Goal is to to predict CEO departure (ceo_dismissal).

Import Data

library(tidyverse)
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library(readr)
library(correlationfunnel)
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## Missing values and cleaning data are critical to getting great correlations. :)
library(tidymodels)
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library(textrecipes)
library(tidytext)
library(usemodels)
library(xgboost)
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## The following object is masked from 'package:dplyr':
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##     slice
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
## 
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data %>% skimr::skim()
Data summary
Name Piped 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

Clean Dataset

data_clean <- data %>%
    
    # Clean the target
    filter(!is.na(ceo_dismissal)) %>%
    mutate(ceo_dismissal = if_else(ceo_dismissal == 1, "dismissed", "not_dis")) %>%
    mutate(ceo_dismissal = as.factor(ceo_dismissal)) %>%
    
    # Address too many missing values
    select(-still_there, -interim_coceo, -eight_ks) %>%
    
    # Remove irrelevant variables
    select(-`_merge`, -sources) %>%
    
    # Remove variables that can't be used
    select(-departure_code) %>%
    
    # Remove redundant variables
    select(-cik, -gvkey, -co_per_rol, -fyear, -leftofc) %>%
    
    # Remove duplicates in the id variable
    distinct(dismissal_dataset_id, .keep_all = TRUE) %>%
    
    # Remove 2997 in fyear_gone
    filter(fyear_gone < 2025) %>%
    
    # Convert character columns to factors
    mutate(across(c(max_tenure_ceodb, tenure_no_ceodb, fyear_gone), as.factor)) %>%

    # Convert character columns to factors
    mutate(across(where(is.character), as.factor)) %>%
    
    mutate(notes = as.character(notes)) %>%

    # Omit missing values
    na.omit()
    
data_clean %>% skimr::skim()
Data summary
Name Piped data
Number of rows 7458
Number of columns 8
_______________________
Column type frequency:
character 1
factor 6
numeric 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
notes 0 1 5 3117 0 7448 0

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
coname 0 1 FALSE 3427 BAR: 8, CLA: 8, FED: 8, NTN: 8
exec_fullname 0 1 FALSE 6961 Joh: 4, Mel: 4, Alb: 3, Ami: 3
ceo_dismissal 0 1 FALSE 2 not: 5976, dis: 1482
tenure_no_ceodb 0 1 FALSE 3 1: 7274, 2: 177, 3: 7
max_tenure_ceodb 0 1 FALSE 4 1: 7123, 2: 317, 3: 15, 4: 3
fyear_gone 0 1 FALSE 34 200: 378, 199: 350, 200: 332, 200: 320

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
dismissal_dataset_id 0 1 5570.24 25786.43 1 2170.25 4321.5 6575.75 559044 ▇▁▁▁▁

Explore Data

data_clean %>% count(ceo_dismissal)
## # A tibble: 2 × 2
##   ceo_dismissal     n
##   <fct>         <int>
## 1 dismissed      1482
## 2 not_dis        5976
data_clean %>%
    ggplot(aes(ceo_dismissal)) +
    geom_bar()

ceo_dismissal vs. fyear_gone

data_clean %>%
    ggplot(aes(group = ceo_dismissal, fyear_gone)) +
    geom_boxplot()

correlation plot

# Step 1: Binarize
data_binarized <- data_clean %>%
    select(-dismissal_dataset_id, -notes) %>%
    na.omit() %>%
    binarize()

data_binarized %>% glimpse()
## Rows: 7,458
## Columns: 40
## $ coname__BARRICK_GOLD_CORP   <dbl> 0, 0, 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, 1, 1, …
## $ exec_fullname__John_W._Rowe <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `exec_fullname__-OTHER`     <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ ceo_dismissal__dismissed    <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ ceo_dismissal__not_dis      <dbl> 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, …
## $ tenure_no_ceodb__1          <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ tenure_no_ceodb__2          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `tenure_no_ceodb__-OTHER`   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ max_tenure_ceodb__1         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ max_tenure_ceodb__2         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `max_tenure_ceodb__-OTHER`  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ fyear_gone__1993            <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, …
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## $ `fyear_gone__-OTHER`        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
# Step 2: Correlation
data_correlation <- data_binarized %>%
    correlate(ceo_dismissal__dismissed)

data_correlation
## # A tibble: 40 × 3
##    feature          bin       correlation
##    <fct>            <chr>           <dbl>
##  1 ceo_dismissal    dismissed      1     
##  2 ceo_dismissal    not_dis       -1     
##  3 max_tenure_ceodb 1              0.0577
##  4 max_tenure_ceodb 2             -0.0533
##  5 fyear_gone       1999          -0.0390
##  6 fyear_gone       2002           0.0378
##  7 fyear_gone       2003           0.0303
##  8 fyear_gone       2009           0.0292
##  9 fyear_gone       2008           0.0261
## 10 fyear_gone       1997          -0.0255
## # ℹ 30 more rows
# Step 3: Plot
data_correlation %>%
    correlationfunnel::plot_correlation_funnel()
## Warning: ggrepel: 28 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps