Expected Population Growth (Inspired by TidyTuesday dataset)

Predictive Analytics

This analysis examines the total figures for the global population, focusing on how the size of the population is expected to evolve in different regions and countries of the world.

Overview

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library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
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library(lubridate)
library(skimr)
library(survival)
library(survminer)
Loading required package: ggpubr

Attaching package: 'survminer'

The following object is masked from 'package:survival':

    myeloma
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library(plotly)

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout
Show the code 
library(DT)

# Load the data
thePath <- "/Users/cordarrylhall/CorDarryl Hall Data Portfolio/CorDarryl Hall Data Portfolio"
ds <- read_csv(paste(thePath, "Projections-of-the-world-population-until-2100-by-the-Wittgenstein-Centre.csv", sep="/"))
Rows: 189 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): Entity, Code
dbl (2): Year, PopulationTotal_SSP2

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
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# Display basic info about the data
names(ds)
[1] "Entity"               "Code"                 "Year"                
[4] "PopulationTotal_SSP2"
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head(ds)
# A tibble: 6 × 4
  Entity Code   Year PopulationTotal_SSP2
  <chr>  <chr> <dbl>                <dbl>
1 Africa <NA>   1970            368002720
2 Africa <NA>   1975            420166976
3 Africa <NA>   1980            482569664
4 Africa <NA>   1985            553231936
5 Africa <NA>   1990            634977088
6 Africa <NA>   1995            720581760
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skim(ds)
Data summary
Name ds
Number of rows 189
Number of columns 4
_______________________
Column type frequency:
character 2
numeric 2
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
Entity 0 1.00 4 31 0 7 0
Code 162 0.14 8 8 0 1 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Year 0 1 2035 3.90500e+01 1970 2000 2035 2070 2100 ▇▇▇▇▇
PopulationTotal_SSP2 0 1 2184638392 2.72418e+09 19334460 425366016 737849088 3680742912 9397164032 ▇▁▂▁▁

Key Insights

Population Growth by Region: Different regions are expected to experience varying rates of population growth.

Top Growing Regions: Identify the regions with the highest expected population growth.

Distribution of Population Growth: Understanding the distribution and density of expected population growth across different regions.

Interactive Boxplot of Expected Population Growth by Region

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# Interactive boxplot
plot_ly(ds, x = ~Entity, y = ~PopulationTotal_SSP2, type = 'box', name = 'Population') %>%
  layout(title = "Expected Population Growth by Region",
         xaxis = list(title = "Region"),
         yaxis = list(title = "Population"))

Expected Population Growth by Region (Histogram)

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# Histogram of expected population growth
p <- ds %>%
  ggplot(aes(x = PopulationTotal_SSP2, fill = Entity)) +
  geom_histogram(color = "black", bins = 20) +
  ggtitle("Expected Population Growth by Region") +
  facet_wrap(. ~ Entity) +
  theme_minimal()

ggplotly(p)

Trend Analysis: Population Growth Over Time

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# Line plot for population growth over time
trend_plot <- ds %>%
  ggplot(aes(x = Year, y = PopulationTotal_SSP2, color = Entity)) +
  geom_line() +
  ggtitle("Population Growth Over Time by Region") +
  xlab("Year") +
  ylab("Population") +
  theme_minimal()

ggplotly(trend_plot)

Comparative Analysis

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# Compare population growth in top regions
top_regions <- ds %>%
  group_by(Entity) %>%
  summarize(Total_Population = sum(PopulationTotal_SSP2)) %>%
  arrange(desc(Total_Population)) %>%
  head(10)

datatable(top_regions, options = list(pageLength = 5, autoWidth = TRUE))

Detailed Analysis

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# Regional breakdown plot
regional_breakdown <- ds %>%
  ggplot(aes(x = Entity, y = PopulationTotal_SSP2, fill = Entity)) +
  geom_col() +
  coord_flip() +
  ggtitle("Regional Breakdown of Expected Population Growth") +
  xlab("Region") +
  ylab("Total Population") +
  theme_minimal()

print(regional_breakdown)

Conclusion

The analysis provides insights into the expected population growth across various regions and countries. By leveraging interactive visualizations, trend analysis, and comparative studies, we can better understand the global population dynamics and prepare for future demographic shifts.

Feel free to explore the interactive elements and delve deeper into the analysis