Import Data

library(tidyverse)
## Warning: package 'ggplot2' was built under R version 4.3.3
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(correlationfunnel)
## Warning: package 'correlationfunnel' was built under R version 4.3.3
## ══ correlationfunnel Tip #3 ════════════════════════════════════════════════════
## Using `binarize()` with data containing many columns or many rows can increase dimensionality substantially.
## Try subsetting your data column-wise or row-wise to avoid creating too many columns.
## You can always make a big problem smaller by sampling. :)
members <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-22/members.csv')
## Rows: 76519 Columns: 21
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): expedition_id, member_id, peak_id, peak_name, season, sex, citizen...
## dbl  (5): year, age, highpoint_metres, death_height_metres, injury_height_me...
## lgl  (6): hired, success, solo, oxygen_used, died, injured
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
members
## # A tibble: 76,519 × 21
##    expedition_id member_id    peak_id peak_name   year season sex     age
##    <chr>         <chr>        <chr>   <chr>      <dbl> <chr>  <chr> <dbl>
##  1 AMAD78301     AMAD78301-01 AMAD    Ama Dablam  1978 Autumn M        40
##  2 AMAD78301     AMAD78301-02 AMAD    Ama Dablam  1978 Autumn M        41
##  3 AMAD78301     AMAD78301-03 AMAD    Ama Dablam  1978 Autumn M        27
##  4 AMAD78301     AMAD78301-04 AMAD    Ama Dablam  1978 Autumn M        40
##  5 AMAD78301     AMAD78301-05 AMAD    Ama Dablam  1978 Autumn M        34
##  6 AMAD78301     AMAD78301-06 AMAD    Ama Dablam  1978 Autumn M        25
##  7 AMAD78301     AMAD78301-07 AMAD    Ama Dablam  1978 Autumn M        41
##  8 AMAD78301     AMAD78301-08 AMAD    Ama Dablam  1978 Autumn M        29
##  9 AMAD79101     AMAD79101-03 AMAD    Ama Dablam  1979 Spring M        35
## 10 AMAD79101     AMAD79101-04 AMAD    Ama Dablam  1979 Spring M        37
## # ℹ 76,509 more rows
## # ℹ 13 more variables: citizenship <chr>, expedition_role <chr>, hired <lgl>,
## #   highpoint_metres <dbl>, success <lgl>, solo <lgl>, oxygen_used <lgl>,
## #   died <lgl>, death_cause <chr>, death_height_metres <dbl>, injured <lgl>,
## #   injury_type <chr>, injury_height_metres <dbl>

Clean Data

skimr::skim(members)
Data summary
Name members
Number of rows 76519
Number of columns 21
_______________________
Column type frequency:
character 10
logical 6
numeric 5
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
expedition_id 0 1.00 9 9 0 10350 0
member_id 0 1.00 12 12 0 76518 0
peak_id 0 1.00 4 4 0 391 0
peak_name 15 1.00 4 25 0 390 0
season 0 1.00 6 7 0 5 0
sex 2 1.00 1 1 0 2 0
citizenship 10 1.00 2 23 0 212 0
expedition_role 21 1.00 4 25 0 524 0
death_cause 75413 0.01 3 27 0 12 0
injury_type 74807 0.02 3 27 0 11 0

Variable type: logical

skim_variable n_missing complete_rate mean count
hired 0 1 0.21 FAL: 60788, TRU: 15731
success 0 1 0.38 FAL: 47320, TRU: 29199
solo 0 1 0.00 FAL: 76398, TRU: 121
oxygen_used 0 1 0.24 FAL: 58286, TRU: 18233
died 0 1 0.01 FAL: 75413, TRU: 1106
injured 0 1 0.02 FAL: 74806, TRU: 1713

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
year 0 1.00 2000.36 14.78 1905 1991 2004 2012 2019 ▁▁▁▃▇
age 3497 0.95 37.33 10.40 7 29 36 44 85 ▁▇▅▁▁
highpoint_metres 21833 0.71 7470.68 1040.06 3800 6700 7400 8400 8850 ▁▁▆▃▇
death_height_metres 75451 0.01 6592.85 1308.19 400 5800 6600 7550 8830 ▁▁▂▇▆
injury_height_metres 75510 0.01 7049.91 1214.24 400 6200 7100 8000 8880 ▁▁▂▇▇

Notes: * drop death_cause, highpoint_metres, death_height_metres, injury_height_metres, injury_type (missing values) * filter missing observations out- age, peak_name, sex, citizenship, expedition_role * target variable- died- only small amount die * member_id is id variable (one repeat- remove one obs) * drop peak_id- redundant * convert character variables to factor

data <- members %>%
    
    # Drop variables with too many missing values
    select(-death_cause, -injury_type, -highpoint_metres, -death_height_metres, -injury_height_metres) %>%
    
    # Drop observations with missing values
    drop_na() %>%
    
    # Remove a redundant variable
    select(-peak_id, -expedition_id) %>%
    
    # Remove one of the two members with the identical id
    distinct(member_id, .keep_all = TRUE) %>%
    
    # Recode died
    mutate(died = case_when(died == "TRUE" ~ "died", died == "FALSE" ~ "no")) %>%
    
    # Convert character variables to factors
    mutate(across(where(is.character), factor)) %>%
    
    # Convert logical variables to factors
    mutate(across(where(is.logical), factor)) %>%
    
    # id should be character
    mutate(member_id = as.character(member_id))

skimr::skim(data)
Data summary
Name data
Number of rows 72984
Number of columns 14
_______________________
Column type frequency:
character 1
factor 11
numeric 2
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
member_id 0 1 12 12 0 72984 0

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
peak_name 0 1 FALSE 390 Eve: 20994, Cho: 8608, Ama: 8235, Man: 4510
season 0 1 FALSE 4 Spr: 36150, Aut: 34186, Win: 2011, Sum: 637
sex 0 1 FALSE 2 M: 66150, F: 6834
citizenship 0 1 FALSE 207 Nep: 14367, USA: 6318, Jap: 6188, UK: 5071
expedition_role 0 1 FALSE 483 Cli: 43315, H-A: 13033, Lea: 9884, Exp: 1411
hired 0 1 FALSE 2 FAL: 59006, TRU: 13978
success 0 1 FALSE 2 FAL: 44913, TRU: 28071
solo 0 1 FALSE 2 FAL: 72868, TRU: 116
oxygen_used 0 1 FALSE 2 FAL: 55215, TRU: 17769
died 0 1 FALSE 2 no: 72055, die: 929
injured 0 1 FALSE 2 FAL: 71333, TRU: 1651

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
year 0 1 2001.00 14.12 1905 1992 2004 2012 2019 ▁▁▁▃▇
age 0 1 37.34 10.39 7 29 36 44 85 ▁▇▅▁▁

Explore Data

Correlation funnel

data_binarized <- data %>%
    select(-member_id) %>%
    binarize()

data_binarized %>% glimpse()
## Rows: 72,984
## Columns: 67
## $ peak_name__Ama_Dablam          <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ peak_name__Annapurna_I         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Annapurna_IV        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Baruntse            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Cho_Oyu             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Dhaulagiri_I        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Everest             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Himlung_Himal       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Kangchenjunga       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Lhotse              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Makalu              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Manaslu             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_name__Pumori              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `peak_name__-OTHER`            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `year__-Inf_1992`              <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ year__1992_2004                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ year__2004_2012                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ year__2012_Inf                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ season__Autumn                 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, …
## $ season__Spring                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, …
## $ season__Winter                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `season__-OTHER`               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ sex__F                         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ sex__M                         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ `age__-Inf_29`                 <dbl> 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, …
## $ age__29_36                     <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ age__36_44                     <dbl> 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, …
## $ age__44_Inf                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Australia         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Austria           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Canada            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__China             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__France            <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, …
## $ citizenship__Germany           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__India             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Italy             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Japan             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Nepal             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Netherlands       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__New_Zealand       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Poland            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Russia            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__S_Korea           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Spain             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__Switzerland       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__UK                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ citizenship__USA               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, …
## $ citizenship__W_Germany         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, …
## $ `citizenship__-OTHER`          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ expedition_role__Climber       <dbl> 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, …
## $ expedition_role__Deputy_Leader <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ expedition_role__Exp_Doctor    <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `expedition_role__H-A_Worker`  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ expedition_role__Leader        <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `expedition_role__-OTHER`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, …
## $ hired__FALSE                   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ hired__TRUE                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ success__FALSE                 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, …
## $ success__TRUE                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, …
## $ solo__FALSE                    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ `solo__-OTHER`                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ oxygen_used__FALSE             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ oxygen_used__TRUE              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ died__died                     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ died__no                       <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ injured__FALSE                 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ injured__TRUE                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
data_correlation <- data_binarized %>%
    correlate(died__died)
## Warning: correlate(): [Data Imbalance Detected] Consider sampling to balance the classes more than 5%
##   Column with imbalance: died__died
data_correlation
## # A tibble: 67 × 3
##    feature   bin          correlation
##    <fct>     <chr>              <dbl>
##  1 died      died              1     
##  2 died      no               -1     
##  3 year      -Inf_1992         0.0519
##  4 peak_name Annapurna_I       0.0336
##  5 success   FALSE             0.0332
##  6 success   TRUE             -0.0332
##  7 peak_name Dhaulagiri_I      0.0290
##  8 peak_name Ama_Dablam       -0.0281
##  9 peak_name Cho_Oyu          -0.0241
## 10 year      2004_2012        -0.0211
## # ℹ 57 more rows
data_correlation %>%
    correlationfunnel::plot_correlation_funnel()
## Warning: ggrepel: 41 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Notes: * strongest correlation appears to be year (1992_2004) * look further into- peak_name, sex, solo

Year - is it safer with modern equipment?

data %>%
    ggplot(aes(died, year)) +
    geom_boxplot()

Notes: fewer average deaths

Peak_name - are some peaks more dangerous?

top10_peaks <- data %>% count(peak_name, sort = TRUE) %>% slice_max(order_by = n, n = 10) %>% pull(peak_name)

data %>%
    filter(peak_name %in% top10_peaks) %>%
    count(died, peak_name) %>%
    pivot_wider(names_from = died, values_from = n, values_fill = 0) %>%
    mutate(percent_died = (died / (no + died)) * 100) %>%
    
    ggplot(aes(percent_died, fct_reorder(peak_name, percent_died))) +
    geom_col() +
    
    labs(y = "Peak Names", x = "Percent Died")

Notes: Annapurna has most deaths, followed by Dhaulgiri

Sex

data %>%
    count(died, sex) %>%
    pivot_wider(names_from = died, values_from = n) %>%
    mutate(percent_died = (died / (no + died)) * 100) %>%
    ggplot(aes(percent_died, sex)) +
    geom_col()

Notes: Men have higher percentage of deaths