Aon_PA_CS
KC
2023-02-01
#Read in Libraries
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## select## Loading required package: factoextra## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
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## between, first, last#Read and Join two datasets on Job Code #Replace names of columns so there are no spaces
aon_all <- read_csv("case_study_aon.csv")## Rows: 63396 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): CoName, JobCode
## dbl (3): SurveyYear, EmpID, ManagerID
##
## 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.radford <- read_csv("radford.csv")## New names:
## Rows: 12625 Columns: 19
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (18): Job Comment, Job Module, Job Code, Job Title, Job Function, Job Ar... dbl
## (1): ...1
## 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.
## • `` -> `...1`full_data <- left_join(
aon_all,
radford,
by = c("JobCode"="Job Code"),
copy = FALSE,
suffix = c(".x", ".y"),
)
names(full_data)<-str_replace_all(names(full_data), c(" " = "." , "," = "" ))#Create a variety of indicator variables
#manager_ind = 1 if the employee has a manager, 0 if the employee does not have a manager #change_ind = 1 if there was a change to the job in 2022, 0 if there was not a chagne in the job for 2022 #category_level = level associated within each job seniority level #Co_E_Ind <- whether the company is company E or not #Eng_Ind = 1 if the role is engineering, 0 if not #Sales_Ind = 1 if role is sales, 0 if not
#Took all job modules and converted them to indicators for Tech, Sales, Infrastructure, Media and Gaming, Executive, Energy, Business Process Outsourcing, and Manufacturing
#Create an indicator variable for the manager variable (1 if the employee has a manager, 0 if the employee does not have a manager)
full_data$manager_ind <- ifelse(is.na(full_data$ManagerID), 0, 1)
full_data$change_ind <- ifelse(full_data$Job.Comment == 'No Change for 2022', 0, 1)
#Identify the different job levels and split them by their category and category level
#unique(full_data$Job.Module)
#Separate the Job level to be level (in text), category, and category level
full_data <- full_data %>% separate(Job.Level, c('Level', 'RoleType'), sep = '\\(')
full_data<- full_data %>% separate(RoleType, c('Role_Type','Role_Type2'))
full_data$Category <- stringr::str_extract(full_data$Role_Type, "^.{1}")
full_data$Category_Level <- substring(full_data$Role_Type, 2, 2)
full_data$Co_E_Ind<- ifelse(full_data$CoName == 'E', 1, 0)
full_data$Module_Tech <- ifelse(full_data$Job.Module== 'Technology', 1, 0)
full_data$Module_Sales <-ifelse(full_data$Job.Module== 'Sales', 1, 0)
full_data$Module_Infrastructure <- ifelse(full_data$Job.Module== 'Infrastructure', 1, 0)
full_data$Module_M_G <- ifelse(full_data$Job.Module== 'Media & Gaming', 1, 0)
full_data$Module_Exec <- ifelse(full_data$Job.Module== 'Executive', 1, 0)
full_data$Module_Energy <- ifelse(full_data$Job.Module== 'Energy', 1, 0)
full_data$Module_BPO <- ifelse(full_data$Job.Module== 'Business Process Outsourcing', 1, 0)
full_data$Module_Manu <- ifelse(full_data$Job.Module== 'Manufacturing', 1, 0)
full_data$Eng_Ind<- ifelse(full_data$Job.Function == 'Engineering', 1, 0)
full_data$Sales_Ind <- ifelse(full_data$Job.Function == 'Engineering', 1, 0)
full_data$Category_Professional <- ifelse(full_data$Category == 'P', 1, 0)
full_data$Category_Management<- ifelse(full_data$Category == 'M', 1, 0)
full_data$Category_Support <- ifelse(full_data$Category == 'S', 1, 0)
full_data$Category_Executive <- ifelse(full_data$Category == 'E', 1, 0) head(full_data)## # A tibble: 6 × 44
## SurveyYear CoName EmpID JobCode Manag…¹ ...1 Job.C…² Job.M…³ Job.T…⁴ Job.F…⁵
## <dbl> <chr> <dbl> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
## 1 2022 A 765970 EN.SOD… 632603 6336 No Cha… Techno… Softwa… Engine…
## 2 2022 E 599690 SA.ISW… 229430 5630 No Cha… Sales Inside… Sales
## 3 2022 B 466405 EN.PMP… 305361 6216 Job Mo… Techno… Projec… Engine…
## 4 2022 B 190066 SA.ZZM… 569213 5839 No Cha… Sales Sales … Sales
## 5 2022 G 744444 PS.COC… 737020 6464 No Cha… Techno… Consul… Profes…
## 6 2022 G 358223 TE.DMD… 572976 4916 No Cha… Infras… Data M… Inform…
## # … with 34 more variables: Job.Area <chr>, Job.Focus <chr>, Level <chr>,
## # Role_Type <chr>, Role_Type2 <chr>, Module.Code <chr>, Function.Code <chr>,
## # Area.Code <chr>, Focus.Code <chr>, Level.Code <chr>,
## # Module.Definition <chr>, Function.Definition <chr>, Area.Definition <chr>,
## # Focus.Definition <chr>, Detailed.Definition <chr>, manager_ind <dbl>,
## # change_ind <dbl>, Category <chr>, Category_Level <chr>, Co_E_Ind <dbl>,
## # Module_Tech <dbl>, Module_Sales <dbl>, Module_Infrastructure <dbl>, …#Create Subsets based on Company Names to build profiles and easily compare
#Company A = 9729
full_data_A <- full_data %>% filter(full_data$CoName == 'A')
dim(full_data_A)## [1] 9279 44#Company B = 11829
full_data_B <- full_data %>% filter(full_data$CoName == 'B')
dim(full_data_B)## [1] 11829 44#Company C = 8331
full_data_C <- full_data %>% filter(full_data$CoName == 'C')
dim(full_data_C)## [1] 8331 44#Company D = 7272
full_data_D <- full_data %>% filter(full_data$CoName == 'D')
dim(full_data_D)## [1] 7272 44#Company E 5683
full_data_E <- full_data %>% filter(full_data$CoName == 'E')
dim(full_data_E)## [1] 5704 44#Company F 270
full_data_F <- full_data %>% filter(full_data$CoName == 'F')
dim(full_data_F)## [1] 270 44#Company G 15068
full_data_G <- full_data %>% filter(full_data$CoName == 'G')
dim(full_data_G)## [1] 15068 44#Company H 5643
full_data_H <- full_data %>% filter(full_data$CoName == 'H')
dim(full_data_H)## [1] 5643 44library(plotly)
fig <- plot_ly(alpha = 0.6, name = "Seniority Category")
fig <- fig %>% add_histogram(x=full_data_A$Category, name = "Company A")
fig <- fig %>% add_histogram(x=full_data_B$Category, name = "Company B")
fig <- fig %>% add_histogram(x=full_data_C$Category, name = "Company C")
fig <- fig %>% add_histogram(x=full_data_D$Category, name = "Company D")
fig <- fig %>% add_histogram(x=full_data_E$Category, name = "Company E")
fig <- fig %>% add_histogram(x=full_data_F$Category,name = "Company F")
fig <- fig %>% add_histogram(x=full_data_G$Category,name = "Company G")
fig <- fig %>% layout(barmode="overlay")
fig## Warning: Ignoring 21 observationslibrary(plotly)
fig <- plot_ly(alpha = 0.6, name = "Role_Type")
fig <- fig %>% add_histogram(x=full_data_A$Role_Type, name = "Company A")
fig <- fig %>% add_histogram(x=full_data_B$Role_Type, name = "Company B")
fig <- fig %>% add_histogram(x=full_data_C$Role_Type, name = "Company C")
fig <- fig %>% add_histogram(x=full_data_D$Role_Type, name = "Company D")
fig <- fig %>% add_histogram(x=full_data_E$Role_Type, name = "Company E")
fig <- fig %>% add_histogram(x=full_data_F$Role_Type,name = "Company F")
fig <- fig %>% add_histogram(x=full_data_G$Role_Type,name = "Company G")
fig <- fig %>% add_histogram(x=full_data_H$Role_Type,name = "Company H")
fig <- fig %>% layout(barmode="overlay")
fig## Warning: Ignoring 21 observationslibrary(plotly)
full_data_A_Sales <- full_data_A %>% filter(full_data_A$Job.Function == 'Sales')
full_data_B_Sales <- full_data_B %>% filter(full_data_B$Job.Function == 'Sales')
full_data_C_Sales <- full_data_C %>% filter(full_data_C$Job.Function == 'Sales')
full_data_D_Sales <- full_data_D %>% filter(full_data_D$Job.Function == 'Sales')
full_data_E_Sales <- full_data_E %>% filter(full_data_E$Job.Function == 'Sales')
full_data_F_Sales <- full_data_F %>% filter(full_data_F$Job.Function == 'Sales')
full_data_G_Sales <- full_data_G %>% filter(full_data_G$Job.Function == 'Sales')
full_data_H_Sales <- full_data_H %>% filter(full_data_H$Job.Function == 'Sales')
fig <- plot_ly(alpha = 0.6, name = "Job.Focus")
fig <- fig %>% add_histogram(x=full_data_A_Sales$Job.Focus, name = "Company A")
fig <- fig %>% add_histogram(x=full_data_B_Sales$Job.Focus, name = "Company B")
fig <- fig %>% add_histogram(x=full_data_C_Sales$Job.Focus, name = "Company C")
fig <- fig %>% add_histogram(x=full_data_D_Sales$Job.Focus, name = "Company D")
fig <- fig %>% add_histogram(x=full_data_E_Sales$Job.Focus, name = "Company E")
#fig <- fig %>% add_histogram(x=full_data_F_Sales$Job.Focus,name = "Company F")
fig <- fig %>% add_histogram(x=full_data_G_Sales$Job.Focus,name = "Company G")
fig <- fig %>% add_histogram(x=full_data_H_Sales$Job.Focus,name = "Company H")
fig <- fig %>% layout(barmode="overlay")
figfig <- plot_ly(alpha = 0.6, name = "Job Function")
fig <- fig %>% add_histogram(x=full_data_A$Job.Function, name = "Company A")
fig <- fig %>% add_histogram(x=full_data_B$Job.Function, name = "Company B")
fig <- fig %>% add_histogram(x=full_data_C$Job.Function, name = "Company C")
fig <- fig %>% add_histogram(x=full_data_D$Job.Function, name = "Company D")
fig <- fig %>% add_histogram(x=full_data_E$Job.Function, name = "Company E")
fig <- fig %>% add_histogram(x=full_data_F$Job.Function,name = "Company F")
fig <- fig %>% add_histogram(x=full_data_G$Job.Function,name = "Company G")
fig <- fig %>% layout(barmode="overlay")
fig## Warning: Ignoring 21 observations#Run a variety of Frequency Charts to understand the data better
#JOB FUNCTION
#We see when looking at all companies, the average frequency is 37% engineering, 24% sales, 10% customer service
library(dplyr)
full_data_job_func <- full_data %>%
group_by(Job.Function) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_func %>% arrange(desc(freq)) ## # A tibble: 25 × 3
## Job.Function total freq
## <chr> <int> <dbl>
## 1 Engineering 23366 36.9
## 2 Sales 15251 24.1
## 3 Customer Service / Support 6497 10.2
## 4 Professional Services / Consulting 3644 5.75
## 5 Information Technology 3149 4.97
## 6 Marketing 3017 4.76
## 7 Corporate & Business Services 2842 4.48
## 8 Human Resources 1820 2.87
## 9 Finance 1727 2.72
## 10 Strategic Planning & Corporate Development 783 1.24
## # … with 15 more rows#However, Company E does not follow the same distribution! Interesting! There are more Sales folks and less engineers than all companies. Let’s look closely and derive differences from all other company profiles #Sales 28%, Engineering 23%
dim(full_data_E)## [1] 5704 44full_data_job_func <- full_data_E %>%
group_by(Job.Function) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_func %>% arrange(desc(freq)) ## # A tibble: 17 × 3
## Job.Function total freq
## <chr> <int> <dbl>
## 1 Sales 1600 28.1
## 2 Engineering 1342 23.5
## 3 Customer Service / Support 1167 20.5
## 4 Marketing 444 7.78
## 5 Human Resources 327 5.73
## 6 Information Technology 327 5.73
## 7 Professional Services / Consulting 154 2.70
## 8 Corporate & Business Services 123 2.16
## 9 Finance 119 2.09
## 10 Legal 34 0.596
## 11 <NA> 21 0.368
## 12 Strategic Planning & Corporate Development 19 0.333
## 13 Creative 13 0.228
## 14 Media 5 0.0877
## 15 Supply Chain 4 0.0701
## 16 Leadership 3 0.0526
## 17 Manufacturing 2 0.0351full_data_job_func <- full_data_E %>%
group_by(Job.Module) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_func %>% arrange(desc(freq)) ## # A tibble: 7 × 3
## Job.Module total freq
## <chr> <int> <dbl>
## 1 Infrastructure 2556 44.8
## 2 Sales 1591 27.9
## 3 Technology 1466 25.7
## 4 Executive 50 0.877
## 5 <NA> 21 0.368
## 6 Media & Gaming 18 0.316
## 7 Manufacturing 2 0.0351#Company A Job Function - 60% Engineering
# Change this code to summarize by year
library(dplyr)
dim(full_data_A)## [1] 9279 44full_data_job_func <- full_data_A %>%
group_by(Job.Function) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_func %>% arrange(desc(freq)) ## # A tibble: 13 × 3
## Job.Function total freq
## <chr> <int> <dbl>
## 1 Engineering 5316 57.3
## 2 Customer Service / Support 1036 11.2
## 3 Sales 899 9.69
## 4 Marketing 427 4.60
## 5 Human Resources 420 4.53
## 6 Information Technology 416 4.48
## 7 Corporate & Business Services 309 3.33
## 8 Finance 175 1.89
## 9 Strategic Planning & Corporate Development 160 1.72
## 10 Legal 75 0.808
## 11 Corporate Affairs 35 0.377
## 12 Leadership 9 0.0970
## 13 Product Design 2 0.0216#Company B Job Function - 35% Engineering, 21% sales
# Change this code to summarize by year
library(dplyr)
dim(full_data_B)## [1] 11829 44full_data_job_func <- full_data_B %>%
group_by(Job.Function) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_func %>% arrange(desc(freq)) ## # A tibble: 19 × 3
## Job.Function total freq
## <chr> <int> <dbl>
## 1 Engineering 4225 35.7
## 2 Sales 2501 21.1
## 3 Customer Service / Support 1237 10.5
## 4 Corporate & Business Services 913 7.72
## 5 Marketing 777 6.57
## 6 Information Technology 603 5.10
## 7 Finance 405 3.42
## 8 Professional Services / Consulting 359 3.03
## 9 Strategic Planning & Corporate Development 244 2.06
## 10 Human Resources 199 1.68
## 11 Research & Development 120 1.01
## 12 Legal 113 0.955
## 13 Corporate Affairs 60 0.507
## 14 Manufacturing 27 0.228
## 15 Media 18 0.152
## 16 Supply Chain 11 0.0930
## 17 Environmental Health & Safety 10 0.0845
## 18 Leadership 6 0.0507
## 19 Broadcast 1 0.00845#Company C Job Function - 33% Engineering, 29% sales
# Change this code to summarize by year
library(dplyr)
dim(full_data_C)## [1] 8331 44full_data_job_func <- full_data_C %>%
group_by(Job.Function) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_func %>% arrange(desc(freq)) ## # A tibble: 18 × 3
## Job.Function total freq
## <chr> <int> <dbl>
## 1 Engineering 2798 33.6
## 2 Sales 2433 29.2
## 3 Customer Service / Support 1211 14.5
## 4 Information Technology 472 5.67
## 5 Professional Services / Consulting 312 3.75
## 6 Corporate & Business Services 274 3.29
## 7 Marketing 265 3.18
## 8 Finance 249 2.99
## 9 Human Resources 144 1.73
## 10 Legal 62 0.744
## 11 Strategic Planning & Corporate Development 62 0.744
## 12 Supply Chain 14 0.168
## 13 Manufacturing 13 0.156
## 14 Corporate Affairs 7 0.0840
## 15 Leadership 6 0.0720
## 16 Media 5 0.0600
## 17 Environmental Health & Safety 3 0.0360
## 18 Product Design 1 0.0120#Company D Job Function - 50% Sales, 20% Engineering
# Change this code to summarize by year
library(dplyr)
dim(full_data_D)## [1] 7272 44full_data_job_func <- full_data_D %>%
group_by(Job.Function) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_func %>% arrange(desc(freq)) ## # A tibble: 18 × 3
## Job.Function total freq
## <chr> <int> <dbl>
## 1 Sales 3630 49.9
## 2 Engineering 1490 20.5
## 3 Professional Services / Consulting 387 5.32
## 4 Customer Service / Support 354 4.87
## 5 Information Technology 323 4.44
## 6 Marketing 257 3.53
## 7 Finance 208 2.86
## 8 Human Resources 205 2.82
## 9 Corporate & Business Services 163 2.24
## 10 Strategic Planning & Corporate Development 124 1.71
## 11 Legal 101 1.39
## 12 Business Process Outsourcing 10 0.138
## 13 Corporate Affairs 8 0.110
## 14 Manufacturing 7 0.0963
## 15 Media 2 0.0275
## 16 Environmental Health & Safety 1 0.0138
## 17 Leadership 1 0.0138
## 18 Supply Chain 1 0.0138#Company F Job Function - 31% Finance, 27% Business Ops
# Change this code to summarize by year
library(dplyr)
dim(full_data_F)## [1] 270 44full_data_job_func <- full_data_F %>%
group_by(Job.Function) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_func %>% arrange(desc(freq)) ## # A tibble: 6 × 3
## Job.Function total freq
## <chr> <int> <dbl>
## 1 Finance 85 31.5
## 2 Business Operations 75 27.8
## 3 Legal 60 22.2
## 4 Information Technology 25 9.26
## 5 Engineering 20 7.41
## 6 Energy & Mining Operations 5 1.85#Company G Job Function - 43% Finance, 16% Business Ops
# Change this code to summarize by year
library(dplyr)
dim(full_data_G)## [1] 15068 44full_data_job_func <- full_data_G %>%
group_by(Job.Function) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_func %>% arrange(desc(freq)) ## # A tibble: 18 × 3
## Job.Function total freq
## <chr> <int> <dbl>
## 1 Engineering 6492 43.1
## 2 Sales 2543 16.9
## 3 Professional Services / Consulting 2092 13.9
## 4 Customer Service / Support 1034 6.86
## 5 Corporate & Business Services 744 4.94
## 6 Information Technology 723 4.80
## 7 Marketing 457 3.03
## 8 Human Resources 335 2.22
## 9 Finance 293 1.94
## 10 Legal 145 0.962
## 11 Strategic Planning & Corporate Development 98 0.650
## 12 Corporate Affairs 52 0.345
## 13 Creative 22 0.146
## 14 Environmental Health & Safety 17 0.113
## 15 Supply Chain 15 0.0995
## 16 Leadership 3 0.0199
## 17 Media 2 0.0133
## 18 Manufacturing 1 0.00664full_data_job_func <- full_data_G %>%
group_by(Job.Module) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_func %>% arrange(desc(freq)) ## # A tibble: 5 × 3
## Job.Module total freq
## <chr> <int> <dbl>
## 1 Technology 8366 55.5
## 2 Infrastructure 4009 26.6
## 3 Sales 2505 16.6
## 4 Executive 164 1.09
## 5 Media & Gaming 24 0.159JOB FUNCTION ENGINEERING
Company A - G Taking a look at role types. Analysis can be found in deck.
full_data_A_Eng <- full_data_A %>% filter(full_data_A$Job.Function == 'Engineering')
full_data_job_lvl <- full_data_A_Eng %>%
group_by(Job.Title) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 70 × 3
## Job.Title total freq
## <chr> <int> <dbl>
## 1 Software - Software Development Engineering - P3 1434 27.0
## 2 Software - Software Development Engineering - P4 1110 20.9
## 3 Software - Software Development Engineering - P2 659 12.4
## 4 Software - Software Development Engineering - M3 415 7.81
## 5 Software - Software Development Engineering - P5 241 4.53
## 6 Product Management - Technology Product Management - P4 153 2.88
## 7 User Interface/User Experience (UI/UX) - UI / UX Design - P4 104 1.96
## 8 Software - Software Development Engineering - M4 99 1.86
## 9 Product Management - Technology Product Management - P3 97 1.82
## 10 Project Management - Project / Program (Design) - P5 84 1.58
## # … with 60 more rowsfull_data_B_Eng <- full_data_B %>% filter(full_data_B$Job.Function == 'Engineering')
full_data_job_lvl <- full_data_B_Eng %>%
group_by(Job.Title) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 102 × 3
## Job.Title total freq
## <chr> <int> <dbl>
## 1 Software - Software Development Engineering - P3 992 23.5
## 2 Software - Software Development Engineering - P4 626 14.8
## 3 Software - Software Development Engineering - P2 285 6.75
## 4 Software - Quality Assurance Engineering - P3 216 5.11
## 5 Software - Software Development Engineering - M4 191 4.52
## 6 Software - Software Development Engineering - M3 177 4.19
## 7 Software - Software Development Engineering - P5 170 4.02
## 8 Product Management - Technology Product Management - P4 158 3.74
## 9 User Interface/User Experience (UI/UX) - UI / UX Engineering - P3 118 2.79
## 10 Product Management - Technology Product Management - P3 116 2.75
## # … with 92 more rowsfull_data_C_Eng <- full_data_C %>% filter(full_data_C$Job.Function == 'Engineering')
full_data_job_lvl <- full_data_C_Eng %>%
group_by(Job.Title) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 79 × 3
## Job.Title total freq
## <chr> <int> <dbl>
## 1 Software - Software Development Engineering - P3 855 30.6
## 2 Software - Software Development Engineering - P4 361 12.9
## 3 Software - Software Development Engineering - P2 317 11.3
## 4 Software - Software Development Engineering - P5 208 7.43
## 5 Software - Software Development Engineering - P1 127 4.54
## 6 Software - Software Development Engineering - M4 79 2.82
## 7 Software - Quality Assurance Engineering - P3 69 2.47
## 8 Software - Software Development Engineering - M3 67 2.39
## 9 Engineering - Other Area - Multi-Focus - S5 49 1.75
## 10 Software - Software Development Engineering - P6 43 1.54
## # … with 69 more rowsfull_data_D_Eng <- full_data_D %>% filter(full_data_D$Job.Function == 'Engineering')
full_data_job_lvl <- full_data_D_Eng %>%
group_by(Job.Title) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 90 × 3
## Job.Title total freq
## <chr> <int> <dbl>
## 1 Software - Software Development Engineering - P4 231 15.5
## 2 Software - Software Development Engineering - P3 141 9.46
## 3 Software - Software Development Engineering - P5 105 7.05
## 4 Software - Software Development Engineering - P2 89 5.97
## 5 Software - Software Development Engineering - M4 57 3.83
## 6 Software - Software Development Engineering - P1 57 3.83
## 7 Software - Quality Assurance Engineering - P3 47 3.15
## 8 Software - Quality Assurance Engineering - P4 44 2.95
## 9 Software - Software Development Engineering - M5 44 2.95
## 10 User Interface/User Experience (UI/UX) - UI / UX Engineering - P4 41 2.75
## # … with 80 more rowsfull_data_E_Eng <- full_data %>% filter(full_data$Job.Function == 'Engineering')
full_data_job_lvl <- full_data_E_Eng %>%
group_by(Job.Title) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 175 × 3
## Job.Title total freq
## <chr> <int> <dbl>
## 1 Software - Software Development Engineering - P3 5434 23.3
## 2 Software - Software Development Engineering - P4 3769 16.1
## 3 Software - Software Development Engineering - P2 2260 9.67
## 4 Software - Software Development Engineering - P5 1186 5.08
## 5 Software - Software Development Engineering - M3 1071 4.58
## 6 Product Management - Technology Product Management - P4 722 3.09
## 7 Software - Quality Assurance Engineering - P3 673 2.88
## 8 Software - Software Development Engineering - M4 644 2.76
## 9 Product Management - Technology Product Management - P3 573 2.45
## 10 Software - Quality Assurance Engineering - P4 463 1.98
## # … with 165 more rowsfull_data_F_Eng <- full_data_F %>% filter(full_data_F$Job.Function == 'Engineering')
full_data_job_lvl <- full_data_F_Eng %>%
group_by(Job.Title) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 2 × 3
## Job.Title total freq
## <chr> <int> <dbl>
## 1 Software - Software Development Engineering - P3 15 75
## 2 Software - Software Development Engineering - M3 5 25full_data_G_Eng <- full_data_G %>% filter(full_data_G$Job.Function == 'Engineering')
full_data_job_lvl <- full_data_G_Eng %>%
group_by(Job.Title) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 112 × 3
## Job.Title total freq
## <chr> <int> <dbl>
## 1 Software - Software Development Engineering - P4 1105 17.0
## 2 Software - Software Development Engineering - P3 895 13.8
## 3 Software - Software Development Engineering - P2 473 7.29
## 4 Software - Software Development Engineering - P5 431 6.64
## 5 Software - Quality Assurance Engineering - P4 336 5.18
## 6 Software - Quality Assurance Engineering - P3 331 5.10
## 7 Product Management - Technology Product Management - P4 247 3.80
## 8 Software - Software Development Engineering - M3 243 3.74
## 9 Product Management - Technology Product Management - P3 205 3.16
## 10 Software - Software Development Engineering - M4 186 2.87
## # … with 102 more rowsJOB FOCUS Sales
Company A - G Taking a look at role types. Analysis can be found in deck.
full_data_A_Sales <- full_data_A %>% filter(full_data_A$Job.Function == 'Sales')
full_data_job_lvl <- full_data_A_Sales %>%
group_by(Job.Focus) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 17 × 3
## Job.Focus total freq
## <chr> <int> <dbl>
## 1 Post-Sales - Non-Sales Plan 196 21.8
## 2 Customer Success 190 21.1
## 3 Direct Sales - New Accounts 165 18.4
## 4 Alliance Partnership 85 9.45
## 5 Inside Sales - Own Quota 74 8.23
## 6 Pre-Sales - Sales Plan 71 7.90
## 7 Sales Enablement 44 4.89
## 8 Direct Sales 33 3.67
## 9 Multi-Focus 16 1.78
## 10 Deal Desk 13 1.45
## 11 Product / Overlay Specialist Sales 4 0.445
## 12 Field Sales - Sales Account Management - Executive 3 0.334
## 13 Alliance Partnership - Executive 1 0.111
## 14 Sales - Executive 1 0.111
## 15 Sales Administration 1 0.111
## 16 Sales Commissions 1 0.111
## 17 Top Sales - Corporate 1 0.111full_data_B_Sales <- full_data_B %>% filter(full_data_B$Job.Function == 'Sales')
full_data_job_lvl <- full_data_B_Sales %>%
group_by(Job.Focus) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 26 × 3
## Job.Focus total freq
## <chr> <int> <dbl>
## 1 Pre-Sales - Sales Plan 359 14.4
## 2 Multi-Channel Sales 348 13.9
## 3 Multi-Focus 326 13.0
## 4 Inside Sales - Own Quota 192 7.68
## 5 Telemarketing / Lead Development 166 6.64
## 6 OEM/VAR Sales 137 5.48
## 7 Direct Sales - Existing Accounts 127 5.08
## 8 Product / Overlay Specialist Sales 121 4.84
## 9 Sales Operations 121 4.84
## 10 Pre & Post-Sales - Sales Plan 115 4.60
## # … with 16 more rowsfull_data_C_Sales <- full_data_C %>% filter(full_data_C$Job.Function == 'Sales')
full_data_job_lvl <- full_data_C_Sales %>%
group_by(Job.Focus) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 29 × 3
## Job.Focus total freq
## <chr> <int> <dbl>
## 1 Pre-Sales - Sales Plan 492 20.2
## 2 Inside Sales - Overlay Quota 319 13.1
## 3 Direct Sales - Existing Accounts 287 11.8
## 4 OEM/VAR Sales 281 11.5
## 5 Customer Success 206 8.47
## 6 Multi-Focus 179 7.36
## 7 Product / Overlay Specialist Sales 85 3.49
## 8 Sales Operations 81 3.33
## 9 Alliance Partnership 76 3.12
## 10 Contract Renewal 64 2.63
## # … with 19 more rowsfull_data_D_Sales <- full_data_D %>% filter(full_data_D$Job.Function == 'Sales')
full_data_job_lvl <- full_data_D_Sales %>%
group_by(Job.Focus) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 28 × 3
## Job.Focus total freq
## <chr> <int> <dbl>
## 1 Telemarketing / Lead Development 1042 28.7
## 2 Direct Sales - New Accounts 1020 28.1
## 3 Pre-Sales - Sales Plan 317 8.73
## 4 Direct Sales 244 6.72
## 5 Contract Renewal 243 6.69
## 6 Customer Success 219 6.03
## 7 Multi-Focus 126 3.47
## 8 Direct Sales - Existing Accounts 90 2.48
## 9 Alliance Partnership 64 1.76
## 10 Sales Operations 47 1.29
## # … with 18 more rowsfull_data_E_Sales <- full_data_E %>% filter(full_data_E$Job.Function == 'Sales')
dim(full_data_E_Sales)## [1] 1600 44full_data_job_lvl <- full_data_E_Sales %>%
group_by(Job.Focus) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 12 × 3
## Job.Focus total freq
## <chr> <int> <dbl>
## 1 Inside Sales - Own Quota 956 59.8
## 2 Telemarketing / Lead Development 241 15.1
## 3 Multi-Focus 191 11.9
## 4 Contract Renewal 92 5.75
## 5 Pre-Sales - Sales Plan 61 3.81
## 6 Sales Operations 40 2.5
## 7 Customer Success 7 0.438
## 8 Sales - Executive 5 0.312
## 9 Sales Commissions 3 0.188
## 10 Inside Sales / Telemarketing - Executive 2 0.125
## 11 Sales Operations - Executive 1 0.0625
## 12 Top Sales - Corporate 1 0.0625full_data_F_Sales <- full_data_F %>% filter(full_data_F$Job.Function == 'Sales')
full_data_job_lvl <- full_data_F_Sales %>%
group_by(Job.Focus) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 0 × 3
## # … with 3 variables: Job.Focus <chr>, total <int>, freq <dbl>full_data_G_Sales <- full_data_G %>% filter(full_data_G$Job.Function == 'Sales')
full_data_job_lvl <- full_data_G_Sales %>%
group_by(Job.Focus) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 26 × 3
## Job.Focus total freq
## <chr> <int> <dbl>
## 1 Pre-Sales - Sales Plan 639 25.1
## 2 Direct Sales - New Accounts 490 19.3
## 3 Direct Sales 235 9.24
## 4 Telemarketing / Lead Development 194 7.63
## 5 Direct Sales - Existing Accounts 187 7.35
## 6 Multi-Focus 148 5.82
## 7 Consulting Services Sales 140 5.51
## 8 Inside Sales - Overlay Quota 63 2.48
## 9 Sales Force Effectiveness / Training 61 2.40
## 10 Sales Operations 59 2.32
## # … with 16 more rowsfull_data_H_Sales <- full_data_H %>% filter(full_data_H$Job.Function == 'Sales')
full_data_job_lvl <- full_data_H_Sales %>%
group_by(Job.Focus) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 23 × 3
## Job.Focus total freq
## <chr> <int> <dbl>
## 1 Direct Sales - New Accounts 450 27.4
## 2 Telemarketing / Lead Development 283 17.2
## 3 Customer Success 196 11.9
## 4 Inside Sales - Own Quota 192 11.7
## 5 Multi-Focus 124 7.54
## 6 Sales Operations 80 4.86
## 7 Direct Sales 56 3.40
## 8 Sales Administration 49 2.98
## 9 Alliance Partnership 46 2.80
## 10 Contract Renewal 44 2.67
## # … with 13 more rowsfull_data_Sales <- full_data %>% filter(full_data$Job.Function == 'Sales')
dim(full_data_Sales)## [1] 15251 44full_data_job_lvl <- full_data_Sales %>%
group_by(Job.Focus) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 43 × 3
## Job.Focus total freq
## <chr> <int> <dbl>
## 1 Direct Sales - New Accounts 2162 14.2
## 2 Telemarketing / Lead Development 1984 13.0
## 3 Pre-Sales - Sales Plan 1965 12.9
## 4 Inside Sales - Own Quota 1414 9.27
## 5 Multi-Focus 1110 7.28
## 6 Customer Success 827 5.42
## 7 Direct Sales - Existing Accounts 691 4.53
## 8 Direct Sales 635 4.16
## 9 Contract Renewal 579 3.80
## 10 OEM/VAR Sales 430 2.82
## # … with 33 more rows#Two Proportion Test- Sales Company E vs Full Company
#E compared to A
res <- prop.test(x = c(956, 5316), n = c(1600, 15251))
res##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(956, 5316) out of c(1600, 15251)
## X-squared = 382.96, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## 0.2233962 0.2744692
## sample estimates:
## prop 1 prop 2
## 0.5975000 0.3485673Role Types By Company
full_data_job_lvl <- full_data_A %>%
group_by(Role_Type) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 19 × 3
## Role_Type total freq
## <chr> <int> <dbl>
## 1 P3 3003 32.4
## 2 P4 2103 22.7
## 3 P2 1645 17.7
## 4 M3 779 8.40
## 5 P5 612 6.60
## 6 M4 431 4.64
## 7 M5 186 2.00
## 8 M2 99 1.07
## 9 P1 98 1.06
## 10 M6 96 1.03
## 11 P6 95 1.02
## 12 E1 60 0.647
## 13 S3 19 0.205
## 14 S1 16 0.172
## 15 E2 14 0.151
## 16 S2 13 0.140
## 17 E3 6 0.0647
## 18 E6 2 0.0216
## 19 S4 2 0.0216full_data_job_lvl <- full_data_B %>%
group_by(Role_Type) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 23 × 3
## Role_Type total freq
## <chr> <int> <dbl>
## 1 P3 3390 28.7
## 2 P4 2242 19.0
## 3 P2 1386 11.7
## 4 P5 1275 10.8
## 5 M4 788 6.66
## 6 M3 674 5.70
## 7 P1 454 3.84
## 8 M5 412 3.48
## 9 P6 296 2.50
## 10 M2 186 1.57
## # … with 13 more rowsfull_data_job_lvl <- full_data_C %>%
group_by(Role_Type) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 23 × 3
## Role_Type total freq
## <chr> <int> <dbl>
## 1 P3 2325 27.9
## 2 P4 1997 24.0
## 3 P2 944 11.3
## 4 P5 879 10.6
## 5 M4 345 4.14
## 6 M3 313 3.76
## 7 M5 300 3.60
## 8 P1 284 3.41
## 9 M6 203 2.44
## 10 M2 150 1.80
## # … with 13 more rowsfull_data_job_lvl <- full_data_D %>%
group_by(Role_Type) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 23 × 3
## Role_Type total freq
## <chr> <int> <dbl>
## 1 P4 1343 18.5
## 2 P3 1148 15.8
## 3 P5 728 10.0
## 4 S2 712 9.79
## 5 P2 706 9.71
## 6 P1 610 8.39
## 7 M3 285 3.92
## 8 M5 279 3.84
## 9 S1 273 3.75
## 10 M4 248 3.41
## # … with 13 more rowsfull_data_job_lvl <- full_data_E %>%
group_by(Role_Type) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 22 × 3
## Role_Type total freq
## <chr> <int> <dbl>
## 1 P3 1886 33.1
## 2 P2 878 15.4
## 3 P4 838 14.7
## 4 P1 574 10.1
## 5 M3 382 6.70
## 6 M4 265 4.65
## 7 S1 191 3.35
## 8 P5 164 2.88
## 9 M5 133 2.33
## 10 S2 127 2.23
## # … with 12 more rowsfull_data_job_lvl <- full_data_F %>%
group_by(Role_Type) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 6 × 3
## Role_Type total freq
## <chr> <int> <dbl>
## 1 P3 110 40.7
## 2 P2 70 25.9
## 3 P4 35 13.0
## 4 M5 30 11.1
## 5 M3 15 5.56
## 6 E3 10 3.70full_data_job_lvl <- full_data_G %>%
group_by(Role_Type) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 21 × 3
## Role_Type total freq
## <chr> <int> <dbl>
## 1 P4 4995 33.1
## 2 P3 3321 22.0
## 3 P5 1901 12.6
## 4 P2 1123 7.45
## 5 M3 861 5.71
## 6 M4 626 4.15
## 7 M5 619 4.11
## 8 M6 432 2.87
## 9 P6 385 2.56
## 10 P1 181 1.20
## # … with 11 more rows#Convert indicator variables to integers so we can attempt to cluster them
full_data## # A tibble: 63,396 × 44
## SurveyY…¹ CoName EmpID JobCode Manag…² ...1 Job.C…³ Job.M…⁴ Job.T…⁵ Job.F…⁶
## <dbl> <chr> <dbl> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
## 1 2022 A 765970 EN.SOD… 632603 6336 No Cha… Techno… Softwa… Engine…
## 2 2022 E 599690 SA.ISW… 229430 5630 No Cha… Sales Inside… Sales
## 3 2022 B 466405 EN.PMP… 305361 6216 Job Mo… Techno… Projec… Engine…
## 4 2022 B 190066 SA.ZZM… 569213 5839 No Cha… Sales Sales … Sales
## 5 2022 G 744444 PS.COC… 737020 6464 No Cha… Techno… Consul… Profes…
## 6 2022 G 358223 TE.DMD… 572976 4916 No Cha… Infras… Data M… Inform…
## 7 2022 A 651685 EN.SOD… 142353 6335 No Cha… Techno… Softwa… Engine…
## 8 2022 H 640998 CB.PMP… 912532 2001 Job Mo… Infras… Projec… Corpor…
## 9 2022 G 155035 PS.PSS… 251729 6573 Job Mo… Techno… Profes… Profes…
## 10 2022 A 162447 EN.SOD… 328949 6336 No Cha… Techno… Softwa… Engine…
## # … with 63,386 more rows, 34 more variables: Job.Area <chr>, Job.Focus <chr>,
## # Level <chr>, Role_Type <chr>, Role_Type2 <chr>, Module.Code <chr>,
## # Function.Code <chr>, Area.Code <chr>, Focus.Code <chr>, Level.Code <chr>,
## # Module.Definition <chr>, Function.Definition <chr>, Area.Definition <chr>,
## # Focus.Definition <chr>, Detailed.Definition <chr>, manager_ind <dbl>,
## # change_ind <dbl>, Category <chr>, Category_Level <chr>, Co_E_Ind <dbl>,
## # Module_Tech <dbl>, Module_Sales <dbl>, Module_Infrastructure <dbl>, …full_data$change_ind <- as.integer(full_data$change_ind)
full_data$manager_ind <- as.integer(full_data$manager_ind)
full_data$Category_Level <- as.integer(full_data$Category_Level)
full_data$Eng_Ind <- as.integer(full_data$Eng_Ind)
full_data$Sales_Ind <- as.integer(full_data$Sales_Ind)
full_data$Category_Professional <- as.integer(full_data$Category_Professional)
full_data$Category_Management <- as.integer(full_data$Category_Management)
full_data$Category_Support <- as.integer(full_data$Category_Support)
full_data$Category_Executive <- as.integer(full_data$Category_Executive)final <- full_data %>% dplyr::select(Co_E_Ind, change_ind, manager_ind, Category_Level, Eng_Ind, Sales_Ind, Category_Professional, Category_Management, Category_Support, Category_Executive)
final <- as.matrix(final)
final <- prop.table(final, margin=1)
final_set <- bind_cols(as.data.frame(full_data[,2]), as.data.frame(final))
head(final_set)## CoName Co_E_Ind change_ind manager_ind Category_Level Eng_Ind Sales_Ind
## 1 A 0.0000000 0.0 0.1250000 0.5000000 0.125 0.125
## 2 E 0.1666667 0.0 0.1666667 0.5000000 0.000 0.000
## 3 B 0.0000000 0.1 0.1000000 0.5000000 0.100 0.100
## 4 B 0.0000000 0.0 0.2000000 0.6000000 0.000 0.000
## 5 G 0.0000000 0.0 0.1428571 0.7142857 0.000 0.000
## 6 G 0.0000000 0.0 0.1666667 0.6666667 0.000 0.000
## Category_Professional Category_Management Category_Support Category_Executive
## 1 0.1250000 0 0 0
## 2 0.1666667 0 0 0
## 3 0.1000000 0 0 0
## 4 0.2000000 0 0 0
## 5 0.1428571 0 0 0
## 6 0.1666667 0 0 0#Running a clustering analysis to see how the data would be clustered without company names attached
final_set <- na.omit(final_set)
library(cluster) # Needed for silhouette function
require(purrr)## Loading required package: purrr##
## Attaching package: 'purrr'## The following object is masked from 'package:data.table':
##
## transposekmeansDat <- final_set[,-(1)] # Extract only customer columns
kmeansDat.t <- t(kmeansDat) # Get customers in rows and products in columns
set.seed(123)
wss <- function(k) {
kmeans(na.omit(kmeansDat), k, nstart = 4 )$tot.withinss
}
# Compute and plot wss for k = 1 to k = 15
k.values <- 1:15
# extract wss for 2-15 clusters
wss_values <- map_dbl(k.values, wss)
plot(k.values, wss_values,
type="b", pch = 19, frame = FALSE,
xlab="Number of clusters K",
ylab="Total within-clusters sum of squares")
#Final Clustering Results
#Final
set.seed(123)
final <- kmeans(na.omit(kmeansDat), 4, nstart = 25)
require(readr)
require(dplyr)
require(ggplot2)
require(factoextra)
fviz_cluster(final, data = na.omit(kmeansDat))
#Obtain Manager to Employee Ratios #Assume mu = 6.579008 which is the mean of Company E Managers to Employees ratio #Perform Wilcox test
emp_manager <- full_data %>% dplyr::select(CoName, ManagerID)
emp_manager_counts <- setDT(emp_manager)[,list(Count=.N) ,names(emp_manager)]
emp_manager_count <- emp_manager_counts %>% filter(CoName=='E')
mean(as.integer(na.omit(emp_manager_count$Count)))## [1] 6.579008#Assume mu = 6.579008 which is the mean of Company E Managers to Employees ratio
emp_manager_count <- emp_manager_counts %>% filter(CoName=='A')
mean(as.integer(na.omit(emp_manager_count$Count)))## [1] 5.654479result = wilcox.test(na.omit(emp_manager_count)$Count, mu = 6.579008)
result##
## Wilcoxon signed rank test with continuity correction
##
## data: na.omit(emp_manager_count)$Count
## V = 368900, p-value < 2.2e-16
## alternative hypothesis: true location is not equal to 6.579008emp_manager_count <- emp_manager_counts %>% filter(CoName=='B')
mean(as.integer(na.omit(emp_manager_count$Count)))## [1] 5.083369result = wilcox.test(na.omit(emp_manager_count)$Count, mu = 6.579008)
result##
## Wilcoxon signed rank test with continuity correction
##
## data: na.omit(emp_manager_count)$Count
## V = 529654, p-value < 2.2e-16
## alternative hypothesis: true location is not equal to 6.579008emp_manager_count <- emp_manager_counts %>% filter(CoName=='C')
mean(as.integer(na.omit(emp_manager_count$Count)))## [1] 6.103297result = wilcox.test(na.omit(emp_manager_count)$Count, mu = 6.579008)
result##
## Wilcoxon signed rank test with continuity correction
##
## data: na.omit(emp_manager_count)$Count
## V = 333010, p-value < 2.2e-16
## alternative hypothesis: true location is not equal to 6.579008emp_manager_count <- emp_manager_counts %>% filter(CoName=='D')
mean(as.integer(na.omit(emp_manager_count$Count)))## [1] 5.48003result = wilcox.test(na.omit(emp_manager_count)$Count, mu = 6.579008)
result##
## Wilcoxon signed rank test with continuity correction
##
## data: na.omit(emp_manager_count)$Count
## V = 216840, p-value < 2.2e-16
## alternative hypothesis: true location is not equal to 6.579008#emp_manager_count <- emp_manager_counts %>% filter(CoName=='F')
#mean(as.integer(na.omit(emp_manager_count$Count)))
#result = wilcox.test(na.omit(emp_manager_count)$Count, mu = 6.579008)
#result
emp_manager_count <- emp_manager_counts %>% filter(CoName=='G')
mean(as.integer(na.omit(emp_manager_count$Count)))## [1] 5.345158result = wilcox.test(na.omit(emp_manager_count)$Count, mu = 6.579008)
result##
## Wilcoxon signed rank test with continuity correction
##
## data: na.omit(emp_manager_count)$Count
## V = 805573, p-value < 2.2e-16
## alternative hypothesis: true location is not equal to 6.579008emp_manager_count <- emp_manager_counts %>% filter(CoName=='H')
mean(as.integer(na.omit(emp_manager_count$Count)))## [1] 4.539823result = wilcox.test(na.omit(emp_manager_count)$Count, mu = 6.579008)
result##
## Wilcoxon signed rank test with continuity correction
##
## data: na.omit(emp_manager_count)$Count
## V = 103971, p-value < 2.2e-16
## alternative hypothesis: true location is not equal to 6.579008full_data_E_Eng <- full_data %>% filter(full_data$CoName == 'C')
dim(full_data_E_Eng)## [1] 8331 44full_data_job_lvl <- full_data_E_Eng %>%
group_by(Job.Function) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 18 × 3
## Job.Function total freq
## <chr> <int> <dbl>
## 1 Engineering 2798 33.6
## 2 Sales 2433 29.2
## 3 Customer Service / Support 1211 14.5
## 4 Information Technology 472 5.67
## 5 Professional Services / Consulting 312 3.75
## 6 Corporate & Business Services 274 3.29
## 7 Marketing 265 3.18
## 8 Finance 249 2.99
## 9 Human Resources 144 1.73
## 10 Legal 62 0.744
## 11 Strategic Planning & Corporate Development 62 0.744
## 12 Supply Chain 14 0.168
## 13 Manufacturing 13 0.156
## 14 Corporate Affairs 7 0.0840
## 15 Leadership 6 0.0720
## 16 Media 5 0.0600
## 17 Environmental Health & Safety 3 0.0360
## 18 Product Design 1 0.0120#Engineering Proportion Tests
#Engineering Chi-Squared Tests
#E compared to A
res <- prop.test(x = c(1342, 5316), n = c(5704, 9279))
res##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(1342, 5316) out of c(5704, 9279)
## X-squared = 1629.6, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.3526900 -0.3225762
## sample estimates:
## prop 1 prop 2
## 0.2352735 0.5729066#Engineering Chi-Squared Tests
#E compared to B
res <- prop.test(x = c(1342, 4225), n = c(5704, 11829))
res##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(1342, 4225) out of c(5704, 11829)
## X-squared = 263.32, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.1360199 -0.1077792
## sample estimates:
## prop 1 prop 2
## 0.2352735 0.3571730#Engineering Chi-Squared Tests
#E compared to C
res <- prop.test(x = c(1342, 2798), n = c(5704, 8331))
res##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(1342, 2798) out of c(5704, 8331)
## X-squared = 164.22, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.1156956 -0.0854655
## sample estimates:
## prop 1 prop 2
## 0.2352735 0.3358540#Engineering Chi-Squared Tests
#E compared to D
res <- prop.test(x = c(1342, 1490), n = c(5704, 7272))
res##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(1342, 1490) out of c(5704, 7272)
## X-squared = 17.112, df = 1, p-value = 3.523e-05
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## 0.01582611 0.04492989
## sample estimates:
## prop 1 prop 2
## 0.2352735 0.2048955#Engineering Chi-Squared Tests
#E compared to F
res <- prop.test(x = c(1342, 20), n = c(5704, 270))
res##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(1342, 20) out of c(5704, 270)
## X-squared = 37.15, df = 1, p-value = 1.094e-09
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## 0.1261389 0.1962599
## sample estimates:
## prop 1 prop 2
## 0.23527349 0.07407407#Engineering Chi-Squared Tests
#E compared to G
res <- prop.test(x = c(1342, 3450), n = c(5704, 15068))
res##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(1342, 3450) out of c(5704, 15068)
## X-squared = 0.89348, df = 1, p-value = 0.3445
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.00670036 0.01932327
## sample estimates:
## prop 1 prop 2
## 0.2352735 0.2289620#Engineering Chi-Squared Tests
#E compared to H
res <- prop.test(x = c(1342, 1683), n = c(5704, 5643))
res##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(1342, 1683) out of c(5704, 5643)
## X-squared = 57.211, df = 1, p-value = 3.915e-14
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.07938561 -0.04655864
## sample estimates:
## prop 1 prop 2
## 0.2352735 0.2982456#P value is less than 0.05, we reject the null hypothesis and conclude that the engineering job function is independent of company E.
full_data$eng_ind <- ifelse(is.na(full_data$Job.Function == 'Engineering'), 1, 0)
full_data$Co_E_Ind## [1] 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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## [63361] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0table(full_data$Eng_Ind, full_data$Co_E_Ind)##
## 0 1
## 0 35668 4341
## 1 22024 1342chisq.test(full_data$Eng_Ind, full_data$Co_E_Ind)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: full_data$Eng_Ind and full_data$Co_E_Ind
## X-squared = 470.62, df = 1, p-value < 2.2e-16full_data_E_Eng <- full_data %>% filter(full_data$Job.Function == 'Engineering')
full_data_E_Eng <- full_data_E_Eng %>% filter(full_data_E_Eng$CoName == 'H')
dim(full_data_E_Eng)## [1] 1683 45full_data_job_lvl <- full_data_E_Eng %>%
group_by(Role_Type) %>%
summarise(total = n()) %>%
mutate(freq = (total / sum(total))*100)
full_data_job_lvl %>% arrange(desc(freq)) ## # A tibble: 13 × 3
## Role_Type total freq
## <chr> <int> <dbl>
## 1 P3 519 30.8
## 2 P4 450 26.7
## 3 P2 267 15.9
## 4 M2 94 5.59
## 5 M3 91 5.41
## 6 P5 87 5.17
## 7 M4 42 2.50
## 8 M5 33 1.96
## 9 M6 33 1.96
## 10 P6 28 1.66
## 11 E1 22 1.31
## 12 P1 15 0.891
## 13 E3 2 0.119#Compare Level P2 and P3 for Company A and E
#Engineering Chi-Squared Tests
#E compared to A
res <- prop.test(x = c(977, 2473), n = c(1342, 5316))
res##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(977, 2473) out of c(1342, 5316)
## X-squared = 295.39, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## 0.2350283 0.2906087
## sample estimates:
## prop 1 prop 2
## 0.7280179 0.4651994#Compare Level P2 and P3 for Company B and E #SIGNIFICANT
#Engineering Chi-Squared Tests
#E compared to B
res <- prop.test(x = c(977, 2003), n = c(1342, 4225))
res##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(977, 2003) out of c(1342, 4225)
## X-squared = 263, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## 0.2252752 0.2825949
## sample estimates:
## prop 1 prop 2
## 0.7280179 0.4740828#Compare Level P2 and P3 for Company C and E #SIGNIFICANT
#Engineering Chi-Squared Tests
#E compared to C
res <- prop.test(x = c(977, 1439), n = c(1342, 2798))
res##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(977, 1439) out of c(1342, 2798)
## X-squared = 169.6, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## 0.1830087 0.2444352
## sample estimates:
## prop 1 prop 2
## 0.7280179 0.5142959#Compare Level P2 and P3 for Company D and E #SIGNIFICANT
#Engineering Chi-Squared Tests
#E compared to D
res <- prop.test(x = c(977, 445), n = c(1342, 1490))
res##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(977, 445) out of c(1342, 1490)
## X-squared = 518.95, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## 0.3953831 0.4633372
## sample estimates:
## prop 1 prop 2
## 0.7280179 0.2986577#Engineering Chi-Squared Tests
#E compared to F
res <- prop.test(x = c(977, 15), n = c(1342, 20))
res##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(977, 15) out of c(1342, 20)
## X-squared = 7.7109e-31, df = 1, p-value = 1
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.2352245 0.1912602
## sample estimates:
## prop 1 prop 2
## 0.7280179 0.7500000#Compare Level P2 and P3 for Company G and E #SIGNIFICANT
#Engineering Chi-Squared Tests
#E compared to G
res <- prop.test(x = c(977, 2367), n = c(1342, 6492))
res##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(977, 2367) out of c(1342, 6492)
## X-squared = 598.87, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## 0.3364350 0.3903956
## sample estimates:
## prop 1 prop 2
## 0.7280179 0.3646026#Compare Level P2 and P3 for Company H and E #SIGNIFICANT
#Engineering Chi-Squared Tests
#E compared to G
res <- prop.test(x = c(977, 786), n = c(1342, 1683))
res##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(977, 786) out of c(1342, 1683)
## X-squared = 208.1, df = 1, p-value < 2.2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## 0.2266362 0.2953532
## sample estimates:
## prop 1 prop 2
## 0.7280179 0.4670232