This 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"
#>
#> $sector_profile_companies
#> [1] "sector_profile_companies.csv"
#>
#> $sector_profile_any_scenarios
#> [1] "sector_profile_any_scenarios.csv"
options(
# Determines the number of chunks
tiltWorkflows.chunks = params$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$input)) use_toy_input()
if (!dir_exists(params$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
#> ├── emissions_profile_at_company_level.csv
#> ├── emissions_profile_at_product_level.csv
#> ├── emissions_profile_upstream_at_company_level.csv
#> ├── emissions_profile_upstream_at_product_level.csv
#> ├── sector_profile_upstream_at_company_level.csv
#> └── sector_profile_upstream_at_product_level.csv
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
#> 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$isic))Data specific to this indicator.
Sector profile
For this TILT indicator, compute results both at product and company level.
sector_profile <- profile_sector(
companies = sector_profile_companies,
scenarios = sector_profile_any_scenarios,
europages_companies = europages_companies,
ecoinvent_activities = ecoinvent_activities,
ecoinvent_europages = ecoinvent_europages,
isic = isic
)
#> Warning: Splitting `companies` into 4 chunks.Results
Overview and save results at each level.
sector_profile |>
unnest_product() |>
print() |>
write_csv(path(params$output, "sector_profile_at_product_level.csv"))
#> # A tibble: 304 × 28
#> companies_id company_name country sector_profile reduction_targets scenario year ep_product matched_activity_name matched_reference_product unit tilt_sector tilt_subsector multi_match matching_certainty matching_certainty_c…¹ company_city postcode address main_activity activity_uuid_produc…² isic_4digit sector_scenario subsector_scenario min_headcount max_headcount ei_geography isic_4digit_name
#> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 antimonarchy_canine NA NA medium 0.18 1.5C RPS 2030 tent market for shed, large, wood, non-insulated, fire-unprotected shed, large, wood, non-insulated, fire-unprotected m2 construction construction residential NA NA NA NA NA NA NA 76269c17-78d6-420b-99… '4100' buildings NA NA NA tilt_world NA
#> 2 antimonarchy_canine NA NA high 0.98 1.5C RPS 2050 tent market for shed, large, wood, non-insulated, fire-unprotected shed, large, wood, non-insulated, fire-unprotected m2 construction construction residential NA NA NA NA NA NA NA 76269c17-78d6-420b-99… '4100' buildings NA NA NA tilt_world NA
#> 3 antimonarchy_canine NA NA high 0.4 NZ 2050 2030 tent market for shed, large, wood, non-insulated, fire-unprotected shed, large, wood, non-insulated, fire-unprotected m2 construction construction residential NA NA NA NA NA NA NA 76269c17-78d6-420b-99… '4100' total residential NA NA tilt_world NA
#> 4 antimonarchy_canine NA NA high 0.97 NZ 2050 2050 tent market for shed, large, wood, non-insulated, fire-unprotected shed, large, wood, non-insulated, fire-unprotected m2 construction construction residential NA NA NA NA NA NA NA 76269c17-78d6-420b-99… '4100' total residential NA NA tilt_world NA
#> 5 celestial_lovebird NA NA medium 0.18 1.5C RPS 2030 table hire for parties market for shed, large, wood, non-insulated, fire-unprotected shed, large, wood, non-insulated, fire-unprotected m2 construction construction residential NA NA NA NA NA NA NA 76269c17-78d6-420b-99… '4100' buildings NA NA NA tilt_world NA
#> 6 celestial_lovebird NA NA high 0.98 1.5C RPS 2050 table hire for parties market for shed, large, wood, non-insulated, fire-unprotected shed, large, wood, non-insulated, fire-unprotected m2 construction construction residential NA NA NA NA NA NA NA 76269c17-78d6-420b-99… '4100' buildings NA NA NA tilt_world NA
#> 7 celestial_lovebird NA NA high 0.4 NZ 2050 2030 table hire for parties market for shed, large, wood, non-insulated, fire-unprotected shed, large, wood, non-insulated, fire-unprotected m2 construction construction residential NA NA NA NA NA NA NA 76269c17-78d6-420b-99… '4100' total residential NA NA tilt_world NA
#> 8 celestial_lovebird NA NA high 0.97 NZ 2050 2050 table hire for parties market for shed, large, wood, non-insulated, fire-unprotected shed, large, wood, non-insulated, fire-unprotected m2 construction construction residential NA NA NA NA NA NA NA 76269c17-78d6-420b-99… '4100' total residential NA NA tilt_world NA
#> 9 nonphilosophical_llama NA NA low 0.09 1.5C RPS 2030 surface engineering market for deep drawing, steel, 10000 kN press, automode deep drawing, steel, 10000 kN press, automode kg metals other metals NA NA NA NA NA NA NA 833caa78-30df-4374-90… '2591' industry other industry NA NA tilt_world NA
#> 10 nonphilosophical_llama NA NA high 0.95 1.5C RPS 2050 surface engineering market for deep drawing, steel, 10000 kN press, automode deep drawing, steel, 10000 kN press, automode kg metals other metals NA NA NA NA NA NA NA 833caa78-30df-4374-90… '2591' industry other industry NA NA tilt_world NA
#> # ℹ 294 more rows
#> # ℹ abbreviated names: ¹matching_certainty_company_average, ²activity_uuid_product_uuid
sector_profile |>
unnest_company() |>
print() |>
write_csv(path(params$output, "sector_profile_at_company_level.csv"))
#> # A tibble: 1,152 × 13
#> companies_id company_name country sector_profile_share sector_profile scenario year matching_certainty_company_average company_city postcode address main_activity reduction_targets_avg
#> <chr> <chr> <chr> <dbl> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 antimonarchy_canine NA NA 0 high 1.5C RPS 2030 NA NA NA NA NA 0.18
#> 2 antimonarchy_canine NA NA 1 medium 1.5C RPS 2030 NA NA NA NA NA 0.18
#> 3 antimonarchy_canine NA NA 0 low 1.5C RPS 2030 NA NA NA NA NA 0.18
#> 4 antimonarchy_canine NA NA 0 NA 1.5C RPS 2030 NA NA NA NA NA 0.18
#> 5 antimonarchy_canine NA NA 1 high 1.5C RPS 2050 NA NA NA NA NA 0.98
#> 6 antimonarchy_canine NA NA 0 medium 1.5C RPS 2050 NA NA NA NA NA 0.98
#> 7 antimonarchy_canine NA NA 0 low 1.5C RPS 2050 NA NA NA NA NA 0.98
#> 8 antimonarchy_canine NA NA 0 NA 1.5C RPS 2050 NA NA NA NA NA 0.98
#> 9 antimonarchy_canine NA NA 1 high NZ 2050 2030 NA NA NA NA NA 0.4
#> 10 antimonarchy_canine NA NA 0 medium NZ 2050 2030 NA NA NA NA NA 0.4
#> # ℹ 1,142 more rowsThe results at product and company level are now saved in the output/ directory.
# NOTE: If other workflows run before this one, this shows the results of all
params$output |> dir_tree()
#> output
#> ├── emissions_profile_at_company_level.csv
#> ├── emissions_profile_at_product_level.csv
#> ├── emissions_profile_upstream_at_company_level.csv
#> ├── emissions_profile_upstream_at_product_level.csv
#> ├── sector_profile_at_company_level.csv
#> ├── sector_profile_at_product_level.csv
#> ├── sector_profile_upstream_at_company_level.csv
#> └── sector_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()