Apply 6
Dante Moretti
2025-07-05
Goal: be able to predict the ceo departure
Import Data
library(tidyverse)## Warning: package 'purrr' was built under R version 4.3.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(textrecipes)## Warning: package 'textrecipes' was built under R version 4.3.3## Loading required package: recipes## Warning: package 'recipes' was built under R version 4.3.3##
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## steplibrary(correlationfunnel)## ══ correlationfunnel Tip #1 ════════════════════════════════════════════════════
## Make sure your data is not overly imbalanced prior to using `correlate()`.
## If less than 5% imbalance, consider sampling. :)data <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2021/2021-04-27/departures.csv')## Rows: 9423 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): coname, exec_fullname, interim_coceo, still_there, notes, sources...
## dbl (10): dismissal_dataset_id, gvkey, fyear, co_per_rol, departure_code, c...
## dttm (1): leftofc
##
## 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 | 9423 |
| Number of columns | 19 |
| _______________________ | |
| Column type frequency: | |
| character | 8 |
| numeric | 10 |
| POSIXct | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| coname | 0 | 1.00 | 2 | 30 | 0 | 3860 | 0 |
| exec_fullname | 0 | 1.00 | 5 | 790 | 0 | 8701 | 0 |
| interim_coceo | 9105 | 0.03 | 6 | 7 | 0 | 6 | 0 |
| still_there | 7311 | 0.22 | 3 | 10 | 0 | 77 | 0 |
| notes | 1644 | 0.83 | 5 | 3117 | 0 | 7755 | 0 |
| sources | 1475 | 0.84 | 18 | 1843 | 0 | 7915 | 0 |
| eight_ks | 4499 | 0.52 | 69 | 3884 | 0 | 4914 | 0 |
| _merge | 0 | 1.00 | 11 | 11 | 0 | 1 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| dismissal_dataset_id | 0 | 1.00 | 5684.10 | 25005.46 | 1 | 2305.5 | 4593 | 6812.5 | 559044 | ▇▁▁▁▁ |
| gvkey | 0 | 1.00 | 40132.48 | 53921.34 | 1004 | 7337.0 | 14385 | 60900.5 | 328795 | ▇▁▁▁▁ |
| fyear | 0 | 1.00 | 2007.74 | 8.19 | 1987 | 2000.0 | 2008 | 2016.0 | 2020 | ▁▆▅▅▇ |
| co_per_rol | 0 | 1.00 | 25580.22 | 18202.38 | -1 | 8555.5 | 22980 | 39275.5 | 64602 | ▇▆▅▃▃ |
| departure_code | 1667 | 0.82 | 5.20 | 1.53 | 1 | 5.0 | 5 | 7.0 | 9 | ▁▃▇▅▁ |
| ceo_dismissal | 1813 | 0.81 | 0.20 | 0.40 | 0 | 0.0 | 0 | 0.0 | 1 | ▇▁▁▁▂ |
| tenure_no_ceodb | 0 | 1.00 | 1.03 | 0.17 | 0 | 1.0 | 1 | 1.0 | 3 | ▁▇▁▁▁ |
| max_tenure_ceodb | 0 | 1.00 | 1.05 | 0.24 | 1 | 1.0 | 1 | 1.0 | 4 | ▇▁▁▁▁ |
| fyear_gone | 1802 | 0.81 | 2006.64 | 13.63 | 1980 | 2000.0 | 2007 | 2013.0 | 2997 | ▇▁▁▁▁ |
| cik | 245 | 0.97 | 741469.17 | 486551.43 | 1750 | 106413.0 | 857323 | 1050375.8 | 1808065 | ▆▁▇▂▁ |
Variable type: POSIXct
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| leftofc | 1802 | 0.81 | 1981-01-01 | 2998-04-27 | 2006-12-31 | 3627 |
data_clean <- data %>%
select(-c(`_merge`,, still_there, sources, eight_ks)) %>%
na.omit() %>%
mutate(across(c(departure_code, ceo_dismissal), as.factor))Explore Data
data_clean %>% count(ceo_dismissal)## # A tibble: 2 × 2
## ceo_dismissal n
## <fct> <int>
## 1 0 253
## 2 1 16data_clean %>%
ggplot(aes(fyear)) +
geom_bar()
correlation plot
data_binarized <- data_clean %>%
select(-leftofc, -notes) %>%
binarize()
data_binarized %>% glimpse()## Rows: 269
## Columns: 48
## $ `dismissal_dataset_id__-Inf_2214` <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, …
## $ dismissal_dataset_id__2214_4496 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, …
## $ dismissal_dataset_id__4496_6636 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ dismissal_dataset_id__6636_Inf <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ coname__BOB_EVANS_FARMS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ coname__NORDSTROM_INC <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `coname__-OTHER` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ `gvkey__-Inf_6802` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, …
## $ gvkey__6802_13700 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, …
## $ gvkey__13700_29791 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ gvkey__29791_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `fyear__-Inf_2001` <dbl> 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, …
## $ fyear__2001_2007 <dbl> 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, …
## $ fyear__2007_2014 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ fyear__2014_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `co_per_rol__-Inf_12685` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ co_per_rol__12685_25457 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ co_per_rol__25457_43559 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ co_per_rol__43559_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ exec_fullname__George_J._Harad <dbl> 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `exec_fullname__-OTHER` <dbl> 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ departure_code__1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ departure_code__3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, …
## $ departure_code__5 <dbl> 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, …
## $ departure_code__6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ departure_code__7 <dbl> 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, …
## $ `departure_code__-OTHER` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ ceo_dismissal__0 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, …
## $ ceo_dismissal__1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, …
## $ `interim_coceo__co-CEO` <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ `interim_coceo__Co-CEO` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `interim_coceo__CO-CEO` <dbl> 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, …
## $ interim_coceo__Interim <dbl> 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, …
## $ `interim_coceo__-OTHER` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tenure_no_ceodb__1 <dbl> 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, …
## $ tenure_no_ceodb__2 <dbl> 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, …
## $ tenure_no_ceodb__3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ max_tenure_ceodb__1 <dbl> 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, …
## $ max_tenure_ceodb__2 <dbl> 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, …
## $ max_tenure_ceodb__3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `fyear_gone__-Inf_2001` <dbl> 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, …
## $ fyear_gone__2001_2008 <dbl> 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, …
## $ fyear_gone__2008_2014 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ fyear_gone__2014_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `cik__-Inf_96287` <dbl> 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, …
## $ cik__96287_833829 <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, …
## $ cik__833829_1042893 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ cik__1042893_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …data_correlation <- data_binarized %>%
correlate(ceo_dismissal__1 )
data_correlation## # A tibble: 48 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 ceo_dismissal 0 -1
## 2 ceo_dismissal 1 1
## 3 departure_code 3 0.966
## 4 departure_code 7 -0.352
## 5 coname -OTHER -0.175
## 6 interim_coceo CO-CEO 0.145
## 7 co_per_rol 43559_Inf -0.145
## 8 departure_code 5 -0.142
## 9 interim_coceo Interim -0.140
## 10 fyear -Inf_2001 0.129
## # ℹ 38 more rowsdata_correlation %>%
correlationfunnel::plot_correlation_funnel()
split data
library(tidymodels)## Warning: package 'tidymodels' was built under R version 4.3.3## ── Attaching packages ────────────────────────────────────── tidymodels 1.3.0 ──## ✔ broom 1.0.7 ✔ rsample 1.2.1
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data_clean <- data_clean %>% sample_n(100)
data_split <- initial_split(data_clean, strata = ceo_dismissal )
data_train <- training(data_split)
data_test <- testing(data_split)
data_cv <- rsample::vfold_cv(data_train, strata = ceo_dismissal)
data_cv## # 10-fold cross-validation using stratification
## # A tibble: 10 × 2
## splits id
## <list> <chr>
## 1 <split [67/8]> Fold01
## 2 <split [67/8]> Fold02
## 3 <split [67/8]> Fold03
## 4 <split [67/8]> Fold04
## 5 <split [67/8]> Fold05
## 6 <split [68/7]> Fold06
## 7 <split [68/7]> Fold07
## 8 <split [68/7]> Fold08
## 9 <split [68/7]> Fold09
## 10 <split [68/7]> Fold10##prepocess data
library(themis)## Warning: package 'themis' was built under R version 4.3.3xgboost_rec <- recipes::recipe(ceo_dismissal ~ ., data = data_train) %>%
update_role(dismissal_dataset_id, new_role = "ID") %>%
step_tokenize(notes) %>%
step_tokenfilter(notes, max_tokens = 50) %>%
step_tf(notes) %>%
step_date(leftofc, keep_original_cols = FALSE) %>%
step_other(coname, exec_fullname) %>%
step_dummy(all_nominal_predictors()) %>%
step_log(gvkey, tenure_no_ceodb, max_tenure_ceodb) %>%
step_smote(ceo_dismissal)
xgboost_rec %>% prep() %>% juice() %>% glimpse()## Rows: 138
## Columns: 89
## $ dismissal_dataset_id <dbl> 4496, 7420, 7503, 423, 5782, 6041, 931, 1404, 58…
## $ gvkey <dbl> 9.545097, 11.082881, 11.092413, 7.667626, 10.130…
## $ fyear <dbl> 2002, 2002, 2010, 2005, 2012, 2014, 2008, 2018, …
## $ co_per_rol <dbl> 20578, 25315, 17477, 19697, 45955, 16402, 13505,…
## $ tenure_no_ceodb <dbl> 0.0000000, 0.0000000, 0.6931472, 0.0000000, 0.00…
## $ max_tenure_ceodb <dbl> 0.0000000, 0.0000000, 0.6931472, 0.0000000, 0.69…
## $ fyear_gone <dbl> 2003, 2003, 2011, 2005, 2012, 2010, 2008, 2019, …
## $ cik <dbl> 819706, 1038339, 1042893, 945489, 883943, 891103…
## $ ceo_dismissal <fct> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ tf_notes_a <dbl> 3, 1, 0, 2, 1, 1, 3, 1, 2, 1, 2, 0, 1, 2, 0, 1, …
## $ tf_notes_after <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, …
## $ tf_notes_also <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, …
## $ tf_notes_and <dbl> 2, 2, 0, 2, 2, 2, 1, 1, 2, 1, 4, 2, 3, 1, 1, 2, …
## $ tf_notes_announced <dbl> 2, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, …
## $ tf_notes_as <dbl> 0, 0, 0, 1, 1, 3, 3, 3, 1, 1, 4, 2, 6, 1, 0, 2, …
## $ tf_notes_be <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, …
## $ tf_notes_been <dbl> 2, 1, 0, 0, 0, 0, 0, 0, 2, 0, 2, 1, 0, 0, 1, 0, …
## $ tf_notes_board <dbl> 2, 1, 0, 1, 1, 0, 0, 3, 0, 0, 3, 2, 3, 1, 3, 1, …
## $ tf_notes_by <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 2, 0, 0, …
## $ tf_notes_ceo <dbl> 1, 0, 0, 0, 2, 0, 1, 1, 0, 2, 4, 1, 0, 2, 0, 1, …
## $ tf_notes_chairman <dbl> 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 2, …
## $ tf_notes_chief <dbl> 1, 1, 0, 2, 0, 1, 2, 3, 1, 1, 1, 2, 3, 0, 1, 1, …
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## $ tf_notes_company <dbl> 0, 0, 0, 1, 0, 4, 0, 1, 0, 1, 2, 1, 1, 1, 0, 0, …
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## $ tf_notes_inc <dbl> 2, 0, 0, 1, 0, 0, 1, 0, 3, 0, 0, 1, 1, 1, 1, 0, …
## $ tf_notes_interim <dbl> 1, 2, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 2, …
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## $ tf_notes_of <dbl> 2, 4, 0, 2, 1, 3, 3, 6, 3, 0, 4, 2, 6, 1, 2, 3, …
## $ tf_notes_officer <dbl> 1, 1, 0, 2, 0, 0, 1, 3, 1, 0, 0, 2, 3, 0, 1, 1, …
## $ tf_notes_on <dbl> 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 2, 0, 1, 2, 0, 0, …
## $ tf_notes_president <dbl> 1, 1, 0, 2, 1, 0, 0, 0, 1, 0, 1, 2, 3, 0, 1, 2, …
## $ tf_notes_served <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, …
## $ tf_notes_since <dbl> 1, 1, 0, 0, 0, 0, 1, 0, 2, 0, 2, 1, 0, 0, 1, 0, …
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## $ tf_notes_with <dbl> 3, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, …
## $ tf_notes_year <dbl> 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ leftofc_year <dbl> 2003, 2003, 2011, 2005, 2012, 2010, 2008, 2019, …
## $ coname_other <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ exec_fullname_other <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ departure_code_X2 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ departure_code_X3 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ departure_code_X4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ departure_code_X5 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, …
## $ departure_code_X6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ departure_code_X7 <dbl> 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, …
## $ interim_coceo_Co.CEO <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ interim_coceo_CO.CEO <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ interim_coceo_interim <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ interim_coceo_Interim <dbl> 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, …
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## $ leftofc_month_Mar <dbl> 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ leftofc_month_Apr <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, …
## $ leftofc_month_May <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ leftofc_month_Jun <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ leftofc_month_Jul <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ leftofc_month_Aug <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ leftofc_month_Sep <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
## $ leftofc_month_Oct <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, …
## $ leftofc_month_Nov <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
## $ leftofc_month_Dec <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …specify model
xgboost_spec <-
boost_tree(trees = tune()) %>%
set_mode("classification") %>%
set_engine("xgboost")
xgboost_workflow <-
workflow() %>%
add_recipe(xgboost_rec) %>%
add_model(xgboost_spec) tune hyperparameters
doParallel::registerDoParallel()
set.seed(65743)
xgboost_tune <-
tune_grid(xgboost_workflow,
resamples = data_cv,
grid = 5)## Warning: ! tune detected a parallel backend registered with foreach but no backend
## registered with future.
## ℹ Support for parallel processing with foreach was soft-deprecated in tune
## 1.2.1.
## ℹ See ?parallelism (`?tune::parallelism()`) to learn more.## Warning: package 'xgboost' was built under R version 4.3.3