> For the complete documentation index, see [llms.txt](https://bouchrabd.gitbook.io/an-r-tutorial-on-on-visualizing-population-pyramid-1/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://bouchrabd.gitbook.io/an-r-tutorial-on-on-visualizing-population-pyramid-1/an-r-tutorial-on-visualizing-population-pyramids-part-2.md).

# An R Tutorial On Visualizing Population Pyramids -Part 2-

### 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:

This section demonstrates how to create an initial population pyramid displaying both male and female populations.

```
# Combine male and female 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/wU1PtH811E1UEXqWDZn7" alt=""><figcaption></figcaption></figure>

### Adjust the Title and Sizes:

Enhance the appearance of the population pyramid by adjusting the title’s position and modifying text sizes.

```
# Enhanced population pyramid plot

b <- 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() +
  theme(
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 14),
    axis.title.y = element_text(size = 14),
    axis.text = element_text(size = 12),
    legend.position = "top",
    legend.title = element_text(size = 14),
    legend.text = element_text(size = 12)
  ) +
  coord_flip()

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

<figure><img src="/files/qLAZuDYewP3JWkWtga25" alt=""><figcaption></figcaption></figure>

### Population Pyramid Using Lines:

Create a population pyramid using lines instead of bars for a smoother representation.

```
# Enhanced population pyramid plot using lines

c <- ggplot(combined, aes(x = age, y = males)) +
  geom_line(aes(color = "Male"), size = 1.2) +
  geom_line(aes(x = age, y = -females, color = "Female"), size = 1.2) +
  scale_color_manual(name = "  ", values = c("Male" = "cyan", "Female" = "pink")) +
  labs(x = "Age", y = "Population", title = "Population Pyramid") +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 14),
    axis.title.y = element_text(size = 14),
    axis.text = element_text(size = 12),
    legend.position = "top",
    legend.title = element_text(size = 14),
    legend.text = element_text(size = 12)
  ) +
  scale_y_continuous(labels = abs) +
  coord_flip()
```

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

<figure><img src="/files/HzqkB0doo9icP51luDAd" alt=""><figcaption></figcaption></figure>

* Explanation of Adjustments:

1. **Line Plot** : Replaced bars with lines for a smoother representation of the population distribution.
2. **Color Customization** : Used blue for males and red for females, making the plot more visually distinct.
3. **Theme Adjustments** : Enhanced the theme with larger and bold titles, adjusted axis labels and text sizes, and repositioned the legend to the top.
4. **Y-axis Labels** : Applied absolute values to y-axis labels for better readability.
5. **Flip Coordinates** : Retained coord\_flip() for the horizontal layout of the pyramid.

### Population Pyramid By Age Groups:

Improve the visualization by defining age groups and categorizing 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:

Create the population pyramid using the defined age groups and population data for males and females.

```
# 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/DCnAJWoRFVhgnrNa5n32" alt=""><figcaption></figcaption></figure>

### Enhanced Population Pyramid Plot Using Lines:

Create an enhanced population pyramid plot using lines for a clearer representation.

```
# Enhanced population pyramid plot using lines
e <- ggplot(combined, aes(x = age_group, y = males)) +
  geom_line(aes(color = "Male"), size = 1.2, group = 1) +
  geom_line(aes(x = age_group, y = -females, color = "Female"), size = 1.2, group = 1) +
  scale_color_manual(name = "  ", values = c("Male" = "cyan", "Female" = "pink")) +
  labs(x = "Age Group", y = "Population", title = "Population Pyramid by Age Group") +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 14),
    axis.title.y = element_text(size = 14),
    axis.text.x = element_text(size = 12, angle = 45, hjust = 1),  # Rotate x-axis text for better readability
    axis.text.y = element_text(size = 12),
    legend.position = "top",
    legend.title = element_text(size = 14),
    legend.text = element_text(size = 12),
    panel.grid.major = element_line(color = "grey80", size = 0.5),  # Add major gridlines
    panel.grid.minor = element_line(color = "grey90", size = 0.25)  # Add minor gridlines
  ) +
  scale_y_continuous(labels = abs) +
  coord_flip()
```

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

<figure><img src="/files/yXI4UrQCi9260fPwj5AQ" alt=""><figcaption></figcaption></figure>

### Combined Population Pyramid Plot:

Incorporate elements from both bar and line plots to create a comprehensive and visually appealing population pyramid.

```
# Combined population pyramid plot using bars and lines

f <- ggplot(combined, aes(x = age_group)) +
  # Bar plots for males and females
  geom_bar(aes(y = males, fill = "Male"), stat = "identity", position = "identity", alpha = 0.4) +
  geom_bar(aes(y = -females, fill = "Female"), stat = "identity", position = "identity", alpha = 0.4) +
  # Line plots for males and females
  geom_line(aes(y = males, color = "Male"), size = 1.2, group = 1) +
  geom_line(aes(y = -females, color = "Female"), size = 1.2, group = 1) +
  # Customizing fill and color for males and females
  scale_fill_manual(name = "  ", values = c("Male" = "lightblue", "Female" = "pink")) +
  scale_color_manual(name = "  ", values = c("Male" = "cyan", "Female" = "red")) +
  # Labels and title
  labs(x = "Age Group", y = "Population", title = "Population Pyramid by Age Group") +
  # Minimal theme and customizations
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 14),
    axis.title.y = element_text(size = 14),
    axis.text.x = element_text(size = 12, angle = 45, hjust = 1),  # Rotate x-axis text for better readability
    axis.text.y = element_text(size = 12),
    legend.position = "top",
    legend.title = element_text(size = 14),
    legend.text = element_text(size = 12),
    panel.grid.major = element_line(color = "grey80", size = 0.5),  # Add major gridlines
    panel.grid.minor = element_line(color = "grey90", size = 0.25)  # Add minor gridlines
  ) +
  scale_y_continuous(labels = abs) +
  coord_flip()

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

<figure><img src="/files/lpe317fLpTCVSkayJAHP" alt=""><figcaption></figcaption></figure>

* Explanation of Combined Elements:

1. **Bar Plots**: Added bar plots for males and females with light colors and semi-transparency (alpha = 0.4) for a subtle background representation of the population.
2. **Line Plots**: Added line plots for males and females with distinct colors for a clear and precise representation of the population trends.
3. **Custom Colors**: Used `scale_fill_manual` and `scale_color_manual` to differentiate between male and female population visually.
4. **Labels and Titles**: Set appropriate labels and titles for clarity.
5. **Theme Customizations**: Included theme settings for better readability and visual appeal, including text rotation and gridlines.
6. **Axis and Coordinates**: Adjusted y-axis labels to show absolute values and flipped coordinates for a horizontal pyramid representation.

### The Modified Combined Plot Code with a Custom Background Color

```
# Combined population pyramid plot using bars and lines with a custom background
g <- ggplot(combined, aes(x = age_group)) +
  # Bar plots for males and females
  geom_bar(aes(y = males, fill = "Male"), stat = "identity", position = "identity", alpha = 0.4) +
  geom_bar(aes(y = -females, fill = "Female"), stat = "identity", position = "identity", alpha = 0.4) +
  # Line plots for males and females
  geom_line(aes(y = males, color = "Male"), size = 1.2, group = 1) +
  geom_line(aes(y = -females, color = "Female"), size = 1.2, group = 1) +
  # Customizing fill and color for males and females
  scale_fill_manual(name = "  ", values = c("Male" = "lightblue", "Female" = "pink")) +
  scale_color_manual(name = "  ", values = c("Male" = "cyan", "Female" = "red")) +
  # Labels and title
  labs(x = "Age Group", y = "Population", title = "Population Pyramid by Age Group") +
  # Custom theme with modified background
  theme_minimal() +
  theme(
    plot.background = element_rect(fill = "lightgrey", color = NA),  # Change background color
    panel.background = element_rect(fill = "white", color = NA),  # Change panel background
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 14),
    axis.title.y = element_text(size = 14),
    axis.text.x = element_text(size = 12, angle = 45, hjust = 1),  # Rotate x-axis text for better readability
    axis.text.y = element_text(size = 12),
    legend.position = "top",
    legend.title = element_text(size = 14),
    legend.text = element_text(size = 12),
    panel.grid.major = element_line(color = "grey", size = 0.5),  # Add major gridlines
    panel.grid.minor = element_line(color = "grey", size = 0.25)  # Add minor gridlines
  ) +
  # Customizing y-axis labels to be absolute values and flipping coordinates
  scale_y_continuous(labels = abs) +
  coord_flip()

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

<figure><img src="/files/1hms71QkZt01NWfv9ZjL" 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).**