Goal: Build a classification model to predict whether the person died in an expedition

Import Data

library(tidyverse)
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## ✔ 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     
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library(correlationfunnel)
## Warning: package 'correlationfunnel' was built under R version 4.4.1
## ══ Using correlationfunnel? ════════════════════════════════════════════════════
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library(tidytext)
## Warning: package 'tidytext' was built under R version 4.4.1
library(xgboost)
## Warning: package 'xgboost' was built under R version 4.4.1
## 
## Attaching package: 'xgboost'
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##     slice
library(textrecipes)
## Warning: package 'textrecipes' was built under R version 4.4.1
## Loading required package: recipes
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## Attaching package: 'recipes'
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##     step
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.

Explore 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 ▁▁▂▇▇ 
str(members)
## spc_tbl_ [76,519 × 21] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ expedition_id       : chr [1:76519] "AMAD78301" "AMAD78301" "AMAD78301" "AMAD78301" ...
##  $ member_id           : chr [1:76519] "AMAD78301-01" "AMAD78301-02" "AMAD78301-03" "AMAD78301-04" ...
##  $ peak_id             : chr [1:76519] "AMAD" "AMAD" "AMAD" "AMAD" ...
##  $ peak_name           : chr [1:76519] "Ama Dablam" "Ama Dablam" "Ama Dablam" "Ama Dablam" ...
##  $ year                : num [1:76519] 1978 1978 1978 1978 1978 ...
##  $ season              : chr [1:76519] "Autumn" "Autumn" "Autumn" "Autumn" ...
##  $ sex                 : chr [1:76519] "M" "M" "M" "M" ...
##  $ age                 : num [1:76519] 40 41 27 40 34 25 41 29 35 37 ...
##  $ citizenship         : chr [1:76519] "France" "France" "France" "France" ...
##  $ expedition_role     : chr [1:76519] "Leader" "Deputy Leader" "Climber" "Exp Doctor" ...
##  $ hired               : logi [1:76519] FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ highpoint_metres    : num [1:76519] NA 6000 NA 6000 NA ...
##  $ success             : logi [1:76519] FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ solo                : logi [1:76519] FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ oxygen_used         : logi [1:76519] FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ died                : logi [1:76519] FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ death_cause         : chr [1:76519] NA NA NA NA ...
##  $ death_height_metres : num [1:76519] NA NA NA NA NA NA NA NA NA NA ...
##  $ injured             : logi [1:76519] FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ injury_type         : chr [1:76519] NA NA NA NA ...
##  $ injury_height_metres: num [1:76519] NA NA NA NA NA NA NA NA NA NA ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   expedition_id = col_character(),
##   ..   member_id = col_character(),
##   ..   peak_id = col_character(),
##   ..   peak_name = col_character(),
##   ..   year = col_double(),
##   ..   season = col_character(),
##   ..   sex = col_character(),
##   ..   age = col_double(),
##   ..   citizenship = col_character(),
##   ..   expedition_role = col_character(),
##   ..   hired = col_logical(),
##   ..   highpoint_metres = col_double(),
##   ..   success = col_logical(),
##   ..   solo = col_logical(),
##   ..   oxygen_used = col_logical(),
##   ..   died = col_logical(),
##   ..   death_cause = col_character(),
##   ..   death_height_metres = col_double(),
##   ..   injured = col_logical(),
##   ..   injury_type = col_character(),
##   ..   injury_height_metres = col_double()
##   .. )
##  - attr(*, "problems")=<externalptr>
variances <- apply(members, 2, var, na.rm = TRUE)
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
## Warning in FUN(newX[, i], ...): NAs introduced by coercion
zero_variance_columns <- names(variances[variances == 0])

print(zero_variance_columns)
##  [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Clean Data

factors_vec <- c("death_cause", "injury_type", "highpoint_metres", "death_height_metres")

members_clean <- members %>% 
  select(-expedition_id, -member_id, -peak_id, -peak_name, -year, -season, -sex, -citizenship, -expedition_role, -hired, -success, -solo, -oxygen_used, -injured)
# Adressing Missing Values
members_clean <- members_clean %>%
  # Impute missing numeric columns with the median
  mutate(across(where(is.numeric), ~ ifelse(is.na(.), median(., na.rm = TRUE), .))) %>%
  # Impute missing categorical columns with "Unknown"
  mutate(across(where(is.character), ~ ifelse(is.na(.), "Unknown", .)))

skimr::skim(members_clean)
Data summary
Name members_clean
Number of rows 76519
Number of columns 7
_______________________
Column type frequency:
character 2
logical 1
numeric 4
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
death_cause 0 1 3 27 0 12 0
injury_type 0 1 3 27 0 12 0

Variable type: logical

skim_variable n_missing complete_rate mean count
died 0 1 0.01 FAL: 75413, TRU: 1106

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
age 0 1 37.27 10.16 7 30 36 43 85 ▁▇▅▁▁
highpoint_metres 0 1 7450.51 879.83 3800 6814 7400 8167 8850 ▁▁▃▇▆
death_height_metres 0 1 6599.90 154.48 400 6600 6600 6600 8830 ▁▁▁▇▁
injury_height_metres 0 1 7099.34 139.48 400 7100 7100 7100 8880 ▁▁▁▇▁

Continue Exploring Data

members_clean %>%
  ggplot(aes(x = age, fill = as.factor(died))) +
  geom_histogram(binwidth = 5, alpha = 0.7, position = "dodge") +
  facet_wrap(~ as.factor(died), scales = "free_y") +
  labs(
    title = "Age Distribution by Survival Status",
    x = "Age",
    y = "count",
    fill = "Died"
  ) +
  theme_minimal()

Prepare DAta

data_binarized_tbl <- members_clean %>% 
  binarize()

data_binarized_tbl %>% glimpse()
## Rows: 76,519
## Columns: 18
## $ `age__-Inf_30`                    <dbl> 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, …
## $ age__30_36                        <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, …
## $ age__36_43                        <dbl> 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, …
## $ age__43_Inf                       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ `highpoint_metres__-Inf_6814`     <dbl> 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, …
## $ highpoint_metres__6814_7400       <dbl> 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, …
## $ highpoint_metres__7400_8167       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ highpoint_metres__8167_Inf        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ died__0                           <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ died__1                           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ death_cause__Unknown              <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ `death_cause__-OTHER`             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `death_height_metres__-Inf_6600`  <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ death_height_metres__6600_Inf     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ injury_type__Unknown              <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ `injury_type__-OTHER`             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `injury_height_metres__-Inf_7100` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ injury_height_metres__7100_Inf    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …

Correlate

data_corr_tbl <- data_binarized_tbl %>% 
  correlate( died__0 )
data_corr_tbl
## # A tibble: 18 × 3
##    feature              bin       correlation
##    <fct>                <chr>           <dbl>
##  1 died                 0            1       
##  2 died                 1           -1       
##  3 death_cause          Unknown      0.995   
##  4 death_cause          -OTHER      -0.995   
##  5 death_height_metres  -Inf_6600    0.686   
##  6 death_height_metres  6600_Inf    -0.686   
##  7 age                  30_36       -0.0328  
##  8 age                  43_Inf       0.0181  
##  9 age                  36_43        0.0175  
## 10 injury_type          -OTHER       0.0161  
## 11 injury_type          Unknown     -0.0161  
## 12 highpoint_metres     7400_8167   -0.0144  
## 13 highpoint_metres     8167_Inf     0.0133  
## 14 injury_height_metres -Inf_7100   -0.00972 
## 15 injury_height_metres 7100_Inf     0.00972 
## 16 highpoint_metres     -Inf_6814   -0.00679 
## 17 highpoint_metres     6814_7400    0.00343 
## 18 age                  -Inf_30     -0.000782

Plot

data_corr_tbl %>%
  plot_correlation_funnel()