Apply 8
Bella Kalinyak
2024-10-31
Import Data
library(tidyverse)## ── 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 errorslibrary(correlationfunnel)## ══ Using correlationfunnel? ════════════════════════════════════════════════════
## You might also be interested in applied data science training for business.
## </> Learn more at - www.business-science.io </>data <-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.Clean Data
skimr::skim(data)| Name | data |
| 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 | ▁▁▂▇▇ |
data_clean <- data %>%
# Logical variables
mutate(across(is.logical, as.factor)) %>%
# Missing values
select(-death_cause, -injury_type, -death_height_metres, -injury_height_metres, -peak_id) %>%
na.omit() %>%
# Duplicate values
filter(member_id !="KANG10101-01") %>%
# Recode died
mutate(died = case_when(died == "TRUE" ~ "died", died == "FALSE" ~ "no"))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `across(is.logical, as.factor)`.
## Caused by warning:
## ! Use of bare predicate functions was deprecated in tidyselect 1.1.0.
## ℹ Please use wrap predicates in `where()` instead.
## # Was:
## data %>% select(is.logical)
##
## # Now:
## data %>% select(where(is.logical))Explore Data
data_clean %>% count(died)## # A tibble: 2 × 2
## died n
## <chr> <int>
## 1 died 744
## 2 no 51639Died
data_clean %>%
ggplot(aes(died)) +
geom_bar()
Deaths vs. Highest point reached
data_clean %>%
ggplot(aes(died, highpoint_metres)) +
geom_boxplot()
Deaths vs. Age
data_clean %>%
ggplot(aes(died, age)) +
geom_boxplot()
Correlation Plot
# Step 1: Binarize
data_binarized <- data_clean %>%
select(-member_id) %>%
binarize()
data_binarized %>% glimpse()## Rows: 52,383
## Columns: 69
## $ expedition_id__HIML13308 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `expedition_id__-OTHER` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ 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__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_1997` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ year__1997_2007 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ year__2007_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, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ season__Spring <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 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, 1, 0, 1, 0, 0, 1, 0, 0, 1, …
## $ age__29_36 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, …
## $ age__36_43 <dbl> 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, …
## $ age__43_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, 0, 0, 0, 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__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, 1, 1, 1, 1, 1, 1, 1, …
## $ `citizenship__-OTHER` <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ expedition_role__Climber <dbl> 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, …
## $ expedition_role__Deputy_Leader <dbl> 1, 0, 0, 0, 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> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `expedition_role__-OTHER` <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 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, …
## $ `highpoint_metres__-Inf_6750` <dbl> 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ highpoint_metres__6750_7400 <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ highpoint_metres__7400_8450 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ highpoint_metres__8450_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ success__FALSE <dbl> 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ success__TRUE <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ 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, …# Step 2: Correlation
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__dieddata_correlation## # A tibble: 69 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 died died 1
## 2 died no -1
## 3 year -Inf_1997 0.0843
## 4 success FALSE 0.0562
## 5 success TRUE -0.0562
## 6 peak_name Annapurna_I 0.0431
## 7 year 2012_Inf -0.0330
## 8 peak_name Ama_Dablam -0.0323
## 9 peak_name Dhaulagiri_I 0.0315
## 10 expedition_role H-A_Worker -0.0309
## # ℹ 59 more rows# Step 3: Plot
data_correlation %>%
correlationfunnel::plot_correlation_funnel()## Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Model Building
Split Data
library(tidymodels)## ── Attaching packages ────────────────────────────────────── tidymodels 1.2.0 ──## ✔ broom 1.0.6 ✔ rsample 1.2.1
## ✔ dials 1.3.0 ✔ tune 1.2.1
## ✔ infer 1.0.7 ✔ workflows 1.1.4
## ✔ modeldata 1.4.0 ✔ workflowsets 1.1.0
## ✔ parsnip 1.2.1 ✔ yardstick 1.3.1
## ✔ recipes 1.0.10## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
## ✖ scales::discard() masks purrr::discard()
## ✖ dplyr::filter() masks stats::filter()
## ✖ recipes::fixed() masks stringr::fixed()
## ✖ dplyr::lag() masks stats::lag()
## ✖ yardstick::spec() masks readr::spec()
## ✖ recipes::step() masks stats::step()
## • Use tidymodels_prefer() to resolve common conflicts.set.seed(1234)
#data_clean <- data_clean %>% group_by(died) %>% sample_n(50) %>% ungroup()
data_split <- initial_split(data_clean, strata = died)
data_train <- training(data_split)
data_test <- testing(data_split)
data_cv <- rsample::vfold_cv(data_train, strata = died)
data_cv## # 10-fold cross-validation using stratification
## # A tibble: 10 × 2
## splits id
## <list> <chr>
## 1 <split [35358/3929]> Fold01
## 2 <split [35358/3929]> Fold02
## 3 <split [35358/3929]> Fold03
## 4 <split [35358/3929]> Fold04
## 5 <split [35358/3929]> Fold05
## 6 <split [35358/3929]> Fold06
## 7 <split [35358/3929]> Fold07
## 8 <split [35359/3928]> Fold08
## 9 <split [35359/3928]> Fold09
## 10 <split [35359/3928]> Fold10Preprocess Data
library(themis)
xgboost_rec <- recipes::recipe(died ~ ., data = data_train) %>%
update_role(member_id, new_role = "ID") %>%
step_other(citizenship, expedition_role, expedition_id) %>%
step_dummy(all_nominal_predictors()) %>%
step_YeoJohnson(year) %>%
step_normalize(all_numeric_predictors()) %>%
step_smote()
xgboost_rec %>% prep() %>% juice() %>% glimpse()## Rows: 39,287
## Columns: 399
## $ member_id <fct> AMAD78301-02, AMAD78301-04, AMAD78…
## $ year <dbl> -1.914587, -1.914587, -1.914587, -…
## $ age <dbl> 0.38170212, 0.28353551, -0.7962971…
## $ highpoint_metres <dbl> -1.426945, -1.426945, -1.426945, -…
## $ died <fct> no, no, no, no, no, no, no, no, no…
## $ expedition_id_other <dbl> 0.03910908, 0.03910908, 0.03910908…
## $ peak_name_Ama.Dablam <dbl> 2.61863, 2.61863, 2.61863, 2.61863…
## $ peak_name_Amotsang <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Amphu.Gyabjen <dbl> -0.01595605, -0.01595605, -0.01595…
## $ peak_name_Amphu.I <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Amphu.Middle <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Anidesh.Chuli <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Annapurna.I <dbl> -0.1383618, -0.1383618, -0.1383618…
## $ peak_name_Annapurna.I.East <dbl> -0.0257336, -0.0257336, -0.0257336…
## $ peak_name_Annapurna.I.Middle <dbl> -0.02199641, -0.02199641, -0.02199…
## $ peak_name_Annapurna.II <dbl> -0.04163904, -0.04163904, -0.04163…
## $ peak_name_Annapurna.III <dbl> -0.0457331, -0.0457331, -0.0457331…
## $ peak_name_Annapurna.IV <dbl> -0.08801348, -0.08801348, -0.08801…
## $ peak_name_Annapurna.South <dbl> -0.04254926, -0.04254926, -0.04254…
## $ peak_name_Api.Main <dbl> -0.02986054, -0.02986054, -0.02986…
## $ peak_name_Arniko.Chuli <dbl> -0.02810103, -0.02810103, -0.02810…
## $ peak_name_Bamongo <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Baruntse <dbl> -0.1713795, -0.1713795, -0.1713795…
## $ peak_name_Baudha <dbl> -0.01888041, -0.01888041, -0.01888…
## $ peak_name_Beding.Go <dbl> -0.01427116, -0.01427116, -0.01427…
## $ peak_name_Bhairab.Takura <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Bhemdang.Ri <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Bhrikuti <dbl> -0.03533777, -0.03533777, -0.03533…
## $ peak_name_Bhrikuti.Shail <dbl> -0.0302845, -0.0302845, -0.0302845…
## $ peak_name_Bhulu.Lhasa <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Bijora.Hiunchuli <dbl> -0.01334928, -0.01334928, -0.01334…
## $ peak_name_Bobaye <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Boktoh <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Burke.Kang <dbl> -0.01888041, -0.01888041, -0.01888…
## $ peak_name_Chago <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Chako <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Chamar.Kang <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Chamar.North <dbl> -0.01513704, -0.01513704, -0.01513…
## $ peak_name_Chamlang <dbl> -0.03605265, -0.03605265, -0.03605…
## $ peak_name_Chandi.Himal <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Changla <dbl> -0.01747942, -0.01747942, -0.01747…
## $ peak_name_Changtse <dbl> -0.03192429, -0.03192429, -0.03192…
## $ peak_name_Changwathang <dbl> -0.02472341, -0.02472341, -0.02472…
## $ peak_name_Chaw.Peak <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Chekigo <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Cheo.Himal <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Chhiv.Himal <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Chhochenphu.Himal <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Chhopa.Bamare <dbl> -0.01595605, -0.01595605, -0.01595…
## $ peak_name_Chhuboche <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Chhukung.Ri <dbl> -0.02140946, -0.02140946, -0.02140…
## $ peak_name_Chhukung.Tse <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Cho.Oyu <dbl> -0.3827439, -0.3827439, -0.3827439…
## $ peak_name_Cho.Polu <dbl> -0.01747942, -0.01747942, -0.01747…
## $ peak_name_Chobuje <dbl> -0.02080599, -0.02080599, -0.02080…
## $ peak_name_Cholatse <dbl> -0.03569699, -0.03569699, -0.03569…
## $ peak_name_Chukyima.Go <dbl> -0.01513704, -0.01513704, -0.01513…
## $ peak_name_Chulu.Central <dbl> -0.01427116, -0.01427116, -0.01427…
## $ peak_name_Chulu.West <dbl> -0.01334928, -0.01334928, -0.01334…
## $ peak_name_Churen.Himal.Central <dbl> -0.02312575, -0.02312575, -0.02312…
## $ peak_name_Churen.Himal.East <dbl> -0.01595605, -0.01595605, -0.01595…
## $ peak_name_Churen.Himal.West <dbl> -0.02855104, -0.02855104, -0.02855…
## $ peak_name_Danga <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Danphe.Shail <dbl> -0.01954333, -0.01954333, -0.01954…
## $ peak_name_Dhampus <dbl> -0.03845075, -0.03845075, -0.03845…
## $ peak_name_Dhaulagiri.I <dbl> -0.1780326, -0.1780326, -0.1780326…
## $ peak_name_Dhaulagiri.II <dbl> -0.03497487, -0.03497487, -0.03497…
## $ peak_name_Dhaulagiri.III <dbl> -0.01888041, -0.01888041, -0.01888…
## $ peak_name_Dhaulagiri.IV <dbl> -0.02943049, -0.02943049, -0.02943…
## $ peak_name_Dhaulagiri.V <dbl> -0.02140946, -0.02140946, -0.02140…
## $ peak_name_Dhaulagiri.VI <dbl> -0.02018452, -0.02018452, -0.02018…
## $ peak_name_Dhechyan.Khang <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Dingjung.North <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Dingjung.Ri <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Dolma.Khang <dbl> -0.02199641, -0.02199641, -0.02199…
## $ peak_name_Dome.Kang <dbl> -0.01747942, -0.01747942, -0.01747…
## $ peak_name_Domo <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Dorje.Lhakpa <dbl> -0.04949222, -0.04949222, -0.04949…
## $ peak_name_Dragmorpa.Ri <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Drangnag.Ri <dbl> -0.01954333, -0.01954333, -0.01954…
## $ peak_name_Drohmo <dbl> -0.01747942, -0.01747942, -0.01747…
## $ peak_name_Dudh.Kundali <dbl> -0.01673506, -0.01673506, -0.01673…
## $ peak_name_Dzanye <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Dzanye.II <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Dzasampatse <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Everest <dbl> -0.6618305, -0.6618305, -0.6618305…
## $ peak_name_Fang <dbl> -0.02472341, -0.02472341, -0.02472…
## $ peak_name_Firnkopf <dbl> -0.01427116, -0.01427116, -0.01427…
## $ peak_name_Firnkopf.West <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Futi.Himal <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Gama.Peak <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Ganchenpo <dbl> -0.03070262, -0.03070262, -0.03070…
## $ peak_name_Gandharva.Chuli <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Ganesh.I <dbl> -0.01427116, -0.01427116, -0.01427…
## $ peak_name_Ganesh.II <dbl> -0.02140946, -0.02140946, -0.02140…
## $ peak_name_Ganesh.III <dbl> -0.02140946, -0.02140946, -0.02140…
## $ peak_name_Ganesh.IV <dbl> -0.02717874, -0.02717874, -0.02717…
## $ peak_name_Ganesh.V <dbl> -0.02140946, -0.02140946, -0.02140…
## $ peak_name_Ganesh.VI <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Gangapurna <dbl> -0.04545281, -0.04545281, -0.04545…
## $ peak_name_Gangapurna.West <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Gaugiri <dbl> -0.01819339, -0.01819339, -0.01819…
## $ peak_name_Gaurishankar <dbl> -0.04163904, -0.04163904, -0.04163…
## $ peak_name_Gaurishankar.South <dbl> -0.01673506, -0.01673506, -0.01673…
## $ peak_name_Gave.Ding <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Ghenge.Liru <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Ghhanyala.Hies <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Ghustang.North <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Ghustang.South <dbl> -0.01334928, -0.01334928, -0.01334…
## $ peak_name_Ghyuthumba.Main <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Gimmigela.Chuli <dbl> -0.02810103, -0.02810103, -0.02810…
## $ peak_name_Gimmigela.Chuli.East <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Glacier.Dome <dbl> -0.03975656, -0.03975656, -0.03975…
## $ peak_name_Gojung <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Gorakh.Himal <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Gorakh.Khang <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Gurja.Himal <dbl> -0.02899409, -0.02899409, -0.02899…
## $ peak_name_Gurkarpo.Ri <dbl> -0.01954333, -0.01954333, -0.01954…
## $ peak_name_Gyachung.Kang <dbl> -0.03111516, -0.03111516, -0.03111…
## $ peak_name_Gyajikang <dbl> -0.05026151, -0.05026151, -0.05026…
## $ peak_name_Gyalzen.Peak <dbl> -0.01513704, -0.01513704, -0.01513…
## $ peak_name_Himalchuli.East <dbl> -0.04344055, -0.04344055, -0.04344…
## $ peak_name_Himalchuli.North <dbl> -0.01673506, -0.01673506, -0.01673…
## $ peak_name_Himalchuli.West <dbl> -0.02199641, -0.02199641, -0.02199…
## $ peak_name_Himjung <dbl> -0.01334928, -0.01334928, -0.01334…
## $ peak_name_Himlung.East <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Himlung.Himal <dbl> -0.1461832, -0.1461832, -0.1461832…
## $ peak_name_Hiunchuli <dbl> -0.01513704, -0.01513704, -0.01513…
## $ peak_name_Hongde <dbl> -0.01747942, -0.01747942, -0.01747…
## $ peak_name_Hongku <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Hongku.Chuli <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Hungchhi <dbl> -0.02472341, -0.02472341, -0.02472…
## $ peak_name_Hunku <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Imjatse <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Jabou.Ri <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Jagdula <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Janak.Chuli <dbl> -0.01954333, -0.01954333, -0.01954…
## $ peak_name_Jannu <dbl> -0.06232088, -0.06232088, -0.06232…
## $ peak_name_Jannu.East <dbl> -0.02472341, -0.02472341, -0.02472…
## $ peak_name_Jasemba.Goth <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Jethi.Bahurani <dbl> -0.01513704, -0.01513704, -0.01513…
## $ peak_name_Jinjang <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Jobo.Rinjang <dbl> -0.01888041, -0.01888041, -0.01888…
## $ peak_name_Jomsom.Himal <dbl> -0.01513704, -0.01513704, -0.01513…
## $ peak_name_Jongsang <dbl> -0.01888041, -0.01888041, -0.01888…
## $ peak_name_Kabru.Dome <dbl> -0.01595605, -0.01595605, -0.01595…
## $ peak_name_Kabru.Main <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Kabru.North <dbl> -0.01747942, -0.01747942, -0.01747…
## $ peak_name_Kabru.South <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Kagmara.I <dbl> -0.01673506, -0.01673506, -0.01673…
## $ peak_name_Kali.Himal <dbl> -0.01334928, -0.01334928, -0.01334…
## $ peak_name_Kande.Hiunchuli <dbl> -0.01888041, -0.01888041, -0.01888…
## $ peak_name_Kande.Hiunchuli.North..I <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Kande.Hiunchuli.North..II <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Kang.Guru <dbl> -0.05394528, -0.05394528, -0.05394…
## $ peak_name_Kang.Kuru <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Kang.Nagchugo <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Kangbachen <dbl> -0.0302845, -0.0302845, -0.0302845…
## $ peak_name_Kangchenjunga <dbl> -0.135954, -0.135954, -0.135954, -…
## $ peak_name_Kangchenjunga.Central <dbl> -0.02472341, -0.02472341, -0.02472…
## $ peak_name_Kangchenjunga.South <dbl> -0.02764372, -0.02764372, -0.02764…
## $ peak_name_Kangchung.Nup <dbl> -0.01334928, -0.01334928, -0.01334…
## $ peak_name_Kangchung.Shar <dbl> -0.01427116, -0.01427116, -0.01427…
## $ peak_name_Kangtega <dbl> -0.04344055, -0.04344055, -0.04344…
## $ peak_name_Kangtokal <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Kangtsune <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Kanjeralwa <dbl> -0.01747942, -0.01747942, -0.01747…
## $ peak_name_Kanjiroba.North <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Kanjiroba.South <dbl> -0.02140946, -0.02140946, -0.02140…
## $ peak_name_Kanta.Gaton <dbl> -0.01513704, -0.01513704, -0.01513…
## $ peak_name_Kanti.Himal <dbl> -0.01819339, -0.01819339, -0.01819…
## $ peak_name_Kaptang <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Karsang.Kang <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Karyolung <dbl> -0.02018452, -0.02018452, -0.02018…
## $ peak_name_Khamjung <dbl> -0.01595605, -0.01595605, -0.01595…
## $ peak_name_Khangri.Shar <dbl> -0.01334928, -0.01334928, -0.01334…
## $ peak_name_Khatang <dbl> -0.0257336, -0.0257336, -0.0257336…
## $ peak_name_Khatung.Khang <dbl> -0.02312575, -0.02312575, -0.02312…
## $ peak_name_Khumbutse <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Kimshung <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Kirat.Chuli <dbl> -0.02256813, -0.02256813, -0.02256…
## $ peak_name_Kojichuwa.Chuli <dbl> -0.02018452, -0.02018452, -0.02018…
## $ peak_name_Korlang.Pari.Tippa <dbl> -0.01513704, -0.01513704, -0.01513…
## $ peak_name_Kotang <dbl> -0.01595605, -0.01595605, -0.01595…
## $ peak_name_Kusum.Kanguru <dbl> -0.03386298, -0.03386298, -0.03386…
## $ peak_name_Kwangde <dbl> -0.02140946, -0.02140946, -0.02140…
## $ peak_name_Kyashar <dbl> -0.02256813, -0.02256813, -0.02256…
## $ peak_name_Kyazo.Ri <dbl> -0.02367026, -0.02367026, -0.02367…
## $ peak_name_Kyungka.Ri.1 <dbl> -0.01334928, -0.01334928, -0.01334…
## $ peak_name_Lachama.Chuli <dbl> -0.01673506, -0.01673506, -0.01673…
## $ peak_name_Lachama.North <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Lamjung.Himal <dbl> -0.02717874, -0.02717874, -0.02717…
## $ peak_name_Langdak <dbl> -0.02312575, -0.02312575, -0.02312…
## $ peak_name_Langdung <dbl> -0.01427116, -0.01427116, -0.01427…
## $ peak_name_Langju <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Langmoche.Ri <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Langpo <dbl> -0.01427116, -0.01427116, -0.01427…
## $ peak_name_Langshisa.Ri <dbl> -0.03111516, -0.03111516, -0.03111…
## $ peak_name_Langtang.Lirung <dbl> -0.05717204, -0.05717204, -0.05717…
## $ peak_name_Langtang.Ri <dbl> -0.01954333, -0.01954333, -0.01954…
## $ peak_name_Langtang.Yubra <dbl> -0.01747942, -0.01747942, -0.01747…
## $ peak_name_Larkya.Peak <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Lashar.II <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Leonpo.Gang <dbl> -0.02472341, -0.02472341, -0.02472…
## $ peak_name_Leonpo.Gang.East <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Lha.Shamma <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Lhayul.Peak <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Lhonak <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Lhotse <dbl> -0.1728688, -0.1728688, -0.1728688…
## $ peak_name_Lhotse.Middle <dbl> -0.01595605, -0.01595605, -0.01595…
## $ peak_name_Lhotse.Shar <dbl> -0.04764941, -0.04764941, -0.04764…
## $ peak_name_Lingtren <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Linkhu.Chuli.Nup <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Linkhu.Chuli.Shar <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Lobuje.East <dbl> -0.02523354, -0.02523354, -0.02523…
## $ peak_name_Lobuje.West <dbl> -0.01334928, -0.01334928, -0.01334…
## $ peak_name_Lugula <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Lunag.II <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Lunag.Ri <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Lunag.West <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Lunchhung.Kamo.East <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Machhapuchhare <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Makalu <dbl> -0.1812036, -0.1812036, -0.1812036…
## $ peak_name_Makalu.II <dbl> -0.05626876, -0.05626876, -0.05626…
## $ peak_name_Malanphulan <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Manapathi <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Manapathi.NW <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Manaslu <dbl> -0.2667035, -0.2667035, -0.2667035…
## $ peak_name_Manaslu.North <dbl> -0.02899409, -0.02899409, -0.02899…
## $ peak_name_Mansail <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Mardi.Himal <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Matathumba <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Mera.Peak <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Merra <dbl> -0.01888041, -0.01888041, -0.01888…
## $ peak_name_Mojca <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Mukut.Himal <dbl> -0.02199641, -0.02199641, -0.02199…
## $ peak_name_Mustang.Himal <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Mustang.Peak <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Myagdi.Matha <dbl> -0.01334928, -0.01334928, -0.01334…
## $ peak_name_Nagoru <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Nalakankar.North <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Nalakankar.South <dbl> -0.01595605, -0.01595605, -0.01595…
## $ peak_name_Nampa <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Nangamari.I <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Nangamari.II <dbl> -0.01427116, -0.01427116, -0.01427…
## $ peak_name_Nangpai.Gosum.I <dbl> -0.01595605, -0.01595605, -0.01595…
## $ peak_name_Nangpai.Gosum.II <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Narphu <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Naulekh <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Nemjung <dbl> -0.03152231, -0.03152231, -0.03152…
## $ peak_name_Nepal.Peak <dbl> -0.02367026, -0.02367026, -0.02367…
## $ peak_name_Ngojumba.Kang.I <dbl> -0.01819339, -0.01819339, -0.01819…
## $ peak_name_Ngojumba.Kang.II <dbl> -0.01747942, -0.01747942, -0.01747…
## $ peak_name_Nilgiri.Central <dbl> -0.01888041, -0.01888041, -0.01888…
## $ peak_name_Nilgiri.North <dbl> -0.03232129, -0.03232129, -0.03232…
## $ peak_name_Nilgiri.South <dbl> -0.02140946, -0.02140946, -0.02140…
## $ peak_name_Nirekha <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Norbu.Kang <dbl> -0.02523354, -0.02523354, -0.02523…
## $ peak_name_Numbur <dbl> -0.03460819, -0.03460819, -0.03460…
## $ peak_name_Numri <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Nup.La.Kang <dbl> -0.01513704, -0.01513704, -0.01513…
## $ peak_name_Nupche.Kang <dbl> -0.02080599, -0.02080599, -0.02080…
## $ peak_name_Nupchu <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Nuptse <dbl> -0.05893761, -0.05893761, -0.05893…
## $ peak_name_Nuptse.East.I <dbl> -0.02018452, -0.02018452, -0.02018…
## $ peak_name_Nuptse.West.II <dbl> -0.02622414, -0.02622414, -0.02622…
## $ peak_name_Ohmi.Kangri <dbl> -0.02140946, -0.02140946, -0.02140…
## $ peak_name_Ombak.Himal <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Ombigaichen <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Omitso.Go <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Pabuk.Kang <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Paldor <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Palung <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Panalotapa <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Panbari <dbl> -0.02199641, -0.02199641, -0.02199…
## $ peak_name_Pandra <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Pangbuk.North <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Pangbuk.Ri <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Pankar.Himal <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Panpoche.1 <dbl> -0.01427116, -0.01427116, -0.01427…
## $ peak_name_Parchamo <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Pasang.Lhamu.Chuli <dbl> -0.0257336, -0.0257336, -0.0257336…
## $ peak_name_Patrasi.Himal <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Pawar.Central <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Peak.29 <dbl> -0.0327135, -0.0327135, -0.0327135…
## $ peak_name_Peak.4 <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Peak.41 <dbl> -0.02018452, -0.02018452, -0.02018…
## $ peak_name_Pemthang.Karpo.Ri <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Peri <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Pethangtse <dbl> -0.02256813, -0.02256813, -0.02256…
## $ peak_name_Pethangtse.East <dbl> -0.01595605, -0.01595605, -0.01595…
## $ peak_name_Pharilapcha <dbl> -0.01427116, -0.01427116, -0.01427…
## $ peak_name_Phu.Kang.Go <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Phungi.Himal <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Phurbi.Chhyachu <dbl> -0.01819339, -0.01819339, -0.01819…
## $ peak_name_Pimu <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Pisang <dbl> -0.01673506, -0.01673506, -0.01673…
## $ peak_name_Pointed.Peak <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Pokalde <dbl> -0.01427116, -0.01427116, -0.01427…
## $ peak_name_Pokharkang <dbl> -0.02018452, -0.02018452, -0.02018…
## $ peak_name_Pota.Himal.North <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Pumori <dbl> -0.1514547, -0.1514547, -0.1514547…
## $ peak_name_Punchen.Himal <dbl> -0.01888041, -0.01888041, -0.01888…
## $ peak_name_Purbung.Himal <dbl> -0.01334928, -0.01334928, -0.01334…
## $ peak_name_Purkhung <dbl> -0.02256813, -0.02256813, -0.02256…
## $ peak_name_Putha.Hiunchuli <dbl> -0.0998714, -0.0998714, -0.0998714…
## $ peak_name_Pyramid.Peak <dbl> -0.02523354, -0.02523354, -0.02523…
## $ peak_name_Raksha.Urai <dbl> -0.02140946, -0.02140946, -0.02140…
## $ peak_name_Ramdung <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Ramtang <dbl> -0.02523354, -0.02523354, -0.02523…
## $ peak_name_Ramtang.Chang <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Rani.Peak <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Rathong <dbl> -0.01427116, -0.01427116, -0.01427…
## $ peak_name_Ratna.Chuli <dbl> -0.04194462, -0.04194462, -0.04194…
## $ peak_name_Raungsiyar <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Ripimo.Shar <dbl> -0.01747942, -0.01747942, -0.01747…
## $ peak_name_Roc.Noir <dbl> -0.0257336, -0.0257336, -0.0257336…
## $ peak_name_Rokapi <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Rolmi <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Rolwaling.Kang <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Roma <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Saipal <dbl> -0.03497487, -0.03497487, -0.03497…
## $ peak_name_Saipal.East.Humla <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Saldim.West <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Samdo <dbl> -0.01595605, -0.01595605, -0.01595…
## $ peak_name_Sano.Kailash <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Saribung <dbl> -0.04488701, -0.04488701, -0.04488…
## $ peak_name_Serku.Dolma <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Shalbachum <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Sharphu.I <dbl> -0.01888041, -0.01888041, -0.01888…
## $ peak_name_Sharphu.II <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Sharphu.IV <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Shartse <dbl> -0.01427116, -0.01427116, -0.01427…
## $ peak_name_Shartse.II <dbl> -0.01595605, -0.01595605, -0.01595…
## $ peak_name_Shershon.Northwest <dbl> -0.0257336, -0.0257336, -0.0257336…
## $ peak_name_Shey.Shikhar <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Simnang.Himal <dbl> -0.01513704, -0.01513704, -0.01513…
## $ peak_name_Singu.Chuli <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Sisne.Himal <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Sita.Chuchura <dbl> -0.01888041, -0.01888041, -0.01888…
## $ peak_name_Sphinx <dbl> -0.02312575, -0.02312575, -0.02312…
## $ peak_name_Swaksa.Kang <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Swelokhan <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Syaokang <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Takargo <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Takargo.East <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Takphu.Himal <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Takphu.North <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Talung <dbl> -0.02523354, -0.02523354, -0.02523…
## $ peak_name_Taple.Shikhar <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Tarke.Kang.Shar <dbl> -0.02080599, -0.02080599, -0.02080…
## $ peak_name_Tashi.Kang <dbl> -0.0302845, -0.0302845, -0.0302845…
## $ peak_name_Tawa <dbl> -0.01513704, -0.01513704, -0.01513…
## $ peak_name_Tawoche <dbl> -0.03778103, -0.03778103, -0.03778…
## $ peak_name_Tengi.Ragi.Tau <dbl> -0.02018452, -0.02018452, -0.02018…
## $ peak_name_Tengkangpoche <dbl> -0.03386298, -0.03386298, -0.03386…
## $ peak_name_Tengkoma <dbl> -0.02764372, -0.02764372, -0.02764…
## $ peak_name_Thamserku <dbl> -0.02717874, -0.02717874, -0.02717…
## $ peak_name_Tharke.Kang <dbl> -0.01128191, -0.01128191, -0.01128…
## $ peak_name_Tharpu.Chuli <dbl> -0.01009072, -0.01009072, -0.01009…
## $ peak_name_Thorong.Peak <dbl> -0.01427116, -0.01427116, -0.01427…
## $ peak_name_Thulagi <dbl> -0.01954333, -0.01954333, -0.01954…
## $ peak_name_Til.Kang <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Tilicho <dbl> -0.08082657, -0.08082657, -0.08082…
## $ peak_name_Tobsar <dbl> -0.02717874, -0.02717874, -0.02717…
## $ peak_name_Tongu <dbl> -0.01513704, -0.01513704, -0.01513…
## $ peak_name_Triangle.Peak <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Tripura.Hiunchuli <dbl> -0.01513704, -0.01513704, -0.01513…
## $ peak_name_Tsartse <dbl> -0.02080599, -0.02080599, -0.02080…
## $ peak_name_Tsaurabong.Peak <dbl> -0.02256813, -0.02256813, -0.02256…
## $ peak_name_Tsisima <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Tso.Karpo.Kang <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Tukuche <dbl> -0.07153084, -0.07153084, -0.07153…
## $ peak_name_Tutse <dbl> -0.008738708, -0.008738708, -0.008…
## $ peak_name_Urkema <dbl> -0.03232129, -0.03232129, -0.03232…
## $ peak_name_Urkinmang <dbl> -0.02199641, -0.02199641, -0.02199…
## $ peak_name_Yakawa.Kang <dbl> -0.01235887, -0.01235887, -0.01235…
## $ peak_name_Yala.Chuli <dbl> -0.007135035, -0.007135035, -0.007…
## $ peak_name_Yalung.Kang <dbl> -0.04373364, -0.04373364, -0.04373…
## $ peak_name_Yanme.Kang <dbl> -0.005045167, -0.005045167, -0.005…
## $ peak_name_Yansa.Tsenji <dbl> -0.01334928, -0.01334928, -0.01334…
## $ peak_name_Yaupa <dbl> -0.01673506, -0.01673506, -0.01673…
## $ season_Spring <dbl> -1.0328792, -1.0328792, -1.0328792…
## $ season_Summer <dbl> -0.08548176, -0.08548176, -0.08548…
## $ season_Winter <dbl> -0.1538121, -0.1538121, -0.1538121…
## $ sex_M <dbl> 0.3112046, 0.3112046, 0.3112046, 0…
## $ citizenship_Japan <dbl> -0.2673052, -0.2673052, -0.2673052…
## $ citizenship_Nepal <dbl> -0.5682997, -0.5682997, -0.5682997…
## $ citizenship_UK <dbl> -0.2736991, -0.2736991, -0.2736991…
## $ citizenship_USA <dbl> -0.3070538, -0.3070538, -0.3070538…
## $ citizenship_other <dbl> -0.9564663, -0.9564663, -0.9564663…
## $ expedition_role_H.A.Worker <dbl> -0.543975, -0.543975, -0.543975, -…
## $ expedition_role_Leader <dbl> -0.4117403, -0.4117403, -0.4117403…
## $ expedition_role_other <dbl> 3.8499370, 3.8499370, -0.2597379, …
## $ hired_TRUE. <dbl> -0.5659087, -0.5659087, -0.5659087…
## $ success_TRUE. <dbl> -1.0758306, -1.0758306, -1.0758306…
## $ solo_TRUE. <dbl> -0.04601171, -0.04601171, -0.04601…
## $ oxygen_used_TRUE. <dbl> -0.7118547, -0.7118547, -0.7118547…
## $ injured_TRUE. <dbl> -0.1553656, -0.1553656, -0.1553656…Specify Model
xgboost_spec <-
boost_tree(trees = tune(), tree_depth = tune()) %>%
set_mode("classification") %>%
set_engine("xgboost")
xgboost_workflow <-
workflow() %>%
add_recipe(xgboost_rec) %>%
add_model(xgboost_spec) Tune Hyperparameters
tree_grid <- grid_regular(trees(),
tree_depth(),
levels = 5)
doParallel::registerDoParallel()
set.seed(68822)
xgboost_tune <-
tune_grid(xgboost_workflow,
resamples = data_cv,
grid = 5,
control = control_grid(save_pred = TRUE))Model Evaluation
Identify optimal values for hyperparameters
collect_metrics(xgboost_tune)## # A tibble: 15 × 8
## trees tree_depth .metric .estimator mean n std_err .config
## <int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 350 9 accuracy binary 0.985 10 0.000734 Preprocessor1_…
## 2 350 9 brier_class binary 0.0142 10 0.000616 Preprocessor1_…
## 3 350 9 roc_auc binary 0.757 10 0.00891 Preprocessor1_…
## 4 716 9 accuracy binary 0.984 10 0.000741 Preprocessor1_…
## 5 716 9 brier_class binary 0.0146 10 0.000622 Preprocessor1_…
## 6 716 9 roc_auc binary 0.752 10 0.00927 Preprocessor1_…
## 7 1111 3 accuracy binary 0.986 10 0.000670 Preprocessor1_…
## 8 1111 3 brier_class binary 0.0136 10 0.000560 Preprocessor1_…
## 9 1111 3 roc_auc binary 0.774 10 0.0116 Preprocessor1_…
## 10 1519 4 accuracy binary 0.984 10 0.000818 Preprocessor1_…
## 11 1519 4 brier_class binary 0.0144 10 0.000683 Preprocessor1_…
## 12 1519 4 roc_auc binary 0.745 10 0.0110 Preprocessor1_…
## 13 1815 13 accuracy binary 0.984 10 0.000816 Preprocessor1_…
## 14 1815 13 brier_class binary 0.0152 10 0.000727 Preprocessor1_…
## 15 1815 13 roc_auc binary 0.748 10 0.0119 Preprocessor1_…collect_predictions(xgboost_tune) %>%
group_by(id) %>%
roc_curve(died, .pred_died) %>%
autoplot()
Fit model for the last time
xgboost_last <- xgboost_workflow %>%
finalize_workflow(select_best(xgboost_tune, metric = "accuracy")) %>%
last_fit(data_split)## → A | warning: ! There are new levels in a factor: `Lashar I`, `Kumlung`, and `Ngojumba Kang
## III`.## There were issues with some computations A: x1There were issues with some computations A: x1collect_metrics(xgboost_last)## # A tibble: 3 × 4
## .metric .estimator .estimate .config
## <chr> <chr> <dbl> <chr>
## 1 accuracy binary 0.985 Preprocessor1_Model1
## 2 roc_auc binary 0.813 Preprocessor1_Model1
## 3 brier_class binary 0.0138 Preprocessor1_Model1collect_predictions(xgboost_last) %>%
yardstick::conf_mat(died, .pred_class) %>%
autoplot()
Variable importance
library(vip)##
## Attaching package: 'vip'## The following object is masked from 'package:utils':
##
## vixgboost_last %>%
workflows::extract_fit_engine() %>%
vip()
Conclusion
The previous model had accuracy value of 0.986, and an AUC value of 0.778 * Featured transformation: normalized numeric data. This did not result in any change. * Featured transformation: YeoJohnson. This did not result in any change. * Featured transformation: PCA. This did not result in any improvement. * Featured transformation: Algorithm tuning. This resulted in an improvement, accuracy became 0.985 and AUC became 0.813