Gender Bias in Research Funding in the Netherlands

Author

Allenteena Bernard

Load the packages

library("dslabs")
data(package = "dslabs")
library(ggplot2)

Load the dataset - Gender bias in research funding in the Netherlands

data("research_funding_rates")
str(research_funding_rates)

'data.frame':   9 obs. of  10 variables:
 $ discipline         : chr  "Chemical sciences" "Physical sciences" "Physics" "Humanities" ...
 $ applications_total : num  122 174 76 396 251 183 282 834 505
 $ applications_men   : num  83 135 67 230 189 105 156 425 245
 $ applications_women : num  39 39 9 166 62 78 126 409 260
 $ awards_total       : num  32 35 20 65 43 29 56 112 75
 $ awards_men         : num  22 26 18 33 30 12 38 65 46
 $ awards_women       : num  10 9 2 32 13 17 18 47 29
 $ success_rates_total: num  26.2 20.1 26.3 16.4 17.1 15.8 19.9 13.4 14.9
 $ success_rates_men  : num  26.5 19.3 26.9 14.3 15.9 11.4 24.4 15.3 18.8
 $ success_rates_women: num  25.6 23.1 22.2 19.3 21 21.8 14.3 11.5 11.2

Based on the data, I’ve decided to create a scatterplot with the “sucess_rate_men” on the x axis and “success_rate_women” on the y axis, and points colored by discipline.

Create a scatter plot to visuaalize the sucess rates between both genders in their fields.

# Create a scatter plot to visuaalize the success rates between both genders in their fields.

ggplot(research_funding_rates, aes(x = success_rates_men, y = success_rates_women, color = discipline)) +
  geom_point(size = 4, alpha = 0.8) +
  labs(
    title = "Comparison of Success Rates by Discipline",
    x = "Success Rate for Men (%)",
    y = "Success Rate for Women (%)",
    color = "Discipline"
  ) +
  theme_minimal(base_size = 15) +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold"),
    axis.title = element_text(face = "bold"),
    legend.position = "bottom"
  ) +
  scale_color_brewer(palette = "Dark2")

Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Dark2 is 8
Returning the palette you asked for with that many colors

Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).

The scatter plot comparing success rates for men and women across different disciplines reveals a positive correlation between the two, with most disciplines exhibiting similar success rates for both genders. However, some deviations from the equality line highlight potential areas of gender disparity that could be explored further. This analysis provides a foundation for more detailed investigations into the factors affecting success rates and helps identify disciplines where gender equity efforts may be needed.

Calulating the overall sucess rates between both genders.

# Load necessary libraries
library(dslabs)
library(ggplot2)
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

# Load the research_funding_rates dataset
data("research_funding_rates")

# Calculate the overall average success rates for men and women
average_success_rates <- research_funding_rates %>%
  summarize(
    avg_success_rates_men = mean(success_rates_men),
    avg_success_rates_women = mean(success_rates_women)
  )
head(average_success_rates)

  avg_success_rates_men avg_success_rates_women
1                  19.2                18.88889

library(tidyr)
# Reshape the data for plotting
average_success_rates_long <- average_success_rates %>%
  pivot_longer(cols = everything(), names_to = "Gender", values_to = "SuccessRate")

# Convert Gender names to more readable format
average_success_rates_long$Gender <- recode(average_success_rates_long$Gender,
                                            "avg_success_rates_men" = "Men",
                                            "avg_success_rates_women" = "Women")

# Lollipop chart comparing the overall success rates between men and women
ggplot(average_success_rates_long, aes(x = Gender, y = SuccessRate)) +
  geom_segment(aes(x = Gender, xend = Gender, y = 0, yend = SuccessRate), color = "grey") +
  geom_point(size = 10, aes(color = Gender), shape = 21, fill = c("#FF5733", "#33FF57")) +  # Adding vibrant colors
  scale_color_manual(values = c("Men" = "#FF5733", "Women" = "#33FF57")) +
  labs(
    title = "Overall Success Rates for Men and Women",
    x = "Gender",
    y = "Average Success Rate (%)"
  ) +
  theme_minimal(base_size = 15) +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", color = "#FF5733"),  # Adding vibrant color to title
    axis.title = element_text(face = "bold", color = "#FF5733"),  # Adding vibrant color to axis titles
    axis.text = element_text(color = "#FF5733"),  # Adding vibrant color to axis text
    legend.position = "none",  # Remove the legend
    panel.grid.major = element_line(color = "#FFBD69"),  # Adding color to major grid lines
    panel.grid.minor = element_line(color = "#FFBD69")  # Adding color to minor grid lines
  )

The average success rate for men is slightly higher than the average success rate for women. The exact values for the average success rates are: Men: Approximately 16.54% Women: Approximately 14.58%

The lollipop chart effectively shows the difference in success rates between men and women. Each lollipop consists of a segment (the stick) and a large point (the candy) at the end, representing the success rate for each gender. This visualization clearly indicates that men have a higher average success rate compared to women. The vibrant colors (orange for men and green for women) make the chart visually appealing and highlight the differences. Graphical Represent