#Read the dataset into R

library (tidyverse)

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library(dlookr)

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library(caret)

## Warning: package 'caret' was built under R version 4.3.3

## Loading required package: lattice
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library(flextable)

## Warning: package 'flextable' was built under R version 4.3.3

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library(gtsummary)

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library(factoextra)

## Warning: package 'factoextra' was built under R version 4.3.3

## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

library(FactoMineR)

## Warning: package 'FactoMineR' was built under R version 4.3.3

library(readxl)
calculateddata <- read_excel("C:/Users/Michael/Desktop/calculateddata.xlsx", skip = 1)

#head(calculateddata)
#dim(calculateddata)

Data consists of 756 rows and 82 columns

#The first column 'JakesNEWCODE' is a row column. Convert the column to row

df <- calculateddata %>% column_to_rownames (var = "JakesNEWCODE")
#head(df)

#look at the colnames

#colnames(df)

Explore the demographic part of the data

The demographic data begins from column 1 to column 44

demo <- df %>% select(1:44) 
#head(demo)
#Replace the column names for the demographics with these
colnames(demo) <- c("CurrentlyHaveMentor", "ServeAsMentor", "PC_ServeAsMentor", "PrimaryJobFunction", "C5", "InstitutionRepresentation", "C7", "JobPath", "C9", "EmploymentStatus", "CurrentPosLevel", "ManagementExp_Years", "YearsToRetirement", "Age", "C15", "C16", "HighestEducationAttained", "C18", "C19", "C20", "C21", "C22", "C23", "C24", "C25", "C26", "C27", "C28", "C29", "C30", "C31", "C32", "C33", "C34", "C35", "C36", "C37", "C38", "C39", "C40", "C41", "Continent", "C43", "C44")

Some columns in the data contains missing values

Select columns that contains little missing data.

subset <- demo %>% select(1, 2, 3, 4, 6, 8, 10, 11, 12, 13, 14, 17, 42)

head(subset)

##   CurrentlyHaveMentor ServeAsMentor PC_ServeAsMentor  PrimaryJobFunction
## 1            Yes I do      Yes I do       Yes I have Facility Operations
## 2          No I don't    No I don't    No I have not Facility Operations
## 3            Yes I do      Yes I do       Yes I have Facility Operations
## 4    Maybe / not sure      Yes I do       Yes I have Facility Operations
## 5            Yes I do      Yes I do       Yes I have Facility Operations
## 6            Yes I do      Yes I do       Yes I have Facility Operations
##                                   InstitutionRepresentation
## 1 Educational (Training Center, K-12, College / University)
## 2 Educational (Training Center, K-12, College / University)
## 3                                        Federal Government
## 4 Educational (Training Center, K-12, College / University)
## 5   Insurance (Health, Life, Auto, Mutual, Casualty, Flood)
## 6 Educational (Training Center, K-12, College / University)
##                                                          JobPath
## 1 Managerial (e.g. supervising people, budgets, and/or projects)
## 2 Managerial (e.g. supervising people, budgets, and/or projects)
## 3 Managerial (e.g. supervising people, budgets, and/or projects)
## 4 Managerial (e.g. supervising people, budgets, and/or projects)
## 5 Managerial (e.g. supervising people, budgets, and/or projects)
## 6 Managerial (e.g. supervising people, budgets, and/or projects)
##                                                                       EmploymentStatus
## 1                              In-house - I am employed directly by the facility owner
## 2                              In-house - I am employed directly by the facility owner
## 3                              In-house - I am employed directly by the facility owner
## 4                              In-house - I am employed directly by the facility owner
## 5 Outsourced - I am employed by a contractor to provide services to the facility owner
## 6                              In-house - I am employed directly by the facility owner
##                                            CurrentPosLevel ManagementExp_Years
## 1            Level 3 - Manage supervisor who manage others         11-15 years
## 2       Level 4 - Manage two or more levels of supervisors  More than 30 years
## 3       Level 4 - Manage two or more levels of supervisors         16-20 years
## 4       Level 4 - Manage two or more levels of supervisors  More than 30 years
## 5 Level 2 - Manage employees but do not manage supervisors                <NA>
## 6       Level 4 - Manage two or more levels of supervisors         26-30 years
##   YearsToRetirement         Age HighestEducationAttained     Continent
## 1 More than 5 years 1979 - 1997        Bachelor's degree        Africa
## 2 More than 5 years 1946 - 1964        Bachelor's degree South America
## 3 More than 5 years 1979 - 1997        Bachelor's degree          Asia
## 4 More than 5 years 1965 - 1978        Bachelor's degree          Asia
## 5         3-5 years 1965 - 1978        Bachelor's degree North America
## 6 More than 5 years 1965 - 1978        Bachelor's degree North America

#Summarise the subsetted data.

-99 has to be removed

subset %>% tbl_summary()

Characteristic	N = 756¹
CurrentlyHaveMentor
-99	3 (0.4%)
Maybe / not sure	67 (8.9%)
No I don't	468 (62%)
Yes I do	218 (29%)
ServeAsMentor
-99	7 (0.9%)
Maybe / not sure	93 (12%)
No I don't	272 (36%)
Yes I do	384 (51%)
PC_ServeAsMentor
Maybe / not sure	53 (7.0%)
No I have not	118 (16%)
Yes I have	585 (77%)
PrimaryJobFunction
Architecture	2 (0.3%)
Construction/Project Management	49 (6.5%)
Consulting	38 (5.0%)
Education	13 (1.7%)
Engineering	18 (2.4%)
Environmental Health and Safety	1 (0.1%)
Facility Operations	545 (72%)
Information Technology	6 (0.8%)
Interior Design/Space Planning	8 (1.1%)
Janitorial	4 (0.5%)
Other	44 (5.8%)
Real Estate	20 (2.6%)
Sales	8 (1.1%)
InstitutionRepresentation
Aircraft/Industrial (industrial Equipment, Aerospace)	9 (1.2%)
Association (Association, Federation, Non-Profit Foundation, Society)	25 (3.3%)
Banking (Consumer, Commercial, Savings, Credit Unions)	40 (5.3%)
Building/Construction (Building, Construction Materials)	21 (2.8%)
Charitable Foundation	11 (1.5%)
Chemical/Pharmaceutical (Chemical, Pharmaceutical, Biotech)	21 (2.8%)
City/County Government (Law Enforcement, Library, Parks / Public Open Space)	56 (7.4%)
Computer (Computer hardware or software)	8 (1.1%)
Consumer Products (Food, Paper, or related)	9 (1.2%)
Corrections (private, state, federal, city, county)	1 (0.1%)
Cultural Facilities (Private, Institutions, Government)	28 (3.7%)
Educational (Training Center, K-12, College / University)	85 (11%)
Electronics (Electronics, Telecommunications Equipment)	7 (0.9%)
Energy (Energy related, mining, or distribution)	12 (1.6%)
Federal Government	30 (4.0%)
Health Care	43 (5.7%)
Hospitality (Hotel, Restaurants, Hospitality-Related)	23 (3.0%)
Information Services (Data Processing, Information Services, E-Commerce)	18 (2.4%)
Insurance (Health, Life, Auto, Mutual, Casualty, Flood)	25 (3.3%)
Investment Services (Securities and Investment Services)	5 (0.7%)
Media (Broadcasting, Entertainment, Gaming, Media, Publishing)	12 (1.6%)
Military	2 (0.3%)
Motor Vehicles	6 (0.8%)
Other Institution:	76 (10%)
Professional Services (Legal, Accounting, Consulting, Engineering, Architecture)	80 (11%)
Religious	12 (1.6%)
Research	17 (2.2%)
Special Districts/ Quasi-government (Transportation Authorities, School Boards)	3 (0.4%)
State/Provincial Government	13 (1.7%)
Telecommunications (Telecommunication, Internet Services/Products)	16 (2.1%)
Trade (Wholesale, Retail)	15 (2.0%)
Transportation (Transportation, Freight)	9 (1.2%)
Utilities (Water, Gas, Electric, Energy Management)	18 (2.4%)
JobPath
Managerial (e.g. supervising people, budgets, and/or projects)	675 (89%)
Other	40 (5.3%)
Technical (e.g. mechanical or systems focus - do not manage people of budgets)	40 (5.3%)
Unknown	1
EmploymentStatus
In-house - I am employed directly by the facility owner	580 (81%)
Outsourced - I am employed by a contractor to provide services to the facility owner	138 (19%)
Unknown	38
CurrentPosLevel
Level 1 - Professional specialist (manage no employees)	55 (8.3%)
Level 2 - Manage employees but do not manage supervisors	136 (21%)
Level 3 - Manage supervisor who manage others	174 (26%)
Level 4 - Manage two or more levels of supervisors	167 (25%)
Level 5 - Senior executive	129 (20%)
Unknown	95
ManagementExp_Years
1-2 years	19 (4.2%)
11-15 years	90 (20%)
16-20 years	74 (16%)
21-25 years	67 (15%)
26-30 years	30 (6.6%)
3-5 years	38 (8.4%)
6-10 years	85 (19%)
More than 30 years	49 (11%)
Unknown	304
YearsToRetirement
1-2 years	31 (4.1%)
3-5 years	106 (14%)
More than 5 years	580 (77%)
Within the next year	36 (4.8%)
Unknown	3
Age
1946 - 1964	193 (26%)
1965 - 1978	319 (42%)
1979 - 1997	232 (31%)
1998 or later	7 (0.9%)
Prior to 1946	1 (0.1%)
Unknown	4
HighestEducationAttained
Associate's degree	43 (5.7%)
Bachelor's degree	289 (38%)
Doctorate degree	12 (1.6%)
High school graduate or equivalent	30 (4.0%)
Master's degree	239 (32%)
Other	5 (0.7%)
Some college, no degree	87 (12%)
Vocational certificate, no degree	50 (6.6%)
Unknown	1
Continent
Africa	35 (4.6%)
Asia	80 (11%)
Australia	5 (0.7%)
Europe	48 (6.4%)
North America	568 (75%)
South America	19 (2.5%)
Unknown	1
¹ n (%)

Demographic data summary based on CurrentPoslevel

subset %>%
  tbl_summary(by = CurrentPosLevel)

## 95 observations missing `CurrentPosLevel` have been removed. To include these observations, use `forcats::fct_na_value_to_level()` on `CurrentPosLevel` column before passing to `tbl_summary()`.

Characteristic	Level 1 - Professional specialist (manage no employees), N = 55¹	Level 2 - Manage employees but do not manage supervisors, N = 136¹	Level 3 - Manage supervisor who manage others, N = 174¹	Level 4 - Manage two or more levels of supervisors, N = 167¹	Level 5 - Senior executive, N = 129¹
CurrentlyHaveMentor
-99	0 (0%)	0 (0%)	2 (1.1%)	1 (0.6%)	0 (0%)
Maybe / not sure	4 (7.3%)	17 (13%)	17 (9.8%)	13 (7.8%)	9 (7.0%)
No I don't	37 (67%)	83 (61%)	111 (64%)	96 (57%)	78 (60%)
Yes I do	14 (25%)	36 (26%)	44 (25%)	57 (34%)	42 (33%)
ServeAsMentor
-99	0 (0%)	1 (0.7%)	2 (1.1%)	0 (0%)	2 (1.6%)
Maybe / not sure	8 (15%)	23 (17%)	22 (13%)	16 (9.6%)	14 (11%)
No I don't	29 (53%)	62 (46%)	65 (37%)	46 (28%)	25 (19%)
Yes I do	18 (33%)	50 (37%)	85 (49%)	105 (63%)	88 (68%)
PC_ServeAsMentor
Maybe / not sure	8 (15%)	16 (12%)	10 (5.7%)	6 (3.6%)	4 (3.1%)
No I have not	12 (22%)	31 (23%)	32 (18%)	14 (8.4%)	8 (6.2%)
Yes I have	35 (64%)	89 (65%)	132 (76%)	147 (88%)	117 (91%)
PrimaryJobFunction
Architecture	1 (1.8%)	0 (0%)	1 (0.6%)	0 (0%)	0 (0%)
Construction/Project Management	9 (16%)	8 (5.9%)	14 (8.0%)	8 (4.8%)	5 (3.9%)
Consulting	7 (13%)	2 (1.5%)	5 (2.9%)	2 (1.2%)	14 (11%)
Education	2 (3.6%)	1 (0.7%)	1 (0.6%)	1 (0.6%)	1 (0.8%)
Engineering	0 (0%)	2 (1.5%)	3 (1.7%)	2 (1.2%)	4 (3.1%)
Environmental Health and Safety	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.8%)
Facility Operations	30 (55%)	109 (80%)	134 (77%)	138 (83%)	84 (65%)
Information Technology	0 (0%)	2 (1.5%)	1 (0.6%)	0 (0%)	1 (0.8%)
Interior Design/Space Planning	0 (0%)	3 (2.2%)	2 (1.1%)	2 (1.2%)	0 (0%)
Janitorial	0 (0%)	0 (0%)	1 (0.6%)	1 (0.6%)	1 (0.8%)
Other	3 (5.5%)	6 (4.4%)	10 (5.7%)	6 (3.6%)	10 (7.8%)
Real Estate	1 (1.8%)	0 (0%)	2 (1.1%)	7 (4.2%)	8 (6.2%)
Sales	2 (3.6%)	3 (2.2%)	0 (0%)	0 (0%)	0 (0%)
InstitutionRepresentation
Aircraft/Industrial (industrial Equipment, Aerospace)	0 (0%)	0 (0%)	4 (2.3%)	5 (3.0%)	0 (0%)
Association (Association, Federation, Non-Profit Foundation, Society)	0 (0%)	3 (2.2%)	8 (4.6%)	9 (5.4%)	4 (3.1%)
Banking (Consumer, Commercial, Savings, Credit Unions)	5 (9.1%)	9 (6.6%)	9 (5.2%)	5 (3.0%)	8 (6.2%)
Building/Construction (Building, Construction Materials)	0 (0%)	4 (2.9%)	4 (2.3%)	4 (2.4%)	3 (2.3%)
Charitable Foundation	1 (1.8%)	2 (1.5%)	6 (3.4%)	0 (0%)	0 (0%)
Chemical/Pharmaceutical (Chemical, Pharmaceutical, Biotech)	1 (1.8%)	6 (4.4%)	4 (2.3%)	6 (3.6%)	3 (2.3%)
City/County Government (Law Enforcement, Library, Parks / Public Open Space)	3 (5.5%)	6 (4.4%)	16 (9.2%)	21 (13%)	8 (6.2%)
Computer (Computer hardware or software)	2 (3.6%)	3 (2.2%)	1 (0.6%)	0 (0%)	1 (0.8%)
Consumer Products (Food, Paper, or related)	3 (5.5%)	3 (2.2%)	2 (1.1%)	0 (0%)	0 (0%)
Corrections (private, state, federal, city, county)	0 (0%)	0 (0%)	1 (0.6%)	0 (0%)	0 (0%)
Cultural Facilities (Private, Institutions, Government)	0 (0%)	8 (5.9%)	5 (2.9%)	5 (3.0%)	6 (4.7%)
Educational (Training Center, K-12, College / University)	4 (7.3%)	10 (7.4%)	17 (9.8%)	27 (16%)	16 (12%)
Electronics (Electronics, Telecommunications Equipment)	0 (0%)	0 (0%)	4 (2.3%)	2 (1.2%)	1 (0.8%)
Energy (Energy related, mining, or distribution)	1 (1.8%)	4 (2.9%)	1 (0.6%)	1 (0.6%)	1 (0.8%)
Federal Government	4 (7.3%)	4 (2.9%)	6 (3.4%)	9 (5.4%)	3 (2.3%)
Health Care	3 (5.5%)	3 (2.2%)	9 (5.2%)	13 (7.8%)	12 (9.3%)
Hospitality (Hotel, Restaurants, Hospitality-Related)	1 (1.8%)	1 (0.7%)	7 (4.0%)	6 (3.6%)	6 (4.7%)
Information Services (Data Processing, Information Services, E-Commerce)	1 (1.8%)	8 (5.9%)	6 (3.4%)	1 (0.6%)	2 (1.6%)
Insurance (Health, Life, Auto, Mutual, Casualty, Flood)	1 (1.8%)	8 (5.9%)	6 (3.4%)	4 (2.4%)	3 (2.3%)
Investment Services (Securities and Investment Services)	1 (1.8%)	0 (0%)	2 (1.1%)	0 (0%)	1 (0.8%)
Media (Broadcasting, Entertainment, Gaming, Media, Publishing)	1 (1.8%)	0 (0%)	1 (0.6%)	1 (0.6%)	5 (3.9%)
Military	0 (0%)	0 (0%)	1 (0.6%)	0 (0%)	1 (0.8%)
Motor Vehicles	0 (0%)	1 (0.7%)	1 (0.6%)	3 (1.8%)	1 (0.8%)
Other Institution:	6 (11%)	10 (7.4%)	13 (7.5%)	15 (9.0%)	12 (9.3%)
Professional Services (Legal, Accounting, Consulting, Engineering, Architecture)	12 (22%)	13 (9.6%)	13 (7.5%)	11 (6.6%)	21 (16%)
Religious	0 (0%)	5 (3.7%)	4 (2.3%)	2 (1.2%)	0 (0%)
Research	1 (1.8%)	4 (2.9%)	4 (2.3%)	3 (1.8%)	2 (1.6%)
Special Districts/ Quasi-government (Transportation Authorities, School Boards)	0 (0%)	1 (0.7%)	0 (0%)	1 (0.6%)	1 (0.8%)
State/Provincial Government	1 (1.8%)	4 (2.9%)	3 (1.7%)	1 (0.6%)	1 (0.8%)
Telecommunications (Telecommunication, Internet Services/Products)	1 (1.8%)	3 (2.2%)	3 (1.7%)	4 (2.4%)	3 (2.3%)
Trade (Wholesale, Retail)	1 (1.8%)	4 (2.9%)	7 (4.0%)	2 (1.2%)	0 (0%)
Transportation (Transportation, Freight)	0 (0%)	2 (1.5%)	3 (1.7%)	3 (1.8%)	0 (0%)
Utilities (Water, Gas, Electric, Energy Management)	1 (1.8%)	7 (5.1%)	3 (1.7%)	3 (1.8%)	4 (3.1%)
JobPath
Managerial (e.g. supervising people, budgets, and/or projects)	55 (100%)	136 (100%)	174 (100%)	167 (100%)	129 (100%)
EmploymentStatus
In-house - I am employed directly by the facility owner	38 (76%)	111 (86%)	139 (82%)	133 (81%)	98 (80%)
Outsourced - I am employed by a contractor to provide services to the facility owner	12 (24%)	18 (14%)	31 (18%)	32 (19%)	25 (20%)
Unknown	5	7	4	2	6
ManagementExp_Years
1-2 years	5 (18%)	7 (7.5%)	3 (2.5%)	3 (2.4%)	0 (0%)
11-15 years	7 (25%)	16 (17%)	28 (23%)	30 (24%)	8 (11%)
16-20 years	3 (11%)	9 (9.7%)	18 (15%)	30 (24%)	13 (17%)
21-25 years	1 (3.6%)	8 (8.6%)	14 (12%)	22 (17%)	19 (25%)
26-30 years	1 (3.6%)	3 (3.2%)	7 (5.8%)	11 (8.7%)	8 (11%)
3-5 years	1 (3.6%)	20 (22%)	11 (9.1%)	2 (1.6%)	4 (5.3%)
6-10 years	8 (29%)	23 (25%)	33 (27%)	12 (9.4%)	8 (11%)
More than 30 years	2 (7.1%)	7 (7.5%)	7 (5.8%)	17 (13%)	16 (21%)
Unknown	27	43	53	40	53
YearsToRetirement
1-2 years	1 (1.8%)	6 (4.4%)	7 (4.0%)	7 (4.2%)	7 (5.5%)
3-5 years	8 (15%)	10 (7.4%)	23 (13%)	31 (19%)	23 (18%)
More than 5 years	43 (78%)	116 (85%)	138 (79%)	120 (73%)	88 (69%)
Within the next year	3 (5.5%)	4 (2.9%)	6 (3.4%)	7 (4.2%)	10 (7.8%)
Unknown	0	0	0	2	1
Age
1946 - 1964	11 (20%)	23 (17%)	38 (22%)	49 (30%)	50 (39%)
1965 - 1978	16 (30%)	57 (42%)	78 (45%)	67 (41%)	61 (47%)
1979 - 1997	24 (44%)	54 (40%)	57 (33%)	48 (29%)	17 (13%)
1998 or later	3 (5.6%)	2 (1.5%)	0 (0%)	1 (0.6%)	0 (0%)
Prior to 1946	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.8%)
Unknown	1	0	1	2	0
HighestEducationAttained
Associate's degree	2 (3.6%)	6 (4.4%)	7 (4.0%)	13 (7.8%)	9 (7.0%)
Bachelor's degree	23 (42%)	60 (44%)	71 (41%)	62 (37%)	36 (28%)
Doctorate degree	0 (0%)	0 (0%)	1 (0.6%)	0 (0%)	8 (6.2%)
High school graduate or equivalent	3 (5.5%)	5 (3.7%)	6 (3.4%)	6 (3.6%)	3 (2.3%)
Master's degree	15 (27%)	30 (22%)	51 (29%)	57 (34%)	63 (49%)
Other	1 (1.8%)	3 (2.2%)	1 (0.6%)	0 (0%)	0 (0%)
Some college, no degree	9 (16%)	24 (18%)	20 (11%)	21 (13%)	3 (2.3%)
Vocational certificate, no degree	2 (3.6%)	8 (5.9%)	17 (9.8%)	7 (4.2%)	7 (5.4%)
Unknown	0	0	0	1	0
Continent
Africa	2 (3.6%)	4 (3.0%)	5 (2.9%)	13 (7.8%)	5 (3.9%)
Asia	4 (7.3%)	8 (5.9%)	17 (9.8%)	19 (11%)	24 (19%)
Australia	1 (1.8%)	1 (0.7%)	1 (0.6%)	2 (1.2%)	0 (0%)
Europe	5 (9.1%)	9 (6.7%)	7 (4.0%)	7 (4.2%)	16 (12%)
North America	43 (78%)	109 (81%)	137 (79%)	121 (72%)	82 (64%)
South America	0 (0%)	4 (3.0%)	7 (4.0%)	5 (3.0%)	2 (1.6%)
Unknown	0	1	0	0	0
¹ n (%)

#Lets explore the Hexaco data**

hexaco <- df %>% select(49,54,59,64,69,74)
head(hexaco)

##   Honesty/Humility Emotionality Extraversion Agreeableness Conscientiousness
## 1             4.42         3.17         3.42          4.67              4.54
## 2             4.25         2.54         4.04          3.38              4.71
## 3             4.29         1.96         3.71          4.13              3.92
## 4             3.79         2.96         3.21          3.38              3.42
## 5             4.08         2.71         3.67          3.58              4.42
## 6             3.79         2.13         3.29          3.58              4.33
##   Openness to Experience
## 1                   3.33
## 2                   3.88
## 3                   4.04
## 4                   2.54
## 5                   2.58
## 6                   2.71

#Summary of hexaco data

flextable(describe(hexaco) %>% select(1,2,3,4,5))

described_variables	n	mean	sd
Honesty/Humility	756	3.907831	0.4800359
Emotionality	756	2.782368	0.5519061
Extraversion	756	3.568135	0.5464286
Agreeableness	756	3.412407	0.5211523
Conscientiousness	756	4.063968	0.4541863
Openness to Experience	756	3.184894	0.4951469

hexaco %>% tbl_summary(statistic = list(
      all_continuous() ~ "{mean} ({sd})"))

Characteristic	N = 756¹
Honesty/Humility	3.91 (0.48)
Emotionality	2.78 (0.55)
Extraversion	3.57 (0.55)
Agreeableness	3.41 (0.52)
Conscientiousness	4.06 (0.45)
Openness to Experience	3.18 (0.50)
¹ Mean (SD)

#Comparison between CurrentPoslevel personality traits.

hexaco_level <- df %>% select(49,54,59,64,69,74)
head(hexaco_level)

##   Honesty/Humility Emotionality Extraversion Agreeableness Conscientiousness
## 1             4.42         3.17         3.42          4.67              4.54
## 2             4.25         2.54         4.04          3.38              4.71
## 3             4.29         1.96         3.71          4.13              3.92
## 4             3.79         2.96         3.21          3.38              3.42
## 5             4.08         2.71         3.67          3.58              4.42
## 6             3.79         2.13         3.29          3.58              4.33
##   Openness to Experience
## 1                   3.33
## 2                   3.88
## 3                   4.04
## 4                   2.54
## 5                   2.58
## 6                   2.71

hexaco_level$CurrentPosLevel <- demo$CurrentPosLevel # add gender column to the hexaco data
head(hexaco_level)

##   Honesty/Humility Emotionality Extraversion Agreeableness Conscientiousness
## 1             4.42         3.17         3.42          4.67              4.54
## 2             4.25         2.54         4.04          3.38              4.71
## 3             4.29         1.96         3.71          4.13              3.92
## 4             3.79         2.96         3.21          3.38              3.42
## 5             4.08         2.71         3.67          3.58              4.42
## 6             3.79         2.13         3.29          3.58              4.33
##   Openness to Experience
## 1                   3.33
## 2                   3.88
## 3                   4.04
## 4                   2.54
## 5                   2.58
## 6                   2.71
##                                            CurrentPosLevel
## 1            Level 3 - Manage supervisor who manage others
## 2       Level 4 - Manage two or more levels of supervisors
## 3       Level 4 - Manage two or more levels of supervisors
## 4       Level 4 - Manage two or more levels of supervisors
## 5 Level 2 - Manage employees but do not manage supervisors
## 6       Level 4 - Manage two or more levels of supervisors

hexaco_level %>%
  tbl_summary(
    by = CurrentPosLevel,
    statistic = list(
      all_continuous() ~ "{mean} ({sd})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = all_continuous() ~ 2,
    
  ) %>% add_p()

## 95 observations missing `CurrentPosLevel` have been removed. To include these observations, use `forcats::fct_na_value_to_level()` on `CurrentPosLevel` column before passing to `tbl_summary()`.

Characteristic	Level 1 - Professional specialist (manage no employees), N = 55¹	Level 2 - Manage employees but do not manage supervisors, N = 136¹	Level 3 - Manage supervisor who manage others, N = 174¹	Level 4 - Manage two or more levels of supervisors, N = 167¹	Level 5 - Senior executive, N = 129¹	p-value²
Honesty/Humility	3.81 (0.61)	3.91 (0.48)	3.91 (0.48)	3.93 (0.45)	3.94 (0.47)	>0.9
Emotionality	2.96 (0.55)	2.82 (0.57)	2.76 (0.58)	2.69 (0.50)	2.74 (0.56)	0.031
Extraversion	3.43 (0.64)	3.48 (0.52)	3.52 (0.55)	3.65 (0.49)	3.74 (0.48)	<0.001
Agreeableness	3.29 (0.57)	3.35 (0.57)	3.45 (0.47)	3.44 (0.50)	3.47 (0.49)	0.3
Conscientiousness	4.01 (0.47)	4.02 (0.45)	4.00 (0.49)	4.10 (0.42)	4.16 (0.45)	0.018
Openness to Experience	3.18 (0.50)	3.15 (0.45)	3.19 (0.52)	3.20 (0.51)	3.22 (0.45)	0.5
¹ Mean (SD)
² Kruskal-Wallis rank sum test

#Impact of Hexaco on Emotional intelligence

hexaco_EQ <- df %>% select(49,54,59,64,69,74,78)
head(hexaco_EQ)

##   Honesty/Humility Emotionality Extraversion Agreeableness Conscientiousness
## 1             4.42         3.17         3.42          4.67              4.54
## 2             4.25         2.54         4.04          3.38              4.71
## 3             4.29         1.96         3.71          4.13              3.92
## 4             3.79         2.96         3.21          3.38              3.42
## 5             4.08         2.71         3.67          3.58              4.42
## 6             3.79         2.13         3.29          3.58              4.33
##   Openness to Experience Overall EQ
## 1                   3.33         87
## 2                   3.88         88
## 3                   4.04         86
## 4                   2.54         79
## 5                   2.58         84
## 6                   2.71         72

Correlation of Hexaco with EQ

library(metan)

## Warning: package 'metan' was built under R version 4.3.3

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

## Warning in check_dep_version(): ABI version mismatch: 
## lme4 was built with Matrix ABI version 1
## Current Matrix ABI version is 0
## Please re-install lme4 from source or restore original 'Matrix' package

## |=========================================================|

## | Multi-Environment Trial Analysis (metan) v1.18.0        |

## | Author: Tiago Olivoto                                   |

## | Type 'citation('metan')' to know how to cite metan      |

## | Type 'vignette('metan_start')' for a short tutorial     |

## | Visit 'https://bit.ly/pkgmetan' for a complete tutorial |

## |=========================================================|

## 
## Attaching package: 'metan'

## The following object is masked from 'package:factoextra':
## 
##     get_dist

## The following object is masked from 'package:caret':
## 
##     progress

## The following object is masked from 'package:dlookr':
## 
##     find_outliers

## The following object is masked from 'package:forcats':
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##     as_factor

## The following object is masked from 'package:dplyr':
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##     recode_factor

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##     replace_na

## The following objects are masked from 'package:tibble':
## 
##     column_to_rownames, remove_rownames, rownames_to_column

corr <- corr_coef(hexaco_EQ)
plot(corr)

Untitled

Michael

2024-05-08

Explore the demographic part of the data

Select columns that contains little missing data.

Demographic data summary based on CurrentPoslevel

Correlation of Hexaco with EQ