# An R Tutorial On Visualizing Population Pyramids

### Summary

In this tutorial, we explored how to create population pyramids using R and ggplot2. Population pyramids are essential tools for visualizing the distribution of population across different age groups and genders. Here’s a breakdown of what we covered:

1. **Introduction**
2. **Loading Libraries**
3. **Data Preparation**
4. **Creating Population Pyramids**
5. **Adjustments and Customization**
6. **Population Projection**

By following this tutorial, you should now have a solid understanding of how to create and customize population pyramids in RMarkdown using ggplot2.

### Introduction

In this tutorial, we will learn how to create population pyramids using the ggplot2 package in Rmarkdown. Population pyramids are graphical illustrations that show the distribution of population numbers by and age groups.

### Load Necessary Libraries

First, we need to load the necessary libraries for data manipulation and visualization.

```
# Set the CRAN mirror
options(repos = c(CRAN = "https://cran.rstudio.com/"))
```

```
# Load necessary packages and suppress warnings
if (!requireNamespace("readxl", quietly = TRUE)) {
  install.packages("readxl")
}
if (!requireNamespace("ggplot2", quietly = TRUE)) {
  install.packages("ggplot2")
}
if (!requireNamespace("reshape", quietly = TRUE)) {
  install.packages("reshape")
}
if (!requireNamespace("reshape2", quietly = TRUE)) {
  install.packages("reshape2")
}
if (!requireNamespace("pyramid", quietly = TRUE)) {
  install.packages("pyramid")
}
if (!requireNamespace("animation", quietly = TRUE)) {
  install.packages("animation")
}
if (!requireNamespace("ggthemes", quietly = TRUE)) {
  install.packages("ggthemes")
}

# Load necessary packages and suppress warnings
suppressWarnings(suppressPackageStartupMessages(library(readxl)))
suppressWarnings(suppressPackageStartupMessages(library(ggplot2)))
suppressWarnings(suppressPackageStartupMessages(library(reshape)))
suppressWarnings(suppressPackageStartupMessages(library(reshape2)))
suppressWarnings(suppressPackageStartupMessages(library(pyramid)))
suppressWarnings(suppressPackageStartupMessages(library(animation)))
suppressWarnings(suppressPackageStartupMessages(library(ggthemes)))
suppressWarnings(suppressPackageStartupMessages(library(tidyverse)))
```

### Upload Population Data

Assume that you have an Excel file.

```
# Load the dataset
proj <- read_excel("C:/Users/ABBAD TECHNOLOGY/Downloads/projection.xlsx")

# View the structure of the dataset
str(proj)
```

```
## tibble [100 × 3] (S3: tbl_df/tbl/data.frame)
##  $ age    : num [1:100] 0 1 2 3 4 5 6 7 8 9 ...
##  $ males  : num [1:100] 525931 505803 494789 479622 455602 ...
##  $ females: num [1:100] 495945 474973 460463 437178 422942 ...
```

### Prepare Data for Visualization

Now, we’ll prepare the data for creating population pyramids. We’ll select data for males and females separately.

```
# Read the data for males
males <- proj %>% select(age, males)

males
```

```
## # A tibble: 100 × 2
##      age  males
##    <dbl>  <dbl>
##  1     0 525931
##  2     1 505803
##  3     2 494789
##  4     3 479622
##  5     4 455602
##  6     5 440379
##  7     6 409459
##  8     7 375371
##  9     8 350141
## 10     9 330236
## # ℹ 90 more rows
```

```
# Read the data for females
females <- proj %>% select(age, females)
females
```

```
## # A tibble: 100 × 2
##      age females
##    <dbl>   <dbl>
##  1     0  495945
##  2     1  474973
##  3     2  460463
##  4     3  437178
##  5     4  422942
##  6     5  400370
##  7     6  388023
##  8     7  376249
##  9     8  350256
## 10     9  330381
## # ℹ 90 more rows
```

### Create Population Pyramid

Now, let’s create an initial population pyramid showing both males and females.

```
# Combine males and females data for plotting
combined <- data.frame(
  age = males$age,
  males = males$males,
  females = females$females
)

# Create the population pyramid plot
p <- ggplot(combined, aes(x = age, y = males)) +
  geom_bar(stat = "identity", aes(fill = "Male"), position = "identity") +
  geom_bar(stat = "identity", aes(x = age, y = -females, fill = "Female"), position = "identity") +
  scale_fill_manual(name = "  ", values = c("Male" = "cyan", "Female" = "pink")) +
  labs(x = "Age", y = "Population", title = "Population Pyramid") +
  theme_minimal() +
  coord_flip()

# Enregistrement du graphique en PNG
png("PopulationPyramid.png", width = 480, height = 300, units = "px")
print(p)
dev.off()
```

<figure><img src="/files/BtKJpBpB9O3yXRaOVJyw" alt=""><figcaption></figcaption></figure>

### Population Pyramid By 5 Age Groups

To improve the visualization, let’s define age groups and categorize the ages accordingly.

```
# Define age groups
combined <- combined %>%
  mutate(age_group = cut(age, breaks = c(-Inf, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, Inf),
                        
                        labels = c("0-4", "5-9", "10-14", "15-19", "20-24", "25-29", "30-34", "35-39", "40-44",
                                   "45-49", "50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85+")))
```

### Create population Pyramid

Now, let’s create the population pyramid using the defined age groups and population data for males and females.

```
# Income
# Plot the population pyramid with age groups

d <- ggplot(combined, aes(x = age_group, y = males)) +
  geom_bar(stat = "identity", aes(fill = "Male"), position = "identity") +
  geom_bar(stat = "identity", aes(x = age_group, y = -females, fill = "Female"), position = "identity") +
  scale_fill_manual(name = "  ", values = c("Male" = "cyan", "Female" = "pink")) +
  labs(x = "Age Group", y = "Population", title = "Population Pyramid by Age Group") +
  theme_minimal() +
  coord_flip()

# Enregistrement du graphique en PNG
png("Plotthepopulationpyramidwithagegroups.png", width = 480, height = 300, units = "px")
print(d)
dev.off()
```

<figure><img src="/files/kCDcXxO91LRaJ2hAzzCR" alt=""><figcaption></figcaption></figure>

### Reshape Data for Population Pyramid Plotting

Next, let’s reshape the `pyram` data frame using the `reshape2` package to prepare it for plotting the population pyramid.

```
pyram <- data.frame(
  age_group = c("0-4", "5-9", "10-14", "15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", "50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85+"),
  male_population = c(2902126, 1786497, 1480079, 1633986, 1823605, 1886390, 1741860, 1399704, 1173186, 1009206, 897000, 784000, 678000, 567000, 467000, 378000, 298000, 210000),
  female_population = c(2728599, 1662222, 1388753, 1563372, 1759641, 1819350, 1679990, 1366123, 1146892, 982000, 874000, 762000, 664000, 555000, 455000, 368000, 288000, 200000)
)

# Convertir age_group en factor avec l'ordre spécifié
pyram$age_group <- factor(pyram$age_group, levels = c("0-4", "5-9", "10-14", "15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", "50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85+"))

# Fusion des données en format long pour le tracé de la pyramide des âges
pyram_long <- melt(pyram, id.vars = "age_group", measure.vars = c("male_population", "female_population"))

# Afficher les données reshaped
print(pyram_long)
```

```
##    age_group          variable   value
## 1        0-4   male_population 2902126
## 2        5-9   male_population 1786497
## 3      10-14   male_population 1480079
## 4      15-19   male_population 1633986
## 5      20-24   male_population 1823605
## 6      25-29   male_population 1886390
## 7      30-34   male_population 1741860
## 8      35-39   male_population 1399704
## 9      40-44   male_population 1173186
## 10     45-49   male_population 1009206
## 11     50-54   male_population  897000
## 12     55-59   male_population  784000
## 13     60-64   male_population  678000
## 14     65-69   male_population  567000
## 15     70-74   male_population  467000
## 16     75-79   male_population  378000
## 17     80-84   male_population  298000
## 18       85+   male_population  210000
## 19       0-4 female_population 2728599
## 20       5-9 female_population 1662222
## 21     10-14 female_population 1388753
## 22     15-19 female_population 1563372
## 23     20-24 female_population 1759641
## 24     25-29 female_population 1819350
## 25     30-34 female_population 1679990
## 26     35-39 female_population 1366123
## 27     40-44 female_population 1146892
## 28     45-49 female_population  982000
## 29     50-54 female_population  874000
## 30     55-59 female_population  762000
## 31     60-64 female_population  664000
## 32     65-69 female_population  555000
## 33     70-74 female_population  455000
## 34     75-79 female_population  368000
## 35     80-84 female_population  288000
## 36       85+ female_population  200000
```

### Plot Population Pyramid

Let’s plot the population pyramid using the reshaped data.

```
# Plot the population pyramid
a <- ggplot(pyram_long, aes(x = age_group, y = ifelse(variable == "female_population", -value, value), fill = variable)) +
  geom_bar(stat = "identity", position = "identity") +
  scale_fill_manual(name = "Sex", values = c("male_population" = "lightblue", "female_population" = "pink"), 
                    labels = c("male_population" = "Males", "female_population" = "Females")) +
  scale_y_continuous(labels = abs) +
  labs(x = "Age Group", y = "Population", title = "Population Pyramid by Age Group") +
  theme_minimal() +
  coord_flip()

# Enregistrement du graphique en PNG
png("Plotthepopulationpyramidwithagegroupsreshapeddata.png", width = 480, height = 300, units = "px")
print(a)
dev.off()
```

<figure><img src="/files/8nQjVGqMbgMvf6SKoyGU" alt=""><figcaption></figcaption></figure>

### More Options

### Adjusted Stacked Population Pyramid

Let’s create a stacked population pyramid with adjusted y-axis breaks and labels.

```
# Adjust the y-axis breaks and labels for better visualization
breaks <- seq(-max(pyram_long$value), max(pyram_long$value), by = 50000)
labels <- abs(breaks)

# Plot the adjusted stacked population pyramid
z <- ggplot(pyram_long, aes(x = age_group, y = ifelse(variable == "female_population", -value, value), fill = variable)) +
  geom_bar(stat = "identity", position = "stack") +
  scale_fill_manual(name = "Sex", values = c("male_population" = "lightblue", "female_population" = "pink"), 
                    labels = c("male_population" = "Males", "female_population" = "Females")) +
  scale_y_continuous(breaks = breaks, labels = labels) +
  labs(x = "Age Group", y = "Population", title = "Adjusted Stacked Population Pyramid by Age Group") +
  theme_minimal() +
  coord_flip()

# Enregistrement du graphique en PNG
png("AdjustedStackedPopulationPyramidbyAgeGroup.png", width = 480, height = 300, units = "px")
print(z)
dev.off()
```

<figure><img src="/files/uIc1dD7YS2hlMhmtqJwr" alt=""><figcaption></figcaption></figure>

### Population Pyramid with ggplot2

Let’s create a population pyramid using `ggplot2`.

```
# Install and load necessary packages if not already installed
if (!require("tidyverse")) install.packages("tidyverse")
library(tidyverse)

# Prepare the data
pyram_long <- melt(pyram, id.vars = "age_group", measure.vars = c("male_population", "female_population"))

# Adjust the data for males to be negative
pyram_long$value <- ifelse(pyram_long$variable == "female_population", pyram_long$value, -pyram_long$value)

# Define the y-axis breaks and labels for better visualization
breaks <- seq(-max(abs(pyram_long$value)), max(abs(pyram_long$value)), by = 50000)
labels <- abs(breaks)

# Plot the population pyramid using ggplot2
r <- ggplot(pyram_long, aes(x = age_group, y = value, fill = variable)) +
  geom_bar(stat = "identity", position = "stack") +
  scale_fill_manual(name = "Sex", values = c("male_population" = "lightblue", "female_population" = "pink"), 
                    labels = c("male_population" = "Males", "female_population" = "Females")) +
  scale_y_continuous(breaks = breaks, labels = labels) +
  labs(x = "Age Group", y = "Population", title = "Population Pyramid by Age Group") +
  theme_minimal() +
  coord_flip()


# Enregistrement du graphique en PNG
png("PopulationPyramidbyAgeGroup.png", width = 480, height = 300, units = "px")
print(r)
dev.off()
```

<figure><img src="/files/KHKYHiWlq5YnW0WVOM5v" alt=""><figcaption></figcaption></figure>

### Population Projection

In this section, we will use the given dataset to create a population projection.

```
# Assuming that we already have the projection data in 'proj'

# Here, we'll simply visualize the projection data for future years (if available)
# For demonstration purposes, let's assume 'proj' already contains future years

# Prepare the data for population projection visualization
projection_long <- proj %>%
  gather(key = "sex", value = "population", -age) %>%
  mutate(sex = recode(sex, "males" = "Male", "females" = "Female"))

# Plot the population projection
s <- ggplot(projection_long, aes(x = age, y = ifelse(sex == "Female", -population, population), fill = sex)) +
  geom_bar(stat = "identity", position = "identity") +
  scale_fill_manual(name = "Sex", values = c("Male" = "lightblue", "Female" = "pink")) +
  scale_y_continuous(labels = abs) +
  labs(x = "Age", y = "Population", title = "Population Projection") +
  theme_minimal() +
  coord_flip()

# Enregistrement du graphique en PNG
png("PopulationProjection.png", width = 480, height = 300, units = "px")
print(s)
dev.off()
```

<figure><img src="/files/cHf3STLMVVzBIH1JKv6g" alt=""><figcaption></figcaption></figure>

**This work has been conducted by Abbad Bouchra under the supervision of Flici Farid at CREAD (Centre de Recherche en Économie Appliquée et du Développement).**


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