30/50 (only my part)
user_2508819
r
6 months ago
30 kB
2
Indexable
Never
### ---------------------- Questions To Analysis -------------------------- ### ## Question 1: What Makes Employees Quit Their Jobs? (Termination) # Analysis 1.1 - Is there correlation between termination type and length of service? # Filter the data to include only relevant columns and drop NA values filtered_data <- new_employee_attrition %>% select(termination_type, length_of_service) %>% drop_na() # Group by termination type and calculate the count of each length of service termtype_los <- filtered_data %>% group_by(termination_type, length_of_service) %>% summarize(count = n()) # Create a line chart with theme and theme_minimal ggplot(termtype_los, aes(x = length_of_service, y = count, color = termination_type)) + geom_line(size = 1) + geom_point(size = 3, shape = 21, fill = "lightskyblue1") + labs(x = "Length of Service", y = "Count", color = "Termination Type", title = "Correlation between Termination Type and Length of Service") + theme_minimal() # Analysis 1.2 - I want to investigate if voluntary termination is related due to length of service? # Filter the data to include only voluntary terminations and length of service columns filtered_data <- new_employee_attrition %>% filter(termination_type == "Voluntary") %>% select(termination_type, length_of_service) # Group by length of service and calculate the count of voluntary terminations voluntary_length_of_service <- filtered_data %>% group_by(length_of_service) %>% summarize(count = n()) # Sort the data by length of service voluntary_length_of_service <- voluntary_length_of_service[order(voluntary_length_of_service$length_of_service), ] # Create an area chart to visualize the relationship ggplot(voluntary_length_of_service, aes(x = length_of_service, y = count)) + geom_area(fill = "steelblue", alpha = 0.7) + labs(x = "Length of Service", y = "Count", title = "Relationship Between Voluntary Termination & Length Of Service") + theme_minimal() + theme(axis.text = element_text(size = 10), axis.title = element_text(size = 12, face = "bold"), plot.title = element_text(size = 16, face = "bold"), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + scale_x_continuous(breaks = seq(0, max(voluntary_length_of_service$length_of_service), by = 5)) + scale_y_continuous(limits = c(0, max(voluntary_length_of_service$count) * 1.1)) + theme(plot.background = element_rect(fill = "white"), panel.background = element_rect(fill = "white"), legend.position = "none") # Analysis 1.3 - I want to investigate if involuntary termination is related due to length of service? # Filter the data to include only involuntary terminations and length of service columns filtered_data <- new_employee_attrition %>% filter(termination_type == "Involuntary") %>% select(termination_type, length_of_service) # Group by length of service and calculate the count of each termination type involuntary_termination <- filtered_data %>% group_by(length_of_service, termination_type) %>% summarize(count = n()) # Create a stacked area chart to visualize the relationship ggplot(involuntary_termination, aes(x = length_of_service, y = count, fill = termination_type)) + geom_area(color = "white") + labs(x = "Length of Service", y = "Count", title = "Relationship between Involuntary Termination and Length of Service") + theme_minimal() + theme(axis.text = element_text(size = 10), plot.title = element_text(size = 14, face = "bold"), axis.title = element_text(size = 11, face = "bold"), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + scale_fill_manual(values = c("darkgreen", "lightgreen")) + theme(plot.background = element_rect(fill = "white"), panel.background = element_rect(fill = "white"), legend.position = "bottom") # Analysis 1.4 - What is the correlation between voluntary termination (termination type) and age of employee? # Filter the data to include only voluntary terminations and age columns filtered_data <- new_employee_attrition %>% filter(termination_type == "Voluntary") %>% select(termination_type, age) # Group by termination type and calculate the count of each age voluntary_age <- filtered_data %>% group_by(age) %>% summarize(count = n()) # Create a connected scatter plot to visualize the correlation ggplot(voluntary_age, aes(x = age, y = count)) + geom_line(color = "steelblue", size = 1) + geom_point(color = "steelblue", size = 3) + labs(x = "Age", y = "Count", title = "Correlation between Voluntary Termination and Age") + theme_minimal() + theme(axis.text = element_text(size = 10), axis.title = element_text(size = 12, face = "bold"), plot.title = element_text(size = 14, face = "bold"), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + scale_color_gradient(low = "darkblue", high = "lightblue") + theme(plot.background = element_rect(fill = "white"), panel.background = element_rect(fill = "white"), legend.position = "none") # Analysis 1.5 - What is the correlation between involuntary termination (termination type) and age of employee? # Filter the data to include only involuntary terminations and age columns, and group by age involuntary_age <- new_employee_attrition %>% filter(termination_type == "Involuntary") %>% group_by(age) %>% summarize(count = n()) # Create a scatter plot with enhanced aesthetics ggplot(involuntary_age, aes(x = age, y = count)) + geom_point(color = "#FF6384", size = 4, alpha = 0.8) + labs(x = "Age", y = "Count", title = "Correlation between Involuntary Termination and Age") + theme_minimal() + theme(axis.text = element_text(size = 10), axis.title = element_text(size = 11, face = "bold"), plot.title = element_text(size = 14, face = "bold"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), legend.position = "bottom", legend.title = element_blank(), legend.text = element_text(size = 10), legend.key.size = unit(0.7, "cm")) + scale_color_gradient(low = "#FF6384", high = "#FFABAB") + guides(color = guide_legend(override.aes = list(size = 4))) + theme(plot.background = element_rect(fill = "white"), panel.background = element_rect(fill = "white")) # Analysis 1.6 - Is there correlation between termination reason and age of employee? # Filter, group, summarize, and arrange the data termreason_age <- new_employee_attrition %>% filter(!is.na(termination_reason)) %>% group_by(age, termination_reason) %>% summarize(count = n()) # Create a stacked bar chart ggplot(termreason_age, aes(x = age, y = count, fill = termination_reason)) + geom_bar(stat = "identity") + labs(x = "Age", y = "Total Count Of Term. Reasons", fill = "Termination Reason") + ggtitle("Correlation Between Termination Reasons & Employee Ages") # Analysis 1.7 - How does employees' genders relate to their reasons for leaving? # Filter, group, summarize, and arrange the data termreason_gender <- employee_attrition %>% filter(!is.na(termination_reason) & !is.na(gender)) %>% group_by(gender, termination_reason) %>% summarize(count = n()) %>% arrange(desc(gender)) # Create a visually appealing grouped bar chart ggplot(termreason_gender, aes(x = gender, y = count, fill = termination_reason)) + geom_bar(stat = "identity", position = "dodge", color = "black", alpha = 0.8) + labs(x = "Gender", y = "Total Count of Term. Reasons", fill = "Termination Reason") + ggtitle("Correlation Between Termination Reasons & Gender") + theme_minimal() + theme(plot.title = element_text(size = 16, face = "bold"), axis.title = element_text(size = 12), axis.text = element_text(size = 10), legend.title = element_text(size = 12), legend.text = element_text(size = 10), legend.position = "bottom") + scale_fill_brewer(palette = "Set2") # Analysis 1.8 - What is the relationship between termination types and gender? # Filter the data to include only relevant columns and non-null values for termination type and gender filtered_data <- new_employee_attrition %>% filter(!is.na(termination_type) & !is.na(gender)) # Group by termination type and gender and calculate the count of each combination grouped_data <- filtered_data %>% group_by(termination_type, gender) %>% summarize(count = n()) # Create a stacked bar chart ggplot(grouped_data, aes(x = termination_type, y = count, fill = gender)) + geom_bar(stat = "identity") + labs(x = "Termination Type", y = "Count", fill = "Gender") + ggtitle("Relationship Between Termination Types & Gender") + theme_minimal() + theme(legend.position = "bottom") # Analysis 1.9 - What is the relationship between voluntary termination and gender? # Filter the data to include only relevant columns and non-null values for termination type and gender filtered_data <- new_employee_attrition %>% filter(!is.na(termination_type) & !is.na(gender)) # Filter the data to include only voluntary terminations voluntary_data <- filtered_data %>% filter(termination_type == "Voluntary") # Group by gender and calculate the count of each gender grouped_data <- voluntary_data %>% group_by(gender) %>% summarize(count = n()) grouped_data # Create a pie chart ggplot(grouped_data, aes(x = "", y = count, fill = gender)) + geom_bar(stat = "identity", width = 1) + coord_polar(theta = "y") + geom_text(aes(label = count), position = position_stack(vjust = 0.5), color = "white", size = 4) + labs(x = "", y = "", fill = "Gender") + ggtitle("Correlation Between Voluntary Termination & Gender") + theme_minimal() + theme(plot.title = element_text(size = 16, face = "bold"), axis.text = element_blank(), axis.title = element_blank(), panel.grid = element_blank()) + scale_fill_manual(values = c("seagreen3", "dimgray")) # Analysis 1.10 - What is the relationship between involuntary termination and gender? # Filter the data to include only involuntary terminations and gender columns filtered_data <- new_employee_attrition %>% filter(termination_type == "Involuntary" & !is.na(gender)) # Group by gender and calculate the count of involuntary terminations grouped_data <- filtered_data %>% group_by(gender) %>% summarize(count = n()) # Create a pie chart with text labels ggplot(grouped_data, aes(x = "", y = count, fill = gender)) + geom_bar(stat = "identity", width = 1) + coord_polar(theta = "y") + geom_text(aes(label = count), position = position_stack(vjust = 0.5), color = "white", size = 4) + labs(x = "", y = "", fill = "Gender") + ggtitle("Correlation Between Involuntary Termination & Gender") + theme_minimal() + theme(legend.position = "bottom", plot.title = element_text(size = 16, face = "bold"), axis.text = element_blank(), panel.grid = element_blank()) + scale_fill_manual(values = c("lightsalmon1", "salmon4")) + coord_polar("y", start = 0, direction = -1) + theme_void() + theme(plot.title = element_text(hjust = 0.5)) # Analysis 1.11 - Are job titles make employees terminated voluntarily? # termination type - voluntary # Filter the data to include only relevant columns and voluntary terminations filtered_data <- new_employee_attrition %>% filter(termination_type == "Voluntary" & !is.na(job_title)) # Group by job title and calculate the count of voluntary terminations grouped_data <- filtered_data %>% group_by(job_title) %>% summarize(count = n()) # Create a horizontal bar chart with a bigger plot title ggplot(grouped_data, aes(x = count, y = job_title, fill = job_title)) + geom_bar(stat = "identity", color = "black") + labs(x = "Count", y = "Job Title", fill = "Job Title") + ggtitle("Relationship Between Voluntary Termination & Job Title") + theme_minimal() + theme(axis.text.y = element_text(hjust = 0), legend.position = "none", plot.title = element_text(hjust = 0.5, size = 16)) # Analysis 1.12 - Are job titles make employees terminated involuntarily? # termination type - Involuntary # Filter the data to include only relevant columns and involuntary terminations filtered_data <- new_employee_attrition %>% filter(termination_type == "Involuntary" & !is.na(job_title)) # Group by job title and calculate the count of involuntary terminations grouped_data <- filtered_data %>% group_by(job_title) %>% summarize(count = n()) # Create a vertical bar chart ggplot(grouped_data, aes(x = job_title, y = count, fill = job_title)) + geom_bar(stat = "identity", color = "white") + labs(x = "Job Title", y = "Count", fill = "Job Title") + ggtitle("Connection Between Involuntary Termination & Job Title") + theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "none", plot.title = element_text(hjust = 0.5)) # Analysis 1.13 - Does the department in which employees work have an impact on their likelihood of termination? # Filter the data to include only relevant columns filtered_data <- new_employee_attrition %>% filter(!is.na(department_name) & !is.na(termination_type)) # Group by department and termination type, and calculate the count of each combination grouped_data <- filtered_data %>% group_by(department_name, termination_type) %>% summarize(count = n()) # Create a stacked bar chart ggplot(grouped_data, aes(x = department_name, y = count, fill = termination_type)) + geom_bar(stat = "identity", color = "black") + labs(x = "Department", y = "Count", fill = "Termination Type") + ggtitle("Connection Between Termination Type & Department") + theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "bottom") # Analysis 1.14 - Does the business unit in which employees work have an influence on the occurrence of terminations? # Filter the data to include only relevant columns filtered_data <- new_employee_attrition %>% filter(!is.na(business_unit) & !is.na(termination_type)) # Group by business unit and calculate the count of terminations grouped_data <- filtered_data %>% group_by(business_unit) %>% summarize(count = n()) # Sort the data by count in descending order grouped_data <- grouped_data[order(grouped_data$count, decreasing = TRUE), ] # Create a treemap chart ggplot(grouped_data, aes(area = count, fill = business_unit, label = business_unit)) + geom_treemap() + geom_treemap_text(fontface = "bold", color = "white", place = "centre", min.size = 0) + labs(fill = "Business Unit") + ggtitle("Connection Between Termination Type & Business Unit") + theme_minimal() + theme(plot.title = element_text(hjust = 0.5), panel.background = element_blank(), axis.text = element_blank(), axis.title = element_blank(), legend.position = "bottom") + scale_fill_viridis_d(option = "D", direction = -1) + guides(fill = guide_legend(reverse = TRUE)) + coord_equal() + theme(legend.key.size = unit(0.7, "cm"), legend.text = element_text(size = 10), legend.title = element_text(size = 12, face = "bold")) # Analysis 1.15 - Does city have to do with the result of terminations from employees? # Filter the data to include only relevant columns filtered_data <- new_employee_attrition %>% filter(!is.na(city) & status == "TERMINATED") # Group by city and calculate the count of terminations grouped_data <- filtered_data %>% group_by(city) %>% summarize(count = n()) # Sort the data by count in descending order grouped_data <- grouped_data[order(grouped_data$count, decreasing = TRUE), ] # Create a bar chart ggplot(grouped_data, aes(x = city, y = count, fill = city)) + geom_bar(stat = "identity", color = "ivory1") + labs(x = "City", y = "Termination Count", fill = "City") + ggtitle("Correlation Between Terminations Count & City") + theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "none") ## Question 2: How is the gender distribution distributed among the employees in the company? # Analysis 2.1 - What are the total male and female of employees in the company? # Convert gender to character type filtered_data$gender <- as.character(filtered_data$gender) # Group by gender and calculate the count of employees grouped_data <- filtered_data %>% group_by(gender) %>% summarize(count = n()) # Create a donut chart plot_ly(grouped_data, labels = ~gender, values = ~count, type = 'pie', text = ~paste(gender, ": ", count), textposition = 'inside', hole = 0.6, marker = list(colors = c("steelblue", "pink"))) %>% layout(title = list(text = "Gender Proportion in the Company", x = 0.5), showlegend = TRUE, legend = list(orientation = "h", x = 0.5, y = -0.15)) # Analysis 2.2 - What is the gender ratio between males and females here? # Filter the data to include only relevant columns filtered_data <- new_employee_attrition %>% filter(!is.na(gender)) # Group by gender and calculate the count of employees grouped_data <- filtered_data %>% group_by(gender) %>% summarize(count = n()) # Calculate the total count of employees total_count <- sum(grouped_data$count) # Calculate the gender ratio grouped_data <- grouped_data %>% mutate(ratio = count / total_count * 100) # Create a stacked bar chart ggplot(grouped_data, aes(x = 1, y = ratio, fill = gender)) + geom_bar(stat = "identity", color = "black") + labs(x = "", y = "Ratio (%)", fill = "Gender") + ggtitle("Gender Ratio in the Company") + theme_minimal() + theme(legend.position = "bottom") + coord_flip() # Analysis 2.3 - How does the gender distribution vary across different departments in the company? # Filter the data to include only relevant columns filtered_data <- new_employee_attrition %>% filter(!is.na(gender) & !is.na(department_name)) # Group by department and gender, and calculate the count of employees grouped_data <- filtered_data %>% group_by(department_name, gender) %>% summarize(count = n()) # Calculate the proportion of each gender within each department proportion_data <- grouped_data %>% group_by(department_name) %>% mutate(proportion = count / sum(count)) # Create a stacked bar chart ggplot(proportion_data, aes(x = department_name, y = proportion, fill = gender)) + geom_bar(stat = "identity") + labs(x = "Department", y = "Proportion", fill = "Gender") + ggtitle("Gender Distribution across Departments") + theme_minimal() + theme(legend.position = "bottom", axis.text.x = element_text(angle = 45, hjust = 1)) # Analysis 2.4 - Is there a difference in the gender distribution between different job titles within the company? # Filter the data to include only relevant columns filtered_data <- new_employee_attrition %>% filter(!is.na(gender) & !is.na(job_title)) # Group by job title and gender, and calculate the count of employees grouped_data <- filtered_data %>% group_by(job_title, gender) %>% summarize(count = n()) # Calculate the proportion of each gender within each job title proportion_data <- grouped_data %>% group_by(job_title) %>% mutate(proportion = count / sum(count)) # Create a grouped dot plot ggplot(proportion_data, aes(x = job_title, y = proportion, color = gender)) + geom_point(size = 3, position = position_dodge(width = 0.5)) + labs(x = "Job Title", y = "Proportion", color = "Gender") + ggtitle("Gender Distribution across Job Titles") + theme_minimal() + theme(legend.position = "bottom", axis.text.x = element_text(angle = 45, hjust = 1), legend.title = element_blank()) # Analysis 2.5 - I want to know if there is connection between length of service and gender. # Filter the data to include only relevant columns filtered_data <- new_employee_attrition %>% filter(!is.na(gender) & !is.na(length_of_service)) # Create a violin plot ggplot(filtered_data, aes(x = gender, y = length_of_service, fill = gender)) + geom_violin(trim = FALSE) + labs(x = "Gender", y = "Length of Service", fill = "Gender") + ggtitle("Length of Service by Gender") + theme_minimal() + theme(legend.position = "none") # Analysis 2.6 - Is there relationship between gender and the age of employees in the company? # Filter the data to include only relevant columns filtered_data <- new_employee_attrition %>% filter(!is.na(gender) & !is.na(age)) # Create a box plot ggplot(filtered_data, aes(x = gender, y = age, fill = gender)) + geom_boxplot(color = "black", outlier.shape = NA) + labs(x = "Gender", y = "Age", fill = "Gender") + ggtitle("Relationship between Gender and Age") + theme_minimal() + theme(legend.position = "none", plot.title = element_text(hjust = 0.5, size = 16, face = "bold", color = "slategrey"), axis.text = element_text(size = 12, color = "gray40"), axis.title = element_text(size = 14, face = "bold", color = "steelblue"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), plot.margin = unit(c(1, 1, 1, 1), "cm"), plot.background = element_rect(fill = "bisque1"), # plot.border = element_rect(color = "steelblue", fill = NA, size = 1), panel.background = element_rect(fill = "white"), panel.border = element_rect(color = "gray80", fill = NA), legend.background = element_rect(fill = "white"), legend.title = element_text(size = 12, face = "bold"), legend.text = element_text(size = 12), legend.key = element_rect(fill = "lightgray", color = NA), strip.background = element_rect(fill = "steelblue", color = NA), strip.text = element_text(size = 12, face = "bold", color = "white")) # Analysis 2.7 - Is there a relationship between gender and departments in the company? # Filter the data to include only relevant columns filtered_data <- new_employee_attrition %>% filter(!is.na(gender) & !is.na(department_name)) # Group by gender and department, and calculate the count of employees grouped_data <- filtered_data %>% group_by(gender, department_name) %>% summarize(count = n()) # Create a heatmap chart with red color palette ggplot(grouped_data, aes(x = department_name, y = gender, fill = count)) + geom_tile() + labs(x = "Department", y = "Gender", fill = "Count") + ggtitle("Correlation between Gender and Department") + theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "right") + scale_fill_gradient(low = "red4", high = "red") # Analysis 2.8 - Investigate the gender distribution of Cashier (Job Title). # Filter the data to include only employees with the job title "Cashier" filtered_data <- new_employee_attrition %>% filter(job_title == "Cashier") # Group by gender and calculate the count of employees grouped_data <- filtered_data %>% group_by(gender) %>% summarize(count = n()) # Create a pie chart ggplot(grouped_data, aes(x = "", y = count, fill = gender)) + geom_bar(stat = "identity", width = 1) + coord_polar(theta = "y") + labs(x = NULL, y = NULL, fill = "Gender") + ggtitle("Gender Distribution of Cashiers") + theme_minimal() + theme(legend.position = "bottom", plot.title = element_text(size = 16, face = "bold")) + scale_fill_manual(values = c("steelblue", "pink")) # Analysis 2.9 - Identify the gender proportion of current active employees. # Filter the data to include only current active employees filtered_data <- new_employee_attrition %>% filter(status == "ACTIVE") # Create a boxplot ggplot(filtered_data, aes(x = gender, y = age, fill = gender)) + geom_boxplot() + labs(x = "Gender", y = "Age", fill = "Gender") + ggtitle("Gender Distribution of Current Active Employees") + theme_minimal() + theme(legend.position = "none", plot.title = element_text(hjust = 0.5)) # Analysis 2.10 - Identify the gender proportion of past employees. # Filter the data to include only past employees filtered_data <- new_employee_attrition %>% filter(status == "TERMINATED") # Group by gender and calculate the count of past employees grouped_data <- filtered_data %>% group_by(gender) %>% summarize(count = n()) # Calculate the proportion of each gender grouped_data <- grouped_data %>% mutate(proportion = count / sum(count)) # Create a horizontal bar chart ggplot(grouped_data, aes(x = proportion, y = reorder(gender, proportion), fill = gender)) + geom_col() + labs(x = "Proportion", y = "Gender", fill = "Gender") + ggtitle("Gender Proportion of Past Employees") + theme_minimal() + theme(legend.position = "none", plot.title = element_text(hjust = 0.5), axis.text.y = element_text(hjust = 0)) # Analysis 2.11 - Does the gender ratio change over time? (e.g., analyzing gender distribution by year)? # Filter the data to include only relevant columns filtered_data <- new_employee_attrition %>% filter(!is.na(gender) & !is.na(status_year)) # Group by status_year and gender, and calculate the count of employees grouped_data <- filtered_data %>% group_by(status_year, gender) %>% summarize(count = n()) # Calculate the proportion of each gender within each year proportion_data <- grouped_data %>% group_by(status_year) %>% mutate(proportion = count / sum(count)) # Create a bar chart ggplot(proportion_data, aes(x = as.factor(status_year), y = proportion, fill = gender)) + geom_bar(stat = "identity", position = "stack") + labs(x = "Year", y = "Proportion", fill = "Gender") + ggtitle("Gender Distribution Over Time") + theme_minimal() + theme(legend.position = "bottom") # Analysis 2.12 - Are there any variations in the gender distribution based on the business units in the company? # Filter the data to include only relevant columns filtered_data <- new_employee_attrition %>% filter(!is.na(gender) & !is.na(business_unit)) # Group by business_unit and gender, and calculate the count of employees grouped_data <- filtered_data %>% group_by(business_unit, gender) %>% summarize(count = n()) # Calculate the proportion of each gender within each business unit proportion_data <- grouped_data %>% group_by(business_unit) %>% mutate(proportion = count / sum(count)) # Define a custom color palette colors <- brewer.pal(3, "Set2") # Create a stacked bar chart with custom colors ggplot(proportion_data, aes(x = business_unit, y = proportion, fill = gender)) + geom_bar(stat = "identity", position = "fill") + labs(x = "Business Unit", y = "Proportion", fill = "Gender") + ggtitle("Gender Distribution by Business Unit") + scale_fill_manual(values = colors) + theme_minimal() + theme(legend.position = "bottom", axis.text.x = element_text(angle = 45, hjust = 1)) # Analysis 2.13 - How many female employees are still active in this company from 2010 to 2011? # Convert status_year to numeric new_employee_attrition$status_year <- as.numeric(as.character(new_employee_attrition$status_year)) # Filter the data to include only relevant columns and years 2010-2011 filtered_data <- new_employee_attrition %>% filter(gender == "Female" & status_year >= 2010 & status_year <= 2011) # Group by status_year and calculate the count of active females grouped_data <- filtered_data %>% group_by(status_year) %>% summarize(count = n()) # Create a scatter plot with encircling ggplot(grouped_data, aes(x = status_year, y = count)) + geom_point() + geom_encircle(data = grouped_data[grouped_data$status_year >= 2010 & grouped_data$status_year <= 2011, ], aes(x = status_year, y = count), color = "blue", expand = 0.1) + labs(x = "Year", y = "Count", title = "Number of Active Females (2010-2011)") + theme_minimal() # Analysis 2.14 - Which gender dominates from the highest job positions in the company? # Define the job titles of interest higher_positions <- c("CEO", "Chief Information Officer", "VP Finances", "VP Human Resources", "VP Stores") # Filter the data to include only relevant job titles filtered_data <- new_employee_attrition %>% filter(job_title %in% higher_positions) # Group by gender and job title, and calculate the count of employees grouped_data <- filtered_data %>% group_by(gender, job_title) %>% summarize(count = n()) # Order the job titles by count in descending order grouped_data <- grouped_data %>% arrange(job_title, desc(count)) # Create the lollipop chart ggplot(grouped_data, aes(x = reorder(job_title, count), y = count, fill = gender)) + geom_segment(aes(xend = reorder(job_title, count), yend = 0), color = "black") + geom_point(size = 3, color = "black", shape = 21) + labs(x = "Job Title", y = "Count", fill = "Gender") + ggtitle("Gender Distribution in Higher Job Positions") + theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "bottom") # Analysis 2.15 - I want to know gender distribution from the stores? # Filter the data to include only relevant columns filtered_data <- new_employee_attrition %>% filter(!is.na(store_id) & !is.na(gender)) # Group by store_id and gender, and calculate the count of employees grouped_data <- filtered_data %>% group_by(store_id, gender) %>% summarize(count = n()) # Create a bar chart ggplot(grouped_data, aes(x = store_id, y = count, fill = gender)) + geom_bar(stat = "identity", color = "ivory1") + labs(x = "Store ID", y = "Count", fill = "Gender") + ggtitle("Gender Distribution from Stores") + theme_minimal() + theme(legend.position = "bottom", axis.text.x = element_text(angle = 45, hjust = 1))