# Load the cleaned dataset
library(readxl)
## Warning: package 'readxl' was built under R version 4.4.2
Cleaned_TNA_Questionnaire <- read_excel("C:/Users/Mr Cowboy/Downloads/Cleaned_TNA_Questionnaire.xlsx")
data <- Cleaned_TNA_Questionnaire
attach(data)
#str(data)
head(data)
## # A tibble: 6 × 37
## ID Name Gender Age Directorate Department `Work Unit/section`
## <dbl> <lgl> <chr> <chr> <chr> <chr> <chr>
## 1 1 NA Male 36 – 45 years Finance an… Finance a… Finance and Account
## 2 2 NA Male 36 – 45 years Finance an… Finance a… Finance and Accoun…
## 3 3 NA Female 36 – 45 years Human reso… Records m… Registry
## 4 4 NA Male 46 – 55 years Registrati… Benefits Data
## 5 5 NA Female 25 years & below Registrati… Benefits Customer care
## 6 6 NA Female 26 – 35 years Human reso… Administr… Kitchen
## # ℹ 30 more variables: `Date of first appointment` <dttm>,
## # `Designation at first appointment` <chr>, `Current Designation` <chr>,
## # `Date of current appointment` <dttm>,
## # `Length of Service at the department/work section/Institution/organization` <chr>,
## # `Length of service in the public/private Sector` <chr>,
## # `Academic qualifications (Tick the highest level attained)` <chr>,
## # `If selected others kindly specify` <chr>, …
# Load required libraries
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
# Summary of Gender
gender_summary <- data %>% group_by(Gender) %>% summarise(Count = n())
print(gender_summary)
## # A tibble: 2 × 2
## Gender Count
## <chr> <int>
## 1 Female 17
## 2 Male 23
# Summary of Age
age_summary <- data %>% group_by(Age) %>% summarise(Count = n())
print(age_summary)
## # A tibble: 5 × 2
## Age Count
## <chr> <int>
## 1 25 years & below 1
## 2 26 – 35 years 10
## 3 36 – 45 years 16
## 4 46 – 55 years 11
## 5 55 years and above 2
# Save summaries to CSV
write.csv(gender_summary, "Gender_Summary.csv", row.names = FALSE)
write.csv(age_summary, "Age_Summary.csv", row.names = FALSE)
# Summary of Directorate
directorate_summary <- data %>% group_by(Directorate) %>% summarise(Count = n())
print(directorate_summary)
## # A tibble: 34 × 2
## Directorate Count
## <chr> <int>
## 1 Adm 1
## 2 BENEFITS 1
## 3 BENEFITS ADMIISTRATION 1
## 4 BENEFITS ADMINISTRATION 1
## 5 BENEFITS AND REGISTRATION 1
## 6 Benefits 1
## 7 Benefits Administration 3
## 8 Benefits Administration and Registration 1
## 9 CEO 1
## 10 CORPORATION SECRETARY & LEGAL SERVICES 1
## # ℹ 24 more rows
write.csv(directorate_summary, "Directorate_Summary.csv", row.names = FALSE)
# Summary of Department
department_summary <- data %>% group_by(Department) %>% summarise(Count = n())
print(department_summary)
## # A tibble: 31 × 2
## Department Count
## <chr> <int>
## 1 ADMINISTRATION 2
## 2 Administration 1
## 3 BENEFITS 3
## 4 Benefits 4
## 5 Benefits & Customer Experience 1
## 6 Benefits Administration 1
## 7 Benefits department 1
## 8 CEO 1
## 9 CORPORATION SECRETARY & LEGAL SERVICES 1
## 10 Corporate Communication 1
## # ℹ 21 more rows
# Summary of sponsor of all courses
sponsor_summary <- data %>% group_by(`Indicate sponsor of all the courses/seminars/workshops you have attended in the last two years. (e.g. Employer, Self etc)`) %>% summarise(Count = n())
print(sponsor_summary)
## # A tibble: 18 × 2
## Indicate sponsor of all the courses/seminars/workshops you have atten…¹ Count
## <chr> <int>
## 1 "1. The Actuarial Society of Kenya (TASK) 9th Annual seminar - PSSF\r\… 1
## 2 "1.PSSF" 1
## 3 "EMPLOYER" 1
## 4 "Employer" 10
## 5 "Employer \r\nSelf" 1
## 6 "NONE" 1
## 7 "None" 2
## 8 "None sponsored me" 1
## 9 "PSSF" 7
## 10 "PSSS" 1
## 11 "Pss fund." 1
## 12 "Pssf" 2
## 13 "Pssf \r\nThe National Treasury" 1
## 14 "Public service superannuation fund" 1
## 15 "Self" 3
## 16 "Self sponsorship" 1
## 17 "Unknown" 4
## 18 "employer" 1
## # ℹ abbreviated name:
## # ¹​`Indicate sponsor of all the courses/seminars/workshops you have attended in the last two years. (e.g. Employer, Self etc)`
# Summary of Work unit
workunit_summary <- data %>% group_by(`Work Unit/section`) %>% summarise(Count = n())
print(workunit_summary)
## # A tibble: 33 × 2
## `Work Unit/section` Count
## <chr> <int>
## 1 Administration 1
## 2 BENEFITS 2
## 3 Benefits 2
## 4 Benefits Administration 1
## 5 Benefits Claims Unit 1
## 6 CEO 1
## 7 CEO'S OFFICE 2
## 8 CORPORATION SECRETARY & LEGAL SERVICES 1
## 9 CUSTOMER EXPERIENCE 1
## 10 Clerical 1
## # ℹ 23 more rows
# Combine summaries into one list
summary_report <- list(
Gender = gender_summary,
Age = age_summary,
Directorate = directorate_summary,
Department = department_summary,
WorkUnit = workunit_summary
)
# Save the summaries to a single file
print(summary_report)
## $Gender
## # A tibble: 2 × 2
## Gender Count
## <chr> <int>
## 1 Female 17
## 2 Male 23
##
## $Age
## # A tibble: 5 × 2
## Age Count
## <chr> <int>
## 1 25 years & below 1
## 2 26 – 35 years 10
## 3 36 – 45 years 16
## 4 46 – 55 years 11
## 5 55 years and above 2
##
## $Directorate
## # A tibble: 34 × 2
## Directorate Count
## <chr> <int>
## 1 Adm 1
## 2 BENEFITS 1
## 3 BENEFITS ADMIISTRATION 1
## 4 BENEFITS ADMINISTRATION 1
## 5 BENEFITS AND REGISTRATION 1
## 6 Benefits 1
## 7 Benefits Administration 3
## 8 Benefits Administration and Registration 1
## 9 CEO 1
## 10 CORPORATION SECRETARY & LEGAL SERVICES 1
## # ℹ 24 more rows
##
## $Department
## # A tibble: 31 × 2
## Department Count
## <chr> <int>
## 1 ADMINISTRATION 2
## 2 Administration 1
## 3 BENEFITS 3
## 4 Benefits 4
## 5 Benefits & Customer Experience 1
## 6 Benefits Administration 1
## 7 Benefits department 1
## 8 CEO 1
## 9 CORPORATION SECRETARY & LEGAL SERVICES 1
## 10 Corporate Communication 1
## # ℹ 21 more rows
##
## $WorkUnit
## # A tibble: 33 × 2
## `Work Unit/section` Count
## <chr> <int>
## 1 Administration 1
## 2 BENEFITS 2
## 3 Benefits 2
## 4 Benefits Administration 1
## 5 Benefits Claims Unit 1
## 6 CEO 1
## 7 CEO'S OFFICE 2
## 8 CORPORATION SECRETARY & LEGAL SERVICES 1
## 9 CUSTOMER EXPERIENCE 1
## 10 Clerical 1
## # ℹ 23 more rows
# Gender Distribution
gender_plot <- ggplot(data, aes(x = Gender)) +
geom_bar(fill = "blue", alpha = 0.7) +
theme_minimal() +
labs(title = "Gender Distribution", x = "Gender", y = "Count")
print(gender_plot)
# Save the plot
ggsave("Gender_Distribution.png", plot = gender_plot, width = 6, height = 4)
# Age Distribution
age_plot <- ggplot(data, aes(x = Age)) +
geom_bar(fill = "green", alpha = 0.7) +
theme_minimal() +
labs(title = "Age Distribution", x = "Age Group", y = "Count")
print(age_plot)
# Save the plot
ggsave("Age_Distribution.png", plot = age_plot, width = 6, height = 4)
# Bar plot for Directorate distribution
directorate_plot <- ggplot(data, aes(x = Directorate)) +
geom_bar(fill = "purple", alpha = 0.7) +
theme_minimal() +
labs(title = "Directorate Distribution", x = "Directorate", y = "Count") +
coord_flip()
print(directorate_plot)
ggsave("Directorate_Distribution.png", plot = directorate_plot, width = 6, height = 4)
# Bar plot for Sponsor of all courses distribution
sponsor_plot <- ggplot(data, aes(x = `Indicate sponsor of all the courses/seminars/workshops you have attended in the last two years. (e.g. Employer, Self etc)`)) +
geom_bar(fill = "red", alpha = 0.7) +
theme_minimal() +
labs(title = "Sponsor of all courses Distribution", x = "Sponsor", y = "Count") +
coord_flip()
print(sponsor_plot)
# save the plot
ggsave("Directorate_Distribution.png", plot = directorate_plot, width = 6, height = 4)