065 1 0 1660596 4096 200 0 0 2025-02-19 22:41:20 2025-02-19 22:41:19 2025-02-19 22:41:19
#> 10 2025-02-19 22:41:19 /home/runner/.cache/tiltWorkflows/profile_sector/2.rds file 94.5K rw-r--r-- runner docker 2065 1 0 1660597 4096 192 0 0 2025-02-19 22:41:20 2025-02-19 22:41:19 2025-02-19 22:41:19
#> 11 2025-02-19 22:41:20 /home/runner/.cache/tiltWorkflows/profile_sector/3.rds file 96.2K rw-r--r-- runner docker 2065 1 0 1660598 4096 200 0 0 2025-02-19 22:41:20 2025-02-19 22:41:20 2025-02-19 22:41:20
#> 12 2025-02-19 22:41:20 /home/runner/.cache/tiltWorkflows/profile_sector/4.rds file 94.1K rw-r--r-- runner docker 2065 1 0 1660599 4096 192 0 0 2025-02-19 22:41:20 2025-02-19 22:41:20 2025-02-19 22:41:20
#> 13 2025-02-19 22:41:21 /home/runner/.cache/tiltWorkflows/profile_sector_upstream/1.rds file 100.5K rw-r--r-- runner docker 2065 1 0 1660601 4096 208 0 0 2025-02-19 22:41:21 2025-02-19 22:41:21 2025-02-19 22:41:21
#> 14 2025-02-19 22:41:21 /home/runner/.cache/tiltWorkflows/profile_sector_upstream/2.rds file 119.8K rw-r--r-- runner docker 2065 1 0 1660602 4096 240 0 0 2025-02-19 22:41:21 2025-02-19 22:41:21 2025-02-19 22:41:21
#> 15 2025-02-19 22:41:21 /home/runner/.cache/tiltWorkflows/profile_sector_upstream/3.rds file 101.2K rw-r--r-- runner docker 2065 1 0 1660603 4096 208 0 0 2025-02-19 22:41:21 2025-02-19 22:41:21 2025-02-19 22:41:21
#> 16 2025-02-19 22:41:21 /home/runner/.cache/tiltWorkflows/profile_sector_upstream/4.rds file 135.9K rw-r--r-- runner docker 2065 1 0 1660604 4096 272 0 0 2025-02-19 22:41:21 2025-02-19 22:41:21 2025-02-19 22:41:21If 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()