Returns a tibble where each row represents ONE risk component of ONE
activity. Different risk types (physical, medical, radiation) are never
mixed in the same row. This is the foundational dataset from which
common_risks() aggregates composite values.
Value
A tibble with columns:
- component_id
Unique identifier:
{activity_id}_{component}_{condition}- activity_id
Groups components into one activity
- activity
Human-readable activity name with duration
- component
Risk component:
"all_causes","crash","dvt","radiation", etc.- risk_category
"physical","medical","radiation","environmental","mixed"- component_label
Human-readable label for this component
- micromorts
Risk for this component at this duration for this condition
- duration_hours
Activity duration this row applies to (
NAfor non-duration-dependent)- category
Activity category:
"Travel","Medical","Daily Life", etc.- period
Human-readable period:
"per day","per event", etc.- period_type
"event","day","hour","year","month","period"- hedgeable
Can this component be mitigated?
- hedge_description
How to mitigate (if hedgeable)
- hedge_reduction_pct
Estimated percent reduction from hedging
- condition_variable
What this risk depends on:
"health_profile","geography","country", orNA- condition_value
Condition value:
"healthy","dvt_risk_factors","high_income","low_income","allergic", ISO-2 country codes (e.g."US","UK"), orNA- confidence
Data confidence:
"high","medium","low","estimated"- source_url
Citation URL
- notes
Scaling behavior, caveats
- validation_status
"single_source","corroborated", or"cross_validated"- source_count
Integer count of independent sources checked
- estimate_range
Character range (e.g.
"0.05-0.15") orNAfor point estimates
Details
Activities that have not yet been decomposed use component = "all_causes"
and risk_category = "mixed" as honest placeholders.
See also
common_risks() for the aggregated view.
Examples
atomic_risks()
#> # A tibble: 131 × 22
#> component_id activity_id activity component risk_category component_label
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 mt_everest_asce… mt_everest… Mt. Eve… all_caus… mixed Mt. Everest as…
#> 2 himalayan_mount… himalayan_… Himalay… all_caus… mixed Himalayan moun…
#> 3 covid_19_infect… covid_19_i… COVID-1… all_caus… mixed COVID-19 infec…
#> 4 spanish_flu_inf… spanish_fl… Spanish… all_caus… mixed Spanish flu in…
#> 5 matterhorn_asce… matterhorn… Matterh… all_caus… mixed Matterhorn asc…
#> 6 living_in_us_du… living_in_… Living … all_caus… mixed Living in US d…
#> 7 living_one_day_… living_one… Living … all_caus… mixed Living (one da…
#> 8 base_jumping_pe… base_jumpi… Base ju… all_caus… mixed Base jumping (…
#> 9 first_day_of_li… first_day_… First d… all_caus… mixed First day of l…
#> 10 covid_19_unvacc… covid_19_u… COVID-1… all_caus… mixed COVID-19 unvac…
#> # ℹ 121 more rows
#> # ℹ 16 more variables: micromorts <dbl>, duration_hours <dbl>, category <chr>,
#> # period <chr>, period_type <chr>, hedgeable <lgl>, hedge_description <chr>,
#> # hedge_reduction_pct <dbl>, condition_variable <chr>, condition_value <chr>,
#> # confidence <chr>, source_url <chr>, notes <chr>, validation_status <chr>,
#> # source_count <int>, estimate_range <chr>
atomic_risks() |> dplyr::filter(component != "all_causes")
#> # A tibble: 38 × 22
#> component_id activity_id activity component risk_category component_label
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 flying_2h_2h_cr… flying_2h Flying … crash physical Aircraft crash
#> 2 flying_2h_2h_dv… flying_2h Flying … dvt medical Deep vein thro…
#> 3 flying_2h_2h_ra… flying_2h Flying … radiation radiation Cosmic radiati…
#> 4 flying_2h_2h_dv… flying_2h Flying … dvt medical Deep vein thro…
#> 5 flying_5h_5h_cr… flying_5h Flying … crash physical Aircraft crash
#> 6 flying_5h_5h_dv… flying_5h Flying … dvt medical Deep vein thro…
#> 7 flying_5h_5h_dv… flying_5h Flying … dvt medical Deep vein thro…
#> 8 flying_5h_5h_ra… flying_5h Flying … radiation radiation Cosmic radiati…
#> 9 flying_8h_8h_cr… flying_8h Flying … crash physical Aircraft crash
#> 10 flying_8h_8h_dv… flying_8h Flying … dvt medical Deep vein thro…
#> # ℹ 28 more rows
#> # ℹ 16 more variables: micromorts <dbl>, duration_hours <dbl>, category <chr>,
#> # period <chr>, period_type <chr>, hedgeable <lgl>, hedge_description <chr>,
#> # hedge_reduction_pct <dbl>, condition_variable <chr>, condition_value <chr>,
#> # confidence <chr>, source_url <chr>, notes <chr>, validation_status <chr>,
#> # source_count <int>, estimate_range <chr>
atomic_risks() |> dplyr::filter(hedgeable)
#> # A tibble: 21 × 22
#> component_id activity_id activity component risk_category component_label
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 flying_2h_2h_dv… flying_2h Flying … dvt medical Deep vein thro…
#> 2 flying_2h_2h_dv… flying_2h Flying … dvt medical Deep vein thro…
#> 3 flying_5h_5h_dv… flying_5h Flying … dvt medical Deep vein thro…
#> 4 flying_5h_5h_dv… flying_5h Flying … dvt medical Deep vein thro…
#> 5 flying_8h_8h_dv… flying_8h Flying … dvt medical Deep vein thro…
#> 6 flying_8h_8h_dv… flying_8h Flying … dvt medical Deep vein thro…
#> 7 flying_12h_12h_… flying_12h Flying … dvt medical Deep vein thro…
#> 8 flying_12h_12h_… flying_12h Flying … dvt medical Deep vein thro…
#> 9 airline_pilot_a… airline_pi… Airline… radiation radiation Ionizing radia…
#> 10 xray_tech_annua… xray_tech_… X-ray t… radiation radiation Ionizing radia…
#> # ℹ 11 more rows
#> # ℹ 16 more variables: micromorts <dbl>, duration_hours <dbl>, category <chr>,
#> # period <chr>, period_type <chr>, hedgeable <lgl>, hedge_description <chr>,
#> # hedge_reduction_pct <dbl>, condition_variable <chr>, condition_value <chr>,
#> # confidence <chr>, source_url <chr>, notes <chr>, validation_status <chr>,
#> # source_count <int>, estimate_range <chr>