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```{# Some key packages pkgs00 <- c(“devtools”, “RCurl”) install.packages(pkgs00)

pkg01 <- c( “tidyverse”, #For data science “tidyquant”, #Financial time series “lime”, #Explaining black-box mode “glue”, #Pasting text “fs”, #File system “cowplot”, #Handle multiple ggplot “readxl”, #read excel file “writexl” #write excel file ) install.packages(pkg01)

install.packages(“pacman”) # pacman::p_install(h2o) install.packages(“h2o”) install.packages(“xlsx”) library(h2o) # h2o set-up h2o.no_progress() # turn off h2o progress bars h2o.init() # launch h2o #

library(readxl) library(pacman) p_load(tidyverse, tidyquant, lime, glue, cowplot, ggplot2,fs,readxl, writexl)

load the package

library(xlsx)

Load the necessary packages

library(readr) library(writexl)

Read the CSV file into R

data <- read_csv(“telco_train.csv”)

Write the data to an XLSX file

write_xlsx(data, “telco_train.xlsx”)

Create data directory

fs::dir_create(“data”)

Import telco_train.xlsx data

path_train <- “telco_train.xlsx” train_raw_tbl <- read_excel(path_train) class(train_raw_tbl)

Analysis of attrition by department

dept_job_role_tbl <- train_raw_tbl %>% select(EmployeeNumber, Department, JobRole, PerformanceRating, Attrition)

dept_job_role_tbl %>% group_by(Attrition) %>% summarise(n = n()) %>% ungroup() %>% mutate(pct = n /sum(n)) # department attrition dept_job_role_tbl %>% group_by(Department, Attrition) %>% summarise(n = n()) %>% ungroup() %>% #group_by(Department) %>% mutate(pct = n /sum(n))

JobRole attrition

dept_job_role_tbl %>% group_by(Department, JobRole, Attrition) %>% summarise(n = n()) %>% ungroup() %>% group_by(Department, JobRole) %>% mutate(pct = n /sum(n)) %>% ungroup() %>% filter(Attrition %in% c(“Yes”)) }

```