Apply 6
Spencer Murrin
2024-03-20
Import Data
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.skimr::skim(members)| 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 | ▁▁▂▇▇ |
data_clean <- members %>%
# Treat missing values
select(-death_cause, -injury_type, -highpoint_metres, -death_height_metres, -injury_height_metres) %>%
na.omit() %>%
# Log Transform Variables with pos-skewed Distribution
mutate(across(where(is.logical), as.factor))# Step 1: Prepare data
data_binarized_tbl <- data_clean %>%
select(-peak_name, -expedition_id) %>%
binarize()
data_binarized_tbl %>% glimpse()## Rows: 72,985
## Columns: 69
## $ `member_id__KANG10101-01` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `member_id__-OTHER` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ peak_id__AMAD <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ peak_id__ANN1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__ANN4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__BARU <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__CHOY <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__DHA1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__EVER <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__HIML <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__KANG <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__LHOT <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__MAKA <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__MANA <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ peak_id__PUMO <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `peak_id__-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__FALSE <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ died__TRUE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ 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: Correlate
data_corr_tbl <- data_binarized_tbl %>%
correlate(died__TRUE)## Warning: correlate(): [Data Imbalance Detected] Consider sampling to balance the classes more than 5%
## Column with imbalance: died__TRUEdata_corr_tbl## # A tibble: 69 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 died FALSE -1
## 2 died TRUE 1
## 3 year -Inf_1992 0.0519
## 4 peak_id ANN1 0.0336
## 5 success FALSE 0.0332
## 6 success TRUE -0.0332
## 7 peak_id DHA1 0.0290
## 8 peak_id AMAD -0.0281
## 9 peak_id CHOY -0.0241
## 10 year 2004_2012 -0.0211
## # ℹ 59 more rows# Step 3: Plot
data_corr_tbl %>%
plot_correlation_funnel()## Warning: ggrepel: 41 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Build Model
Split Data
library(tidymodels)## ── Attaching packages ────────────────────────────────────── tidymodels 1.1.1 ──## ✔ broom 1.0.5 ✔ rsample 1.2.0
## ✔ dials 1.2.0 ✔ tune 1.1.2
## ✔ infer 1.0.6 ✔ workflows 1.1.3
## ✔ modeldata 1.3.0 ✔ workflowsets 1.0.1
## ✔ parsnip 1.1.1 ✔ yardstick 1.3.0
## ✔ recipes 1.0.9## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
## ✖ scales::discard() masks purrr::discard()
## ✖ dplyr::filter() masks stats::filter()
## ✖ recipes::fixed() masks stringr::fixed()
## ✖ dplyr::lag() masks stats::lag()
## ✖ xgboost::slice() masks dplyr::slice()
## ✖ yardstick::spec() masks readr::spec()
## ✖ recipes::step() masks stats::step()
## • Learn how to get started at https://www.tidymodels.org/start/#set.seed(2543)
#data_clean <- data_clean %>% sample_n(200)
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 [49264/5474]> Fold01
## 2 <split [49264/5474]> Fold02
## 3 <split [49264/5474]> Fold03
## 4 <split [49264/5474]> Fold04
## 5 <split [49264/5474]> Fold05
## 6 <split [49264/5474]> Fold06
## 7 <split [49264/5474]> Fold07
## 8 <split [49264/5474]> Fold08
## 9 <split [49265/5473]> Fold09
## 10 <split [49265/5473]> Fold10Preprocess Data
library(themis)
xgboost_rec <- recipes::recipe(died ~ ., data = data_train) %>%
update_role(member_id, new_role = "ID") %>%
step_other(expedition_id, threshold = 0.1) %>%
step_dummy(all_nominal_predictors()) %>%
step_smote(died)
#xgboost_rec %>% prep() %>% juice() %>% glimpse()Specify Model
library(usemodels)
usemodels::use_xgboost(died ~ ., data = data_train)## xgboost_recipe <-
## recipe(formula = died ~ ., data = data_train) %>%
## step_zv(all_predictors())
##
## xgboost_spec <-
## boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(),
## loss_reduction = tune(), sample_size = tune()) %>%
## set_mode("classification") %>%
## set_engine("xgboost")
##
## xgboost_workflow <-
## workflow() %>%
## add_recipe(xgboost_recipe) %>%
## add_model(xgboost_spec)
##
## set.seed(47346)
## xgboost_tune <-
## tune_grid(xgboost_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))xgboost_spec <-
boost_tree(trees = tune()) %>%
set_mode("classification") %>%
set_engine("xgboost")
xgboost_workflow <-
workflow() %>%
add_recipe(xgboost_rec) %>%
add_model(xgboost_spec) Tune Hyperparmeters
doParallel::registerDoParallel()
set.seed(24817)
xgboost_tune <-
tune_grid(xgboost_workflow,
resamples = data_cv,
grid = 5)## Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
## information.