Data-Visualisation

Data Visualization is another element of data exploration. After concluding descriptive statistics, creating visualizations like histograms, scatter plots, and box plots helps to understand distributions, relationships, and patterns in the data.

Background:

Data set used for this analysis is a Glassdoor Gender Pay Gap data where base pay, bonus and other information is provided for each employee for all the roles and departments.

Link to the data set as accessed on the 04.07.2024: https://www.kaggle.com/datasets/nilimajauhari/glassdoor-analyze-gender-pay-gap/data (The reason why the link has not been provided as a hyperlink is to be transparent about the source of the data & security.)

Here's what we'll explore in this project:

1. Import the Data

• Begin by importing the Glassdoor Gender Pay Gap data.

2. BasePay & Bonus by Department and Job Role

• By Department: We'll visualize BasePay and Bonus across departments, utilizing a color palette from R’s Color Brewer.
• By Job Role: We'll then explore the BasePay and Bonus distribution across different job roles.

3. Mean BasePay Visualization

• Bar Chart by Department: Calculate the mean BasePay for each department and visualize it with a bar chart, using a palette from R's Color Brewer.
• BasePay & Bonus by Department: Visualize the average BasePay and Bonus across departments.
• BasePay & Bonus by Job Role: Similarly, visualize the average BasePay and Bonus by job roles.

4. Gender-Based Pay Visualizations

• BasePay by Gender: Use boxplots to compare the distributions of BasePay across genders.
• Bonus by Gender: Visualize the distribution of Bonus amounts across genders using boxplots.
• Scatter Plot: Explore the relationship between BasePay and Bonus, with points colored by gender.
• Facet Grid: View separate scatter plots for each gender, offering a clear comparison.
• Violin Plot: Combine the distributions of BasePay and Bonus into a single plot, visualizing differences by gender.
• Bar Plot: Compare the average BasePay and Bonus between genders using a bar plot.

5. Heat Map Visualizations

• By Job Title and Department: Create a heat map showing the average BasePay by Job Title and Department.
• Count of Job Titles by Department: Visualize the count of job titles across departments using a heat map.
• BasePay by Job Title and Gender: Explore the relationship between BasePay, job titles, and gender with a heat map.

6. Age Distribution

• Histogram: Obtain a histogram to visualize the distribution of employees' ages.

7. Gender Distribution

• Bar Chart: Display the count of each gender within the dataset.

8. Education Level Distribution

• Bar Chart: Visualize the count of employees at different education levels using a bar chart.

Ready to dive in? Just keep scrolling!

Data Visualization

QUESTIONS

0. Importing packages and libraries:

library(dplyr)
library(ggplot2)
library(RColorBrewer)
library(tidyr)

1. Import the Data: Begin by importing the Glassdoor Gender Pay Gap data.

GGPG_data<-read.csv(file.choose(), header=T)

head(GGPG_data)
dim(GGPG_data)
summary(GGPG_data)

2. BasePay & Bonus by Department, Job Role, Seniority:

2.1 By Department: We'll visualize BasePay and Bonus across departments,utilizing a color palette from R’s Color Brewer.

Checking all colour pallets in the package

dev.off() 
display.brewer.all(colorblindFriendly = TRUE)

BasePay by Department

ggplot(GGPG_data, aes(Dept, BasePay, fill = Dept))+
  geom_col()+
  scale_fill_brewer(palette = "Paired") +  
  labs(x = "Dept", y = " Base Pay", title = " Base Pay by Department")+
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Bonus by Department

ggplot(GGPG_data, aes(Dept, Bonus, fill = Dept))+
  geom_col()+
  scale_fill_brewer(palette = "Paired") +  
  labs(x = "Dept", y = " Bonus", title = " Bonus by Department")+
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Box Plot: To compare BasePay across different levels of Seniority

ggplot(GGPG_data, aes(x = factor(Seniority), y = BasePay, fill = factor(Seniority))) +
  geom_boxplot() +
  labs(title = "BasePay by Seniority Level", x = "Seniority Level", y = "BasePay")

Box Plot: To compare Bonus across different levels of Seniority

ggplot(GGPG_data, aes(x = factor(Seniority), y = Bonus, fill = factor(Seniority))) +
  geom_boxplot() +
  labs(title = "BasePay by Seniority Level", x = "Seniority Level", y = "BasePay")

2.2 By Job Role: We'll then explore the BasePay and Bonus distribution across different job roles.

ggplot(GGPG_data, aes(JobTitle, BasePay, fill = JobTitle))+
  geom_col()+
  scale_fill_brewer(palette = "Paired") +  
  labs(x = "JobTitle", y = " Base Pay", title = " Base Pay by JobTitlet")+
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

ggplot(GGPG_data, aes(JobTitle, Bonus, fill = JobTitle))+
  geom_col()+
  scale_fill_brewer(palette = "Paired") +  
  labs(x = "JobTitle", y = " Bonus", title = " Bonus by JobTitle")+
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

3. Mean BasePay Visualization:

3.1 Bar Chart by Department: Calculate the mean BasePay for each department and visualize it with a bar chart, using a palette from R's Color Brewer.

Calculate mean Base Pay per Department

mean_BasePay_per_Dept<-GGPG_data%>%
  group_by(Dept)%>%
  summarise(BasePay_avg=mean(BasePay))
mean_BasePay_per_Dept

Plot bar chart of mean Base Pay per Department

ggplot(mean_BasePay_per_Dept, aes(x = Dept, y = BasePay_avg, fill = Dept)) +
  geom_bar(stat = "identity") +
  scale_fill_brewer(palette = "Paired") +  
  labs(x = "Dept", y = "Mean of Base Pay", title = "Average Base Pay by Department")+
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

3.2 BasePay & Bonus by Department: Visualize the average BasePay and Bonus across departments.

Calculate mean of Base Pay & Bonus per Department

mean_Bonus_per_Dept<-GGPG_data%>%
  group_by(Dept)%>%
  summarise(Bonus_avg=mean(Bonus))
mean_Bonus_per_Dept

Combine the mean base pay and mean bonus datasets into one, pivoting longer

combined_data <- mean_BasePay_per_Dept %>%
  inner_join(mean_Bonus_per_Dept, by = "Dept") %>%
  pivot_longer(cols = c("BasePay_avg", "Bonus_avg"),
               names_to = "Pay_Type",
               values_to = "Average")
head(combined_data)

Average Base Pay and Bonus by Department

ggplot(combined_data, aes(x = Dept, y = Average, fill = Pay_Type)) +
  geom_bar(stat = "identity", position = "dodge") +
  scale_fill_brewer(palette = "Paired") +
  labs(x = "Department", y = "Average Pay", 
       title = "Average Base Pay and Bonus by Department") +
  theme_minimal()

3.3 BasePay & Bonus by Job Role: Similarly, visualize the average BasePay and Bonus by job roles.

Mean of Base Pay & Bonus by Job Role

mean_JobRole <- GGPG_data %>%
  group_by(JobTitle) %>%
  summarise(avg_BasePay = mean(BasePay),
            avg_Bonus = mean(Bonus))

mean_JobRole_long <- mean_JobRole %>%
  pivot_longer(cols = c("avg_BasePay", "avg_Bonus"),
               names_to = "Pay_Type2",
               values_to = "Average2")
mean_JobRole
mean_JobRole_long

ggplot(mean_JobRole_long, aes(x = JobTitle, y = Average2, fill = Pay_Type2 ))+
  geom_bar(stat = "identity", position = "dodge")+
  scale_fill_brewer(palette = "Paired") +
  labs(x = "Job Title", y = "Average Pay", 
       title = "Average Base Pay and Bonus by Job Title") +
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

4. Gender-Based Pay Visualizations:

4.1 BasePay by Gender: Use boxplots to compare the distributions of BasePay across genders.

ggplot(GGPG_data, aes(x = Gender, y = BasePay, fill = Gender)) +
  geom_boxplot() +
  scale_fill_brewer(palette = "Paired") +
  labs(title = "BasePay Distribution by Gender",
       x = "Gender",
       y = "Base Pay") +
  theme_minimal()

4.2 Bonus by Gender: Visualize the distribution of Bonus amounts across genders using boxplots.

ggplot(GGPG_data, aes(x = Gender, y = Bonus, fill = Gender)) +
  geom_boxplot() +
  scale_fill_brewer(palette = "Paired") +
  labs(title = "Bonus Distribution by Gender",
       x = "Gender",
       y = "Bonus") +
  theme_minimal()

4.3 Scatter Plot: Explore the relationship between BasePay and Bonus, with points colored by gender.

ggplot(GGPG_data, aes(x = BasePay, y = Bonus, color = Gender)) +
  geom_point(alpha = 0.7) +
  scale_color_brewer(palette = "Paired") +
  labs(title = "Scatter Plot of BasePay vs. Bonus by Gender",
       x = "Base Pay",
       y = "Bonus") +
  theme_minimal()

4.4 Facet Grid: View separate scatter plots for each gender, offering a clear comparison.

ggplot(GGPG_data, aes(x = BasePay, y = Bonus)) +
  geom_point(alpha = 0.7) +
  facet_wrap(~Gender) +
  labs(title = "BasePay vs. Bonus Faceted by Gender",
       x = "Base Pay",
       y = "Bonus") +
  theme_minimal()

4.5 Violin Plot: Combine the distributions of BasePay and Bonus into a single plot, visualizing differences by gender.

Melting the data to combine BasePay and Bonus into one variable

library(reshape2)
melted_data <- melt(GGPG_data, id.vars = "Gender", measure.vars = c("BasePay", "Bonus"))

Violin plot for BasePay and Bonus by Gender

ggplot(melted_data, aes(x = Gender, y = value, fill = Gender)) +
  geom_violin(trim = FALSE) +
  facet_wrap(~variable, scales = "free_y") +
  scale_fill_brewer(palette = "Paired") +
  labs(title = "Distribution of BasePay and Bonus by Gender",
       x = "Gender",
       y = "Value") +
  theme_minimal()

4.6 Bar Plot: Compare the average BasePay and Bonus between genders using a bar plot.

mean_pay_bonus <- GGPG_data %>%
  group_by(Gender) %>%
  summarise(BasePay_avg = mean(BasePay),
            Bonus_avg = mean(Bonus))

Melt the data for ggplot

mean_pay_bonus_melted <- melt(mean_pay_bonus, id.vars = "Gender")

Grouped bar plot

ggplot(mean_pay_bonus_melted, aes(x = Gender, y = value, fill = variable)) +
  geom_bar(stat = "identity", position = "dodge") +
  scale_fill_brewer(palette = "Paired") +
  labs(title = "Average BasePay and Bonus by Gender",
       x = "Gender",
       y = "Average Value",
       fill = "Variable") +
  theme_minimal()

5. Heat Map Visualizations:

5.1 By Job Title and Department: Create a heat map showing the average BasePay by Job Title and Department.

avgBasePay_Dept_JobTitle<-GGPG_data %>%
  group_by(Dept, JobTitle)%>%
  summarise(avg_BasePay = mean(BasePay))
avgBasePay_Dept_JobTitle

Creating the heat map of Average BasePay by JobTitle and Dept:

ggplot(avgBasePay_Dept_JobTitle, aes(x = Dept, y = JobTitle, fill = avg_BasePay)) +
  geom_tile(color = "white") +
  labs(x = "Dept", y = "Job Title", title = "Heatmap of Job Title and Department with Average Base Pay") +
  scale_fill_gradientn(colors = c("orange", "lightyellow", "steelblue")) +  # Three-color gradient
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

--> This heat map would show the distribution of average BasePay or Bonus across different

job titles within each department. It could help you identify which departments have higher

or lower pay for specific job titles.

5.2 Count of Job Titles by Department: Visualize the count of job titles across departments using a heat map.

JobTitle_count <- GGPG_data %>%
  group_by(Dept, JobTitle) %>%
  summarise(JT_count = length(JobTitle))
JobTitle_count
ggplot(JobTitle_count, aes(x = JobTitle, y = Dept, fill = JT_count)) +
  geom_tile(color = "white") +  
  geom_text(aes(label = JT_count), size = 4, color = "black") + 
  scale_fill_gradient(low = "lightgreen", high = "darkgreen") +  
  labs(x = "Job Title", y = "Department", fill = "Count",
       title = "Heat Map of Job Title Counts by Department") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  

5.3 BasePay by Job Title and Gender: Explore the relationship between BasePay, job titles, and gender with a heat map.

ggplot(GGPG_data, aes(x = JobTitle, y = Gender, fill = BasePay)) +
  geom_tile(color = "white") +
  labs(x = "Job Title", y = "Dept", title = "Heatmap of Job Title and Department with Job Titles Count") +
  scale_fill_gradient(low = "yellow2", high = "turquoise4")+
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  

6. Age Distribution:

6.1 Histogram: Obtain a histogram to visualize the distribution of employees' ages.

  geom_histogram(binwidth = 5, fill = "skyblue", color = "black") +
  labs(title = "Distribution of Employee Ages", x = "Age", y = "Count")

7. Gender Distribution:

7.1 Bar Chart: Display the count of each gender within the dataset.

ggplot(GGPG_data, aes(x = Gender)) +
  geom_bar(fill = "lightgreen", color = "black") +
  labs(title = "Gender Distribution", x = "Gender", y = "Count")

8. Education Level Distribution:

8.1 Bar Chart: Visualize the count of employees at different education levels using a bar chart.

ggplot(GGPG_data, aes(x = Education)) +
  geom_bar(fill = "purple4", color = "black") +
  labs(title = "Distribution of Education Levels", x = "Education Level", y = "Count")