Guiding question: How does the mean age at marriage differ between genders across countries?

I’m using this data set from World Bank’s Gender Data Portal.

Load libraries and import data

library(readr)
library(tidyverse)
library(dplyr)
library(ggplot2)
mean_age_at_first_marriage <- read_csv("mean age at first marriage.csv")

Review the data

#take a look at variables 
colnames(mean_age_at_first_marriage)
## [1] "Indicator Name" "Indicator Code" "Country Name"   "Country Code"  
## [5] "Year"           "Value"          "Disaggregation"
#check for NAs 
sum(is.na(mean_age_at_first_marriage))
## [1] 0

Use dplyr for analysis

Compute average age at first marriage by gender (all countries)

# use group_by and summarize
gender_summary <- mean_age_at_first_marriage %>%
group_by(Disaggregation) %>%
summarize(avg_age = round(mean(Value), 0), median_age = round(median(Value), 0), min_age = min(Value), max_age = max(Value))

View results of group_by and summarize

gender_summary
## # A tibble: 2 × 5
##   Disaggregation avg_age median_age min_age max_age
##   <chr>            <dbl>      <dbl>   <dbl>   <dbl>
## 1 female              24         24    14.4    34.1
## 2 male                28         28    20.8    36.9

Count available observations per country

# use count and order by highest reporter at top
country_counts <- mean_age_at_first_marriage %>%
  count(`Country Name`, name = "total_records") %>%
  arrange(desc(total_records))
# taking a look at the difference from top and lowest reporting countries
range(country_counts$total_records)
## [1]  1 96
# taking a look at the distribution of reponse counts
table(country_counts$total_records)
## 
##  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 
##  1  3  2  8  4  9  7 19  8 14  9 13 10  8 13  5  6  9  6  5  3  3  5  3  3  1 
## 27 28 29 30 31 32 34 37 38 41 44 46 56 60 61 63 68 78 84 86 88 96 
##  1  3  1  3  1  3  3  1  2  1  1  2  1  1  1  2  1  1  2  2  1  1

View results of count

# using head here as it's a big data set
head(country_counts, 10)
## # A tibble: 10 × 2
##    `Country Name` total_records
##    <chr>                  <int>
##  1 Hungary                   96
##  2 Denmark                   88
##  3 Norway                    86
##  4 Sweden                    86
##  5 Finland                   84
##  6 Netherlands               84
##  7 Iceland                   78
##  8 Canada                    68
##  9 Greenland                 63
## 10 San Marino                63

These insights indicate that across countries, women tend to be younger at their first marriage, but also that there’s inconsistency in reporting across countries over the years, with more reporting from countries in Europe than anywhere else (this last point deduced just from looking at the country counts data frame, not from performing analysis on it; with more time I’d have added a column to assign each observation a continent so I could do that kind of analysis, and which also may have provided more plot opportunities for that data).

Use ggplot2 for visualization

Gender Summary - Marriage Age by Gender

I decided to try a density plot, comparing the density of the mean age responses for each gender. I don’t think it necessarily makes the most sense as a way to visualize this information (maybe actual number of reports on the y would be better) but I wanted to try it out.

ggplot(mean_age_at_first_marriage, aes(x = Value, fill = Disaggregation)) +
  geom_density(alpha = 0.5) +
  labs(
    title = "Marriage Age by Gender Across Countries",
    x = "Mean Age at First Marriage (Years)",
    y = "Density",
    fill = "Gender"
  ) +
  theme_minimal()

Country Count - Response Distribution

I also wanted to visualize the country count, for which a histogram seemed to make the most sense, giving us a sense of how many data points countries typically contribute.

ggplot(country_counts, aes(x = total_records)) +
  geom_histogram(binwidth = 2, fill = "darkgreen", color = "white") +
  labs(
    title = "Distribution of Responses Across All Countries",
    x = "Number of Survey Responses per Country",
    y = "Number of Countries"
  ) +
  theme_minimal()